Soybean (Glycine max L.) Leaf Moisture Estimation Based on Multisource Unmanned Aerial Vehicle Image Feature Fusion.

Efficient acquisition of crop leaf moisture information holds significant importance for agricultural production. This information provides farmers with accurate data foundations, enabling them to implement timely and effective irrigation management strategies, thereby maximizing crop growth efficiency and yield. In this study, unmanned aerial vehicle (UAV) multispectral technology was employed. Through two consecutive years of field experiments (2021-2022), soybean (Glycine max L.) leaf moisture data and corresponding UAV multispectral images were collected. Vegetation indices, canopy texture features, and randomly extracted texture indices in combination, which exhibited strong correlations with previous studies and crop parameters, were established. By analyzing the correlation between these parameters and soybean leaf moisture, parameters with significantly correlated coefficients (p < 0.05) were selected as input variables for the model (combination 1: vegetation indices; combination 2: texture features; combination 3: randomly extracted texture indices in combination; combination 4: combination of vegetation indices, texture features, and randomly extracted texture indices). Subsequently, extreme learning machine (ELM), extreme gradient boosting (XGBoost), and back propagation neural network (BPNN) were utilized to model the leaf moisture content. The results indicated that most vegetation indices exhibited higher correlation coefficients with soybean leaf moisture compared with texture features, while randomly extracted texture indices could enhance the correlation with soybean leaf moisture to some extent. RDTI, the random combination texture index, showed the highest correlation coefficient with leaf moisture at 0.683, with the texture combination being Variance1 and Correlation5. When combination 4 (combination of vegetation indices, texture features, and randomly extracted texture indices) was utilized as the input and the XGBoost model was employed for soybean leaf moisture monitoring, the highest level was achieved in this study. The coefficient of determination (R2) of the estimation model validation set reached 0.816, with a root-mean-square error (RMSE) of 1.404 and a mean relative error (MRE) of 1.934%. This study provides a foundation for UAV multispectral monitoring of soybean leaf moisture, offering valuable insights for rapid assessment of crop growth.

Monitoring of Chlorophyll Content of Potato in Northern Shaanxi Based on Different Spectral Parameters.

Leaf chlorophyll content (LCC) is an important physiological index to evaluate the photosynthetic capacity and growth health of crops. In this investigation, the focus was placed on the chlorophyll content per unit of leaf area (LCCA) and the chlorophyll content per unit of fresh weight (LCCW) during the tuber formation phase of potatoes in Northern Shaanxi. Ground-based hyperspectral data were acquired for this purpose to formulate the vegetation index. The correlation coefficient method was used to obtain the "trilateral" parameters with the best correlation between potato LCCA and LCCW, empirical vegetation index, any two-band vegetation index constructed after 0-2 fractional differential transformation (step size 0.5), and the parameters with the highest correlation among the three spectral parameters, which were divided into four combinations as model inputs. The prediction models of potato LCCA and LCCW were constructed using the support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) algorithms. The results showed that, compared with the "trilateral" parameter and the empirical vegetation index, the spectral index constructed by the hyperspectral reflectance after differential transformation had a stronger correlation with potato LCCA and LCCW. Compared with no treatment, the correlation between spectral index and potato LCC and the prediction accuracy of the model showed a trend of decreasing after initial growth with the increase in differential order. The highest correlation index after 0-2 order differential treatment is DI, and the maximum correlation coefficients are 0.787, 0.798, 0.792, 0.788 and 0.756, respectively. The maximum value of the spectral index correlation coefficient after each order differential treatment corresponds to the red edge or near-infrared band. A comprehensive comparison shows that in the LCCA and LCCW estimation models, the RF model has the highest accuracy when combination 3 is used as the input variable. Therefore, it is more recommended to use the LCCA to estimate the chlorophyll content of crop leaves in the agricultural practices of the potato industry. The results of this study can enhance the scientific understanding and accurate simulation of potato canopy spectral information, provide a theoretical basis for the remote sensing inversion of crop growth, and promote the development of modern precision agriculture.

Efficient Homolytic Cleavage of H2O2 on Hydroxyl-Enriched Spinel CuFe2O4 with Dual Lewis Acid Sites.

Traditional H2O2 cleavage mediated by macroscopic electron transfer (MET) not only has low utilization of H2O2, but also sacrifices the stability of catalysts. We present a non-redox hydroxyl-enriched spinel (CuFe2O4) catalyst with dual Lewis acid sites to realize the homolytic cleavage of H2O2. The results of systematic experiments, in situ characterizations, and theoretical calculations confirm that tetrahedral Cu sites with optimal Lewis acidity and strong electron delocalization can synergistically elongate the O-O bonds (1.47 Š→ 1.87 Å) in collaboration with adjacent bridging hydroxyl (another Lewis acid site). As a result, the free energy of H2O2 homolytic cleavage is decreased (1.28 eV → 0.98 eV). H2O2 can be efficiently split into ⋅OH induced by hydroxyl-enriched CuFe2O4 without MET, which greatly improves the catalyst stability and the H2O2 utilization (65.2 %, nearly 2 times than traditional catalysts). The system assembled with hydroxyl-enriched CuFe2O4 and H2O2 affords exceptional performance for organic pollutant elimination. The scale-up experiment using a continuous flow reactor realizes long-term stability (up to 600 mL), confirming the tremendous potential of hydroxyl-enriched CuFe2O4 for practical applications.

Emission and spatial variation characteristics of odorous pollutants in the aerobic tank of an underground wastewater treatment plant (UWWTP) in southern China.

In this study, the spatial concentration of odorous pollutants in the aerobic tank of an underground wastewater treatment plant (UWWTP) in southern China is monitored. The odour activity value, odour contribution rate, and chemical concentration contribution rate are used to evaluate the degree of contribution of odorous substances. Computational fluid dynamics (CFD) simulations of odorous pollutant diffusion are also established. The study shows that the odorous substances detected in the aerobic tank mainly included ammonia (NH3), hydrogen sulfide (H2S), trimethylamine (C3H9N), and methanethiol (CH3SH), and their concentrations are 1.160, 0.778, 0.022, and 0.0006 mg/m3, respectively. The total odour activity value of the aerobic tank is 450.72 (dimensionless), of which the odour activity value of H2S is 432.22, and the contribution rate reaches 95.9%. H2S is the main contributor to odour and a key controlled substance. The air inlets and exhaust outlets in the aerobic tank are cross-arranged at the top of the space, and the CFD model of odorous pollutant diffusion shows that the gas flow organization determines the odorous pollutant diffusion. The spatial distribution of gas flow and odorous substances in the aerobic tank is relatively uniform, and the odour collection efficiency is higher. The production flux and production coefficient of H2S in the aerobic tank are calculated as 25.831 mg/(m2·h) and 14.149 mg/t, respectively. This study determines the reasonable air supply and exhaust design of the aerobic tank, the number of odour pollutants, and the key controlled substances. These findings offer guidance and serve as useful references for the prevention and control of odour pollution in aerobic tanks of the same type of UWWTPs.

Association Between Family Atmosphere and Internet Addiction Among Adolescents: The Mediating Role of Self-Esteem and Negative Emotions.

Objectives: Family atmosphere is a significant predictor of internet addiction in adolescents. Based on the vulnerability model of emotion and the compensatory internet use theory, this study examined whether self-esteem and negative emotions (anxiety, depression) mediated the relationship between family atmosphere and internet addiction in parallel and sequence. Methods: A total of 3,065 Chinese middle school and high school students (1,524 females, mean age = 13.63 years, SD = 4.24) participated. They provided self-reported data on demographic variables, family atmosphere, self-esteem, anxiety, depression, and internet addiction through the Scale of Systemic Family Dynamic, Self-Esteem Scale, Self-Rating Anxiety Scale, Self-Rating Depression Scale, and Internet Addiction Test, respectively. We employed Hayes PROCESS macro for the SPSS program to scrutinize the suggested mediation model. Results: It revealed that self-esteem, anxiety, and depression mediated the relationship between family atmosphere and internet addiction in parallel and sequence. The pathway of family atmosphere-self-esteem-internet addiction played a more important role than others. Conclusion: The present study confirmed the mediating role of self-esteem and negative emotions between family atmosphere and internet addiction, providing intervention studies with important targeting factors.

Dual role of O2 concentration on the reducing gases produced and NO reduction during sewage sludge combustion in pilot scale cement precalciner.

The reducing gases produced and NO reduction by sewage sludge combustion were investigated in a self-made cement precalciner. The dual role of O2 concentration (0-5 vol%) in the production characteristics of reducing gases and the reduction efficiency of NO were evaluated experimentally. TG-FTIR analysis demonstrated that the key reducing gaseous species produced by sewage sludge combustion were HCN, NH3, CO, and CH4. And experiments demonstrated that O2 concentration had pronounced effects on NH3 distribution, the maximum production rate was obtained at an O2 concentration of 3 vol%. Meanwhile, the reducing gases NH3 and CO were the key species for NO reduction in the cement precalciner, and the reduction efficiency of NO, when reduced by NH3, increased with an increase in O2 concentration, while the reduction performance of NO by CO was limited by O2 concentration. Therefore, O2 concentration greatly influences NO reduction efficiency by sewage sludge combustion; the maximum NO reduction efficiency was 61.67% at an O2 concentration of 3 vol%. The difference in NO reduction by sewage sludge combustion under different O2 concentrations was primarily attributed to NH3 production rate and NO reduction by NH3 and CO, which is greatly affected by O2 concentration. Sewage sludge combustion can result in NO reduction in the cement kiln flue gas and resource utilisation of sewage sludge.

Long noncoding RNA CASC2 protect ROS-induced oxidative stress in myocardial infarction by miR-18a/SIRT2.

We aimed to investigate the function and its possible mechanisms of long noncoding RNA (lncRNA) in acute myocardial infarction (AMI) model. Patients with AMI and normal volunteers were selected from our hospital. Sprague-Dawley rats were induced into in vivo model of AMI. H9c2 cells were treated with H2 O2 to generate injury model. A significantly lower serum gene expression of lncRNA CASC2 was detected. In rat models of AMI, lncRNA CASC2 gene expressions in heart tissue of mice with AMI were decreased. In in vitro model, downregulation of lncRNA CASC2 increased reactive oxygen species (ROS)-induced oxidative stress; lncRNA CASC2 induced NADPH oxidase (NOX-2) expression and suppressed miR-18a expression; MiR-18a promoted ROS-induced oxidative stress; downregulation of miR-18a decreased ROS-induced oxidative stress. The inhibition of miR-18a reversed the effects of CASC2 downregulation on ROS-induced oxidative stress in in vitro model of AMI. The activation of miR-18a reversed the effects of CASC2 on ROS-induced oxidative stress in in vitro model of AMI. These data for the first time suggest that lncRNA CASC2 have better protective effects on AMI, which could reduce oxidative stress through their carried miR-18a and subsequently downregulating the SIRT2/ROS pathway.

The Tetramethylpyrazine Derivative Statmp-151: A Novel Small Molecule Stat3 Inhibitor With Promising Activity Against Breast Cancer.

Breast cancer is the most common malignancy in women and is a molecularly heterogeneous disease. Signal transducer and activator of transcription 3 (Stat3) is overexpressed and hyperactivated in a variety of human tumours, including breast cancer, thus representing a promising target for breast cancer treatment. In the present study, we evaluated the activities of a novel Stat3 inhibitor named Statmp-151 in the human breast cancer cell lines MCF-7 and MDA-MB-231 and the murine mammary carcinoma cell line 4T1. The in vitro results showed that Statmp-151 inhibited the proliferation of breast cancer cell lines in a dose- and time-dependent manner and suppressed the phosphorylation of Stat3 in a dose-dependent manner. Flow cytometry (FCM) assays revealed that Statmp-151 affected mitochondrial membrane potential and reactive oxygen species (ROS) production. Furthermore, Statmp-151 inhibited cell migration, as shown by analysis of the matrix metalloproteinases MMP2 and MMP9. Finally, in a 4T1 tumour-bearing mouse model, intraperitoneal injection of 30 mg/kg/day Statmp-151 significantly suppressed the growth of tumours without obvious toxicity. These results indicated that Statmp-151 might be a potential candidate for the treatment of breast cancer.

Effect of Ca(OH)2 on the Release Characteristics of HCl during Sludge Combustion.

With the addition of Ca(OH)2, the effects of combustion temperature, moisture, sludge particle size, and chlorine-containing additives on the removal of HCl during sludge combustion were studied. The experimental results showed that combustion temperature and moisture content promoted the formation of HCl and Ca(OH)2 played a key role in the formation of HCl during sludge combustion. Under the best conditions of a sludge particle size of 380-250 µm, moisture content of 5%, temperature of 850 °C, and Ca(OH)2/sludge weight ratio of 3/10, the HCl capture efficiency was 79.81%. In addition, the effect of PVC on the production of HCl was greater than that of NaCl, probably because the lattice energy of NaCl was much higher, indicating that inorganic chlorine was not the main source of HCl. Ca(OH)2 can effectively inhibit the formation of HCl, which had practical guiding significance for the formation of HCl during the sludge combustion, especially the sludge containing chlorine.

Simultaneous Removal of SO2 and NO by O3 Oxidation Combined with Wet Absorption.

The effects of ozone concentration, NaOH concentration, type and concentration of additives, initial pH, temperature, and NO and SO2 concentration on simultaneous removal of NO and SO2 were studied through ozone oxidation combined with wet absorption. Results indicated that ozone concentration and the type and concentration of additives had the most significant effect on NO removal. The optimal ozone concentration was 250 ppm (NO/NO2 = 1), and the best additive was KMnO4. The removal efficiency of NO x was as high as 97.86% when NO/NO2 = 1, and the concentration of KMnO4 was 0.025 mol/L. Considering economic and other factors, the KMnO4 concentration was selected to be 0.006 mol/L. At this time, the removal efficiencies of NO x and SO2 were 81.35 and 100%, respectively. This method has potential application prospects for simultaneous removal of SO2 and NO in the industrial flue gas.

Correction: Mechanistic study on NO reduction by sludge reburning in a pilot scale cement precalciner with different CO2 concentrations.

[This corrects the article DOI: 10.1039/C9RA04065J.].

Data on species and concentration of the main gaseous products during sludge combustion to support the feasibility of using sludge as a flue gas denitration agent for the cement industry.

The dataset presented in this article is the supplementary data for the research article Fang et al., 2019 [1] and provided detailed data profile to support that sludge is an effective NOX reducing agent, as reductive gas components produce during sludge combustion. The instantaneous concentrations of the main gaseous products during sludge combustion were detected by using Fourier transform infrared spectroscopy (FTIR, DX-4000, Gasmet Technologies). The results showed the distribution and concentration level of gaseous products during sludge combustion and evidenced the feasibility of using sludge as a deNOX agent in cement industry.

Mechanistic study on NO reduction by sludge reburning in a pilot scale cement precalciner with different CO2 concentrations.

An experimental study on the effects of CO2 concentration on the release of reducing gases and the NO reduction efficiency by sludge reburning was carried out in a pilot scale cement precalciner. The results indicate that sludge reburning shows an ideal NO reduction activity. The best NO reduction efficiency of 54% is reached when the CO2 concentration is 25 vol%. Characteristic analysis of the sludge shows that the main types of reducing gases generated by sludge reburning are HCN, NH3, CO and CH4. Among them, CO2 concentration plays a crucial role in the release of HCN, CO and CH4. The mechanistic study indicates that NO reduction is dominated by homogeneous reduction during the sludge reburning process, in particular the reducing gases of CO and NH3 have significant influences on the NO reduction. Meanwhile, the effect of CO2 concentration on NO reduction is mainly due to the difference in CO release. The results of the present study not only provide insight into the mechanism of NO reduction by sludge reburning, but could also contribute to the development of NO X removal technology in the cement industry.

Research progress in machine learning methods for gene-gene interaction detection.

Complex diseases are results of gene-gene and gene-environment interactions. However, the detection of high-dimensional gene-gene interactions is computationally challenging. In the last two decades, machine-learning approaches have been developed to detect gene-gene interactions with some successes. In this review, we summarize the progress in research on machine learning methods, as applied to gene-gene interaction detection. It systematically examines the principles and limitations of the current machine learning methods used in genome wide association studies (GWAS) to detect gene-gene interactions, such as neural networks (NN), random forest (RF), support vector machines (SVM) and multifactor dimensionality reduction (MDR), and provides some insights on the future research directions in the field.