4 7 OPENThe snub-nosed monkey genus Rhinopithecus includes five closely related species distributed across altitudinal gradients from 800 to 4,500 m. Rhinopithecus bieti, Rhinopithecus roxellana, and Rhinopithecus strykeri inhabit high-altitude habitats, whereas Rhinopithecus brelichi and Rhinopithecus avunculus inhabit lowland regions. We report the de novo whole-genome sequence of R. bieti and genomic sequences for the four other species. Eight shared substitutions were found in six genes related to lung function, DNA repair, and angiogenesis in the high-altitude snub-nosed monkeys. Functional assays showed that the high-altitude variant of CDT1 (Ala537Val) renders cells more resistant to UV irradiation, and the high-altitude variants of RNASE4 (Asn89Lys and Thr128Ile) confer enhanced ability to induce endothelial tube formation in vitro. Genomic scans in the R. bieti and R. roxellana populations identified signatures of selection between and within populations at genes involved in functions relevant to high-altitude adaptation. These results provide valuable insights into the adaptation to high altitude in the snub-nosed monkeys.

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To evaluate the fatigue damage accumulation and predict the residual life of components at different stress levels, this paper proposed a modified cumulative damage model based on the strain energy density parameter. Noting that mean stress and load interaction under uniaxial fatigue loading exhibit significant effects on fatigue damage accumulation and life prediction. According to this, a new model based on damaged stress model which considers the effects of mean stress and load interaction was presented in this paper. The proposed model was verified by using four experimental data sets of aluminium alloys and steels. The experimental results are compared with those of the Miner’s rule, damaged stress model (DSM) and damaged energy model (DEM). Results show that the proposed model agrees better with the experimental observations than others.

Background: Verifying interactions between drugs and targets is key to discover new drugs. Many computational methods have been developed to predict drug-target interactions and performed successfully, but challenges still exist in the field. Objective: We try to develop a machine learning method to predict drug-target affinity, which can determine the strength of the binding relationship between drug and target. Method: This paper proposes an integrated machine learning system for drug-target binding affinity prediction based on network fusion. First, multiple similarity networks representing drugs or targets are calculated. Second, multiple networks representing drugs (targets) are fused separately. Finally, the characteristic information of splicing drugs and targets was used for model construction and training. By integrating multiple similarity networks, the model fully embodies the complementarity of network information, and the most complete features of information can be obtained after the redundancy is removed. Results: Experimental results showed that our model obtained good results for DTI binding affinity. Conclusion: It is still challenging to predict drug-target affinity. This paper proposes to use an integrated system of fusion network information for addressing the issue, and the proposed method performs well, which can provide a certain data basis for the subsequent work. Website: https://www.dlearningapp.com/web/inmpba.htm

When carrying on laboratory experimental studies on broken piles, the test results depend on the technique used in simulating the model piles. Based on laboratory model tests, a complete set of making process of broken piles used in the study of the working behavior and mechanism of broken piles was presented in detail. The techniques include the pore-forming process and the simulation of the broken defects. The load vs. settlement curves for the vertically loaded broken piles obtained through the model pile tests were adopted and demonstrated the feasibility of the production process.