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Abstract Objective This study aims to conduct an extensive analysis of autoimmune bullous diseases, particularly pemphigus vulgaris and bullous pemphigoid, in Shanghai, China, from 2016 to 2023. It seeks to understand the demographic profiles, comorbidities, mortality rates, risk factors, and socioeconomic impacts associated with autoimmune bullous disease. Methods A cross-sectional study design was employed, enrolling 1,072 patients. Diagnostic measures included clinical manifestations, histopathology, direct immunofluorescence, and serologic tests. The study also involved a detailed socioeconomic analysis and evaluation of occupational risks. Results The findings highlight a significant occupational risk in industries requiring enhanced safety measures, with a notable prevalence of autoimmune bullous disease among workers in these sectors. A considerable portion of the patients were from low-income backgrounds with limited literacy, indicating the economic burden of autoimmune bullous disease. A key discovery of the study is the potential pathological link between autoimmune bullous disease and interstitial lung disease. Conclusion This research, one of the first comprehensive studies on autoimmune bullous disease in China, underscores the need for targeted healthcare strategies and further investigation into autoimmune bullous disease, particularly its relationship with interstitial lung disease.

With the rapid development of deep neural networks, salient object detection has achieved great success in natural images. However, detecting salient objects from optical remote sensing images still remains a challenging task due to the diversity of object types, scale, shape and orientation variations, as well as cluttered backgrounds. Therefore, it is impractical to directly leverage methods designed for natural images to detect salient objects in optical remote sensing images. In this work, we present an end-to-end deep neural network for salient object detection in optical remote sensing images via global context relation-guided feature aggregation. Since the objects in remote sensing images often have a scattered distribution, we design a global context relation module to capture the global relationships between different spatial positions. In order to effectively integrate low-level appearance features as well as high-level semantic features for enhancing the final performance, we develop a feature aggregation module with the global context relation information as guidance and embed it into the backbone network to refine the deep features in a progressive manner. Instead of using traditional binary cross entropy as a training loss which treats all pixels equally, we design a weighted binary cross entropy to capture local surrounding information of different pixels. Extensive experiments on three public datasets are conducted to validate the efficiency of the proposed network and the results demonstrate that our proposed method consistently outperforms other competitors.

The utilization of solid waste as a resource is a beneficial approach to achieve pollution reduction and carbon reduction simultaneously. In this paper, we developed a quaternary solid waste-based cementitious materials (SWCMs) that can be used as a substitute for cement by utilizing four types of solid waste, namely ground granulated blast furnace slag (GGBS), steel slag (SS), ammonia-soda residue (ASR) and desulfurization gypsum (DG). The performance optimization and carbon emissions of SWCMs are investigated by response surface methodology and emission factor calculations. The results showed that a second-order polynomial model can accurately predict the compressive strength of mortar specimens of SWCMs, with prediction accuracies of 96.78 % and 87.17 % for compressive strengths at 3 days and 28 days, respectively. In terms of raw materials, DG content positively correlates with the compressive strength of the mortar containing SWCMs, moreover, ratios of GGBS to ASR of less than two or more than eight are beneficial. In addition, the production process of each ton of SWCMs emits 71.51 kg CO2, which is only 10 % of the production process of ordinary Portland cement. Overall, this work elucidates the influence of raw materials on the mechanical properties of quaternary SWCMs and quantifies their carbon reduction effects as a substitute for traditional cement, advancing the investigation and application of SWCMs in the realm of low-carbon materials.

Against the backdrop of global warming, intensifying regional conflicts, deglobalization, and the spread of diseases, global food security is facing severe challenges. Studying the food security situation in China and the United States in depth can provide practical experience for formulating food security policies for countries around the world and promoting global food security governance. On the basis of a meticulous review of the evolving connotations of food security, this study adopts six dimensions—quantity security, quality security, circulation security, economic security, ecological resource security, and policy security—as breakthrough points to construct a framework consisting of food security evaluation indicator system comprising 29 specific indicators. The CRITIC–MEREC–MARCOS model is applied to evaluate the status of food security in China and the United States from 2000 to 2022, while the obstacle degree model (ODM) model is utilized to identify factors impeding food security between the two countries. The results indicate that the level of food security in China has shown slight fluctuations initially, followed by a steady upward trend. The gap with the United States is continuously narrowing. However, significant differences between China and the United States still exist in terms of economic security, ecological resource security, and policy security. Furthermore, due to the limited productivity of agricultural labor, scarcity of water and soil resources, and low efficiency in the use of fertilizers and pesticides, China’s food security is subject to economic and environmental constraints. The restrictions imposed by economic security and ecological resource security on China’s food security are showing an increasing trend year by year. For the United States, with the obstruction of grain exports and the increasing frequency of drought disasters, the impact of circulation security and ecological resource security on food security is becoming increasingly prominent. In the future, China and the United States should join hands to address challenges, actively promote international cooperation in food security, and drive sustainable development for humanity.

Abstract The rapid advancement of the Internet has brought a exponential growth in network traffic. At present, devices deployed at edge nodes process huge amount of data, extract key features of network traffic and then forward them to the cloud server/data center. However, since the efficiency of mobile terminal devices in identifying and classifying encrypted and malicious traffic lags behind, how to identify network traffic more efficiently and accurately remains a challenging problem. We design a convolutional neural network model: One-dimensional convolutional neural network with hexadecimal data (HexCNN-1D) that combines normalized processing and attention mechanisms. By adding the attention mechanism modules Global Attention Block (GAB) and Category Attention Block (CAB), network traffic is classified and identified. By extracting effective load information from hexadecimal network traffic, our model can identify most categories of network traffic including encrypted and malicious traffic data. The experimental results show that the average accuracy is 98.8%. Our model can greatly improve the accuracy of network traffic data recognition.

Immune memory is the ability of organisms to elicit potentiated immune responses at secondary infection. Current studies have revealed that similar to adaptive immunity, innate immunity exhibits memory characteristics (called "innate immune memory"). Although epigenetic reprogramming plays an important role in innate immune memory, the underlying mechanisms have not been elucidated, especially at the individual level. Here, we established experimental systems for detecting innate immune memory in Drosophila melanogaster. Training infection with low-pathogenic bacteria enhanced the survival rate of the flies at subsequent challenge infection with high-pathogenic bacteria. Among low-pathogenic bacteria, Micrococcus luteus (Ml) and Salmonella typhimurium (St) exerted apparent training effects in the fly but exhibited different mechanisms of action. Ml exerted training effects even after its clearance from flies, while live St persisted in the flies for a prolonged duration. RNA sequencing (RNA-Seq) analysis revealed that Ml training enhanced the expression of the immune-related genes under the challenge condition but not under the non-challenge condition. In contrast, St training upregulated the expression of the immune-related genes independent of challenge. These results suggest that training effects with Ml and St are due to memory and persistence of immune responses, respectively. Furthermore, we searched for the gene involved in immune memory, and identified a candidate gene, Ada2b, which encodes a component of the histone modification complex. The Ada2b mutant suppressed Ml training effects on survival and disrupted the expression of some genes under the training + challenge condition. These results suggest that the gene expression regulated by Ada2b may contribute to innate immune memory in Drosophila.

Current studies have demonstrated that innate immunity possesses memory characteristics. Although the molecular mechanisms underlying innate immune memory have been addressed by numerous studies, genetic variations in innate immune memory and the associated genes remain unclear. Here, we explored innate immune memory in 163 lines of Drosophila melanogaster from the Drosophila Synthetic Population Resource. In our assay system, prior training with low pathogenic bacteria (Micrococcus luteus) increased the survival rate of flies after subsequent challenge with highly pathogenic bacteria (Staphylococcus aureus). This positive training effect was observed in most lines, but some lines exhibited negative training effects. Survival rates under training and control conditions were poorly correlated, suggesting that distinct genetic factors regulate training effects and normal immune responses. Subsequent quantitative trait loci analysis suggested that four loci containing 80 genes may be involved in regulating innate immune memory. Among them, Adgf-A, which encodes an extracellular adenosine deaminase-related growth factor, was shown to be associated with training effects. Our study findings help to elucidate the genetic architecture of innate immune memory in Drosophila and may provide insight for new therapeutic treatments aimed at boosting immunity.

Abstract The traffic congestion in the Hsuehshan tunnel and at the Toucheng interchange has led to traffic-related air pollution with increasing concern. To ensure the authenticity of our simulation, the concentration of the last 150 m in Hsuehshan tunnel was simulated using the computational fluid dynamics fluid model. The air quality at the Toucheng interchange along a 2 km length highway was simulated using the California Line Source Dispersion Model. The differences in air quality between rush hours and normal traffic conditions were also investigated. An unmanned aerial vehicle (UAV) with installed PM2.5 sensors was developed to obtain the three-dimensional distribution of pollutants. On different roads, during the weekend, the concentrations of pollutants such as SOx, CO, NO, and PM2.5 were observed to be in the range of 0.003–0.008, 7.5–15, 1.5–2.5 ppm, and 40–80 μg m− 3, respectively. On weekdays, the vehicle speed and the natural wind were 60 km h− 1 and 2.0 m s− 1, respectively. On weekdays, the SOx, CO, NO, and PM2.5 concentrations were found to be in the range of 0.002–0.003, 3–9, 0.7–1.8 ppm, and 35–50 μg m− 3, respectively. The UAV was used to verify that the PM2.5 concentrations of vertical changes at heights of 9.0, 7.0, 5.0, and 3.0 m were 45–48, 30–35, 25–30, and 50–52 μg m− 3, respectively. In addition, the predicted PM2.5 concentrations were 40–45, 25–30, 45–48, and 45–50 μg m− 3 on weekdays. These results provide a reference model for environmental impact assessments of long tunnels and traffic jam-prone areas. These models and data are useful for transportation planners in the context of creating traffic management plans.

As an important pre-processing step in clinical applications, automatic and accurate 3D cardiovascular image segmentation has attracted more and more attention. However, cardiovascular structures are often with high diversity, blood pool and myocardium shapes are also with large variability, and ambiguous cardiac borders make the segmentation task very challenging. In this paper, a novel deep neural network to segment the blood pool and myocardium from three dimensional cardiovascular images is introduced by fully exploiting the global context and complementary information encoded in different feature extraction layers, referred to as GCEFG-R2Net briefly. In order to semantically locate the two kinds of regions in a global manner, we design a global context pooling module which can effectively learn context information in a global manner from the deep features extracted from the last two deep layers. Instead of directly using or combining different levels of deep features, we develop an interactive feature aggregation strategy to enhance different levels of deep features by embedding a series of interactive feature aggregation modules. By using the enhanced features, a residual feature refining branch is designed for refining the side outputs in a top-down stream with the guidance of global context features. Finally, the refined side outputs of different layers and the enhanced deep features are combined to generate the final segmentation result by using a feature fusion module. Extensive experiments on two challenge datasets are conducted to demonstrate that the proposed GCEFG-R2Net can obtain appealing segmentation results for the blood pool and myocardium and performs better than other state-of-the-art methods.

Summary: The nuclear factor-kappa B (NF-κB) pathway is an evolutionarily conserved signaling pathway that plays a central role in immune responses and inflammation. Here, we show that Drosophila NF-κB signaling is activated via a pathway in parallel with the Toll receptor by receptor-type guanylate cyclase, Gyc76C. Gyc76C produces cyclic guanosine monophosphate (cGMP) and modulates NF-κB signaling through the downstream Tollreceptor components dMyd88, Pelle, Tube, and Dif/Dorsal (NF-κB). The cGMP signaling pathway comprises a membrane-localized cGMP-dependent protein kinase (cGK) called DG2 and protein phosphatase 2A (PP2A) and is crucial for host survival against Gram-positive bacterial infections in Drosophila. A membrane-bound cGK, PRKG2, also modulates NF-κB activation via PP2A in human cells, indicating that modulation of NF-κB activation in innate immunity by the cGMP signaling pathway is evolutionarily conserved.

As an auxiliary means of remote sensing (RS) intelligent interpretation, remote sensing scene classification (RSSC) attracts considerable attention and its performance has been improved significantly by the popular deep convolutional neural networks (DCNNs). However, there are still several challenges that hinder the practical applications of RSSC, such as complex composition of land cover, scale-variation of objects, and redundant and noisy areas for scene classification. In order to mitigate the impact of these issues, we propose an adaptive discriminative regions learning network for RSSC, referred as ADRL-Net briefly, which locates discriminative regions effectively for boosting the performance of RSSC by utilizing a novel self-supervision mechanism. Our proposed ADRL-Net consists of three main modules, including a discriminative region generator, a region discriminator, and a region scorer. Specifically, the discriminative region generator first generates some candidate regions which could be informative for RSSC. Then, the region discriminator evaluates the regions generated by region generator and provides feedback for the generator to update the informative regions. Finally, the region scorer makes prediction scores for the whole image by using the discriminative regions. In such a manner, the three modules of ADRL-Net can cooperate with each other and focus on the most informative regions of an image and reduce the interference of redundant regions for final classification, which is robust to the complex scene composition, object scales, and irrelevant information. In order to validate the efficacy of the proposed network, we conduct experiments on four widely used benchmark datasets, and the experimental results demonstrate that ADRL-Net consistently outperforms other state-of-the-art RSSC methods.

Single-cell RNA-sequencing (scRNA-seq) data provide opportunities to reveal new insights into many biological problems such as elucidating cell types. An effective approach to elucidate cell types in complex tissues is to partition the cells into several separated subgroups via clustering techniques, where the cells in a specific cluster belong to the same cell type based on gene expression patterns. In this work, we present a novel multiple kernel clustering framework for scRNA-seq data clustering via locality preserving kernel alignment. Specifically, we first generate a series of similarity kernel matrices by using different kernel functions. Then we transfer the clustering task to a multiple kernel k-means problem with the kernels aligned in a local manner, i.e., the similarity of a sample to its k-nearest neighbours are aligned with the ideal similarity matrix. In our method, the clustering process focuses on closer sample pairs that shall stay together, and avoids involving unreliable similarity evaluation for farther sample pairs. In addition, we construct a local Laplacian matrix for each sample to constrain that closer samples should be allocated similar labels. In such a manner, the local structure of the data can be well preserved and utilized to produce better alignment for clustering. An alternate updating algorithm with theoretical analysis is developed to solve the proposed problem. We evaluate the performance of the proposed method on various real scRNA-seq data, and the results show that our method can obtain superior results when compared with other state-of-the-art approaches.

This paper attempts to examine the formation path of the United Temple. Since research on the United Temple has focused more on its organization and religious practice in contemporary Singapore, the paper looks at the sub-temples under the Singapore United Temple, analyzing their paths toward unification from a more extended historical perspective. The authors divide sub-temples into three categories: ancestral temples (血缘庙), geographic temples (地缘庙), and deity-related temples (神缘庙) and compare their flexible strategies. This paper tries to explain how the formation of the United Temples was influenced by multiple spatial, social, and cultural factors. The blood lineage, religion, and regional ties from the homeland could still be essential when the localized, community-based social links beyond the boundaries play an equally crucial integrative role in forming United Temples. It is the contention of the authors of this paper to study the United Temple—the unique religious space in Singapore—as the potential syncretic field of the present and the past.

Multi-view clustering, which exploits multi-view information to improve the clustering performance has attracted much attention in recent years. However, existing methods seldom consider the diverse quality of data points in different views, and assign each data point with the same importance for clustering. This way degrades the clustering performance due to the interference of low quality data points on the learned clustering indicators. In this paper, a novel robust multi-view clustering method with a unified weight learning paradigm is proposed to address this issue. The unified weight learning paradigm adaptively learns the quality of data points and the clustering capability of each view. Specifically, the reconstruction error of each data point in each view is treated as a factor to depict the quality of data point in this view. Afterwards, the clustering capability of each view is captured from the diverse quality of data points in each view. The clustering capability of each view in turn improves the learning process of data quality. An alternating iterative optimization algorithm with theoretical convergence guarantee and complexity analysis is designed to optimize the objective function. Experimental results on real-world benchmark datasets demonstrate the superiority of the proposed method.

This article examines the life and times of Lee Choon Seng (李俊承), exploring his role as a prominent lay Buddhist leader in Singapore and the broader Chinese Buddhist world. Lee Choon Seng’s influence in society, as well as his adherence to Chinese Mahayana Buddhism, laid an important foundation for him to carry out philanthropic activities within the Buddhist community in Singapore. Before World War II, Lee Choon Seng acted as a patron of the Dharma, sponsoring Buddhist activities and advocating for the idea of revitalizing China in the spirit of Buddhism. During the Japanese Occupation, Lee Choon Seng initiated and led the Buddhist philanthropy movement, which converged into a huge developmental trend in society. In the postwar period, Lee Choon Seng established and served as the inaugural chairman of the Singapore Buddhist Federation, making him a key figure in unifying the Buddhist community in Singapore. As this article demonstrates, Lee Choon Seng’s socio-religious model, which was comprised of a combination of Mahayana Buddhism with social charity and welfare, was an important factor for the promotion of Buddhism among the Chinese community in Singapore.

Prostate cancer is a common carcinoma in males, the development of which involves the androgen receptor (AR) as a key regulator. AR transactivation induces the high expression of androgen-regulated genes, including transmembrane protease serine 2 (TMPRSS2) and long noncoding RNA prostate cancer-associated transcript 38 (PRCAT38). PRCAT38 and TMPRSS2 are both located on chromosome 21, separated by a series of enhancers. PRCAT38 is a prostate-specific long noncoding RNA that is highly expressed in cancer tissue as compared to normal tissue. Here, we show chromatin looping by enhancers E1 and E2 with the promoters for PRCAT38 and TMPRSS2, indicating the co-regulation of PRCAT38 and TMPRSS2 by the same enhancers. The knockout of enhancer E1 or E2 simultaneously impaired the transcription of PRCAT38 and TMPRSS2 and inhibited cell growth and migration. Moreover, the loop formation and enhancer activity were mediated by AR/FOXA1 binding and the activity of acetyltransferase p300. Our findings demonstrate the utilization of shared enhancers in the joint regulation of two oncogenes in prostate cancer cells.

TiO2 has been studied most commonly because it has high stability, nontoxicity, high catalytic activity, and high conductivity. Many studies have shown that TiO2 would generate electron-hole pairs illuminated with UV and surround more energy than that before being illuminated. In this study, the titanium nanotube (TNT) photocatalysts were prepared to increase the surface area and adsorption capacity. The Fe TNT was also prepared from a slag iron since many slag irons cause waste treatment problems. In this study, a different Fe loading was also assessed since TNT doped with metals can be used to improve the degradation efficiency. Furthermore, five kinds of dye concentration, including 10, 20, 100, 200, and 400 ppm, and five kinds of Fe-doped content, including 0, 0.77, 1.13, 2.24, and 4.50%, were tested. Different kinds of reaction time and dye species were also assessed. In this result, Direct Black 22 was the most difficult to be degraded, although the concentration was decreased or the dose amount was increased. The degradation efficiency of 10 ppm Direct Black 22 was below 40% with 0.04 gL−1 TNT under 365 nm UV irradiation.

While various studies have focused on the significance of computational thinking (CT) for the future career paths of individuals in science, technology, engineering, and mathematics (STEM), few studies have focused on computational thinking for non-STEM college students. This study explores the relationship between computational thinking and learning satisfaction for non-STEM-major college students. A conceptual model is proposed to examine the structural relationships among computational thinking, self-efficacy, self-exploration, enjoyment and learning satisfaction in an AppInventor-based liberal education course. Collecting data from 190 undergraduate students from Taiwan and analyzing the data by using partial least squares (PLS) methods, the research framework confirms the six proposed hypotheses. These results show that both computational thinking and enjoyment play significant roles in both self-exploration and digital self-efficacy. Moreover, digital self-efficacy and self-exploration also have a significant positive influence on learning satisfaction. These findings have implications for influencing the learning outcomes of non-STEM-major college students, computational thinking course instructors, and computational thinking relevant policies.

Arsenic contamination in groundwater poses serious health risks, as exemplified by the Blackfoot disease epidemic in Taiwan, which was caused by prolonged arsenic exposure. This study investigates the use of biochar derived from the wastewater treatment sludge of the Far Eastern Memorial Hospital (New Taipei City, Taiwan) as an efficient adsorbent for arsenic removal. A novel iron-doped sludge biochar (Fe-SBC) was developed to enhance arsenic adsorption efficiency, facilitate adsorbent recovery, and reduce operational costs. The adsorption mechanism of arsenic on Fe-SBC, modified with iron hydroxide complexes, was examined through Density Functional Theory (DFT) simulations. The results demonstrate a high arsenic removal efficiency of approximately 90% using continuous adsorption systems. The DFT calculations revealed strong chemical interactions between arsenic and the biochar, evidenced by high adsorption energy (−156.8 kJ mol−1) and a short bond distance (1.48 Å), correlating with the high adsorption performance observed experimentally. Additionally, arsenic byproducts desorbed from the adsorbent were repurposed into antibacterial agents and pigments. Four distinct pigment colors—green, blue, gray, and orange—were produced through different preparation methods, with the antibacterial agents showing effective antimicrobial properties. This study highlights the potential of Fe-SBC for sustainable arsenic remediation and resource recovery. [ABSTRACT FROM AUTHOR]

Background: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and often arises in the context of chronic liver disease, such as hepatitis B or C infection, and cirrhosis. Advanced unresectable HCC (uHCC) presents significant treatment challenges due to its advanced stage and inoperability. One efficient treatment method for advanced uHCC is the use of hepatic arterial infusion chemotherapy (HAIC) combined with transcatheter arterial embolization (TAE). Patients and Methods: In this study, we conducted a retrospective collection of clinical data, including basic information, radiological data, and blood test parameters, for patients with advanced uHCC who underwent TAE + HAIC treatment from August 2020 to February 2023. A total of 743 cases involving 262 patients were included. Ultimately, the covariates included in the analysis were the Child-Pugh score, extrahepatic metastasis, tumor number, tumor size, and treatment method. Results: In the study, we performed univariable and multivariable analysis on 23 clinical factors that were screened by LASSO regression, indicating that the five variables aforementionedly were identified as independent factors influencing patient prognosis. Then we developed a nomogram of the sensitive model and calculated concordance indices of prognostic survival models. Conclusion: Based on the uHCC patient cohort, we have developed a prognostic model for OS in patients who received TAE + HAIC treatment. This model can accurately predict OS and has the potential to assist in personalized clinical decision-making. [ABSTRACT FROM AUTHOR]