The Future of the Journal? Integrating research data with scientific discourse

To advance the pace of scientific discovery we propose a conceptual format that forms the basis of a truly new way of publishing science. In our proposal, all scientific communication objects (including experimental workflows, direct results, email conversations, and all drafted and published information artifacts) are labeled and stored in a great, big, distributed data store (or many distributed data stores that are all connected). Each item has a set of metadata attached to it, which includes (at least) the person and time it was created, the type of object it is, and the status of the object including intellectual property rights and ownership. Every researcher can (and must) deposit every knowledge item that is produced in the lab into this repository. With this deposition goes an essential metadata component that states who has the rights to see, use, distribute, buy or sell this item. Into this grand (and system-wise distributed, cloud-based) architecture, all items produced by a single lab, or several labs, are stored, labeled and connected

Contextualizing Citations for Scientific Summarization using Word Embeddings and Domain Knowledge

Citation texts are sometimes not very informative or in some cases inaccurate by themselves; they need the appropriate context from the referenced paper to reflect its exact contributions. To address this problem, we propose an unsupervised model that uses distributed representation of words as well as domain knowledge to extract the appropriate context from the reference paper. Evaluation results show the effectiveness of our model by significantly outperforming the state-of-the-art. We furthermore demonstrate how an effective contextualization method results in improving citation-based summarization of the scientific articles.Comment: SIGIR 201

How learning style affects evidence-based medicine:a survey study

BACKGROUND: Learning styles determine how people manage new information. Evidence-based medicine (EBM) involves the management of information in clinical practice. As a consequence, the way in which a person uses EBM can be related to his or her learning style. In order to tailor EBM education to the individual learner, this study aims to determine whether there is a relationship between an individual's learning style and EBM competence (knowledge/skills, attitude, behaviour). METHODS: In 2008, we conducted a survey among 140 novice GP trainees in order to assess their EBM competence and learning styles (Accommodator, Diverger, Assimilator, Converger, or mixed learning style). RESULTS: The trainees' EBM knowledge/skills (scale 0-15; mean 6.8; 95%CI 6.4-7.2) were adequate and their attitudes towards EBM (scale 0-100; mean 63; 95%CI 61.3-64.3) were positive. We found no relationship between their knowledge/skills or attitudes and their learning styles (p = 0.21; p = 0.19). Of the trainees, 40% used guidelines to answer clinical questions and 55% agreed that the use of guidelines is the most appropriate way of applying EBM in general practice. Trainees preferred using evidence from summaries to using evidence from single studies. There were no differences in medical decision-making or in EBM use (p = 0.59) for the various learning styles. However, we did find a link between having an Accommodating or Converging learning style and making greater use of intuition. Moreover, trainees with different learning styles expressed different ideas about the optimal use of EBM in primary care. CONCLUSIONS: We found that EBM knowledge/skills and EBM attitudes did not differ with respect to the learning styles of GP trainees. However, we did find differences relating to the use of intuition and the trainees' ideas regarding the use of evidence in decision-making

Data Discovery Paradigms: User Requirements and Recommendations for Data Repositories

As data repositories make more data openly available it becomes challenging for researchers to find what they need either from a repository or through web search engines. This study attempts to investigate data users’ requirements and the role that data repositories can play in supporting data discoverability by meeting those requirements. We collected 79 data discovery use cases (or data search scenarios), from which we derived nine functional requirements for data repositories through qualitative analysis. We then applied usability heuristic evaluation and expert review methods to identify best practices that data repositories can implement to meet each functional requirement. We propose the following ten recommendations for data repository operators to consider for improving data discoverability and user’s data search experience: 1. Provide a range of query interfaces to accommodate various data search behaviours. 2. Provide multiple access points to find data. 3. Make it easier for researchers to judge relevance, accessibility and reusability of a data collection from a search summary. 4. Make individual metadata records readable and analysable. 5. Enable sharing and downloading of bibliographic references. 6. Expose data usage statistics. 7. Strive for consistency with other repositories. 8. Identify and aggregate metadata records that describe the same data object. 9. Make metadata records easily indexed and searchable by major web search engines. 10. Follow API search standards and community adopted vocabularies for interoperability

Dynamic enhancement of drug product labels to support drug safety, efficacy, and effectiveness

Out-of-date or incomplete drug product labeling information may increase the risk of otherwise preventable adverse drug events. In recognition of these concerns, the United States Federal Drug Administration (FDA) requires drug product labels to include specific information. Unfortunately, several studies have found that drug product labeling fails to keep current with the scientific literature. We present a novel approach to addressing this issue. The primary goal of this novel approach is to better meet the information needs of persons who consult the drug product label for information on a drug’s efficacy, effectiveness, and safety. Using FDA product label regulations as a guide, the approach links drug claims present in drug information sources available on the Semantic Web with specific product label sections. Here we report on pilot work that establishes the baseline performance characteristics of a proof-of-concept system implementing the novel approach. Claims from three drug information sources were linked to the Clinical Studies, Drug Interactions, and Clinical Pharmacology sections of the labels for drug products that contain one of 29 psychotropic drugs. The resulting Linked Data set maps 409 efficacy/effectiveness study results, 784 drug-drug interactions, and 112 metabolic pathway assertions derived from three clinically-oriented drug information sources (ClinicalTrials.gov, the National Drug File – Reference Terminology, and the Drug Interaction Knowledge Base) to the sections of 1,102 product labels. Proof-of-concept web pages were created for all 1,102 drug product labels that demonstrate one possible approach to presenting information that dynamically enhances drug product labeling. We found that approximately one in five efficacy/effectiveness claims were relevant to the Clinical Studies section of a psychotropic drug product, with most relevant claims providing new information. We also identified several cases where all of the drug-drug interaction claims linked to the Drug Interactions section for a drug were potentially novel. The baseline performance characteristics of the proof-of-concept will enable further technical and user-centered research on robust methods for scaling the approach to the many thousands of product labels currently on the market

Software Citation Checklist for Developers

This document provides a minimal, generic checklist that developers of software (either open or closed source) used in research can use to ensure they are following good practice around software citation. This will help developers get credit for the software they create, and improve transparency, reproducibility, and reuse

Tissue-specific suppression of thyroid hormone signaling in various mouse models of aging

DNA damage contributes to the process of aging, as underscored by premature aging syndromes caused by defective DNA repair. Thyroid state changes during aging, but underlying mechanisms remain elusive. Since thyroid hormone (TH) is a key regulator of metabolism, changes in TH signaling have widespread effects. Here, we reveal a significant common transcriptomic signature in livers from hypothyroid mice, DNA repair-deficient mice with severe (Csbm/m/Xpa-/-) or intermediate (Ercc1-/Δ-7) progeria and naturally aged mice. A strong induction of TH-inactivating deiodinase D3 and decrease of TH-activating D1 activities are observed in Csbm/m/Xpa-/- livers. Similar findings are noticed in Ercc1-/Δ-7, in naturally aged animals and in wild-type mice exposed to a chronic subtoxic dose of DNAdamaging agents. In contrast, TH signaling in muscle, heart and brain appears unaltered. These data show a strong suppression of TH signaling in specific peripheral organs in premature and normal aging, probably lowering metabolism, while other tissues appear to preserve metabolism. D3-mediated TH inactivation is unexpected, given its expression mainly in fetal tissues. Our studies highlight the importance of DNA damage as the underlying mechanism of changes in thyroid state. Tissue-specific regulation of deiodinase activities, ensuring diminished TH signaling, may contribute importantly to the protective metabolic response in aging

COVID19 Disease Map, a computational knowledge repository of virus-host interaction mechanisms.

Funder: Bundesministerium für Bildung und ForschungFunder: Bundesministerium für Bildung und Forschung (BMBF)We need to effectively combine the knowledge from surging literature with complex datasets to propose mechanistic models of SARS-CoV-2 infection, improving data interpretation and predicting key targets of intervention. Here, we describe a large-scale community effort to build an open access, interoperable and computable repository of COVID-19 molecular mechanisms. The COVID-19 Disease Map (C19DMap) is a graphical, interactive representation of disease-relevant molecular mechanisms linking many knowledge sources. Notably, it is a computational resource for graph-based analyses and disease modelling. To this end, we established a framework of tools, platforms and guidelines necessary for a multifaceted community of biocurators, domain experts, bioinformaticians and computational biologists. The diagrams of the C19DMap, curated from the literature, are integrated with relevant interaction and text mining databases. We demonstrate the application of network analysis and modelling approaches by concrete examples to highlight new testable hypotheses. This framework helps to find signatures of SARS-CoV-2 predisposition, treatment response or prioritisation of drug candidates. Such an approach may help deal with new waves of COVID-19 or similar pandemics in the long-term perspective

Control of paratuberculosis: who, why and how. A review of 48 countries

Paratuberculosis, a chronic disease affecting ruminant livestock, is caused by Mycobacterium avium subsp. paratuberculosis (MAP). It has direct and indirect economic costs, impacts animal welfare and arouses public health concerns. In a survey of 48 countries we found paratuberculosis to be very common in livestock. In about half the countries more than 20% of herds and flocks were infected with MAP. Most countries had large ruminant populations (millions), several types of farmed ruminants, multiple husbandry systems and tens of thousands of individual farms, creating challenges for disease control. In addition, numerous species of free-living wildlife were infected. Paratuberculosis was notifiable in most countries, but formal control programs were present in only 22 countries. Generally, these were the more highly developed countries with advanced veterinary services. Of the countries without a formal control program for paratuberculosis, 76% were in South and Central America, Asia and Africa while 20% were in Europe. Control programs were justified most commonly on animal health grounds, but protecting market access and public health were other factors. Prevalence reduction was the major objective in most countries, but Norway and Sweden aimed to eradicate the disease, so surveillance and response were their major objectives. Government funding was involved in about two thirds of countries, but operations tended to be funded by farmers and their organizations and not by government alone. The majority of countries (60%) had voluntary control programs. Generally, programs were supported by incentives for joining, financial compensation and/or penalties for non-participation. Performance indicators, structure, leadership, practices and tools used in control programs are also presented. Securing funding for long-term control activities was a widespread problem. Control programs were reported to be successful in 16 (73%) of the 22 countries. Recommendations are made for future control programs, including a primary goal of establishing an international code for paratuberculosis, leading to universal acknowledgment of the principles and methods of control in relation to endemic and transboundary disease. An holistic approach across all ruminant livestock industries and long-term commitment is required for control of paratuberculosis