Diversity, extinction, and threat status in Lagomorphs

A quarter of all lagomorphs (pikas, rabbits, hares and jackrabbits) are threatened with extinction, including several genera that contain only one species. The number of species in a genus correlates with extinction risk in lagomorphs, but not in other mammal groups, and this is concerning because the non-random extinction of small clades disproportionately threatens genetic diversity and phylogenetic history. Here, we use phylogenetic analyses to explore the properties of the lagomorph phylogeny and test if variation in evolution, biogeography and ecology between taxa explains current patterns of diversity and extinction risk. Threat status was not related to body size (and, by inference, its biological correlates), and there was no phylogenetic signal in extinction risk. We show that the lagomorph phylogeny has a similar clade-size distribution to other mammals, and found that genus size was unrelated to present climate, topography, or geographic range size. Extinction risk was greater in areas of higher human population density and negatively correlated with anthropogenically modified habitat. Consistent with this, habitat generalists were less likely to be threatened. Our models did not predict threat status accurately for taxa that experience region-specific threats. We suggest that pressure from human populations is so severe and widespread that it overrides ecological, biological, and geographic variation in extant lagomorphs.

An Intelligent Threat Prevention Framework with Heterogeneous Information

Three issues usually are associated with threat prevention intelligent surveillance systems. First, the fusion and interpretation of large scale incomplete heterogeneous information; second, the demand of effectively predicting suspects’ intention and ranking the potential threats posed by each suspect; third, strategies of allocating limited security resources (e.g., the dispatch of security team) to prevent a suspect’s further actions towards critical assets. However, in the literature, these three issues are seldomly considered together in a sensor network based intelligent surveillance framework. To address
this problem, in this paper, we propose a multi-level decision support framework for in-time reaction in intelligent surveillance. More specifically, based on a multi-criteria event modeling framework, we design a method to predict the most plausible intention of a suspect. Following this, a decision support model is proposed to rank each suspect based on their threat severity and to determine resource allocation strategies. Finally, formal properties are discussed to justify our framework.

The European Union funded NEOShield project: A global approach to near-Earth object impact threat mitigation

Although discussions are underway within the Action Team 14 of the United Nations COPUOS, there is currently no concerted international plan addressing the impact threat from near-Earth objects (NEOs) and how to organize, prepare and implement mitigation measures. We report on a new international project to address impact hazard mitigation issues, being the subject of a proposal submitted to the European Commission in response to the 2011 FP7 Call "Prevention of impacts from near-Earth objects on our planet". Our consortium consists of 13 research institutes, universities, and industrial partners from 6 countries and includes leading US and Russian space organizations. The primary aim of the project, NEOShield, is to investigate in detail the three most promising mitigation techniques: the kinetic impactor, blast deflection,and the gravity tractor, and devise feasible demonstration missions. Furthermore, we will investigate options for an international strategy for implementation when an actual impact threat arises. The NEOShield project was formally accepted by the European Commission on 17 November 2011 and funded with a total of 5.8 million Euros for a period of 3.5 years. The kick-off meeting took place at the DLR Institute of Planetary Research, Berlin, in January 2012. In this paper we present a brief overview of the planned scope of the project.

Outperformance in exchange-traded fund pricing deviations: Generalized control of data snooping bias

An investigation into exchange-traded fund (ETF) outperforrnance during the period 2008-2012 is undertaken utilizing a data set of 288 U.S. traded securities. ETFs are tested for net asset value (NAV) premium, underlying index and market benchmark outperformance, with Sharpe, Treynor, and Sortino ratios employed as risk-adjusted performance measures. A key contribution is the application of an innovative generalized stepdown procedure in controlling for data snooping bias. We find that a large proportion of optimized replication and debt asset class ETFs display risk-adjusted premiums with energy and precious metals focused funds outperforming the S&P 500 market benchmark. 

Lack of genetic structure and evidence for long-distance dispersal in ash (Fraxinus excelsior) populations under threat from an emergent fungal pathogen: implications for restorative planting

Genetic analysis on populations of European ash (Fraxinus excelsior) throughout Ireland was carried out to determine the levels and patterns of genetic diversity in naturally seeded trees in ash woodlands and hedgerows, with the aim of informing conservation and replanting strategies in the face of potential loss of trees as a result of ash dieback. Samples from 33 sites across Northern Ireland and three sites in the Republic of Ireland were genotyped for eight nuclear and ten chloroplast microsatellites. Levels of diversity were high (mean A R = 10.53; mean H O = 0.709; mean H E = 0.765) and were similar to those in Great Britain and continental Europe, whilst levels of population genetic differentiation based on nuclear microsatellites were extremely low (Φ ST = 0.0131). Levels of inbreeding (mean F IS = 0.067) were significantly lower than those reported for populations from Great Britain. Fine-scale analysis of seed dispersal indicated potential for dispersal over hundreds of metres. Our results suggest that ash woodlands across Ireland could be treated as a single management unit, and thus native material from anywhere in Ireland could be used as a source for replanting. In addition, high potential for dispersal has implications for recolonization processes post-ash dieback (Chalara fraxinea) infection, and could aid in our assessment of the capacity of ash to shift its range in response to global climate change.

Failure of a numerical quality assessment scale to identify potential risk of bias in a systematic review: a comparison study

BACKGROUND: Assessing methodological quality of primary studies is an essential component of systematic reviews. Following a systematic review which used a domain based system [United States Preventative Services Task Force (USPSTF)] to assess methodological quality, a commonly used numerical rating scale (Downs and Black) was also used to evaluate the included studies and comparisons were made between quality ratings assigned using the two different methods. Both tools were used to assess the 20 randomized and quasi-randomized controlled trials examining an exercise intervention for chronic musculoskeletal pain which were included in the review. Inter-rater reliability and levels of agreement were determined using intraclass correlation coefficients (ICC). Influence of quality on pooled effect size was examined by calculating the between group standardized mean difference (SMD).

RESULTS: Inter-rater reliability indicated at least substantial levels of agreement for the USPSTF system (ICC 0.85; 95% CI 0.66, 0.94) and Downs and Black scale (ICC 0.94; 95% CI 0.84, 0.97). Overall level of agreement between tools (ICC 0.80; 95% CI 0.57, 0.92) was also good. However, the USPSTF system identified a number of studies (n = 3/20) as "poor" due to potential risks of bias. Analysis revealed substantially greater pooled effect sizes in these studies (SMD -2.51; 95% CI -4.21, -0.82) compared to those rated as "fair" (SMD -0.45; 95% CI -0.65, -0.25) or "good" (SMD -0.38; 95% CI -0.69, -0.08).

CONCLUSIONS: In this example, use of a numerical rating scale failed to identify studies at increased risk of bias, and could have potentially led to imprecise estimates of treatment effect. Although based on a small number of included studies within an existing systematic review, we found the domain based system provided a more structured framework by which qualitative decisions concerning overall quality could be made, and was useful for detecting potential sources of bias in the available evidence.

Data-Augmentation for Reducing Dataset Bias in Person Re-identification

In this paper we explore ways to address the issue of dataset bias in person re-identification by using data augmentation to increase the variability of the available datasets, and we introduce a novel data augmentation method for re-identification based on changing the image background. We show that use of data augmentation can improve the cross-dataset generalisation of convolutional network based re-identification systems, and that changing the image background yields further improvements.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Alcohol attention bias in adolescent social drinkers: an eye tracking study

Rationale
Previous research on attention bias in nondependent social drinkers has focused on adult samples with limited focus on the presence of attention bias for alcohol cues in adolescent social drinkers.
Objectives
The aim of this study was to examine the presence of alcohol attention bias in adolescents and the relationship of this cognitive bias to alcohol use and alcohol-related expectancies.
Methods
Attention bias in adolescent social drinkers and abstainers was measured using an eye tracker during exposure to alcohol and neutral cues. Questionnaires measured alcohol use and explicit alcohol expectancies.
Results
Adolescent social drinkers spent significantly more time fixating to alcohol stimuli compared to controls. Total fixation time to alcohol stimuli varied in accordance with level of alcohol consumption and was significantly associated with more positive alcohol expectancies. No evidence for automatic orienting to alcohol stimuli was found in adolescent social drinkers.
Conclusion
Attention bias in adolescent social drinkers appears to be underpinned by controlled attention suggesting that whilst participants in this study displayed alcohol attention bias comparable to that reported in adult studies, the bias has not developed to the point of automaticity. Initial fixations appeared to be driven by alternative attentional processes which are discussed further.