Fryecurrin4521

The prevalence of autism spectrum disorder is increasing. It usually presents in childhood with abnormal behaviour and development The diagnosis can be difficult. There are often comorbidities which can cause confusion Non-drug treatments are first line. Drug treatment is not effective for the core symptoms of autism spectrum disorder. However, drugs may have a role in managing comorbidities and related symptoms, such as irritability and aggression Anxiety is a common comorbidity. Cognitive behaviour therapy can be effective, but in some cases selective serotonin reuptake inhibitors may have a role Most patients have problems sleeping, but drugs are not usually used to treat sleep disorders in children Antipsychotics, such as risperidone, may be considered for irritability and aggression. Clonidine is first line for children with Tourette syndrome. ABT-199 nmr Patients need regular monitoring because of the adverse effects of these drugsPsychological distress is a priority health issue in low- and middle-income countries; however, it is inadequately addressed among vulnerable youth living in extremely underserved communities (i.e., on the streets and in the slums) who are at a high risk of experiencing adversity. The purpose of this study was to compute the prevalence of self-reported psychological distress among youth living in the slums of Kampala, Uganda, and examine how orphan status and commercial sexual exploitation (CSE) are related to youth psychological distress. Analyses are based on a 2014 cross-sectional survey of service-seeking youth (N = 1134) in Kampala, Uganda. Bivariate and multivariable multinomial regression analyses were used to determine associations between orphan status, sexual exploitation, and psychological distress (defined as experiencing the following proxy variables for more complex psychopathology hopelessness and/or worry). Among all youth participants, 83.2% (n = 937) reported at least one type of psychologicd population.Although first-order stochastic algorithms, such as stochastic gradient descent, have been the main force to scale up machine learning models, such as deep neural nets, the second-order quasi-Newton methods start to draw attention due to their effectiveness in dealing with ill-conditioned optimization problems. The L-BFGS method is one of the most widely used quasi-Newton methods. We propose an asynchronous parallel algorithm for stochastic quasi-Newton (AsySQN) method. Unlike prior attempts, which parallelize only the calculation for gradient or the two-loop recursion of L-BFGS, our algorithm is the first one that truly parallelizes L-BFGS with a convergence guarantee. Adopting the variance reduction technique, a prior stochastic L-BFGS, which has not been designed for parallel computing, reaches a linear convergence rate. We prove that our asynchronous parallel scheme maintains the same linear convergence rate but achieves significant speedup. Empirical evaluations in both simulations and benchmark datasets demonstrate the speedup in comparison with the non-parallel stochastic L-BFGS, as well as the better performance than first-order methods in solving ill-conditioned problems.A 9-week feeding trial was conducted with juvenile red drum, Sciaenops ocellatus, to evaluate the use of soy oil as a fish oil replacement. Three primary protein sources (fishmeal - FM, soybean meal - SBM, and soy protein concentrate - SPC) were utilized with 100% fish oil (FM, SBM, SPC), 75% fish oil (SBM, SPC), or 50% fish oil (FM, SBM, SPC) as the lipid source. Traditional growth and performance metrics (specific growth rate, feed consumption, feed conversion ratio) were tracked and tissue samples (liver, muscle, plasma, adipose, and brain) were collected for gas chromatography-based fatty acid profiling. Ten lipid metabolism related genes were analyzed for potential expression differences between dietary treatments in liver and muscle tissues and whole body and fillet tissues were sampled for proximate composition analyses. Forty- four fatty acids were measured by gas chromatography-flame ionization detector (GC-FID) and evaluated with principle component analysis and ANOVA to understand the dietary influence on lipid metabolism and health. Significant differences in growth rate were observed with the SBM 50% fish oil diet outperforming the FM 100% fish oil reference diet. All other soy protein-based diets performed statistically equivalent to both FM reference diets (100% and 50% fish oil) in regard to growth, however all soy protein-based formulations had significantly lower feed conversion ratios than the fishmeal-based references (p less then .001). Gene expression differences were not significant in most cases, however often trended similarly as the observed performance. Fatty acid profiles differed as a function of oil source, with no apparent influence by protein source, with C182n-6 (linoleic acid) being-the primary differentiator. Overall, the six soy protein, fishmeal-free formulations performed equivalently or better than the fishmeal references with up to 50% of fish oil replaced by soybean oil.Deep learning has shown a great promise in classifying brain disorders due to its powerful ability in learning optimal features by nonlinear transformation. However, given the high-dimension property of neuroimaging data, how to jointly exploit complementary information from multimodal neuroimaging data in deep learning is difficult. In this paper, we propose a novel multilevel convolutional neural network (CNN) fusion method that can effectively combine different types of neuroimage-derived features. Importantly, we incorporate a sequential feature selection into the CNN model to increase the feature interpretability. To evaluate our method, we classified two symptom-related brain disorders using large-sample multi-site data from 335 schizophrenia (SZ) patients and 380 autism spectrum disorder (ASD) patients within a cross-validation procedure. Brain functional networks, functional network connectivity, and brain structural morphology were employed to provide possible features. As expected, our fusion method outperformed the CNN model using only single type of features, as our method yielded higher classification accuracy (with mean accuracy >85%) and was more reliable across multiple runs in differentiating the two groups.