Challenging Behaviors in Children with Comorbid Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder

Challenging behaviors, such as aggression, destruction, self-injurious behaviors, or stereotypic movements, affect the majority of individuals with Autism Spectrum Disorder. One factor that is known to influence the frequency at which challenging behaviors occur is the presence of a comorbid disorder. Attention-deficit/hyperactivity disorder (ADHD) is thought to be one such disorder. This study aimed to compare the prevalence rates of challenging behaviors, according to the Autism Spectrum Disorder-Behavior Problems, Child Version (ASD-BPC), in children ages 6-16 with parent reported symptoms of ASD, ADHD, comorbid ASD/ADHD, and no diagnosis. Differences existing overall were examined as well as differences on the ASD-BPC’s two factors, behaviors directed towards self and behaviors directed towards others. Results indicate that individuals with symptoms of ASD/ADHD display significantly higher rates of challenging behavior than those with symptoms of ADHD only and no diagnosis. They also indicate that those with symptoms of ADHD displayed higher rates of challenging behavior than those with no diagnosis. A better understanding of challenging behaviors in individuals with comorbid ASD/ADHD will assist in more accurate differential diagnoses

Autonomous analysis to identify bijels from two-dimensional images

Bicontinuous interfacially jammed emulsion gels (bijels) are novel composite materials that can be challenging to manufacture. As a step towards automating production, we have developed a machine learning tool to classify fabrication attempts. We use training and testing data in the form of confocal images from both successful and unsuccessful attempts at bijel fabrication. We then apply machine learning techniques to this data in order to classify whether an image is a bijel or a non-bijel. Our principal approach is to process the images to find their autocorrelation function and structure factor, and from these functions we identify variables that can be used for training a supervised machine learning model to identify a bijel image. We are able to categorise images with reasonable accuracies of 85.4% and 87.5% for two different approaches. We find that using both the liquid and particle channels helps to achieve optimal performance and that successful classification relies on the bijel samples sharing a characteristic length scale. Our second approach is to classify the shapes of the liquid domains directly; the shape descriptors are then used to classify fabrication attempts via a decision tree. We have used an adaptive design approach to find an image pre-processing step that yields the optimal classification results. Again, we find that the characteristic length scale of the images is crucial in performing the classification

Rheological Behavior and in Situ Confocal Imaging of Bijels Made by Mixing

International audienc

Nanostructured, Fluid-Bicontinuous Gels for Continuous-Flow Liquid–Liquid Extraction

Fluid-bicontinuous gels are unique materials that allow two distinct fluids to interact through a percolating, rigid scaffold. Current restrictions for their use are the large fluid-channel sizes (>5 µm), limiting the fluid–fluid interaction surface-area, and the inability to flow liquids through the channels. In this work a scalable synthesis route of nanoparticle stabilized fluid-bicontinuous gels with channels sizes below 500 nm and specific surface areas of 2 m2 cm−3 is introduced. Moreover, it is demonstrated that liquids can be pumped through the fluid-bicontinuous gels via electroosmosis. The fast liquid flow in the fluid-bicontinuous gel facilitates their use for molecular separations in continuous-flow liquid–liquid extraction. Together with the high surface areas, liquid flow through fluid-bicontinuous gels enhances their potential as highly permeable porous materials with possible uses as microreaction media, fuel-cell components, and separation membranes

Landscape and Residential Variables Associated with Plague-Endemic Villages in the West Nile Region of Uganda

Plague, caused by the bacteria Yersinia pestis , is a severe, often fatal disease. This study focuses on the plagueendemic West Nile region of Uganda, where limited information is available regarding environmental and behavioral risk factors associated with plague infection. We conducted observational surveys of 10 randomly selected huts within historically classified case and control villages (four each) two times during the dry season of 2006 ( N = 78 case huts and N = 80 control huts), which immediately preceded a large plague outbreak. By coupling a previously published landscape-level statistical model of plague risk with this observational survey, we were able to identify potential residence-based risk factors for plague associated with huts within historic case or control villages (e.g., distance to neighboring homestead and presence of pigs near the home) and huts within areas previously predicted as elevated risk or low risk (e.g., corn and other annual crops grown near the home, water storage in the home, and processed commercial foods stored in the home). The identified variables are consistent with current ecologic theories on plague transmission dynamics. This preliminary study serves as a foundation for future case control studies in the area

An active learning approach to home heating in the smart grid

A key issue for the realization of the smart grid vision is the implementation of effective demand-side management. One possible approach involves exposing dynamic energy prices to end-users. In this paper, we consider a resulting problem on the user’s side: how to adaptively heat a home given dynamic prices. The user faces the challenge of having to react to dynamic prices in real time, trading off his comfort with the costs of heating his home to a certain temperature. We propose an active learning approach to adjust the home temperature in a semiautomatic way. Our algorithm learns the user’s preferences over time and automatically adjusts the temperature in real-time as prices change. In addition, the algorithm asks the user for feedback once a day. To find the best query time, the algorithm solves an optimal stopping problem. Via simulations, we show that our algorithm learns users’ preferences quickly, and that using the expected utility loss as the query criterion outperforms standard approaches from the active learning literature

Flea Diversity as an Element for Persistence of Plague Bacteria in an East African Plague Focus

Plague is a flea-borne rodent-associated zoonotic disease that is caused by Yersinia pestis and characterized by long quiescent periods punctuated by rapidly spreading epidemics and epizootics. How plague bacteria persist during inter-epizootic periods is poorly understood, yet is important for predicting when and where epizootics are likely to occur and for designing interventions aimed at local elimination of the pathogen. Existing hypotheses of how Y. pestis is maintained within plague foci typically center on host abundance or diversity, but little attention has been paid to the importance of flea diversity in enzootic maintenance. Our study compares host and flea abundance and diversity along an elevation gradient that spans from low elevation sites outside of a plague focus in the West Nile region of Uganda (∼725–1160 m) to higher elevation sites within the focus (∼1380–1630 m). Based on a year of sampling, we showed that host abundance and diversity, as well as total flea abundance on hosts was similar between sites inside compared with outside the plague focus. By contrast, flea diversity was significantly higher inside the focus than outside. Our study highlights the importance of considering flea diversity in models of Y. pestis persistence