The Effect of Stress on the Job Satisfaction and Productivity of Construction Professionals in the Ghanaian Construction Industry

The economic conditions prevailing in Ghana currently is making it increasingly difficult than ever for workers to cope with job challenges. Most often stress starts from the house and continues at the workplace and all workers feel the added pressure. This is because most employees experience stress as the demands made on them do not match the resources available to meet their needs, thereby negatively affect productivity. This paper seeks to study professionals in the Ghanaian construction industry and determine if they are undergoing any form of stress. If so, to determine if they are adopting the right strategies to solve their stress challenges. The survey method included a structured questionnaire and semi structured interviews and site visits. Questionnaires were sent to 180 construction professionals selected through simple random sampling as they are knowledgeable and qualified enough as respondents for the study. 115 were returned and used for the analysis. The research findings indicate that 62% of the respondents believe their job satisfaction and productivity will increase if stress is reduced. It is recommended that the workload of the construction professionals must match their abilities and resources whilst unrealistic targets must be avoided. The construction professionals must make effective stress management an ongoing process. Keywords: Stress, Job satisfaction, Productivity, Construction professionals, Ghan

Conserved co-expression for candidate disease gene prioritization

Contains fulltext : 71114.pdf ( ) (Open Access)BACKGROUND: Genes that are co-expressed tend to be involved in the same biological process. However, co-expression is not a very reliable predictor of functional links between genes. The evolutionary conservation of co-expression between species can be used to predict protein function more reliably than co-expression in a single species. Here we examine whether co-expression across multiple species is also a better prioritizer of disease genes than is co-expression between human genes alone. RESULTS: We use co-expression data from yeast (S. cerevisiae), nematode worm (C. elegans), fruit fly (D. melanogaster), mouse and human and find that the use of evolutionary conservation can indeed improve the predictive value of co-expression. The effect that genes causing the same disease have higher co-expression than do other genes from their associated disease loci, is significantly enhanced when co-expression data are combined across evolutionarily distant species. We also find that performance can vary significantly depending on the co-expression datasets used, and just using more data does not necessarily lead to better prioritization. Instead, we find that dataset quality is more important than quantity, and using a consistent microarray platform per species leads to better performance than using more inclusive datasets pooled from various platforms. CONCLUSION: We find that evolutionarily conserved gene co-expression prioritizes disease candidate genes better than human gene co-expression alone, and provide the integrated data as a new resource for disease gene prioritization tools.13 p

Analysis of genome-wide association study data using the protein knowledge base

Article 9

Gentrepid V2.0: a web server for candidate disease gene prediction

Contains fulltext : 124935.pdf (publisher's version ) (Open Access)BACKGROUND: Candidate disease gene prediction is a rapidly developing area of bioinformatics research with the potential to deliver great benefits to human health. As experimental studies detecting associations between genetic intervals and disease proliferate, better bioinformatic techniques that can expand and exploit the data are required. DESCRIPTION: Gentrepid is a web resource which predicts and prioritizes candidate disease genes for both Mendelian and complex diseases. The system can take input from linkage analysis of single genetic intervals or multiple marker loci from genome-wide association studies. The underlying database of the Gentrepid tool sources data from numerous gene and protein resources, taking advantage of the wealth of biological information available. Using known disease gene information from OMIM, the system predicts and prioritizes disease gene candidates that participate in the same protein pathways or share similar protein domains. Alternatively, using an ab initio approach, the system can detect enrichment of these protein annotations without prior knowledge of the phenotype. CONCLUSIONS: The system aims to integrate the wealth of protein information currently available with known and novel phenotype/genotype information to acquire knowledge of biological mechanisms underpinning disease. We have updated the system to facilitate analysis of GWAS data and the study of complex diseases. Application of the system to GWAS data on hypertension using the ICBP data is provided as an example. An interesting prediction is a ZIP transporter additional to the one found by the ICBP analysis. The webserver URL is https://www.gentrepid.org/

Computational disease gene identification: a concert of methods prioritizes type 2 diabetes and obesity candidate genes

Genome-wide experimental methods to identify disease genes, such as linkage analysis and association studies, generate increasingly large candidate gene sets for which comprehensive empirical analysis is impractical. Computational methods employ data from a variety of sources to identify the most likely candidate disease genes from these gene sets. Here, we review seven independent computational disease gene prioritization methods, and then apply them in concert to the analysis of 9556 positional candidate genes for type 2 diabetes (T2D) and the related trait obesity. We generate and analyse a list of nine primary candidate genes for T2D genes and five for obesity. Two genes, LPL and BCKDHA, are common to these two sets. We also present a set of secondary candidates for T2D (94 genes) and for obesity (116 genes) with 58 genes in common to both diseases

Prediction of Human Disease Genes by Human-Mouse Conserved Coexpression Analysis

One of the most limiting aspects of biological research in the post-genomic era is the capability to integrate massive datasets on gene structure and function for producing useful biological knowledge. In this report we have applied an integrative approach to address the problem of identifying likely candidate genes within loci associated with human genetic diseases. Despite the recent progress in sequencing technologies, approaching this problem from an experimental perspective still represents a very demanding task, because the critical region may typically contain hundreds of positional candidates. We found that by concentrating only on genes sharing similar expression profiles in both human and mouse, massive microarray datasets can be used to reliably identify disease-relevant relationships among genes. Moreover, we found that integrating the coexpression criterion with systematic phenome analysis allows efficient identification of disease genes in large genomic regions. Using this approach on 850 OMIM loci characterized by unknown molecular basis, we propose high-probability candidates for 81 genetic diseases

Calibration of multi-layered probes with low/high magnetic moments

We present a comprehensive method for visualisation and quantification of the magnetic stray field of magnetic force microscopy (MFM) probes, applied to the particular case of custom-made multi-layered probes with controllable high/low magnetic moment states. The probes consist of two decoupled magnetic layers separated by a non-magnetic interlayer, which results in four stable magnetic states: ±ferromagnetic (FM) and ±antiferromagnetic (A-FM). Direct visualisation of the stray field surrounding the probe apex using electron holography convincingly demonstrates a striking difference in the spatial distribution and strength of the magnetic flux in FM and A-FM states. In situ MFM studies of reference samples are used to determine the probe switching fields and spatial resolution. Furthermore, quantitative values of the probe magnetic moments are obtained by determining their real space tip transfer function (RSTTF). We also map the local Hall voltage in graphene Hall nanosensors induced by the probes in different states. The measured transport properties of nanosensors and RSTTF outcomes are introduced as an input in a numerical model of Hall devices to verify the probe magnetic moments. The modelling results fully match the experimental measurements, outlining an all-inclusive method for the calibration of complex magnetic probes with a controllable low/high magnetic moment

Sequence variation between 462 human individuals fine-tunes functional sites of RNA processing

A. Palotie on työryhmän GEUVADIS Consortium jäsen.Recent advances in the cost-efficiency of sequencing technologies enabled the combined DNA-and RNA-sequencing of human individuals at the population-scale, making genome-wide investigations of the inter-individual genetic impact on gene expression viable. Employing mRNA-sequencing data from the Geuvadis Project and genome sequencing data from the 1000 Genomes Project we show that the computational analysis of DNA sequences around splice sites and poly-A signals is able to explain several observations in the phenotype data. In contrast to widespread assessments of statistically significant associations between DNA polymorphisms and quantitative traits, we developed a computational tool to pinpoint the molecular mechanisms by which genetic markers drive variation in RNA-processing, cataloguing and classifying alleles that change the affinity of core RNA elements to their recognizing factors. The in silico models we employ further suggest RNA editing can moonlight as a splicing-modulator, albeit less frequently than genomic sequence diversity. Beyond existing annotations, we demonstrate that the ultra-high resolution of RNA-Seq combined from 462 individuals also provides evidence for thousands of bona fide novel elements of RNA processing-alternative splice sites, introns, and cleavage sites-which are often rare and lowly expressed but in other characteristics similar to their annotated counterparts.Peer reviewe

Echocardiographic measurements in a preclinical model of chronic chagasic cardiomyopathy in dogs : validation and reproducibility.

Background: The failure to translate preclinical results to the clinical setting is the rule, not the exception. One reason that is frequently overlooked is whether the animal model reproduces distinctive features of human disease. Another is the reproducibility of the method used to measure treatment effects in preclinical studies. Left ventricular (LV) function improvement is the most common endpoint in preclinical cardiovascular disease studies, while echocardiography is the most frequently used method to evaluate LV function. In this work, we conducted a robust echocardiographic evaluation of LV size and function in dogs chronically infected by Trypanosoma cruzi. Methods and Results: Echocardiography was performed blindly by two distinct observers in mongrel dogs before and between 6 and 9 months post infection. Parameters analyzed included end-systolic volume (ESV), end-diastolic volume (EDV), ejection fraction (EF), and fractional shortening (FS). We observed a significant LVEF and FS reduction in infected animals compared to controls, with no significant variation in volumes. However, the effect of chronic infection in systolic function was quite variable, with EF ranging from 17 to 66%. Using the cut-off value of EF ? 40%, established for dilated cardiomyopathy (DCM) in dogs, only 28% of the infected dogs were affected by the chronic infection. Conclusions: The canine model of CCC mimics human disease, reproducing the percentage of individuals that develop heart failure during the chronic infection. It is thus mandatory to establish inclusion criteria in the experimental design of canine preclinical studies to account for the variable effect that chronic infection has on systolic function

Data resource profile: network for analysing longitudinal population-based HIV/AIDS data on Africa (ALPHA Network)

The Network for Analysing Longitudinal Population-based HIV/AIDS data on Africa (ALPHA Network, http://alpha.lshtm.ac.uk/) brings together ten population-based HIV surveillance sites in eastern and southern Africa, and is coordinated by the London School of Hygiene and Tropical Medicine (LSHTM). It was established in 2005 and aims to (i) broaden the evidence base on HIV epidemiology for informing policy, (ii) strengthen the analytical capacity for HIV research, and (iii) foster collaboration between network members. All study sites, some starting in the late 1980s and early 1990s, conduct demographic surveillance in populations that range from approximately 20 to 220 thousand individuals. In addition, they conduct population-based surveys with HIV testing, and verbal autopsy interviews with relatives of deceased residents. ALPHA Network datasets have been used for studying HIV incidence, sexual behaviour and the effects of HIV on mortality, fertility, and household composition. One of the network’s substantive focus areas is the monitoring of AIDS mortality and HIV services coverage in the era of antiretroviral therapy. Service use data are retrospectively recorded in interviews and supplemented by information from record linkage with medical facilities in the surveillance areas. Data access is at the discretion of each of the participating sites, but can be coordinated by the network