Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016–2020 and exhibited a significant reduction trend across the entire study area during 2019–2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization

Clinical characteristics of patients with non-tuberculous mycobacterial pulmonary disease: a seven-year follow-up study conducted in a certain tertiary hospital in Beijing

BackgroundThe incidence of non-tuberculous mycobacterial pulmonary disease (NTM-PD) has increased in recent years. However, the clinical and immunologic characteristics of NTM-PD patients have received little attention.MethodsNTM strains, clinical symptoms, underlying diseases, lung CT findings, lymphocyte subsets, and drug susceptibility tests (DSTs) of NTM-PD patients were investigated. Then, the counts of immune cells of NTM-PD patients and their correlation were evaluated using principal component analysis (PCA) and correlation analysis.Results135 NTM-PD patients and 30 healthy controls (HCs) were enrolled from 2015 to 2021 in a certain tertiary hospital in Beijing. The number of NTM-PD patients increased every year, and Mycobacterium intracellulare (M. intracellulare), M. abscessus, M. avium, and M. kansasii were the major pathogens of NTM-PD. The main clinical symptoms of NTM-PD patients were cough and sputum production, and the primary lung CT findings were thin-walled cavity, bronchiectasis, and nodules. In addition, we identified 23 clinical isolates from 87 NTM-PD patients with strain records. The DST showed that almost all of M. abscessus and M. avium and more than half of the M. intracellulare and M. avium complex groups were resistant to anti-tuberculosis drugs tested in this study. M. xenopi was resistant to all aminoglycosides. M. kansasii was 100% resistant to kanamycin, capreomycin, amikacin, and para-aminosalicylic acid, and sensitive to streptomycin, ethambutol, levofloxacin, azithromycin, and rifamycin. Compared to other drugs, low resistance to rifabutin and azithromycin was observed among NTM-PD isolates. Furthermore, the absolute counts of innate and adaptive immune cells in NTM-PD patients were significantly lower than those in HCs. PCA and correlation analysis revealed that total T, CD4+, and CD8+ T lymphocytes played an essential role in the protective immunity of NTM-PD patients, and there was a robust positive correlation between them.ConclusionThe incidence of NTM-PD increased annually in Beijing. Individuals with bronchiectasis and COPD have been shown to be highly susceptible to NTM-PD. NTM-PD patients is characterized by compromised immune function, non-specific clinical symptoms, high drug resistance, thin-walled cavity damage on imaging, as well as significantly reduced numbers of both innate and adaptive immune cells

Association between a novel mutation in SLC20A2 and familial idiopathic basal ganglia calcification.

Familial idiopathic basal ganglia calcification (FIBGC) is a rare, autosomal dominant disorder involving bilateral calcification of the basal ganglia. To identify gene mutations related to a Chinese FIBGC lineage, we evaluated available individuals in the family using CT scans. DNA was extracted from the peripheral blood of available family members, and both exonic and flanking intronic sequences of the SLC20A2 gene were amplified by PCR and then sequenced. Non-denaturing polyacrylamide gel electrophoresis (PAGE) was used to confirm the presence of mutations. Allele imbalances of the SLC20A2 gene or relative quantity of SLC20A2 transcripts were evaluated using qRT-PCR. A novel heterozygous single base-pair deletion (c.510delA) within the SLC20A2 gene was identified. This deletion mutation was found to co-segregate with basal ganglia calcification in all of the affected family members but was not detected in unaffected individuals or in 167 unrelated Han Chinese controls. The mutation will cause a frameshift, producing a truncated SLC20A2 protein with a premature termination codon, most likely leading to the complete loss of function of the SLC20A2 protein. This mutation may also lead to a reduction in SLC20A2 mRNA expression by approximately 30% in cells from affected individuals. In conclusion, we identified a novel mutation in SLC20A2 that is linked to FIBGC. In addition to the loss of function at the protein level, decreasing the expression of SLC20A2 mRNA may be another mechanism that can regulate SLC20A2 function in IBGC individuals. We propose that the regional expression pattern of SLC20A1 and SLC20A2 might explain the unique calcification pattern observed in FIBGC patients

Using a Two-Stage Scheme to Map Toxic Metal Distributions Based on GF-5 Satellite Hyperspectral Images at a Northern Chinese Opencast Coal Mine

Toxic metals have attracted great concern worldwide due to their toxicity and slow decomposition. Although metal concentrations can be accurately obtained with chemical methods, it is difficult to map metal distributions on a large scale due to their inherently low efficiency and high cost. Moreover, chemical analysis methods easily lead to secondary contamination. To address these issues, 110 topsoil samples were collected using a soil sampler, and positions for each sample were surveyed using a global navigation satellite system (GNSS) receiver from a coal mine in northern China. Then, the metal contents were surveyed in a laboratory via a portable X-ray fluorescence spectroscopy (XRF) device, and GaoFen-5 (GF-5) satellite hyperspectral images were used to retrieve the spectra of the soil samples. Furthermore, a Savitzky–Golay (SG) filter and continuous wavelet transform (CWT) were selected to smooth and enhance the soil reflectance. Competitive adaptive reweighted sampling (CARS) and Boruta algorithms were utilized to identify the feature bands. The optimum two-stage method, consisting of the random forest (RF) and ordinary kriging (OK) methods, was used to infer the metal concentrations. The following outcomes were achieved. Firstly, both zinc (Zn) (68.07 mg/kg) and nickel (Ni) (26.61 mg/kg) surpassed the regional background value (Zn: 48.60 mg/kg, Ni: 19.5 mg/kg). Secondly, the optimum model of RF, combined with the OK (RFOK) method, with a relatively higher coefficient of determination (R2) (R2 = 0.60 for Zn, R2 = 0.30 for Ni), a lower root-mean-square error (RMSE) (RMSE = 12.45 mg/kg for Zn, RMSE = 3.97 mg/kg for Ni), and a lower mean absolute error (MAE) (MAE = 9.47 mg/kg for Zn, MAE = 3.31mg/kg for Ni), outperformed the other four models, including the RF, OK, inverse distance weighted (IDW) method, and the optimum model of RF combined with IDW (RFIDW) method in estimating soil Zn and Ni contents, respectively. Thirdly, the distribution of soil Zn and Ni concentrations obtained from the best-predicted method and the GF-5 satellite hyperspectral images was in line with the actual conditions. This scheme proves that satellite hyperspectral images can be used to directly estimate metal distributions, and the present study provides a scientific base for mapping heavy metal spatial distribution on a relatively large scale

mRNA expression analysis of the <i>SLC20A2</i> mutation.

<p>(A) Sequencing showing an imbalance of the c.510delA (p.R172fsX19) mutant and wild-type alleles in cDNA templates. (B) Relative quantity (mean ± SD) of <i>SLC20A2</i> transcripts derived from real-time quantitative polymerase chain reactions in 3 affected individuals and a normal individual.</p

Identification of the c.510delA <i>SLC20A2</i> mutation.

<p>(A) Sequencing chromatogram showing the heterozygous c.510delA mutation in <i>SLC20A2</i> (right) and the wild type sequence (left). (B) Heteroduplex mobility assay of the 171 bp PCR product derived from all the affected individuals and some unrelated normal individuals. After denaturation (95°C), re-annealed reactions were run under non-denaturing conditions. M, 100 bp DNA marker; N, normal individuals. The bands were visualized by silver-staining. (C) Schematic diagram of the wild-type and mutant SLC20A2 proteins. Purple regions represent the L183–V483 fragment of SLC20A2, which are important for the Pi transportation activity. The blue circle indicates the mutated amino acid residue. Amino-acid residues of the novel C-terminal peptides in the p.R172fsX19 mutant are given with the 19 new residues in red. The structure model was drawn using TOPO2 software (<a href="http://www.sacs.ucsf.edu/TOPO2/" target="_blank">http://www.sacs.ucsf.edu/TOPO2/</a>).</p

Sequences and positions of the primers used for mutation analysis of <i>SLC20A2</i>.

<p>Genomic position of PCR primers corresponding to the Feb 2009 human genome reference sequence GRCh37.</p

Study subjects and imaging evaluation.

<p>(A) Pedigree of family PJ-IBGC. The proband is indicated by an arrow. Filled symbols represent affected individuals, including both symptomatic (black) and asymptomatic (gray). (B) CT scan of the affected individuals. (C) MRS examination of a 4-year-old girl. I 1: a 54-year-old man; II 1: a 37-year-old woman; II:3: a 24-year-old man; III 1: an 11-year-old girl; III 2: a 4-year-old girl.</p

Identifying Population Hollowing Out Regions and Their Dynamic Characteristics across Central China

Continuous urbanization and industrialization lead to plenty of rural residents migrating to cities for a living, which seriously accelerated the population hollowing issues. This generated series of social issues, including residential estate idle and numerous vigorous laborers migrating from undeveloped rural areas to wealthy cities and towns. Quantitatively determining the population hollowing characteristic is the priority task of realizing rural revitalization. However, the traditional field investigation methods have obvious deficiencies in describing socio-economic phenomena, especially population hollowing, due to weak efficiency and low accuracy. Here, this paper conceives a novel scheme for representing population hollowing levels and exploring the spatiotemporal dynamic of population hollowing. The nighttime light images were introduced to identify the potential hollowing areas by using the nightlight decreasing trend analysis. In addition, the entropy weight approach was adopted to construct an index for evaluating the population hollowing level based on statistical datasets at the political boundary scale. Moreover, we comprehensively incorporated physical and anthropic factors to simulate the population hollowing level via random forest (RF) at a grid-scale, and the validation was conducted to evaluate the simulation results. Some findings were achieved. The population hollowing phenomenon decreasing gradually was mainly distributed in rural areas, especially in the north of the study area. The RF model demonstrated the best accuracy with relatively higher R2 (Mean = 0.615) compared with the multiple linear regression (MLR) and the geographically weighted regression (GWR). The population hollowing degree of the grid-scale was consistent with the results of the township scale. The population hollowing degree represented an obvious trend that decreased in the north but increased in the south during 2016&ndash;2020 and exhibited a significant reduction trend across the entire study area during 2019&ndash;2020. The present study supplies a novel perspective for detecting population hollowing and provides scientific support and a first-hand dataset for rural revitalization