Institution
University of Alcalá
Education•Alcalá de Henares, Spain•
About: University of Alcalá is a education organization based out in Alcalá de Henares, Spain. It is known for research contribution in the topics: Population & Context (language use). The organization has 10795 authors who have published 20718 publications receiving 410089 citations. The organization is also known as: University of Alcala & University of Alcala de Henares.
Topics: Population, Context (language use), Medicine, Receptor, Computer science
Papers published on a yearly basis
Papers
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University of London1, Stanford University2, University of Maryland, Baltimore3, Emory University4, Brown University5, Katholieke Universiteit Leuven6, University of Alcalá7, Federal University of Rio de Janeiro8, University of Granada9, University of Zurich10, University of Paris11, Harvard University12, Médecins Sans Frontières13, Israel Ministry of Health14, Erasmus University Rotterdam15, University of Rome Tor Vergata16, Karolinska Institutet17, University of Modena and Reggio Emilia18, Kaiser Permanente19, Centers for Disease Control and Prevention20, Public Health England21, Robert Koch Institute22, University College London23, Medical Research Council24, IBM25, University of the Free State26, University of Cologne27, Chulalongkorn University28
TL;DR: A global assessment of drug resistance after virological failure with first-line tenofovir-containing ART, defined as presence of K65R/N or K70E/G/Q mutations in the reverse transcriptase ( RT ) gene.
Abstract: Summary Background Antiretroviral therapy (ART) is crucial for controlling HIV-1 infection through wide-scale treatment as prevention and pre-exposure prophylaxis (PrEP). Potent tenofovir disoproxil fumarate-containing regimens are increasingly used to treat and prevent HIV, although few data exist for frequency and risk factors of acquired drug resistance in regions hardest hit by the HIV pandemic. We aimed to do a global assessment of drug resistance after virological failure with first-line tenofovir-containing ART. Methods The TenoRes collaboration comprises adult HIV treatment cohorts and clinical trials of HIV drug resistance testing in Europe, Latin and North America, sub-Saharan Africa, and Asia. We extracted and harmonised data for patients undergoing genotypic resistance testing after virological failure with a first-line regimen containing tenofovir plus a cytosine analogue (lamivudine or emtricitabine) plus a non-nucleotide reverse-transcriptase inhibitor (NNRTI; efavirenz or nevirapine). We used an individual participant-level meta-analysis and multiple logistic regression to identify covariates associated with drug resistance. Our primary outcome was tenofovir resistance, defined as presence of K65R/N or K70E/G/Q mutations in the reverse transcriptase ( RT ) gene. Findings We included 1926 patients from 36 countries with treatment failure between 1998 and 2015. Prevalence of tenofovir resistance was highest in sub-Saharan Africa (370/654 [57%]). Pre-ART CD4 cell count was the covariate most strongly associated with the development of tenofovir resistance (odds ratio [OR] 1·50, 95% CI 1·27–1·77 for CD4 cell count Interpretation We recorded drug resistance in a high proportion of patients after virological failure on a tenofovir-containing first-line regimen across low-income and middle-income regions. Effective surveillance for transmission of drug resistance is crucial. Funding The Wellcome Trust.
213 citations
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TL;DR: In this paper, a polyethersulfone (PES) flat-sheet membrane was manufactured by the phase inversion method for wastewater treatment application, and the nanoparticles size distribution was characterized by scanning electron microscope (SEM) and dynamic light scattering (DLS) method to explore the effect of nanoparticle aggregation.
213 citations
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TL;DR: An attitude heading reference system (AHRS) based on the unscented Kalman filter (UKF) using the three-axis attitude determination (TRIAD) algorithm as the observation model is introduced.
Abstract: A main problem in autonomous vehicles in general, and in unmanned aerial vehicles (UAVs) in particular, is the determination of the attitude angles. A novel method to estimate these angles using off-the-shelf components is presented. This paper introduces an attitude heading reference system (AHRS) based on the unscented Kalman filter (UKF) using the three-axis attitude determination (TRIAD) algorithm as the observation model. The performance of the method is assessed through simulations and compared to an AHRS based on the extended Kalman filter (EKF). The paper presents field experiment results using a real fixed-wing UAV. The results show good real-time performance with low computational cost in a microcontroller.
212 citations
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17 Jun 2015TL;DR: The challenging problem that this paper is to precisely estimate the number of vehicles in an image of a traffic congestion situation is explored and TRANCOS, a novel database for extremely overlapping vehicle counting, is introduced.
Abstract: The challenging problem that we explore in this paper is to precisely estimate the number of vehicles in an image of a traffic congestion situation We start introducing TRANCOS, a novel database for extremely overlapping vehicle counting It provides more than 1200 images where the number of vehicles and their locations have been annotated We establish a clear experimental setup which will let others evaluate their own vehicle counting approaches We also propose a novel evaluation metric, the Grid Average Mean absolute Error (GAME), which overcomes the limitations of previously proposed metrics for object counting Finally, we perform an experimental validation, using the proposed TRANCOS dataset, for two types of vehicle counting strategies: counting by detection; and counting by regression Our results show that counting by regression strategies are more precise localizing and estimating the number of vehicles The TRANCOS database and the source code for reproducing the results are available at http://agamenontscuahes/Personales/rlopez/data/trancos
212 citations
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University of Amsterdam1, Conservation International2, Woodrow Wilson International Center for Scholars3, Higher University of San Andrés4, Martin Luther University of Halle-Wittenberg5, Spanish National Research Council6, University of Alcalá7, Florida Museum of Natural History8, Cornell University9, Zoological Society of London10, Helmholtz Centre for Environmental Research - UFZ11, University of Paris12, University of Gothenburg13, University of Twente14, Commonwealth Scientific and Industrial Research Organisation15, International Center for Tropical Agriculture16, Aston University17, University of Melbourne18, Global Biodiversity Information Facility19, University of Bari20, University of Eastern Finland21, University of Toulouse22, University of Trento23, Australian Museum24, Office of Environment and Heritage25, Cardiff University26
TL;DR: The challenges of a ‘Big Data’ approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance are assessed.
Abstract: Much biodiversity data is collected worldwide, but it remains challenging to assemble the scattered knowledge for assessing biodiversity status and trends. The concept of Essential Biodiversity Variables (EBVs) was introduced to structure biodiversity monitoring globally, and to harmonize and standardize biodiversity data from disparate sources to capture a minimum set of critical variables required to study, report and manage biodiversity change. Here, we assess the challenges of a 'Big Data' approach to building global EBV data products across taxa and spatiotemporal scales, focusing on species distribution and abundance. The majority of currently available data on species distributions derives from incidentally reported observations or from surveys where presence-only or presence-absence data are sampled repeatedly with standardized protocols. Most abundance data come from opportunistic population counts or from population time series using standardized protocols (e.g. repeated surveys of the same population from single or multiple sites). Enormous complexity exists in integrating these heterogeneous, multi-source data sets across space, time, taxa and different sampling methods. Integration of such data into global EBV data products requires correcting biases introduced by imperfect detection and varying sampling effort, dealing with different spatial resolution and extents, harmonizing measurement units from different data sources or sampling methods, applying statistical tools and models for spatial inter- or extrapolation, and quantifying sources of uncertainty and errors in data and models. To support the development of EBVs by the Group on Earth Observations Biodiversity Observation Network (GEO BON), we identify 11 key workflow steps that will operationalize the process of building EBV data products within and across research infrastructures worldwide. These workflow steps take multiple sequential activities into account, including identification and aggregation of various raw data sources, data quality control, taxonomic name matching and statistical modelling of integrated data. We illustrate these steps with concrete examples from existing citizen science and professional monitoring projects, including eBird, the Tropical Ecology Assessment and Monitoring network, the Living Planet Index and the Baltic Sea zooplankton monitoring. The identified workflow steps are applicable to both terrestrial and aquatic systems and a broad range of spatial, temporal and taxonomic scales. They depend on clear, findable and accessible metadata, and we provide an overview of current data and metadata standards. Several challenges remain to be solved for building global EBV data products: (i) developing tools and models for combining heterogeneous, multi-source data sets and filling data gaps in geographic, temporal and taxonomic coverage, (ii) integrating emerging methods and technologies for data collection such as citizen science, sensor networks, DNA-based techniques and satellite remote sensing, (iii) solving major technical issues related to data product structure, data storage, execution of workflows and the production process/cycle as well as approaching technical interoperability among research infrastructures, (iv) allowing semantic interoperability by developing and adopting standards and tools for capturing consistent data and metadata, and (v) ensuring legal interoperability by endorsing open data or data that are free from restrictions on use, modification and sharing. Addressing these challenges is critical for biodiversity research and for assessing progress towards conservation policy targets and sustainable development goals.
212 citations
Authors
Showing all 10907 results
Name | H-index | Papers | Citations |
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José Luis Zamorano | 105 | 695 | 133396 |
Jesús F. San Miguel | 97 | 527 | 44918 |
Sebastián F. Sánchez | 96 | 629 | 32496 |
Javier P. Gisbert | 95 | 990 | 33726 |
Luis M. Ruilope | 94 | 841 | 97778 |
Luis M. Garcia-Segura | 88 | 484 | 27077 |
Alberto Orfao | 85 | 597 | 37670 |
Amadeo R. Fernández-Alba | 83 | 318 | 21458 |
Rafael Luque | 80 | 693 | 28395 |
Francisco Rodríguez | 79 | 748 | 24992 |
Andrea Negri | 79 | 242 | 35311 |
Rafael Cantón | 78 | 575 | 29702 |
David J. Grignon | 78 | 301 | 23119 |
Christophe Baudouin | 74 | 553 | 22068 |
Josep M. Argilés | 73 | 310 | 19675 |