Women's job quality across family life stages: An analysis of female employees across 27 European countries

There is little empirical evidence on how working conditions affect women’s employment and fertility choices, despite a number of studies on the impact of individual-level and institutional factors. The article addresses this gap by examining how family life stages are related to particular aspects of job quality among employed women in 27 European countries. The central argument of the analysis is that high-quality jobs are conducive to both transitions to motherhood and employment after childbirth as women select into these roles. Accordingly, mothers of young children, if employed, are expected to have relatively better quality jobs. Four dimensions of job quality are considered: job security, career progression, working time and intrinsic job quality. The results indicate that mothers with young children are more likely to hold high-quality jobs than women at other life stages with respect to working time quality and job security, but with some variation across countries for job security. The findings highlight the importance of high-quality jobs for women’s fertility decisions and labour market attachment after childbirth, with implications for European employment policy

OrthoNets: simultaneous visual analysis of orthologs and their interaction neighborhoods across different organisms

Motivation: Protein interaction networks contain a wealth of biological information, but their large size often hinders cross-organism comparisons. We present OrthoNets, a Cytoscape plugin that displays protein–protein interaction (PPI) networks from two organisms simultaneously, highlighting orthology relationships and aggregating several types of biomedical annotations. OrthoNets also allows PPI networks derived from experiments to be overlaid on networks extracted from public databases, supporting the identification and verification of new interactors. Any newly identified PPIs can be validated by checking whether their orthologs interact in another organism

Increased peri-ductal collagen micro-organization may contribute to raised mammographic density

BACKGROUND: High mammographic density is a therapeutically modifiable risk factor for breast cancer. Although mammographic density is correlated with the relative abundance of collagen-rich fibroglandular tissue, the causative mechanisms, associated structural remodelling and mechanical consequences remain poorly defined. In this study we have developed a new collaborative bedside-to-bench workflow to determine the relationship between mammographic density, collagen abundance and alignment, tissue stiffness and the expression of extracellular matrix organising proteins. METHODS: Mammographic density was assessed in 22 post-menopausal women (aged 54–66 y). A radiologist and a pathologist identified and excised regions of elevated non-cancerous X-ray density prior to laboratory characterization. Collagen abundance was determined by both Masson’s trichrome and Picrosirius red staining (which enhances collagen birefringence when viewed under polarised light). The structural specificity of these collagen visualisation methods was determined by comparing the relative birefringence and ultrastructure (visualised by atomic force microscopy) of unaligned collagen I fibrils in reconstituted gels with the highly aligned collagen fibrils in rat tail tendon. Localised collagen fibril organisation and stiffness was also evaluated in tissue sections by atomic force microscopy/spectroscopy and the abundance of key extracellular proteins was assessed using mass spectrometry. RESULTS: Mammographic density was positively correlated with the abundance of aligned periductal fibrils rather than with the abundance of amorphous collagen. Compared with matched tissue resected from the breasts of low mammographic density patients, the highly birefringent tissue in mammographically dense breasts was both significantly stiffer and characterised by large (>80 μm long) fibrillar collagen bundles. Subsequent proteomic analyses not only confirmed the absence of collagen fibrosis in high mammographic density tissue, but additionally identified the up-regulation of periostin and collagen XVI (regulators of collagen fibril structure and architecture) as potential mediators of localised mechanical stiffness. CONCLUSIONS: These preliminary data suggest that remodelling, and hence stiffening, of the existing stromal collagen microarchitecture promotes high mammographic density within the breast. In turn, this aberrant mechanical environment may trigger neoplasia-associated mechanotransduction pathways within the epithelial cell population. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13058-015-0664-2) contains supplementary material, which is available to authorized users

Can Parents Afford to Work? Childcare Costs, Tax-Benefit Policies and Work Incentives

Childcare policies play a crucial role in helping parents reconcile care and employment-related tasks. This paper quantifies the net cost of purchasing full-time centre-based childcare in OECD countries taking into account a wide range of influences on household budgets, including fees charged by childcare providers as well as childcare-related tax concessions and cash benefits available to parents. Building on these calculations, family resources are evaluated for different employment situations in order to assess the financial trade-offs between work and staying at home. Results are disaggregated to identify the policy features that present barriers to work for parents whose employment decisions are known to be particularly responsive to financial work incentives: lone parents and second earners with young children requiring care. The results indicate that the cost of purchasing childcare services should be analysed in conjunction with other social and fiscal policies that affect family incomes. While childcare fees can be very high, high prices may not impede employment if tax-benefit systems incorporate well-balanced provisions that help parents pay for these services. Conversely, even highly subsidised childcare markets can leave parents with little financial gain from employment if high tax burdens or benefit claw-back rates give rise to adverse work incentives

Prior knowledge based mining functional modules from Yeast PPI networks with gene ontology

<p>Abstract</p> <p>Background</p> <p>In the literature, there are fruitful algorithmic approaches for identification functional modules in protein-protein interactions (PPI) networks. Because of accumulation of large-scale interaction data on multiple organisms and non-recording interaction data in the existing PPI database, it is still emergent to design novel computational techniques that can be able to correctly and scalably analyze interaction data sets. Indeed there are a number of large scale biological data sets providing indirect evidence for protein-protein interaction relationships.</p> <p>Results</p> <p>The main aim of this paper is to present a prior knowledge based mining strategy to identify functional modules from PPI networks with the aid of Gene Ontology. Higher similarity value in Gene Ontology means that two gene products are more functionally related to each other, so it is better to group such gene products into one functional module. We study (i) to encode the functional pairs into the existing PPI networks; and (ii) to use these functional pairs as pairwise constraints to supervise the existing functional module identification algorithms. Topology-based modularity metric and complex annotation in MIPs will be used to evaluate the identified functional modules by these two approaches.</p> <p>Conclusions</p> <p>The experimental results on Yeast PPI networks and GO have shown that the prior knowledge based learning methods perform better than the existing algorithms.</p

Methods for visual mining of genomic and proteomic data atlases

<p>Abstract</p> <p>Background</p> <p>As the volume, complexity and diversity of the information that scientists work with on a daily basis continues to rise, so too does the requirement for new analytic software. The analytic software must solve the dichotomy that exists between the need to allow for a high level of scientific reasoning, and the requirement to have an intuitive and easy to use tool which does not require specialist, and often arduous, training to use. Information visualization provides a solution to this problem, as it allows for direct manipulation and interaction with diverse and complex data. The challenge addressing bioinformatics researches is how to apply this knowledge to data sets that are continually growing in a field that is rapidly changing.</p> <p>Results</p> <p>This paper discusses an approach to the development of visual mining tools capable of supporting the mining of massive data collections used in systems biology research, and also discusses lessons that have been learned providing tools for both local researchers and the wider community. Example tools were developed which are designed to enable the exploration and analyses of both proteomics and genomics based atlases. These atlases represent large repositories of raw and processed experiment data generated to support the identification of biomarkers through mass spectrometry (the PeptideAtlas) and the genomic characterization of cancer (The Cancer Genome Atlas). Specifically the tools are designed to allow for: the visual mining of thousands of mass spectrometry experiments, to assist in designing informed targeted protein assays; and the interactive analysis of hundreds of genomes, to explore the variations across different cancer genomes and cancer types.</p> <p>Conclusions</p> <p>The mining of massive repositories of biological data requires the development of new tools and techniques. Visual exploration of the large-scale atlas data sets allows researchers to mine data to find new meaning and make sense at scales from single samples to entire populations. Providing linked task specific views that allow a user to start from points of interest (from diseases to single genes) enables targeted exploration of thousands of spectra and genomes. As the composition of the atlases changes, and our understanding of the biology increase, new tasks will continually arise. It is therefore important to provide the means to make the data available in a suitable manner in as short a time as possible. We have done this through the use of common visualization workflows, into which we rapidly deploy visual tools. These visualizations follow common metaphors where possible to assist users in understanding the displayed data. Rapid development of tools and task specific views allows researchers to mine large-scale data almost as quickly as it is produced. Ultimately these visual tools enable new inferences, new analyses and further refinement of the large scale data being provided in atlases such as PeptideAtlas and The Cancer Genome Atlas.</p

Cardiac contractile dysfunction in insulin-resistant rats fed a high-fat diet is associated with elevated CD36-mediated fatty acid uptake and esterification

Changes in cardiac substrate utilisation leading to altered energy metabolism may underlie the development of diabetic cardiomyopathy. We studied cardiomyocyte substrate uptake and utilisation and the role of the fatty acid translocase CD36 in relation to in vivo cardiac function in rats fed a high-fat diet (HFD).Rats were exposed to an HFD or a low-fat diet (LFD). In vivo cardiac function was monitored by echocardiography. Substrate uptake and utilisation were determined in isolated cardiomyocytes.Feeding an HFD for 8 weeks induced left ventricular dilation in the systolic phase and decreased fractional shortening and the ejection fraction. Insulin-stimulated glucose uptake and proline-rich Akt substrate 40 phosphorylation were 41% (p <0.001) and 45% (p <0.05) lower, respectively, in cardiomyocytes from rats on the HFD. However, long-chain fatty acid (LCFA) uptake was 1.4-fold increased (p <0.001) and LCFA esterification into triacylglycerols and phospholipids was increased 1.4- and 1.5-fold, respectively (both p <0.05), in cardiomyocytes from HFD compared with LFD hearts. In the presence of the CD36 inhibitor sulfo-N-succinimidyloleate, LCFA uptake and esterification were similar in LFD and HFD cardiomyocytes. In HFD hearts CD36 was relocated to the sarcolemma, and basal phosphorylation of a mediator of CD36-trafficking, i.e. protein kinase B (PKB/Akt), was increased.Feeding rats an HFD induced cardiac contractile dysfunction, which was accompanied by the relocation of CD36 to the sarcolemma, and elevated basal levels of phosphorylated PKB/Akt. The permanent presence of CD36 at the sarcolemma resulted in enhanced rates of LCFA uptake and myocardial triacylglycerol accumulation, and may contribute to the development of insulin resistance and diabetic cardiomyopathy

Which clustering algorithm is better for predicting protein complexes?

<p>Abstract</p> <p>Background</p> <p>Protein-Protein interactions (PPI) play a key role in determining the outcome of most cellular processes. The correct identification and characterization of protein interactions and the networks, which they comprise, is critical for understanding the molecular mechanisms within the cell. Large-scale techniques such as pull down assays and tandem affinity purification are used in order to detect protein interactions in an organism. Today, relatively new high-throughput methods like yeast two hybrid, mass spectrometry, microarrays, and phage display are also used to reveal protein interaction networks.</p> <p>Results</p> <p>In this paper we evaluated four different clustering algorithms using six different interaction datasets. We parameterized the MCL, Spectral, RNSC and Affinity Propagation algorithms and applied them to six PPI datasets produced experimentally by Yeast 2 Hybrid (Y2H) and Tandem Affinity Purification (TAP) methods. The predicted clusters, so called protein complexes, were then compared and benchmarked with already known complexes stored in published databases.</p> <p>Conclusions</p> <p>While results may differ upon parameterization, the MCL and RNSC algorithms seem to be more promising and more accurate at predicting PPI complexes. Moreover, they predict more complexes than other reviewed algorithms in absolute numbers. On the other hand the spectral clustering algorithm achieves the highest valid prediction rate in our experiments. However, it is nearly always outperformed by both RNSC and MCL in terms of the geometrical accuracy while it generates the fewest valid clusters than any other reviewed algorithm. This article demonstrates various metrics to evaluate the accuracy of such predictions as they are presented in the text below. Supplementary material can be found at: <url>http://www.bioacademy.gr/bioinformatics/projects/ppireview.htm</url></p