Microsatellites within genes and ESTs of the Pacific oyster Crassostrea gigas and their transferability in five other Crassostrea species

We developed 15 novel polymorphic microsatellites for the Pacific oyster Crassostrea gigas by screening genes and expressed sequence tags (ESTs) found in GenBank. The number of alleles per locus ranged from two to 24 with an average of 8.7, and the values of observed heterozygosity (Ho) and expected heterozygosity (He) ranged from 0.026 to 0.750 and from 0.120 to 0.947, respectively. No significant pairwise linkage disequilibrium was detected among loci and eight loci conformed to Hardy-Weinberg equilibrium. Transferability of the markers was examined on five other Crassostrea species and all the markers were amplified successfully in at least one species. These new microsatellites should be useful for population genetics, parentage analysis and genome mapping studies of C. gigas and closely related species. The nine markers identified from known genes are expected to be especially valuable for comparative mapping as type I markers

Evaluation of the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems in predicting postoperative mortality and morbidity in gastric cancer patients

SummaryBackground/ObjectiveGastric cancer is the fourth most prevalent cancer worldwide. The ability to accurately predict surgery-related morbidity and mortality is critical in deciding both the timing of surgery and choice of surgical procedure. The aim of this study is to compare the POSSUM, p-POSSUM, o-POSSUM, and APACHE II scoring systems for predicting surgical morbidity and mortality in Chinese gastric cancer patients, as well as to create new scoring systems to achieve better prediction.MethodsData from 612 gastric cancer patients undergoing gastrectomy between January 2007 and December 2011 were included in this study. The predictive abilities of the four scoring systems were compared by examining observed-to-expected (O/E) ratios, the receiver operating characteristic curve, Student t test, and χ2 test results.ResultsThe observed complication rate of 34% (n = 208) did not differ significantly from the rate of 36.6% (n = 208) predicted by the POSSUM scoring system (O/E ratio = 0.93). The observed mortality rate was 2.9% (n = 18). For predicting mortality, POSSUM had an O/E ratio of 0.34 as compared with p-POSSUM (O/E ratio = 0.91), o-POSSUM (O/E ratio = 1.26), and APACHE II (O/E ratio = 0.28).ConclusionThe POSSUM scoring system performed well with respect to predicting morbidity risk following gastric cancer resection. For predicting postoperative mortality, p-POSSUM and o-POSSUM exhibited superior performance relative to POSSUM and APACHE II

Effective Semisupervised Community Detection Using Negative Information

The semisupervised community detection method, which can utilize prior information to guide the discovery process of community structure, has aroused considerable research interests in the past few years. Most of the former works assume that the exact labels of some nodes are known in advance and presented in the forms of individual labels and pairwise constraints. In this paper, we propose a novel type of prior information called negative information, which indicates whether a node does not belong to a specific community. Then the semisupervised community detection algorithm is presented based on negative information to efficiently make use of this type of information to assist the process of community detection. The proposed algorithm is evaluated on several artificial and real-world networks and shows high effectiveness in recovering communities

UnitedHuman: Harnessing Multi-Source Data for High-Resolution Human Generation

Human generation has achieved significant progress. Nonetheless, existing methods still struggle to synthesize specific regions such as faces and hands. We argue that the main reason is rooted in the training data. A holistic human dataset inevitably has insufficient and low-resolution information on local parts. Therefore, we propose to use multi-source datasets with various resolution images to jointly learn a high-resolution human generative model. However, multi-source data inherently a) contains different parts that do not spatially align into a coherent human, and b) comes with different scales. To tackle these challenges, we propose an end-to-end framework, UnitedHuman, that empowers continuous GAN with the ability to effectively utilize multi-source data for high-resolution human generation. Specifically, 1) we design a Multi-Source Spatial Transformer that spatially aligns multi-source images to full-body space with a human parametric model. 2) Next, a continuous GAN is proposed with global-structural guidance and CutMix consistency. Patches from different datasets are then sampled and transformed to supervise the training of this scale-invariant generative model. Extensive experiments demonstrate that our model jointly learned from multi-source data achieves superior quality than those learned from a holistic dataset.Comment: Accepted by ICCV2023. Project page: https://unitedhuman.github.io/ Github: https://github.com/UnitedHuman/UnitedHuma