Enhancing Reproducibility and Collaboration via Management of R Package Cohorts

Science depends on collaboration, result reproduction, and the development of supporting software tools. Each of these requires careful management of software versions. We present a unified model for installing, managing, and publishing software contexts in R. It introduces the package manifest as a central data structure for representing versionspecific, decentralized package cohorts. The manifest points to package sources on arbitrary hosts and in various forms, including tarballs and directories under version control. We provide a high-level interface for creating and switching between side-by-side package libraries derived from manifests. Finally, we extend package installation to support the retrieval of exact package versions as indicated by manifests, and to maintain provenance for installed packages. The provenance information enables the user to publish libraries or sessions as manifests, hence completing the loop between publication and deployment. We have implemented this model across three software packages, switchr, switchrGist and GRANBase, and have released the source code under the Artistic 2.0 license

Full Issue: Journal on Empowering Teaching Excellence, Volume 7, Issue 2, Fall 2023

The full-length Fall 2023 issue (Volume 7, Issue 2) of the Journal on Empowering Teaching Excellence Access the online Pressbooks version (with downloadable EPUB format) here. The Fall 2023 issue presents research and guidance on topics related to educational adaptation. The first article by C. Farrell describes an adaptation of the interteaching method to the hybrid delivery method. The second article by C. C. Loose and R. Jagielo-Manion describes a study of modules on personalized learning to preservice teachers and its impact on their comfort level and preparation to implement personalized learning in their classrooms. The third article by B. Bean presents a case study in which students in an introductory data science course are asked to complete a reproducible final project, with proposed adaptations for non-data-science courses. The fourth article by K. Klein et al. reports the results of a study evaluating the effectiveness of traditional and active lecture methods in higher education using a multiple group convergent parallel mixed method design. The final article by S. L. Brosi et al., provides a book review of Rural Education in America, What Works for Our Students, Teachers, and Communities, by G. Marietta and S. Marietta

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

Reproducible science: What, why, how

Most scientific papers are not reproducible: it is really hard, if not impossible, to understand how results are derived from data, and being able to regenerate them in the future (even by the same researchers). However, traceability and reproducibility of results are indispensable elements of highquality science, and an increasing requirement of many journals and funding sources. Reproducible studies include code able to regenerate results from the original data. This practice not only provides a perfect record of the whole analysis but also reduces the probability of errors and facilitates code reuse, thus accelerating scientific progress. But doing reproducible science also brings many benefits to the individual researcher, including saving time and effort, improved collaborations, and higher quality and impact of final publications. In this article we introduce reproducible science, why it is important, and how we can improve the reproducibility of our work. We introduce principles and tools for data management, analysis, version control, and software management that help us achieve reproducible workflows in the context of ecology.Peer Reviewe

Comprehensive Assessment of Incidence, Risk Factors, and Mechanisms of Impaired Medical and Psychosocial Health Outcomes among Adolescents and Young Adults with Cancer:Protocol of the Prospective Observational COMPRAYA Cohort Study

Simple Summary Adolescents and young adults (AYA), aged 18-39 years at first cancer diagnosis, are recognized as a distinct population within the oncology community due to the unique challenges they encounter including recognition, diagnosis, treatment, and monitoring of their disease. It is imperative for advances in the field of AYA oncology to pool data sources (patient-reported outcomes, clinical, treatment, genetic, and biological data) across institutions and countries and create large cohorts that include the full range of AYA ages and diagnoses to be able to address the many pressing questions that remain unanswered in this vulnerable population. The Dutch COMPRAYA study aims to examine the incidence, risk factors, and mechanisms of impaired health outcomes (short- and long-term medical and psychosocial effects) over time among AYA cancer patients. The overarching aim is to provide a research infrastructure for (future) data analyses and observational retrospective/prospective ancillary studies and to expand data collection to other countries. Adolescent and young adult (AYA) cancer patients suffer from delay in diagnosis, and lack of centralized cancer care, age-adjusted expertise, and follow-up care. This group presents with a unique spectrum of cancers, distinct tumor biology, cancer risk factors, developmental challenges, and treatment regimens that differ from children and older adults. It is imperative for advances in the field of AYA oncology to pool data sources across institutions and create large cohorts to address the many pressing questions that remain unanswered in this vulnerable population. We will create a nationwide infrastructure (COMPRAYA) for research into the incidence, predictive/prognostic markers, and underlying mechanisms of medical and psychosocial outcomes for AYA between 18-39 years diagnosed with cancer. A prospective, observational cohort of (n = 4000), will be established. Patients will be asked to (1) complete patient-reported outcome measures; (2) donate a blood, hair, and stool samples (to obtain biochemical, hormonal, and inflammation parameters, and germline DNA); (3) give consent for use of routinely archived tumor tissue and clinical data extraction from medical records and registries; (4) have a clinic visit to assess vital parameters. Systematic and comprehensive collection of patient and tumor characteristics of AYA will support the development of evidence-based AYA care programs and guidelines

A review of the changes to the licensing of influenza vaccines in Europe

In 2014, the European Committee for Medicinal Products for Human Use (CHMP) published a draft regulatory guideline for the evaluation of influenza vaccines. Following a public consultation round, the final guidance will be published in the near future. Here, we highlight the main changes in the clinical section in this guideline and discuss the background to these changes and whether the new consolidated guidance document can be expected to achieve a better understanding of the performance of seasonal, zoonotic and pandemic influenza vaccines during the regulatory licensing process. The new influenza guideline reflects a changed approach to the regulatory assessment of influenza vaccines, resulting in the abolition of serological criteria, known as the CHMP criteria, which have been the mainstay for evaluating the influenza vaccine immunogenicity for several decades. The new guideline adopts a more diversified approach to the measurement and reporting of the immune response to influenza vaccines and sets a requirement to conduct clinical outcome trials in young children. Importantly, more emphasis is placed on the post-licensure monitoring of the benefit risk of influenza vaccines, including a request for continuous monitoring of efficacy and enhanced safety surveillance. Despite the improvements these new requirements will expectedly bring to the regulatory assessment of influenza vaccines, major challenges remain which cannot be overcome by new guidance alone. Ongoing initiatives in which academia, manufacturers, public health institutes and regulators work together to address these challenges are central to the development of robust tools to evaluate and monitor performance of influenza vaccines in the future

Radiomic signatures of posterior fossa ependymoma: Molecular subgroups and risk profiles

BACKGROUND: The risk profile for posterior fossa ependymoma (EP) depends on surgical and molecular status [Group A (PFA) versus Group B (PFB)]. While subtotal tumor resection is known to confer worse prognosis, MRI-based EP risk-profiling is unexplored. We aimed to apply machine learning strategies to link MRI-based biomarkers of high-risk EP and also to distinguish PFA from PFB. METHODS: We extracted 1800 quantitative features from presurgical T2-weighted (T2-MRI) and gadolinium-enhanced T1-weighted (T1-MRI) imaging of 157 EP patients. We implemented nested cross-validation to identify features for risk score calculations and apply a Cox model for survival analysis. We conducted additional feature selection for PFA versus PFB and examined performance across three candidate classifiers. RESULTS: For all EP patients with GTR, we identified four T2-MRI-based features and stratified patients into high- and low-risk groups, with 5-year overall survival rates of 62% and 100%, respectively (p < 0.0001). Among presumed PFA patients with GTR, four T1-MRI and five T2-MRI features predicted divergence of high- and low-risk groups, with 5-year overall survival rates of 62.7% and 96.7%, respectively (p = 0.002). T1-MRI-based features showed the best performance distinguishing PFA from PFB with an AUC of 0.86. CONCLUSIONS: We present machine learning strategies to identify MRI phenotypes that distinguish PFA from PFB, as well as high- and low-risk PFA. We also describe quantitative image predictors of aggressive EP tumors that might assist risk-profiling after surgery. Future studies could examine translating radiomics as an adjunct to EP risk assessment when considering therapy strategies or trial candidacy

Current Applications and Future Development of Magnetic Resonance Fingerprinting in Diagnosis, Characterization, and Response Monitoring in Cancer

Magnetic resonance imaging (MRI) has enabled non-invasive cancer diagnosis, monitoring, and management in common clinical settings. However, inadequate quantitative analyses in MRI continue to limit its full potential and these often have an impact on clinicians' judgments. Magnetic resonance fingerprinting (MRF) has recently been introduced to acquire multiple quantitative parameters simultaneously in a reasonable timeframe. Initial retrospective studies have demonstrated the feasibility of using MRF for various cancer characterizations. Further trials with larger cohorts are still needed to explore the repeatability and reproducibility of the data acquired by MRF. At the moment, technical difficulties such as undesirable processing time or lack of motion robustness are limiting further implementations of MRF in clinical oncology. This review summarises the latest findings and technology developments for the use of MRF in cancer management and suggests possible future implications of MRF in characterizing tumour heterogeneity and response assessment