Non-equilibrium dynamics and floral trait interactions shape extant angiosperm diversity.

Why are some traits and trait combinations exceptionally common across the tree of life, whereas others are vanishingly rare? The distribution of trait diversity across a clade at any time depends on the ancestral state of the clade, the rate at which new phenotypes evolve, the differences in speciation and extinction rates across lineages, and whether an equilibrium has been reached. Here we examine the role of transition rates, differential diversification (speciation minus extinction) and non-equilibrium dynamics on the evolutionary history of angiosperms, a clade well known for the abundance of some trait combinations and the rarity of others. Our analysis reveals that three character states (corolla present, bilateral symmetry, reduced stamen number) act synergistically as a key innovation, doubling diversification rates for lineages in which this combination occurs. However, this combination is currently less common than predicted at equilibrium because the individual characters evolve infrequently. Simulations suggest that angiosperms will remain far from the equilibrium frequencies of character states well into the future. Such non-equilibrium dynamics may be common when major innovations evolve rarely, allowing lineages with ancestral forms to persist, and even outnumber those with diversification-enhancing states, for tens of millions of years

Combinatorial quorum sensing allows bacteria to resolve their social and physical environment

Quorum sensing (QS) is a cell–cell communication system that controls gene expression in many bacterial species, mediated by diffusible signal molecules. Although the intracellular regulatory mechanisms of QS are often well-understood, the functional roles of QS remain controversial. In particular, the use of multiple signals by many bacterial species poses a serious challenge to current functional theories. Here, we address this challenge by showing that bacteria can use multiple QS signals to infer both their social (density) and physical (mass-transfer) environment. Analytical and evolutionary simulation models show that the detection of, and response to, complex social/physical contrasts requires multiple signals with distinct half-lives and combinatorial (nonadditive) responses to signal concentrations. We test these predictions using the opportunistic pathogen Pseudomonas aeruginosa and demonstrate significant differences in signal decay betweeallyn its two primary signal molecules, as well as diverse combinatorial responses to dual-signal inputs. QS is associated with the control of secreted factors, and we show that secretome genes are preferentially controlled by synergistic “AND-gate” responses to multiple signal inputs, ensuring the effective expression of secreted factors in high-density and low mass-transfer environments. Our results support a new functional hypothesis for the use of multiple signals and, more generally, show that bacteria are capable of combinatorial communication

Meningococcal disease in children in Merseyside, England:a 31 year descriptive study

Meningococcal disease (MCD) is the leading infectious cause of death in early childhood in the United Kingdom, making it a public health priority. MCD most commonly presents as meningococcal meningitis (MM), septicaemia (MS), or as a combination of the two syndromes (MM/MS). We describe the changing epidemiology and clinical presentation of MCD, and explore associations with socioeconomic status and other risk factors. A hospital-based study of children admitted to a tertiary children's centre, Alder Hey Children's Foundation Trust, with MCD, was undertaken between 1977 to 2007 (n = 1157). Demographics, clinical presentations, microbiological confirmation and measures of deprivation were described. The majority of cases occurred in the 1-4 year age group and there was a dramatic fall in serogroup C cases observed with the introduction of the meningococcal C conjugate (MCC) vaccine. The proportion of MS cases increased over the study period, from 11% in the first quarter to 35% in the final quarter. Presentation with MS (compared to MM) and serogroup C disease (compared to serogroup B) were demonstrated to be independent risk factors for mortality, with odds ratios of 3.5 (95% CI 1.18 to 10.08) and 2.18 (95% CI 1.26 to 3.80) respectively. Cases admitted to Alder Hey were from a relatively more deprived population (mean Townsend score 1.25, 95% CI 1.09 to 1.41) than the Merseyside reference population. Our findings represent one of the largest single-centre studies of MCD. The presentation of MS is confirmed to be a risk factor of mortality from MCD. Our study supports the association between social deprivation and MCD

Cholera outbreak in districts around Lake Chilwa, Malawi: Lessons learned

BackgroundCholera is endemic in Malawi with seasonal outbreaks during the wet season. People living around Lake Chilwa rely on the lake for their water supply. From May 2009 to May 2010, a cholera outbreak occurred in fishing communities around Lake Chilwa. This paper describes the outbreak response and lessons learned for prevention and management of future outbreaks.MethodsStarting in January 2010, Médecins Sans Frontières (MSF) helped District Health Management Teams (DHMTs) to distribute educational materials, water disinfectant and hygiene supplies, and oral rehydration solution (ORS) in fishing communities. MSF also supported case management by mentoring health workers and providing equipment and supplies.ResultsA total of 1,171 cholera cases and 21 deaths were reported in the districts around the lake, with cases also being reported on the Mozambican side of the lake. The attack rate was highest among people living on or around the lake, particularly among fishermen. Samples of lake water had high turbidity conducive to the propagation of Vibrio cholerae.ConclusionA number of practical measures could be taken to prevent future outbreaks and to manage outbreaks more effectively. These measures should address surveillance, environmental management, outbreak preparedness, and case management

Qualitative exploration of the potential for adverse events when using an online peer support network for mental health: Cross-sectional survey

Background: Online peer support networks are a growing area of mental health support for offering social connection, identity, and support. However, it has been reported that not all individuals have a positive experience on such networks. The potential for adverse events within a moderated online peer support network is a new area of research exploration. Objective: The objective of the study was to determine if use of an online moderated peer networks leads to adverse events for users. Methods: Four biannual online surveys (October 2014 to March 2016) were conducted by a large national UK mental health charity, with users of their online peer support network exploring personal safety, moderation, experiences on the site, and how the site could be improved. Data were analyzed using thematic analysis by 2 independent researchers using a priori themes: negative experiences of moderation, social exclusion, contagion, negative interactions with other users, online relationships, co-rumination and collusion, and other. Results: In total, 2353 survey responses were logged with 197 (8.37%) documenting an adverse event of negative experience. A dominant theme of negative experiences of moderation emerged (73/197, 37.1%) with evidence of social exclusion (50/197, 25.4%). Reading user posts was shown to be a cause of worry and distress for a few users, and analysis highlighted several instances of depressogenic and emotional contagion as well as some limited evidence of behavioral contagion (46/197, 23.4%). Very limited evidence of co-rumination (1/197, 0.5%) and no evidence of collusion were identified. Conclusions: Evidence of adverse events was identified at low levels in the sample of respondents, although we have no comparison data to indicate if levels are low compared with comparable platforms. Not all users of online peer support networks find them wholly beneficial. Research must explore what works for whom. The next stage of service development should consider which users may be likely to receive no benefit, or even deteriorate, as a result of using the service

Spatial vegetation patterns and neighborhood competition among woody plants in an East African savanna

The majority of research on savanna vegetation dynamics has focused on the coexistence of woody and herbaceous vegetation. Interactions among woody plants in savannas are relatively poorly understood. We present data from a 10-year longitudinal study of spatially explicit growth patterns of woody vegetation in an East African savanna following exclusion of large herbivores and in the absence of fire. We examined plant spatial patterns and quantified the degree of competition among woody individuals. Woody plants in this semi-arid savanna exhibit strongly clumped spatial distributions at scales of 1 - 5 m. However, analysis of woody plant growth rates relative to their conspecific and heterospecific neighbors revealed evidence for strong competitive interactions at neighborhood scales of up to 5 m for most woody plant species. Thus, woody plants were aggregated in clumps despite significantly decreased growth rates in close proximity to neighbors, indicating that the spatial distribution of woody plants in this region depends on dispersal and establishment processes rather than on competitive, density-dependent mortality. However, our documentation of suppressive effects of woody plants on neighbors also suggests a potentially important role for tree-tree competition in controlling vegetation structure and indicates that the balanced-competition hypothesis may contribute to well-known patterns in maximum tree cover across rainfall gradients in Africa

Vitamin D Status and its Association with Morbidity including Wasting and Opportunistic Illnesses in HIV-Infected Women in Tanzania.

Vitamin D has a potential role in preventing HIV-related complications, based on its extensive involvement in immune and metabolic function, including preventing osteoporosis and premature cardiovascular disease. However, this association has not been examined in large studies or in resource-limited settings. Vitamin D levels were assessed in 884 HIV-infected pregnant women at enrollment in a trial of multivitamin supplementation (excluding vitamin D) in Tanzania. Information on HIV related complications was recorded during follow-up (median, 70 months). Proportional hazards models and generalized estimating equations were used to assess the relationship of vitamin D status with these outcomes. Women with low vitamin D status (serum 25-hydroxyvitamin D<32 ng/mL) had 43% higher risk of reaching a body mass index (BMI) less than 18 kg/m(2) during the first 2 years of follow-up, compared to women with adequate vitamin D levels (hazard ratio [HR]: 1.43; 95% confidence intervals: [1.03-1.99]). The relationship between continuous vitamin D levels and risk of BMI less than 18 kg/m(2) during follow-up was inverse and linear (p=0.03). Women with low vitamin D levels had significantly higher incidence of acute upper respiratory infections (HR: 1.27 [1.04-1.54]) and thrush (HR: 2.74 [1.29-5.83]) diagnosed during the first 2 years of follow-up. Low vitamin D status was a significant risk factor for wasting and HIV-related complications such as thrush during follow-up in this prospective cohort in Tanzania. If these protective associations are confirmed in randomized trials, vitamin D supplementation could represent a simple and inexpensive method to improve health and quality of life of HIV-infected patients, particularly in resource-limited settings

Nonparametric directionality measures for time series and point process data

The need to determine the directionality of interactions between neural signals is a key requirement for analysis of multichannel recordings. Approaches most commonly used are parametric, typically relying on autoregressive models. A number of concerns have been expressed regarding parametric approaches, thus there is a need to consider alternatives. We present an alternative nonparametric approach for construction of directionality measures for bivariate random processes. The method combines time and frequency domain representations of bivariate data to decompose the correlation by direction. Our framework generates two sets of complementary measures, a set of scalar measures, which decompose the total product moment correlation coefficient summatively into three terms by direction and a set of functions which decompose the coherence summatively at each frequency into three terms by direction: forward direction, reverse direction and instantaneous interaction. It can be undertaken as an addition to a standard bivariate spectral and coherence analysis, and applied to either time series or point-process (spike train) data or mixtures of the two (hybrid data). In this paper, we demonstrate application to spike train data using simulated cortical neurone networks and application to experimental data from isolated muscle spindle sensory endings subject to random efferent stimulation

Generalized estimating equations to estimate the ordered stereotype logit model for panel data

By modeling the effects of predictor variables as a multiplicative function of regression parameters being invariant over categories, and category-specific scalar effects, the ordered stereotype logit model is a flexible regression model for ordinal response variables. In this article, we propose a generalized estimating equations (GEE) approach to estimate the ordered stereotype logit model for panel data based on working covariance matrices, which are not required to be correctly specified. A simulation study compares the performance of GEE estimators based on various working correlation matrices and working covariance matrices using local odds ratios. Estimation of the model is illustrated using a real-world dataset. The results from the simulation study suggest that GEE estimation of this model is feasible in medium-sized and large samples and that estimators based on local odds ratios as realized in this study tend to be less efficient compared with estimators based on a working correlation matrix. For low true correlations, the efficiency gains seem to be rather small and if the working covariance structure is too flexible, the corresponding estimator may even be less efficient compared with the GEE estimator assuming independence. Like for GEE estimators more generally, if the true correlations over time are high, then a working covariance structure which is close to the true structure can lead to considerable efficiency gains compared with assuming independence.Peer ReviewedPostprint (published version

Bayesian spatial extreme value analysis of maximum temperatures in County Dublin, Ireland

In this study, we begin a comprehensive characterisation of temperature extremes in Ireland for the period 1981-2010. We produce return levels of anomalies of daily maximum temperature extremes for an area over Ireland, for the 30-year period 1981-2010. We employ extreme value theory (EVT) to model the data using the generalised Pareto distribution (GPD) as part of a three-level Bayesian hierarchical model. We use predictive processes in order to solve the computationally difficult problem of modelling data over a very dense spatial field. To our knowledge, this is the first study to combine predictive processes and EVT in this manner. The model is fit using Markov chain Monte Carlo (MCMC) algorithms. Posterior parameter estimates and return level surfaces are produced, in addition to specific site analysis at synoptic stations, including Casement Aerodrome and Dublin Airport. Observational data from the period 2011-2018 is included in this site analysis to determine if there is evidence of a change in the observed extremes. An increase in the frequency of extreme anomalies, but not the severity, is observed for this period. We found that the frequency of observed extreme anomalies from 2011-2018 at the Casement Aerodrome and Phoenix Park synoptic stations exceed the upper bounds of the credible intervals from the model by 20% and 7% respectively