218 resultados para DYNAMIC FEATURES


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IMPORTANCE: Depression and obesity are 2 prevalent disorders that have been repeatedly shown to be associated. However, the mechanisms and temporal sequence underlying this association are poorly understood. OBJECTIVE: To determine whether the subtypes of major depressive disorder (MDD; melancholic, atypical, combined, or unspecified) are predictive of adiposity in terms of the incidence of obesity and changes in body mass index (calculated as weight in kilograms divided by height in meters squared), waist circumference, and fat mass. DESIGN, SETTING, AND PARTICIPANTS: This prospective population-based cohort study, CoLaus (Cohorte Lausannoise)/PsyCoLaus (Psychiatric arm of the CoLaus Study), with 5.5 years of follow-up included 3054 randomly selected residents (mean age, 49.7 years; 53.1% were women) of the city of Lausanne, Switzerland (according to the civil register), aged 35 to 66 years in 2003, who accepted the physical and psychiatric baseline and physical follow-up evaluations. EXPOSURES: Depression subtypes according to the DSM-IV. Diagnostic criteria at baseline and follow-up, as well as sociodemographic characteristics, lifestyle (alcohol and tobacco use and physical activity), and medication, were elicited using the semistructured Diagnostic Interview for Genetic Studies. MAIN OUTCOMES AND MEASURES: Changes in body mass index, waist circumference, and fat mass during the follow-up period, in percentage of the baseline value, and the incidence of obesity during the follow-up period among nonobese participants at baseline. Weight, height, waist circumference, and body fat (bioimpedance) were measured at baseline and follow-up by trained field interviewers. RESULTS: Only participants with the atypical subtype of MDD at baseline revealed a higher increase in adiposity during follow-up than participants without MDD. The associations between this MDD subtype and body mass index (β = 3.19; 95% CI, 1.50-4.88), incidence of obesity (odds ratio, 3.75; 95% CI, 1.24-11.35), waist circumference in both sexes (β = 2.44; 95% CI, 0.21-4.66), and fat mass in men (β = 16.36; 95% CI, 4.81-27.92) remained significant after adjustments for a wide range of possible cofounding. CONCLUSIONS AND RELEVANCE: The atypical subtype of MDD is a strong predictor of obesity. This emphasizes the need to identify individuals with this subtype of MDD in both clinical and research settings. Therapeutic measures to diminish the consequences of increased appetite during depressive episodes with atypical features are advocated.

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Lentivector-mediated transgenesis is increasingly used, whether for basic studies as an alternative to pronuclear injection of naked DNA or to test candidate gene therapy vectors. In an effort to characterize the genetic features of this approach, we first measured the frequency of germ line transmission of individual proviruses established by infection of fertilized mouse oocytes. Seventy integrants from 11 founder (G0) mice were passed to 111 first generation (G1) pups, for a total of 255 events corresponding to an average rate of transmission of 44%. This implies that integration had most often occurred at the one- or two-cell stage and that the degree of genotypic mosaicism in G0 mice obtained through this approach is generally minimal. Transmission analysis of eight individual proviruses in 13 G2 mice obtained by a G0-G1 cross revealed only 8% of proviral homozygosity, significantly below the 25% expected from purely Mendelian transmission, suggesting counter-selection due to interference with the functions of targeted loci. Mapping of 239 proviral integration sites in 49 founder animals revealed that about 60% resided within annotated genes, with a marked tendency for clustering in the middle of the transcribed region, and that integration was not influenced by the transcriptional orientation. Transcript levels of a set of arbitrarily chosen target genes were significantly higher in two-cell embryos than in embryonic stem cells or adult somatic cells, suggesting that, as previously noted in other settings, lentiviral vectors integrate preferentially into regions of the genome that are transcriptionally active or poised for activation.

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OBJECTIVE: Previous studies reported that the severity of cognitive deficits in euthymic patients with bipolar disorder (BD) increases with the duration of illness and postulated that progressive neuronal loss or shrinkage and white matter changes may be at the origin of this phenomenon. To explore this issue, the authors performed a case-control study including detailed neuropsychological and magnetic resonance imaging analyses in 17 euthymic elderly patients with BD and 17 healthy individuals. METHODS: Neuropsychological evaluation concerned working memory, episodic memory, processing speed, and executive functions. Volumetric estimates of the amygdala, hippocampus, entorhinal cortex, and anterior cingulate cortex were obtained using both voxel-based and region of interest morphometric methods. Periventricular and deep white matter were assessed semiquantitatively. Differences in cognitive performances and structural data between BD and comparison groups were analyzed using paired t-test or analysis of variance. Wilcoxon test was used in the absence of normal distribution. RESULTS: Compared with healthy individuals, patients with BD obtained significantly lower performances in processing speed, working memory, and episodic memory but not in executive functions. Morphometric analyses did not show significant volumetric or white matter differences between the two groups. CONCLUSIONS: Our results revealed impairment in verbal memory, working memory, and processing speed in euthymic older adults with BD. These cognitive deficits are comparable both in terms of affected functions and size effects to those previously reported in younger cohorts with BD. Both this observation and the absence of structural brain abnormalities in our cohort do not support a progressively evolving neurotoxic effect in BD.

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Animal dispersal in a fragmented landscape depends on the complex interaction between landscape structure and animal behavior. To better understand how individuals disperse, it is important to explicitly represent the properties of organisms and the landscape in which they move. A common approach to modelling dispersal includes representing the landscape as a grid of equal sized cells and then simulating individual movement as a correlated random walk. This approach uses a priori scale of resolution, which limits the representation of all landscape features and how different dispersal abilities are modelled. We develop a vector-based landscape model coupled with an object-oriented model for animal dispersal. In this spatially explicit dispersal model, landscape features are defined based on their geographic and thematic properties and dispersal is modelled through consideration of an organism's behavior, movement rules and searching strategies (such as visual cues). We present the model's underlying concepts, its ability to adequately represent landscape features and provide simulation of dispersal according to different dispersal abilities. We demonstrate the potential of the model by simulating two virtual species in a real Swiss landscape. This illustrates the model's ability to simulate complex dispersal processes and provides information about dispersal such as colonization probability and spatial distribution of the organism's path.

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To test hypotheses about the universality of personality traits, college students in 50 cultures identified an adult or college-aged man or woman whom they knew well and rated the 11,985 targets using the 3rd-person version of the Revised NEO Personality Inventory. Factor analyses within cultures showed that the normative American self-report structure was clearly replicated in most cultures and was recognizable in all. Sex differences replicated earlier self-report results, with the most pronounced differences in Western cultures. Cross-sectional age differences for 3 factors followed the pattern identified in self-reports, with moderate rates of change during college age and slower changes after age 40. With a few exceptions, these data support the hypothesis that features of personality traits are common to all human groups.

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The aim of this study was to propose a methodology allowing a detailed characterization of body sit-to-stand/stand-to-sit postural transition. Parameters characterizing the kinematics of the trunk movement during sit-to-stand (Si-St) postural transition were calculated using one initial sensor system fixed on the trunk and a data logger. Dynamic complexity of these postural transitions was estimated by fractal dimension of acceleration-angular velocity plot. We concluded that this method provides a simple and accurate tool for monitoring frail elderly and to objectively evaluate the efficacy of a rehabilitation program.

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The international Functional Annotation Of the Mammalian Genomes 4 (FANTOM4) research collaboration set out to better understand the transcriptional network that regulates macrophage differentiation and to uncover novel components of the transcriptome employing a series of high-throughput experiments. The primary and unique technique is cap analysis of gene expression (CAGE), sequencing mRNA 5'-ends with a second-generation sequencer to quantify promoter activities even in the absence of gene annotation. Additional genome-wide experiments complement the setup including short RNA sequencing, microarray gene expression profiling on large-scale perturbation experiments and ChIP-chip for epigenetic marks and transcription factors. All the experiments are performed in a differentiation time course of the THP-1 human leukemic cell line. Furthermore, we performed a large-scale mammalian two-hybrid (M2H) assay between transcription factors and monitored their expression profile across human and mouse tissues with qRT-PCR to address combinatorial effects of regulation by transcription factors. These interdependent data have been analyzed individually and in combination with each other and are published in related but distinct papers. We provide all data together with systematic annotation in an integrated view as resource for the scientific community (http://fantom.gsc.riken.jp/4/). Additionally, we assembled a rich set of derived analysis results including published predicted and validated regulatory interactions. Here we introduce the resource and its update after the initial release.

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Uncertainty quantification of petroleum reservoir models is one of the present challenges, which is usually approached with a wide range of geostatistical tools linked with statistical optimisation or/and inference algorithms. Recent advances in machine learning offer a novel approach to model spatial distribution of petrophysical properties in complex reservoirs alternative to geostatistics. The approach is based of semisupervised learning, which handles both ?labelled? observed data and ?unlabelled? data, which have no measured value but describe prior knowledge and other relevant data in forms of manifolds in the input space where the modelled property is continuous. Proposed semi-supervised Support Vector Regression (SVR) model has demonstrated its capability to represent realistic geological features and describe stochastic variability and non-uniqueness of spatial properties. On the other hand, it is able to capture and preserve key spatial dependencies such as connectivity of high permeability geo-bodies, which is often difficult in contemporary petroleum reservoir studies. Semi-supervised SVR as a data driven algorithm is designed to integrate various kind of conditioning information and learn dependences from it. The semi-supervised SVR model is able to balance signal/noise levels and control the prior belief in available data. In this work, stochastic semi-supervised SVR geomodel is integrated into Bayesian framework to quantify uncertainty of reservoir production with multiple models fitted to past dynamic observations (production history). Multiple history matched models are obtained using stochastic sampling and/or MCMC-based inference algorithms, which evaluate posterior probability distribution. Uncertainty of the model is described by posterior probability of the model parameters that represent key geological properties: spatial correlation size, continuity strength, smoothness/variability of spatial property distribution. The developed approach is illustrated with a fluvial reservoir case. The resulting probabilistic production forecasts are described by uncertainty envelopes. The paper compares the performance of the models with different combinations of unknown parameters and discusses sensitivity issues.

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Psychodynamic therapists are often suspicious of positive emotions and consider them to be nothing more than a form of denial or of another defense aiming to diminish painful or difficult affects. Positive emotions seem to exist only through the absence of negative emotions or as something that may happen outside of therapy. On the other hand, clinicians also agree that psychoanalytic work could not be successful without such positive emotions as interest, pleasure, surprise and creativity. Contemporary psychoanalytic thinking and new research findings in the area of relationship regulation are likely to give positive emotions an increasingly prominent place in dynamically oriented therapies. With today's emphasis on the therapeutic relationship and intersubjectivity, the time appears right to integrate positive emotions more formally into psychodynamic clinical theories.