Machine-learned molecular mechanics force fields from large-scale quantum chemical data.

The development of reliable and extensible molecular mechanics (MM) force fields-fast, empirical models characterizing the potential energy surface of molecular systems-is indispensable for biomolecular simulation and computer-aided drug design. Here, we introduce a generalized and extensible machine-learned MM force field, espaloma-0.3, and an end-to-end differentiable framework using graph neural networks to overcome the limitations of traditional rule-based methods. Trained in a single GPU-day to fit a large and diverse quantum chemical dataset of over 1.1 M energy and force calculations, espaloma-0.3 reproduces quantum chemical energetic properties of chemical domains highly relevant to drug discovery, including small molecules, peptides, and nucleic acids. Moreover, this force field maintains the quantum chemical energy-minimized geometries of small molecules and preserves the condensed phase properties of peptides and folded proteins, self-consistently parametrizing proteins and ligands to produce stable simulations leading to highly accurate predictions of binding free energies. This methodology demonstrates significant promise as a path forward for systematically building more accurate force fields that are easily extensible to new chemical domains of interest.

Spatial-aware contrastive learning for cross-domain medical image registration.

BACKGROUND: With the rapid advancement of medical imaging technologies, precise image analysis and diagnosis play a crucial role in enhancing treatment outcomes and patient care. Computed tomography (CT) and magnetic resonance imaging (MRI), as pivotal technologies in medical imaging, exhibit unique advantages in bone imaging and soft tissue contrast, respectively. However, cross-domain medical image registration confronts significant challenges due to the substantial differences in contrast, texture, and noise levels between different imaging modalities. PURPOSE: The purpose of this study is to address the major challenges encountered in the field of cross-domain medical image registration by proposing a spatial-aware contrastive learning approach that effectively integrates shared information from CT and MRI images. Our objective is to optimize the feature space representation by employing advanced reconstruction and contrastive loss functions, overcoming the limitations of traditional registration methods when dealing with different imaging modalities. Through this approach, we aim to enhance the model's ability to learn structural similarities across domain images, improve registration accuracy, and provide more precise imaging analysis tools for clinical diagnosis and treatment planning. METHODS: With prior knowledge that different domains of images (CT and MRI) share same content-style information, we extract equivalent feature spaces from both images, enabling accurate cross-domain point matching. We employ a structure resembling that of an autoencoder, augmented with designed reconstruction and contrastive losses to fulfill our objectives. We also propose region mask to solve the conflict between spatial correlation and distinctiveness, to obtain a better representation space. RESULTS: Our research results demonstrate the significant superiority of the proposed spatial-aware contrastive learning approach in the domain of cross-domain medical image registration. Quantitatively, our method achieved an average Dice similarity coefficient (DSC) of 85.68%, target registration error (TRE) of 1.92 mm, and mean Hausdorff distance (MHD) of 1.26 mm, surpassing current state-of-the-art methods. Additionally, the registration processing time was significantly reduced to 2.67 s on a GPU, highlighting the efficiency of our approach. The experimental outcomes not only validate the effectiveness of our method in improving the accuracy of cross-domain image registration but also prove its adaptability across different medical image analysis scenarios, offering robust support for enhancing diagnostic precision and patient treatment outcomes. CONCLUSIONS: The spatial-aware contrastive learning approach proposed in this paper introduces a new perspective and solution to the domain of cross-domain medical image registration. By effectively optimizing the feature space representation through carefully designed reconstruction and contrastive loss functions, our method significantly improves the accuracy and stability of registration between CT and MRI images. The experimental results demonstrate the clear advantages of our approach in enhancing the accuracy of cross-domain image registration, offering significant application value in promoting precise diagnosis and personalized treatment planning. In the future, we look forward to further exploring the application of this method in a broader range of medical imaging datasets and its potential integration with other advanced technologies, contributing more innovations to the field of medical image analysis and processing.

The Open Force Field Initiative: Open Software and Open Science for Molecular Modeling.

Force fields are a key component of physics-based molecular modeling, describing the energies and forces in a molecular system as a function of the positions of the atoms and molecules involved. Here, we provide a review and scientific status report on the work of the Open Force Field (OpenFF) Initiative, which focuses on the science, infrastructure and data required to build the next generation of biomolecular force fields. We introduce the OpenFF Initiative and the related OpenFF Consortium, describe its approach to force field development and software, and discuss accomplishments to date as well as future plans. OpenFF releases both software and data under open and permissive licensing agreements to enable rapid application, validation, extension, and modification of its force fields and software tools. We discuss lessons learned to date in this new approach to force field development. We also highlight ways that other force field researchers can get involved, as well as some recent successes of outside researchers taking advantage of OpenFF tools and data.

An in-depth analysis of postoperative insomnia in elderly patients and its implications on rehabilitation.

OBJECTIVES: (1) Assess the prevalence of postoperative insomnia; (2) identify the risk factors for postoperative insomnia before exposure to surgery; (3) explore the impact of postoperative insomnia on rehabilitation. METHODS: A study was conducted with 132 participants aged ≥ 65 undergoing spine interbody fusion. We collected the basic demographic data, Numeric Rating Scales (NRS), Pittsburgh Sleep Quality Index (PSQI), Geriatric Depression Scale (GDS), and Beck Anxiety Inventory (BAI). We measured Quality of Recovery 40 (QoR-40), GDS, BAI, NRS, and PSQI on the first and third nights post-surgery, followed by QoR-40 and NRS assessments two weeks after surgery. RESULTS: The cases of postoperative insomnia on the first and third nights and after two weeks were 81 (61.36%), 72 (54.55%), and 64 (48.48%), respectively, and the type of insomnia was not significantly different (P = 0.138). Sleep efficiency on the first night was 49.96% ± 23.51. On the first night of postoperative insomnia, 54 (66.67%) cases were depression or anxiety, and the PSQI was higher in this group than in the group without anxiety or depression (P < 0.001). PSQI, GDS, and the time of surgery were related factors for postoperative insomnia (PPSQI < 0.001, PGDS = 0.008, and PTime = 0.040). Postoperative rehabilitation showed differences between the insomnia and non-insomnia groups (P < 0.001). CONCLUSIONS: The prevalence of postoperative insomnia in the elderly was high, and postoperative insomnia had a significant correlation with postoperative rehabilitation. Interventions that target risk factors may reduce the prevalence of postoperative insomnia and warrant further research. CLINICAL TRIAL REGISTRATION: Multivariate analysis of postoperative insomnia in elderly patients with spinal surgery and its correlation with postoperative rehabilitation ( https://www.chictr.org.cn/bin/project/edit?pid=170201 ; #ChiCTR2200059827).

Genome-Wide Detection of Copy Number Variations and Their Potential Association with Carcass and Meat Quality Traits in Pingliang Red Cattle.

Copy number variation (CNV) serves as a significant source of genetic diversity in mammals and exerts substantial effects on various complex traits. Pingliang red cattle, an outstanding indigenous resource in China, possess remarkable breeding value attributed to their tender meat and superior marbling quality. However, the genetic mechanisms influencing carcass and meat quality traits in Pingliang red cattle are not well understood. We generated a comprehensive genome-wide CNV map for Pingliang red cattle using the GGP Bovine 100K SNP chip. A total of 755 copy number variable regions (CNVRs) spanning 81.03 Mb were identified, accounting for approximately 3.24% of the bovine autosomal genome. Among these, we discovered 270 potentially breed-specific CNVRs in Pingliang red cattle, including 143 gains, 73 losses, and 54 mixed events. Functional annotation analysis revealed significant associations between these specific CNVRs and important traits such as carcass and meat quality, reproduction, exterior traits, growth traits, and health traits. Additionally, our network and transcriptome analysis highlighted CACNA2D1, CYLD, UBXN2B, TG, NADK, and ITGA9 as promising candidate genes associated with carcass weight and intramuscular fat deposition. The current study presents a genome-wide CNV map in Pingliang red cattle, highlighting breed-specific CNVRs, and transcriptome findings provide valuable insights into the underlying genetic characteristics of Pingliang red cattle. These results offer potential avenues for enhancing meat quality through a targeted breeding program.

DrugGym: A testbed for the economics of autonomous drug discovery.

Drug discovery is stochastic. The effectiveness of candidate compounds in satisfying design objectives is unknown ahead of time, and the tools used for prioritization-predictive models and assays-are inaccurate and noisy. In a typical discovery campaign, thousands of compounds may be synthesized and tested before design objectives are achieved, with many others ideated but deprioritized. These challenges are well-documented, but assessing potential remedies has been difficult. We introduce DrugGym, a framework for modeling the stochastic process of drug discovery. Emulating biochemical assays with realistic surrogate models, we simulate the progression from weak hits to sub-micromolar leads with viable ADME. We use this testbed to examine how different ideation, scoring, and decision-making strategies impact statistical measures of utility, such as the probability of program success within predefined budgets and the expected costs to achieve target candidate profile (TCP) goals. We also assess the influence of affinity model inaccuracy, chemical creativity, batch size, and multi-step reasoning. Our findings suggest that reducing affinity model inaccuracy from 2 to 0.5 pIC50 units improves budget-constrained success rates tenfold. DrugGym represents a realistic testbed for machine learning methods applied to the hit-to-lead phase. Source code is available at www.drug-gym.org.

Rb-Doped Perovskite Oxides: Surface Enrichment and Structural Reconstruction During the Oxygen Evolution Reaction.

Alkali-metal doped perovskite oxides have emerged as promising materials due to their unique properties and broad applications in various fields, including photovoltaics and catalysis. Understanding the complex interplay between alkali metal doping, structural modifications, and their impact on performance remains a crucial challenge. In this study, this challenge is addressed by investigating the synthesis and properties of Rb-doped perovskite oxides. These results reveal that the doping of Rb into perovskite oxides function as a structural modifier in the as-synthesized samples and during the oxygen evolution reaction (OER) as well. Electron microscopy and first-principles calculations confirm the enrichment of Rb on the surface of the as-synthesized sample. Further investigations into the electrocatalytic reaction revealed that the Rb-doped perovskite underwent drastic restructuring with Rb leaching and formation of strontium oxide.

EspalomaCharge: Machine Learning-Enabled Ultrafast Partial Charge Assignment.

Atomic partial charges are crucial parameters in molecular dynamics simulation, dictating the electrostatic contributions to intermolecular energies and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of ab initio semiempirical quantum chemical methods such as AM1-BCC and is expensive for large systems or large numbers of molecules. We propose a hybrid physical/graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserve total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling for the first time the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package EspalomaCharge, this approach provides drop-in replacements for both AmberTools antechamber and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at https://github.com/choderalab/espaloma-charge.

Modeling and optimizing through plenoptic function for the dual lenticular lens-based directional autostereoscopic display system.

We propose an autostereoscopic display system that ensures full resolution for multiple users by directional backlight and eye tracking technology. The steerable beam formed by directional backlight can be regarded as the result of sparsely sampling the light field in space. Therefore, we intuitively propose an optimization algorithm based on the characterization for the state of the steerable beams, which is computed in matrix form using the plenoptic function. This optimization algorithm aims to optimize the exit pupil quality and ultimately enhancing the viewing experience of stereoscopic display. Numerical simulations are conducted and the improvement in exit pupil quality achieved by the optimization scheme is verified. Furthermore, a prototype of the stereoscopic display that employs dual-lenticular lens sheets for the directional backlight has been constructed using the optimized optical parameters. It provides 9 independent exit pupils at the optimal viewing distance of 400 mm, with an exit pupil resolution of 1/30. The field of view is ±16.7°, the viewing distance range is 380 mm to 440 mm. At the optimal viewing distance 400 mm, the average crosstalk of the system is 3%, and the dynamic brightness uniformity across the entire viewing plane reaches 85%. The brightness uniformity of the display at each exit pupil is higher than 88%.

A review of social-ecological system vulnerability in desertified regions: Assessment, simulation, and sustainable management.

Desertified regions face considerable vulnerability due to the combined effects of climate change and human activities, which threaten regional ecological security and societal development. It is therefore necessary to assess, simulate, and manage the vulnerability of desertified regions from the perspective of the social-ecological system, to support desertification control and sustainable development. This study is a systematic review of the vulnerability of the social-ecological system in desertified regions (SESDR) based on a bibliometric analysis, and a summary of the research progresses in vulnerability assessment, simulation, and sustainable management is provided. It was found that SESDR vulnerability research started relatively late, but has developed rapidly in recent years, with an emphasis on the coupling between natural systems and human activities, and multi-scale interactions and dynamics. Using various indicators at different scales, SESDR vulnerability could be assessed in terms of exposure, sensitivity, and adaptability. Modeling the complex interactions among natural and human factors across multiple scales is essential to simulate the vulnerability dynamics of the SESDR. The sustainable management of SESDR vulnerability focuses on rational spatial planning to achieve the maximum benefits, with the right measures in the right places. Four priority research directions were proposed to develop a better understanding of the mechanisms of vulnerability and smart restoration of desertified land. The findings of this study will enable researchers, land managers, and policymakers to develop a more comprehensive understanding of SESDR vulnerability, thereby enabling them to better address the challenges posed by complex resource and environmental issues.

Deep profiling of potential substrate atlas of porcine epidemic diarrhea virus 3C-like protease.

Coronavirus (CoV) 3C-like protease (3CLpro) is essential for viral replication and is involved in immune escape by proteolyzing host proteins. Deep profiling the 3CLpro substrates in the host proteome extends our understanding of viral pathogenesis and facilitates antiviral drug discovery. Here, 3CLpro from porcine epidemic diarrhea virus (PEDV), an enteropathogenic CoV, was used as a model which to identify the potential 3CLpro cleavage motifs in all porcine proteins. We characterized the selectivity of PEDV 3CLpro at sites P5-P4'. We then compiled the 3CLpro substrate preferences into a position-specific scoring matrix and developed a 3CLpro profiling strategy to delineate the protein substrate landscape of CoV 3CLpro. We identified 1,398 potential targets in the porcine proteome containing at least one putative cleavage site and experimentally validated the reliability of the substrate degradome. The PEDV 3CLpro-targeted pathways are involved in mRNA processing, translation, and key effectors of autophagy and the immune system. We also demonstrated that PEDV 3CLpro suppresses the type 1 interferon (IFN-I) cascade via the proteolysis of multiple signaling adaptors in the retinoic acid-inducible gene I (RIG-I) signaling pathway. Our composite method is reproducible and accurate, with an unprecedented depth of coverage for substrate motifs. The 3CLpro substrate degradome establishes a comprehensive substrate atlas that will accelerate the investigation of CoV pathogenicity and the development of anti-CoV drugs.IMPORTANCECoronaviruses (CoVs) are major pathogens that infect humans and animals. The 3C-like protease (3CLpro) encoded by CoV not only cleaves the CoV polyproteins but also degrades host proteins and is considered an attractive target for the development of anti-CoV drugs. However, the comprehensive characterization of an atlas of CoV 3CLpro substrates is a long-standing challenge. Using porcine epidemic diarrhea virus (PEDV) 3CLpro as a model, we developed a method that accurately predicts the substrates of 3CLpro and comprehensively maps the substrate degradome of PEDV 3CLpro. Interestingly, we found that 3CLpro may simultaneously degrade multiple molecules responsible for a specific function. For instance, it cleaves at least four adaptors in the RIG-I signaling pathway to suppress type 1 interferon production. These findings highlight the complexity of the 3CLpro substrate degradome and provide new insights to facilitate the development of anti-CoV drugs.

LEIFR-Net: light estimation for implicit face relight network.

Relighting facial images based on estimated lighting distribution and intensity from image backgrounds and environments can lead to more natural and convincing effects across diverse settings. In this paper, we introduce the Light Estimation for Implicit Face Relight Network (LEIFR-Net), which we believe to be a novel approach that significantly improves upon current methodologies. Initially, we present a method to estimate global illumination from a single image. We then detail our approach for structurally disentangled relighting of faces using pixel-aligned implicit functions. Furthermore, we elaborate on constructing a paired synthetic dataset, which includes environments, maps of lighting distribution, albedo and relighted faces, utilizing a process we refer to as stable diffusion. Our experimental results, evaluated against specific benchmarks, demonstrate the effectiveness of LEIFR-Net in achieving more harmonious alignment of highlights and shadows with environmental lighting, surpassing the performance of other contemporary methods in this domain.

In-Situ Derived Defective Ru Particles Anchored on Ru-Ni Layered Double Hydroxides for Enhanced Alkaline Hydrogen Evolution.

Developing active, stable, and cost-efficient electrocatalysts to replace platinum for the alkaline hydrogen evolution reaction (HER) is highly desirable yet represents a great challenge. Here, it is reported on a facile one-pot synthesis of RuxNi layered double hydroxides (RuxNi-LDHs) that exhibit remarkable HER activity and stability after an in-situ activation treatment, surpassing most state-of-the-art Ru-based catalysts as well as commercial Ru/C and Pt/C catalysts. The structural and chemical changes triggered by in-situ activation are systematically investigated, and the results clearly show that the pristine, less-active RuxNi-LDHs are transformed into a highly active catalyst characterized by raft-like, defect-rich Ru° particles decorated on the surface of RuxNi-LDHs. Density functional theory (DFT) calculations reveal that the defective Ru sites can effectively optimize the reaction pathway and lower the free energies of the elemental steps involved, leading to enhanced intrinsic activity. This work highlights the importance of the currently understudied strategy of defect engineering in boosting the HER activity of Ru-based catalysts and offers an effective approach involving in-situ electrochemical activation for the development of high-performance alkaline HER catalysts.

OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials.

Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use of machine learning potentials. Arbitrary PyTorch models can be added to a simulation and used to compute forces and energy. A higher-level interface allows users to easily model their molecules of interest with general purpose, pretrained potential functions. A collection of optimized CUDA kernels and custom PyTorch operations greatly improves the speed of simulations. We demonstrate these features in simulations of cyclin-dependent kinase 8 (CDK8) and the green fluorescent protein chromophore in water. Taken together, these features make it practical to use machine learning to improve the accuracy of simulations with only a modest increase in cost.

Genetic Origin and Introgression Pattern of Pingliang Red Cattle Revealed Using Genome-Wide SNP Analyses.

The Pingliang red cattle, an outstanding indigenous resource in China, possesses an exceptional breeding value attributed to its tender meat and superior marbling quality. Currently, research efforts have predominantly concentrated on exploring its maternal origin and conducting conventional phenotypic studies. However, there remains a lack of comprehensive understanding regarding its genetic basis. To address this gap, we conducted a thorough whole-genome analysis to investigate the population structure, phylogenetic relationships, and gene flows of this breed using genomic SNP chip data from 17 bovine breeds. The results demonstrate that Pingliang red cattle have evolved distinct genetic characteristics unique to this breed, clearly distinguishing it from other breeds. Based on the analysis of the population structure and phylogenetic tree, it can be classified as a hybrid lineage between Bos taurus and Bos indicus. Furthermore, Pingliang red cattle display a more prominent B. taurus pedigree in comparison with Jinnan, Qinchuan, Zaosheng, Nanyang, and Luxi cattle. Moreover, this study also revealed closer genetic proximity within the Chinese indigenous cattle breed, particularly Qinchuan cattle, which shares the longest identical by descent (IBD) fragment with Pingliang red cattle. Gene introgression analysis shows that Pingliang red cattle have undergone gene exchange with South Devon and Red Angus cattle from Europe. Admixture analysis revealed that the proportions of East Asian taurine and Chinese indicine in the ancestry of Pingliang red cattle are approximately 52.44% and 21.00%, respectively, while Eurasian taurine, European taurine, and Indian indicine account for approximately 17.55%, 7.27%, and 1.74%. Our findings unveil distinct genetic characteristics in Pingliang red cattle and attribute their origin to B. taurus and B. indicus ancestry, as well as contributions from Qinchuan cattle, South Devon, and Red Angus.

Targeting LRRC41 as a potential therapeutic approach for hepatocellular carcinoma.

Introduction: Hepatocellular carcinoma (HCC) is the most common primary liver cancer, characterized by high mortality rate. In clinical practice, several makers of liver cancer, such as VEGFR1, FGFR1 and PDGFRα, were identified and their potentials as a therapeutic target were explored. However, the unsatisfied treatment results emphasized the needs of new therapeutic targets. Methods: 112 HCC patients samples were obtained to evaluate the expression of LRRC41, SOX9, CD44, and EPCAM in HCC, combined with prognosis analysis. A DEN-induced HCC rat model was constructed to verify the expression of LRRC41 and SOX9 in HCC and lung metastasis tissues. Immune score evaluation was analysized by bioinformatics methods. Network pharmacology was performed to explored the potential FDA-approved drugs targeting LRRC41. Results: Through analysis of the Timer database and tissue micro-array, we confirmed that LRRC41 was over-expressed in HCC and exhibited a significant positive correlation with recurrence and metastasis. Immunohistochemistry staining of human HCC tissue samples revealed significant upregulation of LRRC41, SOX9, CD44, and EPCAM, with LRRC41 showing a positive correlation with SOX9, CD44, and EPCAM expression. UALCAN database analysis indicated that LRRC41 and SOX9 contribute to poor prognosis whereas CD44 and EPCAM did not demonstrate the same significance. Furthermore, analysis of a DEN-induced HCC rat model confirmed the significantly elevated expression of LRRC41 and SOX9 in HCC and lung metastasis tissues. Drug sensitivity analysis and molecular docking targeting LRRC41 identified several FDA-approved drugs, which may have potential antitumor effects on HCC by targeting LRRC41. Conclusion: Our findings highlight the role of LRRC41 overexpression in promoting HCC progression and its association with a poor prognosis. Drug sensitivity analysis and molecular docking shows several FDA-approved drugs may be potential therapeutic targets for HCC. Targeting LRRC41 may hold promise as a potential therapeutic strategy for HCC.