ROBustness In Network (robin): an R Package for Comparison and Validation of Communities

In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset

UK investment trust portfolio strategies before the First World War

UK investment trust companies were at the forefront of financial innovation during the so-called first globalization era before the First World War. This study examines in detail their portfolio strategies using a unique dataset of 115 portfolio observations for 30 different investment trust companies, comprising a total of 32,708 portfolio holdings. Our results reveal strong performance and relatively sophisticated asset management, which was based on a mixture of a buy-and-hold investment strategy and active portfolio management. Investment trusts employed global rather than domestic diversification. The early predominant investment in bonds in the 1880s gradually declined in favour of ordinary and preferred shares. North and Latin American markets were the main geographical target of UK investment trusts, with less appetite for domestic investments and negligible interest in continental European financial securities. There is significant cross-sectional variation in asset allocation between investment trusts; they thus avoided herding behaviour in portfolio choice and developed a wide range of different portfolio strategies

Violent and non-violent crimes against sex workers : the influence of the sex market on reporting practices in the United Kingdom

Previous research has shown that sex workers experience extremely high rates of victimization but are often reluctant to report their experiences to the police. This paper explores how the markets in which sex workers operate in the United Kingdom impact upon the violent and non-violent crimes they report to a national support organization and their willingness to report victimization to the police. We use a secondary quantitative data analysis of 2,056 crime reports submitted to the UK National Ugly Mugs (NUM) scheme between 2012 and 2016. The findings indicate that although violence is the most common crime type reported to NUM, sex workers operating in different markets report varying relative proportions of different types of victimization. We also argue that there is some variation in the level of willingness to share reports with the police across the different sex markets, even when the type crime, presence of violence, and other variables are taken into account. Our finding that street sex workers are most likely to report victimization directly to the police challenges previously held assumptions that criminalization is the key factor preventing sex workers from engaging with the police. Key words: sex work; violence; policing; reported victimizatio

Coping with poachers in European stalked barnacle fisheries: Insights from a stakeholder workshop.

In January 2020, a stakeholder workshop was organized as a knowledge sharing strategy among European stalked barnacle fisheries. Management of this fishery differs greatly among regions and ranges from less organized and governed at large scales (>100 km, coasts of SW Portugal and Brittany in France) to highly participatory systems which are co-managed at small spatial scales (10′s km and less, Galicia and Asturias). Discussions revealed that poaching is ubiquitous, hard to eradicate, and adapts to all types of management. The stakeholders identified some key management initiatives in the fight against poaching: granting professional harvesters with exclusive access to the resource, increasing social capital among harvesters through tenure systems (e.g. Territorial Use Rights in Fisheries) that empower them as stewards of their resource and intensi- fication of surveillance with the active participation of the harvesters. Furthermore, increased cooperation be- tween fishers associations and regional fisheries authorities, improved legal frameworks, adoption of new technologies and the implementation of market-based solutions can also help coping with this systemic problem

How Do They Do It? – Understanding the Success of Marine Invasive Species

From the depths of the oceans to the shallow estuaries and wetlands of our coasts, organisms of the marine environment are teeming with unique adaptations to cope with a multitude of varying environmental conditions. With millions of years and a vast volume of water to call their home, they have become quite adept at developing specialized and unique techniques for survival and – given increasing human mediated transport – biological invasions. A growing world human population and a global economy drives the transportation of goods across the oceans and with them invasive species via ballast water and attached to ship hulls. In any given 24-hour period, there are about 10,000 species being transported across different biogeographic regions. If any of them manage to take hold and establish a range in an exotic habitat, the implications for local ecosystems can be costly. This review on marine invasions highlights trends among successful non-indigenous species (NIS), from vectors of transport to ecological and physiological plasticity. Apart from summarizing patterns of successful invasions, it discusses the implications of how successfully established NIS impact the local environment, economy and human health. Finally, it looks to the future and discusses what questions need to be addressed and what models can tell us about what the outlook on future marine invasions is

ROBustness In Network(robin): an R package for Comparison and Validation of Communities

In network analysis, many community detection algorithms have been developed. However, their implementation leaves unaddressed the question of the statistical validation of the results. Here, we present robin (ROBustness In Network), an R package to assess the robustness of the community structure of a network found by one or more methods to give indications about their reliability. The procedure initially detects if the community structure found by a set of algorithms is statistically significant and then compares two selected detection algorithms on the same graph to choose the one that better fits the network of interest. We demonstrate the use of our package on the American College Football benchmark dataset

Decoding gender dimorphism of the human brain using multimodal anatomical and diffusion MRI data

The female brain contains a larger proportion of gray matter tissue, while the male brain comprises more white matter. Findings like these have sparked increasing interest in studying dimorphism of the human brain: the general effect of gender on aspects of brain architecture. To date, the vast majority of imaging studies is based on unimodal MR images and typically limited to a small set of either gray or white matter regions-of-interest. The morphological content of magnetic resonance (MR) images, however, strongly depends on the underlying contrast mechanism. Consequently, in order to fully capture gender-specific morphological differences in distinct brain tissues, it might prove crucial to consider multiple imaging modalities simultaneously. This study introduces a novel approach to perform such multimodal classification incorporating the relative strengths of each modality-specific physical aperture to tissue properties. To illustrate our approach, we analyzed multimodal MR images (T(1)-, T(2)-, and diffusion-weighted) from 121 subjects (67 females) using a linear support vector machine with a mass-univariate feature selection procedure. We demonstrate that the combination of different imaging modalities yields a significantly higher balanced classification accuracy (96%) than any one modality by itself (83%-88%). Our results do not only confirm previous morphometric findings; crucially, they also shed new light on the most discriminative features in gray-matter volume and microstructure in cortical and subcortical areas. Specifically, we find that gender disparities are primarily distributed along brain networks thought to be involved in social cognition, reward-based learning, decision-making, and visual-spatial skills

FTO gene variant modulates the neural correlates of visual food perception

Variations in the fat mass and obesity associated (FTO) gene are currently the strongest knowngenetic factor predisposing humans to non-monogenic obesity. Recent experiments have linked these variants to a broad spectrum of behavioural alterations, including food choice and substance abuse. Yet, the underlying neurobiological mechanisms by which these genetic variations influence body weight remain elusive. Here, we explore the brain structural substrate of the obesity-predisposing rs9939609 T/A variant of the FTO gene in non-obese subjects bymeans ofmultivariate classification and use fMRI to investigate genotype-specific differences in neural food-cue reactivity by analysing correlates of a visual food perception task. Our findings demonstrate that MRI-derived measures of morphology along middle and posterior fusiform gyrus (FFG) are highly predictive for FTO at-risk allele carriers, who also show enhanced neural responses elicited by food cues in the same posterior FFG area. In brief, these findings provide first-time evidence for FTO-specific differences in both brain structure and function already in non-obese individuals, thereby contributing to amechanistic understanding of why FTO is a predisposing factor for obesity. (C) 2015 Elsevier Inc. All rights reserved