Fluoxetine during Development Reverses the Effects of Prenatal Stress on Depressive-Like Behavior and Hippocampal Neurogenesis in Adolescence

Depression during pregnancy and the postpartum period is a growing health problem, which affects up to 20% of women. Currently, selective serotonin reuptake inhibitor (SSRIs) medications are commonly used for treatment of maternal depression. Unfortunately, there is very little research on the long-term effect of maternal depression and perinatal SSRI exposure on offspring development. Therefore, the aim of this study was to determine the role of exposure to fluoxetine during development on affective-like behaviors and hippocampal neurogenesis in adolescent offspring in a rodent model of maternal depression. To do this, gestationally stressed and non-stressed Sprague-Dawley rat dams were treated with either fluoxetine (5 mg/kg/day) or vehicle beginning on postnatal day 1 (P1). Adolescent male and female offspring were divided into 4 groups: 1) prenatal stress+fluoxetine exposure, 2) prenatal stress+vehicle, 3) fluoxetine exposure alone, and 4) vehicle alone. Adolescent offspring were assessed for anxiety-like behavior using the Open Field Test and depressive-like behavior using the Forced Swim Test. Brains were analyzed for endogenous markers of hippocampal neurogenesis via immunohistochemistry. Results demonstrate that maternal fluoxetine exposure reverses the reduction in immobility evident in prenatally stressed adolescent offspring. In addition, maternal fluoxetine exposure reverses the decrease in hippocampal cell proliferation and neurogenesis in maternally stressed adolescent offspring. This research provides important evidence on the long-term effect of fluoxetine exposure during development in a model of maternal adversity

TRY plant trait database - enhanced coverage and open access

Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

The Evolution of Compact Binary Star Systems

We review the formation and evolution of compact binary stars consisting of white dwarfs (WDs), neutron stars (NSs), and black holes (BHs). Binary NSs and BHs are thought to be the primary astrophysical sources of gravitational waves (GWs) within the frequency band of ground-based detectors, while compact binaries of WDs are important sources of GWs at lower frequencies to be covered by space interferometers (LISA). Major uncertainties in the current understanding of properties of NSs and BHs most relevant to the GW studies are discussed, including the treatment of the natal kicks which compact stellar remnants acquire during the core collapse of massive stars and the common envelope phase of binary evolution. We discuss the coalescence rates of binary NSs and BHs and prospects for their detections, the formation and evolution of binary WDs and their observational manifestations. Special attention is given to AM CVn-stars -- compact binaries in which the Roche lobe is filled by another WD or a low-mass partially degenerate helium-star, as these stars are thought to be the best LISA verification binary GW sources.Comment: 105 pages, 18 figure

Facial Cosmetics and Attractiveness: Comparing the Effect Sizes of Professionally-Applied Cosmetics and Identity

Forms of body decoration exist in all human cultures. However, in Western societies, women are more likely to engage in appearance modification, especially through the use of facial cosmetics. How effective are cosmetics at altering attractiveness? Previous research has hinted that the effect is not large, especially when compared to the variation in attractiveness observed between individuals due to differences in identity. In order to build a fuller understanding of how cosmetics and identity affect attractiveness, here we examine how professionally-applied cosmetics alter attractiveness and compare this effect with the variation in attractiveness observed between individuals. In Study 1, 33 YouTube models were rated for attractiveness before and after the application of professionally-applied cosmetics. Cosmetics explained a larger proportion of the variation in attractiveness compared with previous studies, but this effect remained smaller than variation caused by differences in attractiveness between individuals. Study 2 replicated the results of the first study with a sample of 45 supermodels, with the aim of examining the effect of cosmetics in a sample of faces with low variation in attractiveness between individuals. While the effect size of cosmetics was generally large, between-person variability due to identity remained larger. Both studies also found interactions between cosmetics and identity-more attractive models received smaller increases when cosmetics were worn. Overall, we show that professionally- applied cosmetics produce a larger effect than self-applied cosmetics, an important theoretical consideration for the field. However, the effect of individual differences in facial appearance is ultimately more important in perceptions of attractiveness

Opposing Effects of Sirtuins on Neuronal Survival: SIRT1-Mediated Neuroprotection Is Independent of Its Deacetylase Activity

Background: Growing evidence suggests that sirtuins, a family of seven distinct NAD-dependent enzymes, are involved in the regulation of neuronal survival. Indeed, SIRT1 has been reported to protect against neuronal death, while SIRT2 promotes neurodegeneration. The effect of SIRTs 3–7 on the regulation of neuronal survival, if any, has yet to be reported. Methodology and Principal Findings: We examined the effect of expressing each of the seven SIRT proteins in healthy cerebellar granule neurons (CGNs) or in neurons induced to die by low potassium (LK) treatment. We report that SIRT1 protects neurons from LK-induced apoptosis, while SIRT2, SIRT3 and SIRT6 induce apoptosis in otherwise healthy neurons. SIRT5 is generally localized to both the nucleus and cytoplasm of CGNs and exerts a protective effect. In a subset of neurons, however, SIRT5 localizes to the mitochondria and in this case it promotes neuronal death. Interestingly, the protective effect of SIRT1 in neurons is not reduced by treatments with nicotinamide or sirtinol, two pharmacological inhibitors of SIRT1. Neuroprotection was also observed with two separate mutant forms of SIRT1, H363Y and H355A, both of which lack deacetylase activity. Furthermore, LK-induced neuronal death was not prevented by resveratrol, a pharmacological activator of SIRT1, at concentrations at which it activates SIRT1. We extended our analysis to HT-22 neuroblastoma cells which can be induced to die by homocysteic acid treatment. While the effects of most of the SIRT proteins were similar to that observed in CGNs, SIRT6 was modestly protective against homocysteic acid toxicity in HT-22 cells. SIRT5 was generally localized in th

The proteome of neural stem cells from adult rat hippocampus

BACKGROUND: Hippocampal neural stem cells (HNSC) play an important role in cerebral plasticity in the adult brain and may contribute to tissue repair in neurological disease. To describe their biological potential with regard to plasticity, proliferation, or differentiation, it is important to know the cellular composition of their proteins, subsumed by the term proteome. RESULTS: Here, we present for the first time a proteomic database for HNSC isolated from the brains of adult rats and cultured for 10 weeks. Cytosolic proteins were extracted and subjected to two-dimensional gel electrophoresis followed by protein identification through mass spectrometry, database search, and gel matching. We could map about 1141 ± 209 (N = 5) protein spots for each gel, of which 266 could be identified. We could group the identified proteins into several functional categories including metabolism, protein folding, energy metabolism and cellular respiration, as well as cytoskeleton, Ca(2+ )signaling pathways, cell cycle regulation, proteasome and protein degradation. We also found proteins belonging to detoxification, neurotransmitter metabolism, intracellular signaling pathways, and regulation of DNA transcription and RNA processing. CONCLUSIONS: The HNSC proteome database is a useful inventory which will allow to specify changes in the cellular protein expression pattern due to specific activated or suppressed pathways during differentiation or proliferation of neural stem cells. Several proteins could be identified in the HNSC proteome which are related to differentiation and plasticity, indicating activated functional pathways. Moreover, we found a protein for which no expression has been described in brain cells before

The Netherlands study of depression in older persons (NESDO); a prospective cohort study

<p>Abstract</p> <p>Background</p> <p>To study late-life depression and its unfavourable course and co morbidities in The Netherlands.</p> <p>Methods</p> <p>We designed the Netherlands Study of Depression in Older Persons (NESDO), a multi-site naturalistic prospective cohort study which makes it possible to examine the determinants, the course and the consequences of depressive disorders in older persons over a period of six years, and to compare these with those of depression earlier in adulthood.</p> <p>Results</p> <p>From 2007 until 2010, the NESDO consortium has recruited 510 depressed and non depressed older persons (≥ 60 years) at 5 locations throughout the Netherlands. Depressed persons were recruited from both mental health care institutes and general practices in order to include persons with late-life depression in various developmental and severity stages. Non-depressed persons were recruited from general practices. The baseline assessment included written questionnaires, interviews, a medical examination, cognitive tests and collection of blood and saliva samples. Information was gathered about mental health outcomes and demographic, psychosocial, biological, cognitive and genetic determinants. The baseline NESDO sample consists of 378 depressed (according to DSM-IV criteria) and 132 non-depressed persons aged 60 through 93 years. 95% had a major depression and 26.5% had dysthymia. Mean age of onset of the depressive disorder was around 49 year. For 33.1% of the depressed persons it was their first episode. 41.0% of the depressed persons had a co morbid anxiety disorder. Follow up assessments are currently going on with 6 monthly written questionnaires and face-to-face interviews after 2 and 6 years.</p> <p>Conclusions</p> <p>The NESDO sample offers the opportunity to study the neurobiological, psychosocial and physical determinants of depression and its long-term course in older persons. Since largely similar measures were used as in the Netherlands Study of Depression and Anxiety (NESDA; age range 18-65 years), data can be pooled thus creating a large longitudinal database of clinically depressed persons with adequate power and a large set of neurobiological, psychosocial and physical variables from both younger and older depressed persons.</p

Climate-Based Models for Understanding and Forecasting Dengue Epidemics

Dengue fever is a major public health problem in the tropics and subtropics. Since no vaccine exists, understanding and predicting outbreaks remain of crucial interest. Climate influences the mosquito-vector biology and the viral transmission cycle. Its impact on dengue dynamics is of growing interest. We analyzed the epidemiology of dengue in Noumea (New Caledonia) from 1971 to 2010 and its relationships with local and remote climate conditions using an original approach combining a comparison of epidemic and non epidemic years, bivariate and multivariate analyses. We found that the occurrence of outbreaks in Noumea was strongly influenced by climate during the last forty years. Efficient models were developed to estimate the yearly risk of outbreak as a function of two meteorological variables that were contemporaneous (explicative model) or prior (predictive model) to the outbreak onset. Local threshold values of maximal temperature and relative humidity were identified. Our results provide new insights to understand the link between climate and dengue outbreaks, and have a substantial impact on dengue management in New Caledonia since the health authorities have integrated these models into their decision making process and vector control policies. This raises the possibility to provide similar early warning systems in other countries

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice