Explosives and pyrotechnic propellants for use in long term deep space missions

Explosives and pyrotechnic propellant materials which will withstand heat sterilization cycling at 125 C and ten year deep space aging under 10 to the minus 6th power torr and 66 C have been selected. The selection was accomplished through a detailed literature survey and an analytical evaluation of the physicochemical properties of the materials. The chemical components of the electroexplosive devices used in U.S. missiles and spacecraft were categorized into primary explosives, secondary explosives, and propellant ingredients. Kinetic data on such parameters as thermal decomposition and sublimation were obtained for these materials and used as a basis for the ten year life prediction. From these experimental data and some analytical calculations, a listing of candidate materials for deep space missions was made

Remote measurement utilizing NASA's scanning laser Doppler systems. Volume 2: Laser Doppler dust devil velocity profile measurement program

The first detailed velocity profile data on thermally induced dust vortices are presented. These dust devils will be analyzed and studied to determine their flow fields and origin in an effort to correlate this phenomena with the generation and characteristics of tornadoes. A continuing effort to increase mankind's knowledge of vortex and other meteorological phenomena will hopefully allow the prediction of tornado occurrence, their path, and perhaps eventually even lead to some technique for their destruction

New biphenyl iminium salt catalysts for highly enantioselective asymmetric epoxidation: role of additional substitution and dihedral angle

New biaryl iminium salt catalysts for enantioselective alkene epoxidation containing additional substitution in the heterocyclic ring are reported. The effects upon conformation and enantioselectivity of this additional substitution, and the influence of dihedral angle in these systems, has been investigated using a synthetic approach supported by density functional theory. Enantioselectivities of up to 97% ee were observed

Population analysis of Legionella pneumophila reveals a basis for resistance to complement-mediated killing

Legionella pneumophila is the most common cause of the severe respiratory infection known as Legionnaires' disease. However, the microorganism is typically a symbiont of free-living amoeba, and our understanding of the bacterial factors that determine human pathogenicity is limited. Here we carried out a population genomic study of 902 L. pneumophila isolates from human clinical and environmental samples to examine their genetic diversity, global distribution and the basis for human pathogenicity. We find that the capacity for human disease is representative of the breadth of species diversity although some clones are more commonly associated with clinical infections. We identified a single gene (lag-1) to be most strongly associated with clinical isolates. lag-1, which encodes an O-acetyltransferase for lipopolysaccharide modification, has been distributed horizontally across all major phylogenetic clades of L. pneumophila by frequent recent recombination events. The gene confers resistance to complement-mediated killing in human serum by inhibiting deposition of classical pathway molecules on the bacterial surface. Furthermore, acquisition of lag-1 inhibits complement-dependent phagocytosis by human neutrophils, and promoted survival in a mouse model of pulmonary legionellosis. Thus, our results reveal L. pneumophila genetic traits linked to disease and provide a molecular basis for resistance to complement-mediated killing. The bacterium Legionella pneumophila can cause severe respiratory infection, but is typically a symbiont of free-living amoeba. Here, the authors analyse the genomes of 902 clinical and environmental isolates, and identify a bacterial gene that is strongly associated with human infection and confers resistance to complement-mediated killing.Peer reviewe

Epidemiological analysis of Legionnaires' disease in Scotland: a genomic study

Background: Legionella pneumophila is the main cause of a severe pneumonic illness known as Legionnaires' disease and is a global public health threat. Whole-genome sequencing (WGS) can be applied to trace environmental origins of L pneumophila infections, providing information to guide appropriate interventions. We aim to explore the evolutionary and epidemiological relationships in a 36-year Scottish L pneumophila reference isolate collection. Methods: We investigated the genomic epidemiology of Legionnaires' disease over 36 years in Scotland, comparing genome sequences for all clinical L pneumophila isolates (1984–2020) with a sequence dataset of 3211 local and globally representative isolates. We used a stratified clustering approach to capture epidemiological relationships by core genome Multi-locus Sequence Typing, followed by high-resolution phylogenetic analysis of clusters to measure diversity and evolutionary relatedness in context with epidemiological metadata. Findings: Clustering analysis showed that 111 (57·5 %) of 193 of L pneumophila infections in Scotland were caused by ten endemic lineages with a wide temporal and geographical distribution. Phylogenetic analysis of L pneumophila identified hospital-associated sublineages that had been detected in the hospital environment up to 19 years. Furthermore, 12 (30·0%) of 40 community-associated infections (excluding a single, large outbreak) that occurred over a 13 year period (from 2000 to 2013) were caused by a single widely distributed endemic clone (ST37), consistent with enhanced human pathogenicity. Finally, our analysis revealed clusters linked by national or international travel to distinct geographical regions, indicating several previously unrecognised travel links between closely related isolates (fewer than five single nucleotide polymorphisms) connected by geography. Interpretation: Our analysis reveals the existence of previously undetected endemic clones of L pneumophila that existed for many years in hospital, community, and travel-associated environments. In light of these findings, we propose that cluster and outbreak definitions should be reconsidered, and propose WGS-based surveillance as a critical public health tool for real-time identification and mitigation of clinically important endemic clones. Funding: Chief Scientist Office, Biotechnology and Biological Sciences Research Council (UK), Medical Research Council Precision Medicine Doctoral Training Programme, Wellcome Trust, and Medical Research Council (UK)

Accelerated Identification of Disease-Causing Variants With Ultra-Rapid Nanopore Genome Sequencing

Whole-genome sequencing (WGS) can identify variants that cause genetic disease, but the time required for sequencing and analysis has been a barrier to its use in acutely ill patients. In the present study, we develop an approach for ultra-rapid nanopore WGS that combines an optimized sample preparation protocol, distributing sequencing over 48 flow cells, near real-time base calling and alignment, accelerated variant calling and fast variant filtration for efficient manual review. Application to two example clinical cases identified a candidate variant inprioritization, and accelerates diagnostic clinical genome sequencing twofold compared with previous approaches

Using symptom-based case predictions to identify host genetic factors that contribute to COVID-19 susceptibility

Epidemiological and genetic studies on COVID-19 are currently hindered by inconsistent and limited testing policies to confirm SARS-CoV-2 infection. Recently, it was shown that it is possible to predict COVID-19 cases using cross-sectional self-reported disease-related symptoms. Here, we demonstrate that this COVID-19 prediction model has reasonable and consistent performance across multiple independent cohorts and that our attempt to improve upon this model did not result in improved predictions. Using the existing COVID- 19 prediction model, we then conducted a GWAS on the predicted phenotype using a total of 1,865 predicted cases and 29,174 controls. While we did not find any common, largeeffect variants that reached genome-wide significance, we do observe suggestive genetic associations at two SNPs (rs11844522, p = 1.9x10-7; rs5798227, p = 2.2x10-7). Explorative analyses furthermore suggest that genetic variants associated with other viral infectious diseases do not overlap with COVID-19 susceptibility and that severity of COVID-19 may have a different genetic architecture compared to COVID-19 susceptibility. This study represents a first effort that uses a symptom-based predicted phenotype as a proxy for COVID-19 in our pursuit of understanding the genetic susceptibility of the disease. We conclude that the inclusion of symptom-based predicted cases could be a useful strategy in a scenario of limited testing, either during the current COVID-19 pandemic or any future viral outbreak