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The present study punctuates the importance of using nuclear genome-wide information and representative sampling for resolving phylogenetic relationships, especially in broadly distributed species and those with complex evolutionary histories.The Asian water snake genus Trimerodytes (formerly Sinonatrix) is endemic to East and Southeast Asia. Although several species have been included in various phylogenetic studies previously, the evolution and relationships among members of this genus as a whole remain unexplored. In this study, we report the sequencing two protein-coding mitochondrial gene fragments (MTCYB and ND2) and three nuclear genes (c-mos, NT3, and Rag1), reconstruct interspecific phylogeny, and explore biogeography for the genus Trimerodytes. Both Bayesian inference and maximum likelihood analyses consistently recover the monophyly of Trimerodytes with strong support, with T. yapingi the sister-group to the remaining species. The divergence date and ancestral area estimation suggest that Trimerodytes likely originated in Hengduan Mountains (eastern Tibetan Plateau) in western China at 23.93 Ma (95% HPD 17.09-31.30), and intraspecific divergence began at about 4.23 Ma (95% HPD 2.74-6.10). Analyses support the validity of T. yunnanensis.Comparative phylogeography is a powerful approach to investigate the role of historical and environmental processes in the evolution of biodiversity within a region. In this regard, comparative studies of species with similar habitat preferences are valuable to reduce the confounding influence of habitat association when interpreting phylogeographic patterns. In the Atlantic Forest of South America, phylogeographic studies of highland and lowland species have shown distinct population structure patterns so far, suggesting that such species have responded differently to Pleistocene glacial cycles. Herein, we performed a comparative analysis using molecular data and paleodistribution models of two Montane Atlantic Forest (MAF) co-distributed passerine birds with similar habitat requirements but with distinct life-history traits and ecologies the frugivore lek-breeding Blue Manakin (Chiroxiphia caudata) and the insectivore and socially monogamous Drab-Breasted Bamboo Tyrant (Hemitriccus diops). We aimed to shed a lek-breeding species, it has a longer generation time than H. diops. These traits could hinder genetic differentiation when populations become geographically isolated. Nonetheless, both species showed some common biological features, such as signatures of synchronous population expansion and larger distribution ranges during the Last Glacial Maximum, possibly due to similar cold tolerance.Neurofibromatosis type 1 is the most common neurocutaneous syndrome with a frequency of 1 in 2500 persons. Diagnosis is paramount in the pretumor stage in order to provide proper anticipatory guidance for a number of neoplasms, both benign and malignant. Loss of function mutations in the NF1 gene result in a truncated and non-functional production of neurofibromin, a tumor suppressor protein involved in downregulating the RAS signaling pathway. New therapeutic and preventive options include tyrosine kinase inhibitors, mTOR inhibitors, interferons, and radiofrequency therapy. This review summarizes recent updates in genetics, mutation analysis assays, and treatment options targeting aberrant genetic pathways. We also propose modified diagnostic criteria and provide an algorithm for surveillance of patients with neurofibromatosis type 1.Causal inference often relies on the counterfactual framework, which requires that treatment assignment is independent of the outcome, known as strong ignorability. Approaches to enforcing strong ignorability in causal analyses of observational data include weighting and matching methods. Effect estimates, such as the average treatment effect (ATE), are then estimated as expectations under the re-weighted or matched distribution, P. The choice of P is important and can impact the interpretation of the effect estimate and the variance of effect estimates. In this work, instead of specifying P, we learn a distribution that simultaneously maximizes coverage and minimizes variance of ATE estimates. In order to learn this distribution, this research proposes a generative adversarial network (GAN)-based model called the Counterfactual χ-GAN (cGAN), which also learns feature-balancing weights and supports unbiased causal estimation in the absence of unobserved confounding. Our model minimizes the Pearson χ2-divergence, which we show simultaneously maximizes coverage and minimizes the variance of importance sampling estimates. To our knowledge, this is the first such application of the Pearson χ2-divergence. selleck kinase inhibitor We demonstrate the effectiveness of cGAN in achieving feature balance relative to established weighting methods in simulation and with real-world medical data.

Artificial intelligence in healthcare increasingly relies on relations in knowledge graphs for algorithm development. However, many important relations are not well covered in existing knowledge graphs. We aim to develop a novel long-distance relation extraction algorithm that leverages the article section structure and is trained with bootstrapped noisy data to identify important relations for diagnosis, including may cause, may be caused by, and differential diagnosis.

Known relations were extracted from semistructured web pages and a relational database and were paired with sentences containing corresponding medical concepts to form training data. The sentence form was extended to allow one concept to be in the title. An attention mechanism was applied to reduce the effect of noisily labeled sentences. Section structure embedding was added to provide additional context for relation expressions. Graph information was further incorporated into the model to differentiate the target relations whose expressions were often similar and interwoven.

The extended sentence form allowed 1.75 times as many relations and 2.17 times as many sentences to be found compared to the conventional form. The various components of the proposed model all added to the accuracy. Overall, the positive sample accuracy of the proposed model was 9 percentage points higher than baseline deep learning models and 13 percentage points higher than naïve Bayes and support vector machines.

Our bootstrap data preparation method and the extended sentence form could form a large training dataset to enable algorithm development and data mining efforts. Section structure embedding and graph information significantly increased prediction accuracy.

Our bootstrap data preparation method and the extended sentence form could form a large training dataset to enable algorithm development and data mining efforts. Section structure embedding and graph information significantly increased prediction accuracy.