BACKGROUND There are high attrition rates observed in efficacy studies for social anxiety disorder, and research has not identified consistent nor theoretically meaningful predictors of dropout. Pre-treatment symptom severity and demographic factors, such as age and gender, are sometimes predictive of dropout. The current study examines a theoretically meaningful predictor of attrition based on...

Development of antiobesity drugs is continuously challenged by high dropout rates during clinical trials. The objective was to develop a population pharmacodynamic model that describes the temporal changes in body weight, considering disease progression, lifestyle intervention, and drug effects. Markov modeling (MM) was applied for quantification and characterization of responder and nonrespond...

We explore a recently proposed Variational Dropout technique that provided an elegant Bayesian interpretation to Gaussian Dropout. We extend Variational Dropout to the case when dropout rates are unbounded, propose a way to reduce the variance of the gradient estimator and report first experimental results with individual dropout rates per weight. Interestingly, it leads to extremely sparse sol...

Augmented accuracy in prediction of diabetes will open up new frontiers in health prognostics. Data overfitting is a performance-degrading issue in diabetes prognosis. In this study, a prediction system for the disease of diabetes is presented where the issue of overfitting is minimized by using the dropout method. Deep learning neural network is used where both fully connected layers are follo...

The aural cavity magnetic susceptibility artifact leads to significant echo planar imaging (EPI) signal dropout in rat deep brain that limits acquisition of functional connectivity fcMRI data. In this study, we provide a method that recovers much of the EPI signal in deep brain. Needle puncture introduction of a liquid-phase fluorocarbon into the middle ear allows acquisition of rat fcMRI data ...

Dropout is a method that prevents overfitting when training deep neural networks. It involves sampling different sub-networks by temporarily removing nodes at random. Dropout works well in practice, but its properties have not been fully explored or theoretically justified. Our project explores the properties of dropout by applying methods used in optimization such as simulated annealing and lo...

We present the results of colour selection of candidate high redshift galaxies in Hub-ble Deep Field South (HDF-S) using the Lyman dropout scheme. The HDF-S data we discuss were taken in a number of different filters extending from the near–UV (F300W) to the infrared (F222M) in two different fields. This allows us to select candidates with redshifts from z∼3 to z∼12. We find 15 candidate z∼3 ob...

A growing number of population genetic studies utilize nuclear DNA microsatellite data from museum specimens and noninvasive sources. Genotyping errors are elevated in these low quantity DNA sources, potentially compromising the power and accuracy of the data. The most conservative method for addressing this problem is effective, but requires extensive replication of individual genotypes. In se...

BACKGROUND Immunization against childhood diseases is one of the most important ways of preventing childhood morbidity and mortality. AIMS The objective of this study is to review the dropout rates of immunization of children in a health center using a single dose of diphtheria-pertussis-tetanus (DPT1) and three doses of diphtheria-pertussis-tetanus (DPT3) vaccine. SUBJECTS AND METHODS This...

Dropout from school hinders development as it makes human potentialities unexplored. This creates all round backwardness. Muslim communities are more backward particularly in Malda district, W.B., where the dropout rate is high. Parental decision to dropout their wards from primary education does not depend upon gender, but depend upon their economic status. Poverty influences the parents to wi...