Knowledge Graph Embedding for Ecotoxicological Effect Prediction

Exploring the effects a chemical compound has on a species takes a considerable experimental effort. Appropriate methods for estimating and suggesting new effects can dramatically reduce the work needed to be done by a laboratory. In this paper we explore the suitability of using a knowledge graph embedding approach for ecotoxicological effect prediction. A knowledge graph has been constructed from publicly available data sets, including a species taxonomy and chemical classification and similarity. The publicly available effect data is integrated to the knowledge graph using ontology alignment techniques. Our experimental results show that the knowledge graph based approach improves the selected baselines

Feature of adhesins produced by human clinical isolates of mycobacterium intracellulare, mycobacterium intracellulare subsp. chimaera and closely related species

International audienceThe Mycobacterium avium complex includes two closely related species, Mycobacterium avium and Mycobacterium intracellulare. They are opportunistic pathogens in humans and responsible for severe disease in a wide variety of animals. Yet, little is known about factors involved in their pathogenicity. Here, we identified, purified and characterized adhesins belonging to the heparin-binding hemagglutinin (HBHA) and laminin-binding protein (LBP) family from M. intracellulare ATCC13950 and examined clinical isolates from patients with different pathologies associated with M. intracellulare infection for the presence and conservation of HBHA and LBP. Using a recombinant derivative strain of M. intracellulare ATCC13950 producing green fluorescent protein and luciferase, we found that the addition of heparin inhibited mycobacterial adherence to A549 cells, whereas the addition of laminin enhanced adherence. Both HBHA and LBP were purified by heparin-Sepharose chromatography and their methylation profiles were determined by mass spectrometry. Patients with M. intracellulare infection mounted strong antibody responses to both proteins. By using PCR and immunoblot analyses, we found that both proteins were highly conserved among all 17 examined clinical M. intracellulare isolates from patients with diverse disease manifestations, suggesting a conserved role of these adhesins in M. intracellulare virulence in humans and their potential use as a diagnostic tool

The semantic sensor network ontology, revamped

The Semantic Sensor Network Ontology, popularly known as SSN, was developed by an Incubator Group of the World Wide Web Consortium (W3C) over 2009 to 2011. Subsequently, the W3C and the Open Geospatial Consortium (OGC) joined forces to update the SSN as informed by experience, to harmonize it with OGC's O&M, and to publish a new version to be endorsed as both a W3C Recommendation and an OGC standard in late 2017. The major contribution of the new SSN is a modular structure designed to be more convenient for ontology engineers and data custodians. It also slightly extends the coverage of the previous SSN with new terms for sampling and actuation. SSN re- tains the ability to comprehensively represent: sensors in terms of what they can sense, and what and how they do sense; observations in terms of what they measure and what values they find; systems (or networks) of sensors in terms of sensor components and how they are deployed; and real-world objects (called features of interest, OGC-style) in terms of their physical properties, what can sense them, and what observations of them have been made. A few little-used SSN terms have been deprecated, and several others have been renamed. For a comprehensive description of new SSN the reader is referred to the specification [10]. A full descrip- tion of the scope, design rationale and additions, with examples of its application are presented in [11

The Modular SSN Ontology: A Joint W3C and OGC Standard Specifying the Semantics of Sensors, Observations, Sampling, and Actuation

The joint W3C (World Wide Web Consortium) and OGC (Open Geospatial Consortium) Spatial Data on the Web (SDW) Working Group developed a set of ontologies to describe sensors, actuators, samplers as well as their observations, actuation, and sampling activities. The ontologies have been published both as a W3C recommendation and as an OGC implementation standard. The set includes a lightweight core module called SOSA (Sensor, Observation, Sampler, and Actuator) available at: http://www.w3.org/ns/sosa/, and a more expressive extension module called SSN (Semantic Sensor Network) available at: http://www.w3.org/ns/ssn/. Together they describe systems of sensors and actuators, observations, the used procedures, the subjects and their properties being observed or acted upon, samples and the process of sampling, and so forth. The set of ontologies adopts a modular architecture with SOSA as a self-contained core that is extended by SSN and other modules to add expressivity and breadth. The SOSA/SSN ontologies are able to support a wide range of applications and use cases, including satellite imagery, large-scale scientific monitoring, industrial and household infrastructures, social sensing, citizen science, observation-driven ontology engineering, and the Internet of Things. In this paper we give an overview of the ontologies and discuss the rationale behind key design decisions, reporting on the differences between the new SSN ontology presented here and its predecessor [Web Semantics: Science, Services and Agents on the World Wide Web 17 (2012), 25–32] developed by the W3C Semantic Sensor Network Incubator group (the SSN-XG). We present usage examples and describe alignment modules that foster interoperability with other ontologies.The authors acknowledge partial support from NSF (award number 1540849) and CSIRO (Oznome project

Mortality reduction by post-dilution online-haemodiafiltration : A cause-specific analysis

Background. From an individual participant data (IPD) meta-analysis from four randomized controlled trials comparing haemodialysis (HD) with post-dilution online-haemodiafiltration (ol-HDF), previously it appeared that HDF decreases all-cause mortality by 14% (95% confidence interval 25; 1) and fatal cardiovascular disease (CVD) by 23% (39; 3). Significant differences were not found for fatal infections and sudden death. So far, it is unclear, however, whether the reduced mortality risk of HDF is only due to a decrease in CVD events and if so, which CVD in particular is prevented, if compared with HD. Methods. The IPD base was used for the present study. Hazard ratios and 95% confidence intervals for cause-specific mortality overall and in thirds of the convection volume were calculated using the Cox proportional hazard regression models. Annualized mortality and numbers needed to treat (NNT) were calculated as well. Results. Besides 554 patients dying from CVD, fatal infections and sudden death, 215 participants died from 'other causes', such as withdrawal from treatment and malignancies. In this group, the mortality risk was comparable between HD and ol-HDF patients, both overall and in thirds of the convection volume. Subdivision of CVD mortality in fatal cardiac, non-cardiac and unclassified CVD showed that ol-HDF was only associated with a lower risk of cardiac casualties [0.64 (0.61; 0.90)]. Annual mortality rates also suggest that the reduction in CVD death is mainly due to a decrease in cardiac fatalities, including both ischaemic heart disease and congestion. Overall, 32 and 75 patients, respectively, need to be treated by high-volume HDF (HV-HDF) to prevent one all-cause and one CVD death, respectively, per year. Conclusion. The beneficial effect of ol-HDF on all-cause and CVD mortality appears to be mainly due to a reduction in fatal cardiac events, including ischaemic heart disease as well as congestion. In HV-HDF, the NNT to prevent one CVD death is 75 per year

Mortality reduction by post-dilution online-haemodiafiltration : A cause-specific analysis

Background. From an individual participant data (IPD) meta-analysis from four randomized controlled trials comparing haemodialysis (HD) with post-dilution online-haemodiafiltration (ol-HDF), previously it appeared that HDF decreases all-cause mortality by 14% (95% confidence interval 25; 1) and fatal cardiovascular disease (CVD) by 23% (39; 3). Significant differences were not found for fatal infections and sudden death. So far, it is unclear, however, whether the reduced mortality risk of HDF is only due to a decrease in CVD events and if so, which CVD in particular is prevented, if compared with HD. Methods. The IPD base was used for the present study. Hazard ratios and 95% confidence intervals for cause-specific mortality overall and in thirds of the convection volume were calculated using the Cox proportional hazard regression models. Annualized mortality and numbers needed to treat (NNT) were calculated as well. Results. Besides 554 patients dying from CVD, fatal infections and sudden death, 215 participants died from 'other causes', such as withdrawal from treatment and malignancies. In this group, the mortality risk was comparable between HD and ol-HDF patients, both overall and in thirds of the convection volume. Subdivision of CVD mortality in fatal cardiac, non-cardiac and unclassified CVD showed that ol-HDF was only associated with a lower risk of cardiac casualties [0.64 (0.61; 0.90)]. Annual mortality rates also suggest that the reduction in CVD death is mainly due to a decrease in cardiac fatalities, including both ischaemic heart disease and congestion. Overall, 32 and 75 patients, respectively, need to be treated by high-volume HDF (HV-HDF) to prevent one all-cause and one CVD death, respectively, per year. Conclusion. The beneficial effect of ol-HDF on all-cause and CVD mortality appears to be mainly due to a reduction in fatal cardiac events, including ischaemic heart disease as well as congestion. In HV-HDF, the NNT to prevent one CVD death is 75 per year

Low incidence of SARS-CoV-2, risk factors of mortality and the course of illness in the French national cohort of dialysis patients

International audienceThe aim of this study was to estimate the incidence of COVID-19 disease in the French national population of dialysis patients, their course of illness and to identify the risk factors associated with mortality. Our study included all patients on dialysis recorded in the French REIN Registry in April 2020. Clinical characteristics at last follow-up and the evolution of COVID-19 illness severity over time were recorded for diagnosed cases (either suspicious clinical symptoms, characteristic signs on the chest scan or a positive reverse transcription polymerase chain reaction) for SARS-CoV-2. A total of 1,621 infected patients were reported on the REIN registry from March 16th, 2020 to May 4th, 2020. Of these, 344 died. The prevalence of COVID-19 patients varied from less than 1% to 10% between regions. The probability of being a case was higher in males, patients with diabetes, those in need of assistance for transfer or treated at a self-care unit. Dialysis at home was associated with a lower probability of being infected as was being a smoker, a former smoker, having an active malignancy, or peripheral vascular disease. Mortality in diagnosed cases (21%) was associated with the same causes as in the general population. Higher age, hypoalbuminemia and the presence of an ischemic heart disease were statistically independently associated with a higher risk of death. Being treated at a selfcare unit was associated with a lower risk. Thus, our study showed a relatively low frequency of COVID-19 among dialysis patients contrary to what might have been assumed