Service Interaction Flow Analysis Technique for Service Personalization

Abstract Service interaction flows are difficult to capture, analyze, outline, and represent for research and design purposes. We examine how variation of personalized service flows in technology-mediated service interaction can be modeled and analyzed to provide information on how service personalization could support interaction. We have analyzed service interaction cases in a context of technology-mediated car rental service. With the analysis technique we propose, inspired by Interaction Analysis method, we were able to capture and model the situational service interaction. Our contribution regarding technology-mediated service interaction design is twofold: First, with the increased understanding on the role of personalization in managing variation in technology-mediated service interaction, our study contributes to designing service management information systems and human-computer interfaces that support personalized service interaction flows. Second, we provide a new analysis technique for situated interaction analysis, particularly when the aim is to understand personalization in service interaction flows

The evolutionary signal in metagenome phyletic profiles predicts many gene functions

Background. The function of many genes is still not known even in model organisms. An increasing availability of microbiome DNA sequencing data provides an opportunity to infer gene function in a systematic manner. Results. We evaluated if the evolutionary signal contained in metagenome phyletic profiles (MPP) is predictive of a broad array of gene functions. The MPPs are an encoding of environmental DNA sequencing data that consists of relative abundances of gene families across metagenomes. We find that such MPPs can accurately predict 826 Gene Ontology functional categories, while drawing on human gut microbiomes, ocean metagenomes, and DNA sequences from various other engineered and natural environments. Overall, in this task, the MPPs are highly accurate, and moreover they provide coverage for a set of Gene Ontology terms largely complementary to standard phylogenetic profiles, derived from fully sequenced genomes. We also find that metagenomes approximated from taxon relative abundance obtained via 16S rRNA gene sequencing may provide surprisingly useful predictive models. Crucially, the MPPs derived from different types of environments can infer distinct, non-overlapping sets of gene functions and therefore complement each other. Consistently, simulations on > 5000 metagenomes indicate that the amount of data is not in itself critical for maximizing predictive accuracy, while the diversity of sampled environments appears to be the critical factor for obtaining robust models. Conclusions. In past work, metagenomics has provided invaluable insight into ecology of various habitats, into diversity of microbial life and also into human health and disease mechanisms. We propose that environmental DNA sequencing additionally constitutes a useful tool to predict biological roles of genes, yielding inferences out of reach for existing comparative genomics approaches

Are there any differences in clinical and laboratory findings on admission between H1N1 positive and negative patients with flu-like symptoms?

<p>Abstract</p> <p>Background</p> <p>The World Health Organization alert for the H1N1 influenza pandemic led to the implementation of certain measures regarding admission of patients with flu-like symptoms. All these instructions were adopted by the Greek National Health System. The aim of this study was to retrospectively examine the characteristics of all subjects admitted to the Unit of Infectious Diseases with symptoms indicating H1N1 infection, and to identify any differences between H1N1 positive or negative patients. Patients from the ED (emergency department) with flu-like symptoms (sore throat, cough, rhinorhea, or nasal congestion) and fever >37.5°C were admitted in the Unit of Infectious diseases and gave pharyngeal or nasopharyngeal swabs. Swabs were tested with real-time reverse-transcriptase-polymerase-chain-reaction (RT-PCR).</p> <p>Findings</p> <p>Patients were divided into two groups. Group A comprised 33 H1N1 positive patients and Group B (control group) comprised of 27 H1N1 negative patients. The two groups did not differ in terms of patient age, co-morbidities, length of hospitalization, temperature elevation, hypoxemia, as well as renal and liver function. There were also no significant differences in severity on admission. C-reactive protein (CRP) (mean 12.8 vs. 5.74) and white blood count (WBC) (mean 10.528 vs. 7.114) were significantly higher in group B than in group A upon admission. Obesity was noted in 8 patients of Group A (mean 31.67) and 14 patients of Group B (mean 37.78). Body mass index (BMI) was lower in H1N1 positive than in H1N1 negative patients (mean 31.67 vs. 37.78, respectively; p = 0.009).</p> <p>Conclusions</p> <p>The majority of patients in both groups were young male adults. CRP, WBC and BMI were higher among H1N1 negative patients. Finally, clinical course of patients in both groups was mild and uneventful.</p

Online detection and quantification of epidemics

<p>Abstract</p> <p>Background</p> <p>Time series data are increasingly available in health care, especially for the purpose of disease surveillance. The analysis of such data has long used periodic regression models to detect outbreaks and estimate epidemic burdens. However, implementation of the method may be difficult due to lack of statistical expertise. No dedicated tool is available to perform and guide analyses.</p> <p>Results</p> <p>We developed an online computer application allowing analysis of epidemiologic time series. The system is available online at <url>http://www.u707.jussieu.fr/periodic_regression/</url>. The data is assumed to consist of a periodic baseline level and irregularly occurring epidemics. The program allows estimating the periodic baseline level and associated upper forecast limit. The latter defines a threshold for epidemic detection. The burden of an epidemic is defined as the cumulated signal in excess of the baseline estimate. The user is guided through the necessary choices for analysis. We illustrate the usage of the online epidemic analysis tool with two examples: the retrospective detection and quantification of excess pneumonia and influenza (P&I) mortality, and the prospective surveillance of gastrointestinal disease (diarrhoea).</p> <p>Conclusion</p> <p>The online application allows easy detection of special events in an epidemiologic time series and quantification of excess mortality/morbidity as a change from baseline. It should be a valuable tool for field and public health practitioners.</p

Identification of novel biomarker candidates by proteomic analysis of cerebrospinal fluid from patients with moyamoya disease using SELDI-TOF-MS

<p>Abstract</p> <p>Background</p> <p>Moyamoya disease (MMD) is an uncommon cerebrovascular condition with unknown etiology characterized by slowly progressive stenosis or occlusion of the bilateral internal carotid arteries associated with an abnormal vascular network. MMD is a major cause of stroke, specifically in the younger population. Diagnosis is based on only radiological features as no other clinical data are available. The purpose of this study was to identify novel biomarker candidate proteins differentially expressed in the cerebrospinal fluid (CSF) of patients with MMD using proteomic analysis.</p> <p>Methods</p> <p>For detection of biomarkers, CSF samples were obtained from 20 patients with MMD and 12 control patients. Mass spectral data were generated by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) with an anion exchange chip in three different buffer conditions. After expression difference mapping was undertaken using the obtained protein profiles, a comparative analysis was performed.</p> <p>Results</p> <p>A statistically significant number of proteins (34) were recognized as single biomarker candidate proteins which were differentially detected in the CSF of patients with MMD, compared to the control patients (p < 0.05). All peak intensity profiles of the biomarker candidates underwent classification and regression tree (CART) analysis to produce prediction models. Two important biomarkers could successfully classify the patients with MMD and control patients.</p> <p>Conclusions</p> <p>In this study, several novel biomarker candidate proteins differentially expressed in the CSF of patients with MMD were identified by a recently developed proteomic approach. This is a pilot study of CSF proteomics for MMD using SELDI technology. These biomarker candidates have the potential to shed light on the underlying pathogenesis of MMD.</p

Epistasis: Obstacle or Advantage for Mapping Complex Traits?

Identification of genetic loci in complex traits has focused largely on one-dimensional genome scans to search for associations between single markers and the phenotype. There is mounting evidence that locus interactions, or epistasis, are a crucial component of the genetic architecture of biologically relevant traits. However, epistasis is often viewed as a nuisance factor that reduces power for locus detection. Counter to expectations, recent work shows that fitting full models, instead of testing marker main effect and interaction components separately, in exhaustive multi-locus genome scans can have higher power to detect loci when epistasis is present than single-locus scans, and improvement that comes despite a much larger multiple testing alpha-adjustment in such searches. We demonstrate, both theoretically and via simulation, that the expected power to detect loci when fitting full models is often larger when these loci act epistatically than when they act additively. Additionally, we show that the power for single locus detection may be improved in cases of epistasis compared to the additive model. Our exploration of a two step model selection procedure shows that identifying the true model is difficult. However, this difficulty is certainly not exacerbated by the presence of epistasis, on the contrary, in some cases the presence of epistasis can aid in model selection. The impact of allele frequencies on both power and model selection is dramatic

Genome-Wide Screen in Saccharomyces cerevisiae Identifies Vacuolar Protein Sorting, Autophagy, Biosynthetic, and tRNA Methylation Genes Involved in Life Span Regulation

The study of the chronological life span of Saccharomyces cerevisiae, which measures the survival of populations of non-dividing yeast, has resulted in the identification of homologous genes and pathways that promote aging in organisms ranging from yeast to mammals. Using a competitive genome-wide approach, we performed a screen of a complete set of approximately 4,800 viable deletion mutants to identify genes that either increase or decrease chronological life span. Half of the putative short-/long-lived mutants retested from the primary screen were confirmed, demonstrating the utility of our approach. Deletion of genes involved in vacuolar protein sorting, autophagy, and mitochondrial function shortened life span, confirming that respiration and degradation processes are essential for long-term survival. Among the genes whose deletion significantly extended life span are ACB1, CKA2, and TRM9, implicated in fatty acid transport and biosynthesis, cell signaling, and tRNA methylation, respectively. Deletion of these genes conferred heat-shock resistance, supporting the link between life span extension and cellular protection observed in several model organisms. The high degree of conservation of these novel yeast longevity determinants in other species raises the possibility that their role in senescence might be conserved

Specific versus Non-Specific Immune Responses in an Invertebrate Species Evidenced by a Comparative de novo Sequencing Study

Our present understanding of the functioning and evolutionary history of invertebrate innate immunity derives mostly from studies on a few model species belonging to ecdysozoa. In particular, the characterization of signaling pathways dedicated to specific responses towards fungi and Gram-positive or Gram-negative bacteria in Drosophila melanogaster challenged our original view of a non-specific immunity in invertebrates. However, much remains to be elucidated from lophotrochozoan species. To investigate the global specificity of the immune response in the fresh-water snail Biomphalaria glabrata, we used massive Illumina sequencing of 5′-end cDNAs to compare expression profiles after challenge by Gram-positive or Gram-negative bacteria or after a yeast challenge. 5′-end cDNA sequencing of the libraries yielded over 12 millions high quality reads. To link these short reads to expressed genes, we prepared a reference transcriptomic database through automatic assembly and annotation of the 758,510 redundant sequences (ESTs, mRNAs) of B. glabrata available in public databases. Computational analysis of Illumina reads followed by multivariate analyses allowed identification of 1685 candidate transcripts differentially expressed after an immune challenge, with a two fold ratio between transcripts showing a challenge-specific expression versus a lower or non-specific differential expression. Differential expression has been validated using quantitative PCR for a subset of randomly selected candidates. Predicted functions of annotated candidates (approx. 700 unisequences) belonged to a large extend to similar functional categories or protein types. This work significantly expands upon previous gene discovery and expression studies on B. glabrata and suggests that responses to various pathogens may involve similar immune processes or signaling pathways but different genes belonging to multigenic families. These results raise the question of the importance of gene duplication and acquisition of paralog functional diversity in the evolution of specific invertebrate immune responses

Quantitative modeling of the physiology of ascites in portal hypertension

Although the factors involved in cirrhotic ascites have been studied for a century, a number of observations are not understood, including the action of diuretics in the treatment of ascites and the ability of the plasma-ascitic albumin gradient to diagnose portal hypertension. This communication presents an explanation of ascites based solely on pathophysiological alterations within the peritoneal cavity. A quantitative model is described based on experimental vascular and intraperitoneal pressures, lymph flow, and peritoneal space compliance. The model's predictions accurately mimic clinical observations in ascites, including the magnitude and time course of changes observed following paracentesis or diuretic therapy