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. MSignVGAE employs a graph variational autoencoder to model noisy signed association data and extends the multi-scale concept to enhance representation capabilities. A novel strategy for propagating signed message in signed networks addresses heterogeneity and consistency among nodes connected by signed edges. Additionally, we utilize the idea of denoising autoencoder to handle the noise in similarity feature information, which helps overcome biases in the fused similarity data. MSignVGAE represents microbe-disease associations as a heterogeneous graph using similarity information as node features. The multi-class classifier XGBoost is utilized to predict sign associations between diseases and microbes. CONCLUSIONS: MSignVGAE achieves AUROC and AUPR values of 0.9742 and 0.9601, respectively. Case studies on three diseases demonstrate that MSignVGAE can effectively capture a comprehensive distribution of associations by leveraging signed information.

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.

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.

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.

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.

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. The initial visual acuity was 0.99 logMAR. The test dataset consisted of 60 patients with 100 eyes collected while the model was optimized. Four different machine-learning algorithms (Extreme Gradient Boosting, support vector regression, Bayesian ridge, and random forest regressor) were used to predict BCVA, and four algorithms (Extreme Gradient Boosting, support vector machine, logistic regression, and random forest classifier) were used to classify BCVA in patients with ocular trauma after treatment for 6mo or longer. Clinical features were obtained from outpatient records, and ocular parameters were extracted from optical coherence tomography images and fundus photographs. These features were put into different machine-learning models, and the obtained predicted values were compared with the actual BCVA values. The best-performing model and the best variable selected were further evaluated in the test dataset. RESULTS: There was a significant correlation between the predicted and actual values [all Pearson correlation coefficient (PCC)>0.6]. Considering only the data from the traumatic group (group A) into account, the lowest mean absolute error (MAE) and root mean square error (RMSE) were 0.30 and 0.40 logMAR, respectively. In the traumatic and healthy groups (group B), the lowest MAE and RMSE were 0.20 and 0.33 logMAR, respectively. The sensitivity was always higher than the specificity in group A, in contrast to the results in group B. The classification accuracy and precision were above 0.80 in both groups. The MAE, RMSE, and PCC of the test dataset were 0.20, 0.29, and 0.96, respectively. The sensitivity, precision, specificity, and accuracy of the test dataset were 0.83, 0.92, 0.95, and 0.90, respectively. CONCLUSION: Predicting BCVA using machine-learning models in patients with treated ocular trauma is accurate and helpful in the identification of visual dysfunction.

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.

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.

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.

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.