Development Of Workflow Research Articles

Introducing the Automated Ligand Searcher.

The Automated Ligand Searcher (ALISE) is designed as an automated computational drug discovery tool. To approximate the binding free energy of ligands to a receptor, ALISE includes a three-stage workflow, with each stage involving an increasingly sophisticated computational method: molecular docking, molecular dynamics, and free energy perturbation, respectively. To narrow the number of potential ligands, poorly performing ligands are gradually segregated out. The performance and usability of ALISE are benchmarked for a case study containing known active ligands and decoys for the HIV protease. The example illustrates that ALISE filters the decoys successfully and demonstrates that the automation, comprehensiveness, and user-friendliness of the software make it a valuable tool for improved and faster drug development workflows.

Development of a portable on-site applicable metagenomic data generation workflow for enhanced pathogen and antimicrobial resistance surveillance

Rapid, accurate and comprehensive diagnostics are essential for outbreak prevention and pathogen surveillance. Real-time, on-site metagenomics on miniaturized devices, such as Oxford Nanopore Technologies MinION sequencing, could provide a promising approach. However, current sample preparation protocols often require substantial equipment and dedicated laboratories, limiting their use. In this study, we developed a rapid on-site applicable DNA extraction and library preparation approach for nanopore sequencing, using portable devices. The optimized method consists of a portable mechanical lysis approach followed by magnetic bead-based DNA purification and automated sequencing library preparation, and resulted in a throughput comparable to a current optimal, laboratory-based protocol using enzymatic digestion to lyse cells. By using spike-in reference communities, we compared the on-site method with other workflows, and demonstrated reliable taxonomic profiling, despite method-specific biases. We also demonstrated the added value of long-read sequencing by recovering reads containing full-length antimicrobial resistance genes, and attributing them to a host species based on the additional genomic information they contain. Our method may provide a rapid, widely-applicable approach for microbial detection and surveillance in a variety of on-site settings.

Development and evaluation of a concise nurse-driven non-pharmacological delirium reduction workflow for hospitalized patients: An interrupted time series study.

We created a concise nurse-driven delirium reduction workflow with the aim of reducing delirium rates and length of stay for hospitalized adults. Our nurse-driven workflow included five evidence-based daytime “sunrise” interventions (patient room lights on, blinds up, mobilization/out-of-bed, water within patient's reach and patient awake) and five nighttime “turndown” interventions (patient room lights off, blinds down, television off, noise reduction and pre-set bedtime). Interventions were also chosen because fidelity could be quickly monitored twice daily without patient interruption from outside the room. To evaluate the workflow, we used an interrupted time series study design between 06/01/17 and 05/30/22 to determine if the workflow significantly reduced the unit's delirium rate and average length of stay. Our workflow is feasible to implement and monitor and initially significantly reduced delirium rates but not length of stay. However, the reduction in delirium rates were not sustained following the emergence of the COVID-19 pandemic.

Building expertise on FAIR through evolving Bring Your Own Data (BYOD) workshops: describing the data, software, and management- focused approaches and their evolution

Abstract Since 2014, “Bring Your Own Data” workshops (BYODs) have been organised to inform people about the process and benefits of making resources Findable, Accessible, Interoperable, and Reusable (FAIR, and the FAIRification process). The BYOD workshops’ content and format differ depending on their goal, context, and the background and needs of participants. Data-focused BYODs educate domain experts on how to make their data FAIR to find new answers to research questions. Management-focused BYODs promote the benefits of making data FAIR and instruct project managers and policy-makers on the characteristics of FAIRification projects. Software-focused BYODs gather software developers and experts on FAIR to implement or improve software resources that are used to support FAIRification. Overall, these BYODs intend to foster collaboration between different types of stakeholders involved in data management, curation, and reuse (e.g. domain experts, trainers, developers, data owners, data analysts, FAIR experts). The BYODs also serve as an opportunity to learn what kind of support for FAIRification is needed from different communities and to develop teaching materials based on practical examples and experience. In this paper, we detail the three different structures of the BYODs and describe examples of early BYODs related to plant breeding data, and rare disease registries and biobanks, which have shaped the structure of the workshops. We discuss the latest insights into making BYODs more productive by leveraging our almost ten years of training experience in these workshops, including successes and encountered challenges. Finally, we examine how the participants’ feedback has motivated the research on FAIR, including the development of workflows and software.

Development of a redox proteomics workflow to identify cell surface proteins susceptible to thiol oxidation in endothelial cells

Development of a redox proteomics workflow to identify cell surface proteins susceptible to thiol oxidation in endothelial cells

An Approach for Automatic Generating RESTFul API Code based on SQL DDL Code

RESTFul API is an emerging technology that is used for communication between various applications or systems. RESTFul API is commonly used in the development of systems and applications. Various application, such as mobile, web, or desktop, are use RESTFul API to handle and compute business processes. Currently, Development of the RESTFul API is easier with the code generator tools and some approaches that will generate RESTful API application automatically. With that tools, developers more easier dan efficient when developing their applications. In this research, we try to create a new approach to create a RESTFul API code generator. This approach is based on the SQL DDL code in the SQL dump file as input. We use this approach because, in the initial phase of building a system, developers must provide their database structure before building their applications. With proposed approach, RESTFul API code generator is more effective and suitable with the developer's workflow. Based on testing result, The proposed approach can perform well for automatic generating code for RESTful API based on SQL DDL given. And provide basic functionality in RESTFul API such as create, read, update, and delete data.

Development of a Three-Dimensional Carotid Ultrasound Image Segmentation Workflow for Improved Efficiency, Reproducibility and Accuracy in Measuring Vessel Wall and Plaque Volume and Thickness.

Automated segmentation of carotid lumen-intima boundary (LIB) and media-adventitia boundary (MAB) by deep convolutional neural networks (CNN) from three-dimensional ultrasound (3DUS) images has made assessment and monitoring of carotid atherosclerosis more efficient than manual segmentation. However, training of CNN still requires manual segmentation of LIB and MAB. Therefore, there is a need to improve the efficiency of manual segmentation and develop strategies to improve segmentation accuracy by the CNN for serial monitoring of carotid atherosclerosis. One strategy to reduce segmentation time is to increase the interslice distance (ISD) between segmented axial slices of a 3DUS image while maintaining the segmentation reliability. We, for the first time, investigated the effect of ISD on the reproducibility of MAB and LIB segmentations. The intra-observer reproducibility of LIB and MAB segmentations at ISDs of 1 mm and 2 mm was not statistically significantly different, whereas the reproducibility at ISD = 3 mm was statistically lower. Therefore, we conclude that segmentation with an ISD of 2 mm provides sufficient reliability for CNN training. We further proposed training the CNN by the baseline images of the entire cohort of patients for automatic segmentation of the follow-up images acquired for the same cohort. We validated that segmentation with this time-based partitioning approach is more accurate than that produced by patient-based partitioning, especially at the carotid bifurcation. This study forms the basis for an efficient, reproducible, and accurate 3DUS workflow for serial monitoring of carotid atherosclerosis useful in risk stratification of cardiovascular events and in evaluating the efficacy of new treatments.

AGouTI-Flexible Annotation of Genomic and Transcriptomic Intervals.

The recent development of high-throughput workflows in genomics and transcriptomics revealed that efficient annotation of such results is essential for researchers to draw conclusions from obtained results. Although some tools are available, their functionality is limited. Here, we present AGouTI-a universal tool for flexible annotation of any genomic or transcriptomic coordinates using known genomic features deposited in different publicly available databases in the form of GTF or GFF files. In contrast to currently available tools, AGouTI is designed to provide a flexible selection of genomic features overlapping or adjacent to annotated intervals and can be used on custom column-based text files obtained from different data analysis pipelines. Although providing many unique options, AGouTI is straightforward in installation and usage, enabling effortless integration into existing data analysis workflows.

A strategic approach towards mass balance investigations in pharmaceutical drug substance release testing: A peculiar out of specification case study encountered during API manufacture

Mass balance in drug substances release testing is a critical quality attribute in pharmaceutical manufacturing that continues to challenge modern analytical characterization. This specific perspective of mass balance is lacking in literature, and the following work addresses the knowledge gap related to this topic by examining an in-depth case study and detailing the systematic investigation into mass imbalance observed during release testing of a small molecule API. The process followed a logical stepwise progression beginning with most probable causes and expanded to more obscure causes that require a deeper examination of the API in question until the undetected impurity in question was finally identified. The discovered impurity was eventually found to be formed from a unique side reaction that led to the formation of API-related oligomer impurities, which had eluded conventional small molecule release testing strategies. Ultimately, the characterization gap was traced back to deficiency in the LC results of the developed API purity methods. More importantly, this gap provides an ideal opportunity to highlight common oversights and pitfalls encountered in early phase pharmaceutical development especially as it relates to the method development of truly representative chromatography methods in the API characterization. The work reflects on the key lessons learned from the highlighted pitfalls that were encountered in this case study and offers strategic insights to guide and to improve the development workflow for drug substance characterization strategies.

Harnessing Variabilities in Digital Melt Curves for Accurate Identification of Bacteria.

Digital PCR combined with high resolution melt (HRM) is an emerging method for identifying pathogenic bacteria with single cell resolution via species-specific digital melt curves. Currently, the development of such digital PCR-HRM assays entails first identifying PCR primers to target hypervariable gene regions within the target bacteria panel, next performing bulk-based PCR-HRM to examine whether the resulting species-specific melt curves possess sufficient interspecies variability (i.e., variability between bacterial species), and then digitizing the bulk-based PCR-HRM assays with melt curves that have high interspecies variability via microfluidics. In this work, we first report our discovery that the current development workflow can be inadequate because a bulk-based PCR-HRM assay that produces melt curves with high interspecies variability can, in fact, lead to a digital PCR-HRM assay that produces digital melt curves with unwanted intraspecies variability (i.e., variability within the same bacterial species), consequently hampering bacteria identification accuracy. Our subsequent investigation reveals that such intraspecies variability in digital melt curves can arise from PCR primers that target nonidentical gene copies or amplify nonspecifically. We then show that computational in silico HRM opens a window to inspect both interspecies and intraspecies variabilities and thus provides the missing link between bulk-based PCR-HRM and digital PCR-HRM. Through this new development workflow, we report a new digital PCR-HRM assay with improved bacteria identification accuracy. More broadly, this work can serve as the foundation for enhancing the development of future digital PCR-HRM assays toward identifying causative pathogens and combating infectious diseases.

Pseudo-3D cubes from densely spaced subbottom profiles via projection onto convex sets interpolation: An open-source workflow applied to a pockmark field

High-resolution subbottom profiler (SBP) data are commonly recorded during academic or industrial surveys and analyzed as 2D profiles. However, there is growing interest in 3D imaging of the shallow seafloor subsurface, e.g., site surveys prior to the installation of offshore wind farms. Despite ongoing advancements in 3D systems, SBPs continue to be used mainly for 2D profiling. In other seismic imaging applications, “pseudo-3D” seismic cubes have been successfully generated from dense 2D seismic surveys. We developed a novel open-source Python-based workflow to interpolate densely spaced 2D SBP profiles acquired with a hull-mounted parametric SBP into pseudo-3D cubes. This workflow is applied to a study area on the Chatham Rise east of New Zealand’s South Island, comprising numerous seafloor pockmarks and subsurface paleo-pockmarks. Our workflow consists of two stages: (1) processing of individual 2D profiles to compensate for existing vertical offsets and (2) sparse data interpolation by applying the iterative projection onto convex sets algorithm. We correct the residual static effect caused by the sea state during acquisition, compensate for the varying tidal elevations, and apply vertical time shifts to individual profiles to correct misties at profile intersections. The interpolation of the binned sparse 3D cube is performed in the frequency domain using the fast Fourier transform and executed in parallel processes, which significantly increases the computational efficiency. We validate our workflow by testing different bin sizes, sparse transforms, thresholding approaches, and varying total iterations, which noticeably impact the resulting interpolation quality. Our developed workflow generates pseudo-3D subsurface images from densely spaced SBP profiles, with numerous potential applications for academic and industrial surveys.

Development and Validation of an Enzymatic Assay for TMPRSS4: Evaluation of Molecular Inhibitors

TMPRSS4, a member of the transmembrane serine protease (TTSP) family, has earned significant attention due to its pronounced overexpression in various carcinoma types, involvement in disease processes, and viral entry. However, despite its pivotal role in disease biology, little is known about its structural characteristics and potential drug targets. Accurate measurement of TMPRSS4 activity is crucial for understanding its role in these disease processes and for developing potential therapeutic interventions. In this context, we have expressed and purified TMPRSS4 protein and report the development of an enzymatic assay for the quantitative measurement of TMPRSS4 activity. The assay is based on a synthetic fluorogenic peptide substrate that mimics the natural cleavage site of TMPRSS4/serine proteases substrates. The Assay development workflow includes enzyme preparation, optimization of assay materials, reagents, and conditions. The assay is further validated using a known serine protease inhibitor, Aprotinin. Our results demonstrate that the developed enzymatic assay exhibits a wide dynamic range and successfully applied the assay to assess molecular inhibitors predicted by our computational studies. In conclusion, our study presents a robust enzymatic assay for TMPRSS4 activity that provides researchers and clinicians with a valuable tool for studying this protease's function and potential clinical applications.

Research competencies in university students: Intertwining complex thinking and Education 4.0

Research competencies are skills that university students must develop to create and socialize scientific products during their academic live. In this research, an experience was implemented to improve the students’ competency levels through its imbrication with complex thinking and the use of Education 4.0 applications, such as remote team workflow development apps, web-based virtual reality, and social robotics. The study was sequential-quantitative and descriptive. A questionnaire was applied before and after the experience to know the perception of 105 Mexican university students, later a rubric was implemented for the teacher’s assessment. The results indicate that the students perceived an improvement in their research skills, however, the evaluation showed a difference between the student’s perception and the teacher’s regarding improvement in said skills. The experience can be scaled to other scenarios, where disruptive teaching strategies can support research skills development.

TopDownApp: An open and modular platform for analysis and visualisation of top-down proteomics data.

Although Top-down (TD) proteomics techniques, aimed at the analysis of intact proteins and proteoforms, are becoming increasingly popular, efforts are needed at different levels to generalise their adoption. In this context, there are numerous improvements that are possible in the area of open science practices, including a greater application of the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. These include, for example, increased data sharing practices and readily available open data standards. Additionally, the field would benefit from the development of open data analysis workflows that can enable data reuse of public datasets, something that is increasingly common in other proteomics fields.

Improving Sexual Orientation and Gender Identity Documentation at an NCI-Designated Comprehensive Cancer Center

To increase the rate of sexual orientation and gender identity (SOGI) patient data collection in healthcare software for new patients at a NCI-Designated Comprehensive Cancer Center, via targeted identification of Advanced Practice Providers (APP), development of novel SOGI collection workflows, creation of a real-time SOGI data dashboard, and tailored training utilizing existing High Reliability Organization (HRO) huddles in order to measure, analyze, and improve the quality of care, safety, and patient experiences for Sexual and Gender Minority (SGM) patients. Prior to interventions, project leadership (PL) and data analytics staff obtained SOGI baseline data, while cancer center clinical leadership, registration, and administrative staff identified APPs as the most appropriate staff to collect patient SOGI data. Intervention 1: PL met with Disease Management Groups (DMG)/HRO leads of thoracic medical oncology to identify a practice-level APP champion. PL and the APP champion scripted language for multiple scenarios when asking SOGI questions, and served as a resource for additional practice APPs. PL provided SGM-focused training during HRO huddles, which included how to ask and document SOGI, and significance of asking SOGI information. SOGI data completion rate was tracked on a HRO scorecard. This intervention was replicated across additional DMG/HROs during the study period. Intervention 2: Breast surgical oncology completed Intervention 1 and added SOGI questions to their new patient-facing intake form, allowing patients to self-identify. PL and data analytics team provided continuous feedback to DMG/HRO leads on provider-level completion rates and additional education as needed. At the end of the Interventions, there were 9 LGBTQ+ Knowledge and Awareness HRO training sessions completed resulting in over 300 unique individuals receiving advanced SOGI documentation education. For intervention 1, there were 12,322 new patients asked their SOGI information, which was a greater than 300% increase in SOGI documentation (baseline completion rate for sexual orientation and gender identity was 17% and 21%, respectively, which improved to 77% and 84%). For intervention 2, there were 3,217 new patients asked their SOGI information, which was a greater than 400% increase (baseline completion rate for sexual orientation and gender identity was 13% and 16%, respectively, which improved to 81% and 81%). Together, patient self-reporting quickly increased data completion rates from baseline, comparable to clinical sites from Intervention 1, and may decrease the burden of APPs collecting SOGI history; however, clinical review of questions is important and may further increase SOGI data completion. Leadership buy-in, site champions, and active data monitoring are essential to measurable change. Together, improved SOGI data completion will allow for improved equitable cancer care and increased assessment of SGM cancer disparities.

Ten quick tips for building FAIR workflows.

Research data is accumulating rapidly and with it the challenge of fully reproducible science. As a consequence, implementation of high-quality management of scientific data has become a global priority. The FAIR (Findable, Accesible, Interoperable and Reusable) principles provide practical guidelines for maximizing the value of research data; however, processing data using workflows-systematic executions of a series of computational tools-is equally important for good data management. The FAIR principles have recently been adapted to Research Software (FAIR4RS Principles) to promote the reproducibility and reusability of any type of research software. Here, we propose a set of 10 quick tips, drafted by experienced workflow developers that will help researchers to apply FAIR4RS principles to workflows. The tips have been arranged according to the FAIR acronym, clarifying the purpose of each tip with respect to the FAIR4RS principles. Altogether, these tips can be seen as practical guidelines for workflow developers who aim to contribute to more reproducible and sustainable computational science, aiming to positively impact the open science and FAIR community.

Quantitative Validation of Gas-Liquid Flow Regime Transition Using Eulerian-Eulerian CFD Models

Quantitative validation of a computational fluid dynamics (CFD) code is about making point-to-point comparisons over fields of data to make statements about predictive fidelity. By the very nature of CFD, these comparisons are high-performance computing and data intensive. This presentation provides an overview of workflow development toward quantitative development of two-phase CFD codes. The focus here is on multifield two-fluid model capability implemented in the commercial CFD code ANSYS CFX applicable to boiling gas-liquid two-phase flows. Applications to validating three-dimensional predictions that span two-phase flow regimes are discussed.

B-220 Development of Reference Materials for Detection of Monkeypox

Abstract Background Monkeypox virus (MPXV) is a member of Poxviridae family and belongs to the genus of orthopoxvirus. The Monkeypox virus has a double stranded DNA genome of approximately 197 kb with almost 190 nonoverlapping ORFs. Monkeypox virus encodes all the enzymes needed for transcription and replication process of the viral genome. It has been hypothesized that progressive gene loss, primarily at the terminal end of the genome, has been the justification behind evolution of a new adaptive virus capable of attacking humans and causing serious illness. Monkeypox virus infection in human can lead to a smallpox-like illness with almost 11% fatality rate in individuals who are non-vaccinated. Monkeypox virus infects a wide range of mammalian cells without the need for specific host receptors, cell entry molecules and replication machinery. No specific approved treatment exists for Monkeypox. The symptoms could be managed by treating secondary bacterial infections, reducing symptoms, and providing supportive care. ST-246 (tecovirimat), FDA approved drug against Smallpox virus, has shown some efficacy against Monkeypox, and therefore, accurate diagnosis, usually through PCR-based testing is critical. LGC Clinical Diagnostics has developed AccuPlex Monkeypox Reference Material, which is a non-infectious, stable, and reproducible standard to aid nucleic acid testing for Monkeypox virus. Methods AccuPlex™ Monkeypox Reference Material consists of recombinant Adeno virus bearing the complete Monkeypox genes: J2L (TNF receptor gene), F8L (DNA polymerase gene), F3L (Double-stranded RNA-binding protein, inhibitor of IFN signaling), and N3R (orthopoxvirus MHC class I-like protein -OMCP). The design of the recombinant virus is based on the sequence of ON585038.1 USA strain from the recent outbreak. A digital PCR assay was developed using the E9L Non-variola (NVAR) Orthopox Generic Assay design described in Y. Li et al. /Journal of Clinical Virology 36 (2006) 194–203. Digital PCR testing using the BioRad QX-200 droplet digital PCR system was used to quantitate the viral copies/mL and guide formulation. The final product is targeted as a low positive control, approximately 2–3 times the detection limit of commercial PCR testing methods and is formulated in defibrinated human plasma. Results AccuPlex™ Monkeypox Reference Material gave an average value of 5.33E+03 copies/mL ± 6.34E+02 copies/mL by digital PCR. Along with the undiluted sample, 1:2 and 1:4 dilutions were made which were then tested on Quest Diagnostics Qualitative RT-PCR platform to verify performance and compatibility with the assay detection limits. All three samples were positively detected. Conclusion LGC Clinical Diagnostics has developed a stable, non-infectious reference material using recombinant Adenovirus technology for assays that detect Monkeypox DNA. Our study helps ensure that the control will be low positive for Monkeypox virus and could be used for monitoring the sensitivity of the assay. Data presented indicates the compatibility of the reference material with commercially available real time PCR assays. These materials may be used by labs for development, validation, training, and ongoing QC of Monkeypox detection assays and workflows.

Stripped and layered fabrication of minimal surface tectonics using parametric algorithms

Abstract This article describes a parametric design and fabrication workflow influenced by Frei Otto’s form-finding experiments on soap films. The research investigates minimal surface geometry by combining physical and digital experiments in a computational framework. Operating on mesh topology, various parametric design tools and plug-ins in Rhinoceros/Grasshopper are presented to discuss the translation of minimal surfaces to flat strips suitable for planar fabrication using flexible materials. These tools are tested on a case study to show the automated design and manufacture of double-curved surfaces as double-layered strips running in perpendicular directions that can be affixed at point connections for structural stability. The development of the parametric workflow, material constraints, and stripped fabrication of layers are discussed.

ENERGYSIM: techniques for advancing building energy education through immersive virtual reality (VR) simulation

An important practice for reducing the effects of global warming is the design and construction of energy-efficient buildings. In design education, the full comprehension of thermal behavior in buildings based on their geometry and material composition is required. The complexity of energy simulation principles, vis-a-vis the number of elements that impact the energy loads, their linkages, and their relationships to one another all combine to make this a challenging subject to absorb. Virtual Reality (VR) provides an immersive way to learn the concepts of building energy responses; however, the development of VR applications for education is difficult due to the knowledge, skill, and performance resource-related gaps. Unoptimized VR applications can adversely impact learning if user experiences are broken due to performance lags. This research, therefore, explores VR as a teaching tool for building energy education while showcasing the development process toward a visually accurate simulation and performant application. We developed EnergySIM; a multi-user VR building energy simulation prototype of the famous Farnsworth House. Using this prototype, we document rigorously tested development workflows for improved VR game performance, high visual fidelity, and user interaction, the three key factors which positively contribute to user knowledge retention. The study combines menu-driven interaction, virtual exploration, and miniature model manipulation approaches with the aim of testing user understanding and knowledge retention. Highlighted results provide reduced barriers of entry for educators towards developing higher quality educational VR applications. EnergySIM showcases pre-simulated building exterior surface heatmaps response from four seasons (winter, summer, fall, and spring) alongside an all-year-round sun-hour scenario. Four different material pre-simulated scenarios (single glazing, double glazing, concrete, and wood) for interior atmospheric temperature mapping are also explored. Preferred interaction methods are documented by allowing users’ visual appraisal of alternative building materials based on insulation capacity or resistance to heat flow (R-value). The significance of this work lies in its potential to revolutionize how students, designers, and instructors approach building energy education in today’s world. EnergySIM provides a hands-on and visually engaging learning experience towards the enhancement of knowledge retention and understanding. It pushes the boundaries of development for visual fidelity using geometry/mesh modeling input from various software into game engines and optimizing game performance using the HTC Vive Pro Eye and Meta Quest Pro headsets.