Znaczna poprawa kliniczna u chorego z niewydolnością serca i zespołem obturacyjnego bezdechu sennego po właściwym ustaleniu stałego, dodatniego ciśnienia w drogach oddechowych

Zaburzenia oddychania w czasie snu często towarzyszą niewydolności krążenia i mogą przybierać postać zespołu obturacyjnego i ośrodkowego bezdechu sennego. Użycie aparatów wytwarzających stałe dodatnie ciśnienie w drogach oddechowych (CPAP, continuous positive airway pressure) u chorych na niewydolność krążenia ze współistniejącym zespołem obturacyjnego bezdechu sennego prowadzi do poprawy profilu neuroendokrynnego i polepszenia funkcji serca. Obturacja dróg oddechowych (jak również ciśnienie CPAP konieczne do jej pokonania) u chorego na niewydolność krążenia może podlegać dużym zmianom spowodowanym zmiennym obrzękiem dróg oddechowych. W niniejszej pracy przedstawiono opis pacjenta z niewydolnością krążenia, którego objawy sercowo-naczyniowe znacząco poprawiły się w czasie leczenia współistniejacego zespołu obturacyjnego bezdechu sennego po właściwym ustaleniu ciśnienia przy użyciu aparatu automatycznie dostosowującego dodatnie ciśnienie w drogach oddechowych (AutoPAP). Pneumonol. Alergol. Pol. 2011; 79, 1: 48-51Sleep disordered breathing (SDB) is frequently present in heart failure (HF), and it may take the form of obstructive (OSA) and central (CSA) sleep apnea. The use of continuous positive airway pressure (CPAP) in patients with OSA and HF is associated with an improved neuroendocrine profile and cardiac function. The degree of upper airway obstruction and the airway closing pressure (and the PAP pressure used to relieve it) may all be highly variable in a setting of uncontrolled HF, mostly due to variable airway oedema. We present a case of a man with HF whose cardiac symptoms radically improved after adequate treatment of his OSA with an auto-adjusting PAP device. Pneumonol. Alergol. Pol. 2011; 79, 1: 48-5

Multi-netclust: an efficient tool for finding connected clusters in multi-parametric networks

Summary: Multi-netclust is a simple tool that allows users to extract connected clusters of data represented by different networks given in the form of matrices. The tool uses user-defined threshold values to combine the matrices, and uses a straightforward, memory-efficient graph algorithm to find clusters that are connected in all or in either of the networks. The tool is written in C/C++ and is available either as a form-based or as a command-line-based program running on Linux platforms. The algorithm is fast, processing a network of > 106 nodes and 108 edges takes only a few minutes on an ordinary computer

Interoperability and FAIRness through a novel combination of Web technologies

Data in the life sciences are extremely diverse and are stored in a broad spectrum of repositories ranging from those designed for particular data types (such as KEGG for pathway data or UniProt for protein data) to those that are general-purpose (such as FigShare, Zenodo, Dataverse or EUDAT). These data have widely different levels of sensitivity and security considerations. For example, clinical observations about genetic mutations in patients are highly sensitive, while observations of species diversity are generally not. The lack of uniformity in data models from one repository to another, and in the richness and availability of metadata descriptions, makes integration and analysis of these data a manual, time-consuming task with no scalability. Here we explore a set of resource-oriented Web design patterns for data discovery, accessibility, transformation, and integration that can be implemented by any general- or special-purpose repository as a means to assist users in finding and reusing their data holdings. We show that by using off-the-shelf technologies, interoperability can be achieved atthe level of an individual spreadsheet cell. We note that the behaviours of this architecture compare favourably to the desiderata defined by the FAIR Data Principles, and can therefore represent an exemplar implementation of those principles. The proposed interoperability design patterns may be used to improve discovery and integration of both new and legacy data, maximizing the utility of all scholarly outputs

eggNOG v3.0: orthologous groups covering 1133 organisms at 41 different taxonomic ranges

Orthologous relationships form the basis of most comparative genomic and metagenomic studies and are essential for proper phylogenetic and functional analyses. The third version of the eggNOG database (http://eggnog.embl.de) contains non-supervised orthologous groups constructed from 1133 organisms, doubling the number of genes with orthology assignment compared to eggNOG v2. The new release is the result of a number of improvements and expansions: (i) the underlying homology searches are now based on the SIMAP database; (ii) the orthologous groups have been extended to 41 levels of selected taxonomic ranges enabling much more fine-grained orthology assignments; and (iii) the newly designed web page is considerably faster with more functionality. In total, eggNOG v3 contains 721 801 orthologous groups, encompassing a total of 4 396 591 genes. Additionally, we updated 4873 and 4850 original COGs and KOGs, respectively, to include all 1133 organisms. At the universal level, covering all three domains of life, 101 208 orthologous groups are available, while the others are applicable at 40 more limited taxonomic ranges. Each group is amended by multiple sequence alignments and maximum-likelihood trees and broad functional descriptions are provided for 450 904 orthologous groups (62.5%)

PhylomeDB v3.0: an expanding repository of genome-wide collections of trees, alignments and phylogeny-based orthology and paralogy predictions

The growing availability of complete genomic sequences from diverse species has brought about the need to scale up phylogenomic analyses, including the reconstruction of large collections of phylogenetic trees. Here, we present the third version of PhylomeDB (http://phylomeDB.org), a public database for genome-wide collections of gene phylogenies (phylomes). Currently, PhylomeDB is the largest phylogenetic repository and hosts 17 phylomes, comprising 416 093 trees and 165 840 alignments. It is also a major source for phylogeny-based orthology and paralogy predictions, covering about 5 million proteins in 717 fully-sequenced genomes. For each protein-coding gene in a seed genome, the database provides original and processed alignments, phylogenetic trees derived from various methods and phylogeny-based predictions of orthology and paralogy relationships. The new version of phylomeDB has been extended with novel data access and visualization features, including the possibility of programmatic access. Available seed species include model organisms such as human, yeast, Escherichia coli or Arabidopsis thaliana, but also alternative model species such as the human pathogen Candida albicans, or the pea aphid Acyrtosiphon pisum. Finally, PhylomeDB is currently being used by several genome sequencing projects that couple the genome annotation process with the reconstruction of the corresponding phylome, a strategy that provides relevant evolutionary insights

eggNOG v2.0: extending the evolutionary genealogy of genes with enhanced non-supervised orthologous groups, species and functional annotations

The identification of orthologous relationships forms the basis for most comparative genomics studies. Here, we present the second version of the eggNOG database, which contains orthologous groups (OGs) constructed through identification of reciprocal best BLAST matches and triangular linkage clustering. We applied this procedure to 630 complete genomes (529 bacteria, 46 archaea and 55 eukaryotes), which is a 2-fold increase relative to the previous version. The pipeline yielded 224 847 OGs, including 9724 extended versions of the original COG and KOG. We computed OGs for different levels of the tree of life; in addition to the species groups included in our first release (i.e. fungi, metazoa, insects, vertebrates and mammals), we have now constructed OGs for archaea, fishes, rodents and primates. We automatically annotate the non-supervised orthologous groups (NOGs) with functional descriptions, protein domains, and functional categories as defined initially for the COG/KOG database. In-depth analysis is facilitated by precomputed high-quality multiple sequence alignments and maximum-likelihood trees for each of the available OGs. Altogether, eggNOG covers 2 242 035 proteins (built from 2 590 259 proteins) and provides a broad functional description for at least 1 966 709 (88%) of them. Users can access the complete set of orthologous groups via a web interface at: http://eggnog.embl.de

Finished genome of the fungal wheat pathogen Mycosphaerella graminicola Reveals dispensome structure, chromosome plasticity, and stealth pathogenesis.

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MSOAR 2.0: Incorporating tandem duplications into ortholog assignment based on genome rearrangement

<p>Abstract</p> <p>Background</p> <p>Ortholog assignment is a critical and fundamental problem in comparative genomics, since orthologs are considered to be functional counterparts in different species and can be used to infer molecular functions of one species from those of other species. MSOAR is a recently developed high-throughput system for assigning one-to-one orthologs between closely related species on a genome scale. It attempts to reconstruct the evolutionary history of input genomes in terms of genome rearrangement and gene duplication events. It assumes that a gene duplication event inserts a duplicated gene into the genome of interest at a random location (<it>i.e.</it>, the random duplication model). However, in practice, biologists believe that genes are often duplicated by tandem duplications, where a duplicated gene is located next to the original copy (<it>i.e.</it>, the tandem duplication model).</p> <p>Results</p> <p>In this paper, we develop MSOAR 2.0, an improved system for one-to-one ortholog assignment. For a pair of input genomes, the system first focuses on the tandemly duplicated genes of each genome and tries to identify among them those that were duplicated after the speciation (<it>i.e.</it>, the so-called inparalogs), using a simple phylogenetic tree reconciliation method. For each such set of tandemly duplicated inparalogs, all but one gene will be deleted from the concerned genome (because they cannot possibly appear in any one-to-one ortholog pairs), and MSOAR is invoked. Using both simulated and real data experiments, we show that MSOAR 2.0 is able to achieve a better sensitivity and specificity than MSOAR. In comparison with the well-known genome-scale ortholog assignment tool InParanoid, Ensembl ortholog database, and the orthology information extracted from the well-known whole-genome multiple alignment program MultiZ, MSOAR 2.0 shows the highest sensitivity. Although the specificity of MSOAR 2.0 is slightly worse than that of InParanoid in the real data experiments, it is actually better than that of InParanoid in the simulation tests.</p> <p>Conclusions</p> <p>Our preliminary experimental results demonstrate that MSOAR 2.0 is a highly accurate tool for one-to-one ortholog assignment between closely related genomes. The software is available to the public for free and included as online supplementary material.</p