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. RESULTS: In this work, we proposed a novel framework, Multi-scale Variational Graph AutoEncoder embedding Wasserstein distance (MVGAEW) to predict disease-related microbes, which had the ability to resist data perturbation and effectively generate latent representations for both microbes and diseases from the perspective of distribution. First, we calculated multiple similarities and integrated them through similarity network confusion. Subsequently, we obtained node latent representations by improved variational graph autoencoder. Ultimately, XGBoost classifier was employed to predict potential disease-related microbes. We also introduced multi-order node embedding reconstruction to enhance the representation capacity. We also performed ablation studies to evaluate the contribution of each section of our model. Moreover, we conducted experiments on common drugs and case studies, including Alzheimer's disease, Crohn's disease, and colorectal neoplasms, to validate the effectiveness of our framework. CONCLUSIONS: Significantly, our model exceeded other currently state-of-the-art methods, exhibiting a great improvement on the HMDAD database.

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. For charged NaAOT and CTABr RMs, the localization of hydronium near a counterion or conjugate base reduces the Eigen and Zundel configurations that enable forward hopping. Thus, localized oscillatory hopping dominates, an effect that is most extreme for CTABr in which the proton residence time increases dramatically such that even oscillatory hopping is slow.

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. Herein, we formulate, implement, and investigate decomposing the forces associated with intermolecular interactions into the same components. The resulting force decomposition analysis (FDA) is potentially useful as a complement to the EDA to understand chemistry, while also providing far more information than an EDA for data analysis purposes such as training physics-based force fields. We apply the FDA based on absolutely localized molecular orbitals (ALMOs) to analyze interactions of water with sodium and chloride ions as well as in the water dimer. We also analyze the forces responsible for geometric changes in carbon dioxide upon adsorption onto (and activation by) gold and silver anions. We also investigate how the force components of an EDA-based force field for water clusters, namely MB-UCB, compare to those from force decomposition analysis.

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. In recent years, the rapid development of machine learning has significantly improved the effectiveness and reliability of living age estimation, which is one of the main development directions of current research. This paper summarizes the analysis methods of age estimation by knee joint MRI, introduces the current research trends, and future application trend.

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 ≥0.5) were selected to simulate three groups of monocular refractive visual impairment by interpolation method. The sum of near and far vision ≤0.2 was Group A, the severe visual impairment group; the sum of near and far vision <0.8 was Group B, the moderate visual impairment group; and the sum of near and far vision ≥0.8 was Group C, the mild visual impairment group. The volunteers' binocular normal visions were set as the control group. The VR-PVEP P100 peak times measured by simultaneous binocular perception and monocular stimulation were compared at four spatial frequencies 16×16, 24×24, 32×32 and 64×64. RESULTS: In Group A, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 24×24, 32×32 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group B, the differences between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at 16×16, 24×24 and 64×64 spatial frequencies were statistically significant (P<0.05); and the P100 peak time of normal vision eyes at 64×64 spatial frequency was significantly different from the simulant visual impairment eyes (P<0.05). In Group C, there was no significant difference between P100 peak times of simulant visual impairment eyes and simultaneous binocular perception at all spatial frequencies (P>0.05). There was no significant difference in the P100 peak times measured at all spatial frequencies between simulant visual impairment eyes and simultaneous binocular perception in the control group (P>0.05). CONCLUSIONS: VR-PVEP can be used for visual acuity evaluation of patients with severe and moderate monocular visual impairment, which can reflect the visual impairment degree caused by ametropia. VR-PVEP has application value in the objective evaluation of visual function and forensic clinical identification.

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. Traditional experimental techniques to identify D are laborious and time-consuming. In addition, there are few computational tools for such analysis. In this study, we utilized eleven sequence-derived feature extraction methods and implemented five popular machine algorithms to identify an optimal model. During data preprocessing, data were partitioned for training and testing. Oversampling was also adopted to reduce the effect of the imbalance between positive and negative samples. The best-performing model was obtained through a combination of random forest and nucleotide chemical property modeling. The optimized model presented high sensitivity and specificity values of 0.9688 and 0.9706 in independent tests, respectively. Our proposed model surpassed published tools in independent tests. Furthermore, a series of validations across several aspects was conducted in order to demonstrate the robustness and reliability of our model.

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. The experimental groups were as follows: Control, Oxymatrine only group (100 mg/kg/d), Gentamicin only group (100 mg/kg/d), Gentamicin (100 mg/kg/d) plus Oxymatrine (100 mg/kg/d) group (n = 10). All rats were treated for seven continuous days. The results indicated that oxymatrine alleviated gentamicin-induced kidney injury, and decreased rats' kidney indices and NAG (N-acetyl-beta-d-glucosaminidase), BUN (blood urea nitrogen) and CRE (creatine) serum levels. The oxymatrine-treated group sustained less histological damage. Oxymatrine also relived gentamicin-induced oxidative and nitrative stress, indicated by the increased SOD (superoxidase dismutase), GSH (glutathione) and CAT (catalase) activities and decreased MDA (malondialdehyde), iNOS (inducible nitric oxide synthase) and NO (nitric oxide) levels. Caspase-9 and -3 activities were also decreased in the oxymatrine-treated group. Oxymatrine exhibited a potent anti-inflammatory effect on gentamicin-induced kidney injury, down-regulated the Bcl-2ax and NF-κB mRNAs, and upregulated Bcl-2, HO-1 and Nrf2 mRNAs in the kidney tissue. Our investigation revealed the renal protective effect of oxymatrine in gentamicin-induced kidney injury for the first time. The effect was achieved through activation of the Nrf2/HO-1 pathways. The study underlines the potential clinical application of oxymatrine as a renal protectant agent for gentamicin therapy.

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 vitro 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. RESULTS: 1) Compared to the toxicity of 15 mM formic acid on RGCs, 30 mM of formic acid environment significantly promoted apoptosis, and cell death occurred in the 60-mM formic acid group 24 h later. The toxicity of methanol for inducing apoptosis was not as obvious as formic acid. 2) In the 15-mM group, the level of CRYB protein was down-regulated after stimulating with both methanol and formic acid for 48 h, and ATP5A1 protein level decreased significantly with formic but not methanol. No change in ALDH2 was observed in methanol or formic acid. With a prolonged duration (>7 d) or high concentration (>30 mM) stimulation, cells treated with both methanol and formic acid showed severe apoptosis, rendering it challenging to collect a sufficient number of cells for protein detection. 3) In the 48-h group, no significant effect was detected on the mRNA of CRYB, ATP5A1, and ALDH2 by both 15/30 mM formic acid and 15 mM methanol. Conversely, 30 mM methanol had a significant up-regulation effect on the expression of the three genes, while no significant effect was observed in the 7-d groups. CONCLUSIONS: Formic acid exerted stronger toxicity on CRYB, ATP5A1, and ALDH2 than methanol and played a regulatory role at the translation level, while the effect of methanol is still uncertain, needing additional investigation.

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. Through CT image input, preprocessing, feature extraction, feature information dimension reduction, feature selection, parameter selection, model establishment and model comparison, a discriminative model of pelvic injury was established. RESULTS: The PLS dimension reduction method was better than the PCA method and the SVM model was better than the naive Bayesian classifier (NBC) model. The accuracy of the modeling set, leave-one-out cross validation and testing set of the SVM classification model based on 12 PLS factors was 100%, 100% and 93.33%, respectively. CONCLUSIONS: In the evaluation of pelvic injury, the pelvic injury data mining model based on CT images reaches high accuracy, which lays a foundation for automatic and rapid identification of pelvic injuries.

Diels-Alder Reactions in Water Are Determined by Microsolvation.

Nanoconfined aqueous environments and the recent advent of accelerated chemistry in microdroplets are increasingly being investigated for catalysis. The mechanisms underlying the enhanced reactivity in alternate solvent environments and whether the enhanced reactivity due to nanoconfinement is a universal phenomenon are not fully understood. Here, we use ab initio molecular dynamics simulations to characterize the free energy of a retro-Diels-Alder reaction in bulk water at very different densities and in water nanoconfined by parallel graphene sheets. We find that the broadly different global solvation environments accelerate the reactions to a similar degree with respect to the gas-phase reaction, with activation free energies that do not differ by more than kbT from each other. The reason for the same acceleration factor in the extremely different solvation environments is that it is the microsolvation of the dienophile's carbonyl group that governs the transition-state stabilization and mechanism, which is not significantly disrupted by either the lower density in bulk water or the strong nanoconfinement conditions used here. Our results also suggest that significant acceleration of Diels-Alder reactions in microdroplets or on-water conditions cannot arise from local microsolvation when water is present but instead must come from highly altered reaction environments that drastically change the reaction mechanisms.

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 W0 =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. These findings have implications for atmospheric chemistry, biochemical and biophysical environments, and energy materials, as transport of protons vital to these processes can be suppressed due to confinement, aggregation, and/or concentration.

QMCPACK: Advances in the development, efficiency, and application of auxiliary field and real-space variational and diffusion quantum Monte Carlo.

We review recent advances in the capabilities of the open source ab initio Quantum Monte Carlo (QMC) package QMCPACK and the workflow tool Nexus used for greater efficiency and reproducibility. The auxiliary field QMC (AFQMC) implementation has been greatly expanded to include k-point symmetries, tensor-hypercontraction, and accelerated graphical processing unit (GPU) support. These scaling and memory reductions greatly increase the number of orbitals that can practically be included in AFQMC calculations, increasing the accuracy. Advances in real space methods include techniques for accurate computation of bandgaps and for systematically improving the nodal surface of ground state wavefunctions. Results of these calculations can be used to validate application of more approximate electronic structure methods, including GW and density functional based techniques. To provide an improved foundation for these calculations, we utilize a new set of correlation-consistent effective core potentials (pseudopotentials) that are more accurate than previous sets; these can also be applied in quantum-chemical and other many-body applications, not only QMC. These advances increase the efficiency, accuracy, and range of properties that can be studied in both molecules and materials with QMC and QMCPACK.

Deep Learning-Assisted Three-Dimensional Fluorescence Difference Spectroscopy for Identification and Semiquantification of Illicit Drugs in Biofluids.

The fast identification and quantification of illicit drugs in biofluids are of great significance in clinical detection. However, existing drug detection strategies cannot fully meet clinical needs, and the on-site identification and quantification of various illicit drugs in biofluids remain a great challenge. Here, we report the development of a deep learning-assisted three-dimensional (3D) fluorescence difference spectroscopy for rapid identification and semiquantification of illicit drugs in biofluids. This strategy introduces highly fluorescent silver nanoclusters into the biofluids with illicit drugs as signal sources. The interaction between silver nanoclusters and drug molecules changed the fluorescence performance of the mixture. Deep learning methods were applied to grasp the subtle fingerprint information from the 3D fluorescence difference spectra to identify and semiquantify various illicit drugs in biofluids, including codeine, 4,5-methylene-dioxy amphetamine, 3,4-methylene dioxy methamphetamine, meperidine, and methcathinone. This approach can achieve a high prediction accuracy rate of 88.07% and a broad detection range from 2 µg/mL to 100 mg/mL. It opens up a new way for the detection of small molecules with or without fluorescence in complicated matrixes.

N-Heterocyclic Carbene Self-Assembled Monolayers on Gold as Surface Plasmon Resonance Biosensors.

Surface plasmon resonance (SPR)-based biosensing is a powerful tool to study the recognition processes between biomolecules in real-time without need for labels. The use of thiol chemistry is a critical component in surface functionalization of various SPR biosensor surfaces on gold. However, its use is hampered by the high propensity for oxidation of the gold-thiol linkage even in ambient atmosphere, resulting in a short lifetime of SPR sensor chips unless strict precautions are taken. Herein, we describe an approach to overcome this limitation by employing highly robust self-assembled monolayers (SAMs) of alkylated N-heterocyclic carbenes (NHCs) on gold. An alkylated NHC sensor surface was developed and its biosensing capabilities were compared to a commercial thiol-based analogue-a hydrophobic association (HPA) chip-in terms of its ability to act as a reliable platform for biospecific interaction analysis under a wide range of conditions. The NHC-based SPR sensor outperforms related thiol-based sesnsors in several aspects, including lower nonspecific binding capacity, better chemical stability, higher reproducibility, shorter equilibration time, and longer life span. We also demonstrate that the NHC-based sensor can be used for rapid and efficient formation of a hybrid lipid bilayer for use in membrane interaction studies. Overall, this work identifies the great promise in designing NHC-based surfaces as a new technology platform for SPR-based biosensing.

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. Participants in different context groups were tasked to identify testing strategies for carbon monoxide and opiate drugs. The results of chi-squared tests for their selections and two context groups exhibited statistically significant differences (p < 0.05 or p < 0.01). These findings show contextual information can bias forensic toxicology decisions about testing strategies, despite it is a relatively objective domain in forensic science.

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. Participants were divided into different context groups and tasked with examining whether questioned signatures with ambiguous features matched reference signatures. The results of independent-samples t-tests for their decision score data in the two context groups exhibited a statistically significant difference (p < 0.05, Cohen's d > 0.8). Moreover, the submitted forensic reports by participants disclosed a biased evaluation of handwriting features. These findings show how contextual information can bias forensic decision-making in handwriting examination. Context management with complementary strategies such as case triage, cognitive training and decision-making transparency must be implemented to minimize bias in handwriting examination.

Conserved glycine at position 45 of major cochlear connexins constitutes a vital component of the Ca²âº sensor for gating of gap junction hemichannels.

Mutations in gap junction (GJ) family of proteins, especially in the connexin (Cx) 26, are responsible for causing severe congenital hearing loss in a significant portion of patients (30-50% in various ethnic groups). Substitution of glycine at the position 45 of Cx26 to glutamic acid (p.G45E mutation) causes the Keratitis-ichthyosis-deafness (KID) syndrome. Previous studies have suggested that this point mutation caused a gain-of-function defect. However, the molecular mechanism of KID syndrome remains unclear. Since glycine at this position is conserved in many Cxs expressed in the cochlea, we tested the hypothesis that glycine at position 45 is an important component of the sensor regulating the Ca(2+) gating of GJ hemichannels. Using reconstituted Cx30, 32 and 43 expressed in the HEK 293 cells, we compared the functions of wild type and p.G45E mutant Cxs. We found that G45E in Cx30 resulted in similar deleterious cellular effects as Cx26 did. Cell death occurred within 24h of transfection, which was rescued by increasing extracellular Ca(2+) concentration ([Ca(2+)]o). Dye loading assay showed that Cx30 G45E, similar to Cx26 G45E, had leaky hemichannels at physiological [Ca(2+)]o (1.2 mM). Higher [Ca(2+)]o reduced the dye loading in a dose-dependent manner. Whole cell membrane current recordings also indicated that G45E caused increased hemichannel activities. p.G45E mutations of Cx32 and 43 also resulted in leaky hemichannels compared to their respective wild types in lower [Ca(2+)]o. Our data in this study provided further support for the hypothesis that glycine at position 45 is a conserved Ca(2+) sensor for the gating of GJ hemichannels among multiple Cx subtypes expressed in the cochlea.

Self-assembly of Au15 into single-cluster-thick sheets at the interface of two miscible high-boiling solvents.

Wet (nano)blanket: The self-assembly of Au nanoclusters into single-cluster-thick nanosheets is performed in two miscible high-boiling solvents with a slight polarity difference, which generates microphase separation and acts as a soft template to direct the self-assembly in a two-dimensional orientation.

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. During this study, bioinformatics analysis was conducted on the RNA sequencing data and DNA methylation sites from the public database to determine the prognostic genes related to sepsis, and a 9-gene prognostic signature for sepsis was constructed. According to the risk score, all sepsis samples were divided into two groups. Then, the prediction effect of the 9-gene signature was verified in two cohorts, and the association between these genes and sepsis was further revealed through immune infiltration analysis, gene set enrichment analysis and the relationship between clinical phenotype and survival rate. Our study provided a reliable prognostic signature for sepsis. The signature could predict the survival of patients with sepsis and serve as a predictor.

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. At present M-Chem is a developer friendly environment in which we encourage new software contributors from diverse fields to build their algorithms, models, and methods in our modular framework. The long-term objective of M-Chem is to create an interdisciplinary platform for computational methods with applications ranging from biomolecular simulations, reactive chemistry, to materials research.