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.

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.

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.

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.

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.

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.

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.

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 H2O2 mechanism. We find that the vertical ionization energies (VIEs) of partially solvated OH- ions are greatly lowered relative to the average VIE in the bulk liquid, unlike the case of the Cl- anion which shows no reduction in the VIEs regardless of solvation environment. Overall reduced hydrogen-bonding and undercoordination of OH- are structural features that are more readily present at the air-water interface, where the energy scale for ionization can be matched by statistically probable electric field values.

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 ab initio 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 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.

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. 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.

Novel Organic-Inorganic Hybrid Polystyrene Nanoparticles with Trichromatic Luminescence for the Detection of Latent Fingerprints.

This article explored the application of novel organic-inorganic hybrid polystyrene nanoparticles (PSNPs) with trichromatic luminescence for the detection of latent fingerprints. The PSNPs were synthesized by encapsulated Eu(DBM)3phen, coumarin 6, and FDBT into the polystyrene nanoparticles through the swelling method and applied them to visualize latent fingerprints. The PSNPs had a spherical morphology with an average diameter of 310.7 nm, and they emitted trichromatic fluorescence (525 nm/570 nm/610 nm) under 365 nm excitation wavelength with green/yellow/red color under filters. They were less likely to aggregate, float or stain the background when treating fingerprints. The developed fingerprints with excellent clarity of ridges and contrast could be viewed, and the digital magnification of fluorescence-developed fingerprints provided more minutiae details about some regional patterns. The colorimetric and fluorescent trichromatic light could provide complementary signals without the background interference from fluorescent substrates and/or complex multicolor surfaces, which improved the applicability of fluorescent nanoparticles for fingerprints development. PSNPs are promising for the detection of latent fingerprints and practical criminal investigations with their ease of operation, eco-friendly properties, and excellent trichromatic optical performance.

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., fingerprints, handwritings, and tool marks), and has been demonstrated in objective analytical disciplines based on quantitative instruments (e.g., DNA typing). Recently, several publications suggested that contextual bias can even affect decision-makings in forensic toxicology. To obtain more extensive evidence of contextual bias, we surveyed 200 forensic toxicology practitioners from China for 1) unconscious bias in two hypothetical forensic toxicology cases with contextual information, 2) understanding of the concept and nature of contextual bias, 3) communication between investigators and forensic examiners, 4) and perception of the task-relevance of contextual information. The results revealed that 1) most of the participants made a decision deviating from standard processes under the potentially biasing context; 2) they also showed a low level of familiarity with the concept and nature of contextual bias; 3) they had a close contact with police investigators and even a dual role as a crime scene investigator and laboratory examiner, especially those affiliated with police departments; 4) and there was a general opinion that all available case information should be considered in the forensic toxicological analysis, even if the information should be specifically identified as task-irrelevant. As the first empirical work in China, this study attempted to draw more attention to the potential impact of Chinese forensic scientists' lack of appreciation of cognitive factors in forensic practice, and promote more extensive empirical research in various forensic domains.

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. Furthermore, electric field distributions can be an order of magnitude larger than the average due to non-linear coupling of intramolecular solvent polarization with intermolecular solvent modes which may contribute to even greater surface reactivity for weakening or breaking chemical bonds at the droplet surface.

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.