Monotony in Service Orchestrations

Web Service orchestrations are compositions of different Web Services to form a new service. The services called during the orchestration guarantee a given performance to the orchestrater, usually in the form of contracts. These contracts can be used by the orchestrater to deduce the contract it can offer to its own clients, by performing contract composition. An implicit assumption in contract based QoS management is: "the better the component services perform, the better the orchestration's performance will be". Thus, contract based QoS management for Web services orchestrations implicitly assumes monotony. In some orchestrations, however, monotony can be violated, i.e., the performance of the orchestration improves when the performance of a component service degrades. This is highly undesirable since it can render the process of contract composition inconsistent. In this paper we define monotony for orchestrations modelled by Colored Occurrence Nets (CO-nets) and we characterize the classes of monotonic orchestrations. We show that few orchestrations are indeed monotonic, mostly since latency can be traded for quality of data. We also propose a sound refinement of monotony, called conditional monotony, which forbids this kind of cheating and show that conditional monotony is widely satisfied by orchestrations. This finding leads to reconsidering the way SLAs should be formulated

RSV-specific airway resident memory CD8+ T cells and differential disease severity after experimental human infection

In animal models, resident memory CD8+ T (Trm) cells assist in respiratory virus elimination but their importance in man has not been determined. Here, using experimental human respiratory syncytial virus (RSV) infection, we investigate systemic and local virus-specific CD8+ T cell responses in adult volunteers. Having defined the immunodominance hierarchy, we analyze phenotype and function longitudinally in blood and by serial bronchoscopy. Despite rapid clinical recovery, we note surprisingly extensive lower airway inflammation with persistent viral antigen and cellular infiltrates. Pulmonary virus-specific CD8+ T cells display a CD69+CD103+ Trm phenotype and accumulate to strikingly high frequencies into convalescence without continued proliferation. These are more highly differentiated but express fewer cytotoxicity markers than in blood, but their abundance prior to infection correlates with protection from more severe disease

Atorvastatin attenuation of ABCB1 expression is mediated by microRNA miR-491-3p in Caco-2 cells

AimAtorvastatin, a HMG-CoA reductase inhibitor, used in the treatment of hypercholesterolemia, has been previously shown to regulate ABCB1 expression in vivo and in vitro. We hypothesized that the statin could regulate gene expression of ABCB1 transporter via microRNAs.MethodsExpression of microRNAs and ABCB1 mRNA was examined in atorvastatin-treated and control cells using real-time PCR. miR-491-3P mimic and inhibitor were transfected in Caco-2 and ABCB1 expression was monitored by western blot and real-time PCR.ResultsIn HepG2 cells, none of the microRNAs predicted to target ABCB1 3'UTR was regulated by atorvastatin treatment. In agreement with this, ABCB1 3'UTR activity was not modulated in HepG-2 cells after 48h-treatment as measured by luciferase assay. In Caco-2 cells, atorvastatin treatment provoked a decrease in luciferase activity and, accordingly, miR-491-3p was upregulated about 2.7 times after 48h-statin treatment. Luciferase analysis of miR-491-3p with a mimetic or inhibitor of miR-491-3p revealed that this microRNA could target ABCB1 3'UTR, as after miR-491-3p inhibition, ABCB1 levels were increased by two-fold, and miR-491-3p superexpression decreased ABCB1 3'UTR activity. Finally, functional analysis revealed that treatment with miR-491-3p inhibitor could reverses atorvastatin attenuation of ABCB1 (Pg-p) protein levels.ConclusionOur results suggest atorvastatin control ABCB1 expression via miR-491-3p in Caco-2 cells. This finding may be an important mechanism of statin drug-drug interaction, since common concomitant drugs used in the prevention of cardiovascular diseases are ABCB1 substrates

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe