Malaria vector species in Colombia: a review

Here we present a comprehensive review of the literature on the vectorial importance of the major Anopheles malaria vectors in Colombia. We provide basic information on the geographical distribution, altitudinal range, immature habitats, adult behaviour, feeding preferences and anthropophily, endophily and infectivity rates. We additionally review information on the life cycle, longevity and population fluctuation of Colombian Anopheles species. Emphasis was placed on the primary vectors that have been epidemiologically incriminated in malaria transmission: Anopheles darlingi, Anopheles albimanus and Anopheles nuneztovari. The role of a selection of local, regional or secondary vectors (e.g., Anopheles pseudopunctipennis and Anopheles neivai) is also discussed. We highlight the importance of combining biological, morphological and molecular data for the correct taxonomical determination of a given species, particularly for members of the species complexes. We likewise emphasise the importance of studying the bionomics of primary and secondary vectors along with an examination of the local conditions affecting the transmission of malaria. The presence and spread of the major vectors and the emergence of secondary species capable of transmitting human Plasmodia are of great interest. When selecting control measures, the anopheline diversity in the region must be considered. Variation in macroclimate conditions over a species' geographical range must be well understood and targeted to plan effective control measures based on the population dynamics of the local Anopheles species

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