Modelling verbal aggression, physical aggression and inappropriate sexual behaviour after acquired brain injury

Understanding the underpinnings of behavioural disturbances following brain injury is of considerable importance, but little at present is known about the relationships between different types of behavioural disturbances. Here, we take a novel approach to this issue by using confirmatory factor analysis to elucidate the architecture of verbal aggression, physical aggression and inappropriate sexual behaviour using systematic records made across an eight-week observation period for a large sample (n = 301) of individuals with a range of brain injuries. This approach offers a powerful test of the architecture of these behavioural disturbances by testing the fit between observed behaviours and different theoretical models. We chose models that reflected alternative theoretical perspectives based on generalized disinhibition (Model 1), a difference between aggression and inappropriate sexual behaviour (Model 2), or on the idea that verbal aggression, physical aggression and inappropriate sexual behaviour reflect broadly distinct but correlated clinical phenomena (Model 3). Model 3 provided the best fit to the data indicating that these behaviours can be viewed as distinct, but with substantial overlap. These data are important both for developing models concerning the architecture of behaviour as well as for clinical management in individuals with brain injury

EMA - A R package for Easy Microarray data analysis

<p>Abstract</p> <p>Background</p> <p>The increasing number of methodologies and tools currently available to analyse gene expression microarray data can be confusing for non specialist users.</p> <p>Findings</p> <p>Based on the experience of biostatisticians of Institut Curie, we propose both a clear analysis strategy and a selection of tools to investigate microarray gene expression data. The most usual and relevant existing R functions were discussed, validated and gathered in an easy-to-use R package (EMA) devoted to gene expression microarray analysis. These functions were improved for ease of use, enhanced visualisation and better interpretation of results.</p> <p>Conclusions</p> <p>Strategy and tools proposed in the EMA R package could provide a useful starting point for many microarrays users. EMA is part of Comprehensive R Archive Network and is freely available at <url>http://bioinfo.curie.fr/projects/ema/</url>.</p

Regional differences in mitochondrial DNA methylation in human post-mortem brain tissue

Background: DNA methylation is an important epigenetic mechanism involved in gene regulation, with alterations in DNA methylation in the nuclear genome being linked to numerous complex diseases. Mitochondrial DNA methylation is a phenomenon that is receiving ever-increasing interest, particularly in diseases characterized by mitochondrial dysfunction; however, most studies have been limited to the investigation of specific target regions. Analyses spanning the entire mitochondrial genome have been limited, potentially due to the amount of input DNA required. Further, mitochondrial genetic studies have been previously confounded by nuclear-mitochondrial pseudogenes. Methylated DNA Immunoprecipitation Sequencing is a technique widely used to profile DNA methylation across the nuclear genome; however, reads mapped to mitochondrial DNA are often discarded. Here, we have developed an approach to control for nuclear-mitochondrial pseudogenes within Methylated DNA Immunoprecipitation Sequencing data. We highlight the utility of this approach in identifying differences in mitochondrial DNA methylation across regions of the human brain and pre-mortem blood. Results: We were able to correlate mitochondrial DNA methylation patterns between the cortex, cerebellum and blood. We identified 74 nominally significant differentially methylated regions (p < 0.05) in the mitochondrial genome, between anatomically separate cortical regions and the cerebellum in matched samples (N = 3 matched donors). Further analysis identified eight significant differentially methylated regions between the total cortex and cerebellum after correcting for multiple testing. Using unsupervised hierarchical clustering analysis of the mitochondrial DNA methylome, we were able to identify tissue-specific patterns of mitochondrial DNA methylation between blood, cerebellum and cortex. Conclusions: Our study represents a comprehensive analysis of the mitochondrial methylome using pre-existing Methylated DNA Immunoprecipitation Sequencing data to identify brain region-specific patterns of mitochondrial DNA methylation

Adjustment for time-invariant and time-varying confounders in ‘unexplained residuals’ models for longitudinal data within a causal framework and associated challenges

‘Unexplained residuals’ models have been used within lifecourse epidemiology to model an exposure measured longitudinally at several time points in relation to a distal outcome. It has been claimed that these models have several advantages, including: the ability to estimate multiple total causal effects in a single model, and additional insight into the effect on the outcome of greater-than-expected increases in the exposure compared to traditional regression methods. We evaluate these properties and prove mathematically how adjustment for confounding variables must be made within this modelling framework. Importantly, we explicitly place unexplained residual models in a causal framework using directed acyclic graphs. This allows for theoretical justification of appropriate confounder adjustment and provides a framework for extending our results to more complex scenarios than those examined in this paper. We also discuss several interpretational issues relating to unexplained residual models within a causal framework. We argue that unexplained residual models offer no additional insights compared to traditional regression methods, and, in fact, are more challenging to implement; moreover, they artificially reduce estimated standard errors. Consequently, we conclude that unexplained residual models, if used, must be implemented with great care

Cryptic multiple hypotheses testing in linear models: overestimated effect sizes and the winner's curse

Fitting generalised linear models (GLMs) with more than one predictor has become the standard method of analysis in evolutionary and behavioural research. Often, GLMs are used for exploratory data analysis, where one starts with a complex full model including interaction terms and then simplifies by removing non-significant terms. While this approach can be useful, it is problematic if significant effects are interpreted as if they arose from a single a priori hypothesis test. This is because model selection involves cryptic multiple hypothesis testing, a fact that has only rarely been acknowledged or quantified. We show that the probability of finding at least one ‘significant’ effect is high, even if all null hypotheses are true (e.g. 40% when starting with four predictors and their two-way interactions). This probability is close to theoretical expectations when the sample size (N) is large relative to the number of predictors including interactions (k). In contrast, type I error rates strongly exceed even those expectations when model simplification is applied to models that are over-fitted before simplification (low N/k ratio). The increase in false-positive results arises primarily from an overestimation of effect sizes among significant predictors, leading to upward-biased effect sizes that often cannot be reproduced in follow-up studies (‘the winner's curse’). Despite having their own problems, full model tests and P value adjustments can be used as a guide to how frequently type I errors arise by sampling variation alone. We favour the presentation of full models, since they best reflect the range of predictors investigated and ensure a balanced representation also of non-significant results

Community views about routine HIV testing and antiretroviral treatment in Botswana: signs of progress from a cross sectional study

<p>Abstract</p> <p>Background</p> <p>The Botswana government began providing free antiretroviral therapy (ART) in 2002 and in 2004 introduced routine HIV testing (RHT) in government health facilities, aiming to increase HIV testing and uptake of ART. There have been concerns that the RHT programme might be coercive, lead to increased partner violence, and drive people away from government health services.</p> <p>Methods</p> <p>We conducted a household survey of 1536 people in a stratified random sample of communities across Botswana, asking about use and experience of government health services, views about RHT, views about ART, and testing for HIV in the last 12 months. Focus groups further discussed issues about ART.</p> <p>Results</p> <p>Some 81% of respondents had visited a government clinic within the last 24 months. Of these 92% were satisfied with the service, 96% felt they were treated with respect and 90% were comfortable about confidentiality. Almost all respondents said they would choose a government clinic for treatment of AIDS.</p> <p>Nearly one half (47%) thought they were at risk of HIV. Those who had experienced partner violence within the last 12 months were more likely to think themselves at risk. One half of those who had visited a government facility in the last 24 months were offered HIV tests, and nearly half were tested. A few (8%) of those who were not asked thought they were tested. Most people (79%) had heard of RHT and 94% were in favour of it. Over one half (55%) of the entire sample had been tested for HIV within the last 12 months, one half of these through RHT. Women were more likely to have been tested.</p> <p>Nearly everyone (94%) had heard of ART and thought it could help AIDS. Focus groups identified problems of access to ART due to distance from treatment centres and long queues in the centres.</p> <p>Conclusion</p> <p>Public awareness and approval of RHT was very high. The high rate of RHT has contributed to the overall high rate of HIV testing. The government's programme to increase HIV testing and uptake of ART is apparently working well. However, turning the tide of the epidemic will also require further concerted efforts to reduce the rate of new HIV infections.</p

Both male and female identity influence variation in male signalling effort

<p>Abstract</p> <p>Background</p> <p>Male sexual displays play an important role in sexual selection by affecting reproductive success. However, for such displays to be useful for female mate choice, courtship should vary more among than within individual males. In this regard, a potentially important source of within male variation is adjustment of male courtship effort in response to female traits. Accordingly, we set out to dissect sources of variation in male courtship effort in a fish, the desert goby (<it>Chlamydogobius eremius</it>). We did so by designing an experiment that allowed simultaneous estimation of within and between male variation in courtship, while also assessing the importance of the males and females as sources of courtship variation.</p> <p>Results</p> <p>Although males adjusted their courtship depending on the identity of the female (a potentially important source of within-male variation), among-male differences were considerably greater. In addition, male courtship effort towards a pair of females was highly repeatable over a short time frame.</p> <p>Conclusion</p> <p>Despite the plasticity in male courtship effort, courtship displays had the potential to reliably convey information about the male to mate-searching females. Our experiment therefore underscores the importance of addressing the different sources contributing to variation in the expression of sexually-selected traits.</p

Spanish Validation of the Flourishing Scale in the General Population

Well-being research and its measurement have grown in the last two decades. The objective of this study was to adapt and validate the Flourishing Scale in a sample of Spanish adults. This was a cross-sectional study using a non-probabilistic sample of 999 Spanish general adult population participants. The psychometric properties of the scale were analysed from an exploratory and confirmatory perspective. Exploratory factor analysis showed a one-factor solution explaining 42.3% of the variance; an internal consistency of .846; temporal reliability correlation of .749; convergent validity with the Satisfaction with Life Scale of .521 and criterion validity with positive and negative affect (PANAS), pessimism and optimism (LOT-R) ranging from .270 to .488. Confirmatory factor analysis testing the one-factor solution showed a χ2 of 65.57 df = 20; CFI of .982, RMSEA of .06, average variance extracted index of .518 and composite reliability index of .841. Results showed that the Spanish version of the FS is a reliable and valid method for measuring high levels of well-bein

Quantitative measurements of inequality in geographic accessibility to pediatric care in Oita Prefecture, Japan: Standardization with complete spatial randomness

<p>Abstract</p> <p>Background</p> <p>A quantitative measurement of inequality in geographic accessibility to pediatric care as well as that of mean distance or travel time is very important for priority setting to ensure fair access to pediatric facilities. However, conventional techniques for measuring inequality is inappropriate in geographic settings. Since inequality measures of access distance or travel time is strongly influenced by the background geographic distribution patterns, they cannot be directly used for regional comparisons of geographic accessibility. The objective of this study is to resolve this issue by using a standardization approach.</p> <p>Methods</p> <p>Travel times to the nearest pediatric care were calculated for all children in Oita Prefecture, Japan. Relative mean differences were considered as the inequality measure for secondary medical service areas, and were standardized with an expected value estimated from a Monte Carlo simulation based on complete spatial randomness.</p> <p>Results</p> <p>The observed mean travel times in the area considered averaged 4.50 minutes, ranging from 1.83 to 7.02 minutes. The mean of the observed inequality measure was 1.1, ranging from 0.9 to 1.3. The expected values of the inequality measure varied according to the background geographic distribution pattern of children, which ranged from 0.3 to 0.7. After standardizing the observed inequality measure with the expected one, we found that the ranks of the inequality measure were reversed for the observed areas.</p> <p>Conclusions</p> <p>Using the indicator proposed in this paper, it is possible to compare the inequality in geographic accessibility among regions. Such a comparison may facilitate priority setting in health policy and planning.</p

Immune cell constitution in bone marrow microenvironment predicts outcome in adult ALL

As novel immunological treatments are gaining a foothold in the treatment of acute lymphoblastic leukemia (ALL), it is elemental to examine ALL immunobiology in more detail. We used multiplexed immunohistochemistry (mIHC) to study the immune contexture in adult precursor B cell ALL bone marrow (BM). In addition, we developed a multivariate risk prediction model that stratified a poor survival group based on clinical parameters and mIHC data. We analyzed BM biopsy samples of ALL patients (n = 52) and healthy controls (n = 14) using mIHC with 30 different immunophenotype markers and computerized image analysis. In ALL BM, the proportions of M1-like macrophages, granzyme B+CD57+CD8+ T cells, and CD27+ T cells were decreased, whereas the proportions of myeloid-derived suppressor cells and M2-like macrophages were increased. Also, the expression of checkpoint molecules PD1 and CTLA4 was elevated. In the multivariate model, age, platelet count, and the proportion of PD1+TIM3+ double-positive CD4+ T cells differentiated a poor survival group. These results were validated by flow cytometry in a separate cohort (n = 31). In conclusion, the immune cell contexture in ALL BM differs from healthy controls. CD4+PD1+TIM3+ T cells were independent predictors of poor outcome in our multivariate risk model, suggesting that PD1 might serve as an attractive immuno-oncological target in B-ALL.Peer reviewe