Computational Data Research Articles

Sustainable solutions: using MCDM to choose the best location for green energy projects

Purpose This study aims to find the best location for constructing green energy facilities in India and reducing CO2 emissions. Incorporating green energy is a priority for many countries under the Paris Agreement. This task is challenging due to factors that affect implementation, and making the wrong decision wastes resources. India’s goals are net-zero emissions by 2070 and 50% renewable electricity by 2030. Other developing nations should emulate India’s renewable energy strategy. India ranks fourth in renewable energy and wind power, and fifth in solar power capacity. This research aims to identify the best locations in India for implementing green energy projects. Design/methodology/approach To identify the optimal green energy implementation sites in India, this research uses the hybrid multicriteria decision analysis (MCDA) in an uncertain environment. This research uses the Delphi method to identify the most suitable green energy implementation sites in India. It adapts the elements for this investigation. In addition, the utilization of the Fermatean fuzzy weighted aggregated sum product assessment technique is implemented to effectively prioritize the factors that impact the selection of these sites. This study used the MEREC method (method based on the removal effects of criteria) to identify the most suitable areas in India for implementing green energy. The highest accuracy is attained through the amalgamation of these hybrid methods. Findings Following the computation data by hybrid MCDA in uncertainty environment, NP Kunta in Andhra Pradesh emerges as the recommended green energy site among the 11 considered. Also among the factors political strategies and objectives hold the highest priority among them. Originality/value This study is pioneering in its efforts to provide a comprehensive perspective on the development and management of green energy operations in India. The study proves advantageous for diverse sites in the successful adoption and management of green energy. The study is additionally valuable in informing policy development aimed at promoting the use of green energy by employees through the utilization of MCDA methods in uncertain environments.

Exploring quantum confinement signature in nitrogen-functionalized graphene quantum dots: Effective mass approximation (EMA) model insights from computational and experimental analyses

A well-established relationship between bandgap energy and Quantum Dots (QDs) dimension, predicted by Effective Mass Approximation (EMA) models, is a cost-effective and reliable method to evaluate the bandgap energy of QDs. The EMA model sheds light on linearity between bandgap energy (Egap) to their inverse square diameter (1/dn), as a signature of the quantum confinement, with the n value reflecting the dimension of the quantum confinement. This study systematically investigated the power parameter "n" dependence in the bandgap energy of Nitrogen-functionalized Graphene Quantum Dots (N-GQDs) with distinct C–N configurations, i.e., Pyridinic-N, Pyrrolic-N, Graphitic-N based on computational and experimental data. The results showed that the calculated bandgap energy values reveal a notable weakening of the size dependence in N-GQDs compared to pristine GQDs, particularly in Pyrrolic-N GQDs, indicative of a unique geometric dimensionality of confinement. This systematic exploration advances our understanding of "n" dependence and bandgap energy in N-GQDs, laying a solid groundwork for modulating the bandgap energy of N-GQDs for optoelectronics and quantum device applications.

Transcriptomics and epigenetic data integration learning module on Google Cloud.

Abstract Multi-omics (genomics, transcriptomics, epigenomics, proteomics, metabolomics, etc.) research approaches are vital for understanding the hierarchical complexity of human biology and have proven to be extremely valuable in cancer research and precision medicine. Emerging scientific advances in recent years have made high-throughput genome-wide sequencing a central focus in molecular research by allowing for the collective analysis of various kinds of molecular biological data from different types of specimens in a single tissue or even at the level of a single cell. Additionally, with the help of improved computational resources and data mining, researchers are able to integrate data from different multi-omics regimes to identify new prognostic, diagnostic, or predictive biomarkers, uncover novel therapeutic targets, and develop more personalized treatment protocols for patients. For the research community to parse the scientifically and clinically meaningful information out of all the biological data being generated each day more efficiently with less wasted resources, being familiar with and comfortable using advanced analytical tools, such as Google Cloud Platform becomes imperative. This project is an interdisciplinary, cross-organizational effort to provide a guided learning module for integrating transcriptomics and epigenetics data analysis protocols into a comprehensive analysis pipeline for users to implement in their own work, utilizing the cloud computing infrastructure on Google Cloud. The learning module consists of three submodules that guide the user through tutorial examples that illustrate the analysis of RNA-sequence and Reduced-Representation Bisulfite Sequencing data. The examples are in the form of breast cancer case studies, and the data sets were procured from the public repository Gene Expression Omnibus. The first submodule is devoted to transcriptomics analysis with the RNA sequencing data, the second submodule focuses on epigenetics analysis using the DNA methylation data, and the third submodule integrates the two methods for a deeper biological understanding. The modules begin with data collection and preprocessing, with further downstream analysis performed in a Vertex AI Jupyter notebook instance with an R kernel. Analysis results are returned to Google Cloud buckets for storage and visualization, removing the computational strain from local resources. The final product is a start-to-finish tutorial for the researchers with limited experience in multi-omics to integrate transcriptomics and epigenetics data analysis into a comprehensive pipeline to perform their own biological research. This manuscript describes the development of a resource module that is part of a learning platform named ``NIGMS Sandbox for Cloud-based Learning'' https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox [16] at the beginning of this Supplement. This module delivers learning materials on the analysis of bulk and single-cell ATAC-seq data in an interactive format that uses appropriate cloud resources for data access and analyses. Highlights

A comprehensive survey on client selection strategies in federated learning

Federated learning (FL) has emerged as a promising paradigm for collaborative model training while preserving data privacy. Client selection plays a crucial role in determining the overall performance and efficiency of the FL training process. However, the heterogeneity of clients in terms of computational resources, data quality, and network conditions poses significant challenges in selecting the optimal set of clients for each training round. This survey provides a comprehensive review of client selection methodologies in FL, addressing the challenges and opportunities in this field. We present a systematic categorization of existing client selection techniques based on their underlying principles and objectives, and discuss the key challenges, including resource constraints, data heterogeneity, and security concerns. Additionally, we provide a comparative analysis of the surveyed techniques, highlighting their strengths, limitations, and suitability for various FL scenarios. Furthermore, we identify open research problems and propose future research directions, emphasizing the need for more efficient, adaptive, and secure selection strategies. This survey serves as a comprehensive framework for understanding client selection in FL, bridging gaps in the existing literature and providing guidance for future research, including ethical considerations and domain-specific applications. Our work aims to support practitioners in making informed decisions and to stimulate further research in this critical area of FL.

A global plasma and surface model of hydrogen/methane inductively coupled discharge to analyze hydrocarbon plasma–surface interactions in extreme-ultraviolet lithography machines

Hydrocarbon contamination is associated with light transmission losses in modern lithography machines, which contain extreme-ultraviolet-induced plasma. A volume-averaged global and deposition/etch surface model of a reference hydrogen/methane inductive discharge is developed to investigate the plasma–surface interactions. The simulation results are validated against a wide variety of experiments and verified with respect to multiple sets of computational data. The deposition rate is calculated for a variation in methane impurity (10–10 000 ppm), power, pressure, and net mass flow. The simulations conclude that the hydrocarbon plasma deposition can be minimized by reducing methane impurity and excluding solid organic structures.

High‐Throughput First‐Principles Calculations and Machine Learning of Grain Boundary Segregation in Metals

The segregation of solute elements to defects in metals plays a fundamental role for microstructure evolution and the material performance. However, the available computational data are scattered and inconsistent due to the use of different simulation parameters and methods. A high‐throughput study is presented on grain boundary and surface segregation together with their effect on grain boundary embrittlement using a consistent first‐principles methodology. The data are evaluated for most technologically relevant metals including Al, Cu, Fe, Mg, Mo, Nb, Ni, Ta, Ti, and W with the majority of the elements from the periodic table treated as segregating elements. Trends among the solute elements are analyzed and explained in terms of phenomenological models and the computed data are compared to the available literature data. The computed first‐principles data are used for a machine learning investigation, showing the capabilities for extrapolation from first‐principles calculation to the whole periodic table of solutes. The present work allows for comprehensive screening of new alloys with improved interface properties.

Prediction of Steam Turbine Blade Erosion Using Computational Fluid Dynamics Simulation Data and Hierarchical Machine Learning

Abstract The information of the degree of blade erosion is vital for the efficient operation of steam turbines. However, it is nearly impossible to directly measure the degree of blade erosion during operation. Moreover, collecting sufficient data of eroded cases for predictive analysis is challenging. Therefore, this paper proposes a blade erosion prediction method using numerical simulation and machine learning. Pressure data of several blade erosion cases are collected from the numerical turbine simulation. The machine learning approach involves training on collected simulation data to predict the degree of erosion for the first-stage stator (1S) and the first-stage rotor blade (1R) from internal pressure data. The proposed erosion prediction model employs a two-step hierarchical approach. First, the proposed model predicts the 1S erosion degree using the k-nearest neighbor (k-NN) regression. Second, the proposed model estimates the 1R erosion degree with linear regression models. These models are tailored for each of the 1S erosion degrees, utilizing pressure data processed through fast Fourier transform (FFT). The evaluation shows that the proposed method achieves the prediction of the 1S erosion with a mean absolute error (MAE) of 0.000693 mm and the 1R erosion with an MAE of 0.458 mm. The evaluation results indicate that the proposed method can accurately capture the degree of turbine blade erosion from internal pressure data. As a result, the proposed method suggests that the erosion prediction method can be effectively used to determine the optimal timing for maintenance, repair, and overhaul (MRO).

Transcriptome-centric approach to the derivation of adverse outcome pathway networks of vascular dysfunction after long-term low-level exposure of human endothelial cells to dibutyl phthalate

Dibutyl phthalate (DBP) is an endocrine disruptor that adversely affects reproduction; however, evidence suggests it can also impact other systems, including vascular function. The mechanisms underlying DBP-induced vascular dysfunction, particularly after long-term low-level exposure of endothelial cells to this phthalate, remain largely unknown. To address this gap, we used experimentally derived data on differentially expressed genes (DEGs) obtained after 12 weeks of exposure of human vascular endothelial cells EA.hy926 to the concentrations of DBP to which humans are routinely exposed (10−9 M, 10−8 M, and 10−7 M) and various computational tools and manual data curation to build the first adverse outcome pathway (AOP) network relevant to DBP-induced vascular toxicity. DEGs were used to infer transcription factors (molecular initiating events) and molecular functions and biological processes (key events, KEs) using the Enrichr database. The AOP-helpFinder 2.0, an artificial intelligence-based web tool, was used to link genes and KEs and assign confidence scores to co-occurred terms. We constructed the AOP networks using Cytoscape and then manually arranged KEs to depict the flow of mechanistic information across different levels of network organization. An AOP network was created for each DBP concentration, revealing several distinct high-confidence subnetworks that could be involved in DBP-induced vascular toxicity: the insulin-like growth factor subnetwork for 10−7 M DBP, the CXCL8-dependent chemokine subnetwork for 10−8 M DBP, and the fatty acid subnetwork for 10−9 M DBP. We also developed an AOP network providing a mechanistic insight into the dose-dependent effects of DBP in endothelial cells leading to vascular dysfunction. In summary, we present novel putative AOP networks describing the mechanistic flow of information involved in DBP-induced vascular dysfunction in a long-term low-level exposure scenario.

A Time-Domain Calculation Method for Gust Aerodynamics in Flight Simulation

Gusts have a significant impact on aircraft and need to be analyzed through flight simulations. The solution for time-domain gust aerodynamic forces stands as a pivotal stage in this process. With the increasing demand for flight simulations within gusty environments, traditional methods related to gust aerodynamics cannot fail to balance computational accuracy and efficiency. A method that can be used to quickly and accurately calculate the time-domain gust aerodynamic force is needed. This study proposes the fitting strip method, a gust aerodynamic force solution method that is suitable for real-time flight simulations. It only requires the current and previous gust information to calculate the aerodynamic force and is suitable for different configurations of aircraft and different kinds of gusts. Firstly, the fitting strip method requires the division of fitting strips and the calculation of the aerodynamic force under calibration conditions. In this study, the double-lattice method and computational fluid dynamics are used to calculate the aerodynamic force of the strips. Then, the amplitude coefficients and time-delay coefficients are obtained through a fitting calculation. Finally, the coefficients and gust information are put into the formula to calculate the gust aerodynamic force. An example of a swept wing is used for validation, demonstrating congruence between the computational results and experimental data across subsonic and transonic speeds, which proves the accuracy of the fitting strip method in both discrete gusts and continuous gusts. Compared with other methods, the fitting strip method uses the shortest time. Furthermore, the results of a calculation for normal-layout aircraft show that this method avoids the shortcomings of the rational function approximation method and is more accurate than the gust grouping method. Concurrently, gust aerodynamic force calculations were performed on aircraft with large aspect ratios and used in a real-time flight simulation.

Applications of Finite-Time Lyapunov Exponent in detecting Lagrangian Coherent Structures for coastal ocean processes: a review

Addressing the threats of climate change, pollution, and overfishing to marine ecosystems necessitates a deeper understanding of coastal and oceanic fluid dynamics. Within this context, Lagrangian Coherent Structures (LCS) emerge as essential tools for elucidating the complexities of marine fluid dynamics. Methods used to detect LCS include geometric, probabilistic, cluster-based and braid-based approaches. Advancements have been made to employ Finite-time Lyapunov Exponents (FTLE) to detect LCS due to its high efficacy, reliability and simplicity. It has been proven that the FTLE approach has provided invaluable insights into complex oceanic phenomena like shear, confluence, eddy formations, and oceanic fronts, which also enhanced the understanding of tidal-/wind-driven processes. Additionally, FTLE-based LCS were crucial in identifying barriers to contaminant dispersion and assessing pollutant distribution, aiding environmental protection and marine pollution management. FTLE-based LCS has also contributed significantly to understanding ecological interactions and biodiversity in response to environmental issues. This review identifies pressing challenges and future directions of FTLE-based LCS. Among these are the influences of external factors such as river discharges, ice formations, and human activities on ocean currents, which complicate the analysis of ocean fluid dynamics. While 2D FTLE methods have proven effective, their limitations in capturing the full scope of oceanic phenomena, especially in 3D environments, are evident. The advent of 3D LCS analysis has marked progress, yet computational demands and data quality requirements pose significant hurdles. Moreover, LCS extracted from FTLE fields involves establishing an empirical threshold that introduces considerable variability due to human judgement. Future efforts should focus on enhancing computational techniques for 3D analyses, integrating FTLE and LCS into broader environmental models, and leveraging machine learning to standardize LCS detection.

Metabolomics-based profiling of five Salvia L. (Lamiaceae) species using untargeted data analysis workflow.

The genus Salvia L., a member of the family Lamiaceae, is a keystone genus with a wide range of medicinal properties. It possesses a rich metabolite source that has long been used to treat different disorders. Due to a deficiency of untargeted metabolomic profiling in the genus Salvia, this work attempts to investigate a comprehensive mass spectral library matching, computational data annotations, exclusive biomarkers, specific chemotypes, intraspecific metabolite profile variation, and metabolite enrichment by a case study of five medicinal species of Salvia. Aerial parts of each species were subjected to QTRAP liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis workflow based on untargeted metabolites. A comprehensive and multivariate analysis was acquired on the metabolite dataset utilizing MetaboAnalyst 6.0 and the Global Natural Products Social Molecular Networking (GNPS) Web Platform. The untargeted approach empowered the identification of 117 metabolites by library matching and 92 nodes annotated by automated matching. A machine learning algorithm as substructural topic modeling, MS2LDA, was further implemented to explore the metabolite substructures, resulting in four Mass2Motifs. The automated library newly discovered a total of 23 metabolites. In addition, 87 verified biomarkers of library matching, 58 biomarkers of GNPS annotations, and 11 specific chemotypes were screened. Integrative spectral library matching and automated annotation by the GNPS platform provide comprehensive metabolite profiling through a workflow. In addition, QTRAP LC-MS/MS with multivariate analysis unveiled reliable information about inter and intraspecific levels of differentiation. The rigorous investigation of metabolite profiling presents a large-scale overview and new insights for chemotaxonomy and pharmaceutical studies.

IDRWalker: A Random Walk Based Tool for Generating Intrinsically Disordered Regions in Large Protein Complexes.

Intrinsically disordered regions (IDRs), which may be functionally important, are common in proteins. However, the structures of IDRs are often missing due to their highly dynamic nature. In the study of IDRs, integrative modeling combining computational simulations and experimental data is a common approach, for which initial structures of the IDRs need to be built. However, applying this method to large protein complexes is challenging because existing structure generation tools are sometimes unsuitable for IDRs in large systems. To facilitate convenient and rapid structure generation of IDRs in large protein complexes, we developed a computational tool named IDRWalker based on self-avoiding random walks. Three protein complexes were used to illustrate the efficiency of the tool, and it was found that IDRs in more than 800 chains of the nuclear pore complex could be generated in minutes. These structures of large protein complexes with added IDRs can be further used to run computational simulations for integrative modeling.

Reverse vaccinology approach for identification of epitopes from E1 protein as peptide vaccine against HCV: A proof of concept

The development of effective vaccines against Hepatitis C Virus (HCV) remains a global health priority and challenge. In this study, we employed an integrative approach combining computational epitope prediction with experimental validation to identify immunogenic peptides targeting the E1 glycoprotein of HCV. In the present report, computational data from various epitope prediction algorithms such as IEDB and SYFPEITHI, followed by molecular dynamics (MD) simulations and immuno-informatics analysis is presented. Through computational screening, we identified potential epitope candidates, with QVRNSSGLY (P3) and QLFTFSPRH (P7) emerging as promising candidates. MD simulations revealed stable interactions between these epitopes and MHC molecule, further validated by free energy estimations using MMPBSA method. Immuno-informatics analysis supported these findings, showing high binding potential and immunogenicity scores for the selected peptides. Subsequent synthesis and characterization of epitope peptides confirmed their structural integrity and purity required for conducting immune activation assays. Experimental immunological assays carried out in this study involved epitope peptide induced activation of CD8 + and CD4 + T cells from healthy human subjects and HCV- recovered patients. Data from experimental validation revealed significant cytokine release upon exposure to epitope peptides, particularly TNF-a, IL-6, and GM-CSF, indicative of robust immune responses. Notably, peptides P3 and P7 exhibited the most pronounced cytokine induction profiles, underscoring their potential as vaccine candidates. Further investigations addressing the mechanism of action of these epitope peptides under preclinical and clinical settings may help in developing effective vaccine against HCV.

Zemax Modeling Of A Self-Aligned Focusing Schlieren System With Simulated Compressible Flow

A self-aligned focusing schlieren system is simulated in the ANSYS Zemax OpticStudio software environment. The model uses a custom scatter-defining dynamic link library on the retroreflective background in order to scatter rays in the direction of the incident ray rather than the specular ray. Simulated compressible flow refractive index fields are imported into Zemax by use of the grid gradient object and a workaround to the maximum point limit is introduced. The Ronchi ruling is built as a high resolution bitmap image in MATLAB and set in Zemax as a slide object allowing the bitmap image to either transmit or terminate rays at each line pair. The Zemax simulations are compared to computational numerical schlieren and experimental conventional, path-integrated schlieren results from a hypersonic stagnation point injection flow configuration and an experimental image of a shock wave from a laser-induced breakdown. In the stagnation point injection simulations, the Zemax schlieren images capture complex flow features such as the Mach disk, shear layers, contact surface, and bow shock well, but contains long, curved features (striations) between the model and the bow shock that are not present in the synthetic schlieren image from the CFD or the experimental image. The image from Zemax of the spark-induced shock wave captures the shape and curvature of the shock but appears blurry due to grid discretization in the computational data.

Divalent Titanium via Reductive N-C Coupling of a TiIV Nitrido with π-Acids.

The nitrido-ate complex [(PN)2Ti(N){μ2-K(OEt2)}]2 (1) (PN-=(N-(2-PiPr2-4-methylphenyl)-2,4,6-Me3C6H2) reductively couples CO and isocyanides in the presence of DME or cryptand (Kryptofix222), to form rare, five-coordinate TiII complexes having a linear cumulene motif, [K(L)][(PN)2Ti(NCE)] (E=O, L=Kryptofix222, (2); E=NAd, L=3 DME, (3); E=NtBu, L=3 DME, (4); E=NAd, L=Kryptofix222, (5)). Oxidation of 2-5 with [Fc][OTf] afforded an isostructural TiIII center containing a neutral cumulene, [(PN)2Ti(NCE)] (E=O, (6); E=NAd (7), NtBu (8)) and characterization by CW X-band EPR spectroscopy, revealed unpaired electron to be metal centric. Moreover, 1e- reduction of 6 and 7 in the presence of Kryptofix222cleanly reformed corresponding discrete TiII complexes 2 and 5, which were further characterized by solution magnetization measurements and high-frequency and -field EPR (HFEPR) spectroscopy. Furthermore, oxidation of 7 with [Fc*][B(C6F5)4] resulted in a ligand disproportionated TiIV complex having transoid carbodiimides, [(PN)2Ti(NCNAd)2] (9). Comparison of spectroscopic, structural, and computational data for the divalent, trivalent, and tetravalent systems, including their 15N enriched isotopomers demonstrate these cumulenes to decrease in order of backbonding as TiII→TiIII→TiIV and increasing order of π-donation as TiII→TiIII→TiIV, thus displaying more covalency in TiIII species. Lastly, we show a synthetic cycle whereby complex 1 can deliver an N-atom to CO and CNAd.

Computational studies on a selection of phosphite esters as antioxidants for polymeric materials.

Phosphite esters, a class of organo-phosphorus compounds, are widely used as non-discolouring antioxidants in many polymeric products. Apart from normal radical scavenging, they prevent the splitting of hydroperoxides (ROOH), one of the initial products of autoxidation, from forming extremely reactive free radicals such as alkoxy (RO.) and hydroxy (.OH) radicals. The inherent molecular properties of antioxidants and the chemistry of their action are essential for researchers working in this field of science. Four organo-phosphorous compounds well-known for their antioxidant activity are selected here for theoretical analysis: Tri(m-methylphenyl) phosphite (m-TMPP), Tri(4-methyl-2,6-di-tert-butylphenyl) phosphite (TMdtBPP), Tri(allylphenyl) phosphite (TAPP) and Tri(mercaptobenzothiazoyl) thiophosphate (TMBTTP). The antioxidant activity exhibited by these compounds is theoretically verified, and the results are consistent with the available experimental data. Such theoretical predictions offer advantages in scientific research, particularly when researchers need to select certain molecules as antioxidants for experiments from a pool of molecular systems. The chemical computations presented in this report are done in Gaussian 16 program package. The procedure of density functional theory (DFT) with the model chemistry B3LYP/6-31G(d,p) is used to generate computational data. Global reactivity indices, thermochemical data, Fukui functions, molecular electrostatic potential and NMR spectra are computed for the chosen molecular systems from their optimized geometries.

Divalent Titanium via Reductive N−C Coupling of a TiIV Nitrido with π‐Acids

AbstractThe nitrido‐ate complex [(PN)2Ti(N){μ2‐K(OEt2)}]2 (1) (PN−=(N‐(2‐PiPr2‐4‐methylphenyl)‐2,4,6‐Me3C6H2) reductively couples CO and isocyanides in the presence of DME or cryptand (Kryptofix222), to form rare, five‐coordinate TiII complexes having a linear cumulene motif, [K(L)][(PN)2Ti(NCE)] (E=O, L=Kryptofix222, (2); E=NAd, L=3 DME, (3); E=NtBu, L=3 DME, (4); E=NAd, L=Kryptofix222, (5)). Oxidation of 2–5 with [Fc][OTf] afforded an isostructural TiIII center containing a neutral cumulene, [(PN)2Ti(NCE)] (E=O, (6); E=NAd (7), NtBu (8)) and characterization by CW X‐band EPR spectroscopy, revealed unpaired electron to be metal centric. Moreover, 1e− reduction of 6 and 7 in the presence of Kryptofix222cleanly reformed corresponding discrete TiII complexes 2 and 5, which were further characterized by solution magnetization measurements and high‐frequency and ‐field EPR (HFEPR) spectroscopy. Furthermore, oxidation of 7 with [Fc*][B(C6F5)4] resulted in a ligand disproportionated TiIV complex having transoid carbodiimides, [(PN)2Ti(NCNAd)2] (9). Comparison of spectroscopic, structural, and computational data for the divalent, trivalent, and tetravalent systems, including their 15N enriched isotopomers demonstrate these cumulenes to decrease in order of backbonding as TiII→TiIII→TiIV and increasing order of π‐donation as TiII→TiIII→TiIV, thus displaying more covalency in TiIII species. Lastly, we show a synthetic cycle whereby complex 1 can deliver an N‐atom to CO and CNAd.

Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers.

Critical evaluation of computational tools for predicting variant effects is important considering their increased use in disease diagnosis and driving molecular discoveries. In the sixth edition of the Critical Assessment of Genome Interpretation (CAGI) challenge, a dataset of 28 STK11 rare variants (27 missense, 1 single amino acid deletion), identified in primary non-small cell lung cancer biopsies, was experimentally assayed to characterize computational methods from four participating teams and five publicly available tools. Predictors demonstrated a high level of performance on key evaluation metrics, measuring correlation with the assay outputs and separating loss-of-function (LoF) variants from wildtype-like (WT-like) variants. The best participant model, 3Cnet, performed competitively with well-known tools. Unique to this challenge was that the functional data was generated with both biological and technical replicates, thus allowing the assessors to realistically establish maximum predictive performance based on experimental variability. Three out of the five publicly available tools and 3Cnet approached the performance of the assay replicates in separating LoF variants from WT-like variants. Surprisingly, REVEL, an often-used model, achieved a comparable correlation with the real-valued assay output as that seen for the experimental replicates. Performing variant interpretation by combining the new functional evidence with computational and population data evidence led to 16 new variants receiving a clinically actionable classification of likely pathogenic (LP) or likely benign (LB). Overall, the STK11 challenge highlights the utility of variant effect predictors in biomedical sciences and provides encouraging results for driving research in the field of computational genome interpretation.

Preface

From 22nd to 24th December 2023, the 2023 3rd International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA 2023) was held in Sanya, China via virtual form. The Conference attracted about 150 researchers, engineers, teachers and students worldwide working in or interested in the fields related to computational modeling, simulation and data analysis. The general objectives of the CMSDA 2023 were: To generate an adequate academic and scientific forum to present the work and experiences of researchers/teachers/students, contributing, in this way, to the production of new knowledge in the fields of computational modeling and data analysis. To create a path of establishing certain relationships for the speakers, authors and listeners, and providing opportunities for collaboration among the universities and institutions, in order to promote scientific research activities and develop relevant technologies. The subject areas in which the accepted papers were presented cover but are but not limited to: Perceptual Issues in Visualization and Modeling; Mathematical and Numerical Methods in Simulation and Modeling; Parallel and Distributed Computing Simulation; Big Data Visual Analysis; Data Analysis Algorithms and Systems, etc. We have invited renowned professors at home and abroad in these academic fields who shared with the attendees the latest innovations and research results in the domains of computational modeling, data mining and analysis. The Conference mainly featured keynote speeches by renowned experts and presentations of peer-reviewed papers by the authors. Among them, Prof. Daowen Qiu (Sun Yat-Sen University, China) shared his research on Distributed Quantum Algorithm for Simon’s Problem. Limited by today’s physical devices, quantum circuits are usually noisy and difficult to be designed deeply. The novel computing architecture of distributed quantum computing is expected to reduce the noise and depth of quantum circuits. And in his study, he presented the Simon’s problem in distributed scenarios and designed a distributed quantum algorithm to solve the problem. The algorithm proposed by him has the advantage of not only exponential acceleration compared with the classical distributed computing, but also square acceleration compared with the best distributed quantum algorithm proposed before. The Conference Proceedings was made so fruitful with all the excellent contributions collected from both domestic and international channels including the wonderful presentations in the Conference. It is a pleasure to thank the organizers, sponsors and all the participants and contributors for making the Conference possible and interesting. And our appreciation also to the members of Journal of Physics: Conference Series for providing us help in publishing this paper volume. The Committee of CMSDA 2023 List of Committee Member is available in this pdf.

Applying Machine Learning for Antibiotic Development and Prediction of Microbial Resistance.

Antimicrobial resistance (AMR) poses a serious threat to human health worldwide. It is now more challenging than ever to introduce a potent antibiotic to the market considering rapid emergence of antimicrobial resistance, surpassing the rate of antibiotic drug discovery. Hence, new approaches need to be developed to accelerate the rate of drug discovery process and meet the demands for new antibiotics, while reducing the cost of their development. Machine learning holds immense promise of becoming a useful tool, especially since in the last two decades, exponential growth has occurred in computational power and biological big data analytics. Recent advancements in machine learning algorithms for drug discovery have provided significant clues for potential antibiotic classes. Apart from discovery of new scaffolds, the machine learning protocols will significantly impact prediction of AMR patterns and drug metabolism. In this review, we outline power of machine learning in antibiotic drug discovery, metabolic fate, and AMR prediction to support researchers engaged and interested in this field.