Scientific Software Development Research Articles

Full Native timsTOF PASEF-Enabled Quantitative Proteomics with the i2MassChroQ Software Package.

Ion mobility mass spectrometry has become popular in proteomics lately, in particular because the Bruker timsTOF instruments have found significant adoption in proteomics facilities. The Bruker's implementation of the ion mobility dimension generates massive amounts of mass spectrometric data that require carefully designed software both to extract meaningful information and to perform processing tasks at reasonable speed. In a historical move, the Bruker company decided to harness the skills of the scientific software development community by releasing to the public the timsTOF data file format specification. As a proteomics facility that has been developing Free Open Source Software (FOSS) solutions since decades, we took advantage of this opportunity to implement the very first FOSS proteomics complete solution to natively read the timsTOF data, low-level process them, and explore them in an integrated quantitative proteomics software environment. We dubbed our software i2MassChroQ because it implements a (peptide)identification-(protein)inference-mass-chromatogram-quantification processing workflow. The software benchmarking results reported in this paper show that i2MassChroQ performed better than competing software on two critical characteristics: (1) feature extraction capability and (2) protein quantitative dynamic range. Altogether, i2MassChroQ yielded better quantified protein numbers, both in a technical replicate MS runs setting and in a differential protein abundance analysis setting.

The tale of HORTON: Lessons learned in a decade of scientific software development.

HORTON is a free and open-source electronic-structure package written primarily in Python 3 with some underlying C++ components. While HORTON's development has been mainly directed by the research interests of its leading contributing groups, it is designed to be easily modified, extended, and used by other developers of quantum chemistry methods or post-processing techniques. Most importantly, HORTON adheres to modern principles of software development, including modularity, readability, flexibility, comprehensive documentation, automatic testing, version control, and quality-assurance protocols. This article explains how the principles and structure of HORTON have evolved since we started developing it more than a decade ago. We review the features and functionality of the latest HORTON release (version 2.3) and discuss how HORTON is evolving to support electronic structure theory research for the next decade.

Holistic Person-Centered Care in Radiotherapy: Protocol for a Scoping Review.

Several types of health care professionals are responsible for the care of patients with cancer throughout their engagement with the health care system. One such type is the radiotherapist. The radiotherapist not only administers treatment but is also directly involved with the patient during treatment. Despite this direct contact with the patient, the narrative tends to focus more on technical tasks than the actual patient. This task-focused interaction is often due to the highly sophisticated equipment and complex radiotherapy treatment processes involved. This often results in not meeting the psychosocial needs of the patient, and patients have acknowledged noncompliance and delayed treatment as a result. The scoping review aims to explore, chart, and map the available literature on holistic person-centered care in radiotherapy and to identify and present key concepts, definitions, methodologies, knowledge gaps, and evidence related to holistic person-centered care in radiotherapy. This protocol was developed using previously described methodological frameworks for scoping studies. The review will include both peer-reviewed and gray literature regarding holistic, person-centered care in radiotherapy. A comprehensive search strategy has been developed for MEDLINE (Ovid), which will be translated into the other included databases: Scopus, CINAHL (EBSCO), MEDLINE (PubMed), Embase (Elsevier), Cochrane Library, and the Directory of Open Access Journals. Gray literature searching will include Google (Google Books and Google Scholar), ProQuest, the WorldWideScience website, the OpenGrey website, and various university dissertation and thesis repositories. The title and abstract screening, full-text review, and relevant data extraction will be performed independently by all 3 reviewers using the Covidence (Veritas Health Innovation) software, which will also be used to guide the resolution of conflicts. Sources selected will be imported into ATLAS.ti (ATLAS.ti Scientific Software Development GmbH) for analysis, which will consist of content analysis, narrative analysis, and descriptive synthesis. Results will be presented using narrative, diagrammatic, and tabular formats. The review is expected to identify research gaps that will inform current and future holistic, person-centered care in radiotherapy. The review commenced in November 2023, and the formal literature search was completed by the end of February 2024. Final results are expected to be published in a peer-reviewed journal by 2025. The findings of this review are expected to provide a wide variety of strategies aimed at providing holistic, person-centered care in radiotherapy, as well as to identify some gaps in the literature. These findings will be used to inform future studies aimed at designing, developing, evaluating, and implementing strategies toward improved holistic, person-centered care in radiotherapy. DERR1-10.2196/51338.

PyRTlib: an educational Python-based library for non-scattering atmospheric microwave radiative transfer computations

Abstract. This article introduces PyRTlib, a new standalone Python package for non-scattering line-by-line microwave radiative transfer simulations. PyRTlib is a flexible and user-friendly tool for computing down- and upwelling brightness temperatures and related quantities (e.g., atmospheric absorption, optical depth, opacity, mean radiating temperature) written in Python, a language commonly used nowadays for scientific software development, especially by students and early-career scientists. PyRTlib allows for simulating observations from ground-based, airborne, and satellite microwave sensors in clear-sky and in cloudy conditions (under non-scattering Rayleigh approximation). The intention for PyRTlib is not to be a competitor to state-of-the-art atmospheric radiative transfer codes that excel in speed and/or versatility (e.g., ARTS, Atmospheric Radiative Transfer Simulator; RTTOV, Radiative Transfer for TOVS (Television Infrared Observation Satellite (TIROS) Operational Vertical Sounder)). The intention is to provide an educational tool, completely written in Python, to readily simulate atmospheric microwave radiative transfer from a variety of input profiles, including predefined climatologies, global radiosonde archives, and model reanalysis. The paper presents quick examples for the built-in modules to access popular open data archives. The paper also presents examples for computing the simulated brightness temperature for different platforms (ground-based, airborne, and satellite), using various input profiles, showing how to easily modify other relevant parameters, such as the observing angle (zenith, nadir, slant), surface emissivity, and gas absorption model. PyRTlib can be easily embedded in other Python codes needing atmospheric microwave radiative transfer (e.g., surface emissivity models and retrievals). Despite its simplicity, PyRTlib can be readily used to produce present-day scientific results, as demonstrated by two examples showing (i) an absorption model comparison and validation with ground-based radiometric observations and (ii) uncertainty propagation of spectroscopic parameters through the radiative transfer calculations following a rigorous approach. To our knowledge, the uncertainty estimate is not provided by any other currently available microwave radiative transfer code, making PyRTlib unique for this aspect in the atmospheric microwave radiative transfer code scenario.

Assessing Biomedical and Psychosocial Factors in a Cross-Sectional Study of School Dropouts Among Pregnant Adolescents and Young Mothers in Quito, Ecuador.

Over nearly three decades, Ecuador experienced a significant rise in adolescent motherhood. By focusing on social, health, and psychological aspects, the research aims to reveal the complex factors influencing the decision to discontinue education. The emphasis on providing a platform for direct expression of personal experiences not only adds qualitative depth to the study but also ensures that the voices of those involved are heard authentically. Employing a nonexperimental, descriptive, cross-sectional approach with qualitative and quantitative methods, the research delves into the interplay of biological, psychological, and social factors. Descriptive statistics, presented through tables and graphs, were used for variable analysis, complemented by inferential statistics to validate hypotheses. Focus group sessions, processed with ATLAS.ti(ATLAS.ti Scientific Software Development GmbH, Berlin, Germany) underwent a thorough review in workshops with Servicio de Atención Integral para Adolescentes(SAIA) experts. Adolescent participants were randomly recruited through the hospital's system. The findings unveiled a significant dropout rate among adolescents, where pregnancy was just one factor influencing their decision. Those discontinuing education often embraced a life project centered on motherhood and domestic roles, facing domestic violence and mental health disorders. In contrast, those persisting with education were driven by professional development, facing challenges but benefiting from family support. Despite unwanted pregnancies and low contraceptive use, many found personal growth and identity affirmation in motherhood. Our research highlights key insights into factors like pregnancy desire, contraception, reactions, and challenges. Urgent action is needed to address systemic problems and provide holistic support, acknowledging the resilience and validity of choices made by adolescent mothers in balancing motherhood with education and career goals.

PatentInspector: An Open-Source Tool for Applied Patent Analysis and Information Extraction

Patent analysis is a field that concerns the analysis of patent records, for the purpose of extracting insights and trends, and it is widely used in various fields. Despite the abundance of proprietary software employed for this purpose, there is currently a lack of easy-to-use and publicly available software that can offer simple and intuitive visualizations, while advocating for open science and scientific software development. In this study, we attempt to fill this gap by offering PatentInspector, an open-source, public tool that, by leveraging patent data from the United States Trademark and Patent Office, is able to produce descriptive analytics, thematic axes and citation network analysis. The use and interpretability of PatentInspector is illustrated through a use case on human resource management-related patents, highlighting its functionalities. The results indicate that PatentInspector is a practical resource for conducting patent analytics and can be used by individuals with a limited or no background in coding and software development.

Assumptions, Perceptions, and Experiences of Behavioral Health Providers Using Telemedicine: Qualitative Study.

The urgent and reactive implementation of telemedicine during the pandemic does not represent a long-term, strategic, and proactive approach to optimizing this technology. The assumptions, perceptions, and experiences of the behavioral health providers using telemedicine can inform system-wide and institutional-level strategies to promote longitudinal maintenance of care delivery, which can reduce the use of high-cost care due to new symptom onset and symptom exacerbation related to service interruptions. We aim to identify the assumptions, perspectives, and experiences of behavioral health clinicians and providers using telemedicine to inform the development of an optimized, sustainable approach to telemedicine implementation. This qualitative study applies the domains of the Consolidated Framework for Implementation Research (CFIR) to structure data collection and analysis from behavioral health providers using telemedicine via an audiovisual connection in the New England region. In total, 12 providers across levels of care were recruited for a 60-minute interview, developed from the CFIR interview guide. Atlas Ti Qualitative Software (version 23; ATLAS.ti Scientific Software Development GmbH) was used to coordinate and facilitate coding among 3 reviewers. Deductive coding was provided from the CFIR interview guide, allowing for data to be categorized by domain and construct. Constructs were analyzed for descriptive themes and tabulated for response frequency. Uncoded data were reviewed and coded in vivo to explore variables contributing to participant perceptions of experience with telemedicine use. Descriptive themes, then analytical themes, were identified. Analytical themes and tabulated frequency of response data were summarized. Finally, a sentiment analysis was completed to derive tone and meaning from the data. Results are reported within the CFIR domains: intervention characteristic, outer setting, inner setting, characteristics of individuals, and process. The findings with ≥90% agreement include "best practice standards were not known"; "telemedicine was believed to be efficient and time-saving for the patient and provider, maximizing productivity and thus increasing access to care"; "telemedicine provided an additional option for patients to access services, promoting sustained continuity and timeliness of care"; "participants did not identify any clear goals related to telemedicine use"; "demonstrated positive affective responses to telemedicine use"; "expressed high efficacy with telemedicine utilization"; and "strong leadership support." These findings support the development of interstate compacts advancing licensure across state lines; payment parity across modalities of care to ensure the financial vitality of behavioral health services; improved dissemination of telehealth training and resources, and telehealth training in academic programs of the health professions; seamless, dynamic workflows to accommodate the changing needs of patient and care continuity; emergency response protocols; and community partnerships to provide private spaces needed for a therapeutic encounter. Future research exploring the patient's experience with telemedicine is needed for all stakeholders to be represented in developing a sustainable, integrated system.

A Unified Framework for Manipulating N-dimensional Astronomical Data and Coordinate Transformations in Python: The NDCube 2 and Astropy APE-14 World Coordinate System APIs

The NDCube 2 API is a Python application programming interface (API) for storing and manipulating N-dimensional coordinate-aware astronomical data. While there are Python packages for handling astronomical data and coordinate transformations separately and for handling specific combinations of dimensions and transformations, none provide a unified and agnostic way of handling them simultaneously. This leads to a proliferation of different APIs for conducting the same analysis tasks on similar types of observations and introduces technical barriers between multi-instrument studies and cross-community collaboration. In this paper, we outline how the NDCube 2 API and its implementation in the open-source, community-developed ndcube package, together with the AstroPy WCS API, help to solve this problem. We discuss the guiding principles underpinning the API design and provide examples of how it is already being used to serve broad sections of the astronomy community, including agency-funded missions. The aim of this paper is to help users better understand the purpose and potential of the NDCube 2 API and ndcube package and hence how to more effectively deploy them in scientific analyses and software development.

Redesigning Graphical User Interface of Open-Source Geospatial Software in a Community-Driven Way: A Case Study of GRASS GIS

Learning to use geographic information system (GIS) software effectively may be intimidating due to the extensive range of features it offers. The GRASS GIS software, in particular, presents additional challenges for first-time users in terms of its complex startup procedure and unique terminology associated with its data structure. On the other hand, a substantial part of the GRASS user community including us as developers recognized and embraced the advantages of the current approach. Given the controversial nature of the whole issue, we decided to actively involve regular users by conducting several formal surveys and by performing usability testing. Throughout this process, we discovered that resolving specific software issues through pure user-centered design is not always feasible, particularly in the context of open-source scientific software where the boundary between users and developers is very fuzzy. To address this challenge, we adopted the user-centered methodology tailored to the requirements of open-source scientific software development, which we refer to as community-driven design. This paper describes the community-driven redesigning process on the GRASS GIS case study and sets a foundation for applying community-driven design in other open-source scientific projects by providing insights into effective software development practices driven by the needs and input of the project’s community.

Patients' Experiences Using a Mobile Health App for Self-Care of Heart Failure in a Real-World Setting: Qualitative Analysis.

Publicly available patient-focused mobile health (mHealth) apps are being increasingly integrated into routine heart failure (HF)-related self-care. However, there is a dearth of research on patients' experiences using mHealth apps for self-care in real-world settings. The purpose of this study was to explore patients' experiences using a commercially available mHealth app, OnTrack to Health, for HF self-care in a real-world setting. Patient satisfaction, measured with a 5-point Likert scale, and an open-ended survey were used to gather data from 23 patients with HF who were provided the OnTrack to Health app as a part of routine HF management. A content analysis of patients' responses was conducted with the qualitative software Atlas.ti (version 8; ATLAS.ti Scientific Software Development GmbH). Patients (median age 64, IQR 57-71 years; 17/23, 74% male) used OnTrack to Health for a median 164 (IQR 51-640) days before the survey. All patients reported excellent experiences related to app use and would recommend the app to other patients with HF. Five themes emerged from the responses to the open-ended questions: (1) features that enhanced self-care of HF (medication tracker, graphic performance feedback and automated alerts, secured messaging features, and HF self-care education); (2) perceived benefits (provided assurance of safety, improved HF self-care, and decreased hospitalization rates); (3) challenges with using apps for self-care (giving up previous self-care strategies); (4) facilitators (perceived ease of use and availability of technical support); and (5) suggested improvements (streamlining data entry, integration of apps with an electronic medical record, and personalization of app features). Patients were satisfied with using OnTrack to Health for self-care. They perceived the features of the app as valuable tools for improving self-care ability and decreasing hospitalization rates. The development of apps in collaboration with end users is essential to ensure high-quality patient experiences related to app use for self-care.

Underestimated Factors Regarding the Use of Technology in Daily Practice of Long-Term Care: Qualitative Study Among Health Care Professionals.

Increasing life expectancy is resulting in a growing demand for long-term care; however, there is a shortage of qualified health care professionals (HCPs) to deliver it. If used optimally, technology can provide a solution to this challenge. HCPs play an important role in the use of technology in long-term care. However, technology influences several core aspects of the work that HCPs do, and it is therefore important to have a good understanding of their viewpoint regarding the use of technology in daily practice of long-term care. The aim of this study was to identify the factors that HCPs consider as relevant for using technology in daily practice of long-term care. In this qualitative study, 11 focus groups were organized with 73 HCPs. The focus group discussions were guided by an innovative game, which was specifically developed for this study. The content of the game was categorized into 4 categories: health care technology and me; health care technology, the patient, and me; health care technology, the organization, and me; and facilitating conditions. The perspectives of HCPs about working with technology were discussed based on this game. The focus groups were recorded and transcribed, followed by an inductive thematic analysis using ATLAS.ti 9x (ATLAS.ti Scientific Software Development GmbH). Overall, 2 main domain summaries were developed from the data: technology should improve the quality of care and acceptance and use of technology in care. The first factor indicates the need for tailored and personalized care and balance between human contact and technology. The second factor addresses several aspects regarding working with technology such as trusting technology, learning to work with technology, and collaboration with colleagues. HCPs are motivated to use technology in daily practice of long-term care when it adds value to the quality of care and there is sufficient trust, expertise, and collaboration with colleagues. Their perspectives need to be considered as they play a crucial part in the successful use of technology, transcending their role as an actor in implementation. On the basis of the findings from this study, we recommend focusing on developing technology for situations where both efficiency and quality of care can be improved; redefining the roles of HCPs and the impact of technology hereon; involving HCPs in the design process of technology to enable them to link it to their daily practice; and creating ambassadors in care teams who are enthusiastic about working with technology and can support and train their colleagues.

Intergenerational Reminiscence Approach in Improving Emotional Well-Being of Older Asian Americans in Early-Stage Dementia Using Virtual Reality: Protocol for an Explanatory Sequential Mixed Methods Study.

After a dementia diagnosis, Asian Americans experience anxiety, feelings of shame, and other negative effects. Emotional well-being is not only an important aspect of mental health, but also a quality of resilience that helps people bounce back faster from difficulties. However, few studies have addressed issues in developing, implementing, and testing intervention strategies to promote emotional well-being among older adults. Intergenerational solidarity between grandparents and grandchildren has been emphasized in Asian families and is beneficial for the health of persons with dementia. Reminiscence and life review have been identified as potentially effective intervention strategies for helping depression and emotional well-being for older adults. This proposed study aims to develop and implement an intergenerational reminiscence approach and evaluate its potential feasibility and effectiveness in improving the emotional well-being of older Asian American adults who have a recent dementia diagnosis. An explanatory sequential mixed methods design will be used in which quantitative data will first be collected and analyzed to identify subsamples of participants who report the greatest and least change in emotional well-being; then, these subsamples will be interviewed to further understand why or why not this intervention works for them. Older adults will receive 6 sessions of life review with grandchildren in virtual reality (VR; 1-1.5 hours each week for 6 weeks), aided by pictures and virtually traveling to important places in their life using Google Earth to look around at those places and remember important times. Quantitative survey data will be collected pre- and postintervention and at a 3-month follow-up. Qualitative interviews with selected participants will also be integrated into the study design. The quantitative data from the surveys will be entered into SPSS (IBM Corp) and analyzed using descriptive analyses, Pearson chi-square tests, nonparametric Friedman tests, or nonparametric Wilcox signed-rank tests (2-tailed). The qualitative data will be transcribed by research assistants, coded by the investigators independently, and analyzed with guidance from content analysis software (Atlas.ti; Atlas.ti Scientific Software Development GmbH). The project was delayed due to the COVID-19 pandemic. Data collection started in late 2021, and 26 participants were recruited as of December 2022. While we are still cleaning and analyzing the quantitative data, the qualitative interviews showed promising results of this intergenerational reminiscence approach in improving emotional well-being among older Asian American adults who have cognitive impairment. Intergenerational reminiscence provided by grandchildren is promising in improving the emotional well-being of grandparents. VR technology is likely to be accepted by older adults. Future research may consider scaling up this pilot into a trackable, replicable model that includes more participants and develops a more rigorous study design with control groups to test the effectiveness of this intervention for older adults with dementia. DERR1-10.2196/48927.

A Software Engineering Competency Inventory for Scientific Software Development Processes

Araştırma amaçlı bilimsel yazılım geliştirme süreçleri, yazılım yaşam döngüsü açısından hem süreç hem girdi ve çıktılar bakımından iş veya endüstri amaçlı yazılımlardan bazı farklılıklar göstermektedir. Bilimsel yazılım geliştiricilerin, kod yazma deneyimi olsa da yapısal ve güncel yazılım mühendisliği yetkinliklerinin olmama olasılığı, bilimsel yazılımların gereksinimleri karşılaması ve sürdürülebilirliği açısından sorun yaratabilmektedir. Bu çalışmada literatürde bilimsel yazılım geliştirme alanında gereksinim duyulduğu belirtilen pratikler ile temelde IEEE Yazılım Mühendisliği Yetkinlik Modeli’nin (IEEE’s Software Engineering Competency Model -SWECOM) yetkinlikleri eşleştirilmiş; ve bilimsel yazılım geliştirme açısından en gereksinim duyulan yazılım mühendisliği pratikleri belirlenmiştir. Yapılan sıklık analiziyle özellikle yazılım tasarımı ve yapımı sırasındaki detaylı tasarım ve planlama yetkinliklerine ihtiyaç duyulduğu belirlenmiştir. Üretilen envanter, Ar-Ge destekleri çerçevesinde eğitim programları geliştirmek ve iyileştirmek için kullanılabilecektir.

Acceptability of an mHealth App for Monitoring Perinatal and Postpartum Mental Health: Qualitative Study With Women and Providers.

Up to 15% of pregnant and postpartum women commonly experience undiagnosed and untreated mental health conditions, such as depression and anxiety, which may result in serious health complications. Mobile health (mHealth) apps related to mental health have been previously used for early diagnosis and intervention but not among pregnant and postpartum women. This study aims to assess the acceptability of using mHealth to monitor and assess perinatal and postpartum depression and anxiety. Focus group discussions with pregnant and postpartum women (n=20) and individual interviews with health care providers (n=8) were conducted to inform the acceptability of mHealth and determine its utility for assessing perinatal and postpartum mood symptoms. Participants were recruited via purposive sampling from obstetric clinics and the surrounding community. A semistructured interview guide was developed by an epidemiologist with qualitative research training in consultation with an obstetrician. The first author conducted all focus group discussions and provider interviews either in person or via Zoom (Zoom Video Communications, Inc) depending on the COVID-19 protocol that was in place during the study period. All interviews were audio recorded with consent; transcribed; and uploaded for coding to ATLAS.ti 8 (ATLAS.ti Scientific Software Development Gmb H), a qualitative data analysis and retrieval software. Data were analyzed using the deductive content analysis method using a set of a priori codes developed based on the interview guide. Methodological rigor and quality were ensured by adopting a systematic approach during the implementation, data collection, data analysis, and reporting of the data. Almost all women and providers had downloaded and used at least 1 health app. The respondents suggested offering short questions in layperson language that could be understood by women of all educational levels and offering no more than 2 to 3 assessments per day at preferred timings decided by the women themselves. They also suggested that the women themselves receive the alerts first, with other options being family members, spouses, or friends if the women themselves did not respond within 24 to 72 hours. Customization and snooze features were strongly endorsed by women and providers to improve acceptability and utility. Women mentioned competing demands on their time during the postpartum period, fatigue, privacy, and the security of mental health data as concerns. Health care professionals highlighted the long-term sustainability of app-based mood assessment and monitoring as an important challenge. The findings from this study show that mHealth would be acceptable to pregnant and postpartum women for monitoring mood symptoms. This could inform the development of clinically meaningful and inexpensive tools for facilitating the continuous monitoring of, the early diagnosis of, and an early intervention for mood disorders in this vulnerable population.

Centering Frontline Health Care Workers in Digital Innovation Design to Inform the Optimization of an App for Improved Male Circumcision Follow-up in South Africa: Qualitative Usability Study.

Voluntary medical male circumcision (VMMC) is a safe and effective HIV prevention strategy. However, adherence to recommended in-person, postoperative follow-up is inefficient for procedures with few adverse events. Two-way texting (2wT)-based follow-up appears to be a safe and an efficient alternative to scheduled clinic visits for low-risk patients who underwent VMMC. To ensure that 2wT responds to the needs of health care workers (HCWs) and patients, HCWs were closely involved in app design using a human-centered design (HCD) approach. Embedded within an ongoing randomized controlled trial of 2wT in South Africa and complementary HCD processes of 2wT app optimization, this qualitative study aimed to use key informant interviews (KIIs) to explore the thoughts, suggestions, and opinions on and perceptions of 2wT's usability and acceptability among HCWs involved in 2wT implementation in both urban and rural South Africa. A total of 7 HCWs using 2wT in Gauteng and the North West province participated in KIIs regarding the usability and acceptability of 2wT. HCWs were asked for their opinions on 2wT as a viable, useful, appropriate, and accessible method of postoperative VMMC care. They were also asked about 2wT-related working, exploring areas where 2wT could add to or reduce their daily tasks. The KII data were explored, coded, and analyzed by 3 qualitative researchers using thematic content analysis and the ATLAS.ti (ATLAS.ti Scientific Software Development GmbH) software. Most HCWs felt confident, comfortable, satisfied, and well supported using a 2wT-based follow-up as an alternative to in-person clinical reviews. They felt that 2wT was easy to use and required little technical support after initial mentoring on how to use the 2wT system. Few noted safety concerns, as men can receive clinical guidance, reassurance, and referral via 2wT. Although fewer in-person visits reduced the in-person review workload and eased clinical flow, HCWs noted the added burden of having to interact with clients via SMS text messages on evenings or weekends. HCWs reinforced the need for enhanced postoperative counseling to ensure that 2wT patients could recognize and understood how to respond to early signs of complications. HCWs suggested a rotation to spread the evening and weekend workload and ensure swift patient responses. In this formative qualitative study focused on HCWs, 2wT was a highly usable alternative to in-person postoperative reviews for patients who underwent VMMC in South Africa. The HCD processes likely improved the usability and acceptability of 2wT for HCWs. HCWs supported the scale-up of 2wT given the distance from the clinic to the men's homes and the potential for reducing workload. To ensure success, providers urged sensitizing patients to the fact that 2wT augments, but does not replace, the existing after-hours and emergency care services.

Synthetic biology open language (SBOL) version 3.1.0

Abstract Synthetic biology builds upon genetics, molecular biology, and metabolic engineering by applying engineering principles to the design of biological systems. When designing a synthetic system, synthetic biologists need to exchange information about multiple types of molecules, the intended behavior of the system, and actual experimental measurements. The Synthetic Biology Open Language (SBOL) has been developed as a standard to support the specification and exchange of biological design information in synthetic biology, following an open community process involving both bench scientists and scientific modelers and software developers, across academia, industry, and other institutions. This document describes SBOL 3.1.0, which improves on version 3.0.0 by including a number of corrections and clarifications as well as several other updates and enhancements. First, this version includes a complete set of validation rules for checking whether documents are valid SBOL 3. Second, the best practices section has been moved to an online repository that allows for more rapid and interactive of sharing these conventions. Third, it includes updates based upon six community approved enhancement proposals. Two enhancement proposals are related to the representation of an object’s namespace. In particular, the Namespace class has been removed and replaced with a namespace property on each class. Another enhancement is the generalization of the CombinatorialDeriviation class to allow direct use of Features and Measures. Next, the Participation class now allow Interactions to be participants to describe higher-order interactions. Another change is the use of Sequence Ontology terms for Feature orientation. Finally, this version of SBOL has generalized from using Unique Reference Identifiers (URIs) to Internationalized Resource Identifiers (IRIs) to support international character sets.

The State of Trajectory Visualization in Notebook Environments

Gaining insights from trajectory datasets is a challenging task that requires suitable visual data representations. There is a considerable gap between the state-of-the-art cartographic techniques presented in the literature and currently available spatial data science toolboxes. This review paper presents the current state of geospatial visualization tools for trajectory data, focusing on the Python and Jupyter notebooks ecosystem. The shortcomings identified provide pointers for further scientific software development, as well as a reference for data scientists in choosing the best-fitting tool for a specific job.

Methodological Frameworks and Dimensions to Be Taken Into Consideration in Digital Health Technology Assessment: Protocol for a Scoping Review.

BackgroundHealth technology assessment (HTA) is one of the main tools that health systems have to appraise evidence and determine the value of a given health technology. Although the existing HTA frameworks are useful tools for the evaluation of a wide range of health technologies, more and more experts, organizations across the world, and HTA agencies are highlighting the need to update or develop specific methodological frameworks for the evaluation of digital health technologies in order to take into account additional domains that cover these technologies’ intrinsic characteristics.ObjectiveThe purpose of our scoping review is to identify the methodological frameworks that are used worldwide for the assessment of digital health technologies; determine what dimensions and aspects are being considered; and generate, through a thematic analysis, a proposal for a methodological framework that is based on the most frequently described dimensions in the literature.MethodsThe scoping review will be performed in accordance with the guidelines established in the updated statement of the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). We will search for peer-reviewed and grey literature published between 2011 and the date of the search execution. The retrieved references will be reviewed in a single-blind manner by 2 independent authors, and their quality will be assessed by using the Critical Appraisal Skills Program tool. The ATLAS.ti software (Scientific Software Development GmbH) will be used for data extraction and to perform the thematic analysis.ResultsThe scoping review is currently (May 2022) in progress. It is expected to be completed in October 2022, and the final results of the research will be presented and published by November 2022.ConclusionsTo our knowledge, no studies have been published to date that identify the existing methodological frameworks for digital HTA, determine which dimensions must be evaluated for correct decision-making, and serve as a basis for the development of a methodological framework of reference that health care systems can use to carry out this kind of assessment. This work is intended to address this knowledge gap of key relevance for the field of HTA.International Registered Report Identifier (IRRID)DERR1-10.2196/39905

Barriers to Care for Nursing Home Residents With Substance Use Disorders: A Qualitative Study.

Over the past decade, the numbers of older adults with opioid and substance use disorders (OUD/SUD) have increased. As this population enters nursing homes (NHs) in increasing numbers, it is crucial to consider their capacity to manage issues related to OUD/SUD. This study aimed to examine current NH protocols for care coordination of residents with OUD/SUD as well as facility-related barriers to providing care to this vulnerable population within the NH. Twenty-four semistructured interviews were conducted with NH staff including directors of nursing, administrators, nurses, and physicians in July 2020. Staff were recruited from 11 different post-acute care and long-term care facilities located in urban and suburban communities of Chicago. Interviews were conducted virtually (via teleconference platform or by telephone) and subsequently coded using ATLAS.ti 8 (ATLAS.ti Scientific Software Development GmbH, Berlin, Germany) using constant comparative method. Qualitative analyses identified 3 themes around NH barriers to care for residents with SUD/OUD: (1) staff preparedness, (2) staff perceptions of addiction, and (3) overall lack of resources. Results revealed a strong need for the development of consistent policies, as well as standardized, educational interventions for NH staff that target SUD/OUD management in this vulnerable population. The evaluation and impact of persons with SUD/OUD entering NHs are an important topic that requires further study. More resources and staff training are necessary to ensure that residents with SUD/OUD have access to appropriate care within these settings.

Self-admitted technical debt in R: detection and causes

Self-Admitted Technical Debt (SATD) is primarily studied in Object-Oriented (OO) languages and traditionally commercial software. However, scientific software coded in dynamically-typed languages such as R differs in paradigm, and the source code comments’ semantics are different (i.e., more aligned with algorithms and statistics when compared to traditional software). Additionally, many Software Engineering topics are understudied in scientific software development, with SATD detection remaining a challenge for this domain. This gap adds complexity since prior works determined SATD in scientific software does not adjust to many of the keywords identified for OO SATD, possibly hindering its automated detection. Therefore, we investigated how classification models (traditional machine learning, deep neural networks, and deep neural Pre-Trained Language Models (PTMs)) automatically detect SATD in R packages. This study aims to study the capabilities of these models to classify different TD types in this domain and manually analyze the causes of each in a representative sample. Our results show that PTMs (i.e., RoBERTa) outperform other models and work well when the number of comments labelled as a particular SATD type has low occurrences. We also found that some SATD types are more challenging to detect. We manually identified sixteen causes, including eight new causes detected by our study. The most common cause was failure to remember, in agreement with previous studies. These findings will help the R package authors automatically identify SATD in their source code and improve their code quality. In the future, checklists for R developers can also be developed by scientific communities such as rOpenSci to guarantee a higher quality of packages before submission.