Soil-Transmitted Helminth Infections, Nutrition and Growth in School-age Children from Rural Communities in Honduras

Background: Soil-transmitted helminth (STH) infections are endemic in Honduras but their impact on children’s health is not well studied. Objectives: To evaluate the prevalence and intensity of STH infections and their association with nutrition and growth in a sample of Honduran children. Methodology: A cross-sectional study was done among Honduran rural school-age children in 2011. Blood and stool samples and anthropometric measurements were obtained to determine nutritional status, STH infection and growth status, respectively. Results: The STH prevalence among 320 studied children was 72.5%. Prevalence by species was 30%, 67% and 16% for Ascaris, Trichuris and 16% hookworms, respectively. High intensity infections were associated with decreased growth scores but regardless of intensity, co-infections negatively affected growth indicators. Conclusions: The health burden of STH infections is related to high parasitic load but also to the presence of low-intensity concurrent infections. The synergistic effects of polyparasitism in underprivileged children warrants more attention.

Developing Physical Activity Habit in Schools for Active Lifestyle Among Children and Adolescents

This study sought to identify and suggest ways to develop physical activity habits in school-aged children and adolescents that could help them continue healthy active practices throughout their lifespan. A systematic review of the literature identified 4 key factors that may influence school-based physical activity habit formation—motivation, enjoyment, commitment, and sustainment—and how each may be achieved in schools. The research paper begins by exploring the definitions and meaning of a habit, how it is developed, and its effect on a healthy active lifestyle. The study proposes a framework comprising 3 major components (i.e., programs, teachers, students) and offers practical strategies that support and nurture the development of students’ physical activity habits in schools. The study concludes by making recommendations for further study.

Statistical Machine Learning Techniques for the Prediction of Learning Disabilities in School-Age Children

Learning Disability (LD) is a general term that describes specific kinds of learning problems. It is a neurological condition that affects a child's brain and impairs his ability to carry out one or many specific tasks. The learning disabled children are neither slow nor mentally retarded. This disorder can make it problematic for a child to learn as quickly or in the same way as some child who isn't affected by a learning disability. An affected child can have normal or above average intelligence. They may have difficulty paying attention, with reading or letter recognition, or with mathematics. It does not mean that children who have learning disabilities are less intelligent. In fact, many children who have learning disabilities are more intelligent than an average child. Learning disabilities vary from child to child. One child with LD may not have the same kind of learning problems as another child with LD. There is no cure for learning disabilities and they are life-long. However, children with LD can be high achievers and can be taught ways to get around the learning disability. In this research work, data mining using machine learning techniques are used to analyze the symptoms of LD, establish interrelationships between them and evaluate the relative importance of these symptoms. To increase the diagnostic accuracy of learning disability prediction, a knowledge based tool based on statistical machine learning or data mining techniques, with high accuracy,according to the knowledge obtained from the clinical information, is proposed. The basic idea of the developed knowledge based tool is to increase the accuracy of the learning disability assessment and reduce the time used for the same. Different statistical machine learning techniques in data mining are used in the study. Identifying the important parameters of LD prediction using the data mining techniques, identifying the hidden relationship between the symptoms of LD and estimating the relative significance of each symptoms of LD are also the parts of the objectives of this research work. The developed tool has many advantages compared to the traditional methods of using check lists in determination of learning disabilities. For improving the performance of various classifiers, we developed some preprocessing methods for the LD prediction system. A new system based on fuzzy and rough set models are also developed for LD prediction. Here also the importance of pre-processing is studied. A Graphical User Interface (GUI) is designed for developing an integrated knowledge based tool for prediction of LD as well as its degree. The designed tool stores the details of the children in the student database and retrieves their LD report as and when required. The present study undoubtedly proves the effectiveness of the tool developed based on various machine learning techniques. It also identifies the important parameters of LD and accurately predicts the learning disability in school age children. This thesis makes several major contributions in technical, general and social areas. The results are found very beneficial to the parents, teachers and the institutions. They are able to diagnose the child’s problem at an early stage and can go for the proper treatments/counseling at the correct time so as to avoid the academic and social losses.

Prediction of Key Symptoms of Learning Disabilities in School-Age Children Using Rough Sets

This paper highlights the prediction of learning disabilities (LD) in school-age children using rough set theory (RST) with an emphasis on application of data mining. In rough sets, data analysis start from a data table called an information system, which contains data about objects of interest, characterized in terms of attributes. These attributes consist of the properties of learning disabilities. By finding the relationship between these attributes, the redundant attributes can be eliminated and core attributes determined. Also, rule mining is performed in rough sets using the algorithm LEM1. The prediction of LD is accurately done by using Rosetta, the rough set tool kit for analysis of data. The result obtained from this study is compared with the output of a similar study conducted by us using Support Vector Machine (SVM) with Sequential Minimal Optimisation (SMO) algorithm. It is found that, using the concepts of reduct and global covering, we can easily predict the learning disabilities in children

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

Performance Improvement of Fuzzy and Neuro Fuzzy Systems: Prediction of Learning Disabilities in School-age Children

Learning Disability (LD) is a classification including several disorders in which a child has difficulty in learning in a typical manner, usually caused by an unknown factor or factors. LD affects about 15% of children enrolled in schools. The prediction of learning disability is a complicated task since the identification of LD from diverse features or signs is a complicated problem. There is no cure for learning disabilities and they are life-long. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. The aim of this paper is to develop a new algorithm for imputing missing values and to determine the significance of the missing value imputation method and dimensionality reduction method in the performance of fuzzy and neuro fuzzy classifiers with specific emphasis on prediction of learning disabilities in school age children. In the basic assessment method for prediction of LD, checklists are generally used and the data cases thus collected fully depends on the mood of children and may have also contain redundant as well as missing values. Therefore, in this study, we are proposing a new algorithm, viz. the correlation based new algorithm for imputing the missing values and Principal Component Analysis (PCA) for reducing the irrelevant attributes. After the study, it is found that, the preprocessing methods applied by us improves the quality of data and thereby increases the accuracy of the classifiers. The system is implemented in Math works Software Mat Lab 7.10. The results obtained from this study have illustrated that the developed missing value imputation method is very good contribution in prediction system and is capable of improving the performance of a classifier.

A performance test for school age children wtih a language handicap

This paper discusses language and intelligence tests for hearing impaired children.

Noise exposure in high school aged youths and distortion product otoacoustic emissions

This paper discusses the effect of noise exposure on high school aged boys' hearing levels and how to measure the effects.

Noise exposure in school-age children

This paper discusses a study to determine the average level of noise exposure for school children on a typical school day.

The effects of maternal depression and parental conflict on children's peer play

Background Little is known about the relative effects of exposure to postnatal depression and parental conflict on the social functioning of school-aged children. This is, in part, because of a lack of specificity in the measurement of child and parental behaviour and a reliance on children's reports of their hypothetical responses to conflict in play. Methods In the course of a prospective longitudinal study of children of postnatally depressed and well women, 5-year-old children were videotaped at home with a friend in a naturalistic dressing-up play setting. As well as examining possible associations between the occurrence of postnatal depression and the quality of the children's interactions, we investigated the influence of parental conflict and co-operation, and the continuity of maternal depression. The quality of the current mother-child relationship was considered as a possible mediating factor. Results Exposure to postnatal depression was associated with increased likelihood, among boys, of displaying physical aggression in play with their friend. However, parental conflict mediated the effects of postnatal depression on active aggression during play, and was also associated with displays of autonomy and intense conflict. While there were no gender effects in terms of the degree or intensity of aggressive behaviours, girls were more likely to express aggression verbally using denigration and gloating whereas boys were more likely to display physical aggression via interpersonal and object struggles. Conclusions The study provided evidence for the specificity of effects, with strong links between parental and child peer conflict. These effects appear to arise from direct exposure to parental conflict, rather than indirectly, through mother-child interactions.

Time-based prospective memory in children with autism spectrum disorder

In this study, for the first time, prospective memory was investigated in 11 school-aged children with autism spectrum disorders and 11 matched neurotypical controls. A computerised time-based prospective memory task was embedded in a visuospatial working memory test and required participants to remember to respond to certain target times. Controls had significantly more correct prospective memory responses than the autism spectrum group. Moreover, controls checked the time more often and increased time-monitoring more steeply as the target times approached. These differences in time-checking may suggest that prospective memory in autism spectrum disorders is affected by reduced self-initiated processing as indicated by reduced task monitoring.