Social Capital in Transition(s) to Early Adulthood: A Longitudinal and Mixed-Methods Approach

Social capital captures the value of relationships. Although research has examined social capital among adults, comparatively little attention has been paid to social capital among young adults—particularly from a longitudinal and mixed-methods perspective. As social capital predicts educational achievement, employment, and psychosocial well-being, it is an important construct to study alongside youth transition(s). Following a Bourdieusian approach, we define social capital as the resources potentially available in our ties that can be mobilized when necessary. To examine social capital in transition to adulthood, we draw on survey (n = 1,650, at ages 17 and 21) and interview (n = 70, at age 24) data from a cohort of Portuguese youth. We study the two main dimensions of social capital: bonding and bridging. Survey data were analyzed with latent class modeling, logistic regressions, and Wilcoxon signed-rank tests, and interviews with thematic analysis. Findings show that respondents reported receiving more emotional support than financial support from their networks, but that both types of support increased over time. Perceived bonding and bridging also changed positively in transition to adulthood. In addition, gender and parental education predicted bonding and bridging. We contextualize these results with qualitative meanings and experiences of social capital.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was funded by the Portuguese Foundation for Science and Technology (2013-2015), Grant No. PTDC/IVCSOC/4943/2012

Cortisol and insulin behaviors during an ultramarathon event: are they real markers of extreme exertion?

"BACKGROUND: The current work aimed to describe and compare the cortisol and insulin concentrations behavior and rate of perceived exertion (RPE) during a 115 km ultramarathon race. METHODS: Nine ultrarunners (eight males) were evaluated six times (0, 37, 60, 76, 89 and 115 km). At each moment, saliva samples (for cortisol and insulin assessment) and RPE (CR10 scale) were collected. Statistical analysis included correlation, one-way repeated measure ANOVA, and Statistical Parametric Mapping to define discrete and continues changes and compare cortisol, insulin and RPE profiles. RESULTS: Our main findings revealed an early peak in cortisol and RPE, accompanied by a decline in insulin responses (402±49 min of the race, P<0.05). Cortisol and insulin only showed magnitude differences with inverse behaviors until ~6% (7 km) of the ultramarathon duration. Cortisol and RPE presented similar behaviors, rising from the beginning of the race and remaining elevated throughout the race (η2=0.91 and η2=1.0, P<0.001). Insulin levels decreased when the race started, remaining below 60% of baseline values from the midpoint to the end of the race (P=0.04). CONCLUSIONS: The study showed an imbalance in the catabolic/anabolic hormone profile during an ultramarathon race, with a prominence in catabolic state. It should be considered in the ultramarathon races preparation and participation due to its possible detrimental effect on the athlete’s health.

Unsupervised Bayesian linear unmixing of gene expression microarrays

Background: This paper introduces a new constrained model and the corresponding algorithm, called unsupervised Bayesian linear unmixing (uBLU), to identify biological signatures from high dimensional assays like gene expression microarrays. The basis for uBLU is a Bayesian model for the data samples which are represented as an additive mixture of random positive gene signatures, called factors, with random positive mixing coefficients, called factor scores, that specify the relative contribution of each signature to a specific sample. The particularity of the proposed method is that uBLU constrains the factor loadings to be non-negative and the factor scores to be probability distributions over the factors. Furthermore, it also provides estimates of the number of factors. A Gibbs sampling strategy is adopted here to generate random samples according to the posterior distribution of the factors, factor scores, and number of factors. These samples are then used to estimate all the unknown parameters. Results: Firstly, the proposed uBLU method is applied to several simulated datasets with known ground truth and compared with previous factor decomposition methods, such as principal component analysis (PCA), non negative matrix factorization (NMF), Bayesian factor regression modeling (BFRM), and the gradient-based algorithm for general matrix factorization (GB-GMF). Secondly, we illustrate the application of uBLU on a real time-evolving gene expression dataset from a recent viral challenge study in which individuals have been inoculated with influenza A/H3N2/Wisconsin. We show that the uBLU method significantly outperforms the other methods on the simulated and real data sets considered here. Conclusions: The results obtained on synthetic and real data illustrate the accuracy of the proposed uBLU method when compared to other factor decomposition methods from the literature (PCA, NMF, BFRM, and GB-GMF). The uBLU method identifies an inflammatory component closely associated with clinical symptom scores collected during the study. Using a constrained model allows recovery of all the inflammatory genes in a single factor

Monitoring frequency influences the analysis of resting behaviour in a forest carnivore

Resting sites are key structures for many mammalian species, which can affect reproduction, survival, population density, and even species persistence in human-modified landscapes. As a consequence, an increasing number of studies has estimated patterns of resting site use by mammals, as well as the processes underlying these patterns, though the impact of sampling design on such estimates remain poorly understood. Here we address this issue empirically, based on data from 21 common genets radiotracked during 28 months in Mediterranean forest landscapes. Daily radiotracking data was thinned to simulate every other day and weekly monitoring frequencies, and then used to evaluate the impact of sampling regime on estimates of resting site use. Results showed that lower monitoring frequencies were associated with major underestimates of the average number of resting sites per animal, and of site reuse rates and sharing frequency, though no effect was detected on the percentage use of resting site types. Monitoring frequency also had a major impact on estimates of environmental effects on resting site selection, with decreasing monitoring frequencies resulting in higher model uncertainty and reduced power to identify significant explanatory variables. Our results suggest that variation in monitoring frequency may have had a strong impact on intra- and interspecific differences in resting site use patterns detected in previous studies. Given the errors and uncertainties associated with low monitoring frequencies, we recommend that daily or at least every other day monitoring should be used whenever possible in studies estimating resting site use patterns by mammals

Fluorescence studies on new potential antitumoral benzothienopyran-1-ones in solution and in liposomes

Fluorescence properties of four new potential antitumoral compounds, 3-arylbenzothieno[2,3-c]pyran-1-ones, were studied in solution and in lipid membranes of dipalmitoyl phosphatidylcholine (DPPC), egg yolk phosphatidylcholine (Egg-PC) and dioctadecyldimethylammonium bromide (DODAB). The 3-(4-methoxyphenyl)benzothieno[2,3-c]pyran-1-one (1c) exhibits the higher fluorescence quantum yields in all solvents studied. All compounds present a solvent sensitive emission, with significant red shifts in polar solvents for the methoxylated compounds. The results point to an ICT character of the excited state, more pronounced for compound 1c. Fluorescence (steady-state) anisotropy measurements of the compounds incorporated in liposomes of DPPC, DODAB and Egg-PC indicate that all compounds have two different locations, one due to a deep penetration in the lipid membrane and another corresponding to a more hydrated environment. In general, the methoxylated compounds prefer hydrated environments inside the liposomes. The 3-(4- fluorophenyl)benzothieno[2,3-c]pyran-1-one (1a) clearly prefers a hydrated environment, with some molecules located at the outer part of the liposome interface. On the contrary, the preferential location of 3-(2-fluorophenyl)benzothieno[2,3-c]pyran-1-one (1b) is in the region of lipid hydrophobic tails. Compounds with a planar geometry (1a and 1c) have higher mobility in the lipid membranes when phase transition occurs.Portugal and FEDER (Fundo Europeu de Desenvolvimento Regional), for financial support through Centro de Física (CFUM) and Centro de Química (CQ-UM) of University of Minho and through the Project PTDC/QUI/81238/2006. M.S.D. Carvalho and R.C. Calhelha acknowledge FCT for their PhD grants SFRH/BD/47052/2008 and SFRH/BD/29274/2006, respectively.Fundação para a Ciência e a Tecnologia (FCT

Analysis of Population Structure: A Unifying Framework and Novel Methods Based on Sparse Factor Analysis

We consider the statistical analysis of population structure using genetic data. We show how the two most widely used approaches to modeling population structure, admixture-based models and principal components analysis (PCA), can be viewed within a single unifying framework of matrix factorization. Specifically, they can both be interpreted as approximating an observed genotype matrix by a product of two lower-rank matrices, but with different constraints or prior distributions on these lower-rank matrices. This opens the door to a large range of possible approaches to analyzing population structure, by considering other constraints or priors. In this paper, we introduce one such novel approach, based on sparse factor analysis (SFA). We investigate the effects of the different types of constraint in several real and simulated data sets. We find that SFA produces similar results to admixture-based models when the samples are descended from a few well-differentiated ancestral populations and can recapitulate the results of PCA when the population structure is more “continuous,” as in isolation-by-distance models