Underwater acoustic sensor networks node localization based on compressive sensing in water hydrology

Peer reviewedPublisher PD

Comparison of two novel methods for counting wheat ears in the field with terrestrial LiDAR

Abstract Background The metrics for assessing the yield of crops in the field include the number of ears per unit area, the grain number per ear, and the thousand-grain weight. Typically, the ear number per unit area contributes the most to the yield. However, calculation of the ear number tends to rely on traditional manual counting, which is inefficient, labour intensive, inaccurate, and lacking in objectivity. In this study, two novel extraction algorithms for the estimation of the wheat ear number were developed based on the use of terrestrial laser scanning (TLS) in conjunction with the density-based spatial clustering (DBSC) algorithm based on the normal and the voxel-based regional growth (VBRG) algorithm. The DBSC involves two steps: (1) segmentation of the point clouds using differences in the normal vectors and (2) clustering of the segmented point clouds using a density clustering algorithm to calculate the ear number. The VBRG involves three steps: (1) voxelization of the point clouds, (2) construction of the topological relationships between the voxels as a connected region using the k-dimensional tree, and (3) detection of the wheat ears in the connected areas using a regional growth algorithm. Results The results demonstrated that DBSC and VBRG were promising in estimating the number of ears for different cultivars, planting densities, N fertilization rates, and growth stages of wheat (RMSE = 76 ~ 114 ears/m2, rRMSE = 18.62 ~ 27.96%, r = 0.76 ~ 0.84). Comparing the performance of the two algorithms, the overall accuracy of the DBSC (RMSE = 76 ears/m2, rRMSE = 18.62%, r = 0.84) was better than that of the VBRG (RMSE = 114 ears/m2, rRMSE = 27.96%, r = 0.76). It was found that with the DBSC, the calculation in points as units permitted more detailed information to be retained, and this method was more suitable for estimation of the wheat ear number in the field. Conclusions The algorithms adopted in this study provide new approaches for non-destructive measurement and efficient acquisition of the ear number in the assessment of the wheat yield phenotype

Genome-Wide Identification of Histone Deacetylases and Their Roles Related with Light Response in Tartary Buckwheat (<i>Fagopyrum tataricum</i>)

Histone deacetylases (HDACs), known as histone acetylation erasers, function crucially in plant growth and development. Although there are abundant reports focusing on HDACs of Arabidopsis and illustrating their important roles, the knowledge of HDAC genes in Tartary buckwheat (Polygonales Polygonaceae Fagopyrum tataricum (L.) Gaertn) is still scarce. In the study, a total of 14 HDAC genes were identified and divided into three main groups: Reduced Potassium Dependency-3/His-52 tone Deacetylase 1 (RPD3/HDA1), Silent Information Regulator 2 (SIR2), and the plant-53 specific HD2. Domain and motif composition analysis showed there were conserved domains and motifs in members from the same subfamilies. The 14 FtHDACs were distributed asymmetrically on 7 chromosomes, with three segmental events and one tandem duplication event identified. The prediction of the cis-element in promoters suggested that FtHDACs probably acted in numerous biological processes including plant growth, development, and response to environmental signals. Furthermore, expression analysis based on RNA-seq data displayed that all FtHDAC genes were universally and distinctly expressed in diverse tissues and fruit development stages. In addition, we found divergent alterations in FtHDACs transcript abundance in response to different light conditions according to RNA-seq and RT-qPCR data, indicating that five FtHDACs might be involved in light response. Our findings could provide fundamental information for the HDAC gene family and supply several targets for future function analysis of FtHDACs related with light response of Tartary buckwheat

Comprehensive proteomic analysis of exosome mimetic vesicles and exosomes derived from human umbilical cord mesenchymal stem cells

Abstract Background Exosomes derived from mesenchymal stem cells (MSCs) have shown to have effective application prospects in the medical field, but exosome yield is very low. The production of exosome mimetic vesicles (EMVs) by continuous cell extrusion leads to more EMVs than exosomes, but whether the protein compositions of MSC-derived EMVs (MSC-EMVs) and exosomes (MSC-exosomes) are substantially different remains unknown. The purpose of this study was to conduct a comprehensive proteomic analysis of MSC-EMVs and MSC-exosomes and to simply explore the effects of exosomes and EMVs on wound healing ability. This study provides a theoretical basis for the application of EMVs and exosomes. Methods In this study, EMVs from human umbilical cord MSCs (hUC MSCs) were isolated by continuous extrusion, and exosomes were identified after hUC MSC ultracentrifugation. A proteomic analysis was performed, and 2315 proteins were identified. The effects of EMVs and exosomes on the proliferation, migration and angiogenesis of human umbilical vein endothelial cells (HUVECs) were evaluated by cell counting kit-8, scratch wound, transwell and tubule formation assays. A mouse mode was used to evaluate the effects of EMVs and exosomes on wound healing. Results Bioinformatics analyses revealed that 1669 proteins in both hUC MSC-EMVs and hUC MSC-exosomes play roles in retrograde vesicle-mediated transport and vesicle budding from the membrane. The 382 proteins unique to exosomes participate in extracellular matrix organization and extracellular structural organization, and the 264 proteins unique to EMVs target the cell membrane. EMVs and exosomes can promote wound healing and angiogenesis in mice and promote the proliferation, migration and angiogenesis of HUVECs. Conclusions This study presents a comprehensive proteomic analysis of hUC MSC-derived exosomes and EMVs generated by different methods. The tissue repair function of EMVs and exosomes was herein verified by wound healing experiments, and these results reveal their potential applications in different fields based on analyses of their shared and unique proteins

Transferring from ex-vivo to in-vivo: instrument localization in 3D cardiac ultrasound using Pyramid-UNet with hybrid loss

Automated instrument localization during cardiac interventions is essential to accurately and efficiently interpret a 3D ultrasound (US) image. In this paper, we propose a method to automatically localize the cardiac intervention instrument (RF-ablation catheter or guidewire) in a 3D US volume. We propose a Pyramid-UNet, which exploits the multi-scale information for better segmentation performance. Furthermore, a hybrid loss function is introduced, which consists of contextual loss and class-balanced focal loss, to enhance the performance of the network in cardiac US images. We have collected a challenging ex-vivo dataset to validate our method, which achieves a Dice score of 69.6% being 18.8% higher than the state-of-the-art methods. Moreover, with the pre-trained model on the ex-vivo dataset, our method can be easily adapted to the in-vivo dataset with several iterations and then achieves a Dice score of 65.8% for a different instrument. With segmentation, instruments can be localized with an average error less than 3 voxels in both datasets. To the best of our knowledge, this is the first work to validate the image-based method on in-vivo cardiac datasets