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The Yangtze River is the mother river of China. To promote the aquatic ecosystem protection of the great river, the Project of Yangtze Fisheries Resources and Environment Investigation (2017–2021) supported by the Ministry of Agriculture and Rural Affairs, P. R. China carried out by 24 institutes and universities that located in the Yangtze River basin surveys the status of (1) fish species composition and spatial distribution, (2) current fish abundance, (3) endangered fishes, (4) Yangtze finless porpoise, (5) aquatic eco-environments, (6) water-level fluctuation areas, (7) capture fisheries and recreational fisheries of the Yangtze River mainstream and 10 of its main tributaries, including Yalong River, Heng River, Min River (including Dadu River), Chishui River, Tuo River, Jialing River, Wu River, Han River, Dongting Lake and Poyang Lake. The results showed that there were 443 fish species (belonging to 163 genus, 37 families, and 18 orders) before 2017, but only 323 fish species (including 15 new recorded exotic species) were recorded in the project of 2017–2021. Among them, Cyprinus carpio, Carassius auratus, Hypophthalmichthys molitrix, Pelteobagrus fulvidraco, Coilia brachygnathus, Silurus asotus, Saurogobio dabryi, Ctenopharyngodon idellus, Pelteobagrus nitidus, Hemiculter leucisculus, Siniperca chuatsi, Coreius heterodon, Culter alburnus, Parabramis pekinensis, and Aristichthys nobilis were the dominant species across the whole Yangtze River system. It is estimated that there were 886 million individuals weighing 124.8 million kg, merely equivalent to 27.3% of the resources in 1950s, 30.9% of the resources in 1960s, or 58.7% of the resources in 1980s. In the new list of protected fishes that recorded in the Yangtze River system, only 15 of 29 were collected in this project of 2017–2021. Psephurus gladius has been affirmed to be Extinct by International Union for Conservation of Nature (IUCN). The wild individuals of Tenualosa reevesii and Luciobrama macrocephalus have disappeared for many years and maybe have been extinct already. Acipenser dabryanus has been affirmed to be Extinct in the Wild by IUCN. The natural propagations of A. sinensis, Myxocyprinus asiaticus and Trachidermus fasciatus have been interrupted for many years. The populations of Yangtze finless porpoise in the Yangtze River mainstream, Dongting Lake and Poyang Lake have steadily rising sizes and expanding distributions in 2017–2021. Parts of them migrate from one region to another with the seasons, which would result in the fluctuation of Yangtze finless porpoise population within some regions. The conventional indicators of water quality in the Yangtze River system were good and conformed to the water quality criteria of fishery in 2017–2021. In the last 40 years, the maximum surface water area in the Yangtze River basin extended to approximately 63,360 km2, the minimum surface water area covered approximately 26,396 km2, and the seasonal water-level fluctuation areas occupied approximately 36,964 km2. Compared with 1984–2000 period, the 2001–2020 period witnessed an overall decreasing trend in the frequency of surface water occurrence within about 25,869 km2 of aquatic areas. From 1984 to 2000 period to 2001–2020 period, permanent surface water has decreased by nearly 8,750 km2. In 2017, the fishermen were mainly 40–60 years old and their educational levels were mainly lower than junior high school. In 2017, most anglers were older than 40 and used hand rod and/or sea rod. Their average catch of each time was mainly less than 1 kg. Results suggested that the fishing ban in key waters of the Yangtze River basin is not only an ecological project of aquatic ecosystem conservation, but also a livelihood project for the fisherman to embrace industrial transformation and improve their living conditions. After the implementation of the fishing ban, the fisheries resources would gradually recover, and the Yangtze finless porpoise population size would also see a steady increase. However, the endangered species would remain threatened for a long time. The degraded waterbodies and water-level fluctuation areas would be the key restrictive factors for future aquatic ecosystem recovery in the Yangtze River basin. Since this survey was carried out before the fishing ban, the current results could provide a baseline for future evaluation of the effect of the Yangtze River fishing ban.

Abstract Venous thromboembolism (VTE) is a prevalent complication among patients with cancer, contributing significantly to morbidity and mortality. However, the relationship between VTE-related genes (VRGs) and their potential impact on prognosis, immune response, and therapeutic targets in various cancer types remains unclear. Based on the coagulation and complement pathways, we identified hub VRGs that play a role in regulating the immune response in cancer. Specifically, coagulation factor III (F3), plasminogen activator (PLAT) and complement C1s (C1S) were identified as genes that exhibit high expression levels, positively correlating with tumor stemness and copy number variations, while inversely correlating with methylation levels, in particular cancer types. Pan-cancer survival analysis revealed detrimental effects of these VRGs in several cancer types, notably in glioblastoma and lower grade glioma (GMBLGG). Further analysis using receiver operating characteristic (ROC) curves demonstrated a high accuracy of F3, PLAT and C1S in predicting outcomes in GBMLGG, with area under the curve (AUC) values ranging from 0.78 to 0.9. Validation of the prognostic value of these three genes in GMBLGG was conducted using an independent Gene Expression Omnibus (GEO) dataset. Additionally, gene–drug association analysis identified ciclosporin, ouabain and 6- mercaptopurine, which all exhibit immunosuppressive properties, as potential therapeutic options for tumor patients exhibiting high F3, PLAT or C1S expression, respectively. In summary, our findings provide a bioinformatics perspective on VRGs in pan-cancer, highlighting the pivotal roles of F3, PLAT and C1S, which could potentially be therapeutically exploited and targeted in several cancers, especially in GBMLGG.

Abstract Objective To investigate the extent of knowledge about breastfeeding and attitudes towards infant feeding among spouses of puerperas at the time of discharge from hospital, and explore the factors influencing spousal attitudes toward breastfeeding. Methods We conducted a questionnaire survey among 204 spouses of puerperas who were admitted in the maternity wards at a tertiary hospital in Shaanxi Province between October 2021 and December 2021. Respondents who fulfilled the inclusion criteria were identified using convenient sampling. Results (1) The score of breastfeeding knowledge among spouses prior to discharge from the hospital was (10.56 ± 3.78), with an accuracy rate of 59.6%, and the lowest accuracy rate was for Item 1 “Newborns should be fed on time, not on demand” (42.6%) and Item 5 “Breastfeeding can prevent infant rickets” (49.5%). (2) The average score of spouses’ infant feeding attitudes was (58.15 ± 5.55), and the lowest scoring was for Item 17 “Daily urine volume of infants is a reliable indicator to judge whether they get enough breast milk” (1.99 ± 1.14). (3) Generalized linear model analysis showed a more positive attitude (higher score) among spousal attitudes towards infant feeding in those who had received breastfeeding education [OR = 4.588, 95% CI (0.160 ∼ 3.598)] and those with a master’s degree or above [OR = 18.278, 95% CI (3.471 ∼ 9.346)]. Conclusion (1) Spouses that received breastfeeding education and those that had a Masters Degree and above had more positive attitude towards infant feeding. (2) Medical staff should focus on puerperas’spouses with degrees below master’s level who had not received breastfeeding education. We recommend using a variety of education methods to enable them to acquire more knowledge on breastfeeding and develop a more positive attitude towards breastfeeding, which will further enhance spousal support for breastfeeding, thus positivizing postpartum co-parenting attitudes and improving the rate of exclusive breastfeeding.

Abstract Excessive production of reactive oxygen species (ROS) and inflammation are the key problems that impede diabetic wound healing. In particular, dressings with ROS scavenging capacity play a crucial role in the process of chronic wound healing. Herein, Zr-based large-pore mesoporous metal–organic frameworks (mesoMOFs) were successfully developed for the construction of spatially organized cascade bioreactors. Natural superoxide dismutase (SOD) and an artificial enzyme were spatially organized in these hierarchical mesoMOFs, forming a cascade antioxidant defense system, and presenting efficient intracellular and extracellular ROS scavenging performance. In vivo experiments demonstrated that the SOD@HMUiO-MnTCPP nanoparticles (S@M@H NPs) significantly accelerated diabetic wound healing. Transcriptomic and western blot results further indicated that the nanocomposite could inhibit fibroblast senescence and ferroptosis as well as the stimulator of interferon genes (STING) signaling pathway activation in macrophages mediated by mitochondrial oxidative stress through ROS elimination. Thus, the biomimetic multi-enzyme cascade catalytic system with spatial ordering demonstrated a high potential for diabetic wound healing, where senescence, ferroptosis, and STING signaling pathways may be potential targets. Graphical Abstract Schematic diagram showing the proposed mechanism that S@M@H NPs promoted diabetic wound healing

Supervised machine learning (SML) has become a crucial tool for estimating water quality parameters (WQPs) from satellite images. Its effectiveness relies heavily on synchronised in-situ datasets covering diverse water bodies. However, collecting such datasets is time-consuming, resulting in temporal gaps between sampling and imaging. In addition, the in situ dataset may exhibit an imbalance. These imperfections could introduce uncertainties to SML-derived models, compromising the accuracy of the WQP estimates. Using in situ data collected automatically every four hours, the estimation of both optically active parameters (OAPs) and non-optically active parameters (nOAPs) in the Middle Reaches of the Yangtze River (MRYR) serves as an example to illustrate the importance of this challenge in freshwater remote sensing. Additionally, the investigation was extended to estimate OAPs and nOAPs in lakes of Wuhan through manual sampling measurements, thereby bridging theoretical insights with real-world applications. Employing four ML algorithms, the SML-based models for each WQP were calibrated using in situ datasets with different spatio-temporal distributions. The results demonstrated that precision decreased with increasing time gaps, whereas most nOAPs (COD, TP, TN, pH, and DO) showed greater robustness to the time gap than the OAPs (turbidity, Secchi depth, Chl-a, and algae density). The mean absolute percentage errors (MAPEs) of these nOAPs were as follows: for all models, pH MAPEs

In order to ensure the accuracy of laboratory test results, the uncertainty of the determination of indicative polychlorinated biphenyls in fish by gas chromatography was evaluated. According to the national Food Safety standard, National Food Safety Standard Maximum Levels of Contaminants in Foods (GB 2762-2022), the content of PCB28, PCB52, PCB101, PCB118, PCB138, PCB153 and PCB180) in the "measured" samples was determined as the total content of PCBs. Then, the polychlorinated biphenyls (PCBs) measurement model was re-established according to the "measured" and detection standard, and then the weight of uncertainty affected by the measurement model on test results were analyzed and evaluated. After evaluation, the measurement result of PCBs in quality control fish samples was 149.1 μg/kg, and the relative extended uncertainty was Ur=2.9%, k=2. The contribution of uncertainty was measurement repeatability 70.8%, sample weight 0.025%, internal standard volume 1.8%, sum of concentration ratios of each target in sample solution 27.3%.

While it is crucial to monitor the spatio-temporal dynamics of forests at the subpixel scale, most available nonlinear methods are used to predict forest cover fraction maps only at the acquisition time of the training samples and are, thus, unable to estimate time-series forest cover fraction beyond the acquisition time. Based on MODIS NDVI, VCF and Landsat tree canopy height data, we developed a spatio-temporal regression (STR) method to estimate annual forest cover fraction maps during 2000–2020 on China's Han River Basin. Results obtained by the proposed STR method achieved significantly higher accuracy (R2 = 0.897, RMSE = 0.1364, MAE = 0.077) than that obtained by a traditional nonlinear regression method. Moreover, the STR exhibits increased accuracy when using training samples from both 2000 and 2020 compared to those using training samples solely from either 2000 or 2020. We also introduced Landsat tree cover maps in 2005, 2010, and 2015 as reference data to verify the effectiveness of the STR method. The STR method was employed to produce annual forest cover fraction maps from 2000 to 2020. The outcomes revealed a distinct overall recovery trend of forest cover at both the pixel and subpixel scales in China's Han River Basin during 2000–2020, notably around the vicinity of the Danjiangkou reservoir area. In general, the STR method proposed in this research is superior in accurately tracking time-series high-intensity and low-intensity forest cover fraction changes in a complex forest ecosystem, which is composed of various forest types in a subtropical monsoon climate.

Abstract Rice farming threatens freshwater resources, while also being increasingly vulnerable to drought due to climate change. Rice farming needs to become more sustainable and resilient to climate change by improving irrigation drainage systems. Small water bodies, used to store drainage water and supply irrigation in traditional rice farming systems have gradually been abandoned in recent decades. This has resulted in a higher water footprint (WF) associated with rice farming due to increased freshwater usage and wastewater release, also leaving rice production more vulnerable to extreme weather events. Here, we propose how protecting and reactivating small water bodies for rice irrigation and drainage can decrease rice production WF in China by 30%, save 9% of China’s freshwater consumption, increase irrigation self-sufficiency from 3% to 31%, and alleviate yield loss in dry years by 2–3%. These findings show that redesigning rice irrigation drainage systems can help meet water scarcity challenges posed by climate change.

Abstract Background We aim to clarify the controversial associations between EBV-related antibodies and gastric cancer risk. Methods We analysed the associations between serological Epstein-Barr nuclear antigen 1 immunoglobulin A (EBNA1-IgA) and viral capsid antigen immunoglobulin A (VCA-IgA) by enzyme-linked immunosorbent assay and the risk of gastric cancer in a nested case-control study originated from a population-based nasopharyngeal carcinoma (NPC) screening cohort in Zhongshan, a city of southern China, including 18 gastric cancer cases and 444 controls. Conditional logistic regression was used to calculate the odds ratios (ORs) and corresponding 95% confidence intervals (CIs). Results All the sera of cases were sampled before diagnosis and the median time interval was 3.04 (range: 0.04, 7.59) years. Both increased relative optical density (rOD) values of EBNA1-IgA and VCA-IgA were associated with higher risks of gastric cancer with age adjusted ORs of 1.99 (95%CI: 1.07, 3.70) and 2.64 (95%CI: 1.33, 5.23), respectively. Each participant was further classified as high or medium/low risk based on a combination of two anti-EBV antibody levels. Participants in the high-risk group had substantially higher odds of developing gastric cancer than that in the medium/low risk group with an age adjusted OR of 6.53 (95%CI: 1.69, 25.26). Conclusions Our research reveals positive associations between EBNA1-IgA and VCA-IgA and gastric cancer risk in southern China. We thus postulate that EBNA1-IgA and VCA-IgA might appear to be potential biomarkers for gastric cancer. More research to further validate the results among diverse populations and investigate its underlying biological mechanism is needed.

Small water bodies (SWBs), such as ponds and on-farm reservoirs, are a key part of the hydrological system and play important roles in diverse domains from agriculture to conservation. The monitoring of SWBs has been greatly facilitated by medium-spatial-resolution satellite images, but the monitoring accuracy is considerably affected by the mixed-pixel problem. Although various spectral unmixing methods have been applied to map sub-pixel surface water fractions for large water bodies, such as lakes and reservoirs, it is challenging to map SWBs that are small in size relative to the image pixel and have dissimilar spectral properties. In this study, a novel regression-based surface water fraction mapping method (RSWFM) using a random forest and a synthetic spectral library is proposed for mapping 10 m spatial resolution surface water fractions from Sentinel-2 imagery. The RSWFM inputs a few endmembers of water, vegetation, impervious surfaces, and soil to simulate a spectral library, and considers spectral variations in endmembers for different SWBs. Additionally, RSWFM applies noise-based data augmentation on pure endmembers to overcome the limitation often arising from the use of a small set of pure spectra in training the regression model. RSWFM was assessed in ten study sites and compared with the fully constrained least squares (FCLS) linear spectral mixture analysis, multiple endmember spectral mixture analysis (MESMA), and the nonlinear random forest (RF) regression without data-augmentation. The results showed that RSWFM decreases the water fraction mapping errors by ~ 30%, ~15%, and ~ 11% in root mean square error compared with the linear FCLS, MESMA unmixings, and the nonlinear RF regression without data-augmentation respectively. RSWFM has an accuracy of approximately 0.85 in R2 in estimating the area of SWBs smaller than 1 ha.

Abstract Background We aimed to investigate associations between pre-diagnostic anti-Epstein-Barr virus (EBV) antibodies, including interactions with hepatitis B virus (HBV), and risk of primary liver cancer in southern China. Methods In a population-based nested case-control study, we measured pre-diagnostic immunoglobulin A (IgA) against EBV nuclear antigen 1 (EBNA1) and viral capsid antigen (VCA) in 125 primary liver cancer cases and 2077 matched controls. We also explored the interaction between HBV surface antigen (HBsAg) and anti-EBV antibodies. Results Participants with positive EBNA1-IgA, positive VCA-IgA or single-positive anti-EBV antibodies had two-fold odds of developing liver cancer, compared with seronegative subjects. The odds ratios (ORs) between the relative optical density of EBNA1-IgA and VCA-IgA and primary cancer, controlling for age and HBsAg, were 1.59 (95% confidence interval (CI): 1.17, 2.14) and 1.60 (95% CI: 1.07, 2.41), respectively. Subjects with both HBsAg and anti-EBV antibody seropositivity were at 50-fold increased risk compared with those negative for both biomarkers (OR: 50.67, 95% CI: 18.28, 140.46), yielding a relative excess risk due to interaction of 30.81 (95% CI: 3.42, 114.93). Conclusion Pre-diagnostic seropositivity for EBNA1-IgA and/or VCA-IgA was positively associated with primary liver cancer risk, especially in combination with HBsAg positivity. EBV may interact with HBV in the development of primary liver cancer, and anti-EBV antibodies might be potential biomarkers for primary liver cancer in this high-risk population.

A proportional integral differential (PID) trajectory tracking control strategy based on fuzzy logic and RBF neural network is proposed for the trajectory stability tracking control of the quadrotor unmanned aerial vehicle (UAV) control system. Firstly, the trajectory tracking problem of UAV is transformed into the command tracking control problem of PID position control loop and PID attitude control loop by transformation. Then, the fuzzy control theory is used to adjust the PID parameter gain adaptively in real time, so as to overcome the shortcomings of the traditional PID parameters relying on experience and unable to adjust in real time according to the change of the system. At the same time, the online compensator of PID parameter gain is designed by using the attention mechanism of radial basis function (RBF) neural network, and the disturbance caused by the environmental impact of the system is suppressed by online learning and adjustment. Finally, the designed controller (RFPID) is compared with the PID controller and the fuzzy-PID (FPID) controller in three numerical simulation. The experimental results show that the proposed controller can significantly improve the robustness and accuracy of the trajectory tracking control of the quadrotor UAV.

Aiming at the issues of complex calculation and low accuracy of two-dimensional (2D) Otsu segmentation images, an image threshold segmentation means of 2D Otsu ground on a modified sparrow search algorithm is proposed. Firstly, in the initialization stage, the tent chaos mapping is added to enhance the multiformity of the population, and the population elite strategy is introduced to enhance the quality of the initial solution. Secondly, in the local search stage, the elite reverse learning strategy is applied to renewal the sparrow location to solve the issue of getting into local optimality. Eventually, the modified sparrow search algorithm is fused with 2D Otsu and the image threshold is segmented to enhance the accuracy of image segmentation. Compared with the traditional 2D Otsu algorithm, 2D Otsu genetic algorithm (GA-Otsu), 2D Otsu seagull optimization algorithm (SOA-Otsu), 2D Otsu particle swarm algorithm (PSO-Otsu) and 2D Otsu sparrow search algorithm (SSA-Otsu), the mean square error (MSE) value is reduced by 40.84%, 2.68%, 1.57%, 0.77% and 1.04%, respectively, and the peak signal-to-noise ratio (PSNR) value is increased by 24.48%, 1.24%, 0.83%, 0.40% and 0.45%, respectively. Moreover, the optimal threshold of the proposed algorithm is better than the other five algorithms. It is verified that the algorithm in this paper has faster convergence speed and higher accuracy, and effectively improves the quality of image segmentation.

Super-resolution mapping (SRM) can effectively predict the spatial distribution of land cover classes within mixed pixels at a higher spatial resolution than the original remotely sensed imagery. The uncertainty of land cover fraction errors within mixed pixels is one of the most important factors affecting SRM accuracy. Studies have shown that SRM methods using deep learning techniques have significantly improved land cover mapping accuracy but have not coped well with spectral–spatial errors. This study proposes an end-to-end SRM model using a spectral–spatial generative adversarial network (SGS) with the direct input of multispectral remotely sensed imagery, which deals with spectral–spatial error. The proposed SGS comprises the following three parts: first, cube-based convolution for spectral unmixing is adopted to generate land cover fraction images. Second, a residual-in-residual dense block fully and jointly considers spectral and spatial information and reduces spectral errors. Third, a relativistic average GAN is designed as a backbone to further improve the super-resolution performance and reduce spectral–spatial errors. SGS was tested in one synthetic and two realistic experiments with multi/hyperspectral remotely sensed imagery as the input, comparing the results with those of hard classification and several classic SRM methods. The results showed that SGS performed well at reducing land cover fraction errors, reconstructing spatial details, removing unpleasant and unrealistic land cover artifacts, and eliminating false recognition.

Solar energy, as an environmentally sustainable power source, is gaining increasing popularity worldwide, driving a surge in the number of solar photovoltaic (PV) plants. China, which has a prominent role in this domain, requires continuous updates to its PV plant data for spatiotemporal analyses. However, there remains an absence of a comprehensive and timely dataset of PV plants across China, leaving PV installation dates and other crucial attributes for comprehensive analyses underexplored. This study leverages Sentinel-2 data as a primary source to propose an optimized deep learning approach for PV plant extraction in China. Statistical analyses of PV plant attributes, including its installation date, size, site slope, and site land cover, were implemented from multiple data sources. Comprehensive analyses were conducted to unravel their spatiotemporal development patterns in the 21st century. The results indicate that as of 2023, China boasts 4347 PV plants, collectively spanning 4146 km ^2 , which are predominantly concentrated in Northwest and North China. 2016 and 2017 marked substantial growth in China’s PV plants, while other years exhibited stability. These plants exhibit the distinct spatial characteristics of installing smaller PV plants on flat terrain covered by vegetation or barren land. Over time, a notable trend in the installation of China’s PV plants has been the increasing preference for establishing larger ones in smooth terrain, with a focus on preventing damage to natural resources. The results reveal China’s optimization of PV plant site selection and construction strategies, aligning with global environmental goals and sustainable energy practices.

Abstract Background Dietary management is the most important and effective treatment for citrin deficiency, as well as a decisive factor in the clinical outcome of patients. However, the dietary management ability of caregivers of children with citrin deficiency is generally poor, especially in East Asia where carbohydrate-based diets are predominant. The aim of this study was to identify the difficulties that caregivers encounter in the process of home-based dietary management, and the reasons responsible for these challenges. Results A total of 26 caregivers of children with citrin deficiency were recruited, including 24 mothers, one father, and one grandmother. Grounded theory was employed to identify three themes (covering 12 sub-themes) related to the dilemma of dietary management: dietary management that is difficult to implement; conflicts with traditional concepts; and the notion that children are only a part of family life. The first theme describes the objective difficulties that caregivers encounter in the process of dietary management; the second theme describes the underlying reasons responsible for the non-adherent behavior of caregivers; the third theme further reveals the self-compromise by caregivers in the face of multiple difficulties. Conclusions This study reflects the adverse effects of multi-dimensional contradictions on the adherence of caregivers to dietary management. These findings reveal that the dietary management of citrin deficiency is not only a rational process, rather it is deeply embedded in family, social, and dietary traditions.

In view of the disadvantage that UAV is usually regarded as a particle in trajectory planning with particle swarm optimization algorithm,the flight time,angle and other parameters of UAV are ignored,a trajectory planning method combining numerical method with particle swarm optimization algorithm was proposed.Firstly,considering that the optimization of each control variable will cost a lot of time,the flight time of UAV was discretized as a certain number of Chebyshev points,and control variables were optimized at these discrete collocations to reduce the computational burden.Secondly,the angular velocity was taken as the control variable,the function of angular velocity and time was solved by curve fitting,and the function of angular and position of UAV with time was obtained by integration.Thirdly,the angular velocity,angle and position were calculated according to the allocated time.Finally,the simulation of UAV trajectory planning in complex environment was carried out,and the effectiveness and feasibility of the proposed method were verified by comparing the Monte-Carlo simulation results with the results of existing methods.The results show that the proposed method can calculate the kinematic parameters including position,plan a smooth trajectory and successfully avoid the obstacles in the process of forward.Therefore,the combination of particle swarm optimization algorithm and numerical method can solve the kinematics parameters of UAV and provide certain reference value for the improvement of the solution dimension of trajectory planning and the realization of intelligent autonomous flight.

Background: Cytokines play a role in the progression of idiopathic-nephrotic syndrome (INS). Objectives: To investigate the association of different cytokine genes polymorphisms with INS incidence and response to steroid therapy in Chinese children. Methods: 182 children with INS and 100 healthy controls were enrolled in this study. Blood genomic DNAs were used to analyze20 single nucleotide polymorphisms (SNPs) in 8 cytokine genes includingIL-21, IL-18, IL-6, IFN-γ, IL-4, IL-10, IL-17F, IL-17A d by multi-PCR with next-generation sequencing. Results: Among 182 children with INS, 89 (48.6%) were steroid-sensitive (SS), 73 (39.9%) were steroid-dependent (SD) and 21 (11.5%) were steroid-resistant (SR). In 20 SNPs, IL-4-rs2243283 exhibited a significantly different genotype distribution between INS and the healthy controls (CC is a risk genotype: 66.5% of INS VS 51% of the control; OR=1.91, p=0.012). Patients carrying AG genotype (rs2275913, IL-17A) had a significantly higher risk of steroid-dependent response (69.1% of SD VS 46.4% of SS; OR=2.58, p=0.014). Similarly, patients carrying A allele of IL-10-rs1800872 (39.0% of SD VS 26.7% of SS; OR=1.76, p=0.018) and C allele of IL-10-rs1800896 (12.3% of SD VS 3.9% of SS; OR=3.44, p=0.004) had a higher risk of steroid-dependent response. However, none of these 20 SNPs showed a significant difference between SS group and SR group. Conclusion: Among the 20 cytokine gene SNPs, IL-4-rs2243283 might increase the susceptibility to INS in Chinese children; rs2275913 of IL-17A, rs1180972, and rs1800896 of IL-10 show association with the steroid -response in Chinese INS children.

Abstract An algorithm that integrates the improved artificial fish swarm algorithm with continuous segmented Bézier curves is proposed, aiming at the path planning and smoothing of mobile robots. On the one hand, to overcome the low accuracy problems, more inflection points and relatively long planning paths in the traditional artificial fish swarm algorithm for path planning, feasible solutions and a range of step sizes are introduced based on Dijkstra's algorithm. To solve the problems of poor convergence and degradation that hinder the algorithm's ability to find the best in the later stage, a dynamic feedback horizon and an adaptive step size are introduced. On the other hand, to ensure that the planned paths are continuous in both orientation and curvature, the Bessel curve theory is introduced to smooth the planned paths. This is demonstrated through a simulation that shows the improved artificial fish swarm algorithm achieving 100% planning accuracy, while ensuring the shortest average path in the same grid environment. Additionally, the smoothed path is continuous in both orientation and curvature, which satisfies the kinematic characteristics of the mobile robot.

Abstract Background Primary dysmenorrhea (PD) is one of the main gynecological complaints in women of child-bearing age, but limited effective treatments are available. Guizhi Fuling Wan (GFW), one of the most widely known traditional Chinese medicine (TCM) formulations, has been commonly used in clinical practice to treat gynecological disorders in China. In recent years, a growing number of studies have shown that GFW is beneficial for patients with PD. However, the quality of evidence is limited, and there are few studies on specific TCM syndromes of GFW for PD. Therefore, we plan to conduct a randomized controlled trial to explore the efficacy and safety of GFW for PD patients with heat-burning blood-stasis syndrome. Methods and analysis The clinical study is a randomized, double-blinded, placebo-controlled trial. Eligible patients will be randomly assigned to the GFW group (treated with GFW) and the control group (treated with a matching placebo) in a 1:1 ratio for three menstrual cycles with a 3-month follow-up. The primary outcome will be the mean change of pain intensity measured by the visual analog scale (VAS). The secondary outcomes will include the Cox Menstrual Symptom Scale (CMSS), the Self-rating Depression Scale (SDS), the Self-rating Anxiety Scale (SAS), and the TCM syndrome scale. Adverse events will also be reported. Discussion This randomized trial will be the first rigorous study designed to assess the efficacy and safety of GFW in treating PD with heat-burning blood-stasis syndrome. The finding of this study will provide an objective clinical basis for the use of GFW for PD in the future. Trial registration Chinese Clinical Trial Registry ChiCTR2000034118 . Registered on 24 June 2020

Abstract Aims We aimed to investigate whether sacubitril–valsartan could further improve the prognosis, cardiac function, and left ventricular (LV) remodelling in patients following acute myocardial infarction (AMI). Methods and results We searched the PubMed, Embase, Cochrane Library, and China National Knowledge Infrastructure (CNKI) from inception to 10 May 2021 to identify potential articles. Randomized controlled trials (RCTs) meeting the inclusion criteria were included and analysed. Thirteen RCTs, covering 1358 patients, were analysed. Compared with angiotensin‐converting enzyme inhibitors (ACEI)/angiotensin receptor blockers (ARB), sacubitril–valsartan did not significantly reduced the cardiovascular mortality [risk ratio (RR) 0.65, 95% confidence interval (CI) 0.22 to 1.93, P = 0.434] and the rate of myocardial reinfarction (RR 0.65, 95% CI 0.29 to 1.46, P = 0.295) of patients following AMI, but the rate of hospitalization for heart failure (HF) (RR 0.48, 95% CI 0.35 to 0.66, P

The Forel–Ule index (FUI) is one of the indicators used to assess the trophic state of inland waters, which can be objectively determined based on satellite-retrieved corrected hue angles (α). However, the functions for α correction have been proposed based on a dataset from Dutch and the North Sea, which might be inadequate for the precise FUI-derived trophic state. This study proposed a method for FUI retrieval through improved α correction. First, the daily surface spectral reflectance product (MOD09GA) was corrected through simple band subtraction. The results showed that the FUI retrieved from the corrected MOD09GA was more reliable than those without corrections. Second, we obtained new offset functions through the fit between the sensor (α) and the error with α derived from the corrected MOD09GA. The results showed that: (1) for three atmospheric correction methods, the C2RCC (case 2 regional coast color) processor and the iCOR (image correction for atmospheric effects) performed better than 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) in FUI calculations; and (2) with Pearson’s r square (r2) of 0.72, and percentage error (PE) values of 5.80% for FUI derived from Landsat 8, r2 of 0.52 and PE of 7.81% for results derived from Sentinel-2, r2 of 0.60 and PE of 19.40% for results retrieved from Sentinel-3, the FUI determined by the new offset-corrected greatly improved the assessment of trophic states for five waterbodies in and around Wuhan, China. Hue angles retrieved by the corrected-MOD09GA are available as references and the results also imply that hue angle correction the functions can be achieved in any place, even if without an in-situ hyperspectral dataset. The waters of the Xiajiasi Reservoir and the Daoguanhe Reservoir were mostly in mesotrophic status; while East Lake, Tangxun Lake and Liangzi Lake were eutrophic.

Abstract This study investigated the outcomes and major adverse cardiovascular events (MACEs) incurred by acute myocardial infarction (AMI) patients comorbiding with hypertension and hyperhomocysteinemia (HHcy) during hospitalization and 1-year follow-up. 648 consecutive AMI patients were divided into four categories: (1) hypertension with Hcy ≥ 15 µmol/L; (2) hypertension with Hcy

Abstract Disturbed blood flow (d-flow) has been known to induce changes of the cells in the arterial wall, increasing the risk of atherosclerosis. However, the heterogeneity of the vascular cell populations under d-flow remains less understood. To generate d-flow in vivo, partial carotid artery ligation (PCL) was performed. Seven days after ligation, single-cell RNA sequencing of nine left carotid arteries (LCA) from the PCL group (10,262 cells) or control group (14,580 cells) was applied and a single-cell atlas of gene expression was constructed. The integrated analysis identified 15 distinct carotid cell clusters, including 10 d-flow-relevant subpopulations. Among endothelial cells, at least four subpopulations were identified, including Klk8hi ECs, Lrp1hi ECs, Dkk2hi ECs, and Cd36hi ECs. Analysis of GSVA and single-cell trajectories indicated that the previously undescribed Dkk2hi ECs subpopulation was mechanosensitive and potentially transformed from Klk8hi ECs under d-flow. D-flow-induced Spp1hi VSMCs subpopulation that appeared to be endowed with osteoblast differentiation, suggesting a role in arterial stiffness. Among the infiltrating cell subpopulations, Trem2hi Mφ, Birc5hi Mφ, DCs, CD4+ T cells, CXCR6+ T cells, NK cells, and granulocytes were identified under d-flow. Of note, the novel Birc5hi Mφ was identified as a potential contributor to the accumulation of macrophages in atherosclerosis. Finally, Dkk2hi ECs, and Cd36hi ECs were also found in the proatherosclerotic area of the aorta where the d-flow occurs. In conclusion, we presented a comprehensive single-cell atlas of all cells in the carotid artery under d-flow, identified previously unrecognized cell subpopulations and their gene expression signatures, and suggested their specialized functions.

ObjectiveThe study aimed to ascertain the comparative efficacy of these two forms on reducing anxiety scores of scales in patients with a generalized anxiety disorder (GAD) by examining the available evidence for face-to-face cognitive behavior therapy (CBT) and internet-based cognitive behavior therapy (ICBT). Moreover, this study attempted to determine whether ICBT can obtain similar benefits as CBT for GAD patients during coronavirus disease 2019 (COVID-19) due to the quarantine policy and the requirement of social distance.MethodsThis meta-analysis was registered with the International Prospective Register of Systematic Reviews (PROSPERO) according to the guidelines in the Preferred Reporting Items for Systematic Reviews and Meta-analyses statement (registration number CRD42021241938). Therefore, a meta-analysis of randomized controlled trials (RCTs) examining CBT or ICBT was conducted in this study to treat GAD patients diagnosed with DMS-IV. The researchers searched PubMed, MEDLINE, Embase, PsycINFO, and the Cochrane Database of Systematic Reviews for relevant studies published from 2000 to July 5, 2022. Evidence from RCTs was synthesized by Review Manager 5.4 as mean difference (MD) for change in scores of scales through a random-effects meta-analysis.ResultsA total of 26 trials representing 1,687 participants were pooled. The results demonstrated that ICBT and CBT were very close in the effect size of treating GAD (MD = −2.35 vs. MD = −2.79). Moreover, they still exhibited a similar response (MD = −3.45 vs. MD = −2.91) after studies with active control were removed.ConclusionRegarding the treatment of GAD, ICBT can achieve a similar therapeutic effect as CBT and could be CBT's candidate substitute, especially in the COVID-19 pandemic era, since the internet plays a crucial role in handling social space constraints.Systematic Review Registrationhttps://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=241938, identifier CRD42021241938.

Pervious concrete is an important material for permeable pavement, while the clogging phenomenon is deleterious to the service performance of the pervious concrete pavement. In this paper, the pervious concrete is proportioned by introducing a parameter of filling ratio, and the influences of filling ratio and W/C ratio on compressive strength and permeability of pervious concrete are investigated. A novel method using saturated slurry (soil suspension) as clogging media is proposed to evaluate the clogging resistance of pervious concrete. The pore characteristics are analyzed by the image processing to interpret the clogging phenomenon. The results indicate that the filling ratio is an essential parameter in the mix design of pervious concrete, while the only decrease of W/C is not always effective to prepare high performance pervious concrete. The proposed method could evaluate the clogging risk with clogging cycles for pervious concrete with different pore structures. According to image processing analysis, large pores are beneficial to the permeability, but on the contrary, excessive large pores would reduce the permeability due to the increase of pore tortuosity. More importantly, except for porosity and tortuosity, the pore size distribution needs to be sufficiently considered to design an unclogged pervious concrete.

Monitoring the spatio-temporal dynamic changes of reservoir surface water area (RSWA) values is crucial when investigating global water resource reserves. Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed imagery has been extensively used for this purpose, but the coarse spatial resolution limits the accuracy when monitoring the small water area variations of reservoirs. In this paper, we introduce a novel approach for the monitoring of RSWA variation by integrating MODIS and Landsat imagery to overcome this problem. In the proposed method, the sub-pixel water fraction information in the MODIS images is explored based on the C/M ratio model, where C/M represents the ratio between the reflectance of a land pixel and that of a water pixel. By using the series of RSWA values extracted from Landsat images as the reference, the C/M values extracted from MODIS images are used to construct regression functions with the overall RSWA for each MODIS pixel. An RSWA time series is then predicted from the extracted daily C/M values with the use of the regression functions constructed in the study. In a case study of Dale Hollow Reservoir in the United States, the performance of the proposed method was assessed, and the resulting Pearson's correlation coefficient between the predicted RSWA and water level (WL) reached 0.88, which was higher than that of the conventional pixel analysis based method and the spectral unmixing based sub-pixel analysis method, confirming the effectiveness of the proposed method.

BackgroundThe Coronavirus Disease-19 (COVID-19) pandemic negatively impacts mental health. Some published studies have investigated the prevalence of depression among children and adolescents in China during the pandemic. However, the results vary widely. We aimed to systematically analyze and estimate the prevalence of depressive symptoms and attempted to reveal the reasons for prevalence variety in previous studies.MethodsPublished studies were searched in PubMed, Embase, Cochrane Central, the Chinese Scientific Journal Database (VIP Database), China National Knowledge database (CNKI), and the WanFang database from December 2019 to May 2021. The quality of all included studies was assessed by the Joanna Briggs Institute (JBI) checklist and the American Agency for Health Care Quality and Research’s (AHRQ) cross-sectional study quality evaluation items. Meta-analysis was performed using random-effects modeling.ResultsOf the 1,708 references screened, 13 related reports that involve 41,729 participants were included. The results suggested that the pooled prevalence of depressive symptoms among Chinese children and adolescents during the COVID-19 epidemic was 28.6%. Subgroup analyses showed that the pooled prevalence was highest among the studies using the Patient Health Questionnaire (PHQ)-9 (46.8%) and lowest among these using Depression Self-Rating Scale for Children (DSRSC) (11.4%). All studies using PHQ-9 set the cutoff at 5 points instead of 10. The pooled prevalence of studies that include primary school students was lower (16.5%) than that of studies excluding primary school students (39.1%).ConclusionThe meta-analysis suggests that depressive symptoms were relatively prevalent among Chinese children and adolescents during COVID-19, especially among the secondary school students. The suitable screening tools and cutoff should be carefully chosen in the survey.

Multi-constraint trajectory planning for unmanned aerial vehicles (UAVs) has been widely used in military and civil fields. The existing path planning methods, such as swarm intelligence algorithm and graph-based algorithm, cannot incorporate the flying time and UAV kinematic model into evolution. To overcome such disadvantage, a method of solving trajectory planning under obstacles and multi-constraint is investigated in this paper. Firstly, the flying time is discretized as a certain number of Chebyshev points which are the optimized moments of control variable, and they can reduce the computational burden. The process of solution is divided into multi-phase, i.e., two points as a phase to generate the trajectory. Then, angular velocity is taken as control variable, and function of angular velocity is solved by cubic spline interpolation. Besides, functions of angle and position are obtained by integration. The results are substituted into the model consisted by particle swarm optimization (PSO) and the UAV kinematic model to optimize. On this basis, the angular velocity, angle and position are calculated according to the allocated moments. Finally, Monte-Carlo simulation and comparison with existed method are carried out in obstacle environment. All the results illustrate that the multi-phase method can calculate the kinematic parameters of UAV accurately and plan smooth trajectories. Meanwhile, the proposed method is easier to meet the complicated constraints than the single-phase method. In addition, the dimensionality of solution is also enriched effectively.

Accurate extraction of river network from the Digital Elevation Model (DEM) is a significant content in the application of a distributed hydrological model. However, the study of river network extraction based on DEM has some limitations, such as location offset, inaccurate parallel channel and short circuit of meandering channels. In this study, we proposed a new enhancement method for NASADEM V001 in the Danjiangkou Reservoir area. We used Surface Water Occurrence (SWO) and Sentinel-2 data to describe vertical limit differences between morphological units to complement actual flow path information from NASADEM data by a stream burning method. The differences between the extracted river network and the actual river network were evaluated in three different geographical regions. Compared with the actual river centerline, the location error of the river network extraction was significantly reduced. The average offset distances between river network extraction and the actual river network were 68.38, 36.99, and 21.59 m in the three test areas. Compared with NASADEM V001, the average offset distances in the three test areas were reduced by 7.26, 40.29, and 42.35%, respectively. To better estimate accuracy, we also calculated and compared the accuracy of the river network based on MERIT Hrdro and HydroSHEDS. The experimental results demonstrated that the method can effectively improve the accuracy of river network extraction and meet the needs of hydrological simulation.

Abstract Background Monascus azaphilone pigments (MonAzPs), which were produced by Monascus species, have been used as important food colorant and food supplements for more than one billion people during their daily life. Moreover, MonAzPs recently have received more attention because of their diverse physiological activities. However, the high microbial production of MonAzPs is still not always guaranteed. Herein, the aim of this study was to develop an efficient biotechnological process for MonAzPs production. Results In this study, exogenous cyclic adenosine monophosphate (cAMP) treatment not only induced MonAzPs production, but also stimulated the expression of a cAMP phosphodiesterase gene, named as mrPDE, in M. purpureus HJ11. Subsequently, MrPDE was identified as a cAMP phosphodiesterase by in vitro enzymatic reaction with purified enzyme. Further, a gene knockout mutant of mrPDE was constructed to verify the activation of cAMP signalling pathway. Deletion of mrPDE in M. purpureus HJ11 improved cAMP concentration by 378% and enhanced PKA kinase activity 1.5-fold, indicating that activation of cAMP signalling pathway was achieved. The ΔmrPDE strain produced MonAzPs at 8563 U/g, with a 2.3-fold increase compared with the WT strain. Moreover, the NAPDH/NADP+ ratio of the ΔmrPDE strain was obviously higher than that of the wild type strain, which led to a higher proportion of yellow MonAzPs. With fed-batch fermentation of the ΔmrPDE strain, the production and yield of MonAzPs achieved 332.1 U/mL and 8739 U/g. Conclusions A engineered M. purpureus strain for high MonAzPs production was successfully developed by activating the cAMP signalling pathway. This study not only describes a novel strategy for development of MonAzPs-producing strain, but also provides a roadmap for engineering efforts towards the production of secondary metabolism in other filamentous fungi.

BackgroundThe human microbiome has been reported to mediate the response to anticancer therapies. However, research about the influence of the oral microbiome on nasopharyngeal carcinoma (NPC) survival is lacking. We aimed to explore the effect of oral microbiota on NPC prognosis.MethodsFour hundred eighty-two population-based NPC cases in southern China between 2010 and 2013 were followed for survival, and their saliva samples were profiled using 16s rRNA sequencing. We analyzed associations of the oral microbiome diversity with mortality from all causes and NPC.ResultsWithin- and between-community diversities of saliva were associated with mortality with an average of 5.29 years follow-up. Lower Faith’s phylogenetic diversity was related to higher all-cause mortality [adjusted hazard ratio (aHR), 1.52 (95% confidence interval (CI), 1.06–2.17)] and NPC-specific mortality [aHR, 1.57 (95% CI, 1.07–2.29)], compared with medium diversity, but higher phylogenetic diversity was not protective. The third principal coordinate (PC3) identified from principal coordinates analysis (PCoA) on Bray–Curtis distance was marginally associated with reduced all-cause mortality [aHR, 0.85 (95% CI, 0.73–1.00)], as was the first principal coordinate (PC1) from PCoA on weighted UniFrac [aHR, 0.86 (95% CI, 0.74–1.00)], but neither was associated with NPC-specific mortality. PC3 from robust principal components analysis was associated with lower all-cause and NPC-specific mortalities, with HRs of 0.72 (95% CI, 0.61–0.85) and 0.71 (95% CI, 0.60–0.85), respectively.ConclusionsOral microbiome may be an explanatory factor for NPC prognosis. Lower within-community diversity was associated with higher mortality, and certain measures of between-community diversity were related to mortality. Specifically, candidate bacteria were not related to mortality, suggesting that observed associations may be due to global patterns rather than particular pathogens.

Adrenal Vein Sampling (AVS) is the gold standard for categorizing primary aldosteronism (PA). However, catheterization of the right adrenal vein (RAV) is challenging due the small size and variable location. This study aims to explore the relationship between the RAV orifice and the right kidney contour (RKC) on fluoroscopy, thus evaluating the potential of use the RKC as an anatomic marker for localizing RAV. Imaging data of 107 PA patients with successful bilateral AVS were retrospectively analyzed. Based on the body mass index (BMI), all patients were divided into the Normal Group (BMI < 24 kg/m2), Overweight Group (24 kg/m2 ≤ BMI < 28 kg/m2) and Obese Group (BMI ≥ 28 kg/m2). At the anterior view, the height level of RAV orifice was determined relative to vertebral bodies and disks. The distance from the RAV orifice to the upper edge of RKC was measured manually. The RAV orifice height level was mainly distributed from vertebral T11 to T12 (90.6%), and tended to be higher in patients with a larger BMI. The mean distance from the RAV orifice to the upper edge of RKC was 13.9±7.8mm, and had no difference among Normal group (n = 53, 14.1±8.2mm), Overweight group (n = 39, 13.7±8.0mm), and Obese group (n = 15, 13.9±5.5mm) (p = 0.981). Based on these findings, the RKC might be used as a landmark for localizing RAV on fluoroscopy, which is conductive to narrow down the exploration range and increase the success rate of RAV catheterization.

Objective: To evaluate the efficacy and safety of Morinda officinalis oligosaccharide (MOO) capsules for depressive disorder. Methods: Eight electronic databases were searched for relevant studies from inception to April 19, 2020. Randomized controlled trials comparing MOO capsules with antidepressants were included. Data analysis was conducted using Review Manager 5.3 software. The risk of bias was assessed using the Cochrane Risk of Bias Tool, and the quality of the studies was evaluated by two researchers using the Grading of Recommendation, Assessment, Development and Evaluations (GRADE) software. Results: Seven studies involving 1,384 participants were included in this study. The effect of MOO capsules for moderate depressive disorder was not different from that of antidepressants (risk ratio [RR] = 0.99, 95%CI 0.92-1.06). Regarding adverse events, no significant difference was found between MOO capsules and antidepressants (RR = 0.84, 95%CI 0.65-1.07). In addition, the quality of evidence related to these adverse events was rated as low. Conclusion: This systematic review suggests that the efficacy of MOO capsules in the treatment of mild to moderate depression is not inferior to that of conventional antidepressants, which may provide a new direction for clinical alternative selection of antidepressants. However, more high-quality research and detailed assessments are needed.

In order to solve the problems of large computation amount, long operation time and difficulty in ensuring optima-lity, an improved method combining particle swarm optimization algorithm and Gaussian pseudospectral method was proposed. Firstly, particle swarm optimization algorithm was used for track pre-planning to ensure the fast realization of approximate optimal solution. Secondly, according to the selection of relative positions of collocation points of Gaussian pseudospectral method, the path points of pre-planned particle swarm optimization were fitted, which was used as the initial reference instruction of Gaussian pseudospectral method, so as to solve the problem of initial value sensitivity of pseudospectral method and accelerate the convergence speed of optimization algorithm. Finally, simulation experiments were carried out by taking the performance index of unmanned aerial vehicle formation, flight environment and cooperative flight constraints into consideration. Simulation results verify the importance of the selection of initial value and show that the designed algorithm could improve the optimization and convergence speed of the solution. The research results can be used to quickly plan multi-dimensional and high-precision guidance instructions for coordinated flight control of multiple UAVs, and have certain reference value for the realization of intelligent autonomous flight.

Optical remote sensing imagery is commonly used to monitor the spatial and temporal distribution patterns of inland waters. Its usage, however, is limited by cloud contamination, which results in low-quality images or missing values. Selecting cloud-free scenes or combining multi-temporal images to produce a cloud-free composite image can partially overcome this problem at the cost of the monitoring frequency. Predicting the spectral values of cloudy areas based on the spectral characteristics is a possible solution; however, this is not appropriate for water because it changes rapidly. Reconstructing cloud-covered water areas using historical water-distribution data has good performance, but such methods are typically only suitable for lakes and reservoirs, not over vast and complex terrain. This paper proposes a category-based approach to reconstruct the water distribution in cloud-contaminated images using a spatiotemporal dependence model. The proposed method predicts the class label (water or land) of a cloudy pixel based on the neighboring pixel labels and those at the same position in images acquired on other dates according to historical spatiotemporal water-distribution data. The method was evaluated through eight experiments in different study regions using Landsat and Sentinel-2 images. The results demonstrated that the proposed method could yield high-quality cloud-free classification maps and provide good water-extraction accuracy and consistency in most hydrological conditions, with an overall accuracy of up to 98%. The accuracy and practicality of the method render it promising for applications across a wide range of future research and monitoring efforts.

An increasing number of studies have emphasized the role of autophagy in cancer cell metastasis and treatment of malignant tumors. Autophagy inhibitors have been widely used in combination therapies to treat advanced malignancies. Several lung adenocarcinoma cells harbor epidermal growth factor receptor (EGFR) gene mutations, and EGFR tyrosine kinase inhibitors (TKIs) are routinely used in the treatment of lung adenocarcinoma. However, a number of lung adenocarcinoma tumors do not respond or develop resistance to EGFR TKIs. The aim of the present study was to explore the effect of autophagy inhibition on the biological behavior of lung adenocarcinoma cells. In addition, whether autophagy inhibition increases the efficacy of gefitinib in lung adenocarcinoma was investigated. The activation of autophagy was inhibited via the reduction of the expression of ATG5 in A549, H1975 and HCC827 cells. ATG5 knockdown using ATG5 siRNA partially suppressed the LC3B-II expression, decreased the LC3B-I/II conversion rate and enhanced the P62 expression. Cell scratch test and Transwell assay showed that the inhibition of autophagy could impair the migration and invasion ability of cells. These studies suggested that autophagy may play a pro-survival role in lung adenocarcinoma.

High-temporal-resolution inundation maps play an important role in surface water monitoring, especially in lake sites where water bodies change tremendously. Synthetic Aperture Radar (SAR) that guarantees a full time-series in monitoring surface water due to its cloud-penetrating capability is preferred in practice. To date, the methods of extracting and analyzing inundation maps of lake sites have been widely discussed, but the method of extracting surface water maps refined by inundation frequency map and the distinction of inundation frequency map from different datasets have not been fully explored. In this study, we leveraged the Google Earth Engine platform to compare and evaluate the effects of a method combining a histogram-based algorithm with a temporal-filtering algorithm in order to obtain high-quality surface water maps. Both algorithms were conducted on Sentinel-1 images over Poyang Lake and Dongting Lake, the two largest lakes in China, respectively. High spatiotemporal time-series analyses of both lakes were implemented between 2017 and 2021, while the inundation frequency maps extracted from Sentinel-1 data were compared with those extracted from Landsat images. It was found that Sentinel-1 can monitor water inundation with a substantially higher accuracy, although minor differences were found between the two sites, with the overall accuracy for Poyang Lake (95.38–98.69%) being higher than that of Dongting Lake (95.05–97.5%). The minimum and maximum water areas for five years were 1232.96 km2 and 3828.36 km2 in Poyang Lake, and 624.7 km2 and 2189.17 km2 in Dongting Lake. Poyang Lake was frequently inundated with 553.03 km2 of permanent water and 3361.39 km2 of seasonal water while Dongting Lake was less frequently inundated with 320.09 km2 of permanent water and 2224.53 km2 of seasonal water. The inundation frequency maps from different data sources had R2 values higher than 0.8, but there were still significant differences between them. The overall inundation frequency values of the Sentinel-1 inundation frequency maps were lower than those of the Landsat inundation frequency maps due to the severe contamination from cloud cover in Landsat imagery, which should be paid attention in practical application.

AIM: To investigate the difference and consistency between iTrace and Pentacam in measuring corneal spherical aberration(SphA)and curvature.METHODS: The corneal spherical aberration and curvature were measured by iTrace and Pentacam in 63 cases(125 eyes)separately, and the SphA, the minimum corneal curvature(K1), maximum corneal curvature(K2)and corneal astigmatism(CA)were recorded respectively. Paired t-test was used for statistical analysis of econometric data, Bland-Altman analysis was used to evaluate the consistency of measurement results(SphA and curvature)measured by iTrace and Pentacam.RESULTS: The measurement result of SphA, K1, K2 and CA were(0.230±0.086)μm,(43.01±2.38)D,(44.08±2.48)D and(1.03±0.57)D measured by iTrace, and(0.247±0.114)μm,(42.88±1.44)D,(43.86±1.51)D and(1.07±0.62)D measured by Pentacam, there was no significant difference of the measurement result of SphA, K1, K2 and CA between iTrace and Pentacam(P>0.05). The difference of SphA, K1, K2 and CA between iTrace and Pentacam was close to 0, the consistency was good because of the narrow 95% confidence interval.CONCLUSION:There is no significant difference of the measurement result between iTrace and Pentacam, the consistency is good, and both can be used as a mutual reference in clinical.

Aiming at the problem that the feature extraction of miners' facial expressions is slower and the recognition accuracy is not high for the traditional miner facial expression recognition method, based on the principal component analysis method, Fisher's linear discriminant method is used to improve the traditional principal component analysis method. Firstly, based on the principal component analysis method, an inter-class discrete matrix is added to make the distance between the feature points of different categories become larger after projection, and the distance between the feature points of the same category is more compact, so that the result of feature extraction to the miners' facial expression images is more representative and targeted. Then, the radial basis network is used to map the low-dimensional and nonlinear separable miner's facial expression feature matrix to the high-dimensional spatially separable class to realize the identification and classification of miners' facial expressions. The experimental results show that the recognition rate of the miner's facial expression reaches 89.0%, which is superior to the traditional miner's facial expression recognition algorithms. The method has a good application prospect in the fields of mine safety monitoring and fatigue driving.

A Au-stained Au nanoparticle (Aus)/pyridine (Py)/carboxylated multiwalled carbon nanotubes (C-MWCNTs)/glassy carbon electrode (GCE) was prepared for the sensitive analysis of As(III) by cast-coating of C-MWCNTs on a GCE, electroreduction of 4-cyanopyridine (cPy) to Py, adsorption of gold nanoparticles (AuNPs), and gold staining. The Py/C-MWCNTs/GCE can provide abundant active surface sites for the stable loading of AuNPs and then the AuNPs-initiated Au staining in HAuCl4 + NH2OH solution, giving a large surface area of Au on the Aus/Py/C-MWCNTs/GCE for the linear sweep anodic stripping voltammetry (LSASV) analysis of As(III). At a high potential-sweep rate of 5 V s−1, sharp two-step oxidation peaks of As(0) to As(III) and As(III) to As(V) were obtained to realize the sensitive dual-signal detection of As(III). Under optimal conditions, the ASLSV peak currents for oxidation of As(0) to As(III) and of As(III) to As(V) are linear with a concentration of As(III) from 0.01 to 8 μM with a sensitivity of 0.741 mA μM−1 and a limit of detection (LOD) of 3.3 nM (0.25 ppb) (S/N = 3), and from 0.01 to 8.0 μM with a sensitivity of 0.175 mA μM−1 and an LOD of 16.7 nM (1.20 ppb) (S/N = 3), respectively. Determination of As(III) in real water samples yielded satisfactory results.

Accurately mapping surface water fractions is essential to understanding the distribution and area of small water bodies (SWBs), which are numerous and widespread. Traditional spectral unmixings based on the linear mixture model require high-quality prior endmember information, and are not appropriate in situations such as dealing with multiple scattering effects. To overcome difficulties with unknown mixing mechanisms and parameters, a novel automated and hierarchical surface water fraction mapping (AHSWFM) for mapping SWBs from Sentinel-2 images was proposed. AHSWFM is automated, requires no endmember prior knowledge and uses self-trained regression using scalable algorithms and random forest to construct relationships between the multispectral data and water fractions. AHSWFM uses a hierarchical structure that divides pixels into pure water, pure land and mixed water-land pixels, and predicts their water fractions separately to avoid overestimating water fractions for pure land pixels and underestimating water fractions for pure water pixels. Results show that using the hierarchical strategy can increase the accuracy in estimating SWB areas. AHSWFM predicted SWB areas with a root mean square error of approximately 0.045 ha in a region using more than 1200 SWB samples that were mostly smaller than 0.75 ha.

Plant diversity is an important parameter in maintaining forest ecosystem services, functions and stability. Timely and accurate monitoring and evaluation of large-area wall-to-wall maps on plant diversity and its spatial heterogeneity are crucial for the conservation and management of forest resources. However, traditional botanical field surveys designed to estimate plant diversity are usually limited in their spatiotemporal resolutions. Using Sentinel-1 (S-1) and Sentinel-2 (S-2) data at high spatiotemporal scales, combined with and referenced to botanical field surveys, may be the best choice to provide accurate plant diversity distribution information over a large area. In this paper, we predicted and mapped plant diversity in a subtropical forest using 24 months of freely and openly available S-1 and S-2 images (10 m × 10 m) data over a large study area (15,290 km2). A total of 448 quadrats (10 m × 10 m) of forestry field surveys were captured in a subtropical evergreen-deciduous broad-leaved mixed forest to validate a machine learning algorithm. The objective was to link the fine Sentinel spectral and radar data to several ground-truthing plant diversity indices in the forests. The results showed that: (1) The Simpson and Shannon-Wiener diversity indices were the best predicted indices using random forest regression, with ȓ2 of around 0.65; (2) The use of S-1 radar data can enhance the accuracy of the predicted heterogeneity indices in the forests by approximately 0.2; (3) As for the mapping of Simpson and Shannon-Wiener, the overall accuracy was 67.4% and 64.2% respectively, while the texture diversity’s overall accuracy was merely 56.8%; (4) From the evaluation and prediction map information, the Simpson, Shannon-Wiener and texture diversity values (and its confidence interval values) indicate spatial heterogeneity in pixel level. The large-area forest plant diversity indices maps add spatially explicit information to the ground-truthing data. Based on the results, we conclude that using the time-series of S-1 and S-2 radar and spectral characteristics, when coupled with limited ground-truthing data, can provide reasonable assessments of plant spatial heterogeneity and diversity across wide areas. It could also help promote forest ecosystem and resource conservation activities in the forestry sector.

The monitoring of impervious surfaces in urban areas using remote sensing with fine spatial and temporal resolutions is crucial for monitoring urban development and environmental changes in urban areas. Spatiotemporal super-resolution mapping (STSRM) fuses fine-spatial-coarse-temporal remote sensing data with coarse-spatial-fine-temporal data, allowing for urban impervious surface mapping at both fine-spatial and fine-temporal resolutions. The STSRM involves two main steps: unmixing the coarse-spatial-fine-temporal remote sensing data to class fraction images, and downscaling the fraction images to sub-pixel land cover maps. Yet, challenges exist in each step when applying STSRM in mapping impervious surfaces. First, the impervious surfaces have high spectral variability (i.e., high intra-class and low inter-class variability), which impacts the accurate extraction of sub-pixel scale impervious surface fractions. Second, downscaling the fraction images to sub-pixel land cover maps is an ill-posed problem and would bring great uncertainty and error in the predictions. This paper proposed a new Spatiotemporal Continuous Impervious Surface Mapping (STCISM) method to deal with these challenges in fusing Landsat and Google Earth imagery. The STCISM used the Multiple Endmember Spectral Mixture Analysis and the Fisher Discriminant Analysis to minimize the within-class variability and maximize the between-class variability to reduce the spectral unmixing uncertainty. In addition, the STCISM adopted a new temporal consistency check model to incorporate temporal contextual information to reduce the uncertainty in the time-series impervious surface prediction maps. Unlike the traditional temporal consistency check model that assumed the impervious-to-pervious conversion is unlikely to happen, the new model allowed the bidirectional conversions between pervious and impervious surfaces. The temporal consistency check was used as a post-procession method to correct the errors in the prediction maps. The proposed STCISM method was used to predict time-series impervious surface maps at 5 m resolution of Google Earth image at the Landsat frequency. The results showed that the proposed STCISM outperformed the STSRM model without using the temporal consistency check and the STSRM model using the temporal consistency check based on the unidirectional pervious-to-impervious surface conversion rule.

A study using four Holstein cows with ruminal cannulas was conducted to evaluate the degradability of different moisture content or particle size of maize silage and alfalfa haylage. The maize silage (MS; 20-mm length) and alfalfa haylage (AH; 40-mm length) samples were wet (wet maize silage, MSW; wet alfalfa haylage, AHW), dried (dried maize silage, MSD; dried alfalfa haylage, AHD), or ground to pass through a 2.5-mm screen (dried ground maize silage, MSG; dried ground alfalfa haylage, AHG). Samples were incubated in the rumen for 2, 6, 12, 24, 36, 48, and 72 h. Cows were fed ad libitum and allowed free access to water. High moisture content treatment of MSW expressed a lower rinsing NDF and ADF degradability at 2 h (P

The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms.

A new design strategy was proposed to improve the mechanical performance of double network (DN) hydrogels by introducing polyhydroxy compounds into the DN structure and form a physically linked double network through the interaction of hydrogen bonding. Herein, agar/poly(acrylic acid)/hydroxyethyl cellulose composite hydrogels could be prepared by a simple one-pot method. The resulting hydrogels exhibit highly mechanical properties and excellent recoverability, which have potential applications in biomedical fields.