Sources of variability of speech, spatial, and qualities of hearing scale (SSQ) scores in normal-hearing and hearing-impaired populations.

OBJECTIVE: To identify and quantify sources of variability in scores on the speech, spatial, and qualities of hearing scale (SSQ) and its short forms among normal-hearing and hearing-impaired subjects using a French-language version of the SSQ. DESIGN: Multi-regression analyses of SSQ scores were performed using age, gender, years of education, hearing loss, and hearing-loss asymmetry as predictors. Similar analyses were performed for each subscale (Speech, Spatial, and Qualities), for several SSQ short forms, and for differences in subscale scores. STUDY SAMPLE: One hundred normal-hearing subjects (NHS) and 230 hearing-impaired subjects (HIS). RESULTS: Hearing loss in the better ear and hearing-loss asymmetry were the two main predictors of scores on the overall SSQ, the three main subscales, and the SSQ short forms. The greatest difference between the NHS and HIS was observed for the Speech subscale, and the NHS showed scores well below the maximum of 10. An age effect was observed mostly on the Speech subscale items, and the number of years of education had a significant influence on several Spatial and Qualities subscale items. CONCLUSION: Strong similarities between SSQ scores obtained across different populations and languages, and between SSQ and short forms, underline their potential international use.

Identification of tumor-associated antigens by large-scale analysis of genes expressed in human colorectal cancer.

Despite the high prevalence of colon cancer in the world and the great interest in targeted anti-cancer therapy, only few tumor-specific gene products have been identified that could serve as targets for the immunological treatment of colorectal cancers. The aim of our study was therefore to identify frequently expressed colon cancer-specific antigens. We performed a large-scale analysis of genes expressed in normal colon and colon cancer tissues isolated from colorectal cancer patients using massively parallel signal sequencing (MPSS). Candidates were additionally subjected to experimental evaluation by semi-quantitative RT-PCR on a cohort of colorectal cancer patients. From a pool of more than 6000 genes identified unambiguously in the analysis, we found 2124 genes that were selectively expressed in colon cancer tissue and 147 genes that were differentially expressed to a significant degree between normal and cancer cells. Differential expression of many genes was confirmed by RT-PCR on a cohort of patients. Despite the fact that deregulated genes were involved in many different cellular pathways, we found that genes expressed in the extracellular space were significantly over-represented in colorectal cancer. Strikingly, we identified a transcript from a chromosome X-linked member of the human endogenous retrovirus (HERV) H family that was frequently and selectively expressed in colon cancer but not in normal tissues. Our data suggest that this sequence should be considered as a target of immunological interventions against colorectal cancer.

Poor value of pulsed-field gel electrophoresis to investigate long-term scale epidemiology of methicillin-resistant Staphylococcus aureus.

Pulsed-field gel electrophoresis (PFGE) is widely used for epidemic investigations of methicillin-resistant Staphylococcus aureus (MRSA). In the present study, we evaluated its use in a long-term epidemiological setting (years to few decades, country to continent level). The clustering obtained from PFGE patterns after SmaI digestion of the DNA of 20 strains was compared to that obtained using a phylogenetic typing method (multiprimer RAPD). The results showed that the analysis of small PFGE bands (10-85kb) correlates better with multiprimer RAPD than the analysis of large PFGE bands (>85-700kb), suggesting that the analysis of small bands would be more suitable for the investigation of long-term epidemiological setting. However, given the technical difficulties to obtain a good resolution of these bands and the putative presence of plasmids among them, PFGE does not appear to be a method of choice for the long-term epidemiology analysis of MRSA.

Bats of Borneo: diversity, distributions and representation in protected areas

Protected areas are valuable in conserving tropical biodiversity, but an insufficient understanding of species diversity and distributions makes it difficult to evaluate their effectiveness. This is especially true on Borneo, a species rich island shared by three countries, and is particularly concerning for bats, a poorly known component of mammal diversity that may be highly susceptible to landscape changes. We reviewed the diversity, distributions and conservation status of 54 bat species to determine the representation of these taxa in Borneo's protected areas, and whether these reserves complement each other in terms of bat diversity. Lower and upper bound estimates of bat species composition were characterised in 23 protected areas and the proposed boundaries of the Heart of Borneo conservation area. We used lower and upper bound estimates of species composition. By using actual inventories, species representation was highly irregular, and even if some reserves were included in the Heart of Borneo, the protected area network would still exhibit low complementarity. By inferring species presence from distributions, composition between most reserves was similar, and complementarity was much higher. Predicting species richness using abundance information suggested that bat species representation in reserves may lie between these two extremes. We recommend that researchers better sample biodiversity over the island and address the conservation threats faced in Borneo both within and outside protected areas. While the Heart of Borneo Initiative is commendable, it should not divert attention from other conservation areas.

The fine-scale architecture of structural variants in 17 mouse genomes.

BACKGROUND: Accurate catalogs of structural variants (SVs) in mammalian genomes are necessary to elucidate the potential mechanisms that drive SV formation and to assess their functional impact. Next generation sequencing methods for SV detection are an advance on array-based methods, but are almost exclusively limited to four basic types: deletions, insertions, inversions and copy number gains. RESULTS: By visual inspection of 100 Mbp of genome to which next generation sequence data from 17 inbred mouse strains had been aligned, we identify and interpret 21 paired-end mapping patterns, which we validate by PCR. These paired-end mapping patterns reveal a greater diversity and complexity in SVs than previously recognized. In addition, Sanger-based sequence analysis of 4,176 breakpoints at 261 SV sites reveal additional complexity at approximately a quarter of structural variants analyzed. We find micro-deletions and micro-insertions at SV breakpoints, ranging from 1 to 107 bp, and SNPs that extend breakpoint micro-homology and may catalyze SV formation. CONCLUSIONS: An integrative approach using experimental analyses to train computational SV calling is essential for the accurate resolution of the architecture of SVs. We find considerable complexity in SV formation; about a quarter of SVs in the mouse are composed of a complex mixture of deletion, insertion, inversion and copy number gain. Computational methods can be adapted to identify most paired-end mapping patterns.

Regional-scale integration of multi-scale hydrological and geophysical data using a two-step Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale for the purpose of improving predictions of groundwater flow and solute transport. However, extending corresponding approaches to the regional scale still represents one of the major challenges in the domain of hydrogeophysics. To address this problem, we have developed a regional-scale data integration methodology based on a two-step Bayesian sequential simulation approach. Our objective is to generate high-resolution stochastic realizations of the regional-scale hydraulic conductivity field in the common case where there exist spatially exhaustive but poorly resolved measurements of a related geophysical parameter, as well as highly resolved but spatially sparse collocated measurements of this geophysical parameter and the hydraulic conductivity. To integrate this multi-scale, multi-parameter database, we first link the low- and high-resolution geophysical data via a stochastic downscaling procedure. This is followed by relating the downscaled geophysical data to the high-resolution hydraulic conductivity distribution. After outlining the general methodology of the approach, we demonstrate its application to a realistic synthetic example where we consider as data high-resolution measurements of the hydraulic and electrical conductivities at a small number of borehole locations, as well as spatially exhaustive, low-resolution estimates of the electrical conductivity obtained from surface-based electrical resistivity tomography. The different stochastic realizations of the hydraulic conductivity field obtained using our procedure are validated by comparing their solute transport behaviour with that of the underlying ?true? hydraulic conductivity field. We find that, even in the presence of strong subsurface heterogeneity, our proposed procedure allows for the generation of faithful representations of the regional-scale hydraulic conductivity structure and reliable predictions of solute transport over long, regional-scale distances.

Turnover and accumulation of genetic diversity across large time-scale cycles of isolation and connection of populations.

Major climatic and geological events but also population history (secondary contacts) have generated cycles of population isolation and connection of long and short periods. Recent empirical and theoretical studies suggest that fast evolutionary processes might be triggered by such events, as commonly illustrated in ecology by the adaptive radiation of cichlid fishes (isolation and reconnection of lakes and watersheds) and in epidemiology by the fast adaptation of the influenza virus (isolation and reconnection in hosts). We test whether cyclic population isolation and connection provide the raw material (standing genetic variation) for species evolution and diversification. Our analytical results demonstrate that population isolation and connection can provide, to populations, a high excess of genetic diversity compared with what is expected at equilibrium. This excess is either cyclic (high allele turnover) or cumulates with time depending on the duration of the isolation and the connection periods and the mutation rate. We show that diversification rates of animal clades are associated with specific periods of climatic cycles in the Quaternary. We finally discuss the importance of our results for macroevolutionary patterns and for the inference of population history from genomic data.

Large-scale analysis of orthologs and paralogs under covarion-like and constant-but-different models of amino acid evolution.

Functional divergence between homologous proteins is expected to affect amino acid sequences in two main ways, which can be considered as proxies of biochemical divergence: a "covarion-like" pattern of correlated changes in evolutionary rates, and switches in conserved residues ("conserved but different"). Although these patterns have been used in case studies, a large-scale analysis is needed to estimate their frequency and distribution. We use a phylogenomic framework of animal genes to answer three questions: 1) What is the prevalence of such patterns? 2) Can we link such patterns at the amino acid level with selection inferred at the codon level? 3) Are patterns different between paralogs and orthologs? We find that covarion-like patterns are more frequently detected than "constant but different," but that only the latter are correlated with signal for positive selection. Finally, there is no obvious difference in patterns between orthologs and paralogs.

Role of high normal gamma-glutamyltransferase level in identifying heavy alcohol use in young men.

The objective of the current study was to determine the predictive value of high normal gamma-glutamyltransferase (GGT) level as an indication of heavy drinking in young men. In a sample of 577 men attending a one-day army recruitment process mandatory for all Swiss men at age 19 years, GGT level was evaluated as the dependent variable for each of eight dichotomous classifications of individuals on the basis of meeting cut-off criteria for five indexes of alcohol use, two indexes of alcohol-related problems, and one index of body mass. The sensitivity, specificity, and predictive values of GGT level in identifying subjects as either heavy drinkers or being overweight were determined. Compared with findings for their counterparts, GGT level was higher in subjects reporting consumption of more than 14 drinks per week (20.5 +/- 7.81 vs. 18.9 +/- 7.60, P <.05), in those reporting being drunk at least once during the past 30 days (20.3 +/- 7.80 vs. 18.3 +/- 7.43, P <.001), and in individuals with body mass indexes >or=25 kg/m(2) (25.8 +/- 10.84 vs. 18.3 +/- 6.59, P <.001). At a GGT level cut-off of 20 U/l, the sensitivity, specificity, and positive and negative predictive values of either being a heavy drinker or overweight were 48.2%, 70.2%, 67.7%, and 51.2%, respectively. Exclusion of subjects with body mass indexes of >or=25 kg/m(2) revealed similar results. High normal GGT level in young men is indicative of heavy alcohol use or being overweight; when present, subjects should be screened further for potential concomitant drinking problems.

Large scale assessment of regulatory evolution and transcriptome complexity in mammals

AbstractIn addition to genetic changes affecting the function of gene products, changes in gene expression have been suggested to underlie many or even most of the phenotypic differences among mammals. However, detailed gene expression comparisons were, until recently, restricted to closely related species, owing to technological limitations. Thus, we took advantage of the latest technologies (RNA-Seq) to generate extensive qualitative and quantitative transcriptome data for a unique collection of somatic and germline tissues from representatives of all major mammalian lineages (placental mammals, marsupials and monotremes) and birds, the evolutionary outgroup.In the first major project of my thesis, we performed global comparative analyses of gene expression levels based on these data. Our analyses provided fundamental insights into the dynamics of transcriptome change during mammalian evolution (e.g., the rate of expression change across species, tissues and chromosomes) and allowed the exploration of the functional relevance and phenotypic implications of transcription changes at a genome-wide scale (e.g., we identified numerous potentially selectively driven expression switches).In a second project of my thesis, which was also based on the unique transcriptome data generated in the context of the first project we focused on the evolution of alternative splicing in mammals. Alternative splicing contributes to transcriptome complexity by generating several transcript isoforms from a single gene, which can, thus, perform various functions. To complete the global comparative analysis of gene expression changes, we explored patterns of alternative splicing evolution. This work uncovered several general and unexpected patterns of alternative splicing evolution (e.g., we found that alternative splicing evolves extremely rapidly) as well as a large number of conserved alternative isoforms that may be crucial for the functioning of mammalian organs.Finally, the third and final project of my PhD consisted in analyzing in detail the unique functional and evolutionary properties of the testis by exploring the extent of its transcriptome complexity. This organ was previously shown to evolve rapidly both at the phenotypic and molecular level, apparently because of the specific pressures that act on this organ and are associated with its reproductive function. Moreover, my analyses of the amniote tissue transcriptome data described above, revealed strikingly widespread transcriptional activity of both functional and nonfunctional genomic elements in the testis compared to the other organs. To elucidate the cellular source and mechanisms underlying this promiscuous transcription in the testis, we generated deep coverage RNA-Seq data for all major testis cell types as well as epigenetic data (DNA and histone methylation) using the mouse as model system. The integration of these complete dataset revealed that meiotic and especially post-meiotic germ cells are the major contributors to the widespread functional and nonfunctional transcriptome complexity of the testis, and that this "promiscuous" spermatogenic transcription is resulting, at least partially, from an overall transcriptionally permissive chromatin state. We hypothesize that this particular open state of the chromatin results from the extensive chromatin remodeling that occurs during spermatogenesis which ultimately leads to the replacement of histones by protamines in the mature spermatozoa. Our results have important functional and evolutionary implications (e.g., regarding new gene birth and testicular gene expression evolution).Generally, these three large-scale projects of my thesis provide complete and massive datasets that constitute valuables resources for further functional and evolutionary analyses of mammalian genomes.

MBIS: multivariate Bayesian image segmentation tool.

We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering tool based on the mixture of multivariate normal distributions model. MBIS supports multichannel bias field correction based on a B-spline model. A second methodological novelty is the inclusion of graph-cuts optimization for the stationary anisotropic hidden Markov random field model. Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data. We first validate the accuracy of segmentation and the estimated bias field for each channel. MBIS outperforms a widely used segmentation tool in a cross-comparison evaluation. The second experiment demonstrates the robustness of results on atlas-free segmentation of two image sets from scan-rescan protocols on 21 healthy subjects. Multivariate segmentation is more replicable than the monospectral counterpart on T1-weighted images. Finally, we provide a third experiment to illustrate how MBIS can be used in a large-scale study of tissue volume change with increasing age in 584 healthy subjects. This last result is meaningful as multivariate segmentation performs robustly without the need for prior knowledge.

Simulated-annealing-based conditional simulation for the local-scale characterization of heterogeneous aquifers

Simulated-annealing-based conditional simulations provide a flexible means of quantitatively integrating diverse types of subsurface data. Although such techniques are being increasingly used in hydrocarbon reservoir characterization studies, their potential in environmental, engineering and hydrological investigations is still largely unexploited. Here, we introduce a novel simulated annealing (SA) algorithm geared towards the integration of high-resolution geophysical and hydrological data which, compared to more conventional approaches, provides significant advancements in the way that large-scale structural information in the geophysical data is accounted for. Model perturbations in the annealing procedure are made by drawing from a probability distribution for the target parameter conditioned to the geophysical data. This is the only place where geophysical information is utilized in our algorithm, which is in marked contrast to other approaches where model perturbations are made through the swapping of values in the simulation grid and agreement with soft data is enforced through a correlation coefficient constraint. Another major feature of our algorithm is the way in which available geostatistical information is utilized. Instead of constraining realizations to match a parametric target covariance model over a wide range of spatial lags, we constrain the realizations only at smaller lags where the available geophysical data cannot provide enough information. Thus we allow the larger-scale subsurface features resolved by the geophysical data to have much more due control on the output realizations. Further, since the only component of the SA objective function required in our approach is a covariance constraint at small lags, our method has improved convergence and computational efficiency over more traditional methods. Here, we present the results of applying our algorithm to the integration of porosity log and tomographic crosshole georadar data to generate stochastic realizations of the local-scale porosity structure. Our procedure is first tested on a synthetic data set, and then applied to data collected at the Boise Hydrogeophysical Research Site.