University of Jena

About: University of Jena is a education organization based out in Jena, Thüringen, Germany. It is known for research contribution in the topics: Laser & Population. The organization has 22198 authors who have published 45159 publications receiving 1401514 citations. The organization is also known as: Friedrich-Schiller-Universität Jena & Friedrich Schiller University Jena.

Comparison of different efficiency criteria for hydrological model assessment

Abstract: . The evaluation of hydrologic model behaviour and performance is commonly made and reported through comparisons of simulated and observed variables. Frequently, comparisons are made between simulated and measured streamflow at the catchment outlet. In distributed hydrological modelling approaches, additional comparisons of simulated and observed measurements for multi-response validation may be integrated into the evaluation procedure to assess overall modelling performance. In both approaches, single and multi-response, efficiency criteria are commonly used by hydrologists to provide an objective assessment of the "closeness" of the simulated behaviour to the observed measurements. While there are a few efficiency criteria such as the Nash-Sutcliffe efficiency, coefficient of determination, and index of agreement that are frequently used in hydrologic modeling studies and reported in the literature, there are a large number of other efficiency criteria to choose from. The selection and use of specific efficiency criteria and the interpretation of the results can be a challenge for even the most experienced hydrologist since each criterion may place different emphasis on different types of simulated and observed behaviours. In this paper, the utility of several efficiency criteria is investigated in three examples using a simple observed streamflow hydrograph.

Assessment of Global Incidence and Mortality of Hospital-treated Sepsis. Current Estimates and Limitations.

Abstract: Rationale: Reducing the global burden of sepsis, a recognized global health challenge, requires comprehensive data on the incidence and mortality on a global scale.Objectives: To estimate the worldwide incidence and mortality of sepsis and identify knowledge gaps based on available evidence from observational studies.Methods: We systematically searched 15 international citation databases for population-level estimates of sepsis incidence rates and fatality in adult populations using consensus criteria and published in the last 36 years.Measurements and Main Results: The search yielded 1,553 reports from 1979 to 2015, of which 45 met our criteria. A total of 27 studies from seven high-income countries provided data for metaanalysis. For these countries, the population incidence rate was 288 (95% confidence interval [CI], 215–386; τ = 0.55) for hospital-treated sepsis cases and 148 (95% CI, 98–226; τ = 0.99) for hospital-treated severe sepsis cases per 100,000 person-years. Restricted to the last decade, th...

Neuroplasticity: changes in grey matter induced by training.

Abstract: Newly honed juggling skills show up as a transient feature on a brain-imaging scan. Does the structure of an adult human brain alter in response to environmental demands1,2? Here we use whole-brain magnetic-resonance imaging to visualize learning-induced plasticity in the brains of volunteers who have learned to juggle. We find that these individuals show a transient and selective structural change in brain areas that are associated with the processing and storage of complex visual motion. This discovery of a stimulus-dependent alteration in the brain's macroscopic structure contradicts the traditionally held view that cortical plasticity is associated with functional rather than anatomical changes.

International evidence-based recommendations for point-of-care lung ultrasound.

Abstract: The purpose of this study is to provide evidence-based and expert consensus recommendations for lung ultrasound with focus on emergency and critical care settings. A multidisciplinary panel of 28 experts from eight countries was involved. Literature was reviewed from January 1966 to June 2011. Consensus members searched multiple databases including Pubmed, Medline, OVID, Embase, and others. The process used to develop these evidence-based recommendations involved two phases: determining the level of quality of evidence and developing the recommendation. The quality of evidence is assessed by the grading of recommendation, assessment, development, and evaluation (GRADE) method. However, the GRADE system does not enforce a specific method on how the panel should reach decisions during the consensus process. Our methodology committee decided to utilize the RAND appropriateness method for panel judgment and decisions/consensus. Seventy-three proposed statements were examined and discussed in three conferences held in Bologna, Pisa, and Rome. Each conference included two rounds of face-to-face modified Delphi technique. Anonymous panel voting followed each round. The panel did not reach an agreement and therefore did not adopt any recommendations for six statements. Weak/conditional recommendations were made for 2 statements, and strong recommendations were made for the remaining 65 statements. The statements were then recategorized and grouped to their current format. Internal and external peer-review processes took place before submission of the recommendations. Updates will occur at least every 4 years or whenever significant major changes in evidence appear. This document reflects the overall results of the first consensus conference on “point-of-care” lung ultrasound. Statements were discussed and elaborated by experts who published the vast majority of papers on clinical use of lung ultrasound in the last 20 years. Recommendations were produced to guide implementation, development, and standardization of lung ultrasound in all relevant settings.

Deep learning and process understanding for data-driven Earth system science

Abstract: Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.