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Are the variations of the fine predictors at the spatial scale of the target variable to be downscaled helpful for spatial downscaling? However, few studies have explored this topic. In this study, one of the most frequently downscaled satellite products (Tropical Rainfall Measuring Mission (TRMM) precipitation) and one of the most commonly employed downscaled models (geographically weighted regression (GWR)) were chosen as the target variable to be downscaled and the downscaling model, respectively. Three widely adopted auxiliary variables were selected as basic predictors. Variations of the three 1-km basic predictors at the 25-km (a TRMM cell) spatial scale (hereafter termed variation predictors (VP)) were captured by the employment of the “standard deviation” operators. A procedure was designed to determine the monthly optimal trend component model, and area-to-point kriging (ATPK) was applied to retrieve residual components. The monthly TRMM precipitation in the main body of the north-south transitional zone of China (MBNSTZC) from January 2010 to December 2019 (120 months in total) was spatially downscaled. When VP was introduced into the predictor family, performance improvements were observed for more than two-thirds of 120 months, and the average relative improvements in the coefficient of determination $^{}$ ( $\text{R}^{2}$ ), root-mean-square error (RMSE), mean absolute error (MAE), and information entropy (IE) were 9.01%, 9.37%, 10.56%, and 28.21%, respectively. Our study suggests that: i) VP incorporation, which can improve downscaling performance to some extent, is important for GWR downscaling modeling; ii) Residual correction is unnecessary, especially for GWRs with VP incorporation; iii) GWRs with VP incorporation can not only downscale target variable but also have a certain interpolation ability.

Soil biogeochemical processes have the potential to impact water quality in source areas of water diversion projects. This study aimed to explore the differences in biochemical processes and mechanisms at the microaggregate scale for different land use types in the water source area of China's Middle Route of the South-to-North Water Diversion Project. The study compared four typical land use types—forests, shrublands, terraces, and cultivated land—by characterizing the microaggregates using various analytical techniques, including scanning electron microscopy (SEM), SEM-EDS plane scan analysis, X-ray diffraction (XRD), X-ray fluorescence (XRF) spectroscopy, and Fourier transform infrared (FTIR) spectroscopy. Microaggregates from forests and shrublands had a flocculent spatial structure, while those from terraces had a nondirectional plate-like spatial structure, and those from cultivated land had a laminar spatial structure. Additionally, elements such as Mg, Al, and Fe were enriched in clay particles when combined with microaggregates. The mineral composition of clay varied significantly between different land use practices, with illite, chlorite, anorthite, albite, and quartz present in higher proportions in terraces and cultivated land than in forests and shrublands. Moreover, the contents of transitional elements such as Ti, Fe, Zn, Y, and Zr increased with each successive land use practice. Soil organic matter (SOM) was observed to decline in the order of forest > shrubland > terraces > cultivated land. Over-tillage appeared to be the primary mechanism of organic matter loss due to long-term tillage. The results of this study provide valuable insights into soil erosion and chemical transport dynamics. The mineral composition and spatial structure of microaggregates are important determinants of soil biochemical processes and mechanisms, which can influence water quality. The findings suggest that forest and shrubland management practices may be more conducive to maintaining soil health and water quality in source areas of water diversion projects.

Atmospheric aerosols have important effects on the environment and human health. In this study, we analyzed the atmospheric aerosol layers’ optical and physical properties over Africa utilizing CALIPSO level 2 products from 2007 to 2019. Interannual and seasonal variations in aerosol optical parameters were studied: the lowest aerosol layer (AODL), the base height of the lowest aerosol layer (BL), the top height of the lowest aerosol layer (HL), the top height of the highest aerosol layer (HH), the volumetric depolarization ratio of the lowest aerosol layer (DRL), the color ratio of the lowest aerosol layer (CRL), the total AOD of all aerosol layers (AODT), the number of aerosol feature layers (N), the thickness of the lowest aerosol layer (TL), and the AOD proportion of the lowest aerosol layer (PAODL). The annual mean AODT was slightly higher in southern Africa than in northern Africa. HL and HH had strongly positive correlations with landform elevations. However, HL and HH were greater in northern Africa than in southern Africa from March to August. The reason could be that northern Africa is dominated by deserts with high temperatures and intense atmospheric vertical convections leading to dust layers existing in the upper air. PAODL values were lower in northern Africa (daytime: 71%; nighttime: 61%) than in southern Africa (daytime: 78%; nighttime: 69%), revealing that aerosol stratifications were more frequent in northern Africa than in southern Africa. DRL values were higher in northern Africa (daytime: 0.16; nighttime: 0.11) than in southern Africa (daytime: 0.07; nighttime: 0.05) indicating the predominance of non-spherical dust particles in northern Africa. This work can provide an important understanding of regional aerosol layers’ optical and physical properties to scientists and local environmental protection agencies.

Atmospheric aerosols have important impacts on global radiative forcing, air pollution, and human health. This study investigated the optical and physical properties of aerosol layers over Australia from 2007 to 2019 using the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Level 2 aerosol products. Australia was divided into three sub-regions (western highlands, central plains, and eastern ranges). Interannual and seasonal optical property variations in aerosol layers in the three sub-regions were analyzed and compared. Results showed that annual mean values of AODL (lowest aerosol layer AOD) and AODT (total AOD of all aerosol layers) were always higher in the eastern ranges region than the other two regions from 2007 to 2019. The reason could be that Australian population was predominantly located in the eastern ranges region, where more human activities could bring significant aerosol loadings. BL (base height of the lowest aerosol layer), HL (top height of the lowest aerosol layer), and HH (top height of the highest aerosol layer) all showed trends of “western highlands > eastern mountains > central plains”, indicating that the higher the elevation, the higher the BL, HL, and HH. TL (thickness of the lowest aerosol layer) was higher during the day than at night, which might account for increased diurnal atmospheric convection and nocturnal aerosol deposition. DRL (depolarization ratio of the lowest aerosol layer) was higher in the western highlands and central plains than the eastern mountains, probably because these two regions have large deserts with more irregularly shaped dust aerosols. CRL (color ratio of the lowest aerosol layer) had slightly higher values in the eastern ranges than the other two regions, probably due to the wet climate of the eastern ranges, where aerosols were more hygroscopic and had larger particle sizes. This study can provide technical support for the control and management of regional air pollutants.

For research and practice in fields such as the environment and meteorology, nighttime land surface temperature (LST) images at fine resolution provide important basic data. Since only a few satellite sensors can take fine-resolution thermal infrared images at night, such images are rather scarce. Downscaling of coarse-resolution LST images is a potential method for obtaining high-resolution LST images. However, downscaling methods have been mostly proposed for daytime LST images. This study aimed to evaluate the performance of methods that combine multiple explanatory variables and machine learning algorithms for downscaling coarse-resolution nighttime LST images in urban areas. Verification showed that the errors in the downscaling results were acceptable (mean absolute errors within 2 K). The resulting images could depict the spatial pattern of night LST in the study area in great detail. It was demonstrated that visible-near infrared images taken in the daytime could be used for downscaling of nighttime LST images, the rationality of which was deduced. It was also demonstrated that the performance of the spectral explanatory variables in nighttime LST downscaling was not lower than that of the mechanism explanatory variables. This method has high application value in many academic and practical efforts, such as land-atmosphere interface radiation budget studies, suitability assessments of human settlement, and urban environmental planning.

The purpose of this study is to investigate the air quality levels of Nanyang city according to Chinese air quality standards. Therefore, in this study, fine particulate matter (PM2.5), coarse particulate matter (PM10), and total suspended particulate (TSP) were analyzed from 19 November 2021 to 19 March 2022 in Nanyang city. The results show that the average concentrations of PM2.5, PM10, and TSP were 106.47 µg/m3, 137.32 µg/m3, and 283.40 µg/m3, respectively. The numbers of days that meet the national secondary air quality standard of 24-h average concentrations were 29.75% for PM2.5, 63.64% for PM10, and 63.64% for TSP, indicating that most of the time, the air quality of Nanyang city remains polluted in winter, especially with more contributions of PM2.5 compared to PM10 and TSP. The higher concentrations were observed between 07:00 and 08:00, suggesting that vehicular emissions can be a major cause of air pollution in Nanyang city. The results also show a significant positive correlation between particulate matter and relative humidity, and a weak correlation with temperature and wind speed, which suggests that higher relative humidity increases the formation of particulate matter. This study can provide theoretical support for the local government to formulate air pollution prevention and control policies for Nanyang city.

Abstract Background Myopia is a leading cause of preventable blindness. Although, multiple cross-sectional epidemiological studies have confirmed that there is a high prevalence of myopia in high school-aged students in China. However, few longitudinal studies have been performed to assess junior high school students. In the present study, we investigate changes in the prevalence of myopia in third year junior high school (grade 9) students in the Haidian District of Beijing, China, from 2006 to 2015. Methods A retrospective, longitudinal cohort study was performed over 10 years. A total of 37,424 third-year middle school (grade 9) students from 8 junior high schools in Haidian district, Beijing, were included. Participants underwent a comprehensive ophthalmic examination in which they were evaluated using autorefraction under cycloplegia and submitted to retinoscopy to assess accuracy. According to the spherical equivalent refraction (SER) of the right eye, subjects were separated into the following groups: non-myopia, −0.5 ≤ SER diopters (D); low myopia, −3.0 ≤ SER −6.0 D. The following characteristics were measured: refractive error; the proportion of subjects with non- myopia, low myopia, moderate myopia and high myopia; and the difference in the prevalence of myopia between male and female subjects. Results From 2006 to 2015, the prevalence of non-myopia (from 44.05% to 34.52%) and low myopia (from 32.27% to 20.73%) decreased, while the prevalence of moderate myopia (from 19.72% to 38.06%) and high myopia (from 3.96% to 6.69%) significantly increased. For refractive error, the worse eye was −2.23 ± 2.42 D (median, −1.75; range − 12.75 to +8.50) in 2006 and −3.13 ± 2.66 D (median, −2.75; range − 12.75 to +8.50) in 2015. When the entire population was considered, the overall prevalence of myopia increased from 55.95% in 2005 to 65.48% in 2015. There was a significant positive relationship between the year and the prevalence of myopia in both girls and boys. Girls were more likely than boys to have myopia (odds ratio, 1.43 [95% CI, 1.14–1.96]), especially moderate myopia, and the prevalence of moderate and high myopia were higher in girls than in boys. Conclusions During the last 10 years, the prevalence of myopia significantly increased on an annual basis among third-year junior high school students in the Haidian District of Beijing, China. The total prevalence of myopia was significantly higher in girl than in boy participants. The refractive status of this age group deserves particular attention.

Pan evapotranspiration (E) is an important physical parameter in agricultural water resources research. Many climatic factors affect E, and one of the essential challenges is to model or predict E utilizing limited climatic parameters. In this study, the performance of four different artificial neural network (ANN) algorithms i.e., multiple hidden layer back propagation (MBP), generalized regression neural network (GRNN), probabilistic neural networks (PNN), and wavelet neural network (WNN) and one empirical model namely Stephens–Stewart (SS) were employed to predict monthly E. Long-term climatic data (i.e., 1961–2013) was used for the validation of the proposed model in the Henan province of China. It was found that different models had diverse prediction accuracies in various geographical locations, MBP model outperformed other models over almost all stations (maximum R2 = 0.96), and the WNN model was the best over two sites, the accuracies of the five models ranked as MBP, WNN, GRNN, PNN, and SS. The performances of WNN and GRNN were almost the same, five-input ANN models provided better accuracy than the two-input (solar radiation (Ro) and air temperature (T)) SS empirical model (R2 = 0.80). Similarly. the two-input ANN models (maximum R2 = 0.83) also generally performed better than the two-input (Ro and T) SS empirical model. The study could reveal that the above ANN models can be used to predict E successfully in hydrological modeling over Henan Province.

Abstract The development of open-source, multilingual medical language models can benefit a wide, linguistically diverse audience from different regions. To promote this domain, we present contributions from the following: First, we construct a multilingual medical corpus, containing approximately 25.5B tokens encompassing 6 main languages, termed as MMedC, enabling auto-regressive domain adaptation for general LLMs; Second, to monitor the development of multilingual medical LLMs, we propose a multilingual medical multi-choice question-answering benchmark with rationale, termed as MMedBench; Third, we have assessed a number of open-source large language models (LLMs) on our benchmark, along with those further auto-regressive trained on MMedC. Our final model, MMed-Llama 3, with only 8B parameters, achieves superior performance compared to all other open-source models on both MMedBench and English benchmarks, even rivaling GPT-4. In conclusion, in this work, We present a large-scale corpus, a benchmark and a series of models to support the development of multilingual medical LLMs.

Low temperature, rainfall/snowfall and freezing is a complex climate event. Although extreme low temperature, rainfall/snowfall and freezing events occur infrequently, they often cause serious impacts. Researching evolutionary characteristics of low temperature, rainfall/ snowfall and freezing events is of great significance for effective defense and mitigation of disaster impacts. By using daily meteorological observation data from 1981 to 2024 in Hubei and selecting indicators from three aspects: low temperature, rainfall/snowfall and freezing, a comprehensive evaluation model for large-scale sustained low temperature, rainfall/snowfall and freezing events was established. According to the types of disaster-causing factors and the disaster characteristics of the affected bodies, these events were classified and ranked. Combined with the historical disaster data, the disaster characteristics of large-scale sustained low temperature, rainfall/snowfall and freezing events in Hubei since 1981 were then analyzed. The results are as follows. (1) There have been a total of 64 large-scale sustained low temperature, rainfall/snowfall and freezing events in Hubei in the past 44 years, with an average of 1.5 occurrences per year. January has the highest frequency of occurrence, accounting for about 42%, followed by February, accounting for about 34%. (2) The large-scale sustained low temperature, rainfall/snowfall and freezing events in Hubei are classified into six types, with rain and snow type accounting for 40%, low temperature, rainfall/snowfall and freezing type accounting for 31%, and low temperature, rain, and snow type accounting for 16%, other types accounting for a total of 13%. The strongest 10 events all belong to low temperature, rainfall/snowfall and freezing type. (3) The direct economic losses caused by low temperature, rainfall/snowfall and freezing disasters in Hubei have shown an overall increasing trend, but the proportion of direct economic losses to GDP has shown a certain decreasing trend, with a relatively small proportion since 2012. (4) The correlation between the comprehensive intensity index and direct economic losses of large-scale sustained low temperature, rainfall/snowfall and freezing events in Hubei follows a power curve distribution. When the comprehensive intensity index of the event exceeds 1.85, the direct economic losses increase rapidly.

Under the background of climate change, extreme and continuous low temperature rain and snow processes occur from time to time, bringing many adverse effects to social life. In order to further evaluate the continuous low temperature rain and snow weather, . In order to further restore the disaster scenario, the indicators of the evaluation of the continuous low temperature rain and snow process at a single station in Hubei province were revised, and the regional evaluation model of the continuous low temperature rain and snow process was fur? ther established. The revised evaluation index was applied to the evaluation of single station and regional continuous low temperature rain and snow process in Hubei Province. The results showed that: (1) The five most serious regional continuous low temperature rain and snow processes occurred in 1954, 1969, 1977, 1984 and 2008, respectively. The most serious process was from December 24, 1954 to January 18, 1955, followed by January 12, 2008 to February 5, 2008. (2) The regions prone to regional continuous low temperature rain and snow process? es in Hubei Province were concentrated in the alpine or semi-alpine regions of western Hubei, Jingzhou area in Jianghan Plain, Hanjiang River valley, low mountain plain area in Southeastem Hubei and the gap of Mountains in northeast Hubei Province. (3) Under the back? ground of climate warming, both single-station and regional continuous low temperature rain and snow processes in Hubei Province occurred more frequently before the 1980s, and decreased after the 1990s, but the severity of the events became more extrem.

Using the surface meteorological observation data of 76 national meteorological stations in Hubei Province from 1961 to 2022, the drought and flood processes based on the meteorological drought composite index (MCI) and effective precipitation index (EP) are first identified. Then we analyze the spatial-temporal distribution of the frequency of the drought-flood processes, the characteristics of the drought-flood year, the drought-flood transitions over the past 62 years, and the characteristics of drought and flood processes since 2010. The results show that the frequent occurrence area of droughts and floods in Hubei Province is generally characterized by the flaky distribution with floods in the east and droughts in the west, while floods in the south and droughts in the north. The occurrence frequency of droughts and rainstorm floods shows a fluctuating decreasing and increasing trend, respectively. The droughts mainly occur in spring and summer-autumn with a decreasing trend in frequency, but an increasing trend is found in the summer-autumn droughts and extreme droughts. Comparatively, the rainstorms mainly concentrate in summer and show an increasing trend in frequency. The annual variation of drought-flood year is characterized by continuous droughts for 2-4 years, continuous floods for 2-3 years, or alternation of droughts and floods. In some years, droughts and floods coexist in the same year, while abrupt drought-flood transitions are also found. However, there has been no drought-flood coexistence in the same year since 2010. The annual variation of the number of stations with drought-flood transition fluctuates greatly, showing a five-stage increasing characteristic with the maximum value of each stage decreasing. Since 2010, the drought and flood events show characteristics of extreme enhancement, sudden increases, and successive droughts and floods.

Multi-baseline circular synthetic aperture radar (MB CSAR) can be applied to obtain a three-dimensional (3D) image of the observation scene. However, the phase error caused by radar platform motion or atmospheric propagation delay restricts its 3D imaging capabilities. The phase error calibration of MB CSAR data is an essential step in the 3D imaging procedure due to the limited accuracy of positioning sensors. Phase gradient autofocus (PGA) is widely utilized to estimate the phase errors but is subject to shifts in the direction perpendicular to the line of sight and long iteration time in some sub-apertures. In this paper, an improved PGA method for MB CSAR 3D imaging is proposed, which can suppress the shifts and reduce computation time. This method is based on phase gradient estimation, but the prominent units are selected with an energy criterion. Then, weighted phase gradient estimation is presented to suppress the influence of prominent units with poor quality. Finally, a contrast criterion is adopted to reach faster convergence. The experimental results based on the measured MB CSAR data (Gotcha dataset) demonstrate the validity and feasibility of the proposed phase error calibration method for MB CSAR 3D imaging.

In the exploration of humid air discharges at atmospheric pressure using chemical kinetics, the mapping of chemical reaction pathways emerges as a crucial instrument for deciphering the underlying mechanisms of the reaction system. In this study, we employed a force-directed layout method to create diagrams of chemical reaction pathways based on simulation results from a global model of humid air discharges at atmospheric pressure. This innovative approach aids in addressing the challenges traditionally associated with mapping these pathways, notably the difficulty in balancing intuitiveness with the precise representation of physical data. In our method, the mass of each node in the diagram is determined by the species density, and the natural length between nodes is defined according to reaction rates, with the forces acting upon the nodes dictated by the variance between the actual distance and this natural length. The final arrangement of the nodes is established upon reaching a stable equilibrium after undergoing damped motion in response to these forces. This methodology not only provides a tangible and intuitive visualization of the complex interactions within discharge plasmas but also enables a detailed sensitivity analysis to assess the significance of various reactions.

Background and objective There is a high morbidity, mortality, and poor clinical prognosis of lung squamous cell carcinoma (LUSC). However, there is currently no effective targeted treatment plan for LUSC. As a long non-coding RNA (lncRNA), lncRNA miR143HG has been proven to play an important role in the occurrence and development of various tumors. However, the biological role played by lncRNA miR143HG in LUSC cells is still unclear. Therefore, this study aimed to investigate the mechanism of lncRNA miR143HG on regulating the biological behavior of LUSC H520 cells. Methods Pan-cancer analysis and differential expression analysis of lncRNA miR143HG were performed based on The Cancer Genome Atlas (TCGA) database. The predictive effect of lncRNA miR143HG on the diagnosis and prognosis of LUSC was evaluated by adopting the receiver operating characteristic (ROC) curve and timeROC curve. The enrichment degree of each pathway to lncRNA miR143HG was determined. The expression of lncRNA miR143HG and miR-155 in BEAS-2B cells and H520 cells was detected using quantitative real-time polymerase chain reaction (qRT-PCR). H520 cells were randomly divided into blank control group (without any treatment), negative control group (transfected with lncRNA-NC), lncRNA miR143HG group (transfected with lncRNA miR143HG), and lncRNA miR143HG+miR-155 group (co-transfected with lncRNA miR143HG and miR-155). The approaches of CCK-8, wound healing test, Transwell assay, flow cytometry, qRT-PCR, and Western blot were respectively employed to detect the cell proliferation ability, cell migration ability, cell invasion ability, cell apoptosis rate, and expression level of related genes and proteins of the Wnt/β-Catenin pathway. Results The results of pan-cancer analysis and differential analysis collectively showed that except for renal clear cell carcinoma, the expression of lncRNA miR143HG in other cancer tissues was higher than that in healthy tissues, and the differences were significant in LUSC. The evaluation results of the ROC curve and timeROC curve suggested that lncRNA miR143HG was of great significance in the prediction of diagnosis and prognosis of LUSC. The pathways enriched in high expression of lncRNA miR143HG mainly included focal adhesion, vascular smooth muscle contraction, calcium signaling pathways, and so on; the pathways enriched in the low expression of lncRNA miR143HG embraced oxidative phosphorylation, cell cycle, basic transcription factors, etc. The qRT-PCR results showed that lncRNA miR143HG was low expressed but miR-155 was highly expressed in H520 cells when compared to BEAS-2B cells (P

BackgroundCoronavirus disease (COVID-19), caused by SARS-CoV-2, has emerged as a infectious disease, coexisting with widespread seasonal and sporadic influenza epidemics globally. Individuals living with HIV, characterized by compromised immune systems, face an elevated risk of severe outcomes and increased mortality when affected by COVID-19. Despite this connection, the molecular intricacies linking COVID-19, influenza, and HIV remain unclear. Our research endeavors to elucidate the shared pathways and molecular markers in individuals with HIV concurrently infected with COVID-19 and influenza. Furthermore, we aim to identify potential medications that may prove beneficial in managing these three interconnected illnesses.MethodsSequencing data for COVID-19 (GSE157103), influenza (GSE185576), and HIV (GSE195434) were retrieved from the GEO database. Commonly expressed differentially expressed genes (DEGs) were identified across the three datasets, followed by immune infiltration analysis and diagnostic ROC analysis on the DEGs. Functional enrichment analysis was performed using GO/KEGG and Gene Set Enrichment Analysis (GSEA). Hub genes were screened through a Protein-Protein Interaction networks (PPIs) analysis among DEGs. Analysis of miRNAs, transcription factors, drug chemicals, diseases, and RNA-binding proteins was conducted based on the identified hub genes. Finally, quantitative PCR (qPCR) expression verification was undertaken for selected hub genes.ResultsThe analysis of the three datasets revealed a total of 22 shared DEGs, with the majority exhibiting an area under the curve value exceeding 0.7. Functional enrichment analysis with GO/KEGG and GSEA primarily highlighted signaling pathways associated with ribosomes and tumors. The ten identified hub genes included IFI44L, IFI44, RSAD2, ISG15, IFIT3, OAS1, EIF2AK2, IFI27, OASL, and EPSTI1. Additionally, five crucial miRNAs (hsa-miR-8060, hsa-miR-6890-5p, hsa-miR-5003-3p, hsa-miR-6893-3p, and hsa-miR-6069), five essential transcription factors (CREB1, CEBPB, EGR1, EP300, and IRF1), and the top ten significant drug chemicals (estradiol, progesterone, tretinoin, calcitriol, fluorouracil, methotrexate, lipopolysaccharide, valproic acid, silicon dioxide, cyclosporine) were identified.ConclusionThis research provides valuable insights into shared molecular targets, signaling pathways, drug chemicals, and potential biomarkers for individuals facing the complex intersection of COVID-19, influenza, and HIV. These findings hold promise for enhancing the precision of diagnosis and treatment for individuals with HIV co-infected with COVID-19 and influenza.

This study investigated the effects of glucose (GLU), tapioca starch (TS), gelatinized tapioca starch (GTS), potato starch (PS) and gelatinized potato starch (GPS) on growth and physiological responses in juvenile largemouth bass Micropterus salmoides. After 8 weeks, fish fed with starch diets had better weight gain and growth rates. Counts of red blood cells and monocytes were increased in the PS and GPS groups, compared to GLU group. Contents of serum triglyceride and total cholesterol were markedly elevated in the TS, PS and GPS groups. There were lower levels of serum glucose, insulin and cholecystokinin, and higher agouti-related peptide contents in the PS group compared to GLU group. PS and GPS could enhance glycolysis and TCA cycle by increasing their enzyme activities and transcriptional levels. Additionally, starch sources markedly heightened mRNA levels of key genes involved in the respiratory electron transport chain. Additionally, elevated mRNA levels of key antioxidant genes were shown in the TS and GTS groups. Moreover, TS and PS could promote immunity by upregulating transcriptional levels of the complement system, lysozyme and hepcidin. Taken together, starch exhibited better growth via increasing glycolysis and TCA cycle compared with GLU, and PS could improve antioxidant and immune capacities in largemouth bass.

Flooding is one of the main natural disasters that affect agricultural production. The loss assessment of crop flood damage is an important basis for guiding the prevention and mitigation of agricultural disaster, which is of great significance for maintaining the stability of agricultural production and ensuring food security. Firstly, we briefly reviewed the research advances on the formative mechanism in crop flood damage, sorted out the main methods of loss assessment of crop flood damage, and pointed out the advantages and limitations of these methods in various application scenarios. Secondly, based on the comments above, we proposed an integrated framework for dynamic assessment of crop flood damage. This framework is supported by multi-source data, powered by hydrodynamic model, based on the combination of numerical simulation and machine learning, and oriented towards disaster events. Finally, we proposed some practical suggestions and research directions for further study, including (1) constructing multi-parameter loss function and model libraries for different exposures and vulnerability environments, (2) strengthening the application of multi-source data to improve assessment accuracy, and developing parallel computing and artificial intelligence algorithms to improve computational efficiency, (3) constructing comprehensive platform for flood disaster assessment and changing to intelligent applications.