Protocol for a feasibility study of a self help cognitive behavioural therapy resource for the reduction of dental anxiety in young people

Background Childhood dental anxiety is very common, with 10–20 % of children and young people reporting high levels of dental anxiety. It is distressing and has a negative impact on the quality of life of young people and their parents as well as being associated with poor oral health. Affected individuals may develop a lifelong reliance on general anaesthetic or sedation for necessary dental treatment thus requiring the support of specialist dental services. Children and young people with dental anxiety therefore require additional clinical time and can be costly to treat in the long term. The reduction of dental anxiety through the use of effective psychological techniques is, therefore, of high importance. However, there is a lack of high-quality research investigating the impact of cognitive behavioural therapy (CBT) approaches when applied to young people’s dental anxiety. Methods/design The first part of the study will develop a profile of dentally anxious young people using a prospective questionnaire sent to a consecutive sample of 100 young people referred to the Paediatric Dentistry Department, Charles Clifford Dental Hospital, in Sheffield. The second part will involve interviewing a purposive sample of 15–20 dental team members on their perceptions of a CBT self-help resource for dental anxiety, their opinions on whether they might use such a resource with patients, and their willingness to recruit participants to a future randomised controlled trial (RCT) to evaluate the resource. The third part of the study will investigate the most appropriate outcome measures to include in a trial, the acceptability of the resource, and retention and completion rates of treatment with a sample of 60 dentally anxious young people using the CBT resource. Discussion This study will provide information on the profile of dentally anxious young people who could potentially be helped by a guided self-help CBT resource. It will gain the perceptions of dental care team members of guided self-help CBT for dental anxiety in young people and their willingness to recruit participants to a trial. Acceptability of the resource to participants and retention and completion rates will also be investigated to inform a future RCT.

L3 acquisition: a focus on cognitive approaches

Interest in third language (L3) acquisition has increased exponentially in recent years, due to its potential to inform long-lasting debates in theoretical linguistics, language acquisition and psycholinguistics. Researchers investigating child and adult L3 acquisition have, from the very beginning, considered the many different cognitive factors that constrain and condition the initial state and development of newly acquired languages, and their models have duly evolved to incorporate insights from the most recent findings in psycholinguistics, neurolinguistics and cognitive psychology. The articles in this Special Issue of Bilingualism: Language and Cognition, in dealing with issues such as age of acquisition, attrition, relearning, cognitive economy or the reliance on different memory systems –to name a few–, provide an accurate portrayal of current inquiry in the field, and are a particularly fine example of how instrumental research in language acquisition and other cognitive domains can be to one another.

Anticipating future risk in social-ecological systems using fuzzy cognitive mapping: the case of wildfire in the Chiquitania, Bolivia

Understanding complex social-ecological systems, and anticipating how they may respond to rapid change, requires an approach that incorporates environmental, social, economic, and policy factors, usually in a context of fragmented data availability. We employed fuzzy cognitive mapping (FCM) to integrate these factors in the assessment of future wildfire risk in the Chiquitania region, Bolivia. In this region, dealing with wildfires is becoming increasingly challenging due to reinforcing feedbacks between multiple drivers. We conducted semi-structured interviews and constructed different FCMs in focus groups to understand the regional dynamics of wildfire from diverse perspectives. We used FCM modelling to evaluate possible adaptation scenarios in the context of future drier climatic conditions. Scenarios also considered possible failure to respond in time to the emergent risk. This approach proved of great potential to support decision-making for risk management. It helped identify key forcing variables and generate insights into potential risks and trade-offs of different strategies. All scenarios showed increased wildfire risk in the event of more droughts. The ‘Hands-off’ scenario resulted in amplified impacts driven by intensifying trends, affecting particularly the agricultural production. The ‘Fire management’ scenario, which adopted a bottom-up approach to improve controlled burning, showed less trade-offs between wildfire risk reduction and production compared to the ‘Fire suppression’ scenario. Findings highlighted the importance of considering strategies that involve all actors who use fire, and the need to nest these strategies for a more systemic approach to manage wildfire risk. The FCM model could be used as a decision-support tool and serve as a ‘boundary object’ to facilitate collaboration and integration of different forms of knowledge and perceptions of fire in the region. This approach has also the potential to support decisions in other dynamic frontier landscapes around the world that are facing increased risk of large wildfires.

Peripheral Oxidative Stress Biomarkers in Mild Cognitive Impairment and Alzheimer`s Disease

Oxidative stress has been associated with normal aging and Alzheimer`s disease (AD). However, little is known about oxidative stress in mild cognitive impairment (MCI) patients who present a high risk for developing AD. The aim of this study was to investigate plasma production of the lipid peroxidation marker, malonaldehyde (MDA) and to determine, in erythrocytes, the enzymatic antioxidant activity of catalase, glutathione peroxidase (GPx), glutathione reductase (GR), and glutathione S-transferase (GST) in 33 individuals with MCI, 29 with mild probable AD and 26 healthy aged subjects. GR/GPx activity ratio was calculated to better assess antioxidant defenses. The relationship between oxidative stress and cognitive performance was also evaluated by the Mini Mental State Examination (MMSE). AD patients showed higher MDA levels than both MCI and healthy elderly subjects. MCI subjects also exhibited higher MDA levels compared to controls. Catalase and GPx activity were similar in MCI and healthy individuals but higher in AD. GR activity was lower in MCI and AD patients than in healthy aged subjects. Additionally, GR/GPx ratio was higher in healthy aged subjects, intermediate in MCI and lower in AD patients. No differences in GST activity were detected among the groups. MMSE was negatively associated with MDA levels (r = -0.31, p = 0.028) and positively correlated with GR/GPx ratio in AD patients (r = 0.68, p < 0.001). MDA levels were also negatively correlated to GR/GPx ratio (r = -0.31, p = 0.029) in the AD group. These results suggest that high lipid peroxidation and decreased antioxidant defenses may be present early in cognitive disorders.

Symptomatic expression of depression among Jewish adolescents: effects of gender and age

Background Depression symptomatology was assessed with the Beck Depression Inventory (BDI) in a sample of Jewish adolescents, in order to compare the frequency and severity of depression with non-Jewish adolescents as well as examine gender difference of the expression of depressive symptomatology. Method Subjects comprised 475 students from Jewish private schools, aged 13-17 years, who were compared with an age-matched non-Jewish sample (n = 899). Kendall`s definition was adopted to classify these adolescents according to level of depressive symptoms. The frequency of depression was calculated for ethnicity, gender and age strata. Discriminant analysis and principal component analysis were performed to assess the importance of depression-specific and non-specific items, along with the factor structure of the BDI, respectively. Results The overall mean score on the BDI in the Jewish and the non-Jewish sample was 9.0 (SD = 6.4) and 8.6 (SD = 7.2), respectively. Jewish girls and boys had comparable mean BDI scores, contrasting with non-Jewish sample, where girls complained more of depressive symptoms than boys (p < 0.001). The frequency of depression, adopting a BDI cutoff of 20, was 5.1% for the Jewish sample and 6.3% for the non-Jewish sample. The frequency of depression for Jewish girls and boys was 5.5% (SE = 1.4) and 4.6% (SE = 1.5), respectively. On the other hand, the frequency of depression for non-Jewish girls and boys was 8.4% (SE = 1.2) and 4.0% (SE = 1.0), respectively. The female/male ratio of frequency of BDI-depression was 1.2 in the Jewish sample, but non-Jewish girls were twice (2.1) as likely to report depression as boys. Discriminant analysis showed that the BDI highly discriminates depressive symptomatology among Jewish adolescents, and measured specific aspects of depression. Factor analysis revealed two meaningful factors for the total sample and each gender (cognitive-affective dimension and somatic dimension), evidencing a difference between Jewish boys and Jewish girls in the symptomatic expression of depression akin to non-Jewish counterparts. Conclusions Ethnic-cultural factor might play a role in the frequency, severity and symptomatic expression of depressive symptoms in Jewish adolescents. The lack of gender effect on depression, which might persist from adolescence to adulthood among Jewish people, should be investigated in prospective studies.

Sparse block-Jacobi matrices with arbitrarily accurate Hausdorff dimension

We show that the Hausdorff dimension of the spectral measure of a class of deterministic, i.e. nonrandom, block-Jacobi matrices may be determined with any degree of precision, improving a result of Zlatos [Andrej Zlatos,. Sparse potentials with fractional Hausdorff dimension, J. Funct. Anal. 207 (2004) 216-252]. (C) 2010 Elsevier Inc. All rights reserved.

Shape classification using complex network and Multi-scale Fractal Dimension

Shape provides one of the most relevant information about an object. This makes shape one of the most important visual attributes used to characterize objects. This paper introduces a novel approach for shape characterization, which combines modeling shape into a complex network and the analysis of its complexity in a dynamic evolution context. Descriptors computed through this approach show to be efficient in shape characterization, incorporating many characteristics, such as scale and rotation invariant. Experiments using two different shape databases (an artificial shapes database and a leaf shape database) are presented in order to evaluate the method. and its results are compared to traditional shape analysis methods found in literature. (C) 2009 Published by Elsevier B.V.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Fractal dimension applied to plant identification

This article discusses methods to identify plants by analysing leaf complexity based on estimating their fractal dimension. Leaves were analyzed according to the complexity of their internal and external shapes. A computational program was developed to process, analyze and extract the features of leaf images, thereby allowing for automatic plant identification. Results are presented from two experiments, the first to identify plant species from the Brazilian Atlantic forest and Brazilian Cerrado scrublands, using fifty leaf samples from ten different species, and the second to identify four different species from genus Passiflora, using twenty leaf samples for each class. A comparison is made of two methods to estimate fractal dimension (box-counting and multiscale Minkowski). The results are discussed to determine the best approach to analyze shape complexity based on the performance of the technique, when estimating fractal dimension and identifying plants. (C) 2008 Elsevier Inc. All rights reserved.

Influence of education and depressive symptoms on cognitive function in the elderly

Objective: The purpose of the present study was to investigate the influence that education and depression have on the performance of elderly people in neuropsychological tests. Methods: The study was conducted at the Institute of Psychiatry, University of Sao Paulo School of Medicine, Hospital das Clinicas. All of the individuals evaluated were aged 60 or older. The study sample consisted of 59 outpatients with depressive disorders and 51 healthy controls. We stratified the sample by level of education: low = 1-4 years of schooling; high = 5 or more years of schooling. Evaluations consisted of psychiatric assessment, cognitive assessment, laboratory tests and cerebral magnetic resonance imaging. Results: We found that level of education influenced all the measures of cognitive domains investigated (intellectual efficiency, processing speed, attention, executive function and memory) except the Digit Span Forward and Fuld Object Memory Evaluation (immediate and delayed recall), whereas depressive symptoms influenced some measures of memory, attention, executive function and processing speed. Although the combination of a low level of education and depression had a significant negative influence on Stroop Test part B, Trail Making Test part B and Logical Memory (immediate recall), we found no other significant effects of the interaction between level of education and depression. Conclusion: The results of this study underscore the importance of considering the level of education in the analysis of cognitive performance in depressed elderly patients, as well as the relevance of developing new cognitive function tests in which level of education has a reduced impact on the results.

Financial time series analysis : Chaos and neurodynamics approach

This work aims at combining the Chaos theory postulates and Artificial Neural Networks classification and predictive capability, in the field of financial time series prediction. Chaos theory, provides valuable qualitative and quantitative tools to decide on the predictability of a chaotic system. Quantitative measurements based on Chaos theory, are used, to decide a-priori whether a time series, or a portion of a time series is predictable, while Chaos theory based qualitative tools are used to provide further observations and analysis on the predictability, in cases where measurements provide negative answers. Phase space reconstruction is achieved by time delay embedding resulting in multiple embedded vectors. The cognitive approach suggested, is inspired by the capability of some chartists to predict the direction of an index by looking at the price time series. Thus, in this work, the calculation of the embedding dimension and the separation, in Takens‘ embedding theorem for phase space reconstruction, is not limited to False Nearest Neighbor, Differential Entropy or other specific method, rather, this work is interested in all embedding dimensions and separations that are regarded as different ways of looking at a time series by different chartists, based on their expectations. Prior to the prediction, the embedded vectors of the phase space are classified with Fuzzy-ART, then, for each class a back propagation Neural Network is trained to predict the last element of each vector, whereas all previous elements of a vector are used as features.