Fusion with extracellular domain of cytotoxic T-lymphocyte-associated-antigen 4 leads to enhancement of immunogenicity of Hantaan virus DNA vaccines in C57BL/6 mice

<p>Abstract</p> <p>Background</p> <p>Hantaan virus (HTNV) is the causative agent of the most severe form of a rodent-borne disease known as hemorrhagic fever with renal syndrome (HFRS). A safe and effective HTNV vaccine is needed. Vaccination with DNA constructs expressing fused antigen with bioactive factors, has shown promising improvement of immunogenicity for viral agents in animal models, but the effect of fusion strategy on HTNV DNA vaccine has not been investigated.</p> <p>Results</p> <p>DNA plasmids encoding the HTNV nucleocapsid protein (N) and glycoprotein (Gn and Gc) in fusion to the extracellular domain of cytotoxic T-lymphocyte-associated-antigen 4 (eCTLA-4) targeting to antigen presenting cells (APCs) were constructed. Intramuscular immunization of mice with plasmids expressing eCTLA-4-HTNV-N/GP fusion proteins leads to a significant enhancement of the specific antibody response as well as cytotoxic T-lymphocyte (CTL) response in C57BL/6 mice. Moreover, this effect could be further augmented when co-administered with CpG motifs.</p> <p>Conclusions</p> <p>Modification of viral antigen in fusion to bioactive factor will be promising to confer efficient antigen presentation and improve the potency of DNA vaccine in mice.</p

2020 taxonomic update for phylum Negarnaviricota (Riboviria: Orthornavirae), including the large orders Bunyavirales and Mononegavirales.

In March 2020, following the annual International Committee on Taxonomy of Viruses (ICTV) ratification vote on newly proposed taxa, the phylum Negarnaviricota was amended and emended. At the genus rank, 20 new genera were added, two were deleted, one was moved, and three were renamed. At the species rank, 160 species were added, four were deleted, ten were moved and renamed, and 30 species were renamed. This article presents the updated taxonomy of Negarnaviricota as now accepted by the ICTV

Fast Localization and Sectioning of Mouse Locus Coeruleus

The locus nucleus (LC) is a multifunctional nucleus which is also the source of norepinephrine in the brain. To date, there is no simple and easy method to locate the small LC in brain sectioning. Here we report a fast, accurate, and easy-to-follow protocol for the localization of mice LC in frozen sectioning. After fixation and dehydration, the intact brains of adult mice were placed on a horizontal surface and vertically cut along the posterior margin of the bilateral cerebral cortex. In the coronal cutting plane, the aqueduct of midbrain can be seen easily with the naked eyes. After embedding the cerebellum part with optimal cutting temperature (OCT) compound, coronal brain slices were cut from the cutting plane, within 1 mm, the aqueduct of midbrain disappeared and the fourth ventricle appeared, then the brain slices contained LC and were collected. From the first collection, at ~200 μm, the noradrenergic neurons’ most enriched brain slices can be collected. The tyrosine hydroxylase immunofluorescence staining confirmed that the localization of LC with this method is accurate and the noradrenergic neuron most abundant slices can be determined with this method

Compensating group delay distortion of signals based on engineered material dispersion

Dispersion, which originates from the total frequency responses of materials, devices and transmission lines, makes envelope distortion of signals inevitable in transmission systems. In this study, we investigate the group delay distortion of a signal due to the presence of dispersion in transmission systems, and propose an approach to eliminate the distortion by compensation based on engineered material dispersion. We demonstrate theoretically and experimentally that utilizing the anomalous frequency response of a dispersive material, envelope distortion of a signal passing through a given transmission system can be fully compensated. Compared with previous researches on dispersion compensation using grating compressors or chirp compressors in optics and non-Foster circuits in microwave bands, the proposed approach is robust and scalable to other frequency bands

Discovery and Validation of a CT-Based Radiomic Signature for Preoperative Prediction of Early Recurrence in Hypopharyngeal Carcinoma

Purpose. In the clinical management of hypopharyngeal squamous cell carcinoma (HSCC), preoperative identification of early recurrence (≤2 years) after curative resection is essential. Thus, we aimed to develop a CT-based radiomic signature to predict early recurrence in HSCC patients preoperatively. Methods. In total, 167 HSCC patients who underwent partial surgery were enrolled in this retrospective study and divided into two groups, i.e., the training cohort (n=133) and the validation cohort (n=34). Each individual was followed up for at least for 2 years. Radiomic features were extracted from CT images, and the radiomic signature was built with the least absolute shrinkage and selection operator (LASSO) logistic regression (LR) model. The associations of preoperative clinical factors with early recurrence were evaluated. A radiomic signature-combined model was built, and the area under the curve (AUC) was used to explore their performance in discriminating early recurrence. Results. Among the 1415 features, 335 of them were selected using the variance threshold method. Then, the SelectKBest method was further used for the selection of 31 candidate features. Finally, 11 out of 31 optimal features were identified with the LASSO algorithm. In the LR classifier, the AUCs of the training and validation sets in discriminating early recurrence were 0.83 (95% CI: 0.76-0.90) (sensitivity 0.8 and specificity 0.83) and 0.83 (95% CI: 0.67-0.99) (sensitivity 0.69 and specificity 0.71), respectively. Conclusions. Using the radiomic signature, we developed a radiomic signature to preoperatively predict early recurrence in patients with HSCC, which may serve as a potential noninvasive tool to guide personalized treatment

Weed Detection in Peanut Fields Based on Machine Vision

The accurate identification of weeds in peanut fields can significantly reduce the use of herbicides in the weed control process. To address the identification difficulties caused by the cross-growth of peanuts and weeds and by the variety of weed species, this paper proposes a weed identification model named EM-YOLOv4-Tiny incorporating multiscale detection and attention mechanisms based on YOLOv4-Tiny. Firstly, an Efficient Channel Attention (ECA) module is added to the Feature Pyramid Network (FPN) of YOLOv4-Tiny to improve the recognition of small target weeds by using the detailed information of shallow features. Secondly, the soft Non-Maximum Suppression (soft-NMS) is used in the output prediction layer to filter the best prediction frames to avoid the problem of missed weed detection caused by overlapping anchor frames. Finally, the Complete Intersection over Union (CIoU) loss is used to replace the original Intersection over Union (IoU) loss so that the model can reach the convergence state faster. The experimental results show that the EM-YOLOv4-Tiny network is 28.7 M in size and takes 10.4 ms to detect a single image, which meets the requirement of real-time weed detection. Meanwhile, the mAP on the test dataset reached 94.54%, which is 6.83%, 4.78%, 6.76%, 4.84%, and 9.64% higher compared with YOLOv4-Tiny, YOLOv4, YOLOv5s, Swin-Transformer, and Faster-RCNN, respectively. The method has much reference value for solving the problem of fast and accurate weed identification in peanut fields

Weed Detection in Peanut Fields Based on Machine Vision

The accurate identification of weeds in peanut fields can significantly reduce the use of herbicides in the weed control process. To address the identification difficulties caused by the cross-growth of peanuts and weeds and by the variety of weed species, this paper proposes a weed identification model named EM-YOLOv4-Tiny incorporating multiscale detection and attention mechanisms based on YOLOv4-Tiny. Firstly, an Efficient Channel Attention (ECA) module is added to the Feature Pyramid Network (FPN) of YOLOv4-Tiny to improve the recognition of small target weeds by using the detailed information of shallow features. Secondly, the soft Non-Maximum Suppression (soft-NMS) is used in the output prediction layer to filter the best prediction frames to avoid the problem of missed weed detection caused by overlapping anchor frames. Finally, the Complete Intersection over Union (CIoU) loss is used to replace the original Intersection over Union (IoU) loss so that the model can reach the convergence state faster. The experimental results show that the EM-YOLOv4-Tiny network is 28.7 M in size and takes 10.4 ms to detect a single image, which meets the requirement of real-time weed detection. Meanwhile, the mAP on the test dataset reached 94.54%, which is 6.83%, 4.78%, 6.76%, 4.84%, and 9.64% higher compared with YOLOv4-Tiny, YOLOv4, YOLOv5s, Swin-Transformer, and Faster-RCNN, respectively. The method has much reference value for solving the problem of fast and accurate weed identification in peanut fields