When who and how matter: explaining the success of referendums in Europe

This article aims to identify the institutional factors that make a referendum successful. This comparative analysis seeks to explain the success of top-down referendums organized in Europe between 2001 and 2013. It argues and tests for the main effect of three institutional factors (popularity of the initiator, size of parliamentary majority, and political cues during referendum campaigns) and controls for the type of referendum and voter turnout. The analysis uses data collected from referendums and electoral databases, public opinion surveys, and newspaper articles. Results show that referendums proposed by a large parliamentary majority or with clear messages from political parties during campaign are likely to be successful

Taxonomy of the family Arenaviridae and the order Bunyavirales : update 2018

In 2018, the family Arenaviridae was expanded by inclusion of 1 new genus and 5 novel species. At the same time, the recently established order Bunyavirales was expanded by 3 species. This article presents the updated taxonomy of the family Arenaviridae and the order Bunyavirales as now accepted by the International Committee on Taxonomy of Viruses (ICTV) and summarizes additional taxonomic proposals that may affect the order in the near future.Peer reviewe

Comparative performances of machine learning methods for classifying Crohn Disease patients using genome-wide genotyping data

Abstract: Crohn Disease (CD) is a complex genetic disorder for which more than 140 genes have been identified using genome wide association studies (GWAS). However, the genetic architecture of the trait remains largely unknown. The recent development of machine learning (ML) approaches incited us to apply them to classify healthy and diseased people according to their genomic information. The Immunochip dataset containing 18,227 CD patients and 34,050 healthy controls enrolled and genotyped by the international Inflammatory Bowel Disease genetic consortium (IIBDGC) has been re-analyzed using a set of ML methods: penalized logistic regression (LR), gradient boosted trees (GBT) and artificial neural networks (NN). The main score used to compare the methods was the Area Under the ROC Curve (AUC) statistics. The impact of quality control (QC), imputing and coding methods on LR results showed that QC methods and imputation of missing genotypes may artificially increase the scores. At the opposite, neither the patient/control ratio nor marker preselection or coding strategies significantly affected the results. LR methods, including Lasso, Ridge and ElasticNet provided similar results with a maximum AUC of 0.80. GBT methods like XGBoost, LightGBM and CatBoost, together with dense NN with one or more hidden layers, provided similar AUC values, suggesting limited epistatic effects in the genetic architecture of the trait. ML methods detected near all the genetic variants previously identified by GWAS among the best predictors plus additional predictors with lower effects. The robustness and complementarity of the different methods are also studied. Compared to LR, non-linear models such as GBT or NN may provide robust complementary approaches to identify and classify genetic markers

Community Mobilization and Credit: The Impact of Nonprofits and Social Capital on Community Reinvestment Act Lending

Recent trends in urban research emphasize the importance of local nonprofits and social capital in the revitalization of poor and minority neighborhoods. This article tests the idea that urban communities able to mobilize themselves by establishing development nonprofits and overcoming collective action problems will be better able to make use of urban-development policies. Copyright (c) 2004 by the Southwestern Social Science Association.

Possibility and Challenges of Conversion of Current Virus Species Names to Linnaean Binomials.

Botanical, mycological, zoological, and prokaryotic species names follow the Linnaean format, consisting of an italicized Latinized binomen with a capitalized genus name and a lower case species epithet (e.g., Homo sapiens). Virus species names, however, do not follow a uniform format, and, even when binomial, are not Linnaean in style. In this thought exercise, we attempted to convert all currently official names of species included in the virus family Arenaviridae and the virus order Mononegavirales to Linnaean binomials, and to identify and address associated challenges and concerns. Surprisingly, this endeavor was not as complicated or time-consuming as even the authors of this article expected when conceiving the experiment. [Arenaviridae; binomials; ICTV; International Committee on Taxonomy of Viruses; Mononegavirales; virus nomenclature; virus taxonomy.]

Possibility and Challenges of Conversion of Current Virus Species Names to Linnaean Binomials

Botanical, mycological, zoological, and prokaryotic species names follow the Linnaean format, consisting of an italicized Latinized binomen with a capitalized genus name and a lower-case species epithet (e.g., Homo sapiens). Virus species names, however, do not follow a uniform format, and even when binomial, are not Linnaean in style. In this thought exercise, we attempted to convert all currently official names of species included in the virus family Arenaviridae and the virus order Mononegavirales to Linnaean binomials, and to identify and address associated challenges and concerns. Surprisingly, this endeavor was not as complicated or time-consuming as even the authors of this article expected when conceiving the experiment