Factor Decomposition Analysis of Industrial Wastewater Discharge: A Case of China’s Jiangxi Province

Industrial wastewater discharge is a serious problem for the environmental management of water in Jiangxi, China. In order to analyze the drivers of industrial wastewater discharge in 2004-2015, we used a Logarithmic Mean Divisia Index (LMDI) method to examine four effects including scale , structure , discharge intensity, and technology on the discharges. In 2004-2015, the wastewater discharge from Jiangxi’s industrial sectors increased by 2348.918 ×104 tons. The expansions of production scale and discharge intensity were the main factors leading to increases in industrial wastewater discharges, by1.392-fold and 1.028-fold, respectively. Improvements in abatement technology and adjustment of industrial structure was most important role in decreasing industrial wastewater discharge by 0.659-fold and 0.996-fold, respectively. The main industries that led to increased wastewater discharge during 2004-2015 were WC, MPNFMO, PFAP, Mte, MPPP, MRCMCP, MM, and MCCOEE. We showed that if control of wastewater discharge in Jiangxi’s industrial sectors is to be achieved in future, the industrial sectors should continue to rely on advanced technology to improve efficiency of water use in the short term, and should simultaneously limit expansion of energy-intensive and highly polluting industries. This may help accelerate restructuring and upgrading of the industrial sector in China in the long run. Keywords: decomposition factors, wastewater discharge, Jiangxi, industrial sectors, LMD

Validation of the GALAD model and establishment of a new model for HCC detection in Chinese patients

BackgroundGALAD model is a statistical model used to estimate the possibility of hepatocellular carcinoma (HCC) in patients with chronic liver disease. Many studies with other ethnic populations have shown that it has high sensitivity and specificity. However, whether this model can be used for Chinese patients remains to be determined. Our study was conducted to verify the performance of GALAD model in a Chinese cohort and construct a new model that is more appropriately for Chinese populations.MethodsThere are total 512 patients enrolled in the study, which can be divided into training set and validation set. 80 patients with primary liver cancer, 139 patients with chronic liver disease and 87 healthy people were included in the training set. Through the ROC(receiver operating characteristic) curve analysis, the recognition performance of GALAD model for liver cancer was evaluated, and the GAADPB model was established by logistic regression, including gender, age, AFP, DCP, total protein, and total bilirubin. The validation set (75 HCC patients and 130 CLD patients) was used to evaluate the performance of the GAADPB model.ResultThe GALAD and GAADPB achieved excellent performance (area under the receiver operating characteristic curve [AUC], 0.925, 0.945), and were better than GAAP, Doylestown, BALAD-2, aMAP, AFP, AFP-L3%, DCP and combined detection of AFP, AFP-L3 and DCP (AUCs: 0.894, 0.870, 0.648, 0.545, 0.879, 0.782, 0.820 and 0.911) for detecting HCC from CLD in the training set. As for early stage of HCC (BCLC 0/A), GAADPB had the best sensitivity compared to GALAD, ADP and DCP (56.3%, 53.1%, 40.6%, 50.0%). GAADPB had better performance than GALAD in the test set, AUC (0.896 vs 0.888).ConclusionsThe new GAADPB model was powerful and stable, with better performance than the GALAD and other models, and it also was promising in the area of HCC prognosis prediction. Further study on the real-world HCC patients in China are needed

Decomposing the decoupling of water consumption and economic growth in Jiangxi, China

Current population growth coupled with industrial growth has caused water supply to be outstripped by human demand. Understanding water consumption (WC) decoupling patterns and the factors affecting the decoupling status are essential for balancing economic growth and WC. This study determines the decoupling relationship between WC and economic growth in Jiangxi Province, China, and the driving factors were determined by the Tapio decoupling model and the logarithmic mean Divisia index method. Results showed that changes in the industrial structure in Jiangxi Province resulted in corresponding changes in WC structure. Analysis of the decoupling relationship showed that the decoupling state between WC and economic growth for primary industry was very unstable and largely volatile from 1999 to 2015, but showed a good decoupling status for secondary and tertiary industries. The largest cumulative effects on WC were economic development and technology, which were positive and negative drivers of WC changes, contributing 1,406.14% and −902.96% to the total effect of WC, respectively. The findings can help Jiangxi government identify the key factors influencing the decoupling effect, and formulate effective policies to reduce WC, which will benefit the harmonious development of economy, society and water resources in Jiangxi Province

Evaluation of Sea Surface Wind Products from Scatterometer Onboard the Chinese HY-2D Satellite

The Chinese new marine dynamic environment satellite HY-2D was launched on 19 May 2021, carrying a Ku-band scatterometer (named HSCAT-D). In this study, wind products observed by the HSCAT-D were validated by comparing with wind data from the U.S. National Data Buoy Center (NDBC) buoys and European Centre for Medium-Range Weather Forecasts (ECMWF) model. The statistical results show that the HSCAT-D winds have a good agreement with the buoys’ wind measurements: in comparison with buoy winds, the wind speed standard deviation (STD) and root-mean-squared errors (RMSE) of direction were 0.78 m/s and 14.10°, respectively. Other scatterometers’ wind data are also employed for comparisons, including the HY-2B scatterometer (HSCAT-B), HY-2C scatterometer (HSCAT-C), and MetOp-B scatterometer (ASCAT-B) winds. The statistical results indicate that errors for HSCAT-D winds are smaller than HSCAT-C but a little bit larger than HSCAT-B. The spectral analysis shows that the HSCAT-D wind products contain less small-scale information than ASCAT-B. Moreover, the Extended Triple Collocation (ETC) results show that the HSCAT-D wind product is of good quality and well-calibrated. We believe that the HSCAT-D wind products will be helpful for the scientific community, as shown by the encouraging validation results

CRISPR/Cas14 and G-Quadruplex DNAzyme-Driven Biosensor for Paper-Based Colorimetric Detection of African Swine Fever Virus.

The highly contagious nature and 100% fatality rate contribute to the ongoing and expanding impact of the African swine fever virus (ASFV), causing significant economic losses worldwide. Herein, we developed a cascaded colorimetric detection using the combination of a CRISPR/Cas14a system, G-quadruplex DNAzyme, and microfluidic paper-based analytical device. This CRISPR/Cas14a-G4 biosensor could detect ASFV as low as 5 copies/μL and differentiate the wild-type and mutated ASFV DNA with 2-nt difference. Moreover, this approach was employed to detect ASFV in porcine plasma. A broad linear detection range was observed, and the limit of detection in spiked porcine plasma was calculated to be as low as 42-85 copies/μL. Our results indicate that the developed paper platform exhibits the advantages of high sensitivity, excellent specificity, and low cost, making it promising for clinical applications in the field of DNA disease detection and suitable for popularization in low-resourced areas

Utilizing Machine Learning Techniques to Predict the Efficacy of Aerobic Exercise Intervention on Young Hypertensive Patients Based on Cardiopulmonary Exercise Testing

Recently, the incidence of hypertension has significantly increased among young adults. While aerobic exercise intervention (AEI) has long been recognized as an effective treatment, individual differences in response to AEI can seriously influence clinicians’ decisions. In particular, only a few studies have been conducted to predict the efficacy of AEI on lowering blood pressure (BP) in young hypertensive patients. As such, this paper aims to explore the implications of various cardiopulmonary metabolic indicators in the field by mining patients’ cardiopulmonary exercise testing (CPET) data before making treatment plans. CPET data are collected “breath by breath” by using an oxygenation analyzer attached to a mask and then divided into four phases: resting, warm-up, exercise, and recovery. To mitigate the effects of redundant information and noise in the CPET data, a sparse representation classifier based on analytic dictionary learning was designed to accurately predict the individual responsiveness to AEI. Importantly, the experimental results showed that the model presented herein performed better than the baseline method based on BP change and traditional machine learning models. Furthermore, the data from the exercise phase were found to produce the best predictions compared with the data from other phases. This study paves the way towards the customization of personalized aerobic exercise programs for young hypertensive patients

Epidemiological Characteristics of Lung Cancer Incidence in the Tumor Registration Area of Gansu Province from 2010 to 2019

Background and objective Lung cancer is the malignant tumor with the highest incidence rate and the heaviest disease burden in China. In recent years, lung cancer has shown a high incidence trend, seriously affecting the health of the population. In this paper, we analyze the characteristics of lung cancer incidence in 2019 and the trend of incidence rate from 2010-2019 in the tumor registration area of Gansu province, in order to provide a reference basis for the development of lung cancer prevention and control strategies in Gansu province. Methods By analyzing the cases of lung cancer incidence in the tumor registration area of Gansu province in 2019, we calculated the incidence rate, medium incidence rate, world incidence rate and other related indexes; we used Joinpoint to calculate the annual percentage change (APC) for trend analysis. Results In 2019, a total of 3757 new cases of lung cancer were reported in Gansu province, accounting for 14.96% of all new malignant tumors. The incidence rate, medium incidence rate and world incidence rate and world rate of lung cancer were 40.52/105, 25.78/105, 25.86/105; and the cumulative rate of 0-74 years old, and the truncation rate of 35-64 years old were 3.23%, 40.03/105, respectively. The incidence of lung cancer rises with age, and is high in the age group of 40 years and above, and the incidence peaks in the male and female populations in the group of 75 years and above, and the group of 80 years and above, respectively. The crude incidence rate of lung cancer in the tumor registration area of Gansu province from 2010-2019 showed an overall increasing trend, and the rate of increase was relatively fast, with an APC 5.39% (P<0.05); Separately, according to gender, urban and rural areas, the incidence of lung cancer in all populations showed an increasing trend, and the APC of male, female, urban and rural populations were 4.98%, 6.39%, 6.26%, and 4.64%, respectively (all P<0.05). According to the trend analysis of lung cancer incidence rate by age group, only lung cancer incidence in the age group of 65 years and above increased at an annual average rate of 4.15% (P<0.05). Conclusion The incidence rate of lung cancer in the tumor registration area of Gansu province from 2010 to 2019 shows a rising trend year by year, and there are differences in the incidence of lung cancer in people of different genders, regions and age groups, so comprehensive prevention and control work should be carried out for the key populations of lung cancer incidence

Different Appearance of Chest CT Images of T2DM and NDM Patients with COVID-19 Pneumonia Based on an Artificial Intelligent Quantitative Method

COVID-19 is a kind of pneumonia with new coronavirus infection, and the risk of death in COVID-19 patients with diabetes is four times higher than that in healthy people. It is unclear whether there is a difference in chest CT images between type 2 diabetes mellitus (T2DM) and non-diabetes mellitus (NDM) COVID-19 patients. The aim of this study was to investigate the differences in chest CT images between T2DM and NDM patients with COVID-19 based on a quantitative method of artificial intelligence. A total of 62 patients with COVID-19 pneumonia were retrospectively enrolled and divided into group A (T2DM COVID-19 pneumonia group, n = 15) and group B (NDM COVID-19 pneumonia group, n = 47). The clinical and laboratory examination information of the two groups was collected. Quantitative features (volume of consolidation shadows and ground glass shadows, proportion of consolidation shadow (or ground glass shadow) to lobe volume, total volume, total proportion, and number) of chest spiral CT images were extracted using Dr. Wise @Pneumonia software. The results showed that among the 26 CT image features, the total volume and proportion of bilateral pulmonary consolidation shadow in group A were larger than those in group B (P=0.031 and 0.019, respectively); there was no significant difference in the total volume and proportion of bilateral pulmonary ground glass density shadow between the two groups (P>0.05). In group A, the blood glucose level was correlated with the volume of consolidation shadow and the proportion of consolidation shadow to right middle lobe volume, and higher than those patients in group B. In conclusion, the inflammatory exudation in the lung of COVID-19 patients with diabetes is more serious than that of patients without diabetes based on the quantitative method of artificial intelligence. Moreover, the blood glucose level is positively correlated with pulmonary inflammatory exudation in COVID-19 patients