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Abstract Diabetic kidney disease (DKD) is a prevalent microvascular complication of diabetes mellitus and a primary cause of end-stage renal disease (ESRD). Increasing studies suggest that immune cells are involved in regulating renal inflammation, which contributes to the progression of DKD. Compared with conventional methods, single-cell sequencing technology is more developed technique that has advantages in resolving cellular heterogeneity, parallel multi-omics studies, and discovering new cell types. ScRNA-seq helps researchers to analyze specifically gene expressions, signaling pathways, intercellular communication as well as their regulations in various immune cells of kidney biopsy and urine samples. It is still challenging to investigate the function of each cell type in the pathophysiology of kidney due to its complex and heterogeneous structure and function. Here, we discuss the application of single-cell transcriptomics in the field of DKD and highlight several recent studies that explore the important role of immune cells including macrophage, T cells, B cells etc. in DKD through scRNA-seq analyses. Through combing the researches of scRNA-seq on immune cells in DKD, this review provides novel perspectives on the pathogenesis and immune therapeutic strategy for DKD.

Species diversity at different altitudes exhibits different variation patterns. In alpine ecosystems, species migration from low to high altitudes has changed the intensity of interspecific competition and association, inevitably adjusting or changing the existing prevention and control objects and strategies. In this context, selecting a typical area to probe the variations in species diversity and interspecific competition and association is imperative. Thus, this study investigated the diversity of locusts and grasshoppers at 400 m intervals in the grasslands at different altitudes in the Ili River basin of China. The responses of locusts (or grasshoppers) diversity, ecological niche, and interspecific association to altitudinal variations were comprehensively analyzed using univariate linear regression and redundancy analysis. The results showed that the diversity of locusts and grasshoppers declined with rising altitude. Average vegetation height and wind velocity significantly affected the diversity of locusts and grasshoppers at altitudes of 1,000–1,400 and 1,400–1,800 m, respectively. The vegetation dominance, aspect, and relative humidity significantly affected the diversity of locusts and grasshoppers at 1,800–2,200 m. Nonetheless, no ecological factor significantly affected the diversity of locusts and grasshoppers at ≤ 1,000 m. Before 1,400 m, the rise in altitude intensified the interspecific competition and tightened interspecific relationships. From 1,400 m, the interspecific competition was weakened, and interspecific relationships were loosened. Calliptamus italicus, Oedaleus decorus, and Calliptamus barbarus were the dominant locust taxa in the Ili River basin, with C. italicus and O. decorus being distributed at multiple altitudes, especially within ≤ 1,800 m. The high population density of C. italicus and O. decorus within this range required intensive monitoring and control. By contrast, Egnatius apicalis and Conophyma sp. were sparsely distributed within the > 1,400 m range, posing challenges in collection of locusts and grasshoppers. Therefore, protective measures should be taken to increase insect diversity in grasslands.

Background: The composition of the gut microbiome has been recorted to be strongly associated with gestational diabetes mellitus (GDM), but mutational characterization of the microbiome in patients with GDM has been overlooked. Here, we revealed the genetic variation landscape of the gut microbiome and assessed its clinical significance in a cohort of patients with GDM. Methods: We employed a macrogenomic dataset made up of a discovery cohort of 54 cases and a validation cohort of 220 cases to screen for high-abundance microbial flora and identified single nucleotide variants (SNVs) and insertions/deletions (indels). Subsequently, we analyzed the mutation spectra of genomes of the intestinal flora by using the previously identified SNVs and identified mutation signatures. Additionally, we utilized the Random Forest algorithm to identify key differential SNVs and elucidated their biological functions and associations with the clinicopathological parameters of GDM. Results: We screened 15 key microbial flora and found that the GDM group had more SNVs and indels in the intestinal flora than the control group, with a significant increase in C > T and T > C base mutations and were more susceptible to sequence mutations. Compared to the control group, the GDM group underwent a more significant evolution, as evidenced by the presence of a unique mutational spectrum and mutational characteristics. Random Forest algorithm analysis showed that the combined characterization of five gut microbial species and 21 SNV-related markers was effective in distinguishing between GDM and control subjects in both discovery (area under the curve (AUC) = 0.86) and validation (AUC = 0.73) sets. These markers also revealed that GDM is strongly associated with sphingolipids, galactose, and proteins containing the DUF structural domain. Conclusions: The GDM intestinal flora has unique mutational features that correlate significantly with clinicopathological involvement and may be involved in the development of the disease.

The microwave-derived vegetation optical depth (VOD) products were used to monitor aboveground biomass (AGB) at regional to global scales, but the ability of VOD to monitor AGB in China is uncertain. This study evaluated the sensitivity of four VOD products (e.g. L-VOD, IB-VOD, LPDR-VOD, and Liu-VOD) and optical vegetation indices (VI) (e.g. NDVI, EVI, LAI, and tree cover from MODIS) to the AGB across China. Our results showed tree cover product has the highest spatial agreement with reference AGBs (indicated by the median correlation value of 0.85), followed by L-VOD (with a median correlation value of 0.80), which performs better than other VIs and VODs. Further comparisons between reference and estimated AGB computed using the fitted logistic regression showed that AGB estimations from tree cover and L-VOD outperformed the estimations from other VIs and VODs over most vegetation types (except forest), indicated by the higher median correlation value of 0.86 and 0.83 and lower RMSD of 23.9 and 27.3 Mg/ha, respectively. The good performance of tree cover could be partly due to that tree cover product is not independent from the reference AGBs. The good performance of L-VOD can be explained by its higher sensitivity to the vegetation characteristics of the entire canopy (including woody component), relative to other VODs and VIs. Among the six reference AGB products, Saatchi-WT and Saatchi-RF products were found to have the best correlations with VIs and VODs. This study demonstrates that microwave VODs, particularly L-VOD, are effective proxies for large-scale monitoring of vegetation AGB in China.

The application of time series forecasting utilizing historical data has become increasingly essential across a variety of industries including finance, healthcare, meteorology, and industrial sectors. The assessment of bond transaction rates in the interbank bond market serves as a crucial indicator for assessing bank risk. In this paper, we proposed a composite model to forecast the transaction interest rates of China’s interbank bonds over a long period. Specifically, our model integrates an intrinsic complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) model along with various long-term prediction models including long short-term memory network, temporal convolutional network, transformer, and autoformer. Our findings reveal that: 1) predictive performance of different long-term prediction models varies across different frequencies of single time series data; 2) predictive efficacy of diverse model combinations differs across varying prediction time lengths; 3) best results can be realized by using different prediction model combinations for high-frequency, medium-frequency and low-frequency data under different time steps.

As an important part of proliferation and discharge, crustaceans can effectively conserve and supplement fishery resources in natural waters. Based on the data of crustacean proliferation and release in Zhejiang Province from 2017 to 2021 and related literature, this paper systematically reviewed the annual variation trend of crustacean release type and quantity, release capital, and the spatial and quantitative distribution of release area and release specifications. The results show that: from 2017 to 2021, six species of crustacean proliferate in Zhejiang Province, including Penaeus japonicus, Portunus trituberculatus and Penaeus chinensis, etc.. A total of 5220, 455, 000 were released, and the investment was 33, 202, 000 yuan. Among them, the largest amount of release and investment was Japanese prawnaeus, the least amount of release was green crab, and the least amount of investment was Prawnaeus puerarii. The release area involved Wenzhou, Ningbo, Taizhou, Zhoushan and other cities, and the distribution of release was unbalanced between regions, and mainly concentrated in small and medium sizes. In this paper, some specific suggestions will be put forward on problems such as insufficient assessment of the effect of crustacean release species, poor release effect of some proliferating species, and few reserve species of crustacean release in the coastal waters of Zhejiang Province, so as to provide a basis for improving the scientific management of crustacean release species in the future.

Vegetation Optical Depth (VOD), a vegetation parameter that quantifies the extinction effect of microwaves penetrating the canopy, plays a crucial role in global-scale biomass monitoring and climate change research. However, the spatial gridding of existing long-term VOD products is relatively coarse (approximately 25 km), with restrictions on their application at a regional scale. High-resolution active-microwave proxies and optical vegetation indices can potentially be used to disaggregate coarse-resolution VOD, but it is unclear which proxy is optimal. In this paper, the Normalized Difference Vegetation Index (NDVI) and active-microwave proxies (VH, VV, and cross-polarization ratio CR) from Sentinel-1 were quantitatively assessed with VOD products at various frequencies (L-/C-/X-VOD) across the contiguous United States (U.S.). The results showed that VH (R = 0.80) and NDVI (R = 0.77) exhibit a high spatial correlation with L-VOD products. For temporal correlation, NDVI had the highest overall performances with all VOD products, but good correlations were also achieved with CR and, to a lesser extent, VH. Further comparisons of the performance between Brightness Temperature (TB) and VOD revealed that while TB displayed a strong temporal correlation with active-microwave proxies, its spatial correlations with such proxies were low. In contrast, VOD had good correlations both temporally and spatially with active-microwave proxies (e.g., VH). These evidences suggested that the downscaling of VOD using the combination of VH and other proxies could be an alternative promising method to estimate high-resolution VOD.

Radiotherapy stands as a cornerstone in the treatment of numerous malignant tumors, including non-small cell lung cancer. However, the critical challenge of amplifying the tumoricidal effectiveness of radiotherapy while minimizing collateral damage to healthy tissues remains an area of significant research interest. Radiosensitizers, by methods such as amplifying DNA damage and fostering the creation of free radicals, play a pivotal role in enhancing the destructive impact of radiotherapy on tumors. Over recent decades, nano-dimensional radiosensitizers have emerged as a notable advancement. Their mechanisms include cell cycle arrest in the G2/M phase, combating tumor hypoxia, and others, thereby enhancing the efficacy of radiotherapy. This review delves into the evolving landscape of nanomaterials used for radiosensitization in non-small cell lung cancer. It provides insights into the current research progress and critically examines the challenges and future prospects within this burgeoning field.

To unravel the general relationship between bus travel and land use around bus stops and along bus routes and to promote their coordinated development, this paper explores a method to estimate passenger flow volumes from and to bus stops based on land use types, intensities, and spatial distributions around bus stops and along bus routes. Firstly, following the principle of the gravity model, which considers traffic volumes analogous to gravity based on trip generation and distance impedance between traffic analysis zones (TAZs), a gravitational logic estimation method for passenger flow volumes from and to bus stops was constructed with land use elements between bus stop TAZs and the upstream and downstream collections of bus stop TAZs. Building upon this, the passenger flow volumes from and to 38 bus stops in the Xueyuan Square area of Dalian during weekday morning peak hours were taken as the experimental objects. The basic estimation models of two gravity sets corresponding to passenger flow volumes from and to bus stops were constructed using the bus travel generation based on the aggregation of area-based origin unit method and the bus travel distance impedance based on the probability density method. Finally, the reliability of the estimation method of passenger flow volumes from and to bus stops was verified by regression fitting between the surveyed values of passenger flow volume and the estimated values of the basic models. The results indicate that the fuzzy estimation and transformation of bus travel based on land use elements, which serves as a crucial lever for facilitating strategic alignment in transit-oriented development (TOD), can be effectively achieved by using the area-based origin unit method to aggregate bus travel generation and the probability density method to evaluate the bus travel distance impedance.

Since the 1980s, the main fishery resources in Zhejiang offshore have shown a declining trend. Therefore, since 2000, relevant units have continued to carry out artificial proliferation and release to repair fishery resources. Based on the statistical data of proliferation and release of Zhejiang offshore fish from 2017 to 2021, this paper compared the trends of release funds, release quantity and release species. It was concluded that 16 kinds of marine fish such as Larimichthys crocea, Acanthopagrus schlegelii, Nibea albiflora and Oplegnathus fasciatus were mainly released in 12 important proliferation and release sea areas of Zhejiang offshore from 2017 to 2021. The total investment in releasing funds was 112.859 million yuan, and a total of 467.998 million marine fish fry were released, of which 3.301 million yuan were marked fish and 488,000 fry were released. Investigation and literature reports show that stock enhancement plays a positive role in increasing the biological resources of marine fish in Zhejiang offshore, restoring the community structure of marine fish, protecting marine biodiversity, and promoting the development of marine fishery. Finally, according to the investigation and related literature, the author sorted out the problems existing in the proliferation and release and put forward relevant suggestions for improvement, in order to provide reference for the future scientific and standardized implementation of proliferation and release.

Abstract Exceptional points pose exceptional difficulties to access and encircle. By simply gating graphene, it is now easier to hit the exceptional point.

North American boreal forests are known to be an important carbon pool in boreal ecosystems, but have experienced extensive tree mortality and carbon loss due to multiple agents of stand-replacing disturbances in recent decades. However, the impacts of these stand-replacing disturbances on forest dynamics are still unknown. We used a recently developed remote-sensing based stand-replacing disturbance product, coupled with aboveground biomass (AGB), gross primary productivity (GPP) and leaf area index (LAI) datasets to estimate the impacts of stand-replacing disturbances (e.g., fires, logging and insect outbreaks) on the carbon balance of western North American boreal forests during 2000–2012. Our results showed that fire, logging and insect outbreaks resulted in AGB losses of 23.4, 16.6, and 4.7 Tg/yr, respectively. In the post-disturbance periods, AGB did not recover to its pre-disturbed levels in the 10th year, which is longer than the recovery time of GPP and LAI. Furthermore, the losses of AGB, GPP and LAI in fire events were the dominant factors for forest recovery after stand-replacing fire. Vapor Pressure Deficit (VPD), soil clay content, temperature and precipitation were the important factors for forest recovery after stand-replacing insect outbreaks and stand-replacing logging. When removing the impact of environmental factors, our results showed a smaller magnitude of AGB, GPP and LAI loss relative to the results including these factors, although similar recovery trajectories were observed among the two results. The results have important implications for understanding the effects of stand-replacing disturbances on the carbon dynamics of boreal forests, which is required to adopt effective forest management strategies after disturbance.

Systematically and comprehensively enhancing road traffic safety using artificial intelligence (AI) is of paramount importance, and it is gradually becoming a crucial framework in smart cities. Within this context of heightened attention, we propose to utilize machine learning (ML) to optimize and ameliorate pedestrian crossing predictions in intelligent transportation systems, where the crossing process is vital to pedestrian crossing behavior. Compared with traditional analytical models, the application of OpenCV image recognition and machine learning methods can analyze the mechanisms of pedestrian crossing behaviors with greater accuracy, thereby more precisely judging and simulating pedestrian violations in crossing. Authentic pedestrian crossing behavior data were extracted from signalized intersection scenarios in Chinese cities, and several machine learning models, including decision trees, multilayer perceptrons, Bayesian algorithms, and support vector machines, were trained and tested. In comparing the various models, the results indicate that the support vector machine (SVM) model exhibited optimal accuracy in predicting pedestrian crossing probabilities and speeds, and it can be applied in pedestrian crossing prediction and traffic simulation systems in intelligent transportation.

Abstract Background Calliptamus italicus is a dominant species in the desert and semi-desert grassland. It is widely distributed throughout many regions such as Asia, Europe, North Africa and the Mediterranean, and has enormous destructive potential for agriculture and animal husbandry. The C. italicus overwintering as eggs in the soil through diapause, and the cold tolerance of locust eggs is the key to their ability to survive the winter smoothly to maintain the population. Results Transcriptome analysis of C. italicus eggs was carried out in this paper in constant low temperature acclimation, natural low temperature acclimation and room temperature. The differentially expressed genes related to cold tolerance were screened out, the differences in expression patterns under different low temperature acclimation were analyzed, and the genes in the significantly up-regulated pathways may play an important role in cold tolerance. The results show that different domestication modes can induce C. italicus eggs to express a large number of genes to alleviate low temperature damage, but C. italicus eggs are more sensitive to changes in temperature. Compared with the control, there are 8689 DEGs at constant low temperature and 14,994 DEGs at natural low temperature. KEGG analysis showed that DEGs were mainly enriched in pathways related to metabolism and biological systems under constant low temperature, and were mainly enriched in pathways related to biological systems and environmental information processing under natural low temperature. In addition, RNAi technology was used to further verify the regulation of genes in the significantly enriched up-regulated pathways on C. italicus eggs, and it was confirmed that the hatching rate of C. italicus eggs at low temperature was significantly reduced after interference. Conclusions Transcriptome analysis of C. italicus eggs treated at different temperatures provided a theoretical basis for further understanding the adaptation mechanism of C. italicus eggs to low temperature. In addition, four potential RNAi target genes were verified in the eggs of C. italicus for the first time, providing new ideas for effective control of this species.

This paper explores the hazard classification method of blasting vibration on nearby buildings. The ratio of the self-vibration frequency f 0 of the protected buildings to the main vibration frequency f of blasting vibration and the ratio of the actual measured peak blasting vibration velocity to the permissible vibration velocity of blasting safety regulations are used as two indicators of the blasting vibration hazard classification method, the blasting vibration hazard index calculation formula was proposed, and the blasting vibration hazard was classified into four levels according to the blasting vibration hazard index. An open pit mine blasting project was used for monitoring, the collected data were processed by random forest method. After processing, bursting heart distance, the total explosive quantity required for one blast, the hole distance meter delay, and the row distance meter delay was selected as the input parameters; the optimal kernel function parameter gamma and c were applied to the SVR (Support Vector Regression) model by changing the search step to expand the parameter search, and improved GS-SVR (Grid Search-Support Vector Regression) model was constructed, through which peak blasting vibration velocity and vibration frequency values were predicted. The results show: improved GS-SVR is effective in predicting peak blasting vibration velocity, with the lowest relative error of 0.15% and the average error of 7.96%; the minimum relative error for the prediction of vibration frequency is 0.03% and the average error is 2.54%. The measured data in the literature of related scholars verified that the blasting vibration hazard classification method is scientific and feasible. It can provide reference and reference for blasting vibration hazard classification and prediction of similar blasting projects.

The upper Luanhe River Basin is a significant ecological barrier guarding the Beijing–Tianjin–Hebei region in China. Quantitative measures of vegetation productivity can be used to assess ecosystem carbon sequestration capacity and monitor regional ecological environmental health. Although several vegetation productivity products have been generated, poor spatiotemporal resolution limits their application in ecosystem service assessment. In this article, vegetation net primary productivity (NPP) from 2000 to 2017 with a resolution of 30 m in the upper Luanhe River Basin was generated based on a data fusion model and the multisource data synergized quantitative (MuSyQ) NPP model. Then, the variation trend of NPP and its climate controls were analyzed. Compared with forest NPP observation data, we derived an R2 of 0.68 and the root-mean-square error of 81.70 gC.m−2.yr−1. Annual NPP had a fluctuating increasing trend from 2001 to 2017, with values ranging between 3.43 and 5.00 TgC.yr−1, with an annual increase trend of 0.04 TgC.yr−1. Precipitation was significantly correlated with NPP in the upper part of the Luanhe River basin, which is an important reason for the interannual variation of NPP. Grassland had a stronger correlation to precipitation than forest because it is more sensitive to precipitation. The area where the temperature is significantly correlated with annual NPP only accounts for 2% of the study area, indicating that temperature has a weak influence on NPP. Furthermore, human activities, such as forest management, fertilization, and irrigation, can change the trend of annual NPP.

A surface soil moisture (SM) condition at high spatiotemportal resolutions is required by regional Earth system applications. Here, we mapped daily 1-km SM in the Babao River Basin in the northwest of China during the summers from 2013 to 2015 using a random forest (RF) method by merging SM information retrieved from in situ measurements, optical/thermal remote sensing, and topographical indices. Relative importance analysis was used to determine the optimal predictors for estimating high-resolution SM. A specific RF model (RFVI+sup) was constructed using the optimal predictors including remote sensing albedo, apparent thermal inertia (ATI), normalized difference vegetation index, normalized difference infrared index 5, soil adjusted vegetation index, and topographical indices (aspect and elevation). The RFVI+sup also accounted for missing observations of the thermal index (e.g., ATI) over the mountainous regions. In the comparison between the SM estimates using the new RFVI+sup model and other RF models, the spatial coverage of available estimates increased from 14% to 64% over the study region, the correlation coefficient values were improved to 0.75, the unbiased root-mean-squared difference values decreased to 0.032 m3/m3. Thus, the proposed RF method provided accurate SM estimates with high spatiotemporal resolution over the mountainous regions, by merging multiresource datasets from in situ measurements, remotely-sensed, and topographical indices.

Over the past 2 to 3 decades, Chinese forests are estimated to act as a large carbon sink, yet the magnitude and spatial patterns of this sink differ considerably among studies. Using 3 microwave (L- and X-band vegetation optical depth [VOD]) and 3 optical (normalized difference vegetation index, leaf area index, and tree cover) remote-sensing vegetation products, this study compared the estimated live woody aboveground biomass carbon (AGC) dynamics over China between 2013 and 2019. Our results showed that tree cover has the highest spatial consistency with 3 published AGC maps (mean correlation value R = 0.84), followed by L-VOD (R = 0.83), which outperform the other VODs. An AGC estimation model was proposed to combine all indices to estimate the annual AGC dynamics in China during 2013 to 2019. The performance of the AGC estimation model was good (root mean square error = 0.05 Pg C and R2 = 0.90 with a mean relative uncertainty of 9.8% at pixel scale [0.25°]). Results of the AGC estimation model showed that carbon uptake by the forests in China was about +0.17 Pg C year−1 from 2013 to 2019. At the regional level, provinces in southwest China including Guizhou (+22.35 Tg C year−1), Sichuan (+14.49 Tg C year−1), and Hunan (+11.42 Tg C year−1) provinces had the highest carbon sink rates during 2013 to 2019. Most of the carbon-sink regions have been afforested recently, implying that afforestation and ecological engineering projects have been effective means for carbon sequestration in these regions.

Abstract Hybrid japonica cultivars, such as the Yongyou series, have shown high yield potential in the field in both the early and late growing seasons. Moreover, understanding the responses of rice flowering dates to temperature and light is critical for improving yield performance. However, few studies have analyzed flowering genes in high-yielding japonica cultivars. Based on the five sowing date experiments from 2019 to 2020, select the sensitive cultivar Yongyou 538 and the insensitive cultivar Ninggeng 4 and take their flag leaves and panicles for transcriptome analysis. The results showed that compared with sowing date 1 (6/16), after the sowing date was postponed (sowing date 5, 7/9), 4480 and 890 differentially expressed genes (DEGs) were detected in the leaves and panicles in Ninggeng 4, 9275 and 2475 DEGs were detected in the leaves and panicles in Yongyou 538, respectively. KEGG pathway analysis showed that both Ninggeng 4 and Yongyou 538 regulated rice flowering through the plant circadian rhythm and plant hormone signal transduction pathways. Gene expression analysis showed that Os01g0566050 (OsELF3-2), Os01g0182600 (OsGI), Os11g0547000 (OsFKF1), Os06g0275000 (Hd1), and Os09g0513500 (FT-1) were expressed higher and Os02g0771100 (COP1-1) was expressed lower in Yongyou 538 compared with Ninggeng 4 as the climate conditions changed, which may be the key genes that regulate the flowering process with the change of temperature and light resources in sensitive cultivar Yongyou 538 in the late season.

Poststroke depression significantly affects health and quality of life of stroke patients. This study evaluates the role of social support in influencing poststroke depression. The literature search was conducted in electronic databases and study selection was based on precise eligibility criteria. The prevalence rates reported by individual studies were pooled. A meta-analysis of standardized mean differences (SMD) in social support between depressed and non-depressed stroke patients was performed. The odds ratios and correlation coefficients showing the relationship between social support and depression were pooled to achieve overall estimates. Twenty-five studies (9431 patients) were included. The prevalence of depression was 36% [95% confidence interval (CI): 28, 45]. Patients with poststroke depression had significantly lower social support in comparison with patients with no or lower levels of depression [SMD in social support scores −0.338 (95% CI: −0.589, −0.087); p = 0.008]. The odds of depression were lower in patients receiving higher levels of social support [OR 0.82 (95% CI: 0.69, 0.95)] but were higher in patients who were receiving weaker social support [OR 5.22 (95% CI: −0.87, 11.31)]. A meta-analysis of correlation coefficients found a significantly inverse correlation between social support and poststroke depression [r −0.336 (95% CI: −0.414, −0.254)]. Poststroke depression has a significant independent inverse association with social support.

Long time series of vegetation productivity products are significant for the research of global carbon cycle and climate change. In this article, the 0.05° global gross primary productivity (GPP) and net primary productivity (NPP) products from 1981 to 2018 were estimated by using the improved multisource data synergized quantitative (MuSyQ) NPP algorithm. The model was based on the fraction of absorbed photosynthetically active radiation (FPAR) and leaf area index (LAI) data from the global land surface satellite (GLASS) dataset, the light use efficiency (LUE) from the parameterization approach with the clearness index (CI), the ERA-Interim meteorological data, and other environmental factors. The results suggested that the accuracy of the MuSyQ GPP product was slightly higher than that of the MOD17 GPP product when compared with the FLUXNET GPP, especially for the evergreen broadleaf forest (EBF), deciduous broadleaf forest (DBF), wetland (WET), cropland (CRO), woody savanna (WSAV), and closed shrubland (CSH) land types. MuSyQ NPP product also has higher accuracy [R2 = 0.81, RMSE = 214.6 gC/(m2year)] than MOD17 NPP [R2 = 0.55, RMSE = 214.7 gC/(m2year)] when compared with the BigFoot NPP, which indicated the reliability of the improved MuSyQ-NPP algorithm in estimating global NPP. Our results showed a significant upward trend in global NPP, which was most affected by FPAR, followed by LUE, temperature, and PAR. The average NPP declined significantly in Asia and Amazon tropical rainforests and increased significantly in Africa tropical rainforest, which were affected by the local deforestation or the forest expansion, and also the climate factors.

Observations of the effects of long-term paddy-upland rotations on soil carbon (C) and nitrogen (N) stocks are scarce. This experiment aimed to examine soil C and N stock characteristics and their relationships with rice yield using four long-term rotation systems. The results showed that in the topsoil at 50 cm, compared to rice-fallow (RF), rice-potato with rice straw mulch and return (RP) and rice-Chinese milk vetch (RC) increased soil organic carbon (SOC) stock by 19.67% and 15.29%, total nitrogen (TN) stock by 20.40% and 18.54%, and available nitrogen (AN) density by 61.54% and 34.44%, respectively; while rice-wheat (RW) increased AN density by 30.24%. Furthermore, the increases in densities of soil C and N at 20–30 cm were likely to increase rice above biomass and yield. The highest soil C and N densities were observed in RP, which was the best choice for increasing soil nutrients and rice yield. In addition, SOC, TN, and AN had obvious stratification and surface aggregation characteristics, and more than 70% of the total stocks were concentrated at the top 30 cm. These results provide a theoretical basis for the high-yield cultivation of rice in the middle and lower reaches of the Yangtze River.

Mid−season drainage (MSD) is a widely used water management practice in rice (Oryza sativa L.) cultivation. However, the timing of the initiation and termination of MSD is highly arbitrary and subjective, and a quantitative indicator is lacking in precision agronomic practice. In this study, datasets (91 cases) from previous field experiments were obtained and used to fit adjusted exponential growth models, incorporating rice canopy light interception (CLI) and tillering development dynamics. Different criteria for initiating and terminating MSD were developed based on CLI indicators. The results showed that the CLI indicator at 80% of the projected panicle could be used to predict the initiation of MSD; however, it was highly variable, depending on the growing season and rice cultivated variety. The values for Indica rice were 0.26 to 0.31 and 0.31 to 0.42 in the late and single seasons, respectively, while the values for Japonica rice were 0.15 to 0.29 and 0.23 to 0.33, respectively. In addition, the CLI values at 7 to 10 days prior to panicle initiation (PI) ranged from 0.77 to 0.87 and from 0.56 to 0.83 for the Indica and Japonica varieties, respectively, and were calculated to determine MSD termination. The CLI indicators for MSD were greatly dependent on the cultivated variety, growing season, and planting method. The results of the correlation study and principal component analysis (PCA) indicated that the differences in CLI values attributed to rice type and growing season were driven by the tillering and canopy characteristic traits, respectively. Therefore, the current parameters could provide a reference for subsequent field applications in specific areas, but further optimization is needed to increase their robustness. To evaluate the usefulness of the CLI indicator for determining MSD initiation and termination, a set of devices for monitoring canopy light interception and water level was developed, and an on-farm trial was carried out in the middle and lower reaches of the Yangtze River in China in 2022. The field application demonstrated that MSD could be scheduled automatically based on the current system, and that the effect was consistent in practice.

Vegetation optical depth (VOD) retrieved from microwave remote sensing techniques has been used as an important proxy for monitoring the vegetation dynamics at large scales. In a first study we developed a new X-VOD indice from inversion of the X-MEB model from the Microwave Scanning Radiometer 2 (AMSR2, 2012-present) observations. The Advanced Microwave Scanning Radiometer for EOS (AMSR-E, 2002–2011) and AMSR2 share many characteristics, such as local crossing time, incidence angle, frequency at X-band, etc, which makes it possible to produce a consistent long-term X-VOD product over a long period spanning from 2002 to present. The objective of this study was to merge the AMSR-E and AMSR2 observations in order, in a second step, to produce a consistent long-term X-VOD product spanning over the combined periods of AMSR-E and AMSR2.The main challenges in retrieving a consistent X-VOD data set from AMSR-E and AMSR2 are that (i) there is a bias between observations from the two sensors; and (ii) the lack of overlapping observations between the two sensors makes it impossible to achieve a direct inter-calibration. Here, to overcome this problem, we used AMSR-E slow rotation data (AMSR-E L1S), which has similar characteristics as AMSR-E and provided observations concurrently with AMSR2, as a bridge to calibrate the AMSR-E brightness temperature (TB) observations with AMSR2 TB.As our main objective in this TB calibration study was to produce a consistent long-term AMSR-E/AMSR2 X-VOD product, we evaluated the retrieved X-VOD product based on different inter-calibration, either global-based or pixel-based, methods. Both AMSR-E and AMSR2 X-VOD were evaluated against the Aboveground Biomass (AGB), Leaf Area Index (LAI) and the Normalized Difference Vegetation Index (NDVI). The results suggest that global-based inter-calibration methods using homogeneous and temporally stable land covers (Evergreen Broadleaf Forests and Snow and Ice) as reference calibration data sets provided the best results. For instance, the spatial relationships between X-VOD and AGB/LAI/NDVI are highly consistent over the AMSR-E and AMSR2 periods after the calibration work. This study laid a solid foundation for monitoring the dynamics of X-VOD, as a proxy of AGB, over the combined periods of AMSR-E and AMSR2 sensors (almost 20 years).

The magnetic properties of 50 nm Ni80Fe20 deposited on Cu underlayers with different thicknesses by obliquely sputtering were studied. It was found that the in-plane uniaxial magnetic anisotropy (IPUMA) of the Ni80Fe20 film can be induced by the obliquely sputtered Cu underlayer deposited under the NiFe layer. The IPUMA field of NiFe film varies between 20 Oe and 40 Oe when the thickness of Cu underlayer varies from 5 nm to 50 nm. The permeability spectrum results show that the damping factor increases with increasing Cu underlayer thickness. This indicates that changing the thickness of the Cu underlayer of obliquely sputtering is an effective method to adjust the damping factor in the dynamic magnetization process of Ni80Fe20 thin films.

Vegetation is a key element in the energy, water and carbon balances over the land surfaces and is strongly impacted by climate change and anthropogenic effects. Remotely sensed observations are commonly used for the monitoring of vegetation dynamics and its temporal changes from regional to global scales. Among the different indices derived from Earth observation satellites to study the vegetation, the vegetation optical depth (VOD), which is related to the intensity of extinction effects within the vegetation canopy layer in the microwave domain and which can be derived from both passive and active microwave observations, is increasingly used for monitoring a wide range of ecological vegetation variables. Based on different frequency bands used to derive VOD, from L- to Ka-bands, these variables include, among others, the vegetation water content/status and the above ground biomass. In this review, the theoretical bases of VOD estimates for both the passive and active microwave domains are presented and the global long-term VOD products computed from various groups in the world are described. Then, major findings obtained using VOD are reviewed and the perspectives offered by methodological improvements and by new sensors onboard satellite missions recently launched or to be launched in a close future are presented.

Pseudomonas aeruginosa is an opportunistic pathogen using virulence factors and biofilm regulated by quorum sensing (QS) systems to infect patients and protect itself from environmental stress and antibiotics. Interfering with QS systems is a novel approach to combat P. aeruginosa infections without killing the bacteria, meaning that it is much harder for bacteria to develop drug resistance. A marine fungus Cladosporium sp. Z148 with anti-QS activity was obtained from Jiaozhou Bay, China. Cladodionen, a novel QS inhibitor, was isolated from the extracts of this fungus. Cladodionen had a better inhibitory effect than pyocyanin on the production of elastase and rhamnolipid. It also inhibited biofilm formation and motilities. The mRNA expressions of QS-related genes, including receptor proteins (lasR, rhlR and pqsR), autoinducer synthases (lasI, rhlI and pqsA) and virulence factors (lasB and rhlA) were down-regulated by cladodionen. Molecular docking analysis showed that cladodionen had better binding affinity to LasR and PqsR than natural ligands. Moreover, the binding affinity of cladodionen to LasR was higher than to PqsR. Cladodionen exhibits potential as a QS inhibitor against P. aeruginosa, and its structure–activity relationships should be further studied to illustrate the mode of action, optimize its structure and improve anti-QS activity.

Light use efficiency (LUE), which characterizes the efficiency with which vegetation converts captured/absorbed radiation into organic dry matter through photosynthesis, is a key parameter for estimating vegetation gross primary productivity (GPP). Studies suggest that diffuse radiation induces a higher LUE than direct radiation in short-term and site-scale experiments. The clearness index (CI), described as the fraction of solar incident radiation on the surface of the earth to the extraterrestrial radiation at the top of the atmosphere, is added to the parameterization approach to explain the conditions of diffuse and direct radiation in this study. Machine learning methods—such as the Cubist regression tree approach—are also popular approaches for studying vegetation carbon uptake. This paper aims to compare and analyze the performances of three different approaches for estimating global LUE and GPP. The methods for collecting LUE were based on the following: (1) parameterization approach without CI; (2) parameterization approach with CI; and (3) Cubist regression tree approach. We collected GPP and meteorological data from 180 FLUXNET sites as calibration and validation data and the Global Land Surface Satellite (GLASS) products and ERA-interim data as input data to estimate the global LUE and GPP in 2014. Site-scale validation with FLUXNET measurements indicated that the Cubist regression approach performed better than the parameterization approaches. However, when applying the approaches to global LUE and GPP, the parameterization approach with the CI became the most reliable approach, then closely followed by the parameterization approach without the CI. Spatial analysis showed that the addition of the CI improved the LUE and GPP, especially in high-value zones. The results of the Cubist regression tree approach illustrate more fluctuations than the parameterization approaches. Although the distributions of LUE presented variations over different seasons, vegetation had the highest LUE, at approximately 1.5 gC/MJ, during the whole year in equatorial regions (e.g., South America, middle Africa and Southeast Asia). The three approaches produced roughly consistent global annual GPPs ranging from 109.23 to 120.65 Pg/yr. Our results suggest the parameterization approaches are robust when extrapolating to the global scale, of which the parameterization approach with CI performs slightly better than that without CI. By contrast, the Cubist regression tree produced LUE and GPP with lower accuracy even though it performed the best for model validation at the site scale.

Mapping the regional distribution of forest canopy height and aboveground biomass is worthwhile and necessary for estimating the carbon stocks on Earth and assessing the terrestrial carbon flux. In this study, we produced maps of forest canopy height and the aboveground biomass at a 30 m spatial resolution in Maryland by combining Geoscience Laser Altimeter System (GLAS) data and Landsat spectral imageries. The processes for calculating the forest biomass included the following: (i) processing the GLAS waveform and calculating spatially discrete forest canopy heights; (ii) developing canopy height models from Landsat imagery and extrapolating them to spatially contiguous canopy heights in Maryland; and, (iii) estimating forest aboveground biomass according to the relationship between canopy height and biomass. In our study, we explore the ability to use the GLAS waveform to calculate canopy height without ground-measured forest metrics (R2 = 0.669, RMSE = 4.82 m, MRE = 15.4%). The machine learning models performed better than the principal component model when mapping the regional forest canopy height and aboveground biomass. The total forest aboveground biomass in Maryland reached approximately 160 Tg. When compared with the existing Biomass_CMS map, our biomass estimates presented a similar distribution where higher values were in the Western Shore Uplands region and Folded Application Mountain section, while lower values were located in the Delmarva Peninsula and Allegheny Mountain regions.

ABSTRACT Background Objectives Methods Results Conclusion Relevance to Clinical Practice Patient or Public Contribution Leukaemia children often encounter various social adaptation issues stemming from changes in their living environment. Identifying and optimising the factors associated with social adaptation is crucial in improving social adaptation in children with leukaemia.To investigate social adaptation in children with leukaemia and to clarify the impact pathway of cognitive flexibility, problem behaviour, prosocial behaviour, parenting style and social support on social adaptation.Using a convenience sampling method, 264 leukaemia children completed a cross‐sectional survey. Data were collected using the Social Adaptation Assessment Scale for Children with Leukaemia, Strengths and Difficulties Questionnaire, Cognitive Flexibility Scale, Short‐Egna Minnenav Barndoms Uppfostran and Multi‐Dimensional Scale of Perceived Social Support. Structural equation modelling was used to test the influencing factors of social adaptation.Social support directly influenced social adaptation (β = 0.19, p < 0.01) and indirectly influenced social adaptation through prosocial behaviour (β = 0.06, p < 0.01) and problem behaviour (β = 0.18, p < 0.01). Prosocial behaviour (β = 0.17, p < 0.01) and problem behaviour (β = −0.61, p < 0.01) directly influenced social adaptation. Negative parenting directly influenced social adaptation (β = 0.12, p < 0.01) and indirectly influenced social adaptation (β = −0.23, p < 0.01) through problem behaviour.Our finding indicated that there are prominent social adjustment problems in the recovery process of children with leukaemia. These findings suggest that targeted education is needed to enhance social adaptation among children with leukaemia.The final model should serve as a framework to guide intervention research targeting social support and negative parenting to improve the social adaptation of children with leukaemia. During the recovery process, nurses need to regularly follow up on the children's social adaptation and encourage them to participate in social activities to increase social support while providing family guidance based on improving parenting styles.The subjects of this study were children with leukaemia aged 8–18 years. All authors read and approved the final manuscript. [ABSTRACT FROM AUTHOR]