Search

Messenger ribonucleic acid (mRNA) technology has been rapidly applied for the development of the COVID-19 vaccine. However, naked mRNA itself is inherently unstable. Lipid nanoparticles (LNPs) protect mRNAs from extracellular ribonucleases and facilitate mRNA trafficking. For mRNA vaccines, antigen-presenting cells utilize LNPs through uptake to elicit antigen-specific immunity. There are reports on the impact of various physical characteristics of LNPs, particularly those with sizes less than 200 nm, especially 50 to 150 nm, on the overall stability and protective efficacy of mRNA vaccines. To address this, a single change in the size of LNPs using the same mRNA stock solution was assessed for the physicochemical characterization of the resulting mRNA-LNPs vaccine, along with the evaluation of their protective efficacy. Particles of smaller sizes generally disperse more effectively in solutions, with minimized occurrence of particle precipitation and aggregation. Here, we demonstrate that the vaccine containing 80–100 nm mRNA-LNPs showed the best stability and protection at 4°C and −20°C. Furthermore, we can conclude that freezing the vaccine at −20°C is more appropriate for maintaining stability over the long term. This effort is poised to provide a scientific basis for improving the quality of ongoing mRNA vaccine endeavors and providing information on the development of novel products.

Abstract Objective Cuproptosis is a novel cell death dependent on mitochondrial respiration and regulated by copper. This study aimed to investigate the cuproptosis‐related gene DLAT potential value in gastric cancer (GC). Methods Bioinformatics was used to analyze DLAT expression. DLAT expression in GC cell lines was detected using qRT‐PCR. Cell proliferation ability was assessed using CCK8 and cell cycle assay. Cell migration and invasion were assessed using wound healing and transwell assay. A prognostic assessment was performed through survival and Cox regression analysis. DLAT protein expression was analyzed through HPA immunohistochemistry. Biological functions and processes were analyzed through GO and KEGG enrichment analysis and PPI. Correlation with immune cell infiltration and immune checkpoint genes was analyzed for DLAT. Results DLAT expression was upregulated in GC tissues and cells and correlated with shorter survival for patients. Age, gender, histological typing, lymph node metastasis, and distant metastasis were identified as independent prognostic factors affecting OS in GC. DLAT protein was upregulated in GC. The biological functions and pathways enriched in DLAT were mainly linked to mitochondrial respiration and the TCA cycle. The expression of DLAT was found to be positively correlated with the infiltration of Th and Th2 immune cells and only positively correlated with the expression of the BTN2A1 immune checkpoint gene. Conclusion DLAT has the potential to serve as a prognostic assessment factor in GC. The expression of DLAT was correlated with immune infiltration and tumor immune escape, providing a new target for immunotherapy of GC.

Harvesting robots had difficulty extracting filament phenotypes for small, numerous filaments, heavy cross-obscuration, and similar phenotypic characteristics with organs. Robots experience difficulty in localizing under near-colored backgrounds and fuzzy contour features. It cannot accurately harvest filaments for robots. Therefore, a method for detecting and locating filament picking points based on an improved DeepLabv3+ algorithm is proposed in this study. A lightweight network structure, ShuffletNetV2, was used to replace the backbone network Xception of the traditional DeepLabv3+. Convolutional branches for 3 different sampling rates were added to extract information on the safflower features under the receptive field. Convolutional block attention was incorporated into feature extraction at the coding and decoding layers to solve the interference problem of the near-color background in the feature-fusion process. Then, using the region of interest of the safflower branch obtained by the improved DeepLabv3+, an algorithm for filament picking-point localization was designed based on barycenter projection. The tests demonstrated that this method was capable of accurately localizing the filament. The mean pixel accuracy and mean intersection over union of the improved DeepLabv3+ were 95.84% and 96.87%, respectively. The detection rate and weights file size required were superior to those of other algorithms. In the localization test, the depth-measurement distance between the depth camera and target safflower filament was 450 to 510 mm, which minimized the visual-localization error. The average localization and picking success rates were 92.50% and 90.83%, respectively. The results show that the proposed localization method offers a viable approach for accurate harvesting localization.

The accurate acquisition of safflower filament information is the prerequisite for robotic picking operations. To detect safflower filaments accurately in different illumination, branch and leaf occlusion, and weather conditions, an improved Faster R-CNN model for filaments was proposed. Due to the characteristics of safflower filaments being dense and small in the safflower images, the model selected ResNeSt-101 with residual network structure as the backbone feature extraction network to enhance the expressive power of extracted features. Then, using Region of Interest (ROI) Align improved ROI Pooling to reduce the feature errors caused by double quantization. In addition, employing the partitioning around medoids (PAM) clustering was chosen to optimize the scale and number of initial anchors of the network to improve the detection accuracy of small-sized safflower filaments. The test results showed that the mean Average Precision (mAP) of the improved Faster R-CNN reached 91.49%. Comparing with Faster R-CNN, YOLOv3, YOLOv4, YOLOv5, and YOLOv6, the improved Faster R-CNN increased the mAP by 9.52%, 2.49%, 5.95%, 3.56%, and 1.47%, respectively. The mAP of safflower filaments detection was higher than 91% on a sunny, cloudy, and overcast day, in sunlight, backlight, branch and leaf occlusion, and dense occlusion. The improved Faster R-CNN can accurately realize the detection of safflower filaments in natural environments. It can provide technical support for the recognition of small-sized crops.

Understanding the biomechanical properties of safflowers is essential for appropriately designing harvesting machinery and optimizing the harvesting process. Safflower is a flexible crop that lacks a basis for relevant simulation parameters, which causes difficulties in designing harvesting machinery. In this study, a calibration method for safflowers was proposed. First, a discrete element model was established by measuring the intrinsic parameters of a safflower, such as its geometric parameters, density, Poisson's ratio, and modulus of elasticity. Second, the contact and bonding parameters were calibrated using a combination of physical and simulation tests. In the contact parameter tests, the Hertz-Mindlin (no-slip) model was implemented for the stacking angle tests conducted regarding the safflower filament. A regular two-level factorial design was used to determine the important factors and perform the steepest climb test. Moreover, the Box-Behnken design was adopted to obtain the optimal contact parameters. In the bonding parameter tests, the Hertz-Mindlin model with bonding contact was applied for the safflower shear simulation tests; moreover, the optimum bonding parameters were obtained through the central composite design test. The results demonstrated that the relative errors between the simulated and measured stacking angles and maximum shear were 3.19% and 5.29%, respectively. As a result, the safflower simulation parameters were accurately calibrated, providing a reference for appropriately setting the simulation parameters and designing key mechanical components. [ABSTRACT FROM AUTHOR]