Investigation of catalytic combustion of impurities in air Final report, 24 Mar. - 28 Dec. 1966

Catalytic coil to oxidize carbon monoxide, hydrogen, and methane present as impurities in ai

Real-Time Risk and Fault Management in the Mission Evaluation Room for the International Space Station

Effective anomaly resolution in the Mission Evaluation Room (MER) of the International Space Station (ISS) requires consideration of risk in the process of identifying faults and developing corrective actions. Risk models such as fault trees from the ISS Probabilistic Risk Assessment (PRA) can be used to support anomaly resolution, but the functionality required goes significantly beyond what the PRA could provide. Methods and tools are needed that can systematically guide the identification of root causes for on-orbit anomalies, and to develop effective corrective actions that address the event and its consequences without undue risk to the crew or the mission. In addition, an overall information management framework is needed so that risk can be systematically incorporated in the process, and effectively communicated across all the disciplines and levels of management within the space station program. The commercial nuclear power industry developed such a decision making framework, known as the critical safety function approach, to guide emergency response following the accident at Three Mile Island in 1979. This report identifies new methods, tools, and decision processes that can be used to enhance anomaly resolution in the ISS Mission Evaluation Room. Current anomaly resolution processes were reviewed to identify requirements for effective real-time risk and fault management. Experience gained in other domains, especially the commercial nuclear power industry, was reviewed to identify applicable methods and tools. Recommendations were developed for next-generation tools to support MER anomaly resolution, and a plan for implementing the recommendations was formulated. The foundation of the proposed tool set will be a ''Mission Success Framework'' designed to integrate and guide the anomaly resolution process, and to facilitate consistent communication across disciplines while focusing on the overriding importance of mission success

Identification and Analysis of Behavioral Phenotypes in Autism Spectrum Disorder via Unsupervised Machine Learning

Background and objective: Autism spectrum disorder (ASD) is a heterogeneous disorder. Research has explored potential ASD subgroups with preliminary evidence supporting the existence of behaviorally and genetically distinct subgroups; however, research has yet to leverage machine learning to identify phenotypes on a scale large enough to robustly examine treatment response across such subgroups. The purpose of the present study was to apply Gaussian Mixture Models and Hierarchical Clustering to identify behavioral phenotypes of ASD and examine treatment response across the learned phenotypes. Materials and methods: The present study included a sample of children with ASD (N = 2400), the largest of its kind to date. Unsupervised machine learning was applied to model ASD subgroups as well as their taxonomic relationships. Retrospective treatment data were available for a portion of the sample (n =1034). Treatment response was examined within each subgroup via regression. Results: The application of a Gaussian Mixture Model revealed 16 subgroups. Further examination of the subgroups through Hierarchical Agglomerative Clustering suggested 2 overlying behavioral phenotypes with unique deficit profiles each composed of subgroups that differed in severity of those deficits. Furthermore, differentiated response to treatment was found across subtypes, with a substantially higher amount of variance accounted for due to the homogenization effect of the clustering. Discussion: The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning

The Effect of Hospital Volume on Mortality in Patients Admitted with Severe Sepsis

Importance The association between hospital volume and inpatient mortality for severe sepsis is unclear. Objective: To assess the effect of severe sepsis case volume and inpatient mortality. Design Setting and Participants Retrospective cohort study from 646,988 patient discharges with severe sepsis from 3,487 hospitals in the Nationwide Inpatient Sample from 2002 to 2011. Exposures The exposure of interest was the mean yearly sepsis case volume per hospital divided into tertiles. Main Outcomes and Measures Inpatient mortality. Results: Compared with the highest tertile of severe sepsis volume (>60 cases per year), the odds ratio for inpatient mortality among persons admitted to hospitals in the lowest tertile (≤10 severe sepsis cases per year) was 1.188 (95% CI: 1.074–1.315), while the odds ratio was 1.090 (95% CI: 1.031–1.152) for patients admitted to hospitals in the middle tertile. Similarly, improved survival was seen across the tertiles with an adjusted inpatient mortality incidence of 35.81 (95% CI: 33.64–38.03) for hospitals with the lowest volume of severe sepsis cases and a drop to 32.07 (95% CI: 31.51–32.64) for hospitals with the highest volume. Conclusions and Relevance We demonstrate an association between a higher severe sepsis case volume and decreased mortality. The need for a systems-based approach for improved outcomes may require a high volume of severely septic patients

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: A Review

Autism spectrum disorder (ASD) research has yet to leverage big data on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data