Exploring the Influence of Trust in the Development of Transactive Memory Systems in Virtual Project Teams

Virtual project teams fulfill business strategies and realize business objectives in a competitive business environment. However, these teams work together over a limited period of time across geographical distances and varied time zones, which pose risks on the team’s performance as knowledge that is necessary to accomplish crucial project tasks may be impeded. Through information technology, these teams are able to communicate and develop their respective transactive memory systems—a concept known to help teams pool together a collective working knowledge to improve team performance. In addition, trust among team members plays an equally crucial role in the development of transactive memory systems. By presenting a conceptual model and a set of propositions, this study explores the interrelationships between trust, transactive memory systems, information technology and their consequential impacts to team performance in a virtual project context

Onto Collab: Strategic review oriented collaborative knowledge modeling using ontologies

Modeling efficient knowledge bases for improving the semantic property of the World Wide Web is mandatory for promoting innovations and developments in World Wide Web. There is a need for efficient and organized modeling of the knowledge bases. In this paper, a strategy Onto Collab is proposed for construction of knowledge bases using ontology modeling. Ontologies are visualized as the basic building blocks of the knowledge in the web. The cognitive bridge between the human conceptual understanding of real world data and the processable data by computing systems is represented by Ontologies. A domain is visualized as a collection of similar ontologies. A review based strategy is proposed over a secure messaging system to author ontologies and a platform for retracing the domain ontologies as individuals and as a team is proposed. Evaluations for ontologies constructed pertaining to a domain for non-wiki knowledge bases is carried out

Pathophysiological and pharmacological modulation of melatonergic system

Pineal gland once considered as rudimentary or vestigial, has become a principal endocrine gland that regulates the body’s internal environment, after the discovery of melatonin - a hormone produced by it. Melatonin is also synthesized from extra-pineal sites such as retina, skin, platelets, bone marrow, and gastrointestinal tract. The chronobiological property of this hormone in maintaining the circadian rhythm by synchronizing with the dark-light cycle is well-established. Melatonin also possesses anti-inflammatory, anti-depressant, anti-oxidant, oncostatic, immunomodulatory, antiepileptic, and glucose-regulating properties. These pleiotropic effects of melatonin on diverse organ systems either through a receptor or non-receptor mediated pathways are under investigation. This review highlights the pathophysiological and pharmacological actions of melatonin along with melatonergic agonists in “real life” clinical practice

Cost Impact Analysis of Using The Improve® Tool For Venous Thromboembolism Risk Assessment In Medical Patients Admitted to The UK NHS Hospitals to Inform NICE Clinical Guideline Recommendation

© 2018 The Author(s). This an open access work distributed under the terms of the Creative Commons Attribution Licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.Peer reviewedFinal Accepted Versio

A Hybridized Framework for Ontology Modeling incorporating Latent Semantic Analysis and Content based Filtering

In the era of Semantic Web, organization of the necessary Semantic Information becomes quite vital for improving overall retrieval efficiency of the Semantic Web contents. Ontologies are one of the most important and yet the most primary entities of the semantic web which is used for representing and modeling knowledge. Authoring of ontologies must be done in a highly systematic and an organized manner in order to validate the correctness of the ontologies authored. Several traditional ontology authoring systems are based on Semantic Wikis which use graphs to store the ontological entities that increase the overall complexity of ontologies which needs to be overcome. A Hash Table based ontology organization strategy is proposed which is further empowered by a Semantic Latent Analysis to compute the ontological relevance. Several agents are incorporated to check the correctness of ontologies. The proposed framework is further enhanced with Content Based Filtering for yielding better results. The proposed methodology yields an accuracy percentage of 88.9

ENHANCED NEIGHBORHOOD NORMALIZED POINTWISE MUTUAL INFORMATION ALGORITHM FOR CONSTRAINT AWARE DATA CLUSTERING

Clustering of similar data items is an important technique in mining useful patterns. To enhance the performance of Clustering, training or learning is an important task. A constraint learning semi-supervised methodology is proposed which incorporates SVM and Normalized Pointwise Mutual Information Computation Strategy to increase the relevance as well as the performance efficiency of clustering. The SVM Classifier is of Hard Margin Type to roughly classify the initial set. A recursive re-clustering approach is proposed for achieving higher degree of relevance in the final clustered set by incorporating ENNPI algorithm. An overall enriched F-Measure value of 94.09% is achieved as compared to existing algorithms

Enhanced neighborhood normalized pointwise mutual information algorithm for constraint aware data clustering

Clustering of similar data items is an important technique in mining useful patterns. To enhance the performance of Clustering, training or learning is an important task. A constraint learning semi-supervised methodology is proposed which incorporates SVM and Normalized Point wise Mutual Information Computation Strategy to increase the relevance as well as the performance efficiency of clustering. The SVM Classifier is of Hard Margin Type to roughly classify the initial set. A recursive re-clustering approach is proposed for achieving higher degree of relevance in the final clustered set by incorporating ENNPI algorithm. An overall enriched F-Measure value of 94.09% is achieved as compared to existing algorithms

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture

The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require datamanagement plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices