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Accurate pathogenicity prediction of missense variants is critically important in genetic studies and clinical diagnosis. Previously published prediction methods have facilitated the interpretation of missense variants but have limited performance. Here, we describe MVP (Missense Variant Pathogenicity prediction), a new prediction method that uses deep residual network to leverage large training data sets and many correlated predictors. We train the model separately in genes that are intolerant of loss of function variants and the ones that are tolerant in order to take account of potentially different genetic effect size and mode of action. We compile cancer mutation hotspots and de novo variants from developmental disorders for benchmarking. Overall, MVP achieves better performance in prioritizing pathogenic missense variants than previous methods, especially in genes tolerant of loss of function variants. Finally, using MVP, we estimate that de novo coding variants contribute to 7.8% of isolated congenital heart disease, nearly doubling previous estimates. Accurate prediction of variant pathogenicity is essential to understanding genetic risks in disease. Here, the authors present a deep neural network method for prediction of missense variant pathogenicity, MVP, and demonstrate its utility in prioritizing de novo variants contributing to developmental disorders.

A class of boundary value problems for fractional differential systems involving variable-order derivatives is considered. Such problems can be transformed into some boundary value problems for nonlinear Caputo fractional differential systems. Here, the relations between linear Caputo fractional differential equations and their corresponding linear integral equations are investigated, and the results demonstrate that a proper Lipschitz-type condition is needed for studying nonlinear Caputo fractional differential equations. Then, an existence and uniqueness result is established in some vector subspaces by Banach’s fixed-point theorem and ∥·∥e norm. In addition, two examples are presented to illustrate the theoretical conclusions.

Accurate prediction of the functional impact of missense variants is important for disease gene discovery, clinical genetic diagnostics, therapeutic strategies, and protein engineering. Previous efforts have focused on predicting a binary pathogenicity classification, but the functional impact of missense variants is multi-dimensional. Pathogenic missense variants in the same gene may act through different modes of action (i.e., gain/loss-of-function) by affecting different aspects of protein function. They may result in distinct clinical conditions that require different treatments. We develop a new method, PreMode, to perform gene-specific mode-of-action predictions. PreMode models effects of coding sequence variants using SE(3)-equivariant graph neural networks on protein sequences and structures. Using the largest-to-date set of missense variants with known modes of action, we show that PreMode reaches state-of-the-art performance in multiple types of mode-of-action predictions by efficient transfer-learning. Additionally, PreMode’s prediction of G/LoF variants in a kinase is consistent with inactive-active conformation transition energy changes. Finally, we show that PreMode enables efficient study design of deep mutational scans and can be expanded to fitness optimization of non-human proteins with active learning. Accurate prediction of the functional impact of missense variants is important in clinical genetic diagnostics and therapeutic strategies. Here the authors introduce a largest-to-date dataset of human missense variants labeled with their mode-of-action and a deep learning method to predict mode-of-action effects with state-of-the-art performance.

The electrocatalysts are widely applied in lithium–sulfur (Li–S) batteries to selectively accelerate the redox kinetics behavior of Li2S, in which bifunctional active sites are established, thereby improving the electrochemical performance of the battery. Considering that the Li–S battery is a complex closed “black box” system, the internal redox reaction routes and active sites cannot be directly observed and monitored especially due to the distribution of potential active‐site structures and their dynamic reconstruction. Empirical evidence demonstrates that traditional electrochemical test methods and theoretical calculations only probe the net result of multi‐factors on an average and whole scale. Herein, based on the amorphous TiO2‐x@Ni selective bifunctional model catalyst, these limitations are overcome by developing a system that couples the light field and in situ irradiated X‐ray photoelectron spectroscopy to synergistically convert the “black box” battery into a “see‐through” battery for direct observation of the charge transportation, thus revealing that amorphous TiO2‐x and Ni nanoparticle as the oxidation and reduction sites selectively promote the decomposition and nucleation of Li2S, respectively. This work provides a universal method to achieve a deeper mechanistic understanding of bidirectional sulfur electrochemistry. In situ irradiated X‐ray photoelectron spectroscopy coupled with the light field is hired to synergistically convert the “black box” battery into a “see‐through” battery for direct observation of the charge transportation, thus revealing that amorphous TiO2‐x and Ni nanoparticle as the oxidation and reduction sites selectively promote the decomposition and nucleation of Li2S, respectively.

Accurately predicting the effect of missense variants is important in discovering disease risk genes and clinical genetic diagnostics. Commonly used computational methods predict pathogenicity, which does not capture the quantitative impact on fitness in humans. We develop a method, MisFit, to estimate missense fitness effect using a graphical model. MisFit jointly models the effect at a molecular level ( d ) and a population level (selection coefficient, s ), assuming that in the same gene, missense variants with similar d have similar s . We train it by maximizing probability of observed allele counts in 236,017 individuals of European ancestry. We show that s is informative in predicting allele frequency across ancestries and consistent with the fraction of de novo mutations in sites under strong selection. Further, s outperforms previous methods in prioritizing de novo missense variants in individuals with neurodevelopmental disorders. In conclusion, MisFit accurately predicts s and yields new insights from genomic data. Predicting the effects of missense variants is important in discovering disease risk genes and clinical genetic diagnostics. Here the authors introduce a deep learning method (MisFit) to estimate the fitness effects of all possible human missense variants using large population data, achieving superior performance in prioritizing risk variants in early-onset conditions.

In this paper, a class of nonlinear fractional differential equations with periodic boundary condition is investigated. Although the nonlinearity of the equation and the Green’s function are sign-changing, the results of the existence and nonexistence of positive solutions are obtained by using the Schaefer’s fixed-point theorem. Finally, two examples are given to illustrate the main results.

Sophisticated efficient electrocatalysts are essential to rectifying the shuttle effect and realizing the high performance of flexible lithium‐sulfur batteries (LSBs). Phase transformation of MoSe2 from the 2H phase to the 1T phase has been proven to be a significant method to improve the catalytic activity. However, precisely controllable phase engineering of MoSe2 has rarely been reported. Herein, by in situ Li ions intercalation in MoSe2, a precisely controllable phase evolution from 2H‐MoSe2 to 1T‐MoSe2 was realized. More importantly, the definite functional relationship between cut‐off voltage and phase structure was first identified for phase engineering through in situ observation and modulation methods. The sulfur host (CNFs/1T‐MoSe2) presents high charge density, strong polysulfides adsorption, and catalytic kinetics. Moreover, Li‐S cells based on it display capacity retention of 875.3 mAh g−1 after 500 cycles at 1 C and an areal capacity of 8.71 mAh cm−2 even at a high sulfur loading of 8.47 mg cm−2. Furthermore, the flexible pouch cell exhibiting decent performance will endow a promising potential in the wearable energy storage field. This study proposes an effective strategy to precisely control the phase structure of MoSe2, which may provide the reference to fabricate the highly efficient electrocatalysts for LSBs and other energy systems. A definite functional relationship between cut‐off voltage and phase structure was first proposed for MoSe2 phase engineering, which realizes the control of the MoSe2 electrocatalyst microstructure at the macro level, enabling a stable cycle life and high energy density in Li‐S batteries.

The main contribution of this paper is to establish some new criteria, which ensure that every solution of third-order neutral delay differential equations with distributed deviating arguments is either oscillatory or tends to zero. The obtained theorems extend and improve several known results in the literature. Two examples are provided to illustrate the main results.

Background: Acute mountain sickness (AMS) is a prevalent and potentially life-threatening condition caused by rapid exposure to high-altitude hypoxia, affecting pulmonary and neurological functions. Tongqiao Jiuxin Oil (TQ), a traditional Chinese medicine formula composed of aromatic and resinous ingredients such as sandalwood, agarwood, frankincense, borneol, and musk, has been widely used in the treatment of cardiovascular and cerebrovascular disorders. Clinical observations suggest its potential efficacy against AMS, yet its pharmacological mechanisms remain poorly understood. Methods: The chemical profile of TQ was characterized using UHPLC-Q-Exactive Orbitrap HRMS. Network pharmacology was applied to predict the potential targets and pathways involved in AMS. A rat model of AMS was established by exposing animals to hypobaric hypoxia (~10% oxygen), simulating an altitude of approximately 5500 m. TQ was administered at varying doses. Physiological indices, oxidative stress markers (MDA, SOD, GSH), histopathological changes, and the expression of hypoxia- and apoptosis-related proteins (HIF-1α, VEGFA, EPO, Bax, Bcl-2, Caspase-3) in lung and brain tissues were assessed. Results: A total of 774 chemical constituents were identified from TQ. Network pharmacology predicted the involvement of multiple targets and pathways. TQ significantly improved arterial oxygenation and reduced histopathological damage in both lung and brain tissues. It enhanced antioxidant activity by elevating SOD and GSH levels and reducing MDA content. Mechanistically, TQ downregulated the expression of HIF-1α, VEGFA, EPO, and pro-apoptotic markers (Bax/Bcl-2 ratio, Caspase-3), while upregulated Bcl-2, the anti-apoptotic protein expression. Conclusions: TQ exerts protective effects against AMS-induced tissue injury by improving oxygen homeostasis, alleviating oxidative stress, and modulating hypoxia-related and apoptotic signaling pathways. This study provides pharmacological evidence supporting the potential of TQ as a promising candidate for AMS intervention, as well as the modern research method for multi-component traditional Chinese medicine.

Acral melanoma (AM) is a subtype of melanoma with high prevalence in East Asians. AM is characterized by greater aggressiveness and lower survival rates. However, there are still fewer studies on immune mechanisms of AM especially subungual melanoma (SM) versus non-subungual melanoma (NSM). In order to explore tumor heterogeneity and immune microenvironment in different subtypes of AM, we applied single-cell RNA sequencing to 24,789 single cells isolated from the SM and plantar melanoma (PM) patients. Aspects of tumor heterogeneity, melanocytes from PM and SM had significant differences in gene expression, CNV and pathways in which tumor-associated such as NF-kb and Wnt were involved. Regarding the immune microenvironment, PM contained more fibroblasts and T/NK cells. The EPHA3-EFNA1 axis was expressed only in cancer-associated fibroblast (CAF) and melanocytes of PM, and the TIGIT-NECTIN2 axis was expressed in both AM subtypes of T/NK cells and melanocytes. Altogether, our study helps to elucidate the tumor heterogeneity in AM subpopulations and provides potential therapeutic targets for clinical research.