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Post-chemoradiation T4 rectal cancer remains a therapeutic challenge and an aggressive surgical approach is the only chance for a cure. Rectal lesions infiltrating the upper vaginal wall and uterine cervix are usually treated by low anterior resection with en bloc removal of the vagina and uterus. However, failure can occur when one is trying to access the anterior recto-vaginal plane below the tumor, especially in obese patients with a narrow pelvis. The remaining surgical alternatives are aggressive and debilitating. The objective of the study is to describe a modified supralevator pelvic exenteration for selected patients. A new surgical option is added to the armamentarium of the oncologic surgeon. The discussion focuses on the indications for this surgical technique and its advantages, such as the preservation of the anal sphincter and the vagina, thus allowing for fecal continence and sexual activity.

In structural biology area, protein residue-residue contacts play a crucial role in protein structure prediction. Some researchers have found that the predicted residue-residue contacts could effectively constrain the conformational search space, which is significant for de novo protein structure prediction. In the last few decades, related researchers have developed various methods to predict residue-residue contacts, especially, significant performance has been achieved by using fusion methods in recent years. In this work, a novel fusion method based on rank strategy has been proposed to predict contacts. Unlike the traditional regression or classification strategies, the contact prediction task is regarded as a ranking task. First, two kinds of features are extracted from correlated mutations methods and ensemble machine-learning classifiers, and then the proposed method uses the learning-to-rank algorithm to predict contact probability of each residue pair. First, we perform two benchmark tests for the proposed fusion method (RRCRank) on CASP11 dataset and CASP12 dataset respectively. The test results show that the RRCRank method outperforms other well-developed methods, especially for medium and short range contacts. Second, in order to verify the superiority of ranking strategy, we predict contacts by using the traditional regression and classification strategies based on the same features as ranking strategy. Compared with these two traditional strategies, the proposed ranking strategy shows better performance for three contact types, in particular for long range contacts. Third, the proposed RRCRank has been compared with several state-of-the-art methods in CASP11 and CASP12. The results show that the RRCRank could achieve comparable prediction precisions and is better than three methods in most assessment metrics. The learning-to-rank algorithm is introduced to develop a novel rank-based method for the residue-residue contact prediction of proteins, which achieves state-of-the-art performance based on the extensive assessment.