Comparison of the Neutralization Power of Sotrovimab Against SARS-CoV-2 Variants: Development of a Rapid Computational Method.

BACKGROUND: The rapid evolution of SARS-CoV-2 imposed a huge challenge on disease control. Immune evasion caused by genetic variations of the SARS-CoV-2 spike protein's immunogenic epitopes affects the efficiency of monoclonal antibody-based therapy of COVID-19. Therefore, a rapid method is needed to evaluate the efficacy of the available monoclonal antibodies against the new emerging variants or potential novel variants. OBJECTIVE: The aim of this study is to develop a rapid computational method to evaluate the neutralization power of anti-SARS-CoV-2 monoclonal antibodies against new SARS-CoV-2 variants and other potential new mutations. METHODS: The amino acid sequence of the extracellular domain of the spike proteins of the severe acute respiratory syndrome coronavirus (GenBank accession number YP_009825051.1) and SARS-CoV-2 (GenBank accession number YP_009724390.1) were used to create computational 3D models for the native spike proteins. Specific mutations were introduced to the curated sequence to generate the different variant spike models. The neutralization potential of sotrovimab (S309) against these variants was evaluated based on its molecular interactions and Gibbs free energy in comparison to a reference model after molecular replacement of the reference receptor-binding domain with the variant's receptor-binding domain. RESULTS: Our results show a loss in the binding affinity of the neutralizing antibody S309 with both SARS-CoV and SARS-CoV-2. The binding affinity of S309 was greater to the Alpha, Beta, Gamma, and Kappa variants than to the original Wuhan strain of SARS-CoV-2. However, S309 showed a substantially decreased binding affinity to the Delta and Omicron variants. Based on the mutational profile of Omicron subvariants, our data describe the effect of the G339H and G339D mutations and their role in escaping antibody neutralization, which is in line with published clinical reports. CONCLUSIONS: This method is rapid, applicable, and of interest to adapt the use of therapeutic antibodies to the treatment of emerging variants. It could be applied to antibody-based treatment of other viral infections.

Revolutionising Neurological Therapeutics: Investigating Drug Repurposing Strategies.

Repurposing drugs (DR) has become a viable approach to hasten the search for cures for neurodegenerative diseases (NDs). This review examines different off-target and on-target drug discovery techniques and how they might be used to find possible treatments for non-diagnostic depressions. Off-target strategies look at the known or unknown side effects of currently approved drugs for repositioning, whereas on-target strategies connect disease pathways to targets that can be treated with drugs. The review highlights the potential of experimental and computational methodologies, such as machine learning, proteomic techniques, network and genomics-based approaches, and in silico screening, in uncovering new drug-disease correlations. It also looks at difficulties and failed attempts at drug repurposing for NDs, highlighting the necessity of exact and standardised procedures to increase success rates. This review's objectives are to address the purpose of drug repurposing in human disorders, particularly neurological diseases, and to provide an overview of repurposing candidates that are presently undergoing clinical trials for neurological conditions, along with any possible causes and early findings. We then include a list of drug repurposing strategies, restrictions, and difficulties for upcoming research.

Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections.

The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning techniques, such as random forest analysis, can significantly improve diagnostic accuracy. We conducted a retrospective analysis of clinical and laboratory data from 214 patients with acute respiratory infections, collected between October 2022 and October 2023 at the Second Hospital of Nanping. The study population was categorized into three groups: COVID-19 positive (n = 52), MP positive (n = 140), and co-infected (n = 22). Key biomarkers, including C-reactive protein (CRP), procalcitonin (PCT), interleukin- 6 (IL-6), and white blood cell (WBC) counts, were evaluated. Correlation analyses were conducted to assess relationships between biomarkers within each group. The random forest analysis was applied to evaluate the discriminative power of these biomarkers. The random forest model demonstrated high classification performance, with area under the ROC curve (AUC) scores of 0.86 (95% CI: 0.70-0.97) for COVID-19, 0.79 (95% CI: 0.64-0.92) for MP, 0.69 (95% CI: 0.50-0.87) for co-infections, and 0.90 (95% CI: 0.83-0.95) for the micro-average ROC. Additionally, the precision-recall curve for the random forest classifier showed a micro-average AUC of 0.80 (95% CI: 0.69-0.91). Confusion matrices highlighted the model's accuracy (0.77) and biomarker relationships. The SHAP feature importance analysis indicated that age (0.27), CRP (0.25), IL6 (0.14), and PCT (0.14) were the most significant predictors. The integration of computational methods, particularly random forest analysis, in evaluating clinical and biomarker data presents a promising approach for enhancing diagnostic processes for infectious diseases. Our findings support the use of specific biomarkers in differentiating between COVID-19 and MP, potentially leading to more targeted and effective diagnostic strategies. This study underscores the potential of machine learning techniques in improving disease classification in the era of precision medicine.

A novel degradable PEG superparamagnetic iron oxide capsule coupled with a polyphenolic nano-enzymatic conjugate (PSPM-NE), to treat ROS-driven cardiovascular-diseases, tested in atherosclerosis as a model disease, and hypothesizing autoimmunity as an atheroma's trigger.

Cardiovascular diseases account for a significant portion of the worldwide mortality rate. This aroused interest among the specialised scientific community, seeking for solutions based on non-clinical and clinical investigations, to shed light onto the physio-pathology of cardiovascular impairment. It is proven challenging managing chronic cardiovascular illnesses like atherosclerosis, arrhythmias, and diverse cardiomyopathies. In certain cases, there is no approved treatment. In other cases, the need for combining therapeutic components, when dealing with co-morbidities, may increase the risk of toxicity-driven cardiovascular impairment. In this case, because the risk of cardiac events correlates with the QT prolongation rates, the QT or QTc interval prolongation has become an important biomarker to access drug-related cardio-toxicity. Several approaches have been found in the current literature, aiming at improving physiological acceptance, i.e., to reduce toxicity. Nanotechnology has increasingly appeared as a promising ally to modulate active substances, preserving cardiovascular function and optimising drug effectiveness, i.e., acting as a cardio-protective mechanism, leveraging the effects of drug-driven cardio-toxicity. In this manuscript, the author combines plant active compounds and nanotechnological strategies, e.g., nano-encapsulation, nano-enzymes, magnetically driven nano-delivery systems, applied in regenerative medicine, and assesses their effects on the cardiovascular system, e.g., as cardio-protective factors, reducing cardio-toxicity. The aim is to propose a new strategy to tackle atherosclerosis initiation and progression, in a drug design that targets ROS-removal and reduces inflammation, using auto-immunity biomarkers to select key atheroma-related signalling cascades. To analyse physiological phenomena related to atherosclerosis initiation and progression, the author proposes both experimental observations and a new haemorheological computational model of arterial constriction. The results of such analysis are used as motivators in the design of the here presented strategy to tackle atheroma. This novel design is based on degradable polyethylene glycol (PEG) superparamagnetic iron oxide capsule coupled with a polyphenolic nano-enzymatic conjugate (PSPM-NE).

Visual Computation of Material Microstructure and Deformation.

The experimentally obtained material microstructure can be used to calculate a material's properties and identify microstructure-property relationships. The key procedure to enable this is to interpret the observed microstructure accurately. This work reports on a newly developed computational method to serve such a purpose. The method is based on cubic spline interpolation and a simple search algorithm. Parameterisation was accomplished via the comparison between its preliminary statistical results and the information in a phase diagram. The method was applied to analyse the quenched microstructure of multicomponent and multiphase metallic-oxide materials. The importance of adequate parameterisation is demonstrated. The results provide a good explanation for the experimentally measured electric conductance behaviour. Further application of the method to the deformation of materials is discussed. The algorithms are directly available for the analysis of the three-dimensional microstructure of materials.

Prediction of cell-type-specific cohesin-mediated chromatin loops based on chromatin state.

Chromatin loop is of crucial importance for the regulation of gene transcription. Cohesin is a type of chromatin-associated protein that mediates the interaction of chromatin through the loop extrusion. Cohesin-mediated chromatin interactions have strong cell-type specificity, posing a challenge for predicting chromatin loops. Existing computational methods perform poorly in predicting cell-type-specific chromatin loops. To address this issue, we propose a random forest model to predict cell-type-specific cohesin-mediated chromatin loops based on chromatin states identified by ChromHMM and the occupancy of related factors. Our results show that chromatin state is responsible for cell-type-specificity of loops. Using only chromatin states as features, the model achieved high accuracy in predicting cell-type-specific loops between two cell types and can be applied to different cell types. Furthermore, when chromatin states are combined with the occurrence frequency of CTCF, RAD21, YY1, and H3K27ac ChIP-seq peaks, more accurate prediction can be achieved. Our feature extraction method provides novel insights into predicting cell-type-specific chromatin loops and reveals the relationship between chromatin state and chromatin loop formation.

Experimental, Computational, and Machine Learning Methods for Prediction of Residual Stresses in Laser Additive Manufacturing: A Critical Review.

In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

Integrative analysis of senescence-related genes identifies robust prognostic clusters with distinct features in hepatocellular carcinoma.

INTRODUCTION: Senescence refers to a state of permanent cell growth arrest and is regarded as a tumor suppressive mechanism, whereas accumulative evidence demonstrate that senescent cells play an adverse role during cancer progression. The scarcity of specific and reliable markers reflecting senescence level in cancer impede our understanding of this biological basis. OBJECTIVES: Senescence-related genes (SRGs) were collected for integrative analysis to reveal the role of senescence in hepatocellular carcinoma (HCC). METHODS: Consensus clustering was used to subtype HCC based on SRGs. Several computational methods, including single sample gene set enrichment analysis (ssGSEA), fuzzy c-means algorithm, were performed. Data of drug sensitivities were utilized to screen potential therapeutic agents for different senescence patients. Additionally, we developed a method called signature-related gene analysis (SRGA) for identification of markers relevant to phenotype of interest. Experimental strategies consisting quantitative real-time PCR (qRT-PCR), ß-galactosidase assay, western blot, and tumor-T cell co-culture system were used to validate the findings in vitro. RESULTS: We identified three robust prognostic clusters of HCC patients with distinct survival outcome, mutational landscape, and immune features. We further extracted signature genes of senescence clusters to construct the senescence scoring system and profile senescence level in HCC at bulk and single-cell resolution. Senescence-induced stemness reprogramming was confirmed both in silico and in vitro. HCC patients with high senescence were immune suppressed and sensitive to Tozasertib and other drugs. We suggested that MAFG, PLIN3, and 4 other genes were pertinent to HCC senescence, and MAFG potentially mediated immune suppression, senescence, and stemness. CONCLUSION: Our findings provide insights into the role of SRGs in patients stratification and precision medicine.

Integrated annotation prioritizes metabolites with bioactivity in inflammatory bowel disease.

Microbial biochemistry is central to the pathophysiology of inflammatory bowel diseases (IBD). Improved knowledge of microbial metabolites and their immunomodulatory roles is thus necessary for diagnosis and management. Here, we systematically analyzed the chemical, ecological, and epidemiological properties of ~82k metabolic features in 546 Integrative Human Microbiome Project (iHMP/HMP2) metabolomes, using a newly developed methodology for bioactive compound prioritization from microbial communities. This suggested >1000 metabolic features as potentially bioactive in IBD and associated ~43% of prevalent, unannotated features with at least one well-characterized metabolite, thereby providing initial information for further characterization of a significant portion of the fecal metabolome. Prioritized features included known IBD-linked chemical families such as bile acids and short-chain fatty acids, and less-explored bilirubin, polyamine, and vitamin derivatives, and other microbial products. One of these, nicotinamide riboside, reduced colitis scores in DSS-treated mice. The method, MACARRoN, is generalizable with the potential to improve microbial community characterization and provide therapeutic candidates.

Coregulatory effects of multiple histone modifications in key ferroptosis-related genes for lung adenocarcinoma.

Aim: The present study was designed to investigate the coregulatory effects of multiple histone modifications (HMs) on gene expression in lung adenocarcinoma (LUAD). Materials & methods: Ten histones for LUAD were analyzed using ChIP-seq and RNA-seq data. An innovative computational method is proposed to quantify the coregulatory effects of multiple HMs on gene expression to identify strong coregulatory genes and regions. This method was applied to explore the coregulatory mechanisms of key ferroptosis-related genes in LUAD. Results: Nine strong coregulatory regions were identified for six ferroptosis-related genes with diverse coregulatory patterns (CA9, PGD, CDKN2A, PML, OTUB1 and NFE2L2). Conclusion: This quantitative method could be used to identify important HM coregulatory genes and regions that may be epigenetic regulatory targets in cancers.

Assessment of Hydrazone Derivatives for Enhanced Steel Corrosion Resistance in 15 wt.% HCl Environments: A Dual Experimental and Theoretical Perspective.

This study evaluates the corrosion inhibition capabilities of two novel hydrazone derivatives, (E)-2-(5-methoxy-2-methyl-1H-indol-3-yl)-N'-(4-methylbenzylidene)acetohydrazide (MeHDZ) and (E)-N'-benzylidene-2-(5-methoxy-2-methyl-1H-indol-3-yl)acetohydrazide (HHDZ), on carbon steel in a 15 wt.% HCl solution. A comprehensive suite of analytical techniques, including gravimetric analysis, potentiodynamic polarization (PDP), electrochemical impedance spectroscopy (EIS), and scanning electron microscopy (SEM), demonstrates their significant inhibition efficiency. At an optimal concentration of 5 × 10-3 mol/L, MeHDZ and HHDZ achieve remarkable inhibition efficiencies of 98% and 94%, respectively. EIS measurements reveal a dramatic reduction in effective double-layer capacitance (from 236.2 to 52.8 and 75.3 µF/cm2), strongly suggesting inhibitor adsorption on the steel surface. This effect is further corroborated by an increase in polarization resistance and a significant decrease in corrosion current density at optimal concentrations. Moreover, these inhibitors demonstrate sustained corrosion mitigation over extended exposure durations and maintain effectiveness even under elevated temperatures, highlighting their potential for diverse operational conditions. The adsorption process of these inhibitors aligns well with the Langmuir adsorption isotherm, implying physicochemical interactions at the carbon steel surface. Density functional tight-binding (DFTB) calculations and molecular dynamics simulations provide insights into the inhibitor-surface interaction mechanism, further elucidating the potential of these hydrazone derivatives as highly effective corrosion inhibitors in acidic environments.

AmyloComp: A Bioinformatic Tool for Prediction of Amyloid Co-aggregation.

Typically, amyloid fibrils consist of multiple copies of the same protein. In these fibrils, each polypeptide chain adopts the same ß-arc-containing conformation and these chains are stacked in a parallel and in-register manner. In the last few years, however, a considerable body of data has been accumulated about co-aggregation of different amyloid-forming proteins. Among known examples of the co-aggregation are heteroaggregates of different yeast prions and human proteins Rip1 and Rip3. Since the co-aggregation is linked to such important phenomena as infectivity of amyloids and molecular mechanisms of functional amyloids, we analyzed its structural aspects in more details. An axial stacking of different proteins within the same amyloid fibril is one of the most common type of co-aggregation. By using an approach based on structural similarity of the growing tips of amyloids, we developed a computational method to predict amyloidogenic ß-arch structures that are able to interact with each other by the axial stacking. Furthermore, we compiled a dataset consisting of 26 experimentally known pairs of proteins capable or incapable to co-aggregate. We utilized this dataset to test and refine our algorithm. The developed method opens a way for a number of applications, including the identification of microbial proteins capable triggering amyloidosis in humans. AmyloComp is available on the website: https://bioinfo.crbm.cnrs.fr/index.php?route=tools&tool=30.

Adsorption of some cationic dyes onto two models of graphene oxide.

CONTEXT: The search for highly efficient adsorbent materials remains a significant requirement in the field of adsorption for wastewater treatment. Computational study can highly contribute to the identification of efficient material. In this work, we propose a computational approach to study the adsorption of four cationic basic dyes, basic blue 26 (BB26), basic green 1 (BG1), basic yellow 2 (BY2), and basic red 1 (BR1), onto two models of graphene oxide as adsorbents. The main objectives of this study are the assessment of the adsorption capacity of the graphene oxide towards basic dyes and the evaluation of the environmental and temperature effects on the adsorption capacity. Quantum theory of atoms in molecules (QTAIM) analysis has been used to understand the interactions between the dyes and graphene oxides. In addition, adsorption free energies of the dyes onto graphene oxides are calculated in gas and solvent phases for temperatures varying from 200 to 400 K. As a result, the adsorption free energy varies linearly depending on the temperature, highlighting the importance of temperature effects in the adsorption processes. Furthermore, the results indicate that the environment (through the solvation) considerably affects the calculated adsorption free energies. Overall, the results show that the two models of graphene oxide used in this work are efficient for removing dyes from wastewater. METHODS: We have optimized the complexes formed by the interaction of dyes with graphene oxides at the PW6B95-D3/def2-SVP level of theory. The SMD solvation model realizes the implicit solvation, and water is used as the solvent. Calculations are performed using the Gaussian 16 suite of program. QTAIM analysis is performed using the AIMAll program. Gibbs free energies as function of temperature are calculated using the TEMPO program.

Exploring Political Mistrust in Pandemic Risk Communication: Mixed-Method Study Using Social Media Data Analysis.

BACKGROUND: This research extends prior studies by the Finnish Institute for Health and Welfare on pandemic-related risk perception, concentrating on the role of trust in health authorities and its impact on public health outcomes. OBJECTIVE: The paper aims to investigate variations in trust levels over time and across social media platforms, as well as to further explore 12 subcategories of political mistrust. It seeks to understand the dynamics of political trust, including mistrust accumulation, fluctuations over time, and changes in topic relevance. Additionally, the study aims to compare qualitative research findings with those obtained through computational methods. METHODS: Data were gathered from a large-scale data set consisting of 13,629 Twitter and Facebook posts from 2020 to 2023 related to COVID-19. For analysis, a fine-tuned FinBERT model with an 80% accuracy rate was used for predicting political mistrust. The BERTopic model was also used for superior topic modeling performance. RESULTS: Our preliminary analysis identifies 43 mistrust-related topics categorized into 9 major themes. The most salient topics include COVID-19 mortality, coping strategies, polymerase chain reaction testing, and vaccine efficacy. Discourse related to mistrust in authority is associated with perceptions of disease severity, willingness to adopt health measures, and information-seeking behavior. Our findings highlight that the distinct user engagement mechanisms and platform features of Facebook and Twitter contributed to varying patterns of mistrust and susceptibility to misinformation during the pandemic. CONCLUSIONS: The study highlights the effectiveness of computational methods like natural language processing in managing large-scale engagement and misinformation. It underscores the critical role of trust in health authorities for effective risk communication and public compliance. The findings also emphasize the necessity for transparent communication from authorities, concluding that a holistic approach to public health communication is integral for managing health crises effectively.

scTOP: physics-inspired order parameters for cellular identification and visualization.

Advances in single-cell RNA sequencing provide an unprecedented window into cellular identity. The abundance of data requires new theoretical and computational frameworks to analyze the dynamics of differentiation and integrate knowledge from cell atlases. We present 'single-cell Type Order Parameters' (scTOP): a statistical, physics-inspired approach for quantifying cell identity given a reference basis of cell types. scTOP can accurately classify cells, visualize developmental trajectories and assess the fidelity of engineered cells. Importantly, scTOP does this without feature selection, statistical fitting or dimensional reduction (e.g. uniform manifold approximation and projection, principle components analysis, etc.). We illustrate the power of scTOP using human and mouse datasets. By reanalyzing mouse lung data, we characterize a transient hybrid alveolar type 1/alveolar type 2 cell population. Visualizations of lineage tracing hematopoiesis data using scTOP confirm that a single clone can give rise to multiple mature cell types. We assess the transcriptional similarity between endogenous and donor-derived cells in the context of murine pulmonary cell transplantation. Our results suggest that physics-inspired order parameters can be an important tool for understanding differentiation and characterizing engineered cells. scTOP is available as an easy-to-use Python package.

Decoding Human Biology and Disease Using Single-cell Omics Technologies.

Over the past decade, advances in single-cell omics (SCO) technologies have enabled the investigation of cellular heterogeneity at an unprecedented resolution and scale, opening a new avenue for understanding human biology and disease. In this review, we summarize the developments of sequencing-based SCO technologies and computational methods, and focus on considerable insights acquired from SCO sequencing studies to understand normal and diseased properties, with a particular emphasis on cancer research. We also discuss the technological improvements of SCO and its possible contribution to fundamental research of the human, as well as its great potential in clinical diagnoses and personalized therapies of human disease.

Enrichment analysis of phenotypic data for drug repurposing in rare diseases.

Drug-induced Behavioral Signature Analysis (DBSA), is a machine learning (ML) method for in silico screening of compounds, inspired by analytical methods quantifying gene enrichment in genomic analyses. When applied to behavioral data it can identify drugs that can potentially reverse in vivo behavioral symptoms in animal models of human disease and suggest new hypotheses for drug discovery and repurposing. We present a proof-of-concept study aiming to assess Drug-induced Behavioral Signature Analysis (DBSA) as a systematic approach for drug discovery for rare disorders. We applied Drug-induced Behavioral Signature Analysis to high-content behavioral data obtained with SmartCube®, an automated in vivo phenotyping platform. The therapeutic potential of several dozen approved drugs was assessed for phenotypic reversal of the behavioral profile of a Huntington's Disease (HD) murine model, the Q175 heterozygous knock-in mice. The in silico Drug-induced Behavioral Signature Analysis predictions were enriched for drugs known to be effective in the symptomatic treatment of Huntington's Disease, including bupropion, modafinil, methylphenidate, and several SSRIs, as well as the atypical antidepressant tianeptine. To validate the method, we tested acute and chronic effects of tianeptine (20 mg/kg, i. p.) in vivo, using Q175 mice and wild type controls. In both experiments, tianeptine significantly rescued the behavioral phenotype assessed with the SmartCube® platform. Our target-agnostic method thus showed promise for identification of symptomatic relief treatments for rare disorders, providing an alternative method for hypothesis generation and drug discovery for disorders with huge disease burden and unmet medical needs.

Leveraging Gene Redundancy to Find New Histone Drivers in Cancer.

Histones play a critical role in chromatin function but are susceptible to mutagenesis. In fact, numerous mutations have been observed in several cancer types, and a few of them have been associated with carcinogenesis. Histones are peculiar, as they are encoded by a large number of genes, and the majority of them are clustered in three regions of the human genome. In addition, their replication and expression are tightly regulated in a cell. Understanding the etiology of cancer mutations in histone genes is impeded by their functional and sequence redundancy, their unusual genomic organization, and the necessity to be rapidly produced during cell division. Here, we collected a large data set of histone gene mutations in cancer and used it to investigate their distribution over 96 human histone genes and 68 different cancer types. This analysis allowed us to delineate the factors influencing the probability of mutation accumulation in histone genes and to detect new histone gene drivers. Although no significant difference in observed mutation rates between different histone types was detected for the majority of cancer types, several cancers demonstrated an excess or depletion of mutations in histone genes. As a consequence, we identified seven new histone genes as potential cancer-specific drivers. Interestingly, mutations were found to be distributed unevenly in several histone genes encoding the same protein, pointing to different factors at play, which are specific to histone function and genomic organization. Our study also elucidated mutational processes operating in genomic regions harboring histone genes, highlighting POLE as a factor of potential interest.

CIARA: a cluster-independent algorithm for identifying markers of rare cell types from single-cell sequencing data.

A powerful feature of single-cell genomics is the possibility of identifying cell types from their molecular profiles. In particular, identifying novel rare cell types and their marker genes is a key potential of single-cell RNA sequencing. Standard clustering approaches perform well in identifying relatively abundant cell types, but tend to miss rarer cell types. Here, we have developed CIARA (Cluster Independent Algorithm for the identification of markers of RAre cell types), a cluster-independent computational tool designed to select genes that are likely to be markers of rare cell types. Genes selected by CIARA are subsequently integrated with common clustering algorithms to single out groups of rare cell types. CIARA outperforms existing methods for rare cell type detection, and we use it to find previously uncharacterized rare populations of cells in a human gastrula and among mouse embryonic stem cells treated with retinoic acid. Moreover, CIARA can be applied more generally to any type of single-cell omic data, thus allowing the identification of rare cells across multiple data modalities. We provide implementations of CIARA in user-friendly packages available in R and Python.

Iterative multiscale and multi-physics computations for operando catalyst nanostructure elucidation and kinetic modeling.

Modern heterogeneous catalysis has benefitted immensely from computational predictions of catalyst structure and its evolution under reaction conditions, first-principles mechanistic investigations, and detailed kinetic modeling, which are rungs on a multiscale workflow. Establishing connections across these rungs and integration with experiments have been challenging. Here, operando catalyst structure prediction techniques using density functional theory simulations and ab initio thermodynamics calculations, molecular dynamics, and machine learning techniques are presented. Surface structure characterization by computational spectroscopic and machine learning techniques is then discussed. Hierarchical approaches in kinetic parameter estimation involving semi-empirical, data-driven, and first-principles calculations and detailed kinetic modeling via mean-field microkinetic modeling and kinetic Monte Carlo simulations are discussed along with methods and the need for uncertainty quantification. With these as the background, this article proposes a bottom-up hierarchical and closed loop modeling framework incorporating consistency checks and iterative refinements at each level and across levels.