Policy-based SLA storage management model for distributed data storage services

There is  high demand for storage related services supporting scientists in their research activities. Those services are expected to provide not only capacity but also features allowing for more flexible and cost efficient usage. Such features include easy multiplatform data access, long term data retention, support for performance and cost differentiating of SLA restricted data access. The paper presents a policy-based SLA storage management model for distributed data storage services. The model allows for automated management of distributed data aimed at QoS provisioning with no strict resource reservation. The problem of providing  users with the required QoS requirements is complex, and therefore the model implements heuristic approach  for solving it. The corresponding system architecture, metrics and methods for SLA focused storage management are developed and tested in a real, nationwide environment

the facial expression of emotions recognition in patients with polycystic ovary syndrome

Background. A facial expression of emotions recognition is one of the basic psychological abilities. Sex steroids are able to strongly modulate the process of interpretation of facial expressions, as it has been shown in Turner syndrome patients.Objective. The aim of this study was the assessment of ability to interpret the facial emotions in women with polycystic ovary syndrome (PCOS).Methods. Participants completed a visual emotional task in which they were asked to recognize the emotion expressed of 80 randomly chosen facial expressions from NimStim set (Tottenham et al., 2009). With dedicated software we were able to assess the accuracy of patients facial emotion recognition (in comparison to NimStim validation set) and time required to provide the answer. Patients with psychotic personality have been excluded using Eysenck Personality Questionnaire (EPQ). All the patients underwent also hormonal tests including gonadotropins, estradiol and androgen concentrations.Patients. 80 women diagnosed with PCOS and hyperandrogenemia were included to the study. The control group consisted of 60 healthy, euovulatory women matched by age.Intervention. Each patient underwent visual emotional and EPQ tasks using specifically designed software.Main outcome measures. The accuracy rate (AR) and time required to recognize emotion (TE) of following emotions: anger, disgust, fear, happiness, sadness, surprise, calm and neutral has been measured.Results. Patients with PCOS showed significantly reduced AR for calm (0.76¬+/-0.09) and surprise (0.67+/-0.18) emotions in comparison to controls (0.81+/-0.09, 0.79+/-0.08 respectively). The TE for the anger was higher in PCOS group. Estradiol concentrations showed a statistic tendency (p=0.07) for correlation with TE for the happiness in controls. Conclusions. In this study we showed for the first time that patients affected by hyperandrogenism shows signs of disturbed recognition of facial expression of emotions

The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms

<p>Abstract</p> <p>Background</p> <p>The distribution of isoelectric point (pI) of proteins in a proteome is universal for all organisms. It is bimodal dividing the proteome into two sets of acidic and basic proteins. Different species however have different abundance of acidic and basic proteins that may be correlated with taxonomy, subcellular localization, ecological niche of organisms and proteome size.</p> <p>Results</p> <p>We have analysed 1784 proteomes encoded by chromosomes of Archaea, Bacteria, Eukaryota, and also mitochondria, plastids, prokaryotic plasmids, phages and viruses. We have found significant correlation in more than 95% of proteomes between the protein length and pI in proteomes – positive for acidic proteins and negative for the basic ones. Plastids, viruses and plasmids encode more basic proteomes while chromosomes of Archaea, Bacteria, Eukaryota, mitochondria and phages more acidic ones. Mitochondrial proteomes of Viridiplantae, Protista and Fungi are more basic than Metazoa. It results from the presence of basic proteins in the former proteomes and their absence from the latter ones and is related with reduction of metazoan genomes. Significant correlation was found between the pI bias of proteomes encoded by prokaryotic chromosomes and proteomes encoded by plasmids but there is no correlation between eukaryotic nuclear-coded proteomes and proteomes encoded by organelles. Detailed analyses of prokaryotic proteomes showed significant relationships between pI distribution and habitat, relation to the host cell and salinity of the environment, but no significant correlation with oxygen and temperature requirements. The salinity is positively correlated with acidicity of proteomes. Host-associated organisms and especially intracellular species have more basic proteomes than free-living ones. The higher rate of mutations accumulation in the intracellular parasites and endosymbionts is responsible for the basicity of their tiny proteomes that explains the observed positive correlation between the decrease of genome size and the increase of basicity of proteomes. The results indicate that even conserved proteins subjected to strong selectional constraints follow the global trend in the pI distribution.</p> <p>Conclusion</p> <p>The distribution of pI of proteins in proteomes shows clear relationships with length of proteins, subcellular localization, taxonomy and ecology of organisms. The distribution is also strongly affected by mutational pressure especially in intracellular organisms.</p

Relationship between the pI bias and: (A) logarithm of proteome size and (B) genomic GC content for different ecological groups of prokaryotes

<p><b>Copyright information:</b></p><p>Taken from "The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms"</p><p>http://www.biomedcentral.com/1471-2164/8/163</p><p>BMC Genomics 2007;8():163-163.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1905920.</p><p></p

Distributions of the correlation coefficients between pI value and length of proteins calculated separately for acidic and basic sets of proteomes

<p><b>Copyright information:</b></p><p>Taken from "The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms"</p><p>http://www.biomedcentral.com/1471-2164/8/163</p><p>BMC Genomics 2007;8():163-163.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1905920.</p><p></p

Statistical analysis of the pI bias of different groups of proteomes and their UPGMA-based clustering according to the median of the pI bias

<p><b>Copyright information:</b></p><p>Taken from "The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms"</p><p>http://www.biomedcentral.com/1471-2164/8/163</p><p>BMC Genomics 2007;8():163-163.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1905920.</p><p></p> Numbers at nodes mean the percentage support based on subsampling method and asterisks denote results of WLS-LRT/F tests (both with p < 0.001)

Statistical analysis of the pI bias of mitochondrial proteomes and their UPGMA-based clustering according to the median of the pI bias

<p><b>Copyright information:</b></p><p>Taken from "The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms"</p><p>http://www.biomedcentral.com/1471-2164/8/163</p><p>BMC Genomics 2007;8():163-163.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1905920.</p><p></p> Numbers at nodes mean the percentage support based on subsampling method and asterisks denote results of WLS-LRT/F tests (both with p < 0.001)

Ratios of the observed to expected number of proteomes in a given class of pI bias for different ecological classifications: (A) oxygen, (B) temperature, (C) salinity, (D) habitat and (E) relation to host cell

<p><b>Copyright information:</b></p><p>Taken from "The relationships between the isoelectric point and: length of proteins, taxonomy and ecology of organisms"</p><p>http://www.biomedcentral.com/1471-2164/8/163</p><p>BMC Genomics 2007;8():163-163.</p><p>Published online 12 Jun 2007</p><p>PMCID:PMC1905920.</p><p></p