A parenting intervenion for childhood behavioral problems: a randomised controlled trial in disadvantaged Community-based settings:A randomized controlled trial in disadvantaged community-based settings

Objective: A community-based randomized controlled trial (RCT) was conducted in urban areas characterized by high levels of disadvantage to test the effectiveness of the Incredible Years BASIC parent training program (IYBP) for children with behavioral problems. Potential moderators of intervention effects on child behavioral outcomes were also explored. Method: Families were included if the child (aged 32-88 months) scored above a clinical cutoff on the Eyberg Child Behavior Inventory (ECBI). Participants (n = 149) were randomly allocated on a 2:1 ratio to an intervention group (n = 103) or a waiting-list control group (n = 46). Child behavior, parenting skills, and parent well-being were assessed at baseline and 6 months later using parent-report and independent observations. An intention-to-treat analysis of covariance was used to examine postintervention differences between groups. Results: Statistically significant differences in child disordered behavior favored the intervention group on the ECBI Intensity (effect size = 0.7, p

A qualitative study of psychological, social and behavioral barriers to appropriate food portion size control

Background: Given the worldwide prevalence of overweight and obesity, there is a clear need for meaningful practical healthy eating advice - not only in relation to food choice, but also on appropriate food portion sizes. As the majority of portion size research to date has been overwhelmingly quantitative in design, there is a clear need to qualitatively explore consumers’ views in order to fully understand how food portion size decisions are made. Using qualitative methodology this present study aimed to explore consumers’ views about factors influencing their portion size selection and consumption and to identify barriers to appropriate portion size control.

Methods: Ten focus groups with four to nine participants in each were formed with a total of 66 persons (aged 19–64 years) living on the island of Ireland. The semi-structured discussions elicited participants’ perceptions of suggested serving size guidance and explored the influence of personal, social and environmental factors on their food portion size consumption. Audiotapes of the discussions were professionally transcribed verbatim, loaded into NVivo 9, and analysed using an inductive thematic analysis procedure.
Results: The rich descriptive data derived from participants highlight that unhealthy portion size behaviors emanate from various psychological, social and behavioral factors. These bypass reflective and deliberative control, and converge to constitute significant barriers to healthy portion size control. Seven significant barriers to healthy portion size control were apparent: (1) lack of clarity and irrelevance of suggested serving size guidance; (2) guiltless eating; (3) lack of self-control over food cues; (4) distracted eating; (5) social pressures; (6) emotional eating rewards;
and (7) quantification habits ingrained from childhood.

Conclusions: Portion size control strategies should empower consumers to overcome these effects so that the consumption of appropriate food portion sizes becomes automatic and habitual.
Keywords: Food portion size, Barriers, Obesity, Consumers, Qualitative study. © 2013 Spence et al.; licensee BioMed Central Ltd

Multi-Dimensional Deprivation in Ireland Among 9-Year-Olds in Ireland:An Analysis of the Growing Up in Ireland Survey’

In this paper we make use of the 9-year old wave of the Growing Up in Ireland study to analyse multidimensional deprivation in Ireland. The Alkire and Foster adjusted head count ratio approach (AHCR; 2007, 2011a, 2011b) applied here constitutes a significant improvement on union and intersection approaches and allows for the decomposition of multidimensional poverty in terms of dimensions and sub-groups. The approach involves a censoring of data such that deprivations count only for those above the specified multidimensional threshold leading to a stronger set of interrelationships between deprivation dimensions. Our analysis shows that the composition of the adjusted head ratio is influenced by a range of socio-economic factors. For less-favoured socio-economic groups dimensions relating to material deprivation are disproportionately represented while for the more advantaged groups, those relating to behavioral and emotional issues and social interaction play a greater role. Notwithstanding such variation in composition, our analysis showed that the AHCR varied systematically across categories of household type, and the social class, education and age group of the primary care giver. Furthermore, these variables combined in a cumulative manner. The most systematic variation was in relation to the head count of those above the multidimensional threshold rather than intensity, conditional on being above that cut-off point. Without seeking to arbitrate on the relative value of composite indices versus disaggregated profiles, our analysis demonstrates that there is much to be gained from adopting an approach with clearly understood axiomatic properties. Doing so allows one to evaluate the consequences of the measurement strategy employed for the understanding of levels of multidimensional deprivation, the nature of such deprivation profiles and socio-economic risk patterns. Ultimately it permits an informed assessment of the strengths and weaknesses of the particular choices made.

Nonlinear behavioral modeling of mixed signal RF-digital I/O devices

Esta tese investiga a caracterização (e modelação) de dispositivos que realizam o interface entre os domínios digital e analógico, tal como os buffers de saída dos circuitos integrados (CI). Os terminais sem fios da atualidade estão a ser desenvolvidos tendo em vista o conceito de rádio-definido-por-software introduzido por Mitola. Idealmente esta arquitetura tira partido de poderosos processadores e estende a operação dos blocos digitais o mais próximo possível da antena. Neste sentido, não é de estranhar que haja uma crescente preocupação, no seio da comunidade científica, relativamente à caracterização dos blocos que fazem o interface entre os domínios analógico e digital, sendo os conversores digital-analógico e analógico-digital dois bons exemplos destes circuitos. Dentro dos circuitos digitais de alta velocidade, tais como as memórias Flash, um papel semelhante é desempenhado pelos buffers de saída. Estes realizam o interface entre o domínio digital (núcleo lógico) e o domínio analógico (encapsulamento dos CI e parasitas associados às linhas de transmissão), determinando a integridade do sinal transmitido. Por forma a acelerar a análise de integridade do sinal, aquando do projeto de um CI, é fundamental ter modelos que são simultaneamente eficientes (em termos computacionais) e precisos. Tipicamente a extração/validação dos modelos para buffers de saída é feita usando dados obtidos da simulação de um modelo detalhado (ao nível do transístor) ou a partir de resultados experimentais. A última abordagem não envolve problemas de propriedade intelectual; contudo é raramente mencionada na literatura referente à caracterização de buffers de saída. Neste sentido, esta tese de Doutoramento foca-se no desenvolvimento de uma nova configuração de medição para a caracterização e modelação de buffers de saída de alta velocidade, com a natural extensão aos dispositivos amplificadores comutados RF-CMOS. Tendo por base um procedimento experimental bem definido, um modelo estado-da-arte é extraído e validado. A configuração de medição desenvolvida aborda não apenas a integridade dos sinais de saída mas também do barramento de alimentação. Por forma a determinar a sensibilidade das quantias estimadas (tensão e corrente) aos erros presentes nas diversas variáveis associadas ao procedimento experimental, uma análise de incerteza é também apresentada.

Behavioral modeling optimization and enhancement for high-speed analog mixed-signal I/O interfaces

A integridade do sinal em sistemas digitais interligados de alta velocidade, e avaliada através da simulação de modelos físicos (de nível de transístor) é custosa de ponto vista computacional (por exemplo, em tempo de execução de CPU e armazenamento de memória), e exige a disponibilização de detalhes físicos da estrutura interna do dispositivo. Esse cenário aumenta o interesse pela alternativa de modelação comportamental que descreve as características de operação do equipamento a partir da observação dos sinais eléctrico de entrada/saída (E/S). Os interfaces de E/S em chips de memória, que mais contribuem em carga computacional, desempenham funções complexas e incluem, por isso, um elevado número de pinos. Particularmente, os buffers de saída são obrigados a distorcer os sinais devido à sua dinâmica e não linearidade. Portanto, constituem o ponto crítico nos de circuitos integrados (CI) para a garantia da transmissão confiável em comunicações digitais de alta velocidade. Neste trabalho de doutoramento, os efeitos dinâmicos não-lineares anteriormente negligenciados do buffer de saída são estudados e modulados de forma eficiente para reduzir a complexidade da modelação do tipo caixa-negra paramétrica, melhorando assim o modelo standard IBIS. Isto é conseguido seguindo a abordagem semi-física que combina as características de formulação do modelo caixa-negra, a análise dos sinais eléctricos observados na E/S e propriedades na estrutura física do buffer em condições de operação práticas. Esta abordagem leva a um processo de construção do modelo comportamental fisicamente inspirado que supera os problemas das abordagens anteriores, optimizando os recursos utilizados em diferentes etapas de geração do modelo (ou seja, caracterização, formulação, extracção e implementação) para simular o comportamento dinâmico não-linear do buffer. Em consequência, contributo mais significativo desta tese é o desenvolvimento de um novo modelo comportamental analógico de duas portas adequado à simulação em overclocking que reveste de um particular interesse nas mais recentes usos de interfaces de E/S para memória de elevadas taxas de transmissão. A eficácia e a precisão dos modelos comportamentais desenvolvidos e implementados são qualitativa e quantitativamente avaliados comparando os resultados numéricos de extracção das suas funções e de simulação transitória com o correspondente modelo de referência do estado-da-arte, IBIS.

Authentic Leadership and Behavioral Integrity as Drivers of Follower Commitment and Performance

The literatures on both authentic leadership and behavioral integrity have argued that leader integrity drives follower performance. Yet, despite overlap in conceptualization and mechanisms, no research has investigated how authentic leadership and behavioral integrity relate to one another in driving follower performance. In this study, we propose and test the notion that authentic leadership behavior is an antecedent to perceptions of leader behavioral integrity, which in turn affects follower affective organizational commitment and follower work role performance. Analysis of a survey of 49 teams in the service industry supports the proposition that authentic leadership is related to follower affective organizational commitment, fully mediated through leader behavioral integrity. Next, we found that authentic leadership and leader behavioral integrity are related to follower work role performance, fully mediated through follower affective organizational commitment. These relationships hold when controlling for ethical organizational culture.

Superior outcome of women with stage I/II cutaneous melanoma: pooled analysis of four European Organisation for Research and Treatment of Cancer phase III trials.

PURPOSE: Several studies observed a female advantage in the prognosis of cutaneous melanoma, for which behavioral factors or an underlying biologic mechanism might be responsible. Using complete and reliable follow-up data from four phase III trials of the European Organisation for Research and Treatment of Cancer (EORTC) Melanoma Group, we explored the female advantage across multiple end points and in relation to other important prognostic indicators. PATIENTS AND METHODS: Patients diagnosed with localized melanoma were included in EORTC adjuvant treatment trials 18832, 18871, 18952, and 18961 and randomly assigned during the period of 1984 to 2005. Cox proportional hazard models were used to calculate hazard ratios (HRs) and 95% CIs for women compared with men, adjusted for age, Breslow thickness, body site, ulceration, performed lymph node dissection, and treatment. RESULTS: A total of 2,672 patients with stage I/II melanoma were included. Women had a highly consistent and independent advantage in overall survival (adjusted HR, 0.70; 95% CI, 0.59 to 0.83), disease-specific survival (adjusted HR, 0.74; 95% CI, 0.62 to 0.88), time to lymph node metastasis (adjusted HR, 0.70; 95% CI, 0.51 to 0.96), and time to distant metastasis (adjusted HR, 0.69; 95% CI, 0.59 to 0.81). Subgroup analysis showed that the female advantage was consistent across all prognostic subgroups (with the possible exception of head and neck melanomas) and in pre- and postmenopausal age groups. CONCLUSION: Women have a consistent and independent relative advantage in all aspects of the progression of localized melanoma of approximately 30%, most likely caused by an underlying biologic sex difference.

Meta-analysis and imputation refines the association of 15q25 with smoking quantity.

Smoking is a leading global cause of disease and mortality. We established the Oxford-GlaxoSmithKline study (Ox-GSK) to perform a genome-wide meta-analysis of SNP association with smoking-related behavioral traits. Our final data set included 41,150 individuals drawn from 20 disease, population and control cohorts. Our analysis confirmed an effect on smoking quantity at a locus on 15q25 (P = 9.45 x 10(-19)) that includes CHRNA5, CHRNA3 and CHRNB4, three genes encoding neuronal nicotinic acetylcholine receptor subunits. We used data from the 1000 Genomes project to investigate the region using imputation, which allowed for analysis of virtually all common SNPs in the region and offered a fivefold increase in marker density over HapMap2 (ref. 2) as an imputation reference panel. Our fine-mapping approach identified a SNP showing the highest significance, rs55853698, located within the promoter region of CHRNA5. Conditional analysis also identified a secondary locus (rs6495308) in CHRNA3.

"I might be a duck, but I'm human" : an analysis of clothing in Disney cartoons

Mickey Mouse, one of the world's most recognizable cartoon characters, did not wear a shirt in his earliest incarnation in theatrical shorts and, for many years, Donald Duck did not wear pants and still rarely does so. Especially when one considers the era in which these figures were first created by the Walt Disney Studio, in the 1920s and 1930s, why are they portrayed without full clothing? The obvious answer, of course, is that they are animals, and animals do not wear clothes. But these are no ordinary animals: in most cases, they do wear clothing - some clothing, at least - and they walk on two legs, talk in a more or less intelligible fashion, and display a number of other anthropomorphic traits. If they are essentially animals, why do they wear clothing at all? On the other hand, if these characters are more human than animal, as suggested by other behavioral traits - they walk, talk, work, read, and so on - why are they not more often fully clothed? To answer these questions I undertook three major research strategies used to gather evidence: interpretive textual analysis of 321 cartoons; secondary analysis of interviews conducted with the animators who created the Disney characters; and historical and archival research on the Disney Company and on the times and context in which it functioned. I was able to identify five themes that played a large part in what kind of clothing a character wore; first, the character's gender and/or sexuality; second, what species or "race" the character was; third, the character's socio-economic status; fourth, the degree to which the character was anthropomorphized; and, fifth, the context in which the character and its clothing appeared in a particular scene or narrative. I concluded that all of these factors played a part in determining, to some extent, the clothing worn by particular characters at particular times. However, certain patterns emerged from the analysis that could not be explained by these factors alone or in combination. Therefore, my analysis also investigates the individual and collective attitudes and desires of the men in the Disney studio who were responsible for creating these characters and the cultural conditions under which they were created. Drawing on literature from the psychoanalytic approach to film studies, I argue that the clothing choices spoke to an idealized fantasy world to which the animators (most importantly, Walt Disney himself), and possibly wider society, wanted to return.

“Weathering a Hidden Storm”: An Application of Andersen’s Behavioral Model of Health, and Health Services Use for Those with Diagnosable Anxiety Disorder

Abstract: Research has primarily focused on depression and mood disorders, but little research has been devoted to an examination of mental health services use amongst those with diagnosable anxiety disorder (Wittchen et al., 2002; Bergeron et al., 2005). This study examined the possible predicting factors for mental health services utilization amongst those with identifiable anxiety disorder in the Canadian population. The methods used for this study was the application of Andersen’s Behavioral Model of Health Services Use, where predisposing, need and enabling characteristics were regressed on the dependent variable of mental health services use. This study used the Canadian Community Health Survey (cycle 1.2: Mental Health and Well-Being) in a secondary data analysis. Several multiple logistics models predicted the likelihood to seek and use mental health services. Predisposing characteristics of gender and age, Enabling characteristics of education and geographical location, and those with co-occurring mood disorders were at the greatest increased likelihood to seek and use mental health services.

"Weathering a hidden storm" : an application of Andersen's behavioral model of health, and health services use for those with diagnosable anxiety disorder

"Weathering a Hidden Storm": An App~ication of Andersen's Behaviora~ Mode~ of Hea~th, and Hea~th Services Use for Those With Diagnosab~e Anxiety Disorder Research has primarily focused on depression and mood disorders, but little research has been devoted to an examination of mental health services use amongst those with diagnosable anxiety disorder (Wittchen et al., 2002; Bergeron et al., 2005). This study examined the possible predicting factors for mental health services utilization amongst those with identifiable anxiety disorder in the Canadian population. The methods used for this study was the application of Andersen's Behavioral Model of Health Services Use, where predisposing, need and enabling 111 characteristics were regressed on the dependent variable of mental health services use. This study used the Canadian Community Health Survey (cycle 1.2: Mental Health and Well- Being) in a secondary data analysis. Several multiple logistics models predicted the likelihood to seek and use mental health services. Predisposing characteristics of gender and age, Enabling characteristics of education and geographical location, and those with co-occurring mood disorders were at the greatest increased likelihood to seek and use mental health services.

Cognitive Behavior Therapy for Obsessive-Compulsive Behavior in Children with Autism Spectrum Disorder: A Comparison of Hierarchical Linear Modelling and Visual Analysis

Behavioral researchers commonly use single subject designs to evaluate the effects of a given treatment. Several different methods of data analysis are used, each with their own set of methodological strengths and limitations. Visual inspection is commonly used as a method of analyzing data which assesses the variability, level, and trend both within and between conditions (Cooper, Heron, & Heward, 2007). In an attempt to quantify treatment outcomes, researchers developed two methods for analysing data called Percentage of Non-overlapping Data Points (PND) and Percentage of Data Points Exceeding the Median (PEM). The purpose of the present study is to compare and contrast the use of Hierarchical Linear Modelling (HLM), PND and PEM in single subject research. The present study used 39 behaviours, across 17 participants to compare treatment outcomes of a group cognitive behavioural therapy program, using PND, PEM, and HLM on three response classes of Obsessive Compulsive Behaviour in children with Autism Spectrum Disorder. Findings suggest that PEM and HLM complement each other and both add invaluable information to the overall treatment results. Future research should consider using both PEM and HLM when analysing single subject designs, specifically grouped data with variability.

Partner behavioral responses to pain mediate the relationship between partner pain cognitions and pain outcomes in women with provoked vestibulodynia.

Partner behavioral responses to pain can have a significant impact on patient pain and depression, but little is known about why partners respond in specific ways. Using a cognitive-behavioral model, the present study examined whether partner cognitions were associated with partner behavioral responses, which prior work has found to predict patient pain and depressive symptoms. Participants were 354 women with provoked vestibulodynia and their partners. Partner pain-related cognitions were assessed using the partner versions of the Pain Catastrophizing Scale and Extended Attributional Style Questionnaire, whereas their behavioral responses to pain were assessed with the Multidimensional Pain Inventory. Patient pain was measured using a numeric rating scale, and depressive symptoms were assessed using the Beck Depression Inventory–II. Path analysis was used to examine the proposed model. Partner catastrophizing and negative attributions were associated with negative partner responses, which were associated with higher patient pain. It was also found that partner pain catastrophizing was associated with solicitous partner responses, which in turn were associated with higher patient pain and depressive symptoms. The effect of partner cognitions on patient outcomes was partially mediated by partner behavioral responses. Findings highlight the importance of assessing partner cognitions, both in research and as a target for intervention. Perspective The present study presents a cognitive-behavioral model to partially explain how significant others' thoughts about pain have an effect on patient pain and depressive symptoms. Findings may inform cognitive-behavioral therapy for couples coping with PVD.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

CAPM und Behavioral Finance - Versuch einer Synthese

Seit Etablierung der ersten Börsen als Marktplatz für fungible Güter sind Marktteilnehmer und die Wissenschaft bemüht, Erklärungen für das Zustandekommen von Marktpreisen zu finden. Im Laufe der Zeit wurden diverse Modelle entwickelt. Allen voran ist das neoklassische Capital Asset Pricing Modell (CAPM) zu nennen. Die Neoklassik sieht den Akteur an den Finanzmärkten als emotionslosen und streng rationalen Entscheider, dem sog. homo oeconomicus. Psychologische Einflussfaktoren bei der Preisbildung bleiben unbeachtet. Mit der Behavioral Finance hat sich ein neuer Zweig zur Erklärung von Börsenkursen und deren Bewegungen entwickelt. Die Behavioral Finance sprengt die enge Sichtweise der Neoklassik und geht davon aus, dass psychologische Effekte die Entscheidung der Finanzakteure beeinflussen und dabei zu teilweise irrational und emotional geprägten Kursänderungen führen. Eines der Hauptprobleme der Behavioral Finance liegt allerdings in der fehlenden formellen Ermittelbarkeit und Testbarkeit der einzelnen psychologischen Effekte. Anders als beim CAPM, wo die einzelnen Parameter klar mathematisch bestimmbar sind, besteht die Behavioral Finance im Wesentlichen aus psychologischen Definitionen von kursbeeinflussenden Effekten. Die genaue Wirkrichtung und Intensität der Effekte kann, mangels geeigneter Modelle, nicht ermittelt werden. Ziel der Arbeit ist es, eine Abwandlung des CAPM zu ermitteln, die es ermöglicht, neoklassische Annahmen durch die Erkenntnisse des Behavioral Finance zu ergänzen. Mittels der technischen Analyse von Marktpreisen wird versucht die Effekte der Behavioral Finance formell darstellbar und berechenbar zu machen. Von Praktikern wird die technische Analyse dazu verwendet, aus Kursverläufen die Stimmungen und Intentionen der Marktteilnehmer abzuleiten. Eine wissenschaftliche Fundierung ist bislang unterblieben. Ausgehend von den Erkenntnissen der Behavioral Finance und der technischen Analyse wird das klassische CAPM um psychologische Faktoren ergänzt, indem ein Multi-Beta-CAPM (Behavioral-Finance-CAPM) definiert wird, in das psychologisch fundierte Parameter der technischen Analyse einfließen. In Anlehnung an den CAPM-Test von FAMA und FRENCH (1992) werden das klassische CAPM und das Behavioral-Finance-CAPM getestet und der psychologische Erklärungsgehalt der technischen Analyse untersucht. Im Untersuchungszeitraum kann dem Behavioral-Finance-CAPM ein deutlich höherer Erklärungsgehalt gegenüber dem klassischen CAPM zugesprochen werden.