Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices

A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the spatial bioclimatic probability distribution of each bioclimatic index, which allows researchers to obtain the probability of each location belonging to different bioclimates. The methodology is evaluated on two indices in the Island of Cyprus.Peer Reviewe

Deciphering Genomic Heterogeneity and the Internal Composition of Tumour Activities through a Hierarchical Factorisation Model

Genomic heterogeneity constitutes one of the most distinctive features of cancer diseases, limiting the efficacy and availability of medical treatments. Tumorigenesis emerges as a strongly stochastic process, producing a variable landscape of genomic configurations. In this context, matrix factorisation techniques represent a suitable approach for modelling such complex patterns of variability. In this work, we present a hierarchical factorisation model conceived from a systems biology point of view. The model integrates the topology of molecular pathways, allowing to simultaneously factorise genes and pathways activity matrices. The protocol was evaluated by using simulations, showing a high degree of accuracy. Furthermore, the analysis with a real cohort of breast cancer patients depicted the internal composition of some of the most relevant altered biological processes in the disease, describing gene and pathway level strategies and their observed combinations in the population of patients. We envision that this kind of approaches will be essential to better understand the hallmarks of cancer.Peer ReviewedPostprint (published version

Applied approach to the spaces constitutive of collective identity in social movement: case study of the anti-eviction movement and the squatting movement in Spain

Cuando las experiencias sociales no se corresponden con las expectativas, es decir, cuando la regulación social dista de la esperada emancipación social, se descubre que determinados colectivos vinculan sus luchas sociales a la consecución de unos derechos subjetivamente usurpados. Para ello constituyen espacios múltiples en los cuales se ponen en común las problemáticas y se comienza, por ende, a visibilizar sus posibles soluciones. En esta línea, vislumbramos la forma en que estos espacios de socialización terminan por re-definirse en espacios de acción colectiva. Teniendo esto en cuenta, en el presente artículo presentamos el resultado de una investigación etnográfica comparativa de dos movimientos sociales con manifiestas diferencias simbólicas, políticas, culturales y sociales, pero que confluyen en un eje vertebrador común: el derecho a una vivienda digna. Estos son, por una parte, el movimiento de okupación y, por otra, la lucha antidesahucios, materializada en la Plataforma de Afectados por la Hipoteca (PAH). Ambos desarrollan prácticas culturales similares en los distintos espacios de socialización a través de sus respectivos repertorios y espacios de acción colectiva. Tras un análisis desde la antropología implicada, se concluye que estos espacios, ya sean físicos —centros sociales, bares, plazas, etc.— o simbólicos —manifestaciones, concentraciones, fiestas, etc.—, sirven para reconfigurar y reforzar las identidades personales y colectivas a través de la puesta en común de prácticas y experiencias reivindicativas.When social experiences do not correspond to expectations, that is, when social regulation is far from the expected social emancipation, we discover that certain communities link their social struggles to the achievement of some subjectively usurped rights. To this end, they constitute multiple spaces in which problems are shared and thus begin to make visible their possible solutions. In this sense, we discern the way in which these spaces of socialization end up redefining themselves in spaces of collective action. With this in mind, in this article we present the result of a comparative ethnographic research of two social movements with obvious symbolic, political, cultural and social differences, but which converge in a common mainstay: the right to decent housing. These are, on the one hand, the squatting movement and, on the other, the anti-eviction struggle, materialized in the Platform of People Affected by Mortgages (PAH, by its Spanish initials). Both develop similar cultural practices in the different spaces of socialization through their respective struggles and spaces of collective action. After an analysis from the applied anthropology, it is concluded that these spaces, whether physical — social centers, bars, squares, etc. — or symbolic —demonstrations, rallies, parties, etc. — serve to reconfigure and reinforce personal and collective identities through the sharing of demanding practices and experiences

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We provide some figures and tables with several indexes and indicators as well as an Analysissection that discusses a specific topic related with the pandemic. As for the predictions, we employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-14 days later. We show an individual report with 8 graphs and a summary table with the main indicators for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-10 days later. The model and predictions are based on two parameters that are daily fitted to available data: a: the velocity at which spreading specific rate slows down; the higher the value, the better the control. K: the final number of expected cumulated cases, which cannot be evaluated at the initial stages because growth is still exponential. We show an individual report with 8 graphs and a table with the short-term predictions for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases. The predicted period of a country depends on the number of datapoints over this 100 cases threshold, and is of 5 days for those that have reported more than 100 cumulated cases for 10 consecutive days or more. For short-term predictions, we assign higher weight to last 3 points in the fittings, so that changes are rapidly captured by the model. The whole methodology employed in the inform is explained in the last pages of this document. In addition to the individual reports, the reader will find an initial dashboard with a brief analysis of the situation in EU-EFTA-UK countries, some summary figures and tables as well as long-term predictions for some of them, when possible. These long-term predictions are evaluated without different weights to datapoints. We also discuss a specific issue every day.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version

Analysis and prediction of COVID-19 for EU-EFTA-UK and other countries

The present report aims to provide a comprehensive picture of the pandemic situation of COVID-19 in the EU countries, and to be able to foresee the situation in the next coming days. We provide some figures and tables with several indexes and indicators as well as an Analysissection that discusses a specific topic related with the pandemic. As for the predictions, we employ an empirical model, verified with the evolution of the number of confirmed cases in previous countries where the epidemic is close to conclude, including all provinces of China. The model does not pretend to interpret the causes of the evolution of the cases but to permit the evaluation of the quality of control measures made in each state and a short-term prediction of trends. Note, however, that the effects of the measures’ control that start on a given day are not observed until approximately 7-14 days later. We show an individual report with 8 graphs and a summary table with the main indicators for different countries and regions. We are adjusting the model to countries and regions with at least 4 days with more than 100 confirmed cases and a current load over 200 cases.These reports are funded by the European Commission (DG CONNECT, LC-01485746) PJC and MC received funding from “la Caixa” Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; CP, DL, SA, MC, received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00Postprint (published version