213 research outputs found

    A meta-analysis of pharmacotherapy for social anxiety disorder: an examination of efficacy, moderators, and mediators

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    INTRODUCTION: Social anxiety disorder (SAD) is among the most prevalent mental disorders, associated with impaired functioning and poor quality of life. Pharmacotherapy is the most widely utilized treatment option. The current study provides an updated meta-analytic review of the efficacy of pharmacotherapy and examines moderators and mediators of treatment efficacy. Areas Covered: A comprehensive search of the current literature yielded 52 randomized, pill placebo-controlled trials of pharmacotherapy for adults diagnosed with SAD. Data on potential mediators of treatment outcome were collected, as well as data necessary to calculate pooled correlation matrices to compute indirect effects. Expert Opinion: The overall effect size of pharmacotherapy for SAD is small to medium (Hedges' g = 0.41). Effect sizes were not moderated by age, sex, length of treatment, initial severity, risk of study bias, or publication year. Furthermore, reductions in symptoms mediated pharmacotherapy's effect on quality of life. Support was found for reverse mediation. Future directions may include sustained efforts to examine treatment mechanisms of pharmacotherapy using rigorous longitudinal methodology to better establish temporal precedence

    Using machine learning to characterize solar wind driving of convection in the terrestrial magnetotail lobes

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    In order to quantitatively investigate the mechanism of how magnetospheric convection is driven in the region of magnetotail lobes on a global scale, we analyzed data from the ARTEMIS spacecraft in the deep tail and data from the Cluster spacecraft in the near and mid-tail regions. Our previous work revealed that, in the lobes near the Moon’s orbit, the convection can be estimated by using ARTEMIS measurements of lunar ions’ velocity. Based on that, in this paper, we applied machine learning models to these measurements to determine which upstream solar wind parameters significantly drive the lobe convection in magnetotail regions, to help us understand the mechanism that controls the dynamics of the tail lobes. The results demonstrate that the correlations between the predicted and measured convection velocities for the machine learning models (>0.75) are superior to those of the multiple linear regression model (∼0.23–0.43) in the testing dataset. The systematic analysis shows that the IMF and magnetospheric activity play an important role in influencing plasma convection in the global magnetotail lobes

    Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

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    Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis

    PHUSER (Primer Help for USER): a novel tool for USER fusion primer design

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    Uracil-Specific Exision Reagent (USER) fusion is a recently developed technique that allows for assembly of multiple DNA fragments in a few simple steps. However, designing primers for USER fusion is both tedious and time consuming. Here, we present the Primer Help for USER (PHUSER) software, a novel tool for designing primers specifically for USER fusion and USER cloning applications. We also present proof-of-concept experimental validation of its functionality. PHUSER offers quick and easy design of PCR optimized primers ensuring directionally correct fusion of fragments into a plasmid containing a customizable USER cassette. Designing primers using PHUSER ensures that the primers have similar annealing temperature (Tm), which is essential for efficient PCR. PHUSER also avoids identical overhangs, thereby ensuring correct order of assembly of DNA fragments. All possible primers are individually analysed in terms of GC content, presence of GC clamp at 3′-end, the risk of primer dimer formation, the risk of intra-primer complementarity (secondary structures) and the presence of polyN stretches. Furthermore, PHUSER offers the option to insert linkers between DNA fragments, as well as highly flexible cassette options. PHUSER is publicly available at http://www.cbs.dtu.dk/services/phuser/

    Theoretical Studies of Spectroscopy and Dynamics of Hydrated Electrons.

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    Analysis of reflex modulation with a biologically realistic neural network

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    In this study, a neuromusculoskeletal model was built to give insight into the mechanisms behind the modulation of reflexive feedback strength as experimentally identified in the human shoulder joint. The model is an integration of a biologically realistic neural network consisting of motoneurons and interneurons, modeling 12 populations of spinal neurons, and a one degree-of-freedom musculoskeletal model, including proprioceptors. The model could mimic the findings of human postural experiments, using presynaptic inhibition of the Ia afferents to modulate the feedback gains. In a pathological case, disabling one specific neural connection between the inhibitory interneurons and the motoneurons could mimic the experimental findings in complex regional pain syndrome patients. It is concluded that the model is a valuable tool to gain insight into the spinal contributions to human motor control. Applications lay in the fields of human motor control and neurological disorders, where hypotheses on motor dysfunction can be tested, like spasticity, clonus, and tremor
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