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ABSTRACTDesert locust outbreaks pose severe threats to agricultural production and ecological environments. Hence, understanding the distribution of habitat suitability is crucial for establishing effective prevention and control measures. We introduce an innovative method for analyzing the temporal and spatial variations of desert locust habitat suitability in Yemen. This method integrates ecological niche models with space–time cube analysis. First, we gathered key environmental variables that affect desert locust distribution, including vegetation and soil types, precipitation, and temperature. Subsequently, we employed the MaxEnt model to assess habitat suitability for desert locusts in Yemen for 2010 and 2013–2021. Finally, we applied the space–time cube method for spatiotemporal analysis to reveal the spatiotemporal dynamics of habitat suitability distribution. The MaxEnt model results showed that the kappa coefficients exceeded 0.46 and the area under the curve exceeded 0.75. The spatiotemporal analysis results showed that from 2010 to 2021, the Red Sea coastal plains in western Yemen exhibited the highest habitat suitability levels, with significant increases in habitat suitability in the western and central regions. This study offers a new perspective on studying the spatial and temporal characteristics of desert locust habitat suitability distribution and can provides a scientific basis for the management of desert locusts.

Grasshoppers, as pernicious pests, pose a formidable threat to the advancement of agriculture and animal husbandry. Their presence can elicit a cascade of environmental challenges, underscoring the pressing need for effective control measures. However, grasshopper development is an intricate process influenced by diverse environmental factors with varying weights across regions, making it difficult to prevent and control. Therefore, this study focused on the prevalent infestation region, Xilingol, and selected Dasyhippus barbipes as the research subject because of its highest density and largest damaged area. Initially, according to the development mechanisms of D. barbipes, 31 habitat factors from five categories (meteorology, vegetation, soil, topography, and ecology) were selected; then, difference tests, correlation analysis, importance tests, and principal component analysis were applied to construct representative indicators for monitoring the habitat of D. barbipes (HDB). Subsequently, employing the occurrence data of D. barbipes from 2018 to 2023, a spatial pattern analysis was conducted to explore the hotspot aggregation area (HAA) and spatiotemporal characteristics of D. barbipes. Finally, considering landscape and spatial heterogeneity, the Landscape-based Geographically Weighted Logistic Regression (L-GWLR) model for HDB was constructed to achieve adaptive changes in factor weights across regions. The indicators included minimum temperature during the egg stage, precipitation, and soil temperature during the spawning stage, slope, fractional vegetation coverage in the nymph stage, soil moisture in the 1st to 3rd nymph instar, patch area, and gyration radius. The spatial pattern analysis revealed a significant spatial autocorrelation in the distribution of D. barbipes at a 90 % confidence interval (z > 1.65 and p

The desert locust, Schistocerca gregaria (Forsk., 1775), stands as one of the most pervasive pests globally, inflicting extensive damage across Asia and Africa. Facilitated by intercontinental migration, the desert locust engages in population exchange between different source areas, perpetuating its widespread proliferation. Despite the wind being recognized as a key factor during migration events, elucidating its precise influence on intercontinental migration has remained elusive. In this study, we scrutinized monitoring data sourced from the FAO monitoring system, pinpointing 13 desert locust events featuring intercontinental migrations since 1967. From these events, four migration routes were summarized, traversing the Red Sea (RS-WE and RS-EW) and the northern Indian Ocean (IO-WE and IO-EW). Typically, RS-WE and IO-EW migrations occurred between December and March, whereas RS-EW and IO-WE migrations were observed from May to June and April to July, respectively. Our examination of wind field data spanning the past 15 years revealed that wind direction and speed facilitated intercontinental migrations. Furthermore, migration trajectory modeling indicated that desert locusts might exhibit migratory behavior both during the day and at night in the cases of RS-WE and RS-EW, with cross-oceanic migration potentially lasting for a week for IO-WE and IO-EW. In summary, our study identifies four migration routes for the intercontinental migration of the desert locust, providing crucial support for the scientific prediction of its occurrence and contributing to international food security efforts.

The fall armyworm (Spodoptera frugiperda) (J. E. Smith) is a widespread, polyphagous, and highly destructive agricultural pest. Global climate change may facilitate its spread to new suitable areas, thereby increasing threats to host plants. Consequently, predicting the potential suitable distribution for the fall armyworm and its host plants under current and future climate scenarios is crucial for assessing its outbreak risks and formulating control strategies. This study, based on remote sensing assimilation data and plant protection survey data, utilized machine learning methods (RF, CatBoost, XGBoost, LightGBM) to construct potential distribution prediction models for the fall armyworm and its 120 host plants. Hyperparameter methods and stacking ensemble method (SEL) were introduced to optimize the models. The results showed that SEL demonstrated optimal performance in predicting the suitable distribution for the fall armyworm, with an AUC of 0.971 ± 0.012 and a TSS of 0.824 ± 0.047. Additionally, LightGBM and SEL showed optimal performance in predicting the suitable distribution for 47 and 30 host plants, respectively. Overlay analysis suggests that the overlap areas and interaction links between the suitable areas for the fall armyworm and its host plants will generally increase in the future, with the most significant rise under the RCP8.5 climate scenario, indicating that the threat to host plants will further intensify due to climate change. The findings of this study provide data support for planning and implementing global and intercontinental long-term pest management measures aimed at mitigating the impact of the fall armyworm on global food production.

Summary: The biological rhythms governed by negative feedback loops have undergone extensive investigation. However, developing reliable and versatile warning signals to predict periodic fluctuations in physiological processes and behaviors associated with these rhythms remains a challenge. Here, we monitored the heart rate and tracked ovulation dates of 91 fertile women. The finding strongly links the velocity (derivative) of heart rate with ovulation in menstrual cycles, providing a predictive warning signal. Similarly, an analysis of calcium signaling in the suprachiasmatic nucleus (SCN) of mice reveals that the maximum velocity of rising calcium signal aligns with locomotor activity offsets. To demonstrate the generality of derivative-transitions link, numerical simulations using a negative feedback loop model were conducted. Statistical analysis indicated that over 90% of the oscillations exhibited a correlation between maximum velocity and transition points. Consequently, the maximum velocity derived from oscillatory curves holds significant potential as an early warning signal for critical transitions.

Powdery mildew significantly impacts the yield of natural rubber by being one of the predominant diseases that affect rubber trees. Accurate, non-destructive recognition of powdery mildew in the early stage is essential for the cultivation management of rubber trees. The objective of this study is to establish a technique for the early detection of powdery mildew in rubber trees by combining spectral and physicochemical parameter features. At three field experiment sites and in the laboratory, a spectroradiometer and a hand-held optical leaf-clip meter were utilized, respectively, to measure the hyperspectral reflectance data (350–2500 nm) and physicochemical parameter data of both healthy and early-stage powdery-mildew-infected leaves. Initially, vegetation indices were extracted from hyperspectral reflectance data, and wavelet energy coefficients were obtained through continuous wavelet transform (CWT). Subsequently, significant vegetation indices (VIs) were selected using the ReliefF algorithm, and the optimal wavelengths (OWs) were chosen via competitive adaptive reweighted sampling. Principal component analysis was used for the dimensionality reduction of significant wavelet energy coefficients, resulting in wavelet features (WFs). To evaluate the detection capability of the aforementioned features, the three spectral features extracted above, along with their combinations with physicochemical parameter features (PFs) (VIs + PFs, OWs + PFs, WFs + PFs), were used to construct six classes of features. In turn, these features were input into support vector machine (SVM), random forest (RF), and logistic regression (LR), respectively, to build early detection models for powdery mildew in rubber trees. The results revealed that models based on WFs perform well, markedly outperforming those constructed using VIs and OWs as inputs. Moreover, models incorporating combined features surpass those relying on single features, with an overall accuracy (OA) improvement of over 1.9% and an increase in F1-Score of over 0.012. The model that combines WFs and PFs shows superior performance over all the other models, achieving OAs of 94.3%, 90.6%, and 93.4%, and F1-Scores of 0.952, 0.917, and 0.941 on SVM, RF, and LR, respectively. Compared to using WFs alone, the OAs improved by 1.9%, 2.8%, and 1.9%, and the F1-Scores increased by 0.017, 0.017, and 0.016, respectively. This study showcases the viability of early detection of powdery mildew in rubber trees.

To ensure that a variable-rate spray (VRS) system can perform unmanned aerial spray in accordance with a prescription map at different flight speeds, we examine in this paper such significant factors as the response time of the VRS system and the pressure fluctuation of the nozzle during the variable-rate spraying process. The VRS system uses a lag compensation algorithm (LCA) to counteract the droplet deposition position lag caused by the system response delay. In addition, pulse width modulated (PWM) solenoid valves are used for controlling the flowrates of the nozzles on the variable-rate spray system, and a mathematical model was constructed for the spray rate (L min-1) and the relative proportion of time (duty cycle) each solenoid valve is open. The pressure drop and solenoid valve response time at different duty cycles (50%~90%) were measured by indoor experiments. Meanwhile, the lag distance (LD), spray accuracy, and droplet deposition characteristics of the VRS system were tested by conducting outdoor experiments at different flight speeds (4m s-1, 5m s-1, 6m s-1). The results show that LCA can effectively reduce the lag distance. The lag distance (LD) values of the VRS system with LCA ranged from -0.27 to 0.78m with an average value of 0.32m, while without LCA, the LD values increased to 3.5~4.3m with an average value of 3.87m. The overall spray position accuracy was in the range of 91.56%~97.32%. Furthermore, the spray coverage and deposition density, determined using water sensitive paper (WSP), were used to evaluate the spray application performance taking into account the spray volume applied. The VRS system can provide the most suitable spray volumes for insecticide and fungicide plant protection products. Based on a prescription map, the optimized VRS system can achieve accurate pesticide spraying as well as desirable spray coverage and deposition density.

The drug pair consisting of Sophora flavescens Aiton (Sophorae flavescentis radix, Kushen) and Coptis chinensis Franch. (Coptidis rhizoma, Huanglian), as described in Prescriptions for Universal Relief (Pujifang), is widely used to treat laxation. Matrine and berberine are the major active components of Kushen and Huanglian, respectively. These agents have shown remarkable anti-cancer and anti-inflammatory effects. A mouse model of colorectal cancer was used to determine the most effective combination of Kushen and Huanglian against anti-colorectal cancer. The results showed that the combination of Kushen and Huanglian at a 1:1 ratio exerted the best anti-colorectal cancer effect versus other ratios. Moreover, the anti-colorectal cancer effect and potential mechanism underlying the effects of matrine and berberine were evaluated by the analysis of combination treatment or monotherapy. In addition, the chemical constituents of Kushen and Huanglian were identified and quantified by liquid chromatography-tandem mass spectrometry (LC-MS/MS). A total of 67 chemical components were identified from the Kushen–Huanglian drug pair (water extraction), and the levels of matrine and berberine were 129 and 232 µg/g, respectively. Matrine and berberine reduced the growth of colorectal cancer and relieved the pathological conditions in mice. In addition, the combination of matrine and berberine displayed better anti-colorectal cancer efficacy than monotherapy. Moreover, matrine and berberine reduced the relative abundance of Bacteroidota and Campilobacterota at phylum level and that of Helicobacter, Lachnospiraceae_NK4A136_group, Candidatus_Arthromitus, norank_f_Lachnospiraceae, Rikenella, Odoribacter, Streptococcus, norank_f_Ruminococcaceae, and Anaerotruncus at the genus level. Western blotting results demonstrated that treatment with matrine and berberine decreased the protein expressions of c-MYC and RAS, whereas it increased that of sirtuin 3 (Sirt3). The findings indicated that the combination of matrine and berberine was more effective in inhibiting colorectal cancer than monotherapy. This beneficial effect might depend on the improvement of intestinal microbiota structure and regulation of the RAS/MEK/ERK-c-MYC-Sirt3 signaling axis.

Grasshoppers have profound effects on both grassland ecosystems and livestock production. Despite commendable efforts made by China in grasshopper control, completely eradicating or preventing them still remains a distant prospect. This study aims to analyze the ecological distribution and patterns of grasshopper occurrences in order to provide more accurate monitoring techniques and preventive measures. By considering four types of environmental determinants—meteorology, vegetation, soil, and topography—we systematically identified 18 key influencing factors. These factors encompass various developmental stages of grasshoppers, including variables such as temperature, precipitation, vegetation coverage, vegetation type, soil moisture, soil salinity, soil type, and terrain characteristics. The MaxEnt model is employed in this study to comprehensively capture complex ecological interactions. Omission curves, Receiver Operating Characteristic curves (ROC curves), and the Area Under the Curve (AUC values) demonstrate the robustness and high accuracy of the MaxEnt model. Our research results indicate that meteorological factors are the primary influencing factors for the distribution of grasshoppers, surpassing the effects of vegetation, soil, and terrain. Precipitation and vegetation type emerge as key factors shaping their distributional patterns. Integrating the Sen-MK trend method, our findings identify the epicenter of damage primarily within the central, southern, and northeastern regions, notably affecting locales such as New Barag East County and the Ewenki Autonomous Banner. While their impact in 2012 was particularly severe, temporal trends indicate a decreasing risk of grasshoppers in specific regions, with escalated activity observed in other areas. The empirical insights from this study lay a solid foundation for the development of monitoring and control strategies concerning grasshoppers. Furthermore, the derived theoretical framework serves as a valuable foundation for future research endeavors addressing grasshopper infestations.

The present study investigated the impact of quinoa protein (QP) on the physicochemical properties, sensory quality, and oxidative stability of myofibrillar protein (MP) in pork patties during five freeze–thaw (F-T) cycles. It was observed that repeated F-T cycles resulted in a deterioration of pork patty quality; however, the incorporation of QP effectively mitigated these changes. Throughout the F-T cycles, the sensory quality of the QP-treated group consistently surpassed that of the control group. After five F-T cycles, the thiobarbituric acid reactive substance (TBARS) content in the control group was measured at 0.423 mg/kg, whereas it significantly decreased to 0.347 mg/kg in the QP-treated group (p < 0.05). Furthermore, QP inclusion led to a decrease in pH and an increase in water-holding capacity (WHC) within pork patties. Following five F-T cycles, Ca2+-ATPase activity exhibited a significant increase of 11.10% in the QP-treated group compared to controls (p < 0.05). Additionally, supplementation with QP resulted in elevated total sulfhydryl content and reduced carbonyl content, Schiff base content, and dityrosine content within myofibrillar proteins (MPs), indicating its inhibitory effect on MP oxidation. In particular, after five F-T cycles, total sulfhydryl content reached 58.66 nmol/mL for the QP-treated group significantly higher than that observed for controls at 43.65 nmol/mL (p < 0.05). While carbonyl content increased from 2.37 nmol/mL to 4.63 nmol/mL between the first and fifth F-T cycle for controls; it only rose from 2.15 nmol/mL to 3.47 nmol/mL in the QP-treated group. The endogenous fluorescence levels were significantly higher (p < 0.05) in the QP-treated group compared to controls. In conclusion, the addition of QP enhanced the quality of pork patties and effectively inhibited the oxidative denaturation of MP during F-T cycles.

Powdery mildew is one of the most significant rubber tree diseases, with a substantial impact on the yield of natural rubber. This study aims to establish a detection approach that coupled continuous wavelet transform (CWT) and machine learning for the accurate assessment of powdery mildew severity in rubber trees. In this study, hyperspectral reflectance data (350–2500 nm) of healthy and powdery mildew-infected leaves were measured with a spectroradiometer in a laboratory. Subsequently, three types of wavelet features (WFs) were extracted using CWT. They were as follows: WFs dimensionally reduced by the principal component analysis (PCA) of significant wavelet energy coefficients (PCA-WFs); WFs extracted from the top 1% of the determination coefficient between wavelet energy coefficients and the powdery mildew disease class (1%R2-WFs); and all WFs at a single decomposition scale (SS-WFs). To assess the detection capability of the WFs, the three types of WFs were input into the random forest (RF), support vector machine (SVM), and back propagation neural network (BPNN), respectively. As a control, 13 optimal traditional spectral features (SFs) were extracted and combined with the same classification methods. The results revealed that the WF-based models all performed well and outperformed those based on SFs. The models constructed based on PCA-WFs had a higher accuracy and more stable performance than other models. The model combined PCA-WFs with RF exhibited the optimal performance among all models, with an overall accuracy (OA) of 92.0% and a kappa coefficient of 0.90. This study demonstrates the feasibility of combining CWT with machine learning in rubber tree powdery mildew detection.

Abstract Exploring the functions of human-specific genes (HSGs) is challenging due to the lack of a tractable genetic model system. Testosterone is essential for maintaining human spermatogenesis and fertility, but the underlying mechanism is unclear. Here, we identified Cancer/Testis Antigen gene family 47 (CT47) as an essential regulator of human-specific spermatogenesis by stabilizing arginine methyltransferase 5 (PRMT5). A humanized mouse model revealed that CT47 functions to arrest spermatogenesis by interacting with and regulating CT47/PRMT5 accumulation in the nucleus during the leptotene/zygotene-to-pachytene transition of meiosis. We demonstrate that testosterone induces nuclear depletion of CT47/PRMT5 and rescues leptotene-arrested spermatocyte progression in humanized testes. Loss of CT47 in human embryonic stem cells (hESCs) by CRISPR/Cas9 led to an increase in haploid cells but blocked the testosterone-induced increase in haploid cells when hESCs were differentiated into haploid spermatogenic cells. Moreover, CT47 levels were decreased in nonobstructive azoospermia. Together, these results established CT47 as a crucial regulator of human spermatogenesis by preventing meiosis initiation before the testosterone surge.

With the development of globalization and agriculture trade, as well as its own strong migratory capacity, fall armyworm (FAW) (Spodoptera frugiperda) (J.E. Smith) has invaded more than 70 countries, posing a serious threat to the production of major crops in these areas. FAW has now also been detected in Egypt in North Africa, putting Europe, which is separated from it only by the Mediterranean Sea, at high risk of invasion. Therefore, this study integrated multiple factors of insect source, host plant, and environment to provide a risk analysis of the potential trajectories and time periods of migration of FAW into Europe in 2016~2022. First, the CLIMEX model was used to predict the annual and seasonal suitable distribution of FAW. The HYSPLIT numerical trajectory model was then used to simulate the possibility of the FAW invasion of Europe through wind-driven dispersal. The results showed that the risk of FAW invasion between years was highly consistent (P

Inonotus obliquus is anatural plant fungal resource with a variety of biological activities. Triterpenoids are the main functional components. Because triterpenoids have excellent anticancer, hypoglycemic and antioxidant properties, the related research and development has attracted much attention in recent years. In this paper, the current research status of Inonotus obliquus triterpenoid compounds are summarized and analyzed from the aspects of induction fermentation production, separation and purification of sporophore triterpenoid compounds, chemical composition and structure analysis, functional characteristics and application development, among which the use of plant commensal microbial extracts as inducers in induction fermentation production is the most significant. The separation and purification techniques have respective advantages according to different purposes. The separation and purification technologies with high separation efficiency such as high-performance counter-current chromatography and high-performance liquid chromatography are more suitable for chemical composition and structure analysis, and macroporous resin has more advantages in large-scale application. Based on the above analysis, the future research direction is prospected, which provides a theoretical basis for subsequent development and research and application in that medicine and food industry.

Abstract Severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) can cause gastrointestinal (GI) symptoms that often correlate with the severity of COVID‐19. Here, we explored the pathogenesis underlying the intestinal inflammation in COVID‐19. Plasma VEGF level was particularly elevated in patients with GI symptoms and significantly correlated with intestinal edema and disease progression. Through an animal model mimicking intestinal inflammation upon stimulation with SARS‐CoV‐2 spike protein, we further revealed that VEGF was over‐produced in the duodenum prior to its ascent in the circulation. Mechanistically, SARS‐CoV‐2 spike promoted VEGF production through activating the Ras‐Raf‐MEK‐ERK signaling in enterocytes, but not in endothelium, and inducing permeability and inflammation. Blockage of the ERK/VEGF axis was able to rescue vascular permeability and alleviate intestinal inflammation in vivo. These findings provide a mechanistic explanation and therapeutic targets for the GI symptoms of COVID‐19.

Locust plagues inflict severe agricultural damage. Climate change-induced extreme events like rainfall and droughts have expanded locust habitats. These new areas, often beyond routine monitoring, could become potential high-risk locust areas (PHRLA). Quantitatively understanding the outbreak mechanism driving drought and flood dynamics is crucial for identifying PHRLA, but such studies are scarce. To address this gap, we conducted a case study on locust outbreaks in Xiashan Reservoir, the largest reservoir in Shandong Province, China, in 2017 and 2018. Using time series satellite imagery and meteorological products, we quantitatively analyzed how drought–flood dynamics and temperature affect locust habitats, reproduction, and aggregation. Employing an object-oriented random forest classifier, we generated locust habitat classification maps with 93.77% average overall accuracy and Kappa coefficient of 0.90. Combined with meteorological analysis, we found that three consecutive drought years from 2014 to 2016 reduced the water surface area by 75%, expanding suitable habitats (primarily reeds and weeds) to cover 60% of the reservoir. Warm winters and high temperatures during locust key growth periods, coupled with expanding suitable habitats, promoted multi-generational locust reproduction. However, substantial flooding events in 2017 and 2018, driven by plentiful rainfall during key growth periods, reduced suitable habitats by approximately 54% and 29%, respectively. This compression led to high locust density, causing the locust plague and high-density spots of locusts (HDSL). Our study elucidates locust plague outbreak mechanisms under dynamic drought and flood environments. Based on this, we propose an approach to identify PHRLA by monitoring changes in drought and flood patterns around water bodies and variations in suitable habitat size and distribution, as well as surrounding topography. These findings hold significant implications for enhancing locust monitoring and early warning capabilities, reducing pesticide usage, and ensuring food and ecological security and sustainable agriculture.

The development of unmanned aerial vehicles (UAV) and artificial intelligence (AI) techniques has drawn increasing interest and started a novel area of research applications [...]

Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions.

Oedaleus decorus asiaticus is one of the dominant harmful pests in central Inner Mongolia, China. Large-scale outbreaks of this pest create many serious problems in animal husbandry and agriculture. Therefore, understanding the underlying mechanisms between plant losses and Odecorus at different density levels and growth stages can guide the development of monitoring and prediction measures to reduce damage. In this study, an unmanned aerial vehicle (UAV) carrying a camera was employed to collect multi-spectral data. Further, nine vegetation indices (VIs) were analyzed to explore the most suitable indices for estimating plant loss caused by O. decorus in different growth stages. The following results were obtained: (1) The second instar nymphs of O. decorus could promote vegetation growth. As the density level in each cage increased, the biomass of each cage increased (nymph density < 30 nymphs/m2) and then decreased (nymph density ≥ 30 nymphs/m2). When nymph density was greater than 60 nymphs/m2, the biomass in those cages decreased significantly. (2) With respect to the control group, large damage began to emerge during the third instar nymphal stage. In particular, the largest vegetation loss was caused by fourth nymphal larvae. (3) The ratio vegetation index (RVI) appeared as the most excellent index for reflecting Leymus chinensis loss caused by O. decorus at different growth stages. Nevertheless, the difference vegetation index (DVI) was better than the RVI in the fifth instar nymphal stage.

The behavior of droplets collision in a flash evaporation ambient widely exists in various fields. In this work, the deformation analysis and thermal analysis models were established under the condition of flash via a computational fluid dynamics (CFD) method. First, the effects of initial temperature and collision velocity on heat and mass transfer during evaporation were considered. Then, the morphology change of the liquid phase, the mass change, and their influencing factors during the droplet evaporation process were analyzed. A very good agreement is observed between the results of this paper and the published literature. The results show that the interaction between the initial collision velocity and the initial temperature affects the heat and mass transfer performance. The initial collision velocity influences the heat and mass transfer process of the evaporating droplet by affecting the deformation characteristics of the droplet. The collision velocity and the liquid temperature have a competitive relationship with the evaporation process. Under a low-initial temperature, the collision velocity played a leading role in the evaporation of the liquid phase and the mass transfer of steam.

AimsWe assessed the efficacy of the traditional Chinese medicine formulation Jia-Wei-Si-Miao-Yong-An decoction (HJ11) in the treatment of acute coronary syndrome and evaluated its impact on the intestinal microbiota and their metabolites.MethodsAn acute coronary syndrome model was established in rats, which were randomly assigned to the model, HJ11 treatment, and atorvastatin treatment groups. Rats were then administered saline solution (model and sham operation control groups) or drugs by oral gavage for 28 d. Echocardiography was performed and serum creatine kinase-MB and cardiac troponin I levels were monitored to examine the cardiac function. Inflammation was evaluated using hematoxylin and eosin staining of heart tissue, and serum interleukin-2, interleukin-6, tumor necrosis factor alpha, and high-sensitivity C-reactive protein measurements. Gut microbiota composition was analyzed via 16S rRNA gene sequencing. Metabolomics was used to determine fecal metabolites and elucidate the modes of action of HJ11 in acute coronary syndrome treatment.ResultsHJ11 improved cardiac function and attenuated inflammation in rats with acute coronary syndrome. Relative to the untreated model group, the HJ11-treated group presented normalized Firmicutes/Bacteroidetes ratio and reduced abundances of the bacterial genera norank_f__Ruminococcaceae, Desulfovibrio, Clostridium_sensu_stricto_1, Adlercreutzia, Staphylococcus, Bacteroides, Prevotella, Rikenellaceae_RC9_gut_group, unclassified_o__Bacteroidales, and Ruminococcus_gauvreauii_group. We found 23 differentially expressed intestinal metabolites, and the enriched metabolic pathways were mainly related to amino acid metabolism. We also discovered that asymmetric dimethylarginine levels were strongly associated with cardiovascular disease. Correlation analyses revealed strong associations among intestinal microflora, their metabolites, proinflammatory factors, and cardiac function. Hence, the therapeutic effects of HJ11 on acute coronary syndrome are related to specific alterations in gut microbiota and their metabolites.ConclusionThis work demonstrated that HJ11 effectively treats acute coronary syndrome. HJ11 seems to increase the abundance of beneficial bacterial taxa (Bacteroides and Rikenellaceae_RC9_gut_group), mitigate the risk factors associated with cardiovascular disease, alter bacterial metabolites, lower asymmetric dimethylarginine levels, and effectively treat acute coronary syndrome.

Fusarium head blight (FHB) is one of the major fungal diseases affecting wheat production worldwide, influencing kernel development and producing poisonous mycotoxins. Mechanistic models have been extensively used for plant disease simulation; however, regional disease prediction using these models is difficult because they simplify the heterogeneous plant growth conditions. Herein, we present a remote sensing based Susceptible-Exposed-Infectious-Removed (SEIR) model for regional prediction of FHB occurrence in wheat. Plant properties that are key to the development of FHB are extracted from remote sensing data or data products to initialize or drive the model. Fractional vegetation cover products, time-series curves from satellite images, and vegetation indices were used to indicate plant density, phenology, and vegetation vigor. We applied our model to a plain region in China that suffers greatly from FHB annually. The SEIR model was parameterized by incorporating remote sensing data products, and then calibrated and verified for regional FHB prediction. The model was trained and evaluated by comparing the results of its prediction of FHB incidence to field observations during the susceptible period for wheat; satisfactory results were observed with a correlation coefficient of 0.804, root mean-square error of 0.131, classification accuracy of 0.860, and missed detection rate of 0.035 when the model was initialized with the Optimized Soil Adjusted Vegetation Index (OSAVI). The disease progress curves furnished by our model display an S-shape—a characteristic of polycyclic diseases—which matches the wheat FHB epidemiology. These results indicate that our remote sensing-based SEIR model is promising for the regional prediction of FHB occurrence in wheat.

Infection caused by Fusarium head blight (FHB) has severely damaged the quality and yield of wheat in China and threatened the health of humans and livestock. Inaccurate disease detection increases the use cost of pesticide and pollutes farmland, highlighting the need for FHB detection in wheat fields. The combination of spectral and spatial information provided by image analysis facilitates the detection of infection-related damage in crops. In this study, an effective detection method for wheat FHB based on unmanned aerial vehicle (UAV) hyperspectral images was explored by fusing spectral features and image features. Spectral features mainly refer to band features, and image features mainly include texture and color features. Our aim was to explain all aspects of wheat infection through multi-class feature fusion and to find the best FHB detection method for field wheat combining current advanced algorithms. We first evaluated the quality of the two acquired UAV images and eliminated the excessively noisy bands in the images. Then, the spectral features, texture features, and color features in the images were extracted. The random forest (RF) algorithm was used to optimize features, and the importance value of the features determined whether the features were retained. Feature combinations included spectral features, spectral and texture features fusion, and the fusion of spectral, texture, and color features to combine support vector machine, RF, and back propagation neural network in constructing wheat FHB detection models. The results showed that the model based on the fusion of spectral, texture, and color features using the RF algorithm achieved the best performance, with a prediction accuracy of 85%. The method proposed in this study may provide an effective way of FHB detection in field wheat.

Rice is an important food crop in China, and diseases and pests are the main factors threatening its safety, ecology, and efficient production. The development of remote sensing technology provides an important means for non-destructive and rapid monitoring of diseases and pests that threaten rice crops. This paper aims to provide insights into current and future trends in remote sensing for rice crop monitoring. First, we expound the mechanism of remote sensing monitoring of rice diseases and pests and introduce the applications of different commonly data sources (hyperspectral data, multispectral data, thermal infrared data, fluorescence, and multi-source data fusion) in remote sensing monitoring of rice diseases and pests. Secondly, we summarize current methods for monitoring rice diseases and pests, including statistical discriminant type, machine learning, and deep learning algorithm. Finally, we provide a general framework to facilitate the monitoring of rice diseases or pests, which provides ideas and technical guidance for remote sensing monitoring of unknown diseases and pests, and we point out the challenges and future development directions of rice disease and pest remote sensing monitoring. This work provides new ideas and references for the subsequent monitoring of rice diseases and pests using remote sensing.

Leaf area index (LAI) and canopy chlorophyll density (CCD) are key indicators of crop growth status. In this study, we compared several vegetation indices and their red-edge modified counterparts to evaluate the optimal red-edge bands and the best vegetation index at different growth stages. The indices were calculated with Sentinel-2 MSI data and hyperspectral data. Their performances were validated against ground measurements using R2, RMSE, and bias. The results suggest that indices computed with hyperspectral data exhibited higher R2 than multispectral data at the late jointing stage, head emergence stage, and filling stage. Furthermore, red-edge modified indices outperformed the traditional indices for both data genres. Inversion models indicated that the indices with short red-edge wavelengths showed better estimation at the early jointing and milk development stage, while indices with long red-edge wavelength estimate the sought variables better at the middle three stages. The results were consistent with the red-edge inflection point shift at different growth stages. The best indices for Sentinel-2 LAI retrieval, Sentinel-2 CCD retrieval, hyperspectral LAI retrieval, and hyperspectral CCD retrieval at five growth stages were determined in the research. These results are beneficial to crop trait monitoring by providing references for crop biophysical and biochemical parameters retrieval.

Stripe rust is one of the most destructive wheat diseases in China, negatively affecting the production safety and causing yield losses of wheat. Thus, it is important to analyze the environmental suitability and dynamic changes of wheat stripe rust in China. The occurrence of stripe rust is affected by multiple factors. Therefore, this study combined data from various disciplinary fields such as remote sensing, meteorology, biology, and plant protection to evaluate the environmental suitability of stripe rust in China using species distribution models. The study also discusses the importance and effect of various variables. Results revealed that meteorological factors had the greatest impact on the occurrence of stripe rust, especially temperature and precipitation. Wheat growth factors have a greater impact from April to August. Elevation has a greater impact in summer. The ensemble model results were better than the single model, with TSS and AUC greater than 0.851 and 0.971, respectively. Overlapping analysis showed that the winter stripe rust suitable areas were mainly in the Sichuan Basin, Northwestern Hubei, Southern Shaanxi, and Southern Henan wheat areas. In spring, the suitable areas of stripe rust increased in Huang-Huai-Hai and the middle and lower reaches of the Yangtze River and Guanzhong Plain, and the development of northwestern wheat areas such as Xinjiang and Gansu slightly lagged behind. In summer, wheat threatened by stripe rust is mainly in late-ripening spring wheat areas in Gansu, Ningxia, Qinghai, and Xinjiang. This study can provide a scientific basis for optimizing and improving the comprehensive management strategy of stripe rust.

Circadian factors likely influence the occurrence, development, therapy, and prognosis of cardiovascular diseases (CVDs). To determine the association between the heart rate (HR) diurnal parameters and CVD risks, we designed an analytical strategy to detect diurnal rhythms of HR using longitudinal data collected by clinically used Holter monitors and wearable devices. By combining in-house developed algorithms with existing analytical tools, we obtained trough phase and nocturnal variation in HR for different purposes. The analytical strategy is robust and also sensitive enough to identify variations in HR rhythms influenced by multiple effectors such as jet lag, geological location and altitude, and age from total 211 volunteers. A total of 10,094 sets of 24-h Holter ECG data were analyzed by stepwise partial correlation to determine the critical points of HR trough phase and nocturnal variation. The following HR diurnal patterns correlate with high CVD risk: arrhythmic pattern, anti-phase pattern, rhythmic patterns with trough phase less than 0 (extremely advanced diurnal pattern) or more than 5 (extremely delayed diurnal pattern), and nocturnal variation less than 2.75 (extremely low) or more than 26 (extremely high). In addition, HR trough phases from wearable devices were nearly identical to those from 24-h Holter monitoring from 12 volunteers by linear correlation and Bland-Altman analysis. Our analytical system provides useful information to identify functional diurnal patterns and parameters by monitoring personalized, HR-based diurnal changes. These findings have important implications for understanding how a regular heart diurnal pattern benefits cardiac function and raising the possibility of non-pharmacological intervention against circadian related CVDs. With the rapid expansion of wearable devices, public cardiovascular health can be promoted if the analytical strategy is widely applied.

Abstract Background Hepatic fibrosis is a pathological response of the liver to a variety of chronic stimuli. Hepatic stellate cells (HSCs) are the major source of myofibroblasts in the liver. Follistatin like 1 (Fstl1) is a secreted glycoprotein induced by transforming growth factor-β1 (TGF-β1). However, the precise functions and regulation mechanisms of Fstl1 in liver fibrogenesis remains unclear. Methods Hepatic stellate cell (HSC) line LX-2 stimulated by TGF-β1, primary culture of mouse HSCs and a model of liver fibrosis induced by CCl4 in mice was used to assess the effect of Fstl1 in vitro and in vivo. Results Here, we found that Fstl1 was significantly up regulated in human and mouse fibrotic livers, as well as activated HSCs. Haplodeficiency of Fstl1 or blockage of Fstl1 with a neutralizing antibody 22B6 attenuated CCl4-induced liver fibrosis in vivo. Fstl1 modulates TGF-β1 classic Samd2 and non-classic JNK signaling pathways. Knockdown of Fstl1 in HSCs significantly ameliorated cell activation, cell migration, chemokines C-C Motif Chemokine Ligand 2 (CCL2) and C-X-C Motif Chemokine Ligand 8 (CXCL8) secretion and extracellular matrix (ECM) production, and also modulated microRNA-29a (miR29a) expression. Furthermore, we identified that Fstl1 was a target gene of miR29a. And TGF-β1 induction of Fstl1 expression was partially through down regulation of miR29a in HSCs. Conclusions Our data suggests TGF-β1-miR29a-Fstl1 regulatory circuit plays a key role in regulation the HSC activation and ECM production, and targeting Fstl1 may be a strategy for the treatment of liver fibrosis. Video Abstract Graphical abstract

Limited-angle computed tomography (CT) has arisen in some medical and industrial applications. It is also a challenging problem since some scan views are missing and the directly reconstructed images often suffer from severe distortions. For such kind of problems, we analyze the features of limited-angle CT images and propose a multi-scale dilated convolution neural network (MSD-CNN) to correct the artifacts and to restore the image. In this network, the dilated convolution layer and multi-scale pooling layer are combined to form a group and exited in the whole encoder-decoder process. Since the dilated convolutions support an exponential expansion of the receptive field without losing resolution and coverage, the obtained artifact features possess the multi-scale characteristic. Furthermore, to improve the effectiveness and accuracy of the training step, we employ a preprocessing method, which extracts image patches. Numerical experiments verify the out-performance of the proposed method compared with some conventional methods, such as Unet based deep learning,TV- and L0 -based optimization methods.

Rapid, non-destructive detection of wheat Fusarium head blight (FHB) is an important tool for disease control. Red-edge (RE) is a prominent spectral feature for determining crop conditions with the potential to enhance the accuracy of monitoring FHB regionally. This study explored the potential of RE for FHB monitoring based on Sentinel-2 Multispectral Instrument (MSI) data. The novel red-edge head blight index (REHBI) was developed to detect FHB at a regional scale. Hyperspectral data at the canopy scale was integrated to simulate Sentinel-2 multispectral reflectance using the relative spectral response (RSR) function of the sensor. Then, many differential and ratio combinations of Sentinel-2 bands that were sensitive to FHB severity were selected. REHBI was established based on these basic vegetation indexes (VIs), and the model developed from REHBI performed best in monitoring FHB severity (R2 = 0.82, RMSE = 10.1). Additionally, the infected canopies with disease index (DI) values between 10 and 50 were classified as slightly diseased canopies. Ordinary least square (OLS) was used to test the performance of REHBI and two conventional VIs, i.e., OSAVI and RDVI, in monitoring slightly diseased canopies; REHBI outperformed these alternatives (R2 = 0.69, RMSE = 3.6). To approximate real agricultural conditions, Poisson noise was added to the simulated Sentinel-2 multispectral data and generalized performance of VIs was evaluated again; REHBI still had the highest R2 and lowest RMSE values (0.74 and 12.6, respectively). Finally, to validate REHBI's ability to detect FHB infection in agricultural production, it was applied to monitoring FHB in the wheat planting areas of Changfeng and Dingyuan counties from Sentinel-2 imagery. Generally, REHBI performed better in disease monitoring than OSAVI and RDVI. The overall accuracy was up to 78.6%, and the kappa coefficient was 0.51. Experimental results demonstrate that REHBI can be used to monitor FHB.

The outbreak of Oriental Migratory Locust(Locusta migratoria manilensis) causes devastating disasters to agriculture. With the impact of climate changes and human activities, the distribution of locust habitat (locust habitat is the environment in which locusts live and survive) in China is constantly changing. Monitoring and extracting locust habitat are of great significance for guiding large-scale agricultural production. The occurrence of the locust is closely related to their habitat. Therefore, a comprehensive analysis of habitat factors that affect locust survival is carried out to monitor locust habitat distribution. Besides, the landscape structure also affects distribution. This study explored a model for analyzing multi-temporal Landsat and MODIS images, which combined multiple habitat factors and landscape structure to analyze locust habitat. The locust habitat near North Dagang Reservoir in Tianjin is the research object. First, the habitat factors that affect locust oviposition and growth were analyzed, and vegetation coverage, land cover class, soil moisture, soil salinity, and land surface temperature were selected as five habitat factors. The weights of five habitat factors were evaluated according to the Analytic Hierarchy Process (AHP) model. Then, considering the impact of landscape structure on locust habitat, a moving-window was used to correlate locust habitat factors at pixel scale with locust habitat at patch scale. Finally, the distribution map of the locust habitat at patch scale was generated. The Analytic Hierarchy Process(AHP) was used to compare and test the results. Our research shows that the Patch based - Analytic Hierarchy Process (PB-AHP) can monitor locust habitat. The overall accuracy reached 88%, which is 10% higher than the result based on the Analytic Hierarchy Process(AHP). These results show that the Patch based - Analytic Hierarchy Process (PB-AHP) model has strong robustness and generalization ability in identifying locust habitat and can provide scientific guidance for locust monitoring and control.

Infected areas and damage levels due to crop pest and disease have been growing seriously according to the climate change. We aim to develop an automatic system to provide national pest and disease dynamic monitoring and early forecasting products, by integrating multisource information (Earth Observation, meteorological, ecological, entomological, and plant pathological, etc.) and cutting edge research on pest and disease modeling to support decision making in the sustainable management of pest and disease. First, we selected the sensitive indexes for pest and disease habitat monitoring and early forecasting, and then optimized the forecasting model's parameters to enhance its applicability in national level. Second, we developed an automatic system based on web GIS platform to efficiently realize the national pest and disease dynamic habitat monitoring and early forecasting. Finally, we released the pest and disease forecasting thematic maps. China's national disease wheat yellow rust (Puccinia striiformis) and national pest oriental migratory locust (Locusta migratoria manilensis (Meyen)) are taking as the experimental objects. Based on the developed system, we forecasted the infected areas of rust and locust in China, in 2019, with these R-square values are higher than 0.87. This system would not only promote the efficacy of pest and disease management and prevention by improving accuracy of monitoring and forecasting, but also help to reduce the amount of chemical pesticides, which could thus guarantee food security and agriculture sustainable development in China.

Grasshoppers can greatly interfere with agriculture and husbandry, and they will breed and grow rapidly in suitable habitats. Therefore, it is necessary to extract the distribution of the grasshopper potential habitat (GPH), analyze the spatial-temporal characteristics of the GPH, and detect the different effects of key environmental factors in the meadow and typical steppe. To achieve the goal, this study took the two steppe types of Xilingol (the Inner Mongolia Autonomous Region of China) as the research object and coupled them with the MaxEnt and multisource remote sensing data to establish a model. First, the environmental factors, including meteorological, vegetation, topographic, and soil factors, that affect the developmental stages of grasshoppers were obtained. Secondly, the GPH associated with meadow and typical steppes from 2018 to 2022 were extracted based on the MaxEnt model. Then, the spatial-temporal characteristics of the GPHs were analyzed. Finally, the effects of the habitat factors in two steppe types were explored. The results demonstrated that the most suitable and moderately suitable areas were distributed mainly in the southern part of the meadow steppe and the eastern and southern parts of the typical steppe. Additionally, most areas in the town of Gaorihan, Honggeergaole, Jirengaole, as well as the border of Wulanhalage and Haoretugaole became more suitable for grasshoppers from 2018 to 2022. This paper also found that the soil temperature in the egg stage, the vegetation type, the soil type, and the precipitation amount in the nymph stage were significant factors both in the meadow and typical steppes. The slope and precipitation in the egg stage played more important roles in the typical steppe, whereas the aspect had a greater contribution to the meadow steppe. These findings can provide a methodical guide for grasshopper control and management and for further ensuring the security of agriculture and husbandry.

Numerous studies have discovered that chronic stress induces metabolic disorders by affecting iron and zinc metabolism, but the relationship between chronic stress and copper metabolism remains unclear. Here, we explore the influence of chronic corticosterone (CORT) exposure on copper metabolism and its regulatory mechanism in mice. Mice were treated with 100 μg/mL CORT in drinking water for a 4-week trial. We found that CORT treatment resulted in a significant decrease in plasma copper level, plasma ceruloplasmin activity, plasma and liver Cu/Zn-SOD activity, hepatic copper content, and liver metallothionein content in mice. CORT treatment led to the reduction in duodenal expression of copper transporter 1 (CTR1), duodenal cytochrome b (DCYTB), and ATPase copper-transporting alpha (ATP7A) at the mRNA and protein level in mice. CORT treatment activated nuclear glucocorticoid receptor (GR) and down-regulated CRT1 expression in Caco-2 cells, whereas these phenotypes were reversible by an antagonist of GR, RU486. Chromatin immunoprecipitation analysis revealed that GR bound to the Ctr1 promoter in Caco-2 cells. Transient transfection assays in Caco-2 cells demonstrated that the Ctr1 promoter was responsive to the CORT-activated glucocorticoid receptor, whereas mutation/deletion of the glucocorticoid receptor element (GRE) markedly impaired activation of the Ctr1 promoter. In addition, CORT-induced downregulation of Ctr1 promoter activity was markedly attenuated in Caco-2 cells when RU486 was added. These findings present a novel molecular target for CORT that down-regulates intestinal CTR1 expression via GR-mediated trans-repression in mice.

In this study, a modified horizontal settling tank with rotating biological discs was developed to treat slightly polluted surface water, and its performance on the simultaneous removal of turbidity, organics, and NH4+-N was investigated on a lab scale. Results show that the effluent quality of the modified settling tank is stable in more than two months of continuous operation. At a hydraulic retention time (HRT) of 2.0 h, 73.65 ± 5.15% turbidity, 53.98 ± 5.17% TOC, and 77.01 ± 10.02%, NH4+-N could be removed by the modified settling tank with an average of 1.96 NTU turbidity, 1.98 mg/L TOC, and 0.46 mg/L NH4+-N residue in the effluent. Due to the improvement in DO supply, higher removal efficiencies of both organics and NH4+-N were achieved with increased disc rotating speed (r < 4 r/min). Further study showed that the genus Hyphomicrobium dominant on the posterior discs and the genus Nitrospira dominant on the anterior discs mainly contributed to the enhanced bio-oxidation of organics and NH4+-N, respectively.

Yellow rust, caused by Puccinia striiformis f. sp. Tritici, is a serious disease attacking wheat (Triticum aestivum L.) across the globe. The occurrence of yellow rust can result in severe yield reduction and economic loss. Hyperspectral remote sensing has demonstrated potential in detecting yellow rust, with the majority of studies distinguishing qualitatively between diseased and healthy individuals or performing simple grading of disease severity. However, research on the quantification of the severity of yellow rust is limited. To fill this gap in the literature, in the current study, we constructed a new spectral index, the yellow rust optimal index (YROI), using the hyperspectral data obtained by ASD field spectrometer to quantitatively estimate yellow rust severity. The index is based on the spectral response of spores, and vegetation biophysical and biochemical parameters (VPCPs); and integrated with the PROSPECT-D model. We evaluated the new index and compared it with 11 commonly used yellow rust detection indices using experimental leaf- and canopy-scale spectral datasets. Results demonstrated the superior accuracy of YROI for both the leaf (R2 = 0.822, RMSE = 0.070) and canopy (R2 = 0.542, RMSE = 0.085) scales. In this research, we quantitatively analyzed the spectral response mechanism of wheat yellow rust, which provided a new idea for the quantitative identification of crop diseases. Moreover, our results can be employed as a reference and theoretical basis for the accurate and timely quantitative identification of crop diseases over the large areas in the future.

In comparison with conventional machine learning algorithms, deep learning can effectively express the deep features of remote sensing images. Considering the rich spectral and spatial information contained in hyperspectral images (HSIs), a combination method was proposed for HSI classification based on stacked autoencoder (SAE) and 3D deep residual network (3DDRN). Specifically, a SAE neural network was first built to reduce the dimensions of original HSIs. A 3D convolutional neural network (3DCNN) was then designed and the residual network module was introduced to build a 3DDRN. The dimension-reduced 3D HSI cubes were input into the 3DDRN to extract identifiable joint spectral-spatial features. Finally, the deep features continuously identified by the 3DDRN were input to Softmax classification layer to realize the classification. In addition, Batch Normalization (BN) and Dropout were used during the learning process to avoid overfitting on training data. The training and test sets of Indian Pines (IP), Pavia University (PU) and Salinas (SA) hyperspectral data sets were selected as the modeling and verification data sources. Six classical classification algorithms were adopted for comparing our proposed method, specifically including conventional machine learning algorithms of Radial Basis Function-Support Vector Machine (RBF-SVM), Kernel Simultaneous Orthogonal Matching Pursuit (KSOMP) and Local Binary Pattern-K-Nearest Neighbor (LBP-KNN), and mainstream deep learning algorithms of Variational Autoencoder (VAE), Convolutional Neural Network (CNN) and Spectral-Spatial Residual Network (SSRN). The results showed that the overall accuracy (OA) reached 98.97%, 99.69% and 99.24%, respectively, only based on 10%, 5% and 1% of training samples for IP, PU and SA. Consequently, the proposed method shows a better classification performance, even in the case of limited samples.

With the Delta variant currently fueling a resurgence of new infections in the fully vaccinated population, developing an effective therapeutic drug is especially critical and urgent in fighting COVID-19. In contrast to the many efforts to repurpose existing drugs or address only one aspect of COVID-19, we are developing a novel agent with first-in-class mechanisms of action that address both the viral infection and the overactive immune system in the pathogenesis of the disease.

The fall armyworm (FAW) (Spodoptera frugiperda) (J. E. Smith) is a migratory pest that lacks diapause and has raised widespread concern in recent years due to its global dispersal and infestation. Seasonal environmental changes lead to its large-scale seasonal activities, and quantitative simulations of its dispersal patterns and spatiotemporal distribution facilitate integrated pest management. Based on remote sensing data and meteorological assimilation products, we constructed a mechanistic model of the dynamic distribution of FAW (FAW-DDM) by integrating weather-driven flight of FAW with host plant phenology and environmental suitability. The potential distribution of FAW in China from February to August 2020 was simulated. The results showed a significant linear relationship between the dates of the first simulated invasion and the first observed invasion of FAW in 125 cities (R2 = 0.623; p < 0.001). From February to April, FAW was distributed in the Southwestern and Southern Mountain maize regions mainly due to environmental influences. From May to June, FAW spread rapidly, and reached the Huanghuaihai and North China maize regions between June to August. Our results can help in developing pest prevention and control strategies with data on specific times and locations, reducing the impact of FAW on food security.

The outbreak of the Asian migratory locust (Locusta migratoria migratoria) (AML) can deal a great blow to agriculture and grassland farming. The emergence of high-density locusts facilitates the outbreak of locusts. Understanding the forming mechanism of the high-density spot of locust (HDSL) is very important for locust monitoring and control. To achieve this goal, this paper took Nong’an County, which used to form an HDSL in 2017, as the study area. Firstly, based on the habitat classification system, support vector machine (SVM), random forest (RF), and maximum likelihood (ML) methods were employed to explore the best classification method for locust habitats. Then, the optimal method was applied to monitor habitat dynamic changes from 2014 to 2017 in the HDSL in Nong’an. Finally, the HDSL forming mechanism was clarified coupled with habitat dynamic changes and meteorological data. The results showed that the SVM method was the optimal method, with an accuracy of 95.28%, which is higher than the RF and ML methods by 0.25% and 8.52%, respectively. The annual increased barren land and sufficient reeds provided adequate suitable habitats for the breeding of AML. From 2014 to 2016, the temperatures during the overwintering and hatching periods were higher than the 2010–2018 average, and the precipitation during the spawning period was lower than the 2010–2018 average. The precipitation during the growing period in 2017 was 30.8 mm less than the average from 2010 to 2018. All these characteristics were conducive to the reproduction of locusts. We concluded that the suitable habitat and meteorological conditions increased the locust quantity yearly, resulting in the formation of HDSL. These results are instrumental for monitoring potential high-risk outbreak areas, which is important to improve locust control and ensure food security.

Wheat Fusarium head blight (FHB) can be effectively controlled through prediction. To address the low accuracy and poor stability of model predictions of wheat FHB, a prediction method of wheat FHB that couples a logistic regression mechanism-based model and k-nearest neighbours (KNN) model is proposed in this paper. First, we selected predictive factors, including remote sensing-based and meteorological factors. Then, we quantitatively expressed the factor weights of the disease occurrence and development mechanisms in the disease prediction model by using a logistic model. Subsequently, we integrated the obtained factor weights into the predictive factors and input the predictive factors with weights into the KNN model to predict the incidence of wheat FHB. Finally, the accuracy and generalizability of the models were evaluated. Wheat fields in Changfeng, Dingyuan, Fengyuan, and Feidong counties, Anhui Province, where wheat FHB often occurs, were used as the study area. The incidences of wheat FHB on 29 April and 10 May 2021 were predicted. Compared with a model that did not consider disease mechanism, the accuracy of our model increased by approximately 13%. The overall accuracies of the models for the two dates were 0.88 and 0.92, and the F1 index was 0.86 and 0.94, respectively. The results show that the predictions made with the logistic-KNN model had higher accuracy and better stability than those made with the KNN model, thus achieving remote sensing-based high-precision prediction of wheat FHB.

With the application of the concept of supramolecular chemistry to various fields, a large number of supramolecules have been discovered. The chemical components of traditional Chinese medicine have various sources and unique structures. During the high-temperature boiling process, various active components form supramolecules due to complex interactions. The supramolecular structure in a traditional Chinese medicine decoction can not only be used as a drug carrier to promote the absorption and distribution of medicinal components but may also have biological activities superior to those of single active ingredients or their physical mixtures. By summarizing the relevant research results over recent years, this paper introduces the research progress regarding supramolecules in various decoctions, laying a foundation for further research into supramolecules in traditional Chinese medicine decoctions, and provides a new perspective for revealing the compatibility mechanisms of traditional Chinese medicine, guiding clinical medications, and developing new nanometers materials.

Wheat stripe rust poses a serious threat to wheat production. An effective prediction method is important for food security. In this study, we developed a prediction model for wheat stripe rust based on vegetation indices and meteorological features. First, based on time-series Sentinel-2 remote sensing images and meteorological data, wheat phenology (jointing date) was estimated using the harmonic analysis of time-series combined with average cumulative temperature. Then, vegetation indices were extracted based on phenological information. Meteorological features were screened using correlation analysis combined with independent t-test analysis. Finally, a random forest (RF) was used to construct a prediction model for wheat stripe rust. The results showed that the RF model using the input combination (phenological information-based vegetation indices and meteorological features) produced a higher prediction accuracy and a kappa coefficient of 88.7% and 0.772, respectively. The prediction model using phenological information-based vegetation indices outperformed the prediction model using single-date image-based vegetation indices, and the overall accuracy improved from 62.9% to 78.4%. These results indicated that the method combining phenological information-based vegetation indices and meteorological features can be used for wheat stripe rust prediction. The results of the prediction model can provide guidance and suggestions for disease prevention in the study area.

Circadian systems provide a fitness advantage to organisms by allowing them to adapt to daily changes of environmental cues, such as light/dark cycles. The molecular mechanism underlying the circadian clock has been well characterized. However, how internal circadian clocks are entrained with regular daily light/dark cycles remains unclear. By collecting and analyzing indirect calorimetry (IC) data from more than 2000 wild-type mice available from the International Mouse Phenotyping Consortium (IMPC), we show that the onset time and peak phase of activity and food intake rhythms are reliable parameters for screening defects of circadian misalignment. We developed a machine learning algorithm to quantify these two parameters in our misalignment screen (SyncScreener) with existing datasets and used it to screen 750 mutant mouse lines from five IMPC phenotyping centres. Mutants of five genes (Slc7a11, Rhbdl1, Spop, Ctc1 and Oxtr) were found to be associated with altered patterns of activity or food intake. By further studying the Slc7a11tm1a/tm1a mice, we confirmed its advanced activity phase phenotype in response to a simulated jetlag and skeleton photoperiod stimuli. Disruption of Slc7a11 affected the intercellular communication in the suprachiasmatic nucleus, suggesting a defect in synchronization of clock neurons. Our study has established a systematic phenotype analysis approach that can be used to uncover the mechanism of circadian entrainment in mice.

Desert locust plagues can easily cause a regional food crisis and thus affect social stability. Preventive control of the disaster highlights the early detection of hopper gregarization before they form devastating swarms. However, the response of hopper band emergence to environmental fluctuation exhibits a time lag. To realize the dynamic forecast of band occurrence with optimal temporal predictors, we proposed an SVM-based model with a temporal sliding window technique by coupling multisource time-series imagery with historical locust ground survey observations from between 2000–2020. The sliding window method was based on a lagging variable importance ranking used to analyze the temporal organization of environmental indicators in band-forming sequences and eventually facilitate the early prediction of band emergence. Statistical results show that hopper bands are more likely to occur within 41–64 days after increased rainfall; soil moisture dynamics increasing by approximately 0.05 m³/m³ then decreasing may enhance the chance of observing bands after 73–80 days. While sparse vegetation areas with NDVI increasing from 0.18 to 0.25 tend to witness bands after 17–40 days. The forecast model combining the optimal time lags of these dynamic indicators with other static indicators allows for a 16-day extended outlook of band presence in Somalia, Ethiopia, and Kenya. Monthly predictions from February to December 2020 display an overall accuracy of 77.46%, with an average ROC-AUC of 0.767 and a mean F-score close to 0.772. The multivariate forecast framework based on the lagging effect can realize the early warning of band presence in different spatiotemporal scenarios, supporting early decisions and response strategies for desert locust preventive management.

For the problem of multi-dimensional feature redundancy in remote sensing detection of wheat stripe rust using reflectance spectrum and solar-induced chlorophyll fluorescence (SIF), a feature selection and disease index (DI) monitoring model combining mRMR and XGBoost algorithm was proposed in this study. Firstly, characteristic wavelengths selected by successive projections algorithm (SPA) were combined with the vegetation indices, trilateral parameters, and canopy SIF parameters to constitute the initial feature set. Then, the max-relevance and min-redundancy (mRMR) algorithm and correlation coefficient (CC) analysis were used to reduce the dimensionality of the initial feature set, respectively. Features selected by mRMR and CC were input as independent variables into the extreme gradient boosting regression (XGBoost) and gradient boosting regression tree (GBRT) to monitor the severity of stripe rust. The experimental results show that, compared with CC analysis, the monitoring accuracy of the features selected by mRMR in the XGBoost and GBRT models increased by 12% and 17% on average, respectively. Meanwhile, the mRMR-XGBoost model achieved the best monitoring accuracy (R2 = 0.8894, RMSE = 0.1135). The R2 between the measured DI and predicted DI of mRMR-XGBoost was improved by an average of 5%, 12%, and 22% compared with mRMR-GBRT, CC-XGBoost, and CC-GBRT models. These results suggested that XGBoost is more suitable for the remote sensing monitoring of wheat stripe rust, and mRMR has more advantages than the commonly used CC analysis in feature selection. Field survey data validation results also confirm that the mRMR-XGBoost algorithm has excellent monitoring applicability and scalability. The proposed model could provide a reference for data dimensionality reduction and crop disease index monitoring based on hyperspectral data.

The Oriental migratory locust is a destructive agricultural pest in China. Large-scale locust area (the area possessing suitable breeding habitat for locusts and has locust infestation) extraction and its evolution analysis are essential for locust ecological control. Existing methods seldom consider the spatial differences in the locust development and habitat landscape structures in large areas. To analyze these effects, our study proposed a landscape-based habitat suitability model (LHS model) for large-scale locust area extraction based on remote sensing data, taking the middle and lower reaches of the Yellow River (MLYR) as an example. Firstly, the DD model was used to simulate locust development and obtain habitat factors of the corresponding dates; secondly, the patch distribution of different land cover classes and their adjacent landscape characteristics were analyzed to determine the landscape-based factors memberships; finally, the habitat suitability index was calculated by combining the factors memberships and weights to extract the locust area. Compared with the patch-based model using moving windows (patch based-analytic hierarchy process model, R2 = 0.77), the LHS model accuracy improved significantly (R2 = 0.83). Our results showed that the LHS model has a better application prospect in large-scale locust area extraction. By analyzing the locust areas evolution along the MLYR extracted using the LHS model, we found human activities were the main factors affecting the locust areas evolution from 2016 to 2020, including: (1) planting the plants that locusts do not like and urbanization caused the decrease of the locust area; (2) the wetland protection policies may cause the increase of the locust area. The model and research results help locust control and prevention to realize the sustainable development of agriculture.

Appropriate modeling methods and feature selection algorithms must be selected to improve the accuracy of early and mid-term remote sensing detection of wheat stripe rust. In the current study, we explored the effectiveness of the random forest (RF) algorithm combined with the extreme gradient boosting (XGboost) method for early and mid-term wheat stripe rust detection based on the vegetation indices extracted from canopy level hyperspectral measurements. Initially, 21 vegetation indices that were related to the early and mid-term winter wheat stripe rust were calculated on the basis of canopy level hyperspectral reflectance. Subsequently, the optimal vegetation index combination for disease detection was determined using correlation analysis (CA) combined with RF algorithms. Then, the disease severity detection model of early and mid-term winter wheat stripe rust was constructed using XGBoost method based on the optimal vegetation index combination. For the evaluation and comparison of the initial results, three commonly used classification methods, namely, RF, backpropagation neural network (BPNN), and support vector machine (SVM), were utilized. The vegetation index combinations determined by the single CA algorithm were also used to construct detection models. Compared with the detection models based on the vegetation index combination obtained using the single CA algorithm, the overall accuracy of the four detection models based on the optimal vegetation index combination based on CA combined with RF algorithms increased by 16.1% (XGBoost), 9.7% (RF), 8.1% (SVM), and 8.1% (BPNN). Among the eight models, the XGBoost detection model based on the optimal vegetation index combination using CA combined with RF algorithms, CA-RF-XGBoost, achieved the highest overall accuracy of 87.1% and the highest kappa coefficient of 0.798. Our results indicate that the RF combined with XGBoost can improve the detection accuracy of early and mid-term winter wheat stripe rust effectively at canopy scale.