Antimicrobial surfaces for use on inhabited space craft: A review.

Biodegradation of materials on crewed spacecraft can cause disruption, loss of function and lost crew time. Cleaning of surfaces is only partially effective due in accessibility and resource concerns. Commonly affected surfaces are hand-touch sites, waste disposal systems and liquid-handling systems, including condensing heat exchangers. The use of materials on and within such affected systems that reduce the attachment of and degradation by microbes, is an innovative solution to this problem. This review aims to examine both terrestrial and space-based experiments that have aimed to reduce microbial growth which are applicable to the unique conditions of crewed spacecraft. Traditional antimicrobial surfaces such as copper and silver, as well as nanoparticles, long-chain organic molecules and surface topographical features, as well as novel "smart" technologies are discussed. Future missions to cis-lunar and Martian destinations will depend on materials that retain their function and reliability for their success; thus, the use of antimicrobial and antifouling materials is a pivotal one.

Planetary protection technologies for planetary science instruments, spacecraft, and missions: Report of the NASA Planetary Protection Technology Definition Team (PPTDT).

Planetary bodies like Mars, Europa, and Enceladus pose the question, "How to study them without contaminating them and destroying future prospects to detect life, if it is there?" The natural trade-off, of course, is that the cleaner your spacecraft, the more you can explore such a body without risk of contaminating it. As chartered by NASA Headquarters, the Planetary Protection Technology Definition Team (PPTDT) was asked to provide a report covering six different areas related to the engineering and technology challenges of implementing planetary protection requirements on solar system exploration missions.

Microbiomes of China's Space Station During Assembly, Integration, and Test Operations.

Sufficient evidence indicates that orbiting space stations contain diverse microbial populations, which may threaten astronaut health and equipment reliability. Understanding the composition of microbial communities in space stations will facilitate further development of targeted biological safety prevention and maintenance practices. Therefore, this study systematically investigated the microbial community of China's Space Station (CSS). Air and surface samples from 46 sites on the CSS and Assembly Integration and Test (AIT) center were collected, from which 40 bacteria strains were isolated and identified. Most isolates were cold- and desiccation-resistant and adapted to oligotrophic conditions. Bacillus was the dominant bacterial genus detected by both cultivation-based and Illumina MiSeq amplicon sequencing methods. Microbial contamination on the CSS was correlated with encapsulation staff activities. Analysis by spread plate and qPCR revealed that the CSS surface contained 2.24 × 103-5.47 × 103 CFU/100 cm2 culturable bacteria and 9.32 × 105-5.64 × 106 16S rRNA gene copies/100cm2; BacLight™ analysis revealed that the viable/total bacterial cell ratio was 1.98-13.28%. This is the first study to provide important systematic insights into the microbiome of the CSS during assembly that describes the pre-launch microbial diversity of the space station. Our findings revealed the following. (1) Bacillus strains and staff activities should be considered major concerns for future biological safety. (2) Autotrophic and multi-resistant microbial communities were widespread in the AIT environment. Although harsh cleaning methods reduced the number of microorganisms, stress-resistant strains were not completely removed. (3) Sampling, storage and analytical methods for the space station were thoroughly optimized, and are expected to be applicable to low-biomass environments in general. Microbiology-related future works will follow up to comprehensively understand the changing characteristics of microbial communities in CSS.

Multi-drug resistant Enterobacter bugandensis species isolated from the International Space Station and comparative genomic analyses with human pathogenic strains.

BACKGROUND: The antimicrobial resistance (AMR) phenotypic properties, multiple drug resistance (MDR) gene profiles, and genes related to potential virulence and pathogenic properties of five Enterobacter bugandensis strains isolated from the International Space Station (ISS) were carried out and compared with genomes of three clinical strains. Whole genome sequences of ISS strains were characterized using the hybrid de novo assembly of Nanopore and Illumina reads. In addition to traditional microbial taxonomic approaches, multilocus sequence typing (MLST) analysis was performed to classify the phylogenetic lineage. Agar diffusion discs assay was performed to test antibiotics susceptibility. The draft genomes after assembly and scaffolding were annotated with the Rapid Annotations using Subsystems Technology and RNAmmer servers for downstream analysis. RESULTS: Molecular phylogeny and whole genome analysis of the ISS strains with all publicly available Enterobacter genomes revealed that ISS strains were E. bugandensis and similar to the type strain EB-247T and two clinical isolates (153_ECLO and MBRL 1077). Comparative genomic analyses of all eight E. bungandensis strains showed, a total of 4733 genes were associated with carbohydrate metabolism (635 genes), amino acid and derivatives (496 genes), protein metabolism (291 genes), cofactors, vitamins, prosthetic groups, pigments (275 genes), membrane transport (247 genes), and RNA metabolism (239 genes). In addition, 112 genes identified in the ISS strains were involved in virulence, disease, and defense. Genes associated with resistance to antibiotics and toxic compounds, including the MDR tripartite system were also identified in the ISS strains. A multiple antibiotic resistance (MAR) locus or MAR operon encoding MarA, MarB, MarC, and MarR, which regulate more than 60 genes, including upregulation of drug efflux systems that have been reported in Escherichia coli K12, was also observed in the ISS strains. CONCLUSION: Given the MDR results for these ISS Enterobacter genomes and increased chance of pathogenicity (PathogenFinder algorithm with > 79% probability), these species pose important health considerations for future missions. Thorough genomic characterization of the strains isolated from ISS can help to understand the pathogenic potential, and inform future missions, but analyzing them in in-vivo systems is required to discern the influence of microgravity on their pathogenicity.

Dynamics of space particles and spacecrafts passing by the atmosphere of the Earth.

The present research studies the motion of a particle or a spacecraft that comes from an orbit around the Sun, which can be elliptic or hyperbolic, and that makes a passage close enough to the Earth such that it crosses its atmosphere. The idea is to measure the Sun-particle two-body energy before and after this passage in order to verify its variation as a function of the periapsis distance, angle of approach, and velocity at the periapsis of the particle. The full system is formed by the Sun, the Earth, and the particle or the spacecraft. The Sun and the Earth are in circular orbits around their center of mass and the motion is planar for all the bodies involved. The equations of motion consider the restricted circular planar three-body problem with the addition of the atmospheric drag. The initial conditions of the particle or spacecraft (position and velocity) are given at the periapsis of its trajectory around the Earth.

Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.

Research in scientific, public health, and policy disciplines relating to the environment increasingly makes use of high-dimensional remote sensing and the output of numerical models in conjunction with traditional observations. Given the public health and resultant public policy implications of the potential health effects of particulate matter (PM*) air pollution, specifically fine PM with an aerodynamic diameter < or = 2.5 pm (PM2.5), there has been substantial recent interest in the use of remote-sensing information, in particular aerosol optical depth (AOD) retrieved from satellites, to help characterize variability in ground-level PM2.5 concentrations in space and time. While the United States and some other developed countries have extensive PM monitoring networks, gaps in data across space and time necessarily occur; the hope is that remote sensing can help fill these gaps. In this report, we are particularly interested in using remote-sensing data to inform estimates of spatial patterns in ambient PM2.5 concentrations at monthly and longer time scales for use in epidemiologic analyses. However, we also analyzed daily data to better disentangle spatial and temporal relationships. For AOD to be helpful, it needs to add information beyond that available from the monitoring network. For analyses of chronic health effects, it needs to add information about the concentrations of long-term average PM2.5; therefore, filling the spatial gaps is key. Much recent evidence has shown that AOD is correlated with PM2.5 in the eastern United States, but the use of AOD in exposure analysis for epidemiologic work has been rare, in part because discrepancies necessarily exist between satellite-retrieved estimates of AOD, which is an atmospheric-column average, and ground-level PM2.5. In this report, we summarize the results of a number of empirical analyses and of the development of statistical models for the use of proxy information, in particular satellite AOD, in predicting PM2.5 concentrations in the eastern United States. We analyzed the spatiotemporal structure of the relationship between PM2.5 and AOD, first using simple correlations both before and after calibration based on meteorology, as well as large-scale spatial and temporal calibration to account for discrepancies between AOD and PM2.5. We then used both raw and calibrated AOD retrievals in statistical models to predict PM2.5 concentrations, accounting for AOD in two ways: primarily as a separate data source contributing a second likelihood to a Bayesian statistical model, as well as a data source on which we could directly regress. Previous consideration of satellite AOD has largely focused on the National Aeronautics and Space Administration (NASA) moderate resolution imaging spectroradiometer (MODIS) and multiangle imaging spectroradiometer (MISR) instruments. One contribution of our work is more extensive consideration of AOD derived from the Geostationary Operational Environmental Satellite East Aerosol/Smoke Product (GOES GASP) AOD and its relationship with PM2.5. In addition to empirically assessing the spatiotemporal relationship between GASP AOD and PM2.5, we considered new statistical techniques to screen anomalous GOES reflectance measurements and account for background surface reflectance. In our statistical work, we developed a new model structure that allowed for more flexible modeling of the proxy discrepancy than previous statistical efforts have had, with a computationally efficient implementation. We also suggested a diagnostic for assessing the scales of the spatial relationship between the proxy and the spatial process of interest (e.g., PM2.5). In brief, we had little success in improving predictions in our eastern-United States domain for use in epidemiologic applications. We found positive correlations of AOD with PM2.5 over time, but less correlation for long-term averages over space, unless we used calibration that adjusted for large-scale discrepancy between AOD and PM2.5 (see sections 3, 4, and 5). Statistical models that combined AOD, PM2.5 observations, and land-use and meteorologic variables were highly predictive of PM2.5 observations held out of the modeling, but AOD added little information beyond that provided by the other sources (see sections 5 and 6). When we used PM2.5 data estimates from the Community Multiscale Air Quality model (CMAQ) as the proxy instead of using AOD, we similarly found little improvement in predicting held-out observations of PM2.5, but when we regressed on CMAQ PM2.5 estimates, the predictions improved moderately in some cases. These results appeared to be caused in part by the fact that large-scale spatial patterns in PM2.5 could be predicted well by smoothing the monitor values, while small-scale spatial patterns in AOD appeared to weakly reflect the variation in PM2.5 inferred from the observations. Using a statistical model that allowed for potential proxy discrepancy at both large and small spatial scales was an important component of our modeling. In particular, when our models did not include a component to account for small-scale discrepancy, predictive performance decreased substantially. Even long-term averages of MISR AOD, considered the best, albeit most sparse, of the AOD products, were only weakly correlated with measured PM2.5 (see section 4). This might have been partly related to the fact that our analysis did not account for spatial variation in the vertical profile of the aerosol. Furthermore, we found evidence that some of the correlation between raw AOD and PM2.5 might have been a function of surface brightness related to land use, rather than having been driven by the detection of aerosol in the AOD retrieval algorithms (see sections 4 and 7). Difficulties in estimating the background surface reflectance in the retrieval algorithms likely explain this finding. With regard to GOES, we found moderate correlations of GASP AOD and PM2.5. The higher correlations of monthly and yearly averages after calibration reflected primarily the improved large-scale correlation, a necessary result of the calibration procedure (see section 3). While the results of this study's GOES reflectance screening and surface reflection correction appeared sensible, correlations of our proposed reflectance-based proxy with PM2.5 were no better than GASP AOD correlations with PM2.5 (see section 7). We had difficulty improving spatial prediction of monthly and yearly average PM2.5 using AOD in the eastern United States, which we attribute to the spatial discrepancy between AOD and measured PM2.5, particularly at smaller scales. This points to the importance of paying attention to the discrepancy structure of proxy information, both from remote-sensing and deterministic models. In particular, important statistical challenges arise in accounting for the discrepancy, given the difficulty in the face of sparse observations of distinguishing the discrepancy from the component of the proxy that is informative about the process of interest. Associations between adverse health outcomes and large-scale variation in PM2.5 (e.g., across regions) may be confounded by unmeasured spatial variation in factors such as diet. Therefore, one important goal was to use AOD to improve predictions of PM2.5 for use in epidemiologic analyses at small-to-moderate spatial scales (within urban areas and within regions). In addition, large-scale PM2.5 variation is well estimated from the monitoring data, at least in the United States. We found little evidence that current AOD products are helpful for improving prediction at small-to-moderate scales in the eastern United States and believe more evidence for the reliability of AOD as a proxy at such scales is needed before making use of AOD for PM2.5 prediction in epidemiologic contexts. While our results relied in part on relatively complicated statistical models, which may be sensitive to modeling assumptions, our exploratory correlation analyses (see sections 3 and 5) and relatively simple regression-style modeling of MISR AOD (see section 4) were consistent with the more complicated modeling results. When assessing the usefulness of AOD in the context of studying chronic health effects, we believe efforts need to focus on disentangling the temporal from the spatial correlations of AOD and PM2.5 and on understanding the spatial scale of correlation and of the discrepancy structure. While our results are discouraging, it is important to note that we attempted to make use of smaller-scale spatial variation in AOD to distinguish spatial variations of relatively small magnitude in long-term concentrations of ambient PM2.5. Our efforts pushed the limits of current technology in a spatial domain with relatively low PM2.5 levels and limited spatial variability. AOD may hold more promise in areas with higher aerosol levels, as the AOD signal would be stronger there relative to the background surface reflectance. Furthermore, for developing countries with high aerosol levels, it is difficult to build statistical models based on PM2.5 measurements and land-use covariates, so AOD may add more incremental information in those contexts. More generally, researchers in remote sensing are involved in ongoing efforts to improve AOD products and develop new approaches to using AOD, such as calibration with model-estimated vertical profiles and the use of speciation information in MISR AOD; these efforts warrant continued investigation of the usefulness of remotely sensed AOD for public health research.

Application of satellite precipitation data to analyse and model arbovirus activity in the tropics.

BACKGROUND: Murray Valley encephalitis virus (MVEV) is a mosquito-borne Flavivirus (Flaviviridae: Flavivirus) which is closely related to Japanese encephalitis virus, West Nile virus and St. Louis encephalitis virus. MVEV is enzootic in northern Australia and Papua New Guinea and epizootic in other parts of Australia. Activity of MVEV in Western Australia (WA) is monitored by detection of seroconversions in flocks of sentinel chickens at selected sample sites throughout WA. Rainfall is a major environmental factor influencing MVEV activity. Utilising data on rainfall and seroconversions, statistical relationships between MVEV occurrence and rainfall can be determined. These relationships can be used to predict MVEV activity which, in turn, provides the general public with important information about disease transmission risk. Since ground measurements of rainfall are sparse and irregularly distributed, especially in north WA where rainfall is spatially and temporally highly variable, alternative data sources such as remote sensing (RS) data represent an attractive alternative to ground measurements. However, a number of competing alternatives are available and careful evaluation is essential to determine the most appropriate product for a given problem. RESULTS: The Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 product was chosen from a range of RS rainfall products to develop rainfall-based predictor variables and build logistic regression models for the prediction of MVEV activity in the Kimberley and Pilbara regions of WA. Two models employing monthly time-lagged rainfall variables showed the strongest discriminatory ability of 0.74 and 0.80 as measured by the Receiver Operating Characteristics area under the curve (ROC AUC). CONCLUSIONS: TMPA data provide a state-of-the-art data source for the development of rainfall-based predictive models for Flavivirus activity in tropical WA. Compared to ground measurements these data have the advantage of being collected spatially regularly, irrespective of remoteness. We found that increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Pilbara at a time-lag of two months. Increases in monthly rainfall and monthly number of days above average rainfall increased the risk of MVEV activity in the Kimberley at a lag of three months.

Satellite derived forest phenology and its relation with nephropathia epidemica in Belgium.

The connection between nephropathia epidemica (NE) and vegetation dynamics has been emphasized in recent studies. Changing climate has been suggested as a triggering factor of recently observed epidemiologic peaks in reported NE cases. We have investigated whether there is a connection between the NE occurrence pattern in Belgium and specific trends in remotely sensed phenology parameters of broad-leaved forests. The analysis of time series of the MODIS Enhanced Vegetation Index revealed that changes in forest phenology, considered in literature as an effect of climate change, may affect the mechanics of NE transmission.

Pico satellite attitude estimation via Robust Unscented Kalman Filter in the presence of measurement faults.

In the normal operation conditions of a pico satellite, a conventional Unscented Kalman Filter (UKF) gives sufficiently good estimation results. However, if the measurements are not reliable because of any kind of malfunction in the estimation system, UKF gives inaccurate results and diverges by time. This study introduces Robust Unscented Kalman Filter (RUKF) algorithms with the filter gain correction for the case of measurement malfunctions. By the use of defined variables named as measurement noise scale factor, the faulty measurements are taken into consideration with a small weight, and the estimations are corrected without affecting the characteristics of the accurate ones. Two different RUKF algorithms, one with single scale factor and one with multiple scale factors, are proposed and applied for the attitude estimation process of a pico satellite. The results of these algorithms are compared for different types of measurement faults in different estimation scenarios and recommendations about their applications are given.

Self-organizing hierarchies in sensor and communication networks.

We consider a hierarchical multicellular sensing and communication network, embedded in an ageless aerospace vehicle that is expected to detect and react to multiple impacts and damage over a wide range of impact energies. In particular, we investigate self-organization of impact boundaries enclosing critically damaged areas, and impact networks connecting remote cells that have detected noncritical impacts. Each level of the hierarchy is shown to have distinct higher-order emergent properties, desirable in self-monitoring and self-repairing vehicles. In addition, cells and communication messages are shown to need memory (hysteresis) in order to retain desirable emergent behavior within and between various hierarchical levels. Spatiotemporal robustness of self-organizing hierarchies is quantitatively measured with graph-theoretic and information-theoretic techniques, such as the Shannon entropy. This allows us to clearly identify phase transitions separating chaotic dynamics from ordered and robust patterns.

[The development of a methodological approach to evaluating the operating efficiency of a system for purifying the atmosphere of harmful trace contaminants]. / Razrabotka metodicheskogo podkhoda k otsenke éffektivnosti raboty sistemy ochistki atmosfery ot vrednykh mikroprimesei.

There has been developed an approach to evaluate an efficiency of the system to purify the atmosphere from the harmful microimpurities (HMI). The methodical approach allows one to isolate 1-2 representatives of each subgroup from the total number of HMIs divided into 12 subgroups in such a way that the determination of the concentration of these compounds would be an indicator of an efficiency of removing all the representatives of a given subgroup. On selecting the representatives there have been took into consideration the values of maximally permissible concentrations of the compounds, the rate of their isolation and the possibility of an effective removal. Data of different authors published in the press have been used.

A comparison of two different types of geosynchronous satellite measurements during the 1989 solar proton events.

The proton telescope aboard the GOES-7 satellite continuously records the proton flux at geosynchronous orbit, and therefore provides a direct measurement of the energetic protons arriving during solar energetic particle (SEP) events. Microelectronic devices are susceptible to single event upset (SEU) caused by both energetic protons and galactic cosmic ray (GCR) ions. Some devices are so sensitive that their upsets can be used as a dosimetric indicator of a high fluence of particles. The 93L422 1K SRAM is one such device. Eight of them are on the TDRS-1 satellite in geosynchronous orbit, and collectively they had been experiencing 1-2 upset/day due to the GCR background. During the large SEP events of 1989 the upset rate increased dramatically, up to about 250 for the week of 19 Oct, due to the arrival of the SEP protons. Using the GOES proton spectra, the proton-induced SEU cross section curve for the 93L422 and the shielding distribution around the 93L422, the calculated upsets based on the GOES satellite data compared well against the log of measured upsets on TDRS-1.