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Computed tomography (CT) scans and magnetic resonance imaging (MRI) are commonly utilized to detect brain gliomas and central nervous system inflammation diseases. However, there are instances where depending solely on medical imaging for a precise diagnosis may result in unsuitable medications or treatments. Pathological analysis is regarded as the definitive method for diagnosing brain gliomas or central nervous system inflammation diseases. To achieve this, a craniotomy or stereotaxic biopsy is necessary to collect brain tissue, which can lead to complications such as cerebral hemorrhage, neurological deficits, cerebrospinal fluid leaks, and cerebral edema. Consequently, the advancement of non-invasive or minimally invasive diagnostic techniques is currently a high priority. This study included samples from four glioma patients and five patients with central nervous system inflammatory diseases, comprising both serum and paired cerebrospinal fluid (CSF). A total of 40 human cytokines were identified in these samples. We utilized a receiver operating characteristic (ROC) analysis to assess the sensitivity and specificity for distinguishing central nervous system inflammation diseases and gliomas. Additionally, we examined the correlation of these factors between serum and CSF in the patients. Ultimately, the identified factors were validated using serum from patients with clinically confirmed gliomas and central nervous system inflammation diseases followed by detection and statistical analysis through ELISA. The levels of serum factors IL-4, IFN-α, IFN-γ, IL-6, TNF-α, CCL4, CCL11, and VEGF were found to be significantly higher in gliomas compared with inflammatory diseases of the central nervous system (p < 0.05). Furthermore, a strong correlation was observed between the levels of CCL4 in serum and CSF, with a correlation coefficient of r = 0.92 (95% CI = 0.20–0.99, p = 0.027). We gathered more clinical samples to provide further validation of the abundance of CCL4 expression. A clinical study analyzing serum samples from 19 glioma patients and 22 patients with central nervous system inflammation diseases revealed that CCL4 levels were notably elevated in the inflammatory group compared with the glioma group (p < 0.001). These results suggest that assessing serum CCL4 levels may be useful in distinguishing those patients for clinical diagnostic purposes.

In recent years, object detection in Synthetic-Aperture Radar (SAR) images still faces many challenges, especially for ship detection. Small or dense ships are vulnerable to the interference of complex scenes such as ports and land. In feature extraction, a large amount of redundant information on the feature map will further reduce the network’s attention to small-sized ships. Therefore, in this paper, we propose a network called neighborhood removal-and-emphasis network (NRE-Net), including an object neighborhood removal (ONR) strategy and a neighborhood feature emphasis (NFE) module. Among them, the ONR strategy directly removes the complex background information around the ship before feature extraction, only retaining effective contextual neighborhoods conducive to ship detection and avoiding the interference of complex background information on the network. The NFE module is based on ONR to extract features and form a weight map of small-sized ships or complex images. This module can adaptively recognize the detection neighborhood of each ship and highlight the detection ship on the feature map. Our network has validated the effectiveness of the method on multiple SAR ship datasets and improved the object AP for each size. Our code is available at: https://github.com/Xidian-AIGroup190726/NRENet.

In recent years, owing to the swift progression in sensor technology and the extensive utilization of remote sensing imagery, obtaining and using high-quality remote sensing images is increasingly important. Among them, it is necessary to address the classification problem of panchromatic remote sensing images and multispectral remote sensing images. In this field of research, cleverly eliminating modal differences, removing redundancy, and better integrating information has become a challenge. In this article, we propose a DBFR-AENet for the multisource remote sensing image classification task. First, the IFFS strategy aims to design different feature branches to pick up the advantageous features of multispectral and panchromatic images separately. It filters redundant information and obtains useful information with higher purity. Second, the Bi-VAE strategy aims to eliminate modal differences by constructing a low-dimensional shared space. The dual-source image is input into the encoder to obtain the latent encoding in the latent space. The goal of feature alignment can be achieved in the potential shared space. Then, perform feature fusion. Finally, classify the image after feature fusion.

Self-supervised contrastive learning can help alleviating the meet of large numbers of annotated samples and learning high-level representations from unlabeled data. However, the high diversities in ground objects make it difficult to learn the features at more robust and refined level in synthetic aperture radar (SAR) image analysis. To alleviate this issue, we propose a self-supervised weighted contrastive learning method with context-augmented transformer for change detection in multiresolution SAR images. First, a weighted contrastive learning framework is built by introducing a weighted contrastive loss, which can reduce the influence of changed pixels in the process of self-supervised feature learning and align feature representations of image pairs. Then, to model complex and rich context information, a context-augmented swin transformer is proposed to aggregate contextual information and compute hierarchical representations, which are beneficial for dense prediction. Specially, global channel-wise aggregation module and multiscale fusion structure are designed to enhance global features and capture fine-scale features, respectively. Thus, rich local, global and multiscale context information can be modeled jointly to achieve fine and robust feature expression. Compared with other network, our network gives full play to the advantages of CL and transformer to extract representations with rich context information in unsupervised scenes, with good generalization. Experiments on real SAR images with different resolutions demonstrate the effectiveness and superiority of the proposed method.

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.

Objective: Here, we aimed to explore the effect of LBP in combination with Oxaliplatin (OXA) on reversing drug resistance in colon cancer cells through in vitro and in vivo experiments. We also aimed to explore the possible mechanism underlying this effect. Finally, we aimed to determine potential targets of Lycium barbarum polysaccharide (LBP) in colon cancer (CC) through network pharmacology and molecular docking.Methods: The invasion ability of colon cancer cells was assessed using the invasion assay. The migration ability of these cells was assessed using the migration assay and wound healing assay. Cell cycle analysis was carried out using flow cytometry. The expression levels of phosphomannose isomerase (PMI) and ATP-binding cassette transport protein of G2 (ABCG2) proteins were determined using immunofluorescence and western blotting. The expression levels of phosphatidylinositol3-kinase (PI3K), protein kinase B (AKT), B-cell lymphoma 2 (Bcl-2), and BCL2-Associated X (Bax) were determined using western blotting. Forty BALB/c nude mice purchased from Weitong Lihua, Beijing, for the in vivo analyses. The mice were randomly divided into eight groups. They were administered HCT116 and HCT116-OXR cells to prepare colon cancer xenograft models and then treated with PBS, LBP (50 mg/kg), OXA (10 mg/kg), or LBP + OXA (50 mg/kg + 10 mg/kg). The tumor weight and volume of treated model mice were measured, and organ toxicity was evaluated using hematoxylin and eosin staining. The expression levels of PMI, ABCG2, PI3K, and AKT proteins were then assessed using immunohistochemistry. Moreover, PMI and ABCG2 expression levels were analyzed using immunofluorescence and western blotting. The active components and possible targets of LBP in colon cancer were explored using in silico analysis. GeneCards was used to identify CC targets, and an online Venn analysis tool was used to determine intersection targets between these and LBP active components. The PPI network for intersection target protein interactions and the PPI network for interactions between the intersection target proteins and PMI was built using STRING and Cytoscape. To obtain putative targets of LBP in CC, we performed GO function enrichment and KEGG pathway enrichment analyses.Results: Compared with the HCT116-OXR blank treatment group, both invasion and migration abilities of HCT116-OXR cells were inhibited in the LBP + OXA (2.5 mg/mL LBP, 10 μΜ OXA) group (p < 0.05). Cells in the LBP + OXA (2.5 mg/mL LBP, 10 μΜ OXA) group were found to arrest in the G1 phase of the cell cycle. Knockdown of PMI was found to downregulate PI3K, AKT, and Bcl-2 (p < 0.05), while it was found to upregulate Bax (p < 0.05). After treatment with L. barbarum polysaccharide, 40 colon cancer subcutaneous tumor models showed a decrease in tumor size. There was no difference in the liver index after LBP treatment (p > 0.05). However, the spleen index decreased in the OXA and LBP + OXA groups (p < 0.05), possibly as a side effect of oxaliplatin. Immunohistochemistry, immunofluorescence, and western blotting showed that LBP + OXA treatment decreased PMI and ABCG2 expression levels (p < 0.05). Moreover, immunohistochemistry showed that LBP + OXA treatment decreased the expression levels of PI3K and AKT (p < 0.05). Network pharmacology analysis revealed 45 active LBP components, including carotenoids, phenylpropanoids, quercetin, xanthophylls, and other polyphenols. It also revealed 146 therapeutic targets of LBP, including AKT, SRC, EGFR, HRAS, STAT3, and MAPK3. KEGG pathway enrichment analysis showed that the LBP target proteins were enriched in pathways, including cancer-related signaling pathways, PI3K/AKT signaling pathway, and IL-17 signaling pathways. Finally, molecular docking experiments revealed that the active LBP components bind well with ABCG2 and PMI.conclusion: Our in vitro experiments showed that PMI knockdown downregulated PI3K, AKT, and Bcl-2 and upregulated Bax. This finding confirms that PMI plays a role in drug resistance by regulating the PI3K/AKT pathway and lays a foundation to study the mechanism underlying the reversal of colon cancer cell drug resistance by the combination of LBP and OXA. Our in vivo experiments showed that LBP combined with oxaliplatin could inhibit tumor growth. LBP showed no hepatic or splenic toxicity. LBP combined with oxaliplatin could downregulate the expression levels of PMI, ABCG2, PI3K, and AKT; it may thus have positive significance for the treatment of advanced metastatic colon cancer. Our network pharmacology analysis revealed the core targets of LBP in the treatment of CC as well as the pathways they are enriched in. It further verified the results of our in vitro and in vivo experiments, showing the involvement of multi-component, multi-target, and multi-pathway synergism in the drug-reversing effect of LBP in CC. Overall, the findings of the present study provide new avenues for the future clinical treatment of CC.

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.

Abstract Using differential privacy to provide privacy protection for classification algorithms has become a research hotspot in data mining. In this paper, we analyze the defects in the differentially private decision tree named Maxtree, and propose an improved model DPtree. DPtree can use the Fayyad theorem to process continuous features quickly, and can adjust privacy budget adaptively according to sample category distributions in leaf nodes. Moreover, to overcome the inevitable decline of classification ability of differentially private decision trees, we propose an ensemble learning model for DPtree, namely En-DPtree. In the voting process of En-DPtree, we propose a multi-population quantum genetic algorithm, and introduce immigration operators and elite groups to search the optimal weights for base classifiers. Experiments show that the performance of DPtree is better than Maxtree, and En-DPtree is always superior to other competitive algorithms.

Abstract In ensemble learning, random subspace technology not only easily loses some important features but also easily produces some redundant subspaces, inevitably leading to the decline of ensemble learning performance. In order to overcome the shortcomings, we propose a new selective quantum ensemble learning model inspired by improved AdaBoost based on local sample information (SELA). Firstly, SELA combines information entropy and random subspace to ensure that the important features of the classification task in each subspace are preserved. Then, we select the base classifier that can balance accuracy and diversity among a group of base classifiers generated based on local AdaBoost in each iteration. Finally, we utilize the quantum genetic algorithm to search optimal weights for base learners in the label prediction process. We use UCI datasets to analyze the impact of important parameters in SELA on classification performance and verify that SELA is usually superior to other competitive algorithms.

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.

Measurement(s) glioma Technology Type(s) Whole Exome Sequencing Sample Characteristic - Organism Homo sapiens

Pancreatic β-cells death mediated by apoptosis plays a key role in the development of diabetes. Pro-inflammatory cytokines activate the apoptotic program in pancreatic β-cells. Therefore, inhibiting inflammatory cytokines induced apoptosis could preserve pancreatic β-cells. The fruit of Lycium is widely used as an antioxidant food and its pharmacological action is partially mediated by Lycium barbarum polysaccharides (LBP). However, whether LBP can inhibit inflammatory cytokines-induced β-cells apoptosis has not been reported. In this study, we showed that LBP could alleviate the apoptosis of pancreatic β-cells through the inhibition of interferon-γ (IFN-γ) pathway. In healthy mice, LBP administration could improve glucose tolerance. In streptozotocin-induced β-cells injury mice, preventive and continuous administration of LBP could alleviate hyperglycemia and protect pancreatic insulin content. Our study identified a novel function of LBP in prevention and protection of pancreatic β-cells apoptosis, which was partially through the inhibition of IFN-γ pathway.

Conversion of syngas or CO2 greenhouse gas derived from various carbon-containing materials including coal, natural gas, biomass, waste plastics and biogas, or power plant, and petroleum is paramount to ensure global energy security, and recycle carbon in the earth and atmosphere and reach the global goal of carbon neutrality by 2050 [...]

Postharvest decay of goji berries, mainly caused by Alternaria alternata, results in significant economic losses. To investigate the effects of melatonin (MLT) on resistance to Alternaria rot in goji berries, the fruits were immersed in the MLT solutions with varying concentrations (0, 25, 50, and 75 μmol L−1) and then inoculated with A. alternata. The results showed that the fruits treated with 50 μmol L−1 MLT exhibited the lowest disease incidence and least lesion diameter. Meanwhile, endogenous MLT in the fruits treated with 50 μmol L−1 MLT showed higher levels than in the control fruits during storage at 4 ± 0.5 °C. Further, the enzymatic activities and expressions of genes encoding peroxidase, phenylalanine ammonia-lyase, cinnamate 4-hydroxylase, 4-coumarate-CoA ligase, chalcone synthase, chalcone isomerase, and cinnamyl alcohol dehydrogenase were induced in the treated fruit during storage. UPLC-ESI-MS/MS revealed that secondary metabolites in the fruits on day 0, in order of highest to lowest levels, were rutin, p-coumaric acid, chlorogenic acid, ferulic acid, caffeic acid, naringenin, quercetin, kaempferol, and protocatechuic acid. MLT-treated fruits exhibited higher levels of secondary metabolites than the control. In conclusion, MLT treatment contributed to controlling the postharvest decay of goji fruit during storage by boosting endogenous MLT levels, thus activating the antioxidant system and secondary metabolism.

BackgroundWe aimed to describe the epidemiological characteristics, clinical presentations, and prognoses in a national health center for children.MethodsFrom January 2015 to December 2020, 484 patients aged 0-16 years, who were diagnosed with brain tumors and received neurosurgery treatment, were enrolled in the study. Pathology was based on the World Health Organization 2021 nervous system tumor classification, and tumor behaviors were classified according to the International Classification of Diseases for Oncology, third edition.ResultsAmong the 484 patients with brain tumors, the median age at diagnosis was 4.62 [2.19, 8.17] years (benign tumors 4.07 [1.64, 7.13] vs. malignant tumors 5.36 [2.78, 8.84], p=0.008). The overall male-to-female ratio was 1.33:1(benign 1.09:1 vs. malignant 1.62:1, p=0.029). Nausea, vomiting, and headache were the most frequent initial symptoms. The three most frequent tumor types were embryonal tumors (ET, 22.8%), circumscribed astrocytic gliomas (20.0%), and pediatric-type diffuse gliomas (11.0%). The most common tumor locations were the cerebellum and fourth ventricle (38.67%), the sellar region (22.9%) and ventricles (10.6%). Males took up a higher proportion than females in choroid plexus tumors (63.6%), ET (61.1%), ependymal tumors (68.6%), and germ cell tumors (GCTs, 78.1%). Patients were followed for 1 to 82 months. The overall 5-year survival rate was 77.5%, with survival rates of 91.0% for benign tumors and 64.6% for malignant tumors.ConclusionBrain tumors presented particularly sex-, age-, and regional-dependent epidemiological characteristics. Our results were consistent with previous reports and might reflect the real epidemiological status in China.

Abstract Glioma is the most common primary malignant brain tumor with limited treatment options and poor prognosis. To investigate the potential relationships between transcriptional characteristics and clinical phenotypes, we applied weighted gene co-expression network analysis (WGCNA) to construct a free-scale gene co-expression network yielding four modules in gliomas. Turquoise and yellow modules were positively correlated with the most malignant glioma subtype (IDH-wildtype glioblastomas). Of them, genes in turquoise module were mainly involved in immune-related terms and were regulated by NFKB1, RELA, SP1, STAT1 and STAT3. Meanwhile, genes in yellow module mainly participated in cell-cycle and division processes and were regulated by E2F1, TP53, E2F4, YBX1 and E2F3. Furthermore, 14 genes in turquoise module were screened as hub genes. Among them, five prognostic hub genes (TNFRSF1B, LAIR1, TYROBP, VAMP8, and FCGR2A) were selected to construct a prognostic risk score model via LASSO method. The risk score of this immune-related gene signature is associated with clinical features, malignant phenotype, and somatic alterations. Moreover, this signature showed an accurate prediction of prognosis across different clinical and pathological subgroups in three independent datasets including 1619 samples. Our results showed that the high-risk group was characterized by active immune-related activities while the low-risk group enriched in neurophysiological-related pathway. Importantly, the high-risk score of our immune signature predicts an enrichment of glioma-associated microglia/macrophages and less response to immune checkpoint blockade (ICB) therapy in gliomas. This study not only provides new insights into the molecular pathogenesis of glioma, but may also help optimize the immunotherapies for glioma patients.

The effect of activation environment (N2, H2 and H2S/H2) on the hydrocracking performance of a NiMo/Al catalyst was studied at 380 °C and 3.5 MPa using octacosane (C28). The catalyst physical structure and acidity were characterized by BET, XRD, SEM-EDX and FTIR techniques. The N2 activation generated more active nonsulfided NiMo/Al catalyst relative to the H2 or H2S activation (XC28, 70–80% versus 6–10%). For a comparison, a NiMo/Si-Al catalyst was also tested after normal H2 activation and showed higher activity at the same process conditions (XC28, 81–99%). The high activity of the NiMo/Al (N2 activation) and NiMo/Si-Al catalysts was mainly ascribed to a higher number of Brønsted acid sites (BAS) on the catalysts. The hydrocracking of cobalt wax using Pt/Si-Al and Pt/Al catalysts confirmed the superior activity of the Si-Al support. A double-peak product distribution occurred at C4–C6 and C10–C16 on all catalysts, which illustrates secondary hydrocracking and faster hydrocracking at the middle of the chain. The nonsulfided NiMo/Al and Pt/Al catalysts, and NiMo/Si-Al catalyst produced predominantly diesel (sel. 50–70%) and gasoline range (sel. > 50%) hydrocarbons, respectively, accompanied by some CH4 and light hydrocarbons C2–C4. On the other hand, the hydrocarbon distribution of the Pt/Si-Al varied with conditions (i.e., diesel sel. 87–90% below 290 °C or gasoline sel. 60–70% above 290 °C accompanied by little CH4) The dependence of the isomer/paraffin ratio on chain length was studied as well. The peak iso/paraffin value was observed at C10–C13 for the SiAl catalyst.

Light olefins as one the most important building blocks in chemical industry can be produced via Fischer–Tropsch synthesis (FTS) from syngas. FT synthesis conducted at high temperature would lead to light paraffins, carbon dioxide, methane, and C5+ longer chain hydrocarbons. The present work focuses on providing a critical review on the light olefin production using Fischer–Tropsch synthesis. The effects of metals, promoters and supports as the most influential parameters on the catalytic performance of catalysts are discussed meticulously. Fe and Co as the main active metals in FT catalysts are investigated in terms of pore size, crystal size, and crystal phase for obtaining desirable light olefin selectivity. Larger pore size of Fe-based catalysts is suggested to increase olefin selectivity via suppressing 1-olefin readsorption and secondary reactions. Iron carbide as the most probable phase of Fe-based catalysts is proposed for light olefin generation via FTS. Smaller crystal size of Co active metal leads to higher olefin selectivity. Hexagonal close-packed (HCP) structure of Co has higher FTS activity than face-centered cubic (FCC) structure. Transition from Co to Co3C is mainly proposed for formation of light olefins over Co-based catalysts. Moreover, various catalysts’ deactivation routes are reviewed. Additionally, techno-economic assessment of FTS plants in terms of different costs including capital expenditure and minimum fuel selling price are presented based on the most recent literature. Finally, the potential for global environmental impacts associated with FTS plants including atmospheric and toxicological impacts is considered via lifecycle assessment (LCA).

Background: Lycium barbarum (also called wolfberry), a famous Chinese traditional medicine and food ingredient, is well recognized for its significant role in preventing obesity; however, the molecular mechanisms underlying its preventive effects on fat accumulation are not well understood yet. The aim of this study was to determine the effects and mechanism of Lycium barbarum polysaccharides (LBP) on the proliferation and differentiation of 3T3-L1 preadipocytes. MTT was used to detect the proliferation of 3T3-Ll preadipocytes. Oil red O staining and colorimetric analysis were used to detect cytosolic lipid accumulation during 3T3-L1 preadipocyte differentiation. Real-time fluorescent quantitative PCR (qPCR) technology was used to detect peroxisome proliferator-activated receptor γ (PPARγ), CCAAT/enhancer-binding protein α (C/EBPα), adipocyte fatty-acid-binding protein (aP2), fatty acid synthase (FAS), and lipoprotein lipase (LPL) expression. Results: The concentration of LBP from 25 to 200 μg/mL showed a tendency to inhibit the growth of preadipocytes at 24 h, and it inhibited the differentiation of 3T3-L1 preadipocytes in a dose-dependent manner. In the preadipocytes treated with 200 μg/mL LBP, there were reduced lipid droplets in the cytoplasm, and its effect was opposite to that of rosiglitazone (ROS), which significantly reduced the PPARγ, C/EBPα, aP2, FAS, and LPL mRNA expression of adipocytes. Conclusions: LBP exerts inhibitive effects on the proliferation and differentiation of 3T3-L1 preadipocytes and decreases the cytoplasm accumulation of lipid droplets during induced differentiation of preadipocytes toward mature cells. Above phenomenon might link to lowered expression of PPARγ, C/EBPα, aP2, FAS, and LPL after LBP treatment. Thus, LBP could serve as a potential plant extract to treat human obesity or improve farm animal carcass quality via adjusting lipid metabolism.How to cite: Xu X, Chen W, Yu S, et al. Inhibition of preadipocyte differentiation by Lycium barbarum polysaccharide treatment in 3T3-L1 cultures. Electron J Biotechnol 2021;50. https://doi.org/10.1016/j.ejbt.2021.01.003

This review emphasizes the importance of the catalytic conversion techniques in the production of clean liquid and hydrogen fuels (XTF) and chemicals (XTC) from the carbonaceous materials including coal, natural gas, biomass, organic wastes, biogas and CO2. Dependence of the performance of Fischer–Tropsch Synthesis (FTS), a key reaction of the XTF/XTC process, on catalyst structure (crystal and size) is comparatively examined and reviewed. The contribution illustrates the very complicated crystal structure effect, which indicates that not only the particle type, but also the particle shape, facets and orientation that have been evidenced recently, strongly influence the catalyst performance. In addition, the particle size effects over iron, cobalt and ruthenium catalysts were carefully compared and analyzed. For all Fe, Co and Ru catalysts, the metal turnover frequency (TOF) for CO hydrogenation increased with increasing metal particle size in the small size region i.e., less than the size threshold 7–8 nm, but was found to be independent of particle size for the catalysts with large particle sizes greater than the size threshold. There are some inconsistencies in the small particle size region for Fe and Ru catalysts, i.e., an opposite activity trend and an abnormal peak TOF value were observed on a Fe catalyst and a Ru catalyst (2 nm), respectively. Further study from the literature provides deeper insights into the catalyst behaviors. The intrinsic activity of Fe catalysts (10 nm) at 260–300 °C is estimated in the range of 0.046–0.20 s−1, while that of the Co and Ru catalysts (7–70 nm) at 220 °C are 0.1 s−1 and 0.4 s−1, respectively.

In this article, we propose an effective method for remote sensing image registration. Point features are robust to remote sensing images with low quality, small overlapping area, and local deformation. Therefore, we extract point features from remote sensing images and convert the problem of remote sensing image registration into the problem of feature point matching. A correspondence set constructed solely on the similar of features often contains many false correspondences or outliers, so our key idea is to remove the mismatches in the initial correspondence set and obtain a stable correspondence through a two-step strategy. First, we use two constraints to construct the optimization model which can solve in linear time. The first constraint is that the topology of the points and their neighbors can be maintained after the spatial transformation. Another constraint is that the feature distance of the correct matches are similar to the neighbors. Then, we design a strategy to increase the number of inliers and raise the precision by a global constraint calculated from the solution in the previous step. Experiments on a variety of remote sensing image datasets demonstrate that our method is more robust and accurate than state-of-the-art methods.

The aim of this study was to investigate the effects of fermented Caragana korshinskii feed on meat quality in three muscle tissues (longissimus dorsi m., triceps brachii and biceps femoris) in Tan sheep. Sheep were randomly divided into two groups: the control group was fed a basic diet, and the experimental group was fed the basic diet supplemented with 10% fermented Caragana korshinskii. The results for the meat quality characteristics showed that the fermented Caragana korshinskii group had significantly greater more crude protein in all three muscles and more ash in the longissimus dorsi m. and triceps brachii than the control group. The fermented Caragana korshinskii group had a significantly higher ultimate pH in the biceps femoris and triceps brachii than the control group. The fermented Caragana korshinskii group had greater inosinic acid content in the longissimus dorsi m. and triceps brachii than the control group. The fermented Caragana korshinskii group had significantly higher levels of Asp, Thr, Glu and Ser in the triceps brachii than the control group. The fermented Caragana korshinskii group had significantly higher levels of C12:0 in the triceps brachii, of C15:0 and C16:0 in the biceps femoris, of C16:1 in the longissimus dorsi m. and triceps brachii, of C18:0 in the triceps brachii than the control group. Therefore, adding 10% fermented Caragana korshinskii to the diet of the sheep significantly improved the crude protein, ash, inosinic acid, partial amino acid and fatty acid contents as well as the ultimate pH and L* values in Tan sheep muscles.HIGHLIGHTS Fermentative caragana korshinskii was partially substituted for corn stalk to feed Tan sheep could significantly improve crude protein, ash, ultimate pH, L* value, inosinic acid, partial amino acid and fat acid species and content of Tan sheep. It was provided scientific basis for the rational utilization of caragana korshinskii resources by studying its effect on meat quality of Tan sheep. Fermented Caragana korshinskii solves the problem of lack of forage resources in China and the imbalance of forage supply in winter and spring in China.

The aim of this study was to investigate the effects of fermented Caragana korshinskii on the intramuscular fat content and varied expression of the intramuscular fat deposition-related genes FABP3, UBE3C, ADRB3, LIPE, and SCD among four muscle tissues (m. psoas, gluteus, quadriceps, and supraspinatus) of Tan sheep. Twenty-eight male animals of similar age (270 ± 10 days) and weight (24.6 ± 1.06 kg) were randomly divided into a control group (fed the basal diet) and an experimental group (fed the same diet except 10% of corn stalks were replaced with fermented C. korshinskii). Soxhlet petroleum-ether extraction and quantitative real-time PCR were applied to evaluate the fat content and gene expression in tissues, respectively. We observed a significant improvement (P < 0.05) in the intramuscular fat contents in the m. gluteus and supraspinatus of treated sheep compared to those of non treated sheep. The FABP3 mRNA level was markedly higher (P < 0.05) in the m. quadriceps and supraspinatus of treated sheep than in the control sheep. UBE3C mRNA levels were significantly decreased in the m. gluteus, quadriceps, and supraspinatus (P < 0.05) of treated sheep compared with those of the control sheep. ADRB3 mRNA levels were significantly lower (P < 0.05) in the m. psoas, gluteus, and supraspinatus of sheep fed fermented C. korshinskii than in the control group, whereas LIPE mRNA levels were significantly increased (P < 0.05) in the m. gluteus and quadriceps of sheep fed fermented C. korshinskii. The SCD mRNA levels in m. psoas, quadriceps, and supraspinatus of sheep fed fermented C. korshinskii were significantly higher than those of the control group (P < 0.05). Our results indicated that fermented C. korshinskii could partially replace the roughage used in Tan sheep feed, and its substitution affected the intramuscular fat content and altered the expression of intramuscular fat deposition-related genes. The present study lays a solid foundation for further exploring the utilization of C. korshinskii in ruminant husbandry.

The robustness and accuracy of feature descriptor are two essential factors in the process of image registration. Existing feature descriptors can extract important image features, but it may be difficult to find enough correct correspondences for sophisticated images. And these feature descriptors often require domain expertise and human intervention. The aim of this paper is to utilise Genetic Programming (GP) to automatically evolve feature descriptors which are adaptive to various images including remote sensing images and optical images. In this paper, a novel GP-based method (GPFD) is proposed to extract feature vectors and evolve image descriptors for image registration without supervision. The proposed method designs a set of simple arithmetic operators and first-order statistics to construct feature descriptors in order to reduce noise interference. The performance of the proposed method is evaluated and compared against five methods including SIFT, SURF, RIFT, GLPM and GP. These results demonstrate that the feature descriptors evolved by GPFD are robust to complex geometric transformation, the illumination difference and noise.

This article presents a two-stage evolutionary fuzzy clustering framework for noisy image segmentation. It is a bi-stage system comprising a multi-objective optimization stage and a fuzzy clustering segmentation stage. In the multi-objective optimization stage, the fuzzy clustering on pixels in inhomogeneous regions is converted into a multi-objective problem, which can preserve image details while restraining noise. The multi-objective problem is decomposed into several sub-problems by the Tchebycheff approach. A trade-off can be obtained by optimizing these sub-problems simultaneously. In the fuzzy clustering segmentation stage, fuzzy clustering with the trade-off between preserving image details and restraining noise is performed on the whole observed image. To deal with this fuzzy clustering problem, an adaptive evolutionary fuzzy clustering algorithm with spatial information is proposed. Experiment results on synthetic and real images illustrate the effectiveness of the proposed framework for noisy image segmentation.

Diffuse glioma is the most common primary tumor of the central nervous system. The prognosis of the individual tumor is heavily dependent on its grade and subtype. Homeobox B7 (HOXB7), a member of the homeobox family, is abnormally overexpressed in a variety of tumors. However, its function in glioma is unclear. In this study, HOXB7 mRNA and protein expression levels were analyzed in 401 gliomas from the CGGA RNA-seq database (325 cases) and our hospital (76 cases). HOXB7 expression, at both mRNA and protein levels, were upregulated in glioblastoma (GBM) and isocitrate dehydrogenase 1 (IDH1) wild-type glioma tissues. Kaplan–Meier with log-rank test showed that patients with high HOXB7 expression had a poor prognosis (p < 0.0001). Moreover, HOXB7 protein was deleted in 90.9% (20/22) of oligodendrogliomas and 13.0% (3/23) of astrocytomas. The sensitivity and specificity of HOXB7 protein deletion in oligodendroglioma were 90.9% (20/22) and 87.0% (20/23), respectively. To verify the reliability of using HOXB7 in differentiating oligodendroglioma, we used 1p/19q fluorescence in situ hybridization (FISH) testing as a positive control. The Cohen’s kappa coefficient of HOXB7 immunohistochemistry staining and 1p/19q FISH testing was 0.778 (95% CI: 0.594–0.962, p < 0.001). In conclusion, HOXB7 is an independent predictor of poor prognosis in all grade gliomas. Additionally, HOXB7 is also a highly sensitive and specific indicator to differentiate oligodendroglioma from astrocytoma.

Abstract This paper first gives a regularity theorem and its corollary. Then, a new construction of generating hard random lattices with short bases is obtained by using this corollary. This construction is from a new perspective and uses a random matrix whose entries obeyed Gaussian sampling which ensures that the corresponding schemes have a wider application future in cryptography area. Moreover, this construction is more specific than the previous constructions, which makes it can be implemented easier in practical applications.

In this paper, we present a novel convolutional neural network (CNN)-based model for change detection in synthetic aperture radar (SAR) images. Considering that change detection task takes image pairs as an input, we first explore multiple neural network architectures, which are specifically adapted to the change detection task. There are several ways in which patch pairs can be processed by the network and how information sharing can efficiently learn the semantic difference between the changed and unchanged pixels. For this reason, we then design a “Siamese samples” CNN, which treats patch pairs as indiscriminate samples to extract descriptors and then joins for their outputs. During training, the two patch features are extracted by the same network instead of separate sub-networks, while the joining neuron measures the distance between the two feature vectors. Due to “pseudo-labels” with high accuracy that is difficult to obtain, we modify a joint classifier based on the fuzzy c-means method into joint-similarity classifier as preclassification to obtain coarse “pseudo labels,” and discard sample selection. Thus, the preclassification labels with a low accuracy are used to fine-tune the network. Finally, a significantly improved change detection result can be obtained from the network. The proposed architecture provides a better trade-off in terms of speed and accuracy among its counterparts (Siamese, Pseudo-Siamese, and 2-Channel networks). The experiments on several real SAR data sets demonstrate the state-of-the-art performance of the proposed method compared with the advanced change detection methods.

Nowadays, with the development of remote sensing technology, very-high-resolution (VHR) remote sensing image object detection technology attracts more and more attention. However, various challenges still exist in remote sensing image object detection field, such as the complex and varied appearances, the expensive manual annotation, and difficult in fast detecting the large scene image. In this paper, we propose a multi-scale image block-level fully convolutional neural network (MIF-CNN) for remote sensing image object detection, which can solve the above problems to a certain degree. First, the training data sets which only require class labels and do not need the bounding box label can reduce the spend of manual annotation during training. Second, the MIF-CNN is designed to extract the multi-scale-based high-level feature which can better represent the various types of objects. The image block-level fully convolutional network contributes to improving computing efficiency and can directly detect any size of the input image, including a large scene remote sensing image. In the testing phase, the large scene image is directly input to MIF-CNN model, and the detection results are generated from the MIF-CNN output maps which are improved by the proposed bounding boxes modification strategy and local re-recognized strategy. The experiments on NWPU VHR-10 [1] and two Airports data sets demonstrate the effectiveness of the proposed method.

Telecare Medical Information System (TMIS) integrates various types of mobile devices and communication technologies to upgrade the traditional face-to-face medical treatment model to intelligent one, which can provide the flexible and convenient e-health care. Due to the complexity and openness of Internet, e-health care data is grabbing the interest of cyber attackers. Hence, security and privacy are still our dominant concerns. Fortunately, blockchain technology leverages decentralized or distributed process to ensure data security. A MediBchain-based privacy-preserving mutual authentication for mobile medical cloud architecture (abbreviated to MBPA) is proposed in this paper. MBPA scheme not only mitigates the weaknesses of existing ones, but has other advantages. First, MBPA scheme supports patients anonymity and traceability since the patient's identity is hidden in two dynamic anonyms and a static anonym and only the trusted center can recover his/her real identity. Second, each MediBchain node shares a secret value, which realizes authentication with extremely low computional cost between terminals and MediBchain nodes. Finally, MBPA scheme is proven safely against passive and active attacks under elliptic curve computational Diffie-Hellman problem (ECDHP) assumption in random oracle model. Hence, these features make MBPA scheme very suitable for computation-limited mobile devices compared with other related existing schemes.

Immunoreactions regulated by TAMs (Tumor-associated macrophages) play a pivotal role in tumorigenesis and metastasis. In recent decades, treatments based on immune regulation have achieved revolutionary breakthroughs in cancer targeted therapies. The phenotypes of TAMs in gliomas are more heterogeneous and inherently complex than can be simply defined by classification into the M1 and M2 polarized states. The detailed mechanisms surrounding infiltrating macrophage phenotype and glioma characteristics remain undefined. SAMD9 (Sterile Alpha Motif Domain-Containing Protein 9) was found to be highly expressed in glioma and closely related to histological and genetic features in CGGA and TCGA databases. Simultaneously, we present evidence to show that there was a positive association between SAMD9 and malignancy characters in LGG. Univariable and Multivariate proportional hazard Cox analysis showed that SAMD9 was an independent prognostic factor for LGG. Surprisingly, Gene Ontology (GO) analysis showed SAMD9 expression level was remarkably well correlated with immunological responses and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis supported the connection with immune responses and tumorigenesis. Immune infiltration analysis demonstrated that high SAMD9 expression resulted in an accumulation of macrophages by CIBERSORT and TIMER databases, especially positively related to macrophage total marker gene AIF1 and Macrophage M2 marker gene CD163. IHC staining further indicated a high correlation of SAMD9 with those specific macrophage markers in the immune response. Human THP-1 cells were induced into M2 macrophages, which were then co-cultured with LN229 cells. Silencing of SAMD9 by shRNA in LN229 cells attenuated the infiltration abilities of M2 macrophage. SAMD9 explored immune response via relating of M2 macrophage in vitro. Our results revealed SAMD9 acted as the malignancy characters in LGG, enrichment with M2 macrophage.

Recently, femtocells are expected to be densely deployed to meet the booming demands for wireless data traffic, which inevitably causes serious co-layer interference due to the unplanned deployment. Hence, how to efficiently combat interference while furthest guaranteeing the quality of service (QoS) becomes an open question. To address this issue, in this paper, we present a joint admission control and a resource allocation strategy for an orthogonal frequency-division multiple access-based femtocell network. Particularly, we consider the scenario, where users are classified into two types, of which high priority ones have priority to access the network and enjoy high-resolution video streams. To perfectly eliminate the colayer interference with limited spectrum while maximizing the number of users whose QoS requirements can be guaranteed, we formulate an integer nonlinear programming problem, followed by a graph-based algorithm with polynomial computational complexity to solve it. The basic principle behind the proposed algorithm is to first chordalize the given conflict graph, and then perform admission control with priority differentiation-based admission control sub-algorithm, followed by the final resource block assignment with maximum effect rank allocation sub-algorithm. Furthermore, we illustrate how our proposal could be implemented in the long-term evolution system. Finally, through extensive simulations, we show that our proposal outperforms other two schemes in terms of guaranteeing QoS requirements.

Quantum state sharing (QSTS) is a hot topic in quantum cryptography. However, majority of the current structures cannot resist the participant's fraud well during the initial state reconstruction phase. Especially, verifiable QSTS scheme which can be used to share unknown quantum state has yet to emerge. In this paper, we will propose the first verifiable structure by improving an existing threshold QSTS scheme. Our scheme has three merits: 1) it is easily realized in physical experiment because only the simple oneparticle unitary operations were involved; 2) each dishonest behavior of one participant could be detected by the next client; and 3) the private key of the dealer could be reused for many times.

Medical cloud blends medical treatment, cloud computing, and cloud storage together via the Internet to achieve stakeholders' benefits (healthcare center, patient, doctor, and cloud server). It also enables bunch of shared computing-storage resources to end users at anytime and anywhere. Due to the complexity and openness of the Internet, assorted challenges are there. Hence, security and privacy are the dominant concerns. Therefore, a strong privacy protection authentication protocol is desired. Very recently, Cheng et al. proposed a cloud-based authentication protocol and claimed that their protocol was against mentioned weaknesses in their paper. However, the analysis shows that Cheng et al.'s protocol is insecure against message confidentiality and patient's anonymity. Then, a novel energy-efficient and traceable authentication protocol (ETAP) is proposed. The ETAP not only mitigates the weaknesses, but has other advantages. First, the ETAP realizes authentication with extremely low computational cost between stakeholders. Second, the ETAP can enable patients to enjoy the remote services with privacy protection. Finally, the ETAP is proven to be safe against passive and active attacks under the elliptic curve discrete logarithm problem assumption in random oracle model. Hence, these features make the ETAP very suitable for computationlimited mobile devices (such as smartphone and PAD) compared with other related existing protocols.

Vehicular networking involves the storage, compute, and analysis of massive vehicular data. Vehicular cloud computing, as a special cloud computing platform, seamlessly combines vehicular ad hoc networks and conventional cloud computing. However, in the vehicular cloud computing, there is still the problem of unauthorized users accessing and stealing data. In the traditional ciphertext-policy attribute-based encryption (CP-ABE) scheme, a trusted central authority is employed to manage attributes and distribute keys. Based on multi-authority (MA) CP-ABE, we propose a secure and revocable access control scheme for vehicular cloud computing in this paper, in which the requester can decrypt the ciphertext with only a small amount of computation. We show that our MA-CP-ABE scheme can prevent static corruption of authorities in the standard model under the decisional q-parallel bilinear Diffie-Hellman exponent assumption. Theoretical analysis and experimental simulation results show that our scheme has lower communication cost and lower computational complexity than other schemes.

In this paper, a novel remote sensing image registration method based on phase congruency (PC) and spatial constraint is proposed. PC can provide intrinsic and meaningful image features, even when there are complex intensity changes or noise. Image features will be well detected from the corresponding PC images by the SAR-SIFT operator. It means that the feature detection methods in the frequency domain (PC) and the spatial domain (SAR-SIFT operator) are combined. To further improve the result of registration, spatial constraints, including point and line constraint, are established by utilizing the position and orientation information. Then, one to more matches can be removed and the influence of adjacent point can be greatly eliminated. The experimental results demonstrate that our method can obtain a better registration performance with higher accuracy and more correct correspondences than the state-of-the-art methods, such as SIFT, SAR-SIFT, SURF, PSO-SIFT, RIFT, and GLPM.

Abstract Background H19 is a well-characterized Long noncoding RNA (lncRNA) that has been proven to promote myoblast differentiation in humans and mice. However, its mechanism of action is still not fully interpreted. Methods Using RT-qPCR, we examined H19 RNA levels in various tissues from 1-week, 1-month, 6-month and 36-month old male cattle (i.e., newborn, infant, young and adult). The protein and mRNA levels of MyoG, MyHC, Sirt1 and FoxO1 in the satellite and C2C12 cells with an H19 silencing or overexpression vector were respectively detected using western blot and real-time qPCR. Results H19 was highly expressed in skeletal muscle at all the studied ages. High expression of H19 was required for the differentiation of bovine satellite cells. Knockdown of H19 caused a remarkable increase in the myoblast-inhibitory genes Sirt1/FoxO1, suggesting that H19 suppresses Sirt1/FoxO1 expression during myogenesis. Western blotting analysis of co-transfection of Sirt1 or FoxO1 expression vectors with pcDNA-H19 indicated that Sirt1/FoxO1 overexpression neutralized the promotion of myoblast differentiation through transfection of pcDNA-H19. Conclusion H19 promoted the differentiation of bovine skeletal muscle satellite cells by suppressing Sirt1/FoxO1.

Recently, with the popularity of space-borne earth satellites, the resolution of high-resolution panchromatic (PAN) and multispectral (MS) remote sensing images is also increasing year by year, multiresolution remote sensing classification has become a research hotspot. In this paper, from the perspective of deep learning, we design a dual-branch interactive spatial-channel collaborative attention enhancement network (SCCA-net) for multiresolution classification. It aims to combine sample enhancement and feature enhancement to improve classification accuracy. In the part of sample enhancement, we propose an adaptive neighbourhood transfer sampling strategy (ANTSS). Different from the traditional pixel-centric sampling strategy with orthogonal sampling angle, our algorithm allows each patch to adaptively transfer the neighbourhood range by finding the homogeneous region of the pixel to be classified. And it also adaptively adjust the sampling angle according to the texture distribution of the homogeneous region to capture neighbourhood information that is more conducive for classification. Moreover, in the part of feature enhancement part, we design a local spatial attention module (LSA-module) for PAN data to highlight the spatial resolution advantages and a global channel attention module (GCA-module) for MS data to improve the multi-channel representation. It not only highlights the spatial resolution advantage of PAN data and the multi-channel advantage of MS data, but also improves the difference between features through the interaction between the two modules. Quantitative and qualitative experimental results verify the robustness and effectiveness of the method.

Traditional change detection (CD) methods operate in the simple image domain or hand-crafted features, which has less robustness to the inconsistencies (e.g., brightness and noise distribution, etc.) between bitemporal satellite images. Recently, deep learning techniques have reported compelling performance on robust feature learning. However, generating accurate semantic supervision that reveals real change information in satellite images still remains challenging, especially for manual annotation. To solve this problem, we propose a novel self-supervised representation learning method based on temporal prediction for remote sensing image CD. The main idea of our algorithm is to transform two satellite images into more consistent feature representations through a self-supervised mechanism without semantic supervision and any additional computations. Based on the transformed feature representations, a better difference image (DI) can be obtained, which reduces the propagated error of DI on the final detection result. In the self-supervised mechanism, the network is asked to identify different sample patches between two temporal images, namely, temporal prediction. By designing the network for the temporal prediction task to imitate the discriminator of generative adversarial networks, the distribution-aware feature representations are automatically captured and the result with powerful robustness can be acquired. Experimental results on real remote sensing data sets show the effectiveness and superiority of our method, improving the detection precision up to 0.94–35.49%.

Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based on generative adversarial networks (GANs). The generator captures the distribution of the bitemporal multi-spectral image data and transforms it into change detection results, and these change detection results (as the fake data) are input into the discriminator to train the discriminator. The results obtained by pre-classification are also input into the discriminator as the real data. The adversarial training can facilitate the generator learning the transformation from a bitemporal image to a change map. When the generator is trained well, the generator has the ability to generate the final result. The bitemporal multi-spectral images are input into the generator, and then the final change detection results are obtained from the generator. The proposed method is completely unsupervised, and we only need to input the preprocessed data that were obtained from the pre-classification and training sample selection. Through adversarial training, the generator can better learn the relationship between the bitemporal multi-spectral image data and the corresponding labels. Finally, the well-trained generator can be applied to process the raw bitemporal multi-spectral images to obtain the final change map (CM). The effectiveness and robustness of the proposed method were verified by the experimental results on the real high-resolution multi-spectral image data sets.

Because there are many unlabeled samples in hyperspectral images and the cost of manual labeling is high, this paper adopts semi-supervised learning method to make full use of many unlabeled samples. In addition, those hyperspectral images contain much spectral information and the convolutional neural networks have great ability in representation learning. This paper proposes a novel semi-supervised hyperspectral image classification framework which utilizes self-training to gradually assign highly confident pseudo labels to unlabeled samples by clustering and employs spatial constraints to regulate self-training process. Spatial constraints are introduced to exploit the spatial consistency within the image to correct and re-assign the mistakenly classified pseudo labels. Through the process of self-training, the sample points of high confidence are gradually increase, and they are added to the corresponding semantic classes, which makes semantic constraints gradually enhanced. At the same time, the increase in high confidence pseudo labels also contributes to regional consistency within hyperspectral images, which highlights the role of spatial constraints and improves the HSIc efficiency. Extensive experiments in HSIc demonstrate the effectiveness, robustness, and high accuracy of our approach.

With the increasing resolution of optical remote sensing images, ship detection in optical remote sensing images has attracted a lot of research interests. The current ship detection methods usually adopt the coarse-to-fine detection strategy, which firstly extracts low-level and manual features, and then performs multi-step training. Inadequacies of this strategy are that it would produce complex calculation, false detection on land and difficulty in detecting the small size ship. Aiming at these problems, a sea-land separation algorithm that combines gradient information and gray information is applied to avoid false alarms on land, the feature pyramid network (FPN) is used to achieve small ship detection, and a multi-scale detection strategy is proposed to achieve ship detection with different degrees of refinement. Then the feature extraction structure is adopted to fuse different hierarchical features to improve the representation ability of features. Finally, we propose a new coarse-to-fine ship detection network (CF-SDN) that directly achieves an end-to-end mapping from image pixels to bounding boxes with confidences. A coarse-to-fine detection strategy is applied to improve the classification ability of the network. Experimental results on optical remote sensing image set indicate that the proposed method outperforms the other excellent detection algorithms and achieves good detection performance on images including some small-sized ships and dense ships near the port.

This focused review article underscores how metal reduction promoters can impact deactivation phenomena associated with cobalt Fischer-Tropsch synthesis catalysts. Promoters can exacerbate sintering if the additional cobalt metal clusters, formed as a result of the promoting effect, are in close proximity at the nanoscale to other cobalt particles on the surface. Recent efforts have shown that when promoters are used to facilitate the reduction of small crystallites with the aim of increasing surface Co0 site densities (e.g., in research catalysts), ultra-small crystallites (e.g.,

The effects of 1% of Cd, In and Sn additives on the physicochemical properties and Fischer-Tropsch synthesis (FTS) performance of a 15% Co/Al2O3 catalyst were investigated. The fresh and spent catalysts were characterized by BET, temperature programmed reduction (TPR), H2-chemisorption, NH3 temperature programmed desorption (TPD), X-ray absorption near edge spectroscopy (XANES), and X ray diffraction (XRD). The catalysts were tested in a 1 L continuously stirred tank reactor (CSTR) at 220 °C, 2.2 MPa, H2/CO = 2.1 and 20–55% CO conversion. Addition of 1% of Cd or In enhanced the reduction degree of 15%Co/Al2O3 by ~20%, while addition of 1% Sn slightly hindered it. All three additives adversely impacted Co dispersion by 22–32% by increasing apparent Co cluster size based on the H2-chemisorption measurements. However, the decreased Co active site density resulting from the additives did not result in a corresponding activity loss; instead, the additives decreased the activity of the Co catalysts to a much greater extent than expected, i.e., 82–93%. The additional detrimental effect on catalyst activity likely indicates that the Cd, In and Sn additives migrated to and covered active sites during reaction and/or provided an electronic effect. XANES results showed that oxides of the additives were present during the reaction, but that a fraction of metal was also likely present based on the TPR and reaction testing results. This is in contrast to typical promoters that become metallic at or below ~350 °C, such as noble metal promoters (e.g., Pt, Ru) and Group 11 promoters (e.g., Ag, Au) on Co catalysts in earlier studies. In the current work, all three additives remarkably increased CH4 and CO2 selectivities and decreased C5+ selectivity, with the Sn and In additives having a greater effect. Interestingly, the Cd, In, or Sn additives were found to influence hydrogenation and isomerization activities. At a similar conversion level (i.e., in the range of 40–50%), the additives significantly increased 2-C4 olefin content from 3.8 to 10.6% and n-C4 paraffin from 50 to 61% accompanied by decreases in 1-C4 olefin content from 48 to 30%. The Sn contributed the greatest impact on the secondary reactions of 1-olefins, followed by the In and Cd. NH3-TPD results suggest enhanced acid sites on cobalt catalysts resulting from the additives, which likely explains the change in selectivities for the different catalysts.

Recently, Hyperspectral Image (HSI) classification has gradually been getting attention from more and more researchers. HSI has abundant spectral and spatial information; thus, how to fuse these two types of information is still a problem worth studying. In this paper, to extract spectral and spatial feature, we propose a Double-Branch Multi-Attention mechanism network (DBMA) for HSI classification. This network has two branches to extract spectral and spatial feature respectively which can reduce the interference between the two types of feature. Furthermore, with respect to the different characteristics of these two branches, two types of attention mechanism are applied in the two branches respectively, which ensures to extract more discriminative spectral and spatial feature. The extracted features are then fused for classification. A lot of experiment results on three hyperspectral datasets shows that the proposed method performs better than the state-of-the-art method.

Homogeneous image change detection research has been well developed, and many methods have been proposed. However, change detection between heterogeneous images is challenging since heterogeneous images are in different domains. Therefore, direct heterogeneous image comparison in the way that we do it is difficult. In this paper, a method for heterogeneous synthetic aperture radar (SAR) image and optical image change detection is proposed, which is based on a pixel-level mapping method and a capsule network with a deep structure. The mapping method proposed transforms an image from one feature space to another feature space. Then, the images can be compared directly in a similarly transformed space. In the mapping process, some image blocks in unchanged areas are selected, and these blocks are only a small part of the image. Then, the weighted parameters are acquired by calculating the Euclidean distances between the pixel to be transformed and the pixels in these blocks. The Euclidean distance calculated according to the weighted coordinates is taken as the pixel gray value in another feature space. The other image is transformed in a similar manner. In the transformed feature space, these images are compared, and the fusion of the two different images is achieved. The two experimental images are input to a capsule network, which has a deep structure. The image fusion result is taken as the training labels. The training samples are selected according to the ratio of the center pixel label and its neighboring pixels’ labels. The capsule network can improve the detection result and suppress noise. Experiments on remote sensing datasets show the final detection results, and the proposed method obtains a satisfactory performance.

Object detection in optical remote sensing images is still a challenging task because of the complexity of the images. The diversity and complexity of geospatial object appearance and the insufficient understanding of geospatial object spatial structure information are still the existing problems. In this paper, we propose a novel multi-model decision fusion framework which takes contextual information and multi-region features into account for addressing those problems. First, a contextual information fusion sub-network is designed to fuse both local contextual features and object-object relationship contextual features so as to deal with the problem of the diversity and complexity of geospatial object appearance. Second, a part-based multi-region fusion sub-network is constructed to merge multiple parts of an object for obtaining more spatial structure information about the object, which helps to handle the problem of the insufficient understanding of geospatial object spatial structure information. Finally, a decision fusion is made on all sub-networks to improve the stability and robustness of the model and achieve better detection performance. The experimental results on a publicly available ten class data set show that the proposed method is effective for geospatial object detection.