Ecological vulnerability of China's Yellow River Basin: evaluation and socioeconomic driving factors.

The Yellow River Basin (YRB) is a typical ecologically fragile area in both the Chinese and global contexts. This paper built a sensitivity-resilience-pressure model to evaluate the ecological vulnerability (EV) of the YRB at the grid and prefecture-level regional scales; explored the spatiotemporal characteristics of EV; and scrutinized the impacts of socioeconomic driving factors on EV. The results showed that (1) at the grid and prefecture-level regional scales, EV decreased from the upper to lower reaches, and high vulnerability was observed in the provincial capital city. Mild and severe vulnerability constituted the main EV types in the YRB. (2) The EV index of the YRB decreased from 2.71 to 2.56 in the study period, indicating that the ecological environment improved in the YRB. The slightly and lightly vulnerable areas experienced shrinking-expansion changes, and the overall areas of these two EV types showed expanding trends, while the areal changes and overall trends of the moderately, severely, and extremely vulnerable areas were contrary to those of the slightly and lightly vulnerable areas. (3) The impacts of socioeconomic driving factors on EV dynamically strengthened with the improvement of the socioeconomic level. After considering the interactions of all socioeconomic factors, the explanatory power of the spatial differentiation of EV was enhanced, and the influence of these factors became more prominent.

Preconception exposure to environmental antibiotics among childbearing couples in Anhui and health risk assessment: A multicenter population-based representative study.

Only few studies have assessed the health effects due to preconception exposure to antibiotics among childbearing couples. This study investigated the status of preconception exposure to antibiotics among childbearing couples in Anhui, associated with health risks, and influencing factors. Overall, 1500 childbearing couples were randomly selected from the Reproductive Health of Childbearing Couples - Anhui Cohort (RHCC-AC). The urinary levels of 40 antibiotics and 2 metabolites were determined, and specific gravity (SG) adjusted concentrations of antibiotics were measured to assess health risks. Generalized linear models were used to assess the associations of urinary SG-adjusted concentration of antibiotics with demographic parameters and diet frequency. The total detection rates of all antibiotics were 98.9 % and 99.3 % in wives and husbands, respectively. The detection rates of veterinary antibiotics (VAs) and preferred as VAs (PVAs) were above 90 %. Among eight antibiotics, sulfonamides (95.1 %) and fluoroquinolones (87.6 %) had the highest detection rates in couples. Approximately four-fifths of couples were simultaneously exposed to at least three different antibiotics, and more than half of them were exposed to low concentrations of antibiotics. 8.9 % and 9.2 % of wives and husbands had hazard index value of antibiotics exposure greater than 1. Antibiotic concentrations were associated with residence, sampling season, and diet frequency. In Anhui, nearly 98 % of childbearing couples have environmental exposure to antibiotics, and VAs and PVAs are the primary antibiotics. More than 8 % of couples had health risks due to antibiotic exposure. Several potential determinants of urinary antibiotics deserve more attention in future research.

Changes in PM2.5-related health burden in China's poverty and non-poverty areas during 2000-2020: A health inequality perspective.

China suffers from severe PM2.5 pollution that has resulted in a huge health burden. Such PM2.5-related health burden has long been suspected to differ between China's poverty-stricken areas (PAs) and non-poverty-stricken areas (NPAs). Yet, evidence-based examination of this long-held belief, which is critical as a barrier of environmental injustice to advancing China's sustainability, is still missing. Here our study shows that the PM2.5 pollution is more serious in China's NPAs than PAs-with their annual averages being respectively 54.83 µg/m3 and 43.63 µg/m3-causing higher premature mortality in the NPAs. Compared to economic inequality, China's total PM2.5-related premature mortality was relatively evenly distributed during 2000-2015 across regions of varying levels of gross domestic product (GDP) per capita but increased slightly in 2015-2020 owing to the dramatic change in age structure. The elderly population increased by 31 %. PM2.5-related premature deaths were more severe for populations of low socioeconomic status, and such environmental health inequalities could be amplified by population aging. Additionally, population migration from China's PAs to developed cities contributed to 638, 779, 303, 954, and 896 premature deaths in 2000, 2005, 2010, 2015, and 2020, respectively. Changes in the age structure (53 %) and PM2.5 concentration (28 %) had the greatest impact on premature deaths, followed by changes in population (12 %) and baseline mortality (8 %). The contribution rate of changes in the age structure and PM2.5 concentration was higher in PAs than in NPAs. Our findings provide insight into PM2.5-related premature death and environmental inequality, and may inform more equitable clean air policies to achieve China's sustainable development goals.

Comprehensive assessment of protein loop modeling programs on large-scale datasets: prediction accuracy and efficiency.

Protein loops play a critical role in the dynamics of proteins and are essential for numerous biological functions, and various computational approaches to loop modeling have been proposed over the past decades. However, a comprehensive understanding of the strengths and weaknesses of each method is lacking. In this work, we constructed two high-quality datasets (i.e. the General dataset and the CASP dataset) and systematically evaluated the accuracy and efficiency of 13 commonly used loop modeling approaches from the perspective of loop lengths, protein classes and residue types. The results indicate that the knowledge-based method FREAD generally outperforms the other tested programs in most cases, but encountered challenges when predicting loops longer than 15 and 30 residues on the CASP and General datasets, respectively. The ab initio method Rosetta NGK demonstrated exceptional modeling accuracy for short loops with four to eight residues and achieved the highest success rate on the CASP dataset. The well-known AlphaFold2 and RoseTTAFold require more resources for better performance, but they exhibit promise for predicting loops longer than 16 and 30 residues in the CASP and General datasets. These observations can provide valuable insights for selecting suitable methods for specific loop modeling tasks and contribute to future advancements in the field.

Farmland quality assessment using deep fully convolutional neural networks.

Farmland is the cornerstone of agriculture and is important for food security and social production. Farmland assessment is essential but traditional methods are usually expensive and slow. Deep learning methods have been developed and widely applied recently in image recognition, semantic understanding, and many other application domains. In this research, we used fully convolutional networks (FCN) as the deep learning model to evaluate farmland grades. Normalized difference vegetation index (NDVI) derived from Landsat images was used as the input data, and the China National Cultivated Land Grade Database within Jiangsu Province was used to train the model on cloud computing. We also applied an image segmentation method to improve the original results from the FCN and compared the results with classical machine learning (ML) methods. Our research found that the FCN can predict farmland grades with an overall F1 score (the harmonic mean of precision and recall) of 0.719 and F1 score of 0.909, 0.590, 0.740, 0.642, and 0.023 for non-farmland, level I, II, III, and IV farmland, respectively. Combining the FCN and image segmentation method can further improve prediction accuracy with results of fewer noise pixels and more realistic edges. Compared with conventional ML, at least in farmland evaluation, FCN provides better results with higher precision, recall, and F1 score. Our research indicates that by using remote sensing NDVI data, the deep learning method can provide acceptable farmland assessment without fieldwork and can be used as a novel supplement to traditional methods. The method used in this research will save a lot of time and cost compared with traditional means.

Assessment of corneal biomechanics, tonometry and pachymetry with the Corvis ST in myopia.

To evaluate the repeatability of Corvis ST corneal biomechanical, tonometry and pachymetry measurements, and agreement of pachymetry measures with the Pentacam HR and RTVue OCT. Three consecutive measurements of the right eye of 238 myopic subjects were acquired with the Corvis ST, Pentacam HR, and RTVue OCT. Repeatability of Corvis ST was evaluated by within-subject standard deviation [Sw] and repeatability limit [r]. The agreement of central corneal thickness (CCT) measurements were compared among the three instruments using the Bland-Altman limits of agreement. Comparisons were further stratified by CCT (Corneathin ≤ 500 µm; Corneanormal = 500-550 µm; Corneathick > 550 µm). Sw was below 1 mmHg in Corneathin, Corneanormal, and Corneathick groups for IOP and bIOP. Sw for SP-A1 were 4.880, 6.128, 7.719 mmHg/mm respectively. Sw for CBI were 0.228, 0.157, 0.076, and correspondingly Sw for TBI and SSI were 0.094 and 0.056, 0.079 and 0.053, 0.070 and 0.053. The Bland-Altman plots for CCT implied poor agreement with mean differences of 29.49 µm between Corvis and OCT, 9.33 µm between Pentacam and OCT, and 20.16 µm between Corvis and Pentacam. The Corvis ST showed good repeatability with the exception of CBI in the various CCT groups. The CCT measured by Corvis ST was not interchangeable with Pentacam HR and RTVue OCT.

Development and evaluation of a rapid CRISPR-based diagnostic for COVID-19.

The recent outbreak of human infections caused by SARS-CoV-2, the third zoonotic coronavirus has raised great public health concern globally. Rapid and accurate diagnosis of this novel pathogen posts great challenges not only clinically but also technologically. Metagenomic next-generation sequencing (mNGS) and reverse-transcription PCR (RT-PCR) have been the most commonly used molecular methodologies. However, each has their own limitations. In this study, we developed an isothermal, CRISPR-based diagnostic for COVID-19 with near single-copy sensitivity. The diagnostic performances of all three technology platforms were also compared. Our study aimed to provide more insights into the molecular detection of SARS-CoV-2, and also to present a novel diagnostic option for this new emerging virus.

Influence of Industrialization and Environmental Protection on Environmental Pollution: A Case Study of Taihu Lake, China.

In order to quantitatively study the effect of environmental protection in China since the twenty-first century and the environmental pollution projected for the next ten years (under the model of extensive economic development), this paper establishes a Bayesian regulation back propagation neural network (BRBPNN) to analyze the typical pollutants (i.e., cadmium (Cd) and benzopyrene (BaP)) for Taihu Lake, a typical Chinese freshwater lake. For the periods 1950⁻2003 and 1950⁻2015, the neural network model estimated the BaP concentration for the database with Nash-Sutcliffe model efficiency (NS) = 0.99 and 0.99 and root-mean-square error (RMSE) = 3.1 and 9.3 for the total database and the Cd concentration for the database with NS = 0.93 and 0.98 and RMSE = 45.4 and 65.7 for the total database, respectively. In the model of extensive economic development, the concentration of pollutants in the sediments of Taihu reached the maximum value at the end of the twentieth century and early twenty-first century, and there was an inflection point. After the early twenty-first century, the concentration of pollutants was controlled under various environmental policies and measures. In 2015, the environmental protection ratio of Cd and BaP reached 52% and 89%, respectively. Without environmental protection measures, the concentrations of Cd and BaP obtained from the neural network model is projected to reach 2015.5 µg kg-1 and 407.8 ng g-1, respectively, in 2030. Based on the results of this study, the Chinese government will need to invest more money and energy to clean up the environment.

Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations.

With China's rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity.