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The Yihe River Basin is a key area for water conservation and soil erosion control in northern China. The excessive development of land resources is a major factor causing soil erosion and ecological degradation. However, the impacts of land use change on soil erosion in the basin are not yet clearly. Understanding the complex relationship between land use and soil erosion is an important way to promote the development of land resources utilization and ecological construction from cognition to decision-making. This study simulated the temporal-spatial changes of soil erosion in the basin from 1956 to 2020 using Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, and evaluated the changes of soil erosion under different land use scenarios from 2020 to 2050 using Future Land Use Simulation (FLUS) model. From 1956 to 2020, the overall soil erosion intensity showed a slight decreasing trend, and the average annual soil erosion modulus was 38.21 t/ha/year. Soil erosion intensity was higher in the central and northern mountainous areas, while it was lower in the flat alluvial plains in the south. Arable land (4.07 t/ha/year) was the largest contributor to the amount of soil erosion, and land use changes caused the soil erosion intensity to fluctuate and decrease after 1995. From 2020 to 2050, soil erosion varied widely under different land use scenarios, and the land use pattern targeting ecological priority development would effectively mitigate soil erosion. Therefore, optimizing land use patterns and structures are critical initiatives to prevent soil erosion.

Abstract Structural annotation of small molecules in tandem mass spectrometry has always been a central challenge in mass spectrometry analysis, especially using a miniaturized mass spectrometer for on-site testing. Here, we propose the Transformer enabled Fragment Tree (TeFT) method, which combines various types of fragmentation tree models and a deep learning Transformer module. It is aimed to generate the specific structure of molecules de novo solely from mass spectrometry spectra. The evaluation results on different open-source databases indicated that the proposed model achieved remarkable results in that the majority of molecular structures of compounds in the test can be successfully recognized. Also, the TeFT has been validated on a miniaturized mass spectrometer with low-resolution spectra for 16 flavonoid alcohols, achieving complete structure prediction for 8 substances. Finally, TeFT confirmed the structure of the compound contained in a Chinese medicine substance called the Anweiyang capsule. These results indicate that the TeFT method is suitable for annotating fragmentation peaks with clear fragmentation rules, particularly when applied to on-site mass spectrometry with lower mass resolution.

Choroid plexus papilloma (CPP) is a rare benign intracranial tumor origin that predominantly manifests in the lateral ventricle in children, accounting for 0.3%–0.6% of all primary intracranial tumors. It is extremely rare to have the CPP in the trigone of the lateral ventricle through the contralateral posterior interhemispheric transfalcine transprecuneus approach (PITTA). Herein, we report this rare case. A 7-year-old girl presented with headache. Magnetic resonance imaging of the brain showed periatrial lesions, and histopathological examination confirmed CPP (WHO grade I). The contralateral PITTA is a safe, effective, reasonable, and appropriate for some lesions in the trigone of the lateral ventricle. It provides a wider surgical angle (especially for the lateral extension) and reduces the risk of disturbance of the optic radiation compared with the conventional approaches. The use of multiple modern neurosurgical techniques, including interventional embolization, intraoperative navigation, microscope, and electrophysiological monitoring, make the procedure much easier and more accurate, and the neuroendoscope adds to the visualization of the microscope and can reduce surgical complications.

Abstract Compared to commercial chickens, local breeds exhibit better in meat quality and flavour, but the productivity (e.g., growth rate, body weight) of local chicken breeds is rather low. Genetic analysis based on whole-genome sequencing contributes to elucidating the genetic markers or putative candidate genes related to some economic traits, facilitating the improvement of production performance, the acceleration of breeding progress, and the conservation of genetic resources. Here, a total of 209 local chickens from 13 breeds were investigated, and the observation of approximately 91.4% high-quality sequences (Q30 > 90%) and a mapping rate over 99% for each individual indicated good results of this study, as confirmed by a genome coverage of 97.6%. Over 19 million single nucleotide polymorphisms (SNPs) and 1.98 million insertion-deletions (InDels) were identified using the reference genome (GRCg7b), further contributing to the public database. This dataset provides valuable resources for studying genetic diversity and adaptation and for the cultivation of new chicken breeds/lines.

Polarimetric synthetic aperture radar (PolSAR) image interpretation is widely used in various fields. Recently, deep learning has made significant progress in PolSAR image classification. Supervised learning (SL) requires a large amount of labeled PolSAR data with high quality to achieve better performance, however, manually labeled data are insufficient. This causes the SL to fail into overfitting and degrades its generalization performance. Furthermore, the scattering confusion problem is also a significant challenge that attracts more attention. To solve these problems, this article proposes a heterogeneous contrastive learning method (HCLNet). It aims to learn high-level representation from unlabeled PolSAR data for few-shot classification according to multifeatures and superpixels. Beyond the conventional CL, HCLNet introduces the heterogeneous architecture for the first time to utilize heterogeneous PolSAR features better. And it develops two easy-to-use plugins to narrow the domain gap between optics and PolSAR, including feature filter and superpixel-based instance discrimination, which the former is used to enhance the complementarity of multifeatures, and the latter is used to increase the diversity of negative samples. Experiments demonstrate the superiority of HCLNet on three widely used PolSAR benchmark datasets compared with state-of-the-art methods. Ablation studies also verify the importance of each component. Besides, this work has implications for how to efficiently utilize the multifeatures of PolSAR data to learn better high-level representation in CL and how to construct networks suitable for PolSAR data better.

The advent of the sixth generation of mobile communications (6G) ushers in an era of heightened demand for advanced network intelligence to tackle the challenges of an expanding network landscape and increasing service demands. Deep Learning (DL), as a crucial technique for instilling intelligence into 6G, has demonstrated powerful and promising development. This paper provides a comprehensive overview of the pivotal role of DL in 6G, exploring the myriad opportunities and challenges that arise. Firstly, we present a detailed vision for DL in 6G, emphasizing areas such as adaptive resource allocation, intelligent network management, robust signal processing, ubiquitous edge intelligence, and endogenous security. Secondly, this paper reviews how DL models leverage their unique learning capabilities to solve complex service demands in 6G. The models discussed include Convolutional Neural Networks (CNN), Generative Adversarial Networks (GAN), Graph Neural Networks (GNN), Deep Reinforcement Learning (DRL), Transformer, Federated Learning (FL), and Meta Learning. Additionally, we examine the specific challenges each DL model faces within the 6G context. Moreover, we delve into the rapidly evolving field of Artificial Intelligence Generated Content (AIGC), examining its development and impact within the 6G framework. Finally, this paper culminates in a detailed discussion of ten critical open problems in integrating DL with 6G, setting the stage for future research and development in this field.

Complex-valued convolutional neural networks (CV-CNN) have recently gained recognition in feature representation learning. It implements the repeated application of the operations in convolution, local average pooling, and the absolute value of the resulting vectors. However, it is only conducted in the complex spatial domain, and lacks effective representation of directionality, singularity, and regularity in the complex spectral domain for anomaly detection of images. This is the key to feature learning representation of high-order singularity. To solve this problem, a complex-valued contourlet neural network (C$^{2}$N$^{2}$) is proposed in this article. It is novel in this sense that, different from the CV-CNN in the spatial domain, the spectral stream of C$^{2}$N$^{2}$ can enhance the multiresolution sparse representation of nonsubsampled contourlet (NSCT) with multiscales and multidirections for images. Furthermore, the spectral feature integration module is proposed to capture the statistical properties of the NSCT coefficients. It is shown that the proposed network can improve the distinguishability of feature learning and classification ability in theoretical analysis and experiments on three benchmark datasets (Flevoland, Xi'an, and Germany) compared with developed methods. Polarimetric synthetic aperture radar image classification is widely used in the fields of agriculture, forestry, and military. It must be emphasized that there is potential in effective feature learning representation and the generalization capability of C$^{2}$N$^{2}$ in deep learning, recognition, and interpretation.

Abstract Chinese torreya nut (Torreya grandis cv. Merrillii) is a unique edible nut that grows in China, which is known for its ability to reduce blood lipids, prevent blood sclerosis, and coronary heart disease. However, its effect on reproduction is not well understood. In this study, the comprehensive effects of Chinese torreya nut on the sex hormones and endometrium in female mice and their uterine expression profiles were investigated. Here, feeding of mice with Chinese torreya nut resulted in a decrease in uterine coefficients and serum progesterone levels, thickening of the endometrium, and an increase in estrogen levels. The enrichment analyses indicated that the differentially expressed genes between the Chinese torreya nut addition and control mice were enriched in NOD‐like receptor signaling pathway, complement and coagulation cascades and p53 pathway. Quantitative real time PCR and western blot results showed that there were significant differences in the transcripts and protein expression of estrogen receptor alpha and progestogen receptor associated with G protein coupled estrogen receptor. These findings suggest that Chinese torreya nut may have a potentially beneficial effect on coordinating uterine contractions in mice, rendering it a promising functional food.

Abstract Background Restrictive spirometry pattern (RSP), defined as reduced forced vital capacity (FVC) in absence of airflow obstruction (AFO), is associated with increased risk of mortality in general population. However, evidence in the patients with silicosis is limited. This study was aimed to investigate the relationship between RSP and the risk of death in a silicotic cohort. Method This retrospective cohort study used data from the Pneumoconiosis Clinic, Hong Kong Department of Health that containing 4315 patients aged 18–80 years and diagnosed with silicosis during 1981–2019, with a follow-up till 31 December 2019. Spirometry was carried out at the diagnostic examination of silicosis. Lung function categories were classified as normal spirometry (FEV1/FVC ≥ 0.7, FVC ≥ 80% predicted), RSP only (FEV1/FVC ≥ 0.7, FVC

(1) Background: The understory planting of Chinese herbal medicine is a common soil and water conservation farming measure, and this approach makes full use of the natural conditions of the understory. However, a large number of studies on soil erosion have focused on the simulation of natural indoor conditions, and there are very few investigations on soil erosion caused by understory planting in the field. This study aims to investigate the effects of different slopes on soil and water and nitrogen–phosphorus nutrient loss from understory planting of Gastrodia elata Bl by changing the vegetation structure and soil structure of forest land. (2) Methods: To reveal the nitrogen and phosphorus loss and flow and sediment characteristics of the understory planting of Gastrodia elata Bl, runoff plots were set up in a field, and three surface slopes (5°, 15°, and 20°) were designed to collect runoff sediments and compare the soil and water loss between the natural slopes and those with Gastrodia elata Bl. This provides a basis for the restoration of vegetation cover and the enhancement of soil fertility. (3) Results: The total loss of soil, water, nitrogen, and phosphorus in the forested land with Gastrodia elata Bl increased significantly compared with that in the natural forested land, and the greater the slope was, the greater the loss was. (4) Conclusions: Planting Gastrodia elata Bl should be avoided in areas with steep slopes and serious soil erosion. However, some soil and water conservation engineering measures can be taken, such as the construction of retaining walls, drainage ditches, etc., to minimize the scouring and erosion of soil by rainwater.

Deep learning relies on learning from extensive data to generate prediction results. This approach may inadvertently capture spurious correlations within the data, leading to models that lack interpretability and robustness. Researchers have developed more profound and stable causal inference methods based on cognitive neuroscience. By replacing the correlation model with a stable and interpretable causal model, it is possible to mitigate the misleading nature of spurious correlations and overcome the limitations of model calculations. In this survey, we provide a comprehensive and structured review of causal inference methods in deep learning. Brain-like inference ideas are discussed from a brain-inspired perspective, and the basic concepts of causal learning are introduced. The article describes the integration of causal inference with traditional deep learning algorithms and illustrates its application to large model tasks as well as specific modalities in deep learning. The current limitations of causal inference and future research directions are discussed. Moreover, the commonly used benchmark datasets and the corresponding download links are summarized.

Nature, with its numerous surprising rules, serves as a rich source of creativity for the development of artificial intelligence, inspiring researchers to create several nature-inspired intelligent computing paradigms based on natural mechanisms. Over the past decades, these paradigms have revealed effective and flexible solutions to practical and complex problems. This paper summarizes the natural mechanisms of diverse advanced nature-inspired intelligent computing paradigms, which provide valuable lessons for building general-purpose machines capable of adapting to the environment autonomously. According to the natural mechanisms, we classify nature-inspired intelligent computing paradigms into 4 types: evolutionary-based, biological-based, social-cultural-based, and science-based. Moreover, this paper also illustrates the interrelationship between these paradigms and natural mechanisms, as well as their real-world applications, offering a comprehensive algorithmic foundation for mitigating unreasonable metaphors. Finally, based on the detailed analysis of natural mechanisms, the challenges of current nature-inspired paradigms and promising future research directions are presented.

BackgroundStudies in general population reported a positive association between tobacco smoking and airflow obstruction (AFO), a hallmark of chronic obstructive pulmonary disease (COPD). However, this attempt was less addressed in silica dust-exposed workers.MethodsThis retrospective cohort study consisted of 4481 silicotic workers attending the Pneumoconiosis Clinic during 1981-2019. The lifelong work history and smoking habits of these workers were extracted from medical records. Spirometry was carried out at the diagnosis of silicosis (n = 4177) and reperformed after an average of 9.4 years of follow-up (n = 2648). AFO was defined as forced expiratory volume in one second (FEV1)/force vital capacity (FVC) less than lower limit of normal (LLN). The association of AFO with smoking status was determined using multivariate logistics regression, and the effect of smoking cessation on the development of AFO was evaluated Cox regression.ResultsSmoking was significantly associated with AFO (current smokers: OR = 1.92, 95% CI 1.51-2.44; former smokers: OR = 2.09, 95% CI 1.65-2.66). The risk of AFO significantly increased in the first 3 years of quitting smoking (OR = 1.23, 95% CI 1.02-1.47) but decreased afterwards with increasing years of cessation. Smoking cessation reduced the risk of developing AFO no matter before or after the confirmation of silicosis (pre-silicosis cessation: HR = 0.58, 95% CI 0.46-0.74; post-silicosis cessation: HR = 0.62, 95% CI 0.48-0.79).ConclusionsSmoking cessation significantly reduced the risk of AFO in the workers with silicosis, although the health benefit was not observed until 3 years of abstinence. These findings highlight the importance of early and long-term smoking cessation among silicotic or silica dust-exposed workers.

As a typical ecologically fragile area, the ecological security of the Loess Plateau has been seriously threatened. Ecological security patterns (ESP) have gradually become an effective method for protecting ecological security and supporting the management and sustainable development of ecosystems. Therefore, this study constructed a novel ESP based on ecological “function–structure”, utilizing minimum cumulative resistance (MCR) to identify ecological source areas and corridors. Additionally, time scales were introduced into the ESP, allowing for a comprehensive analysis of the spatiotemporal heterogeneity of ecological security in the Loess Plateau. The study revealed that the number of ecological sources decreased from southeast to northwest, with 27, 41, and 77 sources covering total areas of 4263.810 km2, 18,566.034 km2, and 113,209.595 km2 from 2000 to 2020, respectively. Similarly, the number and complexity of ecological corridors increased over the same time period, with 64, 85, and 105 corridors totaling lengths of 4579.326 km, 6526.996 km, and 7015.174 km, respectively. The expansion of ecological security zones was mainly observed in the southeast part of the Loess Plateau. Overall, the ESP of the Loess Plateau saw an improvement, with the southeastern part showing better ecological security than the northwestern part. These findings hold great significance for regional ecological security evaluations and are crucial for promoting ecological management and healthy development in the Loess Plateau.

Background: Preserved ratio impaired spirometry (PRISm) is prevalent within the general population. Increased mortality has been reported among subjects with PRISm, but the evidence has never been summarised. This systematic review aims to synthesise evidence on the association between PRISm and the risk of all-cause, cardiovascular and respiratory-related mortality. Methods: We systematically searched MEDLINE, Embase and Web of Science for population-based cohort studies from inception to April 2023 using the terms related to impaired spirometry and mortality. Titles and abstracts were screened to identify eligible studies that reported mortality estimates for individuals with PRISm. We excluded studies that adopted other definitions of impaired spirometry, had a specific study setting (e.g. HIV patients), had an insufficient follow-up period (

The foundation model (FM) has garnered significant attention for its remarkable transfer performance in downstream tasks. Typically, it undergoes task-agnostic pretraining on a large dataset and can be efficiently adapted to various downstream applications through fine-tuning. While FMs have been extensively explored in language and other domains, their potential in remote sensing has also begun to attract scholarly interest. However, comprehensive investigations and performance comparisons of these models on remote sensing tasks are currently lacking. In this survey, we provide essential background knowledge by introducing key technologies and recent developments in FMs. Subsequently, we explore essential downstream applications in remote sensing, covering classification, localization, and understanding. Our analysis encompasses over 30 FMs in both natural and remote sensing fields, and we conduct extensive experiments on more than 10 datasets, evaluating global feature representation, local feature representation, and target localization. Through quantitative assessments, we highlight the distinctions among various FMs and confirm that pretrained large-scale natural FMs can also deliver outstanding performance in remote sensing tasks. After that, we systematically presented a brain-inspired framework for remote sensing foundation models (RSFMs). We delve into the brain-inspired characteristics in this framework, including structure, perception, learning, and cognition. To conclude, we summarize 12 open problems in RSFMs, providing potential research directions. Our survey offers valuable insights into the burgeoning field of RSFMs and aims to foster further advancements in this exciting area.

Transformer has shown excellent performance in remote sensing field with long-range modeling capabilities. Remote sensing video (RSV) moving object detection and tracking play indispensable roles in military activities as well as urban monitoring. However, transformers in these fields are still at the exploratory stage. In this survey, we comprehensively summarize the research prospects of transformers in RSV moving object detection and tracking. The core designs of remote sensing transformers and advanced transformers are first analyzed. It mainly includes the attention mechanism evolution for specific tasks, the fitting ability design of input mapping, diverse feature representation, model optimization, etc. The architectural characteristics of RSV detection and tracking are then described across two aspects. One is moving object detection for motion-based traditional background subtractions and appearance-based deep learning models. The other is object tracking for single and multiple targets. The research difficulties mainly include the blurred foreground in RSV data, the irregular object movement in traditional background subtraction, and the severe object occlusion in object tracking. Following that, the potential significance of transformers is discussed according to some thorny problems in RSV. Finally, we summarize ten open challenges of transformers in RSV, which may be used as a reference for promoting future research.

Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This article summarizes and analyzes the essential properties of brain cognize learning and the recent advance of remote sensing interpretation. First, this article introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this article summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation, and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

Abstract There has been no validated tool to assess workplace infection control towards SARS-Cov-2 in non-healthcare industries. In this first year survey during 07/2020–04/2021, 6684 workers were recruited from varied non-healthcare settings of Hong Kong, Nanjing and Wuhan of China and responded standard questionnaires containing information of prevention measures and policies implemented by companies and personal preventive behaviour towards infection control. All participants were randomly stratified into two sub-samples as training and validation sample. Workplace safety index towards SARS-Cov-2 (WSI-SC2) was developed and validated using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We identified 14 manifest variables in WSI-SC2, with three sub-indices named “Workplace infection control measures and prevention”, “Company occupational safety and health management and commitment” and “Worker’s personal preventive behavior and awareness towards infectious control”. WSI-SC2 obtained a good internal consistency reliability (Cronbach's alpha coefficients ranged: 0.76–0.91), good composite reliability (composite reliability ranged: 0.70–0.95) and satisfactory fit of the model (GFI = 0.95; SRMR = 0.05; RMSEA = 0.07). We further performed stratified analysis according to cities, and the index remained stable. Workers with higher scores of WSI-SC2 were more likely to uptake COVID-19 test. This multi-city large study developed a novel and validated tool that could horizontally measure the workplace safety towards SARS-Cov-2 in non-healthcare workers.

Affordable, polyvalent meningococcal vaccines are needed for use in emergency reactive immunization campaigns. A phase IV randomized, observer-blind, controlled study compared the safety and immunogenicity of a quadrivalent meningococcal polysaccharide vaccine (MPV-4, MPV ACYW135) and quadrivalent meningococcal ACWY conjugate vaccine (MCV-4, Menactra®). Healthy, 2- to 10-year-old children in Bamako, Mali, were randomized 1:1 to receive one dose of MPV-4 or MCV-4. Safety outcomes were evaluated for 6 months post-immunization. Immunogenicity for all serogroups was assessed for non-inferiority between MPV-4 and MCV-4 30 days post immunization by serum bactericidal antibody assay using baby rabbit complement (rSBA). From December 2020 to July 2021, 260 healthy subjects were consented and randomized. At Day 30 post-immunization, the proportions of subjects with rSBA titers ≥ 128 for all serogroups in the MPV-4 group were non-inferior to those in MCV-4 group. The proportions of subjects with rSBA ≥ 4-fold increase and rSBA titers ≥ 8 for all serogroups were similar among vaccine groups (P > .05). Geometric Mean Titers and Geometric Mean Fold Increases for all serogroups in both vaccine groups were similar (P > .05). Few local and systemic post-immunization reactions of similar severity and duration were observed within 7 days and were similar in both groups (P > .05). All resolved without sequelae. Unsolicited adverse events were similar in both groups regarding relationship to study vaccine, severity and duration. No serious adverse events were reported during the study period. MPV ACYW135 showed a non-inferior immunogenicity profile and a comparable reactogenicity profile to MCV-4 in Malian children aged 2–10 years. Clinical Trial Registration: NCT04450498.

Abstract Food production is one of the main contributors to greenhouse gas emissions and climate change. China, as a rapidly developing economy, contributes to an unsustainable food system as its consumption of animal products and meat has continued to grow in recent decades. Using the extended theory of planned behavior as the conceptual framework, this paper examines factors influencing consumers' intention to purchase sustainable food in China. To this end, a population‐based face‐to‐face survey was conducted with 2422 respondents in five provinces spanning the north and south of China. The results showed that the traditional constructs of behavioral attitude, subjective norms, perceived behavioral control, and the additional construct of perceived quality are significant in inducing such intentions. This paper suggests that to enhance consumers' willingness to shift to sustainable food consumption, appropriate regulation and monitoring framework is needed to increase consumers' trust toward sustainable food. The government can also cooperate with the media, experts, and social media opinion leaders to ensure that messages on sustainable development are promoted in effective ways.

This paper presents a pansharpening technique based on the non-subsampled contourlet transform (NSCT) and convolutional autoencoder (CAE). NSCT is exceptionally proficient at presenting orientation information and capturing the internal geometry of objects. First, it’s used to decompose the multispectral (MS) and panchromatic (PAN) images into high-frequency and low-frequency components using the same number of decomposition levels. Second, a CAE network is trained to generate original low-frequency PAN images from their spatially degraded versions. Low-resolution multispectral images are then fed into the trained convolutional autoencoder network to generate estimated high-resolution multispectral images. Third, another CAE network is trained to generate original high-frequency PAN images from their spatially degraded versions. The result of low-pass CAE is fed to the trained high-pass CAE to generate estimated high-resolution multispectral images. The final pan-sharpened image is accomplished by injecting the detailed map of the spectral bands into the corresponding estimated high-resolution multispectral bands. The proposed method is tested on QuickBird datasets and compared with some existing pan-sharpening techniques. Objective and subjective results demonstrate the efficiency of the proposed method.

With the development of science and technology and the continuous changes of social environment, the development prospect of traditional cinema is worrying. This work aims to improve the publicity effect of movie posters and optimize the marketing efficiency of movie posters and promote the development of film and television industry. First, the design concept of high grossing movie posters is discussed. Then, the concept of movie poster analysis based on Deep Learning (DL) technology is analyzed under Big Data Technology. Finally, a movie poster analysis model is designed based on Convolutional Neural Network (CNN) technology under DL and is evaluated. The results demonstrate that the learning curve of the CNN model reported here is the best in the evaluation of model performance in movie poster analysis. Besides, the learning rate of the model is basically stable when the number of iterations is about 500. The final loss value is around 0.5. Meanwhile, the accuracy rate of the model is also stable at the number of iterations of about 500, and the accuracy rate of the model is around 0.9. In addition, the recognition accuracy of the model designed here in movie poster classification recognition is generally between 60% and 85% in performing theme, style, composition, color scheme, set, and product recognition of movie posters. Moreover, the evaluation of the model in the movie poster style composition suggests that the style composition of movie poster production dramatically varies in different films, in which movie posters focus most on movie product, style, and theme. Compared with other models, the performance of this model is more outstanding in all aspects, which shows that this work has achieved a great technical breakthrough. This work provides a reference for the optimization of the design method of movie posters and contributes to the development of the movie industry.

IntroductionSoil bacteria not only maintain the biodiversity of forest ecosystems but also affect soil nutrient cycling and ecosystem function. Nonetheless, the spatial pattern and patchy distribution of dominant bacterial community components in soil are still rarely explored.MethodThe spatial pattern and distribution of the dominant bacterial community components and their influential factors were investigated using traditional statistics, geostatistics, and kriging spatial interpolation methods in the Huoditang region of the Qinling Mountains, China.ResultsThe dominant bacterial phyla were Proteobacteria, Acidobacteria, Chloroflexi, Rokubacteria, Actinobacteria, and Verrucomicrobia in this region. Among the bacterial phyla, Proteobacteria occupied an area of 2.56 km2 (the greatest) in the highest patch category, followed by Planctomycetes. Moreover, among the lowest patch category, Firmicutes occupied the lowest area (11.93 km2). The results of kriging maps showed that the dominant bacterial group presented “peak,” “bimodal,” and “multimodal” distributions in Huoditang. Proteobacteria, Acidobacteria, Chloroflexi, Gemmatimonadetes, Nitrospirae, and ASV (amplicon sequence variants) had significant spatial autocorrelation (

Abstract Remote sensing image fusion plays important roles in numerous applications, including monitoring, metrology, and agriculture. Image fusion gathers essential information from several image sources and consolidates them into a single image called a fused image. The fused image involves relevant data, and it is more informative than any other images extracted from one source. This study proposed a pansharpening technique based on image filtering utilising a bilateral filter to generate high‐frequency details from panchromatic image. The various types of side window guided filters are employed to enhance the multispectral band from panchromatic image and then used these filters to adjust spatial data misfortune that happens when images are combined. Experimental results demonstrated that the proposed method provides consistent results concise with reported by the previous research in terms of subjective and objective assessments on remote sensing data.

Trans-10-hydroxy-2-decenoic acid (10-HDA) is a unique fatty acid found in royal jelly that possesses potential health benefits such as anti-inflammatory. However, further research is needed to fully understand its mechanisms of action and therapeutic potential for inflammation-associated diseases. In this present study, liquid chromatography–tandem mass spectrometry (LC-MS/MS) and RNA-seq analyses were conducted to comprehensively analyze the in vitro anti-inflammatory effects of 10-HDA on lipopolysaccharide (LPS)-stimulated RAW 264.7 cells. Our results demonstrated that 128 differentially expressed metabolites and 1721 differentially expressed genes were identified in the 10-HDA-treated groups compared to the LPS groups. Metabolites were significantly enriched in amino acid metabolism pathways, including methionine metabolism, glycine and serine metabolism, and tryptophan metabolism. The differentially expressed genes enrichment analysis indicated that antigen processing and presentation, NOD-like receptor signaling pathway, and arginine biosynthesis were enriched with the administration of 10-had. The correlation analysis revealed that glycerophospholipid metabolism and s-adenosylmethionine-dependent methylation processes might be involved in the response to the 10-HDA treatment. Overall, the findings from this study showed that 10-HDA might involve the modulation of certain signaling pathways involved in the inflammatory response, but further research is needed to determine the safety and efficacy as a therapeutic agent.

Abstract In this paper, we propose a pansharpening method based on a convolutional autoencoder. The convolutional autoencoder is a sort of convolutional neural network (CNN) and objective to scale down the input dimension and typify image features with high exactness. First, the autoencoder network is trained to reduce the difference between the degraded panchromatic image patches and reconstruction output original panchromatic image patches. The intensity component, which is developed by adaptive intensity-hue-saturation (AIHS), is then delivered into the trained convolutional autoencoder network to generate an enhanced intensity component of the multi-spectral image. The pansharpening is accomplished by improving the panchromatic image from the enhanced intensity component using a multi-scale guided filter; then, the semantic detail is injected into the upsampled multi-spectral image. Real and degraded datasets are utilized for the experiments, which exhibit that the proposed technique has the ability to preserve the high spatial details and high spectral characteristics simultaneously. Furthermore, experimental results demonstrated that the proposed study performs state-of-the-art results in terms of subjective and objective assessments on remote sensing data.

BackgroundNeuroinflammation is involved in the mechanisms of levodopa-induced dyskinesia (LID). The canonical NF-κB activation signaling pathway plays a critical role in the neuroinflammation development and BET protein-induced NF-κB-mediated neuroinflammation. The inhibition of the BET protein function has been reported to alleviate LID; however, its association with the canonical NF-κB signaling pathway in the 6-OHDA-lesioned striatum of the LID rat model remains unknown. Accordingly, we identified the status of the canonical NF-κB signaling pathway in the 6-OHDA-lesioned striatum of the LID rat model and whether the anti-dyskinetic effect of the BET inhibitor JQ1 was associated with its suppression on NF-κB-mediated neuroinflammation.Methods6-OHDA PD rat models were treated with either L-dopa plus JQ1 or L-dopa alone. L-dopa treatment was given for 2 weeks, and the JQ1 treatment was given for 3 weeks and was initiated a week prior to L-dopa treatment. As a control, the sham rats were treated with JQ1 or Veh for 3 weeks. The ALO AIM assessment and cylinder test were performed during the treatment. Glial activation markers, pro-inflammatory substances, and critical proteins in the canonical NF-κB signaling pathway were tested in the lesioned striatum after the final treatment.ResultsJQ1 effectively alleviated LID without influencing motor improvement. In the lesioned striatum, L-dopa triggered an overactivation of the canonical NF-κB signaling pathway, with an increase in the phospho-IKKα/β, phospho-IκBα, and NF-κB nuclear translocation and its phosphorylation at Ser 536 and Ser 276 sites (p < 0.01 vs. sham group). L-dopa induced an overexpression of the pro-inflammatory substances of tumor necrosis factor (TNF)-α, interleukin (IL)-1β, IL-6, and inducible nitric oxide synthase (iNOS), and the glial activation markers CD68 and GFAP. All the molecular changes were greatly inhibited by JQ1.ConclusionL-dopa triggered an overactivation of the canonical NF-κB signaling pathway, leading to an enhanced neuroinflammation response in the 6-OHDA-lesioned striatum of LID rat models. The inhibition of the BET protein function significantly suppressed the activation of the canonical NF-κB signaling pathway in the striatum, alleviating the neuroinflammation response and the severity of LID.

Radar emitter identification (REI) aims to extract the fingerprint of an emitter and determine the individual to which it belongs. Although many methods have used deep neural networks (DNNs) for an end-to-end REI, most of them only focus on a single view of signals, such as spectrogram, bi-spectrum, signal waveforms, and so on. When the electromagnetic environment varies, the performance of DNN will be significantly degraded. In this paper, a multi-view adaptive fusion network (MAFN) is proposed by simultaneously exploring the signal waveform and ambiguity function (AF). First, the original waveform and ambiguity function of the radar signals are used separately for feature extraction. Then, a multi-scale feature-level fusion module is constructed for the fusion of multi-view features from waveforms and AF, via the Atrous Spatial Pyramid Pooling (ASPP) structure. Next, the class probability is modeled as Dirichlet distribution to perform adaptive decision-level fusion via evidence theory. Extensive experiments are conducted on two datasets, and the results show that the proposed MAFN can achieve accurate classification of radar emitters and is more robust than its counterparts.

Pixelwise classification plays an important role for image interpretation. The remote sensing images, especially polarimetric synthetic aperture radar (PolSAR), have provided wide applications for both military and civilian users regardless of weather or lighting conditions. However, the classification of heterogeneous imagery is still challenging. In this article, we propose a novel deep fuzzy graph convolutional network (DFGCN) for pixelwise classification of PolSAR imagery. Inspired by the fuzzy logic, the imagery (i.e., a reflection of the backscattering of the ground) is represented by a fuzzy graph, whose node is associated with a feature vector, and fuzzy weights denoting the similarity degrees between nodes are defined by using both feature distance and spatial relation. We also construct a new graph residual module stacking multiple residual layers to extend the depth of our fuzzy graph network. Moreover, we present a graph shrinking strategy to reduce the graph size for training and predicting the labels of large-scale PolSAR imagery, which can cut down the computational cost. The experimental results on various real-world PolSAR datasets indicate that DFGCN dramatically enhances classification accuracies on both heterogeneous and homogeneous regions with limited labeled pixels compared with the state-of-the-art methods.

The recent years has witnessed a rapid development of Deep Learning (DL) based Automation Modulation Classification (AMC) methods, which has proved to outperform traditional classification approaches. In order to disturb the deep neural networks for AMC, in this paper, we propose an adversarial attack method to generate fake signals for fooling DL-based classifiers. Firstly, some constraints on visual difference and recoverability of fake signals are defined. Next, a Few Data Point Attacker (FDPA) is proposed to generate fake signals with few perturbed data points via differential evolution algorithm. Some experiments are taken on a public dataset, RML 2016.10a, and the results show that fake signals generated by the FDPA can remarkably reduce the accuracies of three types of DL-based AMC classifiers, a Convolutional Neural Network (CNN) based classifier, a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) based classifier, and a classifier combined with CNN and LSTM-RNN. The code will be available.

BackgroundTo determine the non-inferiority of the seven common serotypes (4, 6B, 9V, 14, 18C, 19F, and 23F) in the 13-valent pneumococcal conjugate vaccine (PCV13) with each serotype conjugated to a tetanus toxoid carrier protein and adsorbed on aluminum phosphate and the superiority of its six additional serotypes (1, 3, 5, 6A, 7F, and 19A) to the serotypes in the PCV7.MethodsParticipants were evenly randomized in a 1:1 ratio into either the PCV13 or PCV7 groups, to receive three doses of the vaccine at the age of 3, 4, and 5 months, respectively, and a booster dose between 12 and 15 months of age. Serotype-specific antibodies were measured using a standardized enzyme-linked immunosorbent assay (ELISA) and opsonophagocytic activity (OPA) microcolony assay method.ResultsA total of 1,040 healthy infants were enrolled. All the seven common serotypes in the PCV13 were non-inferior to those in the PCV7 in terms of the serotype-specific IgG production induced; however, non-inferiority was not shown for serotype 6B after the infant series. The proportion of subjects who reached OPA antibody titers ≥ 1:8 in the PCV13 group was 89.25% or higher. Local reactions and systemic events were mild or moderate in severity and similar between the two groups. No new safety signals were observed.ConclusionThe newly developed PCV13 was immunogenic for all serotypes and had a comparable safety profile to the marketed PCV7.

Parkinson’s disease (PD) is the second most common neurodegenerative disease. Tianqi Pingchan Granule (TPG) is a clinically effective formula of traditional Chinese medicine to treat PD. However, the therapeutic effect and underlying mechanisms of TPG in PD remain unclear. Based on network pharmacology, the corresponding targets of TPG were identified using the Traditional Chinese Medicine Database and Analysis Platform Database. Differentially expressed genes in PD were obtained from the Therapeutic Target Database, Online Mendelian Inheritance in Man, GeneCards, and DrugBank databases. The protein-protein interaction (PPI) networks of intersected targets were constructed using the STRING database and visualized using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the pathways directly related to the pathogenesis of PD were integrated manually. Furthermore, in vivo studies were carried out based on network pharmacology. The gut microbiota, peripheral inflammatory cytokines, and glia-mediated neuroinflammation in substantia nigra were evaluated. A total of 99 target genes were intersected between targets of TPG and deferentially expressed genes in PD. The PPI network analysis indicated the proinflammatory cytokine as essential targets. GO and KEGG analyses indicated that inflammatory response and its related signaling pathways were closely associated with TPG-mediated PD treatment. In vivo studies revealed that class Negativicutes and order Selenomonadales decreased, whereas class Mollicutes, order Enterobacteriales, and Mycoplasmatales increased in fecal samples of PD rats via 16S rRNA sequence analysis. Furthermore, the function prediction methods purposely revealed that TPG therapy may be involved in flavonoid biosynthesis, which have anti-inflammatory properties. In addition, in vivo studies revealed that TPG exposure was found to not only attenuate the production of peripheral inflammatory cytokines but also inhibit the activation of microglia and astrocytes in substantia nigra of PD rats. Through network pharmacology and in vivo experiment-combined approach, the mechanisms of TPG in the treatment of PD were revealed, and the role of TPG in the regulation of gut microbiota and inflammatory response was confirmed.

Abstract Background The intestinal epithelial barrier, which works as the first line of defense between the luminal environment and the host, once destroyed, it will cause serious inflammation or other intestinal diseases. Tight junctions (TJs) play a vital role to maintain the integrity of the epithelial barrier. Lipopolysaccharide (LPS), one of the most important inflammatory factors will downregulate specific TJ proteins including Occludin and Claudin-1 and impair integrity of the epithelial barrier. Betaine has excellent anti-inflammatory activity but whether betaine has any effect on TJ proteins, particularly on LPS-induced dysfunction of epithelial barriers remains unknown. The purpose of this study is to explore the pharmacological effect of betaine on improving intestinal barrier function represented by TJ proteins. Intestinal porcine epithelial cells (IPEC-J2) were used as an in vitro model. Results The results demonstrated that betaine enhanced the expression of TJ proteins while LPS (1 μg/mL) downregulates the expression of these proteins. Furthermore, betaine attenuates LPS-induced decreases of TJ proteins both shown by Western blot (WB) and Reverse transcription-polymerase chain reaction (RT-PCR). The immunofluorescent images consistently revealed that LPS induced the disruption of TJ protein Claudin-1 and reduced its expression while betaine could reverse these alterations. Similar protective role of betaine on intestinal barrier function was observed by transepithelial electrical resistance (TEER) approach. Conclusion In conclusion, our research demonstrated that betaine attenuated LPS-induced downregulation of Occludin and Claudin-1 and restored the intestinal barrier function.

In this paper a new Sparse Tensor Auto-Encoder (STAE) model is proposed to learn a latent and discriminative feature representations for saliency detection. By formulating the background patches as holistic high-dimensional tensors and learning multi-dimensional dictionary to code image patches, the coding error can precisely reveal the difference between salient object and background. Then a saliency map can be derived by a subsequent refinement of the representation errors of patches via image segmentation. Several benchmark datasets are used to verify the effectiveness of the proposed method. The results show that the proposed STAE can accurately locate saliency region and outperform its counterparts.

Handcraft features are commonly used for signal classification, which is a time-consuming feature engineering. In order to develop a general and robust feature learning method for radio signals, a novel One-dimensional Deep Attention Convolution Network (ODACN) is proposed to automatically extract discriminative features and classify various kinds of signals. First, one-dimensional (1-D) sparse filters are designed to learn hierarchical features of raw signals. Second, an attention layer is constructed to weight and assemble feature maps, to derive more context-relevant representation. By using simple 1-D filtering, ODACN is characteristic of less parameters and lower computation complexity than traditional Convolutional Neural Networks (CNNs). Moreover, feature attention can mimic a succession of partial glimpses of humans and focus on context parts of signals, thus helps in recognizing signals even at low Signal-to-Noise Ratio (SNR). Some experiments are taken to classify 31 kinds of signals with different modulation and channel coding types, and the results show that ODACN can achieve accurate classification of very similar signals, without any prior knowledge and manual operation.

Compared with natural image super-resolution, hyperspectral image super-resolution (HSR) is more complex because the redundancy in spectral bands and spatial information. To overcome the difficulties exist in HSR, in this paper, we propose a tensor spatial-spectral joint correlation based HSR method. Start with the tensor representation, we construct a series of fourth-order tensors to preserve the intrinsic structure of hyperspectral images, and then explore the spatial-spectral joint correlation based on meaningful interpretations of tensor canonical matrices. To further constrain the spectral characteristics, we analyze the sparsity of the spectral gradients and model it with Laplacian prior. Then, the two regularizations are combined with the reconstruction model to develop a new HSR method. Finally, an iterative optimization algorithm based on alternating direction method of multiplier (ADMM) and augmented Lagrangian multiplier method is proposed to reconstruct the high-resolution hyperspectral images. Experimental results on several data sets illustrate the effectiveness of our proposed method both in visual and numerical comparisons.

The past decade has witnessed a prosperity of sparsity-inspired face hallucination methods that use sparse prior and instances to generate High-Resolution (HR) faces. However, they need numerous Low-Resolution (LR) and HR instance pairs and adopt approximate sparse coding, which will bring bias to the recovery and suffer from high computational burden. In this paper we advance a Single Face Image Hallucination (SFIH) method from a new perspective of Non-linear Learning Compressive Sensing (NLCS), which can recover HR faces from a surprisingly small number of HR faces. The nonlinear sparse coding of facial images is explored, and a Deep AutoEncoder (DAE) network is constructed for learning a kernel function from a single HR instance set. SFIH is then reduced to an analytic compressive recovery problem by reformulating linear sparse coding as a nonlinear DAE model. By exploring the nonlinear sparsity in the feature space, NLCS can accurately and rapidly recover HR facial images with large magnification factor and exhibit robustness to LR-HR instance pairs mapping. Some experiments are taken on realizing 3X, 6X, 9X amplification of face images, and the results prove its efficiency and superiority to its counterparts.

Deep Neural Networks (DNNs) have achieved remarkable accuracy improvements for automatic modulation classification. However, the employed networks often have millions of parameters and need very high computation, which makes it difficult to deploy these models on portable devices with limited resources. We propose a cross model deep learning scheme to build a lightweight deep network for accurate modulation classification. Firstly, a large Hybrid DNN (HDNN) that is composed of convolutional and recurrent layers is constructed and trained for automatic and accurate classification of signals. Then we build a smaller Layered Resnet Network (LRN) with shallow layers and few nodes. The HDNN and LRN are taken as a Teacher Model (TM) and a Student Model (SM) respectively. Finally, a knowledge distillation method is proposed to guide the learning of the SM, by formulating a teaching loss from the prediction of the TM to train the SM. The performances of the proposed HDNN and LRN are investigated on the public RadioML2016.10a and RadioML2016.10b data sets. The experimental results show that the trained HDNN presents state-of-the-art classification results and the LRN trained in this scheme takes only about a sixth of the HDNN's inference time and consumes only 472.3KB for storage, with a slight accuracy decrease compared with the large HDNN.

Desired by sparse subset learning, in this paper, a hyperspectral band selection method via pairwise band agreement with spatial-spectral graph regularier, referred as Regularized Band Selection via Learned Pairwise Agreement (RBS-LPA), was proposed. The process was formulated as a graph-regularized row-sparse constrained optimization problem, which select a few representative bands to code the all bands based on the learned pairwise band agreement. In RBS-LPA, a spatial-spectral informative graph, constructed by spatial-spectral neighbor relationship, is incorporated to encode both the spatial and spectral geometrical structure. By combining the learning procedure with graph regularizer jointly, the graph regularizer can adaptively change with the sparse representative bands, which ensures that the selected bands can well preserve the local manifold structure. Experimental results on three real urban hyperspectral data demonstrate the efficiency of the proposed RBS-LPA and achieve convincing performance.

Remote Sensing Images (RSIs) often have extremely wide width and abundant terrain. In order to achieve rapid object detection in large RSIs, in this paper, a Deep Hash Assisted Network (DHAN) is constructed by introducing a hashing encoding of images in a two-stage deep neural network. Different with the available detection networks, DHAN first locates candidate object regions and then transfers the learned features to another Region Proposal Network (RPN) for detection. On the one hand, it can avoid the calculations on the background irrelevant to objects. On the other hand, the built hash encoding layer in DHAN can accelerate the detection via binary hash features. Moreover, a self attention layer is designed and combined with the convolution layer, to distinguish relatively small objects regions from a very large scene. The proposed method is tested on several public data sets, and the comparison results show that DHAN can remarkably improve the detection efficiency on large RSIs and simultaneously achieve high detection accuracy.

Tensor representation is the most natural and effective way to preserve the structural information of hyperspectral image (HSI), and thus is very beneficial to HSI processing. This paper represents the spectral features of each testing pixel and its spatial neighbors as a Spatial Neighbor Tensor (SNT), whose spectral vectors can be simultaneously sparse coded by the spectral vectors of a few common training pixels. The obtained sparse coding coefficients could be regarded as a Slice Sparse Coding Tensor (SSCT), which can be adaptively learnt and utilized to predict the labels of HSI pixels. Furthermore, to improve the efficiency, the Compressive Dimensionality Reduction (CDR) is introduced into tensor slice sparse coding for optimizing SSCT and thus an SSCT based Classifier with CDR (SSCTC-CDR) is proposed for hyperspectral image classification (HIC). The performance of SSCTC-CDR is evaluated on three real HSI data, and the results show that it can obtain high-accurate classification result with relatively low computation lost.

In this paper, a new Separable Attention Capsule Network (SACN) is proposed for signal classification. SACN is a light-weight network composed of multi-channel separable convolution layer, attention module and classification layer. First, depth-wise convolution is employed to extract features of signals in a low-complexity manner, and the multi-channel network structure is designed to increase the network width to improve the diversity of features of signals. Then a channel attention module is followed by a capsule network whose element contains a group of neurons. This attention module can explore the interdependence among channels to use global information to selectively strengthen some important channels, thus achieving the improvement of generalization ability of SACN. Some experiments are taken on several datasets with communication and radar signals, and the comparison results prove the efficiency of SACN and the superiority to its counterparts.

Synthetic aperture radar (SAR) image classification is an important part in the understanding and interpretation of SAR images. Each patch in SAR images has a scene category, but usually contains multiple land-cover classes or latent properties, which can be represented by topics in the probabilistic topic model (PTM). The representation and selection of discriminative features in PTM have a large impact on the classification results. Most of the existing feature learning methods do not make full use of high-level structure feature and the feature correlation within similar images to mine discriminative features. Therefore, this article proposes a discriminative sketch topic model with structural constraint (C-SSTM) for SAR image classification. In the proposed model, each image patch is characterized by structural and texture features. In particular, the sketch structural feature is based on the sketch map to represent the image local structure pattern. Then, the local image manifold information is preserved in terms of structure and texture. In the structural constraint, the texture and structure of each image patch are combined to learn discriminative latent semantic topics between image patches. Finally, each image patch is quantified by discriminative latent semantic topics instead of low-level representation. The experimental results tested on synthetic and real SAR images demonstrate that the proposed C-SSTM is able to learn effective structural feature representation from SAR images. Compared with other related approaches, C-SSTM produces competitive classification accuracies with high time efficiency.

Summary: Recent genomic studies suggest that Asian breast cancer (BC) may have distinct somatic features; however, most comparisons of BC genomic features across populations did not account for differences in age, subtype, and sequencing methods. In this study, we analyzed whole-exome sequencing (WES) data to characterize somatic copy number alterations (SCNAs) and mutation profiles in 98 Hong Kong BC (HKBC) patients and compared with those from The Cancer Genome Atlas of European ancestry (TCGA-EA, N = 686), which had similar distributions of age at diagnosis and PAM50 subtypes as in HKBC. We developed a two-sample Poisson model to compare driver gene selection pressure, which reflects the effect sizes of cancer driver genes, while accounting for differences in sample size, sequencing platforms, depths, and mutation calling methods. We found that somatic mutation and SCNA profiles were overall very similar between HKBC and TCGA-EA. The selection pressure for small insertions and deletions (indels) in GATA3 (false discovery rate (FDR) corrected p < 0.01) and single-nucleotide variants (SNVs) in TP53 (nominal p = 0.02, FDR corrected p = 0.28) was lower in HKBC than in TCGA-EA. Among the 13 signatures of single-base substitutions (SBS) that are common in BC, we found a suggestively higher contribution of SBS18 and a lower contribution of SBS1 in HKBC than in TCGA-EA, while the two APOBEC-induced signatures showed similar prevalence. Our results suggest that the genomic landscape of BC was largely very similar between HKBC and TCGA-EA, despite suggestive differences in some driver genes and mutational signatures that warrant future investigations in large and diverse Asian populations.

A series of effects caused by temperature change are the biggest problems faced by biological systems. These irregular environmental characteristics have brought new challenges to people and even animal groups. The immune function of human intestinal flora has a great protective effect on other organs and the body environment. It can help the human body carry out intestinal digestion, food absorption, nutritional metabolism, and so on. Based on the above situation, this paper uses the time series prediction model to study the factors affecting the imbalance of intestinal flora in the process of temperature change. Firstly, biological experiments are carried out with animals to simulate the human environment. Based on the sequence information of historical temperature change parameters, a temperature prediction device based on time series model is proposed. The effects of air factors and carbon dioxide content on the prediction results are evaluated by statistical analysis. Secondly, in order to ensure the accuracy of the experimental data, the neural network algorithm is used to optimize the model, and the white blood cell count is used to analyze the influence of temperature change on the intestinal flora structure of the two organisms. Finally, the experimental results are applied to the human environment to analyze the research results. The results showed that with the irregular change of temperature, the number of intestinal flora and internal colony structure also changed. The richness index in the normal temperature environment is relatively large, which can effectively explain the high richness of intestinal microflora in the experimental population. Further analyze the subjects and distinguish the animals according to sex. The number of Bacteroides in the intestinal flora of male animals was higher than that of female animals, but this phenomenon disappeared immediately after physical ligation. In addition, the intestinal flora abundance of female animals is higher than that of male animals, and the metabolic level is faster.

Ground clutter data are usually generated using a statistical model, but they cannot effectively reflect the spatial distribution characteristics of ground objects. It is important in practical projects to effectively predict ground clutter when terrain data are known. In this paper, a scheme of ground clutter simulation based on the three-dimensional parabolic equation (3DPE) model is proposed. Radio wave propagation was modeled by the PE model and the spatial field distribution was solved. After the radar cross section (RCS) calculation based on space cells, the ground clutter information data were obtained. Then the radar echo was obtained and the clutter map was simulated. The simulation experimental results show that the ground clutter map simulated by the proposed method has a good reference value, meets the demand of strong clutter area prediction under known terrain conditions, and provides a theoretical basis for radar location and optimal deployment.