Identification of microbe-disease signed associations via multi-scale variational graph autoencoder based on signed message propagation.

Background: Plenty of clinical and biomedical research has unequivocally highlighted the tremendous significance of the human microbiome in relation to human health. Identifying microbes associated with diseases is crucial for early disease diagnosis and advancing precision medicine.

Results: Considering that the information about changes in microbial quantities under fine-grained disease states helps to enhance a comprehensive understanding of the overall data distribution, this study introduces MSignVGAE, a framework for predicting microbe-disease sign associations using signed message propagation.

Contextual bias by Forensic Document Examination trainees: An empirical study from China.

The impact of contextual bias has been repeatedly demonstrated across forensic domains; however, research on this topic in China is scarce. To examine the prevalence of contextual bias in pattern feature-comparison disciplines, we conducted an experiment involving 24 forensic document examination students. The aim was to determine whether knowledge of different contextual information influenced their forensic decision-making.

Contextual bias in forensic toxicology decisions: A follow-up empirical study from China.

The impact of contextual bias has been demonstrated repeatedly across forensic domains; however, research on this topic in forensic toxicology is very limited. In our previous study, experimental data from only one context version were compared with the actual forensic biasing casework. As a follow-up, this controlled experiment with 159 forensic toxicology practitioners was conducted, to test whether knowledge of different contextual information influenced their forensic decision-making.

Identifying disease-related microbes based on multi-scale variational graph autoencoder embedding Wasserstein distance.

Background: Enormous clinical and biomedical researches have demonstrated that microbes are crucial to human health. Identifying associations between microbes and diseases can not only reveal potential disease mechanisms, but also facilitate early diagnosis and promote precision medicine. Due to the data perturbation and unsatisfactory latent representation, there is a significant room for improvement.

The Value of VR-PVEP in Objective Evaluation of Monocular Refractive Visual Impairment.

Objectives: To study the virtual reality-pattern visual evoked potential (VR-PVEP) P100 waveform characteristics of monocular visual impairment with different impaired degrees under simultaneous binocular perception and monocular stimulations.

Methods: A total of 55 young volunteers with normal vision (using decimal recording method, far vision ≥0.8 and near vision ≥0.

M-Chem: a Modular Software Package for Molecular Simulation that Spans Scientific Domains.

We present a new software package called M-Chem that is designed from scratch in C++ and parallelized on shared-memory multi-core architectures to facilitate efficient molecular simulations. Currently, M-Chem is a fast molecular dynamics (MD) engine that supports the evaluation of energies and forces from two-body to many-body all-atom potentials, reactive force fields, coarse-grained models, combined quantum mechanics molecular mechanics (QM/MM) models, and external force drivers from machine learning, augmented by algorithms that are focused on gains in computational simulation times. M-Chem also includes a range of standard simulation capabilities including thermostats, barostats, multi-timestepping, and periodic cells, as well as newer methods such as fast extended Lagrangians and high quality electrostatic potential generation.

Predicting visual acuity with machine learning in treated ocular trauma patients.

Aim: To predict best-corrected visual acuity (BCVA) by machine learning in patients with ocular trauma who were treated for at least 6mo.

Methods: The internal dataset consisted of 850 patients with 1589 eyes and an average age of 44.29y.

Identification of a novel gene signature for the prognosis of sepsis.

Sepsis is a life-threatening organ dysfunction caused by the host's dysfunctional response to infection, and its pathogenesis is still unclear. In view of the complex pathological process of sepsis, finding suitable biomarkers is helpful for the research and treatment of sepsis. This study determined the potential prognostic markers of sepsis by analyzing the molecular characteristics of patients with sepsis.

Research Progress of Age Estimation in the Living by Knee Joint MRI.

Bone development shows certain regularity with age. The regularity can be used to infer age and serve many fields such as justice, medicine, archaeology, etc. As a non-invasive evaluation method of the epiphyseal development stage, MRI is widely used in living age estimation.

Force Decomposition Analysis: A Method to Decompose Intermolecular Forces into Physically Relevant Component Contributions.

Computational quantum chemistry can be more than just numerical experiments when methods are specifically adapted to investigate chemical concepts. One important example is the development of energy decomposition analysis (EDA) to reveal the physical driving forces behind intermolecular interactions. In EDA, typically the interaction energy from a good-quality density functional theory (DFT) calculation is decomposed into multiple additive components that unveil permanent and induced electrostatics, Pauli repulsion, dispersion, and charge-transfer contributions to noncovalent interactions.

Highly Altered State of Proton Transport in Acid Pools in Charged Reverse Micelles.

Transport mechanisms of solvated protons of 1 M HCl acid pools, confined within reverse micelles (RMs) containing the negatively charged surfactant sodium bis(2-ethylhexyl) sulfosuccinate (NaAOT) or the positively charged cetyltrimethylammonium bromide (CTABr), are analyzed with reactive force field simulations to interpret dynamical signatures from TeraHertz absorption and dielectric relaxation spectroscopy. We find that the forward proton hopping events for NaAOT are further suppressed compared to a nonionic RM, while the Grotthuss mechanism ceases altogether for CTABr. We attribute the sluggish proton dynamics for both charged RMs as due to headgroup and counterion charges that expel hydronium and chloride ions from the interface and into the bulk interior, thereby increasing the pH of the acid pools relative to the nonionic RM.

Spontaneous Formation of Hydrogen Peroxide in Water Microdroplets.

There is accumulating evidence that many chemical reactions are accelerated by several orders of magnitude in micrometer-sized aqueous or organic liquid droplets compared to their corresponding bulk liquid phase. However, the molecular origin of the enhanced rates remains unclear as in the case of spontaneous appearance of 1 μM hydrogen peroxide in water microdroplets. In this Letter, we consider the range of ionization energies and whether interfacial electric fields of a microdroplet can feasibly overcome the high energy step from hydroxide ions (OH) to hydroxyl radicals (OH) in a primary HO mechanism.

Oxymatrine Alleviates Gentamicin-Induced Renal Injury in Rats.

Gentamicin is an aminoglycoside antibiotic commonly used to treat Gram-negative bacterial infections that possesses considerable nephrotoxicity. Oxymatrine is a phytochemical with the ability to counter gentamicin toxicity. We investigated the effects and protective mechanism of oxymatrine in rats.

Pelvic Injury Discriminative Model Based on Data Mining Algorithm.

Objectives: To reduce the dimension of characteristic information extracted from pelvic CT images by using principal component analysis (PCA) and partial least squares (PLS) methods. To establish a support vector machine (SVM) classification and identification model to identify if there is pelvic injury by the reduced dimension data and evaluate the feasibility of its application.

Methods: Eighty percent of 146 normal and injured pelvic CT images were randomly selected as training set for model fitting, and the remaining 20% was used as testing set to verify the accuracy of the test, respectively.

NewtonNet: a Newtonian message passing network for deep learning of interatomic potentials and forces.

We report a new deep learning message passing network that takes inspiration from Newton's equations of motion to learn interatomic potentials and forces. With the advantage of directional information from trainable force vectors, and physics-infused operators that are inspired by Newtonian physics, the entire model remains rotationally equivariant, and many-body interactions are inferred by more interpretable physical features. We test NewtonNet on the prediction of several reactive and non-reactive high quality data sets including single small molecules, a large set of chemically diverse molecules, and methane and hydrogen combustion reactions, achieving state-of-the-art test performance on energies and forces with far greater data and computational efficiency than other deep learning models.

Effects of methanol and formic acid on CRYB, ALDH2, and ATP5A1 of RGCs.

Objective: To explore the toxicity of methanol and its metabolite, formic acid on αB-crystallin(CRYB), aldehyde dehydrogenase (ALDH2), and ATPsynthase (ATP5A1) of rat retinal ganglion cells (RGCs).

Methods: RGCs are cultured in a toxic environment with 15/30/60 mM methanol or formic acid, respectively. Then, the morphological changes of RGCs and protein and mRNA levels of ALDH2, ATP5A1, and CRYB in rat RGCs were evaluated.

A benchmark dataset for Hydrogen Combustion.

The generation of reference data for deep learning models is challenging for reactive systems, and more so for combustion reactions due to the extreme conditions that create radical species and alternative spin states during the combustion process. Here, we extend intrinsic reaction coordinate (IRC) calculations with ab initio MD simulations and normal mode displacement calculations to more extensively cover the potential energy surface for 19 reaction channels for hydrogen combustion. A total of ∼290,000 potential energies and ∼1,270,000 nuclear force vectors are evaluated with a high quality range-separated hybrid density functional, ωB97X-V, to construct the reference data set, including transition state ensembles, for the deep learning models to study hydrogen combustion reaction.

Identification of D Modification Sites Using a Random Forest Model Based on Nucleotide Chemical Properties.

Dihydrouridine (D) is an abundant post-transcriptional modification present in transfer RNA from eukaryotes, bacteria, and archaea. D has contributed to treatments for cancerous diseases. Therefore, the precise detection of D modification sites can enable further understanding of its functional roles.

Contextual bias on decision-making in forensic toxicology: First survey from China.

In forensic science, contextual bias has been used to describe the tendency for a forensic analysis to be influenced by task-irrelevant background information. Contextual bias is not limited to subjective pattern matching disciplines based on visual recognition (e.g.

Can electric fields drive chemistry for an aqueous microdroplet?

Reaction rates of common organic reactions have been reported to increase by one to six orders of magnitude in aqueous microdroplets compared to bulk solution, but the reasons for the rate acceleration are poorly understood. Using a coarse-grained electron model that describes structural organization and electron densities for water droplets without the expense of ab initio methods, we investigate the electric field distributions at the air-water interface to understand the origin of surface reactivity. We find that electric field alignments along free O-H bonds at the surface are ~16 MV/cm larger on average than that found for O-H bonds in the interior of the water droplet.

Proton Traffic Jam: Effect of Nanoconfinement and Acid Concentration on Proton Hopping Mechanism.

The properties of the water network in concentrated HCl acid pools in nanometer-sized reverse nonionic micelles were probed with TeraHertz absorption, dielectric relaxation spectroscopy, and reactive force field simulations capable of describing proton hopping mechanisms. We identify that only at a critical micelle size of W =9 do solvated proton complexes form in the water pool, accompanied by a change in mechanism from Grotthuss forward shuttling to one that favors local oscillatory hopping. This is due to a preference for H and Cl ions to adsorb to the micelle interface, together with an acid concentration effect that causes a "traffic jam" in which the short-circuiting of the hydrogen-bonding motif of the hydronium ion decreases the forward hopping rate throughout the water interior even as the micelle size increases.

Recent Advances for Improving the Accuracy, Transferability, and Efficiency of Reactive Force Fields.

Reactive force fields provide an affordable model for simulating chemical reactions at a fraction of the cost of quantum mechanical approaches. However, classically accounting for chemical reactivity often comes at the expense of accuracy and transferability, while computational cost is still large relative to nonreactive force fields. In this Perspective, we summarize recent efforts for improving the performance of reactive force fields in these three areas with a focus on the ReaxFF theoretical model.

Global patent statistical analysis for drug testing technology.

Background: In recent decades, illicit drug testing has become a high priority area in law enforcement and forensic analysis.

Objective: Since patents are the largest source of technical information in the world, patent database analysis for illicit drug testing is extremely important to effectively promote the development and protection of the related intellectual property rights.

Methods: In the present study, we first retrieve a database of 1732 drug detection patents using keywords and logical expressions related to the title, abstract, and claims, and subsequently discuss the current global patent statistics in detail.

A Reactive Force Field with Coarse-Grained Electrons for Liquid Water.

Nonreactive force fields are defined by perturbations of electron density that are relatively small, whereas chemical reactivity involves wholesale electronic rearrangements that make and break bonds. Thus, reactive force fields are incredibly difficult to develop compared to nonreactive force fields, yet at the same time, they fill a critical need when molecular dynamics methods are not affordable. We introduce a new reactive force field model for water that combines modified nonbonded terms of the ReaxFF model and its embedding in the electrostatic interactions described by our recently introduced coarse-grained electron model (C-GeM).