Predictive significance of the six-minute walk distance for long-term survival in chronic hypercapnic respiratory failure

Background: The 6-min walk distance ( 6-MWD) is a global marker of functional capacity and prognosis in chronic obstructive pulmonary disease ( COPD), but less explored in other chronic respiratory diseases. Objective: To study the role of 6-MWD in chronic hypercapnic respiratory failure ( CHRF). Methods: In 424 stable patients with CHRF and non-invasive ventilation ( NIV) comprising COPD ( n = 197), restrictive diseases ( RD; n = 112) and obesity-hypoventilation- syndrome ( OHS; n = 115), the prognostic value of 6-MWD for long- term survival was assessed in relation to that of body mass index (BMI), lung function, respiratory muscle function and laboratory parameters. Results: 6-MWD was reduced in patients with COPD ( median 280 m; quartiles 204/350 m) and RD ( 290 m; 204/362 m) compared to OHS ( 360 m; 275/440 m; p <0.001 each). Overall mortality during 24.9 (13.1/40.5) months was 22.9%. In the 424 patients with CHRF, 6-MWD independently predicted mortality in addition to BMI, leukocytes and forced expiratory volume in 1 s ( p <0.05 each). In COPD, 6-MWD was strongly associated with mortality using the median {[} p <0.001, hazard ratio ( HR) = 3.75, 95% confidence interval (CI): 2.24-6.38] or quartiles as cutoff levels. In contrast, 6-MWD was only significantly associated with impaired survival in RD patients when it was reduced to 204 m or less (1st quartile; p = 0.003, HR = 3.31, 95% CI: 1.73-14.10), while in OHS 6-MWD had not any prognostic value. Conclusions: In patients with CHRF and NIV, 6-MWD was predictive for long- term survival particularly in COPD. In RD only severely reduced 6-MWD predicted mortality, while in OHS 6-MWD was relatively high and had no prognostic value. These results support a disease-specific use of 6-MWD in the routine assessment of patients with CHRF. Copyright (C) 2007 S. Karger AG, Basel

Evolutionary potential and adaptation of Banksia attenuata (Proteaceae) to climate and fire regime in southwestern Australia, a global biodiversity hotspot

Substantial climate changes are evident across Australia, with declining rainfall and rising temperature in conjunction with frequent fires. Considerable species loss and range contractions have been predicted; however, our understanding of how genetic variation may promote adaptation in response to climate change remains uncertain. Here we characterized candidate genes associated with rainfall gradients, temperatures, and fire intervals through environmental association analysis. We found that overall population adaptive genetic variation was significantly affected by shortened fire intervals, whereas declining rainfall and rising temperature did not have a detectable influence. Candidate SNPs associated with rainfall and high temperature were diverse, whereas SNPs associated with specific fire intervals were mainly fixed in one allele. Gene annotation further revealed four genes with functions in stress tolerance, the regulation of stomatal opening and closure, energy use, and morphogenesis with adaptation to climate and fire intervals. B. attenuata may tolerate further changes in rainfall and temperature through evolutionary adaptations based on their adaptive genetic variation. However, the capacity to survive future climate change may be compromised by changes in the fire regime

On the early and developed stages of surface condensation: competition mechanism between interfacial and condensate bulk thermal resistances

Financial supports from the National Natural Science Foundation of China (51406205), the Beijing Natural Science Foundation (3142021) and the Engineering and Physics Science Research Council (EPSRC) of the UK (EP/L001233/1) are acknowledged.Financial supports from the National Natural Science Foundation of China (51406205), the Beijing Natural Science Foundation (3142021) and the Engineering and Physics Science Research Council (EPSRC) of the UK (EP/L001233/1) are acknowledged.Financial supports from the National Natural Science Foundation of China (51406205), the Beijing Natural Science Foundation (3142021) and the Engineering and Physics Science Research Council (EPSRC) of the UK (EP/L001233/1) are acknowledged.We use molecular dynamics simulation to investigate the early and developed stages of surface condensation. We find that the liquid-vapor and solid-liquid interfacial thermal resistances depend on the properties of solid and fluid, which are time-independent, while the condensate bulk thermal resistance depends on the condensate thickness, which is time-dependent. There exists intrinsic competition between the interfacial and condensate bulk thermal resistances in timeline and the resultant total thermal resistance determines the condensation intensity for a given vapor-solid temperature difference. We reveal the competition mechanism that the interfacial thermal resistance dominates at the onset of condensation and holds afterwards while the condensate bulk thermal resistance gradually takes over with condensate thickness growing. The weaker the solid-liquid bonding, the later the takeover occurs. This competition mechanism suggests that only when the condensate bulk thermal resistance is reduced after it takes over the domination can the condensation be effectively intensified. We propose a unified theoretical model for the thermal resistance analysis by making dropwise condensation equivalent to filmwise condensation. We further find that near a critical point (contact angle being ca. 153°) the bulk thermal resistance has the least opportunity to take over the domination while away from it the probability increases.Financial supports from the National Natural Science Foundation of China (51406205), the Beijing Natural Science Foundation (3142021) and the Engineering and Physics Science Research Council (EPSRC) of the UK (EP/L001233/1) are acknowledged

MCL-CAw: A refinement of MCL for detecting yeast complexes from weighted PPI networks by incorporating core-attachment structure

Abstract Background The reconstruction of protein complexes from the physical interactome of organisms serves as a building block towards understanding the higher level organization of the cell. Over the past few years, several independent high-throughput experiments have helped to catalogue enormous amount of physical protein interaction data from organisms such as yeast. However, these individual datasets show lack of correlation with each other and also contain substantial number of false positives (noise). Over these years, several affinity scoring schemes have also been devised to improve the qualities of these datasets. Therefore, the challenge now is to detect meaningful as well as novel complexes from protein interaction (PPI) networks derived by combining datasets from multiple sources and by making use of these affinity scoring schemes. In the attempt towards tackling this challenge, the Markov Clustering algorithm (MCL) has proved to be a popular and reasonably successful method, mainly due to its scalability, robustness, and ability to work on scored (weighted) networks. However, MCL produces many noisy clusters, which either do not match known complexes or have additional proteins that reduce the accuracies of correctly predicted complexes. Results Inspired by recent experimental observations by Gavin and colleagues on the modularity structure in yeast complexes and the distinctive properties of "core" and "attachment" proteins, we develop a core-attachment based refinement method coupled to MCL for reconstruction of yeast complexes from scored (weighted) PPI networks. We combine physical interactions from two recent "pull-down" experiments to generate an unscored PPI network. We then score this network using available affinity scoring schemes to generate multiple scored PPI networks. The evaluation of our method (called MCL-CAw) on these networks shows that: (i) MCL-CAw derives larger number of yeast complexes and with better accuracies than MCL, particularly in the presence of natural noise; (ii) Affinity scoring can effectively reduce the impact of noise on MCL-CAw and thereby improve the quality (precision and recall) of its predicted complexes; (iii) MCL-CAw responds well to most available scoring schemes. We discuss several instances where MCL-CAw was successful in deriving meaningful complexes, and where it missed a few proteins or whole complexes due to affinity scoring of the networks. We compare MCL-CAw with several recent complex detection algorithms on unscored and scored networks, and assess the relative performance of the algorithms on these networks. Further, we study the impact of augmenting physical datasets with computationally inferred interactions for complex detection. Finally, we analyse the essentiality of proteins within predicted complexes to understand a possible correlation between protein essentiality and their ability to form complexes. Conclusions We demonstrate that core-attachment based refinement in MCL-CAw improves the predictions of MCL on yeast PPI networks. We show that affinity scoring improves the performance of MCL-CAw.http://deepblue.lib.umich.edu/bitstream/2027.42/78256/1/1471-2105-11-504.xmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/2/1471-2105-11-504-S1.PDFhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/3/1471-2105-11-504-S2.ZIPhttp://deepblue.lib.umich.edu/bitstream/2027.42/78256/4/1471-2105-11-504.pdfPeer Reviewe

High nutrient-use efficiency during early seedling growth in diverse Grevillea species (Proteaceae)

Several hypotheses have been proposed to explain the rich floristic diversity in regions characterised by nutrient-impoverished soils; however, none of these hypotheses have been able to explain the rapid diversification over a relatively short evolutionary time period of Grevillea, an Australian plant genus with 452 recognised species/subspecies and only 11 million years of evolutionary history. Here, we hypothesise that the apparent evolutionary success of Grevillea might have been triggered by the highly efficient use of key nutrients. The nutrient content in the seeds and nutrient-use efficiency during early seedling growth of 12 species of Grevillea were compared with those of 24 species of Hakea, a closely related genus. Compared with Hakea, the Grevillea species achieved similar growth rates (root and shoot length) during the early stages of seedling growth but contained only approximately half of the seed nutrient content. We conclude that the high nutrient-use efficiency observed in Grevillea might have provided a selective advantage in nutrient-poor ecosystems during evolution and that this property likely contributed to the evolutionary success in Grevillea

GIBA: a clustering tool for detecting protein complexes

Background: During the last years, high throughput experimental methods have been developed which generate large datasets of protein - protein interactions (PPIs). However, due to the experimental methodologies these datasets contain errors mainly in terms of false positive data sets and reducing therefore the quality of any derived information. Typically these datasets can be modeled as graphs, where vertices represent proteins and edges the pairwise PPIs, making it easy to apply automated clustering methods to detect protein complexes or other biological significant functional groupings. Methods: In this paper, a clustering tool, called GIBA (named by the first characters of its developers' nicknames), is presented. GIBA implements a two step procedure to a given dataset of protein-protein interaction data. First, a clustering algorithm is applied to the interaction data, which is then followed by a filtering step to generate the final candidate list of predicted complexes. Results: The efficiency of GIBA is demonstrated through the analysis of 6 different yeast protein interaction datasets in comparison to four other available algorithms. We compared the results of the different methods by applying five different performance measurement metrices. Moreover, the parameters of the methods that constitute the filter have been checked on how they affect the final results. Conclusion: GIBA is an effective and easy to use tool for the detection of protein complexes out of experimentally measured protein - protein interaction networks. The results show that GIBA has superior prediction accuracy than previously published methods

The post-vaccine microevolution of invasive Streptococcus pneumoniae

The 7-valent pneumococcal conjugated vaccine (PCV7) has affected the genetic population of Streptococcus pneumoniae in pediatric carriage. Little is known however about pneumococcal population genomics in adult invasive pneumococcal disease (IPD) under vaccine pressure. We sequenced and serotyped 349 strains of S. pneumoniae isolated from IPD patients in Nijmegen between 2001 and 2011. Introduction of PCV7 in the Dutch National Immunization Program in 2006 preluded substantial alterations in the IPD population structure caused by serotype replacement. No evidence could be found for vaccine induced capsular switches. We observed that after a temporary bottleneck in gene diversity after the introduction of PCV7, the accessory gene pool re-expanded mainly by genes already circulating pre-PCV7. In the post-vaccine genomic population a number of genes changed frequency, certain genes became overrepresented in vaccine serotypes, while others shifted towards non-vaccine serotypes. Whether these dynamics in the invasive pneumococcal population have truly contributed to invasiveness and manifestations of disease remains to be further elucidated. We suggest the use of whole genome sequencing for surveillance of pneumococcal population dynamics that could give a prospect on the course of disease, facilitating effective prevention and management of IPD

TranscriptomeBrowser: A Powerful and Flexible Toolbox to Explore Productively the Transcriptional Landscape of the Gene Expression Omnibus Database

International audienceAs public microarray repositories are constantly growing, we are facing the challenge of designing strategies to provide productive access to the available data.\ We used a modified version of the Markov clustering algorithm to systematically extract clusters of co-regulated genes from hundreds of microarray datasets stored in the Gene Expression Omnibus database (n = 1,484). This approach led to the definition of 18,250 transcriptional signatures (TS) that were tested for functional enrichment using the DAVID knowledgebase. Over-representation of functional terms was found in a large proportion of these TS (84%). We developed a JAVA application, TBrowser that comes with an open plug-in architecture and whose interface implements a highly sophisticated search engine supporting several Boolean operators (http://tagc.univ-mrs.fr/tbrowser/). User can search and analyze TS containing a list of identifiers (gene symbols or AffyIDs) or associated with a set of functional terms.\ As proof of principle, TBrowser was used to define breast cancer cell specific genes and to detect chromosomal abnormalities in tumors. Finally, taking advantage of our large collection of transcriptional signatures, we constructed a comprehensive map that summarizes gene-gene co-regulations observed through all the experiments performed on HGU133A Affymetrix platform. We provide evidences that this map can extend our knowledge of cellular signaling pathways

Rapid Multi-Locus Sequence Typing Using Microfluidic Biochips

sequencing of 6–8 housekeeping loci to assign unique sequence types. In this work we adapted MLST to a rapid microfluidics platform in order to enhance speed and reduce laboratory labor time. isolated in this study from one location in Rockville, Maryland (0.04 substitutions per site) was found to be as great as the global collection of isolates.Biogeographical investigation of pathogens is only one of a panoply of possible applications of microfluidics based MLST; others include microbiologic forensics, biothreat identification, and rapid characterization of human clinical samples

The Potential for pathogenicity was present in the ancestor of the Ascomycete subphylum Pezizomycotina

<p>Abstract</p> <p>Background</p> <p>Previous studies in Ascomycetes have shown that the function of gene families of which the size is considerably larger in extant pathogens than in non-pathogens could be related to pathogenicity traits. However, by only comparing gene inventories in extant species, no insights can be gained into the evolutionary process that gave rise to these larger family sizes in pathogens. Moreover, most studies which consider gene families in extant species only tend to explain observed differences in gene family sizes by gains rather than by losses, hereby largely underestimating the impact of gene loss during genome evolution.</p> <p>Results</p> <p>In our study we used a selection of recently published genomes of Ascomycetes to analyze how gene family gains, duplications and losses have affected the origin of pathogenic traits. By analyzing the evolutionary history of gene families we found that most gene families with an enlarged size in pathogens were present in an ancestor common to both pathogens and non-pathogens. The majority of these families were selectively maintained in pathogenic lineages, but disappeared in non-pathogens. Non-pathogen-specific losses largely outnumbered pathogen-specific losses.</p> <p>Conclusions</p> <p>We conclude that most of the proteins for pathogenicity were already present in the ancestor of the Ascomycete lineages we used in our study. Species that did not develop pathogenicity seemed to have reduced their genetic complexity compared to their ancestors. We further show that expansion of gained or already existing families in a species-specific way is important to fine-tune the specificities of the pathogenic host-fungus interaction.</p