Analyzing the Population Density Pattern in China with a GIS-Automated Regionalization Method: Hu Line Revisited

The famous “Hu Line”, proposed by Hu Huanyong in 1935, divided China into two regions of comparable area sizes that drastically differ in population: about 4% in the northwest part and 96% in the southeast. However, the Hu Line was proposed largely by visual examination of hand-made maps and arduous experiments of numerous configurations, and has been subject to criticism of lack of scientific rigor and accuracy. Furthermore, it has been over eight decades since the Hu Line was proposed. During the time, China sustained several major man-made and natural disasters (e.g., the World War II, the subsequent Civil War and the 1958-62 Great Famine), and also experienced some major government-sponsored migrations, economic growth and unprecedented urbanization. It is necessary to revisit the (in) stability of Hu Line. By using a GIS-automated regionalization method, termed REDCAP (Regionalization with Dynamically Constrained Agglomerative Clustering and Partitioning), this study re-visits the Hu Line in three aspects. First, by reconstructing the demarcation line based on the latest census of 2010 county-level population by REDCAP, this study largely validates and refines the classic Hu Line. Secondly, this research also seeks to uncover the underlying physical environment factors that shape such a contrast by proposing a habitation environment suitability index (HESI) model. In the third part, this study examines the population density change and disparity change over time by using all the six censuses (1953, 1964, 1982, 1990, 2000, and 2010) since the founding of the People’s Republic of China. This study advances the methodological rigor in defining the Hu Line, solidifies the inherent connection between physical environment and population settlement, and strengthens the findings by extending the analysis across time epochs

Human Pose Estimation using Global and Local Normalization

In this paper, we address the problem of estimating the positions of human joints, i.e., articulated pose estimation. Recent state-of-the-art solutions model two key issues, joint detection and spatial configuration refinement, together using convolutional neural networks. Our work mainly focuses on spatial configuration refinement by reducing variations of human poses statistically, which is motivated by the observation that the scattered distribution of the relative locations of joints e.g., the left wrist is distributed nearly uniformly in a circular area around the left shoulder) makes the learning of convolutional spatial models hard. We present a two-stage normalization scheme, human body normalization and limb normalization, to make the distribution of the relative joint locations compact, resulting in easier learning of convolutional spatial models and more accurate pose estimation. In addition, our empirical results show that incorporating multi-scale supervision and multi-scale fusion into the joint detection network is beneficial. Experiment results demonstrate that our method consistently outperforms state-of-the-art methods on the benchmarks.Comment: ICCV201

Ground state solutions for a non-local type problem in fractional Orlicz Sobolev spaces

In this paper, we study the following nonlocal problem in fractional Orlicz Sobolev spaces \begin{eqnarray*} (-\Delta_{\Phi})^{s}u+V(x)a(|u|)u=f(x,u),\quad x\in\mathbb{R}^N, \end{eqnarray*} where $(-\Delta_{\Phi})^{s}(s\in(0, 1))$ denotes the non-local and maybe non-homogeneous operator, the so-called fractional $\Phi$-Laplacian. Without assuming the Ambrosetti-Rabinowitz type and the Nehari type conditions on the nonlinearity, we obtain the existence of ground state solutions for the above problem in periodic case. The proof is based on a variant version of the mountain pass theorem and a Lions' type result for fractional Orlicz Sobolev spaces

Standardized Soil Moisture Index for Drought Monitoring Based on SMAP Observations and 36 Years of NLDAS Data: A Case Study in the Southeast United States

Droughts can severely reduce the productivity of agricultural lands and forests. The United States Department of Agriculture (USDA) Southeast Regional Climate Hub (SERCH) has launched the Lately Identified Geospecific Heightened Threat System (LIGHTS) to inform its users of potential water deficiency threats. The system identifies droughts and other climate anomalies such as extreme precipitation and heat stress. However, the LIGHTS model lacks input from soil moisture observations. This research aims to develop a simple and easy-to-interpret soil moisture and drought warning index - Standardized Soil Moisture Index (SSI) - by fusing the space-borne Soil Moisture Active Passive (SMAP) soil moisture data with the NLDAS climate index. Ground truth soil moisture data from the Soil Climate Analysis Network (SCAN) were collected for validation. As a result, the accuracy of using SMAP to monitor soil moisture content generally displayed a good statistical correlation with the SCAN data. The validation through the Palmer Drought Severity Index (PDSI) and Normalized Difference Water Index (NDWI) suggested that SSI was effective and sensitive for short-term drought monitoring across large areas

Synthesis of Organic Dye-Impregnated Silica Shell-Coated Iron Oxide Nanoparticles by a New Method

A new method for preparing magnetic iron oxide nanoparticles coated by organic dye-doped silica shell was developed in this article. Iron oxide nanoparticles were first coated with dye-impregnated silica shell by the hydrolysis of hexadecyltrimethoxysilane (HTMOS) which produced a hydrophobic core for the entrapment of organic dye molecules. Then, the particles were coated with a hydrophilic shell by the hydrolysis of tetraethylorthosilicate (TEOS), which enabled water dispersal of the resulting nanoparticles. The final product was characterized by X-ray diffraction, transmission electron microscopy, Fourier transform infrared spectroscopy, photoluminescence spectroscopy, and vibration sample magnetometer. All the characterization results proved the final samples possessed magnetic and fluorescent properties simultaneously. And this new multifunctional nanomaterial possessed high photostability and minimal dye leakage

Peanut Frostbite Detection Method Based on Near Infrared Hyperspectral Imaging Technology

Peanuts were susceptible to frost damage during harvesting, transportation, storage, and processing due to temperature and humidity changes, which could affect the quality of peanuts and their products. In order to explore the mechanism of peanut frost damage and improve the detection efficiency of frost-damaged peanuts, this study used near-infrared hyperspectral technology to study the feasibility of non-destructive detection of peanut frost damage, optimization methods based on feature variable screening discriminant models, and the mechanism of peanut frost damage. The effects of five preprocessing methods, including standard normalized variate (SNV), multiplicative scatter correction (MSC), Savizkg-Golag (SG) smoothing, SG smoothing-SNV, and SG smoothing-MSC, on the original data were experimentally studied. Then, eight variable selection methods, including competitive adaptive reweighted sampling (CARS), random frog (RF), variable importance in projection (VIP), successive projections algorithm (SPA), Monte Carlo uninformative variable elimination (MC-UVE), iteration retention information variable (IRIV), variable combination population analysis-iteration retention information variable (VCPA-IRIV), and variable combination population analysis-genetic algorithm (VCPA-GA), were used to screen the feature wavelengths related to peanut frost damage. Support vector machine (SVM) was used to select the feature wavelengths that reached the discrimination accuracy threshold of 90% as the feature wavelengths of peanut frost damage. The results showed that the detection of peanut frost damage based on near-infrared hyperspectral imaging technology was generally feasible and had high accuracy. All variable selection methods can effectively screen the feature wavelengths related to frost damage. Among them, the VCPA-GA algorithm selected the least 7 feature wavelengths, accounting for only 3.125% of all wavelengths in the dataset. The accuracy of the training set and the test set were 91.60% and 90.12%, respectively. The selected frostbite characteristic wavelength reflects information about oleic acid and protein, verifying that excessively low temperatures can lead to a decrease in oleic acid content and an increase in protein content in peanuts. This study provides a theoretical basis and technical support for the rapid non-destructive detection of peanut frost damage

Gibberellins Promote Brassinosteroids Action and Both Increase Heterosis for Plant Height in Maize (Zea mays L.)

Brassinosteroids (BRs) and Gibberellins (GAs) are two classes of plant hormones affecting plant height (PHT). Thus, manipulation of BR and GA levels or signaling enables optimization of crop grain and biomass yields. We established backcross (BC) families, selected for increased PHT, in two elite maize inbred backgrounds. Various exotic accessions used in the germplasm enhancement in maize project served as donors. BC1-derived doubled haploid lines in the same two elite maize inbred backgrounds established without selection for plant height were included for comparison. We conducted genome-wide association studies to explore the genetic control of PHT by BR and GA. In addition, we used BR and GA inhibitors to compare the relationship between PHT, BR, and GA in inbred lines and heterozygotes from a physiological and biological perspective. A total of 73 genomic loci were discovered to be associated with PHT, with seven co-localized with GA, and two co-localized with BR candidate genes. PHT determined in field trials was significantly correlated with seedling stage BR and GA inhibitor responses. However, this observation was only true for maize heterozygotes, not for inbred lines. Path analysis results suggest that heterozygosity increases GA levels, which in turn promote BR levels. Thus, at least part of heterosis for PHT in maize can be explained by increased GA and BR levels, and seedling stage hormone inhibitor response is promising to predict heterosis for PHT

CCNF is a potential pancancer biomarker and immunotherapy target

BackgroundCCNF catalyzes the transfer of ubiquitin molecules from E2 ubiquitin-conjugating enzymes to target proteins, thereby regulating the G1/S or G2/M transition of tumor cells. Thus far, CCNF expression and its potential as a pancancer biomarker and immunotherapy target have not been reported.MethodsTCGA datasets and the R language were used to analyze the pancancer gene expression, protein expression, and methylation levels of CCNF; the relationship of CCNF expression with overall survival (OS), recurrence-free survival (RFS), immune matrix scores, sex and race; and the mechanisms for posttranscriptional regulation of CCNF.ResultsCCNF expression analysis showed that CCNF mRNA expression was higher in cancer tissues than in normal tissues in the BRCA, CHOL, COAD, ESCA, HNSC, LUAD, LUSC, READ, STAD, and UCEC; CCNF protein expression was also high in many cancer tissues, indicating that it could be an important predictive factor for OS and RFS. CCNF overexpression may be caused by CCNF hypomethylation. CCNF expression was also found to be significantly different between patients grouped based on sex and race. Overexpression of CCNF reduces immune and stromal cell infiltration in many cancers. Posttranscriptional regulation analysis showed that miR-98-5p negatively regulates the expression of the CCNF gene.ConclusionCCNF is overexpressed across cancers and is an adverse prognostic factor in terms of OS and RFS in many cancers; this phenomenon may be related to hypomethylation of the CCNF gene, which could lead to cancer progression and worsen prognosis. In addition, CCNF expression patterns were significantly different among patients grouped by sex and race. Its overexpression reduces immune and stromal cell infiltration. miR-98-5p negatively regulates CCNF gene expression. Hence, CCNF is a potential pancancer biomarker and immunotherapy target