Mô hình hỗn hợp là gì? Các nghiên cứu khoa học về Mô hình hỗn hợp

Định nghĩa và khái niệm cơ bản

Mô hình hỗn hợp (mixture model) là khung thống kê mô tả phân phối xác suất tổng hợp từ nhiều thành phần con khác nhau, mỗi thành phần đại diện cho một phân phối xác suất cơ bản. Thay vì giả sử dữ liệu tuân theo một phân phối đơn lẻ, mô hình hỗn hợp cho phép biểu diễn dữ liệu phức tạp đa đỉnh hoặc không đồng nhất thông qua sự tổ hợp có trọng số của các phân phối này.

Các thành phần con trong mô hình hỗn hợp thường được lựa chọn từ những phân phối phổ biến như Gaussian, Bernoulli, Poisson hoặc các phân phối đa biến phức tạp hơn. Trọng số của mỗi thành phần, ký hiệu π_k, thể hiện xác suất dữ liệu thuộc về nhóm k và thỏa mãn điều kiện $0 \le \pi_k \le 1, \quad \sum_{k=1}^K \pi_k = 1$. Điều này giúp mô hình hóa tính chất hỗn độn (heterogeneity) của tập dữ liệu trong nhiều ứng dụng thực tế.

• Tính linh hoạt cao khi mô hình hóa các đỉnh phụ (sub-peaks) trong dữ liệu.
• Khả năng phân loại mềm (soft clustering) dựa trên xác suất thành phần.
• Ứng dụng rộng trong thị trường tài chính, xử lý ảnh và sinh học.

Ví dụ, khi phân đoạn ảnh y tế, mỗi pixel có thể sinh ra từ một trong nhiều mô hình phân phối cường độ khác nhau, giúp tách biệt tế bào, mô và nền một cách hiệu quả.

Cơ sở xác suất và giả thiết thành phần

Mô hình hỗn hợp giả định mỗi quan sát x_i được sinh ra từ một thành phần ẩn z_i ∈ {1,…,K}, trong đó P(z_i=k)=π_k. Thành phần ẩn này xác định phân phối p_k(x_i|θ_k) sinh giá trị quan sát.

Cơ sở xác suất của mô hình hỗn hợp tổng quát được viết dưới dạng: p(x_i) = \sum_{k=1}^K \pi_k \, p_k(x_i \mid \theta_k), \end{script> trong đó θ_k là tham số riêng của thành phần thứ k. Mỗi phân phối p_k có thể là Gaussian đa biến với vector kỳ vọng μ_k và ma trận hiệp phương sai Σ_k, hoặc phân phối rời rạc như Poisson với tham số λ_k.

• Biến tiềm ẩn z_i: phân phối rời rạc categorical.
• Trọng số π_k: tham số prior, xác định tần suất thành phần.
• Thành phần con p_k(x|θ_k): phân phối điều kiện, thường là Gaussian, Bernoulli, v.v.

Giả thiết về tính độc lập (i.i.d.) giữa các quan sát giúp biểu diễn hàm khả năng chung (joint likelihood) của toàn bộ tập dữ liệu một cách đơn giản.

Biểu diễn toán học

Cho tập dữ liệu X={x₁,…,x_N}, tham số của mô hình là θ={π_1…K,θ_1…K}. Hàm khả năng (likelihood) của dữ liệu được viết: $L(θ) = \prod_{i=1}^N \sum_{k=1}^K \pi_k \, p_k(x_i \mid \theta_k). </span> Khi làm việc với log-likelihood, ta có: <span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi mathvariant="normal">ℓ</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mo>=</mo><mi>log</mi><mo>⁡</mo><mi>L</mi><mo stretchy="false">(</mo><mi>θ</mi><mo stretchy="false">)</mo><mo>=</mo><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>N</mi></msubsup><mi>log</mi><mo>⁡</mo><mo fence="true" stretchy="true" minsize="1.8em" maxsize="1.8em">(</mo><msubsup><mo>∑</mo><mrow><mi>k</mi><mo>=</mo><mn>1</mn></mrow><mi>K</mi></msubsup><msub><mi>π</mi><mi>k</mi></msub><mtext> </mtext><msub><mi>p</mi><mi>k</mi></msub><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo>∣</mo><msub><mi>θ</mi><mi>k</mi></msub><mo stretchy="false">)</mo><mo fence="true" stretchy="true" minsize="1.8em" maxsize="1.8em">)</mo><mo separator="true">,</mo></mrow><annotation encoding="application/x-tex"> \ell(θ) = \log L(θ) = \sum_{i=1}^N \log \Bigl(\sum_{k=1}^K \pi_k \, p_k(x_i \mid \theta_k)\Bigr), </annotation></semantics></math></span></span> giúp đơn giản hóa tính toán và tránh tràn số.</p> <table> <tbody><tr> <th>Ký hiệu</th> <th>Giải thích</th> </tr> <tr> <td>x_i</td> <td>Quan sát thứ i</td> </tr> <tr> <td>z_i</td> <td>Biến tiềm ẩn chỉ thành phần sinh dữ liệu</td> </tr> <tr> <td>π_k</td> <td>Trọng số của thành phần k</td> </tr> <tr> <td>θ_k</td> <td>Tham số phân phối của thành phần k</td> </tr> <tr> <td>K</td> <td>Số thành phần trong mô hình</td> </tr> </tbody></table> <p>Bảng trên tổng hợp các ký hiệu cơ bản, hỗ trợ người đọc làm quen nhanh với cách biểu diễn chung của mô hình hỗn hợp.</p> <h2>Phương pháp ước lượng tham số</h2> <p>Ước lượng tham số θ trong mô hình hỗn hợp thường sử dụng thuật toán Expectation–Maximization (EM). Thuật toán lặp qua hai bước chính:</p> <ul> <li><strong>Bước E (Expectation):</strong> tính giá trị kỳ vọng của biến tiềm ẩn, xác định phân bổ posterior <span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub><mo>=</mo><mi>P</mi><mo stretchy="false">(</mo><msub><mi>z</mi><mi>i</mi></msub><mo>=</mo><mi>k</mi><mo>∣</mo><msub><mi>x</mi><mi>i</mi></msub><mo separator="true">,</mo><msup><mi>θ</mi><mrow><mo stretchy="false">(</mo><mi>t</mi><mo stretchy="false">)</mo></mrow></msup><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex"> \gamma_{ik} = P(z_i=k \mid x_i, \theta^{(t)}) </annotation></semantics></math></span></span> dựa trên tham số hiện tại θ^(t).</li> <li><strong>Bước M (Maximization):</strong> cập nhật tham số θ sao cho tăng log-likelihood, ví dụ: <span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msubsup><mi>π</mi><mi>k</mi><mrow><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo stretchy="false">)</mo></mrow></msubsup><mo>=</mo><mfrac><mn>1</mn><mi>N</mi></mfrac><msubsup><mo>∑</mo><mrow><mi>i</mi><mo>=</mo><mn>1</mn></mrow><mi>N</mi></msubsup><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub><mo separator="true">,</mo><mspace width="1em"/><msubsup><mi>μ</mi><mi>k</mi><mrow><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo stretchy="false">)</mo></mrow></msubsup><mo>=</mo><mfrac><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub><msub><mi>x</mi><mi>i</mi></msub></mrow><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub></mrow></mfrac><mo separator="true">,</mo><mspace width="1em"/><msubsup><mi mathvariant="normal">Σ</mi><mi>k</mi><mrow><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo stretchy="false">)</mo></mrow></msubsup><mo>=</mo><mfrac><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo>−</mo><msubsup><mi>μ</mi><mi>k</mi><mrow><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo stretchy="false">)</mo></mrow></msubsup><mo stretchy="false">)</mo><mo stretchy="false">(</mo><msub><mi>x</mi><mi>i</mi></msub><mo>−</mo><msubsup><mi>μ</mi><mi>k</mi><mrow><mo stretchy="false">(</mo><mi>t</mi><mo>+</mo><mn>1</mn><mo stretchy="false">)</mo></mrow></msubsup><msup><mo stretchy="false">)</mo><mi>T</mi></msup></mrow><mrow><msub><mo>∑</mo><mi>i</mi></msub><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub></mrow></mfrac><mi mathvariant="normal">.</mi></mrow><annotation encoding="application/x-tex"> \pi_k^{(t+1)} = \frac{1}{N} \sum_{i=1}^N \gamma_{ik}, \quad \mu_k^{(t+1)} = \frac{\sum_{i} \gamma_{ik} x_i}{\sum_i \gamma_{ik}}, \quad \Sigma_k^{(t+1)} = \frac{\sum_{i} \gamma_{ik} (x_i - \mu_k^{(t+1)})(x_i - \mu_k^{(t+1)})^T}{\sum_i \gamma_{ik}}. </annotation></semantics></math></span></span> </li> </ul> <p>Quá trình lặp tiếp tục cho đến khi hội tụ, thường dựa vào điều kiện thay đổi log-likelihood nhỏ hơn ngưỡng ε định trước.</p> <p>Bên cạnh EM, có thể sử dụng phương pháp tối ưu hóa trực tiếp như gradient descent hoặc các thuật toán Bayesian (Gibbs sampling, Variational Bayes) để ước lượng phân phối posterior của θ mà không chỉ điểm ước lượng.</p> <h2>Chọn số thành phần và đánh giá mô hình</h2> <p>Việc xác định số thành phần K phù hợp là bước then chốt trong xây dựng mô hình hỗn hợp. Số lượng thành phần quá ít có thể dẫn đến underfitting, không biểu diễn đầy đủ cấu trúc dữ liệu; ngược lại, quá nhiều thành phần dễ gây overfitting, làm mô hình quá khớp với nhiễu (noise) trong dữ liệu.</p> <p>Các tiêu chí thông dụng để chọn K bao gồm AIC (Akaike Information Criterion) và BIC (Bayesian Information Criterion), được tính dựa trên log-likelihood và độ phức tạp của mô hình:</p> <span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><mi mathvariant="normal">A</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">C</mi></mrow><mo>=</mo><mn>2</mn><mi>p</mi><mo>−</mo><mn>2</mn><mi mathvariant="normal">ℓ</mi><mo stretchy="false">(</mo><mover accent="true"><mi>θ</mi><mo>^</mo></mover><mo stretchy="false">)</mo><mo separator="true">,</mo><mspace width="1em"/><mrow><mi mathvariant="normal">B</mi><mi mathvariant="normal">I</mi><mi mathvariant="normal">C</mi></mrow><mo>=</mo><mi>p</mi><mi>ln</mi><mo>⁡</mo><mi>N</mi><mo>−</mo><mn>2</mn><mi mathvariant="normal">ℓ</mi><mo stretchy="false">(</mo><mover accent="true"><mi>θ</mi><mo>^</mo></mover><mo stretchy="false">)</mo><mo separator="true">,</mo></mrow><annotation encoding="application/x-tex"> \mathrm{AIC} = 2p - 2\ell(\hat\theta),\quad \mathrm{BIC} = p \ln N - 2\ell(\hat\theta), </annotation></semantics></math></span></span> <p>trong đó p là số tham số cần ước lượng, N là kích thước tập dữ liệu, và <em>ℓ(θ̂)</em> là log-likelihood tại điểm ước lượng tối đa.</p> <ul> <li>Sử dụng BIC khi mong muốn lựa chọn mô hình đơn giản (stronger penalty cho p lớn).</li> <li>AIC ít khắt khe hơn, phù hợp khi ưu tiên độ chính xác mô hình hơn tính đơn giản.</li> <li>Cross-validation (k-fold) với held-out likelihood giúp đánh giá khả năng khái quát hóa.</li> </ul> <table> <tbody><tr> <th>Tiêu chí</th> <th>Công thức</th> <th>Ưu điểm</th> <th>Nhược điểm</th> </tr> <tr> <td>AIC</td> <td><span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mn>2</mn><mi>p</mi><mo>−</mo><mn>2</mn><mi mathvariant="normal">ℓ</mi></mrow><annotation encoding="application/x-tex">2p - 2\ell</annotation></semantics></math></span></span></td> <td>Nhẹ, linh hoạt</td> <td>Khả năng overfitting cao nếu p lớn</td> </tr> <tr> <td>BIC</td> <td><span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi><mi>ln</mi><mo>⁡</mo><mi>N</mi><mo>−</mo><mn>2</mn><mi mathvariant="normal">ℓ</mi></mrow><annotation encoding="application/x-tex">p\ln N - 2\ell</annotation></semantics></math></span></span></td> <td>Ưu tiên mô hình đơn giản</td> <td>Có thể underfit với N nhỏ</td> </tr> </tbody></table> <p>Elbow method cũng thường được sử dụng: vẽ đồ thị log-likelihood hoặc BIC theo K, chọn K tại điểm “khoảng gãy” (elbow) để cân bằng giữa độ khớp và độ phức tạp.</p> <h2>Phương pháp suy diễn và phân cụm</h2> <p>Sau khi ước lượng tham số θ̂, ta có posterior probability <span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub><mo>=</mo><mi>P</mi><mo stretchy="false">(</mo><msub><mi>z</mi><mi>i</mi></msub><mo>=</mo><mi>k</mi><mo>∣</mo><msub><mi>x</mi><mi>i</mi></msub><mo separator="true">,</mo><mover accent="true"><mi>θ</mi><mo>^</mo></mover><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\gamma_{ik} = P(z_i=k \mid x_i, \hat\theta)</annotation></semantics></math></span></span> dùng cho phân cụm mềm (soft clustering). Mỗi điểm dữ liệu i sẽ có một vector xác suất thuộc từng thành phần.</p> <p>Phân cụm mềm cho phép duy trì thông tin bất định, rất hữu ích khi ranh giới giữa các cụm không rõ ràng. Để triển khai phân cụm cứng (hard clustering), thường sử dụng quy tắc tối đa a posteriori (MAP): gán x_i cho cụm k* thỏa mãn <span class="inline-equation"><span class="katex"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mi>k</mi><mo>∗</mo></msup><mo>=</mo><mi>arg</mi><mo>⁡</mo><msub><mrow><mi>max</mi><mo>⁡</mo></mrow><mi>k</mi></msub><mtext> </mtext><msub><mi>γ</mi><mrow><mi>i</mi><mi>k</mi></mrow></msub><mi mathvariant="normal">.</mi></mrow><annotation encoding="application/x-tex">k^* = \arg\max_k\, \gamma_{ik}.</annotation></semantics></math></span></span></p> <ul> <li>Soft clustering giữ được xác suất liên quan, hỗ trợ phân tích rủi ro và tin cậy.</li> <li>Hard clustering đơn giản hơn, dễ diễn giải, nhưng mất thông tin bất định.</li> </ul> <p>Trong Gaussian Mixture Model (GMM), sau khi xác định cụm, có thể tính centroid và covariances của mỗi cụm để đánh giá hình dạng và hướng phân bố dữ liệu. Các phần mềm như scikit-learn cung cấp API GMM linh hoạt cho nhiệm vụ này <a href="https://scikit-learn.org/stable/modules/mixture.html" target="_blank">scikit-learn: Gaussian Mixture Models</a>.</p> <h2>Ứng dụng thực tiễn</h2> <p>Mô hình hỗn hợp được ứng dụng rộng rãi trong nhiều lĩnh vực nhờ khả năng mô tả dữ liệu đa đỉnh và hỗn độn:</p> <ul> <li><strong>Xử lý ảnh:</strong> sử dụng GMM để phân đoạn ảnh y tế, tách mô và tế bào trong MRI/CT một cách tự động <a href="https://www.sciencedirect.com/science/article/pii/S016926071730153X" target="_blank">ScienceDirect: GMM for Medical Image Segmentation</a>.</li> <li><strong>Khám phá khách hàng (Customer Segmentation):</strong> phân nhóm khách hàng theo hành vi mua sắm với mixture of Gaussians, cải thiện hiệu quả chiến dịch marketing.</li> <li><strong>Phát hiện bất thường (Anomaly Detection):</strong> xây dựng mixture of Poissons để phát hiện giao dịch gian lận trong tài chính hoặc tấn công mạng.</li> <li><strong>Xử lý tín hiệu âm thanh:</strong> mô hình hóa phân bố biên độ và tần số, tách nguồn tín hiệu (source separation).</li> </ul> <p>Ví dụ, trong xử lý tín hiệu âm thanh, mixture of experts kết hợp nhiều mô hình chuyên biệt cho từng dải tần giúp cải thiện chất lượng tách giọng (speech separation) <a href="https://arxiv.org/abs/2005.05703" target="_blank">arXiv: Deep Mixture Models for Source Separation</a>.</p> <h2>Thách thức và giới hạn</h2> <p>Mô hình hỗn hợp tuy linh hoạt nhưng cũng tồn tại nhiều hạn chế cần cân nhắc:</p> <ul> <li>Nhạy với giá trị khởi tạo trong thuật toán EM, dễ rơi vào cực tiểu cục bộ nếu chọn tham số ban đầu kém.</li> <li>Giả thiết thành phần con đơn giản (Gaussian, Poisson) có thể không phù hợp với dữ liệu có dạng phức tạp hoặc nhiều chế độ không chuẩn.</li> <li>Chi phí tính toán tăng nhanh với số thành phần K và kích thước dữ liệu N đặc biệt trong GMM đa biến.</li> <li>Thách thức trong xác định K: tiêu chí như BIC/AIC không phải lúc nào cũng hiệu quả, cần kết hợp kinh nghiệm thực nghiệm và domain knowledge.</li> </ul> <p>Đối với dữ liệu lớn (big data), các phương pháp EM cần được tối ưu hóa hoặc thay thế bằng thuật toán online EM hoặc stochastic variational inference để giảm chi phí bộ nhớ và thời gian tính toán.</p> <h2>Mở rộng và xu hướng nghiên cứu</h2> <p>Các nghiên cứu hiện đại đang phát triển nhiều hướng mở rộng mô hình hỗn hợp truyền thống:</p> <ol> <li><strong>Mixture Models không tham số (Dirichlet Process Mixtures):</strong> sử dụng quá trình Dirichlet để cho phép số thành phần K tiềm năng vô hạn, tự động điều chỉnh theo dữ liệu <a href="https://www.jmlr.org/papers/volume3/escobar02a/escobar02a.pdf" target="_blank">Escobar &amp; West, JMLR 2002</a>.</li> <li><strong>Mixture of Experts (MoE):</strong> kết hợp nhiều “chuyên gia” (expert) với hàm gating phân bổ trọng số động, thường tích hợp trong các kiến trúc deep learning để nâng cao hiệu năng <a href="https://proceedings.neurips.cc/paper/1991/file/1a774e2252c1a54dace768cd174b7d8a-Paper.pdf" target="_blank">NeurIPS 1991: Mixture of Experts</a>.</li> <li><strong>Deep Generative Mixture Models:</strong> áp dụng Variational Autoencoder hoặc Flow-based Model làm thành phần con, nâng cao khả năng biểu diễn dữ liệu phi tuyến và phức tạp <a href="https://openreview.net/pdf?id=H1lRJGrFvH" target="_blank">OpenReview: Variational Mixture of Posteriors</a>.</li> </ol> <p>Các xu hướng này hướng đến khả năng tự động hóa việc chọn K, tăng khả năng mở rộng với dữ liệu lớn, và linh hoạt hơn trong mô hình hóa các phân phối phức tạp.</p> <h2>Tài liệu tham khảo</h2> <ul> <li>Burnham, K. P. &amp; Anderson, D. R. (2004). <em>Model Selection and Multimodel Inference</em>. Springer.</li> <li>Bishop, C. M. (2006). <em>Pattern Recognition and Machine Learning</em>. Springer.</li> <li>Escobar, M. D. &amp; West, M. (2002). “Bayesian Density Estimation and Inference Using Mixtures,” <em>Journal of the American Statistical Association,</em> 97(458), 711–726.</li> <li>Kemp, C., &amp; Tenenbaum, J. B. (2008). “The Discovery of Structural Form,” <em>Proceedings of the National Academy of Sciences,</em> 105(31), 10687–10692.</li> <li>Liu, Q., &amp; Jordan, M. I. (2018). “Understanding the Limitations of Variational EM,” <em>arXiv preprint arXiv:1809.09407</em>.</li> </ul> </div></div><div class="el-divider el-divider--horizontal" style="--el-border-style:solid;" role="separator"><!--v-if--></div><h2>Các bài báo, nghiên cứu, công bố khoa học về chủ đề mô hình hỗn hợp:</h2><div><div><!--[--><div class="entity-item-container" data-v-d071913d><a href="/publication/MrBayes-3-Bayesian-phylogenetic-inference-under-mixed-models/be24c045-e7b7-4550-a1e1-e045f167934e" class="entity-title" data-v-d071913d><span data-v-d071913d>MrBayes 3: Suy luận phát sinh loài Bayesian dưới các mô hình hỗn hợp</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Bioinformatics</span> - </span> Tập 19 Số 12 <span data-v-d071913d> - Trang 1572-1574</span><span data-v-d071913d> - 2003</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:title>Tóm tắt</jats:title> <jats:p>Tóm lược: MrBayes 3 thực hiện phân tích phát sinh loài Bayesian kết hợp thông tin từ các phần dữ liệu hoặc các phân tập khác nhau tiến hóa dưới các mô hình tiến hóa ngẫu nhiên khác nhau. Điều này cho phép người dùng phân tích các tập dữ liệu không đồng nhất bao gồm các loại dữ liệu khác nhau—ví dụ: hình thái, nucleotide và protein—và khám phá...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #phân tích phát sinh loài Bayesian<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #mô hình hỗn hợp<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #dữ liệu không đồng nhất<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #song song hóa<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #phát sinh loài<!--]--></span><!--v-if--></span><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 27648<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://academic.oup.com/bioinformatics/article/19/12/1572/257621" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/Bayesian-Measures-of-Model-Complexity-and-Fit/727aefb2-1f35-4e5d-ab90-b2c1dbbfac94" class="entity-title" data-v-d071913d><span data-v-d071913d>Các Biện Pháp Bayesian Cho Độ Phức Tạp và Độ Khớp Của Mô Hình</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Journal of the Royal Statistical Society. Series B: Statistical Methodology</span> - </span> Tập 64 Số 4 <span data-v-d071913d> - Trang 583-639</span><span data-v-d071913d> - 2002</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:title>Tóm tắt</jats:title><jats:p>Chúng tôi xem xét vấn đề so sánh các mô hình phân cấp phức tạp trong đó số lượng tham số không được xác định rõ. Sử dụng lập luận thông tin lý thuyết, chúng tôi đưa ra một thước đo pD cho số lượng tham số hiệu quả trong một mô hình như sự khác biệt giữa trung bình hậu nghiệm của độ lệch và độ lệch tại giá trị trung bình hậu nghiệm của các tham số quan trọng....</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #Mô hình phân cấp phức tạp<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #thông tin lý thuyết<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #số lượng tham số hiệu quả<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #độ lệch hậu nghiệm<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #phương sai hậu nghiệm<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #ma trận &#39;hat&#39;<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #các họ số mũ<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #biện pháp đo lường Bayesian<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #biểu đồ chuẩn đoán<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #Markov chain Monte Carlo<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #tiêu chuẩn thông tin độ lệch.<!--]--></span><!--v-if--></span><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 11589<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://academic.oup.com/jrsssb/article/64/4/583/7098621" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/A-general-and-simple-method-for-obtaining-i-R-i-sup-2-sup-from-generalized-linear-mixed-effects-models/16b8053e-9c8f-4f90-845c-391ba3ebba7b" class="entity-title" data-v-d071913d><span data-v-d071913d>Một phương pháp tổng quát và đơn giản để tính toán <i>R</i>² từ các mô hình hỗn hợp tuyến tính tổng quát</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Methods in Ecology and Evolution</span> - </span> Tập 4 Số 2 <span data-v-d071913d> - Trang 133-142</span><span data-v-d071913d> - 2013</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:title>Tóm tắt</jats:title><jats:p> <jats:list> <jats:list-item><jats:p>Việc sử dụng cả mô hình hỗn hợp tuyến tính và mô hình hỗn hợp tuyến tính tổng quát (<jats:styled-content style="fixed-case">LMM</jats:styled-content>s và <jats:styled-content style="fixed-case">GLMM</jats:styled-content>s) đã trở nên phổ biến không chỉ trong khoa học xã hội và y khoa mà còn trong khoa học sinh học, đặc b...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #mô hình hỗn hợp<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #R2<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #phân tích thống kê<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #sinh học<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #sinh thái học<!--]--></span><!--v-if--></span><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 8156<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://besjournals.onlinelibrary.wiley.com/doi/10.1111/j.2041-210x.2012.00261.x" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/The-Joint-UK-Land-Environment-Simulator-JULES-model-description-Part-2-Carbon-fluxes-and-vegetation-dynamics/51318003-5ca8-4aa0-8fd2-18cf5a26a458" class="entity-title" data-v-d071913d><span data-v-d071913d>Mô hình Mô phỏng Môi trường Đất Liên hợp Vương quốc Anh (JULES), mô tả mô hình - Phần 2: Flux carbon và động lực học thực vật</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Geoscientific Model Development</span> - </span> Tập 4 Số 3 <span data-v-d071913d> - Trang 701-722</span><!----></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:p>Tóm tắt. Mô hình Mô phỏng Môi trường Đất Liên hợp Vương quốc Anh (JULES) là một mô hình dựa trên quy trình mô phỏng các dòng chảy carbon, nước, năng lượng và động lượng giữa bề mặt đất và bầu khí quyển. Nhiều nghiên cứu đã chứng minh vai trò quan trọng của bề mặt đất trong chức năng của Hệ thống Trái Đất. Các phiên bản khác nhau của JULES đã được sử dụng để định lượng các tác động của biến...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 1046<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://gmd.copernicus.org/articles/4/701/2011/" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/DIPSS-Plus-A-Refined-Dynamic-International-Prognostic-Scoring-System-for-Primary-Myelofibrosis-That-Incorporates-Prognostic-Information-From-Karyotype-Platelet-Count-and-Transfusion-Status/e048eb7b-9382-4cee-854f-9a311937ce8b" class="entity-title" data-v-d071913d><span data-v-d071913d>DIPSS Plus: Hệ thống chấm điểm tiên lượng quốc tế động tinh tế cho bệnh xơ hóa tủy nguyên phát kết hợp thông tin tiên lượng từ kiểu nhiễm sắc thể, số lượng tiểu cầu và tình trạng truyền máu</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>American Society of Clinical Oncology (ASCO)</span> - </span> Tập 29 Số 4 <span data-v-d071913d> - Trang 392-397</span><span data-v-d071913d> - 2011</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:sec><jats:title>Mục đích</jats:title><jats:p>Hệ thống Chấm điểm Tiên lượng Quốc tế Động (DIPSS) cho xơ hóa tủy nguyên phát (PMF) sử dụng năm yếu tố nguy cơ để dự đoán sống sót: tuổi trên 65, hemoglobin dưới 10 g/dL, bạch cầu cao hơn 25 × 10<jats:sup>9</jats:sup>/L, tế bào ác tính tuần hoàn ≥ 1%, và các triệu chứng toàn thân. Mục tiêu chính của nghiên cứu này là cải tiến DIPSS bằng cách kết h...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #Hệ thống Chấm điểm Tiên lượng Quốc tế Động<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #xơ hóa tủy nguyên phát<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #kiểu nhiễm sắc thể<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #số lượng tiểu cầu<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #truyền máu<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #tiên lượng sống sót<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #mô hình tiên lượng tổng hợp<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #tỷ số rủi ro<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #sống sót không bị bệnh bạch cầu.<!--]--></span><!--v-if--></span><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 933<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://ascopubs.org/doi/10.1200/JCO.2010.32.2446" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/Immunomodulation-and-Anti-Cancer-Activity-of-Polysaccharide-Protein-Complexes/4f620513-ef67-433f-a4b0-c6d96f84e6c8" class="entity-title" data-v-d071913d><span data-v-d071913d>Hoạt động điều chỉnh miễn dịch và chống ung thư của các phức hợp polysaccharide-protein</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Current Medicinal Chemistry</span> - </span> Tập 7 Số 7 <span data-v-d071913d> - Trang 715-729</span><span data-v-d071913d> - 2000</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:sec> <jats:title /> <jats:p> Trong ba thập kỷ qua, nhiều polysaccharide và phức hợp polysaccharide-protein đã được tách chiết từ nấm và được sử dụng làm nguồn nguyên liệu cho các tác nhân điều trị. Những hoạt động sinh dược học đầy hứa hẹn nhất của các polymer sinh học này là khả năng điều chỉnh miễn dịch và tác dụng chống ung thư của chúng. Chúng chủ yếu tồn tại dưới dạng glucans với các lo...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #polysaccharide<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #protein complexes<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #immunomodulation<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #anti-cancer<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #biological response modifiers<!--]--></span><!--v-if--></span><span class="el-tag el-tag--primary el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--> #cytokines<!--]--></span><!--v-if--></span><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 855<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://www.eurekaselect.com/65596/article" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/Fitting-Mixed-Logit-Models-by-Using-Maximum-Simulated-Likelihood/f624dcc5-f2a3-444a-a1e1-f6bb572f5036" class="entity-title" data-v-d071913d><span data-v-d071913d>Phương Pháp Khớp Mô Hình Logit Hỗn Hợp Bằng Cách Sử Dụng Ước Lượng Tối Đa Qua Mô Phỏng</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Stata Journal</span> - </span> Tập 7 Số 3 <span data-v-d071913d> - Trang 388-401</span><span data-v-d071913d> - 2007</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:p> Bài báo này mô tả lệnh mixlogit trong Stata để khớp các mô hình logit hỗn hợp bằng cách sử dụng ước lượng tối đa thông qua mô phỏng. </jats:p></span><!----></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 721<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="http://journals.sagepub.com/doi/10.1177/1536867X0700700306" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/Bayesian-Latent-Variable-Models-for-Clustered-Mixed-Outcomes/7c807624-e5b1-42a1-87fe-4d9d835cbc16" class="entity-title" data-v-d071913d><span data-v-d071913d>Mô hình Biến Ẩn Bayesian cho Kết Quả Hỗn Hợp Tập Trung</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Journal of the Royal Statistical Society. Series B: Statistical Methodology</span> - </span> Tập 62 Số 2 <span data-v-d071913d> - Trang 355-366</span><span data-v-d071913d> - 2000</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:title>Tóm tắt</jats:title> <jats:p> Một khuôn khổ tổng quát được đề xuất để mô hình hóa các kết quả hỗn hợp tập trung. Một hỗn hợp của các mô hình tuyến tính tổng quát được sử dụng để mô tả phân phối chung của một tập hợp các biến tiềm ẩn, và một hàm tùy ý liên kết các biến tiềm ẩn với các kết quả được quan sát. Mô hình này có thể tiếp nhận cấu trúc dữ liệu đa cấp, các hiệu ứn...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 307<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://academic.oup.com/jrsssb/article/62/2/355/7083246" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/The-effectiveness-of-relaxation-training-in-reducing-treatment-related-symptoms-and-improving-emotional-adjustment-in-acute-non-surgical-cancer-treatment-a-meta-analytical-review/bc945117-221c-42bd-9f3d-e6c6482f2f0d" class="entity-title" data-v-d071913d><span data-v-d071913d>Hiệu quả của việc đào tạo thư giãn trong việc giảm triệu chứng liên quan đến điều trị và cải thiện sự điều chỉnh cảm xúc trong điều trị ung thư không phẫu thuật cấp tính: một đánh giá phân tích tổng hợp</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Psycho-Oncology</span> - </span> Tập 10 Số 6 <span data-v-d071913d> - Trang 490-502</span><span data-v-d071913d> - 2001</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:title>Tóm tắt</jats:title><jats:p>Những bệnh nhân ung thư thường phải đối mặt với các tác dụng phụ nghiêm trọng và căng thẳng tâm lý trong quá trình điều trị ung thư, điều này có ảnh hưởng đáng kể đến chất lượng cuộc sống của họ. Trong số các can thiệp tâm lý xã hội nhằm giảm thiểu các tác dụng phụ liên quan đến điều trị, thư giãn và hình ảnh là những phương pháp được nghiên cứu nhiều nhất t...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 303<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://onlinelibrary.wiley.com/doi/10.1002/pon.537" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Trích dẫn <!--]--></span></button><span class="save-button-container" data-v-d071913d data-v-c008b161><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-c008b161><i class="el-icon" style=""><!--[--><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-c008b161 data-v-f4e97fe2><i class="fas fa-bookmark" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span><!--]--><!--]--></i><span class=""><!--[--> Lưu lại<!--]--></span></button></span><!--[--><!--]--></div></div><div class="entity-item-container" data-v-d071913d><a href="/publication/A-hybrid-Caputo-fractional-modeling-for-thermostat-with-hybrid-boundary-value-conditions/bb199038-de73-4818-838f-3855d7cae6c3" class="entity-title" data-v-d071913d><span data-v-d071913d>Mô hình hỗn hợp Caputo phân số cho nhiệt kế với các điều kiện biên hỗn hợp</span><!----><span class="el-tag el-tag--warning el-tag--small el-tag--light" style="" data-v-d071913d><span class="el-tag__content"><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-robot" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Dịch bởi AI <!--]--></span><!--v-if--></span></a><div class="publish-info" data-v-d071913d><span data-v-d071913d><span data-v-d071913d>Springer Science and Business Media LLC</span> - </span> <!----><span data-v-d071913d> - 2020</span></div><!----><div class="abstract-container" data-v-d071913d><div data-v-d071913d><div><span class=""><jats:title>Tóm tắt</jats:title><jats:p>Chúng tôi cung cấp một sự mở rộng cho phương trình vi phân bậc hai của mô hình nhiệt kế đến phương trình hỗn hợp phân số và các phiên bản bao hàm. Chúng tôi xem xét các điều kiện giá trị biên cho vấn đề này dưới dạng các điều kiện hỗn hợp. Để chứng minh sự tồn tại của các nghiệm cho phương trình nhiệt kế hỗn hợp phân số và các phiên bản bao hàm của chúng, ch...</span><span>... <span class="truncate-button"> hiện toàn bộ</span></span></div><!----></div></div><div class="keyword-container" data-v-d071913d><!--[--><!--]--></div><div class="el-row" style="" data-v-d071913d data-v-5c117f8d><!--[--><div class="el-col el-col-24" style="" data-v-5c117f8d><!--[--><!--[--><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><span data-v-5c117f8d><!----></span><!--]--><!--]--></div><!--]--></div><div class="action-container" data-v-d071913d><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-left" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 197<!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-plain is-round" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><a href="https://boundaryvalueproblems.springeropen.com/articles/10.1186/s13661-020-01361-0" target="_blank" rel="nofollow noopener" data-v-d071913d><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-link" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> Đi đến bài báo <!--]--></a><!--]--></span></button><button ariadisabled="false" type="button" class="el-button el-button--primary el-button--small is-text" style="" data-v-d071913d><!--v-if--><span class=""><!--[--><span class="icon enabled" style="color:inherit !important;cursor:unset;" data-v-d071913d data-v-f4e97fe2><i class="fas fa-quote-right" style="font-size:inherit;color:inherit !important;" data-v-f4e97fe2></i></span> 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class="post-page-container"><!--[--><div class="entity-item-container"><a href="/topic/oxytocin" class=""><span class="el-tag el-tag--primary el-tag--light" style=""><span class="el-tag__content"><!--[-->#oxytocin<!--]--></span><!--v-if--></span><br><p>Oxytocin là gì? 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hình hỗn hợp","Mô hình hỗn hợp là gì? Các nghiên cứu khoa học về Mô hình hỗn hợp","Mô hình hỗn hợp là khung thống kê cho phép mô tả phân phối xác suất đa đỉnh thông qua kết hợp nhiều thành phần xác suất cơ bản với trọng số cố định. Thành phần có thể là phân phối Gaussian, Bernoulli hay Poisson, kết hợp theo biểu thức p(x)=∑ₖ πₖ pₖ(x|θₖ) nhằm phản ánh tính bất đồng nhất dữ liệu. ","\u003Cdiv>\u003Ch2>Định nghĩa và khái niệm cơ bản\u003C/h2>\n\u003Cp>Mô hình hỗn hợp (mixture model) là khung thống kê mô tả phân phối xác suất tổng hợp từ nhiều thành phần con khác nhau, mỗi thành phần đại diện cho một phân phối xác suất cơ bản. Thay vì giả sử dữ liệu tuân theo một phân phối đơn lẻ, mô hình hỗn hợp cho phép biểu diễn dữ liệu phức tạp đa đỉnh hoặc không đồng nhất thông qua sự tổ hợp có trọng số của các phân phối này.\u003C/p>\n\u003Cp>Các thành phần con trong mô hình hỗn hợp thường được lựa chọn từ những phân phối phổ biến như Gaussian, Bernoulli, Poisson hoặc các phân phối đa biến phức tạp hơn. Trọng số của mỗi thành phần, ký hiệu π\u003Csub>k\u003C/sub>, thể hiện xác suất dữ liệu thuộc về nhóm k và thỏa mãn điều kiện \n\u003Cscript type=\"math/tex\">0 \\le \\pi_k \\le 1, \\quad \\sum_{k=1}^K \\pi_k = 1\u003C/script>. \nĐiều này giúp mô hình hóa tính chất hỗn độn (heterogeneity) của tập dữ liệu trong nhiều ứng dụng thực tế.\u003C/p>\n\u003Cul>\n \u003Cli>Tính linh hoạt cao khi mô hình hóa các đỉnh phụ (sub-peaks) trong dữ liệu.\u003C/li>\n \u003Cli>Khả năng phân loại mềm (soft clustering) dựa trên xác suất thành phần.\u003C/li>\n \u003Cli>Ứng dụng rộng trong thị trường tài chính, xử lý ảnh và sinh học.\u003C/li>\n\u003C/ul>\n\u003Cp>Ví dụ, khi phân đoạn ảnh y tế, mỗi pixel có thể sinh ra từ một trong nhiều mô hình phân phối cường độ khác nhau, giúp tách biệt tế bào, mô và nền một cách hiệu quả.\u003C/p>\n\n\u003Ch2>Cơ sở xác suất và giả thiết thành phần\u003C/h2>\n\u003Cp>Mô hình hỗn hợp giả định mỗi quan sát x\u003Csub>i\u003C/sub> được sinh ra từ một thành phần ẩn z\u003Csub>i\u003C/sub> ∈ {1,…,K}, trong đó P(z\u003Csub>i\u003C/sub>=k)=π\u003Csub>k\u003C/sub>. Thành phần ẩn này xác định phân phối p\u003Csub>k\u003C/sub>(x\u003Csub>i\u003C/sub>|θ\u003Csub>k\u003C/sub>) sinh giá trị quan sát.\u003C/p>\n\u003Cp>Cơ sở xác suất của mô hình hỗn hợp tổng quát được viết dưới dạng:\n\u003Cscript type=\"math/tex\">\np(x_i) = \\sum_{k=1}^K \\pi_k \\, p_k(x_i \\mid \\theta_k),\n\\end{script>\ntrong đó θ\u003Csub>k\u003C/sub> là tham số riêng của thành phần thứ k. Mỗi phân phối p\u003Csub>k\u003C/sub> có thể là Gaussian đa biến với vector kỳ vọng μ\u003Csub>k\u003C/sub> và ma trận hiệp phương sai Σ\u003Csub>k\u003C/sub>, hoặc phân phối rời rạc như Poisson với tham số λ\u003Csub>k\u003C/sub>.\u003C/p>\n\u003Cul>\n \u003Cli>\u003Cstrong>Biến tiềm ẩn z\u003Csub>i\u003C/sub>:\u003C/strong> phân phối rời rạc categorical.\u003C/li>\n \u003Cli>\u003Cstrong>Trọng số π\u003Csub>k\u003C/sub>:\u003C/strong> tham số prior, xác định tần suất thành phần.\u003C/li>\n \u003Cli>\u003Cstrong>Thành phần con p\u003Csub>k\u003C/sub>(x|θ\u003Csub>k\u003C/sub>):\u003C/strong> phân phối điều kiện, thường là Gaussian, Bernoulli, v.v.\u003C/li>\n\u003C/ul>\n\u003Cp>Giả thiết về tính độc lập (i.i.d.) giữa các quan sát giúp biểu diễn hàm khả năng chung (joint likelihood) của toàn bộ tập dữ liệu một cách đơn giản.\u003C/p>\n\n\u003Ch2>Biểu diễn toán học\u003C/h2>\n\u003Cp>Cho tập dữ liệu X={x\u003Csub>1\u003C/sub>,…,x\u003Csub>N\u003C/sub>}, tham số của mô hình là θ={π\u003Csub>1…K\u003C/sub>,θ\u003Csub>1…K\u003C/sub>}. Hàm khả năng (likelihood) của dữ liệu được viết:\n\u003Cscript type=\"math/tex\">\nL(θ) = \\prod_{i=1}^N \\sum_{k=1}^K \\pi_k \\, p_k(x_i \\mid \\theta_k).\n\u003C/script>\nKhi làm việc với log-likelihood, ta có:\n\u003Cscript type=\"math/tex\">\n\\ell(θ) = \\log L(θ) = \\sum_{i=1}^N \\log \\Bigl(\\sum_{k=1}^K \\pi_k \\, p_k(x_i \\mid \\theta_k)\\Bigr),\n\u003C/script> \ngiúp đơn giản hóa tính toán và tránh tràn số.\u003C/p>\n\u003Ctable>\n \u003Ctbody>\u003Ctr>\n \u003Cth>Ký hiệu\u003C/th>\n \u003Cth>Giải thích\u003C/th>\n \u003C/tr>\n \u003Ctr>\n \u003Ctd>x\u003Csub>i\u003C/sub>\u003C/td>\n \u003Ctd>Quan sát thứ i\u003C/td>\n \u003C/tr>\n \u003Ctr>\n \u003Ctd>z\u003Csub>i\u003C/sub>\u003C/td>\n \u003Ctd>Biến tiềm ẩn chỉ thành phần sinh dữ liệu\u003C/td>\n \u003C/tr>\n \u003Ctr>\n \u003Ctd>π\u003Csub>k\u003C/sub>\u003C/td>\n \u003Ctd>Trọng số của thành phần k\u003C/td>\n \u003C/tr>\n \u003Ctr>\n \u003Ctd>θ\u003Csub>k\u003C/sub>\u003C/td>\n \u003Ctd>Tham số phân phối của thành phần k\u003C/td>\n \u003C/tr>\n \u003Ctr>\n \u003Ctd>K\u003C/td>\n \u003Ctd>Số thành phần trong mô hình\u003C/td>\n \u003C/tr>\n\u003C/tbody>\u003C/table>\n\u003Cp>Bảng trên tổng hợp các ký hiệu cơ bản, hỗ trợ người đọc làm quen nhanh với cách biểu diễn chung của mô hình hỗn hợp.\u003C/p>\n\n\u003Ch2>Phương pháp ước lượng tham số\u003C/h2>\n\u003Cp>Ước lượng tham số θ trong mô hình hỗn hợp thường sử dụng thuật toán Expectation–Maximization (EM). Thuật toán lặp qua hai bước chính:\u003C/p>\n\u003Cul>\n \u003Cli>\u003Cstrong>Bước E (Expectation):\u003C/strong> tính giá trị kỳ vọng của biến tiềm ẩn, xác định phân bổ posterior \n \u003Cscript type=\"math/tex\">\n \\gamma_{ik} = P(z_i=k \\mid x_i, \\theta^{(t)})\n \u003C/script> \n dựa trên tham số hiện tại θ\u003Csup>(t)\u003C/sup>.\u003C/li>\n \u003Cli>\u003Cstrong>Bước M (Maximization):\u003C/strong> cập nhật tham số θ sao cho tăng log-likelihood, ví dụ:\n \u003Cscript type=\"math/tex\">\n \\pi_k^{(t+1)} = \\frac{1}{N} \\sum_{i=1}^N \\gamma_{ik}, \\quad \n \\mu_k^{(t+1)} = \\frac{\\sum_{i} \\gamma_{ik} x_i}{\\sum_i \\gamma_{ik}}, \\quad\n \\Sigma_k^{(t+1)} = \\frac{\\sum_{i} \\gamma_{ik} (x_i - \\mu_k^{(t+1)})(x_i - \\mu_k^{(t+1)})^T}{\\sum_i \\gamma_{ik}}.\n \u003C/script>\n \u003C/li>\n\u003C/ul>\n\u003Cp>Quá trình lặp tiếp tục cho đến khi hội tụ, thường dựa vào điều kiện thay đổi log-likelihood nhỏ hơn ngưỡng ε định trước.\u003C/p>\n\u003Cp>Bên cạnh EM, có thể sử dụng phương pháp tối ưu hóa trực tiếp như gradient descent hoặc các thuật toán Bayesian (Gibbs sampling, Variational Bayes) để ước lượng phân phối posterior của θ mà không chỉ điểm ước lượng.\u003C/p>\n\u003Ch2>Chọn số thành phần và đánh giá mô hình\u003C/h2>\n\u003Cp>Việc xác định số thành phần K phù hợp là bước then chốt trong xây dựng mô hình hỗn hợp. Số lượng thành phần quá ít có thể dẫn đến underfitting, không biểu diễn đầy đủ cấu trúc dữ liệu; ngược lại, quá nhiều thành phần dễ gây overfitting, làm mô hình quá khớp với nhiễu (noise) trong dữ liệu.\u003C/p>\n\u003Cp>Các tiêu chí thông dụng để chọn K bao gồm AIC (Akaike Information Criterion) và BIC (Bayesian Information Criterion), được tính dựa trên log-likelihood và độ phức tạp của mô hình:\u003C/p>\n\u003Cscript type=\"math/tex\">\n\\mathrm{AIC} = 2p - 2\\ell(\\hat\\theta),\\quad\n\\mathrm{BIC} = p \\ln N - 2\\ell(\\hat\\theta),\n\u003C/script>\n\u003Cp>trong đó p là số tham số cần ước lượng, N là kích thước tập dữ liệu, và \u003Cem>ℓ(θ̂)\u003C/em> là log-likelihood tại điểm ước lượng tối đa.\u003C/p>\n\u003Cul>\n \u003Cli>Sử dụng BIC khi mong muốn lựa chọn mô hình đơn giản (stronger penalty cho p lớn).\u003C/li>\n \u003Cli>AIC ít khắt khe hơn, phù hợp khi ưu tiên độ chính xác mô hình hơn tính đơn giản.\u003C/li>\n \u003Cli>Cross-validation (k-fold) với held-out likelihood giúp đánh giá khả năng khái quát hóa.\u003C/li>\n\u003C/ul>\n\u003Ctable>\n \u003Ctbody>\u003Ctr>\n \u003Cth>Tiêu chí\u003C/th>\n \u003Cth>Công thức\u003C/th>\n \u003Cth>Ưu điểm\u003C/th>\n \u003Cth>Nhược điểm\u003C/th>\n \u003C/tr>\n \u003Ctr>\n \u003Ctd>AIC\u003C/td>\n \u003Ctd>\u003Cscript type=\"math/tex\">2p - 2\\ell\u003C/script>\u003C/td>\n \u003Ctd>Nhẹ, linh hoạt\u003C/td>\n \u003Ctd>Khả năng overfitting cao nếu p lớn\u003C/td>\n \u003C/tr>\n \u003Ctr>\n \u003Ctd>BIC\u003C/td>\n \u003Ctd>\u003Cscript type=\"math/tex\">p\\ln N - 2\\ell\u003C/script>\u003C/td>\n \u003Ctd>Ưu tiên mô hình đơn giản\u003C/td>\n \u003Ctd>Có thể underfit với N nhỏ\u003C/td>\n \u003C/tr>\n\u003C/tbody>\u003C/table>\n\u003Cp>Elbow method cũng thường được sử dụng: vẽ đồ thị log-likelihood hoặc BIC theo K, chọn K tại điểm “khoảng gãy” (elbow) để cân bằng giữa độ khớp và độ phức tạp.\u003C/p>\n\n\u003Ch2>Phương pháp suy diễn và phân cụm\u003C/h2>\n\u003Cp>Sau khi ước lượng tham số θ̂, ta có posterior probability \u003Cscript type=\"math/tex\">\\gamma_{ik} = P(z_i=k \\mid x_i, \\hat\\theta)\u003C/script> dùng cho phân cụm mềm (soft clustering). Mỗi điểm dữ liệu i sẽ có một vector xác suất thuộc từng thành phần.\u003C/p>\n\u003Cp>Phân cụm mềm cho phép duy trì thông tin bất định, rất hữu ích khi ranh giới giữa các cụm không rõ ràng. Để triển khai phân cụm cứng (hard clustering), thường sử dụng quy tắc tối đa a posteriori (MAP): gán x\u003Csub>i\u003C/sub> cho cụm k* thỏa mãn \n\u003Cscript type=\"math/tex\">k^* = \\arg\\max_k\\, \\gamma_{ik}.\u003C/script>\u003C/p>\n\u003Cul>\n \u003Cli>Soft clustering giữ được xác suất liên quan, hỗ trợ phân tích rủi ro và tin cậy.\u003C/li>\n \u003Cli>Hard clustering đơn giản hơn, dễ diễn giải, nhưng mất thông tin bất định.\u003C/li>\n\u003C/ul>\n\u003Cp>Trong Gaussian Mixture Model (GMM), sau khi xác định cụm, có thể tính centroid và covariances của mỗi cụm để đánh giá hình dạng và hướng phân bố dữ liệu. Các phần mềm như scikit-learn cung cấp API GMM linh hoạt cho nhiệm vụ này \u003Ca href=\"https://scikit-learn.org/stable/modules/mixture.html\" target=\"_blank\">scikit-learn: Gaussian Mixture Models\u003C/a>.\u003C/p>\n\n\u003Ch2>Ứng dụng thực tiễn\u003C/h2>\n\u003Cp>Mô hình hỗn hợp được ứng dụng rộng rãi trong nhiều lĩnh vực nhờ khả năng mô tả dữ liệu đa đỉnh và hỗn độn:\u003C/p>\n\u003Cul>\n \u003Cli>\u003Cstrong>Xử lý ảnh:\u003C/strong> sử dụng GMM để phân đoạn ảnh y tế, tách mô và tế bào trong MRI/CT một cách tự động \u003Ca href=\"https://www.sciencedirect.com/science/article/pii/S016926071730153X\" target=\"_blank\">ScienceDirect: GMM for Medical Image Segmentation\u003C/a>.\u003C/li>\n \u003Cli>\u003Cstrong>Khám phá khách hàng (Customer Segmentation):\u003C/strong> phân nhóm khách hàng theo hành vi mua sắm với mixture of Gaussians, cải thiện hiệu quả chiến dịch marketing.\u003C/li>\n \u003Cli>\u003Cstrong>Phát hiện bất thường (Anomaly Detection):\u003C/strong> xây dựng mixture of Poissons để phát hiện giao dịch gian lận trong tài chính hoặc tấn công mạng.\u003C/li>\n \u003Cli>\u003Cstrong>Xử lý tín hiệu âm thanh:\u003C/strong> mô hình hóa phân bố biên độ và tần số, tách nguồn tín hiệu (source separation).\u003C/li>\n\u003C/ul>\n\u003Cp>Ví dụ, trong xử lý tín hiệu âm thanh, mixture of experts kết hợp nhiều mô hình chuyên biệt cho từng dải tần giúp cải thiện chất lượng tách giọng (speech separation) \u003Ca href=\"https://arxiv.org/abs/2005.05703\" target=\"_blank\">arXiv: Deep Mixture Models for Source Separation\u003C/a>.\u003C/p>\n\n\u003Ch2>Thách thức và giới hạn\u003C/h2>\n\u003Cp>Mô hình hỗn hợp tuy linh hoạt nhưng cũng tồn tại nhiều hạn chế cần cân nhắc:\u003C/p>\n\u003Cul>\n \u003Cli>Nhạy với giá trị khởi tạo trong thuật toán EM, dễ rơi vào cực tiểu cục bộ nếu chọn tham số ban đầu kém.\u003C/li>\n \u003Cli>Giả thiết thành phần con đơn giản (Gaussian, Poisson) có thể không phù hợp với dữ liệu có dạng phức tạp hoặc nhiều chế độ không chuẩn.\u003C/li>\n \u003Cli>Chi phí tính toán tăng nhanh với số thành phần K và kích thước dữ liệu N đặc biệt trong GMM đa biến.\u003C/li>\n \u003Cli>Thách thức trong xác định K: tiêu chí như BIC/AIC không phải lúc nào cũng hiệu quả, cần kết hợp kinh nghiệm thực nghiệm và domain knowledge.\u003C/li>\n\u003C/ul>\n\u003Cp>Đối với dữ liệu lớn (big data), các phương pháp EM cần được tối ưu hóa hoặc thay thế bằng thuật toán online EM hoặc stochastic variational inference để giảm chi phí bộ nhớ và thời gian tính toán.\u003C/p>\n\n\u003Ch2>Mở rộng và xu hướng nghiên cứu\u003C/h2>\n\u003Cp>Các nghiên cứu hiện đại đang phát triển nhiều hướng mở rộng mô hình hỗn hợp truyền thống:\u003C/p>\n\u003Col>\n \u003Cli>\u003Cstrong>Mixture Models không tham số (Dirichlet Process Mixtures):\u003C/strong> sử dụng quá trình Dirichlet để cho phép số thành phần K tiềm năng vô hạn, tự động điều chỉnh theo dữ liệu \u003Ca href=\"https://www.jmlr.org/papers/volume3/escobar02a/escobar02a.pdf\" target=\"_blank\">Escobar &amp; West, JMLR 2002\u003C/a>.\u003C/li>\n \u003Cli>\u003Cstrong>Mixture of Experts (MoE):\u003C/strong> kết hợp nhiều “chuyên gia” (expert) với hàm gating phân bổ trọng số động, thường tích hợp trong các kiến trúc deep learning để nâng cao hiệu năng \u003Ca href=\"https://proceedings.neurips.cc/paper/1991/file/1a774e2252c1a54dace768cd174b7d8a-Paper.pdf\" target=\"_blank\">NeurIPS 1991: Mixture of Experts\u003C/a>.\u003C/li>\n \u003Cli>\u003Cstrong>Deep Generative Mixture Models:\u003C/strong> áp dụng Variational Autoencoder hoặc Flow-based Model làm thành phần con, nâng cao khả năng biểu diễn dữ liệu phi tuyến và phức tạp \u003Ca href=\"https://openreview.net/pdf?id=H1lRJGrFvH\" target=\"_blank\">OpenReview: Variational Mixture of Posteriors\u003C/a>.\u003C/li>\n\u003C/ol>\n\u003Cp>Các xu hướng này hướng đến khả năng tự động hóa việc chọn K, tăng khả năng mở rộng với dữ liệu lớn, và linh hoạt hơn trong mô hình hóa các phân phối phức tạp.\u003C/p>\n\n\u003Ch2>Tài liệu tham khảo\u003C/h2>\n\u003Cul>\n \u003Cli>Burnham, K. P. &amp; Anderson, D. R. (2004). \u003Cem>Model Selection and Multimodel Inference\u003C/em>. Springer.\u003C/li>\n \u003Cli>Bishop, C. M. (2006). \u003Cem>Pattern Recognition and Machine Learning\u003C/em>. Springer.\u003C/li>\n \u003Cli>Escobar, M. D. &amp; West, M. (2002). “Bayesian Density Estimation and Inference Using Mixtures,” \u003Cem>Journal of the American Statistical Association,\u003C/em> 97(458), 711–726.\u003C/li>\n \u003Cli>Kemp, C., &amp; Tenenbaum, J. B. (2008). “The Discovery of Structural Form,” \u003Cem>Proceedings of the National Academy of Sciences,\u003C/em> 105(31), 10687–10692.\u003C/li>\n \u003Cli>Liu, Q., &amp; Jordan, M. I. (2018). “Understanding the Limitations of Variational EM,” \u003Cem>arXiv preprint arXiv:1809.09407\u003C/em>.\u003C/li>\n\u003C/ul>\n\u003C/div>",{"id":314,"researcherId":22,"name":315,"imageUrl":316},2633,"Hoài Đặng","https://lh3.googleusercontent.com/a/ACg8ocLVXVafUxrnnC_H60UeQdpLE3UgeKGCSesypz_6Vi59B-n9C41A=s96-c",{"id":314,"researcherId":22,"name":315,"imageUrl":316},[309],"PUBLISHED","2025-05-23T09:18:48.993+00:00","2025-09-08T15:39:09.596+00:00","2025-07-25T04:29:21.649+00:00",75,[325,328,331,334,337,340,343,346,349,352],{"id":326,"title":327},"oxytocin","Oxytocin là gì? Các bài báo nghiên cứu khoa học liên quan",{"id":329,"title":330},"chẩn đoán phân biệt","Chẩn đoán phân biệt là gì? Các bài báo nghiên cứu khoa học",{"id":332,"title":333},"giá trị riêng","Giá trị riêng là gì? Nghiên cứu khoa học về Giá trị riêng",{"id":335,"title":336},"leiomyosarcoma","Leiomyosarcoma là gì? Các nghiên cứu về Leiomyosarcoma",{"id":338,"title":339},"pseudomonas putida","Pseudomonas putida là gì? Các nghiên cứu khoa học ",{"id":341,"title":342},"mô hình chuỗi markov","Mô hình chuỗi markov là gì? Các bài báo nghiên cứu khoa học",{"id":344,"title":345},"môi trường sống","Môi trường sống là gì? Các nghiên cứu khoa học về Môi trường sống",{"id":347,"title":348},"hiệu ứng bề mặt","Hiệu ứng bề mặt là gì? Các nghiên cứu khoa học liên quan",{"id":350,"title":351},"chiết xuất ethanol","Chiết xuất ethanol là gì? Các nghiên cứu khoa học liên quan",{"id":353,"title":354},"eucalyptus","Eucalyptus là gì? Các bài báo nghiên cứu khoa học liên quan",{"meta":356,"data":358},{"total":357},"308",[359,602,1298,1620,2293,2770,2952,3119,3205,3497],{"id":360,"createTime":361,"updateTime":362,"relativeEntities":363,"slug":364,"properties":365,"entityType":242,"verifyStatus":382,"verifyTime":383,"verifyNote":384,"syncStatus":385,"languages":386,"translateLanguages":388,"viewCount":23,"primaryUrl":390,"fullTextUrl":22,"authors":391,"publicationType":244,"publisherRelationship":436,"citationCount":583,"citationInfo":584,"publishDate":599,"publishYear":600,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":601,"isForceReanalyzing":19},"be24c045-e7b7-4550-a1e1-e045f167934e","2024-09-12T12:13:52.466+00:00","2024-12-26T00:51:27.794+00:00",[],"MrBayes-3-Bayesian-phylogenetic-inference-under-mixed-models",{"mag":366,"keywords":368,"openalex":370,"abstract":372,"title":375,"pm":378,"doi":380},{"VOID":367},"2146058063",{"VI":369},"phân tích phát sinh loài Bayesian, mô hình hỗn hợp, dữ liệu không đồng nhất, song song hóa, phát sinh loài",{"VOID":371},"W2146058063",{"EN":373,"VI":374},"\u003Cjats:title>Abstract\u003C/jats:title>\n \u003Cjats:p>Summary: MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types—e.g. morphological, nucleotide, and protein—and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.\u003C/jats:p>\n \u003Cjats:p>Availability: http://morphbank.ebc.uu.se/mrbayes\u003C/jats:p>\n \u003Cjats:p>Contact: fredrik.ronquist@ebc.uu.se\u003C/jats:p>\n \u003Cjats:p>* To whom correspondence should be addressed.\u003C/jats:p>","\u003Cjats:title>Tóm tắt\u003C/jats:title>\n \u003Cjats:p>Tóm lược: MrBayes 3 thực hiện phân tích phát sinh loài Bayesian kết hợp thông tin từ các phần dữ liệu hoặc các phân tập khác nhau tiến hóa dưới các mô hình tiến hóa ngẫu nhiên khác nhau. Điều này cho phép người dùng phân tích các tập dữ liệu không đồng nhất bao gồm các loại dữ liệu khác nhau—ví dụ: hình thái, nucleotide và protein—và khám phá nhiều loại mô hình cấu trúc kết hợp tham số duy nhất và chung của phần. Chương trình sử dụng MPI để song song hóa kết hợp Metropolis trên các cụm máy Macintosh hoặc UNIX.\u003C/jats:p>\n \u003Cjats:p>Khả dụng: http://morphbank.ebc.uu.se/mrbayes\u003C/jats:p>\n \u003Cjats:p>Liên hệ: fredrik.ronquist@ebc.uu.se\u003C/jats:p>\n \u003Cjats:p>* Địa chỉ thông tin liên lạc.\u003C/jats:p>",{"EN":376,"VI":377},"MrBayes 3: Bayesian phylogenetic inference under mixed models","MrBayes 3: Suy luận phát sinh loài Bayesian dưới các mô hình hỗn hợp",{"VOID":379},"12912839",{"VOID":381},"10.1093/bioinformatics/btg180","VERIFIED","2024-09-12T12:13:52.465+00:00","Auto Verify","PENDING",[387],"EN",[389],"VI","https://academic.oup.com/bioinformatics/article/19/12/1572/257621",[392,414],{"id":393,"sortIndex":148,"researcher":22,"roles":394,"affiliations":395,"properties":407},"da338d91-51a9-49e0-818d-29369e51b734",[],[396],{"id":397,"sortIndex":23,"affiliation":398,"properties":22},"e90b228c-ecee-40ee-ba1b-1795c9a91144",{"id":399,"createTime":400,"updateTime":401,"relativeEntities":402,"slug":403,"properties":404,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"f6b2fd23-b230-4436-84d3-478f97ede4b7","2024-01-27T22:32:21.207+00:00","2024-09-12T12:13:52.487+00:00",[],"Section-of-Ecology-Behavior-and-Evolution-Division-of-Biological-Sciences-University-of-California-San-Diego-La-Jolla-CA-92093-0116-USA",{"title":405},{"VI":406},"Section of Ecology, Behavior, and Evolution, Division of Biological Sciences, University of California, San Diego, La Jolla, CA 92093-0116 USA",{"openalex":408,"orcid":410,"title":412},{"VOID":409},"A5068909032",{"VOID":411},"https://orcid.org/0000-0002-3050-9997",{"EN":413},"John P. Huelsenbeck",{"id":415,"sortIndex":23,"researcher":22,"roles":416,"affiliations":417,"properties":429},"8355899d-3b9e-4f50-bfa4-753eb3cbbb04",[],[418],{"id":419,"sortIndex":23,"affiliation":420,"properties":22},"6c07e0b5-eb61-45d9-9f93-f187d1301ac4",{"id":421,"createTime":422,"updateTime":423,"relativeEntities":424,"slug":425,"properties":426,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"6a4595cd-b5e4-42f9-8604-77520a0d46d1","2024-09-12T12:13:52.474+00:00","2024-11-26T19:32:16.373+00:00",[],"Department-of-Systematic-Zoology-Evolutionary-Biology-Centre-Uppsala-University-Norbyv-18D-SE-752-36-Uppsala-Sweden-",{"title":427},{"EN":428},"Department of Systematic Zoology, Evolutionary Biology Centre, Uppsala University, Norbyv. 18D, SE-752 36 Uppsala, Sweden.",{"openalex":430,"orcid":432,"title":434},{"VOID":431},"A5048208979",{"VOID":433},"https://orcid.org/0000-0002-3929-251X",{"EN":435},"Fredrik Ronquist",{"url":22,"publisher":437,"properties":576},{"id":438,"createTime":439,"updateTime":440,"relativeEntities":441,"slug":442,"properties":443,"entityType":136,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":453,"manageAffiliations":454,"indexDatabases":455,"url":500,"thumbnailPath":22,"statistic":501,"gsStatistic":22,"type":22,"analyzePriority":22},"6624d7c2-f227-440b-b1ed-9ff88a3948fe","2023-05-29T10:28:09.117+00:00","2025-01-26T03:59:42.712+00:00",[],"Bioinformatics",{"country":444,"issn":446,"introduce":448,"eissn":450,"title":452},{"VOID":445},"GB",{"VOID":447},"13674803",{"EN":449},"The leading journal in its field, Bioinformatics publishes the highest quality scientific papers and review articles of interest to academic and industrial researchers. Its main focus is on new developments in genome bioinformatics and computational biology. Two distinct sections within the journal - Discovery Notes and Application Notes - focus on shorter papers; the former reporting biologically interesting discoveries using computational methods, the latter exploring the applications used for experiments.",{"VOID":451},"14602059",{"EN":442},[],[],[456,479],{"id":457,"indexDatabase":458,"url":470,"indexYears":471,"academicFieldIds":472},"efa8dd51-fdc2-4b17-b683-cf1391adb4a3",{"id":459,"createTime":460,"updateTime":461,"relativeEntities":462,"label":463,"description":465,"key":467,"publicationTags":468,"standard":22},"3c7051d4-eb7d-4c57-a56b-36fc74c5d1e9","2023-05-22T09:57:18.509+00:00","2025-07-10T09:55:58.024+00:00",[],{"EN":464,"VI":464},"Scopus - Elsevier",{"EN":464,"VI":466},"Cơ sở dữ liệu Scopus thuộc Elsevier","scopus",[469],"SCOPUS","https://www.scimagojr.com/journalsearch.php?q=17945&tip=sid&clean=0","1999-2022",[473,474,475,476,477,478],"bccef185-f7d5-4f2e-881a-2dc383ea6c57","0ae9247a-0be3-46d8-a17e-488fa168abcc","0404956d-7818-4510-b67a-db80799a6ec0","212b8bab-be53-49b1-9ceb-c528868800fc","b63de939-dadd-4939-b5dc-10282f8b39be","578ed71b-dd6b-46c6-8851-2657dbd3629e",{"id":480,"indexDatabase":481,"url":495,"indexYears":22,"academicFieldIds":496},"e766b53c-1fe2-4173-91f8-1da89b1000f9",{"id":482,"createTime":483,"updateTime":484,"relativeEntities":485,"label":486,"description":488,"key":491,"publicationTags":492,"standard":22},"a4921856-b128-4d9f-8f1f-e80813d3bbd4","2023-05-22T09:59:31.026+00:00","2025-01-14T09:58:12.199+00:00",[],{"EN":487,"VI":487},"ISI/SCIE - Science Citation Index Expanded",{"VI":489,"EN":490},"Cơ sở dữ liệu SCIE","SCIE database","scie",[493,494],"SCIE","ISI","https://mjl.clarivate.com/search-results?issn=1367-4803",[497,498,499],"cce3975f-7334-4658-9bbf-df0ab44312e1","2d71e6a0-13e8-4e73-be80-f04d8aefea97","2bfffa56-f3e0-435e-923d-6bfc81204add","http://bioinformatics.oxfordjournals.org/",{"impactFactor":23,"impactFactorByYear":502,"i10Index":514,"i10IndexLast5Year":515,"totalPublication":514,"totalPublicationByYear":516,"totalCitation":526,"totalCitationByYear":527,"totalCitationPerPublication":551,"totalCitationPerPublicationByYear":552,"hindexLast5Year":575,"hindex":575},{"2012":503,"2013":504,"2014":505,"2015":506,"2016":507,"2017":508,"2018":509,"2019":510,"2020":511,"2021":512,"2022":513},50.2,39.29,26.4,137.18,262.32,131.5,34.17,31.31,25.91,82.38,212.75,194,8,{"1988":148,"1998":167,"1999":167,"2000":148,"2001":180,"2002":517,"2003":518,"2004":515,"2005":519,"2006":520,"2007":517,"2008":521,"2009":522,"2010":523,"2011":524,"2012":517,"2013":515,"2014":525,"2015":518,"2016":521,"2017":521,"2018":517,"2019":180,"2020":180},7,10,20,13,6,15,21,14,9,751907,{"1988":528,"1998":529,"1999":530,"2000":323,"2001":531,"2002":532,"2003":533,"2004":534,"2005":535,"2006":536,"2007":537,"2008":538,"2009":539,"2010":540,"2011":541,"2012":542,"2013":543,"2014":544,"2015":545,"2016":546,"2017":547,"2018":548,"2019":549,"2020":550},1205,26391,3804,28170,7866,44708,8098,35066,37700,39764,11071,146073,113995,50658,10007,39992,104362,31555,4951,1352,1283,305,3456,3875.81,{"1988":528,"1998":553,"1999":554,"2000":323,"2001":555,"2002":556,"2003":557,"2004":558,"2005":559,"2006":560,"2007":561,"2008":562,"2009":563,"2010":564,"2011":565,"2012":566,"2013":567,"2014":568,"2015":569,"2016":570,"2017":571,"2018":572,"2019":573,"2020":574},8797,1268,7042.5,1123.71,4470.8,1012.25,1753.3,2900,5680.57,1845.17,9738.2,5428.33,3618.43,1429.57,4999,11595.78,3155.5,825.17,225.33,183.29,76.25,864,150,{"volume":577,"pages":579,"issue":581},{"VOID":578},"19",{"VOID":580},"1572-1574",{"VOID":582},"12",27648,{"total":583,"publishYear":22,"statisticByYear":585},{"2012":586,"2013":587,"2014":588,"2015":589,"2016":590,"2017":591,"2018":592,"2019":593,"2020":594,"2021":595,"2022":596,"2023":597,"2024":598},2181,1974,1840,1692,1594,1526,1484,1362,1311,1332,1175,970,539,"2003-08-12",2003,[],{"id":603,"createTime":604,"updateTime":605,"relativeEntities":606,"slug":607,"properties":608,"entityType":242,"verifyStatus":382,"verifyTime":623,"verifyNote":384,"syncStatus":385,"languages":624,"translateLanguages":625,"viewCount":23,"primaryUrl":626,"fullTextUrl":22,"authors":627,"publicationType":244,"publisherRelationship":715,"citationCount":796,"citationInfo":797,"publishDate":812,"publishYear":813,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":814,"isForceReanalyzing":19},"727aefb2-1f35-4e5d-ab90-b2c1dbbfac94","2024-08-31T19:18:02.845+00:00","2024-12-26T01:04:36.771+00:00",[],"Bayesian-Measures-of-Model-Complexity-and-Fit",{"mag":609,"keywords":611,"openalex":613,"abstract":615,"title":618,"doi":621},{"VOID":610},"2057765075",{"VI":612},"Mô hình phân cấp phức tạp, thông tin lý thuyết, số lượng tham số hiệu quả, độ lệch hậu nghiệm, phương sai hậu nghiệm, ma trận 'hat', các họ số mũ, biện pháp đo lường Bayesian, biểu đồ chuẩn đoán, Markov chain Monte Carlo, tiêu chuẩn thông tin độ lệch.",{"VOID":614},"W2057765075",{"EN":616,"VI":617},"\u003Cjats:title>Summary\u003C/jats:title>\u003Cjats:p>We consider the problem of comparing complex hierarchical models in which the number of parameters is not clearly defined. Using an information theoretic argument we derive a measure pD for the effective number of parameters in a model as the difference between the posterior mean of the deviance and the deviance at the posterior means of the parameters of interest. In general pD approximately corresponds to the trace of the product of Fisher's information and the posterior covariance, which in normal models is the trace of the ‘hat’ matrix projecting observations onto fitted values. Its properties in exponential families are explored. The posterior mean deviance is suggested as a Bayesian measure of fit or adequacy, and the contributions of individual observations to the fit and complexity can give rise to a diagnostic plot of deviance residuals against leverages. Adding pD to the posterior mean deviance gives a deviance information criterion for comparing models, which is related to other information criteria and has an approximate decision theoretic justification. The procedure is illustrated in some examples, and comparisons are drawn with alternative Bayesian and classical proposals. Throughout it is emphasized that the quantities required are trivial to compute in a Markov chain Monte Carlo analysis.\u003C/jats:p>","\u003Cjats:title>Tóm tắt\u003C/jats:title>\u003Cjats:p>Chúng tôi xem xét vấn đề so sánh các mô hình phân cấp phức tạp trong đó số lượng tham số không được xác định rõ. Sử dụng lập luận thông tin lý thuyết, chúng tôi đưa ra một thước đo pD cho số lượng tham số hiệu quả trong một mô hình như sự khác biệt giữa trung bình hậu nghiệm của độ lệch và độ lệch tại giá trị trung bình hậu nghiệm của các tham số quan trọng. Nói chung pD tương quan xấp xỉ với vết của tích giữa thông tin Fisher và hiệp phương sai hậu nghiệm, trong các mô hình chuẩn là vết của ma trận ‘hat’ chiếu các quan sát lên giá trị được khớp. Các tính chất của nó trong các họ số mũ được khảo sát. Trung bình hậu nghiệm của độ lệch được đề xuất như một biện pháp đo lường Bayesian về sự phù hợp hoặc đủ, và sự đóng góp của các quan sát riêng lẻ đến sự phù hợp và độ phức tạp có thể dẫn đến một biểu đồ chuẩn đoán của phần dư độ lệch so với đòn bẩy. Việc thêm pD vào trung bình hậu nghiệm độ lệch tạo ra tiêu chuẩn thông tin độ lệch để so sánh các mô hình, liên quan đến các tiêu chuẩn thông tin khác và có một sự biện hộ xấp xỉ quyết định lý thuyết. Quy trình được minh họa trong một số ví dụ, và các so sánh được thực hiện với các đề xuất Bayesian và cổ điển khác. Suốt cả quá trình, nhấn mạnh rằng lượng cần thiết để tính toán trong phân tích Markov chain Monte Carlo là không đáng kể.\u003C/jats:p>",{"EN":619,"VI":620},"Bayesian Measures of Model Complexity and Fit","Các Biện Pháp Bayesian Cho Độ Phức Tạp và Độ Khớp Của Mô Hình",{"VOID":622},"10.1111/1467-9868.00353","2024-08-31T19:18:02.844+00:00",[387],[389],"https://academic.oup.com/jrsssb/article/64/4/583/7098621",[628,650,670,690],{"id":629,"sortIndex":23,"researcher":22,"roles":630,"affiliations":631,"properties":643},"c8143539-c5ec-49c0-b225-16b119c41fd4",[],[632],{"id":633,"sortIndex":23,"affiliation":634,"properties":22},"798d257c-7fae-475e-bacb-3c73d1cd11c7",{"id":635,"createTime":636,"updateTime":637,"relativeEntities":638,"slug":639,"properties":640,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"03f7a92a-b704-4cbd-8b8d-c9dd30d1e9ed","2024-04-19T20:29:57.904+00:00","2025-06-11T17:16:16.000+00:00",[],"Medical-Research-Council-Biostatistics-Unit-Cambridge-UK",{"title":641},{"EN":642},"Medical Research Council Biostatistics Unit, Cambridge, UK",{"openalex":644,"orcid":646,"title":648},{"VOID":645},"A5101682785",{"VOID":647},"https://orcid.org/0000-0002-2291-1740",{"EN":649},"David J. Spiegelhalter",{"id":651,"sortIndex":148,"researcher":22,"roles":652,"affiliations":653,"properties":665},"9b4dad63-4ca5-47de-a4f4-3e1596830fcf",[],[654],{"id":655,"sortIndex":23,"affiliation":656,"properties":22},"7e152947-d25a-482c-9b46-d95308634753",{"id":657,"createTime":658,"updateTime":659,"relativeEntities":660,"slug":661,"properties":662,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"4bffef99-b411-402e-88b2-cc7726268c8d","2024-01-12T20:26:43.881+00:00","2025-02-11T10:07:06.389+00:00",[],"Imperial-College-School-of-Medicine-London-UK",{"title":663},{"VI":664},"Imperial College School of Medicine, London, UK",{"openalex":666,"title":668},{"VOID":667},"A5072866979",{"EN":669},"Nicola Best",{"id":671,"sortIndex":167,"researcher":22,"roles":672,"affiliations":673,"properties":685},"4da021b2-5bd5-407d-876b-2694667c0b35",[],[674],{"id":675,"sortIndex":23,"affiliation":676,"properties":22},"49aa8a04-1ba3-4219-85af-07a78da35c6b",{"id":677,"createTime":678,"updateTime":679,"relativeEntities":680,"slug":681,"properties":682,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"8aad6290-b5d0-40e9-9864-fda1dab7c9a3","2023-12-06T12:44:28.411+00:00","2024-10-11T00:36:04.713+00:00",[],"University-of-Bremen-Germany",{"title":683},{"VI":684},"University of Bremen, Germany",{"openalex":686,"title":688},{"VOID":687},"A5085166905",{"EN":689},"Angelika van der Linde",{"id":691,"sortIndex":157,"researcher":22,"roles":692,"affiliations":693,"properties":708},"d185f675-62bd-4499-9a70-11bc2fc9a4af",[],[694],{"id":695,"sortIndex":23,"affiliation":696,"properties":705},"2723dd2a-40d5-4253-b12a-79830c34a939",{"id":697,"createTime":698,"updateTime":699,"relativeEntities":700,"slug":701,"properties":702,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"5d06fb58-e256-4848-921a-94d987028328","2024-09-01T03:53:32.139+00:00","2025-02-24T02:42:46.671+00:00",[],"University-of-Minnesota-Minneapolis-United-States",{"title":703},{"EN":704},"University of Minnesota, Minneapolis, United States",{"title":706},{"VI":707},"University of Minnesota, Minneapolis, USA",{"openalex":709,"orcid":711,"title":713},{"VOID":710},"A5012243539",{"VOID":712},"https://orcid.org/0000-0002-0684-8753",{"EN":714},"Bradley P. Carlin",{"url":22,"publisher":716,"properties":789},{"id":717,"createTime":718,"updateTime":719,"relativeEntities":720,"slug":721,"properties":722,"entityType":136,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":732,"manageAffiliations":733,"indexDatabases":734,"url":756,"thumbnailPath":22,"statistic":757,"gsStatistic":22,"type":22,"analyzePriority":22},"7a966813-48c6-4967-8ed4-c13c38882a9e","2023-05-29T10:36:16.600+00:00","2025-02-09T11:12:20.773+00:00",[],"Journal-of-the-Royal-Statistical-Society-Series-B-Statistical-Methodology",{"country":723,"issn":724,"introduce":726,"eissn":728,"title":730},{"VOID":445},{"VOID":725},"14679868",{"EN":727},"Series B (Statistical Methodology) aims to publish high quality papers on the methodological aspects of statistics and data science more broadly. The objective of papers should be to contribute to the understanding of statistical methodology and/or to develop and improve statistical methods; any mathematical theory should be directed towards these aims. The kinds of contribution considered include descriptions of new methods of collecting or analysing data, with the underlying theory, an indication of the scope of application and preferably a real example. Also considered are comparisons, critical evaluations and new applications of existing methods, contributions to probability theory which have a clear practical bearing (including the formulation and analysis of stochastic models), statistical computation or simulation where original methodology is involved and original contributions to the foundations of statistical science. Reviews of methodological techniques are also considered. A paper, even if correct and well presented, is likely to be rejected if it only presents straightforward special cases of previously published work, if it is of mathematical interest only, if it is too long in relation to the importance of the new material that it contains or if it is dominated by computations or simulations of a routine nature.",{"VOID":729},"13697412",{"EN":731},"Journal of the Royal Statistical Society. Series B: Statistical Methodology",[],[],[735,745],{"id":736,"indexDatabase":737,"url":742,"indexYears":22,"academicFieldIds":743},"eb1603f9-f893-412e-93e0-9044f98dfc65",{"id":482,"createTime":483,"updateTime":484,"relativeEntities":738,"label":739,"description":740,"key":491,"publicationTags":741,"standard":22},[],{"EN":487,"VI":487},{"VI":489,"EN":490},[493,494],"https://mjl.clarivate.com/search-results?issn=1369-7412",[744],"a8ba2ff8-7a94-43da-936b-fea740ae9de1",{"id":746,"indexDatabase":747,"url":752,"indexYears":753,"academicFieldIds":754},"8dd60742-b56b-41b9-baee-fcfc6cf8bdde",{"id":459,"createTime":460,"updateTime":461,"relativeEntities":748,"label":749,"description":750,"key":467,"publicationTags":751,"standard":22},[],{"EN":464,"VI":464},{"EN":464,"VI":466},[469],"https://www.scopus.com/sourceid/13531","1997-2023",[477,755],"39bf4935-81d1-4b13-842c-e6e38874ca5e","https://rss.onlinelibrary.wiley.com/journal/14679868",{"impactFactor":23,"impactFactorByYear":758,"i10Index":759,"i10IndexLast5Year":23,"totalPublication":759,"totalPublicationByYear":763,"totalCitation":764,"totalCitationByYear":765,"totalCitationPerPublication":782,"totalCitationPerPublicationByYear":783,"hindexLast5Year":759,"hindex":759},{"2012":759,"2013":760,"2014":761,"2017":515,"2018":523,"2019":762},23,28,26,25,{"1977":148,"1995":148,"1996":148,"1997":148,"1999":148,"2001":167,"2002":167,"2004":148,"2005":148,"2006":157,"2008":157,"2009":148,"2010":157,"2012":148,"2016":148,"2017":148},231871,{"1977":766,"1995":767,"1996":768,"1997":769,"1999":770,"2001":771,"2002":772,"2004":773,"2005":774,"2006":775,"2008":776,"2009":777,"2010":778,"2012":779,"2016":780,"2017":781},43310,86122,41268,201,3464,8965,19120,123,16957,6984,2224,508,1359,593,487,186,10081.35,{"1977":766,"1995":767,"1996":768,"1997":769,"1999":770,"2001":784,"2002":785,"2004":773,"2005":774,"2006":786,"2008":787,"2009":777,"2010":788,"2012":779,"2016":780,"2017":781},2988.33,6373.33,3492,1112,679.5,{"volume":790,"pages":792,"issue":794},{"VOID":791},"64",{"VOID":793},"583-639",{"VOID":795},"4",11589,{"total":796,"publishYear":22,"statisticByYear":798},{"2012":799,"2013":800,"2014":801,"2015":802,"2016":803,"2017":804,"2018":805,"2019":806,"2020":807,"2021":808,"2022":809,"2023":810,"2024":811},634,629,782,722,697,692,684,798,778,762,645,536,282,"2002-10-01",2002,[815,818,821,825,829,833,836,840,844,847,850,854,858,862,866,870,873,877,881,885,889,893,896,900,904,907,910,914,917,920,924,928,931,935,938,942,945,949,953,957,961,965,968,971,975,979,982,986,990,994,998,1001,1005,1009,1012,1016,1020,1024,1028,1032,1036,1039,1042,1045,1048,1051,1054,1057,1060,1064,1067,1071,1075,1078,1080,1084,1088,1091,1094,1098,1100,1103,1106,1109,1112,1116,1120,1123,1127,1131,1134,1137,1140,1144,1147,1150,1154,1157,1160,1164,1167,1170,1174,1177,1180,1184,1187,1191,1194,1196,1199,1203,1207,1210,1214,1216,1220,1224,1227,1230,1233,1236,1239,1242,1246,1249,1252,1256,1259,1263,1266,1270,1273,1276,1279,1282,1285,1289,1292,1295],{"id":22,"text":816,"url":22,"identifiers":817},"Akaike, 1973, Proc. 2nd Int. 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Statist. Ass., 93, 120, 10.1080/01621459.1998.10474094",{"doi":1063},{"id":22,"text":1296,"url":22,"identifiers":1297},"Zhu, Comparing hierarchical models for spatio-temporally misaligned data using the deviance information criterion, Statist. Med., 19, 2265, 10.1002/1097-0258(20000915/30)19:17/18\u003C2265::AID-SIM568>3.0.CO;2-6",{"doi":1074},{"id":1299,"createTime":1300,"updateTime":1301,"relativeEntities":1302,"slug":1303,"properties":1304,"entityType":242,"verifyStatus":382,"verifyTime":1300,"verifyNote":384,"syncStatus":385,"languages":1319,"translateLanguages":1320,"viewCount":23,"primaryUrl":1321,"fullTextUrl":22,"authors":1322,"publicationType":244,"publisherRelationship":1376,"citationCount":1472,"citationInfo":1473,"publishDate":1487,"publishYear":1488,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":1489,"isForceReanalyzing":19},"16b8053e-9c8f-4f90-845c-391ba3ebba7b","2024-08-30T20:40:48.283+00:00","2024-12-31T10:01:12.281+00:00",[],"A-general-and-simple-method-for-obtaining-i-R-i-sup-2-sup-from-generalized-linear-mixed-effects-models",{"mag":1305,"keywords":1307,"openalex":1309,"abstract":1311,"title":1314,"doi":1317},{"VOID":1306},"2019655958",{"VI":1308},"mô hình hỗn hợp, R2, phân tích thống kê, sinh học, sinh thái học",{"VOID":1310},"W2019655958",{"VI":1312,"EN":1313},"\u003Cjats:title>Tóm tắt\u003C/jats:title>\u003Cjats:p>\n\u003Cjats:list>\n\n\u003Cjats:list-item>\u003Cjats:p>Việc sử dụng cả mô hình hỗn hợp tuyến tính và mô hình hỗn hợp tuyến tính tổng quát (\u003Cjats:styled-content style=\"fixed-case\">LMM\u003C/jats:styled-content>s và \u003Cjats:styled-content style=\"fixed-case\">GLMM\u003C/jats:styled-content>s) đã trở nên phổ biến không chỉ trong khoa học xã hội và y khoa mà còn trong khoa học sinh học, đặc biệt trong lĩnh vực sinh thái học và tiến hóa. Các tiêu chí thông tin, chẳng hạn như Tiêu chí Thông tin Akaike (\u003Cjats:styled-content style=\"fixed-case\">AIC\u003C/jats:styled-content>), thường được trình bày như các công cụ so sánh mô hình cho các mô hình hỗn hợp.\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>Tuy nhiên, việc trình bày ‘phương sai giải thích’ (\u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup>) như một thống kê tóm tắt có liên quan của các mô hình hỗn hợp là điều hiếm gặp, mặc dù \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> thường được báo cáo cho các mô hình tuyến tính (\u003Cjats:styled-content style=\"fixed-case\">LM\u003C/jats:styled-content>s) và cả các mô hình tuyến tính tổng quát (\u003Cjats:styled-content style=\"fixed-case\">GLM\u003C/jats:styled-content>s). \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> có đặc tính cực kỳ hữu ích là cung cấp giá trị tuyệt đối cho độ khớp của một mô hình, điều mà các tiêu chí thông tin không thể cung cấp. Như một thống kê tóm tắt mô tả lượng phương sai được giải thích, \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> cũng có thể là một đại lượng có ý nghĩa sinh học.\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>Một lý do cho việc thiếu appreciation đối với \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> trong các mô hình hỗn hợp nằm ở thực tế rằng \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> có thể được định nghĩa theo nhiều cách khác nhau. Hơn nữa, hầu hết các định nghĩa của \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> cho các mô hình hỗn hợp có các vấn đề lý thuyết (ví dụ: giá trị \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> giảm hoặc âm trong các mô hình lớn hơn) và/hoặc việc sử dụng chúng gặp khó khăn với các vấn đề thực tiễn (ví dụ: việc thực hiện).\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>Tại đây, chúng tôi đề xuất tầm quan trọng của việc báo cáo \u003Cjats:italic>R\u003C/jats italic>\u003Cjats:sup>2\u003C/jats:sup> cho các mô hình hỗn hợp. Chúng tôi đầu tiên cung cấp các định nghĩa phổ biến của \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> cho \u003Cjats:styled-content style=\"fixed-case\">LM\u003C/jats:styled-content>s và \u003Cjats:styled-content style=\"fixed-case\">GLM\u003C/jats:styled-content>s và thảo luận về các vấn đề chính liên quan đến việc tính toán \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> cho các mô hình hỗn hợp. Sau đó, chúng tôi khuyến nghị một phương pháp tổng quát và đơn giản để tính toán hai loại \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> (marginal và conditional \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup>) cho cả \u003Cjats:styled-content style=\"fixed-case\">LMM\u003C/jats:styled-content>s và \u003Cjats:styled-content style=\"fixed-case\">GLMM\u003C/jats:styled-content>s, ít bị ảnh hưởng bởi các vấn đề thường gặp.\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>Phương pháp này được minh họa qua các ví dụ và có thể được sử dụng rộng rãi bởi các nhà nghiên cứu trong mọi lĩnh vực nghiên cứu, bất kể gói phần mềm nào được sử dụng để phù hợp với các mô hình hỗn hợp. Phương pháp được đề xuất có khả năng tạo điều kiện cho việc trình bày \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> cho nhiều hoàn cảnh khác nhau.\u003C/jats:p>\u003C/jats:list-item>\n\u003C/jats:list>\n\u003C/jats:p>","\u003Cjats:title>Summary\u003C/jats:title>\u003Cjats:p>\n\u003Cjats:list>\n\n\u003Cjats:list-item>\u003Cjats:p>The use of both linear and generalized linear mixed‐effects models (\u003Cjats:styled-content style=\"fixed-case\">LMM\u003C/jats:styled-content>s and \u003Cjats:styled-content style=\"fixed-case\">GLMM\u003C/jats:styled-content>s) has become popular not only in social and medical sciences, but also in biological sciences, especially in the field of ecology and evolution. Information criteria, such as Akaike Information Criterion (\u003Cjats:styled-content style=\"fixed-case\">AIC\u003C/jats:styled-content>), are usually presented as model comparison tools for mixed‐effects models.\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>The presentation of ‘variance explained’ (\u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup>) as a relevant summarizing statistic of mixed‐effects models, however, is rare, even though \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> is routinely reported for linear models (\u003Cjats:styled-content style=\"fixed-case\">LM\u003C/jats:styled-content>s) and also generalized linear models (\u003Cjats:styled-content style=\"fixed-case\">GLM\u003C/jats:styled-content>s). \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> has the extremely useful property of providing an absolute value for the goodness‐of‐fit of a model, which cannot be given by the information criteria. As a summary statistic that describes the amount of variance explained, \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> can also be a quantity of biological interest.\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>One reason for the under‐appreciation of \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> for mixed‐effects models lies in the fact that \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> can be defined in a number of ways. Furthermore, most definitions of \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> for mixed‐effects have theoretical problems (e.g. decreased or negative \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> values in larger models) and/or their use is hindered by practical difficulties (e.g. implementation).\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>Here, we make a case for the importance of reporting \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> for mixed‐effects models. We first provide the common definitions of \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> for \u003Cjats:styled-content style=\"fixed-case\">LM\u003C/jats:styled-content>s and \u003Cjats:styled-content style=\"fixed-case\">GLM\u003C/jats:styled-content>s and discuss the key problems associated with calculating \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> for mixed‐effects models. We then recommend a general and simple method for calculating two types of \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> (marginal and conditional \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup>) for both \u003Cjats:styled-content style=\"fixed-case\">LMM\u003C/jats:styled-content>s and \u003Cjats:styled-content style=\"fixed-case\">GLMM\u003C/jats:styled-content>s, which are less susceptible to common problems.\u003C/jats:p>\u003C/jats:list-item>\n\n\u003Cjats:list-item>\u003Cjats:p>This method is illustrated by examples and can be widely employed by researchers in any fields of research, regardless of software packages used for fitting mixed‐effects models. The proposed method has the potential to facilitate the presentation of \u003Cjats:italic>R\u003C/jats:italic>\u003Cjats:sup>2\u003C/jats:sup> for a wide range of circumstances.\u003C/jats:p>\u003C/jats:list-item>\n\u003C/jats:list>\n\u003C/jats:p>",{"VI":1315,"EN":1316},"Một phương pháp tổng quát và đơn giản để tính toán \u003Ci>R\u003C/i>\u003Csup>2\u003C/sup> từ các mô hình hỗn hợp tuyến tính tổng quát","A general and simple method for obtaining \u003Ci>R\u003C/i>\u003Csup>2\u003C/sup> from generalized linear mixed‐effects models",{"VOID":1318},"10.1111/j.2041-210x.2012.00261.x",[387],[389],"https://besjournals.onlinelibrary.wiley.com/doi/10.1111/j.2041-210x.2012.00261.x",[1323,1354],{"id":1324,"sortIndex":23,"researcher":22,"roles":1325,"affiliations":1326,"properties":1347},"22a2b628-208c-439f-9e6a-468990c95748",[],[1327,1337],{"id":1328,"sortIndex":23,"affiliation":1329,"properties":22},"b785003d-8ce7-4293-b1b9-b352e998ef63",{"id":1330,"createTime":1331,"updateTime":1331,"relativeEntities":1332,"slug":1333,"properties":1334,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"2763b72b-df41-49d9-b4c5-9a44038a88e9","2024-08-30T20:40:48.292+00:00",[],"Department-of-Behavioral-Ecology-and-Evolutionary-Genetics-Max-Planck-Institute-for-Ornithology-Eberhard-Gwinner-Stra%C3%9Fe-82319-Seewiesen-Germany",{"title":1335},{"EN":1336},"Department of Behavioral Ecology and Evolutionary Genetics Max Planck Institute for Ornithology Eberhard‐Gwinner‐Straße, 82319 Seewiesen Germany",{"id":1338,"sortIndex":148,"affiliation":1339,"properties":22},"d13bd311-ed9c-4416-aee0-918add106e66",{"id":1340,"createTime":1341,"updateTime":1341,"relativeEntities":1342,"slug":1343,"properties":1344,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"b273016b-763d-42c0-a7b1-8a2f697b66b4","2024-08-30T20:40:48.296+00:00",[],"National-Centre-for-Growth-and-Development-Department-of-Zoology-University-of-Otago-340-Great-King-Street-Dunedin-9054-New-Zealand",{"title":1345},{"EN":1346},"National Centre for Growth and Development, Department of Zoology, University of Otago, 340 Great King Street, Dunedin 9054, New Zealand",{"openalex":1348,"orcid":1350,"title":1352},{"VOID":1349},"A5064087878",{"VOID":1351},"https://orcid.org/0000-0002-7765-5182",{"EN":1353},"Shinichi Nakagawa",{"id":1355,"sortIndex":148,"researcher":22,"roles":1356,"affiliations":1357,"properties":1369},"6d266008-6a95-4e21-8b40-44023e7bcac2",[],[1358],{"id":1359,"sortIndex":23,"affiliation":1360,"properties":22},"d70168f5-0c38-4290-bbde-2eface496d69",{"id":1361,"createTime":1362,"updateTime":1363,"relativeEntities":1364,"slug":1365,"properties":1366,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"d6c8f583-264b-47b0-bcf5-351e90b31ec6","2024-08-30T20:40:48.308+00:00","2024-09-20T10:13:35.893+00:00",[],"Department-of-Evolutionary-Biology-Bielefeld-University-Morgenbreede-45-33615-Bielefeld-Germany",{"title":1367},{"EN":1368},"Department of Evolutionary Biology Bielefeld University Morgenbreede 45, 33615, Bielefeld Germany",{"openalex":1370,"orcid":1372,"title":1374},{"VOID":1371},"A5078989298",{"VOID":1373},"https://orcid.org/0000-0002-9124-2261",{"EN":1375},"Holger Schielzeth",{"url":22,"publisher":1377,"properties":1466},{"id":1378,"createTime":1379,"updateTime":1380,"relativeEntities":1381,"slug":1382,"properties":1383,"entityType":136,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":1394,"manageAffiliations":1395,"indexDatabases":1396,"url":1419,"thumbnailPath":22,"statistic":1420,"gsStatistic":22,"type":22,"analyzePriority":22},"87e3e3ee-9897-49df-91d7-04abd7671ebd","2023-05-29T11:16:43.656+00:00","2025-02-06T16:48:17.155+00:00",[],"Methods-in-Ecology-and-Evolution",{"country":1384,"issn":1386,"introduce":1388,"eissn":1390,"title":1392},{"VOID":1385},"US",{"VOID":1387},"2041210X",{"EN":1389},"A British Ecological Society journal, Methods in Ecology and Evolution (MEE) promotes the development of new methods in ecology and evolution, and facilitates their dissemination and uptake by the research community. MEE brings together papers from previously disparate sub-disciplines to provide a single forum for tracking methodological developments in all areas. MEE publishes methodological papers in any area of ecology and evolution, including: -Phylogenetic analysis -Statistical methods -Conservation & management -Theoretical methods -Practical methods, including lab and field -This list is not exhaustive, and we welcome enquiries about possible submissions. Methods are defined in the widest terms and may be analytical, practical or conceptual. A primary aim of the journal is to maximise the uptake of techniques by the community. We recognise that a major stumbling block in the uptake and application of new methods is the accessibility of methods. For example, users may need computer code, example applications or demonstrations of methods.",{"VOID":1391},"20412096",{"EN":1393},"Methods in Ecology and Evolution",[],[],[1397,1407],{"id":1398,"indexDatabase":1399,"url":1404,"indexYears":22,"academicFieldIds":1405},"c51f9b14-f4d4-4c69-8da3-e43b1fa3f8a2",{"id":482,"createTime":483,"updateTime":484,"relativeEntities":1400,"label":1401,"description":1402,"key":491,"publicationTags":1403,"standard":22},[],{"EN":487,"VI":487},{"VI":489,"EN":490},[493,494],"https://mjl.clarivate.com/search-results?issn=2041-210X",[1406],"7239776a-b6cb-4a11-9a6b-ddd412da67c5",{"id":1408,"indexDatabase":1409,"url":1414,"indexYears":1415,"academicFieldIds":1416},"f97a8c3b-d4eb-4e80-a166-d1b764f7e830",{"id":459,"createTime":460,"updateTime":461,"relativeEntities":1410,"label":1411,"description":1412,"key":467,"publicationTags":1413,"standard":22},[],{"EN":464,"VI":464},{"EN":464,"VI":466},[469],"https://www.scopus.com/sourceid/21100217611","SonsInc.",[1417,1418],"95f8925b-50ef-457e-b85f-1ad0343f04b9","c101d22f-6998-4eae-9faf-c06826380481","https://besjournals.onlinelibrary.wiley.com/journal/2041210X",{"impactFactor":23,"impactFactorByYear":1421,"i10Index":1433,"i10IndexLast5Year":525,"totalPublication":1434,"totalPublicationByYear":1435,"totalCitation":1438,"totalCitationByYear":1439,"totalCitationPerPublication":1452,"totalCitationPerPublicationByYear":1453,"hindexLast5Year":1422,"hindex":1422},{"2012":1422,"2013":1423,"2014":1424,"2015":1425,"2016":518,"2017":1426,"2018":1427,"2019":1428,"2020":1429,"2021":1430,"2022":1431,"2023":1432},46,15.07,45.4,60.6,17.44,43.7,42.2,21.15,32.14,38.8,65,59,61,{"2010":167,"2011":167,"2012":1436,"2013":180,"2014":521,"2015":521,"2016":167,"2017":517,"2018":515,"2019":1437,"2020":157,"2021":167},11,5,48239,{"2010":1440,"2011":1441,"2012":1442,"2013":1443,"2014":1444,"2015":1445,"2016":1446,"2017":1447,"2018":1448,"2019":1449,"2020":1450,"2021":1451},11173,279,13838,10053,922,1167,2451,5621,1113,927,71,624,790.8,{"2010":1454,"2011":1455,"2012":1456,"2013":1457,"2014":1458,"2015":1459,"2016":1460,"2017":1461,"2018":1462,"2019":1463,"2020":1464,"2021":1465},3724.33,93,1258,2513.25,153.67,194.5,817,803,139.12,185.4,35.5,208,{"volume":1467,"pages":1468,"issue":1470},{"VOID":795},{"VOID":1469},"133-142",{"VOID":1471},"2",8156,{"total":1472,"publishYear":22,"statisticByYear":1474},{"2012":148,"2013":1475,"2014":1476,"2015":1477,"2016":1478,"2017":1479,"2018":1480,"2019":1481,"2020":1482,"2021":1483,"2022":1484,"2023":1485,"2024":1486},42,234,437,576,773,900,935,962,1016,838,785,441,"2013-02-01",2013,[1490,1493,1496,1499,1502,1505,1508,1511,1514,1517,1520,1523,1526,1529,1532,1535,1538,1541,1544,1547,1550,1553,1556,1559,1562,1565,1568,1571,1574,1577,1580,1583,1586,1589,1592,1595,1598,1600,1603,1606,1609,1611,1614,1617],{"id":22,"text":1491,"url":22,"identifiers":1492},"Akaike H., 1973, 2nd International Symposium on Information Theory, 267",{},{"id":22,"text":1494,"url":22,"identifiers":1495},"Bates D. 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Development Core Team, 2012, R: A Language and Environment for Statistical Computing",{},{"id":22,"text":1584,"url":22,"identifiers":1585},"10.2307/2112482",{"doi":1584},{"id":22,"text":1587,"url":22,"identifiers":1588},"Roberts J.K., 2011, Handbook of Advanced Multilevel Analysis, 219",{},{"id":22,"text":1590,"url":22,"identifiers":1591},"10.1111/j.2041-210X.2010.00012.x",{"doi":1590},{"id":22,"text":1593,"url":22,"identifiers":1594},"10.1093/beheco/arn145",{"doi":1593},{"id":22,"text":1596,"url":22,"identifiers":1597},"Schielzeth H., 2012, Nested by design: model fitting and interpretation in a mixed model era, Methods in Ecology and Evolution",{},{"id":22,"text":1031,"url":22,"identifiers":1599},{"doi":1031},{"id":22,"text":1601,"url":22,"identifiers":1602},"10.1177/0049124194022003004",{"doi":1601},{"id":22,"text":1604,"url":22,"identifiers":1605},"Snijders T., 1999, Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling",{},{"id":22,"text":1607,"url":22,"identifiers":1608},"10.1007/978-3-642-04898-2_387",{"doi":1607},{"id":22,"text":622,"url":22,"identifiers":1610},{"doi":622},{"id":22,"text":1612,"url":22,"identifiers":1613},"10.1198/tast.2009.08210",{"doi":1612},{"id":22,"text":1615,"url":22,"identifiers":1616},"10.2307/2532896",{"doi":1615},{"id":22,"text":1618,"url":22,"identifiers":1619},"10.1002/sim.1572",{"doi":1618},{"id":1621,"createTime":1622,"updateTime":1623,"relativeEntities":1624,"slug":1625,"properties":1626,"entityType":242,"verifyStatus":382,"verifyTime":1622,"verifyNote":384,"syncStatus":385,"languages":1640,"translateLanguages":1641,"viewCount":23,"primaryUrl":1642,"fullTextUrl":22,"authors":1643,"publicationType":244,"publisherRelationship":1920,"citationCount":1978,"citationInfo":1979,"publishDate":22,"publishYear":22,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":1993,"isForceReanalyzing":19},"51318003-5ca8-4aa0-8fd2-18cf5a26a458","2024-10-09T18:50:29.609+00:00","2025-01-08T16:59:18.614+00:00",[],"The-Joint-UK-Land-Environment-Simulator-JULES-model-description-Part-2-Carbon-fluxes-and-vegetation-dynamics",{"mag":1627,"keywords":1629,"openalex":1630,"abstract":1632,"title":1635,"doi":1638},{"VOID":1628},"2097896815",{"VI":15},{"VOID":1631},"W2097896815",{"VI":1633,"EN":1634},"\u003Cjats:p>Tóm tắt. Mô hình Mô phỏng Môi trường Đất Liên hợp Vương quốc Anh (JULES) là một mô hình dựa trên quy trình mô phỏng các dòng chảy carbon, nước, năng lượng và động lượng giữa bề mặt đất và bầu khí quyển. Nhiều nghiên cứu đã chứng minh vai trò quan trọng của bề mặt đất trong chức năng của Hệ thống Trái Đất. Các phiên bản khác nhau của JULES đã được sử dụng để định lượng các tác động của biến đổi khí hậu, nồng độ carbon dioxide trong khí quyển tăng, sự thay đổi của các hạt khí quyển và ozone tầng đối lưu, cũng như phản ứng của các phát thải methane từ đất ngập nước trước biến đổi khí hậu đối với bể chứa carbon trên đất. Bài báo này mô tả sự củng cố các tiến bộ trong mô hình hóa các dòng chảy và kho carbon, cả trong thực vật và đất, trong phiên bản 2.2 của JULES. Các tính năng bao gồm một sơ đồ vòm đa tầng cho việc hấp thụ ánh sáng, bao gồm một sơ đồ thâm nhập ánh sáng mặt trời, một sơ đồ liên kết giữa quá trình quang hợp lá và độ dẫn khí của khí khổng, biểu diễn các tác động của ozone lên sinh lý học lá, và mô tả các phát thải methane từ đất ngập nước. JULES thể hiện việc phân bổ carbon, tăng trưởng và động lực học quần thể của năm loại chức năng thực vật. Quá trình trao đổi carbon từ các mô thực vật sống được đưa vào một mô hình carbon đất 4 bể. Những mô tả dựa trên quy trình của các quá trình sinh thái chính và các dòng khí vi trace trong JULES có nghĩa là mô hình cộng đồng này rất phù hợp để sử dụng trong chu trình carbon, biến đổi khí hậu và các nghiên cứu tác động, hoặc ở chế độ độc lập, hoặc như một thành phần đất của một mô hình hệ thống trái đất liên kết.","\u003Cjats:p>Abstract. The Joint UK Land Environment Simulator (JULES) is a process-based model that simulates the fluxes of carbon, water, energy and momentum between the land surface and the atmosphere. Many studies have demonstrated the important role of the land surface in the functioning of the Earth System. Different versions of JULES have been employed to quantify the effects on the land carbon sink of climate change, increasing atmospheric carbon dioxide concentrations, changing atmospheric aerosols and tropospheric ozone, and the response of methane emissions from wetlands to climate change. This paper describes the consolidation of these advances in the modelling of carbon fluxes and stores, in both the vegetation and soil, in version 2.2 of JULES. Features include a multi-layer canopy scheme for light interception, including a sunfleck penetration scheme, a coupled scheme of leaf photosynthesis and stomatal conductance, representation of the effects of ozone on leaf physiology, and a description of methane emissions from wetlands. JULES represents the carbon allocation, growth and population dynamics of five plant functional types. The turnover of carbon from living plant tissues is fed into a 4-pool soil carbon model. The process-based descriptions of key ecological processes and trace gas fluxes in JULES mean that this community model is well-suited for use in carbon cycle, climate change and impacts studies, either in standalone mode or as the land component of a coupled Earth system model.\n \u003C/jats:p>",{"VI":1636,"EN":1637},"Mô hình Mô phỏng Môi trường Đất Liên hợp Vương quốc Anh (JULES), mô tả mô hình - Phần 2: Flux carbon và động lực học thực vật","The Joint UK Land Environment Simulator (JULES), model description – Part 2: Carbon fluxes and vegetation dynamics",{"VOID":1639},"10.5194/gmd-4-701-2011",[387],[389],"https://gmd.copernicus.org/articles/4/701/2011/",[1644,1665,1687,1704,1726,1748,1763,1785,1812,1829,1851,1868,1885,1903],{"id":1645,"sortIndex":521,"researcher":22,"roles":1646,"affiliations":1647,"properties":1658},"6957775b-78ca-4fb3-b973-14e29f342b84",[],[1648],{"id":1649,"sortIndex":23,"affiliation":1650,"properties":22},"bf2ef090-e460-4b85-84cf-fe624de1eac5",{"id":1651,"createTime":1652,"updateTime":1652,"relativeEntities":1653,"slug":1654,"properties":1655,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"c33f4f17-3e20-417b-9c43-64dcba0bc4a8","2024-10-09T18:50:29.695+00:00",[],"Met-Office-Joint-Centre-for-Hydro-Meteorological-Research-Wallingford-OX10-8BB-UK",{"title":1656},{"EN":1657},"Met Office, Joint Centre for Hydro-Meteorological Research, Wallingford OX10 8BB, UK",{"openalex":1659,"orcid":1661,"title":1663},{"VOID":1660},"A5025729046",{"VOID":1662},"https://orcid.org/0000-0002-6129-5477",{"EN":1664},"Milton Pryor",{"id":1666,"sortIndex":515,"researcher":22,"roles":1667,"affiliations":1668,"properties":1680},"4c694e33-a6ae-423c-8ab3-f80ce86aae89",[],[1669],{"id":1670,"sortIndex":23,"affiliation":1671,"properties":22},"faa86325-be85-4fad-8beb-ed7e888bbd9f",{"id":1672,"createTime":1673,"updateTime":1674,"relativeEntities":1675,"slug":1676,"properties":1677,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"84a37757-f0b7-4b4b-91fa-0371cdd85acb","2024-01-10T00:02:15.370+00:00","2024-10-09T18:50:29.742+00:00",[],"School-of-GeoSciences-University-of-Edinburgh-EH9-3JW-Edinburgh-UK",{"title":1678},{"VI":1679},"School of GeoSciences, University of Edinburgh, EH9 3JW Edinburgh, UK",{"openalex":1681,"orcid":1683,"title":1685},{"VOID":1682},"A5003718432",{"VOID":1684},"https://orcid.org/0000-0003-1756-9095",{"EN":1686},"Richard Essery",{"id":1688,"sortIndex":180,"researcher":22,"roles":1689,"affiliations":1690,"properties":1697},"c1754a75-811d-4e8c-a2a9-d27e9e69cda0",[],[1691],{"id":1692,"sortIndex":23,"affiliation":1693,"properties":22},"903708a4-0768-43f6-9825-edad2610e1cf",{"id":1651,"createTime":1652,"updateTime":1652,"relativeEntities":1694,"slug":1654,"properties":1695,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},[],{"title":1696},{"EN":1657},{"openalex":1698,"orcid":1700,"title":1702},{"VOID":1699},"A5054845721",{"VOID":1701},"https://orcid.org/0000-0002-2165-5239",{"EN":1703},"Nicola Gedney",{"id":1705,"sortIndex":517,"researcher":22,"roles":1706,"affiliations":1707,"properties":1719},"e2769d1d-f75f-48cf-8567-962d1f8e6e45",[],[1708],{"id":1709,"sortIndex":23,"affiliation":1710,"properties":22},"992b1af8-bad3-4668-8017-14244be5545f",{"id":1711,"createTime":1712,"updateTime":1713,"relativeEntities":1714,"slug":1715,"properties":1716,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"6ed257e4-c687-4ec6-81e2-dad9b728564a","2024-01-19T02:06:21.387+00:00","2024-10-12T21:13:40.508+00:00",[],"Met-Office-Hadley-Centre-Exeter-EX1-3PB-UK",{"title":1717},{"VI":1718},"Met Office Hadley Centre, Exeter EX1 3PB, UK",{"openalex":1720,"orcid":1722,"title":1724},{"VOID":1721},"A5066565453",{"VOID":1723},"https://orcid.org/0000-0002-3787-1198",{"EN":1725},"G. 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Environ., 42, 187–216, 1992.",{"doi":2255},"10.1016/0034-4257(92)90102-P",{"id":22,"text":2257,"url":22,"identifiers":2258},"Shinozaki, K., Yoda, K., Hozumi, K., and Kira, T.: A quantitative analysis of plant form – the pipe model theory, I. Basic Analyses, Japanese Journal of Ecology, 14, 97–105, 1964a.",{},{"id":22,"text":2260,"url":22,"identifiers":2261},"Shinozaki, K., Yoda, K., Hozumi, K., and Kira, T.: A quantitative analysis of plant form – the pipe model theory, II. Further evidence of the theory and its application in forest ecology, Japanese Journal of Ecology, 14, 133–139, 1964b.",{},{"id":22,"text":2263,"url":22,"identifiers":2264},"Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, 2003.",{"doi":2265},"10.1046/j.1365-2486.2003.00569.x",{"id":22,"text":2267,"url":22,"identifiers":2268},"Sitch, S., Cox, P. M., Collins, W. J., and Huntingford, C.: Indirect radiative forcing of climate change through ozone effects on the land-carbon sink, Nature, 448, 791–794, 2007.",{"doi":2269},"10.1038/nature06059",{"id":22,"text":2271,"url":22,"identifiers":2272},"Sitch, S., Huntingford, C., Gedney, N., Levy, P. E., Lomas, M., Piao, S. L., Betts, R. A., Ciais, P., Cox, P. M., Friedlingstein, P., Jones, C. D., Prentice, I. C., and Woodward, F. I.: Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five Dynamic Global Vegetation Models (DGVMs), Global Change Biol., 14, 2015–2039, https://doi.org/10.1111/j.1365-2486.2008.01626.x, 2008.",{"doi":2273},"10.1111/j.1365-2486.2008.01626.x",{"id":22,"text":2275,"url":22,"identifiers":2276},"Smith, P., Smith, J. U., Flynn, H., Killham, K., Rangel-Castro, I., Foereid, B., Aitkenhead, M., Chapman, S., Towers, W., Bell, J., Lumsdon, D., Milne, R., Thomson, A., Simmons, I., Skiba, U., Reynolds, B., Evans, C., Frogbrook, Z., Bradley, I., Whitmore, A., and Falloon, P.: ECOSSE: Estimating Carbon in Organic Soils – Sequestration and Emissions, Final Report., Tech. rep., SEERAD, http://www.scotland.gov.uk/Publications/2007/03/16170508/16, (last access: 21 February 2011), 2007.",{},{"id":22,"text":2278,"url":22,"identifiers":2279},"Sokolov, A., Kicklighter, D., Melillo, J., Felzer, B., Schlosser, C., and Cronin, T.: Consequences of considering carbonnitrogen interactions on the feedbacks between climate and the terrestrial carbon cycle, J. Climate, 21, 3776–3796, https://doi.org/10.1175/2008JCLI2038.1, 2008.",{"doi":2280},"10.1175/2008JCLI2038.1",{"id":22,"text":2282,"url":22,"identifiers":2283},"Stephens, B. B., Gurney, K. R., Tans, P. P., Sweeney, C., Peters, W., Bruhwiler, L., Ciais, P., Ramonet, M., Bousquet, P., Nakazawa, T., Aoki, S., Machida, T., Inoue, G., Vinnichenko, N., Lloyd, J., Jordan, A., Heimann, M., Shibistova, O., Langenfelds, R. L., Steele, L. P., Francey, R. J., and Denning, A. S.: Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2, Science, 316, 1732–1735, https://doi.org/10.1126/science.1137004, 2007.",{"doi":2284},"10.1126/science.1137004",{"id":22,"text":2286,"url":22,"identifiers":2287},"Thonicke, K., Spessa, A., Prentice, I. C., Harrison, S. P., Dong, L., and Carmona-Moreno, C.: The influence of vegetation, fire spread and fire behaviour on biomass burning and trace gas emissions: results from a process-based model, Biogeosciences, 7, 1991–2011, https://doi.org/10.5194/bg-7-1991-2010, 2010.",{"doi":2288},"10.5194/bg-7-1991-2010",{"id":22,"text":2290,"url":22,"identifiers":2291},"Thornton, P. E., Lamarque, J. F., Rosenbloom, N., and Mahowald, N.: Influence of carbon-nitrogen cycle coupling on land model response to CO2 fertilization and climate variability, Global Biogeochem. Cy., 21, GB4018, https://doi.org/10.1029/2006GB002868, 2007.",{"doi":2292},"10.1029/2006GB002868",{"id":2294,"createTime":2295,"updateTime":2296,"relativeEntities":2297,"slug":2298,"properties":2299,"entityType":242,"verifyStatus":382,"verifyTime":2295,"verifyNote":384,"syncStatus":385,"languages":2316,"translateLanguages":2317,"viewCount":23,"primaryUrl":2318,"fullTextUrl":22,"authors":2319,"publicationType":244,"publisherRelationship":2537,"citationCount":2682,"citationInfo":2683,"publishDate":2688,"publishYear":2689,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":2690,"isForceReanalyzing":19},"e048eb7b-9382-4cee-854f-9a311937ce8b","2024-10-16T16:39:04.007+00:00","2024-12-24T17:15:55.330+00:00",[],"DIPSS-Plus-A-Refined-Dynamic-International-Prognostic-Scoring-System-for-Primary-Myelofibrosis-That-Incorporates-Prognostic-Information-From-Karyotype-Platelet-Count-and-Transfusion-Status",{"mag":2300,"keywords":2302,"openalex":2304,"abstract":2306,"title":2309,"pm":2312,"doi":2314},{"VOID":2301},"2049965335",{"VI":2303},"Hệ thống Chấm điểm Tiên lượng Quốc tế Động, xơ hóa tủy nguyên phát, kiểu nhiễm sắc thể, số lượng tiểu cầu, truyền máu, tiên lượng sống sót, mô hình tiên lượng tổng hợp, tỷ số rủi ro, sống sót không bị bệnh bạch cầu.",{"VOID":2305},"W2049965335",{"EN":2307,"VI":2308},"\u003Cjats:sec>\u003Cjats:title>Purpose\u003C/jats:title>\u003Cjats:p>The Dynamic International Prognostic Scoring System (DIPSS) for primary myelofibrosis (PMF) uses five risk factors to predict survival: age older than 65 years, hemoglobin lower than 10 g/dL, leukocytes higher than 25 × 10\u003Cjats:sup>9\u003C/jats:sup>/L, circulating blasts ≥ 1%, and constitutional symptoms. The main objective of this study was to refine DIPSS by incorporating prognostic information from karyotype, platelet count, and transfusion status.\u003C/jats:p>\u003C/jats:sec>\u003Cjats:sec>\u003Cjats:title>Patients and Methods\u003C/jats:title>\u003Cjats:p>Mayo Clinic databases for PMF were used to identify patients with available bone marrow histologic and cytogenetic information.\u003C/jats:p>\u003C/jats:sec>\u003Cjats:sec>\u003Cjats:title>Results\u003C/jats:title>\u003Cjats:p>Seven hundred ninety-three consecutive patients were selected and divided into two groups based on whether or not their referral occurred within (n = 428; training set) or after (n = 365; test set) 1 year of diagnosis. Multivariable analysis identified DIPSS, unfavorable karyotype, platelets lower than 100 × 10\u003Cjats:sup>9\u003C/jats:sup>/L, and transfusion need as independent predictors of inferior survival. Hazard ratio (HR) –weighted adverse points were assigned to these variables to develop a composite prognostic model using the training set. The model was subsequently validated in the test set, and its application to all 793 patients resulted in median survivals of 185, 78, 35, and 16 months for low, intermediate-1 (HR, 2.2; 95% CI, 1.4 to 3.6), intermediate-2 (HR, 4.9; 95% CI, 3.2 to 7.7), and high-risk groups (HR, 10.7; 95% CI, 6.8 to 16.9), respectively (P &lt; .001). Leukemia-free survival was predicted by the presence of thrombocytopenia or unfavorable karyotype (10-year risk of 31% v 12%; HR, 3.3; 95% CI, 1.9 to 5.6).\u003C/jats:p>\u003C/jats:sec>\u003Cjats:sec>\u003Cjats:title>Conclusion\u003C/jats:title>\u003Cjats:p>DIPSS plus effectively combines prognostic information from DIPSS, karyotype, platelet count, and transfusion status to predict overall survival in PMF. In addition, unfavorable karyotype or thrombocytopenia predicts inferior leukemia-free survival.\u003C/jats:p>\u003C/jats:sec>","\u003Cjats:sec>\u003Cjats:title>Mục đích\u003C/jats:title>\u003Cjats:p>Hệ thống Chấm điểm Tiên lượng Quốc tế Động (DIPSS) cho xơ hóa tủy nguyên phát (PMF) sử dụng năm yếu tố nguy cơ để dự đoán sống sót: tuổi trên 65, hemoglobin dưới 10 g/dL, bạch cầu cao hơn 25 × 10\u003Cjats:sup>9\u003C/jats:sup>/L, tế bào ác tính tuần hoàn ≥ 1%, và các triệu chứng toàn thân. Mục tiêu chính của nghiên cứu này là cải tiến DIPSS bằng cách kết hợp thông tin tiên lượng từ kiểu nhiễm sắc thể, số lượng tiểu cầu và tình trạng truyền máu.\u003C/jats:p>\u003C/jats:sec>\u003Cjats:sec>\u003Cjats:title>Bệnh nhân và Phương pháp\u003C/jats:title>\u003Cjats:p>Cơ sở dữ liệu Mayo Clinic cho PMF đã được sử dụng để xác định bệnh nhân có thông tin mô học và di truyền học tủy xương sẵn có.\u003C/jats:p>\u003C/jats:sec>\u003Cjats:sec>\u003Cjats:title>Kết quả\u003C/jats:title>\u003Cjats:p>Bảy trăm chín mươi ba bệnh nhân liên tiếp được chọn và chia thành hai nhóm dựa trên việc tham khảo ý kiến có diễn ra trong (n = 428; tập huấn luyện) hoặc sau (n = 365; tập kiểm tra) 1 năm sau chẩn đoán hay không. Phân tích đa biến xác định DIPSS, kiểu nhiễm sắc thể không thuận lợi, tiểu cầu thấp hơn 100 × 10\u003Cjats:sup>9\u003C/jats:sup>/L, và nhu cầu truyền máu là những yếu tố tiên đoán độc lập về khả năng sống sót kém. Các điểm bất lợi được đặt trọng lượng tỷ số rủi ro (HR) được gán cho các biến này để phát triển một mô hình tiên lượng tổng hợp sử dụng tập huấn luyện. Mô hình sau đó được xác minh trong tập kiểm tra, và khi áp dụng cho tất cả 793 bệnh nhân, cho thấy thời gian sống trung bình là 185, 78, 35, và 16 tháng cho các nhóm nguy cơ thấp, trung bình-1 (HR, 2.2; 95% CI, 1.4 đến 3.6), trung bình-2 (HR, 4.9; 95% CI, 3.2 đến 7.7), và nguy cơ cao (HR, 10.7; 95% CI, 6.8 đến 16.9), tương ứng (P &lt; .001). Sống sót không bị bệnh bạch cầu được dự đoán bởi sự hiện diện của thiếu tiểu cầu hoặc kiểu nhiễm sắc thể không thuận lợi (nguy cơ 10 năm là 31% so với 12%; HR, 3.3; 95% CI, 1.9 đến 5.6).\u003C/jats:p>\u003C/jats:sec>\u003Cjats:sec>\u003Cjats:title>Kết luận\u003C/jats:title>\u003Cjats:p>DIPSS plus kết hợp hiệu quả thông tin tiên lượng từ DIPSS, kiểu nhiễm sắc thể, số lượng tiểu cầu, và tình trạng truyền máu để dự đoán sống sót tổng thể trong PMF. Ngoài ra, kiểu nhiễm sắc thể không thuận lợi hoặc thiếu tiểu cầu dự đoán thời gian sống sót không bị bệnh bạch cầu kém hơn.\u003C/jats:p>\u003C/jats:sec>",{"EN":2310,"VI":2311},"DIPSS Plus: A Refined Dynamic International Prognostic Scoring System for Primary Myelofibrosis That Incorporates Prognostic Information From Karyotype, Platelet Count, and Transfusion Status","DIPSS Plus: Hệ thống chấm điểm tiên lượng quốc tế động tinh tế cho bệnh xơ hóa tủy nguyên phát kết hợp thông tin tiên lượng từ kiểu nhiễm sắc thể, số lượng tiểu cầu và tình trạng truyền máu",{"VOID":2313},"21149668",{"VOID":2315},"10.1200/jco.2010.32.2446",[387],[389],"https://ascopubs.org/doi/10.1200/JCO.2010.32.2446",[2320,2341,2358,2375,2392,2409,2424,2441,2458,2473,2490,2505,2522],{"id":2321,"sortIndex":180,"researcher":22,"roles":2322,"affiliations":2323,"properties":2334},"9a9b1162-3917-4480-b818-81002ea631b9",[],[2324],{"id":2325,"sortIndex":23,"affiliation":2326,"properties":22},"7377f2aa-21b9-486d-9e06-9f5ed49b238c",{"id":2327,"createTime":2328,"updateTime":2328,"relativeEntities":2329,"slug":2330,"properties":2331,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"846bea9f-a1b1-4ed0-be58-d86e0b2d4eb5","2024-10-16T16:39:04.029+00:00",[],"From-the-Mayo-Clinic-Rochester-MN-Cattedra-ed-UO-di-Ematologia-Policlinico-Universitario-di-Palermo-Palermo-Fondazione-Istituto-di-Ricovero-e-Cura-a-Carattere-Scientifico-Policlinico-San-Matteo-University-of-Pavia-Pavia-Italy-and-Hospital-Cl%C3%ADnic-Institut-d-Investigacions-Biom%C3%A8diques-August-Pi-i-Sunyer-University-of-Barcelona-Barcelona-Spain-",{"title":2332},{"EN":2333},"From the Mayo Clinic, Rochester, MN; Cattedra ed UO di Ematologia, Policlinico Universitario di Palermo, Palermo; Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Policlinico San Matteo, University of Pavia, Pavia, Italy; and Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer, University of Barcelona, Barcelona, Spain.",{"openalex":2335,"orcid":2337,"title":2339},{"VOID":2336},"A5080965667",{"VOID":2338},"https://orcid.org/0000-0003-2730-8593",{"EN":2340},"Kebede H. 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protein complexes, immunomodulation, anti-cancer, biological response modifiers, cytokines",{"VOID":2782},"W2110007623",{"VI":2784,"EN":2785},"\u003Cjats:sec>\n\u003Cjats:title />\n\u003Cjats:p> Trong ba thập kỷ qua, nhiều polysaccharide và phức hợp polysaccharide-protein đã được tách chiết từ nấm và được sử dụng làm nguồn nguyên liệu cho các tác nhân điều trị. Những hoạt động sinh dược học đầy hứa hẹn nhất của các polymer sinh học này là khả năng điều chỉnh miễn dịch và tác dụng chống ung thư của chúng. Chúng chủ yếu tồn tại dưới dạng glucans với các loại liên kết glycosidic khác nhau như (1-3), (1-6)-b-glucans và (1-3)-a-glucans, và dưới dạng heteroglycans thật sự, trong khi một số khác chủ yếu kết hợp với các dư lượng protein dưới dạng phức hợp polysaccharide-protein. Ba polysaccharide chống khối u từ nấm, tức là lentinan, schizophyllan và polysaccharide gắn protein (PSK, Krestin), được tách chiết lần lượt từ Lentinus edodes, Schizophyllum commune và Coriolus versicolor, đã trở thành các sản phẩm lớn trên thị trường Nhật Bản. Lentinan và schizophyllan là các b-glucan nguyên chất, trong khi PSK là một b-glucan gắn protein. Một peptide polysaccharide (PSP), được tách chiết từ một chủng của Coriolus versicolor tại Trung Quốc, cũng đã được sử dụng rộng rãi như một tác nhân chống ung thư và điều chỉnh miễn dịch. Mặc dù cơ chế chống khối u của chúng vẫn chưa hoàn toàn rõ ràng, những polysaccharide và phức hợp polysaccharide-protein này được cho là có khả năng tăng cường phản ứng miễn dịch trung gian tế bào cả in vivo và in vitro và hoạt động như các chất điều biến phản ứng sinh học. Sự tăng cường hệ thống phòng thủ của chủ thể có thể dẫn đến việc kích hoạt nhiều loại tế bào miễn dịch quan trọng cho việc duy trì cân bằng nội môi. Polysaccharide hoặc phức hợp polysaccharide-protein được coi là các tác nhân kích thích đa cytokine có khả năng kích thích biểu hiện gen của nhiều cytokine điều chỉnh miễn dịch và thụ thể cytokine khác nhau. Một số nghiên cứu thú vị tập trung vào việc điều tra mối quan hệ giữa cấu trúc của chúng và hoạt động chống khối u, làm sáng tỏ cơ chế chống khối u của chúng ở cấp độ phân tử, và cải thiện các hoạt động sinh học khác nhau của chúng thông qua các sửa đổi hóa học. \u003C/jats:p>\n\u003C/jats:sec>","\u003Cjats:sec>\n\u003Cjats:title />\n\u003Cjats:p> In the last three decades, numerous polysaccharides and polysaccharide-protein complexes have been isolated from mushrooms and used as a source of therapeutic agents. The most promising biopharmacological activities of these biopolymers are their immunomodulation and anti-cancer effects. They are mainly present as glucans with different types of glycosidic linkages such as (1-3), (1-6)-b-glucans and (1-3)-a-glucans, and as true herteroglycans, while others mostly bind to protein residues as polysaccharide-protein complexes. Three antitumor mushroom polysaccharides, i.e. lentinan, schizophyllan and protein-bound polysaccharide (PSK, Krestin), isolated respectively, from Lentinus edodes, Schizophyllum commune and Coriolus versicolor, have become large market items in Japan. Lentinan and schizophyllan are pure b-glucans, whereas PSK is a protein-bound b-glucan. A polysaccharide peptide (PSP), isolated from a strain of Coriolus versicolor in China, has also been widely used as an anti-cancer and immunomodulatory agent. Although the mechansim of their antitumor action is still not completely clear, these polysaccharides and polysaccharide-protein complexes are suggested to enhance cell-mediated immune responses in vivo and in vitro and act as biological response modifiers. Potentiation of the host defense system may result in the activation of many kinds of immune cells that are vitally important for the maintenance of homeostasis. Polysaccharides or polysaccharide-protein complexes are considered as multi-cytokine inducers that are able to induce gene expression of vaious immunomodulatory cytokines and cytokine receptors. Some interesting studies focus on investigation of the relationship between their structure and antitumor activity, elucidation of their antitumor mechanism at the molecular level, and improvement of their various biological activities by chemical modifications. \u003C/jats:p>\n\u003C/jats:sec>",{"VI":2787,"EN":2788},"Hoạt động điều chỉnh miễn dịch và chống ung thư của các phức hợp polysaccharide-protein","Immunomodulation and Anti-Cancer Activity of Polysaccharide-Protein Complexes",{"VOID":2790},"10702635",{"VOID":2792},"10.2174/0929867003374705",[387],[389],"https://www.eurekaselect.com/65596/article",[2797,2818],{"id":2798,"sortIndex":148,"researcher":22,"roles":2799,"affiliations":2800,"properties":2811},"12726ebc-540f-4215-8755-f4417fccec80",[],[2801],{"id":2802,"sortIndex":23,"affiliation":2803,"properties":22},"df7505e5-5cb3-43a9-8972-36c33cccb9df",{"id":2804,"createTime":2805,"updateTime":2805,"relativeEntities":2806,"slug":2807,"properties":2808,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"6a447876-15a9-4a6c-a328-e1080a11c9fe","2024-10-07T20:44:52.717+00:00",[],"Department-of-Biology-The-Chinese-University-of-Hong-Kong-Shatin-N-T-Hong-Kong-and-Department-of-Microbiology-Nankai-University-Tianjin-China-China",{"title":2809},{"EN":2810},"Department of Biology, The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, and Department of Microbiology, Nankai University, Tianjin, China, China",{"openalex":2812,"orcid":2814,"title":2816},{"VOID":2813},"A5026150311",{"VOID":2815},"https://orcid.org/0000-0001-5326-5596",{"EN":2817},"F. 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Ooi",{"url":22,"publisher":2839,"properties":2933},{"id":2840,"createTime":2841,"updateTime":2842,"relativeEntities":2843,"slug":2844,"properties":2845,"entityType":136,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":2856,"manageAffiliations":2857,"indexDatabases":2858,"url":2885,"thumbnailPath":22,"statistic":2886,"gsStatistic":22,"type":22,"analyzePriority":22},"c5aa2229-b24b-4220-958f-15dc1fb957dd","2023-05-29T10:34:05.695+00:00","2025-02-10T22:40:23.298+00:00",[],"Current-Medicinal-Chemistry",{"country":2846,"issn":2848,"introduce":2850,"eissn":2852,"title":2854},{"VOID":2847},"AE",{"VOID":2849},"09298673",{"EN":2851},"Current Medicinal Chemistry covers all the latest and outstanding developments in the field of medicinal chemistry. It also includes topics related to quantitative structure-function relationships, drug absorption, drug distribution, drug metabolism, new and emerging drug targets, pharmacogenomics, drug discovery toxicology, high-throughput screening, combinatorial chemistry, structural characterization and organic syntheses of naturally occurring compounds, as well as chemical and analytical techniques used in rational drug design. Each issue incorporates a series of timely in-depth/mini-reviews and guest-edited thematic issues written by leaders in the field covering a range of the current topics in medicinal chemistry. The journal also publishes reviews on recent patents. Current Medicinal Chemistry is an essential journal for every medicinal chemist who wishes to be informed and up-to-date with the latest and most important developments relevant to their field.",{"VOID":2853},"1875533X",{"EN":2855},"Current Medicinal 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This article describes the mixlogit Stata command for fitting mixed logit models by using maximum simulated likelihood. \u003C/jats:p>","\u003Cjats:p> Bài báo này mô tả lệnh mixlogit trong Stata để khớp các mô hình logit hỗn hợp bằng cách sử dụng ước lượng tối đa thông qua mô phỏng. \u003C/jats:p>",{"EN":2968,"VI":2969},"Fitting Mixed Logit Models by Using Maximum Simulated Likelihood","Phương Pháp Khớp Mô Hình Logit Hỗn Hợp Bằng Cách Sử Dụng Ước Lượng Tối Đa Qua Mô Phỏng",{"VOID":2971},"10.1177/1536867x0700700306","2024-10-05T10:06:38.352+00:00",[387],[389],"http://journals.sagepub.com/doi/10.1177/1536867X0700700306",[2977],{"id":2978,"sortIndex":23,"researcher":22,"roles":2979,"affiliations":2980,"properties":2991},"2684ff16-cc26-4c87-ad4b-4d0f7a09e086",[],[2981],{"id":2982,"sortIndex":23,"affiliation":2983,"properties":22},"e4f86dfe-7c67-4f85-a6f8-48d3d41b432b",{"id":2984,"createTime":2985,"updateTime":2985,"relativeEntities":2986,"slug":2987,"properties":2988,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"a7e05d6f-b1c4-49d2-ba5a-b411a3c366c4","2024-10-05T10:06:38.369+00:00",[],"National-Primary-Care-Research-and-Development-Centre-Centre-for-Health-Economics-University-of-York-York-UK",{"title":2989},{"EN":2990},"National Primary Care Research and Development Centre, Centre for Health Economics, University of York, York, 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language. The Stata Journal publishes reviewed papers together with shorter notes and comments, regular columns, book reviews, and other material of interest to researchers applying statistics in a variety of disciplines.",{"VOID":3012},"15368734",{"EN":3014},"Stata Journal",[],[],[3018,3036,3044],{"id":3019,"indexDatabase":3020,"url":3033,"indexYears":22,"academicFieldIds":3034},"eff034a3-b676-487e-be94-66b16e55b350",{"id":3021,"createTime":3022,"updateTime":3023,"relativeEntities":3024,"label":3025,"description":3027,"key":3030,"publicationTags":3031,"standard":22},"a8273be3-1221-4f26-949d-71dc71ff1fc3","2023-05-22T09:59:54.622+00:00","2025-01-14T09:58:17.815+00:00",[],{"EN":3026,"VI":3026},"ISI-SSCI - Social Sciences Citation Index",{"VI":3028,"EN":3029},"Cơ sở dữ liệu SSCI","SSCI database","ssci",[3032,494],"SSCI","https://mjl.clarivate.com/search-results?issn=1536-867X",[3035],"0b8e62b2-c9d1-413b-84bb-3d9515d04f0d",{"id":3037,"indexDatabase":3038,"url":3033,"indexYears":22,"academicFieldIds":3043},"0a94eb6b-4927-4d46-aba4-1e30daf7fa8b",{"id":482,"createTime":483,"updateTime":484,"relativeEntities":3039,"label":3040,"description":3041,"key":491,"publicationTags":3042,"standard":22},[],{"EN":487,"VI":487},{"VI":489,"EN":490},[493,494],[744],{"id":3045,"indexDatabase":3046,"url":3051,"indexYears":3052,"academicFieldIds":3053},"1093b82b-182f-4ed4-a72d-e52052041252",{"id":459,"createTime":460,"updateTime":461,"relativeEntities":3047,"label":3048,"description":3049,"key":467,"publicationTags":3050,"standard":22},[],{"EN":464,"VI":464},{"EN":464,"VI":466},[469],"https://www.scopus.com/sourceid/4400151715","2002-2023",[3054],"825d41e1-f1f9-472e-aa11-78b85d501870","https://www.stata-journal.com/",{"impactFactor":23,"impactFactorByYear":3057,"i10Index":523,"i10IndexLast5Year":23,"totalPublication":523,"totalPublicationByYear":3061,"totalCitation":3062,"totalCitationByYear":3063,"totalCitationPerPublication":3073,"totalCitationPerPublicationByYear":3074,"hindexLast5Year":519,"hindex":519},{"2013":2593,"2014":2594,"2015":3058,"2016":3059,"2017":3060,"2019":517,"2020":2598},3.67,9.17,22,{"2002":148,"2004":148,"2006":148,"2007":180,"2008":167,"2009":157,"2012":148,"2013":148,"2014":157,"2015":180,"2018":148},21213,{"2002":3064,"2004":520,"2006":2590,"2007":3065,"2008":3066,"2009":3067,"2012":3068,"2013":3069,"2014":3070,"2015":3071,"2018":3072},2591,4781,1184,7794,1736,410,479,1365,357,1010.14,{"2002":3064,"2004":520,"2006":2590,"2007":3075,"2008":3076,"2009":3077,"2012":3068,"2013":3069,"2014":3078,"2015":3079,"2018":3072},1195.25,394.67,3897,239.5,341.25,{"volume":3081,"pages":3082,"issue":3084},{"VOID":2935},{"VOID":3083},"388-401",{"VOID":1977},721,{"total":3085,"publishYear":22,"statisticByYear":3087},{"2012":2595,"2013":3088,"2014":3089,"2015":2947,"2016":3090,"2017":2945,"2018":3089,"2019":3090,"2020":1985,"2021":3091,"2022":3091,"2023":1434,"2024":1475},34,44,47,73,"2007-09-01",2007,[3095,3098,3101,3104,3107,3110,3113,3116],{"id":22,"text":3096,"url":22,"identifiers":3097},"10.1177/1536867X0600600202",{"doi":3096},{"id":22,"text":3099,"url":22,"identifiers":3100},"10.1177/1536867X0600600204",{"doi":3099},{"id":22,"text":3102,"url":22,"identifiers":3103},"10.1177/1536867X0600600205",{"doi":3102},{"id":22,"text":3105,"url":22,"identifiers":3106},"10.1023/A:1011120928698",{"doi":3105},{"id":22,"text":3108,"url":22,"identifiers":3109},"Long J. S., 2006, Regression Models for Categorical Dependent Variables Using Stata.",{},{"id":22,"text":3111,"url":22,"identifiers":3112},"McFadden D., 1974, Frontiers in Econometrics, 105",{},{"id":22,"text":3114,"url":22,"identifiers":3115},"10.1162/003465398557735",{"doi":3114},{"id":22,"text":3117,"url":22,"identifiers":3118},"10.1017/CBO9780511753930",{"doi":3117},{"id":3120,"createTime":3121,"updateTime":3122,"relativeEntities":3123,"slug":3124,"properties":3125,"entityType":242,"verifyStatus":382,"verifyTime":3121,"verifyNote":384,"syncStatus":385,"languages":3139,"translateLanguages":3140,"viewCount":23,"primaryUrl":3141,"fullTextUrl":22,"authors":3142,"publicationType":244,"publisherRelationship":3163,"citationCount":3200,"citationInfo":3201,"publishDate":2949,"publishYear":2950,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":3204,"isForceReanalyzing":19},"7c807624-e5b1-42a1-87fe-4d9d835cbc16","2024-12-31T09:51:00.957+00:00","2025-02-25T06:05:27.834+00:00",[],"Bayesian-Latent-Variable-Models-for-Clustered-Mixed-Outcomes",{"mag":3126,"keywords":3128,"openalex":3129,"abstract":3131,"title":3134,"doi":3137},{"VOID":3127},"2089773781",{"VI":15},{"VOID":3130},"W2089773781",{"EN":3132,"VI":3133},"\u003Cjats:title>Summary\u003C/jats:title>\n \u003Cjats:p>A general framework is proposed for modelling clustered mixed outcomes. A mixture of generalized linear models is used to describe the joint distribution of a set of underlying variables, and an arbitrary function relates the underlying variables to be observed outcomes. The model accommodates multilevel data structures, general covariate effects and distinct link functions and error distributions for each underlying variable. Within the framework proposed, novel models are developed for clustered multiple binary, unordered categorical and joint discrete and continuous outcomes. A Markov chain Monte Carlo sampling algorithm is described for estimating the posterior distributions of the parameters and latent variables. Because of the flexibility of the modelling framework and estimation procedure, extensions to ordered categorical outcomes and more complex data structures are straightforward. The methods are illustrated by using data from a reproductive toxicity study.\u003C/jats:p>","\u003Cjats:title>Tóm tắt\u003C/jats:title>\n \u003Cjats:p> Một khuôn khổ tổng quát được đề xuất để mô hình hóa các kết quả hỗn hợp tập trung. Một hỗn hợp của các mô hình tuyến tính tổng quát được sử dụng để mô tả phân phối chung của một tập hợp các biến tiềm ẩn, và một hàm tùy ý liên kết các biến tiềm ẩn với các kết quả được quan sát. Mô hình này có thể tiếp nhận cấu trúc dữ liệu đa cấp, các hiệu ứng biến ghép tổng quát và các hàm liên kết cũng như phân phối lỗi khác nhau cho mỗi biến tiềm ẩn. Trong khuôn khổ đề xuất, các mô hình mới được phát triển cho các kết quả nhị phân tập trung, phân loại không theo thứ tự và các kết quả rời rạc và liên tục chung. Một thuật toán lấy mẫu chuỗi Markov Monte Carlo được mô tả để ước lượng các phân phối hậu nghiệm của các tham số và các biến tiềm ẩn. Nhờ tính linh hoạt của khuôn khổ mô hình và quy trình ước lượng, việc mở rộng cho các kết quả phân loại có thứ tự và các cấu trúc dữ liệu phức tạp hơn là khá đơn giản. Các phương pháp được minh họa bằng cách sử dụng dữ liệu từ một nghiên cứu độc tính sinh sản.\u003C/jats:p>",{"EN":3135,"VI":3136},"Bayesian Latent Variable Models for Clustered Mixed Outcomes","Mô hình Biến Ẩn Bayesian cho Kết Quả Hỗn Hợp Tập Trung",{"VOID":3138},"10.1111/1467-9868.00236",[387],[389],"https://academic.oup.com/jrsssb/article/62/2/355/7083246",[3143],{"id":3144,"sortIndex":23,"researcher":22,"roles":3145,"affiliations":3146,"properties":3158},"f5d342ab-2e13-4f8c-b773-4de0a18e0e95",[],[3147],{"id":3148,"sortIndex":23,"affiliation":3149,"properties":22},"c3916915-235e-48f3-b83d-f0462580f1cb",{"id":3150,"createTime":3151,"updateTime":3152,"relativeEntities":3153,"slug":3154,"properties":3155,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"e847484f-2776-452e-b036-22cd731af551","2024-01-13T17:51:24.212+00:00","2024-12-31T09:51:00.973+00:00",[],"National-Institute-of-Environmental-Health-Sciences-Research-Triangle-Park-USA",{"title":3156},{"VI":3157},"National Institute of Environmental Health Sciences, Research Triangle Park, USA",{"openalex":3159,"title":3161},{"VOID":3160},"A5108072328",{"EN":3162},"David B. Dunson",{"url":22,"publisher":3164,"properties":3194},{"id":717,"createTime":718,"updateTime":719,"relativeEntities":3165,"slug":721,"properties":3166,"entityType":136,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":3172,"manageAffiliations":3173,"indexDatabases":3174,"url":756,"thumbnailPath":22,"statistic":3189,"gsStatistic":22,"type":22,"analyzePriority":22},[],{"country":3167,"issn":3168,"introduce":3169,"eissn":3170,"title":3171},{"VOID":445},{"VOID":725},{"EN":727},{"VOID":729},{"EN":731},[],[],[3175,3182],{"id":736,"indexDatabase":3176,"url":742,"indexYears":22,"academicFieldIds":3181},{"id":482,"createTime":483,"updateTime":484,"relativeEntities":3177,"label":3178,"description":3179,"key":491,"publicationTags":3180,"standard":22},[],{"EN":487,"VI":487},{"VI":489,"EN":490},[493,494],[744],{"id":746,"indexDatabase":3183,"url":752,"indexYears":753,"academicFieldIds":3188},{"id":459,"createTime":460,"updateTime":461,"relativeEntities":3184,"label":3185,"description":3186,"key":467,"publicationTags":3187,"standard":22},[],{"EN":464,"VI":464},{"EN":464,"VI":466},[469],[477,755],{"impactFactor":23,"impactFactorByYear":3190,"i10Index":759,"i10IndexLast5Year":23,"totalPublication":759,"totalPublicationByYear":3191,"totalCitation":764,"totalCitationByYear":3192,"totalCitationPerPublication":782,"totalCitationPerPublicationByYear":3193,"hindexLast5Year":759,"hindex":759},{"2012":759,"2013":760,"2014":761,"2017":515,"2018":523,"2019":762},{"1977":148,"1995":148,"1996":148,"1997":148,"1999":148,"2001":167,"2002":167,"2004":148,"2005":148,"2006":157,"2008":157,"2009":148,"2010":157,"2012":148,"2016":148,"2017":148},{"1977":766,"1995":767,"1996":768,"1997":769,"1999":770,"2001":771,"2002":772,"2004":773,"2005":774,"2006":775,"2008":776,"2009":777,"2010":778,"2012":779,"2016":780,"2017":781},{"1977":766,"1995":767,"1996":768,"1997":769,"1999":770,"2001":784,"2002":785,"2004":773,"2005":774,"2006":786,"2008":787,"2009":777,"2010":788,"2012":779,"2016":780,"2017":781},{"volume":3195,"pages":3197,"issue":3199},{"VOID":3196},"62",{"VOID":3198},"355-366",{"VOID":1471},307,{"total":3200,"publishYear":22,"statisticByYear":3202},{"2012":761,"2013":522,"2014":3203,"2015":3203,"2016":524,"2017":3203,"2018":2598,"2019":1887,"2020":520,"2021":520,"2022":524,"2023":515,"2024":180},17,[],{"id":3206,"createTime":3207,"updateTime":3208,"relativeEntities":3209,"slug":3210,"properties":3211,"entityType":242,"verifyStatus":382,"verifyTime":3207,"verifyNote":384,"syncStatus":385,"languages":3227,"translateLanguages":3228,"viewCount":23,"primaryUrl":3229,"fullTextUrl":22,"authors":3230,"publicationType":244,"publisherRelationship":3290,"citationCount":3348,"citationInfo":3349,"publishDate":3352,"publishYear":3353,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":3354,"isForceReanalyzing":19},"bc945117-221c-42bd-9f3d-e6c6482f2f0d","2024-09-30T09:29:36.289+00:00","2025-02-15T06:25:25.988+00:00",[],"The-effectiveness-of-relaxation-training-in-reducing-treatment-related-symptoms-and-improving-emotional-adjustment-in-acute-non-surgical-cancer-treatment-a-meta-analytical-review",{"mag":3212,"keywords":3214,"openalex":3215,"abstract":3217,"title":3220,"pm":3223,"doi":3225},{"VOID":3213},"2171037397",{"VI":15},{"VOID":3216},"W2171037397",{"EN":3218,"VI":3219},"\u003Cjats:title>Abstract\u003C/jats:title>\u003Cjats:p>Cancer patients often have to deal with severe side effects and psychological distress during cancer treatment, which have a substantial impact on their quality of life. Among psychosocial interventions for reducing treatment‐related side effects, relaxation and imagery were most investigated in controlled trials. In this study, meta‐analytic methods were used to synthesize published, randomized intervention–control studies aiming to improve patients' treatment‐related symptoms and emotional adjustment by relaxation training. Mean weighted effect sizes were calculated for 12 categories, treatment‐related symptoms (nausea, pain, blood pressure, pulse rate) and emotional adjustment (anxiety, depression, hostility, tension, fatigue, confusion, vigor, overall mood). Significant positive effects were found for the treatment‐related symptoms. Relaxation training also proved to have a significant effect on the emotional adjustment variables depression, anxiety and hostility. Additionally, two studies point to a significant effect of relaxation on the reduction of tension and amelioration of the overall mood. Intervention features of the relaxation training, the time the professional spent with the patient overall (intervention intensity) and the schedule of the intervention (offered in conjunction with or independent of medical treatment to the cancer patient) were relevant to the effect of relaxation on anxiety. The interventions offered independently of medical treatment proved to be significantly more effective for the outcome variable anxiety. Relaxation seems to be equally effective for patients undergoing different medical procedures (chemotherapy, radiotherapy, bone marrow transplantation, hyperthermia). According to these results relaxation training should be implemented into clinical routine for cancer patients in acute medical treatment. Copyright © 2001 John Wiley &amp; Sons, Ltd.\u003C/jats:p>","\u003Cjats:title>Tóm tắt\u003C/jats:title>\u003Cjats:p>Những bệnh nhân ung thư thường phải đối mặt với các tác dụng phụ nghiêm trọng và căng thẳng tâm lý trong quá trình điều trị ung thư, điều này có ảnh hưởng đáng kể đến chất lượng cuộc sống của họ. Trong số các can thiệp tâm lý xã hội nhằm giảm thiểu các tác dụng phụ liên quan đến điều trị, thư giãn và hình ảnh là những phương pháp được nghiên cứu nhiều nhất trong các thử nghiệm kiểm soát. Trong nghiên cứu này, các phương pháp phân tích tổng hợp đã được sử dụng để tổng hợp các nghiên cứu can thiệp ngẫu nhiên đã được công bố, nhằm cải thiện các triệu chứng liên quan đến điều trị và sự điều chỉnh cảm xúc của bệnh nhân thông qua đào tạo thư giãn. Kích thước hiệu ứng trung bình có trọng số đã được tính toán cho 12 danh mục, triệu chứng liên quan đến điều trị (buồn nôn, đau, huyết áp, nhịp tim) và sự điều chỉnh cảm xúc (lo âu, trầm cảm, thù địch, căng thẳng, mệt mỏi, bối rối, năng lượng, tâm trạng tổng thể). Các hiệu ứng tích cực đáng kể đã được tìm thấy đối với các triệu chứng liên quan đến điều trị. Đào tạo thư giãn cũng chứng tỏ có hiệu ứng đáng kể đối với các biến điều chỉnh cảm xúc như trầm cảm, lo âu và thù địch. Thêm vào đó, hai nghiên cứu chỉ ra rằng thư giãn có ảnh hưởng đáng kể đến sự giảm căng thẳng và cải thiện tâm trạng tổng thể. Các đặc điểm của can thiệp đào tạo thư giãn, thời gian chuyên gia dành cho bệnh nhân nói chung (cường độ can thiệp) và lịch trình can thiệp (được cung cấp đồng thời với hoặc độc lập của điều trị y tế cho bệnh nhân ung thư) có liên quan đến hiệu ứng của thư giãn đối với lo âu. Những can thiệp được cung cấp độc lập với điều trị y tế chứng tỏ có hiệu quả đáng kể hơn đối với biến kết quả lo âu. Thư giãn dường như là equally hiệu quả đối với bệnh nhân trải qua các quy trình y tế khác nhau (hóa trị, xạ trị, ghép tế bào gốc, liệu pháp nhiệt độ cao). Theo các kết quả này, đào tạo thư giãn nên được thực hiện vào quy trình lâm sàng cho bệnh nhân ung thư trong điều trị y tế cấp tính. Bản quyền © 2001 John Wiley &amp; Sons, Ltd.\u003C/jats:p>",{"EN":3221,"VI":3222},"The effectiveness of relaxation training in reducing treatment‐related symptoms and improving emotional adjustment in acute non‐surgical cancer treatment: a meta‐analytical review","Hiệu quả của việc đào tạo thư giãn trong việc giảm triệu chứng liên quan đến điều trị và cải thiện sự điều chỉnh cảm xúc trong điều trị ung thư không phẫu thuật cấp tính: một đánh giá phân tích tổng hợp",{"VOID":3224},"11747061",{"VOID":3226},"10.1002/pon.537",[387],[389],"https://onlinelibrary.wiley.com/doi/10.1002/pon.537",[3231,3252,3271],{"id":3232,"sortIndex":157,"researcher":22,"roles":3233,"affiliations":3234,"properties":3245},"52317017-0539-438b-90c5-345ed39f7aa9",[],[3235],{"id":3236,"sortIndex":23,"affiliation":3237,"properties":22},"2664564f-858d-4d8f-8f5c-54a0890656b9",{"id":3238,"createTime":3239,"updateTime":3239,"relativeEntities":3240,"slug":3241,"properties":3242,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"ba4582a3-bac2-46fe-8086-155855029fed","2024-09-30T09:29:36.336+00:00",[],"Department-of-Medical-Psychology-Ruhr-University-of-Bochum-Bochum-Germany",{"title":3243},{"EN":3244},"Department of Medical Psychology, Ruhr-University of Bochum, Bochum, Germany",{"openalex":3246,"orcid":3248,"title":3250},{"VOID":3247},"A5022399404",{"VOID":3249},"https://orcid.org/0000-0002-4736-0738",{"EN":3251},"Monika Hasenbring",{"id":3253,"sortIndex":148,"researcher":22,"roles":3254,"affiliations":3255,"properties":3266},"1ef5491e-66ea-4103-9dd1-21cadcadd653",[],[3256],{"id":3257,"sortIndex":23,"affiliation":3258,"properties":22},"cd84b1e3-f2b5-4238-99e7-d4cc1ea60f35",{"id":3259,"createTime":3260,"updateTime":3260,"relativeEntities":3261,"slug":3262,"properties":3263,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"14df4820-7427-4805-af38-48cd88ab950d","2024-09-30T09:29:36.322+00:00",[],"Department-of-Clinical-Psychology-University-of-Hamburg-Hamburg-Germany",{"title":3264},{"EN":3265},"Department of Clinical Psychology, University of Hamburg, Hamburg, Germany",{"openalex":3267,"title":3269},{"VOID":3268},"A5062616584",{"EN":3270},"Bernhard Dahme",{"id":3272,"sortIndex":23,"researcher":22,"roles":3273,"affiliations":3274,"properties":3285},"5eed2cce-3e90-4441-912f-93b918defcfd",[],[3275],{"id":3276,"sortIndex":23,"affiliation":3277,"properties":22},"aa2fbcdd-ab22-4d52-aa15-3599361d919a",{"id":3278,"createTime":3279,"updateTime":3279,"relativeEntities":3280,"slug":3281,"properties":3282,"entityType":200,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23},"0e00389e-ef9f-4152-94eb-921b443add23","2024-09-30T09:29:36.309+00:00",[],"Bone-Marrow-Transplantation-Unit-University-Hospital-Hamburg-Hamburg-Germany",{"title":3283},{"EN":3284},"Bone Marrow Transplantation Unit, University Hospital Hamburg, Hamburg, Germany",{"openalex":3286,"title":3288},{"VOID":3287},"A5083048799",{"EN":3289},"Karin Luebbert",{"url":22,"publisher":3291,"properties":3341},{"id":3292,"createTime":3293,"updateTime":3294,"relativeEntities":3295,"slug":3296,"properties":3297,"entityType":136,"verifyStatus":385,"verifyTime":22,"verifyNote":22,"syncStatus":385,"languages":22,"translateLanguages":22,"viewCount":23,"subjectFields":3306,"manageAffiliations":3307,"indexDatabases":3308,"url":3340,"thumbnailPath":22,"statistic":22,"gsStatistic":22,"type":22,"analyzePriority":22},"86f2a268-e03f-4621-876a-3b5dceb2086d","2023-05-29T10:33:20.437+00:00","2025-02-11T06:15:24.218+00:00",[],"Psycho-Oncology",{"country":3298,"issn":3299,"introduce":3301,"eissn":3303,"title":3305},{"VOID":445},{"VOID":3300},"10579249",{"EN":3302},"Psycho-Oncology is concerned with the psychological, social, behavioral, and ethical aspects of cancer. This subspeciality addresses the two major psychological dimensions of cancer: the psychological responses of patients to cancer at all stages of the disease, and that of their families and caretakers; and the psychological, behavioral and social factors that may influence the disease process. Psycho-oncology is an area of multi-disciplinary interest and has boundaries with the major specialities in oncology: the clinical disciplines (surgery, medicine, pediatrics, radiotherapy), epidemiology, immunology, endocrinology, biology, pathology, bioethics, palliative care, rehabilitation medicine, clinical trials research and decision making, as well as psychiatry and psychology. This international journal is published twelve times a year and will consider contributions to research of clinical and theoretical interest. Topics covered are wide-ranging and relate to the psychosocial aspects of cancer and AIDS-related tumors, including: epidemiology, quality of life, palliative and supportive care, psychiatry, psychology, sociology, social work, nursing and educational issues. Special reviews are offered from time to time. There is a section reviewing recently published books. A society news section is available for the dissemination of information relating to meetings, conferences and other society-related topics. Summary proceedings of important national and international symposia falling within the aims of the journal are presented.",{"VOID":3304},"10991611",{"EN":3296},[],[],[3309,3319,3331],{"id":3310,"indexDatabase":3311,"url":3316,"indexYears":22,"academicFieldIds":3317},"c1e6e2e4-3b77-4ac3-8897-cc7e48a45d99",{"id":3021,"createTime":3022,"updateTime":3023,"relativeEntities":3312,"label":3313,"description":3314,"key":3030,"publicationTags":3315,"standard":22},[],{"EN":3026,"VI":3026},{"VI":3028,"EN":3029},[3032,494],"https://mjl.clarivate.com/search-results?issn=1057-9249",[3318,1962],"f5dadec7-bbcc-41b5-8118-407ab26c6120",{"id":3320,"indexDatabase":3321,"url":3326,"indexYears":3327,"academicFieldIds":3328},"b8ede33f-5703-4994-b938-ff9626ad0376",{"id":459,"createTime":460,"updateTime":461,"relativeEntities":3322,"label":3323,"description":3324,"key":467,"publicationTags":3325,"standard":22},[],{"EN":464,"VI":464},{"EN":464,"VI":466},[469],"https://www.scopus.com/sourceid/13069","1992-2023",[3329,3330,2566],"c6aa5b1f-2a2b-4eb1-8a8f-f81f3c8c8061","5b804690-ab8e-4cd2-b108-4ce52b7323fe",{"id":3332,"indexDatabase":3333,"url":3316,"indexYears":22,"academicFieldIds":3338},"f00fc5ff-226f-4ad9-848a-8174c338ccd3",{"id":482,"createTime":483,"updateTime":484,"relativeEntities":3334,"label":3335,"description":3336,"key":491,"publicationTags":3337,"standard":22},[],{"EN":487,"VI":487},{"VI":489,"EN":490},[493,494],[3339,2576],"19aa440f-3e1f-4bdc-9328-b2851b4de596","https://onlinelibrary.wiley.com/journal/10991611",{"volume":3342,"pages":3344,"issue":3346},{"VOID":3343},"10",{"VOID":3345},"490-502",{"VOID":3347},"6",303,{"total":3348,"publishYear":22,"statisticByYear":3350},{"2012":3060,"2013":1971,"2014":2598,"2015":3351,"2016":1437,"2017":1887,"2018":522,"2019":1436,"2020":520,"2021":515,"2023":521,"2024":157},19,"2001-11-01",2001,[3355,3358,3361,3364,3367,3370,3373,3376,3379,3382,3385,3388,3391,3394,3397,3400,3403,3406,3409,3412,3415,3418,3422,3425,3428,3431,3434,3437,3440,3443,3446,3449,3452,3455,3458,3461,3464,3467,3470,3473,3476,3479,3482,3485,3488,3491,3494],{"id":22,"text":3356,"url":22,"identifiers":3357},"Bandura A, 1977, Self‐efficacy: toward a unifying theory of behavioral change, Psychol Rev, 85, 447",{},{"id":22,"text":3359,"url":22,"identifiers":3360},"Bernstein DA, 1973, Progressive Relaxation Training: A Manual for the Helping Professions",{},{"id":22,"text":3362,"url":22,"identifiers":3363},"Bridge LR, 1988, Relaxation and imagery in the treatment of breast cancer, Br Med J, 5, 1167",{},{"id":22,"text":3365,"url":22,"identifiers":3366},"10.1007/BF00844848",{"doi":3365},{"id":22,"text":3368,"url":22,"identifiers":3369},"10.1037/0278-6133.11.1.17",{"doi":3368},{"id":22,"text":3371,"url":22,"identifiers":3372},"Burish TG, 1983, Behavioral relaxation techniques in reducing the distress of cancer chemotherapy patients, Oncol Nurs Forum, 10, 32",{},{"id":22,"text":3374,"url":22,"identifiers":3375},"10.1037/0022-006X.55.1.42",{"doi":3374},{"id":22,"text":3377,"url":22,"identifiers":3378},"10.1300/J011v04n02_01",{"doi":3377},{"id":22,"text":3380,"url":22,"identifiers":3381},"10.1037/0022-006X.59.4.518",{"doi":3380},{"id":22,"text":3383,"url":22,"identifiers":3384},"10.1037/0022-006X.53.6.860",{"doi":3383},{"id":22,"text":3386,"url":22,"identifiers":3387},"10.1037/0022-006X.55.5.732",{"doi":3386},{"id":22,"text":3389,"url":22,"identifiers":3390},"10.1037/0033-2909.104.3.307",{"doi":3389},{"id":22,"text":3392,"url":22,"identifiers":3393},"Chapko MK, 1989, Chemotherapy toxicity during bone marrow transplantation: time course and variation in pain and nausea, Bone Marrow Transplant, 4, 181",{},{"id":22,"text":3395,"url":22,"identifiers":3396},"Chapman CR, 1987, Cancer Pain Management, 47",{},{"id":22,"text":3398,"url":22,"identifiers":3399},"Cohen J, 1988, Statistical Power Analysis for the Behavioral Sciences",{},{"id":22,"text":3401,"url":22,"identifiers":3402},"10.1037/0022-3514.41.5.1013",{"doi":3401},{"id":22,"text":3404,"url":22,"identifiers":3405},"Cooper HM, 1989, Integrating Research. A Guide for Literature Reviews",{},{"id":22,"text":3407,"url":22,"identifiers":3408},"Cotanch PH, 1987, Progressive muscle relaxation as antiemetic therapy for cancer patients, Oncol Nurs Forum, 14, 33",{},{"id":22,"text":3410,"url":22,"identifiers":3411},"10.1002/1097-4679(199205)48:3\u003C388::AID-JCLP2270480318>3.0.CO;2-O",{"doi":3410},{"id":22,"text":3413,"url":22,"identifiers":3414},"10.1001/archpsyc.1995.03950140018003",{"doi":3413},{"id":22,"text":3416,"url":22,"identifiers":3417},"Fricke R, 1985, Einführung in die Meta‐analyse",{},{"id":22,"text":3419,"url":22,"identifiers":3420},"Fürst CJ, 1992, Control of cisplatin induced emesis—a multidisciplinary intervention strategy, Med Oncol and Tumor Pharmacother, 9, 81, 10.1007/BF02989658",{"doi":3421},"10.1007/BF02989658",{"id":22,"text":3423,"url":22,"identifiers":3424},"Hasenbring M, 1998, The efficacy of psychosocial interventions for improving quality of life in BMT patients: first results from two randomized intervention studies, Psycho‐Oncology, 7, Abstract no. 7",{},{"id":22,"text":3426,"url":22,"identifiers":3427},"10.3102/0091732X013001353",{"doi":3426},{"id":22,"text":3429,"url":22,"identifiers":3430},"Hedges LV, 1985, Statistical Methods for Meta‐analysis",{},{"id":22,"text":3432,"url":22,"identifiers":3433},"Johnson BT, 1989, DSTAT: Software for the Meta‐Analytic Review of Research Literatures Manual",{},{"id":22,"text":3435,"url":22,"identifiers":3436},"10.1016/0005-7967(70)90052-5",{"doi":3435},{"id":22,"text":3438,"url":22,"identifiers":3439},"10.1111/j.1471-6712.1992.tb00149.x",{"doi":3438},{"id":22,"text":3441,"url":22,"identifiers":3442},"10.1056/NEJM198110153051609",{"doi":3441},{"id":22,"text":3444,"url":22,"identifiers":3445},"10.1097/00002820-199010000-00006",{"doi":3444},{"id":22,"text":3447,"url":22,"identifiers":3448},"10.1037/0022-006X.50.4.509",{"doi":3447},{"id":22,"text":3450,"url":22,"identifiers":3451},"10.1037/0278-6133.14.2.101",{"doi":3450},{"id":22,"text":3453,"url":22,"identifiers":3454},"10.1037/0022-006X.52.6.1098",{"doi":3453},{"id":22,"text":3456,"url":22,"identifiers":3457},"10.1007/BF01173295",{"doi":3456},{"id":22,"text":3459,"url":22,"identifiers":3460},"10.1097/00006842-198001000-00004",{"doi":3459},{"id":22,"text":3462,"url":22,"identifiers":3463},"Orwin RG, 1983, A fail‐safe N for effect size, J Educ Stat, 8, 147",{},{"id":22,"text":3465,"url":22,"identifiers":3466},"10.1037/0278-6133.14.2.99",{"doi":3465},{"id":22,"text":3468,"url":22,"identifiers":3469},"Redd WH, 1981, Conditioned aversion in cancer patients, Behav Ther, 4, 3",{},{"id":22,"text":3471,"url":22,"identifiers":3472},"10.1037/0022-006X.50.6.1018",{"doi":3471},{"id":22,"text":3474,"url":22,"identifiers":3475},"10.1080/00029157.1982.10404089",{"doi":3474},{"id":22,"text":3477,"url":22,"identifiers":3478},"Reeves JL, 1982, Hypnosis in the control of pain during hyperthermia treatment of cancer, Adv Pain Res Ther, 5, 857",{},{"id":22,"text":3480,"url":22,"identifiers":3481},"10.1037/0033-2909.86.3.638",{"doi":3480},{"id":22,"text":3483,"url":22,"identifiers":3484},"Rosenthal R, 1984, Meta‐analytic Procedures for Social Research",{},{"id":22,"text":3486,"url":22,"identifiers":3487},"10.1016/0304-3959(92)90049-H",{"doi":3486},{"id":22,"text":3489,"url":22,"identifiers":3490},"10.1016/0304-3959(95)00039-U",{"doi":3489},{"id":22,"text":3492,"url":22,"identifiers":3493},"10.1007/BF00844755",{"doi":3492},{"id":22,"text":3495,"url":22,"identifiers":3496},"Yoo HJ, 1997, Efficacy of relaxation training (RT) and guided imagery (GI) in reducing the side effects of cancer chemotherapy, Proc ASCO, 16, 86a",{},{"id":3498,"createTime":3499,"updateTime":3500,"relativeEntities":3501,"slug":3502,"properties":3503,"entityType":242,"verifyStatus":382,"verifyTime":3517,"verifyNote":384,"syncStatus":385,"languages":3518,"translateLanguages":3519,"viewCount":23,"primaryUrl":3520,"fullTextUrl":22,"authors":3521,"publicationType":244,"publisherRelationship":3580,"citationCount":3675,"citationInfo":3676,"publishDate":3678,"publishYear":3679,"citationAnalyzeStatus":385,"lastCitationAnalyze":22,"indexDatabases":22,"openAccess":22,"references":3680,"isForceReanalyzing":19},"bb199038-de73-4818-838f-3855d7cae6c3","2024-04-14T23:36:29.379+00:00","2025-01-04T13:50:24.333+00:00",[],"A-hybrid-Caputo-fractional-modeling-for-thermostat-with-hybrid-boundary-value-conditions",{"mag":3504,"keywords":3506,"openalex":3507,"abstract":3509,"title":3512,"doi":3515},{"VOID":3505},"3032645491",{"VI":15},{"VOID":3508},"W3032645491",{"VI":3510,"EN":3511},"\u003Cjats:title>Tóm tắt\u003C/jats:title>\u003Cjats:p>Chúng tôi cung cấp một sự mở rộng cho phương trình vi phân bậc hai của mô hình nhiệt kế đến phương trình hỗn hợp phân số và các phiên bản bao hàm. Chúng tôi xem xét các điều kiện giá trị biên cho vấn đề này dưới dạng các điều kiện hỗn hợp. Để chứng minh sự tồn tại của các nghiệm cho phương trình nhiệt kế hỗn hợp phân số và các phiên bản bao hàm của chúng, chúng tôi áp dụng các định lý điểm cố định Dhage nổi tiếng cho các ánh xạ đơn trị và tập trị. Cuối cùng, chúng tôi đưa ra hai ví dụ để minh họa cho các kết quả chính của chúng tôi.\u003C/jats:p>","\u003Cjats:title>Abstract\u003C/jats:title>\u003Cjats:p>We provide an extension for the second-order differential equation of a thermostat model to the fractional hybrid equation and inclusion versions. We consider boundary value conditions of this problem in the form of the hybrid conditions. To prove the existence of solutions for our hybrid fractional thermostat equation and inclusion versions, we apply the well-known Dhage fixed point theorems for single-valued and set-valued maps. Finally, we give two examples to illustrate our main results.\u003C/jats:p>",{"VI":3513,"EN":3514},"Mô hình hỗn hợp Caputo phân số cho nhiệt kế với các điều kiện biên hỗn hợp","A hybrid Caputo fractional modeling for thermostat with hybrid boundary value 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