Efficient Approximations of Complete Interatomic Potentials for Crystal Property Prediction

We study property prediction for crystal materials. A crystal structure consists of a minimal unit cell that is repeated infinitely in 3D space. How to accurately represent such repetitive structures in machine learning models remains unresolved. Current methods construct graphs by establishing edges only between nearby nodes, thereby failing to faithfully capture infinite repeating patterns and distant interatomic interactions. In this work, we propose several innovations to overcome these limitations. First, we propose to model physics-principled interatomic potentials directly instead of only using distances as in many existing methods. These potentials include the Coulomb potential, London dispersion potential, and Pauli repulsion potential. Second, we model the complete set of potentials among all atoms, instead of only between nearby atoms as in existing methods. This is enabled by our approximations of infinite potential summations with provable error bounds. We further develop efficient algorithms to compute the approximations. Finally, we propose to incorporate our computations of complete interatomic potentials into message passing neural networks for representation learning. We perform experiments on the JARVIS and Materials Project benchmarks for evaluation. Results show that the use of interatomic potentials and complete interatomic potentials leads to consistent performance improvements with reasonable computational costs. Our code is publicly available as part of the AIRS library (https://github.com/divelab/AIRS)

An Integrated Clinical-mRNA-lncRNA-miRNA Signature for Muscle-Invasive Bladder Cancer Prognosis

An increasing number of evidence suggests that clinical variables alone are not enough to predict the survival of patients with muscle invasive bladder cancer (MIBC), and the expression of mRNAs, long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) also plays an important role in the onset of MIBC. This study aims to establish a more accurate model for predicting the overall survival of MIBC based on clinical information and genetic characteristics. In this study, the RNAs profiles and clinical variable data of patients with MIBC were downloaded from the Cancer Genome Atlas (TCGA) database. Univariate Cox regression analysis, differential expression analysis and elastic net-regulated Cox regression analysis were used to identify the clinical variables and RNAs (mRNAs, lncRNAs and miRNAs) related to the prognosis of MIBC. Prognostic models of MIBC were established by multivariate Cox regression and ridge regression analysis using the identified prognostic clinical variables and RNAs. Three clinical variables, 25 mRNAs, 3 lncRNAs and 2 miRNAs related to the prognosis of MIBC were identified, and an integrated signature, a clinical variable signature, and an mRNA-lncRNA-miRNA signature were established based on the identified clinical variables and/or RNAs. Among the three models, the integrated signature had the highest predictive accuracy (5-year the area under the curve (AUC)=0.835, 95%CI:0.776-0.894) among the three models (P 0.05). The patients in the TCGA MIBC cohort were classified into high- or low-risk groups by the integrated signature, and it was found that the patients in the low-risk group had a significantly longer overall survival time compared with the patients in the high-risk group (P 0.001). Applying published gene signatures and TCGA data, a new and more accurate integrated clinical-mRNA-lncRNA-miRNA signature for MIBC prognostic was established

QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules

Supervised machine learning approaches have been increasingly used in accelerating electronic structure prediction as surrogates of first-principle computational methods, such as density functional theory (DFT). While numerous quantum chemistry datasets focus on chemical properties and atomic forces, the ability to achieve accurate and efficient prediction of the Hamiltonian matrix is highly desired, as it is the most important and fundamental physical quantity that determines the quantum states of physical systems and chemical properties. In this work, we generate a new Quantum Hamiltonian dataset, named as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics trajectories and 130,831 stable molecular geometries, based on the QM9 dataset. By designing benchmark tasks with various molecules, we show that current machine learning models have the capacity to predict Hamiltonian matrices for arbitrary molecules. Both the QH9 dataset and the baseline models are provided to the community through an open-source benchmark, which can be highly valuable for developing machine learning methods and accelerating molecular and materials design for scientific and technological applications. Our benchmark is publicly available at https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmark

Ubiquitination Is Required for Effective Replication of Coxsackievirus B3

BACKGROUND: Protein ubiquitination and/or degradation by the ubiquitin/proteasome system (UPS) have been recognized as critical mechanisms in the regulation of numerous essential cellular functions. The importance of the UPS in viral pathogenesis has become increasingly apparent. Using murine cardiomyocytes, we have previously demonstrated that the UPS plays a key role in the replication of coxsackievirus B3 (CVB3), an important human pathogen associated with various diseases. To further elucidate the underlying mechanisms, we examined the interplay between the UPS and CVB3, focusing on the role of ubiquitination in viral lifecycle. METHODOLOGY/PRINCIPAL FINDINGS: As assessed by in situ hybridization, Western blot, and plaque assay, we showed that proteasome inhibition decreased CVB3 RNA replication, protein synthesis, and viral titers in HeLa cells. There were no apparent changes in 20S proteasome activities following CVB3 infection. However, we found viral infection led to an accumulation of protein-ubiquitin conjugates, accompanied by a decreased protein expression of free ubiquitin, implicating an important role of ubiquitination in the UPS-mediated viral replication. Using small-interfering RNA, we demonstrated that gene-silencing of ubiquitin significantly reduced viral titers, possibly through downregulation of protein ubiquitination and subsequent alteration of protein function and/or degradation. Inhibition of deubiquitinating enzymes apparently enhances the inhibitory effects of proteasome inhibitors on CVB3 replication. Finally, by immunoprecipitation, we showed that coxsackieviral polymerase 3D was post-translationally modified by ubiquitination and such modification might be a prerequisite for its function in transcriptional regulation of viral genome. CONCLUSION: Coxsackievirus infection promotes protein ubiquitination, contributing to effective viral replication, probably through ubiquitin modification of viral polymerase

A High-Gain Observer-Based Adaptive Super-Twisting Algorithm for DC-Link Voltage Control of NPC Converters

Acting as an interface between the grid and many energy systems, the active front-end (AFE) has been widely used in a large variety of industrial applications. In this paper, in order to ensure the fast dynamic performance and good disturbance rejection ability of the AFE, a high-gain observer (HGO) plus adaptive super-twisting algorithm (STA) for the three-level neutral-point-clamped (NPC) converter is proposed. Comparing with the conventional PI control strategy, the proposed controller implements the adaptive STA in the voltage regulator to provide a faster transient response. The gains of the adaptive STA keep varying according to the rules being reduced in steady state but increasing in transient conditions. Therefore, the chattering phenomenon is mitigated and the dynamic response is guaranteed. Additionally, to undermine the impact of external disturbances on the dc-link voltage, a high-efficiency HGO is designed in the voltage regulation loop to reject it. Experimental results based on a three-level NPC prototype are given and compared with the conventional PI method to validate the fast dynamic performance and high disturbance rejection ability of the proposed approach.National Key R&D Program of China SQ2019YFB130028National Natural Science Foundation of China 61525303National Natural Science Foundation of China 41772377National Natural Science Foundation of China 61673130Self-Planned Task of State Key Laboratory of Robotics and System (HIT) SKLRS201806BMinisterio de Economía y Competitividad TEC2016-78430-RJunta de Andalucía P18-RT-1340Fondo de Investigación Nacional de Qatar NPRP 9-310-2-13

Short-term application of diquafosol ophthalmic solution benefits children with dry eye wearing orthokeratology lens

PurposeThis aim of this study was to evaluate the effect of 3% Diquafosol Ophthalmic Solution (DQS) on children with dry eye from wearing overnight orthokeratology (OrthoK) lenses.MethodsMyopic children aged 8–18 years with dry eye syndrome were enrolled in this prospective observational study, and they were grouped according to their OrthoK treatment history for at least 1 year. All participants received DQS 4 times per day for 1 month. The following indicators were measured at baseline 1 month after treatment: the Dry Eye Questionnaire-5 (DEQ-5), non-invasive tear meniscus height (TMH), non-invasive tear film break-up time (first and average, NIBUT-F and NIBUT-A), meibomian gland score (MG score), conjunctival hyperemia redness score (R-scan), and blink pattern analysis.ResultsA total of 104 participants (189 eyes) including 40 OrthoK wearers (72 eyes) and 64 Orthok candidates (117 eyes) completed the study. Of all, after DQS treatment for 1 month, DEQ-5 scores reduced from 5.54 ± 3.25 to 3.85 ± 2.98 (t = −3.36, p = 0.00). TMH increased from 0.20 ± 0.05 mm to 0.21 ± 0.05 mm (t = 2.59, p = 0.01), NIBUT-F and NIBUT-A were prolonged from 6.67 ± 4.71 s to 10.32 ± 6.19 s and from 8.86 ± 5.25 s to 13.30 ± 6.03 s (all p = 0.00), respectively. R-scan decreased from 0.69 ± 0.28 to 0.50 ± 0.25 (t = −9.01, p = 0.00). Upper MG scores decreased from 1.04 ± 0.32 to 0.97 ± 0.36 (t = −2.14, p = 0.03). Lower MG scores, partial blink rate, partial blinks, and total blinks did not change significantly. Both break-up time (BUT) and R-scan improved significantly after DQS treatment for 1 month (all p = 0.00) in OrthoK candidates and OrthoK wearers. Among the OrthoK wearers, TMH and dry eye symptoms increased significantly (all p = 0.00) but did not increase in OrthoK candidates (p > 0.05). There were no adverse events related to DQS.ConclusionDiquafosol Ophthalmic Solution was effective for children wearing overnight orthokeratology in relieving dry eye symptoms and improving ocular surface parameters, which may help improve children's OrthoK wearing tolerance and compliance

Instant Interfacial Self-Assembly for Homogeneous Nanoparticle Monolayer Enabled Conformal ‘lift on’ Thin Film Technology

Thin film fabrication is of great importance in the modern engineering. Here, we propose a universal and conformal thin film technique enabled by the wetting empowered interfacial self-assembly. By tailoring the contact angle of nanoparticle (NP), a NP monolayer can be assembled instantly (within 5 seconds) with an excellent harvesting efficiency (up to 97.5 wt). This self-assembly strategy presents a universal applicability on various materials, e.g. non-metal, metal and core-shell structures, and can achieve a monolayer with same in-plane area as 4-inch wafer in a single process, indicating great potential for scale-up manufacturing. Through a template transfer, we coat the surface of different substrates (plastic, paper, etc.) with the assembled film in a conformal and non-destructive ‘lift-on’ manner and subsequently demonstrate fluorescent micropatterns. This self-assembly strategy has great implications in advancing thin film technology with a user-friendly and cost-effective fashion, for the applications in anti-counterfeiting, actuators, and wearable/flexible electronic

Gut microbiota and cardiac arrhythmia

One of the most prevalent cardiac diseases is cardiac arrhythmia, however the underlying causes are not entirely understood. There is a lot of proof that gut microbiota (GM) and its metabolites have a significant impact on cardiovascular health. In recent decades, intricate impacts of GM on cardiac arrythmia have been identified as prospective approaches for its prevention, development, treatment, and prognosis. In this review, we discuss about how GM and its metabolites might impact cardiac arrhythmia through a variety of mechanisms. We proposed to explore the relationship between the metabolites produced by GM dysbiosis including short-chain fatty acids(SCFA), Indoxyl sulfate(IS), trimethylamine N-oxide(TMAO), lipopolysaccharides(LPS), phenylacetylglutamine(PAGln), bile acids(BA), and the currently recognized mechanisms of cardiac arrhythmias including structural remodeling, electrophysiological remodeling, abnormal nervous system regulation and other disease associated with cardiac arrythmia, detailing the processes involving immune regulation, inflammation, and different types of programmed cell death etc., which presents a key aspect of the microbial-host cross-talk. In addition, how GM and its metabolites differ and change in atrial arrhythmias and ventricular arrhythmias populations compared with healthy people are also summarized. Then we introduced potential therapeutic strategies including probiotics and prebiotics, fecal microbiota transplantation (FMT) and immunomodulator etc. In conclusion, the GM has a significant impact on cardiac arrhythmia through a variety of mechanisms, offering a wide range of possible treatment options. The discovery of therapeutic interventions that reduce the risk of cardiac arrhythmia by altering GM and metabolites is a real challenge that lies ahead

Pathway-based analyses of gene expression profiles at low doses of ionizing radiation

Radiation exposure poses a significant threat to human health. Emerging research indicates that even low-dose radiation once believed to be safe, may have harmful effects. This perception has spurred a growing interest in investigating the potential risks associated with low-dose radiation exposure across various scenarios. To comprehensively explore the health consequences of low-dose radiation, our study employs a robust statistical framework that examines whether specific groups of genes, belonging to known pathways, exhibit coordinated expression patterns that align with the radiation levels. Notably, our findings reveal the existence of intricate yet consistent signatures that reflect the molecular response to radiation exposure, distinguishing between low-dose and high-dose radiation. Moreover, we leverage a pathway-constrained variational autoencoder to capture the nonlinear interactions within gene expression data. By comparing these two analytical approaches, our study aims to gain valuable insights into the impact of low-dose radiation on gene expression patterns, identify pathways that are differentially affected, and harness the potential of machine learning to uncover hidden activity within biological networks. This comparative analysis contributes to a deeper understanding of the molecular consequences of low-dose radiation exposure

The latest edition of WHO and ELN guidance and a new risk model for Chinese acute myeloid leukemia patients

ObjectiveDiagnosis classification and risk stratification are crucial in the prognosis prediction and treatment selection of acute myeloid leukemia (AML). Here, we used a database of 536 AML patients to compare the 4th and 5th WHO classifications and the 2017 and 2022 versions of ELN guidance.MethodsAML patients were classified according to the 4th and 5th WHO classifications, as well as the 2017 and 2022 versions of the European LeukemiaNet (ELN) guidance. Kaplan–Meier curves with log-rank tests were used for survival analysis.ResultsThe biggest change was that 25 (5.2%), 8 (1.6%), and 1 (0.2%) patients in the AML, not otherwise specified (NOS) group according to the 4th WHO classification, were re-classified into the AML-MR (myelodysplasia-related), KMT2A rearrangement, and NUP98 rearrangement subgroups based on the 5th WHO classification. Referring to the ELN guidance, 16 patients in the favorable group, six patients in the adverse group, and 13 patients in the intermediate group based on the 2017 ELN guidance were re-classified to the intermediate and adverse groups based on the 2022 ELN guidance. Regrettably, the Kaplan–Meier curves showed that the survival of intermediate and adverse groups could not be distinguished well according to either the 2017 or 2022 ELN guidance. To this end, we constructed a risk model for Chinese AML patients, in which the clinical information (age and gender), gene mutations (NPM1, RUNX1, SH2B3, and TP53), and fusions (CBFB::MYH11 and RUNX1::RUNX1T1) were included, and our model could help divide the patients into favorable, intermediate, and adverse groups.ConclusionThese results affirmed the clinical value of both WHO and ELN, but a more suitable prognosis model should be established in Chinese cohorts, such as the models we proposed