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To investigate the effects of buffalo milk exosomes on lipid metabolism disorders in the liver of mice induced by high-fat diet. In the experiment, exosomes were obtained through ultra-high speed centrifugation from buffalo milk, and a total 32 male C57BL/J6 mice were selected and randomly divided into normal control group (ND group), high-fat diet group (HFD group), low-dose exosomes group (HFD-L group), and high-dose exosomes group (HFD-H group). The mice in normal control group and the other groups were respectively fed with a 10% kcal control feed and a 60% kcal high-fat diet for 8 weeks. Subsequently, the mice were gavaged with either PBS or buffalo milk exosomes (5 and 20 mg/kg) every 48 h for a duration of 28 d, based on their respective groupings. The following indexes were detected: body weight, total adipose, liver tissue masses, levels of serum, liver triglycerides (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), as well as serum gluten transaminase (AST) and alanine transaminase (ALT). Furthermore, liver tissue was subjected to HE staining and oil red O staining. The results showed that the intact buffalo milk exosomes could be obtained through ultra-high speed centrifugation, and buffalo milk exosomes could alleviate the weight gain caused by a high-fat diet in mice to a certain degree. Additionally, the exosomes significantly reduced liver and serum levels of TG, TC and LDL-C in a dose-dependent manner in high-fat mice, conversely increased liver and serum levels of HDL-C, and increased the serum levels of AST and ALT (P

Societal Impact Statement Arbuscular mycorrhizal fungi (AMF) impact the relationships between plants, aphids (insects that feed on plant phloem), and their natural enemies (insects that prey on or parasitize aphids). The presence of AMF influences the growth and population of different aphid species and affects the development of aphid‐killing wasps and their attraction to plants. This research has been conducted also considering the insects' feeding strategy and their feeding specialization. This study provides novel perspectives on how these fungi shape interactions in the natural world, offering potential insights for the development of sustainable pest management strategies in agriculture. Summary Arbuscular mycorrhizal fungi (AMF) are major root symbionts regulating plant physiology. Their presence affects the performance of aboveground insect herbivores in relation to their feeding strategy and their feeding specialization. For example, the effect of the arbuscular mycorrhizal (AM) symbiosis on chewing insects, positive for specialists and negative for generalists, has been previously demonstrated. Conversely, the impact of AMF on phloem‐suckers with relatively different levels of specialization remains unexplored. We tested the influence of the AM Funneliformis mosseae on the fitness of the specialist aphid Acyrthosiphon pisum and the generalist aphid Myzus persicae on Vicia faba plants. Further, we investigated the effects of AMF on the higher trophic level, the aphid parasitoids Aphidius ervi (specialist) and Aphidius colemani (generalist), by evaluating plant attractiveness and parasitoid fitness. To support the results of behavioral and biological bioassays we characterized the photosynthetic parameters, the volatilome and the transcriptome of tested plants. Mycorrhizal plants proved unsuitable for the generalist M. persicae but enhanced the fitness of the specialist A. pisum. The AM symbiosis had no effects on the behavioral response of A. colemani and enhanced the attraction and fitness of A. ervi. Volatilome and transcriptome profiling corroborated the results of bioassay highlighting a bottom‐up effect of the AMF across a plant–aphid–parasitoid system.

Abstract Giant trees are pivotal in forest ecosystems, yet our current understanding of their significance is constrained primarily by the limited knowledge of their precise locations and structural characteristics. Amidst escalating human‐induced disturbances globally, there is an urgent need to devise a practical approach to discover and measure giant trees accurately and efficiently. Here, we propose a novel light detection and ranging (lidar)‐based framework designed for the discovery and measurement of giant trees. Our framework integrates cutting‐edge lidar platforms, including spaceborne, Unmanned Aerial Vehicle (UAV), and backpack lidar, to create an end‐to‐end workflow. The algorithm involved in the proposed framework was compiled into a code package and made available as open source. The method successfully identified the tallest trees in China, including the tallest tree in Asia, a Cupressus austrotibetica with a height of 102.3 m, discovered in Yarlung Zangbo Grand Canyon in May 2023. This finding has not only established a new record but also demonstrated the efficacy of our proposed framework. Utilising lidar data, we performed meticulous measurements at both individual and stand levels, revealing the unique characteristics of this giant tree. The new framework for the discovery and measurement of giant trees, encompassing detailed procedures and codes, is expected to facilitate the discovery and measurement of giant trees with high efficiency, thus fostering advancements in giant tree ecology.

This study aims at developing a dataset for determining the presence of carotid artery plaques in ultrasound images, composed of 1761 ultrasound images from 1165 participants. A deep learning architecture that combines bilinear convolutional neural networks with residual neural networks, known as the single-input BCNN-ResNet model, was utilized to aid clinical doctors in diagnosing plaques using carotid ultrasound images. Following training, internal validation, and external validation, the model yielded an ROC AUC of 0.99 (95% confidence interval: 0.91 to 0.84) in internal validation and 0.95 (95% confidence interval: 0.96 to 0.94) in external validation, surpassing the ResNet-34 network model, which achieved an AUC of 0.98 (95% confidence interval: 0.99 to 0.95) in internal validation and 0.94 (95% confidence interval: 0.95 to 0.92) in external validation. Consequently, the single-input BCNN-ResNet network model has shown remarkable diagnostic capabilities and offers an innovative solution for the automatic detection of carotid artery plaques.

Abstract With the modernization of traditional Chinese medicine (TCM), creating devices to digitalize aspects of pulse diagnosis has proved to be challenging. The currently available pulse detection devices usually rely on external pressure devices, which are either bulky or poorly integrated, hindering their practical application. In this work, we propose an innovative wearable active pressure three-channel pulse monitoring device based on TCM pulse diagnosis methods. It combines a flexible pressure sensor array, flexible airbag array, active pressure control unit, advanced machine learning approach, and a companion mobile application for human–computer interaction. Due to the high sensitivity (460.1 kPa−1), high linearity (R 2 > 0.999) and flexibility of the flexible pressure sensors, the device can accurately simulate finger pressure to collect pulse waves (Cun, Guan, and Chi) at different external pressures on the wrist. In addition, by measuring the change in pulse wave amplitude at different pressures, an individual’s blood pressure status can be successfully predicted. This enables truly wearable, actively pressurized, continuous wireless dynamic monitoring of wrist pulse health. The innovative and integrated design of this pulse monitoring platform could provide a new paradigm for digitizing aspects of TCM and other smart healthcare systems.

Abstract China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of China’s planted forest area and its carbon storage remain uncaptured. Here we reveal such changes in China’s planted forests from 1990 to 2020 using satellite and field data. Results show a doubling of planted forest area, a trend that intensified post-2000. These changes lead to China’s planted forest carbon storage increasing from 675.6 ± 12.5 Tg C in 1990 to 1,873.1 ± 16.2 Tg C in 2020, with an average rate of ~ 40 Tg C yr−1. The area expansion of planted forests contributed ~ 53% (637.2 ± 5.4 Tg C) of the total above increased carbon storage in planted forests compared with planted forest growth. This proactive policy-driven expansion of planted forests has catalyzed a swift increase in carbon storage, aligning with China’s Carbon Neutrality Target for 2060.

Abstract α‐halo alkylboronic esters, acting as ambiphilic synthons, play a pivotal role as versatile intermediates in fields like pharmaceutical science and organic chemistry. The sequential transformation of carbon–boron and carbon–halogen bonds into a broad range of carbon–X bonds allows for programmable bond formation, facilitating the incorporation of multiple substituents at a single position and streamlining the synthesis of complex molecules. Nevertheless, the synthetic potential of these compounds is constrained by limited reaction patterns. Additionally, the conventional methods often necessitate the use of bulk toxic solvents, exhibit sensitivity to air/moisture, rely on expensive metal catalysts, and involve extended reaction times. In this report, a ball milling technique is introduced that overcomes these limitations, enabling the external catalyst‐free multicomponent coupling of aryl diazonium salts, alkenes, and simple metal halides. This approach offers a general and straightforward method for obtaining a diverse array of α‐halo alkylboronic esters, thereby paving the way for the extensive utilization of these synthons in the synthesis of fine chemicals.

Over the last decade, a number of techniques for individual tree segmentation has been developed for terrestrial laser scanning data. The superpoint segmentation algorithm based on point cloud has been widely used in individual tree segmentation because of its high efficiency and numerous geometric features. However, this algorithm is generally developed for specific tree species and forest types, limiting its universality and performance for different forest types. To handle this problem, a new method based on the topology of tree branches for individual tree segmentation was proposed. Focusing on the general topological structure of trees, the proposed method iteratively assigns each branch based on its directivity to its upper branch at the superpoint level. The proposed method was tested compared with the original superpoint method and an ecological method in six sample plots with different forest conditions. In such plots, the proposed method achieved anticipated performance with an average accuracy of 40% improvements compared with the other two methods, especially in complicated forest conditions. Experimental results also showed an improved average accuracy of 70% compared with the original superpoint method at the point level. This proposed method can effectively expand the universality of the superpoint method to further advance ecological and forest research.

This experiment aimed to explore the effects of flavonoids in Fructus Aurantii Immaturus (FFAI) on carcass traits, meat quality, and the antioxidant capacity of finishing pigs. The results indicated that the addition of an appropriate amount of FFAI into their diet could significantly reduce the backfat thickness and perirenal fat percentage of finishing pigs, as well as the drip loss, water-holding capacity, shear force, and the levels of lactate, glucose-6-phosphate, glucose, ATP, phosphofructokinase, and pyruvate in the longissimus dorsi (LD) muscle. It also elevated the levels of flavor amino acids such as glutamate, serine, and threonine, and enriched the composition of flavor substances, including benzene and octanal, which significantly contributed to the enhancement of pork flavor. Furthermore, it enhanced the expression levels of MyHC I and MyHC IIa. In summary, the appropriate addition of FFAI to the diet could improve the carcass traits, meat quality, and antioxidant capacity of finishing pigs. The optimal level of FFAI supplementation is 0.12%.

BackgroundCarotid plaques are major risk factors for stroke. Carotid ultrasound can help to assess the risk and incidence rate of stroke. However, large-scale carotid artery screening is time-consuming and laborious, the diagnostic results inevitably involve the subjectivity of the diagnostician to a certain extent. Deep learning demonstrates the ability to solve the aforementioned challenges. Thus, we attempted to develop an automated algorithm to provide a more consistent and objective diagnostic method and to identify the presence and stability of carotid plaques using deep learning.MethodsA total of 3,860 ultrasound images from 1,339 participants who underwent carotid plaque assessment between January 2021 and March 2023 at the Shanghai Eighth People’s Hospital were divided into a 4:1 ratio for training and internal testing. The external test included 1,564 ultrasound images from 674 participants who underwent carotid plaque assessment between January 2022 and May 2023 at Xinhua Hospital affiliated with Dalian University. Deep learning algorithms, based on the fusion of a bilinear convolutional neural network with a residual neural network (BCNN-ResNet), were used for modeling to detect carotid plaques and assess plaque stability. We chose AUC as the main evaluation index, along with accuracy, sensitivity, and specificity as auxiliary evaluation indices.ResultsModeling for detecting carotid plaques involved training and internal testing on 1,291 ultrasound images, with 617 images showing plaques and 674 without plaques. The external test comprised 470 ultrasound images, including 321 images with plaques and 149 without. Modeling for assessing plaque stability involved training and internal testing on 764 ultrasound images, consisting of 494 images with unstable plaques and 270 with stable plaques. The external test was composed of 279 ultrasound images, including 197 images with unstable plaques and 82 with stable plaques. For the task of identifying the presence of carotid plaques, our model achieved an AUC of 0.989 (95% CI: 0.840, 0.998) with a sensitivity of 93.2% and a specificity of 99.21% on the internal test. On the external test, the AUC was 0.951 (95% CI: 0.962, 0.939) with a sensitivity of 95.3% and a specificity of 82.24%. For the task of identifying the stability of carotid plaques, our model achieved an AUC of 0.896 (95% CI: 0.865, 0.922) on the internal test with a sensitivity of 81.63% and a specificity of 87.27%. On the external test, the AUC was 0.854 (95% CI: 0.889, 0.830) with a sensitivity of 68.52% and a specificity of 89.49%.ConclusionDeep learning using BCNN-ResNet algorithms based on routine ultrasound images could be useful for detecting carotid plaques and assessing plaque instability.

Dunes are the primary geomorphological type in deserts, and the distribution of dune morphologies is of significant importance for studying regional characteristics, formation mechanisms, and evolutionary processes. Traditional dune morphology classification methods rely on visual interpretation by humans, which is not only time-consuming and inefficient but also subjective in classification judgment. These issues have impeded the intelligent development of dune morphology classification. However, convolutional neural network (CNN) models exhibit robust feature representation capabilities for images and have achieved excellent results in image classification, providing a new method for studying dune morphology classification. Therefore, this paper summarizes five typical dune morphologies in the deserts of western Inner Mongolia, which can be used to define and describe most of the dune types in Chinese deserts. Subsequently, field surveys and the experimental collection of unmanned aerial vehicle (UAV) orthoimages for different dune types were conducted. Five different types of dune morphology datasets were constructed through manual segmentation, automatic rule segmentation, random screening, and data augmentation. Finally, the classification of dune morphologies and the exploration of dataset construction methods were conducted using the VGG16 and VGG19 CNN models. The classification results of dune morphologies were comprehensively analyzed using different evaluation metrics. The experimental results indicate that when the regular segmentation scale of UAV orthoimages is 1024 × 1024 pixels with an overlap of 100 pixels, the classification accuracy, precision, recall, and F1-Score of the VGG16 model reached 97.05%, 96.91%, 96.76%, and 96.82%, respectively. The method for constructing a dune morphology dataset from automatically segmented UAV orthoimages provides a reference value for the study of large-scale dune morphology classification.

Abstract Waste heat recovery is common in high‐temperature water electrolysis production, but the latent heat from the hydrogen–water mixture is not fully utilized, and the electric heater to generate steam consumes much energy. In this research, a cascade heat pump is proposed to recover the latent heat from electrolysis products and generate steam. The heat pump can save as much as 65% electric energy compared with a single electric heater. Although in some cases, the latent heat is not enough to generate sufficient steam, it can still save 46.1% of energy. Also, this research emphasizes the significant influence of hydrogen and water proportion in electrolysis products. Compared with 80% water proportion, 50% water proportion can save 1.67 MW energy just in the water vaporization process for a 10 MW electrolyzer. The payback period is 3.43 years, which makes it worth investing.

As an effective tool for refining local gravity data with mGal accuracy, vehicle gravimetry system usually consists of Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS). However, the traditional system is unable to achieve gravimetry in an environment without GNSS signal dues to heavy dependence on GNSS signal. To solve this problem, this study proposed a gravimetry method specifically for the environment without GNSS signal, which consists of the high-precision Strapdown Inertial Navigation System (SINS) and Laser Doppler Velocimeter (LDV) developed by our unit. Based on the error analysis, the system index requirements were proposed, and the relevant indexes of the LDV are verified through simulation and vehicle experiment. The simulation results for different velocimeter accuracies show that the internal accord accuracy of the system is within 1mGal when the velocimeter accuracy is 1 ‰, and the internal accord accuracies of the system are more than 1mGal when the velocimeter accuracies are 5 ‰ and 10‰. The results of the vehicle experiment for different dimensional velocimeters show that when the spatial resolution is 1.7 km, the SINS/LDV (2D) and SINS/GNSS integrated systems have an internal accord accuracy of 0.68 mGal and 0.54 mGal, respectively, while the SINS/LDV (1D) integrated system has an internal accord accuracy of 1.50 mGal. The feasibility of the SINS/LDV system and the necessity of the SINS/LDV system for the dimension and accuracy of the velocimeter are illustrated.

Abstract Recently, flexible iontronic pressure sensors (FIPSs) with higher sensitivities and wider sensing ranges than conventional capacitive sensors have been widely investigated. Due to the difficulty of fabricating the nanostructures that are commonly used on electrodes and ionic layers by screen printing techniques, strategies for fabricating such devices using these techniques to drive their mass production have rarely been reported. Herein, for the first time, we employed a 2-dimensional (2D) hexagonal boron nitride (h-BN) as both an additive and an ionic liquid reservoir in an ionic film, making the sensor printable and significantly improving its sensitivity and sensing range through screen printing. The engineered sensor exhibited high sensitivity (Smin> 261.4 kPa−1) and a broad sensing range (0.05–450 kPa), and it was capable of stable operation at a high pressure (400 kPa) for more than 5000 cycles. In addition, the integrated sensor array system allowed accurate monitoring of wrist pressure and showed great potential for health care systems. We believe that using h-BN as an additive in an ionic material for screen-printed FIPS could greatly inspire research on 2D materials for similar systems and other types of sensors. Hexagonal boron nitride (h-BN) was employed for the first time to make iontronic pressure sensor arrays with high sensitivity and a broad sensing range by screen printing.

Accurate and efficient road adhesion coefficient estimation is the premise for the proper functioning of vehicle active safety control system. With the increased application of distributed drive vehicles and on-board sensors, a multi-module self-organizing feedforward neural network (LM-MMSOFNN) based on improved Levenberg-Marquardt (LM) learning algorithm is proposed for online road adhesion coefficient estimation. In this method, the vehicle dynamics model and the Dugoff tire model were well established, and the input and output variables of the neural network model were obtained by Principal Component Analysis (PCA) method. To improve the estimation accuracy, Extended Kalman Filter (EKF) and Moving Average (MA) were used to denoise the measured signal. On this basis, a road adhesion coefficient estimation model based on multi-module self-organizing neural network was established. Both sides of road adhesion coefficients are calculated by multi-module self-organizing neural network simultaneously. Through the increase and decrease of self-organizing neurons and the improved LM learning algorithm, the computational complexity and system hardware storage are reduced, and the algorithm exhibits a good adaptability to different roads. Simulation and vehicle experiments show that the proposed method can fully extract multi-sensor information and adapt to the different road characteristics changes under driving condition. As compared with Kmeans method, it has higher estimation accuracy and stronger adaptability to varying speed.

Because of the excellent thermal conductivity and increased strength, finned special-shaped tube (FSST) is frequently utilized in a variety of industrial applications. However, duo to the complex section structure of FSST, the elastic–plastic evolution of the FSST section is more complex than that of circular and rectangular tubes, when FSST is subjected to various coupling effects of bending moment and torque. Moreover, until now, no literature has studied the bending-torsional-coupled deformation of FSST. In this study, based on the power-law stress–strain curve model and the Henky-Ilyushin equations, an analytical model that can precisely characterize the bending-torsional-coupled deformation of the FSST section is established by using the stress method. According to the boundary conditions divided by the geometric characteristics of FSST, the stress and strain distribution, elastic–plastic evolution, and the coupling relationship between the base circle and fins under different bending radii and unit torsion angles are discussed in detail. Besides, the relationship between bending radius and unit torsion angle and bending moment and torque on the section is presented based on the concept of section moment superposition. Finally, the rationality and accuracy of the proposed analytical model are verified by simplified finite element model (FEM), FEM, and practical tests.

Heat Exchanger Network (HEN) synthesis is primarily formulated as a mixed integer non-linear programming (MINLP) problem based on method of stage-wise superstructure (SWS). Approaches to obtain an optimal HEN configuration can adopt deterministic algorithms. But, as a large scale problem, it is difficult to solve due to the complexities arisen from nonlinearities of SWS. To overcome the model nonlinearities, stochastic algorithms and meta-heuristic approaches have been proposed in the literature to tackle the problem. However, it reaches a near-optimal HEN configuration from a series of stochastic solutions, which are obtained by the execution of many computational procedures with extensive time resource, and a global optimum will not be guaranteed from only randomly generated results. In this paper, an enhanced SWS approach is proposed, in which new temperature and heat duty constraints are updated to reduce the redundant combinations and avoid conflicted calculation of non-isothermal mixing energy balance. The present model is also extended to allow flexible stream splitting for practical applications. Then, a deterministic-based global solver (GAMS/BARON) is applied in solving three case studies. The results show that the proposed approach can provide cost-efficient HEN solutions with lower TAC than using existing stochastic and deterministic algorithms.

In recent years, flexible pressure sensing arrays applied in medical monitoring, human-machine interaction, and the Internet of Things have received a lot of attention for their excellent performance. Epidermal sensing arrays can enable the sensing of physiological information, pressure, and other information such as haptics, providing new avenues for the development of wearable devices. This paper reviews the recent research progress on epidermal flexible pressure sensing arrays. Firstly, the fantastic performance materials currently used to prepare flexible pressure sensing arrays are outlined in terms of substrate layer, electrode layer, and sensitive layer. In addition, the general fabrication processes of the materials are summarized, including three-dimensional (3D) printing, screen printing, and laser engraving. Subsequently, the electrode layer structures and sensitive layer microstructures used to further improve the performance design of sensing arrays are discussed based on the limitations of the materials. Furthermore, we present recent advances in the application of fantastic-performance epidermal flexible pressure sensing arrays and their integration with back-end circuits. Finally, the potential challenges and development prospects of flexible pressure sensing arrays are discussed in a comprehensive manner.

Newcastle disease (ND) and infectious bursal disease (IBD) are two key infectious diseases that significantly threaten the health of the poultry industry. Although existing vaccinations can effectively prevent and treat these two diseases through multiple immunizations, frequent immunization stresses significantly impact chicken growth. In this study, three recombinant adenoviruses, rAd5-F expressing the NDV (genotype VII) F protein, rAd5-VP2 expressing the IBDV VP2 protein, and rAd5-VP2-F2A-F co-expressing F and VP2 proteins, were constructed using the AdEasy system. The F and VP2 genes of the recombinant adenoviruses could be transcribed and expressed normally in HEK293A cells as verified by RT-PCR and Western blot. The three recombinant viruses were shown to have similar growth kinetics as rAd5-EGFP. Compared with the PBS and rAd5-EGFP groups, SPF chickens immunized with recombinant adenoviruses produced higher antibody levels, more significant lymphocyte proliferation, and significantly higher CD4+/CD3+ and CD8+/CD3+ cells in peripheral blood. The survival rate of SPF chickens immunized with rAd5-F and rAd5-VP2-F2A-F after the challenge with DHN3 was 100%, and 86% of SPF chickens showed no viral shedding at 7 dpc. The survival rate of SPF chickens immunized with rAd5-VP2 and rAd5-VP2-F2A-F after the challenge with BC6/85 was 86%. rAd5-VP2 and rAd5-VP2-F2A-F significantly inhibited bursal atrophy and pathological changes compared to the rAd5-EGFP and PBS groups. This study provides evidence that these recombinant adenoviruses have the potential to be developed into safe and effective vaccine candidates for the prevention and control of ND and IBD.

Ni-based composite powder (NCP) has attracted increasing attention due to its unique advantages. Various NCPs have been synthesized successfully by different raw powders, and synthesis strategies and applications of NCP have been designed. However, the mechanism of Ni coating is still in the early stage, and timely updates of recent research progress on new remarkable breakthroughs in NCP are highly desired. In light of the above, the novel synthesis designs and mechanism of NCP were discussed in this review. In addition, this review focused on the application of NCP in electrocatalysis, functional coating, and energy field. The future directions of various NCPs were summarized. Some innovative suggestions were discussed at the end of the article.

For industrial vaccine production, overwhelming the existing antiviral innate immune response dominated by type I interferons (IFN-I) in cells would be a key factor improving the effectiveness and production cost of vaccines. In this study, we report the construction of an IFN-I receptor 1 (IFNAR1)-knockout DF-1 cell line (KO-IFNAR1), which supports much more efficient replication of the duck Tembusu virus (DTMUV), Newcastle disease virus (NDV) and gammacoronavirus infectious bronchitis virus (IBV). Transcriptomic analysis of DTMUV-infected KO-IFNAR1 cells demonstrated that DTMUV mainly activated genes and signaling pathways related to cell growth and apoptosis. Among them, JUN, MYC and NFKBIA were significantly up-regulated. Furthermore, knockdown of zinc-fingered helicase 2 (HELZ2) and interferon-α-inducible protein 6 (IFI6), the two genes up-regulated in both wild type and KO-IFNAR1 cells, significantly increased the replication of DTMUV RNA. This study paves the way for further studying the mechanism underlying the DTMUV-mediated IFN-I-independent regulation of virus replication, and meanwhile provides a potential cell resource for efficient production of cell-based avian virus vaccines.

Avian interferon regulatory factors 1 and 7 (IRF1 and IRF7) play important roles in the host’s innate immunity against viral infection. Our previous study revealed that duck tembusu virus (DTMUV) infection of chicken fibroblasts (DF1) and duck embryo fibroblasts (DEFs) induced the expression of a variety of IFN-stimulated genes (ISGs), including VIPERIN, IFIT5, CMPK2, IRF1, and IRF7. IRF1 was further shown to play a significant role in regulating the up-expression of VIPERIN, IFIT5, and CMPK2 and inhibiting DTMUV replication. In this study, we confirm, through overexpression and knockout approaches, that both IRF1 and IRF7 inhibit DTMUV replication, mainly via regulation of type I IFN expression, as well as the induction of IRF1, VIPERIN, IFIT5, CMPK2, and MX1. In addition, IRF1 directly promoted the expression of VIPERIN and CMPK2 in an IFN-independent manner when IRF7 and type I IFN signaling were undermined. We also found that non-structural protein 2B (NS2B) of DTMUV was able to inhibit the induction of IFN-β mRNA triggered by Newcastle disease virus (NDV) infection or poly(I:C) treatment, revealing a strategy employed by DTMUV to evade host’s immunosurveillance. This study demonstrates that avian IRF7 and IRF1 play distinct roles in the regulation of type I IFN response during DTMUV infection.

Cognitive radio networks (CRNs) have been recognized as a promising technology that allows secondary users (SUs) extensively explore spectrum resource usage efficiency, while not introducing interference to licensed users. Due to the unregulated wireless network environment, CRNs are susceptible to various malicious entities. Thus, it is critical to detect anomalies in the first place. However, from the perspective of intrinsic features of CRNs, there is hardly in existence of an universal applicable anomaly detection scheme. Singular Spectrum Analysis (SSA) has been theoretically proven an optimal approach for accurate and quick detection of changes in the characteristics of a running (random) process. In addition, SSA is a model-free method and no parametric models have to be assumed for different types of anomalies, which makes it a universal anomaly detection scheme. In this paper, we introduce an adaptive parameter and component selection mechanism based on coherence for basic SSA method, upon which we built up a sliding window online anomaly detector in CRNs. Our experimental results indicate great accuracy of the SSA-based anomaly detector for multiple anomalies.

Timely detecting disturbances and locating their sources are critical to the reliable operation of power grids. This capability enables operators to effectively diagnose disturbances over wide areas and earns time for remedial reactions. In this study, a travelling-wave based scheme, namely data-driven online distributed disturbance location (DODDL), is proposed to quickly detect disturbances and determine their geographic location in large-scale power grids when the grids’ topology is not available. The proposed DODDL scheme consists of two function blocks: (i) a singular spectrum analysis-based change-point detection method, which can quickly detect disturbances and determine their arrival time at distributed sensors, and (ii) a novel temporal scanning algorithm, which can accurately determine the geographic location of the disturbance source point. Utilising field measurement data sets recorded by the frequency disturbance recorders from the frequency monitoring network, it is shown that the DODDL scheme is not only quicker and more robust to grid non-homogeneity than existing approaches, but also can capture and locate more subtle and concealed disturbances.