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The green development quality of the construction industry has vital environmental and economic effects. To explore regional differences and convergence, and further provide a reference for the government. Based on the panel data of construction industry development in 31 provinces from 2006 to 2020, this study measured the green development quality of the construction industry in two-dimensional space-time, focuses on the regional differences, the dynamic evolution trend and the convergence of distribution. The findings supported the fact that the green development quality of construction industry was fluctuating upward trend, the overall difference decreased, and the regional difference showed heterogeneity. The convergence rate in the central region was higher than that in the western region, and there was an obvious “catch-up effect.” The green development quality of the construction industry in the national, central region and western region has absolute β convergence. The green development quality of the construction industry in the national, the eastern region and the central region has conditional β convergence. The influence of control variables shows heterogeneity. Based on the conclusions of this study, policy recommendations are put forward from the aspects of science and technology investment, human capital investment and green construction mode.

IFN–stimulated genes (ISGs) are essential effectors of the IFN-dependent antiviral immune response. Dysregulation of ISG expression can cause dysfunctional antiviral responses and autoimmune disorders. Epitranscriptomic regulation, such as N³-methyladenosine (m³A) modification of mRNAs, plays key roles in diverse biological processes. Here, we found that the m³A “reader” YT521-B homology domain-containing family 3 (YTHDF3) suppresses ISG expression under basal conditions by promoting translation of the transcription corepressor forkhead box protein O3 (FOXO3). YTHDF3 cooperates with two cofactors, PABP1 and eIF4G2, to promote FOXO3 translation by binding to the translation initiation region of FOXO3 mRNA. Both the YTH and the P/Q/N-rich domains of YTHDF3 were required for FOXO3 RNA-binding capacity, however, METTL3-mediated m³A modification was not involved in the process observed. Moreover, YTHDF3−/− mice had increased ISG levels and were resistant to several viral infections. Our findings uncover the role of YTHDF3 as a negative regulator of antiviral immunity through the translational promotion of FOXO3 mRNA under homeostatic conditions, adding insight into the networks of RNA-binding protein-RNA interactions in homeostatically maintaining host antiviral immune function and preventing inflammatory response.

Recently, the application of satellite remote sensing images is becoming increasingly popular, but the observed images from satellite sensors are frequently in low-resolution (LR). Thus, they cannot fully meet the requirements of object identification and analysis. To utilize the multi-scale characteristics of objects fully in remote sensing images, this paper presents a multi-scale residual neural network (MRNN). MRNN adopts the multi-scale nature of satellite images to reconstruct high-frequency information accurately for super-resolution (SR) satellite imagery. Different sizes of patches from LR satellite images are initially extracted to fit different scale of objects. Large-, middle-, and small-scale deep residual neural networks are designed to simulate differently sized receptive fields for acquiring relative global, contextual, and local information for prior representation. Then, a fusion network is used to refine different scales of information. MRNN fuses the complementary high-frequency information from differently scaled networks to reconstruct the desired high-resolution satellite object image, which is in line with human visual experience (“look in multi-scale to see better”). Experimental results on the SpaceNet satellite image and NWPU-RESISC45 databases show that the proposed approach outperformed several state-of-the-art SR algorithms in terms of objective and subjective image qualities.

In the field of Industrial Internet of Things (IIoT), existing intrusion detection models face challenges in three main areas: low accuracy in detecting attack traffic, feature redundancy when dealing with high-dimensional and complex attack traffic, making it difficult to capture critical information, and a tendency to favor learning common categories while neglecting rare categories when handling imbalanced data. To tackle these challenges, this study introduces an intrusion detection method that combines an attention mechanism, Bidirectional Gated Recurrent Units (BiGRU), and Inception Convolutional Neural Network (Inception-CNN) to enhance the model’s detection rate. Simultaneously, the method employs a mixed sampling strategy for data resampling to address the bias learning issue caused by data imbalance. Additionally, the method employs a hybrid sampling strategy for data resampling to address the bias learning issue caused by data imbalance. It also incorporates denoising techniques to handle potential dataset noise introduced by hybrid sampling. Furthermore, a feature selection method combining Pearson correlation coefficient and Random Forest is applied to eliminate feature redundancy, enhancing the model’s ability to capture crucial information from high-dimensional attack traffic. Experimental validation on internationally recognized datasets (Edge-IIoTset, CIC-IDS2017, and CIC IoT 2023) affirms the reliability of the proposed intrusion detection method. This approach underscores the significance of intrusion detection in the security of Industrial IoT and showcases its potential in addressing pertinent challenges in network security.

Acute appendicitis is a typical surgical emergency worldwide and one of the common causes of surgical acute abdomen in the elderly. Accurately diagnosing and differentiating acute appendicitis can assist clinicians in formulating a scientific and reasonable treatment plan and providing high-quality medical services for the elderly. In this study, we validated and analyzed the different performances of various machine learning models based on the analysis of clinical data, so as to construct a simple, fast, and accurate estimation method for the diagnosis of early acute appendicitis. The dataset of this paper was obtained from the medical data of elderly patients with acute appendicitis attending the First Affiliated Hospital of Anhui University of Chinese Medicine from January 2012 to January 2022, including 196 males (60.87%) and 126 females (39.13%), including 103 (31.99%) patients with complicated appendicitis and 219 (68.01%) patients with uncomplicated appendicitis. By comparing and analyzing the prediction results of the models implemented by nine different machine learning techniques (LR, CART, RF, SVM, Bayes, KNN, NN, FDA, and GBM), we found that the GBM algorithm gave the optimal results and that sensitivity, specificity, PPV, NPV, precision, recall, F1 and brier are 0.9167, 0.9739, 0.9429, 0.9613, 0.9429, 0.9167, 0.9296, and 0.05649, respectively. The GBM model prediction results are interpreted using the SHAP technology framework. Calibration and Decision curve analysis also show that the machine learning model proposed in this paper has some clinical and economic benefits. Finally, we developed the Shiny application for complicated appendicitis diagnosis to assist clinicians in quickly and effectively recognizing patients with complicated appendicitis (CA) and uncomplicated appendicitis (UA), and to formulate a more reasonable and scientific clinical plan for acute appendicitis patient population promptly.

Object detection in Unmanned Aerial Vehicle (UAV) images plays fundamental roles in a wide variety of applications. As UAVs are maneuverable with high speed, multiple viewpoints, and varying altitudes, objects in UAV images are distributed with great heterogeneity, varying in size, with high density, bringing great difficulty to object detection using existing algorithms. To address the above issues, we propose a novel global density fused convolutional network (GDF-Net) optimized for object detection in UAV images. We test the effectiveness and robustness of the proposed GDF-Nets on the VisDrone dataset and the UAVDT dataset. The designed GDF-Net consists of a Backbone Network, a Global Density Model (GDM), and an Object Detection Network. Specifically, GDM refines density features via the application of dilated convolutional networks, aiming to deliver larger reception fields and to generate global density fused features. Compared with base networks, the addition of GDM improves the model performance in both recall and precision. We also find that the designed GDM facilitates the detection of objects in congested scenes with high distribution density. The presented GDF-Net framework can be instantiated to not only the base networks selected in this study but also other popular object detection models.

Although chiral substituents have been incorporated into ansa chains to stabilize the conformations of cyclophanes and modulate the biological activities of pharmaceuticals, the asymmetric syntheses of these atropisomers relies on substrate-induced diastereoselective macrocyclization. To the best of our knowledge, enantio-, atrop-, and diastereoselective macrocyclizations are yet to be reported. Herein, we describe an N-heterocyclic carbene (NHC) and chiral phosphoric acid (CPA) dual-catalytic process for the desymmetrization of 1,3-diols, to achieve macrocyclization and stereoselective control over two chiral elements. It is deduced that the hydrogen bonding of CPA with the 1,3-diols enhances the diastereoselectivity of the process. As a result, various planar-chiral cyclophanes bearing chiral ansa chains are synthesized. Thermodynamic experiments reveal that the presence of an all-carbon quaternary carbon center on the ansa chain significantly increases the rotational barriers of the cyclophanes. Moreover, density functional theory calculations suggest that the chiral substituent shrinks the ansa chain by compressing the bond angle, thereby rendering the conformational rotation reaction more challenging. Here, the authors describe an N-heterocyclic carbene and chiral phosphoric acid dual-catalytic process for the desymmetrization of 1,3-diols, to achieve macrocyclization and stereoselective control over two chiral elements.

This study employed ultrasound-assisted extraction (UAE) to isolate polysaccharides from Lyophyllum decastes, which were subsequently fractionated into two components, LDP-A1 and LDP-B1, using DEAE cellulose-52 and Sephacryl S-500. The structural characteristics of the polysaccharides were preliminarily analyzed using high-performance liquid chromatography (HPLC), Fourier-transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and Congo red staining. The results indicate significant differences between LDP-A1 and LDP-B1 in terms of molecular weight, monosaccharide composition, and structural features. LDP-A1 (2.27 × 106 Da) exhibits a significantly higher molecular weight compared to LDP-B1 (9.80 × 105 Da), with distinct differences in monosaccharide types and content. Both polysaccharides contain β-glycosidic bonds. LDP-B1 adopts a sheet-like structure with an amorphous internal arrangement and a triple-helix configuration, whereas LDP-A1 is rod-shaped, with a crystalline internal structure, and lacks the triple-helix configuration. In terms of biological activity, both polysaccharides exhibit certain activities, but LDP-B1 shows significantly stronger activity in antioxidant, hypoglycemic, anti-inflammatory, and anticancer effects. In summary, LDPs exhibit significant biological activity, especially outstanding performance in antioxidant, hypoglycemic, anti-inflammatory, and anticancer effects, proving their potential for development in functional foods and pharmaceuticals. Their unique structural characteristics and diverse biological activities provide a solid theoretical foundation for further exploration of LDPs in health promotion and disease prevention, opening up new research directions and application prospects.

Human–computer interaction, an interdisciplinary discipline, has become a frontier research topic in recent years. In the fourth industrial revolution, human–computer interaction has been increasingly applied to construction safety management, which has significantly promoted the progress of hazard recognition in the construction industry. However, limited scholars have yet systematically reviewed the development of human–computer interaction in construction hazard recognition. In this study, we analyzed 274 related papers published in ACM Digital Library, Web of Science, Google Scholar, and Scopus between 2000 and 2021 using bibliometric methods, systematically identified the research progress, key topics, and future research directions in this field, and proposed a research framework for human–computer interaction in construction hazard recognition (CHR-HCI). The results showed that, in the past 20 years, the application of human–computer interaction not only made significant contributions to the development of hazard recognition, but also generated a series of new research subjects, such as multimodal physiological data analysis in hazard recognition experiments, development of intuitive devices and sensors, and the human–computer interaction safety management platform based on big data. Future research modules include computer vision, computer simulation, virtual reality, and ergonomics. In this study, we drew a theoretical map reflecting the existing research results and the relationship between them, and provided suggestions for the future development of human–computer interaction in the field of hazard recognition from a practical perspective.

Pathogenic bacterial infections are the second highest cause of death worldwide and bring severe challenges to public healthcare. Antibiotic resistance makes it urgent to explore new antibacterial therapy. As an essential metal element in both humans and bacteria, zinc ions have various physiological and biochemical functions. They can stabilize the folded conformation of metalloproteins and participate in critical biochemical reactions, including DNA replication, transcription, translation, and signal transduction. Therefore, zinc deficiency would impair bacterial activity and inhibit the growth of bacteria. Interestingly, excess zinc ions also could cause oxidative stress to damage DNA, proteins, and lipids by inhibiting the function of respiratory enzymes to promote the formation of free radicals. Such dual characteristics endow zinc ions with unparalleled advantages in the direction of antibacterial therapy. Based on the fascinating features of zinc ions, nanomaterial-based zinc ion interference therapy emerges relying on the outstanding benefits of nanomaterials. Zinc ion interference therapy is divided into two classes: zinc overloading and zinc deprivation. In this review, we summarized the recent innovative zinc ion interference strategy for the treatment of bacterial infections and focused on analyzing the antibacterial mechanism of zinc overloading and zinc deprivation. Finally, we discuss the current limitations of zinc ion interference antibacterial therapy and put forward problems of clinical translation for zinc ion interference antibacterial therapy.