Identification of Unequally Represented Founder Viruses Among Tissues in Very Early SIV Rectal Transmission

Characterizing the transmitted/founder (T/F) viruses of multi-variant SIV infection may shed new light on the understanding of mucosal transmission.We intrarectally inoculated six Chinese rhesus macaques with a single high dose of SIVmac251 (3.1 × 104 TCID50) and obtained 985 full-length env sequences from multiple tissues at 6 and 10 days post-infection by single genome amplification (SGA). All 6 monkeys were infected with a range of 2 to 8 T/F viruses and the dominant variants from the inoculum were still dominant in different tissues from each monkey. Interestingly, our data showed that a cluster of rare T/F viruses was unequally represented in different tissues. This cluster of rare T/F viruses phylogenetically related to the non-dominant SIV variants in the inoculum and was not detected in any rectum tissues, but could be identified in the descending colon, jejunum, spleen, or plasma. In 2 out of 6 macaques, identical SIVmac251 variants belonging to this cluster were detected simultaneously in descending colon/jejunum and the inoculum.We also demonstrated that the average CG dinucleotide frequency of these rare T/F viruses found in tissues, as well as non-dominant variants in the inoculum, was significantly higher than the dominant T/F viruses in tissues and the inoculum. Collectively, these findings suggest that descending colon/jejunum might be more susceptible than rectum to SIV in the very early phase of infection. And host CG suppression, which was previously shown to inhibit HIV replication in vitro, may also contribute to the bottleneck selection during in vivo transmission

VR-GNN: Variational Relation Vector Graph Neural Network for Modeling both Homophily and Heterophily

Graph Neural Networks (GNNs) have achieved remarkable success in diverse real-world applications. Traditional GNNs are designed based on homophily, which leads to poor performance under heterophily scenarios. Current solutions deal with heterophily mainly by mixing high-order neighbors or passing signed messages. However, mixing high-order neighbors destroys the original graph structure and passing signed messages utilizes an inflexible message-passing mechanism, which is prone to producing unsatisfactory effects. To overcome the above problems, we propose a novel GNN model based on relation vector translation named Variational Relation Vector Graph Neural Network (VR-GNN). VR-GNN models relation generation and graph aggregation into an end-to-end model based on Variational Auto-Encoder. The encoder utilizes the structure, feature and label to generate a proper relation vector. The decoder achieves superior node representation by incorporating the relation translation into the message-passing framework. VR-GNN can fully capture the homophily and heterophily between nodes due to the great flexibility of relation translation in modeling neighbor relationships. We conduct extensive experiments on eight real-world datasets with different homophily-heterophily properties to verify the effectiveness of our model. The experimental results show that VR-GNN gains consistent and significant improvements against state-of-the-art GNN methods under heterophily, and competitive performance under homophily

The Average IFN- γ

Previous studies suggested that both the frequency and the mean fluorescence intensity (MFI) of cytokine secreting T cells could be of great value for immunogenicity evaluation of a vaccine. In this study, by constructing epitope-based DNA vaccines encoding a previously identified CD8+ T cell epitope, we investigated the influence of multiplying epitope copies on both the frequency and the MFI of specific IFN-γ secreting CD8+ T cells. We found that frequencies of specific CD8+ T cell could be improved by multiplying epitope copies, while the MFI of IFN-γ secreted by epitope-specific CD8+ T cells decreased synchronously. And further analysis showed that the decrease of MFI was not caused by the functional avidity variation of CD8+ T cell receptor

Effects of TGF- β

Introduction. This study aimed to explore the effects of TGF-β1 on regulating activities of cementoblasts and osteoblasts with or without stress. Material and Methods. Human recombinant TGF-β1 was added with different doses. Immunohistochemical test of osteoprotegerin (OPG)/receptor activator of nuclear factor-kappaB ligand (RANKL) and Alizarin Red-S staining were conducted. Mechanical compressive stress was obtained by increasing the pressure of gaseous phase. OPG/RANKL expression was detected in both cells through quantitative real-time PCR. Results. Similar significant differences (P<0.05) existed in OPG/RANKL change with increasing concentration of TGF-β1 without mechanical stress for cementoblasts and osteoblasts. However, under 3 h stress, OPG increased and RANKL decreased significantly (P<0.01) but with similar OPG/RANKL change. Moreover, under 24 h stress, OPG change exhibited no difference (P>0.05), but RANKL decreased significantly (P<0.01) at 10 and 100 ng/mL TGF-β1 in cementoblasts. In osteoblasts, OPG increased significantly (P<0.01) at 10 and 100 ng/mL, whereas RANKL decreased with statistical difference (P<0.05) at 1 and 10 ng/mL. Conclusions. The effects of TGF-β1 on OPG/RANKL expression of cementoblasts and osteoblasts are similar even without mechanical stress. However, these effects are different under mechanical compressive stress

The Characteristics, Long-Term Outcomes, Risk Factors, and Antithrombotic Therapy in Chinese Patients With Atrial Fibrillation and Bioprosthetic Valves

Introduction: There were few data about the clinical profiles and long-term outcomes in Chinese patients with atrial fibrillation (AF) and bioprosthetic valves.Methods: The retrospective study enrolled 903 patients with bioprosthetic valve replacement at our hospital and discharged with a diagnosis of AF from January 2010 to December 2018.Results: The median age was 65.6 (61.9–69.1) years, and 548 (60.7%) patients were women. During a follow-up period of 3.84 (2.64–5.51) years, 68 (1.8 per 100 person-years) patients died, 81 (2.1 per 100 person-years) patients developed thromboembolism, and 23 (0.6 per 100 person-years) patients experienced major bleeding. The CHA2DS2-VASc score, as a categorical variable (low, moderate, or high risk), predicted the risk of thromboembolism with the C-statistic of 0.6 (95% CI: 0.511–0.689, p = 0.046). The incidence of the CHA2DS2-VASc score increment was 11.6 per 100 person-years, and the annual reclassification rate of stroke risk (from a low or moderate group to a higher group) was 12.7%. The current proportion of oral anticoagulants was 52.3, 59, and 63.2%, respectively, in the low, moderate, and high stroke risk groups. Age (OR: 1.04, 95% CI: 1.01–1.06, p = 0.01), left atrial size (OR: 1.05, 95% CI: 1.03–1.08, p &lt; 0.001), and rheumatic heart disease (OR: 1.49, 95% CI: 1.05–2.10, p = 0.025) were positively associated with the use of oral anticoagulants. The history of chronic kidney disease (OR: 0.20, 95% CI: 0.05–0.76, p = 0.018), prior surgical ablation (OR: 0.33, 95% CI: 0.24–0.47, p &lt; 0.001), and antiplatelet agent use (OR: 0.08, 95% CI: 0.05–0.13, p &lt; 0.001) were inversely related to the use of oral anticoagulants. Higher admission estimated glomerular filtration rate (HR: 0.515, 95% CI: 0.311–0.853, p = 0.01), left ventricular ejection fraction (HR: 0.961, 95% CI: 0.931–0.992, p = 0.014), concomitant surgical ablation (HR: 0.348, 95% CI: 0.171–0.711, p = 0.004), and rheumatic heart disease history (HR: 0.515, 95% CI: 0.311–0.853, p = 0.01) were associated with a lower risk of death. Surgical ablation (HR: 0.263, 95% CI: 0.133–0.519, p &lt; 0.001) and oral anticoagulants (HR: 0.587, 95% CI: 0.375–0.918, p = 0.019) were related to a lower risk of thromboembolism.Conclusion: Chinese patients with AF and bioprosthetic valve(s) were relatively young and had a high prevalence of rheumatic heart disease with few comorbidities. The percentage of mitral bioprosthetic valve replacement was high. The proportion of concomitant surgical ablation or surgical left atrial appendage occlusion or exclusion was relatively low. The thromboembolic events were the major long-term adverse events. The anticoagulation therapy was underused in patients at moderate or high stroke risk. The CHA2DS2-VASc score was verified to be used for predicting stroke risk in this population. The stroke risk dynamically changed; it needed to be reestimated once the risk factor changed

Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage

IntroductionLeaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider.MethodsTaking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples.ResultsThe ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54).DiscussionThis study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing

Platelet-rich fibrin as an autologous biomaterial for bone regeneration: mechanisms, applications, optimization

Platelet-rich fibrin, a classical autologous-derived bioactive material, consists of a fibrin scaffold and its internal loading of growth factors, platelets, and leukocytes, with the gradual degradation of the fibrin scaffold and the slow release of physiological doses of growth factors. PRF promotes vascular regeneration, promotes the proliferation and migration of osteoblast-related cells such as mesenchymal cells, osteoblasts, and osteoclasts while having certain immunomodulatory and anti-bacterial effects. PRF has excellent osteogenic potential and has been widely used in the field of bone tissue engineering and dentistry. However, there are still some limitations of PRF, and the improvement of its biological properties is one of the most important issues to be solved. Therefore, it is often combined with bone tissue engineering scaffolds to enhance its mechanical properties and delay its degradation. In this paper, we present a systematic review of the development of platelet-rich derivatives, the structure and biological properties of PRF, osteogenic mechanisms, applications, and optimization to broaden their clinical applications and provide guidance for their clinical translation

WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing

Self-supervised learning (SSL) achieves great success in speech recognition, while limited exploration has been attempted for other speech processing tasks. As speech signal contains multi-faceted information including speaker identity, paralinguistics, spoken content, etc., learning universal representations for all speech tasks is challenging. To tackle the problem, we propose a new pre-trained model, WavLM, to solve full-stack downstream speech tasks. WavLM jointly learns masked speech prediction and denoising in pre-training. By this means, WavLM does not only keep the speech content modeling capability by the masked speech prediction, but also improves the potential to non-ASR tasks by the speech denoising. In addition, WavLM employs gated relative position bias for the Transformer structure to better capture the sequence ordering of input speech. We also scale up the training dataset from 60k hours to 94k hours. WavLM Large achieves state-of-the-art performance on the SUPERB benchmark, and brings significant improvements for various speech processing tasks on their representative benchmarks. The code and pre-trained models are available at https://aka.ms/wavlm.Comment: Submitted to the Journal of Selected Topics in Signal Processing (JSTSP

Efficient production of foot-and-mouth disease virus empty capsids in insect cells following down regulation of 3C protease activity

Foot-and-mouth disease virus (FMDV) is a significant economically and distributed globally pathogen of Artiodactyla. Current vaccines are chemically inactivated whole virus particles that require large-scale virus growth in strict bio-containment with the associated risks of accidental release or incomplete inactivation. Non-infectious empty capsids are structural mimics of authentic particles with no associated risk and constitute an alternate vaccine candidate. Capsids self-assemble from the processed virus structural proteins, VP0, VP3 and VP1, which are released from the structural protein precursor P1-2A by the action of the virus-encoded 3C protease. To date recombinant empty capsid assembly has been limited by poor expression levels, restricting the development of empty capsids as a viable vaccine. Here expression of the FMDV structural protein precursor P1-2A in insect cells is shown to be efficient but linkage of the cognate 3C protease to the C-terminus reduces expression significantly. Inactivation of the 3C enzyme in a P1-2A-3C cassette allows expression and intermediate levels of 3C activity resulted in efficient processing of the P1-2A precursor into the structural proteins which assembled into empty capsids. Expression was independent of the insect host cell background and leads to capsids that are recognised as authentic by a range of anti-FMDV bovine sera suggesting their feasibility as an alternate vaccine