Development Of Workflow Research Articles

Fast Preparation of Peptide Tools to address new emerging Viruses: Case example SARS-CoV-2

Abstract The emergence of new virus species or variants poses a major challenge for healthcare systems. Countermeasures, including vaccine development, need to be developed without delay to ensure success. Vaccine and antibody development depend on highly efficient tools for antigen discovery, epitope mapping, diagnostics and (clinical) immune monitoring. Here we present a workflow for peptide tool development that can be rapidly adapted to emerging viruses. It combines bioinformatics algorithms and an ultra-high throughput peptide synthesis method alongside high-content assay formats. Data from different SARS-CoV-2 studies are presented addressing elucidation of both cellular and humoral immune responses. Robust spike-specific cellular immune responses were measured using SARS-CoV-2 Spike PepMix ™peptide pools during infection and/or vaccine response monitoring [ 1]. Using epitope mapping (matrix) pools an immunodominant T-cell epitope was identified that may contribute to the optimization of existing vaccines [ 2]. In addition, the immune response of Covid-19 convalescents against all major SARS-CoV-2 proteins was analyzed [ 2]. Humoral immune response analysis by high-density peptide microarrays identified immunodominant B cell epitopes as starting points for the development of alternative vaccines and diagnostic tests [ 3]. In conclusion, we show that readily available and tailored peptide formats, as exemplified for SARS-CoV-2, are important tools that contribute to the fast development and optimization of vaccines and diagnostic tests for emerging viruses of concern.

One-step endpoint RT-PCR assays for confirmatory body fluid identification.

Messenger RNA (mRNA) expression analysis is increasingly used in casework, in the form of multiplex two-step reverse transcriptase PCR (RT-PCR) assays such as CellTyper 2 (CT2), developed by the Institute of Environmental Science and Research (ESR). This paper presents the development of a one-step endpoint RT-PCR workflow to improve the efficiency and precision of confirmatory body fluid identification. A comparative study of commercial one-step RT-PCR kits was undertaken, with the highest performing kit (RNA to CT) retained for further development. Sensitivity, specificity across body fluids, and precision was assessed simultaneously using receiver operating characteristic (ROC) curves. An optimal RFU cut-off value which maximised sensitivity and specificity was determined for each marker. All assays performed significantly better when compared to the equivalent of a completely uninformative test (area under the curve of 0.5) for their target body fluid. Sensitivity varied between different donors, but the limit of detectionss were estimated as follows; saliva markers HTN3: 1 in 100 dilution of a whole buccal swab and FDCSP: 1 in 10 dilution of a whole buccal swab, circulatory blood marker SLC4A1: 0.1µL blood, menstrual fluid markers STC1, MMP10: 1 in 10 dilution of a whole menstrual swab, spermatozoa markers PRM1, TNP1: 0.1µL semen, seminal fluid markers KLK2: 0.1µL semen and MSMB: 0.01µL semen, and vaginal material marker CYP2B7P: 1 in 1000 dilution of a whole vaginal swab. The method successfully detected most body fluids in a range of simple mixtures with 77 out of 80 markers observed when expected. The developed one-step endpoint RT-PCR assays lack the sensitivity and precision required for forensic casework and provide little benefit when compared with standard two-step endpoint RT-PCR, other than minimal time and cost savings, similar sensitivity, and improved precision for some markers. As both methods utilise endpoint RT-PCR, they have the same narrow linear dynamic range. The novel method is therefore similarly susceptible to varied RNA input, a major disadvantage of this approach. The limited sensitivity and precision consistently encountered with endpoint RT-PCR - regardless of cDNA synthesis strategy - could be addressed by a real-time PCR approach.

Low-Cost Virtual Reality to Support Imaginal Exposure Within PTSD Treatment: A Case Report Study Within a Community Mental Healthcare Setting

Revisiting what happened during (or after) a traumatic event is an important part of the treatment process in trauma-focused cognitive therapy (TF-CT). However, clinicians may have difficulty helping patients to intentionally retrieve these memories in order to engage with their content. As such, clinical tools to support the access and delivery of imaginal exposure content within treatment may prove to be particularly useful for therapists. This case report introduces work undertaken with Mr. A, a 38-year-old male, who 2 years prior had experienced a city centre assault. Initial assessment revealed a PCL-5 score of 64 and he met DSM-5 criteria for posttraumatic stress disorder (PTSD). Mr. A received 10 sessions of TF-CT wherein the traditional imaginal exposure components were implemented via a newly developed virtual reality (VR) development workflow called “VR Photoscan.” After 10 sessions, results showed PCL-5 scores decreased from 64 to 19 and Mr. A no longer met DSM-5 PTSD criteria. VR Photoscan was used during 4/10 sessions and included (1) reliving, (2) memory updating, and (3) stimulus discrimination activities. Mr. A also reported VR Photoscan as helpful regarding preparation for site visits. In conclusion, VR Photoscan technology provided a more visceral exposure experience which supported Mr. A to revisit the trauma memory. He reported high levels of satisfaction with the quality of the virtual environment and no issues using the VR technology. Produced with lower costs and shorter development times than typical computer-generated environments, VR Photoscan may be more easily implemented within routine care, although further research is required.

Improving ICESat‐2‐based boreal forest height estimation by a multivariate sample quality control approach

Abstract Boreal forest heights are associated with global carbon stocks and energy budgets. The launch of the Advanced Topographic Laser Altimeter System (ATLAS) onboard the NASA's Ice, Cloud and Land Elevation Satellite (ICESat‐2) enables canopy vertical structure measurement at a global scale. However, with a photon‐counting laser system, ICESat‐2 contains high uncertainties in the estimated canopy heights, requiring appropriate quality control before being applied to canopy height modelling. We adopted a multivariate quality control approach (i.e. the Cook's distance) to improve the quality of ICESat‐2 samples. The controlled ICESat‐2 data were then input as the response variable for predicting boreal forest heights based on spatially continuous satellite data and machine learning (ML) regression models. The examined ML regressors include artificial neural networks (ANN), gradient boosting machine (GBM), random forest (RF) and support vector regression (SVR). The proposed quality control effectively removes low‐quality ICESat‐2 samples and enhances the correlations between ICESat‐2 and airborne laser scanning (ALS) observations. Moreover, the controlled ICESat‐2 samples help achieve a trade‐off between sample quality and quantity for all ML regressors, generating close canopy heights to ALS‐derived counterparts. Overall, RF and GBM make better canopy height predictions than ANN and SVR. Of all explanatory variables, the normalized difference vegetation index calculated based on the first red edge band of Sentinel‐2 (NDVIredEdge1) is considered the most important. The proposed quality control on ICESat‐2 sample selection and canopy height model (CHM) development workflow will greatly benefit forest structure investigations in the Arctic community.

TargIDe: a machine-learning workflow for target identification of molecules with antibiofilm activity against Pseudomonas aeruginosa

Bacterial biofilms are a source of infectious human diseases and are heavily linked to antibiotic resistance. Pseudomonas aeruginosa is a multidrug-resistant bacterium widely present and implicated in several hospital-acquired infections. Over the last years, the development of new drugs able to inhibit Pseudomonas aeruginosa by interfering with its ability to form biofilms has become a promising strategy in drug discovery. Identifying molecules able to interfere with biofilm formation is difficult, but further developing these molecules by rationally improving their activity is particularly challenging, as it requires knowledge of the specific protein target that is inhibited. This work describes the development of a machine learning multitechnique consensus workflow to predict the protein targets of molecules with confirmed inhibitory activity against biofilm formation by Pseudomonas aeruginosa. It uses a specialized database containing all the known targets implicated in biofilm formation by Pseudomonas aeruginosa. The experimentally confirmed inhibitors available on ChEMBL, together with chemical descriptors, were used as the input features for a combination of nine different classification models, yielding a consensus method to predict the most likely target of a ligand. The implemented algorithm is freely available at https://github.com/BioSIM-Research-Group/TargIDe under licence GNU General Public Licence (GPL) version 3 and can easily be improved as more data become available.

Selection of patients for hybrid ablation procedure.

Catheter ablation for treatment of symptomatic non-paroxysmal atrial fibrillation remains challenging. Clinical failure and need for continued medical therapy or repeat ablation is common, especially in more advanced forms of atrial fibrillation. Hybrid ablation has emerged as a more effective and safe therapy than endocardial-only ablation particularly for longstanding persistent atrial fibrillation as demonstrated by the randomized controlled CONVERGE trial. Hybrid ablation requires collaboration of electrophysiologists and cardiac surgeons to develop specific workflows. This review describes the Hybrid Convergent approach in the context of available ablation options and offers guidance for workflow development and patient selection.

Design of a process development workflow and control strategy for single-pass tangential flow filtration and implementation for integrated and continuous biomanufacturing

Integrated and continuous biomanufacturing (ICB) offers several advantages compared to traditional batch methods, prompting the development of novel technologies and infrastructure for the implementation of ICB in the biopharmaceutical sector. However, most of the advancements have been in the early stages of the purification process, with relatively fewer advancements in the final filtration steps. This work focuses on some of the last major unit operations of a traditional bioprocess: ultrafiltration and diafiltration. Our proposed technological solution involves robust control algorithms for (1) precisely maintaining consistent protein concentration in single-pass ultrafiltration (SP-UF) and (2) achieving the targeted buffer exchange in single-pass diafiltration (SP-DF). Specifically, we adopted an integrated experimental, modeling, and simulation approach to develop a robust process understanding of SP-UF/SP-DF operations via semi-empirical maximum permeate flux models. These models were employed in the optimization of key design variables such as the filter area and the development of control strategies. Finally, we demonstrate the applicability of our proposed solution by operating a single-pass tangential flow filtration (SP-TFF) system in an integrated and continuous manner for six days. We believe that this work is instrumental for the successful realization of SP-UF/SP-DF operations in both batch and ICB processes.

Abstract NG03: Multiomics in disparities research: Identifying African ancestry-specific mechanisms in the triple negative breast cancer tumor microenvironment

Abstract NG03: Multiomics in disparities research: Identifying African ancestry-specific mechanisms in the triple negative breast cancer tumor microenvironment

One Size Does Not Fit All: Common Practices for Standards Collections and Management

There are numerous studies in the engineering literature about standards, though practical information on standards collection management in libraries is somewhat limited. This study builds upon previous studies but with both a broader scope and more granularity. The purpose is to fill gaps and add to the existing literature on standards collections and use. The workflow for collection development and management practices for standards, like other special resources, are according to each institution's preferences, clientele, campus culture, budget, and needs. To deepen the understanding of standards management in libraries, this study uses a larger sample size than previous studies found in the literature, through a survey of 336 academic libraries in the United States at institutions offering four or more Accreditation Board for Engineering and Technology (ABET) accredited engineering programs. The survey includes questions in areas that have not been studied before, such as institutional size, the highest degree offered, collecting intensity, and demand levels. Findings confirm some results of previous studies, but also show some new and unexpected findings.

An Innovative Solution to Manage Projects using Different Javascript Frameworks

The main aim of the paper is to provide some innovative solutions to manage projects by using JavaScript Frameworks. As the world is moving towards high technology in web development. Therefore, the selection of the right JavaScript framework for a project is an extremely challenging task. Due to this, the article will explore some innovative solutions for managing projects that employ various JavaScript frameworks like Angular and Reach within the same project. Furthermore, it will also examine the drawbacks and benefits of each framework in detail by delving into the challenges of integrating them. Also, it will propose some strategies for effective project management. By comparing these two popular frameworks, it will become simple to provide comprehensive insights into optimizing development workflows, achieving better project outcomes, and enhancing collaboration.

Towards a Holistic and Functional Model-Based Design Method for Mechatronic Cyber-Physical Systems

AbstractEngineering cyber-physical systems (CPS) is complex and time-consuming due to the heterogeneity of the involved engineering domains and the high number of physical and logical interactions of their subsystems. Model-based systems engineering (MBSE) approaches tackle the complexity of developing CPS by formally and explicitly modeling subsystems and their interactions. Newer approaches also integrate domain-specific models and modeling languages to cover different aspects of CPS. However, MBSE approaches are currently not fully applicable for CPS development since they do not integrate formal models for physical and mechanical behavior to an extent that allows to seamlessly link mechanical models to the digital models and reuse them. In this paper, we discuss the challenges arising from the missing integration of physics into MBSE and introduce a model-based methodology capable of integrating physical functions and effects into an MBSE approach on a level where detailed physical effects are considered. Our approach offers a fully virtual, model-based development methodology covering the whole development process for the development of CPS. Evaluating this methodology on a real automotive use case demonstrates benefits regarding virtual development and functional testing of CPS. It shows potentials regarding automated development and continuous integration of the whole CPS including all domains. As an outlook of this paper, we discuss potential further research topics extending our development workflow.

PyQMC: An all-Python real-space quantum Monte Carlo module in PySCF.

We describe a new open-source Python-based package for high accuracy correlated electron calculations using quantum Monte Carlo (QMC) in real space: PyQMC. PyQMC implements modern versions of QMC algorithms in an accessible format, enabling algorithmic development and easy implementation of complex workflows. Tight integration with the PySCF environment allows for a simple comparison between QMC calculations and other many-body wave function techniques, as well as access to high accuracy trial wave functions.

Development of an Analytical Workflow to Support the Establishment of Monographs in African Pharmacopoeias - Combretum mucronatum Leaves as Example.

Herbal medicines are invaluable in African medicine, but quality and safety are not documented in many cases. Besides controlled farming, validated quality control methods are needed to ensure identity, purity, and content. Analytical specifications within modern monographs are needed for consistent batch quality. Combretum mucronatum leaves are widely used in West Africa, but state-of-the-art quality control methods and specifications are non-existent. The aim of the following study was the development of ICH-validated chromatographic protocols for identity, purity, content assay, and analytical specifications for consideration into pharmacopoeial monographs. UPLC-ESI-Q-TOF-MS/MS was used for untargeted phytochemical information on composition. Optimisation of extraction was based on phytochemical profiling. HPTLC was used for differentiation of C. mucronatum from other Combretum species and UPLC for simultaneous determination of 7 marker compounds. C. mucronatum batch analyses (n = 49) investigated the influence of harvest time and geographical origin. Pesticides screening from a 349-compound panel were carried out. 30 compounds, identified by LC-MS, were used for characterization of the plant material. Orietin, isoorientin, vitexin and isovitexin were used as specific marker compounds for qualitative and quantitative HPTLC purposes, while UPLC quantified additionally epicatechin, procyanidins B2 and C1. Influence of harvest time and geographic origin on the content of marker compounds was observed. Differences in the metabolite profiles of C. mucronatum compared to related Combretum species were established for quality control purposes. Contamination with high amoounts of chlorpyrifos, and folpet (sum of folpet and phtalimide, expressed as folpet) were also observed.The study provides analytical protocols, analytical specifications and a drafted monograph for consideration for African pharmacopoeias, and reveals potential challenges in the quality of C. mucronatum.

Use of MAXFEA-ANSYS tool to study the Electro-Magnetic behaviour of the new ST40 inner vacuum chamber proposal during a plasma VDE

Tokamak Energy Ltd. is developing compact fusion power plants based on two promising technologies, Spherical Tokamaks (STs) and High Temperature Superconductors (HTS), with the aim to build a first demonstrative plant by the 2030′s. On its path to fusion power, Tokamak Energy is presently operating ST40, a new generation ST that is currently the highest field device of its kind. In the near future, an important ST40 upgrade is planned and foresees the manufacturing and the installation of a new vacuum chamber, called IVC2. This upgrade will allow the exploitation of ST40 to its maximum performances enabling Double Null (DN) diverted operations with up to 2 MA of plasma current and 3 T of toroidal field. IVC2 is currently in the final design stage. The necessity to analyse in detail the Electro-Magnetic (EM) behaviour of this component under the loads coming from plasma disruptions has recently emerged in order to verify the design or drive further modifications. In this context, a study to analyse the EM response of IVC2 during a plasma Vertical Displacement Event (VDE) has been carried out using the MAXFEA code in combination with ANSYS APDL, following a recent workflow development. The main output of this study is the evaluation of the transient EM force density in IVC2 and other components for subsequent mechanical assessments. The aim of this paper is to present the results of this analysis to show the successful application of this procedure in the context of ST40 project.

Initiating a Magnetic Resonance-Guided Focused Ultrasound Program: Comprehensive Workflow and Lessons Learned from the Initial 116 Cases

Introduction: Magnetic resonance-guided focused ultrasound (MRgFUS) represents an incisionless treatment option for essential or parkinsonian tremor. The incisionless nature of this procedure has garnered interest from both patients and providers. As such, an increasing number of centers are initiating new MRgFUS programs, necessitating development of unique workflows to optimize patient care and safety. Herein, we describe establishment of a multi-disciplinary team, workflow processes, and outcomes for a new MRgFUS program. Methods: This is a single-academic center retrospective review of 116 consecutive patients treated for hand tremor between 2020 and 2022. MRgFUS team members, treatment workflow, and treatment logistics were reviewed and categorized. Tremor severity and adverse events were evaluated at baseline, 3, 6, and 12 months post-MRgFUS with the Clinical Rating Scale for Tremor Part B (CRST-B). Trends in outcome and treatment parameters over time were assessed. Workflow and technical modifications were noted. Results: The procedure, workflow, and team members remained consistent throughout all treatments. Technique modifications were attempted to reduce adverse events. A significant reduction in CRST-B score was achieved at 3 months (84.5%), 6 months (79.8%), and 12 months (72.2%) post-procedure (p < 0.0001). The most common post-procedure adverse events in the acute period (<1 day) were gait imbalance (61.1%), fatigue and/or lethargy (25.0%), dysarthria (23.2%), headache (20.4%), and lip/hand paresthesia (13.9%). By 12 months, the majority of adverse events had resolved with a residual 17.8% reporting gait imbalance, 2.2% dysarthria, and 8.9% lip/hand paresthesia. No significant trends in treatment parameters were found. Conclusions: We demonstrate the feasibility of establishing an MRgFUS program with a relatively rapid increase in evaluation and treatment of patients while maintaining high standards of safety and quality. While efficacious and durable, adverse events occur and can be permanent in MRgFUS.

Remote Sensing Data to Support Integrated Decision Making in Cultural and Natural Heritage Management. Impasses and opportunities for collaboration in agricultural areas

Remote and near surface sensing data are widely used in archaeology and heritage management for feature discovery, change detection and monitoring, as an input to predictive modelling, and in the planning process. While global and regional datasets are widely used for some purposes, data are regularly acquired specifically for archaeological projects because of the very high spatial resolution required for feature detection and assessments of archaeological significance and the need for data on subsurface features. The sensing data collected for archaeology cover limited areas and only a few types of sensors, known to produce data efficiently, are regularly employed. Precision agriculture is beginning to produce large quantities of varied sensing data across extensive landscape areas. This situation creates an opportunity to adapt and reuse precision agricultural data for archaeology and heritage work, extending covering and enhancing our understanding of archaeology in contemporary agricultural landscapes. Equally, there is potential for coordinated data collection, collecting data once for multiple applications, and to add value through analyses which bring together perspectives from multiple related domains to model long-term processes in anthropogenic soil systems. This article provides a high-level overview of policy and technological developments which create the potential for sensing data reuse, coordinated data collection, and collaborative analyses across archaeological, agricultural, and agri-environmental applications while underscoring the structural barriers which, at present, constrain this potential. It highlights examples where the development of interoperable data and workflows can promote tighter integration of archaeology and cultural heritage management with sustainable agricultural land management and support integrated decision making.

Knowledge discovery assistants for crash simulations with graph algorithms and energy absorption features

We propose the representation of data from finite element car crash simulations in a graph database to empower analysis approaches. The industrial perspective of this work is to narrow the gap between the uptake of modern machine learning methods and the current computer-aided engineering-based vehicle development workflow. The main goals for the graph representation are to achieve searchability and to enable pattern and trend investigations in the product development history. In this context, we introduce features for car crash simulations to enrich the graph and to provide a summary overview of the development stages. These features are based on the energy output of the finite element solver and, for example, enable filtering of the input data by identifying essential components of the vehicle. Additionally, based on these features, we propose fingerprints for simulation studies that assist in summarizing the exploration of the design space and facilitate cross-platform as well as load-case comparisons. Furthermore, we combine the graph representation with energy features and use a weighted heterogeneous graph visualization to identify outliers and cluster simulations according to their similarities. We present results on data from the real-life development stages of an automotive company.

The use of gas chromatography – high resolution mass spectrometry for suspect screening and non-targeted analysis of per- and polyfluoroalkyl substances

This study is a workflow development for the analysis, identification, and categorization of per- and polyfluoroalkyl substances (PFAS) using gas chromatography-high resolution mass spectrometry (GC-HRMS) with non-targeted analysis (NTA) and suspect screening techniques. The behavior of various PFAS in a GC-HRMS was studied with regards to retention indices, ionization susceptibility, fragmentation patterns, etc. A custom PFAS database was constructed from 141 diverse PFAS. The database contains mass spectra from electron ionization (EI) mode, as well as MS and MS/MS spectra from positive and negative chemical ionization (PCI and NCI, respectively) modes. Common fragments of PFAS were identified across a diverse set of 141 PFAS analyzed. A workflow for suspect screening of PFAS and partially fluorinated products of incomplete combustion/destruction (PICs/PIDs) was developed which utilized both the custom PFAS database and external databases. PFAS and other fluorinated compounds were identified in both a challenge sample (designed to test the identification workflow) and incineration samples suspected to contain PFAS and fluorinated PICs/PIDs. The challenge sample resulted in a 100% true positive rate (TPR) for PFAS which were present in the custom PFAS database. Several fluorinated species were tentatively identified in the incineration samples using the developed workflow.

Advancing Strategies for Proteolysis-Targeting Chimera Design.

Proteolysis-targeting chimeras (PROTACs) have shown great therapeutic potential by degrading various disease-causing proteins, particularly those related to tumors. Therefore, the introduction of PROTACs has ushered in a new chapter of antitumor drug development, marked by significant advances over recent years. Herein, we describe recent developments in PROTAC technology, focusing on design strategy, development workflow, and future outlooks. We also discuss potential opportunities and challenges for PROTAC research.

Experimental design considerations for studies of human tear proteins

PurposeHuman tears contain abundant, diverse sets of proteins that may serve as biomarkers of ocular surface health. There is a need for reproducible methods that consider multiple factors influencing the tear proteome, in addition to the variable of interest. Here we examined a workflow for proteomic analysis of tear proteins without the need to pool tear samples from multiple individuals, thus allowing for analyses based on individual factors, and increasing opportunities for protein biomarker discovery. MethodsTears were collected by Schirmer strip following topical ocular anesthetic application then individually stored at −80 °C prior to processing for proteomics. Tear proteins were extracted from Schirmer strips, digested using suspension trapping spin columns (S-Trap), and labeled with high multiplicity tandem mass tags (TMT). Peptide digests were then extensively fractionated by two-dimensional chromatography and analyzed by mass spectrometry to identify and measure changes in protein abundance in each sample. Analysis of select samples was performed to test protocols and to compare the impact of clinically relevant parameters. To facilitate comparison of separate TMT experiments, common pool samples were included in each TMT instrument run and internal reference scaling (IRS) was performed. ResultsDifferences in subsets of tear proteins were noted for: geographic site of tear collection, contact lens use, and differences in tear fluid volume among individuals. ConclusionThese findings demonstrate that proteomic analysis of human tear proteins can be performed without the need to pool samples, and that development of analytic workflows must consider factors that may affect outcomes in studies focused on diverse clinical samples.