Identification of molecular genetic factors that influence migraine

Migraine is a common neurological disorder with a strong genetic basis. However, the complex nature of the disorder has meant that few genes or susceptibility loci have been identified and replicated consistently to confirm their involvement in migraine. Approaches to genetic studies of the disorder have included analysis of the rare migraine subtype, familial hemiplegic migraine with several causal genes identified for this severe subtype. However, the exact genetic contributors to the more common migraine subtypes are still to be deciphered. Genome-wide studies such as genome-wide association studies and linkage analysis as well as candidate genes studies have been employed to investigate genes involved in common migraine. Neurological, hormonal and vascular genes are all considered key factors in the pathophysiology of migraine and are a focus of many of these studies. It is clear that the influence of individual genes on the expression of this disorder will vary. Furthermore, the disorder may be dependent on gene–gene and gene–environment interactions that have not yet been considered. In addition, identifying susceptibility genes may require phenotyping methods outside of the International Classification of Headache Disorders II criteria, such as trait component analysis and latent class analysis to better define the ambit of migraine expression.

Predictors of mental health in post-menopausal women : results from the Australian healthy aging of women study

Objective: To examine the extent to which socio-demographics, modifiable lifestyle, and physical health status influence the mental health of post-menopausal Australian women. Methods: Cross-sectional data on health status, chronic disease and modifiable lifestyle factors were collected from a random cross-section of 340 women aged 60-70 years, residing in Queensland, Australia. Structural equation modelling (SEM) was used to measure the effect of a range of socio-demographic characteristics, modifiable lifestyle factors, and health markers (self-reported physical health, history of chronic illness) on the latent construct of mental health status. Mental health was evaluated using the Medical Outcomes Study Short Form 12 (SF-12®) which examined and Center for Epidemiologic Studies Depression Scale (CES-D). Results: The model was a good fit for the data (χ2=4.582, df=3, p=0.205) suggesting that mental health is negatively correlated with sleep disturbance (β = -0.612, p <0.001), and a history of depression (β = -0.141, p = 0.024).While mental health was associated with poor sleep, it was not correlated with most lifestyle factors (BMI, alcohol consumption, or cigarette smoking) or socio-demographics like age, income or employment category and they were removed from the final model. Conclusion: Research suggests that it is important to engage in a range of health promoting behaviours to preserve good health. We found that predictors of current mental health status included sleep disturbance, and past mental health problems, while socio-demographics and modifiable lifestyle had little impact. It may be however, that these factors influenced other variables associated with the mental health of post-menopausal women, and these relationships warrant further investigation.

Shouting from the ivory tower : a marketing approach to improve communication of academic research to entrepreneurs

Evidence-based practice in entrepreneurship requires effective communication of research findings. We focus on how research synopses can “promote” research to entrepreneurs. Drawing on marketing communications literature, we examine how message characteristics of research synopses affect their appeal. We demonstrate the utility of conjoint analysis in this context and find message length, media richness and source credibility to have positive influences. We find mixed support for a hypothesized negative influence of jargon, and for our predictions that participants’ involvement with academic research moderates these effects. Exploratory analyses reveal latent classes of entrepreneurs with differing preferences, particularly for message length and jargon.

Towards the retrieval of accurate OD matrices from Bluetooth data : lessons learned from 2 years of data

The Bluetooth technology is being increasingly used to track vehicles throughout their trips, within urban networks and across freeway stretches. One important opportunity offered by this type of data is the measurement of Origin-Destination patterns, emerging from the aggregation and clustering of individual trips. In order to obtain accurate estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. These issues mainly stem from the use of the Bluetooth technology amongst drivers, and the physical properties of the Bluetooth sensors themselves. First, not all cars are equipped with discoverable Bluetooth devices and the Bluetooth-enabled vehicles may belong to some small socio-economic groups of users. Second, the Bluetooth datasets include data from various transport modes; such as pedestrian, bicycles, cars, taxi driver, buses and trains. Third, the Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be extracted from the data. Finally, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold. We first give a comprehensive overview of the aforementioned issues. Further, we propose a methodology that can be followed, in order to cleanse, correct and aggregate Bluetooth data. We postulate that the methods introduced by this paper are the first crucial steps that need to be followed in order to compute accurate Origin-Destination matrices in urban road networks.

ADMRG @ MediaEval 2013 Social Event Detection

This paper elaborates the approach used by the Applied Data Mining Research Group (ADMRG) for the Social Event Detection (SED) Tasks of the 2013 MediaEval Benchmark. We extended the constrained clustering algorithm to apply to the first semi-supervised clustering task, and we compared several classifiers with Latent Dirichlet Allocation as feature selector in the second event classification task. The proposed approach focuses on scalability and efficient memory allocation when applied to a high dimensional data with large clusters. Results of the first task show the effectiveness of the proposed method. Results from task 2 indicate that attention on the imbalance categories distributions is needed.

Retrieving dynamic origin-destination matrices from Bluetooth data

The Bluetooth technology is being increasingly used, among the Automated Vehicle Identification Systems, to retrieve important information about urban networks. Because the movement of Bluetooth-equipped vehicles can be monitored, throughout the network of Bluetooth sensors, this technology represents an effective means to acquire accurate time dependant Origin Destination information. In order to obtain reliable estimations, however, a number of issues need to be addressed, through data filtering and correction techniques. Some of the main challenges inherent to Bluetooth data are, first, that Bluetooth sensors may fail to detect all of the nearby Bluetooth-enabled vehicles. As a consequence, the exact journey for some vehicles may become a latent pattern that will need to be estimated. Second, sensors that are in close proximity to each other may have overlapping detection areas, thus making the task of retrieving the correct travelled path even more challenging. The aim of this paper is twofold: to give an overview of the issues inherent to the Bluetooth technology, through the analysis of the data available from the Bluetooth sensors in Brisbane; and to propose a method for retrieving the itineraries of the individual Bluetooth vehicles. We argue that estimating these latent itineraries, accurately, is a crucial step toward the retrieval of accurate dynamic Origin Destination Matrices.

Pattern-based topic models for information filtering

Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, which has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering is rarely known. Patterns are always thought to be more representative than single terms for representing documents. In this paper, a novel information filtering model, Pattern-based Topic Model(PBTM) , is proposed to represent the text documents not only using the topic distributions at general level but also using semantic pattern representations at detailed specific level, both of which contribute to the accurate document representation and document relevance ranking. Extensive experiments are conducted to evaluate the effectiveness of PBTM by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model achieves outstanding performance.

An investigation of schizotypy in injecting amphetamine users

A fully dimensional view of psychiatric disorder conceptualises schizotypy as both a continuous personality trait and an underlying vulnerability to the development of psychotic illness. Such a model would predict that the structure of schizotypal traits would closely parallel the structure of schizophrenia or psychosis. This was investigated in injecting amphetamine users (N = 322), a clinical population who have high rates of acute psychotic episodes and subclinical schizotypal experiences. Schizotypy was assessed using the Oxford-Liverpool Inventory of Feelings and Experiences (O-LIFE), and psychotic symptoms were assessed using the Brief Psychiatric Rating Scale (BPRS). Using confirmatory factor analysis, O-LIFE subscale scores were mapped onto latent variables with their more clinical counterparts from the BPRS. A four-factor model comprising positive schizotypy, disorganisation, negative schizotypy, and disinhibition provided the best model fit, consistent with prior research into the structure of schizotypy. The model provided a good fit to the data, lending support to the theory that schizotypy and psychotic symptoms map onto common underlying dimensions.

Pattern-based topics for document modelling in information filtering

Many mature term-based or pattern-based approaches have been used in the field of information filtering to generate users’ information needs from a collection of documents. A fundamental assumption for these approaches is that the documents in the collection are all about one topic. However, in reality users’ interests can be diverse and the documents in the collection often involve multiple topics. Topic modelling, such as Latent Dirichlet Allocation (LDA), was proposed to generate statistical models to represent multiple topics in a collection of documents, and this has been widely utilized in the fields of machine learning and information retrieval, etc. But its effectiveness in information filtering has not been so well explored. Patterns are always thought to be more discriminative than single terms for describing documents. However, the enormous amount of discovered patterns hinder them from being effectively and efficiently used in real applications, therefore, selection of the most discriminative and representative patterns from the huge amount of discovered patterns becomes crucial. To deal with the above mentioned limitations and problems, in this paper, a novel information filtering model, Maximum matched Pattern-based Topic Model (MPBTM), is proposed. The main distinctive features of the proposed model include: (1) user information needs are generated in terms of multiple topics; (2) each topic is represented by patterns; (3) patterns are generated from topic models and are organized in terms of their statistical and taxonomic features, and; (4) the most discriminative and representative patterns, called Maximum Matched Patterns, are proposed to estimate the document relevance to the user’s information needs in order to filter out irrelevant documents. Extensive experiments are conducted to evaluate the effectiveness of the proposed model by using the TREC data collection Reuters Corpus Volume 1. The results show that the proposed model significantly outperforms both state-of-the-art term-based models and pattern-based models

Silicate-based bioceramics for periodontal regeneration

Periodontal disease is characterized by the destruction of the tissues that attach the tooth to the alveolar bone. Various methods for regenerative periodontal therapy including the use of barrier membranes, bone replacement grafts, and growth factor delivery have been investigated; however, true regeneration of periodontal tissue is still a significant challenge to scientists and clinicians. The focus on periodontal tissue engineering has shifted from attempting to recreate tissue replacements/constructs to the development of biomaterials that incorporate and release regulatory signals to achieve in situ periodontal regeneration. The release of ions and molecular cues from biomaterials may help to unlock latent regenerative potential in the body by regulating cell proliferation and differentiation towards different lineages (e.g. osteoblasts and cementoblasts). Silicate-based bioactive materials, including bioactive silicate glasses and ceramics, have become the materials of choice for periodontal regeneration, due to their favourable osteoconductivity and bioactivity. This article will focus on the most recent advances in the in vitro and in vivo biological application of silicate-based ceramics, specifically as it relates to periodontal tissue engineering.

Homologs of FT, CEN and FD respond to developmental and environmental signals affecting growth and flowering in the perennial vine kiwifruit

FLOWERING LOCUS T (FT) and CENTRORADIALIS (CEN) homologs have been implicated in regulation of growth, determinacy and flowering. The roles of kiwifruit FT and CEN were explored using a combination of expression analysis, protein interactions, response to temperature in high-chill and low-chill kiwifruit cultivars and ectopic expression in Arabidopsis and Actinidia. The expression and activity of FT was opposite from that of CEN and incorporated an interaction with a FLOWERING LOCUS D (FD)-like bZIP transcription factor. Accumulation of FT transcript was associated with plant maturity and particular stages of leaf, flower and fruit development, but could be detected irrespective of the flowering process and failed to induce precocious flowering in transgenic kiwifruit. Instead, transgenic plants demonstrated reduced growth and survival rate. Accumulation of FT transcript was detected in dormant buds and stem in response to winter chilling. In contrast, FD in buds was reduced by exposure to cold. CEN transcript accumulated in developing latent buds, but declined before the onset of dormancy and delayed flowering when ectopically expressed in kiwifruit. Our results suggest roles for FT, CEN and FD in integration of developmental and environmental cues that affect dormancy, budbreak and flowering in kiwifruit.

Identification and characterization of flowering genes in kiwifruit : sequence conservation and role in kiwifruit flower development

Background Flower development in kiwifruit (Actinidia spp.) is initiated in the first growing season, when undifferentiated primordia are established in latent shoot buds. These primordia can differentiate into flowers in the second growing season, after the winter dormancy period and upon accumulation of adequate winter chilling. Kiwifruit is an important horticultural crop, yet little is known about the molecular regulation of flower development. Results To study kiwifruit flower development, nine MADS-box genes were identified and functionally characterized. Protein sequence alignment, phenotypes obtained upon overexpression in Arabidopsis and expression patterns suggest that the identified genes are required for floral meristem and floral organ specification. Their role during budbreak and flower development was studied. A spontaneous kiwifruit mutant was utilized to correlate the extended expression domains of these flowering genes with abnormal floral development. Conclusions This study provides a description of flower development in kiwifruit at the molecular level. It has identified markers for flower development, and candidates for manipulation of kiwifruit growth, phase change and time of flowering. The expression in normal and aberrant flowers provided a model for kiwifruit flower development.

Measurement equivalence of the dispositional resistance to change scale

Intended to bridge the gap between the latest methodological developments and cross-cultural research, this interdisciplinary resource presents the latest strategies for analyzing cross-cultural data. Techniques are demonstrated through the use of applications that employ cross national data sets such as the latest European Social Survey. With an emphasis on the generalized latent variable approach, internationally?prominent researchers from a variety of fields explain how the methods work, how to apply them, and how they relate to other methods presented in the book. Syntax and graphical and verbal explanations of the techniques are included. [from publisher's website]

Polysubstance use in cannabis users referred for treatment: drug use profiles, psychiatric comorbidity and cannabis-related beliefs

Background: Population-based surveys demonstrate cannabis users are more likely to use both illicit and licit substances, compared with non-cannabis users. Few studies have examined the substance use profiles of cannabis users referred for treatment. Co-existing mental health symptoms and underlying cannabis-related beliefs associated with these profiles remains unexplored. Methods: Comprehensive drug use and dependence severity (Severity of Dependence Scale-Cannabis) data were collected on a sample of 826 cannabis users referred for treatment. Patients completed the General Health Questionnaire, Cannabis Expectancy Questionnaire, Cannabis Refusal Self-Efficacy Questionnaire, and Positive Symptoms and Manic-Excitement subscales of the Brief Psychiatric Rating Scale. Latent class analysis was performed on last month use of drugs to identify patterns of multiple drug use. Mental health comorbidity and cannabis beliefs were examined by identified drug use pattern. Results: A three-class solution provided the best fit to the data: (1) cannabis and tobacco users (n = 176), (2) cannabis, tobacco, and alcohol users (n = 498), and (3) wide-ranging sub- stance users (n = 132). Wide-ranging substance users (3) reported higher levels of cannabis dependence severity, negative cannabis expectancies, lower opportunistic, and emotional relief self-efficacy, higher levels of depression and anxiety and higher manic-excitement and positive psychotic symptoms. Conclusion: In a sample of cannabis users referred for treatment, wide-ranging substance use was associated with elevated risk on measures of cannabis dependence, co-morbid psychopathology, and dysfunctional cannabis cognitions. These findings have implications for cognitive-behavioral assessment and treatment.

MT1-MMP correlates with MMP-2 activation potential seen after epithelial to mesenchymal transition in human breast carcinoma cells

We have previously reported that human breast carcinoma (HBC) cell lines expressing the mesenchymal intermediate filament protein vimentin (VIM+) are highly invasive in vitro, and highly metastatic in nude mice when compared to their VIM- counterparts. Since only VIM+ cell lines can be induced to activate matrix metalloproteinase-2 (MMP-2) upon stimulation with Concanavalin A (Con A), we have examined here membrane type 1 MMP (MT1-MMP), a cell surface activator of MMP-2. Northern analysis reveals baseline expression of MT1-MMP in five of the six VIM+ cell lines studied (MDA-MB-231, MDA-MB-435, BT-549, Hs578T, MCF-7(ADR)), each of which showed variable activation of exogenous MMP-2 after treatment with Con A. In contrast, the four VIM-, poorly invasive HBC cell lines studied (MCF-7, T47D, MDA-MB 468, ZR-75-1) lacked baseline MT1-MMP mRNA expression, and showed no induction of either MT1-MMP expression or MMP-2-activation with Con A. Such differential MT1-MMP expression was confirmed in vivo using in situ hybridization analysis of nude mouse tumor xenografts of representative cell lines. Western analysis of the MDA-MB-231 cells revealed baseline membrane expression of a 60 kDa species, which was strongly induced by Con A treatment along with a weaker band co-migrating with that from MT1-MMP-transfected COS-1 cells (63 kDa), presumably representing latent MT1-MMP. MT1-MMP immunofluorescence strongly decorated Con A-stimulated MDA-MB-231 cells in a manner consistent with membranous staining, but did not decorate the unstimulated MDA-MB-231 cells or MCF-7 cells under either condition. Collectively, the results suggest the constitutive production of active MT1-MMP which is unavailable for either MMP-2 activation or immuno-decoration until Con A treatment. Since VIM expression arises by virtue of the so-called epithelial to mesenchymal transition (EMT) in invasive embryonic epithelia, we propose that this represents a major metastasis mechanism in breast carcinomas. MT1-MMP on the surface of such 'fibroblastoid' carcinoma cells may mediate a paracrine loop for the utilization of stromally produced MMP-2, and contribute to the poorer survival associated with VIM+ breast carcinomas.