Study on the Molding Factors of Preparing High-Strength Laminated Bamboo Composites.

To promote the development of the 'Bamboo as a Substitute for Steel' proposal, rotary cut bamboo veneers were applied to prepare a kind of high-strength laminated bamboo composite, which was achieved through the hot press molding method in this study. Orthogonal experiments of L9 (33) were performed, with hot-pressing temperature, pressure, and time considered as three influencing factors. Physical properties like density and moisture content, and mechanical properties like modulus of rupture (MOR), modulus of elasticity (MOE), shear strength, and compressive strength were tested for the samples. It can be obtained from the results of range analysis and ANOVA that higher density and lower moisture content were correlated with higher mechanical strength. Within the selected range of tested factors, a hot-pressing temperature and time of 150 °C and 10 min can contribute to higher density and lower moisture content, and the combination of 150 °C and 50 MPa can produce greater mechanical strength. In the thickness direction, the laminated bamboo composites displayed a notable compressed structure.

MOCS, a novel classifier system integrated multimoics analysis refining molecular subtypes and prognosis for skin melanoma.

PURPOSE: The present investigation focuses on Skin Cutaneous Melanoma (SKCM), a melanocytic carcinoma characterized by marked aggression, significant heterogeneity, and a complex etiological background, factors which collectively contribute to the challenge in prognostic determinations. We defined a novel classifier system specifically tailored for SKCM based on multiomics. METHODS: We collected 423 SKCM samples with multi omics datasets to perform a consensus cluster analysis using 10 machine learning algorithms and verified in 2 independent cohorts. Clinical features, biological characteristics, immune infiltration pattern, therapeutic response and mutation landscape were compared between subtypes. RESULTS: Based on consensus clustering algorithms, we identified two Multi-Omics-Based-Cancer-Subtypes (MOCS) in SKCM in TCGA project and validated in GSE19234 and GSE65904 cohorts. MOCS2 emerged as a subtype with poor prognosis, characterized by a complex immune microenvironment, dysfunctional anti-tumor immune state, high cancer stemness index, and genomic instability. MOCS2 exhibited resistance to chemotherapy agents like erlotinib and sunitinib while sensitive to rapamycin, NSC87877, MG132, and FH355. Additionally, ELSPBP1 was identified as the target involving in glycolysis and M2 macrophage infiltration in SKCM. CONCLUSIONS: MOCS classification could stably predict prognosis of SKCM; patients with a high cancer stemness index combined with genomic instability may be predisposed to an immune exhaustion state.Communicated by Ramaswamy H. Sarma.

Kisspeptin-10 binding to Gpr54 in osteoclasts prevents bone loss by activating Dusp18-mediated dephosphorylation of Src.

Osteoclasts are over-activated as we age, which results in bone loss. Src deficiency in mice leads to severe osteopetrosis due to a functional defect in osteoclasts, indicating that Src function is essential in osteoclasts. G-protein-coupled receptors (GPCRs) are the targets for ∼35% of approved drugs but it is still unclear how GPCRs regulate Src kinase activity. Here, we reveal that GPR54 activation by its natural ligand Kisspeptin-10 (Kp-10) causes Dusp18 to dephosphorylate Src at Tyr 416. Mechanistically, Gpr54 recruits both active Src and the Dusp18 phosphatase at its proline/arginine-rich motif in its C terminus. We show that Kp-10 binding to Gpr54 leads to the up-regulation of Dusp18. Kiss1, Gpr54 and Dusp18 knockout mice all exhibit osteoclast hyperactivation and bone loss, and Kp-10 abrogated bone loss by suppressing osteoclast activity in vivo. Therefore, Kp-10/Gpr54 is a promising therapeutic target to abrogate bone resorption by Dusp18-mediated Src dephosphorylation.

ImageCAS: A large-scale dataset and benchmark for coronary artery segmentation based on computed tomography angiography images.

Cardiovascular disease (CVD) accounts for about half of non-communicable diseases. Vessel stenosis in the coronary artery is considered to be the major risk of CVD. Computed tomography angiography (CTA) is one of the widely used noninvasive imaging modalities in coronary artery diagnosis due to its superior image resolution. Clinically, segmentation of coronary arteries is essential for the diagnosis and quantification of coronary artery disease. Recently, a variety of works have been proposed to address this problem. However, on one hand, most works rely on in-house datasets, and only a few works published their datasets to the public which only contain tens of images. On the other hand, their source code have not been published, and most follow-up works have not made comparison with existing works, which makes it difficult to judge the effectiveness of the methods and hinders the further exploration of this challenging yet critical problem in the community. In this paper, we propose a large-scale dataset for coronary artery segmentation on CTA images. In addition, we have implemented a benchmark in which we have tried our best to implement several typical existing methods. Furthermore, we propose a strong baseline method which combines multi-scale patch fusion and two-stage processing to extract the details of vessels. Comprehensive experiments show that the proposed method achieves better performance than existing works on the proposed large-scale dataset. The benchmark and the dataset are published at https://github.com/XiaoweiXu/ImageCAS-A-Large-Scale-Dataset-and-Benchmark-for-Coronary-Artery-Segmentation-based-on-CT.