To investigate whether dual-energy multidetector row computed tomographic (CT) imaging with iodine quantification is able to distinguish between clear cell and papillary renal cell carcinoma ( RCC renal cell carcinoma ) subtypes. In this retrospective, HIPAA-compliant, institutional review board-approved study, 88 patients (57 men, 31 women) with diagnosis of either clear cell or papillary RCC renal cell carcinoma at pathologic analysis, who underwent contrast material-enhanced dual-energy nephrographic phase study between December 2007 and June 2013, were included. Five readers, blinded to pathologic diagnosis, independently evaluated all cases by determining the lesion iodine concentration on color-coded iodine maps. The receiving operating characteristic curve analysis was adopted to estimate the optimal threshold for discriminating between clear cell and papillary RCC renal cell carcinoma , and results were validated by using a leave-one-out cross-validation. Interobserver agreement was assessed by using an intraclass correlation coefficient. The correlation between tumor iodine concentration and tumor grade was investigated. A tumor iodine concentration of 0.9 mg/mL represented the optimal threshold to discriminate between clear cell and papillary RCC renal cell carcinoma , and it yielded the following: sensitivity, 98.2% (987 of 1005 [95% confidence interval: 97.7%, 98.7%]); specificity, 86.3% (272 of 315 [95% confidence interval: 85.0%, 87.7%]); positive predictive value, 95.8% (987 of 1030 [95% confidence interval: 95.0%, 96.6%]); negative predictive value, 93.7% (272 of 290 [95% confidence interval: 92.8%, 94.7%]); overall accuracy of 95.3% (1259 of 1320 [95% confidence interval: 94.6%, 96.2%]), with an area under the curve of 0.923 (95% confidence interval: 0.913, 0.933). An excellent agreement was found among the five readers in measured tumor iodine concentration (intraclass correlation coefficient, 0.9990 [95% confidence interval: 0. 9987, 0.9993). A significant correlation was found between tumor iodine concentration and tumor grade for both clear cell (τ = 0.85; P < .001) and papillary RCC renal cell carcinoma (τ = 0.53; P < .001). Dual-energy multidetector CT with iodine quantification can be used to distinguish between clear cell and papillary RCC renal cell carcinoma , and it provides insights regarding the tumor grade.

Accelerometry is used as an objective measure of physical activity in humans and veterinary species. In cats, one important use of accelerometry is in the study of therapeutics designed to treat degenerative joint disease (DJD) associated pain, where it serves as the most widely applied objective outcome measure. These analyses have commonly used summary measures, calculating the mean activity per-minute over days and comparing between treatment periods. While this technique has been effective, information about the pattern of activity in cats is lost. In this study, functional data analysis was applied to activity data from client-owned cats with (n = 83) and without (n = 15) DJD. Functional data analysis retains information about the pattern of activity over the 24-hour day, providing insight into activity over time. We hypothesized that 1) cats without DJD would have higher activity counts and intensity of activity than cats with DJD; 2) that activity counts and intensity of activity in cats with DJD would be inversely correlated with total radiographic DJD burden and total orthopedic pain score; and 3) that activity counts and intensity would have a different pattern on weekends versus weekdays. Results showed marked inter-cat variability in activity. Cats exhibited a bimodal pattern of activity with a sharp peak in the morning and broader peak in the evening. Results further showed that this pattern was different on weekends than weekdays, with the morning peak being shifted to the right (later). Cats with DJD showed different patterns of activity from cats without DJD, though activity and intensity were not always lower; instead both the peaks and troughs of activity were less extreme than those of the cats without DJD. Functional data analysis provides insight into the pattern of activity in cats, and an alternative method for analyzing accelerometry data that incorporates fluctuations in activity across the day.

Costa Rica comprises approximately 6% of the world’s biodiversity. Among these lush ecosystems, wetlands are represented in mangrove forests near the sea, along river lowlands, sedimentary and volcanic mountains, and highland páramo landscapes. In 2018, the Ministry of Environment and Energy (MINAE), through the National System of Conservation Areas (SINAC), the United Nations Development Program (UNDP), and the Global Environment Facility (GEF) carried out the new National Wetlands Inventory (NWI) which identified 10,699 wetland polygons. This assessment collected key information such as location, characteristics of the wetland, land use in the vicinity, threats, and other generalities. Based on these valuable results, we propose a wetland Vulnerability Index composed of a Condition Index and a Hazard Index to determine the different vulnerability conditions of each wetland unit. Our findings provide a better comprehension of the status of wetlands in Costa Rica with an environmental geography perspective. Located in a climate change hotspot, Costa Rica’s conservation policies and actions should consider how to manage the most vulnerable wetlands at different scales. This methodology can improve and generate regional and national wetlands inventories as a basis for evidence-based decision making in other latitudes.

Cooperative games with partial observability are a challenging domain for AI research, especially when the AI should cooperate with a human player. In this paper we investigate one such game, the award-winning card game Hanabi, which has been studied by other researchers before. We present an agent designed to play better with a human cooperator than these previous results by basing it on communication theory and psychology research. To demonstrate that our agent performs better with a human cooperator we ran an experiment in which 224 participants played one or more games of Hanabi with different AIs, and will show that our AI scores higher than previously published work in such a setting.

Trend analyses are common in several types of climate change studies. In many cases, finding evidence that the trends are different from zero in hydroclimate variables is of particular interest. However, when estimating the confidence interval of a set of hydroclimate stations or gridded data the spatial correlation between can affect the significance assessment using for example traditional non-parametric and parametric methods. For this reason, Monte Carlo simulations are needed in order to generate maps of corrected trend significance. In this article, we determined the significance of trends in aridity, modeled runoff using the Variable Infiltration Capacity Macroscale Hydrological model, Hagreaves potential evapotranspiration (PET) and near-surface temperature in Central America. Linear-regression models were fitted considering that the predictor variable is the time variable (years from 1970 to 1999) and predictand variable corresponds to each of the previously mentioned hydroclimate variables. In order to establish if the temporal trends were significantly different from zero, a Mann Kendall and a Monte Carlo test were used. The spatial correlation was calculated first to correct the variance of each trend. It was assumed in this case that the trends form a spatial stochastic process that can be modeled as such. Results show that the analysis considering the spatial correlation proposed here can be used for identifying those extreme trends. However, a set of variables with strong spatial correlation such as temperature can have robust and widespread significant trends assuming independence, but the vast majority of the stations can still fail the Monte Carlo test. We must be vigilant of the statistically robust changes in key primary parameters such as temperature and precipitation, which are the driving sources of hydrological alterations that may affect social and environmental systems in the future.