Protocol for stage 1 of the GaP study (Genetic testing acceptability for Paget's disease of bone): an interview study about genetic testing and preventive treatment: would relatives of people with Paget's disease want testing and treatment if they were available?

BACKGROUND: Paget's disease of bone (PDB) is characterised by focal increases in bone turnover, affecting one or more bones throughout the skeleton. This disrupts normal bone architecture and causes pain, deformity, deafness, osteoarthritis, and fractures. Genetic factors are recognised to play a role in PDB and it is now possible to carry out genetic tests for research. In view of this, it is timely to investigate the clinical potential for a programme of genetic testing and preventative treatment for people who have a family history of PDB, to prevent or delay the development of PDB. Evidence from non-genetic conditions, that have effective treatments, demonstrates that patients' beliefs may affect the acceptability and uptake of treatment. Two groups of beliefs (illness and treatment representations) are likely to be influential. Illness representations describe how people see their illness, as outlined in Leventhal's Self-Regulation Model. Treatment representations describe how people perceive potential treatment for their disease. People offered a programme of genetic testing and treatment will develop their own treatment representations based on what is offered, but the beliefs rather than the objective programme of treatment are likely to determine their willingness to participate. The Theory of Planned Behaviour is a theoretical model that predicts behaviours from people's beliefs about the consequences, social pressures and perceived control over the behaviour, including uptake of treatment. METHODS/DESIGN: This study aims to examine the acceptability of genetic testing, followed by preventative treatment, to relatives of people with PDB. We aim to interview people with Paget's disease, and their families, from the UK. Our research questions are: 1. What do individuals with Paget's disease think would influence the involvement of their relatives in a programme of genetic testing and preventative treatment? 2. What do relatives of Paget's disease sufferers think would influence them in accepting an offer of a programme of genetic testing and preventative treatment? DISCUSSION: Our research will be informed by relevant psychological theory: primarily the Self-Regulation Model and the Theory of Planned Behaviour. The results of these interviews will inform the development of a separate questionnaire-based study to explore these research questions in greater detail

Suicidality among adolescents engaging in nonsuicidal self-injury (NSSI) and firesetting: The role of psychosocial characteristics and reasons for living

Background: Co-occurrence of problem behaviors, particularly across internalizing and externalizing spectra, increases the risk of suicidality (i.e., suicidal ideation and attempt) among youth. Methods: We examined differences in psychosocial risk factors across levels of suicidality in a sample of 77 school-based adolescents engaging in both nonsuicidal self-injury (NSSI) and repeated firesetting. Participants completed questionnaires assessing engagement in problem behaviors, mental health difficulties, negative life events, poor coping, impulsivity, and suicidality. Results: Adolescents endorsing suicidal ideation reported greater psychological distress, physical and sexual abuse, and less problem solving/goal pursuit than those with no history of suicidality; adolescents who had attempted suicide reported more severe NSSI, higher rates of victimization and exposure to suicide, relative to those with suicidal ideation but no history of attempt. Additional analyses suggested the importance of coping beliefs in protecting against suicidality. Conclusions: Clinical implications and suggestions for future research relating to suicide prevention are discussed

Figure Text Extraction in Biomedical Literature

Background: Figures are ubiquitous in biomedical full-text articles, and they represent important biomedical knowledge. However, the sheer volume of biomedical publications has made it necessary to develop computational approaches for accessing figures. Therefore, we are developing the Biomedical Figure Search engin

PDZ domains and their binding partners: structure, specificity, and modification

PDZ domains are abundant protein interaction modules that often recognize short amino acid motifs at the C-termini of target proteins. They regulate multiple biological processes such as transport, ion channel signaling, and other signal transduction systems. This review discusses the structural characterization of PDZ domains and the use of recently emerging technologies such as proteomic arrays and peptide libraries to study the binding properties of PDZ-mediated interactions. Regulatory mechanisms responsible for PDZ-mediated interactions, such as phosphorylation in the PDZ ligands or PDZ domains, are also discussed. A better understanding of PDZ protein-protein interaction networks and regulatory mechanisms will improve our knowledge of many cellular and biological processes

Fast lexicon-based scene text recognition with sparse belief propagation

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Scene Text Recognition Using Similarity and a Lexicon with Sparse Belief Propagation

Scene text recognition (STR) is the recognition of text anywhere in the environment, such as signs and store fronts. Relative to document recognition, it is challenging because of font variability, minimal language context, and uncontrolled conditions. Much information available to solve this problem is frequently ignored or used sequentially. Similarity between character images is often overlooked as useful information. Because of language priors, a recognizer may assign different labels to identical characters. Directly comparing characters to each other, rather than only a model, helps ensure that similar instances receive the same label. Lexicons improve recognition accuracy but are used post hoc. We introduce a probabilistic model for STR that integrates similarity, language properties, and lexical decision. Inference is accelerated with sparse belief propagation, a bottom-up method for shortening messages by reducing the dependency between weakly supported hypotheses. By fusing information sources in one model, we eliminate unrecoverable errors that result from sequential processing, improving accuracy. In experimental results recognizing text from images of signs in outdoor scenes, incorporating similarity reduces character recognition error by 19%, the lexicon reduces word recognition error by 35%, and sparse belief propagation reduces the lexicon words considered by 99.9% with a 12X speedup and no loss in accuracy