Genome-wide Association Study (GWAS) of mesocotyl elongation based on re-sequencing approach in rice

Annotation of candidate genes anchored by associated SNPs. (XLSX 34 kb

3D Morphology of Open Clusters in the Solar Neighborhood with Gaia EDR 3. II. Hierarchical Star Formation Revealed by Spatial and Kinematic Substructures

We identify members of 65 open clusters in the solar neighborhood using the machine-learning algorithm StarGO based on Gaia EDR3 data. After adding members of 20 clusters from previous studies we obtain 85 clusters, and study their morphology and kinematics. We classify the substructures outside the tidal radius into four categories: filamentary (f1) and fractal (f2) for clusters 100 Myr. The kinematical substructures of f1-type clusters are elongated; these resemble the disrupted cluster Group X. Kinematic tails are distinct in t-type clusters, especially Pleiades. We identify 29 hierarchical groups in four young regions (Alessi 20, IC 348, LP 2373, LP 2442); 10 among these are new. The hierarchical groups form filament networks. Two regions (Alessi 20, LP 2373) exhibit global orthogonal expansion (stellar motion perpendicular to the filament), which might cause complete dispersal. Infalling-like flows (stellar motion along the filament) are found in UBC 31 and related hierarchical groups in the IC 348 region. Stellar groups in the LP 2442 region (LP 2442 gp 1–5) are spatially well mixed but kinematically coherent. A merging process might be ongoing in the LP 2442 subgroups. For younger systems (≲30 Myr), the mean axis ratio, cluster mass, and half-mass–radius tend to increase with age values. These correlations between structural parameters may imply two dynamical processes occurring in the hierarchical formation scenario in young stellar groups: (1) filament dissolution and (2) subgroup mergers

Bilingual Content-Based Teaching - an Important Component for Education Globalization

Content-based bilingual teaching is a model of the integrated-skills approach to combined language and content teaching, which integrates the learning of some specific subject-matter content with the learning of foreign language. In this paper, the authors will share with other educators their first-hand experiences in bilingual teaching and observation of science classes in different countries. Through their practice in bilingual teaching, many similarities and variations between the educational components of the two countries have been recognized. Results from the comparison study indicate that bilingual content-based teaching is an important step to fit the current trend of education globalization. It not only provides an English environment for students to strengthen and enhance their technical English skills in science and mathematics, but also creates international exchange opportunities for educators from different countries. It opens the door for educators to see different teaching techniques of others, to exchange their teaching experiences, and to incorporate advanced teaching methods in their own classes. Both educators and students will benefit from the combination of the best features of eastern and western education systems. We believe that successful bilingual content-based instruction will prepare students to be more competitive in their future careers for the rapidly globalizing world

A Deep Learning Localization Method for Acoustic Source via Improved Input Features and Network Structure

Shallow water passive source localization is an essential problem in underwater detection and localization. Traditional matched-field processing (MFP) methods are sensitive to environment mismatches. Many neural network localization methods still have room for improvement in accuracy if they are further adjusted to underwater acoustic characteristics. To address these problems, we propose a deep learning localization method via improved input features and network structure, which can effectively estimate the depth and the closest point of approach (CPA) range of the acoustic source. Firstly, we put forward a feature preprocessing scheme to enhance the localization accuracy and robustness. Secondly, we design a deep learning network structure to improve the localization accuracy further. Finally, we propose a method of visualizing the network to optimize the estimated localization results. Simulations show that the accuracy of the proposed method is better than other compared features and network structures, and the robustness is significantly better than that of the MFP methods. Experimental results further prove the effectiveness of the proposed method

Changes of Microstructures and Mechanical Properties in Commercially Pure Titanium after Different Cycles of Proposed Multi-Directional Forging

A newly proposed multi-directional forging (MDF) was successfully applied to a commercially pure titanium (CP Ti). Severe plastic deformation would result in significant and complex changes of microstructure and mechanical properties, so microstructure characterization and a mechanical test of CP Ti were conducted after different cycles of MDF. The results demonstrated that dynamic recrystallization was the dominant grain refinement mechanism of MDF CP Ti. With increasing the cycles of MDF, grain size, fraction of low angle grain boundaries and dislocations density decreased due to grain refined. After three cycles of MDF, the mean grain size was about 200 nm. The values of tensile strength and hardness increased significantly from zero cycles to one cycle of MDF, but increased slowly after one MDF cycle. Numerous dimples and tear ridges were present, but the dimples were smaller and shallower with increasing cycles of MDF

LRRC superfamily expression in stromal cells predicts the clinical prognosis and platinum resistance of ovarian cancer

Abstract Background Leucine-rich repeat sequence domains are known to mediate protein‒protein interactions. Recently, some studies showed that members of the leucine rich repeat containing (LRRC) protein superfamily may become new targets for the diagnosis and treatment of tumours. However, it is not known whether any of the LRRC superfamily genes is expressed in the stroma of ovarian cancer (OC) and is associated with prognosis. Methods The clinical data and transcriptional profiles of OC patients from the public databases TCGA (n = 427), GTEx (n = 88) and GEO (GSE40266 and GSE40595) were analysed by R software. A nomogram model was also generated through R. An online public database was used for auxiliary analysis of prognosis, immune infiltration and protein‒protein interaction (PPI) networks. Immunohistochemistry and qPCR were performed to determine the protein and mRNA levels of genes in high-grade serous ovarian cancer (HGSC) tissues of participants and the MRC-5 cell line induced by TGF-β. Results LRRC15 and LRRC32 were identified as differentially expressed genes from the LRRC superfamily by GEO transcriptome analysis. PPI network analysis suggested that they were most enriched in TGF-β signalling. The TCGA-GTEx analysis results showed that only LRRC15 was highly expressed in both cancer-associated fibroblasts (CAFs) and the tumour stroma of OC and was related to clinical prognosis. Based on this, we developed a nomogram model to predict the incidence of adverse outcomes in OC. Moreover, LRRC15 was positively correlated with CAF infiltration and negatively correlated with CD8 + T-cell infiltration. As a single indicator, LRRC15 had the highest accuracy (AUC = 0.920) in predicting the outcome of primary platinum resistance. Conclusions The LRRC superfamily is related to the TGF-β pathway in the microenvironment of OC. LRRC15, as a stromal biomarker, can predict the clinical prognosis of HGSC and promote the immunosuppressive microenvironment. LRRC15 may be a potential therapeutic target for reversing primary resistance in OC

The promotion of neural progenitor cells proliferation by aligned and randomly oriented collagen nanofibers through beta 1 integrin/MAPK signaling pathway

In regenerative medicine, accumulating evidence demonstrates that the property of substrates monitors neural stem cells behavior. However, how stem cells sense and interpret biochemical and topographical cues remains elusive. This study aimed to explore the mechanism how nanofibrous scaffold modulated stem cells behavior. Spinal cord derived neural progenitor cells (NPCs) were cultured on electrospun aligned and randomly oriented collagen nanofibrous scaffolds. A 30% increase in proliferation and an elevation of BrdU incorporation were observed in NPCs on collagen nanofibers, compared to that on collagen-coated surface. In particular, NPCs expanded faster on aligned nanofibers in comparison with that on randomly oriented nanofibers. Moreover, an alteration in cell cycle progression with a reduced percentage of cells in G0/G1 phase and increased cell proliferation index (S phase plus G2/M phase) was also detected in NPCs cultured on collagen nanofibers. Incubating NPCs with anti-β1 integrin antibody or U1026 (an inhibitor of mitogen-activated protein kinase kinase, MEK) eliminated the altered cell cycle dynamics and BrdU incorporation induced by collagen nanofibers. In addition, cyclin D1 and cyclin dependent kinase 2 (CDK2), downstream genes of β1 integrin/mitogen-activated protein kinase (MAPK) pathway that control G1/S phase transition, were correspondingly regulated by nanofibers. Collectively, these data suggested that the property of substrate modulated NPCs proliferation by promoting cell cycle through β1 integrin/MAPK pathway. Our findings provide a better understanding of the interaction between NPCs and the substrate and therefore will pave way for regenerative medicine