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Abstract Background Diabetic nephropathy (DN) is a major microvascular complication of diabetes and has become the leading cause of end-stage renal disease worldwide. A considerable number of DN patients have experienced irreversible end-stage renal disease progression due to the inability to diagnose the disease early. Therefore, reliable biomarkers that are helpful for early diagnosis and treatment are identified. The migration of immune cells to the kidney is considered to be a key step in the progression of DN-related vascular injury. Therefore, finding markers in this process may be more helpful for the early diagnosis and progression prediction of DN. Methods The gene chip data were retrieved from the GEO database using the search term ' diabetic nephropathy ‘. The ' limma ' software package was used to identify differentially expressed genes (DEGs) between DN and control samples. Gene set enrichment analysis (GSEA) was performed on genes obtained from the molecular characteristic database (MSigDB. The R package ‘WGCNA’ was used to identify gene modules associated with tubulointerstitial injury in DN, and it was crossed with immune-related DEGs to identify target genes. Gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on differentially expressed genes using the ‘ClusterProfiler’ software package in R. Three methods, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest (RF), were used to select immune-related biomarkers for diagnosis. We retrieved the tubulointerstitial dataset from the Nephroseq database to construct an external validation dataset. Unsupervised clustering analysis of the expression levels of immune-related biomarkers was performed using the ‘ConsensusClusterPlus ‘R software package. The urine of patients who visited Dongzhimen Hospital of Beijing University of Chinese Medicine from September 2021 to March 2023 was collected, and Elisa was used to detect the mRNA expression level of immune-related biomarkers in urine. Pearson correlation analysis was used to detect the effect of immune-related biomarker expression on renal function in DN patients. Results Four microarray datasets from the GEO database are included in the analysis : GSE30122, GSE47185, GSE99340 and GSE104954. These datasets included 63 DN patients and 55 healthy controls. A total of 9415 genes were detected in the data set. We found 153 differentially expressed immune-related genes, of which 112 genes were up-regulated, 41 genes were down-regulated, and 119 overlapping genes were identified. GO analysis showed that they were involved in various biological processes including leukocyte-mediated immunity. KEGG analysis showed that these target genes were mainly involved in the formation of phagosomes in Staphylococcus aureus infection. Among these 119 overlapping genes, machine learning results identified AGR2, CCR2, CEBPD, CISH, CX3CR1, DEFB1 and FSTL1 as potential tubulointerstitial immune-related biomarkers. External validation suggested that the above markers showed diagnostic efficacy in distinguishing DN patients from healthy controls. Clinical studies have shown that the expression of AGR2, CX3CR1 and FSTL1 in urine samples of DN patients is negatively correlated with GFR, the expression of CX3CR1 and FSTL1 in urine samples of DN is positively correlated with serum creatinine, while the expression of DEFB1 in urine samples of DN is negatively correlated with serum creatinine. In addition, the expression of CX3CR1 in DN urine samples was positively correlated with proteinuria, while the expression of DEFB1 in DN urine samples was negatively correlated with proteinuria. Finally, according to the level of proteinuria, DN patients were divided into nephrotic proteinuria group (n = 24) and subrenal proteinuria group. There were significant differences in urinary AGR2, CCR2 and DEFB1 between the two groups by unpaired t test (P

The prevalence of dyslipidemia is increasing, and it has become a significant global public health concern. Some studies have demonstrated contradictory relationships between urinary metals and dyslipidemia, and the combined effects of mixed urinary metal exposure on dyslipidemia remain ambiguous. In this study, we examined how individual and combined urinary metal exposure are associated with the occurrence of dyslipidemia. According to the data from the 2018–2019 baseline survey database of the China Multi-Ethnic Cohort (CMEC) Study, a population of 9348 individuals was studied. Inductively coupled plasmamass spectrometry (ICP-MS) was used to measure 21 urinary metal concentrations in the collected adult urinary samples. The associations between urinary metals and dyslipidemia were analyzed by logistic regression, weighted quantile sum regression (WQS), and quantile-based g-computation (qgcomp), controlled for potential confounders to examine single and combined effects. Dyslipidemia was detected in 3231 individuals, which represented approximately 34.6 % of the total population. According to the single-exposure model, Al and Na were inversely associated with the risk of dyslipidemia (OR = 0.95, 95 % CI: 0.93, 0.98; OR = 0.89, 95 % CI: 0.83, 0.95, respectively), whereas Zn, Ca, and P were positively associated (OR = 1.69, 95 % CI: 1.42, 2.01; OR = 1.12, 95 % CI: 1.06, 1.18; OR = 1.21, 95 % CI: 1.09, 1.34, respectively). Moreover, Zn and P were significantly positively associated even after adjusting for these metals, whereas Al and Cr were negatively associated with the risk of dyslipidemia. The results of the WQS and qgcomp analyses showed that urinary metal mixtures were positively associated with the risk of dyslipidemia (OR = 1.26, 95 % CI: 1.15, 1.38; OR = 1.09, 95 % CI: 1.01, 1.19). This positive association was primarily driven by Zn, P, and Ca. In the sensitivity analyses with collinearity diagnosis, interaction, and stratified analysis, the results remained, confirming the reliability of the study findings. In this study, the individual and combined effects of urinary Zn, P, and Ca on dyslipidemia were determined, which provided novel insights into the link between exposure to metals and dyslipidemia.

Constituents of cigarette smoke are known to be carcinogens. Additionally, there is mounting evidence that the liver is an organ susceptible to tobacco carcinogenicity. Nicotine, the primary constituent of tobacco, plays a role in cancer progression. In our previous study, it was found that nicotine enhances the proliferation of a human normal fetal hepatic (WRL68) cell due to the activation of p53 mutation at Ser249 (p53-RS)/STAT1/CCND1 signaling pathway. Here, we further elucidated the mechanism of regulating this pathway. Firstly, dose-dependent increase of SETDB1 protein level in WRL68 cells upon exposure to nicotine (1.25, 2.5, and 5 μM), significantly enhanced cellular proliferation. In addition, the upregulation of SETDB1 protein was necessary for the nuclear translocation of p53-RS to establish a ternary complex with STAT1 and SETDB1, which facilitated p53-RS di-methylation at K370 (p53-RS/K370me2). After that, the activation of CCND1/PI3K/AKT pathway was initiated when STAT1 stability was enhanced by p53-RS/K370me2, ultimately resulting in cell proliferation. Altogether, the study revealed that the increase in SETDB1 expression could potentially have a significant impact on the activation of CCND1/PI3K/AKT pathway through p53-RS/K370me2, leading to the proliferation of WRL68 cells induced by nicotine, which could contribute to hepatocellular carcinoma for smokers. Besides, the results of this study provided a foundation for the development of anticancer therapies for cancers associated with tobacco use.

Abstract Cone-beam computed tomography (CBCT) is a crucial component of adaptive radiation therapy; however, it frequently encounters challenges such as artifacts and noise, significantly constraining its clinical utility. While CycleGAN is a widely employed method for CT image synthesis, it has notable limitations regarding the inadequate capture of global features. To tackle these challenges, we introduce a refined unsupervised learning model called improved vision transformer CycleGAN (IViT-CycleGAN). Firstly, we integrate a U-net framework that builds upon ViT. Next, we augment the feed-forward neural network by incorporating deep convolutional networks. Lastly, we enhance the stability of the model training process by introducing gradient penalty and integrating an additional loss term into the generator loss. The experiment demonstrates from multiple perspectives that our model-generated synthesizing CT(sCT) has significant advantages compared to other unsupervised learning models, thereby validating the clinical applicability and robustness of our model. In future clinical practice, our model has the potential to assist clinical practitioners in formulating precise radiotherapy plans.

ABSTRACT Understanding changes in the distribution patterns and diversity of soil microbial communities from the perspectives of age-related changes, seasonal variations, and the interaction between the two factors can facilitate the management of plantations. In Chinese fir plantations, we collected soils from different depths in over-mature forests, mature forests, near-mature forests, middle-aged forests, and young forests in summer, autumn, and winter in China’s subtropical regions. As the forests developed, bacterial and fungal communities’ diversity changed, reached a minimum value at near-mature forests, and then increased in mature forests or over-mature forests. Near-mature forests had the lowest topological properties. The Shannon index of microbial communities varied with seasonal changes (P < 0.05). Bacterial and fungal community composition at genus level was more closely related to temperature indicators (including daily average temperature, daily maximum temperature, and daily minimum temperature) (P < 0.01, 0.5554 < R2 0.05, 0.0321 < R2

Arsenic (As) contamination of agricultural soils poses a serious threat to crop productivity and food safety. Zinc oxide nanoparticles (ZnONPs) have emerged as a potential amendment for mitigating the adverse effects of As stress in plants. Soybean crop is mostly grown on marginalized land and is known for high accumulation of As in roots than others tissue. Therefore, this study aimed to elucidate the underlying mechanisms of ZnONPs in ameliorating arsenic toxicity in soybean. Our results demonstrated that ZnOB significantly improved the growth performance of soybean plants exposed to arsenic. This improvement was accompanied by a decrease (55%) in As accumulation and an increase in photosynthetic efficiency. ZnOB also modulated hormonal balance, with a significant increase in auxin (149%), abscisic acid (118%), gibberellin (160%) and jasmonic acid content (92%) under As(V) stress assuring that ZnONPs may enhance root growth and development by regulating hormonal signaling. We then conducted a transcriptomic analysis to understand further the molecular mechanisms underlying the NPs-induced As(V) tolerance. This analysis identified genes differentially expressed in response to ZnONPs supplementation, including those involved in auxin, abscisic acid, gibberellin, and jasmonic acid biosynthesis and signaling pathways. Weighted gene co-expression network analysis identified 37 potential hub genes encoding stress responders, transporters, and signal transducers across six modules potentially facilitated the efflux of arsenic from cells, reducing its toxicity. Our study provides valuable insights into the molecular mechanisms associated with metalloid tolerance in soybean and offers new avenues for improving As tolerance in contaminated soils.

Summary: Microorganisms are critical to the stability of aquatic environments, and understanding the ecological mechanisms of microbial community is essential. However, the distinctions and linkages across biogeographic patterns, ecological processes, and formation mechanisms of microbes in rivers and lakes remain unknown. Accordingly, microbiome-centric analysis was conducted in rivers and lakes in the Yangtze River watershed. Results revealed significant differences in the structure and diversity of microbial communities between rivers and lakes, with rivers showing higher diversity. Lakes exhibited lower community stability, despite higher species interactions. Although deterministic processes dominated microbial community assembly both in rivers and lakes, higher stochastic processes of rare and abundant taxa exhibited in rivers. Spatial factors influenced river microbial community, while environmental factors drove differences in the lake bacterial community. This study deepened the understanding of microbial biogeography and formation mechanisms in large watershed rivers and lakes, highlighting distinct community aggregation patterns between river and lake microorganisms.

[Objective] Digital transformation is an inevitable trend for urban rail transit enterprises during the shift from high-speed growth to high-quality development. It is necessary to study the top-level design, path selection, and implementation strategies for enterprise digital transformation. [Method] Taking the digital transformation practice of Nanjing Metro as example, the steps, working contents, main methods and tools used in the top-level design of digital planning are introduced. The overall framework and application schemes of Nanjing Metro digital transformation are expounded from five levels: digital base layer, digital application layer, digital service layer, security protection system, and IT governance and management system. The implementation strategy of Nanjing Metro digital transformation is made clear. [Result & Conclusion] Nanjing Metro digital transformation is ′customer-oriented′, focusing on digital application scenarios such as happy travel service for passengers, digital intelligent decision-making service for decision makers and managers, and digital collaboration service for employees. The overall architecture and application scheme of digital transformation is designed from the top level. By formulating a scientific and feasible implementation path, Nanjing Metro is advanced towards a ′digitalized, networking, intelligent, and smart′ high-quality development.

Current signal processing for synthetic aperture radar (SAR) cannot satisfy the requirements for high resolution, high squint angle, and wide swath due to the limitations of the traditional stop-and-go model. This model does not take into account the continuous motion between the satellite and the ground, leading to incomplete compensation for the corresponding phase. To address this issue, we first introduce an accurate range equation (ARE) based on the continuous satellite and ground motion, which allows for more precise slant range calculations. Second, we derive the point target spectrum according to the ARE, revealing additional phase components not considered in the traditional stop-and-go model. Third, we propose an advanced two-step processing algorithm to compensate for phase errors due to nonstop-and-go continuous motion. In the first-step processing, we eliminate range-independent components in the two-dimensional frequency domain. In the second-step processing, we compensate for residual phase errors related to range and azimuth frequency in the range-Doppler domain. We verified the proposed compensation method through simulation and compared it with the two-step compensation method used in high-resolution SAR data processing. Experimental results show that we provide an effective compensation method for high-resolution, high-squint-angle, wide-swath spaceborne SAR, which can correct azimuth shifts and solve the problem of defocusing at the edges of large scenes.

Polarimetric synthetic aperture radar (PolSAR) image classification is a critical application of remote sensing image interpretation. Most of the early algorithms that use hand-crafted features to divide the image into various scattering categories have a general classification performance. The convolutional neural networks (CNNs) show superior performance in image processing with powerful nonlinear representation capabilities. However, they also require a large amount of labeled training data, which limit the practical use of CNNs in PolSAR image classification scene where labeled data are rare and expensive. To address the previous issue, this article proposes a deep similarity clustering model with compound regularization for unsupervised PolSAR image classification. The proposed model combines an unsupervised feature extraction pipeline with Wishart distance metric and a deep clustering pipeline with feature similarity metric. The regularization combines two ingredients to preserve both the sharpness of edges and the semantic continuity of the image contents. The first is the differential constraint based on pixel-level features, and the second is the graph partition constraint based on superpixel-level features. Experiments prove the effectiveness of the proposed method on both spaceborne (RadarSat2) and airborne (E-SAR/UAVSAR) PolSAR images. The visual results and quantitative scores show that our method outperforms the traditional unsupervised methods and deep-learning-based unsupervised methods with a large margin.

Abstract Accumulated evidence showed that thyroid diseases induced cognitive decline. However, the relationship between thyroid hormones (THs) and cognition in older euthyroid people is still unclear. Our study aimed to estimate the association between THs within the euthyroid range and cognition in community-dwelling older adults in China. Data were extracted from a cohort study on the health status of rural older adults from the Guizhou province in China (HSRO). Serum thyroid-stimulating hormone (TSH), free thyroxine (FT4), and free triiodothyronine (FT3) were measured using the electrochemiluminescence immunoassay. Cognitive function was evaluated by the Mini-Mental State Examination (MMSE). Linear regression and a binary logistic regression model were used to explore the relationship between THs and cognition in euthyroidism (TSH level of 0.27 ~ 4.20mIU/L). A total of 957 euthyroidism individuals were included in this study, with a mean (SD) age of 71.34 (6.35) years. In individuals with euthyroidism, serum TSH and FT3 levels were positively associated with cognition (TSH:β = 0.06, 95% CI 0.01 ~ 0.11, P = 0.03; FT 3 :β = 0.07, 95% CI 0.01 ~ 0.12, P = 0.01); and serum FT3 and TSH levels were significantly associated with cognitive domains (P

Chlorellacean members are common in aquatic or subaerial habitats, and many of them have significant economic value. Taxonomic reports and organelle genome data for the Nannochloris clade, an important subgroup within this family, are limited, hindering the understanding and exploitation of this clade. In this study, a fusiform-celled strain, FACHB-3607, was isolated from a pond in China. Through examination of morphological characteristics and phylogenetic analyses of rbcL, 18S rDNA, and ITS, it was identified as a new species within the Nannochloris clade, named Koliella bifissiva sp. nov. In addition, this study provided a first insight into the organellar genomes of the genus Koliella. The K. bifissiva chloroplast had a 99.8 kb genome, and the mitochondrion had a 40.8 kb genome, which are moderate sizes within the Nannochloris clade. Phylogenomic analysis showed that K. bifissiva is most closely related to Nannochloris sp. “desiccata”, followed by Marvania. In contrast, Picochlorum was the most distantly related species. The organelle genomes of the Nannochloris clade display dynamic evolution, reflected in variations in genome size, gene content and order, and selection pressure. This research enhances our knowledge of species diversity and evolutionary history in the Nannochloris clade.

Abstract The commercial space industry seems to draw worldwide attention in the recent two decades to respond to the increasingly broad market demand. Compared with Western countries, the development of China’s commercial space industry is still in its infancy and faces many daunting challenges. Few studies have considered the dual perspective of government and market of commercial space industry in China. Aiming at exploring the influencing factors and future directions of the development of China’s commercial space industry, this paper proposed the theoretical analysis framework consisting of Policy, Innovation and Economics, and conducted a synergistic analysis of government and market in China’s commercial space industry. Specifically, based on the interview data, assisted by a machine learning approach, we have conducted an industry analysis of the influencing factors of commercial space in China. The results show that China’s commercial space industry is rapidly developing with many characteristics. Specifically, at present, there is a lack of specific and operational policies at the implementation level. Despite limited disruptive innovation, commercial space companies have achieved breakthroughs in many key areas. The overall industry is exploring more stable and sustainable profit models. The findings of this study contribute to the topic of industry development in business management literature by underpinning the policy discussion, technological analysis and direction for the future development of China’s commercial space industry. The industry practitioners may also benefit from the practices that were discussed in this research.

Infrared (IR) imaging-based detection systems are of vital significance in the domains of early warning and security, necessitating a high level of precision and efficiency in infrared small moving target detection. IR targets often appear dim and small relative to the background and are easily buried by noise and difficult to detect. A novel high-speed spatial–temporal saliency model (HS-STSM) based on a guided filter (GF) is proposed, which innovatively introduces GF into IR target detection to extract the local anisotropy saliency in the spatial domain, and substantially suppresses the background region as well as the bright clutter false alarms present in the background. Moreover, the proposed model extracts the motion saliency of the target in the temporal domain through vectorization of IR image sequences. Additionally, the proposed model significantly improves the detection efficiency through a vectorized filtering process and effectively suppresses edge components in the background by integrating a prior weight. Experiments conducted on five real infrared image sequences demonstrate the superior performance of the model compared to existing algorithms in terms of the detection rate, noise suppression, real-time processing, and robustness to the background.

The combined heating method of seat heating and air conditioning (A/C) was applied in the passenger compartment under different experiment conditions, using thermocouples to continuously measure the wall surfaces and air temperatures in the passenger compartment and the passengers’ skin temperatures of 17 segments. Meanwhile, a subjective evaluation questionnaire survey was conducted using a nine-point evaluation scale on the local and overall thermal sensation and thermal comfort of the passengers, and the data from the questionnaire were analyzed with the ANOVA method. The results showed that the use of the heating pad directly affected the changes in human skin temperature, which in turn affected the local and overall thermal sensation and thermal comfort. For the two thermally stimulated segments of the back and under the thighs, the skin temperature of the back was higher than that of the thighs. Using the heating pad resulted in a rapid increase in the mean skin temperature in the early period of the experiment. Thermal sensation of the back and under-thighs shifted rapidly towards the hot zone in the first 10 min, and then settled around +3, with even more significant differences between the groups. Thermal sensations in non-thermally stimulated segments changed in relation to their position on the heating pad, with slower changes in those at the “distal” end of the body, the head and the feet. Continued use of the heating pads at lower ambient temperatures maintained overall thermal comfort at a neutral level in the range of 0–1, whereas at higher ambient temperatures there was a gradual deterioration of local and overall thermal comfort.

The unique imaging modality of synthetic aperture radar (SAR) has posed significant challenges for object detection, making it more complex to acquire and interpret than optical images. Recently, numerous studies have proposed cross-domain adaptive methods based on convolutional neural networks (CNNs) to promote SAR object detection using optical data. However, existing cross-domain methods focus on image features, lack improvement on input data, and ignore the valuable supervision provided by few labeled SAR images. Therefore, we propose a semi-supervised cross-domain object detection framework that uses optical data and few SAR data to achieve knowledge transfer for SAR object detection. Our method focuses on the data processing aspects to gradually reduce the domain shift at the image, instance, and feature levels. First, we propose a data augmentation method of image mixing and instance swapping to generate a mixed domain that is more similar to the SAR domain. This method fully utilizes few SAR annotation information to reduce domain shift at image and instance levels. Second, at the feature level, we propose an adaptive optimization strategy to filter out mixed domain samples that significantly deviate from the SAR feature distribution to train feature extractor. In addition, we employ Vision Transformer (ViT) as feature extractor to handle the global feature extraction of mixed images. We propose a detection head based on normalized Wasserstein distance (NWD) to enhance objects with smaller effective regions in SAR images. The effectiveness of our proposed method is evaluated on public SAR ship and oil tank datasets.

IntroductionWe aimed to evaluated the effect of premixed insulin (Ins), premixed insulin combined with metformin (Ins+Met) or mulberry twig alkaloids(Ins+SZ-A) on blood glucose fluctuations in patients with type 2 diabetes (T2DM) using continuous glucose monitors (CGM).MethodsThirty patients with T2DM and poor blood glucose control using drugs were evaluated for eligibility during the screening period. Subsequently, their original hypoglycemic drugs were discontinued during the lead-in period, and after receiving Ins intensive treatment for 2 weeks, they were randomly assigned to receive either Ins, Ins+Met, or Ins+SZ-A treatment for the following 12 weeks. The main efficacy endpoint comprised changes in their CGM indicators changes (mean blood glucose level [MBG], standard deviation of blood glucose [SDBG], mean amplitude of glycemic excursions [MAGE], postprandial glucose excursions [PPGE], the largest amplitude of glycemic excursions [LAGE], mean of daily difference [MODD], time in range between 3.9–10.0 mmol/L [TIR] and area under the curve for each meal [AUCpp]) during the screening, lead-in, and after 12-week treatment period. Changes in glycosylated hemoglobin (HbA1c), fasting blood glucose (FBG), 1-h postprandial blood glucose (1h-PBG), 2-h postprandial blood glucose (2h-PBG), fasting blood lipids and postprandial blood lipids were also measured at baseline and after 12 weeks of treatmentResultsThe CGM indicators of the three groups during the lead-in period all showed significant improvements compared to the screening period (P

Tree mortality is a complex process that not only be affected by the various factors, such as stand and climate factors, but also the various long-term effects of the factors to each other. In this study, based on the long-term spacing trials of Chinese fir in four regions of southern China, a Bayesian network was used to model tree mortality in response to stand and climate factors, as well as comparing this approach with logistic regression and random forest method. The results showed that the Bayesian network method had the highest accuracy in predicting tree mortality. In addition, the Bayesian network approach could find the dependency in the relationship between data and provide a theoretical framework for modeling uncertainty by using probabilistic calculus and underlying graph structure. Sensitivity analysis showed relative diameter was the most important factor, and temperature was the most important climate factor. Furthermore, climate factors not only directly affected tree mortality, but also indirectly affected tree mortality through affecting relative diameter, stand density and Gini coefficient. We also found that stand competition, structural heterogeneity and age affected tree mortality under climate change, and a moderate level of competition condition and stand structure heterogeneity weakened the negative impact of climate factors on tree mortality. Old trees were more sensitive to climate change than young trees, especially under extreme climate conditions. Besides, we found that tree mortality was negatively correlated with moderate annual precipitation, winter mean minimum temperature, and stand structure (Gini), and low age, but positively correlated with low relative diameter, high density and age. The results will provide adaptive options for effective forest management of Chinese fir plantations under the backdrop of global climate change in the future.

Geometric structural information of objects in synthetic aperture radar (SAR) images is of great significance in target detection and analysis. The existing orthorectification methods of SAR images generally follow the traditional routes of optical images, resulting in suffering from the influence of layover, and bring difficulties in image analysis or target extraction without sufficient prior knowledge. Therefore, this article proposes a highest-projection-principle-based SAR true digital ortho map (TDOM) production method for the restoration of geometric structural information of targets. First, ground control information extracted from the high-precision digital surface model is applied to calibrate the geolocation error of original SAR images. Second, the highest projection principle and the visibility detection are utilized to reduce the influence of layover. To preserve the structural information of targets, multiview SAR TDOMs are integrated and compensated to generate a fused SAR TDOM image similar to optical view. Two Gaofen-3 (GF-3) SAR spotlight images are included to verify the performance of our proposed method. Experimental results are compared qualitatively in visual effects and quantitatively in geolocation accuracy. The results indicate that the produced SAR TDOMs can preserve the structural information of targets and provide more similar geometric structural information as optical images, which will effectively improve the efficiency in the interpretation of orthorectified SAR images.

Currently, massive Synthetic Aperture Radar (SAR) images acquired from numerous SAR satellites have been widely utilized in various fields, and image mosaicking technology provides important support and assistance for these applications. The traditional mosaic method selects specific SAR images that can cover the region of interest (ROI) from redundant data to produce “One Map”. However, an SAR image suffers from severe geometric distortion, especially in mountainous areas, which inevitably reduces the utilization of mosaic image. Therefore, a multi-view data-based layover information compensation (MDLIC) method for SAR image mosaic is proposed, aiming to take full advantage of multi-view data to compensate for the missing information in the layover area of the SAR image. This is performed to improve the information content of the mosaic image and realize efficient thematic information extraction and analysis. First, the calculation of the object-space extent of all images and the division of object-space grid are completed on the basis of geometric and radiometric preprocessing. Then, according to the transformation relationship between the object-space and the image-space, the sampling rate image of each image corresponding to the object-space grid is generated, which determines the layover area and the layover degree in each image. Finally, the information compensation strategy is implemented in accordance with the sampling rate image to realize the compensation of the layover information. The feasibility and effectiveness of the MDLIC method are verified by using multiple SAR images from the Chinese Gaofen-3 01 satellite as datasets for experiments. The experimental results indicate that the MDLIC method can obtain mosaic images with richer information compared with the traditional method, while still providing satisfactory results.

Ship recognition with optical remote sensing images is currently widely used in fishery management, ship traffic surveillance, and maritime warfare. However, it currently faces two major challenges: recognizing rotated targets and achieving fine-grained recognition. To address these challenges, this paper presents a new model called Related-YOLO. This model utilizes the mechanisms of relational attention to stress positional relationships between the components of a ship, extracting key features more accurately. Furthermore, it introduces a hierarchical clustering algorithm to implement adaptive anchor boxes. To tackle the issue of detecting multiple targets at different scales, a small target detection head is added. Additionally, the model employs deformable convolution to extract the features of targets with diverse shapes. To evaluate the performance of the proposed model, a new dataset named FGWC-18 is established, specifically designed for fine-grained warship recognition. Experimental results demonstrate the excellent performance of the model on this dataset and two other public datasets, namely FGSC-23 and FGSCR-42. In summary, our model offers a new route to solve the challenging issues of detecting rotating targets and fine-grained recognition with remote sensing images, which provides a reliable foundation for the application of remote sensing images in a wide range of fields.

Abstract The activation of pancreatic stellate cells (PSCs) is the key mechanism of pancreatic fibrosis, which can lead to β-cell failure. Oxidative stress is an important risk factor for PSC activation. There is no direct evidence proving if administration of glutathione can inhibit fibrosis and β-cell failure. To explore the role of glutathione in pancreatic fibrosis and β-cell failure induced by hyperglycaemia, we established a rat model of pancreatic fibrosis and β-cell failure. The model was founded through long-term oscillating glucose (LOsG) intake and the setup of a sham group and a glutathione intervention group. In vitro, rat PSCs were treated with low glucose, high glucose, or high glucose plus glutathione to explore the mechanism of high glucose-induced PSC activation and the downstream effects of glutathione. Compared with sham rats, LOsG-treated rats had higher reactive oxygen species (ROS) levels in peripheral leukocytes and pancreatic tissue while TGFβ signalling was upregulated. In addition, as the number of PSCs and pancreatic fibrosis increased, β-cell function was significantly impaired. Glutathione evidently inhibited the upregulation of TGFβ signalling and several unfavourable outcomes caused by LOsG. In vitro treatment of high glucose for 72 h resulted in higher ROS accumulation and potentiated TGFβ pathway activation in PSCs. PSCs showed myofibroblast phenotype transformation with upregulation of α-SMA expression and increased cell proliferation and migration. Treatment with either glutathione or TGFβ pathway inhibitors alleviated these changes. Together, our findings suggest that glutathione can inhibit PSC activation-induced pancreatic fibrosis via blocking ROS/TGFβ/SMAD signalling in vivo and in vitro.

With the rapid development of China’s aerospace information acquisition technology, aerospace data presents the characteristics of large data volume, large number of types, rapid growth and relatively low value density.Remote sensing satellite data is an important part of aerospace big data.How to make use of the scale effect and the complementary advantages of the data of different satellites, so as to improve the processing accuracy and efficiency, is the key problem to be solved in the remote sensing satellite big data processing system.The development history of our country’s remote sensing satellite ground data processing system was reviewed briefly.The core difficulties faced by the ground processing system were pointed out.A high-precision processing technology for remote sensing satellite big data based on stability feature mining was proposed.And a preliminary implementation was given.The method provides a useful reference for the development of our country’s aerospace big data processing system.

On-board processing is increasingly prevalent due to its efficient utilization of satellite resources. Among these resources, geometric rectification can significantly enhance positioning accuracy for subsequent tasks, such as object detection. This approach mitigates the heavy burden on downlink bandwidth and minimizes time delays by transmitting targeted patches rather than raw data. However, existing rectification methods are often unsuitable due to the limitations and conditions imposed on satellites. Factors like hardware quality, heat dissipation, storage space, and geographic positioning are frequently constrained and prone to inaccuracies. This paper proposes a novel on-board rectification method. The method introduces a two-step matching framework to address substantial positioning errors and incorporates a feature-compression strategy to reduce the storage space of reference patches. Quantitative and practical experiments demonstrate the method’s efficacy in terms of storage space, time efficiency, and geometric rectification accuracy.

Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes.

To a certain extent, SAR images are affected by range ambiguity due to antenna sidelobe characteristics and pulse operating system. The work of range ambiguity suppression focuses on SAR system design and signal processing. One type of idea tries to modify the way of transmitting and receiving to block the receiving of the ambiguous energy, such as multiple elevation beams and azimuth phase coding. The other ideas are algorithms that use signal processing technology to reduce the distance ambiguity energy in echo and image domains. This paper proposes a range ambiguity suppression method that combines sparse reconstruction and matched filtering. The method performs the sparse reconstruction of the ambiguity area, estimates the ambiguity area signal using the ambiguity area image and reconstruction matrix, separates it from the echo signal to obtain the primary image signal after range ambiguity suppression, and uses matched filtering to obtain the main area image. In this method, sparse reconstruction ensures the accuracy of fuzzy signal estimation, and matched filtering ensures the efficiency of imaging processing. Simulation results show that the proposed method can effectively suppress range ambiguity, with a suppression effect of 10 dB or higher, and it has a good ability to maintain the weak targets and the details of the main image.

An innovative approach, mainly featured by progressive iteration of partitioning and focusing, to processing very-high-resolution spaceborne synthetic aperture radar (SAR) data is presented in this article. Due to the long integration time endured during data acquisition as well as large scene extensions being common to the advanced SAR systems, spatial dependence of range histories may be severe and must be properly dealt with. In this article, after deriving the exact two dimensional spectrum for an curved satellite orbit, we focus the entire echo data through several iterations of partitioning and focusing. By partitioning, each partitioned data block can be better focused by the following focusing step, and meanwhile, focusing with better quality enables the following partitioning with both higher granularity and less overlapped data region. Besides, a new technique aiming at eliminating spatial dependence inside a data block is also presented and deployed in the processing procedure. Owing to the feature of high granularity partitioning, the spatial dependence of many factors can be accommodated finer and easier compared with other conventional algorithms. What is more, the artifacts caused by subaperture recombination are fundamentally avoided by the fact that the partitioned blocks are not recombined until being processed into fully focused image blocks. Finally, the precision and the efficiency of the proposed methodology are validated by results on a simulated and a real SAR data.

Object detection under weakly supervised learning is a challenging issue. In a remote sensing ship detection task, the weakly supervised learning method requires that the training set only has image-level class annotations. In the absence of location information, it is difficult to locate ships and extract features. Moreover, when the detector extracts more than one candidate region, the image-level annotation makes it difficult to determine whether the candidate regions are multiple ships or mixed with the detected background. To address these issues, this article analyzes the interaction between class information and location information, and proposes a weakly supervised detection method, i.e., PistonNet, based on data-efficient image transformers. PistonNet proposes an artificial point, which is inserted into the feature map. Artificial point can suppress the background and enhance the object by interfering in the weight distribution of object and background in self-attention calculation. PistonNet also proposes joint confidence probability to improve detection accuracy. Experiments on the GF1-LRSD and NWPU VHR-10 datasets show that the proposed method boosts detection performance effectively. The main improvements of PistonNet as well as the contributions of this article are threefold. First, PistonNet is a specific weakly supervised object detection method for single-class detection, which provides an innovative approach to classifying target and background. Second, PistonNet reaches the level of advanced supervised detectors on detection accuracy with fewer parameters. Finally, the objects’ locations detected by PistonNet are obtained by segmenting regions on the heat map. PistonNet’s background suppression ability makes it free from dependence on segmentation threshold.

Colonial Volvocine Algae (CVA) are of great significance for biological evolution study, but little is presently known about their biogeographic distribution. Meanwhile, with the impact of climate change and human activities, their effects on the distribution and structures of CVA communities also remain largely unknown. Herein, the biogeography of CVA was investigated in the Yangtze River basin, 172 sampling sites were set up within a catchment area of 1,800,000 km2, and the distribution and community composition of CVA were studied using single-molecule real-time sequencing and metabarcoding technology based on the full-length 18S sequence. In 76 sampling sites, CVA was discovered in two families, eight genera, and nine species. Eudorina and Colemanosphaera were the main dominant genus. Based on the result of the random forest model and Eta-squared value, the distribution of CVA was significantly influenced by water temperature, altitude, and TP. CVA could be suitably distributed at an average water temperature of 22°C, an average TP concentration of 0.06 mg/L, and an altitude lower than 3,920 m. To assess the effects of anthropogenic pollution on the structures and co-occurrence patterns of CVA communities, we used a stress index calculated by 10 environmental factors to divide the CVA community into low and high pollution group. Network analysis showed that greater pollution levels would have a negative impact on the co-occurrence patterns and diversity of the CVA community. Finally, to study the scientific distribution of CVA under current and future climate change scenarios, we analyzed the climate suitability regionalization of CVA with the maximum entropy model based on 19 climatic factors and four climate scenarios from 2021 to 2040 published by CMIP6. Our results reveal the suitable areas of CVA, and temperature is an important environmental factor affecting the distribution of CVA. With the change of climate in the future, the Three Gorges Reservoir Area, Chaohu Lake, and Taihu Lake are still highly suitable areas for CVA, but the habitat of CVA may be fragmented, and more thorough temporal surveys and sampling of the sediment or mud are needed to investigate the fragmentation of CVA.

Intelligent confrontation has become a vital technology for future air combats. Confrontation games between a penetrating aircraft and an intercepting aircraft are essential for modern air combats. In addition, the performance indexes of both the interceptor and penetrator must be considered. Traditional methods only solve one side's guidance problem without considering the intelligence of the opponent. In this paper, an adaptive heuristic dynamic programming-based algorithm is proposed for aircraft confrontation games. This algorithm constructs a heuristic dynamic programming model for both confrontation aircraft and then updates the critical and action network parameters using the dynamic confrontation state information. Numerical simulations indicate that the proposed algorithm can optimize the guidance law for both the interceptor and penetrator and is therefore superior to traditional proportional navigation methods.

Abstract Background The order Oedogoniales within the single family Oedogoniaceae comprised of three genera, Oedogonium, Oedocladium, and Bulbochaete based on traditional morphological criteria. While several molecular phylogenetic studies have suggested that both Oedogonium and Oedocladium may not be monophyletic, broader taxon sampling and large amounts of molecular data acquisition could help to resolve the phylogeny and evolutionary problems of this order. This study determined five chloroplast (cp) genomes of Oedogonium species and aimed to provide further information on cp genome for a better understanding of the phylogenetic and evolutionary relationships of the order Oedogoniales. Results The five Oedogonium cp genomes showed typical quadripartite and circular structures, and were relatively conserved in their structure, gene synteny, and inverted repeats boundaries in general, except for small variation in genome sizes, AT contents, introns, and repeats. Phylogenetic analyses based on 54 cp protein-coding genes examined by maximum likelihood and Bayesian analyses using amino acid and nucleotide datasets indicated that both Oedocladium and Oedogonium are polyphyletic groups. A positively selected gene (psbA) was identified in the two Oedocladium species and the terrestrial Oedogonium species, indicating that terrestrial Oedogoniales taxa may have undergone adaptive evolution to adjust to the difference in light intensity between aquatic and terrestrial habitats. Conclusions Our results enrich the data on cp genomes of the genus Oedogonium. The availability of these cp genomes can help in understanding the cp genome characteristics and resolve phylogenetic and evolutionary relationships of the order Oedogoniales.

Lyophyllum decastes is a common mushroom that is prone to browning during prolonged storage. In this study, the effects of ultrasonic treatment on metabolic gene expression, enzyme activity, and metabolic compounds related to L. decastes browning were investigated. Treatment of the fruiting body at 35 kHz and 300 W for 10 min reduced the browning index of L. decastes by 21.0 % and increased the L* value by 11.1 %. Ultrasonic treatment of the fruiting body resulted in higher levels of total phenols, flavonoids, and 9 kinds of amino acid with catalase (CAT) and peroxidase (POD) activities maintained at high levels. Higher cytochrome c oxidase (CCO), succinate dehydrogenase (SDH), phosphofructokinase (PFK), and pyruvate kinase (PK) activities may be ascribed to increased antioxidant capacity. Moreover, ultrasonication retained higher adenosine triphosphate (ATP) concentrations with an increased energy charge, while there were lower levels of adenosine diphosphate (ADP) and reduced and oxidized nicotinamide adenine dinucleotide (NADH and NAD+), respectively. Meanwhile, lower lignin contents were observed, along with retarded polyphenol oxidase (PPO) and lipoxygenase (LOX) activities. Lower PPO activity reduced the fruiting body enzymatic browning rate through decreased expression of LdPpo1, LdPpo2, and LdPpo3 during storage at 4 °C for 16 days. This activity may be used to determine the effectiveness of ultrasonication.

Synthetic aperture radar (SAR) automatic target recognition (ATR) has been widely applied in multiple fields. However, the special imaging mechanism of SAR results in different visual features of the same target at different azimuth angles, so single-aspect SAR target recognition has the limitation of observing the target from a single perspective. Multi-aspect SAR target recognition technology can overcome this limitation by utilizing information from different azimuths and effectively improve target recognition performance. Considering the order dependency and data limitation of existing methods, this paper proposes a multi-aspect SAR recognition method based on Non-Local, which applies a self-attention calculation to feature maps to learn the correlation between multi-aspect SAR images. Meanwhile, in order to improve the generalization ability of the proposed method under limited data, a network based on contrastive learning was designed to pre-train the feature extraction part of the whole network. The experimental results using the MSTAR dataset show that the proposed method has excellent recognition accuracy and good robustness.

Abstract Background Long noncoding RNAs (lncRNAs) have been shown to play important roles in the regulation of plant growth and development. Recent transcriptomic analyses have revealed the gene expression profiling in wheat spike development, however, the possible regulatory roles of lncRNAs in wheat spike morphogenesis remain largely unclear. Results Here, we analyzed the genome-wide profiling of lncRNAs during wheat spike development at six stages, and identified a total of 8,889 expressed lncRNAs, among which 2,753 were differentially expressed lncRNAs (DE lncRNAs) at various developmental stages. Three hundred fifteen differentially expressed cis- and trans-regulatory lncRNA-mRNA pairs comprised of 205 lncRNAs and 279 genes were predicted, which were found to be mainly involved in the stress responses, transcriptional and enzymatic regulations. Moreover, the 145 DE lncRNAs were predicted as putative precursors or target mimics of miRNAs. Finally, we identified the important lncRNAs that participate in spike development by potentially targeting stress response genes, TF genes or miRNAs. Conclusions This study outlines an overall view of lncRNAs and their possible regulatory networks during wheat spike development, which also provides an alternative resource for genetic manipulation of wheat spike architecture and thus yield.

The Mediterranean diet (MD) is a healthy diet pattern that can prevent chronic age-related diseases, especially age-related eye diseases (AREDs) including cataract, glaucoma, age-related macular degeneration (AMD), diabetic retinopathy (DR) and dry eye syndrome (DES). In this study, we systematically reviewed studies in the literature that had reported associations between adherence to the MD and the five above-mentioned AREDs. Randomized controlled trials as well as prospective and retrospective observational studies were included; 1164 studies were identified, of which 1, 2, 9, 2 and 4 studies met our eligibility criteria for cataract, glaucoma, AMD, DR, and DES, respectively. According to these studies, higher MD adherence was associated with reduced risks of incident DR, incident AMD and progression to late AMD, but whether early and neovascular AMD could be alleviated remained to be debated. The results regarding the effects of the MD on DES were mixed, with three studies reporting an associations between MD and decreased severity or incidence of DES, whereas one study reported the opposite. No significant associations were observed between the MD and cataract or glaucoma. Generally, convincing evidence suggested a protective effect of the MD against AMD and DR. However, the evidence for cataract, glaucoma, and DES was less conclusive, and high-quality studies are needed for comprehensive evaluations of the potential benefits of MD on these eye diseases.

In the combat system, infrared target detection is an important issue worthy of study. However, due to the small size of the target in the infrared image, the low signal-to-noise ratio of the image and the uncertainty of motion, how to detect the target accurately and quickly is still difficult. Therefore, in this paper, an infrared method of detecting small moving targets based on a coarse-to-fine structure (MCFS) is proposed. The algorithm mainly consists of three modules. The potential target extraction module first smoothes the image through a Laplacian filter and extracts the prior weight of the image by the proposed weighted harmonic method to enhance the target and suppress the background. Then, the local variance feature map and local contrast feature map of the image are calculated through a multiscale three-layer window to obtain the potential target region. Next, a new robust region intensity level (RRIL) algorithm is proposed in the spatial-domain weighting module. Finally, the temporal-domain weighting module is established to enhance the target positions by analyzing the kurtosis features of temporal signals. Experiments are conducted on real infrared datasets. Through scientific analysis, the proposed method can successfully detect the target, at the same time, the ability to suppress the background and the ability to improve the target has reached the maximum, which verifies the effectiveness of the algorithm.

False targets caused by multichannel Synthetic Aperture Radar (SAR) are similar to a defocused ship in both shape and texture, making it difficult to discriminate in the full-aperture SAR image. To address the issue of false alarms caused by such false targets, this paper proposes a multichannel SAR false-target discrimination method based on sub-aperture and full-aperture feature learning. First, amplitude calculation is performed on complex SAR images to obtain the amplitude images, and transfer learning is utilized to extract the full-aperture features from the amplitude images. Then, sub-aperture decomposition is performed on complex SAR images to obtain a series of sub-aperture images, and the Stacked Convolutional Auto-Encoders (SCAE) are applied to extract the sub-aperture features from the sub-aperture images. Finally, the sub-aperture and the full-aperture features are concatenated to form the joint features, which are used to accomplish target discrimination. The accuracy of the method proposed in this paper is 16.32% higher than that of the approach only using the full-aperture feature on GF-3 UFS SAR images.

Deep convolutional neural networks (DCNNs) have attracted extensive attention in synthetic aperture radar (SAR) image target detection. However, there is usually a lack of labeled data in SAR images which results in difficulties to improve the detection performance with DCNN based methods. Transfer learning borrows generic knowledge from other tasks for the target task and can be an effective solution to the small sample problem, but the features' generality and specificity across various models are not discussed in target detection. In addition, conventional DCNN based models have too many parameters related to the prior information of the targets which increase the difficulty of transfer learning between different tasks. In this paper, we mainly conduct research in three folds to address these mentioned issues. First, the generality and specificity of features in different network models are analyzed in the field of SAR target detection. Second, a transitive transfer framework is developed for detection under small sample condition. Third, an anchor free rotatable detector with flexible structure is designed for SAR images detection to suit the transfer detection framework proposed in this paper and a reasonable explanation is given for its upper hand with small samples. Experimental results demonstrate that: 1) transitive transfer framework is valid in SAR target detection; 2) the anchor free detector in this framework is superior to the anchor based one; 3) the proposed detector achieves better performance under small sample condition by comparison with state-of-the-art methods.

Chronic arsenic exposure is associated with the increased risk of several types of cancer, among which, lung cancer is the most deadly one. Nuclear factor erythroid 2 like 1 (NFE2L1), a transcription factor belonging to CNC-bZIP family, regulates multiple important cellular functions in response to acute arsenite exposure. However, the role of NFE2L1 in lung cancer induced by chronic arsenite exposure is unknown. In this study, we firstly showed that chronic arsenite exposure (36 weeks) led to epithelial-mesenchymal transition (EMT) and malignant transformation in human bronchial epithelial cells (BEAS-2B). During the process of malignant transformation, the expression of long isoforms of NFE2L1 (NFE2L1-L) was elevated. Thereafter, BEAS-2B cells with NFE2L1-L stable knockdown (NFE2L1-L-KD) was chronically exposed to arsenite. As expected, silencing of NFE2L1-L gene strikingly inhibited the arsenite-induced EMT and the subsequent malignant transformation. Additionally, NFE2L1-L silencing suppressed the transcription of EMT-inducer SNAIL1 and increased the expression of E-cadherin. Conversely, NFE2L1-L overexpression increased SNAIL1 transcription but decreased E-cadherin expression. Collectively, our data suggest that NFE2L1-L promotes EMT by positively regulating SNAIL1 transcription, and is involved in malignant transformation induced by arsenite.

In the early stage of coronavirus disease 2019 (COVID-19), most cases are identified as mild or moderate illnesses. Approximately 20% of hospitalised patients become severe or critical at the middle or late stage of the disease. The predictors and risk factors for prognosis in those with mild or moderate disease remain to be determined. Of 694 patients with COVID-19, 231 patients with mild or moderate disease, who were hospitalised at 10 hospitals in Wenzhou and nearby counties in China, were enrolled in this retrospective study from 17 January to 20 March 2020. The outcomes of these patients included progression from mild/moderate illness to severe or critical conditions. Among the 231 patients, 49 (21.2%) had a poor prognosis in the hospital. Multivariate logistic regression analysis showed that higher inflammation/coagulopathy/immunology responsive index (ICIRI=[c-reactive protein × fibrinogen × D-dimer]/CD8 T cell count) on admission (OR=345.151, 95% CI=23.014−5176.318) was associated with increased odds ratios for poor prognosis. The area under the receiver operating characteristic curve for ICIRI predicting severe and critical condition progression was 0.65 (95% CI=0.519−0.782) and 0.80 (95% CI=0.647−0.954), with cut-off values of 870.83 and 535.44, respectively. Conversely, age, sex, comorbidity, neutrophil/lymphocyte ratio, CD8 T cell count, and c-reactive protein, fibrinogen, and D-dimer levels alone at admission were not good predictors of poor prognosis in patients with mild or moderate COVID-19. At admission, a novel index, ICIRI, tends to be the most promising predictor of COVID-19 progression from mild or moderate illness to severe or critical conditions.

Abstract Background Order Chaetophorales currently includes six families, namely Schizomeridaceae, Aphanochaetaceae, Barrancaceae, Uronemataceae, Fritschiellaceae, and Chaetophoraceae. The phylogenetic relationships of Chaetophorales have been inferred primarily based on short and less informative rDNA sequences. This study aimed to phylogenetically reconstruct order Chaetophorales and determine the taxonomic scheme, and to further understand the evolution of order Chaetophorales. Results In the present study, seven complete and five fragmentary chloroplast genomes were harvested. Phylogenomic and comparative genomic analysis were performed to determine the taxonomic scheme within Chaetophorales. Consequently, Oedogoniales was found to be a sister to a clade linking Chaetophorales and Chaetopeltidales. Schizomeriaceae, and Aphanochaetaceae clustered into a well-resolved basal clade in Chaetophorales, inconsistent with the results of phylogenetic analysis based on rDNA sequences. Comparative genomic analyses revealed that the chloroplast genomes of Schizomeriaceae and Aphanochaetaceae were highly conserved and homologous, highlighting the closest relationship in this order. Germination types of zoospores precisely correlated with the phylogenetic relationships. Conclusions chloroplast genome structure analyses, synteny analyses, and zoospore germination analyses were concurrent with phylogenetic analyses based on the chloroplast genome, and all of them robustly determined the unique taxonomic scheme of Chaetophorales and the relationships of Oedogoniales, Chaetophorales, and Chaetopeltidales.

Congestion in airports and metroplexes is increasingly becoming a key bottleneck in the global air transport system. It is largely due to inefficient utilization of runway resources and its consequences of imbalance between demand and capacity. Existing studies mainly focus on runway configuration in a single airport system, and little consideration is given to the impact of demand management on runway configuration in metroplexes. Therefore, this paper proposes a methodology and assessment framework for runway configuration with a focus on the exploitation of multiple active runways in metroplex airports. The primary innovation is an integrated runway configuration management formulation with air traffic demand management options. The first is static and dynamic runway configuration management models featuring three optimization objectives, four cases of demand-capacity imbalance, three air traffic demand management options and three assessment scenarios, for eleven priority settings. The second is an efficient multi-objective evolutionary algorithm with a mechanism of objective-guided individual selection, which can obtain close-to optimal solutions with a very low computational cost within 6 seconds. Computational experiments for the real-world case of the Shanghai metroplex airports show that, the inducing strategy of minimizing the total number of adjusted flights is the best mechanism for making satisfactory tradeoffs among multiple objectives in dynamic runway configuration. The proposed model reduces the total number of adjusted flights by an average of 36% compared with the baseline static runway configuration management. Furthermore, unlike the conventional approach of giving priority to arriving aircraft, a higher priority for departures is more effective in enhancing the performance of runway systems and reducing the number of adjusted flights. The proposed framework can be applied at pre-tactical (e.g., one-day planning) as well as tactical (e.g., several-hours rolling horizon) levels.

Azimuth multichannel (AMC) synthetic aperture radar (SAR) is an advanced technology that can prevent the minimum antenna area constraint and provide high-resolution and wide-swath SAR images. The calibration of phase imbalance is an important topic in the AMC SAR signal processing since it has a significant impact on the image quality. For one SAR image, the phase imbalance is usually considered as a constant. However, because of the attitude errors, antenna position errors, target elevation, target motion, and phase mismatch of the antenna pattern, the actual phase imbalance is time-varying in azimuth and space-varying in range. Although the space-time variation of phase imbalances is too tiny to make a little effect on the product quality, it is still meaning to study the variation. On the one hand, it is possible to quickly estimate the phase imbalance of the whole observing operation and eliminate the influences of target motion, on the phase imbalance. On the other hand, some parameters, such as motion, can be retrieved by the phase imbalance. This article first establishes the signal models between phase imbalances and attitude errors, antennal position errors, and target elevation. Then, the signal model is verified by some simulations based on the parameters of Gaofen-3 (GF-3). In addition, the phase imbalance of the real data acquired by GF-3 is processed. Finally, based on the procession results of GF-3 real data, this article makes some discussions and points out the direction for the future work.

Study region: Urban built-up area of Zhengzhou, China. Study focus: A coupled urban flood forecasting model based on WRF-SWMM is proposed. Different spatial resolution data as input for WRF (Weather Research and Forecasting Model) to study the characteristics of forecasting precipitation, combined with measured precipitation series, the SWMM (Storm Water Management Model) was driven to forecast the urban flood process. New hydrological insights for the study region: Using the WRF-SWMM coupling model to simulate flood disasters can effectively improve the timeliness of urban flood prediction. The results indicate that a spatial resolution of 0.25-degree improved the overall precipitation prediction results. With an increase in lead time, the prediction performance of the WRF decreased. The WRF model can simulate results effectively owing to its sensitivity to bimodal rainfall patterns. When the WRF model produces an overestimate (or underestimate), part of the urban canals in the SWMM model will also carry out a high forecast (or low forecast) output of flood peak discharge. The SWMM model also has a cumulative amplification effect on the results of flood simulation. In addition, the quantitative accuracy of precipitation forecasts plays a vital role in urban flood simulation, and the existing coupling models based on WRF-SWMM can generate reasonable forecasts 3–6 h in advance.

Introduction: It is known that the metabolic disorder caused by high glucose is one of pathogenesis in diabetic retinopathy (DR), the leading cause of blindness, due to the main pathological change of apoptosis of endothelial cells (ECs). In previous studies, the potential impact of sodium glucose cotransporter-2 (SGLT-2), whose inhibitors slow the progression of DR, has not been elucidated. The purpose of the presented study was to explore the effect of SGLT-2 inhibitors dapagliflozin (DAPA) on apoptosis of diabetic mice retina and human retinal microvascular endothelial cells (HRMECs), examine the effects of dapagliflozin on HRMECs metabolism, and explore the molecular processes that affect DR.Methods and Results: The eyeballs of male streptozotocin (STZ)-induced diabetic C57BL/6N mice were evaluated. C57BL/6N mice were divided into control group (CON), diabetic untreated group (DM), diabetic dapagliflozin treatment group (DM + DAPA) and diabetic insulin treatment group (DM + INS). Hematoxylin-Eosin (HE) staining was performed to observe the pathological structure of the mice retina, and TUNEL staining to detect apoptosis of mice retinal cells. In vitro, DCFH-DA and western blot (WB) were used to evaluate ROS, Bcl-2, BAX, cleaved-caspase 3 in HRMECs and metabolomics detected the effect of dapagliflozin on the metabolism of HRMECs. And then, we performed correlation analysis and verification functions for significantly different metabolites. In vivo, dapagliflozin reduced the apoptosis of diabetic mice retina independently of hypoglycemic. In vitro, SGLT-2 protein was expressed on HRMECs. Dapagliflozin reduced the level of ROS caused by high glucose, decreased the expression of cleaved-caspase3 and the ratio of BAX/Bcl-2. Metabolomics results showed that dapagliflozin did not affect the intracellular glucose level. Compared with the high glucose group, dapagliflozin reduced the production of arachidonic acid (AA) and inhibited the phosphorylation of ERK1/2, therefore, reducing the phosphorylation of cPLA2, which is a key enzyme for arachidonic acid release.Conclusion: Collectively, results unearthed for the first time that dapagliflozin reduced apoptosis of retina induced by DM whether in vivo or in vitro. Dapagliflozin did not affect the glucose uptake while mitigated intracellular arachidonic acid in HRMECs. Dapagliflozin alleviated HRMECs apoptosis induced by high glucose through ERK/1/2/cPLA2/AA/ROS pathway.

Chronic exposure to arsenic has been associated with a variety of cancers with the mechanisms undefined. Arsenic exposure causes alterations in metabolites in bio-samples. Recent research progress on cancer biology suggests that metabolic reprogramming contributes to tumorigenesis. Therefore, metabolic reprogramming provides a new clue for the mechanisms of arsenic carcinogenesis. In the present manuscript, we review the latest findings in reprogramming of glucose, lipids, and amino acids in response to arsenic exposure. Most studies focused on glucose reprogramming and found that arsenic exposure enhanced glycolysis. However, in vivo studies observed “reverse Warburg effect” in some cases due to the complexity of the disease evolution and microenvironment. Arsenic exposure has been reported to disturb lipid deposition by inhibiting lipolysis, and induce serine-glycine one-carbon pathway. As a dominant mechanism for arsenic toxicity, oxidative stress is considered to link with metabolism reprogramming. Few studies analyzed the causal relationship between metabolic reprogramming and arsenic-induced cancers. Metabolic alterations may vary with exposure doses and periods. Identifying metabolic alterations common among humans and experiment models with human-relevant exposure characteristics may guide future investigations.

Ganoderma resinaceum is a valuable Chinese medicine. This study aimed to investigate whether a G. resinaceum alcohol extract (GRAE) improves sleep, and analyze the potential mechanism. After 30 days of continuous administration of GRAE at various doses, GRAE (1,000 mg/kg.bw) prolonged pentobarbital sodium-induced sleep, increased the rate of sleeping in mice treated with a subthreshold dose of pentobarbital sodium, and shortened sleep latency. The mice brain was analyzed using UPLC-MS/MS and RNA-sequencing. Metabolomics analysis revealed that 73 metabolites in the high-dose (HD) group had changed significantly, mainly in amino acids and their derivatives, especially the accumulation of L-glutamine and PGJ2 (11-oxo-15S-hydroxy-prosta-5Z, 9, 13E-trien-1-oic acid). Transcriptome analysis revealed 500 differential genes between HD and control groups, mainly enriched in neuroactive ligand-receptor interaction, amphetamine addiction, and cocaine addiction pathways. The conjoint analysis of the transcriptome and metabolome showed that the biosynthesis of L-glutamine might be regulated by Homer1, Homer3, and Grin3b. This suggests that GRAE may affect L-glutamine accumulation by regulating the expression of these genes. This study showed that GRAE may prolong the sleep time of mice by reducing the accumulation of L-glutamine and deepens our understanding of the regulatory network between certain genes and L-glutamine.

Although runway separation, based on the probability of collision, has been studied for decades, the mathematical methods proposed by the majority of studies cannot handle complex situations, such as the operation of non-intersecting diverging runways at an airport with multiple runways. By applying a combination method of computer simulation and collision probability calculation, the arrival and departure window (ADW) separation for non-intersecting diverging runways of a multi-runway airport was studied under the emergency avoidance (EA) situation. Combining the example of runways 01L/19R and 11L of Beijing Daxing Airport, the ADW separation settings for the airport’s northward and southward operations were determined to meet the target level of safety. Moreover, the effects of range-type parameters on the ADW separation were quantified. When the EA maximum speed limit and EA minimum climb rate were 200 kt (102.9 m/s) and 10%, respectively, the results were such that no ADW separation was required for northward operation, and the ADW separation was from 3.2 km to 7.1 km for southward operation. Furthermore, the results showed that the proposed method could more accurately describe the nominal trajectories of aircraft and improve the precision of collision probability calculation. Meanwhile, the sensitivity analysis method for range-type parameters could help airports and air traffic control facilities to set reasonable constraints to improve theoretical runway capacity, while satisfying practical feasibility.

Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays a major role in monitoring the ocean, marine rescue activities, and marine safety warnings. However, there are still some factors that restrict further improvements in detecting performance, e.g., multi-scale ship transformation and unfocused images caused by motion. In order to resolve these issues, in this paper, a doppler feature matrix fused with a multi-layer feature pyramid network (D-MFPN) is proposed for SAR ship detection. The D-MFPN takes single-look complex image data as input and consists of two branches: the image branch designs a multi-layer feature pyramid network to enhance the positioning capacity for large ships combined with an attention module to refine the feature map’s expressiveness, and the doppler branch aims to build a feature matrix that characterizes the ship’s motion state by estimating the doppler center frequency and frequency modulation rate offset. To confirm the validity of each branch, individual ablation experiments are conducted. The experimental results on the Gaofen-3 satellite ship dataset illustrate the D-MFPN’s optimal performance in defocused ship detection tasks compared with six other competitive convolutional neural network (CNN)-based SAR ship detectors. Its satisfactory results demonstrate the application value of the deep-learning model fused with doppler features in the field of SAR ship detection.

There are limited studies on the semantic segmentation of high-resolution synthetic aperture radar (SAR) images in building areas due to speckle noise and geometric distortion. For this challenge, we propose the large receptive field feature fusion network (LRFFNet), which contains a feature extractor, a cascade feature pyramid module (CFP), a large receptive field channel attention module (LFCA), and an auxiliary branch. SAR images only contain single-channel information and have a low signal-to-noise ratio. Using only one level of features extracted by the feature extractor will result in poor segmentation results. Therefore, we design the CFP module; it can integrate different levels of features through multi-path connection. Due to the problem of geometric distortion in SAR images, the structural and semantic information is not obvious. In order to pick out feature channels that are useful for segmentation, we design the LFCA module, which can reassign the weight of channels through the channel attention mechanism with a large receptive field to help the network focus on more effective channels. SAR images do not include color information, and the identification of ground object categories is prone to errors, so we design the auxiliary branch. The branch uses the full convolution structure to optimize training results and reduces the phenomenon of recognizing objects outside the building area as buildings. Compared with state-of-the-art (SOTA) methods, our proposed network achieves higher scores in evaluation indicators and shows excellent competitiveness.