On the measurement of inequalities in health

This paper offers a critical appraisal of the various methods employed to date to measure inequalities in health. It suggests that only two of these—the slope index of inequality and the concentration index—are likely to present an accurate picture of socioeconomic inequalities in health. The paper also presents several empirical examples to illustrate of the dangers of using other measures such as the range, the Lorenz curve and the index of dissimilarity

Nitrogen and sulphur management: challenges for organic sources in temperate agricultural systems

A current global trend towards intensification or specialization of agricultural enterprises has been accompanied by increasing public awareness of associated environmental consequences. Air and water pollution from losses of nutrients, such as nitrogen (N) and sulphur (S), are a major concern. Governments have initiated extensive regulatory frameworks, including various land use policies, in an attempt to control or reduce the losses. This paper presents an overview of critical input and loss processes affecting N and S for temperate climates, and provides some background to the discussion in subsequent papers evaluating specific farming systems. Management effects on potential gaseous and leaching losses, the lack of synchrony between supply of nutrients and plant demand, and options for optimizing the efficiency of N and S use are reviewed. Integration of inorganic and organic fertilizer inputs and the equitable re-distribution of nutrients from manure are discussed. The paper concludes by highlighting a need for innovative research that is also targeted to practical approaches for reducing N and S losses, and improving the overall synchrony between supply and demand

Array comparative genomic hybridisation (aCGH) analysis of premenopausal breast cancers from a nuclear fallout area and matched cases from Western New York

High-resolution array comparative genomic hybridisation (aCGH) analysis of DNA copy number aberrations (CNAs) was performed on breast carcinomas in premenopausal women from Western New York (WNY) and from Gomel, Belarus, an area exposed to fallout from the 1986 Chernobyl nuclear accident. Genomic DNA was isolated from 47 frozen tumour specimens from 42 patients and hybridised to arrays spotted with more than 3000 BAC clones. In all, 20 samples were from WNY and 27 were from Belarus. In total, 34 samples were primary tumours and 13 were lymph node metastases, including five matched pairs from Gomel. The average number of total CNAs per sample was 76 (range 35–134). We identified 152 CNAs (92 gains and 60 losses) occurring in more than 10% of the samples. The most common amplifications included gains at 8q13.2 (49%), at 1p21.1 (36%), and at 8q24.21 (36%). The most common deletions were at 1p36.22 (26%), at 17p13.2 (26%), and at 8p23.3 (23%). Belarussian tumours had more amplifications and fewer deletions than WNY breast cancers. HER2/neu negativity and younger age were also associated with a higher number of gains and fewer losses. In the five paired samples, we observed more discordant than concordant DNA changes. Unsupervised hierarchical cluster analysis revealed two distinct groups of tumours: one comprised predominantly of Belarussian carcinomas and the other largely consisting of WNY cases. In total, 50 CNAs occurred significantly more commonly in one cohort vs the other, and these included some candidate signature amplifications in the breast cancers in women exposed to significant radiation. In conclusion, our high-density aCGH study has revealed a large number of genetic aberrations in individual premenopausal breast cancer specimens, some of which had not been reported before. We identified a distinct CNA profile for carcinomas from a nuclear fallout area, suggesting a possible molecular fingerprint of radiation-associated breast cancer

Logical Development of the Cell Ontology

<p>Abstract</p> <p>Background</p> <p>The Cell Ontology (CL) is an ontology for the representation of <it>in vivo </it>cell types. As biological ontologies such as the CL grow in complexity, they become increasingly difficult to use and maintain. By making the information in the ontology computable, we can use automated reasoners to detect errors and assist with classification. Here we report on the generation of computable definitions for the hematopoietic cell types in the CL.</p> <p>Results</p> <p>Computable definitions for over 340 CL classes have been created using a genus-differentia approach. These define cell types according to multiple axes of classification such as the protein complexes found on the surface of a cell type, the biological processes participated in by a cell type, or the phenotypic characteristics associated with a cell type. We employed automated reasoners to verify the ontology and to reveal mistakes in manual curation. The implementation of this process exposed areas in the ontology where new cell type classes were needed to accommodate species-specific expression of cellular markers. Our use of reasoners also inferred new relationships within the CL, and between the CL and the contributing ontologies. This restructured ontology can be used to identify immune cells by flow cytometry, supports sophisticated biological queries involving cells, and helps generate new hypotheses about cell function based on similarities to other cell types.</p> <p>Conclusion</p> <p>Use of computable definitions enhances the development of the CL and supports the interoperability of OBO ontologies.</p

<i>Gaia</i> Data Release 1. Summary of the astrometric, photometric, and survey properties

Context. At about 1000 days after the launch of Gaia we present the first Gaia data release, Gaia DR1, consisting of astrometry and photometry for over 1 billion sources brighter than magnitude 20.7. Aims. A summary of Gaia DR1 is presented along with illustrations of the scientific quality of the data, followed by a discussion of the limitations due to the preliminary nature of this release. Methods. The raw data collected by Gaia during the first 14 months of the mission have been processed by the Gaia Data Processing and Analysis Consortium (DPAC) and turned into an astrometric and photometric catalogue. Results. Gaia DR1 consists of three components: a primary astrometric data set which contains the positions, parallaxes, and mean proper motions for about 2 million of the brightest stars in common with the HIPPARCOS and Tycho-2 catalogues – a realisation of the Tycho-Gaia Astrometric Solution (TGAS) – and a secondary astrometric data set containing the positions for an additional 1.1 billion sources. The second component is the photometric data set, consisting of mean G-band magnitudes for all sources. The G-band light curves and the characteristics of ∼3000 Cepheid and RR-Lyrae stars, observed at high cadence around the south ecliptic pole, form the third component. For the primary astrometric data set the typical uncertainty is about 0.3 mas for the positions and parallaxes, and about 1 mas yr−1 for the proper motions. A systematic component of ∼0.3 mas should be added to the parallax uncertainties. For the subset of ∼94 000 HIPPARCOS stars in the primary data set, the proper motions are much more precise at about 0.06 mas yr−1. For the secondary astrometric data set, the typical uncertainty of the positions is ∼10 mas. The median uncertainties on the mean G-band magnitudes range from the mmag level to ∼0.03 mag over the magnitude range 5 to 20.7. Conclusions. Gaia DR1 is an important milestone ahead of the next Gaia data release, which will feature five-parameter astrometry for all sources. Extensive validation shows that Gaia DR1 represents a major advance in the mapping of the heavens and the availability of basic stellar data that underpin observational astrophysics. Nevertheless, the very preliminary nature of this first Gaia data release does lead to a number of important limitations to the data quality which should be carefully considered before drawing conclusions from the data

Modulation of calcification of vascular smooth muscle cells in culture by calcium antagonists, statins, and their combination

Background Vascular calcification is an organized process in which vascular smooth muscle cells (VSMCs) are implicated primarily. The purpose of the present study was to assess the effects of calcium antagonists and statins on VSMC calcification in vitro. Methods VSMC calcification was stimulated by incubation in growth medium supplemented with 10 mmol/l β-glycerophosphate, 8 mmol/l CaCl2, 10 mmol/l sodium pyruvate, 1 μmol/l insulin, 50 μg/ml ascorbic acid, and 100 nmol/l dexamethasone (calcification medium). Calcification, proliferation, and apoptosis of VSMCs were quantified. Results Calcium deposition was stimulated dose-dependently by β-glycerophosphate, CaCl2, and ascorbic acid (all P < 0.01). Addition of amlodipine (0.01–1 μmol/l) to the calcification medium did not affect VSMC calcification. However, atorvastatin (2–50 μmol/l) stimulated calcium deposition dose-dependently. Combining treatments stimulated calcification to a degree similar to that observed with atorvastatin alone. Both atorvastatin and amlodipine inhibited VSMC proliferation at the highest concentration used. Only atorvastatin (50 μmol/l) induced considerable apoptosis of VSMCs. Conclusion In vitro calcification of VSMCs is not affected by amlodipine, but is stimulated by atorvastatin at concentrations ≥10 μmol/l, which could contribute to the plaque-stabilizing effect reported for statins

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.</p> <p>Results</p> <p>We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC) devoted to BN structure learning.</p> <p>Conclusion</p> <p>We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.</p

CCL5-glutamate cross-talk in astrocyte-neuron communication in multiple sclerosis

The immune system (IS) and the central nervous system (CNS) are functionally coupled, and a large number of endogenous molecules (i.e., the chemokines for the IS and the classic neurotransmitters for the CNS) are shared in common between the two systems. These interactions are key elements for the elucidation of the pathogenesis of central inflammatory diseases. In recent years, evidence has been provided supporting the role of chemokines as modulators of central neurotransmission. It is the case of the chemokines CCL2 and CXCL12 that control pre- and/or post-synaptically the chemical transmission. This article aims to review the functional cross-talk linking another endogenous pro-inflammatory factor released by glial cells, i.e., the chemokine Regulated upon Activation Normal T-cell Expressed and Secreted (CCL5) and the principal neurotransmitter in CNS (i.e., glutamate) in physiological and pathological conditions. In particular, the review discusses preclinical data concerning the role of CCL5 as a modulator of central glutamatergic transmission in healthy and demyelinating disorders. The CCL5-mediated control of glutamate release at chemical synapses could be relevant either to the onset of psychiatric symptoms that often accompany the development of multiple sclerosis (MS), but also it might indirectly give a rationale for the progression of inflammation and demyelination. The impact of disease-modifying therapies for the cure of MS on the endogenous availability of CCL5 in CNS will be also summarized. We apologize in advance for omission in our coverage of the existing literature

HP1α recruitment to DNA damage by p150CAF-1 promotes homologous recombination repair

p150CAF-1-mediated recruitment of HP1α to DNA is essential for efficient assembly of DNA damage response complexes and subsequent homologous recombination repair