Social Media Behaviour Analysis in Disaster-Response Messages of Floods and Heat Waves via Artificial Intelligence

This paper analyses social media data in multiple disaster-related collections of floods and heat waves in the UK. The proposed method uses machine learning classifiers based on deep bidirectional neural networks trained on benchmark datasets of disaster responses and extreme events. The resulting models are applied to perform a qualitative analysis via topic inference in text data. We further analyse a set of behavioural indicators and match them with climate variables via decoding synoptical records to analyse thermal comfort. We highlight the advantages of aligning behavioural indicators along with climate variables to provide with 7 additional valuable information to be considered especially in different phases of a disaster and applicable to extreme weather periods. The positiveness of messages is around 8% for disaster, 1% for disaster and medical response, 7% for disaster and humanitarian related messages. This shows the reliability of such data for our case studies. We show the transferability of this approach to be applied to any social media data collection

CaloriNet: From silhouettes to calorie estimation in private environments

We propose a novel deep fusion architecture, CaloriNet, for the online estimation of energy expenditure for free living monitoring in private environments, where RGB data is discarded and replaced by silhouettes. Our fused convolutional neural network architecture is trainable end-to-end, to estimate calorie expenditure, using temporal foreground silhouettes alongside accelerometer data. The network is trained and cross-validated on a publicly available dataset, SPHERE_RGBD + Inertial_calorie. Results show state-of-the-art minimum error on the estimation of energy expenditure (calories per minute), outperforming alternative, standard and single-modal techniques.Comment: 11 pages, 7 figure

Evaluación y requerimientos nutricionales en pacientes hospitalizados por COVID-19

La COVID-19 desencadena un cuadro clínico que puede impactar negativamente en el estado nutricional y en el pronóstico de los pacientes. La respuesta inflamatoria exacerbada en casos críticos, puede traer consigo una serie de alteraciones metabólicas y catabólicas que afectan directamente el estado nutricional, generando pérdida de masa muscular esquelética y desnutrición, lo que se asocia a peores desenlaces y mayores complicaciones. La valoración del estado nutricional, mediante el tamizaje y la evaluación nutricional en pacientes con COVID-19, nos permite preservar el estado nutricional y/o prevenir o tratar la desnutrición asociada y así, reducir las complicaciones asociadas a la misma. Actualmente no existe tratamiento nutricional específico frente al COVID-19, sin embargo, las diversas sociedades científicas del mundo han establecido una serie de recomendaciones para confrontar la situación nutricional. Así, en el presente trabajo se exponen los requerimientos nutricionales en pacientes con COVID-19 no ventilados en hospitalización general y en la Unidad de Cuidados Intensivos (UCI) en base al aporte de energía, carbohidratos, proteínas, lípidos y micronutrientes (vitaminas y minerales) necesarios para mejorar su pronóstico durante la estancia hospitalaria. Palabras claves: Evaluación Nutricional, Necesidades Nutricionales, Infección por Coronavirus 2019-nCoV. (fuente: DeCS)   DOI: http://dx.doi.org/10.17268/rmt.2021.v16i03.1

Deep sequencing of the Mexican avocado transcriptome, an ancient angiosperm with a high content of fatty acids

Background: Avocado (Persea americana) is an economically important tropical fruit considered to be a good source of fatty acids. Despite its importance, the molecular and cellular characterization of biochemical and developmental processes in avocado is limited due to the lack of transcriptome and genomic information. Results: The transcriptomes of seeds, roots, stems, leaves, aerial buds and flowers were determined using different sequencing platforms. Additionally, the transcriptomes of three different stages of fruit ripening (pre-climacteric, climacteric and post-climacteric) were also analyzed. The analysis of the RNAseqatlas presented here reveals strong differences in gene expression patterns between different organs, especially between root and flower, but also reveals similarities among the gene expression patterns in other organs, such as stem, leaves and aerial buds (vegetative organs) or seed and fruit (storage organs). Important regulators, functional categories, and differentially expressed genes involved in avocado fruit ripening were identified. Additionally, to demonstrate the utility of the avocado gene expression atlas, we investigated the expression patterns of genes implicated in fatty acid metabolism and fruit ripening. Conclusions: A description of transcriptomic changes occurring during fruit ripening was obtained in Mexican avocado, contributing to a dynamic view of the expression patterns of genes involved in fatty acid biosynthesis and the fruit ripening process

Performance levels of four Latin American laboratories for the serodiagnosis of Chagas disease in Mexican sera samples

In nearly all of the previous multicentre studies evaluating serological tests for Trypanosoma cruzi infection, sera samples from Central or South American countries have been used preferentially. In this work we compared the reliability of the serological tests using Mexican sera samples that were evaluated in four independent laboratories. This included a reference laboratory in Brazil and three participant laboratories, including one in Central America and two in Mexico. The kappa index between Brazilian and Honduran laboratories reached 1.0 and the index for the Mexican laboratories reached 0.94. Another finding of this study was that the source of antigen did not affect the performance of the serological tests