Bovine Tuberculosis in Doñana Biosphere Reserve: The Role of Wild Ungulates as Disease Reservoirs in the Last Iberian Lynx Strongholds

Doñana National Park (DNP) in southern Spain is a UNESCO Biosphere Reserve where commercial hunting and wildlife artificial feeding do not take place and traditional cattle husbandry still exists. Herein, we hypothesized that Mycobacterium bovis infection prevalence in wild ungulates will depend on host ecology and that variation in prevalence will reflect variation in the interaction between hosts and environmental risk factors. Cattle bTB reactor rates increased in DNP despite compulsory testing and culling of infected animals. In this study, 124 European wild boar, 95 red deer, and 97 fallow deer were sampled from April 2006 to April 2007 and analyzed for M. bovis infection. Modelling and GIS were used to identify risk factors and intra and inter-species relationships. Infection with M. bovis was confirmed in 65 (52.4%) wild boar, 26 (27.4%) red deer and 18 (18.5%) fallow deer. In the absence of cattle, wild boar M. bovis prevalence reached 92.3% in the northern third of DNP. Wild boar showed more than twice prevalence than that in deer (p<0.001). Modelling revealed that M. bovis prevalence decreased from North to South in wild boar (p<0.001) and red deer (p<0.01), whereas no spatial pattern was evidenced for fallow deer. Infection risk in wild boar was dependent on wild boar M. bovis prevalence in the buffer area containing interacting individuals (p<0.01). The prevalence recorded in this study is among the highest reported in wildlife. Remarkably, this high prevalence occurs in the absence of wildlife artificial feeding, suggesting that a feeding ban alone would have a limited effect on wildlife M. bovis prevalence. In DNP, M. bovis transmission may occur predominantly at the intra-species level due to ecological, behavioural and epidemiological factors. The results of this study allow inferring conclusions on epidemiological bTB risk factors in Mediterranean habitats that are not managed for hunting purposes. Our results support the need to consider wildlife species for the control of bTB in cattle and strongly suggest that bTB may affect animal welfare and conservation

Mining Big Data for Tourist Hot Spots: Geographical Patterns of Online Footprints

Understanding the complex, and often unequal, spatiality of tourist demand in urban contexts requires other methodologies, among which the information base available online and in social networks has gained prominence. Innovation supported by Information and Communication Technologies in terms of data access and data exchange has emerged as a complementary supporting tool for the more traditional data collection techniques currently in use, particularly, in urban destinations where there is the need to more (near)real-time monitoring. The capacity to collect and analise massive amounts of data on individual and group behaviour is leading to new data-rich research approaches. This chapter addresses the potential for discovering geographical insights regarding tourists’ spatial patterns within a destination, based on the analysis of geotagged data available from two social networks. ·info:eu-repo/semantics/publishedVersio

The management of acute venous thromboembolism in clinical practice. Results from the European PREFER in VTE Registry

Venous thromboembolism (VTE) is a significant cause of morbidity and mortality in Europe. Data from real-world registries are necessary, as clinical trials do not represent the full spectrum of VTE patients seen in clinical practice. We aimed to document the epidemiology, management and outcomes of VTE using data from a large, observational database. PREFER in VTE was an international, non-interventional disease registry conducted between January 2013 and July 2015 in primary and secondary care across seven European countries. Consecutive patients with acute VTE were documented and followed up over 12 months. PREFER in VTE included 3,455 patients with a mean age of 60.8 ± 17.0 years. Overall, 53.0 % were male. The majority of patients were assessed in the hospital setting as inpatients or outpatients (78.5 %). The diagnosis was deep-vein thrombosis (DVT) in 59.5 % and pulmonary embolism (PE) in 40.5 %. The most common comorbidities were the various types of cardiovascular disease (excluding hypertension; 45.5 %), hypertension (42.3 %) and dyslipidaemia (21.1 %). Following the index VTE, a large proportion of patients received initial therapy with heparin (73.2 %), almost half received a vitamin K antagonist (48.7 %) and nearly a quarter received a DOAC (24.5 %). Almost a quarter of all presentations were for recurrent VTE, with &gt;80 % of previous episodes having occurred more than 12 months prior to baseline. In conclusion, PREFER in VTE has provided contemporary insights into VTE patients and their real-world management, including their baseline characteristics, risk factors, disease history, symptoms and signs, initial therapy and outcomes

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between data and simulation

Separation of track- and shower-like energy deposits in ProtoDUNE-SP using a convolutional neural network

Liquid argon time projection chamber detector technology provides high spatial and calorimetric resolutions on the charged particles traversing liquid argon. As a result, the technology has been used in a number of recent neutrino experiments, and is the technology of choice for the Deep Underground Neutrino Experiment (DUNE). In order to perform high precision measurements of neutrinos in the detector, final state particles need to be effectively identified, and their energy accurately reconstructed. This article proposes an algorithm based on a convolutional neural network to perform the classification of energy deposits and reconstructed particles as track-like or arising from electromagnetic cascades. Results from testing the algorithm on experimental data from ProtoDUNE-SP, a prototype of the DUNE far detector, are presented. The network identifies track- and shower-like particles, as well as Michel electrons, with high efficiency. The performance of the algorithm is consistent between experimental data and simulation