Multisensory Learning and its Effect on Students with Autism

The growing population of children diagnosed with autism has led to an increasing interest in the sensory processing difficulties experienced by this population. This study examined specific patterns of sensory processing deficits within seven sensory domains. The study involved five children with an autism diagnosis aged 10-12 years. Short Sensory Profiles were completed by each children’s special education teacher, two paraprofessionals, and one parent. Data was collected from four profiles per student for a total of 20 surveys. These surveys revealed probable to definite differences between children with autism and their atypical peers in every sensory domain except Movement Sensitivity. The sensory domain that has been more closely linked to academic performance is Auditory Filtering. Auditory Filtering was found to be the sensory domain with the second greatest deficit when compared to the group percentages and remained in the top three when comparing student’s individual sensory deficits. Throughout this study I sought to determine which sensory domains these children have difficulty with and the types of atypical behaviors that are associated with sensory sensitivity. This paper will discuss current brain-based research in children with ASD, sensory processing and the educational outcomes these deficits have, the use of multi-sensory learning strategies, specific sensory teaching, early intervention, and environmental modifications to provide an ideal learning environment for students with autism. This study indicates that while there is still so much we don’t know about this neurodevelopmental disorder, we can begin to try and understand how the world is perceived in the eyes of a child with autism. By examining the specific patterns of sensory processing and the atypical behaviors that students with autism exhibit in order to cope with the multisensory world around them, we can provide strategies to not only members of the educational field, but strategies to the students themselves so that they can make sense of the constant multisensory stimulation that surrounds them

Scalable Querying of Nested Data

While large-scale distributed data processing platforms have become an attractive target for query processing, these systems are problematic for applications that deal with nested collections. Programmers are forced either to perform non-trivial translations of collection programs or to employ automated flattening procedures, both of which lead to performance problems. These challenges only worsen for nested collections with skewed cardinalities, where both handcrafted rewriting and automated flattening are unable to enforce load balancing across partitions. In this work, we propose a framework that translates a program manipulating nested collections into a set of semantically equivalent shredded queries that can be efficiently evaluated. The framework employs a combination of query compilation techniques, an efficient data representation for nested collections, and automated skew-handling. We provide an extensive experimental evaluation, demonstrating significant improvements provided by the framework in diverse scenarios for nested collection programs

Scalable analysis of multi-modal biomedical data

Background Targeted diagnosis and treatment options are dependent on insights drawn from multi-modal analysis of large-scale biomedical datasets. Advances in genomics sequencing, image processing, and medical data management have supported data collection and management within medical institutions. These efforts have produced large-scale datasets and have enabled integrative analyses that provide a more thorough look of the impact of a disease on the underlying system. The integration of large-scale biomedical data commonly involves several complex data transformation steps, such as combining datasets to build feature vectors for learning analysis. Thus, scalable data integration solutions play a key role in the future of targeted medicine. Though large-scale data processing frameworks have shown promising performance for many domains, they fail to support scalable processing of complex datatypes. Solution To address these issues and achieve scalable processing of multi-modal biomedical data, we present TraNCE, a framework that automates the difficulties of designing distributed analyses with complex biomedical data types. Performance We outline research and clinical applications for the platform, including data integration support for building feature sets for classification. We show that the system is capable of outperforming the common alternative, based on “flattening” complex data structures, and runs efficiently when alternative approaches are unable to perform at all

TraNCE: Transforming Nested Collections Efficiently

Nested relational query languages have long been seen as an attractive tool for scenarios involving large hierarchical datasets. There has been a resurgence of interest in nested relational languages. One driver has been the affinity of these languages for large-scale processing platforms such as Spark and Flink. This demonstration gives a tour of TraNCE, a new system for processing nested data on top of distributed processing systems. The core innovation of the system is a compiler that processes nested relational queries in a series of transformations; these include variants of two prior techniques, shredding and unnesting, as well as a materialization transformation that customizes the way levels of the nested output are generated. The TraNCE platform builds on these techniques by adding components for users to create and visualize queries, as well as data exploration and notebook execution targets to facilitate the construction of large-scale data science applications. The demonstration will both showcase the system from the viewpoint of usability by data scientists and illustrate the data management techniques employed

The Investigation of Rape Complaints: Variables that Best Predict Arrest

I examine the variables that predict arrest in rape cases based on hypotheses derived from the feminist-conflict theory, the consensus perspective, and the liberation hypothesis. Feminist-conflict theorists argue that extralegal variables influence the decisions of the police, irregardless of legal variables. Conversely, the consensus perspective argues that legally relevant variables will have the greatest impact on police decisions. The liberation hypothesis suggests that the influence of extralegal variables on police decisions depend on the strength of evidence and crime seriousness. The results from a logit regression analysis on arrest using police archival data do not support the liberation hypothesis. The feminist-conflict theory correctly predicts a decrease in the likelihood of an arrest as the intimacy between the suspect and victim increases. However, there is more support for the consensus perspective for predicting arrest as evidentiary strength is the strongest predictor of arrest

Financial literacy on college campuses and its relationship to student retention, completion, and debt

This study asked Directors of Financial Aid Office at colleges and universities in Iowa to complete a questionnaire and share how they were implementing collaborative financial literacy programs at their institution. The study sought to answer three questions: (1) To what extent are institutions in Iowa working cross-departmentally to incorporate financial literacy into programming on their campus?, (2) What is the connection between cross-departmental financial literacy programming and student graduation and retention?, and (3) How is cross-departmental financial literacy programming reflected in federal student loan data? The study found that each of the institutions that participated collaborated with at least one other department on campus to provide students financial literacy programming. Programing is also provided in various modalities, including online asynchronous courses, peer-led programs, and in-person workshops. This allows the institutions to reach large portions of their student body each year. Results also showed institutions that collaborated more frequently between departments for financial literacy programming and that cover more concepts during their programing report higher student retention and completion rates compared to institutions who collaborate less frequently or cover fewer topics in their programs. Finally, the study found there is no direct correlation between financial literacy programing and student loan debt amounts, there is a connection between number of collaborations and student loan debt in relation to percentage of tuition, which is even more evident when looking specifically at institution type

Establishing a gold standard for manual cough counting: video versus digital audio recordings

BACKGROUND: Manual cough counting is time-consuming and laborious; however it is the standard to which automated cough monitoring devices must be compared. We have compared manual cough counting from video recordings with manual cough counting from digital audio recordings. METHODS: We studied 8 patients with chronic cough, overnight in laboratory conditions (diagnoses were 5 asthma, 1 rhinitis, 1 gastro-oesophageal reflux disease and 1 idiopathic cough). Coughs were recorded simultaneously using a video camera with infrared lighting and digital sound recording. The numbers of coughs in each 8 hour recording were counted manually, by a trained observer, in real time from the video recordings and using audio-editing software from the digital sound recordings. RESULTS: The median cough frequency was 17.8 (IQR 5.9–28.7) cough sounds per hour in the video recordings and 17.7 (6.0–29.4) coughs per hour in the digital sound recordings. There was excellent agreement between the video and digital audio cough rates; mean difference of -0.3 coughs per hour (SD ± 0.6), 95% limits of agreement -1.5 to +0.9 coughs per hour. Video recordings had poorer sound quality even in controlled conditions and can only be analysed in real time (8 hours per recording). Digital sound recordings required 2–4 hours of analysis per recording. CONCLUSION: Manual counting of cough sounds from digital audio recordings has excellent agreement with simultaneous video recordings in laboratory conditions. We suggest that ambulatory digital audio recording is therefore ideal for validating future cough monitoring devices, as this as this can be performed in the patients own environment

Scalp psoriasis associated with central centrifugal cicatricial alopecia

AbstractScalp psoriasis is a very common dermatological condition with a variety of presentations, but only rarely presents as severe alopecia. We present a case of a 50-year-old female with many years of recalcitrant hair loss that was thought to be secondary to central centrifugal cicatricial alopecia which was later diagnosed as psoriasis. This case highlights an interesting presentation and rare complication of a common disease