Ghent University

About: Ghent University is a education organization based out in Ghent, Belgium. It is known for research contribution in the topics: Population & Context (language use). The organization has 36170 authors who have published 111042 publications receiving 3774501 citations. The organization is also known as: UGent & University of Ghent.

An integrated pan‐tropical biomass map using multiple reference datasets

Abstract: We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan-tropical AGB map at 1-km resolution using an independent reference dataset of field observations and locally calibrated high-resolution biomass maps, harmonized and upscaled to 14 477 1-km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South-East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy-relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country-specific reference datasets.

LOINC, a Universal Standard for Identifying Laboratory Observations: A 5-Year Update

Abstract: The Logical Observation Identifier Names and Codes (LOINC) database provides a universal code system for reporting laboratory and other clinical observations. Its purpose is to identify observations in electronic messages such as Health Level Seven (HL7) observation messages, so that when hospitals, health maintenance organizations, pharmaceutical manufacturers, researchers, and public health departments receive such messages from multiple sources, they can automatically file the results in the right slots of their medical records, research, and/or public health systems. For each observation, the database includes a code (of which 25 000 are laboratory test observations), a long formal name, a "short" 30-character name, and synonyms. The database comes with a mapping program called Regenstrief LOINC Mapping Assistant (RELMA(TM)) to assist the mapping of local test codes to LOINC codes and to facilitate browsing of the LOINC results. Both LOINC and RELMA are available at no cost from http://www.regenstrief.org/loinc/. The LOINC medical database carries records for >30 000 different observations. LOINC codes are being used by large reference laboratories and federal agencies, e.g., the CDC and the Department of Veterans Affairs, and are part of the Health Insurance Portability and Accountability Act (HIPAA) attachment proposal. Internationally, they have been adopted in Switzerland, Hong Kong, Australia, and Canada, and by the German national standards organization, the Deutsches Instituts fur Normung. Laboratories should include LOINC codes in their outbound HL7 messages so that clinical and research clients can easily integrate these results into their clinical and research repositories. Laboratories should also encourage instrument vendors to deliver LOINC codes in their instrument outputs and demand LOINC codes in HL7 messages they get from reference laboratories to avoid the need to lump so many referral tests under the "send out lab" code.

2016 Consensus statement on return to sport from the First World Congress in Sports Physical Therapy, Bern

Abstract: Deciding when to return to sport after injury is complex and multifactorial-an exercise in risk management. Return to sport decisions are made every day by clinicians, athletes and coaches, ideally in a collaborative way. The purpose of this consensus statement was to present and synthesise current evidence to make recommendations for return to sport decision-making, clinical practice and future research directions related to returning athletes to sport. A half day meeting was held in Bern, Switzerland, after the First World Congress in Sports Physical Therapy. 17 expert clinicians participated. 4 main sections were initially agreed upon, then participants elected to join 1 of the 4 groups-each group focused on 1 section of the consensus statement. Participants in each group discussed and summarised the key issues for their section before the 17-member group met again for discussion to reach consensus on the content of the 4 sections. Return to sport is not a decision taken in isolation at the end of the recovery and rehabilitation process. Instead, return to sport should be viewed as a continuum, paralleled with recovery and rehabilitation. Biopsychosocial models may help the clinician make sense of individual factors that may influence the athlete's return to sport, and the Strategic Assessment of Risk and Risk Tolerance framework may help decision-makers synthesise information to make an optimal return to sport decision. Research evidence to support return to sport decisions in clinical practice is scarce. Future research should focus on a standardised approach to defining, measuring and reporting return to sport outcomes, and identifying valuable prognostic factors for returning to sport.

Microbial production and application of sophorolipids

Abstract: Sophorolipids are surface-active compounds synthesized by a selected number of yeast species. They have been known for over 40 years, but because of growing environmental awareness, they recently regained attention as biosurfactants due to their biodegradability, low ecotoxicity, and production based on renewable resources. In this paper, an overview is given of the producing yeast strains and various aspects of fermentative sophorolipid production. Also, the biochemical pathways and regulatory mechanisms involved in sophorolipid biosynthesis are outlined. To conclude, a summary is given on possible applications of sophorolipids, either as native or modified molecules.