Life history and chemical ecology of the Warrior wasp Synoeca septentrionalis (Hymenoptera : Vespidae, Epiponini)

Swarm-founding ‘Warrior wasps’ (Synoeca spp.) are found throughout the tropical regions of South America, are much feared due to their aggressive nest defence and painful sting. There are only five species of Synoeca, all construct distinctive nests that consist of a single sessile comb built onto the surface of a tree or rock face, which is covered by a ribbed envelope. Although locally common, research into this group is just starting. We studied eight colonies of Synoeca septentrionalis, a species recently been described from Brazil. A new colony is established by a swarm of 52 to 140 adults that constructs a colony containing around 200 brood cells. The largest colony collected containing 865 adults and over 1400 cells. The number of queen’s present among the eight colonies varied between 3 and 58 and no clear association between colony development and queen number was detected. Workers and queens were morphologically indistinguishable, but differences in their cuticular hydrocarbons were detected, particularly in their (Z)-9-alkenes. The simple cuticular profile, multiple queens, large size and small number of species makes the ‘Warrior wasps’ an excellent model group for further chemical ecology studies of swarm-founding wasps

Predictors for neonatal death in the rural areas of Shaanxi Province of Northwestern China: a cross-sectional study

Background Almost all (99%) neonatal deaths arise in low-income and middle-income countries. Approximately 450 new-born children die every hour, which is mainly from preventable causes. There has been increased recognition of the need for these countries to implement public health interventions that specifically target neonatal deaths. The purpose of this paper is to identify the predictors of neonatal death in Type 4 rural (poorest) counties in Shaanxi Province of northwestern China. Methods A cross-sectional study was conducted in Shaanxi Province, China. A single-stage survey design was identified to estimate standard errors. Because of concern about the complex sample design, the data were analysed using multivariate logistic regression analysis. Socioeconomic and maternal health service utilization factors were added into the model. Results During the study period, a total of 4750 women who delivered in the past three years were randomly selected for interview in the five counties. There were 4880 live births and 54 neonatal deaths identified. In the multiple logistic regression, the odds of neonatal death was significantly higher for multiparous women (OR = 2.77; 95% CI: 1.34, 5.70) and women who did not receive antennal health care in the first trimester of pregnancy (OR = 2.49; 95% CI: 1.41, 4.40). Women who gave birth in a county-level hospital (OR = 0.18; 95% CI: 0.04, 0.86) and had junior high school or higher education level (OR = 0.20; 95% CI: 0.05, 0.84) were significantly protected from neonatal death. Conclusions Public health interventions directed at reducing neonatal death should address the socioeconomic factors and maternal health service utilization, which significantly influence neonatal mortality in rural China. Multipara, low educational level of the women, availability of prenatal visits in the first trimester of pregnancy and hospital delivery should be considered when planning the interventions to reduce the neonatal mortality in rural areas

Gene Expression Patterns of Dengue Virus-Infected Children from Nicaragua Reveal a Distinct Signature of Increased Metabolism

Dengue is a widespread viral disease for which over 3 billion people are at risk. There are no drug treatments or vaccines available for this disease. It is also difficult for physicians to predict which patients are at highest risk for the severe manifestations known as dengue hemorrhagic fever (DHF) and dengue shock syndrome (DSS). We used genome-wide transcriptional profiling analysis to study peripheral blood responses to dengue among patients from Nicaragua. We found that patients with severe manifestations involving shock had very different transcriptional profiles from dengue patients with mild and moderate illness. We then compared our results with other microarray experiments on dengue patients available from public databases and confirmed that dengue is often associated with large changes to the metabolic processes within cells. This approach could identify prognostic markers for severe dengue as well as provide a better understanding of the pathophysiology associated with different grades of disease severity

Classification of Dengue Fever Patients Based on Gene Expression Data Using Support Vector Machines

Background: Symptomatic infection by dengue virus (DENV) can range from dengue fever (DF) to dengue haemorrhagic fever (DHF), however, the determinants of DF or DHF progression are not completely understood. It is hypothesised that host innate immune response factors are involved in modulating the disease outcome and the expression levels of genes involved in this response could be used as early prognostic markers for disease severity. Methodology/Principal Findings: mRNA expression levels of genes involved in DENV innate immune responses were measured using quantitative real time PCR (qPCR). Here, we present a novel application of the support vector machines (SVM) algorithm to analyze the expression pattern of 12 genes in peripheral blood mononuclear cells (PBMCs) of 28 dengue patients (13 DHF and 15 DF) during acute viral infection. The SVM model was trained using gene expression data of these genes and achieved the highest accuracy of ,85% with leave-one-out cross-validation. Through selective removal of gene expression data from the SVM model, we have identified seven genes (MYD88, TLR7, TLR3, MDA5, IRF3, IFN-a and CLEC5A) that may be central in differentiating DF patients from DHF, with MYD88 and TLR7 observed to be the most important. Though the individual removal of expression data of five other genes had no impact on the overall accuracy, a significant combined role was observed when the SVM model of the two main genes (MYD88 and TLR7) was re-trained to include the five genes, increasing the overall accuracy to ,96%. Conclusions/Significance: Here, we present a novel use of the SVM algorithm to classify DF and DHF patients, as well as to elucidate the significance of the various genes involved. It was observed that seven genes are critical in classifying DF and DHF patients: TLR3, MDA5, IRF3, IFN-a, CLEC5A, and the two most important MYD88 and TLR7. While these preliminary results are promising, further experimental investigation is necessary to validate their specific roles in dengue disease