Optical gas sensing with liquid crystal droplets and convolutional neural networks

UIDB/50009/2020 UIDB/ 04378/2020 SCENT-ERC-2014-STG-639123, 2015-2022Liquid crystal (LC)-based materials are promising platforms to develop rapid, miniaturised and low-cost gas sensor devices. In hybrid gel films containing LC droplets, characteristic optical texture variations are observed due to orientational transitions of LC molecules in the presence of distinct volatile organic compounds (VOC). Here, we investigate the use of deep convolutional neural networks (CNN) as pattern recognition systems to analyse optical textures dynamics in LC droplets exposed to a set of different VOCs. LC droplets responses to VOCs were video recorded under polarised optical microscopy (POM). CNNs were then used to extract features from the responses and, in separate tasks, to recognise and quantify the vapours exposed to the films. The impact of droplet diameter on the results was also analysed. With our classification models, we show that a single individual droplet can recognise 11 VOCs with small structural and functional differences (F1-score above 93%). The optical texture variation pattern of a droplet also reflects VOC concentration changes, as suggested by applying a regression model to acetone at 0.9–4.0% (v/v) (mean absolute errors below 0.25% (v/v)). The CNN-based methodology is thus a promising approach for VOC sensing using responses from individual LC-droplets.publishersversionpublishe

El consumidor del sector moderno y la compra electr?nica en Lima y zona norte del Per?

La presente tesis de investigaci?n, tiene como principal objetivo, estudiar la situaci?n actual del comercio electr?nico en el Per?, en particular a los aspectos relativos al consumidor peruano del sector moderno. Se pretende contribuir con una fuente de informaci?n para organizaciones y personas relacionadas a este tipo de comercio, ya sea en la docencia o en su aplicaci?n en el campo del comercio y asimismo a emprendedores o empresas que deseen incursionar en comercio electr?nico. Se recopil? informaci?n tanto de fuentes primarias como secundarias. En el caso de las fuentes primarias se recurri? a dos herramientas de investigaci?n, la entrevista a profundidad y la encuesta. Ambas permitieron recopilar informaci?n por parte de conocedores del tema y usuarios de internet, referente a temas como situaci?n actual, h?bitos del consumidor, comparativa versus otras econom?as internacionales, tendencias, factores limitantes y aquellos que motivan la compra electr?nica. Las conclusiones del estudio est?n enfocadas en relacionar los hallazgos con los estudios encontrados de otras econom?as del mundo, y las tendencias globales, dando un diagn?stico respecto a la situaci?n actual. Asimismo, se recogen los principales h?bitos del consumidor peruano, as? como sus expectativas sustentadas b?sicamente en reforzar los mecanismos de creaci?n de confianza

Early and Highly Suppressive ART are Main Factors Associated with Low Viral Reservoir in European Perinatally HIV Infected Children

Abstract BACKGROUND: Future strategies aiming to achieve HIV-1 remission are likely to target individuals with small reservoir size. SETTING: We retrospectively investigated factors associated with HIV-1 DNA levels in European, perinatally HIV-infected children starting ART <6 months of age. METHODS: Total HIV-1 DNA was measured from 51 long-term suppressed children 6.3 years (median) after initial viral suppression. Factors associated with log10 total HIV-1 DNA were analyzed using linear regression. RESULTS: At ART initiation, children were aged median [IQR] 2.3 [1.2,4.1] months, CD4% 37 [24,45] %, CD8% 28 [18,36] %, log10 plasma viral load (VL) 5.4 [4.4,5.9] copies/ml. Time to viral suppression was 7.98 [4.6,19.3] months. Following suppression, 13 (25%) children had suboptimal response [ 652 consecutive VL50-400 followed by VL<50] and/or experienced periods of virological failure [ 652 consecutive VL 65400 followed by VL<50]. Median total HIV-1 DNA was 43 [6,195] copies/10 PBMC.Younger age at therapy initiation was associated with lower total HIV-1 DNA (adjusted coefficient [AC] 0.12 per month older, p=0.0091), with a month increase in age at ART start being associated with a 13% increase in HIV DNA. Similarly, a higher proportion of time spent virally suppressed (AC 0.10 per 10% higher, p=0.0022) and absence of viral failure/suboptimal response (AC 0.34 for those with fail/ suboptimal response, p=0.0483) were associated with lower total HIV-1 DNA. CONCLUSION: Early ART initiation and a higher proportion of time suppressed are linked with lower total HIV-1 DNA. Early ART start and improving adherence in perinatally HIV-1 infected children minimize the size of viral reservoir.This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal

Describing variability in pig genes involved in coronavirus infections for a One Health perspective in conservation of animal genetic resources

Coronaviruses silently circulate in human and animal populations, causing mild to severe diseases. Therefore, livestock are important components of a ?One Health? perspective aimed to control these viral infections. However, at present there is no example that considers pig genetic resources in this context. In this study, we investigated the variability of four genes (ACE2, ANPEP and DPP4 encoding for host receptors of the viral spike proteins and TMPRSS2 encoding for a host proteinase) in 23 European (19 autochthonous and three commercial breeds and one wild boar population) and two Asian Sus scrofa populations. A total of 2229 variants were identifed in the four candidate genes: 26% of them were not previously described; 29 variants afected the protein sequence and might potentially interact with the infection mechanisms. The results coming from this work are a frst step towards a ?One Health? perspective that should consider conservation programs of pig genetic resources with twofold objectives: (i) genetic resources could be reservoirs of host gene variability useful to design selection programs to increase resistance to coronaviruses; (ii) the describedFE1B-06B2-126F | Jos? Pedro Pinto de Ara?joN/

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino detector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower- or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Event reconstruction for KM3NeT/ORCA using convolutional neural networks

The KM3NeT research infrastructure is currently under construction at two locations in the Mediterranean Sea. The KM3NeT/ORCA water-Cherenkov neutrino de tector off the French coast will instrument several megatons of seawater with photosensors. Its main objective is the determination of the neutrino mass ordering. This work aims at demonstrating the general applicability of deep convolutional neural networks to neutrino telescopes, using simulated datasets for the KM3NeT/ORCA detector as an example. To this end, the networks are employed to achieve reconstruction and classification tasks that constitute an alternative to the analysis pipeline presented for KM3NeT/ORCA in the KM3NeT Letter of Intent. They are used to infer event reconstruction estimates for the energy, the direction, and the interaction point of incident neutrinos. The spatial distribution of Cherenkov light generated by charged particles induced in neutrino interactions is classified as shower-or track-like, and the main background processes associated with the detection of atmospheric neutrinos are recognized. Performance comparisons to machine-learning classification and maximum-likelihood reconstruction algorithms previously developed for KM3NeT/ORCA are provided. It is shown that this application of deep convolutional neural networks to simulated datasets for a large-volume neutrino telescope yields competitive reconstruction results and performance improvements with respect to classical approaches

Geography and ecology shape the phylogenetic composition of Amazonian tree communities

AimAmazonia hosts more tree species from numerous evolutionary lineages, both young and ancient, than any other biogeographic region. Previous studies have shown that tree lineages colonized multiple edaphic environments and dispersed widely across Amazonia, leading to a hypothesis, which we test, that lineages should not be strongly associated with either geographic regions or edaphic forest types.LocationAmazonia.TaxonAngiosperms (Magnoliids; Monocots; Eudicots).MethodsData for the abundance of 5082 tree species in 1989 plots were combined with a mega-phylogeny. We applied evolutionary ordination to assess how phylogenetic composition varies across Amazonia. We used variation partitioning and Moran's eigenvector maps (MEM) to test and quantify the separate and joint contributions of spatial and environmental variables to explain the phylogenetic composition of plots. We tested the indicator value of lineages for geographic regions and edaphic forest types and mapped associations onto the phylogeny.ResultsIn the terra firme and várzea forest types, the phylogenetic composition varies by geographic region, but the igapó and white-sand forest types retain a unique evolutionary signature regardless of region. Overall, we find that soil chemistry, climate and topography explain 24% of the variation in phylogenetic composition, with 79% of that variation being spatially structured (R2 = 19% overall for combined spatial/environmental effects). The phylogenetic composition also shows substantial spatial patterns not related to the environmental variables we quantified (R2 = 28%). A greater number of lineages were significant indicators of geographic regions than forest types.Main ConclusionNumerous tree lineages, including some ancient ones (&gt;66 Ma), show strong associations with geographic regions and edaphic forest types of Amazonia. This shows that specialization in specific edaphic environments has played a long-standing role in the evolutionary assembly of Amazonian forests. Furthermore, many lineages, even those that have dispersed across Amazonia, dominate within a specific region, likely because of phylogenetically conserved niches for environmental conditions that are prevalent within regions