Ground-based all-sky mid-infrared and visible imagery for purposes of characterizing cloud properties

This paper describes the All Sky Infrared Visible Analyzer (ASIVA), a multi-purpose visible and infrared sky imaging and analysis instrument whose primary function is to provide radiometrically calibrated imagery in the mid-infrared (mid-IR) atmospheric window. This functionality enables the determination of diurnal fractional sky cover and estimates of sky/cloud temperature from which one can derive estimates of sky/cloud emissivity and cloud height. This paper describes the calibration methods and performance of the ASIVA instrument with particular emphasis on data products being developed for the meteorological community. Data presented here were collected during the Solmirus' ASIVA campaign conducted at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) Climate Research Facility from 21 May to 27 July 2009. The purpose of this campaign was to determine the efficacy of IR technology in providing reliable nighttime sky cover data. Significant progress has been made in the analysis of the campaign data over the past several years and the ASIVA has proven to be an excellent instrument for determining sky cover as well as the potential for determining sky/cloud temperature, sky/cloud emissivity, precipitable water vapor (PWV), and ultimately cloud height

Resource Utilization of Patients with Parkinson's Disease in the Late Stages of the Disease in Germany: Data from the CLaSP Study.

Objective: The Care of Late-Stage Parkinsonism (CLaSP) study aimed to collect qualitative and standardized patient data in six European countries (France, Germany, Netherlands, Portugal, UK, Sweden) to enable a detailed evaluation of the underexplored late stages of the disease (Hoehn and Yahr stage > 3) using clinical, neuropsychological, behavioral, and health economic data. The aim of this substudy was to provide a health economic evaluation for the German healthcare system. Methods: In Germany, 228 patients were included in the study. Costs were calculated from a societal perspective for a 3-month period. Univariate analyses were performed to identify cost-driving predictors. Total and direct costs were analyzed using a generalized linear model with a γ-distributed dependent variable and log link function. Indirect costs were analyzed using a binomial generalized linear model with probit link function. Results: The mean costs for the 3-month period were approximately €20,000. Informal care costs and hospitalization are approximately €11,000 and €5000. Direct costs amounted to 89% of the total costs, and the share of indirect costs was 11%. Independent predictors of total costs were the duration of the disease and age. The duration of the disease was the main independent predictor of direct costs, whereas age was an independent predictor of indirect costs. Discussion: Costs in the late stage of the disease are considerably higher than those found in earlier stages. Compared to the latter, the mean number of days in hospital and the need for care is increasing. Informal caregivers provide most of the care

Advancing polar prediction capabilities on daily to seasonal time scales

It is argued that existing polar prediction systems do not yet meet users’ needs; and possible ways forward in advancing prediction capacity in polar regions and beyond are outlined. The polar regions have been attracting more and more attention in recent years, fuelled by the perceptible impacts of anthropogenic climate change. Polar climate change provides new opportunities, such as shorter shipping routes between Europe and East Asia, but also new risks such as the potential for industrial accidents or emergencies in ice-covered seas. Here, it is argued that environmental prediction systems for the polar regions are less developed than elsewhere. There are many reasons for this situation, including the polar regions being (historically) lower priority, with less in situ observations, and with numerous local physical processes that are less well-represented by models. By contrasting the relative importance of different physical processes in polar and lower latitudes, the need for a dedicated polar prediction effort is illustrated. Research priorities are identified that will help to advance environmental polar prediction capabilities. Examples include an improvement of the polar observing system; the use of coupled atmosphere-sea ice-ocean models, even for short-term prediction; and insight into polar-lower latitude linkages and their role for forecasting. Given the enormity of some of the challenges ahead, in a harsh and remote environment such as the polar regions, it is argued that rapid progress will only be possible with a coordinated international effort. More specifically, it is proposed to hold a Year of Polar Prediction (YOPP) from mid-2017 to mid-2019 in which the international research and operational forecasting community will work together with stakeholders in a period of intensive observing, modelling, prediction, verification, user-engagement and educational activities

The Origin and Contribution of Cancer-Associated Fibroblasts in Colorectal Carcinogenesis

Background & Aims: Cancer-associated fibroblasts (CAFs) play an important role in colorectal cancer (CRC) progression and predict poor prognosis in CRC patients. However, the cellular origins of CAFs remain unknown, making it challenging to therapeutically target these cells. Here, we aimed to identify the origins and contribution of colorectal CAFs associated with poor prognosis. Methods: To elucidate CAF origins, we used a colitis-associated CRC mouse model in 5 different fate-mapping mouse lines with 5-bromodeoxyuridine dosing. RNA sequencing of fluorescence-activated cell sorting–purified CRC CAFs was performed to identify a potential therapeutic target in CAFs. To examine the prognostic significance of the stromal target, CRC patient RNA sequencing data and tissue microarray were used. CRC organoids were injected into the colons of knockout mice to assess the mechanism by which the stromal gene contributes to colorectal tumorigenesis. Results: Our lineage-tracing studies revealed that in CRC, many ACTA2+ CAFs emerge through proliferation from intestinal pericryptal leptin receptor (Lepr)+ cells. These Lepr-lineage CAFs, in turn, express melanoma cell adhesion molecule (MCAM), a CRC stroma-specific marker that we identified with the use of RNA sequencing. High MCAM expression induced by transforming growth factor β was inversely associated with patient survival in human CRC. In mice, stromal Mcam knockout attenuated orthotopically injected colorectal tumoroid growth and improved survival through decreased tumor-associated macrophage recruitment. Mechanistically, fibroblast MCAM interacted with interleukin-1 receptor 1 to augment nuclear factor κB–IL34/CCL8 signaling that promotes macrophage chemotaxis. Conclusions: In colorectal carcinogenesis, pericryptal Lepr-lineage cells proliferate to generate MCAM+ CAFs that shape the tumor-promoting immune microenvironment. Preventing the expansion/differentiation of Lepr-lineage CAFs or inhibiting MCAM activity could be effective therapeutic approaches for CRC

A statistical framework to evaluate virtual screening

<p>Abstract</p> <p>Background</p> <p>Receiver operating characteristic (ROC) curve is widely used to evaluate virtual screening (VS) studies. However, the method fails to address the "early recognition" problem specific to VS. Although many other metrics, such as RIE, BEDROC, and pROC that emphasize "early recognition" have been proposed, there are no rigorous statistical guidelines for determining the thresholds and performing significance tests. Also no comparisons have been made between these metrics under a statistical framework to better understand their performances.</p> <p>Results</p> <p>We have proposed a statistical framework to evaluate VS studies by which the threshold to determine whether a ranking method is better than random ranking can be derived by bootstrap simulations and 2 ranking methods can be compared by permutation test. We found that different metrics emphasize "early recognition" differently. BEDROC and RIE are 2 statistically equivalent metrics. Our newly proposed metric SLR is superior to pROC. Through extensive simulations, we observed a "seesaw effect" – overemphasizing early recognition reduces the statistical power of a metric to detect true early recognitions.</p> <p>Conclusion</p> <p>The statistical framework developed and tested by us is applicable to any other metric as well, even if their exact distribution is unknown. Under this framework, a threshold can be easily selected according to a pre-specified type I error rate and statistical comparisons between 2 ranking methods becomes possible. The theoretical null distribution of SLR metric is available so that the threshold of SLR can be exactly determined without resorting to bootstrap simulations, which makes it easy to use in practical virtual screening studies.</p