Reptiles are an important, yet often understudied, taxon in nature conservation. They play a significant role in ecosystems1 and can serve as indicators of environmental health, often responding more rapidly to human pressures than other vertebrate groups.2 At least 21% of reptiles are currently assessed as threatened with extinction by the IUCN.3 However, due to the lack of comprehensive global assessments until recently, they have been omitted from spatial studies addressing conservation or spatial prioritization (e.g., Rosauer et al.,4,5,6,7,8 Fritz and Rahbek,4,5,6,7,8 Farooq et al.,4,5,6,7,8 Meyer et al., 4,5,6,7,8 and Farooq et al.4,5,6,7,8). One important knowledge gap in conservation is the lack of spatially explicit information on the main threats to biodiversity,9 which significantly hampers our ability to respond effectively to the current biodiversity crisis.10,11 In this study, we calculate the probability of a reptile species in a specific location being affected by one of seven biodiversity threats-agriculture, climate change, hunting, invasive species, logging, pollution, and urbanization. We conducted the analysis at a global scale, using a 50 km × 50 km grid, and evaluated the impact of these threats by studying their relationship with the risk of extinction. We find that climate change, logging, pollution, and invasive species are most linked to extinction risk. However, we also show that there is considerable geographical variation in these results. Our study highlights the importance of going beyond measuring the intensity of threats to measuring the impact of these separately for various biogeographical regions of the world, with different historical contingencies, as opposed to a single global analysis treating all regions the same.

Gene selection is an indispensable step for analyzing noisy and high-dimensional single-cell RNA-seq (scRNA-seq) data. Compared with the commonly used variance-based methods, by mimicking the human maker selection in the 2D visualization of cells, a new feature selection method called HRG (Highly Regional Genes) is proposed to find the informative genes, which show regional expression patterns in the cell-cell similarity network. We mathematically find the optimal expression patterns that can maximize the proposed scoring function. In comparison with several unsupervised methods, HRG shows high accuracy and robustness, and can increase the performance of downstream cell clustering and gene correlation analysis. Also, it is applicable for selecting informative genes of sequencing-based spatial transcriptomic data.

BACKGROUND: Lymph node (LN) metastasis is the first site of metastasis of papillary thyroid cancer (PTC). LN status influences clinical management and the prognosis of patients. We explored the relationship between patient obesity and regional patterns of LN involvement in PTC.
METHODS: This study retrospectively analyzed the data from 12,772 PTC patients. The rate of LN metastasis, number of LN metastasis, maximum diameter of positive LN, number of dissected LN, and LN ratios (LNR) were compared between normal-weight and obese patients. Statistical methods have been adjusted for the confounders in hypothesis testing.
RESULTS: Overweight and obesity were independent risk factor for metastatic LNs (OR1 = 1.125, 95% CI 1.042-1.214, P1 = 0.003; OR2 = 1.554, 95% CI 1.339-1.802, P2 <0.001). Obesity was an independent risk factor for the number of metastatic CLNs (OR=1.159, 95% CI 0.975-1.377, P=0.045), however not for number of metastatic lateral LNs (P=0.907). Furthermore, obesity was not an independent risk factor for number of CLNs when dissected more than five (P=0.653), still an independent risk factors for number of metastatic lateral LNs when more than six (OR=1.185, 95% CI 1.010-1.391, P=0.037). As for LNR, obesity was an independent risk factor for the central LNR when more than 0.12 (OR adjusted 1 = 1.099, 95% CI 1.011-1.194, P1 = 0.027; OR adjusted 2 = 1.177, 95% CI 1.003-1.381, P2 = 0.045), for the lateral LNR more than 0.05 (P2 = 0.283).
CONCLUSIONS: Obesity was associated with poor prognoses with PTC respecting LNs. Surgeons should be extreme caution when performing central neck dissection in obese patients.

In Sweden, sarcoidosis prevalence varies geographically, but it is unclear whether diagnosis and treatment patterns vary by geographical area and calendar period. We sought to investigate differences in sarcoidosis diagnosis and treatment by healthcare region and calendar period using nationwide register data.
We included 4777 adults who had at least two ICD-coded visits for sarcoidosis in the National Patient Register (2007-2012). We compared patterns of healthcare use (visits and medication dispensations), and data on sarcoidosis diagnosis and treatment spanning two years before to two years after diagnosis stratified by healthcare region and calendar period at diagnosis.
Compared to other regions, individuals diagnosed in Stockholm were younger, more likely female, and had a higher education level. In all regions, there was an increase in healthcare use at least six months before sarcoidosis diagnosis with small variation among regions. Most patients were diagnosed in pulmonary and internal medicine outpatient clinics, but compared to the national average more patients were diagnosed in rheumatology in the West and ophthalmology and cardiology in the South. Corticosteroid dispensations at diagnosis varied widely by region (48% in the South/Southeast vs. 30% in Stockholm/North). Demographic factors could not explain these differences. We found no differences by calendar period.
Our findings suggest a six-month delay in sarcoidosis diagnosis irrespective of region. The observed regional variation likely reflects differences in diagnosis and treatment patterns. Stakeholders should ensure diagnosis and treatment recommendations are closely followed.

The data presented here represent estimates of the phosphorus content of crop production, phosphorus use efficiency (PUE) and agricultural phosphorus inputs associated with it across the contiguous United States. Net Anthropogenic Phosphorus Input (NAPI) estimates and related data are also provided. Data are presented at county, sub-regional and regional scales. Here, subregions refer to multi-county areas delineated with the goal of obtaining more uniform reporting areas than individual counties. Regions refer to the USDA Farm Resource Regions. The data are reported for 6 agricultural census years, 1987, 1992, 1997, 2002, 2007 and 2012. Estimates of the variables were derived originally from USDA agricultural census data, US population census data, and other sources, using version 3.1 of the NANI/NAPI calculator toolbox (Hong et al.,2011).