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Large-scale, two-sided matching platforms must find market outcomes that align with user preferences while simultaneously learning these preferences from data. Classical notions of stability (Gale and Shapley, 1962; Shapley and Shubik, 1971) are, unfortunately, of limited value in the learning setting, given that preferences are inherently uncertain and destabilizing while they are being learned. To bridge this gap, we develop a framework and algorithms for learning stable market outcomes under uncertainty. Our primary setting is matching with transferable utilities, where the platform both matches agents and sets monetary transfers between them. We design an incentive-aware learning objective that captures the distance of a market outcome from equilibrium. Using this objective, we analyze the complexity of learning as a function of preference structure, casting learning as a stochastic multi-armed bandit problem. Algorithmically, we show that “optimism in the face of uncertainty,” the principle underlying many bandit algorithms, applies to a primal-dual formulation of matching with transfers and leads to near-optimal regret bounds. Our work takes a first step toward elucidating when and how stable matchings arise in large, data-driven marketplaces.

A cluster of vultures, artificially bred in a zoo in Shandong Province, China, displayed signs of emaciation and some even died, which raised the suspicion of an immunosuppressive pathogen. Upon conducting nucleic acid testing on the clinical samples, it was found that the CIAV was present in the tissue of dead vulture. In this study, samples of dead vulture tissue were used to isolate a chicken infectious anemia virus (CIAV) via the MDCC-MSB1 cell line, which was designated the SDTY2021-TJ strain. The full-length genomic sequence of SDTY2021-TJ was determined and analyzed in detail. The full genomic DNA of SDTY2021-TJ was found to be 2298 bp, with no omissions or additions in the coding region. The homology between the full-length genomic sequence of SDTY2021-TJ and the reference strain ranged from 95.5% (Del-Ros) to 98.7% (YN04). In comparison with the reference strain, the VP1 protein of SDTY2021-TJ contained a number of mutations. To assess the virulence of SDTY2021-TJ, one-day-old SPF chickens were inoculated with both high and low doses of the pathogen. The results demonstrated that SDTY2021-TJ had a considerable pathogenicity to SPF chickens, as the high-dose group caused a 50% mortality rate, and even the low-dose group caused a 30% mortality rate. The chickens infected with the disease exhibited paleness in the cockscomb and stunted growth, as well as a compromised response to NDV and AIV-H9 inactivated vaccine. Furthermore, histological observation revealed an atrophy and degeneration of the thymus. To the best of our knowledge, this is the first instance of CIAV being isolated from artificially bred Aegypius monachus, implying that wild birds may be involved in the transmission and spread of CIAV.

The conventional anisotropic P-/S-wave-mode decomposition and angle calculation usually are performed in the wavenumber domain and require multiple Fourier transforms and rely on strong model assumptions. Therefore, it is difficult to use in production due to the prohibitively high computational cost. To tackle this problem, we develop a fast anisotropic P-/S-wave-mode decomposition approach in the space domain and further apply this decomposition to the vertical transversely isotropic (VTI)/tilted transversely isotropic (TTI) elastic reverse time migration (ERTM) and the associated angle-domain common-image gathers (ADCIGs). To start with, we solve the Christoffel equation of the VTI media and obtain an anisotropic-Helmholtz (AH) operator that represents the P- and S-wave polarizations in the wavenumber domain. The decoupled operators for the P-/S-wave decomposition are then derived in terms of the AH operator. To improve the efficiency and model adaptability, we convert the decoupled operators from the wavenumber domain to the space domain as a function of the model parameters and phase angle. By further eliminating the phase-angle term, such space-domain decoupled operators lead to the approximate P- and S-wave components which are proven to achieve the same effect as the accurate P and S wavefields applied in the angle calculation and ERTM images. A fast space-domain AH decomposition approach is thus obtained and can be used for P-/S-wave-mode decomposition, ADCIGs, and ERTM. In addition, we extend our approach to the TTI media by using the coordinate transform. Three examples are used to demonstrate the effectiveness and feasibility of our method.

Fusarium oxysporum f. sp. cucumerinum, which causes root and vascular wilting, is one of the most devastating diseases infecting cucumber. Here, we report the first genome resource with high-quality assembly for F. oxysporum f. sp. cucumerinum strain Race-4, which is primarily endemic to China. The genome was 59.11 Mb in size and consisted of 48 scaffolds with an N50 of 3.87 Mb using PacBio long reads (301.77×) sequencing, and encodes 14,898 proteins from analyzing RNA-seq data. Gene annotations identified pathogen-host interaction genes, fungal virulence factors, secreted proteins, transcription factors, and secondary metabolite biosynthesis gene. Moreover, functional genes reported in previous studies were also identified in the genome of Race-4. These genes and genome resource may play important roles in understanding F. oxysporum f. sp. cucumerinum–cucumber interactions and will be useful for further research.

Feature selection is a crucial method for discovering relevant features in high-dimensional data. However, most studies primarily focus on completely labeled data, ignoring the frequent occurrence of missing labels in real-world problems. To address high-dimensional and label-missing problems in data classification simultaneously, we proposed a semisupervised bacterial heuristic feature selection algorithm. To track the label-missing problem, a k-nearest neighbor semisupervised learning strategy is designed to reconstruct missing labels. In addition, the bacterial heuristic algorithm is improved using hierarchical population initialization, dynamic learning, and elite population evolution strategies to enhance the search capacity for various feature combinations. To verify the effectiveness of the proposed algorithm, three groups of comparison experiments based on eight datasets are employed, including two traditional feature selection methods, four bacterial heuristic feature selection algorithms, and two swarm-based heuristic feature selection algorithms. Experimental results demonstrate that the proposed algorithm has obvious advantages in terms of classification accuracy and selected feature numbers.

Flows are usually represented as vector lines from origins to destinations and can reflect the movements of individuals or groups in space and time. Revealing and analyzing the spatiotemporal flow patterns are conducive to understanding information underlying movements. This paper proposes a new method called the OD – EOF (Origin – Destination – Empirical Orthogonal Function) to discover important spatiotemporal flow patterns on the premise of maintaining the pairwise connections between origins and destinations. We first construct a spatiotemporal flow matrix that contains connection information between origins and destinations and temporal flow information by adding a temporal dimension to the OD map. Then, we decompose the spatiotemporal flow matrix into spatial modes and corresponding time coefficients by EOF decomposition. The decomposition results depict the prominent spatial distribution of and temporal variation in flows, with most of the spatiotemporal characteristics highly concentrated into the first few spatial modes. The method is evaluated by five synthetic datasets and a user study and subsequently applied to analyze the impact of the COVID-19 pandemic on the spatiotemporal patterns of human mobility in China during the Spring Festival travel rush in 2020 and 2021. The results show the prominent spatiotemporal patterns of human mobility during these periods under the influence of the COVID-19 pandemic outbreak and the normalization of pandemic prevention and control.

Despite the rapid development of immunotherapy, low response rates, poor therapeutic outcomes and severe side effects still limit their implementation, making the augmentation of immunotherapy an important goal for current research. DNA, which has principally been recognized for its functions of encoding genetic information, has recently attracted research interest due to its emerging role in immune modulation. Inspired by the intrinsic DNA-sensing signaling that triggers the host defense in response to foreign DNA, DNA or nucleic acid-based immune stimulators have been used in the prevention and treatment of various diseases. Besides that, DNA vaccines allow the synthesis of target proteins in host cells, subsequently inducing recognition of these antigens to provoke immune responses. On this basis, researchers have designed numerous vehicles for DNA and nucleic acid delivery to regulate immune systems. Additionally, DNA nanostructures have also been implemented as vaccine delivery systems to elicit strong immune responses against pathogens and diseased cells. This review will introduce the mechanism of harnessing DNA-mediated immunity for the prevention and treatment of diseases, summarize recent progress, and envisage their future applications and challenges.

The work function and d-band center of a catalyst together determine the catalytic activity of CO2 reduction to formic acid. And it is necessary to consider the solvation effect using an explicit solvent model.

This review summarizes chemical tools for cell engineering, introduces their wide application in diagnosis and therapy, and discusses the challenges and opportunities in precision medicine.

The encapsulation of Au25 nanoclusters in Cu3(BTC)2 metal–organic frameworks can achieve CO2 photoreduction for selective CO production in a gas–solid reaction system with remarkably enhanced durability.

MotivationUsing DNA as the medium to store information has recently been recognized as a promising solution for long-term data storage. While several system prototypes have been demonstrated, the error characteristics in DNA data storage are discussed with limited content. Due to the data and process variations from experiment to experiment, the error variation and its effect on data recovery remain to be uncovered. To close the gap, we systematically investigate the storage channel, i.e., error characteristics in the storage process.ResultsWe first propose a new concept named sequence corruption to unify the error characteristics into the sequence level, easing the channel analysis. Then we derived the formulations of the data imperfection at the decoder including both sequence loss and sequence corruption, revealing the decoding demand and monitoring the data recovery. Furthermore, we extensively explored several data-dependent unevenness observed in the base error patterns and studied a few potential factors and their impacts on the data imperfection at the decoder both theoretically and experimentally. The results presented here introduce a more comprehensive channel model and offer a new angle towards the data recovery issue in DNA data storage by further elucidating the error characteristics of the storage process.Contactpoh.chuehloo@nus.edu.sg

Multigoal reinforcement learning (RL) extends the typical RL with goal-conditional value functions and policies. One efficient multigoal RL algorithm is the hindsight experience replay (HER). By treating a hindsight goal from failed experiences as the original goal, HER enables the agent to receive rewards frequently. However, a key assumption of HER is that the hindsight goals do not change the likelihood of the sampled transitions and trajectories used in training, which is not the fact according to our analysis. More specifically, we show that using hindsight goals changes such a likelihood and results in a biased learning objective for multigoal RL. We analyze the hindsight bias due to this use of hindsight goals and propose the bias-corrected HER (BHER), an efficient algorithm that corrects the hindsight bias in training. We further show that BHER outperforms several state-of-the-art multigoal RL approaches in challenging robotics tasks.

Feline viral diseases, such as feline panleukopenia, feline infectious peritonitis, and feline coronaviral enteritis, seriously endanger the health of cats, and restrict the development of pet industry. Meanwhile, there is a current lack of effective vaccines to protect against feline viral diseases. Thus, effective therapeutic agents are highly desirable. Interferons (IFNs) are important mediators of the antiviral host defense in animals, particularly type I IFNs. In this study, a novel feline IFN omega (feIFN-ω) gene was extracted from the cat stimulated with feline parvovirus (FPV) combined with poly(I:C), and following codon optimization encoding the feIFN-ω, the desired gene (feIFN-ω’) fragment was inserted into plasmid pPICZαA, and transformed into Pichia pastoris GS115, generating a recombinant P. pastoris GS115 strain expressing the feIFN-ω’. After induction, we found that the expression level of the feIFN-ω’ was two times more than that of feIFN-ω (p

With the continuous development of new media technology in current society, most Internet users gradually move from PC to cell phones, and convenient and fast short videos become their primary source of information. Short videos, which are easy to operate, have become their primary choice. At the same time, the aging problem in Chinese society has been intensifying, and the elderly have gradually reached the Internet to meet their psychological needs and social interactions. In this paper, the author studies the relationship between aging groups and short videos, analyses the current situation, motivation and impact through interviews and case studies, and proposes measures to promote older people's better integration into online society. The result shows that for the current situation, the number of elderly users has increased significantly, preferring practicality and entertainment, and older people watch short videos for a more extended period and like to share them with others. The elderly market is enormous, and TikTok shopping has become a new battlefield. The Motivations for older People's Use of TikTok include Curiosity, the diversity of content on TikTok, and the desire to connect with society. The impact of TikTok on older people includes new tools for participation in society, new ways to maintain social circles, and new ideas for older people to communicate with family members.

Manually segmenting medical images is expertise-demanding, time-consuming and laborious. Acquiring massive high-quality labeled data from experts is often infeasible. Unfortunately, without sufficient high-quality pixel-level labels, the usual data-driven learning-based segmentation methods often struggle with deficient training. As a result, we are often forced to collect additional labeled data from multiple sources with varying label qualities. However, directly introducing additional data with low-quality noisy labels may mislead the network training and undesirably offset the efficacy provided by those high-quality labels. To address this issue, we propose a Mean-Teacher-assisted Confident Learning (MTCL) framework constructed by a teacher-student architecture and a label self-denoising process to robustly learn segmentation from a small set of high-quality labeled data and plentiful low-quality noisy labeled data. Particularly, such a synergistic framework is capable of simultaneously and robustly exploiting (i) the additional dark knowledge inside the images of low-quality labeled set via perturbation-based unsupervised consistency, and (ii) the productive information of their low-quality noisy labels via explicit label refinement. Comprehensive experiments on left atrium segmentation with simulated noisy labels and hepatic and retinal vessel segmentation with real-world noisy labels demonstrate the superior segmentation performance of our approach as well as its effectiveness on label denoising.

Bovine viral diarrhoea virus (BVDV) is the etiologic agent of bovine viral diarrhea-mucosal disease, one of the most important viral diseases in cattle, with inflammatory diarrhea, enteritis, and mucosa necrosis as the major clinical manifestations. NF-κB is an important transcription complex that regulates the expression of genes involved in inflammation and immune responses. NLRP3 inflammasome plays a key role in the development of inflammatory diseases. However, whether the activation of NF-κB is crucial for BVDV infection-induced inflammatory responses remains unclear. The results of our present study showed that BVDV infection significantly activated the NF-κB pathway and promoted the expression of NLRP3 inflammasome components (NLRP3, ASC, pro-caspase 1) as well inflammatory cytokine pro-IL-1β in BVDV-infected bovine cells, resulting in the cleavage of pro-caspase 1 and pro-IL-1β into active form caspase 1 and IL-1β. However, the levels of the NLRP3 inflammasome components and inflammatory cytokines were obviously inhibited, as well the cleavage of pro-caspase 1 and pro-IL-1β in the pre-treated bovine cells with NF-κB-specific inhibitors after BVDV infection. Further, cytopathic biotype BVDV (cpBVDV) Erns and NS5A proteins with their key functional domains contributed to BVDV-induced inflammatory responses via activating the NF-κB pathway were confirmed experimentally. Especially, the NS5A can promote cholesterol synthesis and accelerate its augmentation, further activating the NF-κB signalling pathway. Conclusively, our data elucidate that the activation of NF-κB signaling pathway plays a crucial role in cpBVDV infection-induced inflammatory responses.

Anisotropy and Q attenuation bring great challenges to seismic wave migration. On migrated images, anisotropy creates structural and positioning errors, and Q attenuation leads to weak amplitudes and misplacement of reflectors. A 2D Q-compensated least-squares reverse time migration with velocity-anisotropy correction ( QLSRTM-VA) is proposed through the construction of velocity-anisotropic Q-compensated forward modeling, Q-compensated adjoint, and Q-attenuated demigration operators to simultaneously correct velocity-anisotropy and Q-attenuation in the migration process. The preceding operators are derived using first-order velocity-anisotropic viscoacoustic quasi-differential wave equations with variable densities, which are stable, capable of conveniently dealing with variable density media and are easy to transform between velocity-anisotropic Q-compensation and Q-attenuation versions. As exemplified by two synthetic and field data sets, our QLSRTM-VA method increases the imaging resolution, signal-to-noise ratio, and amplitude preservation in deep regions. Our method is capable of producing better images than viscoacoustic isotropic least-squares reverse time migration (LSRTM) and acoustic anisotropic LSRTM.

Cancer immunotherapies have created a new generation of therapeutics to employ the immune system to attack cancer cells. However, these therapies are typically based on biologics that are nonspecific and often exhibit poor tumor penetration and dose-limiting toxicities. Nanocarriers allow the opportunity to overcome these barriers as they have the capabilities to direct immunomodulating drugs to tumor sites via passive and active targeting, decreasing potential adverse effects from nonspecific targeting. In addition, nanocarriers can be multifunctionalized to deliver multiple cancer therapeutics in a single drug platform, offering synergistic potential from co-delivery approaches.This review focuses on the delivery of cancer therapeutics using emerging nanocarriers to achieve synergistic results via co-delivery of immune-modulating components (i.e. chemotherapeutics, monoclonal antibodies, and genes).Nanocarrier-mediated delivery of combinatorial immunotherapy creates the opportunity to fine-tune drug release while achieving superior tumor targeting and tumor cell death, compared to free drug counterparts. As these nanoplatforms are constantly improved upon, combinatorial immunotherapy will afford the greatest benefit to treat an array of tumor types while inhibiting cancer evasion pathways.

We assessed whether an adaptation of value-added analysis (VAA) can provide evidence on the relative effectiveness of interventions implemented in a large school district. We analyzed two datasets, one documenting interventions received by underperforming students, and one documenting interventions received by students in schools benefiting from discretionary funds to invest in specific programs. Results from the former dataset identified several interventions that appear to be more or less effective than the average intervention. Results from the second dataset were counterintuitive. We conclude that, under specific conditions, program VAA can provide evidence to help guide district decision-makers to identify outlier interventions and inform decisions about scaling up or disinvesting in such interventions, with the caveat that if those conditions are not met, the results could be misleading.

Background Phase-contrast cine magnetic resonance imaging (PC-MRI) has been used to measure cerebrospinal fluid (CSF) flow dynamics, but the influence of the area of the aqueduct and region of interest (ROI) on quantification of stroke volume (SV) has not been assessed. Purpose To assess the influence of the area of the ROI in quantifying the aqueductal SV measured with PC-MRI within the cerebral aqueduct. Material and Methods Nine healthy volunteers (mean age = 29.6 years) were enrolled in the study, and brain MRI examinations were performed on a 3.0-T system. Quantitative analysis of the aqueductal CSF flow was performed using manual ROI placement. ROIs were separately drawn for each of the 12 phases of the cardiac cycle, and changes in aqueduct size during the cardiac cycle were determined. The SV was calculated using 12 different aqueductal ROIs and compared with the SV calculated using a fixed ROI size. Results There was variation in the size of the aqueduct during the cardiac cycle. In addition, the measured SV increased with a greater area of the ROI. A significant difference in the calculated SVs with the 12 variable ROIs was observed compared with that using a fixed ROI throughout the cardiac cycle. Conclusion To establish reliable reference values for the SV in future studies, a variable ROI should be considered.

There is an urgent need for animal models of coronavirus disease 2019 to study immunopathogenesis and test therapeutic intervenes. In this study, we showed that NOD/SCID IL2rg

The study aimed to identify BMI-related lipids and to explore the role of lipids linking BMI and gestational diabetes mellitus (GDM).Plasma lipidome, height, and weight were measured in early pregnancy among 1008 women. Pearson correlation analyses and least absolute shrinkage and selection operator regression (LASSO) were performed to identify BMI-associated lipids. Based on these lipids, a lipid score was created using LASSO, and its association with GDM risk was evaluated by conditional logistic regression. The causal relationships between BMI and lipids were tested by Mendelian randomization analysis with genotyping data. Mediation analysis was conducted to evaluate the mediating effect of lipids on the association of BMI with GDM.Of 366 measured lipids, BMI was correlated with 28 lipids, which mainly belong to glycerophospholipids and glycerolipids. A total of 10 lipid species were associated with BMI, and a lipid score was established. A causal relationship between BMI and lysophosphatidylcholine 14:0 was observed. The lipid score was associated with a 1.69-fold increased risk of GDM per 1-point increment (95% CI: 1.33-2.15). Furthermore, BMI-associated lipids might explain 66.4% of the relationship between BMI and GDM.Higher BMI in early pregnancy was associated with altered lipid metabolism that may contribute to the increased risk of GDM.

Background:Education decision makers are increasingly expected to use evidence to inform their actions. However, the majority of educational interventions have not yet been studied and it is challenging to produce high quality research evidence quickly enough to influence policy questions. Aims and objectives:We set out to gather evidence on the efficacy of reading resources implemented at 23 struggling elementary schools in a large, urban district in the US. The schools were at risk of closure by the state. Methods:For each reading resource, we searched for existing effectiveness studies and collected professional judgements by surveying practitioners. We also used an expert survey to collect judgements from three reading experts. We compared the ratings among experts and between practitioners and experts. We also compared practitioner and expert judgements to evidence summaries from research repositories. Findings:We found evidence summaries in research repositories for only five of 23 reading resources used in the 23 schools. Experts showed poor to good agreement on ten questions about each resource. Agreement between practitioners and experts was low with practitioners generally rating resources more positively than reading experts. Discussion and conclusions:Practitioners may be overly optimistic about the efficacy of educational materials while experts have difficulty assessing how well the materials serve an unfamiliar population. In the absence of rigorous evaluations of locally-implemented programmes, district decision makers can review the consistency of evidence collected from practitioners and experts, along with external research evidence, to inform actions towards supporting and guiding struggling schools.

Accurate drug delivery to the lesion has been deliberated for several decades, but one important phenomenon is usually neglected that the immune system can prevent smooth transportation of nanomedicine. Although injection would reduce first-pass effect, macrophages in the blood can still recognize and phagocytose nanomedicine. Here we show that a lubricated nanocontainer, which is prepared based on polyelectrolytes and mesoporous silica nanoparticles, can accurately target muscarinic bioreceptor while escaping from the identification of macrophages. Through

With the rise in piano teaching in recent years, many people have joined the ranks of piano learners. However, the high cost of traditional manual instruction and the exclusive one-on-one teaching model have made learning the piano an extravagant endeavor. Most existing approaches, based on the audio modality, aim to evaluate piano players’ skills. Unfortunately, these methods overlook the information contained in videos, resulting in a one-sided and simplistic evaluation of the piano player’s skills. More recently, multimodal-based methods have been proposed to assess the skill level of piano players by using both video and audio information. However, existing multimodal approaches use shallow networks to extract video and audio features, which limits their ability to extract complex spatio-temporal and time-frequency characteristics from piano performances. Furthermore, the fingering and pitch-rhythm information of the piano performance is embedded within the spatio-temporal and time-frequency features, respectively. Therefore, we propose a ResNet-based audio-visual fusion model that is able to extract both the visual features of the player’s finger movement track and the auditory features, including pitch and rhythm. The joint features are then obtained through the feature fusion technique by capturing the correlation and complementary information between video and audio, enabling a comprehensive and accurate evaluation of the player’s skill level. Moreover, the proposed model can extract complex temporal and frequency features from piano performances. Firstly, ResNet18-3D is used as the backbone network for our visual branch, allowing us to extract feature information from the video data. Then, we utilize ResNet18-2D as the backbone network for the aural branch to extract feature information from the audio data. The extracted video features are then fused with the audio features, generating multimodal features for the final piano skill evaluation. The experimental results on the PISA dataset show that our proposed audio-visual fusion model, with a validation accuracy of 70.80% and an average training time of 74.02 s, outperforms the baseline model in terms of performance and operational efficiency. Furthermore, we explore the impact of different layers of ResNet on the model’s performance. In general, the model achieves optimal performance when the ratio of video features to audio features is balanced. However, the best performance achieved is 68.70% when the ratio differs significantly.

Electrocatalytic reduction of carbon dioxide (CO2RR) employs electricity to store renewable energy in the form of reduction products. The activity and selectivity of the reaction depend on the inherent properties of electrode materials. Single-atom alloys (SAAs) exhibit high atomic utilization efficiency and unique catalytic activity, making them promising alternatives to precious metal catalysts. In this study, density functional theory (DFT) was employed to predict stability and high catalytic activity of Cu/Zn (101) and Pd/Zn (101) catalysts in the electrochemical environment at the single-atom reaction site. The mechanism of C2 products (glyoxal, acetaldehyde, ethylene, and ethane) produced by electrochemical reduction on the surface was elucidated. The C-C coupling process occurs through the CO dimerization mechanism, and the formation of the *CHOCO intermediate proves beneficial, as it inhibits both HER and CO protonation. Furthermore, the synergistic effect between single atoms and Zn results in a distinct adsorption behavior of intermediates compared to traditional metals, giving SAAs unique selectivity towards the C2 mechanism. At lower voltages, the Zn (101) single-atom alloy demonstrates the most advantageous performance in generating ethane on the surface, while acetaldehyde and ethylene exhibit significant certain potential. These findings establish a theoretical foundation for the design of more efficient and selective carbon dioxide catalysts.

Chicken infectious anemia (CIA) is caused by chicken anemia virus (CAV). Recently, severe anemia has emerged in layer chickens (8 to 10-week-old) on poultry farms in China. However, the etiological characteristics and pathogenic potential of CAV in chickens at 6 weeks or older are not well understood. In this study, we isolated a CAV strain, termed SD15, from two-month-old chicken with severe anemia and analyzed the genetic evolution relationship. We found that strain SD15 had the highest homology (98.9%) with CAV18 strain. Comparison with 33 reference strains revealed 16 amino acid mutations in strain SD15, two of which were previously unknown (F210S in VP1 and L25S in Vp3). Compared with low pathogenic strains (Cux-1 and C14), highly pathogenic strains (SDLY08 and SD15) had three base mutations in their noncoding region. To further understand its pathogenicity, 10-week-old specific-pathogen-free (SPF) chickens were challenged with the novel strain and SDLY08. No obvious clinical symptoms were observed in the SDLY08 group. However, SD15-infected chickens showed significant growth retardation and immunosuppression. The main manifestations of immunosuppression were the significantly reduced thymus and bursa indices and AIV-H9 vaccine-induced antibody levels (P < 0.05). The lowest number of red blood cells in the SD15 group was just 60% of that in the control group. Taken together, the novel strain SD15 not only showed higher pathogenicity but also exhibited the potential ability to break the age resistance of older chickens to CAV. Our study enhanced the understanding of the epidemiological characteristics of chickens infected with severe anemia and can facilitate the development of improved control strategies of CIA in China.

With the rise of piano teaching in recent years, many people have joined the team of piano learners. However, the expensive cost of manual instruction and the unique one-on-one teaching model have made piano learning an extravagant event. Most existing approaches based on the audio modality aim to evaluate piano players' skills. Unfortunately, these methods ignored the information contained in the video, which led to a one-sided and simplistic evaluation of the piano player's skills. More recently, multimodal-based methods are proposed to assess the skill level of piano players using both video and audio information. However, existing multimodal approaches use shallow networks to extract video and audio features, which are deficient in extracting complex spatio-temporal and time-frequency features from piano performance. Furthermore, the fingering and the pitch-rhythm information of the piano performance is contained in the spatio-temporal and time-frequency features, respectively. In the paper, we propose a ResNet-based audio-visual fusion model that combines video and audio features to assess the skill level of piano players. Firstly, ResNet18-3D is used as the backbone network for our visual branches, which can extract feature information from the video data. Then, we consider ResNet18-2D as the backbone network of the aural branch and extract the feature information from the audio data. The extracted video features are fused with the audio features to generate multimodal features for the final piano skill evaluation. The experimental results on the PISA dataset show that our proposed audio-visual fusion model, with a validation accuracy of 70.80%, outperforms the state-of-the-art methods in both performance and efficiency. Then, we also explore the impact of different layers of ResNet on model performance, and the experimental results show that the audio-visual fusion model dealing with the piano skill assessment problem can make full use of both feature information when the number of video features is close to the number of audio features.

Previous diffusion tensor imaging (DTI) studies have mainly focused on dose-dependent white matter (WM) alterations 1 month to 1 year after radiation therapy (RT) with a tract-average method. However, WM alterations immediately after RT are subtle, resulting in early WM alterations that cannot be detected by tract-average methods. Therefore, we performed a study with an along-tract method in patients with brain metastases to explore the early dose-response pattern of WM alterations after RT.Sixteen patients with brain metastases underwent DTI before and 1-3 days after brain RT. DTI metrics, such as fractional anisotropy (FA), axial diffusivity (AD), radial diffusivity (RD) and mean diffusivity (MD), were calculated. Along-tract statistics were then used to resample WM fibre streamlines and generate a WM skeleton fibre tract. DTI metric alterations (post_RT-pre_RT DTI metrics) and the planned doses (max or mean doses) were mapped to 18 WM tracts. A linear fixed model was performed to analyse the main effect of dose on DTI metric alterations.AD alterations in the left hemispheric uncinated fasciculus (UNC_L) were associated with max doses, in which decreased AD alterations were associated with higher doses.Our findings may provide pathological insight into early dose-dependent WM alterations and may contribute to the development of max dose-constrained RT techniques to protect brain microstructure in the UNC_L.

To develop and validate a pretreatment magnetic resonance imaging (MRI)-based radiomic-clinical model to assess the treatment response of whole-brain radiotherapy (WBRT) by using SHapley Additive exPlanations (SHAP), which is derived from game theory, and can explain the output of different machine learning models.We retrospectively enrolled 228 patients with brain metastases from two medical centers (184 in the training cohort and 44 in the validation cohort). Treatment responses of patients were categorized as a non-responding group vs. a responding group according to the Response Assessment in Neuro-Oncology Brain Metastases (RANO-BM) criteria. For each tumor, 960 features were extracted from the MRI sequence. The least absolute shrinkage and selection operator (LASSO) was used for feature selection. A support vector machine (SVM) model incorporating clinical factors and radiomic features wase used to construct the radiomic-clinical model. SHAP method explained the SVM model by prioritizing the importance of features, in terms of assessment contribution.Three radiomic features and three clinical factors were identified to build the model. Radiomic-clinical model yielded AUCs of 0.928 (95%CI 0.901-0.949) and 0.851 (95%CI 0.816-0.886) for assessing the treatment response in the training cohort and validation cohort, respectively. SHAP summary plot illustrated the feature's value affected the feature's impact attributed to model, and SHAP force plot showed the integration of features' impact attributed to individual response.The radiomic-clinical model with the SHAP method can be useful for assessing the treatment response of WBRT and may assist clinicians in directing personalized WBRT strategies in an understandable manner.• Radiomic-clinical model can be useful for assessing the treatment response of WBRT. • SHAP could explain and visualize radiomic-clinical machine learning model in a clinician-friendly way.