Safety Case Workshop

In January 2013, a two-day Safety Case Workshop was conducted in Huntsville, Alabama under the sponsorship of the SAE International G-48 System Safety Committee and A-P-T Research, Inc. (APT). Attendees from industry, government and academia participated, with several making formal presentations on various safety methods. Industry focus is turning to international pursuits, which involve a broader understanding of different approaches to ensuring safety. The United States has typically used a process-based approach in managing system safety programs, but there is a current movement to use the evidence-based Safety Case approach to validate the safety of systems. At the conclusion of the workshop, participants reached the consensus view that the Safety Case approach merits being accepted among the best world-wide system safety practices

Machine learning based prediction of COVID-19 mortality suggests repositioning of anticancer drug for treating severe cases

Despite available vaccinations COVID-19 case numbers around the world are still growing, and effective medications against severe cases are lacking. In this work, we developed a machine learning model which predicts mortality for COVID-19 patients using data from the multi-center ‘Lean European Open Survey on SARS-CoV-2-infected patients’ (LEOSS) observational study (>100 active sites in Europe, primarily in Germany), resulting into an AUC of almost 80%. We showed that molecular mechanisms related to dementia, one of the relevant predictors in our model, intersect with those associated to COVID-19. Most notably, among these molecules was tyrosine kinase 2 (TYK2), a protein that has been patented as drug target in Alzheimer's Disease but also genetically associated with severe COVID-19 outcomes. We experimentally verified that anti-cancer drugs Sorafenib and Regorafenib showed a clear anti-cytopathic effect in Caco2 and VERO-E6 cells and can thus be regarded as potential treatments against COVID-19. Altogether, our work demonstrates that interpretation of machine learning based risk models can point towards drug targets and new treatment options, which are strongly needed for COVID-19

An updated PREDICT breast cancer prognostication and treatment benefit prediction model with independent validation

BACKGROUND PREDICT is a breast cancer prognostic and treatment benefit model implemented online. The overall fit of the model has been good in multiple independent case series, but PREDICT has been shown to underestimate breast cancer specific mortality in women diagnosed under the age of 40. Another limitation is the use of discrete categories for tumour size and node status resulting in 'step' changes in risk estimates on moving between categories. We have refitted the PREDICT prognostic model using the original cohort of cases from East Anglia with updated survival time in order to take into account age at diagnosis and to smooth out the survival function for tumour size and node status. METHODS Multivariable Cox regression models were used to fit separate models for ER negative and ER positive disease. Continuous variables were fitted using fractional polynomials and a smoothed baseline hazard was obtained by regressing the baseline cumulative hazard for each patients against time using fractional polynomials. The fit of the prognostic models were then tested in three independent data sets that had also been used to validate the original version of PREDICT. RESULTS In the model fitting data, after adjusting for other prognostic variables, there is an increase in risk of breast cancer specific mortality in younger and older patients with ER positive disease, with a substantial increase in risk for women diagnosed before the age of 35. In ER negative disease the risk increases slightly with age. The association between breast cancer specific mortality and both tumour size and number of positive nodes was non-linear with a more marked increase in risk with increasing size and increasing number of nodes in ER positive disease. The overall calibration and discrimination of the new version of PREDICT (v2) was good and comparable to that of the previous version in both model development and validation data sets. However, the calibration of v2 improved over v1 in patients diagnosed under the age of 40. CONCLUSIONS The PREDICT v2 is an improved prognostication and treatment benefit model compared with v1. The online version should continue to aid clinical decision making in women with early breast cancer

Metapopulation Dynamics Enable Persistence of Influenza A, Including A/H5N1, in Poultry

Thanks to K. Sturm-Ramirez, C. Jessup, J. Rosenthal and the staff of EcoHealth Alliance for feedback. Disclaimer: The contents are the responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government.Conceived and designed the experiments: PRH TF RH DZ CSA AG MJM XX TB PD. Performed the experiments: PRH. Analyzed the data: PRH. Contributed reagents/materials/analysis tools: PRH TF RH DZ CSA AG MJM XX TB JHJ PD. Wrote the paper: PRH TF RH DZ CSA AG MJM XX TB JHJ PD.Highly pathogenic influenza A/H5N1 has persistently but sporadically caused human illness and death since 1997. Yet it is still unclear how this pathogen is able to persist globally. While wild birds seem to be a genetic reservoir for influenza A, they do not seem to be the main source of human illness. Here, we highlight the role that domestic poultry may play in maintaining A/H5N1 globally, using theoretical models of spatial population structure in poultry populations. We find that a metapopulation of moderately sized poultry flocks can sustain the pathogen in a finite poultry population for over two years. Our results suggest that it is possible that moderately intensive backyard farms could sustain the pathogen indefinitely in real systems. This fits a pattern that has been observed from many empirical systems. Rather than just employing standard culling procedures to control the disease, our model suggests ways that poultry production systems may be modified.Yeshttp://www.plosone.org/static/editorial#pee

Effects of Melt-Processing Conditions on the Quality of Poly(ethylene terephthalate) Montmorillonite Clay Nanocomposites

Organically modified montmorillonite was synthesized with a novel 1,2-dimethyl-3-N-alkyl imidazolium salt or a typical quaternary ammonium salt as a control. Poly(ethylene terephthalate) montmorillonite clay nanocomposites were compounded via melt-blending in a corotating mini twin-screw extruder operating at 285 degreesC. The nanocomposites were characterized with thermal analysis, X-ray diffraction, and transmission electron microscopy to determine the extent of intercalation and/or exfoliation present in the system. Nanocomposites produced with N,N-dimethyl-N,N-dioctadecylammonium treated montmorillonite (DMDODA-MMT), which has a decomposition temperature of 250 degreesC, were black, brittle, and tarlike resulting from DMDODA degradation under the processing conditions. Nanocomposites compounded with 1,2-dimethyl-3-N-hexadecyl imidazolium treated MMT, which has a decomposition temperature of 350 degreesC, showed high levels of dispersion and delamination. (C) 2002 Wiley Periodicals, Inc