Shell Scripts Research Articles

Closha 2.0: a bio-workflow design system for massive genome data analysis on high performance cluster infrastructure.

The explosive growth of next-generation sequencing data has resulted in ultra-large-scale datasets and significant computational challenges. As the cost of next-generation sequencing (NGS) has decreased, the amount of genomic data has surged globally. However, the cost and complexity of the computational resources required continue to be substantial barriers to leveraging big data. A promising solution to these computational challenges is cloud computing, which provides researchers with the necessary CPUs, memory, storage, and software tools. Here, we present Closha 2.0, a cloud computing service that offers a user-friendly platform for analyzing massive genomic datasets. Closha 2.0 is designed to provide a cloud-based environment that enables all genomic researchers, including those with limited or no programming experience, to easily analyze their genomic data. The new 2.0 version of Closha has more user-friendly features than the previous 1.0 version. Firstly, the workbench features a script editor that supports Python, R, and shell script programming, enabling users to write scripts and integrate them into their pipelines. This functionality is particularly useful for downstream analysis. Second, Closha 2.0 runs on containers, which execute each tool in an independent environment. This provides a stable environment and prevents dependency issues and version conflicts among tools. Additionally, users can execute each step of a pipeline individually, allowing them to test applications at each stage and adjust parameters to achieve the desired results. We also updated a high-speed data transmission tool called GBox that facilitates the rapid transfer of large datasets. The analysis pipelines on Closha 2.0 are reproducible, with all analysis parameters and inputs being permanently recorded. Closha 2.0 simplifies multi-step analysis with drag-and-drop functionality and provides a user-friendly interface for genomic scientists to obtain accurate results from NGS data. Closha 2.0 is freely available at https://www.kobic.re.kr/closha2 .

Processing and Visualization of Protec-Seq Data for the Generation of Calibrated, Genome-Wide Double Double-Strand Break(dDSB) Maps.

This chapter describes the computational pipeline for the processing and visualization of Protec-Seq data, a method for purification and genome-wide mapping of double-stranded DNA protected by a specific protein at both ends. In the published case, the protein of choice was Saccharomyces cerevisiae Spo11, a conserved topoisomerase-like enzyme that makes meiotic double-strand breaks (DSBs) to initiate homologous recombination, ensuring proper segregation of homologous chromosomes and fertility. The isolated DNA molecules were thus termed double DSB (dDSB) fragments and were found to represent 34 to several hundred base-pair long segments that are generated by Spo11 and are enriched at DSB hotspots, which are sites of topological stress. In order to allow quantitative comparisons between dDSB profiles across experiments, we implemented calibrated chromatin immunoprecipitation sequencing (ChIP-Seq) using the meiosis-competent yeast species Saccharomyces kudriavzevii as calibration strain. Here, we provide a detailed description of the computational methods for processing, analyzing, and visualizing Protec-Seq data, comprising the download of the raw data, the calibrated genome-wide alignments, and the scripted creation of either arc plots or Hi-C-style heatmaps for the illustration of chromosomal regions of interest. The workflow is based on Linux shell scripts (including wrappers for publicly available, open-source software) as well as R scripts and is highly customizable through its modular structure.

An Extensible Evaluation Framework Applied to Clinical Text Deidentification Natural Language Processing Tools: Multisystem and Multicorpus Study.

Clinical natural language processing (NLP) researchers need access to directly comparable evaluation results for applications such as text deidentification across a range of corpus types and the means to easily test new systems or corpora within the same framework. Current systems, reported metrics, and the personally identifiable information (PII) categories evaluated are not easily comparable. This study presents an open-source and extensible end-to-end framework for comparing clinical NLP system performance across corpora even when the annotation categories do not align. As a use case for this framework, we use 6 off-the-shelf text deidentification systems (ie, CliniDeID, deid from PhysioNet, MITRE Identity Scrubber Toolkit [MIST], NeuroNER, National Library of Medicine [NLM] Scrubber, and Philter) across 3 standard clinical text corpora for the task (2 of which are publicly available) and 1 private corpus (all in English), with annotation categories that are not directly analogous. The framework is built on shell scripts that can be extended to include new systems, corpora, and performance metrics. We present this open tool, multiple means for aligning PII categories during evaluation, and our initial timing and performance metric findings. Code for running this framework with all settings needed to run all pairs are available via Codeberg and GitHub. From this case study, we found large differences in processing speed between systems. The fastest system (ie, MIST) processed an average of 24.57 (SD 26.23) notes per second, while the slowest (ie, CliniDeID) processed an average of 1.00 notes per second. No system uniformly outperformed the others at identifying PII across corpora and categories. Instead, a rich tapestry of performance trade-offs emerged for PII categories. CliniDeID and Philter prioritize recall over precision (with an average recall 6.9 and 11.2 points higher, respectively, for partially matching spans of text matching any PII category), while the other 4 systems consistently have higher precision (with MIST's precision scoring 20.2 points higher, NLM Scrubber scoring 4.4 points higher, NeuroNER scoring 7.2 points higher, and deid scoring 17.1 points higher). The macroaverage recall across corpora for identifying names, one of the more sensitive PII categories, included deid (48.8%) and MIST (66.9%) at the low end and NeuroNER (84.1%), NLM Scrubber (88.1%), and CliniDeID (95.9%) at the high end. A variety of metrics across categories and corpora are reported with a wider variety (eg, F2-score) available via the tool. NLP systems in general and deidentification systems and corpora in our use case tend to be evaluated in stand-alone research articles that only include a limited set of comparators. We hold that a single evaluation pipeline across multiple systems and corpora allows for more nuanced comparisons. Our open pipeline should reduce barriers to evaluation and system advancement.

Abstract PO5-06-04: EMSY amplification co-occurs with FGF/FGFR axis amplification in Metastatic Breast Cancer

Abstract Background: Amplification of FGFR and its ligands play an important role in breast cancer onset and progression and mutations of this signaling axis are emerging as mechanisms of resistance to targeted treatments. Understanding the molecular landscape of tumors that harbor aberrations in the FGF/FGFR (FGF/R) axis may help define the biology of these tumors and lead to the development of novel personalized approaches for more effective treatments in this group of patients. Using publicly available datasets, this study aimed at identifying genomic alterations co-occurring with amplification (amp) of the FGF/R axis in Metastatic Breast Cancer (mBC) patients. Methods: We utilized data from three cohorts of mBC patients. The first cohort (c1) included 176 patients with mBC from an ongoing precision medicine program sponsored by the Side-Out Foundation where genomic testing was performed, using a targeted gene panel, in real-world settings by commercial labs based predominantly on tumor specimens collected between 2019 and 2023. Whole exome sequencing data was obtained from AACR Genomics Evidence Neoplasia Information Exchange (GENIE) to establish a second cohort (c2) of 301 patients and a third cohort (c3) of 216 patients with mBC. Lastly, we included a fourth cohort (c4, n=122) of patients with early breast cancer (eBC). Chi2 test and Fisher’s Exact test were used to find significant co-occurring events. Python, pandas, and shell scripting were used for data manipulation, analysis and performing statistical tests. Results: Alterations of the FGF/R axis occurred in 26% (n=46) of patients in c1 of which 76% (n=35) were HR+ tumors and 24% (n=11) were Triple Negative Breast Cancers. Specifically, we detected amp of FGF3 (n=24, 52%), FGF4 (n=23, 50%), FGF19 (n=17, 37%), FGFR1 (n=18, 39%). In patients with alteration of the FGF/R axis, the most frequent aberrations were CCND1 (n=22, 48%), ZNF703 (n=12, 26%), MYC (n=9, 20%), and EMSY (n=8, 17%) amp, along with mutations of PIK3CA (n=17, 37%) and TP53 (n=18, 39%). EMSY amp was found in none of the patients without alterations of the FGF/R axis and co-amplified with the FGF/R axis in 8 patients(100%) (p< 0.0001). Similar results were also detected in the two independent cohorts of patients retrieved from the GENIE database. In c2, co-amp of EMSY and FGF/R axis was found in 53 of the 56 patients (95%) harboring EMSY amp (p< 0.0001). In c3, 17 patients (89%) had co-amp of EMSY and FGF/R axis of the 19 patients with EMSY amp (p< 0.0001). However, when we looked at patients with eBC in c4, of the 10 patients with EMSY amp, 7 patients (70%) had co-amp of EMSY and FGF/R axis and this association was not significant (p = 0.09). Conclusion: Amplification of EMSY has been mostly reported in ovarian (~17%) and sporadic breast cancers (~13%). EMSY has been reported to behave as an oncogene and a transcriptional repressor. It is well known that it can interact with the BRCA2 protein and potentially contributes to the “BRCAness” of tumors harboring its amp. Studies have also suggested that EMSY may act as a transcriptional repressor that modulates the expression of antimetastatic microRNAs in breast cancer. In the three mBC cohorts included in this analysis, EMSY amp was detected in 8, 56, and 19 cases, respectively. However, when the analysis was restricted to tumors harboring amps of both EMSY and FGF/R axis, EMSY was amplified in 100%, 95%, and 89% of those cases. While co-amplification of the EMSY gene with members of the FGF/R axis may be explained by its close proximity to the FGF3/4/19, and CCND1 genes on chromosome 11, in our analysis these co-amplifications were significant only in patients with metastatic disease. As dissecting the complex molecular landscape of mBC continues to be an essential step for advancing personalized treatment and better patient care, understanding the phenotypic effects of these co-amplifications may provide unique opportunities for devising combinatorial treatments for these patients. Citation Format: Deepika Prasad, Edik Blais, Rick Dunetz, Emanuel Petricoin, Mariaelena Pierobon. EMSY amplification co-occurs with FGF/FGFR axis amplification in Metastatic Breast Cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-06-04.

In silico identification of approved drugs for potential prophylactic uses against SARS-CoV-2 infection

The outbreak of Severe Acute Respiratory Syndrome Corona Virus (SARS-CoV-2) responsible for Coronavirus disease 2019 (COVID-19) constitute a major public health threat and economic burden. Despite the multifold research all over the world. COVID-19 has remained without a treatment. Hence, the urgent need to repurpose the FDA-approved drugs through in silico computational technique to identify inhibitors of SARS-CoV-2. Bioinformatics techniques were used to screen 2015 FDA- approved drugs against SARS-CoV-2 proteins and associated human protein, angiotensin-converting enzyme 2 (ACE2) to identify drug for new indication. The ligands were downloaded from Selleckchem drug data bank in SDF format. Open Babel was used to separate the ligands into their compounds and prepared for docking simulation using shell script and saved as PDBQT. Experimental crystal structures of the receptors (ACE2 and Spike proteins) were retrieved from Protein Data Bank (PDB), Autodock tools was used to prepare the proteins and saved as PDBQT files. Docking protocols were validated, molecular docking simulations were performed on a Linux platform using AutoDock Vina in quadruplicate. The binding energies (BEs) kcal/mol were calculated and expressed as mean ± standard deviation. Discovery studio was used for visualization. Density Functional Theory (DFT) was used to determine the predictive maximal inhibitory concentration (pIC50) of best hits. Ninety-one (91) new hits were identified for prophylaxis. Four (4) compounds were subjected to DFT based IC50 calculation with molnupiravir as a reference compound. Significantly, calculated IC50 of molnupiravir in Vero cells and Calu-3 were 0.299 and 0.008µM respectively while the experimental IC50 in Vero cells and Calu-3 as reported in literature were 0.3 and 0.08 µM respectively. The newly identified hits are promising therapeutic agents and there is no significant difference between the experimental IC50 of molnupiravir and the calculated IC50.

Exploring research hotspots and future directions in neural tube defects field by bibliometric and bioinformatics analysis.

Neural tube defects (NTDs) is the most common birth defect of the central nervous system (CNS) which causes the death of almost 88,000 people every year around the world. Much efforts have been made to investigate the reasons that contribute to NTD and explore new ways to for prevention. We trawl the past decade (2013-2022) published records in order to get a worldwide view about NTDs research field. 7,437 records about NTDs were retrieved from the Web of Science (WOS) database. Tools such as shell scripts, VOSviewer, SCImago Graphica, CiteSpace and PubTator were used for data analysis and visualization. Over the past decade, the number of publications has maintained an upward trend, except for 2022. The United States is the country with the highest number of publications and also with the closest collaboration with other countries. Baylor College of Medicine has the closest collaboration with other institutions worldwide and also was the most prolific institution. In the field of NTDs, research focuses on molecular mechanisms such as genes and signaling pathways related to folate metabolism, neurogenic diseases caused by neural tube closure disorders such as myelomeningocele and spina bifida, and prevention and treatment such as folate supplementation and surgical procedures. Most NTDs related genes are related to development, cell projection parts, and molecular binding. These genes are mainly concentrated in cancer, Wnt, MAPK, PI3K-Akt and other signaling pathways. The distribution of NTDs related SNPs on chromosomes 1, 3, 5, 11, 14, and 17 are relatively concentrated, which may be associated with high-risk of NTDs. Bibliometric analysis of the literature on NTDs field provided the current status, hotspots and future directions to some extant. Further bioinformatics analysis expanded our understanding of NTDs-related genes function and revealed some important SNP clusters and loci. This study provided some guidance for further studies. More extensive cooperation and further research are needed to overcome the ongoing challenge in pathogenesis, prevention and treatment of NTDs.

Reducing the user burden when running MELCOR for accident analysis for a tokamak

MELCOR is a software tool for accident analysis with a long history in the fission sector. A model has been developed for use in MELCOR, using proposed layouts of EU DEMO and assumptions based on current design choices, to facilitate safety design and development for fusion power plants.There seems however to be a misconception that MELCOR is difficult to run and that the tasks must be executed sequentially. Accident analysis also requires reruns of multiple slightly different scripts. We have streamlined the running of MELCOR tasks to allow simultaneous execution on a computing cluster. The input decks are version controlled within UKAEA's internal GitLab repository, and shell scripts wrap the separate MELCOR invocations, embed specific job parameters, and keep separate the inputs and outputs of multiple separate instances.We illustrate the streamlined running using two examples: a matrix representing potential responses to an incident by different valves, and a parameter sweep across a range of response times for a single valve. The new technique is shown to be a much faster method of carrying out sensitivity studies and allowing further investigations into specific areas of concern in more detail.

Abstract 5049: Detecting chromosomal copy number variations and point mutations in Glioma using a single assay; sparing tissue while significantly reducing testing time and cost

Abstract We aimed to develop a single streamlined assay to detect all Glioma biomarkers. The current diagnosis of Gliomas incorporates molecular biomarkers that include whole chromosomal copy number variations (CNVs) and single nucleotide variations (SNVs). Testing CNVs and SNVs are traditionally done using different large platforms and are performed in large genomic facilities that have the suitable infrastructure. This creates delays in testing, where samples undergo lengthy referrals and the tissue is often split between institutions, which also adds substantially to the costs. We have previously used Nanopore sequencing to develop an assay to detect IDH SNVs from formalin-fixed paraffin-embedded (FFPE) tissue. Nanopore sequencing platform uses small inexpensive tools and can test for both CNVs and SNVs. However, combining both targets efficiently in FFPE tissue remains a challenge. While nanopore whole genome sequencing can detect CNVs, it does not allow for enough coverage to accurately detect small SNVs. Also, raw FFPE DNA is not compatible with the nanopore technology. To fix both challenges we used a PCR based approach that allows enrichment of the SNV targets, as well as creates new nanopore compatible DNA copies. To counteract the bias that PCR creates with uneven amplification, we used a SNP based method to call chromosomal gains and losses. Statistical analysis of probability showed that targeting 15 heterozygous SNPs per chr arm is sufficient for accurate CNV detection (error rate <0.1%). A total of 57 amplicons targeting CNVs on Chr 1,7, 10, 19, and EGFR gene; and IDH1 and IDH2 SNVs were pooled together in one assay and tested on 4 Glioma samples. Variant calling was performed with a custom Python script. All CNV targets were accurately detected on all samples. Losses in chr 1p, 19q and chr 10 were evident with a loss of heterozygosity (LOH) pattern on the SNP analysis. The gain in chromosome 7 was also observed as an allele gain 2:1 SNP pattern on the corresponding samples. The SNV statuses of these samples were also accurately detected when compared to reference results. Finally, the SNV and CNV calling analysis were streamlined using custom shell script, further reducing total turnaround time (including sequencing and data analysis) to less than a day for the entire batch of samples. Initial cost analysis shows <50% of the traditional testing costs for the assay. This work is the first to develop a streamlined single test to detect CNVs and SNVs in Gliomas using FFPE DNA on the nanopore sequencing platform. The shorter testing time, streamlined workflow, and low assay/capital cost could positively influence the care of brain cancer patients. Notably, nanopore-based sequencing tools can also be implemented in other types of tumor diagnosis, especially by smaller labs, helping to overcome many of the existing diagnostic challenges. Citation Format: Mashiat L. Mimosa, Jared T. Simpson, Karel Boissinot, Mora Tiab, Ramzi Fattouh, Rola M. Saleeb. Detecting chromosomal copy number variations and point mutations in Glioma using a single assay; sparing tissue while significantly reducing testing time and cost [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5049.

BridGE: a pathway-based analysis tool for detecting genetic interactions from GWAS.

Genetic interactions have the potential to modulate phenotypes, including human disease. In principle, genome-wide association studies (GWAS) provide a platform for detecting genetic interactions; however, traditional methods for identifying them, which tend to focus on testing individual variant pairs, lack statistical power. In this protocol, we describe a novel computational approach, called Bridging Gene sets with Epistasis (BridGE), for discovering genetic interactions between biological pathways from GWAS data. We present a Python-based implementation of BridGE along with instructions for its application to a typical human GWAS cohort. The major stages include initial data processing and quality control, construction of a variant-level genetic interaction network, measurement of pathway-level genetic interactions, evaluation of statistical significance using sample permutations and generation of results in a standardized output format. The BridGE software pipeline includes options for running the analysis on multiple cores and multiple nodes for users who have access to computing clusters or a cloud computing environment. In a cluster computing environment with 10 nodes and 100 GB of memory per node, the method can be run in less than 24 h for typical human GWAS cohorts. Using BridGE requires knowledge of running Python programs and basic shell script programming experience.

IBio-GATS-A Semi-Automated Workflow for Structural Modelling of Insect Odorant Receptors.

Insects utilize seven transmembrane (7TM) odorant receptor (iOR) proteins, with an inverted topology compared to G-protein coupled receptors (GPCRs), to detect chemical cues in the environment. For pest biocontrol, chemical attractants are used to trap insect pests. However, with the influx of invasive insect pests, novel odorants are urgently needed, specifically designed to match 3D iOR structures. Experimental structural determination of these membrane receptors remains challenging and only four experimental iOR structures from two evolutionarily distant organisms have been solved. Template-based modelling (TBM) is a complementary approach, to generate model structures, selecting templates based on sequence identity. As the iOR family is highly divergent, a different template selection approach than sequence identity is needed. Bio-GATS template selection for GPCRs, based on hydrophobicity correspondence, has been morphed into iBio-GATS, for template selection from available experimental iOR structures. This easy-to-use semi-automated workflow has been extended to generate high-quality models from any iOR sequence from the selected template, using Python and shell scripting. This workflow was successfully validated on Apocrypta bakeri Orco and Machilis hrabei OR5 structures. iBio-GATS models generated for the fruit fly iOR, OR59b and Orco, yielded functional ligand binding results concordant with experimental mutagenesis findings, compared to AlphaFold2 models.

Cyber5Gym: An Integrated Framework for 5G Cybersecurity Training

The rapid evolution of 5G technology, while offering substantial benefits, concurrently presents complex cybersecurity challenges. Current cybersecurity systems often fall short in addressing challenges such as the lack of realism of the 5G network, the limited scope of attack scenarios, the absence of countermeasures, the lack of reproducible, and open-sourced cybersecurity training environments. Addressing these challenges necessitates innovative cybersecurity training systems, referred to as “cyber ranges”. In response to filling these gaps, we propose the Cyber5Gym, an integrated cyber range that enhances the automation of virtualized cybersecurity training in 5G networks with cloud-based deployment. Our framework leverages open-source tools (i) Open5GS and UERANSIM for realistic emulation of 5G networks, (ii) Docker for efficient virtualization of the training infrastructure, (iii) 5Greply for emulating attack scenarios, and (iv) Shell scripts for automating complex training operations. This integration facilitates a dynamic learning environment where cybersecurity professionals can engage in real-time attack and countermeasure exercises, thus significantly improving their readiness against 5G-specific cyber threats. We evaluated it by deploying our framework on Naver Cloud with 20 trainees, each accessing an emulated 5G network and managing 100 user equipments (UEs), emulating three distinct attack scenarios (SMC-Reply, DoS, and DDoS attacks), and exercising countermeasures, to demonstrate the cybersecurity training. We assessed the effectiveness of our framework through specific metrics such as successfully establishing the 5G network for all trainees, accurate execution of attack scenarios, and their countermeasure implementation via centralized control of the master using automated shell scripts. The open-source foundation of our framework ensures replicability and adaptability, addressing a critical gap in current cybersecurity training methodologies and contributing significantly to the resilience and security of 5G infrastructures.

Ds-Seq: An Integrated Pipeline for In Silico Small RNA Se-quence Analysis for Host-pathogen Interaction Studies

Plant-pathogen interactions activate molecular activities wherein the host defends the pathogen while the pathogen tries to suppress the plant response. Small RNAs (sRNAs) mediate major mechanisms, including post-transcriptional gene silencing, histone modification and DNA methylation by which plants respond to the presence of pathogens. Genome-wide profiling of host and pathogen sRNAs is therefore pivotal to uncovering the mechanisms underlying the host-pathogen interaction and mechanisms for host resistance. sRNA high throughput sequencing (HTS) data analysis often involves multiple stages/tools. Most necessary tools are accessible only through the command line, making it challenging for those without a high level of Unix/Linux skills. Furthermore, installation of some of these tools may become difficult due to dependencies and software version compatibility. We have developed an integrated open-source pipeline, Ds-Seq, for end-to-end in silico analysis of sRNA HTS data with improved reproducibility. The pipeline combines in-house scripts and public tools in a shell script, which can be invoked with a single command. The pipeline's usefulness has been demonstrated with testing on publicly available and published data from independent sRNA-seq datasets of host-pathogen interaction studies. Ds-Seq is available on GitHub, while a Docker image can be obtained from the Docker hub.Availability: Ds-Seq is freely available from the GitHub repository at https://github.com/CEPHAS-01/small-RNASeq.ngs and Docker hub with ID cephas/ds-seq (https://hub.docker.com/r/cephas/ds-seq).

CAT: a computational anatomy toolbox for the analysis of structural MRI data.

A large range of sophisticated brain image analysis tools have been developed by the neuroscience community, greatly advancing the field of human brain mapping. Here we introduce the Computational Anatomy Toolbox (CAT)-a powerful suite of tools for brain morphometric analyses with an intuitive graphical user interface but also usable as a shell script. CAT is suitable for beginners, casual users, experts, and developers alike, providing a comprehensive set of analysis options, workflows, and integrated pipelines. The available analysis streams-illustrated on an example dataset-allow for voxel-based, surface-based, and region-based morphometric analyses. Notably, CAT incorporates multiple quality control options and covers the entire analysis workflow, including the preprocessing of cross-sectional and longitudinal data, statistical analysis, and the visualization of results. The overarching aim of this article is to provide a complete description and evaluation of CAT while offering a citable standard for the neuroscience community.

Revolutionizing Application Monitoring : A Review of DizBoard's Unified Dashboard Approach

In today's software landscape, the continuous and flawless operation of applications stands as a pivotal requirement. The genesis of the "DizBoard: Server and Database Manager" project stemmed from a pressing necessity to guarantee the seamless functionality and resilience of client-server applications. This imperative emerged from firsthand encounters with the escalating intricacies inherent in modern application ecosystems. An exploration of the existing technological milieu unveiled a diverse array of tools and solutions dedicated to application monitoring. However, the discerned need for a unified dashboard catering to databases, web services, and application servers became increasingly apparent. Current fragmented solutions often resulted in monitoring gaps and an absence of holistic insights into overall application health. The implementation of the "DizBoard: Server and Database Manager" project is geared towards bridging these gaps, addressing limitations highlighted in the literature review. Through a fusion of frontend technologies, Python-backed backend logic, and the automation prowess of shell scripting, this endeavor endeavors to craft a unified dashboard offering real-time visibility into critical components' statuses. This review paper embarks on a journey to expound upon the aspirations and methodologies driving the "DizBoard" project. It aims not only to present a comprehensive view of application health but also to delineate the voids that prevailing technologies leave. Envisioned as a user-friendly and automated monitoring dashboard, "DizBoard: Server and Database Manager" aspires to redefine the paradigm of application health management. Its ultimate goal is to elevate reliability, proactively address issues, and ultimately augment user satisfaction in the realm of application management.

A query-response causal analysis of reaction events in biochemical reaction networks

The stochastic kinetics of biochemical reaction networks is described by a chemical master equation (CME) and the underlying laws of mass action. Assuming network-free simulations of the rule-based models of biochemical reaction networks (BRNs), this paper departs from the usual analysis of network dynamics as the time-dependent distributions of chemical species counts, and instead considers statistically evaluating the sequences of reaction events generated from the stochastic simulations. The reaction event-time series can be used for reaction clustering, identifying rare events, and recognizing the periods of increased or steady-state activity. However, the main aim of this paper is to device an effective method for identifying causally and anti-causally related sub-sequences of reaction events using their empirical probabilities. This allows discovering some of the causal dynamics of BRNs as well as uncovering their short-term deterministic behaviors. In particular, it is proposed that the reaction sub-sequences that are conditionally nearly certain or nearly uncertain can be considered as being causally related. Moreover, since the time-ordering of reaction events is locally irrelevant, the reaction sub-sequences can be transformed into the reaction sets or multi-sets. The distance metrics can be then used to define the equivalences among the reaction events. The proposed method for identifying the causally related reaction sub-sequences has been implemented as a computationally efficient query-response mechanism. The method was evaluated for five models of genetic networks in seven defined numerical experiments. The models were simulated in BioNetGen using the open-source network-free simulator NFsim. This simulator had to be modified first to allow recording the traces of reaction events, and it is available in the Github repository, ploskot/nfsim_1.20. The generated event time-series were analyzed with Python and Matlab scripts. The whole process of data generation, analysis and visualization has been nearly fully automated using shell scripts. This demonstrates the opportunities for substantially increasing the research productivity by creating automated data generation and processing pipelines.

A vendor-agnostic, PACS integrated, and DICOM-compatible software-server pipeline for testing segmentation algorithms within the clinical radiology workflow.

Reproducible approaches are needed to bring AI/ML for medical image analysis closer to the bedside. Investigators wishing to shadow test cross-sectional medical imaging segmentation algorithms on new studies in real-time will benefit from simple tools that integrate PACS with on-premises image processing, allowing visualization of DICOM-compatible segmentation results and volumetric data at the radiology workstation. In this work, we develop and release a simple containerized and easily deployable pipeline for shadow testing of segmentation algorithms within the clinical workflow. Our end-to-end automated pipeline has two major components- 1. A router/listener and anonymizer and an OHIF web viewer backstopped by a DCM4CHEE DICOM query/retrieve archive deployed in the virtual infrastructure of our secure hospital intranet, and 2. An on-premises single GPU workstation host for DICOM/NIfTI conversion steps, and image processing. DICOM images are visualized in OHIF along with their segmentation masks and associated volumetry measurements (in mL) using DICOM SEG and structured report (SR) elements. Since nnU-net has emerged as a widely-used out-of-the-box method for training segmentation models with state-of-the-art performance, feasibility of our pipleine is demonstrated by recording clock times for a traumatic pelvic hematoma nnU-net model. Mean total clock time from PACS send by user to completion of transfer to the DCM4CHEE query/retrieve archive was 5 min 32 s (± SD of 1 min 26 s). This compares favorably to the report turnaround times for whole-body CT exams, which often exceed 30 min, and illustrates feasibility in the clinical setting where quantitative results would be expected prior to report sign-off. Inference times accounted for most of the total clock time, ranging from 2 min 41 s to 8 min 27 s. All other virtual and on-premises host steps combined ranged from a minimum of 34 s to a maximum of 48 s. The software worked seamlessly with an existing PACS and could be used for deployment of DL models within the radiology workflow for prospective testing on newly scanned patients. Once configured, the pipeline is executed through one command using a single shell script. The code is made publicly available through an open-source license at "https://github.com/vastc/," and includes a readme file providing pipeline config instructions for host names, series filter, other parameters, and citation instructions for this work.

Technique for preparation of large data for machine learning algorithms to generate intrusion detection system

Machine Learning has become the norm for creating models that predict and detect security breaches in computer systems. Modelling of machine learning systems requires a huge amount of data for training and testing the machine learning algorithm. The present research is concerned with preparing the data prior to feeding it to machine learning algorithms. The necessity for such preparation occurs due to the physical limitations of spreadsheet applications (with regard to the number of records they can handle). This research describes a simple shell scripting approach to combine, extract, and weed out unwanted records and finally prepare the data for feeding to machine learning algorithms.

A Unique Śaṅkhalipi (Shell-script) Inscription from Si Thep, Thailand

Inscriptions in the highly calligraphic and still undeciphered śaṅkhalipi or ‘shell script’ have been found by the hundreds in most parts of India except the far south, typically in conjunction with sites and monuments dating from around the Gupta period and succeeding centuries. To date, four specimens have also been discovered in the Indonesian archipelago, in West Java and West Kalimantan (Borneo). Another specimen of śaṅkhalipi inscription, engraved on a pillar and exceptionally ornate, was recently discovered in Thailand at the site of Si Thep, a moated early settlement in Phetchabun Province. The article reviews the historical and cultural contexts of shell-script inscriptions in India and discusses the significance of this remarkable first specimen found in mainland Southeast Asia.

Simple, Efficient, and Universal Energy Decomposition Analysis Method Based on Dispersion-Corrected Density Functional Theory.

Energy decomposition analysis (EDA) is an important class of methods to explore the nature of interaction between fragments in a chemical system. It can decompose the interaction energy into different physical components to understand the factors that play key roles in the interaction. This work proposes an EDA strategy based on dispersion-corrected density functional theory (DFT), called sobEDA. This method is fairly easy to implement and very universal. It can be used to study weak interactions, chemical bond interactions, open-shell systems, and interactions between multiple fragments. The total time consumption of sobEDA is only about twice that of conventional DFT single-point calculation for the entire system. This work also proposes a variant of the sobEDA method named sobEDAw, which is designed specifically for decomposing weak interaction energies. Through a proper combination of DFT correlation energy and dispersion correction term, sobEDAw gives a ratio between dispersion energy and electrostatic energy that is highly consistent with the symmetry-adapted perturbation theory, which is quite popular and robust in studying weak interactions but expensive. We present a shell script sobEDA.sh to implement the methods proposed in this work based on the very popular Gaussian quantum chemistry program and Multiwfn wavefunction analysis code. Via the script, theoretical chemists can use the sobEDA and sobEDAw methods very conveniently in their study. Through a series of examples, the rationality of the new methods and their implementation are verified, and their great practical values in the study of various chemical systems are demonstrated.

Sequential Data Processing for IMERG Satellite Rainfall Comparison and Improvement Using LSTM and ADAM Optimizer

This study introduces a systematic methodology whereby different technologies were utilized to download, pre-process, and interactively compare the rainfall datasets from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) satellite and rain gauges. To efficiently handle the large volume of data, we developed automated shell scripts for downloading IMERG data and storing it, along with rain gauge data, in a relational database system. Hypertext pre-processor (pHp) programs were built to visualize the result for better analysis. In this study, the performance of IMERG estimations over the east coast of Peninsular Malaysia for the duration of 10 years (2011–2020) against rain gauge observation data is evaluated. Moreover, this study aimed to improve the daily IMERG estimations with long short-term memory (LSTM) developed with Python. Findings show that the LSTM with Adaptive Moment Estimation (ADAM) optimizer trained against the mean square error (MSE) loss enhances the accuracy of satellite estimations. At the point-to-pixel scale, the correlation between satellite estimations and ground observations was increased by about 15%. The bias was reduced by 81–118%, MAE was reduced by 18–59%, the root-mean-square error (RMSE) was reduced by 1–66%, and the Kling–Gupta efficiency (KGE) was increased by approximately 200%. The approach developed in this study establishes a comprehensive and scalable data processing and analysis pipeline that can be applied to diverse datasets and regions encountering similar domain-specific challenges.