University of Valencia

About: University of Valencia is a education organization based out in Valencia, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 27096 authors who have published 65669 publications receiving 1765689 citations. The organization is also known as: Universitat de València & UV.

GW190425: Observation of a Compact Binary Coalescence with Total Mass ∼ 3.4 M O

Abstract: On 2019 April 25, the LIGO Livingston detector observed a compact binary coalescence with signal-to-noise ratio 12.9. The Virgo detector was also taking data that did not contribute to detection due to a low signal-to-noise ratio, but were used for subsequent parameter estimation. The 90% credible intervals for the component masses range from to if we restrict the dimensionless component spin magnitudes to be smaller than 0.05). These mass parameters are consistent with the individual binary components being neutron stars. However, both the source-frame chirp mass and the total mass of this system are significantly larger than those of any other known binary neutron star (BNS) system. The possibility that one or both binary components of the system are black holes cannot be ruled out from gravitational-wave data. We discuss possible origins of the system based on its inconsistency with the known Galactic BNS population. Under the assumption that the signal was produced by a BNS coalescence, the local rate of neutron star mergers is updated to 250-2810.

European Society of Hypertension Position Paper on Ambulatory Blood Pressure Monitoring

Abstract: Ambulatory blood pressure monitoring (ABPM) is being used increasingly in both clinical practice and hypertension research. Although there are many guidelines that emphasize the indications for ABPM, there is no comprehensive guideline dealing with all aspects of the technique. It was agreed at a consensus meeting on ABPM in Milan in 2011 that the 34 attendees should prepare a comprehensive position paper on the scientific evidence for ABPM.This position paper considers the historical background, the advantages and limitations of ABPM, the threshold levels for practice, and the cost-effectiveness of the technique. It examines the need for selecting an appropriate device, the accuracy of devices, the additional information and indices that ABPM devices may provide, and the software requirements.At a practical level, the paper details the requirements for using ABPM in clinical practice, editing considerations, the number of measurements required, and the circumstances, such as obesity and arrhythmias, when particular care needs to be taken when using ABPM.The clinical indications for ABPM, among which white-coat phenomena, masked hypertension, and nocturnal hypertension appear to be prominent, are outlined in detail along with special considerations that apply in certain clinical circumstances, such as childhood, the elderly and pregnancy, and in cardiovascular illness, examples being stroke and chronic renal disease, and the place of home measurement of blood pressure in relation to ABPM is appraised.The role of ABPM in research circumstances, such as pharmacological trials and in the prediction of outcome in epidemiological studies is examined and finally the implementation of ABPM in practice is considered in relation to the issue of reimbursement in different countries, the provision of the technique by primary care practices, hospital clinics and pharmacies, and the growing role of registries of ABPM in many countries.

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.

Vesiclepedia: a compendium for extracellular vesicles with continuous community annotation

Abstract: Extracellular vesicles (EVs) are membraneous vesicles released by a variety of cells into their microenvironment. Recent studies have elucidated the role of EVs in intercellular communication, pathogenesis, drug, vaccine and gene-vector delivery, and as possible reservoirs of biomarkers. These findings have generated immense interest, along with an exponential increase in molecular data pertaining to EVs. Here, we describe Vesiclepedia, a manually curated compendium of molecular data (lipid, RNA, and protein) identified in different classes of EVs from more than 300 independent studies published over the past several years. Even though databases are indispensable resources for the scientific community, recent studies have shown that more than 50% of the databases are not regularly updated. In addition, more than 20% of the database links are inactive. To prevent such database and link decay, we have initiated a continuous community annotation project with the active involvement of EV researchers. The EV research community can set a gold standard in data sharing with Vesiclepedia, which could evolve as a primary resource for the field.