Fake supersymmetry versus Hamilton-Jacobi

We explain when the first-order Hamilton-Jacobi equations for black holes (and domain walls) in (gauged) supergravity, reduce to the usual first-order equations derived from a fake superpotential. This turns out to be equivalent to the vanishing of a newly found constant of motion and we illustrate this with various examples. We show that fake supersymmetry is a necessary condition for having physically sensible extremal black hole solutions. We furthermore observe that small black holes become scaling solutions near the horizon. When combined with fake supersymmetry, this leads to a precise extension of the attractor mechanism to small black holes: The attractor solution is such that the scalars move on specific curves, determined by the black hole charges, that are purely geodesic, although there is a non-zero potential.Comment: 20 pages, v2: Typos corrected, references adde

A systematic review investigating fatigue, psychological and cognitive impairment following TIA and minor stroke:protocol paper

Approximately 20,000 people have a transient ischemic attack (TIA) and 23,375 have a minor stroke in England each year. Fatigue, psychological and cognitive impairments are well documented post-stroke. Evidence suggests that TIA and minor stroke patients also experience these impairments; however, they are not routinely offered relevant treatment. This systematic review aims to: (1) establish the prevalence of fatigue, anxiety, depression, post-traumatic stress disorder (PTSD) and cognitive impairment following TIA and minor stroke and to investigate the temporal course of these impairments; (2) explore impact on quality of life (QoL), change in emotions and return to work; (3) identify where further research is required and to potentially inform an intervention study

Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models

<p>Abstract</p> <p>Background</p> <p>In recent years, artificial neural network is advocated in modeling complex multivariable relationships due to its ability of fault tolerance; while decision tree of data mining technique was recommended because of its richness of classification arithmetic rules and appeal of visibility. The aim of our research was to compare the performance of ANN and decision tree models in predicting hospital charges on gastric cancer patients.</p> <p>Methods</p> <p>Data about hospital charges on 1008 gastric cancer patients and related demographic information were collected from the First Affiliated Hospital of Anhui Medical University from 2005 to 2007 and preprocessed firstly to select pertinent input variables. Then artificial neural network (ANN) and decision tree models, using same hospital charge output variable and same input variables, were applied to compare the predictive abilities in terms of mean absolute errors and linear correlation coefficients for the training and test datasets. The transfer function in ANN model was sigmoid with 1 hidden layer and three hidden nodes.</p> <p>Results</p> <p>After preprocess of the data, 12 variables were selected and used as input variables in two types of models. For both the training dataset and the test dataset, mean absolute errors of ANN model were lower than those of decision tree model (1819.197 vs. 2782.423, 1162.279 vs. 3424.608) and linear correlation coefficients of the former model were higher than those of the latter (0.955 vs. 0.866, 0.987 vs. 0.806). The predictive ability and adaptive capacity of ANN model were better than those of decision tree model.</p> <p>Conclusion</p> <p>ANN model performed better in predicting hospital charges of gastric cancer patients of China than did decision tree model.</p

Grand Challenges: Improving HIV Treatment Outcomes by Integrating Interventions for Co-Morbid Mental Illness.

In the fourth article of a five-part series providing a global perspective on integrating mental health, Sylvia Kaaya and colleagues discuss the importance of integrating mental health interventions into HIV prevention and treatment platforms. Please see later in the article for the Editors' Summary

Use of genomic DNA control features and predicted operon structure in microarray data analysis: ArrayLeaRNA – a Bayesian approach

<p>Abstract</p> <p>Background</p> <p>Microarrays are widely used for the study of gene expression; however deciding on whether observed differences in expression are significant remains a challenge.</p> <p>Results</p> <p>A computing tool (ArrayLeaRNA) has been developed for gene expression analysis. It implements a Bayesian approach which is based on the Gumbel distribution and uses printed genomic DNA control features for normalization and for estimation of the parameters of the Bayesian model and prior knowledge from predicted operon structure. The method is compared with two other approaches: the classical LOWESS normalization followed by a two fold cut-off criterion and the OpWise method (Price, et al. 2006. BMC Bioinformatics. 7, 19), a published Bayesian approach also using predicted operon structure. The three methods were compared on experimental datasets with prior knowledge of gene expression. With ArrayLeaRNA, data normalization is carried out according to the genomic features which reflect the results of equally transcribed genes; also the statistical significance of the difference in expression is based on the variability of the equally transcribed genes. The operon information helps the classification of genes with low confidence measurements.</p> <p>ArrayLeaRNA is implemented in Visual Basic and freely available as an Excel add-in at <url>http://www.ifr.ac.uk/safety/ArrayLeaRNA/</url></p> <p>Conclusion</p> <p>We have introduced a novel Bayesian model and demonstrated that it is a robust method for analysing microarray expression profiles. ArrayLeaRNA showed a considerable improvement in data normalization, in the estimation of the experimental variability intrinsic to each hybridization and in the establishment of a clear boundary between non-changing and differentially expressed genes. The method is applicable to data derived from hybridizations of labelled cDNA samples as well as from hybridizations of labelled cDNA with genomic DNA and can be used for the analysis of datasets where differentially regulated genes predominate.</p

Environmental Costs of Government-Sponsored Agrarian Settlements in Brazilian Amazonia

Brazil has presided over the most comprehensive agrarian reform frontier colonization program on Earth, in which ~1.2 million settlers have been translocated by successive governments since the 1970's, mostly into forested hinterlands of Brazilian Amazonia. These settlements encompass 5.3% of this ~5 million km2 region, but have contributed with 13.5% of all land conversion into agropastoral land uses. The Brazilian Federal Agrarian Agency (INCRA) has repeatedly claimed that deforestation in these areas largely predates the sanctioned arrival of new settlers. Here, we quantify rates of natural vegetation conversion across 1911 agrarian settlements allocated to 568 Amazonian counties and compare fire incidence and deforestation rates before and after the official occupation of settlements by migrant farmers. The timing and spatial distribution of deforestation and fires in our analysis provides irrefutable chronological and spatially explicit evidence of agropastoral conversion both inside and immediately outside agrarian settlements over the last decade. Deforestation rates are strongly related to local human population density and road access to regional markets. Agrarian settlements consistently accelerated rates of deforestation and fires, compared to neighboring areas outside settlements, but within the same counties. Relocated smallholders allocated to forest areas undoubtedly operate as pivotal agents of deforestation, and most of the forest clearance occurs in the aftermath of government-induced migration

Dynamics of direct inter-pack encounters in endangered African wild dogs

Aggressive encounters may have important life history consequences due to the potential for injury and death, disease transmission, dispersal opportunities or exclusion from key areas of the home range. Despite this, little is known of their detailed dynamics, mainly due to the difficulties of directly observing encounters in detail. Here, we describe detailed spatial dynamics of inter-pack encounters in African wild dogs (Lycaon pictus), using data from custom-built high-resolution GPS collars in 11 free-ranging packs. On average, each pack encountered another pack approximately every 7 weeks and met each neighbour twice each year. Surprisingly, intruders were more likely to win encounters (winning 78.6% of encounters by remaining closer to the site in the short term). However, intruders did tend to move farther than residents toward their own range core in the short-term (1 h) post-encounter, and if this were used to indicate losing an encounter, then the majority (73.3%) of encounters were won by residents. Surprisingly, relative pack size had little effect on encounter outcome, and injuries were rare (<15% of encounters). These results highlight the difficulty of remotely scoring encounters involving mobile participants away from static defendable food resources. Although inter-pack range overlap was reduced following an encounter, encounter outcome did not seem to drive this, as both packs shifted their ranges post-encounter. Our results indicate that inter-pack encounters may be lower risk than previously suggested and do not appear to influence long-term movement and ranging