A practical analytic model for daylight

www.cs.utah.edu Figure 1: Left: A rendered image of an outdoor scene with a constant colored sky and no aerial perspective. Right: The same image with a physically-based sky model and physically-based aerial perspective. Sunlight and skylight are rarely rendered correctly in computer graphics. A major reason for this is high computational expense. Another is that precise atmospheric data is rarely available. We present an inexpensive analytic model that approximates full spectrum daylight for various atmospheric conditions. These conditions are parameterized using terms that users can either measure or estimate. We also present an inexpensive analytic model that approximates the effects of atmosphere (aerial perspective). These models are fielded in a number of conditions and intermediate results verified against standard literature from atmospheric science. These models are analytic in the sense that they are simple formulas based on fits to simulated data; no explicit simulation is required to use them. Our goal is to achieve as much accuracy as possible without sacrificing usability

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada

Introduction: This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals.Methods: COVID-19 cases and locations were curated for geostatistical analyses from March 2020 through June 2021, corresponding to the first, second, and third waves of infections. Daily cases were aggregated according to designated forward sortation area (FSA), and postal codes (PC) in municipal regions Hamilton, Kitchener/Waterloo, London, Ottawa, Toronto, and Windsor/Essex county. Hotspots were identified with area-to-area tests including Getis-Ord Gi*, Global Moran’s I spatial autocorrelation, and Local Moran’s I asymmetric clustering and outlier analyses. Case counts were also interpolated across geographic regions by Empirical Bayesian Kriging, which localizes high concentrations of COVID-19 positive tests, independent of FSA or PC boundaries. The Geostatistical Disease Epidemiology Toolbox, which is freely-available software, automates the identification of these regions and produces digital maps for public health professionals to assist in pandemic management of contact tracing and distribution of other resources. Results: This study provided indicators in real-time of likely, community-level disease transmission through innovative geospatial analyses of COVID-19 incidence data. Municipal and provincial results were validated by comparisons with known outbreaks at long-term care and other high density residences and on farms. PC-level analyses revealed hotspots at higher geospatial resolution than public reports of FSAs, and often sooner. Results of different tests and kriging were compared to determine consistency among hotspot assignments. Concurrent or consecutive hotspots in close proximity suggested potential community transmission of COVID-19 from cluster and outlier analysis of neighboring PCs and by kriging. Results were also stratified by population based-categories (sex, age, and presence/absence of comorbidities).Conclusions: Earlier recognition of hotspots could reduce public health burdens of COVID-19 and expedite contact tracing

Improved radiation expression profiling in blood by sequential application of sensitive and specific gene signatures

Purpose. Combinations of expressed genes can discriminate radiation-exposed from normal control blood samples by machine learning based signatures (with 8 to 20% misclassification rates). These signatures can quantify therapeutically-relevant as well as accidental radiation exposures. The prodromal symptoms of Acute Radiation Syndrome (ARS) overlap those present in Influenza and Dengue Fever infections. Surprisingly, these human radiation signatures misclassified gene expression profiles of virally infected samples as false positive exposures. The present study investigates these and other confounders, and then mitigates their impact on signature accuracy. Methods. This study investigated recall by previous and novel radiation signatures independently derived from multiple Gene Expression Omnibus datasets on common and rare non-malignant blood disorders and blood-borne infections (thromboembolism, S. aureus bacteremia, malaria, sickle cell disease, polycythemia vera, and aplastic anemia). Normalized expression levels of signature genes are used as input to machine learning-based classifiers to predict radiation exposure in other hematological conditions. Results. Except for aplastic anemia, these blood-borne disorders modify the normal baseline expression values of genes present in radiation signatures, leading to false-positive misclassification of radiation exposures in 8 to 54% of individuals. Shared changes, predominantly in DNA damage response and apoptosis-related gene transcripts in radiation and confounding hematological conditions, compromise the utility of these signatures for radiation assessment. These confounding conditions (sickle cell disease, thromboembolism, S. aureus bacteremia, malaria) induce neutrophil extracellular traps, initiated by chromatin decondensation, DNA damage response and fragmentation followed by programmed cell death. Riboviral infections (for example, Influenza or Dengue fever) have been proposed to bind and deplete host RNA binding proteins, inducing R-loops in chromatin. R-loops that collide with incoming replication forks can result in incompletely repaired DNA damage, inducing apoptosis and releasing mature virus. To mitigate the effects of confounders, we evaluated predicted radiation-positive samples with novel gene expression signatures derived from radiation-responsive transcripts encoding secreted blood plasma proteins whose expression levels are unperturbed by these conditions. Conclusions. This approach identifies and eliminates misclassified samples with underlying hematological or infectious conditions, leaving only samples with true radiation exposures. Diagnostic accuracy is significantly improved by selecting genes that maximize both sensitivity and specificity in the appropriate tissue using combinations of the best signatures for each of these classes of signatures

Likely community transmission of COVID-19 infections between neighboring, persistent hotspots in Ontario, Canada [version 1; peer review: awaiting peer review]

This study aimed to produce community-level geo-spatial mapping of confirmed COVID-19 cases in Ontario Canada in near real-time to support decision-making. This was accomplished by area-to-area geostatistical analysis, space-time integration, and spatial interpolation of COVID-19 positive individuals

Radiation Exposure Determination in a Secure, Cloudbased Online Environment

Rapid sample processing and interpretation of estimated exposures will be critical for triaging exposed individuals after a major radiation incident. The dicentric chromosome (DC) assay assesses absorbed radiation using metaphase cells from blood. The Automated Dicentric Chromosome Identifier and Dose Estimator System (ADCI) identifies DCs and determines radiation doses. This study aimed to broaden accessibility and speed of this system, while protecting data and software integrity. ADCI Online is a secure web-streaming platform accessible worldwide from local servers. Cloud-based systems containing data and software are separated until they are linked for radiation exposure estimation. Dose estimates are identical to ADCI on dedicated computer hardware. Image processing and selection, calibration curve generation, and dose estimation of 9 test samples completed inframes

Molecular Techniques Reveal Wide Phyletic Diversity of Heterotrophic Microbes Associated with Discodermia spp. (Porifera: Demospongiae)

Sponges are well known to harbor large numbers of heterotrophic microbes within their mesohyl. Studies to determine the diversity of these associated microbes have been attempted for only a few shallow water species. We cultured various microorganisms from several species of Discodermia collected from deep water using the \u27Johnson-Sea-Link\u27 manned submersibles, and characterised them by standard microbiological identification methods. Characterisation of a small proportion (ca. 10%) of the total and potential eubacterial isolate collection with molecular systematics techniques revealed a wide diversity of microbes. Phylogenetic analyses of 32 small subunit (SSU) 16S-like rRNA gene sequences from different micorbes indicated high levels of taxonomic diversity assoiated with this genus of sponge. For example, bacteria from at least five cubacterial subdivisions - gamma, alpha, beta, Cytophaga and Gram positive - were isolated from the mesohyl of Discodermia. Several strains were unidentifiable from current sequence databases. No overlap was found between sequences of 24 isolates and 8 sequences obtained by PCR and cloning directly from sponge samples. The abundance and diversity of microbes associated with sponges such as Discodermia suggest that they may play important roles in marine microbial ecology, dispersal and evolution

A model for 3-methyladenine recognition by 3-methyladenine DNA glycosylase I (TAG) from Staphylococcus aureus

The structure of 3-methyladenine DNA glycosylase I in complex with 3-methyladenine is reported

Motor neuron disease due to neuropathy target esterase gene mutation: Clinical features of the index families

Recently, we reported that mutations in the neuropathy target esterase (NTE) gene cause autosomal recessive motor neuron disease (NTE-MND). We describe clinical, neurophysiologic, and neuroimaging features of affected subjects in the index families. NTE-MND subjects exhibited progressive lower extremity spastic weakness that began in childhood and was later associated with atrophy of distal leg and intrinsic hand muscles. NTE-MND resembles Troyer syndrome, except that short stature, cognitive impairment, and dysmorphic features, which often accompany Troyer syndrome, are not features of NTE-MND. Early onset, symmetry, and slow progression distinguish NTE-MND from typical amyotrophic lateral sclerosis. NTE is implicated in organophosphorus compound–induced delayed neurotoxicity (OPIDN). NTE-MND patients have upper and lower motor neuron deficits that are similar to OPIDN. Motor neuron degeneration in subjects with NTE mutations supports the role of NTE and its biochemical cascade in the molecular pathogenesis of OPIDN and possibly other degenerative neurologic disorders. Muscle Nerve, 2011Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78477/1/21777_ftp.pd

Meeting radiation dosimetry capacity requirements of population-scale exposures by geostatistical sampling.

BACKGROUND: Accurate radiation dose estimates are critical for determining eligibility for therapies by timely triaging of exposed individuals after large-scale radiation events. However, the universal assessment of a large population subjected to a nuclear spill incident or detonation is not feasible. Even with high-throughput dosimetry analysis, test volumes far exceed the capacities of first responders to measure radiation exposures directly, or to acquire and process samples for follow-on biodosimetry testing. AIM: To significantly reduce data acquisition and processing requirements for triaging of treatment-eligible exposures in population-scale radiation incidents. METHODS: Physical radiation plumes modelled nuclear detonation scenarios of simulated exposures at 22 US locations. Models assumed only location of the epicenter and historical, prevailing wind directions/speeds. The spatial boundaries of graduated radiation exposures were determined by targeted, multistep geostatistical analysis of small population samples. Initially, locations proximate to these sites were randomly sampled (generally 0.1% of population). Empirical Bayesian kriging established radiation dose contour levels circumscribing these sites. Densification of each plume identified critical locations for additional sampling. After repeated kriging and densification, overlapping grids between each pair of contours of successive plumes were compared based on their diagonal Bray-Curtis distances and root-mean-square deviations, which provided criteria ( RESULTS/CONCLUSIONS: We modeled 30 scenarios, including 22 urban/high-density and 2 rural/low-density scenarios under various weather conditions. Multiple (3-10) rounds of sampling and kriging were required for the dosimetry maps to converge, requiring between 58 and 347 samples for different scenarios. On average, 70±10% of locations where populations are expected to receive an exposure ≥2Gy were identified. Under sub-optimal sampling conditions, the number of iterations and samples were increased, and accuracy was reduced. Geostatistical mapping limits the number of required dose assessments, the time required, and radiation exposure to first responders. Geostatistical analysis will expedite triaging of acute radiation exposure in population-scale nuclear events