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Background: Rhodojaponin III (RJ-III), a characteristic diterpene of Rhododendron molle G. Don, has a wide range of pharmacological activities including anti-inflammatory, antihypertensive, and analgesic effects. However, further research and development have been limited because of its intense acute toxicity and poor pharmacokinetic profile. Objective: In this study, we propose the construction of folic acid–conjugated mesoporous silica nanoparticles (FA-MSNs) as carriers to deliver RJ-III in an attempt to reduce acute toxicity and improve biomedical applications by prolonging drug release and targeting delivery. Methods: FA-MSNs were synthesized and characterized. RJ-III was then loaded into FA-MSNs (RJIII@ FA-MSNs), and the in vitro drug release profile was assessed. Subsequently, the RJ-III@FAMSNs’ cytotoxicity and targeting efficiency were explored in lipopolysaccharide-activated RAW 264.7 cells, and their acute toxicity was investigated in mice. Results: Spherical FA-MSNs were approximately 122 nm in size. Importantly, the RJ-III@FA-MSNs showed prolonged RJ-III release in vitro. Moreover, in lipopolysaccharide-activated RAW 264.7 cells, RJ-III@FA-MSNs not only reduced the cytotoxicity of RJ-III (P < 0.01), but also showed a good targeting effect from the results of cellular uptake. Additionally, the acute toxicity results demonstrated that RJ-III@FA-MSNs improved the LD50 value of RJ-III in mice by intraperitoneal injection 10-fold. Conclusion: This is the first study to use FA-MSNs as carriers of RJ-III to reduce the acute toxicity of RJ-III. The results confirm the potential for targeted delivery of RJ-III in inflammatory cells to enhance efficacy, as well as providing data for future investigations on anti-inflammatory activity.

Triacylglycerol lipases (TGLs) can catalyze the hydrolysis reaction of triacylglycerol serving multiple functions in most organisms. Based on the genomic and transcriptomic databases of Neocaridina denticulata sinensis, two TGL genes from N. denticulata sinensis designated NdTGL1 and NdTGL2 were identified and characterized. NdTGL1 showed the highest expression in the stomach, followed by the testis and hepatopancreas, and NdTGL2 exhibited the maximum expression in the hepatopancreas, followed by the stomach and heart. Under the stimulation of copper ion, the expression of NdTGL1 peaked at 12 h and the expression of NdTGL2 elevated significantly at 24 h after stimulation (P 0.05). It is speculated that NdTGLs may play an important role in the stress response of N. denticulata sinensis. Challenged with Vibrio parahaemolyticus, the expression profiles of NdTGL1 and NdTGL2 in the hepatopancreas was different, which indicates that the immune response of the V. parahaemolyticus challenge might lead to changes in triglyceride metabolism. The recombinant NdTGL (recNdTGL1 and recNdTGL2) were obtained and the enzymatic characterization of recNdTGL1 and recNdTGL2 were determined. The common maximum activity and stability of the recNdTGL1 and recNdTGL2 were observed at 45 °C and 10 °C, respectively. Both recNdTGL1 and recNdTGL2 exhibited the highest activity at pH 10.0. Furthermore, the recNdTGL1 and recNdTGL2 displayed the maximum stability at pH 5.0 and pH 8.0, respectively. In presence of different metal ions, the enzyme activity of recNdTGL1 and recNdTGL2 were inhibited by Cu

Vibrio parahaemolyticus causes serious economic losses to the shrimp farming industry. There is still a lack of adequate understanding of the changes in the overall response of N. denticulata sinensis caused by V. parahaemolyticus, particularly with respect to gill tissue, which is severely damaged by the pathogen. In this study, a total of 1358 differentially expressed genes were identified between the PBS control and Vibrio stimulation groups using transcriptome sequencing techniques. After further screening and analysis, many immune-related genes were obtained, involving lysosome pathway, metabolic process, chitin-binding protein, and serine protease family members. In addition, we randomly selected six DEGs in the lysosome pathway for qRT-PCR verification, and the results showed that their expression patterns were consistent with the RNA-seq. The results demonstrate the molecular regulation of the gill tissue response to V. parahaemolyticus infection in N. denticulata sinensis, contributing to the understand of the complex and efficient innate immune system and defense mechanisms in crustaceans.

Neocaridina denticulata sinensis possesses characters of rapid growth, tenacious vitality, short growth cycle, transparent, and easy feeding. Therefore, it is gradually being developed into an animal model for basic research on decapod crustaceans. Herein, a Cu/Zn superoxide dismutase (Cu/Zn-SOD), named as Nd-ecCu/Zn-SOD, was identified and characterized from N. denticulata sinensis. The full-length cDNA sequence of Nd-ecCu/Zn-SOD is 829 bp containing a 684 bp open reading frame, which encodes a protein of 227 amino acid residues with a typical Sod_Cu domain. The quantitative real-time PCR analysis showed that Nd-ecCu/Zn-SOD mRNA was expressed in all the tested tissues. Under challenge with copper, the mRNA expression of Nd-ecCu/Zn-SOD reached the maximum at 6 h, and decreased until 24 h. After 24 h of exposure, its expression was up-regulated significantly at 36 h. After then its expression sharply decreased with a comeback at 48 h. The result indicated that Nd-ecCu/Zn-SOD might play an important role in the stress response of N. denticulata sinensis. The expression of Nd-ecCu/Zn-SOD in gills challenged with Vibrio parahaemolyticus changed in a time-dependent manner. Nd-ecCu/Zn-SOD was lowly expressed in early developmental stages by RNA-Seq technology, yet it showed that a cyclical rise and fall occurred between middle stages and late stages. In addition, Nd-ecCu/Zn-SOD was recombinantly expressed using E. coli and the recombinant protein was purified as a single band on SDS-PAGE. The recombinant Nd-ecCu/Zn-SOD (rNd-ecCu/Zn-SOD) existed enzymatic activity under a wide range of temperature and pH. The exposure of metal ions was found that Zn

This study evaluated the eco-geological environment quality of Hunjiang District, Baishan City, Jilin Province. Fifteen indicators were selected from geological, ecological, and social aspects to make the eco-geological environmental quality assessment more comprehensive. On the basis of improved game theory, two weighting methods of FAHP-CV (Fuzzy Analytic Hierarchy Process and Coefficient of Variation) were used to calculate the weight, and finally ArcGIS was used to intuitively understand the eco-geological environment quality of the study area. According to a threshold value, the eco-geological environment quality of the study area was divided into five types: better, good, medium, bad, and worse, and the results show that the overall eco-geological environment quality of the study area is in the middle and upper levels. The eco-geological environment quality of a small part of the area is poor. The classification of the eco-geological environment quality of Hunjiang District provides a scientific basis for the establishment of reasonable eco-geological environment protection and urban planning in the future.

Crustins are a kind of antibacterial peptides (AMP) existing in crustaceans, and their antibacterial abilities are considered to be related to the conserved WAP domain. In this study, a novel type I Crustin gene was identified in Exopalaemon carinicauda, named EcCru. The deduced amino acid sequence revealed that the conserved cysteine at position 7 in the WAP domain was replaced by aspartic acid. The gene is 405 bp in length, encoding 134 amino acids, and is mainly distributed in gills and hepatopancreas. After Vibrio parahaemolyticus and Aeromonas hydrophila stimulation, the expression of EcCru was significantly up-regulated within 12 h, and then returned to normal levels. The recombinant protein was obtained using the Pichia pastoris expression system, and the recombinant protein had neither antibacterial activity against gram-positive or gram-negative bacteria. But the antibacterial ability emerged when Asp

With the rapid development of urbanization and industrialization, water resources are in increasingly short supply, and the construction of sewage treatment plants can ensure the sustainable development of water resources. To eliminate the potential safety hazards of municipal sewage treatment plants and prevent safety accidents from the source, this paper takes a municipal sewage treatment plant in Changchun as the research object, puts forward the evaluation method of the “improved Best-Worst Method (BWM)—fuzzy comprehensive evaluation method”, and carries out safety evaluation research on the research object. Firstly, combined with the technological process of sewage treatment plants, the evaluation index system is constructed from four factors: human factors, material factors, environmental factors, and management factors. Secondly, the improved BWM is used to calculate the weights. Finally, the fuzzy comprehensive evaluation method is used for safety evaluation, and the evaluation of safety status is obtained: the safety level.

Taking Erdaojiang and Dongchang District of Tonghua City, Jilin Province as examples, this paper studies the ecological environment restoration and governance model of their mining areas. In this paper, the vegetation cover (NDVI) data in the past ten years were selected from the study area. The Theil–Sen median and Mann–Kendall (MK) methods were used to analyze the time series of NDVI, and the vegetation cover change trend map of the study area was obtained. Then, the land use data of the study area for 4 years were selected, and the transfer matrix method was used to analyze the land use conversion between the years. Finally, according to the characteristics of the distribution of mines in the study area, for the mining area in different natural geographical locations, it was concluded that the restoration of cultivated land and the restoration of forest land and ecological reconstruction were adopted. Among them, the restoration of forest land includes natural restoration and artificial intervention.

With extreme rainfall events and rapid urbanization, urban flood disaster events are increasing dramatically. As a key flood control city in China, Changchun City suffers casualties and economic losses every year due to floods. The improvement of flood resilience has become an important means for cities to resist flood risks. Therefore, this paper constructs an assessment model of urban flood resilience from four aspects: infrastructure, environment, society and economy. Then, it quantifies infrastructure and environmental vulnerability based on GIS, and uses TOPSIS to quantify social and economic recoverability. Finally, based on k-means clustering of infrastructure and environmental vulnerability and social and economic recoverability, the flood resilience of Changchun City was evaluated. The results show that different factors have different effects on flood resilience, and cities with low infrastructure and environmental vulnerability and high socioeconomic recoverability are more resilient in the face of floods. In addition, cities in the same cluster have the same flood resilience characteristics. The proposed framework can be extended to other regions of China or different countries by simply modifying the indicator system according to different regions, providing experience for regional flood mitigation and improving flood resilience.

Changbai Mountain is an important part of the development and opening pilot area of Changjitu. It is the birthplace of Songhua River, Yalu River, and Tumen River, and is known as the source of the three rivers. Millions of people live in the basin. A volcanic eruption accompanied by earthquakes would lead to a large number of landslides, debris flows, and show a chain effect, the formation of a secondary geological disaster chain, which is a serious threat to people’s lives and property safety. This paper selected indexes from three aspects: the hazard of earthquake-induced geological disaster chain, the exposure and vulnerability of disaster-bearing bodies, and the risk assessment of earthquake-induced geological disaster chain. The sensitivity values of each influence factor were calculated by the certainty factor (CF) using the support vector machine, and then, the susceptibility assessment was obtained. The cumulative displacement calculated by the Newmark model represented the potential risk intensity. We considered the Changbai Mountain volcanic earthquake–landslide disaster chain as an example. The results of risk assessment showed that the extremely high and high risk areas were mainly located within the 12 km radius of Tianchi Lake, and the other areas in the study area were mainly associated with very low to low risk values. The verification results showed that the receiver operating characteristic (ROC) curve area was 0.8373, indicating that the method was very effective in the identification and assessment of seismic hazard chain risk. In these high-risk areas, relevant countermeasures should be formulated to prevent the risk of geological disasters, strengthen the implementation of regional disaster prevention and reduction work, and ensure the safety of residents’ lives and property.

Peroxiredoxin (Prx) is an antioxidant protein family, which widely exists in organisms and plays an important role in innate immunity. In this study, the full-length cDNA of a Prx gene (NdPrx) was obtained from Neocaridina denticulata sinensis, which contains a 735 bp open reading frame (ORF) and encodes a polypeptide of 244 amino acids. It is inferred that the molecular weight of the encoded amino acid is 27261.20 Da and the theoretical isoelectric point is 6.16. Phylogenetic analysis shows that NdPrx and Prx4 have high homology, so it was named NdPrx4. Multiple alignment analysis showed that the amino acid sequence of NdPrx4 had high homology with Prx4 of other species, and the similarity with Homarus americanus was the highest, 92.86%. Quantitative real-time PCR analysis showed that NdPrx4 was expressed in various tissues of N. denticulata sinensis, and the expression in ovary was the highest. It was speculated that NdPrx4 may be related to maternal immune function. Under the stimulation of Cu

As a common aquatic pathogen, Vibrio parahaemolyticus can cause a variety of diseases of shrimp, especially acute hepatopancreatic necrosis disease (AHPND), which leads to great losses to the aquaculture industry around the world. However, the molecular mechanism of V. parahaemolyticus infection is still unclear. Neocaridina denticulate sinensis is a kind of small ornamental shrimp that is popular in aquarium trade, and due to its tenacious vitality, rapid growth, high reproductive capacity, it is very suitable to be developed as an animal model for basic research on decapod crustaceans. Thus, in this paper, transcriptomes of N. denticulate sinensis hepatopancreas with or without V. parahaemolyticus injection were explored. The results showed that a total of 23,624 genes with the N50 of 2705 bp were obtained. Comparative transcriptomic analysis revealed 21,464 differentially expressed genes between the V. parahaemolyticus infected and non-infected group, of which, 11,127 genes were up-regulated and 10,337 genes were down-regulated. Functional enrichment analysis suggested that many DEGs enriched in immune related pathways, including MAPK signaling pathway, Phosphatidylinositol signaling system, Chemokine signaling pathway, Phagosome and Jak-STAT signaling pathway and so on. Eight genes were selected randomly for qRT-PCR to verify the transcriptome sequencing results and the results showed the expression of these genes were consistent with the transcriptome results. Our work provides a unique and important dataset that contributes to the understanding of the molecular mechanisms of the immune response to V. parahaemolyticus infection and may further provide the basis for the prevention and resolution of bacterial diseases.

To estimate the key water quality parameters on a large scale, based on Pearson’s correlation analysis and band ratio, this study first obtains multiple sensitive band combinations (R ≥ 0.30, p < 0.01) for three key water quality parameters: dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Then, principal component analysis is used to reduce the dimensions and analyze multiple optimal combinations, and the first three principal components (PCs) of the optimal combinations are selected to analyze the water quality parameters. Finally, the water quality parameter models of DO, TN, and TP are proposed and compared based on spectral analysis and field measured water quality data respectively using Gaussian process regression and PCs for each parameter. Through model verification and by comparing the performance of the three models, it is found that the TP model performed well (R = 0.9824, p < 0.01), and TP grade accuracy rate is up to 94.97%. Through the error analysis of TN and DO, it is found that 93.0% of error samples occurs when TP < 0.1 mg/L in the water quality. These results would provide a scientific basis for water quality monitoring and water environment management in the study area and could also be used as a reference for water quality monitoring in other basins.

Tetramethylpyrazine (TMP) is commonly used as an antiplatelet drug in clinic. However, the short half-life and low bioavailability limited its applications. 3, 5, 6-Trimethylpyrazine-2-carboxAylic acid (TMP-COOH) and 2-hydroxy-3, 5, 6-trimethylpyrazine (TMP-OH) are the two major active metabolites of TMP in vivo. Both displayed antiplatelet aggregation activity but have obvious disadvantage of rapid metabolism. In this study, conjugates of TMP-COOH/TMP-OH with amino acids were designed to address this issue. We demonstrated that 13 out of 20 conjugates displayed higher antiplatelet aggregation activity than TMP. The optimized compound 4a was further proved to reduce clot retraction, prolong the bleeding time, thrombin time, prothrombin time, activated partial thromboplatin time and reduce fibrinogen content. Taken together, we demonstrated the conjugation strategy could be exploited to develop TMP derivatives for antiplatelet agents.

Global warming has increased the prevalence and severity of natural disasters, such as the increased frequency and intensity of drought events. In this study, we employed the standardized precipitation evapotranspiration index (SPEI) and standardized precipitation index (SPI) to analyze and predict the temporal and spatial variation of drought and its characteristics in the Songliao Plain under two global warming scenarios (1.5 and 2 °C). We used climate model data provided by the Inter-Sectoral Impact Model Intercomparison Project. The results show that drought will become more frequent in the future; it is more serious at representative concentration pathway (RCP) 8.5 than RCP4.5 and under global warming of 1.5 °C than 2 °C. Geographically, the SPEI indicates that there are signs of drought in the northwest and northeast of Songliao Plain while the SPI indicates that drought decreases from north to south. In terms of drought characteristics, the drought barycenter expressed by the SPEI moves to the northeast, while the drought barycenter expressed by the SPI moves to the northwest. The SPI relies on a single meteorological factor, making the SPI mutation test more stable. Finally, compared to the historical period (1976–2005), the frequency and duration of drought have increased, and it is more reliable to use the SPEI to monitor drought in this area.

Using unmanned aerial vehicle (UAV) hyperspectral images to accurately estimate the chlorophyll content of summer maize is of great significance for crop growth monitoring, fertilizer management, and the development of precision agriculture. Hyperspectral imaging data, analytical spectral devices (ASD) data, and SPAD values of summer maize in different key growth periods were obtained under the conditions of a micro-spray strip drip irrigation water supply. The hyperspectral data were preprocessed by spectral transformation methods. Then, several algorithms including Findpeaks (FD), competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and CARS_SPA were used to extract the sensitive characteristic bands related to SPAD values from the hyperspectral image data obtained by UAV. Subsequently, four machine learning regression models including partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), and deep neural network (DNN) were used to establish SPAD value estimation models. The results showed that the correlation coefficient between the ASD and UAV hyperspectral data was greater than 0.96 indicating that UAV hyperspectral image data could be used to estimate maize growth information. The characteristic bands selected by different algorithms were slightly different. The CARS_SPA algorithm could effectively extract sensitive hyperspectral characteristics. This algorithm not only greatly reduced the number of hyperspectral characteristics but also improved the multiple collinearity problem. The low frequency information of SSR in spectral transformation could significantly improve the spectral estimation ability for SPAD values of summer maize. In the accuracy verification of PLSR, RF, XGBoost, and the DNN inversion model based on SSR and CARS_SPA, the determination coefficients (R2) were 0.81, 0.42, 0.65, and 0.82, respectively. The inversion accuracy based on the DNN model was better than the other models. Compared with high-frequency information, low-frequency information (DNN model based on SSR and CARS_SPA) had a strong estimating ability for SPAD values in summer maize canopy. This study provides a reference and technical support for the rapid non-destructive testing of summer maize.

Wnt proteins are a class of hydrophobic secreted glycoproteins involved in diverse important biological processes, such as tissue formation and regeneration, embryonic development and innate immunity. The Wnt gene family has an early origin and is present in all deuterostomes. In the process of evolution, the phenomenon of gene expansion, contraction and adaptive evolution occurs in the Wnt gene family. In the current study, eleven Wnt genes (NdWnt1-2, NdWnt4-7, NdWnt9-11, NdWnt16, and NdWntA) belonging to different subfamilies were obtained based on the genomic and transcriptomic data of Neocaridina denticulata sinensis. Then the expression patterns of all NdWnts were analyzed in various tissues, at different developmental stages and under different stresses. The expression profiles of NdWnts at different developmental stages showed that most NdWnt genes were initially expressed at gastrula stage, and the expression of NdWnt5 and NdWnt16 throughout all developmental stages. The spatial expression of NdWnt genes presented tissue specificity. They were mainly expressed in four tissues, namely gill, intestines, ovary and eyestalk. After Vibrio parahemolyticus infection and under copper exposure, the expression levels of five NdWnts (NdWnt1, NdWnt5, NdWnt10, NdWnt16 and NdWntA) were variable. Our findings enrich the research on the Wnt gene family of N. denticulata sinensis and provide valuable insights into relationship between structure and function of Wnt genes in crustaceans.

The Climate Hazard Group InfraRed Precipitation with Stations (CHIRPS) dataset was examined for its variability and performance in explaining precipitation variations, forecasting, and drought monitoring in Southeast Asia (SEA) for the period of 1981–2020. By using time-series analysis, the Standardized Precipitation Index (SPI), and the Autoregressive Integrated Moving Average (ARIMA) model this study established a data-driven approach for estimating the future trends of precipitation. The ARIMA model is based on the Box Jenkins approach, which removes seasonality and keeps the data stationary while forecasting future patterns. Depending on the series, ARIMA model annual estimates can be read as a blend of recent observations and long-term historical trend. Methods for determining 95 percent confidence intervals for several SEA countries and simulating future annual and seasonal precipitation were developed. The results illustrates that Bangladesh and Sri Lanka were chosen as the countries with the greatest inaccuracies. On an annual basis, Afghanistan has the lowest Mean Absolute Error (MAE) values at 33.285 mm, while Pakistan has the highest at 35.149 mm. It was predicted that these two countries would receive more precipitation in the future as compared to previous years.

Flooding is one of the catastrophic natural hazards worldwide that can easily cause devastating effects on human life and property. Remote sensing devices are becoming increasingly important in monitoring and assessing natural disaster susceptibility and hazards. The proposed research work pursues an assessment analysis of flood susceptibility in a tropical desert environment: a case study of Yemen. The base data for this research were collected and organized from meteorological, satellite images, remote sensing data, essential geographic data, and various data sources and used as input data into four machine learning (ML) algorithms. In this study, RS data (Sentinel-1 images) were used to detect flooded areas in the study area. We also used the Sentinel application platform (SNAP 7.0) for Sentinel-1 image analysis and detecting flood zones in the study locations. Flood spots were discovered and verified using Google Earth images, Landsat images, and press sources to create a flood inventory map of flooded areas in the study area. Four ML algorithms were used to map flash flood susceptibility (FFS) in Tarim city (Yemen): K-nearest neighbor (KNN), Naïve Bayes (NB), random forests (RF), and eXtreme gradient boosting (XGBoost). Twelve flood conditioning factors were prepared, assessed in multicollinearity, and used with flood inventories as input parameters to run each model. A total of 600 random flood and non-flood points were chosen, where 75% and 25% were used as training and validation datasets. The confusion matrix and the area under the receiver operating characteristic curve (AUROC) were used to validate the susceptibility maps. The results obtained reveal that all models had a high capacity to predict floods (AUC > 0.90). Further, in terms of performance, the tree-based ensemble algorithms (RF, XGBoost) outperform other ML algorithms, where the RF algorithm provides robust performance (AUC = 0.982) for assessing flood-prone areas with only a few adjustments required prior to training the model. The value of the research lies in the fact that the proposed models are being tested for the first time in Yemen to assess flood susceptibility, which can also be used to assess, for example, earthquakes, landslides, and other disasters. Furthermore, this work makes significant contributions to the worldwide effort to reduce the risk of natural disasters, particularly in Yemen. This will, therefore, help to enhance environmental sustainability.

In the context of climate change, the remote sensing identification of crops is extremely important for the rapid development of agricultural economy and the detailed assessment of the agro-meteorological disasters. The Jilin Province is the main grain production area in China, with a reputation of being a “golden corn belt”. The main crops in the Jilin Province are rice, corn, and soybean. A large amount of remote sensing data and programming codes from the Google Earth engine (GEE) platform allow for large-area farmland recognition. However, the substantial amount of crop sample information hinders the mapping of crop types over large farmland areas. To save costs and quickly and accurately map the crop types in a study area, multi-source remote sensing data and historical crop labels based on the GEE platform were used in this study, together with the random forest classification method and optimal feature selection to classify farming areas in the Jilin Province. The research steps were as follows: (1) select samples based on the historical crop layer of the farmland; and (2) obtain the classification characteristics of rice, corn, and soybean using multi-source remote sensing data, calculating the feature importance scores. Using different experimental combinations, an optimal classification method was then selected to classify crops in the Jilin Province. The results indicated variable impacts of vegetation indices (of different periods) on crop classification. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and green chlorophyll vegetation index (GCVI) in June exerted a significant impact on the classification of rice, corn, and soybean, respectively. The overall accuracy of crop classification during different periods based on historical crop labels reached 0.70, which is acceptable in crop classification research. The study results demonstrated that the proposed method has promising potential for mapping large-scale crop areas.

Qingyun Yang,&ast; Jian Yang,&ast; Shuigen Sun, Jingyi Zhao, Shuang Liang, Yi Feng, Minchen Liu, Jiquan Zhang Engineering Research Center of Modern Preparation Technology of TCM of Ministry of Education, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yi Feng; Jiquan Zhang, Engineering Research Center of Modern Preparation Technology of TCM of Ministry of Education, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Shanghai, 201203, People’s Republic of China, Tel +86-21-51322431 ; +86 13816062506, Email fyi@vip.sina.com; jqzhang@shutcm.edu.cnBackground: Rhodojaponin III (RJ-III) is a bioactive diterpenoid, which is mainly found in Rhododendron molle G. Don (Ericaceae), a potent analgesia in traditional Chinese medicine with several years of clinical applications in the country. However, its clinical use is limited by its acute toxicity and poor pharmacokinetic profiles. To reduce such limitations, the current study incorporated RJ-III into the colloidal drug delivery system of hydroxypropyl trimethyl ammonium chloride chitosan (HACC)-modified solid lipid nanoparticles (SLNs) to improve its sustained release and antinociceptive effects in vivo for oral delivery.Results: The optimized RJ-III@HACC-SLNs were close to spherical, approximately 134 nm in size, and with a positive zeta potential. In vitro experiments showed that RJ-III@HACC-SLNs were stable in the simulated gastric fluid and had a prolonged release in PBS (pH = 6.8). Pharmacokinetic results showed that after intragastric administration in mice, the relative bioavailability of RJ-III@HACC-SLNs was 87.9%. Further, it was evident that the peak time, half-time, and mean retention time of RJ-III@HACC-SLNs were improved than RJ-III after the administration. In addition, pharmacodynamic studies revealed that RJ-III@HACC-SLNs markedly reduced the acetic acid, hot, and formalin-induced nociceptive responses in mice (P < 0.001), and notably increased the analgesic time (P < 0.01). Moreover, RJ-III@HACC-SLNs not only showed good biocompatibility with Caco-2 cells in vitro but its LD50 value was also increased by 1.8-fold as compared with that of RJ-III in vivo.Conclusion: These results demonstrated that RJ-III@HACC-SLNs improved the pharmacokinetic characteristics of the RJ-III, thereby exhibiting toxicity-attenuating potential and antinociceptive enhancing properties. Consequently, HACC-SLNs loaded with RJ-III could become a promising oral formulation for pain management that deserves further investigation in the future.Graphical Abstract: Keywords: rhodojaponin III, multimodal antinociceptive, oral administration, solid lipid nanoparticles, hydroxypropyl trimethyl ammonium chloride chitosan, pharmacokinetic

Rain-fed agriculture is easily affected by meteorological disasters, especially drought. As an important factor of risk formation, actively carrying out agricultural drought vulnerability assessments is conducive to improving food security and reducing economic losses. In this study, an SE-DEA model with regional exposure and drought risk as input factors and the maize yield reduction rate and drought-affected area as output factors is established. The aim is to evaluate and zone the drought vulnerability of the maize belt in the Songliao Plain. The results show the following: (1) From 2000 to 2019, the drought vulnerability of maize showed a fluctuating increasing trend. The vulnerability in Harbin and central Jilin Province is high, which is extremely unfavorable for maize production. (2) Comparing the historical disaster data with the drought vulnerability map generated using the SE-DEA model, it could be found that the results obtained using the SE-DEA model are reliable. (3) The Tobit model shows that the proportion of the effective irrigated area is more important to alleviate vulnerability. For drought vulnerability zoning using a cluster analysis, we suggest that regulated deficit irrigation should be actively developed in high-vulnerability areas to ensure maize yield while improving water efficiency. The results of this study can provide a basis for the development of drought mitigation and loss reduction strategies, and they provide new ideas for future research.

The negative impact of high-temperature disaster on agricultural production is becoming more and more serious, and reducing the vulnerability to high-temperature disaster is fundamental to achieving sustainable agricultural development. This study is mainly focused on the vulnerability to agricultural high-temperature disaster in Shaanxi Province, China. Firstly, 15 indicators were selected from the perspectives of exposure, sensitivity, and adaptability. Secondly, the combined weighting method (Critic-G1 model) was used to determine the weight of each index. Based on the aforementioned procedures, the Kullback–Leibler (KL)-distance-improved TOPSIS model was utilized to evaluate the vulnerability. Lastly, the obstacle model was used to analyze the influencing factors and to make recommendations for disaster prevention and mitigation. The results show that: (1) The improved TOPSIS model was closer to the results of the synthetical index method. (2) The northern and southern area of Shaanxi is more vulnerable to high-temperature disaster, especially in Ankang and Tongchuan. Low values are distributed in the Guanzhong Plain. (3) Sensitivity is the biggest obstacle to reducing the vulnerability to high-temperature disaster. Among the influencing factors, the meteorological yield reduction coefficient of variation, multiple cropping index and per capita net income of rural residents of the obstacle are high. Decreasing sensitivity should be accompanied by increasing adaptability to improve regional disaster preparedness and mitigation. The results of this study can provide a basis for the development of agricultural high-temperature disaster mitigation and loss reduction strategies and provide new ideas for future research.

Background: Neocaridina denticulata sinensis possesses characters of rapid growth, tenacious vitality, short growth cycle, transparent, and easy feeding. Therefore, it is gradually being developed into an animal model for basic research on decapod crustaceans. Superoxide dismutase (SOD) family, the first line of defense to eliminate ROS, can catalyze ROS into O2 and H2O2. Particularly, Cu/Zn-SOD accounts for about 90% of total SOD, which play an important role in many creatures. Nevertheless, only a small number of related reports have cloned and characterized Cu/Zn-SOD in decapod crustaceans. Herein, a Cu/Zn superoxide dismutase (Cu/Zn-SOD), named as Nd-ecCu/Zn-SOD, was identified and characterized from N. denticulata sinensis. Results: The full-length cDNA sequence of Nd-ecCu/Zn-SOD is 829 bp containing a 684 bp open reading frame, which encoded a protein of 227 amino acid residues with a typical Sod_Cu domain. The quantitative real-time PCR analysis showed that Nd-ecCu/Zn-SOD mRNA was expressed in all the tested tissues. Under challenge with copper, the mRNA expression of Nd-ecCu/Zn-SOD reached the maximum at 6 h, and decreased until 24 h. After 24 h of exposure, its expression was up-regulated significantly at 36 h. After then its expression sharply decreased with a comeback at 48 h. The expression of Nd-ecCu/Zn-SOD in gills challenged with Vibrio parahaemolyticus changed in a time-dependent manner. In addition, Nd-ecCu/Zn-SOD was recombinantly expressed using E. coli and the recombinant protein was purified as a single band on SDS-PAGE. The recombinant Nd-ecCu/Zn-SOD (rNd-ecCu/Zn-SOD) existed enzymatic activity under a wide range of temperature and pH. The exposure of metal ions was found that Zn2+, Mg2+, Ca2+, Ba2+, and Cu2+ could inhibit the enzymatic activity of rNd-ecCu/Zn-SOD, and Mn2+ increased the enzymatic activity of rNd-ecCu/Zn-SOD. Conclusions: the recombinant protein was sensitive to change of pH and addition of different metal ions, and the expression of Nd-ecCu/Zn-SOD challenged with V. parahaemolyticus and exposure of Cu2+changed in a time-dependent manner. The results indicated that Nd-ecCu/Zn-SOD might sever as a biomarker of environment pollution and plays an important role in immune reaction.

PARP (poly ADP-ribose polymerase) family is a crucial DNA repair enzyme that responds to DNA damage, regulates apoptosis, and maintains genome stability; therefore, PARP inhibitors represent a promising therapeutic strategy for the treatment of various human diseases including COVID-19. In this study, a multi-task FP-GNN (Fingerprint and Graph Neural Networks) deep learning framework was proposed to predict the inhibitory activity of molecules against four PARP isoforms (PARP-1, PARP-2, PARP-5A, and PARP-5B). Compared with baseline predictive models based on four conventional machine learning methods such as RF, SVM, XGBoost, and LR as well as six deep learning algorithms such as DNN, Attentive FP, MPNN, GAT, GCN, and D-MPNN, the evaluation results indicate that the multi-task FP-GNN method achieves the best performance with the highest average BA, F1, and AUC values of 0.753 ± 0.033, 0.910 ± 0.045, and 0.888 ± 0.016 for the test set. In addition, Y-scrambling testing successfully verified that the model was not results of chance correlation. More importantly, the interpretability of the multi-task FP-GNN model enabled the identification of key structural fragments associated with the inhibition of each PARP isoform. To facilitate the use of the multi-task FP-GNN model in the field, an online webserver called PARPi-Predict and its local version software were created to predict whether compounds bear potential inhibitory activity against PARPs, thereby contributing to design and discover better selective PARP inhibitors.

The urbanization rate in China has been increasing in recent years, and along with the increasing intensity of human engineering activities, ecological and geological (eco-geological) degradation have become key factors impeding sustainable urban development. Taking the concentrated mineral exploitation area of Tonghua City as an example, the distribution of mines in the area is concentrated and the spatial heterogeneity is significant. This paper includes 14 evaluation indicators in three aspects: eco-geological environment background, anthropogenic and mining engineering activities, and environmental pollution. Then, based on game theory combined with ANP-CV (Analytic Network Process and Coefficient of Variation), two empowerment methods, GIS spatial calculation is used to evaluate the eco-geological environment quality (EEQ). The results showed that the EEQ was divided into grades I–V from high to low, with areas of 21.13%, 30.35%, 27.00%, 14.30%, and 7.22%, respectively; the EEQ of the Hun River basin has a high spatial autocorrelation and low EEQ, and the EEQ grade of mines was divided on this basis; the hot spot analysis is useful for determining the EEQ, as well as for allocating mine restoration resources in a sensible manner. Finally, we propose countermeasures to improve EEQ, and this study can provide a scientific basis for ecological construction and geological environmental protection in Tonghua City.

The agricultural area adjacent to highways experienced high threat ofpersistent organic pollutants (POPs), which are fromvehicle exhaust, asphalt, and tyre wear.To analyze the presence, distributio...

The northern scenic area of Changbai Mountain is a high-incidence area of debris flow disasters, which seriously threaten the safety of tourist’s lives and property. Monitoring debris flow and providing early warning is critical for timely avoidance. Monitoring the change of debris flow source is an effective way to predict debris flow, and the change of source can be reflected in the settlement deformation of the study area. The offset tracking technique (OT) is insensitive to the coherence of SAR images and can resist the decoherence of D-InSAR and SBSA-InSAR to a certain extent. It is a technical means for monitoring large gradient deformation. It has been widely used in the field of seismic activity, glaciers and landslides in recent years, but few scholars have applied this technique in the field of debris flow. In this paper, we use OT techniques in combination with field surveys, Google imagery and Sentinel-1 data to monitor surface deformation in the northern scenic area of Changbai Mountain in 2017 and use D-InSAR techniques to compare and complement the OT monitoring results. The results of this study show that for monitoring surface deformation in the study area after a mudslide, it is better to use both methods to determine the surface deformation in the study area rather than one, and that both methods have their own advantages and disadvantages and yet can complement each other. Finally, we have predicted the development trend of mudflows in the study area by combining the calculated single mudflow solids washout, which will help to improve the long-term monitoring and warning capability of mudflows in the study area. The study also enriches the application of offset-tracking technology and D-InSAR in the field of geohazard monitoring and provides new ideas and methods for the study of mudflow material source changes.

Chain disasters often cause greater casualties and economic losses than single disasters. It plays an important role in the prevention and control to draw the susceptibility map and hazard map of geological hazards. To the best of our knowledge, the existing models are not suitable for the study of earthquake–geological disaster chains. Therefore, this study aims to establish a DNN model suitable for the study of earthquake–geological disaster chains. Firstly, nine key factors affecting geological disasters were selected and multi-source data sets were established based on geological disaster points in the study area. Secondly, the DNN model is trained to calculate the susceptibility of landslides and is discussed with the Support Vector Machine (SVM) model, Logistic Regression (LR) model, and Random Forest (RF) model. Finally, verify with the ROC curve. The verification results show that the DNN model has the highest accuracy among the proposed models. It is suitable for drawing geological hazard susceptibility maps and hazard maps. Therefore, it is proved that the model can be applied for the prediction of chain disasters and is a promising tool for geological hazard assessment.

Peroxiredoxin (Prx) is an antioxidant protein that widely exists in various organisms. To further investigate the role of Prx in the antioxidant and immune responses of Neocaridina denticulata sinensis, the full-length cDNA sequence of a Prx gene (Nd-Prx) from N. denticulata sinensis was obtained. The open reading frame (ORF) of Nd-Prx is 597 bp and encodes 198 amino acids. Amino acid similarity alignment showed that Nd-Prx contained a conserved sequence region "FYPLDFTFVCPTEI". qRT-PCR assay showed that Nd-Prx was expressed in all tested tissues and its expression was highest in the ovary. Nd-Prx was most highly expressed at 36 h after copper stimulation. Nd-Prx expression levels in hepatopancreas were significantly upregulated after Vibrio parahaemolyticus challenge (P 0.05). In addition, the recombinant Nd-Prx was prepared and its enzyme activity was most stable at 70 °C with pH of 6.0. The antioxidant activity and DNA protection of recombinant Nd-Prx was also demonstrated. In summary, this study investigated the role of Prx in antioxidant and immune responses of N. denticulata sinensis, which might provide a foundation for further exploring Prx in immune system of crustaceans and for the application in disease control.

Earth fissures are potential hazards that often cause severe damage and affect infrastructure, the environment, and socio-economic development. Owing to the complexity of the causes of earth fissures, the prediction of earth fissures remains a challenging task. In this study, we assess earth fissure hazard susceptibility mapping through four advanced machine learning algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), Naïve Bayes (NB), and K-nearest neighbor (KNN). Using Qa’ Jahran Basin in Yemen as a case study area, 152 fissure locations were recorded via a field survey for the creation of an earth fissure inventory and 11 earth fissure conditioning factors, comprising of topographical, hydrological, geological, and environmental factors, were obtained from various data sources. The outputs of the models were compared and analyzed using statistical indices such as the confusion matrix, overall accuracy, and area under the receiver operating characteristics (AUROC) curve. The obtained results revealed that the RF algorithm, with an overall accuracy of 95.65% and AUROC, 0.99 showed excellent performance for generating hazard maps, followed by XGBoost, with an overall accuracy of 92.39% and AUROC of 0.98, the NB model, with overall accuracy, 88.43% and AUROC, 0.96, and KNN model with general accuracy, 80.43% and AUROC, 0.88), respectively. Such findings can assist land management planners, local authorities, and decision-makers in managing the present and future earth fissures to protect society and the ecosystem and implement suitable protection measures.

Grassland fire dynamics are subject to myriad climatic, biological, and anthropogenic drivers, thresholds, and feedbacks and therefore do not conform to assumptions of statistical stationarity. The presence of non-stationarity in time series data leads to ambiguous results that can misinform regional-level fire management strategies. This study employs non-stationarity in time series data among multiple variables and multiple intensities using dynamic simulations of autoregressive distributed lag models to elucidate key drivers of climate and ecological change on burned grasslands in Xilingol, China. We used unit root methods to select appropriate estimation methods for further analysis. Using the model estimations, we developed scenarios emulating the effects of instantaneous changes (i.e., shocks) of some significant variables on climate and ecological change. Changes in mean monthly wind speed and maximum temperature produce complex responses on area burned, directly, and through feedback relationships. Our framework addresses interactions among multiple drivers to explain fire and ecosystem responses in grasslands, and how these may be understood and prioritized in different empirical contexts needed to formulate effective fire management policies.

Despite recent advancements in waste management technologies, landfills remain the dominant approach for the final disposal of solid waste (SW) around the world. However, landfills are associated with adverse environmental and health-related impacts. This study is primarily applied to Dar es Salaam, a city in the East African region that has failed to follow appropriate approaches for site selection and that the methods used to select the current sites are manually operated. The study used a geographical information system (GIS) combined with multicriteria decision analysis (MCDA) to objectively determine the optimal landfill sites. A comprehensive list of assessment criteria was examined through a computerized site selection procedure based on GIS to analyze the ground that was surveyed and to remotely obtain data. Analytical hierarchy process (AHP)—one of the best MCDA approaches—was used to assign relative weight and evaluate each criterion. Finally, the study computed site suitability indices (SSIs), which aids in the identification of site suitability levels (SSLs) for the entire region. The final suitability map produced by overlaying the criteria map layers showed that a large part of the study area (60.92%) was completely unsuitable for landfill siting. The remaining areas consisted of land with varying suitability level, which were grouped into four classes: very low (30.92%), low (2.05%), moderate (5.65%), and high (0.46). To this end, only the high suitability class (0.46%) was of acceptable suitability levels (ASLs) (SSI ≥ 2.0). From the numerous identified sites with ASL, only three sites were suggested. Due to concerns regarding the lifespan and environment-related risks associated with these suggested sites, there is a need to further study new environmentally friendly techniques, considering the importance of circular economy agenda of waste to resource/energy for sustainable solid waste management.