Computer Screen Research Articles

Discovering virtual antiperovskites as solid-state electrolytes through active learning

In surveying an extensive library of 18,133 hypothetical antiperovskites (X3BA), we address the challenges posed by conventional experimental and computational screening methods. We introduce a novel computational approach, leveraging an active learning framework that synergistically integrates genetic algorithms with Bayesian optimization. This method efficiently discerns thermodynamically stable antiperovskites, narrowing down the vast initial set to 43 compounds characterized by minimal energy above the hull (Eh<150 meV/atom). Subsequent evaluations of their band structure (Eg), electrochemical stability window (Vw), and room-temperature lithium ion conductivity (σRT), alongside the construction of a 4-dimensional Pareto frontier for Eh, Eg, Vw, and σRT, refined this list to 22 promising candidates, seven of which exhibit outstanding room-temperature ionic conductivity (>4 mS cm−1), marking them as potential candidates. Our methodology not only expedites the identification of superior antiperovskites but also establishes a groundbreaking paradigm for computational exploration in materials science.

Quantifying the Uncertainty of Force Field Selection on Adsorption Predictions in MOFs.

Comparisons between simulated and experimental adsorption isotherms in MOFs are fraught with challenges. On the experimental side, there is significant variation between isotherms measured on the same system, with a significant percentage (∼20%) of published data being considered outliers. On the simulation side, force fields are often chosen "off-the-shelf" with little or no validation. The effect of this choice on the reliability of simulated adsorption predictions has not yet been rigorously quantified. In this work, we fill this gap by systematically quantifying the uncertainty arising from force field selection on adsorption isotherm predictions. We choose methane adsorption, where electrostatic interactions are negligible, to independently study the effect of the framework Lennard-Jones parameters on a series of prototypical materials that represent the most widely studied MOF "families". Using this information, we compute an adsorption "consensus isotherm" from simulations, including a quantification of uncertainty, and compare it against a manually curated set of experimental data from the literature. By considering many experimental isotherms measured by different groups and eliminating outliers in the data using statistical analysis, we conduct a rigorous comparison that avoids the pitfalls of the standard approach of comparing simulation predictions to a single experimental data set. Our results show that (1) the uncertainty in simulated isotherms can be as large as 15% and (2) standard force fields can provide reliable predictions for some systems but can fail dramatically for others, highlighting systematic shortcomings in those models. Based on this, we offer recommendations for future simulation studies of adsorption, including high-throughput computational screening of MOFs.

The medium modulates the medusa effect: Perceived mind in analogue and digital images

We effortlessly attribute mental states to other people. We also attribute minds to people depicted in pictures, albeit at a reduced strength. Intriguingly, this reduction in intensity continues for images of people within a photograph itself—a phenomenon known as the Medusa effect. The present study replicates the Medusa effect for images shown digitally and on paper. Crucially, we demonstrate that we can reduce the magnitude of the Medusa effect by depicting people digitally within a computer screen (e.g., as if one were interacting with a person on a Zoom call). As well as modulating the quantity of the Medusa effect, changes in pictorial medium can affect the quality of the perceived mind. Specifically, the dimension of Experience—what a depicted person can feel—reflected participants' observations that they could interact with an onscreen person embedded in a digital image. This combination of a robust Medusa effect and the ability to control it both quantitatively and qualitatively opens many avenues for its future application, such as manipulating and measuring mind in immersive media.

Superior electro-catalytic performance achieved by the negatively charged boron atom on BC3/TM/graphene sandwich heterostructures

The sandwich heterostructures (SHSs) are novel two-dimensional materials that hold great potential as efficient electro-catalysts. In this work, we computationally designed the BC3/TM/Gr SHSs by intercalating transition metal atoms into the BC3/graphene heterostructure. After the computational screening, only BC3/Sc/Gr, BC3/Ti/Gr, BC3/Y/Gr and BC3/Zr/Gr are validated as stable SHSs. The electron donation from the intercalated TM atom results in the formation of the negatively charged boron atom (Bδ−) and activation of the BC3 surface, making the BC3/TM/Gr SHSs highly promising as single-atom catalysts (SACs). The BC3/Sc/Gr and BC3/Y/Gr SHSs exhibit potential in carbon dioxide reduction reaction (CO2RR) and carbon monoxide reduction reaction (CORR) electro-catalysis. Particularly, when BC3/Y/Gr SHS serves as CORR electro-catalyst, the step (*CHO→*CHOH) is a potential determining step, with an extremely low limiting potential (UL = −0.10 V). The BC3/Ti/Gr and BC3/Zr/Gr SHSs are suitable as hydrogen evolution reaction (HER) electro-catalysts. Specially, the BC3/Ti/Gr SHS serves as an ideal HER electro-catalyst in acid condition, with close-to-zero adsorption free energy (ΔGH = 0.006 eV) and fairly low overall activation barrier (0.20 eV). By analyzing the electronic properties, the unique adsorption activity of the Bδ− on H atom and unsaturated CO2RR intermediates is elucidated as the origin of excellent catalytic activity of BC3/TM/Gr SHSs, which is modulated by the intercalated TM atom. Our work is instructive to rational design of SACs towards energy conversion based on non-metal elements.

Mathematical Modeling Suggests That Monocyte Activity May Drive Sex Disparities during Influenza Infection.

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

A framework for the biophysical screening of antibody mutations targeting solvent-accessible hydrophobic and electrostatic patches for enhanced viscosity profiles

The formulation of high-concentration monoclonal antibody (mAb) solutions in low dose volumes for autoinjector devices poses challenges in manufacturability and patient administration due to elevated solution viscosity. Often many therapeutically potent mAbs are discovered, but their commercial development is stalled by unfavourable developability challenges. In this work, we present a systematic experimental framework for the computational screening of molecular descriptors to guide the design of 24 mutants with modified viscosity profiles accompanied by experimental evaluation. Our experimental observations using a model anti-IL8 mAb and eight engineered mutant variants reveal that viscosity reduction is influenced by the location of hydrophobic interactions, while targeting positively charged patches significantly increases viscosity in comparison to wild-type anti-IL-8 mAb. We conclude that most predicted in silico physicochemical properties exhibit poor correlation with measured experimental parameters for antibodies with suboptimal developability characteristics, emphasizing the need for comprehensive case-by-case evaluation of mAbs. This framework combining molecular design and triage via computational predictions with experimental evaluation aids the agile and rational design of mAbs with tailored solution viscosities, ensuring improved manufacturability and patient convenience in self-administration scenarios.

Computational Screening for the Dipeptidyl Peptidase-IV Inhibitory Peptides from Putative Hemp Seed Hydrolyzed Peptidome as a Potential Antidiabetic Agent.

Dipeptidyl peptidase-IV (DPPIV) inhibitory peptides are a class of antihyperglycemic drugs used in the treatment of type 2 diabetes mellitus, a metabolic disorder resulting from reduced levels of the incretin hormone GLP-1. Given that DPPIV degrades incretin, a key regulator of blood sugar levels, various antidiabetic medications that inhibit DPPIV, such as vildagliptin, sitagliptin, and linagliptin, are employed. However, the potential side effects of these drugs remain a matter of debate. Therefore, we aimed to investigate food-derived peptides from Cannabis sativa (hemp) seeds. Our developed bioinformatics pipeline was used to identify the putative hydrolyzed peptidome of three highly abundant proteins: albumin, edestin, and vicilin. These proteins were subjected to in silico digestion by different proteases (trypsin, chymotrypsin, and pepsin) and then screened for DPPIV inhibitory peptides using IDPPIV-SCM. To assess potential adverse effects, several prediction tools, namely, TOXINpred, AllerCatPro, and HemoPred, were employed to evaluate toxicity, allergenicity, and hemolytic effects, respectively. COPID was used to determine the amino acid composition. Molecular docking was performed using GalaxyPepDock and HPEPDOCK, 3D visualizations were conducted using the UCSF Chimera program, and MD simulations were carried out with AMBER20 MD software. Based on the predictive outcomes, FNVDTE from edestin and EAQPST from vicilin emerged as promising candidates for DPPIV inhibitors. We anticipate that our findings may pave the way for the development of alternative DPPIV inhibitors.

In silico and pharmacokinetic assessment of echinocystic acid effectiveness in Alzheimer's disease like pathology.

Aim: Alzheimer's disease causes dementia which impairs the cognitive domains. Methodology: The pharmacokinetic characteristics and biological activity of echinocystic acid are predicted in this work using in silico or computational approaches, including pkCSM, Swiss ADME, OSIRIS® property explorer, PASS online web resourceand MOLINSPIRATION® software. Results & discussion: The compound has lipid metabolism regulating property as major role in decreasing the progression of Alzheimer's disease and it has no major side effects and ADR. The drug also has anti-inflammatory properties which can help in regulating the innate immunity that plays a major role in Alzheimer's disease. Conclusion: From the computational screening, we infer that, echinocystic acid can regulate memory loss, cognitive disability and also slow down the progression of Alzheimer's disease-like pathology.

USP44 Overexpression Drives a MYC-Like Gene Expression Program in Neuroblastoma through Epigenetic Reprogramming.

Neuroblastoma is an embryonic cancer that contributes disproportionately to death in young children. Sequencing data have uncovered few recurrently mutated genes in this cancer, although epigenetic pathways have been implicated in disease pathogenesis. We used an expression-based computational screen that examined the impact of deubiquitinating enzymes on patient survival to identify potential new targets. We identified the histone H2B deubiquitinating enzyme USP44 as the enzyme with the greatest impact on survival in patients with neuroblastoma. High levels of USP44 significantly correlate with metastatic disease, unfavorable histology, advanced patient age, and MYCN amplification. The subset of patients with tumors expressing high levels of USP44 had significantly worse survival, including those with tumors lacking MYCN amplification. We showed experimentally that USP44 regulates neuroblastoma cell proliferation, migration, invasion, and neuronal development. Depletion of the histone H2B ubiquitin ligase subunit RNF20 resulted in similar findings, strongly implicating this histone mark as the target of USP44 activity in this disease. Integration of transcriptome and epigenome in analyses demonstrates a distinct set of genes that are regulated by USP44, including those in Hallmark MYC target genes in both murine embryonic fibroblasts and the SH-SY5Y neuroblastoma cell line. We conclude that USP44 is a novel epigenetic regulator that promotes aggressive features and may be a novel target in neuroblastoma. Implications: This study identifies a new genetic marker of aggressive neuroblastoma and identifies the mechanisms by which its overactivity contributes to the pathophysiology of this disease.

Targeting SOX13 inhibits assembly of respiratory chain supercomplexes to overcome ferroptosis resistance in gastric cancer

Therapeutic resistance represents a bottleneck to treatment in advanced gastric cancer (GC). Ferroptosis is an iron-dependent form of non-apoptotic cell death and is associated with anti-cancer therapeutic efficacy. Further investigations are required to clarify the underlying mechanisms. Ferroptosis-resistant GC cell lines are constructed. Dysregulated mRNAs between ferroptosis-resistant and parental cell lines are identified. The expression of SOX13/SCAF1 is manipulated in GC cell lines where relevant biological and molecular analyses are performed. Molecular docking and computational screening are performed to screen potential inhibitors of SOX13. We show that SOX13 boosts protein remodeling of electron transport chain (ETC) complexes by directly transactivating SCAF1. This leads to increased supercomplexes (SCs) assembly, mitochondrial respiration, mitochondrial energetics and chemo- and immune-resistance. Zanamivir, reverts the ferroptosis-resistant phenotype via directly targeting SOX13 and promoting TRIM25-mediated ubiquitination and degradation of SOX13. Here we show, SOX13/SCAF1 are important in ferroptosis-resistance, and targeting SOX13 with zanamivir has therapeutic potential.

IN SILICO PREDICTION OF POTENTIAL DERMATOLOGICAL EFFECTS OF A SYNTHETIC ANTIOXIDANT

Nowadays, in silico prediction has become standard in the development of new drugs. Computer screening of known drugs provides an efficient approach to repurposing these agents for new indications. This sparked our interest in conducting an in silico analysis of ethylmethylhydroxypyridine succinate (EMHPS) for pharmacological effects potentially useful in dermatology. The aim of this study is to perform an in silico search for the effects of the synthetic antioxidant EMHPS that are potentially useful for the treatment of skin diseases. Retrospective computer prediction of the EMGPS structure was performed using the Drug2ways methodical approach and the PASS (Prediction of Activity Spectra for Substances) program online. The EMHPS molecule consists of two active fragments, which were analyzed separately. In the structure of 2-ethyl-6-methyl-3-hydroxypyridine, 125 types of activity were predicted, while in succinate - 734 types of activity. From the array of prognostic data, effects that may be relevant for the treatment of skin diseases were extracted. These include anti-seborrheic, anti-alopecia, anti-eczema, anti-pruritic, and anti-infectious effects, as well as photosensitizing action and lack of skin irritation. According to the forecast, these effects may be due to the influence of the components of the EMHPS molecule on membranes, antioxidant protection, the state of connective tissue, enzymes, and signaling pathways involved in the development of inflammation, as well as on the pathogenicity factors of microorganisms. Therefore, in silico screening of new types of activity in EMHPS allowed us to predict some effects useful in the treatment of dermatological pathology. Undoubtedly, the results of the forecast must be verified by practice, but they allow us to create a working hypothesis and approach the planning of experiments more rationally, which corresponds to modern trends in pharmacology.

Single-Atom Substituents in Copper Surfaces May Adsorb Multiple CO Molecules.

Copper is a good CO2 electroreduction catalyst as products beyond CO form, but efficiency and selectivity is low. Experiments have shown that an admixture of other elements can help, and computational screening studies have pointed out various promising candidates based on the adsorption of a single CO molecule as a descriptor. Our calculations of CO adsorption on surfaces where a first row transition metal atom replaces a Cu atom show that multiple CO molecules, not just one, bind to the substitutional atom. For Fe, Co, and Ni atoms, a decrease in binding energy is found, but the reverse trend, namely, increasing bond strength, is found for V, Cr, and Mn and the first three CO molecules. Magnetic moment, charge, and position of the substitutional atom are also strongly affected by the CO adsorption in most cases. Magnetic moment is stepwise reduced to zero, and the outward displacement of the substitutional atom increased.

ВИКОРИСТАННЯ МЕТОДІВ ВІРТУАЛЬНОЇ РЕАЛЬНОСТІ ДЛЯ ОТРИМАННЯ ПРАКТИЧНИХ НАВИКІВ З ВИПРОБУВАНЬ БУДІВЕЛЬНИХ МАТЕРІАЛІВ

Introduction. Current developments that are being implemented by modern organizations must include not only initial methodological complexes of disciplines (modules), but also security programs aimed at mastering professional competencies. The optimal way to develop competencies is through virtual laboratories, which are modeled in an electronic lighting environment on real-world objects. Problem Statement. The problem of this article is the need to promote current technologies of virtual modeling, which is considered as a tool for increasing the brightness of engineering lighting. Purpose. development and implementation of virtual 3D laboratories to obtain practical skills from testing real-life materials. Materials and methods. 3D graphics editor, software Unity 2022.3.4. Results. We also have an analysis of programs for 3D modeling, which can be used not only for the purpose of displaying on printed products or on the computer screen, but also for the development of entire elements. In addition to software products, the authors propose an approach to creating a virtual technological laboratory, which allows industry professionals to learn practical skills from testing real-life materials.

Supramolecular trapping of a cationic all-metal σ-aromatic {Bi4} ring

Aromaticity in organic molecules is well defined, but its role in metal-only rings remains controversial. Here we introduce a supramolecular stabilization approach of a cationic {Bi4} rhomboid within the symmetric charge sphere of two bowl-shaped dianionic calix[4]pyrrolato indinates. Crystallographic and spectroscopic characterization, quantum chemical analysis and magnetically induced ring currents indicate σ-aromaticity in the formally tetracationic 16-valence electron [Bi4]4+ ring. Computational screening for other p-block elements identifies the planar rhomboid as the globally preferred structure for 16-valence electron four-atomic clusters. The aromatic [Bi4]4+ is isoelectronic to the [Al4]4−, a motif previously observed as antiaromatic in Li3[Al4]− in the gas phase. Thus, subtle factors such as charge isotropy seem to decide over aromaticity or antiaromaticity, advising for caution in debates based on the Hückel model—a concept valid for second-row elements but less deterministic for the heavier congeners.

Indolo[3,2-a]carbazoles as Engineered Materials for Optoelectronic Applications: Synthesis, Structural Insights, and Computational Screening.

The biological and medicinal importance of indolocarbazoles has been known for the past several decades. However, in recent times, these compounds have been emerging as potential candidates for optoelectronic applications, although several challenges are associated with their synthesis. We report here a Pd(II)-catalyzed process for the synthesis of indolo[3,2-a]carbazoles. The reaction proceeded under neat conditions and in the presence of aqueous nonmetallic oxidant TBHP, and the products were purified directly after the completion of the reaction. Also, the possibility of employing the present method for reaction with gram-scale feed was investigated. A detailed single-crystal analysis of several indolo[3,2-a]carbazoles revealed how the molecular arrangement can be tuned by altering the functionalization. Finally, the developed molecules were screened computationally to assess their potential for possible use as hole transport materials (HTMs) for organic light-emitting diodes (OLEDs).

Computational screening of metal–organic frameworks for separation of CO2 and N2 from wet flue gas

Computational screening of metal–organic frameworks for separation of CO2 and N2 from wet flue gas

Experimental screening of intermetallic alloys for electrochemical CO2 reduction

In this study, we experimentally screen a promising class of intermetallic alloys for the electrochemical reduction of CO2 toward hydrocarbon products. Based on previous DFT-based screening papers, combinations of strongly CO-binding metals such as iron, cobalt, and nickel with weakly CO-binding metals such as gallium, aluminium or zinc were selected as potentially promising catalytic materials. Despite the challenging production of these alloys, we report a general two-step synthesis method for intermetallic alloys and discuss the specific synthesis conditions that must be taken into account when synthesising these materials. After their synthesis, we use a recently developed differential electrochemical mass spectrometry (DEMS) setup to rapidly quantify the CO2 reduction products over a range of potentials. Almost all newly developed intermetallic catalysts are shown to produce methane and ethylene, while the CoSn catalyst showed higher selectivity towards formate production. However, all tested catalysts mostly produced hydrogen and only reduce CO2 to a small extent, despite the favourable computational screening results. We discuss possible reasons for this discrepancy and outline a more holistic approach for linking future DFT studies with experiments.

Considerations when trying to quantify immersion with secondary tasks

Immersion is a crucial component to characterizing virtual reality (VR) and discovering its many implications for our lives. Studies have defined immersion as the extent that virtual environments submerge users’ perceptual systems, shut out the physical environments’ inputs, and induce engrossment in users. The present study sought to further understand the applications of immersion in virtual environments by leveraging traditional self-report measures, as well as investigating the use of distraction to quantify immersion objectively. We hypothesized that participants in immersive 3-dimensional VR would self-report higher levels of immersion and engagement compared to a non-VR condition. We also predicted that participants in the VR condition would have impaired responses to a secondary task compared to the non-VR condition. Using a within-subjects design, participants engaged in a VR meditation both on a computer screen (non-VR) and through a VR headset (VR). During the meditations, they completed a secondary detection-response task (DRT) by pressing a button in response to a tactile stimulus. We used DRT reaction times to objectively measure immersion. Following the meditations, participants completed questionnaires regarding their experiences. As predicted, participants reported that the VR condition increased their engagement and presence compared to the non-VR version of the same meditation scenario. With regard to the secondary detection task, we did not find a significant effect of VR on DRT performance, although DRT performance was numerically worse in the VR condition compared to the non-VR condition. These mixed results suggest that the use of a secondary task depends upon several factors, which we discuss.

Combining computational screening and machine learning to explore MOFs and COFs for methane purification

Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have great potential to be used as porous adsorbents and membranes to achieve high-performance methane purification. Although the continuous increase in the number and diversity of MOFs and COFs is a great opportunity for the discovery of novel adsorbents and membranes with superior performances, evaluating such a vast number of materials in the quickest and most effective manner requires the development of computational approaches. High-throughput computational screening based on molecular simulations has been extensively used to identify the most promising MOFs and COFs for methane purification. However, the enormous and ever-growing material space necessitates more efficient approaches in terms of time and effort. Combining data science with molecular simulations has recently accelerated the discovery of optimal MOF and COF materials for methane purification and revealed the hidden structure–performance relationships. In this perspective, we highlighted the recent developments in combining high-throughput molecular simulations and machine learning to accurately identify the most promising MOF and COF adsorbents and membranes among thousands of candidates for separating methane from other gases including acetylene, carbon dioxide, helium, hydrogen, and nitrogen. After providing a brief overview of the topic, we reviewed the pioneering contributions in the field and discussed the current opportunities and challenges that we need to direct our efforts for the design and discovery of adsorbent and membrane materials.

Different receptor models show differences in ligand binding strength and location: a computational drug screening for the tick-borne encephalitis virus.

The tick-borne encephalitis virus (TBE) is a neurotrophic disease that has spread more rapidly throughout Europe and Asia in the past few years. At the same time, no cure or specific therapy is known to battle the illness apart from vaccination. To find a pharmacologically relevant drug, a computer-aided drug screening was initiated. Such a procedure probes a possible binding of a drug to the RNA Polymerase of TBE. The crystal structure of the receptor, however, includes missing and partially modeled regions, which rendered the structure incomplete and of questionable use for a thorough drug screening procedure. The quality of the receptor model was addressed by studying three putative structures created. We show that the choice of receptor models greatly influences the binding affinity of potential drug molecules and that the binding location could also be significantly impacted. We demonstrate that some drug candidates are unsuitable for one model but show decent results for another. Without any prejudice on the three employed receptor models, the study reveals the imperative need to investigate the receptor structure before drug binding is probed whether experimentally or computationally.