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Interactions between double-stranded RNA (dsRNA) and proteins play an important role in cellular homeostasis by regulating the editing, stability, and splicing of intracellular RNA. The identification of dsRNA-binding proteins (dsRBPs) is key; however, it has long been challenging to purify dsRBPs from cells. In this study, we developed a novel method, dsRBPC (dsRNA-binding protein capture), to purify cellular dsRBPs based on classic phase separation purification procedures. A global dsRNA-binding proteome of LLC-PK1 cells was obtained, and we identified 1326 dsRBPs, including 1303 putative novel dsRBPs. Functional analyses suggested that these enriched dsRBPs are mainly associated with rRNA processing, RNA splicing, transcriptional regulation, and nucleocytoplasmic transport. We also found that the ARM (armadillo/beta-catenin-like repeats) motif is a previously unknown dsRNA-binding domain, as demonstrated by biochemical experiments. Collectively, this study provides a useful approach for dsRBP identification and the discovery of a global dsRNA-binding proteome to comprehensively map the dsRNA – protein interaction network.

In Plectropomus leopardus, Vibrio disease and Hirudo parasitic disease are relatively common. Timely recognition of these diseases can improve the survival rate of Plectropomus leopardus and prevent their spread. However, early-stage diseases are difficult to distinguish due to their small size and subtle characteristics. Traditional manual recognition methods rely on personal experience and subjective judgment, leading to time-consuming and error-prone diagnoses. To address the challenges in detecting and classifying Plectropomus leopardus diseases, this paper proposes PLDNet (Plectropomus Leopardus Disease Detection Network), a real-time detection and recognition method that provides faster and more accurate diagnoses for fish farms. PLDNet incorporates two significant advancements: First, it employs FocalModulation, which enhances the model’s ability to identify key disease characteristics in images. Second, it introduces the MPDIoU (Minimum Point Distance-based Intersection over Union) for bounding box similarity comparison, optimizing the loss function and improving recognition accuracy. This paper also presents the PLDD (Plectropomus Leopardus Disease Dataset), a newly developed dataset that includes comprehensive images of healthy and diseased specimens. PLDD addresses the scarcity of data for this species and serves as a valuable resource for advancing research in marine fish health. Empirical validation of PLDNet was conducted using the PLDD dataset and benchmarked against leading models, including YOLOv8-n, YOLOv9-m, and YOLOv9-c. The results show that PLDNet achieves superior detection performance, with an average detection accuracy of 84.5%, a recall rate of 86.6%, an mAP@o.5 of 88.1%, and a real-time inference speed of 45 FPS. These metrics demonstrate that PLDNet significantly outperforms other models in both accuracy and efficiency, providing practical solutions for real-time fish disease management.

ABSTRACTCoinfection with multiple viruses is a common phenomenon in clinical settings and is a crucial driver of viral evolution. Although numerous studies have demonstrated viral recombination arising from coinfections of different strains of a specific species, the role of coinfections of different species or genera during viral evolution is rarely investigated. Here, we analyzed coinfections of and recombination events between four different swine enteric coronaviruses that infect the jejunum and ileum in pigs, including porcine epidemic diarrhea virus (PEDV), transmissible gastroenteritis virus (TGEV), and swine acute diarrhea syndrome coronavirus (SADS-CoV), and a deltacoronavirus, porcine deltacoronavirus (PDCoV). Various coinfection patterns were observed in 4,468 fecal and intestinal tissue samples collected from pigs in a 4-year survey. PEDV/PDCoV was the most frequent coinfection. However, recombination analyses have only detected events involving PEDV/TGEV and SADS-CoV/TGEV, indicating that inter-species recombination among coronaviruses is most likely to occur within the same genus. We also analyzed recombination events within the newly identified genus Deltacoronavirus and found that sparrows have played a unique host role in the recombination history of the deltacoronaviruses. The emerging virus PDCoV, which can infect humans, has a different recombination history. In summary, our study demonstrates that swine enteric coronaviruses are a valuable model for investigating the relationship between viral coinfection and recombination, which provide new insights into both inter- and intraspecies recombination events among swine enteric coronaviruses, and extend our understanding of the relationship between coronavirus coinfection and recombination.

Abstract Coronavirus 3C‐like protease (CoV 3CLpro) is essential for viral replication, providing an attractive target for monitoring the evolution of CoV and developing anti‐CoV drugs. Here, the substrate‐binding modes of 3CLpros from four CoV genera are analyzed and found that the S2 pocket in 3CLpro is highly conserved within each genus but differs between genera. Functionally, the S2 pocket, in conjunction with S4 and S1′ pockets, governs the genus‐specific substrate selectivity of 3CLpro. Resurrected ancestral 3CLpros from four CoV genera validate the genus‐specific divergence of S2 pocket. Drawing upon the genus‐specific S2 pocket as evolutionary marker, eight newly identified 3CLpros uncover the ancestral state of modern 3CLpro and elucidate the possible evolutionary process for CoV. It is also demonstrated that the S2 pocket is highly correlated with the genus‐specific inhibitory potency of PF‐07321332 (an FDA‐approved drug against COVID‐19) on different CoV 3CLpros. This study on 3CLpro provides novel insights to inform evolutionary mechanisms for CoV and develop genera‐specific or broad‐spectrum drugs against CoVs.

The Plectropomus leopardus (P. leopardus), a species found in underwater environments, possesses substantial strategic importance due to its rich underwater resources. However, the natural habitat and industrial breeding environment of P. leopardus is generally dark and complex, which presents notable challenges to object detection and recognition. In this research, we propose Plectropomus leopardus recognition using Global Attention mechanism and Transfer learning(PLGAT), integrating a Global Attention Mechanism (GAM) with Transfer Learning to recognize P. leopardus in underwater environments. To resist the color variations, the PLGAT directs the model’s attention toward salient regions by incorporating the output of the GAM with the vectors from the Head convolutional layer. Additionally, transferring knowledge from a large-scale Brackish dataset enhances the model’s discrimination capabilities. To validate the effectiveness of PLGAT, we have created an extensive underwater Plectropomus leopardus Recognition Dataset (PLRD), consisting of 6,058 annotations across 30 distinct object classes. Furthermore, we evaluate the performance of PLGAT on the widely used Brackish underwater dataset. Through comprehensive experiments, our results demonstrate that PLGAT achieves a mean average precision score of approximately 95% on PLRD and 97% on Brackish. These results signify a substantial improvement in recognition performance and highlight the efficacy of our approach compared to the state-of-the-art methods. To foster further research and promote reproducibility, we have made our source code and dataset openly available at https://github.com/wilkinszhang/Recognition-of-Underwater-leopard-coral-grouper.

ElectricalImpedance Myography (EIM) is an innovative, non-invasive technique offering a convenient means of localized exogenous electrophysiological recording. By measuring muscle impedance parameters, this method characterizes the physiological state of muscles, functioning as a biomarker for muscle contractility, injuries, and the progression of neuromuscular diseases. This paper provides an overview of the current state of EIM technology development, along with modeling and data analysis methods, focusing on their application requirements. It further highlights the advancements in EIM research within the realm of sports health, emphasizing its efficacy in identifying injuries and monitoring wound healing, and discusses existing technological limitations. Additionally, the paper explores future research directions. Serving as a transient biosensor during physical activity, EIM holds significant potential in sports health. It presents a promising alternative to invasive and costly clinical assessment methods, positioning itself as a viable personal monitoring tool for both professional athletes and fitness enthusiasts. Nevertheless, the resolution of technical challenges and the establishment of industry-standard implementation programs are essential prerequisites for EIM to evolve into a standard clinical assessment tool.

Urban population explosion may increase ecological environment discomfort, thereby affecting negatively humans’ mental and physical performance. Therefore, it is important to detect and monitor vegetation and predict its ecological benefits. The complex composition of urban environment ground objects, such as steel roofs, plastic courts, and building shadows, significantly interferes with vegetation detection and monitoring. The optimized hyperspectral image-based vegetation index (OHSVI) constructed in this study effectively solves this problem. However, it is difficult to accurately predict the ecological benefits of vegetation based on the two-dimensional vegetation information extracted based on remote sensing images; this is related to the three-dimensional (3D) structure of vegetation and the 3D pattern of buildings. Therefore, we first proposed the vegetation ecological benefits index (VEBI) based on the 3D structure of vegetation to reveal how vegetation acts on its 3D surroundings. The method was tested in a playground, an academic building, and a parking space. The results showed that the vegetation extraction accuracy of the OHSVI exceeded 93%, which is better than that of the existing indices. Our findings suggest that VEBI may be efficient in predicting 3D vegetation ecological benefits combined with remote sensing and lidar datasets.

Porcine deltacoronavirus (PDCoV) is an emerging enteric coronavirus that has been reported to infect a variety of animals and even humans. Cell–cell fusion has been identified as an alternative pathway for the cell-to-cell transmission of certain viruses, but the ability of PDCoV to exploit this transmission model, and the relevant mechanisms, have not been fully elucidated. Herein, we provide evidence that cell-to-cell transmission is the main mechanism supporting PDCoV spread in cell culture and that this efficient spread model is mediated by spike glycoprotein-driven cell–cell fusion. We found that PDCoV efficiently spread to non-susceptible cells via cell-to-cell transmission, and demonstrated that functional receptor porcine aminopeptidase N and cathepsins in endosomes are involved in the cell-to-cell transmission of PDCoV. Most importantly, compared with non-cell-to-cell infection, the cell-to-cell transmission of PDCoV was resistant to neutralizing antibodies and immune sera that potently neutralized free viruses. Taken together, our study revealed key characteristics of the cell-to-cell transmission of PDCoV and provided new insights into the mechanism of PDCoV infection.

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions.

Aircraft propellers produce relatively large in-plane loads, called propeller 1P loads, during maneuvers such as turning, diving, and lifting, and these loads can negatively affect the flight and control of the aircraft. In order to study the change rule of 1P aerodynamic loads, in this paper, a mathematical model of the propeller 1P aerodynamic loads has been developed based on the blade element momentum theory. This mathematical model was then corrected using the Pitt–Peters incoming flow correction method, the Prandtl tip correction method, and the propeller root flow correction method. Based on this mathematical model, a calculation procedure for the propeller 1P aerodynamic loads was developed using MATLAB software, and the accuracy of the procedure was verified by comparing the results with CFD simulation results. Numerical simulations show that the results calculated based on the proposed mathematical model for the coefficients of thrust, power, bending moment, and the tangential force of the propeller have an error of less than ±6.00% compared to the CFD simulation results. For a small inflow angle, the coefficients of bending moment and tangential force of the whole propeller fluctuate in a small range. But, as the inflow angle increases, the fluctuation range of the aerodynamic characteristic parameters of the propeller increases and the fluctuation becomes more complicated. Through numerical calculations, it has been shown that the mathematical model presented herein is reliable and accurate. In addition, it greatly shortens the calculation time and improves the calculation efficiency. It is expected that the proposed model can provide a certain help for the strength design of the propeller structure and the study of the aerodynamic performance of the whole aircraft.

Psoriasis is a chronic immune-mediated inflammatory disease affecting the skin and other systems. Gastrointestinal disease was found to be correlated with psoriasis in previous studies and it can significantly affect the quality of life of psoriasis patients. Despite the importance of the gut microbiome in gut and skin health having already been demonstrated in many research studies, the potential effect of probiotics on GI comorbidities in psoriasis patients is unclear. To investigate the effects of probiotics on functional GI comorbidities including irritable bowel syndrome, functional constipation, and functional diarrhea in psoriasis patients, we conducted a targeted 16S rRNA sequencing and comprehensive bioinformatic analysis among southern Chinese patients to compare the gut microbiome profiles of 45 psoriasis patients over an 8-week course of novel oral probiotics. All the participants were stratified into responders and non-responders according to their improvement in GI comorbidities, which were based on their Bristol Stool Form Scale (BSFS) scores after intervention. The Dermatological Life Quality Index (DLQI) score revealed a significant improvement in quality of life within the responder group (DLQI: mean 10.4 at week 0 vs. mean 15.9 at week 8, p = 0.0366). The proportion of psoriasis patients without GI comorbidity manifestation at week 8 was significantly higher than that at week 0 (week 0: Normal 53.33%, Constipation/Diarrhea 46.67%; week 8: Normal 75.56%, Constipation/Diarrhea 24.44%, p = 0.0467). In addition, a significant difference in the gut microbiome composition between the responders and non-responders was observed according to alpha and beta diversities. Differential abundance analysis revealed that the psoriasis patients exhibited (1) an elevated relative abundance of Lactobacillus acidophilus, Parabacteroides distasonis, and Ruminococcus bromii and (2) a reduced relative abundance of Oscillibacter, Bacteroides vulgatus, Escherichia sp., and Biophila wadsworthia after the 8-week intervention. The responders also exhibited a higher relative abundance of Fusicatenibacter saccharivorans when compared to the non-responders. In summary, our study discovers the potential clinical improvement effects of the novel probiotic formula in improving GI comorbidities and quality of life in psoriasis patients. We also revealed the different gut microbiome composition as well as the gut microbial signatures in the patients who responded to probiotics. These findings could provide insight into the use of probiotics in the management of psoriasis symptoms.

Equine arteritis virus (EAV) and porcine reproductive and respiratory syndrome virus (PRRSV) represent two members of the family Arteriviridae and pose a major threat to the equine- and swine-breeding industries throughout the world. Previously, we and others demonstrated that PRRSV 3C-like protease (3CLpro) had very high glutamic acid (Glu)-specificity at the P1 position (P1-Glu). Comparably, EAV 3CLpro exhibited recognition of both Glu and glutamine (Gln) at the P1 position. However, the underlying mechanisms of the P1 substrate specificity shift of arterivirus 3CLpro remain unclear. We systematically screened the specific amino acids in the S1 subsite of arterivirus 3CLpro using a cyclized luciferase-based biosensor and identified Gly116, His133 and Ser136 (using PRRSV 3CLpro numbering) are important for recognition of P1-Glu, whereas Ser136 is nonessential for recognition of P1-Gln. Molecular dynamics simulations and biochemical experiments highlighted that the PRRSV 3CLpro and EAV 3CLpro formed distinct S1 subsites for the P1 substrate specificity switch. Mechanistically, a specific intermolecular salt bridge between PRRSV 3CLpro and substrate P1-Glu (Lys138/P1-Glu) are invaluable for high Glu-specificity at the P1 position, and the exchange of K138T (salt bridge interruption, from PRRSV to EAV) shifted the specificity of PRRSV 3CLpro toward P1-Gln. In turn, the T139K exchange of EAV 3CLpro showed a noticeable shift in substrate specificity, such that substrates containing P1-Glu are likely to be recognized more efficiently. These findings identify an evolutionarily accessible mechanism for disrupting or reorganizing salt bridge with only a single mutation of arterivirus 3CLpro to trigger a substrate specificity switch.

Nanomedicines, mainly consisting of liposome nanodrugs, polymer nanodrugs, protein nanodrugs, drug nanocrystals and inorganic nanodrugs, usually show higher efficiency than conventional small molecular drugs. Polymeric vesicles, owing to the advantages of good stability, easy functionalization, and large-scale production, have received extensive attention in drug loading and release. The hydrophilic cavity and hydrophobic vesicle wall can simultaneously encapsulate hydrophilic and hydrophobic drugs, which was beneficial to exert the synergistic therapeutic effect of drugs. In this review, we focused on the loading and release of nanodrugs in polymeric vesicles. The application of polymeric vesicles for single, multi-drug loading and co-assembly of polymers and nanoparticles were reviewed. The research progress of polymeric vesicles in the fields of targeted transport, drug release kinetics and stimuli-responsive controlled release of drugs was discussed.

One of the most commonly used techniques in aerospace engineering is the RANS (Reynolds average Navier–Stokes) approach for calculating the transonic compressor flow field, where the accuracy of the computation is significantly affected by the turbulence model used. In this work, we use SA, SST, k-ɛ, and the PAFV turbulence model developed based on the side-biased mean fluctuations velocity and the mean strain rate tensor to numerically simulate the transonic compressor NASA Rotor 67 to evaluate the accuracy of turbulence modeling in numerical calculations of transonic compressors. The simulation results demonstrate that the four turbulence models are generally superior in the numerical computation of NASA Rotor 67, which essentially satisfies the requirements of the accuracy of engineering calculations; by comparing and analyzing the ability of the four turbulence models to predict the aerodynamic performance of transonic compressors and to capture the details of the flow inside the rotor. The errors of the Rotor 67 clogging flow rate calculated by the SA, SST, k-ɛ, and PAFV turbulence models with the experimental data are 0.9%, 0.8%, 0.7%, and 0.6%, respectively. The errors of the calculated peak efficiencies are 2.2%, 1.6%, 0.9%, and 4.9%. The SA and SST turbulence models were developed for the computational characteristics of the aerospace industry. Their computational stability is better and their outputs for Rotor 67 are comparable. The k-ɛ turbulence model calculates the pressure ratio and efficiency that are closest to the experimental data, but the computation of its details of the flow field near the wall surface is not ideal because the k-ɛ turbulence model cannot accurately capture the flow characteristics of the region of high shear stresses. The PAFV turbulence model has a better prediction of complex phenomena such as rotor internal shock wave location, shock–boundary layer interaction, etc., due to the use of a turbulent velocity scale in vector form, but the calculated rotor efficiency is small.

ObjectivesThe hydrodynamic performance of three-dimensional hydrofoils with different shapes is studied in order to find the optimal hydrofoil geometry.Methods NACA 0012 hydrofoil model with different aspect ratios, tip ratios, forward-swept and back-swept shapes are used to simulated the flow field of the hydrofoil with multi-DOF coupling motion based on the NUMECA Fine/Marine solver. The reliability of the numerical method is validated by comparison with the experimental results and analysis of grid independence.ResultsThe results show that a hydrofoil with a larger aspect ratio has better performance. By comparing the results of hydrofoils with the same aspect ratio and different tip ratios, it is found that tip ratio causes performance degradation. By comparing the results of forward-swept, back-swept and trapezoidal hydrofoils with the same tip ratio, it is found that the performance of the back-swept hydrofoil is optimal, and the shape of the trailing edge has great influence on hydrofoil performance.ConclusionsThis study obtains certain optimal geometrical parameters of flapping foil propulsors, and achieves greater understanding of the characteristics of wake vortex structures.

With a close pathogenetic resemblance to human diabetes, canine Diabetes Mellitus, a chronic metabolic disease featuring abnormally high blood sugar levels, is increasing in prevalence worldwide. Unlike humans, canine glycemic control requires life-long insulin injections and dietary control in most cases, thereby jeopardizing diabetic dogs’ quality of life and increasing the difficulty of disease control. While many research studies have focused on elucidating the relationship between the canine gut microbiome and diseases, there is currently no research on the subject of diabetes mellitus in dogs. We hypothesized that the gut microbiome of canines with diabetes mellitus is different from that of healthy controls. Thus, we performed targeted 16S rRNA sequencing and comprehensive bioinformatic analysis to compare the gut microbiome profiles of 16 diabetic dogs with those of 32 healthy dogs. Clostridioides difficile, Phocaeicola plebeius, Lacrimispora indolis, and Butyricicoccus pullicaecorum were found to be enriched in diabetic dogs. A distinct shift towards carbohydrate degradation metabolic pathways was found to be differentially abundant in the diabetic subjects. Alteration of the co-occurrence network was also evident in the diabetic group. In conclusion, our study suggests that the gut microbial landscape differs in diabetic canines at the genera, species, functional, and network levels. These findings have significant implications for disease management, and thus warrant further research.

Urban traffic prediction is essential for intelligent transportation systems. However, traffic data often exhibit highly complex spatio-temporal correlations, posing challenges for accurate forecasting. Graph neural networks have demonstrated an outstanding ability in capturing spatial correlations and are now extensively applied to traffic prediction. However, many graph-based methods neglect the dynamic spatial features between road segments and the continuity of spatial features across adjacent time steps, leading to subpar predictive performance. This paper proposes a Dynamic Spatio-Temporal Graph Fusion Convolutional Network (DSTGFCN) to enhance the accuracy of traffic prediction. Specifically, we designed a dynamic graph fusion module without prior road spatial information, which extracts dynamic spatial information among roads from observed data. Subsequently, we fused the dynamic spatial features of the current time step and adjacent time steps to generate a dynamic graph for each time step. The graph convolutional gated recurrent network was employed to model the spatio-temporal correlations jointly. Additionally, residual connections were added to the model to enhance the ability to extract long-term temporal relationships. Finally, we conducted experiments on six publicly available traffic datasets, and the results demonstrated that DSTGFCN outperforms the baseline models with state-of-the-art predictive performance.

Atopic dermatitis (AD) is a common chronic inflammatory skin disease with a significant association with various type-2 inflammation-related comorbidities. Ongoing research suggests the crucial involvement of gut microbiome, especially in childhood onset AD, and hence, probiotics have emerged as a potential non-steroid-based therapeutics option to complement existing AD management plans. In order to delineate the impact of probiotics in the gut microbiome of pediatric AD patients from southern China, targeted 16S rRNA sequencing and thorough bioinformatic analysis were performed to analyze the gut microbiome profiles of 24 AD children after taking an orally administered novel synbiotics formula with triple prebiotics for 8 weeks. A notable improvement in Eczema Area and Severity Index (EASI) (p = 0.008) was observed after taking an 8-week course of probiotics, with no adverse effects observed. The relative abundances of key microbial drivers including Bacteroides fragilis and Lactobacillus acidophilus were significantly increased at week 8. We also found that the positive responsiveness towards an 8-week course of probiotics was associated with improvements in the gut microbiome profile with a higher relative abundance of probiotic species. Over-represented functional abundance pathways related to vitamin B synthesis and peptidoglycan recycling may imply the underlying mechanism. In summary, our study suggests how the gut microbial landscape shifts upon probiotic supplementation in AD children, and provides preliminary evidence to support targeted probiotic supplementation for the management of childhood AD.

Traffic flow forecasting is the foundation of intelligent transportation systems. Accurate traffic forecasting is crucial for intelligent traffic management and urban development. However, achieving highly accurate traffic flow prediction is challenging due to road networks’ complex dynamic spatial and temporal dependencies. Previous work using predefined static adjacency matrices in graph convolutional networks needs to be revised to reflect the dynamic spatial dependencies in the traffic system. In addition, most current methods ignore the hidden dynamic spatial–temporal correlations between road network nodes as they evolve. We propose a spatial–temporal dynamic graph differential equation network (ST-DGDE) for traffic prediction to address the above problems. First, the model captures the dynamic changes between spatial nodes over time through a dynamic graph learning network. Then, dynamic graph differential equations (DGDE) are used to learn the spatial–temporal dynamic relationships in the global space that change continuously over time. Finally, static adjacency matrices are constructed by static node embedding. The generated dynamic and predefined static graphs are fused and input into a gated temporal causal convolutional network to jointly capture the fixed long-term spatial association patterns and achieve a global receiver domain that facilitates long-term prediction. Experiments of our model on two natural traffic flow datasets show that ST-DGDE outperforms other baselines.

Abstract Deltacoronavirus is the last identified Coronaviridae subfamily genus. Differing from other coronavirus (CoV) genera, which mainly infect birds or mammals, deltacoronaviruses (δ‐CoVs) reportedly infect both animal types. Recent studies show that a novel δ‐CoV, porcine deltacoronavirus (PDCoV), can also infect calves and chickens with the potential to infect humans, raising the possibility of cross‐species transmission of δ‐CoVs. Here, we explored the deep phylogenetic history and cross‐species transmission of δ‐CoVs. Virus–host cophylogenetic analyses showed that δ‐CoVs have undergone frequent host switches in birds, and sparrows may serve as the unappreciated hubs for avian to mammal transmission. Our molecular clock analyses show that PDCoV possibly originated in Southeast Asia in the 1990s and that the PDCoV cluster shares a common ancestor with Sparrow‐CoV of around 1,810. Our findings contribute valuable insights into the diversification, evolution, and interspecies transmission of δ‐CoVs and the origin of PDCoV, providing a model for exploring the relationships of δ‐CoVs in birds and mammals.

Abstract Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security.

Recent studies indicate that bow foil biomimetic systems can significantly improve ship propulsion in waves. In this paper, the DTMB 5415 ship model is taken as the object and a semi-active elastic flapping foil is proposed to install at its bow underwater position. When a ship sails in head wave, heave and pitch motion will occur, which will drive the bow foil to form heave motion. According to the working characteristics of elastic foil, bow foil can generate forward thrust under drive of given heave motion. At first, co-simulation of the ship with self-pitching bow foil in head waves is realized by ISIS-CFD solver and preliminarily realizes drag reduction and thrust increase effect of the bow foil. At the same time, it is found that the effect of bow foil on hull drag reduction is reflected in two aspects, one is the additional thrust generated by the bow foil and the other is that suppression of the bow foil on hull motion also reduces hull resistance in waves. Then, in order to optimize the working characteristics of elastic bow foil, the influence of spring stiffness and span length of the bow foil on drag reduction and thrust increase effect is discussed. A preliminary spring optimization result is obtained, as well as the influence of the span length of the bow foil on the system.

Atopic dermatitis (AD) has been shown to be closely related to gut dysbiosis mediated through the gut–skin axis, and thus the gut microbiome has recently been explored as a potential therapeutic target for the treatment of AD. Contrasting and varying efficacy have been reported since then. In order to investigate the determining factor of probiotics responsiveness in individuals with AD, we initiated the analysis of 41 AD patients with varying disease severity in Hong Kong, whereas the severity was assessed by Eczema Area and Severity Index (EASI) by board certified dermatologist. 16S rRNA sequencing on the fecal samples from AD patients were performed to obtain the metagenomics profile at baseline and after 8 weeks of oral administration of a novel E3 probiotics formula (including prebiotics, probiotics and postbiotics). While EASI of the participants were significantly lower after the probiotics treatment (p < 0.001, paired Wilcoxon signed rank), subjects with mild AD were found to be more likely to respond to the probiotics treatment. Species richness among responders regardless of disease severity were significantly increased (p < 0.001, paired Wilcoxon signed rank). Responders exhibited (1) elevated relative abundance of Clostridium, Fecalibacterium, Lactobacillus, Romboutsia, and Streptococcus, (2) reduced relative abundance of Collinsella, Bifidobacterium, Fusicatenibacter, and Escherichia-Shigella amid orally-intake probiotics identified using the machine learning algorithm and (3) gut microbiome composition and structure resembling healthy subjects after probiotics treatment. Here, we presented the gut microbiome dynamics in AD patients after the administration of the E3 probiotics formula and delineated the unique gut microbiome signatures in individuals with AD who were responding to the probiotics. These findings could guide the future development of probiotics use for AD management.

Accurate urban traffic flow prediction plays a vital role in Intelligent Transportation System (ITS). The complex long-term and long-range spatiotemporal correlations of traffic flow pose a significant challenge to the prediction task. Most current research methods focus only on spatial correlations in local areas, ignoring global geographic contextual information. It is challenging to capture spatial information from distant nodes using shallow graph neural networks (GNNs) to model long-range spatial correlations. To handle this problem, we design a novel spatiotemporal global semantic graph-attentive convolutional network model (STSGAN), which is a deep-level network to achieve the simultaneous modelling of spatiotemporal correlations. First, we propose a graph-attentive convolutional network (GACN) to extract the importance of different spatial features and learn the spatial correlation of local regions and the global spatial semantic information. The temporal causal convolution structure (TCN) is utilized to capture the causal relationships between long-short times, thus enabling an integrated consideration of local and overall spatiotemporal correlations. Several experiments are conducted on two real-world traffic flow datasets, and the results show that our approach outperforms several state-of-the-art baselines.

Taking the rigid NACA0012 airfoil as the object, the key structural parameters of the spring–mass system that govern the dynamics of the double-elastic-constrained flapping hydrofoil are numerically studied in this paper. A two-dimensional numerical model, based on the CFD software FINE/Marine, is established to investigate the influence of the spring stiffness coefficient, frequency ratio, and damping coefficient on the motion and performance of the flapping hydrofoil. This study demonstrates that when the structural parameters are adequately adjusted, the power factor exceeding 1.0 has been achieved, and the corresponding efficiency is up to 37.8%. Moreover, this system can start and work within a wide range of damping coefficients. However, the hydraulic efficiency and power coefficient are sensitive to the change in damping coefficient, so it is very necessary to design an appropriate power output. Lastly, the most obvious parameter affecting the energy acquisition performance is the spring stiffness coefficients. Frequency ratios in the two directions have little influence on the peak value of the power coefficient, but they will cause the change of damping coefficients of the peak point. The key structural parameters studied in this paper provide a useful guideline for an optimized design of this interesting system through searching for the best performance.

Porcine reproductive and respiratory syndrome virus (PRRSV), an important pathogen in the swine industry, is a genetically highly diverse RNA virus. However, the phylogenetic and genomic recombination properties of this virus are not yet fully understood. In this study, we performed an integrated analysis of all available whole-genome sequences of type 2 PRRSV (n = 901) to reveal its evolutionary dynamics. The results showed that there were three distinct phylogenetic lineages of PRRSV in their distribution patterns. We identified that sublineage 2.7 (L2.7), associated with a NADC30 cluster, had the highest substitution rate and higher viral genetic diversity, and inter-lineage recombination is observed more frequently in L2.7 PRRSV compared to other sublineages. Most inter-lineage recombination events detected are observed between L2.7 PRRSVs (as major parents) and L3.4 (a JXA1-R-related cluster)/L3.7 (a WUH3-related cluster) PRRSVs (as minor parents). Moreover, the recombination hotspots are located in the structural protein gene ORF2 and ORF4, or in the non-structural protein gene nsp7. In addition, a GM2-related cluster, L3.2, shows inconsistent recombination modes compared to those of L2.7, suggesting that it may have undergone extensive and unique recombination in their evolutionary history. We also identified several amino acids under positive selection in GP2, GP4 and GP5, the major glycoproteins of PRRSV, showing the driving force behind adaptive evolution. Taken together, our results provide new insights into the evolutionary dynamics of PPRSV that contribute to our understanding of the critical factors involved in its evolution and guide future efforts to develop effective preventive measures against PRRSV.

In cognitive radio ad hoc networks (CRAHNs), the secondary users' links are interrupted by the arrival of primary users, which leads to a significant increase of the number of transmissions per packet. Since network coding and opportunistic routing can reduce the number of transmissions over unreliable wireless links, they are more suitable for CRAHNs. Network coding-based opportunistic routing has been well studied in previous works for traditional ad hoc networks. However, existing approaches have limitations to handle CRAHNs. First, the routing decisions of these methods aim to find one optimal forwarding set for the whole path from the source to the destination. If the channel conditions change dynamically, the pre-selected forwarding set may become unavailable or non-optimal. Second, their coding schemes are not efficient enough to cope with the dynamic spectrum variation in CRAHNs. In this paper, we attempt to overcome these limitations and propose a network-coding-based geographic segmented opportunistic routing scheme for CRAHNs, by fully embracing its characteristics. In our scheme, the whole path from the source to the destination is cut down into several smaller opportunistic route segments, where the packets are transmitted through multiple segments based on a step-by-step forwarding procedure until all the packets are delivered to the destination. Since our scheme utilizes only local spectrum opportunities, topology information, and geometric conditions to compute the forwarding set for each short-term opportunistic route segment, it can better adapt to dynamic spectrum environments and changing network topologies in CRAHNs. Furthermore, we construct a coding graph to show that our coding problem is a reduction from the maximum clique problem, and we propose an efficient network coding strategy to solve it. Simulation results show that our scheme can achieve a considerable performance, compared with the latest opportunistic routing protocols designed for CRAHNs.

In the last decade, a cross-national community of like-minded young men has emerged, commonly known as 'the seduction community'. This community is led by professional 'pick-up artists' who teach these young men a variety of techniques and mindsets with the stated aim of improving their success with women, or 'game'. There has been little research on the men who participate in this community, and none from a mental health angle. As such, this study is propelled by two specific objectives, namely documenting and understanding (i) the reasons why young men join the seduction community; and (ii) the impacts of community involvement on participants' lives. To meet these aims, we used an inductive qualitative methodology giving ample scope for bottom-up understandings to emerge. Specifically, we recruited young men participating in the seduction community for an in-depth qualitative interview (N = 34) to explore self-reported motives and impacts. Interviews were augmented by lengthy participant observation, and data was analyzed by content analysis techniques. The results reveal that men often join the community to address a range of psychosocial deficits, and that community involvement successfully equips participants with numerous valued social and communication skills. The community appears to fill a void in providing a place of hope, fellowship and learning for young (often immigrant) men. The findings are summarized in five themes (i) loneliness and social inclusion; (ii) lack of male role models and need for guidance; (iii) mental health and well-being issues; (iv) skill acquisition and personal development; and (v) the dark side of pick-up. Interestingly, some of the practices commonly taught and utilized within the community resemble aspects of Cognitive Behavioral Therapy and mental health peer support. This may explain its evident appeal. We conclude by reflecting on the implications of the findings for official mental health service provision for young men.

Recently, a local binary patterns-based initialization scheme for robust cascaded pose regression was proposed, which selects the most correlated shapes with the estimated face from training set as the initial shapes. Nevertheless, due to the massive samples in training set, the operation of selecting the most correlated shapes with the estimated shape turns out to be time-consuming. To reduce the quantity of the faces in training set for correlation analyzing in the training set, the faces should be divided into latent classes, and a face in each class could be chosen to form a smaller initial shape pool. In this paper, we view the faces and the latent classes as the latent Dirichlet allocation model. We first extract scale-invariant feature transform features from each face and employ k-means algorithm on the features to obtain a fixed number of clusters. The features in each cluster are represented by its centroid, which can be used to generate the probability distribution of a face belonging to the latent classes via Gibbs sampling. The performance of the proposed scheme is evaluated on the challenging data set of Caltech Occluded Faces in the Wild. The experimental results show that the proposed scheme can significantly reduce time cost on landmark localization by 65.84% without dropping accuracy.

With the rapid development of network technology and cloud computing, more and more organizations and users outsource their data into the cloud server. In order to protect data privacy, the sensitive data have to be encrypted, which increases the heavy computational overhead and brings great challenges to resource-constraint devices. In this paper, we propose secure index based on counting Bloom filter (CBF) for ranked multiple keywords search. In the proposed scheme, several algorithms are designed to maintain and lookup CBF, while a pruning algorithm is used to delete the repeated items for saving the space. Besides, the relevance scores are encrypted by the Paillier cryptosystem. It ensures that the same relevance scores are encrypted into different bits, which can resist the statistical analyses on the ciphertext of the relevance scores. Moreover, since the Paillier cryptosystem supports the homomorphic addition of ciphertext without the knowledge of the private key, the major computing work in ranking could be moved from user side to the cloud server side. Therefore, the proposed scheme has huge potentials in resource-constraint mobile devices such as 5G mobile terminals. Security analyses prove that the proposed scheme can prevent the information leakage. Experiment results guarantee that computation overhead of the proposed scheme in user side is low.

This paper overviews existing digital communication buses which are commonly used in sensor networks, discusses sensor network architectures, and introduces a new sensor bus for low power microsystem applications. The new intra-module multi-element microsystem (IM2) bus is nine-line interface with 8b serial data which implements several advanced features such as power management and plug-n-play while maintaining minimum hardware overhead at the sensor node. Finally, some issues in wireless sensor networking are discussed. The coverage of these issues provides a guideline for choosing the appropriate bus for different sensor network applications.

Four and a Half LIM protein 2 (FHL2) is a LIM domain only protein that is able to form various protein complexes and regulate gene transcription. Recent findings showed that FHL2 is a potential tumor suppressor gene that was down-regulated in hepatocellular carcinoma (HCC). Moreover, FHL2 can bind to and activate the TP53 promoter in hepatic cells. In this study, the activity of the two promoters of FHL2, 1a and 1b, were determined in the human embryonic kidney cell line HEK293 and the activation of these two promoters by p53 was investigated. Our results showed that the 1b promoter has a higher activity than the 1a promoter in HEK 293 cells but the 1a promoter is more responsive to the activation by p53 when compared with the 1b promoter. The regulation of FHL2 by p53 was further confirmed in liver cells by the overexpression of p53 in Hep3B cells and the knockdown of p53 in HepG2 cells. Combining promoter activity results of truncated mutants and predictions by bioinformatics tools, a putative p53 binding site was found in the exon 1a of FHL2 from +213 to +232. The binding between the p53 protein and the putative p53 binding site was then validated by the ChIP assay. Furthermore, the expression of FHL2 and TP53 were down-regulated in majority of HCC tumour samples (n = 41) and significantly correlated (P = 0.026). Finally, we found that the somatic mutation 747 (G→T), a hot spot mutation of the TP53 gene, is potentially associated with a higher expression of FHL2 in HCC tumour samples. Taken together, this is the first in-depth study about the transcriptional regulation of FHL2 by p53.

Graph data is a kind of data composed of nodes and edges, which models the entities as the nodes. Nodes may be connected by edges, and an edge indicates a relationship between entities. By analyzing and mining these data, people can get a lot of valuable information. Meanwhile, it also brings risks of privacy information disclosure for every node in the graph. To address this issue, we propose a graph data publishing method based on the Negative DataBase (NDB). This method transforms the structural characteristics of the graph data into the encoding format of an NDB. Based on this, a generation method for perturbed graphs (NDB-Graph) is designed. Since NDB is a privacy-preserving technique that does not explicitly store the original data and is difficult to reverse, the published graph data ensures the security of the original graph data. Besides, due to the high efficiency of Graph Neural Network (GNN) in relation feature processing in graph data, it is widely used in various task processing modeling on graph data, such as recommendation systems. We also propose a GNN recommendation system based on NDB technology to protect the privacy of graph data for each user. Compared with the publishing method PBCN, the proposed method outperforms it in most cases in experiments on the Karate and Facebook datasets. For example, on Facebook datasets, the smallest L1-error of degree distribution is only 6, which is about 2.6% lower than the PBCN method under the same privacy level, and the worst case is about 1,400, which is about 46.5% lower than the PBCN method under the same privacy level. The experiment of collaborative filtering based on LightGCN also shows that the proposed privacy protection method has high precision. [ABSTRACT FROM AUTHOR]