The Effect of Dynamic Seating on Classroom Behavior for Students in a General Education Classroom

This study examined the effect of using FootFidgets® and standing desks with FootFidgets® on attention and work completion for students in a fourth grade class in a private elementary school. An A-B-C single subject case study design where phases were one week, and students completed daily visual analog scales to examine classroom behavior. The mean attention of students significantly increased while using the standing desk and FootFidget®, t(8) = 2.79, p = .024. One student identified by the Sensory Processing Measure: Home Form as having some problems processing sensory input, increased work completion while using the standing desk and FootFidget®. The FootFidget® alone did not significantly increase attention or work completion of the students. Students reported liking the FootFidget® 90% of the time. The FootFidget® and standing desk may provide increased sensory input compared to the FootFidget® alone. The FootFidget® and standing desk are potential environmental adaptations to improve academic performance

Blood Glucose Forecasting using LSTM Variants under the Context of Open Source Artificial Pancreas System

High accuracy of blood glucose prediction over the long term is essential for preventative diabetes management. The emerging closed-loop insulin delivery system such as the artificial pancreas system (APS) provides opportunities for improved glycaemic control for patients with type 1 diabetes. Existing blood glucose studies are proven effective only within 30 minutes but the accuracy deteriorates drastically when the prediction horizon increases to 45 minutes and 60 minutes. Deep learning, especially for long short term memory (LSTM) and its variants have recently been applied in various areas to achieve state-of-the-art results in tasks with complex time series data. In this study, we present deep LSTM based models that are capable of forecasting long term blood glucose levels with improved prediction and clinical accuracy. We evaluate our approach using 20 cases(878,000 glucose values) from Open Source Artificial Pancreas System (OpenAPS). On 30-minutes and 45-minutes prediction, our Stacked-LSTM achieved the best performance with Root-Mean-Square-Error (RMSE) marks 11.96 & 15.81 and Clark-Grid-ZoneA marks 0.887 & 0.784. In terms of 60-minutes prediction, our ConvLSTM has the best performance with RMSE = 19.6 and Clark-Grid-ZoneA=0.714. Our models outperform existing methods in both prediction and clinical accuracy. This research can hopefully support patients with type 1 diabetes to better manage their behavior in a more preventative way and can be used in future real APS context

Real-World Use of Do-It-Yourself Artificial Pancreas Systems in Children and Adolescents With Type 1 Diabetes: Online Survey and Analysis of Self-Reported Clinical Outcomes

BACKGROUND: Patient-driven initiatives have made uptake of Do-it-Yourself Artificial Pancreas Systems (DIYAPS) increasingly popular among people with diabetes of all ages. Observational studies have shown improvements in glycemic control and quality of life among adults with diabetes. However, there is a lack of research examining outcomes of children and adolescents with DIYAPS in everyday life and their social context. OBJECTIVE: This survey assesses the self-reported clinical outcomes of a pediatric population using DIYAPS in the real world. METHODS: An online survey was distributed to caregivers to assess the hemoglobin A1c levels and time in range (TIR) before and after DIYAPS initiation and problems during DIYAPS use. RESULTS: A total of 209 caregivers of children from 21 countries responded to the survey. Of the children, 47.4% were female, with a median age of 10 years, and 99.4% had type 1 diabetes, with a median duration of 4.3 years (SD 3.9). The median duration of DIYAPS use was 7.5 (SD 10.0) months. Clinical outcomes improved significantly, including the hemoglobin A1c levels (from 6.91% [SD 0.88%] to 6.27% [SD 0.67]; P<.001) and TIR (from 64.2% [SD 15.94] to 80.68% [SD 9.26]; P<.001). CONCLUSIONS: Improved glycemic outcomes were found across all pediatric age groups, including adolescents and very young children. These findings are in line with clinical trial results from commercially developed closed-loop systems

Development and Implementation of an Anthropomorphic Pediatric Spine Phantom for the Assessment of Craniospinal Irradiation Procedures in Proton Therapy

Proton therapy is gaining acceptance as a cancer treatment modality, as it allows for dose deposition to the target volume while sparing the surrounding healthy tissue. This technique is advantageous for craniospinal pediatric patients, as it reduces the radiation side effects that can occur. The purpose of this study is to design an anthropomorphic pediatric spine phantom for use in the evaluation of proton therapy facilities for clinical trial participation by the Imaging and Radiation Oncology Core (IROC) Houston QA Center. It was hypothesized that the designed phantom would evaluate patient simulation, treatment planning and delivery, assuring agreement between the measured and calculated doses within 5%/3mm, with 85% of pixels passing criteria for gamma analysis and also a TLD point dose agreement within 5%. Tissue equivalency was determined by measuring the relative stopping power and Hounsfield unit of potential phantom materials. The materials selected as bone, tissue, and cartilage substitutes were Techron HPV Bearing Grade (RSP 1.3, HU 595.6), solid water (RSP 1.004, HU 16), and blue water (RSP 1.07, HU 86), respectively. The design also incorporates two thermoluminescent dosimeter (TLD)-100 capsules and radiochromic film embedded for dose evaluation. CT images of the phantom were acquired and used to create passive scattering and spot scanning treatment plans. Each plan was delivered three times at a dose of 6 Gy. The following attributes were evaluated: absolute dose agreement, distal range, field width, junction match and right/left dose profile alignment. The hypothesis was accepted for the passive scattering plans, making this phantom and delivery technique suitable for use in IROC Houston proton approval process

Comparison of the Performance of Two Different ALK Antibody Clones (D5F3 and ALK1) in Anaplastic Large Cell Lymphoma (ALCL)

Background:Anaplastic large cell lymphoma (ALCL) is a T-cell lymphoma characterized by CD30 expression and subdivided into anaplastic lymphoma kinase (ALK) positive and negative subtypes that show clinically significant differences in outcomes. The current standard for evaluating ALK status is immunohistochemistry using the mouse monoclonal anti-human CD246 (ALK1) or fluorescence in situ hybridization. The novel rabbit monoclonal anti-human CD246 (D5F3) is proposed as an alternative to ALK1 and FDA approved for diagnosis of ALK-rearranged lung adenocarcinoma. However, its performance has not been systematically tested and compared to ALK1 in ALCL. Design: Twenty-seven cases of ALCL were identified from institutional database searches and retrieved. A representative slide from each case was stained using ALK1 and D5F3 in an automated slide stainer. The intensity of cytoplasmic staining (graded 0-3, none, faint, moderate and strong) and percentage of positive cells (0, \u3c5, 5-50%, 50-75% and \u3e75%) were evaluated for each individual clone and subsequently compared between the two clones. Results: Of the twenty-seven cases, nine were previously diagnosed as ALK expression positive by ALK-1 staining. Nine cases were positive for ALK expression by ALK1 staining (34.6%; 1 1+; 0 2+; 8 3+), while fourteen were positive by D5F3 staining (48.1%; 3 1+; 2 2+; 9 3+). There were no cases that were positive by ALK1, but not by D5F3, which had identified the five additional cases. For three of the nine cases (33.3%) positive by both stains, the D5F3 stained slides showed greater percentage of cells stained. The staining intensity was greater by D5F3 in one of nine cases, the other eight cases showed the same (3+) intensity by D5F3 and ALK1. FISH results are available in five cases (19.2%) and demonstrated 100% concordance with ALK expression by both IHC stains (four positive, one negative). Conclusion: These findings support the use of D5F3 as an equivalent and potentially more sensitive alternative to ALK1 for the evaluation of ALK positivity in ALCL

Do impulsivity and biological sex moderate associations between alcohol-related sexual willingness and behavior among young adults?

This study examined three-way interactions between baseline levels of willingness to engage in alcohol-related sexual behaviors, facets of impulsivity (i.e., urgency, lack of premeditation, and sensation seeking) and biological sex on alcohol-related sexual behaviors 6 months later. Participants were a sample of high-risk 18–25 year olds (N = 321, mean age 22.44) from a larger randomized controlled trial with eligibility criteria including engaging in unprotected sexual behavior after drinking alcohol within the past month at baseline. Results indicated females reporting high urgency and willingness levels were the most likely to engage in alcohol-related sex and to use a condom/dental dam after drinking. Males reporting low urgency levels and high sensation seeking and willingness levels engaged in more alcohol-related sex compared to females. Interventions to decrease alcohol-related sexual behavior by reducing willingness could incorporate sex-specific and impulsivity-related content, particularly related to urgency

Hybrid Model for A Priori Performance Prediction of Multi-Job Type Swarm Search and Service Missions

In Swarm Search and Service (SSS) applications, swarm vehicles are responsible for concurrently searching an area while immediately servicing jobs discovered while searching. Multiple job types may be present in the environment. As vehicles move in and out of the swarm to service jobs, the coverage rate (i.e., area searched by the swarm per time step) changes dynamically to reflect the number of vehicles currently engaged in search. As a result, the arrival rates of jobs also changes dynamically. When planning SSS missions, the resource requirements, such as the swarm size needed to achieve a desired system performance, must be determined. The dynamically changing arrival rates make traditional queuing methods ill-suited to predict the performance of the swarm. This paper presents a hybrid method - Hybrid Model - for predicting the performance of the swarm a priori. It utilizes a Markov model, whose state representation captures the proportion of agents searching or servicing jobs. State-dependent queuing models are used to calculate the state transition function of the Markov states. The model has been developed as a prediction tool to assist mission planners in balancing complex trade-offs, but also provides a basis for optimizing swarm size where cost functions are known. The Hybrid Model is tested in previously considered constant coverage rate scenarios and the results are compared to a previously developed Queuing Model. Additional SSS missions are then simulated and their resulting performance is used to further evaluate the effectiveness of using the Hybrid Model as a prediction tool for swarm performance in more general scenarios with dynamically changing coverage rates

Bus Stop Spacings Statistics: Theory and Evidence

Transit agencies have been removing a large number of bus stops, but discussions around the bus stop spacings exhibit a lack of clarity and data for comparison. This paper proposes new terminology and concepts for statistical consideration of stop spacings, and introduces a python package and open-source database which uses General Transit Feed Specification data to derive real-world stop spacing distributionsComment: 18 pages, 5 tables, 7 figure