User-friendly Software Research Articles

ParthenoGenius: A User-Friendly Heuristic for Inferring Presence and Mechanism of Facultative Parthenogenesis from Genetic and Genomic Data Sets.

Facultative parthenogenesis (FP), or asexual reproduction by sexually-reproducing female animals, has been reported across several clades of vertebrates and is increasingly being recognized as a reproductive mechanism with significant implications for the genetic variation of captive and wild populations. The definitive identification of parthenogens requires molecular confirmation, with large genomic data sets necessary to accurately parse the parthenogenetic mechanism (i.e., endoduplication, gametic duplication, terminal fusion automixis, or central fusion automixis). Current methods for inferring FP from large genomic data sets are statistically intensive, require competency in R scripting for their execution, and are not designed for detection of facultative parthenogenesis or screening of large numbers of mother/offspring pairs, whereas small data sets (i.e., microsatellites) that can be evaluated visually lack the power to discriminate among FP mechanisms. Here, we present the user-friendly software program, ParthenoGenius, that uses intuitive logic to infer presence and mechanism of FP from even large genomic data sets comprising many mothers and offspring. ParthenoGenius runs relatively quickly and does not require the researcher to have knowledge of R scripting or statistics. ParthenoGenius was tested on eight empirical data sets, and in each case identified parthenogens (and parthenogenic mechanism when present) consistent with results of previous studies or corroborating evidence. ParthenoGenius will facilitate the rapid screening of large genomic data sets comprising many mothers and offspring for the presence and mechanism of parthenogenesis, improving our understanding of the frequency and phylogenetic distribution of FP across the animal kingdom.

Deep Learning-based Enhancement of Fluorescence Labeling for Accurate Cell Lineage Tracing During Embryogenesis.

Automated cell lineage tracing throughout embryogenesis plays a key role in the study of regulatory control of cell fate differentiation, morphogenesis and organogenesis in the development of animals, including nematode Caenorhabditis elegans. However, automated cell lineage tracing suffers from an exponential increase in errors at late embryo because of the dense distribution of cells, relatively low signal-to-noise ratio (SNR) and imbalanced intensity profiles of fluorescence images, which demands a huge amount of human effort to manually correct the errors. The existing image enhancement methods are not sensitive enough to deal with the challenges posed by the crowdedness and low signal-to-noise ratio. An alternative method is urgently needed to assist the existing detection methods in improving their detection and tracing accuracy, thereby reducing the huge burden for manual curation. We developed a new method, termed as DELICATE, that dramatically improves the accuracy of automated cell lineage tracing especially during the stage post 350 cells of C. elegans embryo. DELICATE works by increasing the local SNR and improving the evenness of nuclei fluorescence intensity across cells especially in the late embryos. The method both dramatically reduces the segmentation errors by StarryNite and the time required for manually correcting tracing errors up to 550-cell stage, allowing the generation of accurate cell lineage at large-scale with a user-friendly software/interface. All images and data are available at https://doi.org/10.6084/m9.figshare.26778475.v1. The code and user-friendly software are available at https://github.com/plcx/NucApp-develop. Supplementary data are available at Bioinformatics online.

Overcoming bias in estimating epidemiological parameters with realistic history-dependent disease spread dynamics

Epidemiological parameters such as the reproduction number, latent period, and infectious period provide crucial information about the spread of infectious diseases and directly inform intervention strategies. These parameters have generally been estimated by mathematical models that involve an unrealistic assumption of history-independent dynamics for simplicity. This assumes that the chance of becoming infectious during the latent period or recovering during the infectious period remains constant, whereas in reality, these chances vary over time. Here, we find that conventional approaches with this assumption cause serious bias in epidemiological parameter estimation. To address this bias, we developed a Bayesian inference method by adopting more realistic history-dependent disease dynamics. Our method more accurately and precisely estimates the reproduction number than the conventional approaches solely from confirmed cases data, which are easy to obtain through testing. It also revealed how the infectious period distribution changed throughout the COVID-19 pandemic during 2020 in South Korea. We also provide a user-friendly package, IONISE, that automates this method.

NanoCore: core-genome-based bacterial genomic surveillance and outbreak detection in healthcare facilities from Nanopore and Illumina data.

Genomic surveillance involves sequencing the genomes and measuring the relatedness of bacteria from different patients or locations in the same healthcare facility, enabling an improved understanding of pathogen transmission pathways and the detection of "silent" outbreaks that would otherwise go undetected. It has become an indispensable tool for the detection and prevention of healthcare-associated infections and is routinely applied by many healthcare institutions. The earlier an outbreak or transmission chain is detected, the better; in this context, the Oxford Nanopore sequencing technology has important potential advantages over traditionally used short-read sequencing technologies, because it supports "real-time" data generation and the cost-effective "on demand" sequencing of small numbers of bacterial isolates. The analysis of Nanopore sequencing data, however, can be challenging. We present NanoCore, a user-friendly software for genomic surveillance that works directly based on Nanopore sequencing reads in FASTQ format, and demonstrate that its accuracy is equivalent to traditional gold standard short read-based analyses.

Toward widespread use of virtual trials in medical imaging innovation and regulatory science.

The rapid advancement in the field of medical imaging presents a challenge in keeping up to date with the necessary objective evaluations and optimizations for safe and effective use in clinical settings. These evaluations are traditionally done using clinical imaging trials, which while effective, pose several limitations including high costs, ethical considerations for repetitive experiments, time constraints, and lack of ground truth. To tackle these issues, virtual trials (aka in silico trials) have emerged as a promising alternative, using computational models of human subjects and imaging devices, and observer models/analysis to carry out experiments. To facilitate the widespread use of virtual trials within the medical imaging research community, a major need is to establish a common consensus framework that all can use. Based on the ongoing efforts of an AAPM Task Group (TG387), this article provides a comprehensive overview of the requirements for establishing virtual imaging trial frameworks, paving the way toward their widespread use within the medical imaging research community. These requirements include credibility, reproducibility, and accessibility. Credibility assessment involves verification, validation, uncertainty quantification, and sensitivity analysis, ensuring the accuracy and realism of computational models. A proper credibility assessment requires a clear context of use and the questions that the study is intended to objectively answer. For reproducibility and accessibility, this article highlights the need for detailed documentation, user-friendly software packages, and standard input/output formats. Challenges in data and software sharing, including proprietary data and inconsistent file formats, are discussed. Recommended solutions to enhance accessibility include containerized environments and data-sharing hubs, along with following standards such as CDISC (Clinical Data Interchange Standards Consortium). By addressing challenges associated with credibility, reproducibility, and accessibility, virtual imaging trials can be positioned as a powerful and inclusive resource, advancing medical imaging innovation and regulatory science.

B-025 Revolutionizing NGS-driven Laboratory Developed Tests: The impact of automated liquid handling on plate-based bead clean-up

Abstract Background Automated liquid handling systems are proving to be a transformative technology in the landscape of Laboratory Developed Tests (LDTs), especially those utilizing Next-Generation Sequencing (NGS). By enhancing accuracy, boosting efficiency, and offering scalable solutions, these systems enable labs to meet current and future challenges more effectively. The right system not only improves the quality of diagnostics but also positions labs to be agile and adaptive in a rapidly advancing healthcare environment. firefly® is a well-suited liquid handling solution for diagnostics labs developing LDTs due to its compact footprint, user-friendly software and exceptional versatility. Validated protocol templates that support NGS library preparation are readily available from the firefly cloud and can be adapted to meet specific lab needs. For example, automated bead-based clean-ups designed for 96- and 384- NGS applications are available on firefly. These remove DNA fragments of undesirable sizes, enzymes or primers rom the amplified DNA after a PCR reaction. Automating this process facilitates higher throughput and scaling. firefly has a unique combination of 6 positive displacement dispense heads and a 96/384-well air-displacement pipetting head, making it ideal to distribute the reagents required for bead clean-ups. Methods firefly protocols were developed and optimized to be compatible with a range of elution volumes and bead ratios. Protocol performance was tested using library DNA (∼300bp). The same input DNA was plated out and quantified using the QuantiFluor dsDNA system (Promega) before and after running the protocol. This data was used to assess any reduction in DNA concentration and uniformity across the plate. The protocol's ability to successfully modulate the fragment size distribution was demonstrated using a 50bp DNA ladder and different bead ratios. Data was obtained using a Fragment Analyzer (Agilent, DNF-474 HS NGS Fragment Kit). Well-to-well contamination was assessed by running no-template-controls (NTCs) in wells adjacent to library DNA, then analyzed by qPCR using a LightCycler 480 System (Roche, KAPA Library Quantification kit). Results 96 format has a setup time of 10 minutes, run time of 43 and %CV library concentration of <6%. 384 format has a setup time of 10 minutes, run time of 47 and %CV library concentration of <8%. Uniformity of concentration and fragment sizes was seen across the plates. Comparison of the library concentration before and after the clean-up showed a 5% reduction in DNA concentration after the 96-well plate clean-up and an 11% reduction in library concentration when using the 384-well plate clean-up. Fragment size analysis showed that fragment sizes can be successfully modulated on firefly by changing the bead ratio of the firefly run and no detectable well-to-well contamination was detected by qPCR. Conclusions We have demonstrated that automated bead clean-ups on firefly are uniform show uniformity across both 96- and 384-well plates, with no detectable well-to-well contamination, to deliver the consistent and robust results required for an LDT. Bead-based cleanups on firefly offer a fast, 100% walkaway solution. By running 96-samples at once on firefly, we see approximately a 6-fold increase in throughput than when performed manually by a single user.

B-115 Software Data Processing and Review LC-MS/MS Workflow Improvements for Clinical Research

Abstract Background In the field of quantitative LC-MS/MS, the need for efficient, accurate and user-friendly data review software is paramount, with the data generated in the clinical laboratory meeting local guidelines, as well as rigorous method performance characteristics defined by the FDA Bioanalytical Method Validation and CLSI C62-Ed2 guidelines. The quantitative LC-MS/MS workflow is dependent on interfacing to LMS/LIMS for sample traceability, batch analysis using matrix-matched external calibrators and quality controls for quantification and acceptance, and stable-isotope labeled internal standards to compensate for sample extraction, recovery, matrix effects and analytical variability. The demands on the laboratory to run multiplexed analyte panels and analyze a greater number of samples produces large amounts of analytical data which must still go through batch and quality review before release from the laboratory to the clinicians. The Waters Quan Review Software significantly reduces the burdensome aspects of batch quality control by introducing a series of innovative features. These include improvements in workflow, a modernized interface, and the ability to flag exceptions based on acceptance criteria. Methods Samples for three multiplexed LC-MS/MS panels with previously defined analytical method performance characteristics were acquired using Waters IVD Systems controlled by MassLynx software. The data was processed, quantified, and reviewed using both the existing TargetLynx XS IVD Application Manager and the new Quan Review Software. The three quantitative LC-MS/MS methods were the analysis of four immunosuppressants drugs in whole blood, a twelve steroid panel in plasma and a urine dilute and shoot method for a drugs of abuse panel containing greater than 80 analytes. Each method used matrix matched calibrators and QCs for quantification and batch control with performance characteristics including calibration linearity, carryover, precision and accuracy. Results The analytical performance data generated using the new MS Quan data review software met the same performance criteria as the TargetLynx XS data. For immunosuppressants, the % difference of the mean repeatability and total precision (%CV) for the individual analyte QCs processed using both software was within ±1.3% and ±4.4% respectively. The mean % bias of External Quality Assurance Samples (n=40) for each immunosuppressant processed using both software was within ±7.9%. The modernized, task-oriented accelerated workflows and focused review by exception reduced review time by up to 50%. Conclusions The new Quan Review Software generated results equivalent to the existing TargetLynx XS Application Manager. The software’s enhanced workflow capabilities streamlined the review process in a new user-friendly interface which reduced the training burden enabling scientists to generate high-quality data quickly and easily. The exception focused review functionality allowed tailored rule sets which reduced the time spent reviewing data by up to 50%. Whilst the batch-level review and task-oriented workflow improved batch acceptance and simplified the review of complicated datasets into easy tasks. By integrating these features, Data Review software offers a comprehensive solution that not only improves the efficiency and accuracy of batch quality control but also contributes to the overall reliability and integrity of quantitative LC-MS/MS analysis. For Research Use Only. Not for Use in Diagnostic Procedures.

B-026 Transforming PCR protocols in pandemic response with automated non-contact liquid dispensing

Abstract Background During the COVID-19 pandemic, effective Polymerase Chain Reaction (PCR) protocols were crucial for detecting SARS-CoV-2 in clinical samples, playing a key role in rapid infection control and easing the strain on healthcare services. However, with the critical need for fast and accurate pathogen surveillance, the manual execution of PCR workflows presented numerous challenges, including limited throughput, risk of human error and variability, and risk to human health. Several automated liquid handling systems from a range of vendors were considered to prepare extraction plates for this process. Ultimately, RFL decided on dragonfly® discovery, a non-contact positive displacement dispenser. Methods The analytical sensitivity for the UltraDx SARS-CoV-2 N1/N2/RP assay was evaluated by the RFL development team using RNA extracted from a panel of virus inputs from a Qnostics Analytical Q Panel, Product Number: SCV2AQP. Samples were extracted using a modified RNA extraction procedure for Thermo MagMax reagents and consumables with dragonfly discovery. dragonfly discovery was utilized to distribute a combination of inactivation buffer and beads into a 96-well kingfisher flex plate. This process was accomplished using a peristaltic pump and a 6-way Auto Feed Reservoir (AFR) to ensure a constant buffer flow. Simultaneously, the AFR ensured that the beads were kept in constant suspension, resulting in precise dispensing across all wells. The EPCR assay, UltraDx SARS-CoV-2 N1/N2/RP assay (LGC, Biosearch Technologies), utilized two CDC-characterized amplicon primer sets alongside two additional probes using fluorescence-based (FAM dye) detection. The input RNA extracted from clinical samples was combined with RT-PCR reaction mixes, before the reaction arrays were amplified. The reactions were then measured by a single scan on a multi-channel fluorescence reader. Results The UltraDx SARS-CoV-2 N1/N2/RP assay (when performed using the manufacturer method and in the absence of dragonfly discovery) has an LoD of 80 copies per reaction for the N1 assay, and 20 copies per reaction for the N2 assay, across 20 replicates for each copy number tested. Using the same/equivalent reagents and control materials, inclusion of dragonfly discovery showed an LoD of 70 copies/mL (0.9 copies/reaction) sensitivity when compared to the LoD suggested by the manufacture. Conclusions Integrating dragonfly discovery into the UltraDx SARS-CoV-2 N1/N2/RP assay led to an 80-fold reduction in the limit of detection for the N1 assay compared to the reagent manufacturer's recommendation. Some of this improvement can be attributed to dragonfly discovery's non-contact positive displacement dispensing, coupled with the AFR pump mechanism that ensures a continuous suspension of magnetic beads, enhancing accuracy and precision. dragonfly discovery's versatility supports a robust business-as-usual (BAU) process and is easily scalable for pandemic-level testing. Its user-friendly software interface requires zero computer programming knowledge, providing end-users with the flexibility to run multiple protocols on the same instrument, making it ideal for BAU assay processing. The instrument played a pivotal role in the UltraDx SARS-CoV-2 N1/N2/RP assay conducted by UKHSA during the pandemic, and SPT Labtech will continue to support UKHSA in their mission to protect communities from the impact of health threats.

Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond

The application of AI to analytical and separative sciences is a recent challenge that offers new perspectives in terms of data prediction. In this work, we report an AI-based software, named Chrompredict 1.0, which based on chromatographic data of a novel mesogenic crown ether stationary phase (CESP). Its molecular design represents a significant advancement due to the unique combination of properties and binding capabilities, including the formation of a cavity, mesogenic behavior via mobile chains, and a range of polar and non-polar interactions (aromatic rings, N=N and O=O double bonds, alkyl chains, π–π interactions, and hydrogen bonding). The mesogenic phase is effective in both normal and reversed-phase chromatography, enhancing the software's adaptability across diverse datasets.Here we introduce for the first time an unprecedented scientific approach, integrating deep learning techniques with the novel CESP, which demonstrates exceptional thermal and analytical performance in both liquid chromatography modes, especially in the separation of complex hydrocarbon isomers. This ability enables the results obtained with CESP to extend across various types of stationary phases.Leveraging these insights, a comprehensive chromatographic dataset on a series of aromatic and polyaromatic molecules interacting with our CESP was used to train a Deep Learning Model (DLM). This model is embedded within a user-friendly software, Chrompredict 1.0, designed for predicting chromatographic parameters (MAE = 0.042, R² = 0.95) by selecting chemical descriptors directly from SMILES notation. It offers a deeper understanding of molecular structure and interactions through exploratory data analysis, identifying key factors affecting model accuracy and chromatographic behavior.Users can configure hyperparameters, choose from six machine learning models, and compare their performance with DLM. Chrompredict 1.0 excels in retention behavior prediction for compounds with known structures, and it accurately predicts chromatographic retention and thermal characteristics for different temperatures in HPLC and GC. The model has been successfully tested with METLIN database of 1,023 small molecules of diverse structures and polarities (R² > 0.75, error range ±7.8 s). Overall, the CESP, combined with Chrompredict 1.0, offers a robust tool for intelligent chromatographic analysis, encompassing chemo-informatics, statistical analysis, and graphical capabilities across a broad range of compounds and stationary phases.

Large-scale prediction of outer-membrane multiheme cytochromes uncovers hidden diversity of electroactive bacteria and underlying pathways.

Multi-heme cytochromes (MHCs), together with accessory proteins like porins and periplasmic cytochromes, enable microbes to transport electrons between the cytoplasmic membrane and extracellular substrates (e.g., minerals, electrodes, other cells). Extracellular electron transfer (EET) has been described in multiple systems; yet, the broad phylogenetic and mechanistic diversity of these pathways is less clear. One commonality in EET-capable systems is the involvement of MHCs, in the form of porin-cytochrome complexes, pili-like cytochrome polymers, and lipid-anchored extracellular cytochromes. Here, we put forth MHCscan-a software tool for identifying MHCs and identifying potential EET capability. Using MHCscan, we scanned ~60,000 bacterial and 2,000 archaeal assemblies, and identify a diversity of MHCs, many of which represent enzymes with no known function, and many found within organisms not previously known to be electroactive. In total, our scan identified ~1,400 unique enzymes, each encoding more than 10 heme-binding motifs. In our analysis, we also find evidence for modularity and flexibility in MHC-dependent EET pathways, and suggest that MHCs may be far more common than previously recognized, with many facets yet to be discovered. We present MHCscan as a lightweight and user-friendly software tool that is freely available: https://github.com/Arkadiy-Garber/MHCscan.

A MATLAB platform for production planning in batch processing firms producing items that are jointly replenished

Rising levels of poverty have prompted manufacturers of non-durable consumer goods to explore opportunities at the lower end of the wealth spectrum. To enhance affordability for low-income consumers, these goods are often packaged in various sizes, including single-serve sachets. While numerous models and algorithms have been developed to determine the optimal frequency of manufacturing runs and packaging setups for the simultaneous replenishment of different pack sizes, there is a notable lack of literature addressing their implementation by small and medium-sized enterprises (SMEs) in this sector. This gap may be attributed to the absence of accessible source codes, a shortage of model experts, and the complexity involved in manual calculations. To address this issue, we developed a user-friendly MATLAB software platform based on Goyal’s algorithm, tailored specifically for SMEs. The software prioritizes simplicity, interactivity, and flexible visual displays. It was validated against examples from existing literature and through randomly generated scenarios. Users can easily compute the optimal number of manufacturing setups, packaging frequencies for each pack size, and the associated costs by inputting relevant data such as manufacturing setup, inventory holding and packaging setup costs, and demand for each pack size. Importantly, users do not need expertise in modeling to operate the software. However, it is designed for single-product deterministic environments; future research should explore multi-product and stochastic modeling scenarios.

AteMeVs: An R package for the estimation of the average treatment effect with measurement error and variable selection for confounders.

In causal inference, the estimation of the average treatment effect is often of interest. For example, in cancer research, an interesting question is to assess the effects of the chemotherapy treatment on cancer, with the information of gene expressions taken into account. Two crucial challenges in this analysis involve addressing measurement error in gene expressions and handling noninformative gene expressions. While analytical methods have been developed to address those challenges, no user-friendly computational software packages seem to be available to implement those methods. To close this gap, we develop an R package, called AteMeVs, to estimate the average treatment effect using the inverse-probability-weighting estimation method to handle data with both measurement error and spurious variables. This developed package accommodates the method proposed by Yi and Chen (2023) as a special case, and further extends its application to a broader scope. The usage of the developed R package is illustrated by applying it to analyze a cancer dataset with information of gene expressions.

Lessons learned from establishing and running a Mössbauer laboratory in industry

Mössbauer spectroscopy offers unique capabilities for primary industry sectors such as mining, mineral processing, and steelmaking, as well as in environmental services and advanced manufacturing. However, there are three key limiting factors: the time required to record a single spectrum, the challenges of managing an additional radioactive source on company premises, and the lack of personnel skilled in analysing Mössbauer spectra and understanding its operation. More commonly used techniques such as XRD, SEM, or XRF have technical staff who can interpret data with support from standard libraries and user-friendly software packages. In contrast, Mössbauer spectroscopists, who often hold degrees in Physics or Chemistry, typically emerge from academic settings and are less commonly employed in industry. This paper addresses the current challenges limiting the widespread adoption of Mössbauer laboratories in the industry and proposes potential solutions to overcome these obstacles. These solutions include promoting internships for undergraduate and postgraduate students familiar with Mössbauer spectroscopy in industrial settings, increasing investment in software development, hosting seminars and workshops to educate industry professionals on the capabilities of Mössbauer spectroscopy, and providing ongoing support from the scientific community to industries interested in operating spectrometers in their facilities.

ViCE: An automated and quantitative program to assess intestinal tissue morphology

Background and objectiveThe tissue morphology of the intestinal surface is architecturally complex with finger-like projections called villi, and glandular structures called crypts. The ratio of villus height-to-crypt depth ratio (Vh:Cd) is used to quantitatively assess disease severity and response to therapy for intestinal enteropathies, such as celiac disease and is currently quantified manually. Given the time required, manual Vh:Cd measurements have largely been limited to clinical trials and are not used widely in clinical practice. We developed ViCE (Villus Crypt Evaluator), a user-friendly software that automatically quantifies histological parameters in standard hematoxylin and eosin-stained intestinal biopsies. MethodsViCE is based on mathematical morphology operations and is scale and staining agnostic. It evaluates tissue orientation, identifies geometrical structure, and outputs key tissue measurements. ResultsThe output measurements of Vh:Cd are concordant with manual quantifications across multiple datasets. ConclusionsThe underlying mathematical morphological approach for ViCE is robust, and reproducible and easily adaptable for measurement of morphological features in other tissues.

MultiMatch: geometry-informed colocalization in multi-color super-resolution microscopy

With recent advances in multi-color super-resolution light microscopy, it is possible to simultaneously visualize multiple subunits within biological structures at nanometer resolution. To optimally evaluate and interpret spatial proximity of stainings on such an image, colocalization analysis tools have to be able to integrate prior knowledge on the local geometry of the recorded biological complex. We present MultiMatch to analyze the abundance and location of chain-like particle arrangements in multi-color microscopy based on multi-marginal optimal unbalanced transport methodology. Our object-based colocalization model statistically addresses the effect of incomplete labeling efficiencies enabling inference on existent, but not fully observable particle chains. We showcase that MultiMatch is able to consistently recover existing chain structures in three-color STED images of DNA origami nanorulers and outperforms geometry-uninformed triplet colocalization methods in this task. MultiMatch generalizes to an arbitrary number of color channels and is provided as a user-friendly Python package comprising colocalization visualizations.

ALLELE-SPECIFIC QPCR METHOD FOR SNP AND INDEL GENOTYPING BASED ON FRET

The proposed Allele-specific q-PCR (ASQ) method for genotyping of single nucleotide polymorphism (SNP) and insertion-deletion polymorphism (InDel), based on Fluorescence resonance energy transfer (FRET). Initially named by its developers as Kompetitive Allele Specific PCR (KASP), this method is an AS-PCR variant adapted for fluorescence-based detection of amplification results. We developed a bioinformatic tool for designing probe sequences for PCR-based genotyping assays. Probe sequences are designed in both directions, and both single nucleotide polymorphisms (SNPs) and insertion-deletions (InDels) may be targeted. In addition, the tool allows discrimination of up to four allelic variants at a single SNP site. To increase both the reaction specificity and the discriminative power of SNP genotyping, each allele-specific primer is designed such that the penultimate base before the primer’s 3′ end base is positioned at the SNP site. The tool allows the design of custom FRET cassette reporter systems for fluorescence-based assays. FastPCR is a user-friendly and powerful Java-based software that is freely available (http://primerdigital.com/tools/). Using the FastPCR environment and the tool for designing AS-PCR provides unparalleled flexibility for developing genotyping assays and specific and sensitive diagnostic PCR-based tests, translating into a greater likelihood of research success.

Data-driven shear capacity analysis of headed stud in steel-UHPC composite structures

This study employs machine learning (ML) techniques for shear capacity analysis of headed stud in steel-UHPC composite structure. 194 experimental and numerical results of push-out tests are collected and serve as the dataset for ML models training and testing. Six ML algorithms are implemented to train ML models. Comparison analysis is conducted to compare the performance of three empirical formulae and ML models. The results demonstrate that both the Random Forest and eXtreme Gradient Boosting Trees (XGBoost) models exhibit excellent performance, surpassing an R2 value of 97 % on both training and testing datasets. In contrast, the empirical formulae perform less effectively. Besides, the study incorporates the Shapley additive explanations algorithm to ranking the importance of each feature, and carried out parametric analysis to investigate the correlation between each feature and shear capacity using all samples in the collected dataset. Notably, the most influential variables include diameter and ultimate strength of the headed studs, followed by stud height and thickness of UHPC slab. Cover thickness of UHPC layer and steel fiber volume fractions shows little influence on the shear capacity. Furthermore, based on findings of parametric analysis, design recommendations are provided to avoid shear capacity reduction caused by group effect of studs and UHPC damage. Finally, a user-friendly interactive software is developed and provided to facilitate the shear capacity prediction and design of headed studs in steel-UHPC composite structures.

Unmconf : an R package for Bayesian regression with unmeasured confounders

The inability to correctly account for unmeasured confounding can lead to bias in parameter estimates, invalid uncertainty assessments, and erroneous conclusions. Sensitivity analysis is an approach to investigate the impact of unmeasured confounding in observational studies. However, the adoption of this approach has been slow given the lack of accessible software. An extensive review of available R packages to account for unmeasured confounding list deterministic sensitivity analysis methods, but no R packages were listed for probabilistic sensitivity analysis. The R package unmconf implements the first available package for probabilistic sensitivity analysis through a Bayesian unmeasured confounding model. The package allows for normal, binary, Poisson, or gamma responses, accounting for one or two unmeasured confounders from the normal or binomial distribution. The goal of unmconf is to implement a user friendly package that performs Bayesian modeling in the presence of unmeasured confounders, with simple commands on the front end while performing more intensive computation on the back end. We investigate the applicability of this package through novel simulation studies. The results indicate that credible intervals will have near nominal coverage probability and smaller bias when modeling the unmeasured confounder(s) for varying levels of internal/external validation data across various combinations of response-unmeasured confounder distributional families.

VCF observer: a user-friendly software tool for preliminary VCF file analysis and comparison

BackgroundAdvancements over the past decade in DNA sequencing technology and computing power have created the potential to revolutionize medicine. There has been a marked increase in genetic data available, allowing for the advancement of areas such as personalized medicine. A crucial type of data in this context is genetic variant data which is stored in variant call format (VCF) files. However, the rapid growth in genomics has presented challenges in analyzing and comparing VCF files.ResultsIn response to the limitations of existing tools, this paper introduces a novel web application that provides a user-friendly solution for VCF file analyses and comparisons. The software tool enables researchers and clinicians to perform high-level analysis with ease and enhances productivity. The application’s interface allows users to conveniently upload, analyze, and visualize their VCF files using simple drag-and-drop and point-and-click operations. Essential visualizations such as Venn diagrams, clustergrams, and precision–recall plots are provided to users. A key feature of the application is its support for metadata-based file grouping, accomplished through flexible data matrix uploads, streamlining organization and analysis of user-defined categories. Additionally, the application facilitates standardized benchmarking of VCF files by integrating user-provided ground truth regions and variant lists.ConclusionsBy providing a user-friendly interface and supporting essential visualizations, this software enhances the accessibility of VCF file analysis and assists researchers and clinicians in their scientific inquiries.

Digitalization of Analysis of a Concrete Block Layer Using Machine Learning as a Sustainable Approach

The concrete block pavement (CBP) system has a surface layer consisting of concrete block pavers and joint sand over a bedding sand layer. The non-homogeneous nature of the surface course of CBP, along with different laying patterns and shapes of block pavers, makes the analysis of CBP cumbersome. In this study, the surface course of CBP was modeled based on the slab action of the block pavers and joint sand, which are connected together in full contact. Four different laying patterns, including herringbone, stretcher, parquet, and square, were modeled using a finite element model. The elastic moduli of the block pavers varied from 2500 MPa to 45,000 MPa, with thicknesses ranging from 60 mm to 120 mm. As a result, modeling of CBP based on slab action can be considered a realistic strategy. In addition, a dataset was created based on quantitative inputs, e.g., elastic modulus and thickness of the block pavers, and qualitative input, i.e., block laying patterns. The approaches of machine learning adopted were support vector regression, Gaussian process regression, single-layer and deep artificial neural networks, and least squares boosting to implement prediction approach based on input and output. The analyses of statistical accuracy of all five machine learning methods showed high accuracy; however, the Gaussian process and deep artificial neural network methods resulted in the most accurate outputs and are recommended for further studies. Based on the machine learning models, digitalization is achieved through the development of simple, user-friendly software for electronic devices in order to perform a preliminary analysis of different laying patterns of CBP. Such a platform may result in less laboratory work and boosts the level of sustainability in concrete block pavement technology.