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Sjögren's syndrome (SS) is a chronic autoimmune disease that affects the exocrine glands and may lead to a range of systemic symptoms that impact various organs. Both innate and adaptive immune pathways might trigger the disease. Studying the signaling pathways underlying SS is crucial for enhancing diagnostic and therapeutic effectiveness. SS poses an ongoing challenge for medical professionals owing to the limited therapeutic options available. This review offers a comprehensive understanding of the intricate nature of SS, encompassing disease classification criteria, risk factors, and signaling pathways in immunity and inflammation. The advancements summarized herein have the potential to spark new avenues of research into SS.

Gouty arthritis (GA), a metabolic and immunologic disease, primarily affects joints. Dysbiosis of intestinal flora is an important cause of GA. The metabolic disorders of intestinal flora leading to GA and immune disorders might play an important role in patients with hyperuricemia and established GA. However, the exact mechanisms, through which the dysbiosis of intestinal flora causes the development of GA, are not fully understood yet. Moreover, several therapies commonly used to treat GA might alter the intestinal flora, suggesting that modulation of the intestinal flora might help prevent or treat GA. Therefore, a better understanding of the changes in the intestinal flora of GA patients might facilitate the discovery of new diagnostic and therapeutic approaches. The current review article discusses the effects of intestinal flora dysbiosis on the pathogenesis of GA and the cross-regulatory effects between gut flora and drugs for treating GA. This article also highlights the modulatory effects of gut flora by traditional Chinese medicine (TCM) to lower uric acid levels and relieve joint pain as well as provides a summary and outlook, which might help guide future research efforts.

Acquiring a comprehensive understanding of cropping patterns and their spatiotemporal distribution is crucial for sustainable agricultural development and ecological environment protection. However, the similarity of crop spectra and the diversity of ecosystem types hinder the accurate mapping of cropping patterns, especially in agricultural landscape regions. Hence, taking Xinghua County as study area, this article proposed a novel method for integrating multidimensional feature indices and phenological windows, named phenological window feature (PWF), to achieve efficient and accurate mapping of cropping patterns. In this study, we adopt a two-step approach. First, time-series curves of feature indices were constructed using Sentinel-1/2 satellite data to determine the phenological windows of different cropping patterns and construct PWF sets. Then, the ruleset threshold method (RTM) and random forest (RF) algorithms were used to map cropping patterns including wheat-rice, crayfish-rice, vegetable-rice, rice-rapeseed, rapeseed-vegetable, and year-round vegetables. The results indicate that the phenological windows extracted from the cropping patterns in the study area were 30–120, 90–135, and 200–270 days, respectively. The overall accuracies of RTM and RF, based on PWF, were 85.91% and 89.50%, respectively, and the kappa coefficients for RTM and RF were 0.831 and 0.872, respectively. In terms of classification performance, RF slightly outperformed RTM. The study demonstrates that PWF proposed in this article can be effectively utilized for mapping cropping patterns in complex agricultural landscape regions.

Satellite communication links are vulnerable to attacks due to the lack of necessary security protection and can be considered as a white-box environment. In a white-box environment, cryptanalysts can access the intermediate processes of the algorithm and even manipulate or change the operating environment. Cryptanalysts can obtain keys or tamper with important data in many ways, which makes data communication unsecured. To solve the problem, a lightweight white-box cipher over Addition/Rotation/XOR (ARX) structure (LWARX) is proposed, which has 48 rounds of iterations through an unbalanced Feistel structure. On this basis, some of its linear operations are represented as lookup tables, and a secure external coding method is combined to complete the white-box implementation. The design and white-box implementation of the algorithm has resulted in a lighter and more rational structure, and still provides sufficient obfuscation of the data even when the S-box is discarded. The test results show that the average encryption speed is 37.53 Kbps, and average encryption speed after white-box implementation is 30.05 Kbps. And it can resist various attacks. The security of the algorithm against common attack methods such as differential analysis, linear analysis, code lifting attacks and BGE attacks is given in the paper with security analysis and specific values. This scheme balances computing efficiency and security, takes up little space, can be applied to scenarios with limited hardware and software resources, and broadens the application area of white-box cryptography.

Gouty nephropathy (GN) is a metabolic disease with persistently elevated blood uric acid levels. The main manifestations of GN are crystalline kidney stones, chronic interstitial nephritis, and renal fibrosis. Understanding the mechanism of the occurrence and development of GN is crucial to the development of new drugs for prevention and treatment of GN. Currently, most studies exploring the pathogenesis of GN are primarily based on animal and cell models. Numerous studies have shown that inflammation, oxidative stress, and programmed cell death mediated by uric acid and sodium urate are involved in the pathogenesis of GN. In this article, we first review the mechanisms underlying the abnormal intrinsic immune activation and programmed cell death in GN and then describe the characteristics and methods used to develop animal and cell models of GN caused by elevated uric acid and deposited sodium urate crystals. Finally, we propose potential animal models for GN caused by abnormally high uric acid levels, thereby provide a reference for further investigating the methods and mechanisms of GN and developing better prevention and treatment strategies.

Optical remote sensing satellites provide rapid access to regional topsoil salinization mapping. However, mapping topsoil salinization based on spectral reflectance is always affected by background material like vegetation cover, straw mulching and soil types. In light of these challenges, this study investigates the potential of image fusion, where images of original and bare soil pixels were combined, to minimize the impact of vegetation cover on topsoil salinity mapping. A case study was presented for the typical vegetation cover area using synchronized Sentinel-2 MSI image (named original image) and 255 ground-truth data collected in October 2020, aligning with periods of vegetation cover and salt return. Furthermore, to obtain novel bare soil pixels, multi-temporal Sentinel-2 MSI images were acquired during two distinct intervals: March to May and September to November, spanning the years from 2018 to 2021. The synthetic soil image (SYSI) was obtained by extracting bare soil pixels from multi-temporal images. Two images (original, SYSI) were fused with non-negative matrix factorization (NMF) method, named SYSIfused. Then, the stacking machine algorithm was used for soil salinity mapping under different soil types, with evaluating the impact of SYSIfused on the accuracy of soil salinity prediction. The results showed the SYSIfused outperformed the original image (the R2 of the best models increased by 0.054–0.242, RMSE and MAE decreased by 0.049–0.780 and 0.012–0.546, respectively). Based on the SYSIfused, the order of the effect of soil types was coastal bog solonchaks > alluvial soil > cinnamon soil > coral saline soil > overall samples, and their roles in improving the R2 of the model were 0.141, 0.085, 0.022, 0.012, respectively. Besides, stacking models with the SYSIfused provided the best prediction performances (R2 = 0.742, RMSE = 0.377, MAE = 0.362). This study introduces the concept of merging original images with SYSI, resulting in a significant improvement in the accuracy of soil salinity mapping in areas covered by vegetation.

The application field of drones is being broadened with the development of communication and computer technology, but the communication link and data security of drones are facing great threats due to the limited hardware and software resource environment. Attackers can access not only the intermediate data of drone nodes through various means, but even tamper or manipulate their communication environment, when the drone and the control terminal are not trusted and can be regarded as a white-box attack environment. To solve the security problem in drone communication, a lightweight block cipher CARX for drones is proposed and white-boxed for its implementation to cope with the increasingly severe white-box attacks. And over this cryptographic algorithm, a simulated lightweight white-box encryption communication system for drones (LWED) is designed, which can complete the encryption and decryption operations and secure transmission of data and images, etc. The average encryption rate of the 64-bit version of the algorithm is 7.29×10-2Mbps, and the average encryption rate of white-box CARX (WCARX) is 1.18×10-2Mbps, and the average decryption time of the flight status parameters data of the encrypted communication control system is 0.14s. Compared with similar algorithms, the CARX algorithm can guarantee the encryption and decryption efficiency while providing high security and resistance to a variety of attacks, and can fully secure the data during the communication process after being built into the security system of drone.

Soil surveying and mapping provide important support for environmental science research on soil and other resources. Due to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy. Therefore, there is an urgent need for accurate and up-to-date soil maps. Soil has a high correlation with its corresponding environmental factors in space, and typical samples contain an appropriate soil–environment relationship of soil types. Understanding how to extract typical samples according to environmental factors and determine the implied soil–environment relationship is the key to updating soil maps. In this study, a hierarchical typical sample extraction method based on land use type and environmental factors was designed. According to the corresponding relationship between the soil type and the land use type (ST-LU), the outdate soil map patches caused by changes in land use were excluded, follow by typical samples being extracted according to the peak intervals of the soil–environmental factor histograms. Additionally, feature selection was performed through variance analysis and mutual information, and four machine learning models were used to predict soil types. In addition, the influence of environmental factors on soil prediction was discussed, in terms of variable importance analysis. Using an overall common validation set, the results show that the prediction accuracy using typical samples for learning in the modeling set is above 0.8, while the prediction accuracy when using random samples is only about 0.4. Compared with the original soil map, the accuracy and resolution of the predicted soil maps based on typical samples are greatly improved. In general, typical samples can effectively explore the actual soil–environment knowledge implied in the soil type map. By extracting typical samples from historical soil type map and combining them with high-resolution remote sensing data, we can generate new soil type maps with high accuracy and short update cycle. This can provide some references for typical sampling design and soil type prediction.

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping.

Objective: To observe the clinical efficacy and safety of Yiqi Yangxue formula (YQYXF) on knee osteoarthritis (KOA), and to explore the underlying therapeutic mechanism of YQYXF through endogenous differential metabolites and their related metabolic pathways.Methods: A total of 61 KOA patients were recruited and divided into the treatment group (YQYXF, 30 cases) and the control group (celecoxib, Cxb, 31 cases). Effects of these two drugs on joint pain, swelling, erythrocyte sedimentation rate (ESR) and c-reactive protein (CRP) were observed, and their safety and adverse reactions were investigated. In animal experiments, 63 SD rats were randomly divided into normal control (NC) group, sham operation (sham) group, model (KOA) group, Cxb group, as well as low-dose (YL), medium-dose (YM), and high-dose groups of YQYXF (YH). The KOA rat model was established using a modified Hulth method. Ultra-high-performance liquid chromatography/Q Exactive HF-X Hybrid Quadrupole-Orbitrap Mass (UHPLC-QE-MS)-based metabolomics technology was used to analyze the changes of metabolites in plasma samples of rats. Comprehensive (VIP) >1 and t-test p < 0.05 conditions were used to screen the disease biomarkers of KOA, and the underlying mechanisms of YQYXF were explored through metabolic pathway enrichment analysis. The related markers of YQYXF were further verified by ELISA (enzyme-linked immunosorbent assay).Results: YQYXF can improve joint pain, swelling, range of motion, joint function, Michel Lequesen index of severity for osteoarthritis (ISOA) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score, ESR, and CRP. No apparent adverse reactions were reported. In addition, YQYXF can improve cartilage damage in KOA rats, reverse the abnormal changes of 16 different metabolites, and exert an anti-KOA effect mainly through five metabolic pathways. The levels of reactive oxygen species (ROS) and glutathione (GSH) were significantly decreased after the treatment of YQYXF.Conclusion: YQYXF can significantly improve the clinical symptoms of KOA patients without obvious adverse reactions. It mainly improved KOA through modulating lipid metabolism-related biomarkers, reducing lipid peroxidation and oxidative stress.

As natural functional bioactive ingredients found in foods and plants, polyphenols play various antioxidant and anti-inflammatory roles to prevent the development of disease and restore human health. The multi-target modulation of polyphenols provides a novel practical therapeutic strategy for neurodegenerative diseases that are difficult to treat with traditional drugs like glutathione and cholinesterase inhibitors. This review mainly focuses on the efficacy of polyphenols on ischemic stroke, Parkinson's disease and Alzheimer's disease, including in vivo and in vitro experimental studies. It is further emphasized that polyphenols exert neuroprotective effects primarily through inhibiting production of oxidative stress and inflammatory cytokines, which may be the underlying mechanism. However, polyphenols are still rarely used as medicines to treat neurodegenerative diseases. Due to the lack of clinical trials, the mechanism of polyphenols is still in the stage of insufficient exploration. Future large-scale multi-center randomized controlled trials and in-depth mechanism studies are still needed to fully assess the safety, efficacy and side effects of polyphenols.

Depression is an independent mood disorder and one of the most common comorbidities of rheumatoid arthritis (RA). Growing evidence suggests that there is two-way regulation between RA and depression, resulting in a vicious cycle of RA, depression, poor outcomes, and disease burden. The rising prevalence of RA-associated depression warrants a re-examination of the relationships between them. Here we provide an overview of the etiology and pathological mechanisms of RA-associated depression, and recent advances in treatment with biologics, which will facilitate the development of new and effective prevention and treatment strategies.

Autophagy is an intracellular degradation system that maintains the stable state of cell energy metabolism. Some recent findings have indicated that autophagy dysfunction is an important driving factor for the occurrence and development of osteoarthritis (OA). The decrease of autophagy leads to the accumulation of damaged organelles and macromolecules in chondrocytes, which affects the survival of chondrocytes and ultimately leads to OA. An appropriate level of autophagic activation may be a new method to prevent articular cartilage degeneration in OA. This minireview discussed the mechanism of autophagy and OA, key autophagy targets regulating OA progression, and evaluated therapeutic applications of drugs targeting autophagy in preclinical and clinical research. Some critical issues worth paying attention to were also raised to guide future research efforts.

Aim To evaluate the performance of radiomics models with the combination of clinical features in distinguishing non-calcified tuberculosis granuloma (TBG) and lung adenocarcinoma (LAC) in small pulmonary nodules. Methodology We conducted a retrospective analysis of 280 patients with pulmonary nodules confirmed by surgical biopsy from January 2017 to December 2020. Samples were divided into LAC group (n = 143) and TBG group (n = 137). We assigned them to a training dataset (n = 196) and a testing dataset (n = 84). Clinical features including gender, age, smoking, CT appearance (size, location, spiculated sign, lobulated shape, vessel convergence, and pleural indentation) were extracted and included in the radiomics models. 3D slicer and FAE software were used to delineate the Region of Interest (ROI) and extract clinical features. The performance of the model was evaluated by the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Results Based on the model selection, clinical features gender, and age in the LAC group and TBG group showed a significant difference in both datasets (P

Rheumatoid arthritis (RA) is a chronic autoimmune disease that primarily affects the joints. Microbial infection is considered a crucial inducer of RA. Alterations in the composition of intestinal bacteria in individuals with preclinical and established RA suggest a vital role of the gut microbiota in immune dysfunction characteristic of RA. However, the mechanisms by which gut dysbiosis contributes to RA are not fully understood. Furthermore, multiple therapies commonly used to treat RA may alter gut microbiota diversity, suggesting that modulating the gut microbiota may help prevent or treat RA. Hence, a better understanding of the changes in the gut microbiota that accompany RA should aid the development of novel therapeutic approaches. This mini-review discusses the impact of gut dysbiosis in the pathogenesis of RA, the selection of gut microbiota-related biomarkers for diagnosing RA, and provides examples of cross-modulation between the gut microbiota and some drugs commonly used to treat RA. Some suggestions and outlooks are also raised, which may help guide future research efforts.

ObjectivesTo evaluate the immunogenicity of the third dose of inactivated SARS-CoV-2 vaccine in rheumatoid arthritis (RA) patients and explore the effect of RA drugs on vaccine immunogenicity.MethodsWe recruited RA patients (n = 222) and healthy controls (HC, n = 177) who had been injected with a third dose of inactivated SARS-CoV-2 vaccine, and their neutralizing antibody (NAb) titer levels were assessed.ResultsRA patients and HC were age- and gender-matched, and the mean interval between 3rd vaccination and sampling was comparable. The NAb titers were significantly lower in RA patients after the third immunization compared with HC. The positive rate of NAb in HC group was 90.4%, while that in RA patients was 80.18%, and the difference was significant. Furthermore, comparison of NAb titers between RA treatment subgroups and HC showed that the patients in the conventional synthetic (cs) disease-modifying anti-rheumatic drugs (DMARDs) group exhibited no significant change in NAb titers, while in those receiving the treatment of biological DMARDs (bDMARDs), Janus Kinase (JAK) inhibitors, and prednisone, the NAb titers were significantly lower. Spearman correlation analysis revealed that NAb responses to SARS-CoV-2 in HC did differ significantly according to the interval between 3rd vaccination and sampling, but this finding was not observed in RA patients. In addition, NAb titers were not significantly correlated with RA-related laboratory indicators, including RF-IgA, RF-IgG, RF-IgM, anti-CCP antibody; C-RP; ESR; NEUT% and LYMPH%.ConclusionSerum antibody responses to the third dose of vaccine in RA patients were weaker than HC. Our study will help to evaluate the efficacy and safety of booster vaccination in RA patients and provide further guidance for adjusting vaccination strategies.

Rheumatoid arthritis (RA) is a chronic inflammatory autoimmune disease influenced by both genetic and environmental factors. At present, rodent models are primarily used to study the pathogenesis and treatment of RA. However, the genetic divergences between rodents and humans determine differences in the development of RA, which makes it necessary to explore the establishment of new models. Compared to rodents, non-human primates (NHPs) are much more closely related to humans in terms of the immune system, metabolic conditions, and genetic make-up. NHPs model provides a powerful tool to study the development of RA and potential complications, as well as preclinical studies in drug development. This review provides a brief overview of the RA animal models, emphasizes the replication methods, pros and cons, as well as evaluates the validity of the rodent and NHPs models.

ObjectivesAttenuated humoral response to mRNA SARS-CoV-2 vaccines has been reported in some patients with autoimmune disease, e.g., rheumatoid arthritis (RA). However, data of immune responses to inactivated SARS-CoV-2 vaccine in the RA population are still unknown. Herein, the safety and immunogenicity of inactivated SARS-CoV-2 vaccines in RA patients were analyzed.MethodsSeventy five RA patients and 26 healthy controls (HC) were respectively recruited from Yunnan Provincial Hospital of Traditional Chinese Medicine and the community in Kunming city. Neutralizing Antibody (NAb) Test ELISA kit was used to measure the percentage of inhibition. AKA (anti-keratin antibody) positivity was detected using indirect immunofluorescence. Rheumatoid factor (RF)-IgA was detected by ELISA. RF-IgG, RF-IgM, and anti-cyclic citrullinated peptide (CCP) antibodies were measured by chemiluminescence. ESR (erythrocyte sedimentation rate) was detected by ESR analyzer. C-RP (c-reactive protein) was detected by immunoturbidimetry. NEUT% (percentage of neutrophils) and LYMPH% (percentage of percentage) were calculated by a calculation method.ResultsCompared with the HC group, the percentage of inhibition was significantly lower in RA patients receiving two doses of vaccines. Vaccines-induced percentage of inhibition was the lowest in RA patients who had not been vaccinated. In total 80.77% of the HC group had a percentage of inhibition ≧20%, compared with 45.24% of vaccinated RA patients and 6.06% of unvaccinated RA patients. Spearman correlation analysis revealed that antibody responses to SARS-CoV-2 did not differ between RA patients according to their age and disease duration. Furthermore, the results showed that no correlation was found between the percentage of inhibition and indices for RA, including RF-IgA, IgG, IgM; anti-CCP antibody; ESR; C-RP; NEUT% and LYMPH%.ConclusionOur study showed inactivated vaccine-induced SARS-COV-2 antibody responses differ in RA patients and healthy subjects, emphasizing the importance of a third or fourth vaccination in RA patients.

Rice-crayfish field (i.e., RCF) distribution mapping is crucial for the adjustment of the local crop cultivation structure and agricultural development. The single-temporal images of two phenological periods in the year were classified separately, and then the areas where the water disappeared were identified as RCFs in previous studies. However, due to the differences in the segmentation of lakes and rivers between the two images, the incorrect extraction of RCFs is unavoidable. To solve this problem, a bi-temporal-feature-difference-coupling object-based (BTFDOB) algorithm was proposed in order to map RCFs in Sihong County. We mapped RCFs by segmenting the bi-temporal images simultaneously based on the object-based method and selecting appropriate feature differences as the classification features. To evaluate the applicability, the classification results of the previous two years obtained using the single-temporal- and object-based (STOB) method were compared with the results of the BTFDOB method. The results suggested that spectral feature differences showed high feature importance, which could effectively distinguish the RCFs from non-RCFs. Our method worked well, with an overall accuracy (OA) of 96.77%. Compared with the STOB method, OA was improved by up to 2.18% across three years of data. The RCFs were concentrated in the low-lying eastern and southern regions, and the cultivation scale was expanded in Sihong. These findings indicate that the BTFDOB method can accurately identify RCFs, providing scientific support for the dynamic monitoring and rational management of the pattern.

Ferroptosis is one of the newly discovered forms of cell-regulated death characterized by iron-dependent lipid peroxidation. Extensive research has focused on the roles of ferroptosis in tumors, blood diseases, and neurological diseases. Some recent findings have indicated that ferroptosis may also be related to the occurrence and development of inflammatory arthritis. Ferroptosis may be a potential therapeutic target, and few studies in vitro and animal models have shown implications in the pathogenesis of inflammatory arthritis. This mini review discussed the common features between ferroptosis and the pathogenesis of rheumatoid arthritis (RA), and evaluated therapeutic applications of ferroptosis regulators in preclinical and clinical research. Some critical issues worth paying attention to were also raised to guide future research efforts.

With the advantages of high accuracy, low cost, and flexibility, Unmanned Aerial Vehicle (UAV) images are now widely used in the fields of land survey, crop monitoring, and soil property prediction. Since the distribution of soil and landscape are closely related, this study makes use of the advantages of UAV images to classify the landscape to build a landscape classification system for soil investigation. Firstly, land use, object, and topographic factor were selected as landscape factors based on soil-forming factors. Then, based on multispectral images and Digital Elevation Models (DEM) acquired by UAV, object-oriented classification of different landscape factors was carried out. Additionally, we selected 432 sample data and validation data from the field survey. Finally, the landscape factor classification results were superimposed to obtain the landscape unit applicable to the system classification. The landscape classification system oriented to the soil survey was constructed by clustering 11,897 landscape units through the rough K-mean clustering algorithm. Compared to K-mean clustering, the rough K-mean clustering was better, with a Silhouette Coefficient of 0.26247 significantly higher than that of K-mean clustering. From the classification results, it can be found that the overall classification results are somewhat fragmented, but the landscape boundaries at the small area scale are consistent with the actual situation and the fragmented small spots are less. Comparing the small number of landscape boundaries obtained from the actual survey, we can find that the landscape boundaries in the landscape classification map are generally consistent with the actual landscape boundaries. In addition, through the analysis of two soil profile data within a landscape category, we found that the identified soil type of soil formation conditions and the landscape factor type of the landscape category is approximately the same. Therefore, this landscape classification system can be effectively used for soil surveys, and this landscape classification system is important for soil surveys to carry out the selection of survey routes, the setting of profile points, and the determination of soil boundaries.

In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7 vegetation indices, and 2 soil indices as environmental covariates, and according to the law of geographical similarity, we used ML and RK methods to mine the relationship between SOC and environmental covariates to predict the SOC content. Four ensemble models—random forest (RF), Cubist, stochastic gradient boosting (SGB), and Bayesian regularized neural networks (BRNNs)—were used to fit the trend of SOC content, and the simple kriging (SK) method was used to interpolate the residuals of the ensemble models, and then the SOC and residual were superimposed to obtain the RK prediction result. Moreover, the 115 samples were divided into calibration and validation sets at a ratio of 80%, and the tenfold cross-validation method was used to fit the optimal parameters of the model. From the results of four ensemble models: RF performed best in the calibration set (R2c = 0.834) but poorly in the validation set (R2v = 0.362); Cubist had good accuracy and stability in both the calibration and validation sets (R2c = 0.693 and R2v = 0.445); SGB performed poorly (R2c = 0.430 and R2v = 0.336); and BRNN had the lowest accuracy (R2c = 0.323 and R2v = 0.282). The results showed that the R2 of the four RK models in the validation set were 0.718, 0.674, 0.724, and 0.625, respectively. Compared with the ensemble models without superimposed residuals, the prediction accuracy was improved by 0.356, 0.229, 0.388, and 0.343, respectively. In conclusion, Cubist has high prediction accuracy and generalization ability in areas with complex topography, and the RK model can make full use of trends and spatial structural factors that are not easy to mine by ML models, which can effectively improve the prediction accuracy. This provides a reference for soil survey and digital mapping in complex terrain areas.

Rapeseed distribution mapping is a crucial issue for food and oil security, entertainment, and tourism development. Previous studies have used various remote sensing approaches to map rapeseed. However, the time-consuming and labor-intensive sample data used in these supervised classification methods greatly limit the development of large-scale mapping in rapeseed studies. Regarding threshold methods, some empirical thresholding methods still need sample data to select the optimal threshold value, and their accuracies decrease when a fixed threshold is applied in complex and diverse environments. This study first developed the Normalized Difference Rapeseed Index (NDRI), defined as the difference in green and short-wave infrared bands divided by their sum, to find a suitable feature to distinguish rapeseed from other types of crops. Next, a two-stepwise hierarchical adaptive thresholding (THAT) algorithm requiring no training data was used to automatically extract rapeseed in Xinghua. Finally, two adaptive thresholding methods of the standalone Otsu and Otsu with Canny Edge Detection (OCED) were used to extract rapeseed across Jiangsu province. The results show that (1) NDRI can separate rapeseed from other vegetation well; (2) the OCED-THAT method can accurately map rapeseed in Jiangsu with an overall accuracy (OA) of 0.9559 and a Kappa coefficient of 0.8569, and it performed better than the Otsu-THAT method; (3) the OCED-THAT method had a lower but acceptable accuracy than the Random Forest method (OA = 0.9806 and Kappa = 0.9391). This study indicates that the THAT model is a promising automatic method for mapping rapeseed.

Interferon-alpha (IFN-α) is a cytokine widely used in the treatment of brain cancers and virus infections with side effects including causing depression. Monoamine neurotransmitter systems have been found playing important roles in peripheral IFN-α-induced depression, but how peripheral IFN-α accesses the central nervous system and contributes to the development of depression is poorly known. This study aimed to develop a non-human primate model using long-term intracerebroventricular (i.c.v.) administration of IFN-α (5 days/week for 6 weeks), to observe the induced depressive-like behaviors and to explore the contributions of monoamine neurotransmitter systems in the development of depression. In monkeys receiving i.c.v. IFN-α administration, anhedonia was observed as decreases of sucrose consumption, along with depressive-like symptoms including increased huddling behavior, decreases of spontaneous and reactive locomotion in home cage, as well as reduced exploration and increased motionless in the open field. Chronic central IFN-α infusion significantly increased the cerebrospinal fluid (CSF) concentrations of noradrenaline (NA), and 3,4-dihydroxyphenylacetic acid (DOPAC), but not 5-hydroxyindoleacetic acid (5-HIAA) and homovanillic acid (HVA). These CSF monoamine metabolites showed associations with some specific depression-related behaviors. In conclusion, central IFN-α administration induced anhedonia and depression-related behaviors comparable to the results with peripheral administration, and the development of depression was associated with the dysfunction of monoamine neurotransmitters.

It has long been observed in humans that the occurrence of depressive symptoms is often accompanied by the dysfunction of hypothalamic-pituitary-adrenal (HPA) axis. The rodent experiments also showed that chronic corticosterone exposure could induce depression-like phenotype. However, rodents are phylogenetically distant from humans. In contrast, non-human primates bear stronger similarities with humans, suggesting research on primates would provide an important complement. For the first time, we investigated the effects of chronic glucocorticoid exposure on rhesus macaques. Seven male macaques were selected and randomized to glucocorticoid or vehicle groups, which were subjected to either prednisolone acetate or saline injections, respectively. The depression-like behaviors were assessed weekly, and the body weights, HPA axis reactivity, sucrose solution consumption and monoaminergic neurotransmitters were further compared between these two groups. The glucocorticoid group was not found to display more depression-like behaviors than the vehicle group until 7 weeks after treatment. Chronic glucocorticoid exposure significantly decreased the levels of cortisol determined from blood (a biomarker for acute HPA axis reactivity) but increased the hair cortisol concentrations (a reliable indicator of chronic HPA axis reactivity) compared with controls. The glucocorticoid group was also found to consume less sucrose solution than controls, a good manifestation of anhedonia. This could be possibly explained by lower dopamine (DA) levels in cerebrospinal fluid induced by chronic glucocorticoid treatment. The results presented here indicate that chronic glucocorticoid exposure could disturb both the acute and chronic HPA axis reactivity, which eventually disturbed the neurotransmitter system and led monkeys to display depression-like phenotype.

Monitoring spatio-temporal changes in winter wheat planting areas is of high importance for the evaluation of food security. This is particularly the case in China, having the world’s largest population and experiencing rapid urban expansion, concurrently, it puts high pressure on food demands and the availability of arable land. The relatively high spatial resolution of Landsat is required to resolve the historical mapping of smallholder wheat fields in China. However, accurate Landsat-based mapping of winter wheat planting dynamics over recent decades have not been conducted for China, or anywhere else globally. Based on all available Landsat TM/ETM+/OLI images (~28,826 tiles) using Google Earth Engine (GEE) cloud computing and a Random Forest machine-learning classifier, we analyzed spatio-temporal dynamics in winter wheat planting areas during 1999–2019 in the North China Plain (NCP). We applied a median value of 30-day sliding windows to fill in potential data gaps in the available Landsat images, and six EVI-based phenological features were then extracted to discriminate winter wheat from other land cover types. Reference data for training and validation were extracted from high-resolution imagery available via Google Earth™ online mapping service, Sentinel-2 and Landsat imagery. We ran a sensitivity analysis to derive the optimal training sample class ratio (β = 1.8) accounting for the unbalanced distribution of land-cover types. We mapped winter wheat planting areas for 1999–2019 with overall accuracies ranging from 82% to 99% and the user’s/producer’s accuracies of winter wheat range between 90% and 99%. We observed an overall increase in winter wheat planting areas of 1.42 × 106 ha in the NCP as compared to the year 2000, with a significant increase in the Shandong and Hebei provinces (p < 0.05). This result contrasts the general discourse suggesting a decline in croplands (e.g., rapid urbanization) and climate change-induced unfavorable cropping conditions in the NCP. This suggests adjustments of the winter wheat planting area over time to satisfy wheat supply in relation to food security. This study highlights the application of Landsat images through GEE in documenting spatio-temporal dynamics of winter wheat planting areas for adequate management of cropping systems and assessing food security in China.

Winter wheat is one of the major cereal crops in China. The spatial distribution of winter wheat planting areas is closely related to food security; however, mapping winter wheat with time-series finer spatial resolution satellite images across large areas is challenging. This paper explores the potential of combining temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data available via the Google Earth Engine (GEE) platform for mapping winter wheat in Shandong Province, China. First, six phenological median composites of Landsat-8 OLI and Sentinel-2 MSI reflectance measures were generated by a temporal aggregation technique according to the winter wheat phenological calendar, which covered seedling, tillering, over-wintering, reviving, jointing-heading and maturing phases, respectively. Then, Random Forest (RF) classifier was used to classify multi-temporal composites but also mono-temporal winter wheat development phases and mono-sensor data. The results showed that winter wheat could be classified with an overall accuracy of 93.4% and F1 measure (the harmonic mean of producer’s and user’s accuracy) of 0.97 with temporally aggregated Landsat-8 and Sentinel-2 data were combined. As our results also revealed, it was always good to classify multi-temporal images compared to mono-temporal imagery (the overall accuracy dropped from 93.4% to as low as 76.4%). It was also good to classify Landsat-8 OLI and Sentinel-2 MSI imagery combined instead of classifying them individually. The analysis showed among the mono-temporal winter wheat development phases that the maturing phase’s and reviving phase’s data were more important than the data for other mono-temporal winter wheat development phases. In sum, this study confirmed the importance of using temporally aggregated Landsat-8 OLI and Sentinel-2 MSI data combined and identified key winter wheat development phases for accurate winter wheat classification. These results can be useful to benefit on freely available optical satellite data (Landsat-8 OLI and Sentinel-2 MSI) and prioritize key winter wheat development phases for accurate mapping winter wheat planting areas across China and elsewhere.

Industrial wastewater (IW) discharge, which is a known point source of pollution, is a major water pollution source. Increasing IW discharge has imposed considerable pressure on regional or global water environments. It is important to estimate the IW distribution in grid units to improve basin-scale hydrological processes and water quality modeling. For the first time, we use the nighttime light imagery produced by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) to estimate the spatial and temporal variations in the IW distribution from 1992 to 2010 in China. The digital number values per unit area (DNP) of each stable light image were calculated using nighttime light imagery and were regressed against the IW per unit area (IWP) to estimate the total industrial wastewater (TIW) for each province. The results indicated strong positive correlations between the DNP and the IWP for each province during different years. The fitted linear regression models were used to estimate IW discharge in China with reliable accuracy. The IW estimation using the satellite data was consistent with the statistical results. The results also revealed that the IW discharge coverage expanded, whereas the IW discharge intensity decreased from 1992 to 2010 in China.

Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R2 = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R2 = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R2 = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass.