32 research outputs found
Whole-genome sequencing of <em>Oryza brachyantha</em> reveals mechanisms underlying <em>Oryza</em> genome evolution
The wild species of the genus Oryza contain a largely untapped reservoir of agronomically important genes for rice improvement. Here we report the 261-Mb de novo assembled genome sequence of Oryza brachyantha. Low activity of long-terminal repeat retrotransposons and massive internal deletions of ancient long-terminal repeat elements lead to the compact genome of Oryza brachyantha. We model 32,038 protein-coding genes in the Oryza brachyantha genome, of which only 70% are located in collinear positions in comparison with the rice genome. Analysing breakpoints of non-collinear genes suggests that double-strand break repair through non-homologous end joining has an important role in gene movement and erosion of collinearity in the Oryza genomes. Transition of euchromatin to heterochromatin in the rice genome is accompanied by segmental and tandem duplications, further expanded by transposable element insertions. The high-quality reference genome sequence of Oryza brachyantha provides an important resource for functional and evolutionary studies in the genus Oryza
Multidirectional effects of Sr-, Mg-, and Si-containing bioceramic coatings with high bonding strength on inflammation, osteoclastogenesis, and osteogenesis
Ideal coating materials for implants should be able to induce excellent osseointegration, which requires several important parameters, such as good bonding strength, limited inflammatory reaction, balanced osteoclastogenesis and osteogenesis, to gain well-functioning coated implants with long-term life span after implantation. Bioactive elements, like Sr, Mg and Si, have been found to play important roles in regulating the biological responses. It is of great interest to combine bioactive elements for developing bioactive coatings on Ti-6Al-4V orthopedic implants to elicit multidirectional effects on the osseointegration. In this study, Sr, Mg and Si-containing bioactive Sr2MgSi2O7 (SMS) ceramic coatings on Ti-6Al-4V were successfully prepared by plasma-spray coating method. The prepared SMS coatings have significantly higher bonding strength (~37MPa) than conventional pure hydroxyapatite (HA) coatings (mostly in the range of 15-25 MPa). It was also found that the prepared SMS coatings switch the macrophage phenotype into M2 extreme, inhibiting the inflammatory reaction via the inhibition of Wnt5A/Ca2+ and Toll-like receptor (TLR) pathways of macrophages. In addition, the osteoclastic activities were also inhibited by SMS coatings. The expression of osteoclastogenesis related genes (RANKL and MCSF) in bone marrow derived mesenchymal cells (BMSCs) with the involvement of macrophages was decreased, while OPG expression was enhanced on SMS coatings compared to HA coatings, indicating that SMS coatings also downregulated the osteoclastogenesis. However, the osteogenic differentiation of BMSCs with the involvement of macrophages was comparable between SMS and HA coatings. Therefore, the prepared SMS coatings showed multidirectional effects, such as improving bonding strength, reducing inflammatory reaction and downregulating osteoclastic activities, but maintaining a comparable osteogenesis, as compared with HA coatings. The combination of bioactive elements of Sr, Mg and Si into bioceramic coatings can be a promising method to develop bioactive implants with multifunctional properties for orthopaedic application
Nutrient element-based bioceramic coatings on titanium alloy stimulating osteogenesis by inducing beneficial osteoimmmunomodulation
A paradigm shift has taken place in which bone implant materials has gone from being relatively inert to having immunomodulatory properties, indicating the importance of immune response when these materials interact with the host tissues. It has therefore become important to endow the implant materials with immunomodulatory properties favouring osteogenesis and osseointegration. Strontium, zinc and silicon are bioactive elements that have important roles in bone metabolism and that also elicit significant immune responses. In this study, Sr-, Zn- and Si-containing bioactive Sr2ZnSi2O7 (SZS) ceramic coatings on Ti–6Al–4V were successfully prepared by a plasma-spray coating method. The SZS coatings exhibited slow release of the bioactive ions with significantly higher bonding strength than hydroxyapatite (HA) coatings. SZS-coated Ti–6Al–4V elicited significant effects on the immune cells, inhibiting the release of pro-inflammatory cytokines and fibrosis-enhancing factors, while upregulating the expression of osteogenic factors of macrophages; moreover, it could also inhibit the osteoclastic activities. The RANKL/RANK pathway, which enhances osteoclastogenesis, was inhibited by the SZS coatings, whereas the osteogenic differentiation of bone marrow mesenchymal stromal cells (BMSCs) was significantly enhanced by the SZS coatings/macrophages conditioned medium, probably via the activation of BMP2 pathway. SZS coatings are, therefore, a promising material for orthopaedic applications, and the strategy of manipulating the immune response by a combination of bioactive elements with controlled release has the potential to endow biomaterials with beneficial immunomodulatory properties
Design of Line Loss Rate Calculation Method for Low-Voltage Desk Area Based on GA-LMBP Neural Network Model
The distribution network line loss computation method needs to be enhanced in light of the ongoing growth of the national power grid. The study classifies and segments the station data using a decision tree model and a multi-feature volume weighted station clustering algorithm. It then uses a back propagation neural network as a substrate, along with the Levenberg-Marquard algorithm and genetic algorithm for optimization. Collect and organize relevant data on line loss rates in low-voltage substation areas, including information on energy meters, meter boxes, and lines. Next, construct a genetic algorithm neural network model and use the backpropagation algorithm for training. Evaluate the accuracy and stability of various models by comparing the error between predicted and actual line loss rates through experiments. Finally, optimize the neural network parameters and network structure to improve the model’s prediction accuracy and robustness. The experimental data showed that compared to the density-based spatial clustering algorithm for noisy applications, the contour coefficient metrics of the proposed multi-feature volume weighted station clustering algorithm improved by 0.05 and the average consumption time of the algorithm was reduced by 75%. Compared to the back-propagation neural network model optimized by the Levenberg-Marquard algorithm, the root-mean-square error of the neural network model optimized by the addition of the genetic algorithm for the calculation of the line loss rate of the four station samples was reduced by 72%, 55%, 53% and 37%, and the values of R2 were improved by 8.72%, 13.59%, 7.91% and 11.69%, respectively. The testing results demonstrated that the neural network model has good generalization capabilities and a high degree of curve fitting. Also, the relative errors of the calculation of the station area’s line loss rate are mainly within the range of 0% and 10%. For the growth of energy conservation in the country, this innovative technology offers a new way to determine and manage line loss of the station area
SU2GE-Net: a saliency-based approach for non-specific class foreground segmentation
Abstract Salient object detection is vital for non-specific class subject segmentation in computer vision applications. However, accurately segmenting foreground subjects with complex backgrounds and intricate boundaries remains a challenge for existing methods. To address these limitations, our study proposes SU2GE-Net, which introduces several novel improvements. We replace the traditional CNN-based backbone with the transformer-based Swin-TransformerV2, known for its effectiveness in capturing long-range dependencies and rich contextual information. To tackle under and over-attention phenomena, we introduce Gated Channel Transformation (GCT). Furthermore, we adopted an edge-based loss (Edge Loss) for network training to capture spatial-wise structural details. Additionally, we propose Training-only Augmentation Loss (TTA Loss) to enhance spatial stability using augmented data. Our method is evaluated using six common datasets, achieving an impressive F β score of 0.883 on DUTS-TE. Compared with other models, SU2GE-Net demonstrates excellent performance in various segmentation scenarios
STDC-MA Network for Semantic Segmentation
Semantic segmentation is applied extensively in autonomous driving and
intelligent transportation with methods that highly demand spatial and semantic
information. Here, an STDC-MA network is proposed to meet these demands. First,
the STDC-Seg structure is employed in STDC-MA to ensure a lightweight and
efficient structure. Subsequently, the feature alignment module (FAM) is
applied to understand the offset between high-level and low-level features,
solving the problem of pixel offset related to upsampling on the high-level
feature map. Our approach implements the effective fusion between high-level
features and low-level features. A hierarchical multiscale attention mechanism
is adopted to reveal the relationship among attention regions from two
different input sizes of one image. Through this relationship, regions
receiving much attention are integrated into the segmentation results, thereby
reducing the unfocused regions of the input image and improving the effective
utilization of multiscale features. STDC- MA maintains the segmentation speed
as an STDC-Seg network while improving the segmentation accuracy of small
objects. STDC-MA was verified on the verification set of Cityscapes. The
segmentation result of STDC-MA attained 76.81% mIOU with the input of 0.5x
scale, 3.61% higher than STDC-Seg.Comment: 10 pages, 5 figure
Correction: Nutrient element-based bioceramic coatings on titanium alloy stimulating osteogenesis by inducing beneficial osteoimmmunomodulation
Correction for ‘Nutrient element-based bioceramic coatings on titanium alloy stimulating osteogenesis by inducing beneficial osteoimmmunomodulation’ by Zetao Chen et al., J. Mater. Chem. B, 2014, 2, 6030–6043