The Role of Second Generation Antiretroviral Drugs in HIV-1 Subtype B and non-B Variants Harboring Natural Polymorphisms and Drug Resistance Mutations.

Cette thèse traite de la résistance du VIH-1 aux antirétroviraux, en particulier de l'activité antivirale de plusieurs inhibiteurs non nucléosidiques de la transcriptase inverse (INNTI) ainsi que des inhibiteurs de protéase (IP). Nous avons exploré l’émergence et la spécificité des voies de mutations qui confèrent la résistance contre plusieurs nouveaux INNTI (étravirine (ETR) et rilpivirine (RPV)) (chapitres 2 et 3). En outre, le profil de résistance et le potentiel antirétroviral d'un nouvel IP, PL-100, est présenté dans les chapitres 4 et 5. Pour le premier projet, nous avons utilisé des sous-types B et non-B du VIH-1 pour sélectionner des virus résistants à ETR, et ainsi montré que ETR favorise l’émergence des mutations V90I, K101Q, E138K, V179D/E/F, Y181C, V189I, G190E, H221H/Y et M230L, et ce, en 18 semaines. Fait intéressant, E138K a été la première mutation à émerger dans la plupart des cas. Les clones viraux contenant E138K ont montré un faible niveau de résistance phénotypique à ETR (3,8 fois) et une diminution modeste de la capacité de réplication (2 fois) par rapport au virus de type sauvage. Nous avons également examiné les profils de résistance à ETR et RPV dans les virus contenant des mutations de résistance aux INNTI au début de la sélection. Dans le cas du virus de type sauvage et du virus contenant la mutation unique K103N, les premières mutations à apparaître en présence d’ETR ou de RPV ont été E138K ou E138G suivies d’autres mutations de résistance aux INNTI. À l’inverse, dans les mêmes conditions, le virus avec la mutation Y181C a évolué pour produire les mutations V179I/F ou A62V/A, mais pas E138K/G. L'ajout de mutations à la position 138 en présence de Y181C n'augmente pas les niveaux de résistance à ETR ou RPV. Nous avons également observé que la combinaison de Y181C et E138K peut conduire à un virus moins adapté par rapport au virus contenant uniquement Y181C. Sur la base de ces résultats, nous suggérons que les mutations Y181C et E138K peuvent être antagonistes. L’analyse de la résistance au PL-100 des virus de sous-type C et CRF01_AE dans les cellules en culture est décrite dans le chapitre 4. Le PL-100 sélectionne pour des mutations de résistance utilisant deux voies distinctes, l'une avec les mutations V82A et L90M et l'autre avec T80I, suivi de l’addition des mutations M46I/L, I54M, K55R, L76F, P81S et I85V. Une accumulation d'au moins trois mutations dans le rabat protéique et dans le site actif est requise dans chaque cas pour qu’un haut niveau de résistance soit atteint, ce qui démontre que le PL-100 dispose d'une barrière génétique élevée contre le développement de la résistance. Dans le chapitre 5, nous avons évalué le potentiel du PL-100 en tant qu’inhibiteur de protéase de deuxième génération. Les virus résistants au PL-100 émergent en 8-48 semaines alors qu’aucune mutation n’apparaît avec le darunavir (DRV) sur une période de 40 semaines. La modélisation moléculaire montre que la haute barrière génétique du DRV est due à de multiples interactions avec la protéase dont des liaison hydrogènes entre les groupes di-tétrahydrofuranne (THF) et les atomes d'oxygène des acides aminés A28, D29 et D30, tandis que la liaison de PL-100 est principalement basée sur des interactions polaires et hydrophobes délocalisées à travers ses groupes diphényle. Nos données suggèrent que les contacts de liaison hydrogène et le groupe di-THF dans le DRV, ainsi que le caractère hydrophobe du PL-100, contribuent à la liaison à la protéase ainsi qu’à la haute barrière génétique contre la résistance et que la refonte de la structure de PL-100 pour inclure un groupe di-THF pourrait améliorer l’activité antivirale et le profil de résistance.

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.

Working memory in school-aged children: Exhibition intervals and distracters effects in Memonum computerized test

This study assessed visual working memory through Memonum computerized test in schoolchildren. The effects of three exposure times (1, 4 and 8 seconds) have been evaluated, and the presentation of a distractor on the mnemonic performance in the test Memonum in 72 children from a college in the metropolitan area of Bucaramanga, Colombia, aged between 8 and 11 in grades third, fourth and fifth grade. It has been found significant difference regarding the exposure time in the variables number of hits and successes accumulated, showing a better mnemonic performance in participants who took the test during 8 seconds compared to children who took the test during 1 second; in addition, the presence of a distractor showed a significant difference regarding the strengths and successes accumulated. Such distractor is considered a stimulus generator interference that disrupts the storage capacity of working memory in children. Additionally, a significant difference was found with respect to the use of mental rehearsal strategy, indicating that participants who took the test in 4 and 8 seconds, respectively, assigned higher scores than children who took the test in 1 second. A long exposure time to stimuli during Memonum test increases the holding capacity. Also, the use of a distractor affects the storage capacity and this, at the same time, increases the school progression due to the use of mnemonic strategies that children use to ensure the memory of the numerical series

Executive functions in school-aged children: age and socioeconomic status effects

Lately, the study of prefrontal executive functions in grade scholars has noticeably increased. The aim of this study is to investigate the influence of age and socioeconomic status (sEs) on executive tasks performance and to analyze those socioeconomic variables that predict a better execution. A sample of 254 children aged between 7 and 12 years from the city of santa Fe, Argentina and belonging to different socioeconomic status were tested. A bat- tery of executive functions sensitive to prefrontal function was used to obtain the results. These in- dicate a significant influence of age and SES on executive functions. The cognitive patterns follow a different path according to the development and sEs effect. Besides, it is revealed a pattern of low cognitive functioning in low-sEs children in all executive functions. Finally, from the variables included in this study, it was found that only the educational level of the mother and the housing conditions are associated to the children’s executive function. The results are discussed in terms of the influence of the cerebral maturation and the envi- ronmental variables in the executive functioning.

La violencia en el ??mbito escolar = La violence dans l'??cole = Violence in School

Percepciones y preocupaciones del profesorado con respecto a la violencia en la Educaci??n Secundaria y a las posibilidades de la educaci??n para elaborar estrategias y alternativas v??lidas para educar en favor de la paz, la justicia y el desarrollo. 42 profesores de Educaci??n Secundaria, seleccionados en funci??n de: cobertura geogr??fica, disponibilidad y criterios de tipo pr??ctico. La investigaci??n se distribuy?? en cuatro fases, la primera de car??cter exploratorio y de inmersi??n en la comunidad, en la segunda se realiz?? una din??mica con un grupo de discusi??n formado por educadores con circunstancias personales, profesionales y acad??micas distintas, en la tercera la realizaci??n de entrevistas pretest y selecci??n de la muestra y en la cuarta la interpretaci??n y categorizaci??n de la informaci??n obtenida de las entrevistas. Investigaci??n cualitativa de car??cter cuasi-etnogr??fico. No se puede afirmar que se disponga de un paradigma conceptual capaz de interpretar la naturaleza del problema de la violencia escolar en todas sus dimensiones. Su estudio requiere de una reflexi??n profunda sobre el alcance del problema que ponga las bases para comprender su naturaleza y gu??e el camino de la intervenci??n educativa para prevenirla; para ello es necesario multiplicar los procesos de investigaci??n y de intervenci??n que permitan acceder, de forma democr??tica y no traum??tica a su comprensi??n y erradicaci??n.

The effect of nonlinear frequency compression and linear frequency transposition on speech perception in school-aged children

The primary objective of this study is to determine whether nonlinear frequency compression and linear transposition algorithms provide speech perception benefit in school-aged children.

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.

Agency and conformity in school-based teacher training

Concern has been expressed that the current climate in schools militates against trainee teachers' self-directed development. This article explores the issue of trainees' capacity for self-direction through the analysis of interviews with 32 trainees, investigating their perceived proactive social strategies. Three proactive strategies were identified: 'tactical compliance', personalising advice, and seeking out opportunities to exercise control. It is argued that these strategies are indicative of trainees' drive to establish a personal teaching identity through self-directed development and the creation of individual development agendas. The article concludes by emphasising the importance of the development of proactive social skills in beginning teachers. (c) 2008 Elsevier Ltd. All rights reserved.

The effect of cleft lip on socio-emotional functioning in school-aged children

Background: Children with cleft lip are known to be at raised risk for socio-emotional difficulties, but the nature of these problems and their causes are incompletely understood; longitudinal studies are required that include comprehensive assessment of child functioning, and consideration of developmental mechanisms. Method: Children with cleft lip (with and without cleft palate) (N = 93) and controls (N = 77), previously studied through infancy, were followed up at 7 years, and their socio-emotional functioning assessed using teacher and maternal reports, observations of social interactions, and child social representations (doll play). Direct and moderating effects of infant attachment and current parenting were investigated, as was the role of child communication difficulties and attractiveness. Results: Children with clefts had raised rates of teacher-reported social problems, and anxious and withdrawn-depressed behaviour; direct observations and child representations also revealed difficulties in social relationships. Child communication problems largely accounted for these effects, especially in children with cleft palate as well as cleft lip. Insecure attachment contributed to risk in both index and control groups, and a poorer current parenting environment exacerbated the difficulties of those with clefts. Conclusions: Children with clefts are at raised risk for socio-emotional difficulties in the school years; clinical interventions should focus on communication problems and supporting parenting; specific interventions around the transition to school may be required. More generally, the findings reflect the importance of communication skills for children’s peer relations.

The effect of cleft lip on cognitive development in school-aged children: a paradigm for examining sensitive period effects

Background:  Our previous investigation showed that infants with cleft lip who had undergone late (three-month) surgical repair (but not those with early, neonatal, repair) had significantly poorer cognitive development at 18 months than a group of unaffected control children. These differences were mediated by the quality of early mother–infant interactions. The present study examined whether this pattern persisted into later childhood. Method:  At 7 years, 93 index (44 early, and 49 late repair) and 77 control children were followed up and their cognitive development assessed (IQ, language and school achievements). Results:  Index children (particularly those with late lip repair) scored significantly lower than controls on tests of cognitive development. Group differences in Verbal IQ were mediated by 2 months’ maternal sensitivity; this was associated with 7-year Verbal IQ, even after controlling for later mother–child interactions. Conclusions:  Social interactions in the first few months may be of especial importance for child cognitive development. Interventions for infants with cleft lip should be directed at fostering the best possible parental care in infancy.