A Pilot Study: Attention Deficit Hyperactivity Disorder, Sensation Seeking, and Pubertal Changes

This study was designed to examine the relationship of pubertal changes and sensation seeking (SS) in adolescents with Attention Deficit Hyperactivity Disorder (ADHD). Patients with current or past histories of uncomplicated stimulant medication use for ADHD between the ages of 11 and 15 (13 ± 1.5) were recruited from a Child Psychiatry and a General Pediatric Clinic. SS was measured using the SS Scale for Children. Pubertal development was measured using Tanner staging, free testosterone, and DHEAS. Subjects and their parent were interviewed with the Diagnostic Interview Schedule for Children (DISC). SS total score was correlated with Tanner stage, free testosterone, and DHEAS (p ≤ 0.01). The combined parent and child reports of symptoms of Oppositional Defiant Disorder from the DISC were inversely related to age (p ≤ 0.05). Understanding SS in ADHD adolescents as they move through puberty will aid clinicians in monitoring ADHD adolescents and their trajectory into high-risk behaviors

Development of the Guernsey Community Participation and Leisure Assessment – Revised (GCPLA-R).

A sufficiently psychometrically robust measure of community and leisure participation of adults with intellectual disabilities was not in existence, despite research identifying this as an important outcome and a key contributor to quality of life. The current study aimed to update the Guernsey Community Participation and Leisure Assessment (GCPLA). Adults with intellectual disabilities, carers and experts were consulted in creating a revised pool of 46 items. These were then tested and data from 326 adults with intellectual disabilities were analysed for their component structure and psychometric properties. Principal Component analysis discovered a stable set of components describing seven different clusters. This revised measure (the GCPLA-R) was demonstrated to have satisfactory reliability, and scores were related to challenging behaviour and adaptive behaviour in theoretically consistent ways and were correlated with scores on comparable measures

Friendship activities of adults with intellectual disability in supported accommodation in northern England

Background Despite there being considerable evidence to suggest that friendships are central to health and well-being, relatively little attention had been paid to the friendships of people with intellectual disabilities. Methods Friendship activities involving people with and without intellectual disabilities were measured over the preceding month in a sample of 1542 adults with intellectual disabilities receiving supported accommodation in nine geographical localities in Northern England. Results The results of the study indicate: (1) low levels of friendship activities among people with intellectual disabilities in supported accommodation; (2) people with intellectual disabilities are more likely to be involved in activities with friends who also have intel lectual disabilities than with friends who do not have intellectual disabilities; (3) most friendship activities take place in the public domain rather than in more private settings (e.g. at home); (4) the setting in which a person lives is a more significant determinant of the form and content of activities with their friends than the characteristics of participants. Conclusions Further attention needs to be given to research and practice initiatives aimed at increasing the levels of friendship activities of people with intellectual disabilities

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Serum Amyloid A, C-Reactive Protein, and Retinal Microvascular Changes in Hypertensive Diabetic and Nondiabetic Individuals: An Anglo-Scandinavian Cardiac Outcomes Trial (ASCOT) substudy

To study the association of the inflammatory markers serum amyloid A (SAA) and C-reactive protein (CRP) with retinal microvascular parameters in hypertensive individuals with and without type 2 diabetes

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression

Genetic, environmental and stochastic factors in monozygotic twin discordance with a focus on epigenetic differences

PMCID: PMC3566971This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited

Design of Narrow-Band Dielectric Frequency-Selective Surfaces for Microwave Applications

This paper is a postprint of a paper submitted to and accepted for publication in IET Microwaves Antennas and Propagation and is subject to Institution of Engineering and Technology Copyright. The copy of record is available at IET Digital LibraryTwo types of narrow-band dielectric frequency-selective surfaces (DFSSs) have been designed at microwave frequencies. First, a DFSS showing total reflection has been analysed under guided-mode resonance conditions, based on a single dielectric grating which is illuminated by a TM polarised two-dimensional plane wave at Brewster-angle incidence, presenting extremely low-reflectance sidebands adjacent to the resonance peak. Second, a DFSS exhibiting total transmission at normal TE incidence has been designed, by superimposing the resonance condition of a dielectric grating on the classical high-reflectance response of a periodic (band-gap based) structure formed by alternating homogeneous dielectric layers. Finally, the oblique incidence and polarisation effects on the spectral response of the designed DFSSs have been also studied. In addition, dielectric ohmic losses and the problem of the finite size of the periodic structures have been accounted for in both structures. The obtained results have been successfully validated with the commercial software tool high frequency structure simulator.This work was supported in part by Ministerio de Economia y Competitividad (MINECO) under Coordinated Project TEC2013-47037-C5.Coves, A.; Marini, S.; Gimeno, B.; Sánchez-Escuderos, D.; Rodriguez Perez, AM.; Boria Esbert, VE. (2016). Design of Narrow-Band Dielectric Frequency-Selective Surfaces for Microwave Applications. IET Microwaves Antennas and Propagation. 10(3):251-255. https://doi.org/10.1049/iet-map.2015.0121S25125510