Nature and origin of secondary mineral coatings on volcanic rocks of the Black Mountain, Stonewall Mountain, and Kane Springs Wash volcanic centers, southern, Nevada

The following subject areas are covered: (1) genetic, spectral, and LANDSAT Thematic Mapper imagery relationship between desert varnish and tertiary volcanic host rocks, southern Nevada; (2) reconnaissance geologic mapping of the Kane Springs Wash Volcanic Center, Lincoln County, Nevada, using multispectral thermal infrared imagery; (3) interregional comparisons of desert varnish; and (4) airborne scanner (GERIS) imagery of the Kane Springs Wash Volcanic Center, Lincoln County, Nevada

Nature and origin of mineral coatings on volcanic rocks of the Black Mountain, Stonewall Mountain, and Kane Springs, Wash volcanic centers, Southern Nevada

LANDSAT Thematic Mapper imagery was evaluated over 3 Tertiary calderas in southern Nevada. Each volcanic center derived from a highly evolved silici magmatic system represented today by well exposed diverse lithologies. Distinctive imagery contrast between some of the late ash flows and earlier units follows from the high relative reflectance in longer wavelength bands (bands 5 and 7) of the former. Enhancement techniques provide color composite images which highlight some of the units in remarkable color contrast. Inasmuch as coatings on the tuffs are incompletely developed and apparently largely dependent spectrally on rock properties independent of petrochemistry, it is felt that the distinctive imagery characteristics are more a function of primary lithologic or petrochemical properties. Any given outcrop is backdrop for a variety of cover types, of which coatings, at various stages of maturity, are one. Petrographic and X-ray diffraction analysis of the outer air-interface zone of coatings reveal they are composed chiefly of amorphous compounds, probably with varying proportions of iron and manganese. Observations support an origin for some outer (air-interface) coating constituents exogenous to the underlying host

Molecular dynamics symulation of hydrogen diffusion in palladium

In the present work, the diffusion of hydrogen atoms in the crystal lattice of palladium was investigated using the molecular dynamics. The diffusion coefficients, activation energies and preexponential factors were determined for structures with different hydrogen concentration. It is determined that the diffusion coefficients decrease and the activation energy of diffusion grows with increasing concentrations of hydrogen

Past and estimated future impact of invasive alien mammals on insular threatened vertebrate populations

Invasive mammals on islands pose severe, ongoing threats to global biodiversity. However, the severity of threats from different mammals, and the role of interacting biotic and abiotic factors in driving extinctions, remain poorly understood at a global scale. Here we model global extirpation patterns for island populations of threatened and extinct vertebrates. Extirpations are driven by interacting factors including invasive rats, cats, pigs, mustelids and mongooses, native species taxonomic class and volancy, island size, precipitation and human presence. We show that controlling or eradicating the relevant invasive mammals could prevent 41–75% of predicted future extirpations. The magnitude of benefits varies across species and environments; for example, managing invasive mammals on small, dry islands could halve the extirpation risk for highly threatened birds and mammals, while doing so on large, wet islands may have little benefit. Our results provide quantitative estimates of conservation benefits and, when combined with costs in a return-on-investment framework, can guide efficient conservation strategies

Future perspectives in melanoma research. Meeting report from the "Melanoma Research: a bridge Naples-USA. Naples, December 6th-7 th2010"

Progress in understanding the molecular basis of melanoma has made possible the identification of molecular targets with important implications in clinical practice. In fact, new therapeutic approaches are emerging from basic science and it will be important to implement their rapid translation into clinical practice by active clinical investigation

Broad-spectrum CRISPR-mediated inhibition of SARS-CoV-2 variants and endemic coronaviruses in vitro

A major challenge in coronavirus vaccination and treatment is to counteract rapid viral evolution and mutations. Here we demonstrate that CRISPR-Cas13d offers a broad-spectrum antiviral (BSA) to inhibit many SARS-CoV-2 variants and diverse human coronavirus strains with >99% reduction of the viral titer. We show that Cas13d-mediated coronavirus inhibition is dependent on the crRNA cellular spatial colocalization with Cas13d and target viral RNA. Cas13d can significantly enhance the therapeutic effects of diverse small molecule drugs against coronaviruses for prophylaxis or treatment purposes, and the best combination reduced viral titer by over four orders of magnitude. Using lipid nanoparticle-mediated RNA delivery, we demonstrate that the Cas13d system can effectively treat infection from multiple variants of coronavirus, including Omicron SARS-CoV-2, in human primary airway epithelium air-liquid interface (ALI) cultures. Our study establishes CRISPR-Cas13 as a BSA which is highly complementary to existing vaccination and antiviral treatment strategies

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Simple scoring system to predict in-hospital mortality after surgery for infective endocarditis

BACKGROUND: Aspecific scoring systems are used to predict the risk of death postsurgery in patients with infective endocarditis (IE). The purpose of the present study was both to analyze the risk factors for in-hospital death, which complicates surgery for IE, and to create a mortality risk score based on the results of this analysis. METHODS AND RESULTS: Outcomes of 361 consecutive patients (mean age, 59.1\ub115.4 years) who had undergone surgery for IE in 8 European centers of cardiac surgery were recorded prospectively, and a risk factor analysis (multivariable logistic regression) for in-hospital death was performed. The discriminatory power of a new predictive scoring system was assessed with the receiver operating characteristic curve analysis. Score validation procedures were carried out. Fifty-six (15.5%) patients died postsurgery. BMI >27 kg/m2 (odds ratio [OR], 1.79; P=0.049), estimated glomerular filtration rate 55 mm Hg (OR, 1.78; P=0.032), and critical state (OR, 2.37; P=0.017) were independent predictors of in-hospital death. A scoring system was devised to predict in-hospital death postsurgery for IE (area under the receiver operating characteristic curve, 0.780; 95% CI, 0.734-0.822). The score performed better than 5 of 6 scoring systems for in-hospital death after cardiac surgery that were considered. CONCLUSIONS: A simple scoring system based on risk factors for in-hospital death was specifically created to predict mortality risk postsurgery in patients with IE

Quantitation of perivascular monocyte / macrophages around cerebral blood vessels of hypertensive and aged rats

The numbers of monocytes and macrophages in the walls of cerebral blood vessels were counted on perfusion-fixed frozen brain sections (16 JLffi) of spontaneously hypertensive rats (SHR), stroke-prone SHR (SHR-SP), normotensive Wistar-Kyoto (WKY) rats, and young (16-week-old) and old (2-year-old) normotensive Sprague-Dawley rats (SD-l6w and SD-2y, respectively) using monoclonal antiborlies against rat macrophages (ED2). The staining was visualized with fluoresceinlabeled second antiborlies. The ED2-specific staining in brain sections was restricted to macrophages in a perivascular location. The number of perivascular cells per square millimeter of high-power field was significantly greater in SHR-SP (8.6 ± 2.1; n = 4) and SHR (6. 7 ± 0.9; n = 6) than in normotensive WKY (4.0 ± 0.5; n = 6; p <0.01). The number of perivascular macrophages was also greater in SD-2y (7.5 ± 2.7; n = 9) than in SD-l6w (2.9 ± 1.8; n = 8; p < 0.01). No ED2 staining was found in the resident microglia or in the endothelial cells, which were identified by double staining with rhodamine-labeled anti-factor VIII-related antigen antiborlies. The results suggest that the stroke risk factors hypertension and advanced age are associated with increased subendothelial accumulation of monocytes and macrophages. This accumulation could increase the tendency for the endothelium to convert from an anticoagulant to a procoagulant surface in response to mediators released from these subendothelial cells