Recently, metric learning and similarity learning have attracted a large amount of interest. Many models and optimization algorithms have been proposed. However, there is relatively little work on ...the generalization analysis of such methods. In this paper, we derive novel generalization bounds of metric and similarity learning. In particular, we first show that the generalization analysis reduces to the estimation of the Rademacher average over “sums-of-i.i.d.” sample-blocks related to the specific matrix norm. Then, we derive generalization bounds for metric/similarity learning with different matrix-norm regularizers by estimating their specific Rademacher complexities. Our analysis indicates that sparse metric/similarity learning with
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-norm regularization could lead to significantly better bounds than those with Frobenius-norm regularization. Our novel generalization analysis develops and refines the techniques of U-statistics and Rademacher complexity analysis.
•Our paper proposed a method to analyze the urban development utilizing the build-up area maps generated from multiple high-resolution remote sensing (RS) images. The previous researches mainly ...operate on medium-resolution RS images which lack of the details of sub-pixel features, having impacts on the reliability of mapping and analysis. Obviously, high-resolution RS images are more desirable for urban development observation. However, these images with higher resolution but smaller width, bring some challenges to mapping the whole urban with large coverage. Therefore, our paper develops a transferable build-up area extraction (TBUAE) algorithm using multiple satellite images to mapping the build-up area. This algorithm integrates the advantages of deep learning and transfer learning, and efficiently alleviates the pressure of deep learning on the demand for new satellite image samples which require time-and labor-consuming labeling manually. The main contributions are as follows:•Our method of built-up area mapping only requires automatic operation of the acquired remote sensing images, instead of consuming a lot of manpower which convenient to urban development analysis.•The TBUAE algorithm relives the pressure of obtaining high-resolution data in a large area of close time when carrying out urban analysis.•This study extracted the built- up areas of Zhengzhou in 2016, 2018, and 2020 by built-up area mapping to analyze its urban development in the past five years. Accordingly, driven factors are elaborately analyzed from specific aspects of economy and policy, respectively.
Analysis of built-up areas—the most significant artificial urban areas—reveals physical development processes. Unlike previous research involving medium-resolution remote sensing (RS) images, this study used built-up area maps generated from multiple high-resolution RS images with abundant built-up area edge information to analyze development in Zhengzhou. A transferable built-up area extraction (TBUAE) algorithm was developed to map the built-up area maps. The algorithm allows the developed deep learning model used on a certain satellite image to be eligible for other types of satellite images by altering the data distribution with adaptive Wallis filtering (DT-AWF). The proposed method alleviates the pressure of deep learning on the demand for new satellite image samples that are time-consuming and laborious. Additionally, the accuracy of built-up area mapping using this method exceeds 90%. Quantitative and qualitative analyses were conducted on the map results to observe the urban development of Zhengzhou from 2016 to 2020. We found that Zhengzhou has expanded rapidly since it was defined as a central city in central China in 2016. Additionally, the suburban built-up area has expanded rapidly and developed together with the central city. Further, affected by the policy, the built-up areas in different regions of Zhengzhou has changed differently, the urban edge is more simplified, and the urban internal structure is more compact.
Ergothioneine (ET) is a naturally occurring antioxidant that is synthesized by non-yeast fungi and certain bacteria. ET is not synthesized by animals, including humans, but is avidly taken up from ...the diet, especially from mushrooms. In the current study, we elucidated the effect of ET on the hCMEC/D3 human brain endothelial cell line. Endothelial cells are exposed to high levels of the cholesterol oxidation product, 7-ketocholesterol (7KC), in patients with cardiovascular disease and diabetes, and this process is thought to mediate pathological inflammation. 7KC induces a dose-dependent loss of cell viability and an increase in apoptosis and necrosis in the endothelial cells. A relocalization of the tight junction proteins, zonula occludens-1 (ZO-1) and claudin-5, towards the nucleus of the cells was also observed. These effects were significantly attenuated by ET. In addition, 7KC induces marked increases in the mRNA expression of pro-inflammatory cytokines, IL-1β IL-6, IL-8, TNF-α and cyclooxygenase-2 (COX2), as well as COX2 enzymatic activity, and these were significantly reduced by ET. Moreover, the cytoprotective and anti-inflammatory effects of ET were significantly reduced by co-incubation with an inhibitor of the ET transporter, OCTN1 (VHCL). This shows that ET needs to enter the endothelial cells to have a protective effect and is unlikely to act via extracellular neutralizing of 7KC. The protective effect on inflammation in brain endothelial cells suggests that ET might be useful as a nutraceutical for the prevention or management of neurovascular diseases, such as stroke and vascular dementia. Moreover, the ability of ET to cross the blood-brain barrier could point to its usefulness in combatting 7KC that is produced in the CNS during neuroinflammation, e.g. after excitotoxicity, in chronic neurodegenerative diseases, and possibly COVID-19-related neurologic complications.
A new species of Moniliformis, M. tupaia n. sp. is described using integrated morphological methods (light and scanning electron microscopy) and molecular techniques (sequencing and analysing the ...nuclear 18S, ITS, 28S regions and mitochondrial cox1 and cox2 genes), based on specimens collected from the intestine of the northern tree shrew Tupaia belangeri chinensis Anderson (Scandentia: Tupaiidae) in China. Phylogenetic analyses show that M. tupaia n. sp. is a sister to M. moniliformis in the genus Moniliformis, and also challenge the systematic status of Nephridiacanthus major. Moniliformis tupaia n. sp. represents the third Moniliformis species reported from China.
Stainless steel (SS) foils have successful flexible device applications because of their excellent high‐temperature performance and commercial availability, and they are widely used as flexible ...substrate materials for Cu(In,Ga)Se2 (CIGS) solar cells. The method used to control metal impurities is crucial for producing high‐quality cells. Herein, CIGS precursor films are deposited on SS foils coated with a SiOx/Ti compound barrier layer by sputtering a CIGS quaternary target. The absorber layer is recrystallized at a high annealing temperature (≈600 °C). The relationship between cell performance and SiOx layer processes is investigated. The diffusion of Fe and Cr in the annealed films is influenced by SiOx layer processes. The proposed CIGS flexible cells obtain better conversion efficiency when thicker SiOx layers are deposited at high sputtering powers.
Thin silicon oxide and titanium compound layer with thickness of 300 nm can satisfy the preparation requirements of Cu(In,Ga)Se2 flexible cells. The silicon oxide layer processes do not influence the crystalline properties of absorber films but influence the performance of solar cells. Appropriate process can improve the photon absorption and conversion efficiency.
Chlorophyll and nitrogen contents were used as leaf physiological parameters. Based on multispectral images from multiple detection angles and the stoichiometric data of tea (Camellia sinensis) ...leaves in different positions on plants, the spatial differences in tea physiological parameters were explored, and the full channel difference vegetation index was established to effectively remove soil and shadow noise. Support vector machine, random forest (RF), partial least square, and back-propagation algorithms from the multispectral images of leaf and canopy scales were then used to train the tea physiological parameter detection model. Finally, the detection effects of the multispectral images obtained from different angles on the physiological parameters of the top, middle, and bottom tea leaves were analysed and compared. The results revealed distinct spatial differences in the physiological parameters of tea leaves in individual plants. Chlorophyll content was lowest at the top and relatively high at the middle and bottom; nitrogen content was the highest at the top and relatively low at the middle and bottom. The horizontal distribution of physiological parameters was similar, i.e., the values in the east and south were high, whereas those in the west and north were low. The multispectral detection accuracy of the physiological parameters at the leaf scale was better than that at the canopy scale; the model trained by the RF algorithm had the highest comprehensive accuracy. The coefficient of determination between the predicted and measured values of the spad-502 plus instrument was (R2) = 0.79, and the root mean square error (RMSE) was 0.11. The predicted result for the nitrogen content and the measured value was R2 = 0.36 and RMSE = 0.03. The detection accuracy of the multispectral image taken at 60° for the physiological parameters of tea was generally superior to those taken at other shooting angles. These results can guide the high-precision remote sensing detection of tea physiological parameters.
Microglia activation and associated inflammatory response are involved in the pathogenesis of different neurodegenerative diseases. We have reported that Notch-1 and NF-κB/p65 signalling pathways ...operate in synergy in regulating the production of proinflammatory mediators in activated microglia. In the latter, there is also evidence by others that glycogen synthase kinase 3β (GSK-3β) mediates the release of proinflammatory cytokines but the interrelationships between the three signalling pathways have not been fully clarified. This is an important issue as activated microglia are potential therapeutic target for amelioration of microglia mediated neuroinflammation. Here we show that blocking of Notch-1 with N-(3,5-Difluorophenyl) acetyl-L-alanyl-2-phenylglycine-1,1-dimethylethyl ester (DAPT) in LPS activated BV-2 microglia not only suppressed Notch intracellular domain (NICD) and Hes-1 protein expression, but also that of GSK-3β. Conversely, blocking of the latter with lithium chloride (LiCl) decreased NICD expression in a dose-dependent manner; moreover, Hes-1 immunofluorescence was attenuated. Along with this, the protein expression level of p-GSK-3β and p-AKT protein expression was significantly increased. Furthermore, DAPT and LiCl decreased production of IL-1β, TNF-α, IL-6, iNOS, Cox2 and MCP-1; however, IL-10 expression was increased notably in LiCl treated cells. The effects of DAPT and LiCl on changes of the above-mentioned biomarkers were confirmed by immunofluorescence in both BV-2 and primary microglia. Additionally, NF-κB/p65 immunofluorescence was attenuated by DAPT and LiCl; as opposed to this, IκBα protein expression was increased. Taken together, it is suggested that Notch-1, NF-κB/p65 and GSK-3β operate in synergy to inhibit microglia activation. This may be effected via increased expression of phospho-GSK-3β (p-GSK-3β), phospho-protein kinase B (PKB) (p-AKT) and IκBα. It is concluded that the three signalling pathways are functionally interlinked in regulating microglia activation.
Abstract The tumor microenvironment (TME) comprises immune-infiltrating cells that are closely linked to tumor development. By screening and analyzing genes associated with tumor-infiltrating M0 ...cells, we developed a risk model to provide therapeutic and prognostic guidance in clear cell renal cell carcinoma (ccRCC). First, the infiltration abundance of each immune cell type and its correlation with patient prognosis were analyzed. After assessing the potential link between the depth of immune cell infiltration and prognosis, we screened the infiltrating M0 cells to establish a risk model centered on three key genes (TMEN174, LRRC19, and SAA1). The correlation analysis indicated a positive correlation between the risk score and various stages of the tumor immune cycle, including B-cell recruitment. Furthermore, the risk score was positively correlated with CD8 expression and several popular immune checkpoints (ICs) (TIGIT, CTLA4, CD274, LAG3, and PDCD1). Additionally, the high-risk group (HRG) had higher scores for tumor immune dysfunction and exclusion (TIDE) and exclusion than the low-risk group (LRG). Importantly, the risk score was negatively correlated with the immunotherapy-related pathway enrichment scores, and the LRG showed a greater therapeutic benefit than the HRG. Differences in sensitivity to targeted drugs between the HRG and LRG were analyzed. For commonly used targeted drugs in RCC, including axitinib, pazopanib, temsirolimus, and sunitinib, LRG had lower IC50 values, indicating increased sensitivity. Finally, immunohistochemistry results of 66 paraffin-embedded specimens indicated that SAA1 was strongly expressed in the tumor samples and was associated with tumor metastasis, stage, and grade. SAA1 was found to have a significant pro-tumorigenic effect by experimental validation. In summary, these data confirmed that tumor-infiltrating M0 cells play a key role in the prognosis and treatment of patients with ccRCC. This discovery offers new insights and directions for the prognostic prediction and treatment of ccRCC.
Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) ...classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI), gray-level co-occurrence matrix (GLCM) and digital surface model (DSM) are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC), support vector machine (SVM) and multinomial logistic regression (MLR) are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF). The experimental results confirm that the proposed algorithm is very effective in urban LULC classification.
A method of establishing a prediction model of the greenhouse temperature based on time-series analysis and the boosting tree model is proposed, aiming at the problem that the temperature of a ...greenhouse cannot be accurately predicted owing to nonlinear changes in the temperature of the closed ecosystem of a greenhouse featuring modern agricultural technology and various influencing factors. This model comprehensively considers environmental parameters, including humidity inside and outside the greenhouse, air pressure inside and outside the greenhouse, and temperature outside the greenhouse, as well as time-series changes, to make a more accurate prediction of the temperature in the greenhouse. Experiments show that the R2 determination coefficients of different prediction models are improved and the mean square error and mean absolute error are reduced after adding time-series features. Among the models tested, LightGBM performs best, with the mean square error of the prediction results of the model decreasing by 18.61% after adding time-series features. Comparing with the support vector machine, radial basis function neural network, back-propagation neural network, and multiple linear regression model after adding time-series features, the mean square error is 11.70% to 29.12% lower. Furthermore, the fitting degree of LightGBM is the best among the models. The prediction results of LightGBM therefore have important application value in greenhouse temperature control.
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