Alcohol-attributable mortality in Switzerland in 2011--age-specific causes of death and impact of heavy versus non-heavy drinking.

BACKGROUND: Alcohol use causes high burden of disease and injury globally. Switzerland has a high consumption of alcohol, almost twice the global average. Alcohol-attributable deaths and years of life lost in Switzerland were estimated by age and sex for the year 2011. Additionally, the impact of heavy drinking (40+grams/day for women and 60+g/day for men) was estimated. METHODS: Alcohol consumption estimates were based on the Addiction Monitoring in Switzerland study and were adjusted to per capita consumption based on sales data. Mortality data were taken from the Swiss mortality register. Methodology of the Comparative Risk Assessment for alcohol was used to estimate alcohol-attributable fractions. RESULTS: Alcohol use caused 1,600 (95% CI: 1,472 - 1,728) net deaths (1,768 deaths caused, 168 deaths prevented) among 15 to 74 year olds, corresponding to 8.7% of all deaths (men: 1,181 deaths; women: 419 deaths). Overall, 42,627 years of life (9.7%, 95% CI: 40,245 - 45,008) were lost due to alcohol. Main causes of alcohol-attributable mortality were injuries at younger ages (15-34 years), with increasing age digestive diseases (mainly liver cirrhosis) and cancers (particularly breast cancers among women). The majority (62%) of all alcohol-attributable deaths was caused by chronic heavy drinking (men: 67%; women: 48 %). CONCLUSION: Alcohol is a major cause of premature mortality in Switzerland. Its impact, among young people mainly via injuries, among men mainly through heavy drinking, calls for a mix of preventive actions targeting chronic heavy drinking, binge drinking and mean consumption.

News in the age of empire : two war scandals

This thesis argues that the motivations underpinning the mainstream news media have fundamentally changed in the 21 sl century. As such, the news is no longer best understood as a tool for propaganda or agenda setting; instead it seems that the news is only motivated by the flow of global network capitalism. The author contrasts the work of Noam Chomsky and Edward S. Herman with that of Gilles Deleuze. Chomsky and Herman's 'Propaganda Model' has been influential within the fields of media studies and popular culture. The 'propaganda model' states that the concentration of ownership of the media has allowed the media elite to exert a disproportionate amount of influence over the mass media. Deleuze, on the other hand, regards the mass media as being yet another cog within the global capitalist mechanism, and is therefore separate from ideology or propaganda. The author proposes that 'propaganda' is no longer a sufficient word to describe the function of the news as terms like 'propaganda' imply some form of national sovereignty or governmental influence. To highlight how the news has 'changed from an instrument of propaganda to an instrument of accumulation, the author compares and contrasts the· coverage of the Abu Ghraib Prison Scandal with that of the Haditha Civilian Massacre. Although similar in nature, the author proposes that the Abu Ghraib Prison Scandal received a disproportionate amount of coverage within the mainstream press because of its exciting and sensational nature.

Bleachers in old Chapman Stadium, Chapman University, Orange, California

Chapman Stadium shortly before demolition, Chapman University, Orange, California, 2005. This stadium was originally constructed in 1934 by Orange Union High School, Orange, California. Ownership transferred to Chapman College in 1954. It was re-named in 2

Bleachers in old Chapman Stadium, Chapman University, Orange, California

Chapman Stadium shortly before demolition, Chapman University, Orange, California, 2005. This stadium was originally constructed in 1934 by Orange Union High School, Orange, California. Ownership transferred to Chapman College in 1954. It was re-named in 2

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.

Soil-Transmitted Helminth Infections and Nutritional Status in School-age Children from Rural Communities in Honduras

Background: Soil-transmitted helminth (STH) infections are endemic in Honduras and efforts are underway to decrease their transmission. However, current evidence is lacking in regards to their prevalence, intensity and their impact on children’s health. Objectives: To evaluate the prevalence and intensity of STH infections and their association with nutritional status in a sample of Honduran children. Methodology: A cross-sectional study was done among school-age children residing in rural communities in Honduras, in 2011. Demographic data was obtained, hemoglobin and protein concentrations were determined in blood samples and STH infections investigated in single-stool samples by Kato-Katz. Anthropometric measurements were taken to calculate heightfor- age (HAZ), BMI-for-age (BAZ) and weight-for-age (WAZ) to determine stunting, thinness and underweight, respectively. Results: Among 320 children studied (48% girls, aged 7–14 years, mean 9.7661.4) an overall STH prevalence of 72.5% was found. Children .10 years of age were generally more infected than 7–10 year-olds (p = 0.015). Prevalence was 30%, 67% and 16% for Ascaris, Trichuris and hookworms, respectively. Moderate-to-heavy infections as well as polyparasitism were common among the infected children (36% and 44%, respectively). Polyparasitism was four times more likely to occur in children attending schools with absent or annual deworming schedules than in pupils attending schools deworming twice a year (p,0.001). Stunting was observed in 5.6% of children and it was associated with increasing age. Also, 2.2% of studied children were thin, 1.3% underweight and 2.2% had anemia. Moderate-to-heavy infections and polyparasitism were significantly associated with decreased values in WAZ and marginally associated with decreased values in HAZ. Conclusions: STH infections remain a public health concern in Honduras and despite current efforts were highly prevalent in the studied community. The role of multiparasite STH infections in undermining children’s nutritional status warrants more research.

Cognition in Seemingly Riskless Choices and Judgments.

We instillate rational cognition and learning in seemingly riskless choices and judgments. Preferences and possibilities are given in a stochastic sense and based on revisable expectations. the theory predicts experimental preference reversals and passes a sharp econometric test of the status quo bias drawn from a field 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.

Evaluación de la psicopatología del preescolar mediante el Early Childhood Inventory-4 (ECI-4): concordancia entre padres y maestros.= Assessment of psychopathology in preschool age children through the Early Childhood Inventory-4 (ECI-4): Agreement among parents and teachers

El objetivo principal de este estudio es conocer la concordancia entre informantes, padres y maestros, en cada una de las dimensiones o categorías diagnósticas del Early Childhood Inventory-4 (ECI-4). Además, se pretende analizar la influencia de la presencia de problemas de salud en los padres en la descripción y valoración de la conducta de una muestra de 204 alumnos de preescolar (3 a 6 años) de perfiles socioeconómicos diferentes. Los resultados indican que los padres tienden a valorar con mayor severidad los síntomas, observándose una mayor concordancia entre informantes en los relativos a los trastornos del desarrollo

Participle Preposing and Other Related Phenomena in Old Spanish and Old Catalan

El participi i altres fenòmens relacionats en el Castellà i el Català antic

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.