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Abstract Corticotropin-releasing hormone-binding protein (CRHBP) is involved in many physiological processes. However, it is still unclear what role CRHBP has in tumor immunity and prognosis prediction. Using databases such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), Tumor Protein Database, Timer Database, and Gene Expression Profiling Interactive Analysis (GEPIA), we evaluated the potential role of CRHBP in diverse cancers. Further research looked into the relationships between CRHBP and tumor survival prognosis, immune infiltration, immune checkpoint (ICP) indicators, tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR), DNA methylation, tumor microenvironment (TME), and drug responsiveness. The anticancer effect of CRHBP in liver hepatocellular carcinoma (LIHC) was shown by Western blotting, EdU staining, JC-1 staining, transwell test, and wound healing assays. CRHBP expression is significantly low in the majority of tumor types and is associated with survival prognosis, ICP markers, TMB, and microsatellite instability (MSI). The expression of CRHBP was found to be substantially related to the quantity of six immune cell types, as well as the interstitial and immunological scores, showing that CRHBP has a substantial impact in the TME. We also noticed a link between the IC50 of a number of anticancer medicines and the degree of CRHBP expression. CRHBP-related signaling pathways were discovered using functional enrichment. Cox regression analysis showed that CRHBP expression was an independent prognostic factor for LIHC. CRHBP has a tumor suppressor function in LIHC, according to cell and molecular biology trials. CRHBP has a significant impact on tumor immunity, treatment, and prognosis, and has the potential as a cancer treatment target and prognostic indicator.

Neural networks have become integral to remote sensing data processing. Among neural networks, convolutional neural networks (CNNs) in deep learning offer numerous advanced algorithms for object detection in remote sensing imagery, which is pivotal in military and civilian contexts. CNNs excel in extracting features from training samples. However, traditional CNN models often lack specific signal assumptions tailored to remote sensing data at the feature level. In this paper, we propose a novel approach aimed at effectively representing and correlating information within CNNs for remote sensing object detection. We introduce object tokens and incorporate global information features in embedding layers, facilitating the comprehensive utilization of features across multiple hierarchical levels. Consideration of feature maps from images as two-dimensional signals, matrix image signal processing is employed to correlate features for diverse representations within the CNN framework. Moreover, hierarchical feature signals are effectively represented and associated during end-to-end network training. Experiments on various datasets demonstrate that the CNN model incorporating feature representation and association outperforms CNN models lacking these elements in object detection from remote sensing images. Additionally, integrating image signal processing enhances efficiency in end-to-end network training. Various signal processing approaches increase the process ability of the network, and the methodology could be transferred to other specific and well-defined task.

The advancement of convolutional neural networks (CNNs) has markedly progressed in the field of face detection, significantly enhancing accuracy and recall metrics. Precision and recall remain pivotal for evaluating CNN-based detection models; however, there is a prevalent inclination to focus on improving true positive rates at the expense of addressing false positives. A critical issue contributing to this discrepancy is the lack of pseudo-face images within training and evaluation datasets. This deficiency impairs the regression capabilities of detection models, leading to numerous erroneous detections and inadequate localization. To address this gap, we introduce the WIDERFACE dataset, enriched with a considerable number of pseudo-face images created by amalgamating human and animal facial features. This dataset aims to bolster the detection of false positives during training phases. Furthermore, we propose a new face detection architecture that incorporates a classification model into the conventional face detection model to diminish the false positive rate and augment detection precision. Our comparative analysis on the WIDERFACE and other renowned datasets reveals that our architecture secures a lower false positive rate while preserving the true positive rate in comparison to existing top-tier face detection models.

Abstract The regulation of PD‐L1 is the key question, which largely determines the outcome of the immune checkpoint inhibitors (ICIs) based therapy. However, besides the transcription level, the protein stability of PD‐L1 is closely correlated with its function and has drawn increasing attention. In this study, EZH2 inhibition enhances PD‐L1 expression and protein stability, and the deubiquitinase ubiquitin‐specific peptidase 22 (USP22) is identified as a key mediator in this process. EZH2 inhibition transcriptionally upregulates USP22 expression, and upregulated USP22 further stabilizes PD‐L1. Importantly, a combination of EZH2 inhibitors with anti‐PD‐1 immune checkpoint blockade therapy improves the tumor microenvironment, enhances sensitivity to immunotherapy, and exerts synergistic anticancer effects. In addition, knocking down USP22 can potentially enhance the therapeutic efficacy of EZH2 inhibitors on colon cancer. These findings unveil the novel role of EZH2 inhibitors in tumor immune evasion by upregulating PD‐L1, and this drawback can be compensated by combining ICI immunotherapy. Therefore, these findings provide valuable insights into the EZH2‐USP22‐PD‐L1 regulatory axis, shedding light on the optimization of combining both immune checkpoint blockade and EZH2 inhibitor‐based epigenetic therapies to achieve more efficacies and accuracy in cancer treatment.

Visible and infrared image fusion is an important image enhancement technique that aims to generate high-quality fused images with prominent targets and rich textures in extreme environments. However, most of the current image fusion methods have poor visual perception of the generated fused images due to severe degradation of texture details of the visible light images in scenes with extreme lighting, which seriously affects the application of subsequent advanced vision tasks such as target detection and tracking. To address these challenges, this paper bridges the gap between image fusion and advanced vision tasks by proposing an efficient fusion method for mutual enhancement of spatial and radiometric information. First, we design the gradient residual dense block (LGCnet) to improve the description of fine spatial details in the fusion network. Then, we developed a cross-modal perceptual fusion (CMPF) module to facilitate modal interactions in the network, which effectively enhances the fusion of complementary information between different modalities and reduces redundant learning. Finally, we designed an adaptive light-aware network (ALPnet) to guide the training of the fusion network to facilitate the fusion network to adaptively select more effective information for fusion under different lighting conditions. Extensive experiments show that the proposed fusion approach has competitive advantages over six current state-of-the-art deep-learning methods in highlighting target features and describing the global scene.

In this work, the formability of copper foils was analyzed based on a series of multi-step micro deep drawing (MMDD) tests. The copper foils were annealed at temperatures ranging from 300 to 700 °C for 5 min, followed by tensile tests and electron backscatter diffraction (EBSD) tests for the exploration of mechanical behavior and microstructural characterization of annealed specimens. The results show that the as-received copper foils exhibit poor formability and cannot form micro cups during MMDD. When the annealing temperature attains 500 °C, a homogeneous microstructure was formed inside copper foils, enhancing the formability of materials and improving the surface quality of formed products. Nevertheless, both the elongation and tensile strength decrease dramatically due to the easy occurrence of incoordination deformation in coarse grain bands with further increase in annealing temperature to 700 °C. Consequently, marked wrinkling is generated on the surface of drawn cups and obvious thinning occurs at the nose radius region due to metal flow. In addition, the results show that the E texture component within the copper foils is favorable for MMDD. Overall, an optimal annealing temperature of 500 °C is obtained with improved formability of copper foils and forming quality of drawn cups.

Abstract Background Male patients with papillary thyroid carcinoma (PTC) tend to have poorer prognosis compared to females, partially attributable to a higher rate of lymph node metastasis (LNM). Developing a precise predictive model for LNM occurrence in male PTC patients is imperative. While preliminary predictive models exist, there is room to improve accuracy. Further research is needed to create optimized prognostic models specific to LNM prediction in male PTC cases. Methods We conducted a comprehensive search of publicly available microarray datasets to identify candidate genes continuously upregulated or downregulated during PTC progression in male patients only. Univariate Cox analysis and lasso regression were utilized to construct an 11-gene signature predictive of LNM. TIPARP emerged as a key candidate gene, which we validated at the protein level using immunohistochemical staining. A prognostic nomogram incorporating the signature and clinical factors was developed based on the TCGA cohort. Results The 11-gene signature demonstrated good discriminative performance for LNM prediction in training and validation datasets. High TIPARP expression associated with advanced stage, high T stage, and presence of LNM. A prognostic nomogram integrating the signature and clinical variables reliably stratified male PTC patients into high and low recurrence risk groups. Conclusions We identified a robust 11-gene signature and prognostic nomogram for predicting LNM occurrence in male PTC patients. We propose TIPARP as a potential contributor to inferior outcomes in males, warranting further exploration as a prognostic biomarker and immunotherapeutic target. Our study provides insights into the molecular basis for gender disparities in PTC.

The Unmanned Aerial Vehicle (UAV) tracking tasks find extensive utility across various applications. However, current Transformer-based trackers are generally tailored for diverse scenarios and lack specific designs for UAV applications. Moreover, due to the complexity of training in tracking tasks, existing models strive to improve tracking performance within limited scales, making it challenging to directly apply lightweight designs. To address these challenges, we introduce an efficient attention mechanism known as Macaron Attention, which we integrate into the existing UAV tracking framework to enhance the model’s discriminative ability within these constraints. Specifically, our attention mechanism comprises three components, fixed window attention (FWA), local squeezing global attention (LSGA), and conventional global attention (CGA), collectively forming a Macaron-style attention implementation. Firstly, the FWA module addresses the multi-scale issue in UAVs by cropping tokens within a fixed window scale in the spatial domain. Secondly, in LSGA, to adapt to the scale variation, we employ an adaptive clustering-based token aggregation strategy and design a “window-to-window” fusion attention model to integrate global attention with local attention. Finally, the CGA module is applied to prevent matrix rank collapse and improve tracking performance. By using the FWA, LSGA, and CGA modules, we propose a brand-new tracking model named MATrack. The UAV123 benchmark is the major evaluation dataset of MATrack with 0.710 and 0.911 on success and precision, individually.

Abstract Abdominal aortic aneurysm (AAA) is a life‐threatening vascular disease but effective drugs for treatment of AAA are still lacking. Recently, erythropoietin (EPO) is reported to induce AAA formation in apolipoprotein‐E knock out (ApoE−/−) mice but an effective antagonist is unknown. In this study, formoterol, a β2 adrenergic receptor (β2AR) agonist, is found to be a promising agent for inhibiting AAA. To test this hypothesis, ApoE−/− mice are treated with vehicle, EPO, and EPO plus low‐, medium‐, and high‐dose formoterol, respectively. The incidence of AAA is 0, 55%, 35%,10%, and 55% in these 5 groups, respectively. Mechanistically, senescence of vascular smooth muscle cell (VSMC) is increased by EPO while decreased by medium‐dose formoterol both in vivo and in vitro, manifested by the altered expression of senescence biomarkers including phosphorylation of H2AXserine139, senescence‐associated β‐galactosidase activity, and P21 protein level. In addition, expression of sirtuin 1 (SIRT1) in aorta is decreased in EPO‐induced AAA but remarkably elevated by medium‐dose formoterol. Knockdown of β2AR and blockage of cyclic adenosine monophosphate (cAMP) attenuate the inhibitory role of formoterol in EPO‐induced VSMC senescence. In summary, medium‐dose formoterol attenuates EPO‐induced AAA via β2AR/cAMP/SIRT1 pathways, which provides a promising medication for the treatment of AAA.

Abstract Background The COVID-19 pandemic led many educational institutions to shift to online courses, making blended education a significant trend in teaching. We examined the effectiveness of blended learning in an evidence-based medicine course. Methods We compared the examination scores of a blended learning group, an online only group, and a traditional offline group and conducted a questionnaire survey on students’ preferences for different learning modes and the reasons for their preferences. A total of 2100 undergraduate students in clinical medicine were included in this cross-sectional study. Examination results were collected, and questionnaires were administered to the study participants. We compared the mean scores and exam pass rates of the three teaching groups using ANOVA and c2test for multiple comparisons. Results The blended group’s exam scores and pass rate were significantly higher than those of the offline and online groups. Furthermore, 71.6% preferred the blended teaching mode. In the survey on " learning effectiveness”, the majority of the students believed that blended education could better enhance the initiative of learning, the interest of the course, the pertinence of the learning content, the comprehension of evidence-based medical thinking, and the basic skills of evidence-based practice. Subsequently, in a questionnaire administered to a blended group of students, their foremost reason for liking online instruction was ‘flexible in time and space’ (99%), followed by ‘can be viewed repeatedly, facilitating a better understanding of knowledge points’ (98%). Their foremost reason for liking offline teaching was ‘helps to create a good learning atmosphere’ (97%), followed by ‘teachers can control students’ learning status in real time’ (89%). Conclusions This study explored the effectiveness of learning in evidence-based medicine courses by comparing the learning outcomes and personal perceptions of three different teaching modes. This is the first cross-sectional study in which three different teaching models are compared and discussed in an evidence-based medicine course. We also elaborate on the specific instructional protocols for each model. This study shows that using a blended education approach in evidence-based medicine courses can improve students’ learning motivation, autonomy, and satisfaction. It also enhances instructional efficiency, thereby improving students’ understanding of the course content.

Abstract In the field of landmark detection based on deep learning, most of the research utilise convolutional neural networks to represent landmarks, and rarely adopt Transformer to represent and encode landmarks. Meanwhile, many works focus on modifying the network structure to improve network performance, and there is little research on the distribution of landmarks. In this article,the authors propose an unsupervised model to extract landmarks of objects in images. First, Transformer structure is combined with the convolutional neural network structure to represent and encode the landmarks; next, positive and negative sample pairs between landmarks are constructed, so that the semantically consistent landmarks on the image are pulled closer in the feature space and the semantically inconsistent landmarks are pushed farther in the feature space; then the authors concentrate their attention on the most active points to distinguish the landmarks of an object from the background; finally, based on the new contrastive loss, the network reconstructs the image by the landmarks of the object that are continuously learnt during training. Experiments show that the proposed model achieves better performance than other unsupervised methods on the CelebA, Annotated Facial Landmarks in the Wild, 300W datasets.

Abstract Noble and commoner in the medieval China capital of Chang’an (modern-day Xi’an) engaged in an intricately intertwined agricultural and pastoral economy. The period represents the peak of the integration of nomadic and agricultural populations in early China. Here we firstly reported human collagen δ 13C and δ 15N values for eight middle age nobles whose lifespans cover roughly a century (AD 503–604) and who include a Northern Zhou, Emperor Wu (北周武帝) and his consort, Empress A’shina (阿史那皇后). We also consider stable isotopic evidence from nine individuals in commoner burials in the Chang’an region, likewise dating to the sixth century. This century-long dietary profile provides direct archeological evidence for dietary variability at this core metropolitan population. Constructing a comparative model using existing historical human isotopic data (n = 1233) from northern China and its northern Steppe, we find a marked regional divergence in noble and commoner dietary patterns. We trace this back to the considerable variety of agricultural and pastoral dietary practices, likely pointing to different economic and geographic backgrounds of the individuals under study. We argue that a two-way relationship between shifting pastoral and agricultural practices was reflected to separate degrees at the elite and commoner level. This study offers an improved understanding of the multiplicity of dietary patterns and associated lifeways for the metropolitan societies of medieval northern China.

In the realm of visual tracking, remote sensing videos captured by Unmanned Aerial Vehicles (UAVs) have seen significant advancements with wide applications. However, there remain challenges to conventional Transformer-based trackers in balancing tracking accuracy and inference speed. This problem is further exacerbated when Transformers are extensively implemented at larger model scales. To address this challenge, we present a fast and efficient UAV tracking framework, denoted as SiamPT, aiming to reduce the number of Transformer layers without losing the discriminative ability of the model. To realize it, we transfer the conventional prompting theories in multi-model tracking into UAV tracking, where a novel self-prompting method is proposed by utilizing the target’s inherent characteristics in the search branch to discriminate targets from the background. Specifically, a self-distribution strategy is introduced to capture feature-level relationships, which segment tokens into distinct smaller patches. Subsequently, salient tokens within the full attention map are identified as foreground targets, enabling the fusion of local region information. These fused tokens serve as prompters to enhance the identification of distractors, thereby avoiding the demand for model expansion. SiamPT has demonstrated impressive results on the UAV123 benchmark, achieving success and precision rates of 0.694 and 0.890 respectively, while maintaining an inference speed of 91.0 FPS.

【Objective】The combining ability and heritability of main agronomic traits of 6 rice restorer lines were analyzed and their application value was evaluated.【Method】6 new rice restorer lines of Shanhui series and 5 three-line sterile lines were selected and 30 hybrid rice combinations were prepared according to the 6×5 incomplete diallel cross design. And combining ability and heritability of 8 main agronomic traits were analyzed.【Result】The contribution rate of GCA genotype variance of restorer line was higher than that of sterile line in yield per plant, plant height and effective panicle number, therefore, the phenotypes of yield per plant, plant height and effective panicle number of hybrid rice combinations were mainly determined by the inheritance of restorer line. Growth period, seed setting rate, panicle length and yield per plant showed lower contribution rate of GCA genotype variance (lower than 50%), therefore, the 4 traits would be mainly influenced by non-additive effect in breeding practice; 1000-grain weight, plant height, effective panicle number and total grain number showed higher contribution rate of GCA genotype variance (higher than 55%), therefore, they are mainly affected by additive effects of genes. The narrow heritability of 1000-grain weight and plant height was 80.99% and 76.03%, which showed good stability in offspring and could be selected in low generation in breeding practice. There was no obvious correlation between GCA and SCA. Both SR02 and SR04 were elite restorer lines. According to the parent utilization value classification method, SR02 would be easy to create large-grain and high-yield hybrid rice combinations in breeding practice, which showed as Class Ⅰ parent in yield per plant and Class Ⅱ parent in seed setting rate and 1000-grain weight; SR04, showed as Class Ⅱ parent in yield per plant, effective panicle number, total grain number and seed setting rate, would be easy to create high-yield hybrid rice combinations with multiple panicles, and SR04 also showed the potential of lodging resistance. New hybrid rice combinations Tianfeng A × SR01, Jinheng A × SR06 and Jinheng A×SR03 showed the highest SCA value in per plant weight, which were 26.320, 26.049 and 20.723, respectively, and all of 3 combinations showed good utilization value.【Conclusion】In the practice of hybrid rice breeding, the selection of parents should not only pay attention to the selection of high GCA parents, but also strengthen the selection of SCA of combinations. At the same time, according to the genetic characteristics of the parents, the design of hybrid rice parental matching should follow the principle of "exploiting strengths and avoiding weaknesses, complementing traits".The inheritance of hybrid rice in yield per plant, plant height and effective panicle number are mainly affected by restorer line. SR02 and SR04 are elite restorer lines, which have good application value. New hybrid rice combinations Tianfeng A × SR01, Jinheng A × SR06 and Jinheng A × SR03 show high SCA value in yield characters, with good application prospects.

During X-ray inspection detection, a detector converts the collected X-rays from objects into electrical signals, which are then transmitted to a computer for image processing and analysis. From the aspect of digital image processing, detection tasks mainly focus on data processing and transformation to identify valuable features, which make the algorithms more effective. The consistent requirement for speed and accuracy in X-ray prohibited item detection is still not fully satisfied, especially in pictures obtained under special imaging conditions. For noisy X-ray images with heavy occlusion, a direct and suitable approach of representation learning is the optimal solution. According to our study, we realized that heterogeneous information fusion from different extraction approaches can be applied effectively to overcome this issue. We proposed two innovative algorithms to extract effective features of X-ray objects to significantly improve the efficiency of X-ray prohibited item detection. The brief model we proposed fuses the representations learned from the noisy X-ray images and outperforms the best model (DOAM-O) so far on OPIXray. Furthermore, the attention module we designed to select information on deep learning and representation strengthens the model; considering this, the model utilizes lesser time for both training and inference, which makes it easier to be trained on a lightweight computing device.

The detection of an infrared small target faces the problems of background interference and non-obvious target features, which have yet to be efficiently solved. By employing the non-local self-correlation characteristic of the infrared images, the principle component pursuit (PCP)-based methods are demonstrated to be applicable to infrared small target detection in a complex scene. However, existing PCP-based methods heavily depend on the uniform distribution of the background pixels and are prone to generating a high number of false alarms under strong clutter situations. In this paper, we propose a group low-rank regularized principle component pursuit model (GPCP) to solve this problem. First, the local image patches are clustered into several groups that correspond to different grayscale distributions. These patch groups are regularized with a group low-rank constraint, enabling an independent recovery of different background regions. Then, GPCP model integrates the group low-rank components with a global sparse component to extract small targets from the background. Different singular value thresholds can be exploited for image groups corresponding to different brightness and grayscale variance, boosting the recovery of background clutters and also enhancing the detection of small targets. Finally, a customized optimization approach based on alternating direction method of multipliers is proposed to solve this model. We set three representative detection scenes, including the ground background, sea background and sky background for experiment analysis and model comparison. The evaluation results show the proposed model has superiority in background suppression and achieves better adaptability for different scenes compared with various state-of-the-art methods.

Due to their devastating ability to extract features, convolutional neural network (CNN)-based approaches have achieved tremendous success in hyperspectral image (HSI) classification. However, previous works have been dedicated to constructing deeper or wider deep learning networks to obtain exceptional classification performance, but as the layers get deeper, the gradient disappearance problem impedes the convergence stability of network models. Additionally, previous works usually focused on utilizing fixed-scale convolutional kernels or multiple available, receptive fields with varying scales to capture features, which leads to the underutilization of information and is vulnerable to feature learning. To remedy the above issues, we propose an innovative hybrid-scale feature enhancement network (HFENet) for HSI classification. Specifically, HFENet contains two key modules: a hybrid-scale feature extraction block (HFEB) and a shuffle attention enhancement block (SAEB). HFEB is designed to excavate spectral–spatial structure information of distinct scales, types, and branches, which can augment the multiplicity of spectral–spatial features while modeling the global long-range dependencies of spectral–spatial informative features. SAEB is devised to adaptively recalibrate spectral-wise and spatial-wise feature responses to generate the purified spectral–spatial information, which effectively filters redundant information and noisy pixels and is conducive to enhancing classification performance. Compared with several sophisticated baselines, a series of experiments conducted on three public hyperspectral datasets showed that the accuracies of OA, AA, and Kappa all exceed 99%, demonstrating that the presented HFENet achieves state-of-the-art performance.

Multiple object tracking (MOT) in videos captured by unmanned aerial vehicle (UAV) is a fundamental aspect of computer vision. Recently, the one-shot tracking paradigm integrates the detection and re-identification (ReID) tasks, striking a balance between tracking accuracy and inference speed. This paradigm alleviates task conflicts and achieves remarkable results through various feature decoupling methods. However, in challenging scenarios like drone movements, lighting changes and object occlusion, it still encounters issues with detection failures and identity switches. In addition, traditional feature decoupling methods directly employ channel-based attention to decompose the detection and ReID branches, without a meticulous consideration of the specific requirements of each branch. To address the above problems, we introduce an asymmetric feature enhancement network with a global coordinate-aware enhancement (GCAE) module and an embedding feature aggregation (EFA) module, aiming to optimize the two branches independently. On the one hand, we develop the GCAE module for the detection branch, which effectively merges rich semantic information within the feature space to improve detection accuracy. On the other hand, we introduce the EFA module for the ReID branch, which highlights the significance of pixel-level features and acquires discriminative identity embedding through a local feature aggregation strategy. By efficiently incorporating the GCAE and EFA modules into the one-shot tracking pipeline, we present a novel MOT framework, named AsyUAV. Extensive experiments have demonstrated the effectiveness of our proposed AsyUAV. In particular, it achieves a MOTA of 38.3% and IDF1 of 51.7% on VisDrone2019, and a MOTA of 48.0% and IDF1 of 67.5% on UAVDT, outperforming existing state-of-the-art trackers.

Palladin (PALLD) belongs to the PALLD/myopalladin (MYPN)/myotilin family of actin-associated immunoglobulin-containing proteins in the sarcomeric Z-line. PALLD is ubiquitously expressed in several isoforms, and its longest 200 kDa isoform, predominantly expressed in striated muscle, shows high structural homology to MYPN. MYPN gene mutations are associated with human cardiomyopathies, whereas the role of PALLD in the heart has remained unknown, partly due to embryonic lethality of PALLD knockout mice. In a yeast two-hybrid screening, CARP/Ankrd1 and FHOD1 were identified as novel interaction partners of PALLD’s N-terminal region. To study the role of PALLD in the heart, we generated conditional (cPKO) and inducible (cPKOi) cardiomyocyte-specific PALLD knockout mice. While cPKO mice exhibited no pathological phenotype, ablation of PALLD in adult cPKOi mice caused progressive cardiac dilation and systolic dysfunction, associated with reduced cardiomyocyte contractility, intercalated disc abnormalities, and fibrosis, demonstrating that PALLD is essential for normal cardiac function. Double cPKO and MYPN knockout (MKO) mice exhibited a similar phenotype as MKO mice, suggesting that MYPN does not compensate for the loss of PALLD in cPKO mice. Altered transcript levels of MYPN and PALLD isoforms were found in myocardial tissue from human dilated and ischemic cardiomyopathy patients, whereas their protein expression levels were unaltered.

Abstract Siamese network‐based object tracking learns features of a target object marked in the first frame and that of the object in subsequent frames simultaneously and then measures similarity between two features to recognize and locate the object. Owing to their efficiency and high accuracy, Siamese networks have attracted much attention recently. However, tracking accuracy decreases significantly when there are scale changes, occlusion, and pose variations due to the way that Siamese networks estimate feature similarity. To address this issue, the authors propose a tracking algorithm, named Semantic and context features integration for robust object tracking that integrates local and global features of the object. Local features provide context information for tracking parts of the object, while global features contain semantic information for tracking the object. The authors meticulously design local and global classification and regression heads and integrate them into one uniform framework to achieve integration tracking. This method effectively alleviates low accuracy in complex scenes such as scale changes, deformation, and occlusion. Numerous experiments demonstrate that this method achieves state‐of‐art (SOTA) performance with 45 FPS on a single RTX2060 Super GPU on public tracking datasets, including VOT2016, VOT2019, OTB100, GOT‐10k, and LaSOT, and its effectiveness and efficiency is confirmed.

Abstract In this work, we synthesized an ionic liquid (IL)—(Dimethylamino)ethyl Methacrylate maleate ([DMAEMA]MA) as the crosslinker, through one-pot to synthesized hydrogels with high adsorption capacity for dye in water. Both anionic dyes (methylene blue, rhodamine B) and cationic dyes (congo red, eosin B) could be adsorbed by this type of hydrogel with different adsorption mechanism, and its adsorption capacity for methylene blue (MB), rhodamine B (RHB), congo red (CR), eosin B (EB) were 489.1, 463.2, 465.5 and 462 mg/g (amount of dye adsorbed per gram of hydrogel), respectively. The surface structure of the hydrogel before and after adsorption was observed and compared by scanning electron microscope (SEM). After studying the adsorption isotherms of the hydrogel adsorbent, it was found that the hydrogel adsorbent had two adsorption mechanisms. This was not found in reported literatures previously.

Convolutional neural networks (CNNs) have shown outstanding feature extraction capability and become a hot topic in the field of hyperspectral image (HSI) classification. However, most of the prior works usually focus on designing deeper or wider network architectures to extract spatial and spectral features, which give rise to difficulty for optimization and more parameters along with higher computation. Moreover, how to learn spatial and spectral information more effectively is still being researched. To tackle the aforementioned problems, a decompressed spectral-spatial multiscale semantic feature network (DSMSFNet) for HSI classification is proposed. This model is composed of a decompressed spectral-spatial feature extraction module (DSFEM) and a multiscale semantic feature extraction module (MSFEM). The former is devised to extract more discriminative and representative global decompressed spectral-spatial features in a lightweight extraction manner, while the latter is constructed to expand the range of available receptive fields and generate clean multiscale semantic features at a granular level to further enhance the classification performance. Compared with progressive classification approaches, abundant experimental results on three benchmark datasets prove the superiority of our developed DSMSFNet model.

Summary: A high point of Tibetan Plateau (TP) civilization, the expansive Tubo Empire (618–842 AD) wielded great influence across ancient western China. However, whether the Tubo expansion was cultural or demic remains unclear due to sparse ancient DNA sampling. Here, we reported ten ancient genomes at 0.017- to 0.867-fold coverages from the Dulan site with typical Tubo archaeological culture dating to 1308–1130 BP. Nine individuals from three different grave types have close relationship with previously reported ancient highlanders from the southwestern Himalayas and modern core-Tibetan populations. A Dulan-related Tubo ancestry contributed overwhelmingly (95%–100%) to the formation of modern Tibetans. A genetic outlier with dominant Eurasian steppe-related ancestry suggesting a potential population movement into the Tubo-controlled regions from Central Asia. Together with archeological evidence from burial styles and customs, our study suggested the impact of the Tubo empire on the northeast edge of the TP involved both cultural and demic diffusion.

Background: Iron overload can accelerate the accumulation of lipid oxides and contribute to the progression of atherosclerosis. Ferritin heavy chain (FT-H) exhibits oxidase activity, which inhibits the toxicity of ferrous ions and reduces oxidative damage. We investigated the effect of the intraperitoneal injection of FT-H on the progression of atherosclerosis in APOE-knockout mice (Apo-E(−/−) mice). Methods: All mice were fed on a high-fat diet. After 10 weeks, the mice were divided into an injection group (n = 4) and a control group (n = 4). The injection group was injected intraperitoneally with FT-H (50 mg/kg, once a week), and the control group was treated with PBS buffer (at an equal volume to the injection group, once a week). After 10 weeks of intervention, MRI of the aortas was performed. Then, the animals were sacrificed, and tissues were taken. Hematoxylin–eosin (HE) staining was used for histomorphometry, Masson staining was used to quantify the collagen content in the arteries, Prussian blue staining was used to visualize iron deposition in the arteries, and MRI was used to analyze the structure of the aorta in vivo. Immunohistochemistry was performed to detect the expression of MCP-1, MMP-2, MMP-9, FT-H, FT-L, TfR1, NRF-2 and GPX-4. Results: The serological results showed that the injection group had lower levels of glucose (Glu), triacylglycerol (TG), cholesterol (CHO), low-density lipoprotein-C (LDL-C) and malondialdehyde (MDA) (p = 0.0058, p = 0.0098, p = 0.0019, p = 0.0368 and p = 0.0025, respectively), and their serum ferritin (SF) and superoxide dismutase (SOD) levels were higher (p = 0.0004 and p < 0.0001). The Masson staining and MRI results showed that the injection group had less collagen deposition (p = 0.0226), a larger arterial lumen area and arterial volume (p = 0.0006 and p = 0.0005), thinner arterial wall thickness (p = 0.0013) and a more stable arterial plaque structure (p < 0.0001). The immunohistochemical results showed reduced expression of FT-H, FT-L, TfR1, MMP-2, MMP-9, MCP-1 and NRF-2 in the injection group (p = 0.0054, p = 0.0242, p = 0.0221, p = 0.0477, p = 0.0131, p = 0.0435 and p = 0.0179). Prussian blue staining showed that the area of iron-positive areas in the aortic plaques of the control group was larger than that of injected group. The expression of GPX-4 was lower in the control group than in the injection group (p = 0.016). Conclusions: The intraperitoneal administration of FT-H to Apo-E(−/−) mice resulted in lower blood glucose and lipid levels; reduced iron and iron metabolism protein deposition in the aorta; reduced indices of their ferroptosis, oxidation and inflammatory aggregation; and reduced collagen deposition in the aorta, which delayed the process of aortic atherosclerosis in mice.

Heavy metal wastewater poses a significant environmental challenge due to its harmful effect on organisms and difficult biodegradation. To address this issue, hydrogel has been used as a promising solution for the adsorption of heavy metal ions in water, offering advantages such as low cost, simple design, and environmental friendliness. In this study, we synthetized a novel poly-acrylamide/acrylic acid/vinyl imidazole bromide (PAM/AA/[Vim]Br2) hydrogel as an effective adsorbent for the removal of NiII, CuII, ZnII, and CrIII from water. The structure of the hydrogel was characterized by using techniques such as Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). By exploring various parameters such as monomer ratio, neutralization degree, crosslinking agent addition amount, and initiator addition amount, the highest swelling ratio of the PAM/AA/[Vim]Br2 hydrogel reached 40,012%. One of the notable aspects of this study lay in the investigation of the adsorption behavior of the hydrogel towards heavy metal ions at different concentrations. The adsorption isotherm calculations and X-ray photoelectron spectroscopy (XPS) analysis revealed distinct adsorption mechanisms. At low concentrations, the hydrogel exhibits a multilayer physical adsorption mechanism, with heavy metal ion removal rates exceeding 80%; while at high concentrations, it demonstrates a monolayer chemical adsorption mechanism, with heavy metal ion removal rates above 90%. This dual mechanism approach distinguishes our study from previous reports on the removal of heavy metal ions using hydrogels and shows good ion adsorption efficiency at both high and low concentrations. To the best of our knowledge, this is the first report to explore the removal of heavy metal ions from water using hydrogels with such intriguing dual mechanisms. Overall, the utilization of the PAM/PAA/[Vim]Br2 hydrogel as an adsorbent for heavy metal ion removal presents a promising and innovative approach, contributing to the development of environmentally friendly solutions for heavy metal wastewater treatment.

Background: Mitophagy is closely related to cancer initiation and progression. However, heterogeneity with reference to mitophagy remains unexplored in pancreatic adenocarcinoma (PAAD).Materials and methods: We used Reactome database to download the mitophagy-related, glycolysis-related and cholesterol biosynthesis-related signaling pathways. Unsupervised clustering using the “ConsensusClusterPlus” R package was performed to identify molecular subtypes related to mitophagy and metabolism. Prognosis-related mitophagy regulators were identified by univariate Cox regression analysis. Receiver operating characteristics (ROC) and Kaplan-Meier (K-M) survival analyses were used to assess the diagnostic and prognostic role of the hub genes and prognosis risk model. Weighted gene co-expression network analysis (WGCNA) was utilized for screening the mitophagy subtype-related hub genes. Metascape was utilized to carry out functional enrichment analysis. The “glmnet” R package was utilised for LASSO, and the “e1071” R package was utilised for SVM. Chemotherapeutic drug sensitivity was estimated using the R package “pRRophetic” and Genomics of Drug Sensitivity in Cancer (GDSC) database. The nomogram was established by the “rms” R package.Results: Three distinct mitophagy subtypes (low, high and intermediate) of PAAD were identified based on the landscape of mitophagy regulators. The high mitophagy subtype had the worst prognosis, highest mRNA expression-based stemness index scores and most hypoxic environment compared to the other subtypes. Additionally, glycolysis and cholesterol biosynthesis were significantly elevated. Three mitophagy subtype-specific gene signatures (CAST, CCDC6, and ERLIN1) were extracted using WGCNA and machine learning. Moreover, PAAD tumours were insensitive to Erlotinib, Sunitinib and Imatinib in the high mitophagy subtype and high CAST, CCDC6, and ERLIN1 expressed subtypes. Furthermore, CAST, CCDC6, and ERLIN1 affected immune cell infiltration (M1 and CD8Tcm), resulting in the altered prognosis of patients with PAAD. A nomogram was constructed to screen patients with the low mitophagy subtype, which showed a higher sensitivity to chemotherapeutic agents.Conclusion: Based on various bioinformatics tools and databases, the PAAD heterogeneity regarding mitophagy was systematically examined. Three different PAAD subtypes having different outcomes, metabolism patterns and chemosensitivity were observed. Moreover, three novel biomarkers that are closely associated with mitophagy and have the potential to guide individualised treatment regimens in PAAD were obtained.

The growing world population has produced an increasing demand for seafood, and the aquaculture industry is under corresponding pressure to fill this demand. The offshore ecology and environment are under significant threat with the continuous expansion of the scale and intensity of aquaculture. Integrated multi-tropic aquaculture (IMTA) is a healthy and sustainable mariculture model based on ecosystem-level management, and has become popular in recent years. It is an effective way to cope with the significant changes in offshore ecosystems under multiple stressors. Phytoplankton bacteria are essential to maintaining the marine ecosystem’s balance and stability. Investigating the changes in the community structure of marine planktonic bacteria can elucidate the impact of mariculture on the marine ecological environment. This study took the fish-shell IMTA system with natural macroalgae nearby as the object, and monitored the plankton community’s structure in the system’s surface seawater for four quarters from July 2020 to April 2021. The space–time distribution characteristics and influencing factors of the plankton community in the surface water were examined. The results showed no significant difference between the planktonic bacterial communities at different sampling sites. There was also no significant difference in the α-diversity index. However, the dominant species and abundance of planktonic bacteria at the sampling sites differed significantly. Proteobacteria and Bacteroides were the dominant groups of planktonic bacteria. The results of the distance-based redundancy analysis demonstrated that chemical oxygen demand, chlorophyll a, and dissolved oxygen constituted the primary environmental factors affecting the planktonic bacterial community structures. The heatmap also showed that NH4+- N, temperature, and salinity levels were also related to certain planktonic bacteria. This study preliminarily identified the distribution of the surface bacterial plankton community and its response to changes in environmental factors in the sea area near Xiasanhengshan Island. The results provide a preliminary basis for assessing the health and stability of the IMTA system in open sea areas.

Background: Although many genes related to epithelial-mesenchymal transition (EMT) have been explored in hepatocellular carcinoma (HCC), their prognostic significance still needs further analysis.Methods: Differentially expressed EMT-related genes were obtained through the integrated analysis of 4 Gene expression omnibus (GEO) datasets. The univariate Cox regression and Lasso Cox regression models are utilized to determine the EMT-related gene signature. Based on the results of multivariate Cox regression, a predictive nomogram is established. Time-dependent ROC curve and calibration curve are used to show the distinguishing ability and consistency of the nomogram. Finally, we explored the correlation between EMT risk score and immune immunity.Results: We identified a nine EMT-related gene signature to predict the survival outcome of HCC patients. Based on the EMT risk score’s median, HCC patients in each dataset were divided into high and low-risk groups. The survival outcomes of HCC patients in the high-risk group were significantly worse than those in the low-risk group. The prediction nomogram based on the EMT risk score has better distinguishing ability and consistency. High EMT risk score was related to immune infiltration.Conclusion: The nomogram based on the EMT risk score can reliably predict the survival outcome of HCC patients, thereby providing benefits for medical decisions.

Objective: The purpose of this study was to establish an N6-methylandenosine (m6A)-related long non-coding RNA (lncRNA) signature to predict the prognosis of hepatocellular carcinoma (HCC).Methods: Pearson correlation analysis was used to identify m6A-related lncRNAs. We then performed univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to construct an m6A-related lncRNA signature. Based on the cutoff value of the risk score determined by the X-title software, we divided the HCC patients into high -and low-risk groups. A time-dependent ROC curve was used to evaluate the predictive value of the model. Finally, we constructed a nomogram based on the m6A-related lncRNA signature.Results: ZEB1-AS1, MIR210HG, BACE1-AS, and SNHG3 were identified to comprise an m6A-related lncRNA signature. These four lncRNAs were upregulated in HCC tissues compared to normal tissues. The prognosis of patients with HCC in the low-risk group was significantly longer than that in the high-risk group. The M6A-related lncRNA signature was significantly associated with clinicopathological features and was established as a risk factor for the prognosis of patients with HCC. The nomogram based on the m6A-related lncRNA signature had a good distinguishing ability and consistency.Conclusion: We identified an m6A-related lncRNA signature and constructed a nomogram model to evaluate the prognosis of patients with HCC.

Coral sand particles with low strength and rich pores affect the mechanical properties and durability of their cement-based materials. This paper investigates the mechanical properties and internal pore evolution of graphene oxide-modified coral sand cement mortar under sulfate erosion environment, aiming to better understand the effect of graphene oxide on the mechanical properties of coral sand cement mortar under an erosion environment, especially in wet and dry cycles. Mechanical properties of coral sand cement mortar were tested with different amounts of graphene oxide and different erosion cycles to understand the relationship between the change of flexural and compressive strength and the content and erosion cycle. The microscopic parameters of hydration products and internal dissolution pores were analyzed by XRD and SEM. The results showed that the degree of erosion performance improvement is related to the content of GO, and the two are positively correlated at first and then negatively correlated. At 0.03 wt% content, the retardation effect is the best. There were more orderly pore arrangement with less morphological complexity, when the dissolution amount of Ca(OH)2, porosity, total pore area, pore number, probability entropy, average form factor, and fractal dimension decreased. Coral sand cement mortar increases resistance to sulfate attack, which graphene oxide inhibits the formation of new dissolution pores and the expansion of original pores.

In the dim-small target detection field, background suppression is a key technique for stably extracting the target. In order to effectively suppress the background to enhance the target, this paper presents a novel background modeling algorithm, which constructs base functions for each pixel based on the local region background and models the background of each pixel, named single pixel background modeling (SPB). In SPB, the low-rank blocks of the local backgrounds are first obtained to construct the background base functions of the center pixel. Then, the background of the center pixel is optimally estimated by the background bases. Experiments demonstrate that in the case of extremely low signal-to-noise ratio (SNR < 1.5 dB) and complex motion state of targets, SPB can stably and effectively separate the target from the strongly undulant sky background. The difference image obtained via SPB background modeling has the characters: the non-target residual could be white noise, and the target is significantly enhanced. Compared with the other typical five algorithms, SPB remarkably outperforms other algorithms to detect the target of a low signal-to-noise ratio.

Sponge city is a method of managing rain floods, proposed by China to deal with urban waterlogging and the overflow pollution of drainage pipe networks, which indicates a more effective strategy to promote urban sustainable development. Due to the diversity of sponge city construction objectives and the complexity of the developmental system, a unified and effective sustainability evaluation method has not yet been formed. Based on the emergy analysis method, the indicators of ecosystem service, the construction cost, the runoff regulation, and the pollutant reduction of sponge city construction are thus included in the evaluation system, and the sustainable evaluation model of a sponge city is fully constructed. Taking the core area in the south of Haicang in Xiamen City as the studying object, the runoff regulation, and the pollutant reduction indicators, are carefully obtained by using Info Works simulation software. The results showed that: ① the quality of COD (Chemical Oxygen Demand) of pollutants discharged from the research object is 409.8t/a, the total runoff is 3.579 million m3/a, the current annual total runoff control rate is 37.15%, and the current emergy index ESI of sponge city system is 0.05 < 1, which is in an unsustainable state, It is necessary to upgrade and transform the urban underlying surface; ② The transformation intensity of three LID (Low Impact Development) facilities, i.e., concave green space, permeable pavement and green roof, is carefully selected as different construction schemes. When the construction intensity of LID is 25%, the emergy index ESI (Emergy Sustainable Index) = 1.08, which meets the basic requirements of sustainable development; As long as the reconstruction construction intensity is 30%, the growth value of ESI, ΔESI, is the largest, the sustainable growth effect of sponge city construction is the most obvious, and the marginal benefit is the largest; ③ As long as the total annual runoff control rate of the research object is 69–82%, its sustainable energy index ESI should be within the range of 1.39–1.83. If ESI is less than 1.39, this indicates that the total annual runoff control rate of the research area cannot adapt to the planning requirements of 69%.

Heterogeneous catalytic ozonation is an alternative approach for the removal of refractory pollutants from water, and the fabrication of mesoporous materials with high dispersibility would enhance the catalytic efficiency. A mesoporous CeO2 was prepared by the nanocasting method with SBA-15 as a hard template, and was investigated in the catalytic ozonation of atrazine. The synthetical CeO2 nanorods have a specific surface area of 95.08 m2/g, a diameter of 10.16 nm, and a spacing of 2.18 nm. The removal rate of atrazine was 85.5%, 64.8%, and 46.4% in the order of catalytic ozonation by synthetical CeO2 > single ozonation > catalytic ozonation by commercial CeO2, respectively. The superior activity of the synthetical CeO2 could be attributed to the well-ordered mesoporous structure, the high surface area, and the redox Ce3+/Ce4+ cycling. Moreover, eight organic intermediates were identified after one minute of catalytic ozonation of atrazine, and the cleavage of the ethylamino group was proposed as the main pathway of atrazine degradation.

Maternal dysglycemia and lipid metabolic dysfunction have been recognized as risk factors for pregnancy complications and adverse perinatal outcome jointly and separately, but current diagnostic window-period which is at the end of the second trimester might be late to avoid chronic adverse impacts on both mother and fetus. A retrospective cohort study involving 48,973 women with fasting blood glucose (FPG) below diagnostic thresholds and lipid screening in early pregnancy was performed. Data of pregnancy outcomes including gestational diabetes mellitus (GDM), hypertensive disorders in pregnancy (HDP), and neonatal outcomes were obtained for multivariable logistic analysis. As a result, higher FPG (≥75th, 4.68 mM) significantly increased risks of GDM (Adjusted odds ratio (AOR), 2.81; 95% CI, 2.60 to 3.05) and HDP (1.98; 1.81 to 2.16), and slightly increased risks of large for gestational age (LGA), macrosomia births and neonatal intensive care unit (NICU) compared to women with low FPG (≤25th, 4.21 mM). High maternal triglyceride (mTG) level had higher risks of GDM and HDP in all maternal FPG strata. Further analysis showed that women of top quartile of glucose combined with upper 10 percentile triglyceride have higher risks for GDM (AOR, 5.97; 95% CI, 5.26 to 6.78; risk difference 30.8, 95% CI 29.2 to 32.3) and HDP (AOR, 2.56; 95% CI, 2.20 to 2.99, risk difference 11.3, 95% CI 9.9 to 12.7) when compared to those in women of the bottom strata after adjustment. Therefore, both the early-pregnancy FPG and mTG levels should be screened among overall population including the low-risk population to reduce the incidence of pregnancy complications.

In this paper, a mutually enhanced modeling method (MEMe) is presented for human pose estimation, which focuses on enhancing lightweight model performance, but with low complexity. To obtain higher accuracy, a traditional model scale is largely expanded with heavy deployment difficulties. However, for a more lightweight model, there is a large performance gap compared to the former; thus, an urgent need for a way to fill it. Therefore, we propose a MEMe to reconstruct a lightweight baseline model, EffBase transferred intuitively from EfficientDet, into the efficient and effective pose (EEffPose) net, which contains three mutually enhanced modules: the Enhanced EffNet (EEffNet) backbone, the total fusion neck (TFNeck), and the final attention head (FAHead). Extensive experiments on COCO and MPII benchmarks show that our MEMe-based models reach state-of-the-art performances, with limited parameters. Specifically, in the same conditions, our EEffPose-P0 with 256 × 192 can use only 8.98 M parameters to achieve 75.4 AP on the COCO val set, which outperforms HRNet-W48, but with only 14% of its parameters.

Graph convolutional networks (GCNs) have made significant progress in the skeletal action recognition task. However, the graphs constructed by these methods are too densely connected, and the same graphs are used repeatedly among channels. Redundant connections will blur the useful interdependencies of joints, and the overly repetitive graphs among channels cannot handle changes in joint relations between different actions. In this work, we propose a novel relation selective graph convolutional network (RS-GCN). We also design a trainable relation selection mechanism. It encourages the model to choose solid edges to work and build a stable and sparse topology of joints. The channel-wise graph convolution and multiscale temporal convolution are proposed to strengthening the model’s representative power. Furthermore, we introduce an asymmetrical module named the spatial-temporal attention module for more stable context modeling. Combining those changes, our model achieves state-of-the-art performance on three public benchmarks, namely NTU-RGB+D, NTU-RGB+D 120, and Northwestern-UCLA.

Blind image deblurring is a well-known ill-posed inverse problem in the computer vision field. To make the problem well-posed, this paper puts forward a plain but effective regularization method, namely spectral norm regularization (SN), which can be regarded as the symmetrical form of the spectral norm. This work is inspired by the observation that the SN value increases after the image is blurred. Based on this observation, a blind deblurring algorithm (BDA-SN) is designed. BDA-SN builds a deblurring estimator for the image degradation process by investigating the inherent properties of SN and an image gradient. Compared with previous image regularization methods, SN shows more vital abilities to differentiate clear and degraded images. Therefore, the SN of an image can effectively help image deblurring in various scenes, such as text, face, natural, and saturated images. Qualitative and quantitative experimental evaluations demonstrate that BDA-SN can achieve favorable performances on actual and simulated images, with the average PSNR reaching 31.41, especially on the benchmark dataset of Levin et al.

Neural network pruning, an important method to reduce the computational complexity of deep models, can be well applied to devices with limited resources. However, most current methods focus on some kind of information about the filter itself to prune the network, rarely exploring the relationship between the feature maps and the filters. In this paper, two novel pruning methods are proposed. First, a new pruning method is proposed, which reflects the importance of filters by exploring the information in the feature maps. Based on the premise that the more information there is, more important the feature map is, the information entropy of feature maps is used to measure information, which is used to evaluate the importance of each filter in the current layer. Further, normalization is used to realize cross layer comparison. As a result, based on the method mentioned above, the network structure is efficiently pruned while its performance is well reserved. Second, we proposed a parallel pruning method using the combination of our pruning method above and slimming pruning method which has better results in terms of computational cost. Our methods perform better in terms of accuracy, parameters, and FLOPs compared to most advanced methods. On ImageNet, it is achieved 72.02% top1 accuracy for ResNet50 with merely 11.41M parameters and 1.12B FLOPs.For DenseNet40, it is obtained 94.04% accuracy with only 0.38M parameters and 110.72M FLOPs on CIFAR10, and our parallel pruning method makes the parameters and FLOPs are just 0.37M and 100.12M, respectively, with little loss of accuracy.

The material performance simulation software JMatPro was used to simulate and calculate equilibrium phase structure and properties of a mixture formed by mixing different proportions of Norem02 alloy and F304 stainless steel. The results show that in the range of 500-1300 °C, equilibrium phases of the mixture include Austenite and Ferrite, and the carbides include M2(C,N), M6C, M7C3 and M23C6. As the content of F304 in the mixture increases, the phase transition temperature A1 of the mixture decreases, and A3 first decreases and then increases; in the equilibrium phase, the content of Austenite and Ferrite increases, and the content of carbide decreases until the carbides M7C3 and M6C gradually disappear. Density, coefficient of linear expansion, thermal conductivity, Young's modulus, Poisson's ratio and specific heat capacity of different mixture materials vary with temperature in a complex manner. Strength and hardness of the mixture formed when the content of F304 is 20 % is the highest. The finer the grain size of the mixture, the higher the strength and hardness of the material. Comparing standard physical property data of F304 stainless steel at room temperature with the simulation calculation results, it is found that the error is small, which verifies the accuracy of the simulation calculation results. [ABSTRACT FROM AUTHOR]