Is Biblioleaks inevitable?

Disease named entity recognition using long-short dependencies

The automatic extraction of disease named entity is a challenging research problem that has attracted attention from the biomedical text mining community. Handcrafted feature methods were employed for this task given a little success since they are limited by the scope of the expert. Lately, deep learning-based methods have been employed to solve this issue. However, most architectures used for this task take into consideration long dependencies only. The proposed method is a two-stage deep neural network model. We start by discovering local dependencies and creating high-level features from word embedding inputs using a deep convolutional neural network. Then we identify long dependencies using a bi-directional recurrent neural network. To solve the problem of unbalanced dataset given by the BMEWO tagging schema and to enforce sequence modeling, we developed a new POS-based tagging schema that subdivides the dominant class into smaller more balanced units. The proposed system was trained and tested on NCBI and achieved an [Formula: see text]-score of 85.59 outperforming the current state-of-the-art methods. Our research results show the effectiveness of using both long and short dependencies. The results also illustrate the benefits of combining different word embedding techniques and the incorporation of morphological features in this task.

Practical Evidence-Based Medicine at the Student-to-Physician Transition: Effectiveness of an Undergraduate Medical Education Capstone Course

INTRODUCTION

Medical information is expanding at exponential rates. Practicing physicians must acquire skills to efficiently navigate large bodies of evidence to answer clinical questions daily. How best to prepare medical students to meet this challenge remains unknown. The authors sought to design, implement, and assess a pragmatic evidence-based medicine (EBM) course engaging students at the transition from undergraduate to graduate medical education.

MATERIALS AND METHODS

An elective course was offered during the required 1-month Capstone medical school curriculum. Participants included one hundred sixty-eight graduating fourth-year medical students at Emory University School of Medicine who completed the course from 2012 to 2018. Through interactive didactics, small groups, and independent work, students actively employed various electronic tools to navigate medical literature and engaged in structured critical appraisal of guidelines and meta-analyses to answer clinical questions.

RESULTS

Assessment data was available for 161 of the 168 participants (95.8%). Pre- and post-assessments demonstrated students' significant improvement in perceived and demonstrated EBM knowledge and skills (p < 0.001), consistent across gender and specialty subgroups.

DISCUSSION

The Capstone EBM course empowered graduating medical students to comfortably navigate electronic medical resources and accurately appraise summary literature. The objective improvement in knowledge, the perceived improvement in skill, and the subjective comments support this curricular approach to effectively prepare graduating students for pragmatic practice-based learning as resident physicians.

Sci-Hub provides access to nearly all scholarly literature

The website Sci-Hub enables users to download PDF versions of scholarly articles, including many articles that are paywalled at their journal's site. Sci-Hub has grown rapidly since its creation in 2011, but the extent of its coverage has been unclear. Here we report that, as of March 2017, Sci-Hub's database contains 68.9% of the 81.6 million scholarly articles registered with Crossref and 85.1% of articles published in toll access journals. We find that coverage varies by discipline and publisher, and that Sci-Hub preferentially covers popular, paywalled content. For toll access articles, we find that Sci-Hub provides greater coverage than the University of Pennsylvania, a major research university in the United States. Green open access to toll access articles via licit services, on the other hand, remains quite limited. Our interactive browser at https://greenelab.github.io/scihub allows users to explore these findings in more detail. For the first time, nearly all scholarly literature is available gratis to anyone with an Internet connection, suggesting the toll access business model may become unsustainable.

The Future of CRISPR Applications in the Lab, the Clinic and Society

CRISPR (clustered regularly interspaced short palindromic repeats) has emerged as one of the premiere biological tools of the century. Even more so than older genome editing techniques such as TALENs and ZFNs, CRISPR provides speed and ease-of-use heretofore unheard of in agriculture, the environment and human health. The ability to map the function of virtually every component of the genome in a scalable, multiplexed manner is unprecedented. Once those regions have been explored, CRISPR also presents an opportunity to take advantage of endogenous cellular repair pathways to change and precisely edit the genome [1-3]. In the case of human health, CRISPR operates as both a tool of discovery and a solution to fundamental problems behind disease and undesirable mutations.

Erratum to: Should We be More "Open" About Publishing Research?

[This corrects the article DOI: 10.1007/s13224-014-0624-2.].

Automatic evidence retrieval for systematic reviews

Background

Snowballing involves recursively pursuing relevant references cited in the retrieved literature and adding them to the search results. Snowballing is an alternative approach to discover additional evidence that was not retrieved through conventional search. Snowballing’s effectiveness makes it best practice in systematic reviews despite being time-consuming and tedious.

Objective

Our goal was to evaluate an automatic method for citation snowballing’s capacity to identify and retrieve the full text and/or abstracts of cited articles.

Methods

Using 20 review articles that contained 949 citations to journal or conference articles, we manually searched Microsoft Academic Search (MAS) and identified 78.0% (740/949) of the cited articles that were present in the database. We compared the performance of the automatic citation snowballing method against the results of this manual search, measuring precision, recall, and F₁ score.

Results

The automatic method was able to correctly identify 633 (as proportion of included citations: recall=66.7%, F₁ score=79.3%; as proportion of citations in MAS: recall=85.5%, F₁ score=91.2%) of citations with high precision (97.7%), and retrieved the full text or abstract for 490 (recall=82.9%, precision=92.1%, F₁ score=87.3%) of the 633 correctly retrieved citations.

Conclusions

The proposed method for automatic citation snowballing is accurate and is capable of obtaining the full texts or abstracts for a substantial proportion of the scholarly citations in review articles. By automating the process of citation snowballing, it may be possible to reduce the time and effort of common evidence surveillance tasks such as keeping trial registries up to date and conducting systematic reviews.