Near surface air temperature (NSAT) is a primary descriptor of terrestrial environmental conditions. In recent decades, many efforts have been made to develop various methods for obtaining spatially continuous NSAT from gauge or station observations. This study compared three spatial interpolation (i.e., Kriging, Spline, and Inversion Distance Weighting (IDW)) and two regression analysis (i.e., Multiple Linear Regression (MLR) and Geographically Weighted Regression (GWR)) models for predicting monthly minimum, mean, and maximum NSAT in China, a domain with a large area, complex topography, and highly variable station density. This was conducted for a period of 12 months of 2010. The accuracy of the GWR model is better than the MLR model with an improvement of about 3 • C in the Root Mean Squared Error (RMSE), which indicates that the GWR model is more suitable for predicting monthly NSAT than the MLR model over a large scale. For three spatial interpolation models, the RMSEs of the predicted monthly NSAT are greater in the warmer months, and the mean RMSEs of the predicted monthly mean NSAT for 12 months in 2010 are 1.56 • C for the Kriging model, 1.74 • C for the IDW model, and 2.39 • C for the Spline model, respectively. The GWR model is better than the Kriging model in the warmer months, while the Kriging model is superior to the GWR model in the colder months. The total precision of the GWR model is slightly higher than the Kriging model. The assessment result indicated that the higher standard deviation and the lower mean of NSAT from sample data would be associated with a better performance of predicting monthly NSAT using spatial interpolation models.

The single‐channel (SC) algorithm has been widely used to retrieve land surface temperature from Landsat series data for its simplicity and requirement of only one thermal infrared channel. The main error sources of the existing SC algorithms are the linearization of the Planck's function and atmospheric correction. This paper proposed a practical SC (PSC) algorithm to retrieve land surface temperature from Landsat series data aiming at avoiding the aforementioned error sources. The sensitivity of the PSC algorithm to the input parameters was analyzed. The performance of the proposed PSC algorithm was compared with the most commonly used SC algorithm (the generalized SC, GSC) using a simulation data set and satellite measurements. Results showed that the PSC algorithm was less sensitive to uncertainties in the input parameters than the GSC algorithm. When validated with the simulation data set, the root‐mean‐square error (RMSE) of the PSC algorithm was 1.23 K, with an improvement by 0.57 K compared with the GSC algorithm. For the validation with 71 clear‐sky Landsat 8 images, the RMSE of the PSC algorithm was 1.77 K when using the measurements from U.S. surface radiation budget network as real values. Compared with the GSC algorithm, the RMSE improvement for the PSC algorithm was 0.47 K. We conclude that the PSC algorithm is more accurate than the GSC algorithm and the sensitivity to input parameters in the PSC algorithm is weaker than in the GSC algorithm.

ObjectiveFrailty is a common geriatric syndrome that is diagnosed and staged based mainly on symptoms. We aimed to evaluate frailty-related alterations of the intestinal permeability and profile fecal microbiota of healthy and frail older adults to identify microbial biomarkers of this syndrome.MethodsWe collected serum and fecal samples from 94 community-dwelling older adults, along with anthropometric, medical, mental health, and lifestyle data. Serum inflammatory cytokines IL-6 and HGMB1 and the intestinal permeability biomarker zonulin were measured using enzyme-linked immunosorbent assays. The 16S rRNA amplicon sequencing method was performed to determine the fecal composition of fecal microbiota. We analyzed the diversity and composition differences of the gut microbiota in the two groups and assessed the relationship between the changes in microbiota structure and clinical biomarkers.ResultsOlder adults with frailty showed higher concentrations of IL-6, HGMB1, and zonulin. Although there were no statistically significant differences in the diversity index and evenness indices or species richness of fecal microbiota between the two groups, we found significant microbiota structure differences. Compared with the control group, fecal samples from the frail group had higher levels of Akkermansia, Parabacteroides, and Klebsiella and lower levels of the commensal genera Faecalibacterium, Prevotella, Roseburia, Megamonas, and Blautia. Spearman’s correlation analysis showed that the intergenus interactions were more common in healthy controls than older adults with frailty. Escherichia/Shigella, Pyramidobacter, Alistipes, and Akkermansia were positively correlated with IL-6, while Faecalibacterium, Prevotella, and Roseburia were negatively correlated with IL-6. Alistipes were found to be positively correlated with HGMB1. Akkermansia and Alistipes were linked to the increased serum level of inflammatory factors and intestinal permeability.ConclusionsFrailty is associated with differences in the composition of fecal microbiota. These findings might aid in the development of probiotics or microbial-based therapies for frailty.

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