Development and validation of the Morphing Fear Questionnaire (MFQ)

Morphing fears (also called transformation obsessions) involve concerns that a person may become contaminated by and acquire undesirable characteristics of others. These symptoms are found in patients with OCD and are thought to be related to mental contamination. Given the high levels of distress and interference morphing fears can cause, a reliable and valid assessment measure is needed. This article describes the development and evaluation of the Morphing Fear Questionnaire (MFQ), a 13-item measure designed to assess for the presence and severity of morphing fears. A sample of 900 participants took part in the research. Of these, 140 reported having a current diagnosis of OCD (SR-OCD) and 760 reported never having had OCD (N-OCD; of whom 24 reported a diagnosis of an anxiety disorder and 23 reported a diagnosis of depression). Factor structure, reliability, and construct and criterion related validity were investigated. Exploratory and confirmatory factor analyses supported a one-factor structure replicable across the N-OCD and SR-OCD group. The MFQ was found to have high internal consistency and good temporal stability, and showed significantly greater associations with convergent measures (assessing obsessive-compulsive symptoms, mental contamination, thought-action fusion and magical thinking) than with divergent measures (assessing depression and anxiety). Moreover, the MFQ successfully discriminated between the SR-OCD sample and the N-OCD group, anxiety disorder sample, and depression sample. These findings suggest that the MFQ has sound psychometric properties and that it can be used to assess morphing fear. Clinical implications are discussed.

Constipation and diet in a community sample of older Australians

Objective: To estimate the prevalence of constipation and laxative use in a sample of people 65 years and over and examine relationships between usual diet and constipation.
Design: A mailed survey using validated instruments to measure bowel habit and laxative use with follow-up interviews to collect dietary data.
Subjects and setting: Three hundred and thirty people aged 65 years and over living at home in Melbourne were randomly selected from the electoral roll of a federal electorate.
Statistical analysis: Descriptive statistics, frequencies and two sample t-tests were used.
Results: Seventy-nine people responded to the mailed bowel survey and 61 were interviewed to collect food intake data. The proportion of constipated people was approximately one quarter (n = 18). Laxative use in the previous 12 months was reported by a fifth of respondents and in these subjects one in four was not constipated. Analysis of the dietary data revealed that the average number of cereal and vegetable serves consumed per day was similar to the national average but less than recommended by nutrition bodies although fruit intake met these recommendations. Constipated subjects consumed fewer serves from the cereals food group than those who were not constipated (2.9 and 3.5 serves respectively, P = 0.03).
Conclusion: Constipation and laxative use appears to be as common in older Australians as in similar populations overseas. Low intake of cereal foods may be a contributing factor.

Alcohol consumption and body composition in a population-based sample of elderly Australian men

Background: Alcohol is calorie dense, and impacts activity, appetite and lipid processing. The aim of this study was to therefore investigate the association between alcohol consumption and components of body composition including bone, fat and lean tissue.

Methods: Participants were recruited from a randomly selected, population-based sample of 534 men aged 65 years and older enrolled in the Geelong Osteoporosis Study. Alcohol intake was ascertained using a food frequency questionnaire and the sample categorised as nondrinkers or alcohol users who consumed B2, 3–4 or C5 standard drinks on a usual drinking day. Bone mineral density (BMD), lean body mass and body fat mass were measured using dual energy X-ray absorptiometry; overall adiposity (%body fat), central adiposity (%truncal fat) and body mass index (BMI) were calculated. Bone quality was determined by quantitative heel ultrasound (QUS).

Results: There were 90 current non-drinkers (16.9 %), 266 (49.8 %) consumed 1–2 drinks/day, 104 (19.5 %) 3–4 drinks/day and 74 (13.8 %) C5 drinks/day. Those consuming C5 drinks/day had greater BMI (?4.8 %), fat mass index (?20.1 %), waist circumference (?5.0 %), %body fat (?15.2 %) and proportion of trunk fat (?5.3 %) and lower lean mass (-5.0 %) than non-drinkers after adjustment for demographic and lifestyle factors. Furthermore, they were more likely to be obese than non-drinkers according to criteria based on BMI (OR = 2.83, 95 %CI 1.10–7.29) or waist circumference (OR = 3.36, 95 %CI 1.32–8.54). There was an inverse relationship between alcohol consumption and QUS parameters and BMD at the mid forearm site; no differences were detected for BMD at other skeletal sites.

Conclusion: Higher alcohol intake was associated with greater total and central adiposity and reduced bone quality.

Validation of patient health questionnaire (PHQ) for major depression in Chinese outpatients with multiple somatic symptoms: A multicenter cross-sectional study

Background: Despite the high co-morbidity of depressive symptoms in patients with multiple somatic symptoms, the validity of the 9-item Patient Health Questionnaire (PHQ-9) has not yet been investigated in Chinese patients with multiple somatic symptoms. Methods: The multicenter cross-sectional study was conducted in ten outpatient departments located in four cities in China. The psychometric properties of the PHQ-9 were examined by confirmative factor analysis (CFA). Criterion validation was undertaken by comparing results with depression diagnoses obtained from the Mini International Neuropsychiatric Interview (MINI) as the gold standard. Results: Overall, 491 patients were recruited of whom 237 had multiple somatic symptoms (SOM+ group, PHQ-15 ≥ 10). Cronbach's α of the PHQ-9 was 0.87, 0.87, and 0.90 for SOM+ patients, SOM- patients, and total sample respectively. All items and the total score were moderately correlated. The factor models of PHQ-9 tested by CFA yielded similar diagnostic performance when compared to sum score estimation. Multi-group confirmatory factor analysis based on unidimensional model showed similar psychometric properties over the groups with low and high somatic symptom burden. The optimal cut-off point to detect depression in Chinese outpatients was 10 for PHQ-9 (sensitivity=0.77, specificity=0.76) and 3 for PHQ-2 (sensitivity=0.77, specificity=0.74). Limitations: Potential selection bias and nonresponse bias with applied sampling method. Conclusions: PHQ-9 (cut-off point=10) and PHQ-2 (cut-off point=3) were reliable and valid to detect major depression in Chinese patients with multiple somatic symptoms.

Influence of sample type and collection method on Streptococcus mutans and Lactobacillus spp. counts in the oral cavity

Objectives: This study compared three methods of Streptococcus mutans and Lactobacillus spp. detection in the oral cavity: saliva swab (SS)-sample of stimulated saliva collected with swab; whole saliva (WS)-sample of 2 ml of stimulated saliva; and the dental plaque method (DP)-plaque sample of all dental surfaces.Methods: Thirty children were included in this study. In the first 15 children, the SS and WS methods were carried out before the dental plaque collection, and in the following 15, the sequence was inverted to evaluate possible interference of the methods sequence. The samples were diluted and inoculated in SB20 and Rogosa agar, respectively for S. mutans and Lactobacillus spp., at 37 degrees C for 48 h.Results: the results (cfu/mL) of S. mutans were analysed by the statistical Friedman's test. The levels of Lactobacillus spp. were analysed by descriptive statistics due to the high proportion of zero counts in the culture. In the first sequence of methods, the number of S. mutans counted for the SS method was inferior to DP and WS (P < 0.05), and the results for the WS and DP methods were similar. The detection of Lactobacillus spp. was observed just by the WS (100 %) and SS (14.3 %) methods. However, in the second experimental set the number of S. mutans detected by the DP method was similar to those of the SS and WS, however, the WS method showed higher values than SS (P < 0.05). A greater number of Lactobacillus spp. was detected by the WS method (100 %), followed by SS (55.5 %) and DP (33.3 %).Conclusions: the dental plaque collection and the sample of stimulated whole saliva presented similar results in the S. mutans count. The most suitable method to detect the Lactobacillus spp. level in the oral cavity is the stimulated whole saliva method. (c) 2004 Elsevier Ltd. All rights reserved.

Expression and purification of human respiratory syncytial virus recombinant fusion protein

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genome-wide association study of reproductive traits in Nellore heifers using Bayesian inference

An important goal of Zebu breeding programs is to improve reproductive performance. A major problem faced with the genetic improvement of reproductive traits is that recording the time for an animal to reach sexual maturity is costly. Another issue is that accurate estimates of breeding values are obtained only a long time after the young bulls have gone through selection. An alternative to overcome these problems is to use traits that are indicators of the reproductive efficiency of the herd and are easier to measure, such as age at first calving. Another problem is that heifers that have conceived once may fail to conceive in the next breeding season, which increases production costs. Thus, increasing heifer's rebreeding rates should improve the economic efficiency of the herd. Response to selection for these traits tends to be slow, since they have a low heritability and phenotypic information is provided only later in the life of the animal. Genome-wide association studies (GWAS) are useful to investigate the genetic mechanisms that underlie these traits by identifying the genes and metabolic pathways involved. Data from 1853 females belonging to the Agricultural Jacarezinho LTDA were used. Genotyping was performed using the BovineHD BeadChip (777 962 single nucleotide polymorphisms (SNPs)) according to the protocol of Illumina - Infinium Assay II ® Multi-Sample HiScan with the unit SQ ™ System. After quality control, 305 348 SNPs were used for GWAS. Forty-two and 19 SNPs had a Bayes factor greater than 150 for heifer rebreeding and age at first calving, respectively. All significant SNPs for age at first calving were significant for heifer rebreeding. These 42 SNPs were next or within 35 genes that were distributed over 18 chromosomes and comprised 27 protein-encoding genes, six pseudogenes and two miscellaneous noncoding RNAs. The use of Bayes factor to determine the significance of SNPs allowed us to identify two sets of 42 and 19 significant SNPs for heifer rebreeding and age at first calving, respectively, which explain 11.35 % and 6.42 % of their phenotypic variance, respectively. These SNPs provide relevant information to help elucidate which genes affect these traits.

Splenogonadal fusion and testicular cancer: case report and review of the literature

A 36 year-old man after tests for assessing male infertility was diagnosed with primary infertility, bilateral cryptorchidism, non-obstructive azoospermia and discontinuous splenogonadal fusion. Carcinoma in situ was found in his left testicle, which was intra-abdominal and associated with splenogonadal fusion. To our knowledge, this is the fourth case of splenogonadal fusion associated with testicular cancer reported. One should always bear in mind the possibility of this association for the left cryptorchid testicle.

The use of remote sensing imagery for monitoring recovery in the aftermath of a natural disaster: the Hurricane Katrina Case

Although Recovery is often defined as the less studied and documented phase of the Emergency Management Cycle, a wide literature is available for describing characteristics and sub-phases of this process. Previous works do not allow to gain an overall perspective because of a lack of systematic consistent monitoring of recovery utilizing advanced technologies such as remote sensing and GIS technologies. Taking into consideration the key role of Remote Sensing in Response and Damage Assessment, this thesis is aimed to verify the appropriateness of such advanced monitoring techniques to detect recovery advancements over time, with close attention to the main characteristics of the study event: Hurricane Katrina storm surge. Based on multi-source, multi-sensor and multi-temporal data, the post-Katrina recovery was analysed using both a qualitative and a quantitative approach. The first phase was dedicated to the investigation of the relation between urban types, damage and recovery state, referring to geographical and technological parameters. Damage and recovery scales were proposed to review critical observations on remarkable surge- induced effects on various typologies of structures, analyzed at a per-building level. This wide-ranging investigation allowed a new understanding of the distinctive features of the recovery process. A quantitative analysis was employed to develop methodological procedures suited to recognize and monitor distribution, timing and characteristics of recovery activities in the study area. Promising results, gained by applying supervised classification algorithms to detect localization and distribution of blue tarp, have proved that this methodology may help the analyst in the detection and monitoring of recovery activities in areas that have been affected by medium damage. The study found that Mahalanobis Distance was the classifier which provided the most accurate results, in localising blue roofs with 93.7% of blue roof classified correctly and a producer accuracy of 70%. It was seen to be the classifier least sensitive to spectral signature alteration. The application of the dissimilarity textural classification to satellite imagery has demonstrated the suitability of this technique for the detection of debris distribution and for the monitoring of demolition and reconstruction activities in the study area. Linking these geographically extensive techniques with expert per-building interpretation of advanced-technology ground surveys provides a multi-faceted view of the physical recovery process. Remote sensing and GIS technologies combined to advanced ground survey approach provides extremely valuable capability in Recovery activities monitoring and may constitute a technical basis to lead aid organization and local government in the Recovery management.

New approaches to open problems in gene expression microarray data

In the past decade, the advent of efficient genome sequencing tools and high-throughput experimental biotechnology has lead to enormous progress in the life science. Among the most important innovations is the microarray tecnology. It allows to quantify the expression for thousands of genes simultaneously by measurin the hybridization from a tissue of interest to probes on a small glass or plastic slide. The characteristics of these data include a fair amount of random noise, a predictor dimension in the thousand, and a sample noise in the dozens. One of the most exciting areas to which microarray technology has been applied is the challenge of deciphering complex disease such as cancer. In these studies, samples are taken from two or more groups of individuals with heterogeneous phenotypes, pathologies, or clinical outcomes. these samples are hybridized to microarrays in an effort to find a small number of genes which are strongly correlated with the group of individuals. Eventhough today methods to analyse the data are welle developed and close to reach a standard organization (through the effort of preposed International project like Microarray Gene Expression Data -MGED- Society [1]) it is not unfrequant to stumble in a clinician's question that do not have a compelling statistical method that could permit to answer it.The contribution of this dissertation in deciphering disease regards the development of new approaches aiming at handle open problems posed by clinicians in handle specific experimental designs. In Chapter 1 starting from a biological necessary introduction, we revise the microarray tecnologies and all the important steps that involve an experiment from the production of the array, to the quality controls ending with preprocessing steps that will be used into the data analysis in the rest of the dissertation. While in Chapter 2 a critical review of standard analysis methods are provided stressing most of problems that In Chapter 3 is introduced a method to adress the issue of unbalanced design of miacroarray experiments. In microarray experiments, experimental design is a crucial starting-point for obtaining reasonable results. In a two-class problem, an equal or similar number of samples it should be collected between the two classes. However in some cases, e.g. rare pathologies, the approach to be taken is less evident. We propose to address this issue by applying a modified version of SAM [2]. MultiSAM consists in a reiterated application of a SAM analysis, comparing the less populated class (LPC) with 1,000 random samplings of the same size from the more populated class (MPC) A list of the differentially expressed genes is generated for each SAM application. After 1,000 reiterations, each single probe given a "score" ranging from 0 to 1,000 based on its recurrence in the 1,000 lists as differentially expressed. The performance of MultiSAM was compared to the performance of SAM and LIMMA [3] over two simulated data sets via beta and exponential distribution. The results of all three algorithms over low- noise data sets seems acceptable However, on a real unbalanced two-channel data set reagardin Chronic Lymphocitic Leukemia, LIMMA finds no significant probe, SAM finds 23 significantly changed probes but cannot separate the two classes, while MultiSAM finds 122 probes with score >300 and separates the data into two clusters by hierarchical clustering. We also report extra-assay validation in terms of differentially expressed genes Although standard algorithms perform well over low-noise simulated data sets, multi-SAM seems to be the only one able to reveal subtle differences in gene expression profiles on real unbalanced data. In Chapter 4 a method to adress similarities evaluation in a three-class prblem by means of Relevance Vector Machine [4] is described. In fact, looking at microarray data in a prognostic and diagnostic clinical framework, not only differences could have a crucial role. In some cases similarities can give useful and, sometimes even more, important information. The goal, given three classes, could be to establish, with a certain level of confidence, if the third one is similar to the first or the second one. In this work we show that Relevance Vector Machine (RVM) [2] could be a possible solutions to the limitation of standard supervised classification. In fact, RVM offers many advantages compared, for example, with his well-known precursor (Support Vector Machine - SVM [3]). Among these advantages, the estimate of posterior probability of class membership represents a key feature to address the similarity issue. This is a highly important, but often overlooked, option of any practical pattern recognition system. We focused on Tumor-Grade-three-class problem, so we have 67 samples of grade I (G1), 54 samples of grade 3 (G3) and 100 samples of grade 2 (G2). The goal is to find a model able to separate G1 from G3, then evaluate the third class G2 as test-set to obtain the probability for samples of G2 to be member of class G1 or class G3. The analysis showed that breast cancer samples of grade II have a molecular profile more similar to breast cancer samples of grade I. Looking at the literature this result have been guessed, but no measure of significance was gived before.

A panchromatic view of the evolution of supermassive black holes

This PhD Thesis is devoted to the accurate analysis of the physical properties of Active Galactic Nuclei (AGN) and the AGN/host-galaxy interplay. Due to the broad-band AGN emission (from radio to hard X-rays), a multi-wavelength approach is mandatory. Our research is carried out over the COSMOS field, within the context of the XMM-Newton wide-field survey. To date, the COSMOS field is a unique area for comprehensive multi-wavelength studies, allowing us to define a large and homogeneous sample of QSOs with a well-sampled spectral coverage and to keep selection effects under control. Moreover, the broad-band information contained in the COSMOS database is well-suited for a detailed analysis of AGN SEDs, bolometric luminosities and bolometric corrections. In order to investigate the nature of both obscured (Type-2) and unobscured (Type-1) AGN, the observational approach is complemented with a theoretical modelling of the AGN/galaxy co-evolution. The X-ray to optical properties of an X-ray selected Type-1 AGN sample are discussed in the first part. The relationship between X-ray and optical/UV luminosities, parametrized by the spectral index αox, provides a first indication about the nature of the central engine powering the AGN. Since a Type-1 AGN outshines the surrounding environment, it is extremely difficult to constrain the properties of its host-galaxy. Conversely, in Type-2 AGN the host-galaxy light is the dominant component of the optical/near-IR SEDs, severely affecting the recovery of the intrinsic AGN emission. Hence a multi-component SED-fitting code is developed to disentangle the emission of the stellar populationof the galaxy from that associated with mass accretion. Bolometric corrections, luminosities, stellar masses and star-formation rates, correlated with the morphology of Type-2 AGN hosts, are presented in the second part, while the final part concerns a physically-motivated model for the evolution of spheroidal galaxies with a central SMBH. The model is able to reproduce two important stages of galaxy evolution, namely the obscured cold-phase and the subsequent quiescent hot-phase.

In defence of modeling simultaneity for a correct approximation of cultural aspects: implications for food consumers studies with latent variables

Dealing with latent constructs (loaded by reflective and congeneric measures) cross-culturally compared means studying how these unobserved variables vary, and/or covary each other, after controlling for possibly disturbing cultural forces. This yields to the so-called ‘measurement invariance’ matter that refers to the extent to which data collected by the same multi-item measurement instrument (i.e., self-reported questionnaire of items underlying common latent constructs) are comparable across different cultural environments. As a matter of fact, it would be unthinkable exploring latent variables heterogeneity (e.g., latent means; latent levels of deviations from the means (i.e., latent variances), latent levels of shared variation from the respective means (i.e., latent covariances), levels of magnitude of structural path coefficients with regard to causal relations among latent variables) across different populations without controlling for cultural bias in the underlying measures. Furthermore, it would be unrealistic to assess this latter correction without using a framework that is able to take into account all these potential cultural biases across populations simultaneously. Since the real world ‘acts’ in a simultaneous way as well. As a consequence, I, as researcher, may want to control for cultural forces hypothesizing they are all acting at the same time throughout groups of comparison and therefore examining if they are inflating or suppressing my new estimations with hierarchical nested constraints on the original estimated parameters. Multi Sample Structural Equation Modeling-based Confirmatory Factor Analysis (MS-SEM-based CFA) still represents a dominant and flexible statistical framework to work out this potential cultural bias in a simultaneous way. With this dissertation I wanted to make an attempt to introduce new viewpoints on measurement invariance handled under covariance-based SEM framework by means of a consumer behavior modeling application on functional food choices.

Rapid fusion and syncytium formation of heterologous cells upon expression of the FGFRL1 receptor

The fusion of mammalian cells into syncytia is a developmental process that is tightly restricted to a limited subset of cells. Besides gamete and placental trophoblast fusion, only macrophages and myogenic stem cells fuse into multinucleated syncytia. In contrast to viral cell fusion, which is mediated by fusogenic glycoproteins that actively merge membranes, mammalian cell fusion is poorly understood at the molecular level. A variety of mammalian transmembrane proteins, among them many of the immunoglobulin superfamily, have been implicated in cell-cell fusion, but none has been shown to actively fuse cells in vitro. Here we report that the FGFRL1 receptor, which is up-regulated during the differentiation of myoblasts into myotubes, fuses cultured cells into large, multinucleated syncytia. We used luciferase and GFP-based reporter assays to confirm cytoplasmic mixing and to identify the fusion inducing domain of FGFRL1. These assays revealed that Ig-like domain III and the transmembrane domain are both necessary and sufficient to rapidly fuse CHO cells into multinucleated syncytia comprising several hundred nuclei. Moreover, FGFRL1 also fused HEK293 and HeLa cells with untransfected CHO cells. Our data show that FGFRL1 is the first mammalian protein that is capable of inducing syncytium formation of heterologous cells in vitro.

QUANTIFYING UNCERTAINTY IN GENOTYPE CALLS

Genome-wide association studies (GWAS) are used to discover genes underlying complex, heritable disorders for which less powerful study designs have failed in the past. The number of GWAS has skyrocketed recently with findings reported in top journals and the mainstream media. Mircorarrays are the genotype calling technology of choice in GWAS as they permit exploration of more than a million single nucleotide polymorphisms (SNPs)simultaneously. The starting point for the statistical analyses used by GWAS, to determine association between loci and disease, are genotype calls (AA, AB, or BB). However, the raw data, microarray probe intensities, are heavily processed before arriving at these calls. Various sophisticated statistical procedures have been proposed for transforming raw data into genotype calls. We find that variability in microarray output quality across different SNPs, different arrays, and different sample batches has substantial inuence on the accuracy of genotype calls made by existing algorithms. Failure to account for these sources of variability, GWAS run the risk of adversely affecting the quality of reported findings. In this paper we present solutions based on a multi-level mixed model. Software implementation of the method described in this paper is available as free and open source code in the crlmm R/BioConductor.