Workflow Process Research Articles

A Bioconductor workflow for processing, evaluating, and interpreting expression proteomics data

Background Expression proteomics involves the global evaluation of protein abundances within a system. In turn, differential expression analysis can be used to investigate changes in protein abundance upon perturbation to such a system. Methods Here, we provide a workflow for the processing, analysis and interpretation of quantitative mass spectrometry-based expression proteomics data. This workflow utilises open-source R software packages from the Bioconductor project and guides users end-to-end and step-by-step through every stage of the analyses. As a use-case we generated expression proteomics data from HEK293 cells with and without a treatment. Of note, the experiment included cellular proteins labelled using tandem mass tag (TMT) technology and secreted proteins quantified using label-free quantitation (LFQ). Results The workflow explains the software infrastructure before focusing on data import, pre-processing and quality control. This is done individually for TMT and LFQ datasets. The application of statistical differential expression analysis is demonstrated, followed by interpretation via gene ontology enrichment analysis. Conclusions A comprehensive workflow for the processing, analysis and interpretation of expression proteomics is presented. The workflow is a valuable resource for the proteomics community and specifically beginners who are at least familiar with R who wish to understand and make data-driven decisions with regards to their analyses.

Validation of Saliva as the Clinical Specimen Type for a University-Wide COVID-19 Surveillance Program.

At the beginning of the COVID-19 pandemic, the Georgia Institute of Technology made the decision to keep the university doors open for on-campus attendance. To manage COVID-19 infection rates, internal resources were applied to develop and implement a mass asymptomatic surveillance program. The objective was to identify infections early for proper follow-on verification testing, contact tracing, and quarantine/isolation as needed. Program success depended on frequent and voluntary sample collection from over 40,000 students, faculty, and staff personnel. At that time, the nasopharyngeal (NP) swab, not saliva, was the main accepted sample type for COVID-19 testing. However, due to collection discomfort and the inability to be self-collected, the NP swab was not feasible for voluntary and frequent self-collection. Therefore, saliva was selected as the clinical sample type and validated. A saliva collection kit and a sample processing and analysis workflow were developed. The results of a clinical sample-type comparison study between co-collected and matched NP swabs and saliva samples showed 96.7% positive agreement and 100% negative agreement. During the Fall 2020 and Spring 2021 semesters, 319,988 samples were collected and tested. The program resulted in maintaining a low overall mean positivity rate of 0.78% and 0.54% for the Fall 2020 and Spring 2021 semesters, respectively. For this high-throughput asymptomatic COVID-19 screening application, saliva was an exceptionally good sample type.

A Clean You Can Trust: Using an Evidence-Based Bundle to Transform Point-of-Use Treatment for Reusable Medical Devices at a Military Treatment Facility

ABSTRACT Introduction Throughout surgical and invasive procedures, reusable instruments and flexible endoscopes become soiled with organic and inorganic materials. When these substances are permitted to dry, a matrix of microbial cells, called biofilm, forms on the surface of devices, irreversibly binding and subsequently impeding the disinfection and sterilization process. To prevent biofilm formation from occurring, devices must be continuously flushed and wiped with water throughout the procedure and at the end of the case. This process, known as point-of-use treatment (POUT), is the critical first step in the decontamination of medical devices. Poor compliance with POUT can increase patient morbidity and mortality and result in failing hospital accreditation. Materials and Methods An interdisciplinary team used the Plan-Do-Study-Act (PDSA) to develop and implement an audit assessing 29 evidence-based criteria for POUT treatment. Each PDSA cycle supported evidence-based opportunities addressing feedback, workflow analysis, policy development, competency assessment creation, training module development, and infection prevention grand rounds. Four audits were performed over ten months to achieve improved organizational POUT compliance. Results Implementing recurrent PDSA cycles that included audits with feedback led to an evidence-based bundle of policies, competencies, and training for 34 different clinical areas. These interventions resulted in a 26% compliance increase (66% to 92%). Program-specific improvements included flexible endoscopes (+4%) and reusable instruments (+20%). Conclusions Multimodal evidence-based initiatives to improve compliance with workflow processes is a translatable POUT evidence-based practice project for similar Defense Health Agengy facilities. Workflow processes can be vetted and distributed using interdisciplinary teams to ensure viability, sustainability, and conformity with organizational requirements, resulting in a more ready force.

Blockchain-Based Fraud Detection System for Healthcare Insurance Claims

Healthcare fraud is a huge concern that affects not only the financial viability of insurance companies but also the well-being of patients who may receive compromised care due to fraudulent acts. Addressing this issue demands novel solutions that can detect and prevent fraudulent conduct in healthcare insurance claims. The project intends to establish an automated fraud detection system using blockchain technology, which has advantages such as security, transparency, and data immutability. By leveraging blockchain's decentralized ledger, the system creates a tamper-proof platform for processing healthcare insurance claims, preventing fraudulent alterations and enhancing trust in the integrity of the claims process. Ethereum's blockchain platform and smart contracts play a critical role in ensuring the secure recording of transactions while preventing retroactive alterations. Moreover, an on-chain database is employed to manage relevant claim data, thereby safeguarding its integrity and ensuring accessibility. The decentralized nature of blockchain technology brings additional advantages by eliminating the need for intermediaries, thereby reducing administrative costs and streamlining the claim processing workflow. The adoption of methodologies such as Personal Extreme Programming (PXP) and Design Science Research Methodology (DSRM) fortifies the project's framework. PXP facilitates continuous improvement through incremental and iterative development, while DSRM ensures a structured approach to problem-solving, yielding reliable results. Through rigorous testing and validation, the automated fraud detection system enhances the efficiency and accuracy of fraud identification in healthcare insurance claims. By combining blockchain technology with methodological frameworks, this project offers a promising solution to combat healthcare fraud, safeguarding insurance systems' integrity and ensuring quality care for patients. Future iterations will focus on expanding the system's capabilities and refining its algorithms to counter the evolving fraudulent tactics prevalent in the healthcare industry.

Evaluating the impact of rapid antimicrobial susceptibility testing for bloodstream infections: a review of actionability, antibiotic use and patient outcome metrics.

Antimicrobial susceptibility testing (AST) is a core function of the clinical microbiology laboratory and is critical to the management of patients with bloodstream infections (BSIs) to facilitate optimal antibiotic therapy selection. Recent technological advances have resulted in several rapid methods for determining susceptibility direct from positive blood culture that can provide turnaround times in under 8 h, which is considerably shorter than conventional culture-based methods. As diagnostic results do not directly produce a medical intervention, actionability is a primary determinant of the effect these technologies have on antibiotic use and ultimately patient outcomes. Randomized controlled trials and observational studies consistently show that rapid AST significantly reduces time to results and improves antimicrobial therapy for patients with BSI across various methods, patient populations and organisms. To date, the clinical impact of rapid AST has been demonstrated in some observational studies, but randomized controlled trials have not been sufficiently powered to validate many of these findings. This article reviews various metrics that have been described in the literature to measure the impact of rapid AST on actionability, antibiotic exposure and patient outcomes, as well as highlighting how implementation and workflow processes can affect these metrics.

Employing automated electrical resistivity tomography for detecting short- and long-term changes in permafrost and active-layer dynamics in the maritime Antarctic

Abstract. Repeated electrical resistivity tomography (ERT) surveys can substantially advance the understanding of spatial and temporal freeze–thaw dynamics in remote regions, such as Antarctica, where the evolution of permafrost has been poorly investigated. To enable time-lapse ERT surveys in Antarctica, an automated ERT (A-ERT) system is required, as regular site visits are not feasible. In this context, we developed a robust A-ERT prototype and installed it at the Crater Lake CALM-S site on Deception Island, Antarctica, to collect quasi-continuous ERT measurements. We developed an automated data processing workflow to efficiently filter and invert the A-ERT datasets and extract the key information required for a detailed investigation of permafrost and active-layer dynamics. In this paper, we report on the results of two complete year-round A-ERT datasets collected in 2010 and 2019 at the Crater Lake CALM-S site and compare them with available climate and borehole data. The A-ERT profile has a length of 9.5 m with an electrode spacing of 0.5 m, enabling a maximum investigation depth of approximately 2 m. Our detailed investigation of the A-ERT data and inverted results shows that the A-ERT system can detect the active-layer freezing and thawing events with high temporal resolution. The resistivity of the permafrost zone in 2019 is very similar to the values found in 2010, suggesting the stability of the permafrost over almost 1 decade at this site. The evolution of thaw depth exhibits a similar pattern in both years, with the active-layer thickness fluctuating between 0.20–0.35 m. However, a slight thinning of the active layer is evident in early 2019, compared to the equivalent period in 2010. These findings show that A-ERT datasets, combined with the new processing workflow that we developed, are an effective tool for studying permafrost and active-layer dynamics with very high resolution and minimal environmental disturbance. The ability of the A-ERT setup to monitor the spatiotemporal progression of thaw depth in two dimensions, and potentially in three dimensions, and to detect brief surficial refreezing and thawing of the active layer reveals the significance of the automatic ERT monitoring system to record continuous resistivity changes. An A-ERT monitoring setup with a longer profile length can investigate greater depths, offering effective monitoring at sites where boreholes are costly and invasive techniques are unsuitable. This shows that the A-ERT setup described in this paper can be a significant addition to the Global Terrestrial Network for Permafrost (GTN-P) and the Circumpolar Active Layer Monitoring (CALM) networks to further investigate the impact of fast-changing climate and extreme meteorological events on the upper soil horizons and to work towards establishing an early warning system for the consequences of climate change.

Improved processing workflow and student transparency with student mistreatment reports leads to graduation questionnaire data gains

Mistreatment of students has been historically documented as common in U.S. medical schools, but graduate questionnaire (GQ) data from the Association of American Medical Colleges (AAMC) displays high numbers of students who have experienced mistreatment but not reported the incident. There are many reasons within the literature as to why students do not report their experiences, including fear of academic repercussion or a misunderstanding of what constitutes as mistreatment. Our institution found through GQ data that there was a shortcoming in understanding policies and knowledge of procedures associated with mistreatment, and student focus group responses showed that many students were not confident that their reports would receive follow-up on the part of the institution. These factors led to the formation of a task force to investigate our school’s workflow once a report of concern for mistreatment is received and examine measures to increase transparency to the student body that their reports are acted upon. We took measures to place a greater emphasis on communication with students during the mistreatment report workflow, as well as releasing name-blinded data within our weekly student communication emails regarding reports that had been processed and resolved. The results after one year of these efforts saw our GQ percentile data jump from falling between the 10th to 25th percentile to the 90th percentile for student awareness of mistreatment policies and from between the 25th to 50th percentile to between the 75th to 90th percentile for student knowledge of mistreatment procedures. These jumps in GQ figures provide insight for policy changes that could benefit other institutions struggling with building a safe environment for students to confidently report incidents of mistreatment with knowledge that their concerns are important and acted upon.

Health professionals’ perceptions of electronic health records system: a mixed method study in Ghana

BackgroundElectronic Health Record systems (EHRs) offer significant benefits and have transformed healthcare in developed countries. However, their implementation and adoption in low- and middle-income countries (LMICs) remains low due to challenges and competing interests. Health professionals’ perception of EHRs can influence their adoption and continued use. The objectives of this study are to explore the perception of health professionals regarding implemented EHR systems in three hospitals in Ghana and identify factors influencing their perception and satisfaction.MethodsIn this study, we employed a concurrent mixed method design to collect data from study participants from May to June 2023. The quantitative part employed a descriptive-survey and the qualitative (in-depth interview) techniques was applied. After obtaining written informed consent from each respondent, a structured survey questionnaire was filled out by the health professionals from three hospitals. An a priori power calculation was used to determine the sample size for the quantitative component. Two hundred and sixty-three (263) health professionals completed the questionnaire from the three facilities. A purposive sampling technique was used to select fifteen [1] participants for the interviews. A semi-structured interview guide was used for the in-depth interviews. The interviews were audio recorded, transcribed, and coded into themes using QSR Nvivo 12 software before thematic content analysis.ResultsOur findings revealed that 213 (80.99%) health professionals perceived the EHRs as beneficial to patients and were generally satisfied. An overwhelming majority, 197 (74.90%) of the health professionals, were satisfied with its use and expressed interest in continuing to use the system. The majority of health professionals viewed the EHRs to have improved their work and workflow processes and provided the desired results. However, few other health professionals were dissatisfied with the system because they viewed the EHRs as frustrating due to unstable internet connectivity and power supply. Other concerns were related to the privacy and confidentiality of patient information. They believe access to patient information should be on a need-to-know basis, and patient information should not be accessible to all other clinicians except those involved directly in their care processes.ConclusionThe study revealed that health professionals have a positive perception of the implemented EHRs, are highly satisfied with them, and are interested in continuing to use them. However, health professionals’ concerns about the unstable power supply, poor internet connectivity, security, and confidentiality of patient’s information need attention, to mitigate their frustrations and boost their confidence in the system.

Abstract B001: Macrophage adipocyte crosstalk as a node of intervention for pancreatic cancer-associated weight loss

Abstract Introduction: Up to 85% of patients with pancreatic ductal adenocarcinoma (PDAC) are afflicted with cachexia - a metabolic syndrome characterized by loss of skeletal muscle mass and adipose tissue wasting. Cachexia worsens patients’ quality of life, their ability to tolerate chemotherapy or surgical procedures, and, ultimately, their survival. Existing therapies often fail as the disease remains insufficiently understood. Drawing insights from obesity research, where changes in the local adipose immune environment play a crucial role in the pathophysiology, we explore the adipose tissue in the context of PDAC cachexia at single-cell resolution to uncover novel therapeutic avenues. Methods: We developed an ex vivo co-culture system to examine the effect of macrophages on adipocyte lipolysis by measuring the amount of released glycerol from adipocytes following incubation with macrophage supernatant. This robust platform can be used to interrogate factors directly affecting glycerol release from adipocytes. To identify candidate mediators of macrophage-adipocyte crosstalk, we profiled murine and human adipose tissue by flow cytometry and single-cell/nucleus RNA sequencing. The tissue samples were obtained from a cohort of genetically engineered KPC mice and a representative group of surgical patients. Patient adipose samples were collected intraoperatively without warm ischemic time at Heidelberg University Hospital. The cohort includes over 60 patients, divided into three groups: (1) PDAC with weight loss, (2) PDAC without weight loss, and (3) control patients without a history of malignancy. Of these, 25 samples are being used for single-nucleus RNA sequencing. The groups are balanced by sex and can be stratified into equally sized subgroups based on whether they had received neoadjuvant treatment. Results: Macrophage- conditioned media significantly elevated lipid release from murine adipocytes, demonstrating that macrophages may play a role in cancer-associated adipose wasting. Single-nucleus RNA sequencing data from our mice and from our human clinical cohort reveals distinct cell clusters with condition-specific subpopulations and differences unique to cancer-associated weight loss. The biggest phenotypic shifts were observed in macrophages and in adipocytes which showed signs of oxidative stress and altered metabolism associated with cachexia. Conclusion: We successfully established reliable workflows for processing and investigating murine and human AT in the context of PDAC cachexia. Our in vitro model highlights a distinct phenotype of murine macrophage supernatant inducing increased glycerol release from adipocytes. The in-depth analysis of our single-nucleus RNA sequencing data from our clinical cohort promises an unprecedentedly detailed and robust understanding of the human adipose immune environment, thereby enhancing our insights into PDAC cachexia. Citation Format:Felix P. Hambitzer, Farhad Faghihi, Brendan D. Parent, Michael Walsh, Susanne Roth, Andrew Aguirre, Christoph W. Michalski, Martin Loos, Stephanie K. Dougan, Evanna Mills, Max Heckler. Macrophage adipocyte crosstalk as a node of intervention for pancreatic cancer-associated weight loss [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B001.

Setting up an institutional OMERO environment for bioimage data: Perspectives from both facility staff and users.

Modern bioimaging core facilities at research institutions are essential for managing and maintaining high-end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage-specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group-specific naming convention, metadata curation with tagging and Key-Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices.

Effect of Data Quality and Data Quantity on the Estimation of Intrinsic Solubility: Analysis Based on a Single-Source Data Set.

Aqueous solubility is one of the most important physicochemical properties of drug molecules and a major driving force for oral drug absorption. To date, the performance of in silico models for the estimation of solubility for novel chemical space is limited. To investigate possible reasons and remedies for this, the Johnson and Johnson in-house aqueous solubility data with over 40,000 compounds was leveraged. All data were generated through the same high-throughput assay, providing a unique opportunity to explore the relationship between data quality, quantity, and model estimations. Six intrinsic solubility data sets with different sizes and noise levels were generated by making use of three different approaches: (i) inclusion or exclusion of amorphous solid residue, (ii) measured or experimental log D to identify the intrinsic solubility, and (iii) adopting or omitting a quality check process in the data processing workflow. A random forest regressor was trained on the data sets with three different sets of descriptors calculated from RDKit, ADMET predictor, or Mordred, and the performances were evaluated with nested cross-validation as well as ten refined test sets. The models confirm, as expected, that with the same data set size, high-quality data leads to better model performance; however, also, models trained with larger data sets containing analytical variability can give equally accurate estimations compared to models trained with small, clean, and diverse data sets. However, noise introduced by including the presence of amorphous solid postsolubility measurement in the training data set cannot be overcome by increasing data size, as they are introducing a biased systematic positive error in the data set, confirming the importance of critical data review. Finally, two top-performing models were tested on the first test set from the second solubility challenge, achieving RMSE values of 0.74 and 0.72 and log S ± 0.5 of 46 and 48%, respectively. These results demonstrated improved performance compared to those reported in the findings of the competition, highlighting that a single-source curated data set can enhance the prediction of intrinsic solubility.

Research on simulation and validation methods of aerosol radiative forcing on the Tibetan Plateau based on satellite and ground-based remote sensing observations over the past 20 years

Atmospheric radiative changes induced by aerosol radiative forcing are the most uncertain factors in climate change, affecting a comprehensive understanding of aerosol's role in the climate system and ecosystem, with current research mainly focused on densely populated and heavily polluted regions. This study utilizes satellite and ground-based remote sensing data to establish a multi-source data processing and analysis workflow suitable for the Qinghai-Tibet Plateau region, and based on atmospheric radiative transfer models, constructs methods for simulating and validating regional aerosol radiative forcing, optimizing the long-term observational, simulation, and variation studies of aerosol radiative forcing at regional scales. The results indicate: (1) Key input parameters for simulating aerosol radiative forcing regions were determined through sensitivity tests of radiative transfer model parameters to be AOD, surface albedo, atmospheric column water vapor content, and total atmospheric ozone. A method for simulating aerosol direct radiative forcing regions was constructed. Comparison and validation against aerosol radiative forcing site simulations based on ground-based remote sensing observations at the Yangbajing station in Tibet showed R2 values above 0.8 and NRMSE values between 0.25 and 0.39, indicating high accuracy of the method, suitable for the Qinghai-Tibet Plateau. (2) Utilizing satellite remote sensing data, aerosol direct radiative forcing simulations for the Qinghai-Tibet Plateau region over the past 20 years were conducted based on the constructed method. Results showed: ① The annual mean aerosol radiative forcing at the top of the atmosphere was −3.03 W/m2, gradually increasing from west to east; monthly means were negative, decreasing by an average of 0.0025 W/m2 per year, with decreases mainly in February to May. ② The annual mean surface aerosol radiative forcing was −13.56 W/m2, gradually increasing from west to east; monthly means were negative, decreasing by an average of 0.015 W/m2 per year, with decreases mainly in February, June to July, and October to December. ③ The annual mean atmospheric aerosol radiative forcing was 10.6 W/m2, gradually increasing from southwest to northeast; monthly means were positive, increasing by 0.007 W/m2 per year, with increases mainly in October to December. Overall, the annual and monthly mean aerosol direct radiative forcing values at the top of the atmosphere and surface were negative, indicating a cooling effect, while those in the atmosphere were positive, indicating a heating effect; the strongest aerosol radiative forcing occurred in summer at the top of the atmosphere, and in spring for both surface and atmosphere; April showed the fastest variation.

Parallel sample processing for mass spectrometry-based single cell proteomics

BackgroundSingle cell mass spectrometry (scMS) has shown great promise for label free proteomics analysis recently. To present single cell samples for proteomics analysis by MS is not a trivial task. Existing methods rely on robotic liquid handlers to scale up sample preparation throughput. The cost associated with specialized equipment hinders the broad adoption of these workflows, and the sequential sample processing nature limits the ultimate throughput. ResultsIn this work, we report a parallel sample processing workflow that can simultaneously process 10 single cells without the need of robotic liquid handlers for scMS. This method utilized 3D printed microfluidic devices to form reagent arrays on a glass slide, and a magnetic beads-based streamlined sample processing workflow to present peptides for LC-MS detection. We optimized key operational parameters of the method and demonstrated the quantification consistency among 10 parallel processed samples. Finally, the utility of the method in differentiating cell lines and studying the proteome change induced by drug treatment were demonstrated. SignificanceThe present method allows parallel sample processing for single cells without the need of expensive liquid handlers, which has great potential to further improve throughput and decrease the barrier for single cell proteomics.

Evaluation of Serum Proteome Sample Preparation Methods to Support Clinical Proteomics Applications.

Serum contains several proteins that are associated with disease-related processes. Mass spectrometry (MS)-based proteomics approaches greatly facilitate serum protein biomarker development. However, the serum proteome complexity presents a technical challenge for the accurate, sensitive, and reproducible quantification of proteins by MS. Thus, efficient sample preparation methods are of critical importance for serum proteome analyses. In this study, we evaluated the technical performance of two serum proteome sample preparation methods using sera from patients with high-grade serous ovarian cancer and patients with benign nongynecological conditions with a goal of providing insight into their compatibility with clinical proteomics workflows. One method entailed the use of immobilized trypsin (SMART Digest Trypsin) with RapiGest SF, an acid-labile surfactant designed to enhance the in-solution enzymatic digestion of proteins. The other method incorporated a commercially available sample preparation kit, iST-BCT, which contains standardized reagents. Significantly higher protein sequence coverage, albeit with lower digestion efficiency, was obtained with the immobilized trypsin + RapiGest SF workflow, whereas the iST-BCT workflow was quicker and had marginally better reproducibility. Protein relative abundance analysis revealed that the serum proteomes clustered primarily by the sample processing workflow and secondarily by disease state. We conducted a time course study to determine whether differences in the relative abundance of diagnostic high-grade serous ovarian cancer serum protein biomarker candidates were biased according to the duration of enzymatic digestion. Our results highlight the importance of optimizing enzymatic digestion kinetics according to the peptide targets of interest while considering the sensitivity of the downstream analytical method utilized in clinical proteomics workflows designed to measure biomarkers.

PharmaBench: Enhancing ADMET benchmarks with large language models

Accurately predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) properties early in drug development is essential for selecting compounds with optimal pharmacokinetics and minimal toxicity. Existing ADMET-related benchmark sets are limited in utility due to their small dataset sizes and the lack of representation of compounds used in drug discovery projects. These shortcomings hinder their application in model building for drug discovery. To address this issue, we propose a multi-agent data mining system based on Large Language Models that effectively identifies experimental conditions within 14,401 bioassays. This approach facilitates merging entries from different sources, culminating in the creation of PharmaBench. Additionally, we have developed a data processing workflow to integrate data from various sources, resulting in 156,618 raw entries. Through this workflow, we constructed PharmaBench, a comprehensive benchmark set for ADMET properties, which comprises eleven ADMET datasets and 52,482 entries. This benchmark set is designed to serve as an open-source dataset for the development of AI models relevant to drug discovery projects.

BrainSuite BIDS App: Containerized Workflows for MRI Analysis.

There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The Anatomical Pipeline extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The Diffusion Pipeline processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for susceptibility-induced geometric image distortion, and fitting diffusion models to the DWI data. The Functional Pipeline performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. It coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. The outputs of each pipeline can then be processed during group-level analysis. The outputs of the Anatomical Pipeline and the Diffusion Pipeline are analyzed using the BrainSuite Statistics Toolbox in R (bstr), which provides functionality for hypothesis testing and statistical modeling. The outputs of the Functional Pipeline can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.

Urine Sample Handling in Laboratory—Process Reengineering is the Need of the Hour

Background and Aims: Monitoring and a continual improvement of turnaround time (TAT) in urine examination and reporting is essential for improved management of laboratory quality as well as ensuring patient satisfaction. TAT can be impacted by multiple factors, some of which appear to be beyond the control of laboratory, especially the preanalytical phase of urine examination such as specimen collection, transportation and sample preparation. The aim of the study was to compare the preanalytical variables like TAT for transportation and incidences of spill between our current urinalysis workflow process and the new urinalysis workflow process using a pneumatic chute-compatible closed urine collection system. Materials and Methods: This observational prospective study was conducted on 4/9/23 from 9 am to 4 pm at the laboratory of Apollo Main Hospital, Chennai. Pneumatic chute compatible closed urine collection cup was given to the patients in Group-A and normal screw cap containers which we are currently using were given to the patients in Group-B. Once the urine samples were collected in the respective containers, they were transported to the laboratory using pneumatic chutes for Group-A samples and manual carry for Group-B samples. Analysis of transportation time using the new collection method (Group-A) when compared with the current method of practice (Group-B) was done. The incidence of spillage was also monitored. Results: A significant improvement was noticed in the transportation process and spill incidence with closed collection devices through the pneumatic chute. Conclusion: To conclude, the implementation of pneumatic chute compatible closed collection tubes for urine improves the transportation and processing time, eliminates the manual carry of urine samples, eliminates the incidences of leakage and spillage, reduces errors and reduces the exposure of urine to healthcare workers, thereby leading to significant improvement in patient care, patient experience and staff satisfaction.

MIGHTEE-H i: deep spectral line observations of the COSMOS field

ABSTRACT The MIGHTEE survey utilizes the South African MeerKAT radio telescope to observe four extragalactic deep fields, with the aim of advancing our understanding of the formation and evolution of galaxies across cosmic time. MIGHTEE’s frequency coverage encompasses the $\textrm {H}\scriptstyle \mathrm{I}$ line to a redshift of z $\simeq$ 0.58, and OH megamasers to z $\simeq$ 0.9. We present the MIGHTEE-$\textrm {H}\scriptstyle \mathrm{I}$ imaging products for the COSMOS field, using a total of 94.2 h on-target and a close-packed mosaic of 15 individual pointings. The spectral imaging covers two broad, relatively interference-free regions (960–1150 and 1290–1520 MHz) within MeerKAT’s L-band, with up to 26 kHz spectral resolution (5.5 km s$^{-1}$ at z = 0). The median noise in the highest spectral resolution data is 74 μJy beam$^{-1}$, corresponding to a 5$\sigma$$\textrm {H}\scriptstyle \mathrm{I}$ mass limit of 10$^{8.5}$ M$_{\odot }$ for a 300 km s$^{-1}$ line at z = 0.07. The mosaics cover $\gt $4 deg$^{2}$, provided at multiple angular resolution / sensitivity pairings, with an angular resolution for $\textrm {H}\scriptstyle \mathrm{I}$ at z = 0 of 12 arcsec. We describe the spectral line processing workflow that will be the basis for future MIGHTEE-$\textrm {H}\scriptstyle \mathrm{I}$ products, and validation of, and some early results from, the spectral imaging of the COSMOS field. We find no evidence for line emission at the position of the z = 0.376 $\textrm {H}\scriptstyle \mathrm{I}$ line reported from the CHILES survey at a $\gt $94 per cent confidence level, placing a 3$\sigma$ upper limit of 8.1 $\times$ 10$^{9}$ M$_{\odot }$ on $M_{\mathrm{HI}}$ for this galaxy. A public data release accompanies this article.

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.

Observations and Considerations for Implementing Vibration Signals as an Input Technique for Mobile Devices

This work examines swipe-based interactions on smart devices, like smartphones and smartwatches, that detect vibration signals through defined swipe surfaces. We investigate how these devices, held in users’ hands or worn on their wrists, process vibration signals from swipe interactions and ambient noise using a support vector machine (SVM). The work details the signal processing workflow involving filters, sliding windows, feature vectors, SVM kernels, and ambient noise management. It includes how we separate the vibration signal from a potential swipe surface and ambient noise. We explore both software and human factors influencing the signals: the former includes the computational techniques mentioned, while the latter encompasses swipe orientation, contact, and movement. Our findings show that the SVM classifies swipe surface signals with an accuracy of 69.61% when both devices are used, 97.59% with only the smartphone, and 99.79% with only the smartwatch. However, the classification accuracy drops to about 50% in field user studies simulating real-world conditions such as phone calls, typing, walking, and other undirected movements throughout the day. The decline in performance under these conditions suggests challenges in ambient noise discrimination, which this work discusses, along with potential strategies for improvement in future research.