A calix[3]carbazole-based cavitand: synthesis, structure and its complexation with fullerenes.

A calix[3]carbazole-based cavitand was conveniently synthesized. It was found that the cavitand with adjustable conformation could show excellent complexation with fullerenes C60 and C70 in both solution and the solid state. Moreover, the crystal structures of the host-guest complexes show that the cavitand can stack into channel-like architectures, in which fullerenes are orderly arranged inside.

Bearing fault detection by using graph autoencoder and ensemble learning.

The research and application of bearing fault diagnosis techniques are crucial for enhancing equipment reliability, extending bearing lifespan, and reducing maintenance expenses. Nevertheless, most existing methods encounter challenges in discriminating between signals from machines operating under normal and faulty conditions, leading to unstable detection results. To tackle this issue, the present study proposes a novel approach for bearing fault detection based on graph neural networks and ensemble learning. Our key contribution is a novel stochasticity-based compositional method that transforms Euclidean-structured data into a graph format for processing by graph neural networks, with feature fusion and a newly proposed ensemble learning strategy for outlier detection specifically designed for bearing fault diagnosis. This approach marks a significant advancement in accurately identifying bearing faults, highlighting our study's pivotal role in enhancing diagnostic methodologies.

Molecular characteristics and pathogenicity of a novel chicken astrovirus variant.

It is well-established that the genetic diversity, regional prevalence, and broad host range of astroviruses significantly impact the poultry industry. In July 2022, a small-scale commercial broiler farm in China reported cases of growth retardation and a 3% mortality rate. From chickens displaying proventriculitis and pancreatitis, three chicken astroviruses (CAstV) isolates were obtained and named SDAU2022-1-3. Complete genomic sequencing and analysis revealed the unique characteristics of these isolates from known CAstV strains in ORF1a, ORF1b, and ORF2 genes, characterized by an unusually high variability. Analysis of amino acid mutations in ORF1a, ORF1b, and ORF2 indicated that the accumulation of these mutations played a pivotal role in the emergence of the variant strain. Inoculation experiments demonstrated that affected chickens exhibited liver and kidney enlargement, localized proventricular hemorrhage, and a dark reddish-brown appearance in about two-thirds of the pancreas. Histopathological examination unveiled hepatic lymphocytic infiltration, renal tubular epithelial cell swelling, along with lymphocytic proventriculitis and pancreatitis. Quantitative reverse transcription-polymerase chain reaction (qRT-PCR) analysis indicated viremia and viral shedding at 3 days post-infection (dpi). The proventriculus displayed the highest viral loads, followed by the liver, kidney, duodenum, and pancreas. Liver parameters (AST and ALT) and kidney parameters (UA and UN) demonstrated mild damage consistent with earlier findings. While the possibility of new mutations in the ORF2 gene of CAstV causing proventriculitis and pancreatitis warrants further investigation, these findings deepen our comprehension of CAstV's pathogenicity in chickens. Additionally, they serve as valuable references for subsequent research endeavors.

miR-155 facilitates the synergistic replication between avian leukosis virus subgroup J and reticuloendotheliosis virus by targeting a dual pathway.

IMPORTANCE: The synergy of two oncogenic retroviruses is an essential phenomenon in nature. The synergistic replication of ALV-J and REV in poultry flocks increases immunosuppression and pathogenicity, extends the tumor spectrum, and accelerates viral evolution, causing substantial economic losses to the poultry industry. However, the mechanism of synergistic replication between ALV-J and REV is still incompletely elusive. We observed that microRNA-155 targets a dual pathway, PRKCI-MAPK8 and TIMP3-MMP2, interacting with the U3 region of ALV-J and REV, enabling synergistic replication. This work gives us new targets to modulate ALV-J and REV's synergistic replication, guiding future research on the mechanism.

Improving the Corrosion Resistance of Zn-Rich Epoxy Coating with Three-Dimensional Porous Graphene.

To improve the corrosion inhibition of zinc-rich epoxy (ZRE) composite coatings and shed light on the influence of the spatial structure of graphene fillers on the coatings' performance, three-dimensional graphene (3DG) and a conventional graphene sheet (G) were used to modify the ZRE composite paint, respectively. The effect of introducing the 2D G fillers on the anti-corrosion behavior of ZRE was studied comprehensively, and its optimal content was determined to be 0.5 wt%. Interestingly, it was found that, comparing with 2D graphene sheets, the corrosion resistance of the ZRE coating could be enhanced more significantly with incorporating even less 3DG. With introducing only 0.1 wt% 3DG, the corrosion current intensity of the resulting 3DG/ZRE coating was reduced to be about 1/10 that of the G/ZRE coating with the same graphene content and 27% of that of the optimized G/ZRE. The corrosion products of the coating were analyzed with the XRD technique. The results indicated that, in contrast to neat ZRE coating, Zn5(CO3)2(OH)6 was absent from the corroded 3DG/ZRE coating, confirming its improved long-term anti-corrosion performance. The porous interconnected framework and high crystallinity of 3DG could contribute to not only its facilely mixing with epoxy resin, but also its effective incorporation into the conductive network of zinc micro-flakes, thus enhancing the corrosion resistance of its ZRE coating at a lower content. The innovative technology to improve the anti-corrosion performance of the ZRE coatings via using the 3D graphene fillers should be capable to be extended to other 2D fillers, such as MXenes.

Applying self-powered sensor and support vector machine in load energy consumption modeling and prediction of relational database.

This study explores the analysis and modeling of energy consumption in the context of database workloads, aiming to develop an eco-friendly database management system (DBMS). It leverages vibration energy harvesting systems with self-sustaining wireless vibration sensors (WVSs) in combination with the least square support vector machine algorithm to establish an energy consumption model (ECM) for relational database workloads. Through experiments, the performance of self-sustaining WVS in providing power is validated, and the accuracy of the proposed ECM during the execution of Structured Query Language (SQL) statements is evaluated. The findings demonstrate that this approach can reliably predict the energy consumption of database workloads, with a maximum prediction error rate of 10% during SQL statement execution. Furthermore, the ECM developed for relational databases closely approximates actual energy consumption for query operations, with errors ranging from 1 to 4%. In most cases, the predictions are conservative, falling below the actual values. This finding underscores the high predictive accuracy of the ECM in anticipating relational database workloads and their associated energy consumption. Additionally, this paper delves into prediction accuracy under different types of operations and reveals that ECM excels in single-block read operations, outperforming multi-block read operations. ECM exhibits substantial accuracy in predicting energy consumption for SQL statements in sequential and random read modes, especially in specialized database management system environments, where the error rate for the sequential read model is lower. In comparison to alternative models, the proposed ECM offers superior precision. Furthermore, a noticeable correlation between model error and the volume of data processed by SQL statements is observed. In summary, the relational database ECM introduced in this paper provides accurate predictions of workload and database energy consumption, offering a theoretical foundation and practical guidance for the development of eco-friendly DBMS.

Modular Synthesis of Biphen[n]arenes Directed by Five-Membered Heterocycles.

Modular synthesis of novel biphen[n]arenes (n = 2-4) with customizable heterocycle blocks, functional skeletons, binding sites, and topological structures could be facilely achieved through the rational design and replacement of reaction modules (furan and thiophene), functional modules (substituted benzene, biphenyl, and naphthalene), and linking modules (methylene). These biphen[n]arenes were characterized by NMR, HRMS, and X-ray crystalline diffraction, complemented by DFT calculations. Their photophysical properties were thoroughly studied.

Complete genome sequence and pathogenicity analysis of a highly pathogenic FAdV-4 strain.

Fowl adenovirus serotype 4 (FAdV-4) is a double-stranded DNA virus that mainly infects broiler chickens and has caused huge economic losses to the poultry industry. Recently, an FAdV-4 strain, SDLC202009, the causative pathogen of hydropericardium-hepatitis syndrome (HHS) in Liaocheng, Shandong, was isolated from commercial laying hens and propagated in specific pathogen free SPF chicken embryos. Pathogenicity studies showed that SDLC202009 could infect SPF chicken embryos and chickens, with a mortality rate of 100%. The complete genome was sequenced, and phylogenetic analysis showed that SDLC202009 belonged to the FAdV-4 cluster, with a genome length of 43, 077 bp. The SDLC202009 had 99.9% identity with the JSJ13 and SD1601, which were recently isolated in China. Compared to the recently isolated strain in China, SDLC202009 had deleted open reading frame 19 (ORF19), ORF27, ORF48, and ORF0. SDLC202009 harbored amino acid site mutations in the main structural proteins hexon, fiber1, and fiber2 similar with those in highly pathogenic strains. Furthermore, SDLC202009 showed unique mutations in hexon A571P, fiber1 E216K, and fiber2 N98K. In summary, our findings provide theoretical support for prevention and control of the HHS.

Incorporation of Graphene Oxide Modified with Polyamide Curing Agent into the Epoxy-Zinc Composite Coating for Promoting Its Corrosion Resistance.

To promote the anticorrosion performance of epoxy/zinc (EP/Zn) coating, graphene oxide (GO) was directly incorporated into dual-component paint. Interestingly, it was found that the method of incorporating GO during the fabrication of the composite paints strongly influenced their performance. The samples were characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), and Raman spectroscopy. The results indicated that GO could be intercalated and modified with the polyamide curing agent while preparing component B of the paint, for which the interlayer spacing of the resulting polyamide modified GO (PGO) increased, and its dispersion in organic solvent was improved. The corrosion resistance of the coatings was studied through potentiodynamic polarization testing, electrochemical impedance spectroscopy (EIS), and immersion testing. Among the three types of as-prepared coatings, i.e., neat EP/Zn coating, GO modified EP/Zn coating (GO/EP/Zn), and PGO-modified EP/Zn coating (PGO/EP/Zn), the order of the corrosion resistance of the coatings was PGO/EP/Zn > GO/EP/Zn > neat EP/Zn. This work demonstrates that although the in situ modification of GO with a curing agent is a simple method, it evidently promotes the shielding effect of the coating and enhances its corrosion resistance.

Fluctuation-based outlier detection.

Outlier detection is an important topic in machine learning and has been used in a wide range of applications. Outliers are objects that are few in number and deviate from the majority of objects. As a result of these two properties, we show that outliers are susceptible to a mechanism called fluctuation. This article proposes a method called fluctuation-based outlier detection (FBOD) that achieves a low linear time complexity and detects outliers purely based on the concept of fluctuation without employing any distance, density or isolation measure. Fundamentally different from all existing methods. FBOD first converts the Euclidean structure datasets into graphs by using random links, then propagates the feature value according to the connection of the graph. Finally, by comparing the difference between the fluctuation of an object and its neighbors, FBOD determines the object with a larger difference as an outlier. The results of experiments comparing FBOD with eight state-of-the-art algorithms on eight real-worlds tabular datasets and three video datasets show that FBOD outperforms its competitors in the majority of cases and that FBOD has only 5% of the execution time of the fastest algorithm. The experiment codes are available at: https://github.com/FluctuationOD/Fluctuation-based-Outlier-Detection .

Serum amyloid A regulates TLR2/4-mediated IFN-ß signaling pathway against Marek's disease virus.

Serum amyloid A (SAA), an acute response phase protein (APP), is crucial for the innate immune response during pathogenic microorganisms' invasion. Marek's disease virus (MDV) is a highly oncogenic alphaherpesvirus that activates multiple innate immune molecules, including SAA, in the host during infection. However, the pathway through which SAA participates in MDV-induced host innate immunity remains unknown. The present study aimed to elucidate the pathway through which SAA exerts its anti-MDV function. We observed that MDV infection in vivo and in vitro significantly elevated SAA expression. Furthermore, through SAA overexpression and knockdown experiments, we demonstrated that SAA could inhibit MDV replication. Subsequently, we found that SAA activated Toll-Like Receptor 2/4 (TLR2/4) -mediated Interferon Beta (IFN-ß) promoter activity and IFN regulatory factor 7 (IRF7) promoter activity. During MDV infection, SAA enhanced TLR2/4-mediated IFN-ß signal transduction and messenger RNAs (mRNAs) expression of type I IFN (IFN-I) and interferon-stimulated genes (ISGs). Finally, TLR2/4 inhibitor OxPAPC inhibits the anti-MDV activity of SAA. These results demonstrated that SAA inhibits MDV replication and enhancing TLR2/4-mediated IFN-ß signal transduction to promote IFNs and ISGs expression. This finding is the first to demonstrate the signaling pathway by which SAA exerts its anti-MDV function. It also provides new insights into the control of oncogenic herpesviruses from the perspective of acute response phase proteins.

Energy saving strategy of cloud data computing based on convolutional neural network and policy gradient algorithm.

Cloud Data Computing (CDC) is conducive to precise energy-saving management of user data centers based on the real-time energy consumption monitoring of Information Technology equipment. This work aims to obtain the most suitable energy-saving strategies to achieve safe, intelligent, and visualized energy management. First, the theory of Convolutional Neural Network (CNN) is discussed. Besides, an intelligent energy-saving model based on CNN is designed to ameliorate the variable energy consumption, load, and power consumption of the CDC data center. Then, the core idea of the policy gradient (PG) algorithm is introduced. In addition, a CDC task scheduling model is designed based on the PG algorithm, aiming at the uncertainty and volatility of the CDC scheduling tasks. Finally, the performance of different neural network models in the training process is analyzed from the perspective of total energy consumption and load optimization of the CDC center. At the same time, simulation is performed on the CDC task scheduling model based on the PG algorithm to analyze the task scheduling demand. The results demonstrate that the energy consumption of the CNN algorithm in the CDC energy-saving model is better than that of the Elman algorithm and the ecoCloud algorithm. Besides, the CNN algorithm reduces the number of virtual machine migrations in the CDC energy-saving model by 9.30% compared with the Elman algorithm. The Deep Deterministic Policy Gradient (DDPG) algorithm performs the best in task scheduling of the cloud data center, and the average response time of the DDPG algorithm is 141. In contrast, the Deep Q Network algorithm performs poorly. This paper proves that Deep Reinforcement Learning (DRL) and neural networks can reduce the energy consumption of CDC and improve the completion time of CDC tasks, offering a research reference for CDC resource scheduling.

Inherently chiral belt-shaped conjugated macrocycles with strong fluorescence and circularly polarized luminescence.

A pair of inherently chiral belt-shaped conjugated macrocycles were conveniently synthesized starting from 2,7-fluoren[3]arene triflate, and they not only exhibited green fluorescence, but also showed circularly polarized luminescence with a |glum| of 2.0 × 10-3.

The Electric-Thermal Effect of a Carbon-Fibre-Reinforced Epoxy Composite and Its Corresponding Mechanical Properties.

In this work, the electric-thermal effect of a carbon-fibre-reinforced epoxy composite (CFRE) panel was studied, as well as the influence of the electric heating treatment on the mechanical properties of the composite. It was observed that the temperature of the composite increased rapidly once the current was loaded, and the equilibrium surface temperature was reached within 2 min. The electric-thermal effect and mechanical properties depended on both the current loading time and the current intensity. At 5A, the flexural modulus and strength of the CFRE increased before decreasing with the current loading time. Under the same treatment time, the flexural strength of the samples treated with 5A was evidently larger than that under the small current, and all the treated samples displayed enhanced flexural strength compared to that of untreated samples. The results depicted that the low-current treatment and short time could improve the interfacial properties between CF/epoxy, along with enhancing the flexural properties of the samples. However, a large amount of the joule heating from the larger current and a more extensive time frame is predicted to cause irreversible defects to the composite, which consequently leads to the reduction in flexural strength of the composite. TGA results indicated decreased thermal stability of the CFRE composite panels after the electric heating treatment was applied.

Musashi-1 and miR-147 Precursor Interaction Mediates Synergistic Oncogenicity Induced by Co-Infection of Two Avian Retroviruses.

Synergism between avian leukosis virus subgroup J (ALV-J) and reticuloendotheliosis virus (REV) has been reported frequently in co-infected chicken flocks. Although significant progress has been made in understanding the tumorigenesis mechanisms of ALV and REV, how these two simple oncogenic retroviruses induce synergistic oncogenicity remains unclear. In this study, we found that ALV-J and REV synergistically promoted mutual replication, suppressed cellular senescence, and activated epithelial-mesenchymal transition (EMT) in vitro. Mechanistically, structural proteins from ALV-J and REV synergistically activated the expression of Musashi-1(MSI1), which directly targeted pri-miR-147 through its RNA binding site. This inhibited the maturation of miR-147, which relieved the inhibition of NF-κB/KIAA1199/EGFR signaling, thereby suppressing cellular senescence and activating EMT. We revealed a synergistic oncogenicity mechanism induced by ALV-J and REV in vitro. The elucidation of the synergistic oncogenicity of these two simple retroviruses could help in understanding the mechanism of tumorigenesis in ALV-J and REV co-infection and help identify promising molecular targets and key obstacles for the joint control of ALV-J and REV and the development of clinical technologies.

Effect of Hydrothermal Environment on Mechanical Properties and Electrical Response Behavior of Continuous Carbon Fiber/Epoxy Composite Plates.

In this work, the effect of a hydrothermal environment on mechanical properties and the electrical response behavior of continuous carbon fiber/epoxy (CFRE) composite produced by the pultrusion method were investigated. Due to the relatively uniform distribution of fibers and lack of resin-rich interlayer area, this effect for the pultruded CFRE composite plates is different from the common CFRE laminated composites. Firstly, its hygroscopicity behavior was studied. The absorption ratio increases rapidly to 1.02% within 3 days before reaching a relatively stable state. A three-point bending test, a Vickers hardness test, a thermogravimetric analysis (TGA), and a scanning electron microscope (SEM) analysis were performed to investigate the effect of the hydrothermal environment on the mechanical properties and thermal stability of the CFRE composite. The results indicated that the bending strength decreased quickly within 3 days of hydrothermal treatment, followed by a stable trend, which coincided with that of the hygroscopicity behavior of the composites. The fracture surface analysis indicated that the interfacial properties of carbon fibers in the epoxy matrix were decreased after the hydrothermal treatment, and more carbon fibers could be pulled out from the CFRE in the hygroscopic state. After the hydrothermal treatment, the micro-hardness of the composites was reduced by 25%. TGA confirmed the decreased thermal stability of the CFRE composites after the hydrothermal treatment as well. Moreover, the hydrothermally treated CFRE composites could a reach stable resistance response more readily. The revealing of the effect of moisture and hot environment on the mechanical properties and electrical response behavior of pultruded CFRE composites prepares the ground for their design and practical application in the corresponding environment.

The In-Situ Mechanical Properties of Carbon Fiber/Epoxy Composite under the Electric-Current Loading.

The Joule heating behavior of the carbon fiber/epoxy composite (CF/EP) was studied in this work, as well as their influence on the in-situ mechanical properties of the composites and their de-icing performance. The equilibrium temperature of the CF/EP composite could be conveniently adjusted by tuning the current according to the Joule's law. Dynamic mechanical analysis (DMA) tests indicated that the rigidity and stiffness of the fiber-reinforced composite decreased with increasing temperature, and the glass transition temperature (Tg) of the composites was around 104 °C. It was found that the flexural properties of the composites in situ, measured under the electric-current loading, depended on the current value in the range of room temperature to Tg. With increasing the loading current, either the flexural modulus or strength of CF/EP decreased gradually. Such results could be explained that the higher current loading, the larger Joule heat, led to the higher operating temperature of the composite samples and the evolution of their mechanical properties accordingly. Vickers hardness tests indicated that the micro-hardness of the composite decreased with the increase of the operating temperature, which coincided with the evolution of its flexural properties with the electric-current loading. The dependence of the failure behaviors of the CF/EP on the loading current was revealed by the analysis of their fractured surface, where micro-buckling, kinking, fiber pull-out and breakage were involved. A preliminary study indicated that less energy was consumed for the deicing of the same amount of the ice with the CF/EP composite in the case of less electric-current loading. The research on the Joule heating effect of CF/EP and their corresponding mechanical properties benefits the design and direct application of the composites under the electric-current loading.

On the Distinctive Hardness, Anti-Corrosion Properties and Mechanisms of Flame-Deposited Carbon Coating with a Hierarchical Structure in Contrast to a Graphene Layer via Chemical Vapor Deposition.

Two carbonaceous (amorphous carbon and graphene) coatings were catalytically grown on bulk Ni plates. It was found that the flame-deposited carbon (FDC) layers exhibited a unique hierarchical structure with the formation of FDC/Ni nano-interlocking interface. The effect of the flame coating time on its corrosion protective efficiency (PE) was studied and compared with that of graphene coating produced via chemical vapor deposition. The FDC grown for 10 min exhibited a PE of 92.7%, which was much greater than that of the graphene coating (75.6%). The anti-corrosive mechanisms of both coatings were revealed and compared. For graphene coatings, the higher reaction temperature than that for FDC resulted in large grain boundaries inherent in the coating. Such boundaries were weak points and easily initiated grain boundary corrosion. In contrast, corrosion started at only certain local defects in FDC layers, whose unique interface structure likely promoted its PE as well. Moreover, after the coating process, the hardness of FDC-coated Ni remained almost unchanged, in contrast to that of graphene-coated samples (reduced by ~30%). This is suggested to be related to the crystal structure evolution of the Ni substrate caused by the heat treatment accompanying the coating process.

RIOK3-Mediated Akt phosphorylation facilitates synergistic replication of Marek's disease and reticuloendotheliosis viruses.

Co-infection of Marek's disease virus (MDV) and reticuloendotheliosis virus (REV) synergistically drives disease progression, yet little is known about the mechanism of the synergism. Here, we found that co-infection of REV and MDV increased their replication via the RIOK3-Akt pathway. Initially, we noticed that the viral titres of MDV and REV significantly increased in REV and MDV co-infected cells compared with single-infected cells. Furthermore, tandem mass tag peptide labelling coupled with LC/MS analysis showed that Akt was upregulated in REV and MDV co-infected cells. Overexpression of Akt promoted synergistic replication of MDV and REV. Conversely, inhibition of Akt suppressed synergistic replication of MDV and REV. However, PI3K inhibition did not affect synergistic replication of MDV and REV, suggesting that the PI3K/Akt pathway is not involved in the synergism of MDV and REV. In addition, we revealed that RIOK3 was recruited to regulate Akt in REV and MDV co-infected cells. Moreover, wild-type RIOK3, but not kinase-dead RIOK3, mediated Akt phosphorylation and promoted synergistic replication of MDV and REV. Our results illustrate that MDV and REV activated a novel RIOK3-Akt signalling pathway to facilitate their synergistic replication.

Anomaly Score-Based Risk Early Warning System for Rapidly Controlling Food Safety Risk.

Food safety is a high-priority issue for all countries. Early warning analysis and risk control are essential for food safety management practices. This paper innovatively proposes an anomaly score-based risk early warning system (ASRWS) via an unsupervised auto-encoder (AE) for the effective early warning of detection products, which classifies qualified and unqualified products by reconstructing errors. The early warning analysis of qualified samples is carried out by early warning thresholds. The proposed method is applied to a batch of dairy product testing data from a Chinese province. Extensive experimental results show that the unsupervised anomaly detection model AE can effectively analyze the dairy product testing data, with a prediction accuracy and fault detection rate of 0.9954 and 0.9024, respectively, within only 0.54 s. We provided an early warning threshold-based method to conduct the risk analysis, and then a panel of food safety experts performed a risk revision on the prediction results produced by the proposed method. In this way, AI improves the panel's efficiency, whereas the panel enhances the model's reliability. This study provides a fast and cost-effective, food safety early warning method for detection data and assists market supervision departments in controlling food safety risk.