Workflow Process Research Articles

Secretome processing for proteomics: A methods comparison.

The cancer cell secretome comprises a treasure-trove for biomarkers since it reflects cross-talk between tumor cells and their surrounding environment with high detectability in biofluids. In this study, we evaluated six secretome sample processing workflows coupled to single-shot mass spectrometry: (1) Protein concentration by ultrafiltration with a molecular weight cut-off (MWCO) filter and sample preparation through in-gel digestion (IGD); (2) Acetone protein precipitation coupled to IGD; (3) MWCO filter-based protein concentration followed by to in-solution digestion (ISD); (4) Acetone protein precipitation coupled to ISD; (5) Direct ISD; (6) Secretome lyophilization and ISD. To this end, we assessed workflow triplicates in terms of total number of protein identifications, unique identifications, reproducibility of protein identification and quantification and detectability of small proteins with important functions in cancer biology such as cytokines, chemokines, and growth factors. Our findings revealed that acetone protein precipitation coupled to ISD outperformed the other methods in terms of the number of identified proteins (2246) and method reproducibility (correlation coefficient between replicates (r=0.94, CV=19%). Overall, especially small proteins such as those from the classes mentioned above were better identified using ISD workflows. Concluding, herein we report that secretome protein precipitation coupled to ISD is the method of choice for high-throughput secretome proteomics via single shot nanoLC-MS/MS.

High-resolution repeat topography of drifting ice floes in the Arctic Ocean from terrestrial laser scanning

Snow and ice topography impact and are impacted by fluxes of mass, energy, and momentum in Arctic sea ice. We measured the topography on approximately a 0.5 km2 drifting parcel of Arctic sea ice on 42 separate days from 18 October 2019 to 9 May 2020 via Terrestrial Laser Scanning (TLS). These data are aligned into an ice-fixed, lagrangian reference frame such that topographic changes (e.g., snow accumulation) can be observed for time periods of up to six months. Using in-situ measurements, we have validated the vertical accuracy of the alignment to ± 0.011 m. This data collection and processing workflow is the culmination of several prior measurement campaigns and may be generally applied for repeat TLS measurements on drifting sea ice. We present a description of the data, a software package written to process and align these data, and the philosophy of the data processing. These data can be used to investigate snow accumulation and redistribution, ice dynamics, surface roughness, and they can provide valuable context for co-located measurements.

EcoPrintAnalyzer: Assessing Sustainability in Material Extrusion Additive Manufacturing for Informed Decision-Making

Sustainability is fundamental in the field of additive manufacturing (AM) for improving eco-consciousness and driving evolution toward environmentally responsible production methods. Compared to traditional manufacturing processes, AM technologies can be more resource-efficient and offer innovative solutions for creating eco-friendly processes and products. Nevertheless, there is significant potential for improvement in additive manufacturing sustainability. The key factors driving this improvement include design optimization and increased awareness. Designers and engineers can create designs that optimize material efficiency and reduce support structures. Raising awareness and educating stakeholders about the environmental benefits of AM can promote responsible choices throughout the industrial process. The development of a tool to assess the environmental impact of AM processes could be a significant contribution to advancing sustainability in the AM field. The EcoPrintAnalyzer, introduced as a complementary plugin for UltiMaker Cura, offers data on the equivalent carbon dioxide footprint and energy consumption in material extrusion additive manufacturing. This tool facilitates informed decision-making regarding materials, designs, and settings, enabling users to optimize their AM processes for reduced waste and enhanced energy efficiency. Beyond aiding decision-making, the EcoPrintAnalyzer fosters environmental consciousness and encourages the adoption of sustainable practices within the AM ecosystem. The efficacy of the tool is demonstrated through the 3DBenchy model case study, showcasing its intuitive interface and seamless integration within the AM process workflow for immediate and comparative environmental impact assessments across different process configurations.

A protocol and training guidelines for mosquito sampling in remote areas with limited power supply

Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas. Specifically, we describe the procedure for using rechargeable lithium batteries and solar panels to power the mosquito traps, demonstrate a workflow for processing and storing the mosquitoes in a -20 °C freezer, data management tools including microclimate data, and quality assurance processes to ensure the validity and reliability of the results. A pre- and post-test was utilized to measure participant knowledge levels. Additional research is needed to validate this protocol for monitoring vector-borne diseases in hard-to-reach areas within other countries and settings.

Overcoming resolution attenuation during tilted cryo-EM data collection

Structural biology efforts using cryogenic electron microscopy are frequently stifled by specimens adopting “preferred orientations” on grids, leading to anisotropic map resolution and impeding structure determination. Tilting the specimen stage during data collection is a generalizable solution but has historically led to substantial resolution attenuation. Here, we develop updated data collection and image processing workflows and demonstrate, using multiple specimens, that resolution attenuation is negligible or significantly reduced across tilt angles. Reconstructions with and without the stage tilted as high as 60° are virtually indistinguishable. These strategies allowed the reconstruction to 3 Å resolution of a bacterial RNA polymerase with preferred orientation, containing an unnatural nucleotide for studying novel base pair recognition. Furthermore, we present a quantitative framework that allows cryo-EM practitioners to define an optimal tilt angle during data acquisition. These results reinforce the utility of employing stage tilt for data collection and provide quantitative metrics to obtain isotropic maps.

Blockchain meets machine learning: a survey

Blockchain and machine learning are two rapidly growing technologies that are increasingly being used in various industries. Blockchain technology provides a secure and transparent method for recording transactions, while machine learning enables data-driven decision-making by analyzing large amounts of data. In recent years, researchers and practitioners have been exploring the potential benefits of combining these two technologies. In this study, we cover the fundamentals of blockchain and machine learning and then discuss their integrated use in finance, medicine, supply chain, and security, including a literature review and their contribution to the field such as increased security, privacy, and decentralization. Blockchain technology enables secure and transparent decentralized record-keeping, while machine learning algorithms can analyze vast amounts of data to derive valuable insights. Together, they have the potential to revolutionize industries by enhancing efficiency through automated and trustworthy processes, enabling data-driven decision-making, and strengthening security measures by reducing vulnerabilities and ensuring the integrity of information. However, there are still some important challenges to be handled prior to the common use of blockchain and machine learning such as security issues, strategic planning, information processing, and scalable workflows. Nevertheless, until the difficulties that have been identified are resolved, their full potential will not be achieved.

Polyester Reçine Üretim Tesisinde Fonksiyonel Rezonans Analiz Yöntemi (FRAM) ile Risk Analizi

There are various hazards and risks in the workflow processes in polyester resin production facilities. The most important of these dangers and risks are; It is related to chemical processes, fire and explosion, equipment malfunctions and storage processes of the products obtained. This study aims to analyze the potential hazards and risks that may be encountered in a polyester resin production facility in the Marmara region, from the arrival of the raw material to the facility, to its processing and storage of the resulting products, with the Functional Resonance Analysis Method (FRAM) and to identify the stages that prevent the processes from being optimized. It has been prepared to make suggestions both for improving workflow processes and reducing risks in the process.

Leveraging Digital Workflows to Transition the Orthotics and Prosthetics Profession Toward a Client-Centric and Values-Based Care Model.

The orthotics and prosthetics (O&P) profession has a history of responding to market demands in a reactive rather than proactive manner. This has created significant impacts including shrinkage in scope of practice and constraint in remuneration for professional services due to a fee-for-device third party payer system. Rapid changes in technology and healthcare combined with an outdated device-centric reimbursement system are creating unprecedented challenges that threaten sustainability of the O&P profession. Hence, a reassessment of the value of O&P care, and the O&P workflow process is necessary to inform an update to the value proposition and practice model for sustainability. This article reviews key factors contributing to the current state of O&P, and potential solutions involving an update in practitioner competencies, and the care delivery model (from device-centric to client-centric and values-based). Updates could be achieved by leveraging the use of digital workflows that increase efficiencies and enhance the value of clinical outcomes. Eventually, these updates could enable the O&P profession to elevate the value proposition that aligns with its most important stakeholders: client-patients and third-party reimbursement agencies in a rapidly changing technology and healthcare landscape.

Development and utility of a clinical research informatics application for participant recruitment and workflow management for a return of results pilot trial in familial hypercholesterolemia in the Million Veteran Program.

The development of clinical research informatics tools and workflow processes associated with re-engaging biobank participants has become necessary as genomic repositories increasingly consider the return of actionable research results. Here we describe the development and utility of an informatics application for participant recruitment and enrollment management for the Veterans Affairs Million Veteran Program Return Of Actionable Results Study, a randomized controlled pilot trial returning individual genetic results associated with familial hypercholesterolemia. The application is developed in Python-Flask and was placed into production in November 2021. The application includes modules for chart review, medication reconciliation, participant contact and biospecimen logging, survey recording, randomization, and documentation of genetic counseling and result disclosure. Three primary users, a genetic counselor and two research coordinators, and 326 Veteran participants have been integrated into the system as of February 23, 2023. The application has successfully handled 3367 task requests involving greater than 95000 structured data points. Specifically, application users have recorded 326 chart reviews, 867 recruitment telephone calls, 158 telephone-based surveys, and 61 return of results genetic counseling sessions, among other available study tasks. The development of usable, customizable, and secure informatics tools will become increasingly important as large genomic repositories begin to return research results at scale. Our work provides a proof-of-concept for developing and using such tools to aid in managing the return of results process within a national biobank.

Evaluation of a prototype reverse fill/flush flow modulation-splitter system for the highly detailed separation required in industrial settings

Reverse fill/flush (RFF) differential flow modulators are a lower cost, consumable free, robust option to achieve comprehensive two-dimensional gas chromatography (GC × GC) with FID and MS dual detection. For the required highly detailed separation of all hydrocarbon classes for a petroleum industry setting thermal-based modulators are considered the “gold standard” for their high performance, but there are substantial costs involved. In an industrial setting, there is a necessary balance between the separation performance, cost of instrumentation and costs of operation. It is uncertain if an RFF modulator chromatographic performance is sufficient to achieve the demands of an industrial research laboratory as a cost-effective alternative to thermal modulation. This paper reports an evaluation of the capabilities of a new prototype RFF flow modulator with flow splitter system and its chromatographic performance relative to thermal modulation for detailed hydrocarbon analysis. The prototype RFF modulator splitter system was easy to use with straightforward method optimization due to the lack of restrictors and implementation of back pressure regulation for variable restriction. It also provided good peak shapes, accurate quantification, and maintained constant split to both detectors throughout the analysis. The data processing workflow was greatly improved with the alignment of the MS and FID traces in the software. However, the chromatographic performance of the RFF was found to be less and insufficient for detailed hydrocarbon analysis when compared to thermal modulation. The greatest discrepancy was found in the differentiation of the saturate class, specifically the alkanes, iso-alkanes, and cycloalkanes from each other and other monoaromatics. A comparable separation was found in the lower carbon range of C7-C14 while the region of C16-C22 was challenging for the RFF modulator. Overall, the detailed analysis of a middle distillate was found to be too complex for the RFF flow modulator in comparison to thermal modulator.

Design and Implementation of an Inpatient Fall Risk Management Information System.

Falls had been identified as one of the nursing-sensitive indicators for nursing care in hospitals. With technological progress, health information systems make it possible for health care professionals to manage patient care better. However, there is a dearth of research on health information systems used to manage inpatient falls. This study aimed to design and implement a novel hospital-based fall risk management information system (FRMIS) to prevent inpatient falls and improve nursing quality. This implementation was conducted at a large academic medical center in central China. We established a nurse-led multidisciplinary fall prevention team in January 2016. The hospital's fall risk management problems were summarized by interviewing fall-related stakeholders, observing fall prevention workflow and post-fall care process, and investigating patients' satisfaction. The FRMIS was developed using an iterative design process, involving collaboration among health care professionals, software developers, and system architects. We used process indicators and outcome indicators to evaluate the implementation effect. The FRMIS includes a fall risk assessment platform, a fall risk warning platform, a fall preventive strategies platform, fall incident reporting, and a tracking improvement platform. Since the implementation of the FRMIS, the inpatient fall rate was significantly lower than that before implementation (P<.05). In addition, the percentage of major fall-related injuries was significantly lower than that before implementation. The implementation rate of fall-related process indicators and the reporting rate of high risk of falls were significantly different before and after system implementation (P<.05). The FRMIS provides support to nursing staff in preventing falls among hospitalized patients while facilitating process control for nursing managers.

Using lean manufacturing to improve process efficiency in a fabrication company

This article presents a case study on improving process efficiency in a mining equipment part fabrication company. The company was facing issues concerning communication, organisation, and workflow processes. This study investigated that ineffective communication among departments was the major weakness which was responsible for the long lead or idle time. This lead time was a waste that affected the company’s productivity. A great amount of time was spent on non-value-added processes. The Kanban Centralised Communication System was implemented. Time study and value stream mapping were also used. A significant improvement in process efficiency from 34% to 85% was achieved by reducing lead time from 4200 minutes to 1680 minutes after streamlining the communication in the company using Kanban.

A Method for Constructing an Intelligent Maintenance Assistance System for Hydroelectric Stations

This paper primarily addresses the work requirements of a certain hydroelectric dam and explores a method for constructing an intelligent assistance system for maintenance in hydroelectric stations. The dam boasts a diverse array of equipment and is situated in a region characterized by complex terrain and structures, posing significant challenges to maintenance work across various disciplines. This study aims to integrate emerging technologies such as big data computation, data analysis, and knowledge graphs, alongside cutting-edge human-computer interaction theories. It seeks to develop advanced theories of maintenance assistance for hydroelectric stations, focusing on the intelligent decision-making and guidance in maintenance operations using digital technology. By achieving digitization and intelligence in maintenance management and workflow processes, this research endeavors to provide participants at every stage of maintenance operations with safe, efficient, and reliable support capabilities. Ultimately, it aims to elevate the level of digitization and intelligence in maintenance operations at hydroelectric stations to unprecedented heights, thereby enhancing the management efficiency of maintenance operations and further improving quality and efficiency.

The ALICE Grid Workflow for LHC Run 3

The ALICE Grid Workflow for LHC Run 3

Parallel DNA/RNA NGS Using an Identical Target Enrichment Panel in the Analysis of Hereditary Cancer Predisposition.

Germline DNA testing using the next-gene-ration sequencing (NGS) technology has become the analytical standard for the diagnostics of hereditary diseases, including cancer. Its increasing use places high demands on correct sample identification, independent confirmation of prioritized variants, and their functional and clinical interpretation. To streamline these processes, we introduced parallel DNA and RNA capture-based NGS using identical capture panel CZECANCA, which is routinely used for DNA analysis of hereditary cancer predisposition. Here, we present the analytical workflow for RNA sample processing and its analytical and diagnostic performance. Parallel DNA/RNA analysis allowed credible sample identification by calculating the kinship coefficient. The RNA capture-based approach enriched transcriptional targets for the majority of clinically relevant cancer predisposition genes to a degree that allowed analysis of the effect of identified DNA variants on mRNA processing. By comparing the panel and whole-exome RNA enrichment, we demonstrated that the tissue-specific gene expression pattern is independent of the capture panel. Moreover, technical replicates confirmed high reproducibility of the tested RNA analysis. We concluded that parallel DNA/RNA NGS using the identical gene panel is a robust and cost-effective diagnostic strategy. In our setting, it allows routine analysis of 48 DNA/RNA pairs using NextSeq 500/550 Mid Output Kit v2.5 (150 cycles) in a single run with sufficient coverage to analyse 226 cancer predisposition and candidate ge-nes. This approach can replace laborious Sanger confirmatory sequencing, increase testing turnaround, reduce analysis costs, and improve interpretation of the impact of variants by analysing their effect on mRNA processing.

Identification of RVFV Host Factors Using Quantitative Interaction Proteomics.

Affinity enrichment coupled with liquid chromatography-tandem mass spectrometry (AE-LC-MS/MS) enables a comprehensive study of virus-host protein-protein interactions in cells and tissues infected with Rift Valley fever virus (RVFV) or ectopically expressing RVFV proteins. Depending on the research question, different experimental setups with carefully chosen controls are needed. Here, we describe the detailed workflow of sample preparation, processing, and cleanup, while also outlining critical points to consider when designing and performing AE-LC-MS/MS experiments.

QSPRmodeler - An open source application for molecular predictive analytics.

The drug design process can be successfully supported using a variety of in silico methods. Some of these are oriented toward molecular property prediction, which is a key step in the early drug discovery stage. Before experimental validation, drug candidates are usually compared with known experimental data. Technically, this can be achieved using machine learning approaches, in which selected experimental data are used to train the predictive models. The proposed Python software is designed for this purpose. It supports the entire workflow of molecular data processing, starting from raw data preparation followed by molecular descriptor creation and machine learning model training. The predictive capabilities of the resulting models were carefully validated internally and externally. These models can be easily applied to new compounds, including within more complex workflows involving generative approaches.

Full daily re-optimization improves plan quality during online adaptive radiotherapy

Background and purposeDaily online treatment plan adaptation requires a fast workflow and planning process. Current online planning consists of adaptation of a predefined reference plan, which might be suboptimal in cases of large anatomic changes. The aim of this study was to investigate plan quality differences between the current online re-planning approach and a complete re-optimization. Material and methodsMagnetic resonance linear accelerator reference plans for ten prostate cancer patients were automatically generated using particle swarm optimization (PSO). Adapted plans were created for each fraction using (1) the current re-planning approach and (2) full PSO re-optimization and evaluated overall compliance with institutional dose-volume criteria compared to (3) clinically delivered fractions. Relative volume differences between reference and daily anatomy were assessed for planning target volumes (PTV60, PTV57.6), rectum and bladder and correlated with dose-volume results. ResultsThe PSO approach showed significantly higher adherence to dose-volume criteria than the reference approach and clinical fractions (p < 0.001). In 74 % of PSO plans at most one criterion failed compared to 56 % in the reference approach and 41 % in clinical plans. A fair correlation between PTV60 D98% and relative bladder volume change was observed for the reference approach. Bladder volume reductions larger than 50 % compared to the reference plan recurrently decreased PTV60 D98% below 56 Gy. ConclusionComplete re-optimization maintained target coverage and organs at risk sparing even after large anatomic variations. Re-planning based on daily magnetic resonance imaging was sufficient for small variations, while large variations led to decreasing target coverage and organ-at-risk sparing.

Research on the application of machine vision in the design of machine automation

Abstract The application of machine vision technology to mechanical automation design is an inevitable measure to improve the comprehensive productivity of mechanical production manufacturing. In this paper, a mechanical automation system based on SOA architecture is designed to cover the image acquisition, processing, and recognition workflow of an image processing module based on machine vision. The binocular camera calibration is completed by the Zhang Zhengyou calibration method. The image is preprocessed by using algorithms such as the mean value method, segmented linear transformation and median filtering, and an algorithm is proposed to improve the ORB feature point extraction, which can extract and match the feature points quickly and efficiently. Meanwhile, a Harris corner point detection algorithm is proposed to improve the SIFT algorithm to enhance the accuracy of target recognition and localization. The designed mechanical automation system is applied to the lychee picking robot, for example, analysis, which shows that the overall recognition P-R value of the system reaches 0.953, and the classification accuracy is above 0.917. In the mechanical automation route localization, the lateral deviation is lower than 4.80cm. The maximum time for image processing and parameter transfer is 117.966ms, which indicates that the system involved in this paper is relatively stable in operation and has a better effect on the localization of the navigation line, which is of certain application value in the field of mechanical automation design.

A decade of technology advancement in seismic processing: A case study from reprocessing legacy sparse OBC data in Kashagan Field

Ceaseless advancements in seismic data processing technology and workflow enable the extraction of increasing amounts of subsurface information essential for derisking and optimizing natural resource development. The Kashagan oil field, one of the world's largest carbonate reservoirs, is faced with significant development optimization challenges due to a combination of complex geology and suboptimal seismic data coverage. The latest processing technologies were applied to the 2001–2002 vintage sparse ocean-bottom cable data and produced step-change improvements over the 2010–2011 legacy processing and imaging results. Such significant image uplift is representative of technology advancements over the span of a decade fully leveraging the newly preprocessed input data, including (1) an integrated model building workflow, (2) adaptive application of full-waveform inversion, (3) geologically constrained reflection tomography, and (4) least-squares imaging. The new seismic results improved the structural and stratigraphic imaging of multiple layers of shallow carbonates, mitigated fault shadow and other complex overburden effects, increased the resolution of subsalt carbonate reservoir features such as karst and internal fractures, improved well marker depth misfit, and ultimately influenced the placement of upcoming wells and reservoir development plans. Further improvements in seismic imaging would be feasible with a modern acquisition of more densely sampled seismic data, which would allow the full potential of the latest seismic processing technologies and workflows to be unlocked.