IDENTIFICATION AND RECOGNIZATION OF BAMBOO BASED ON CROSS-SECTIONAL IMAGES USING COMPUTER VISION

Identification of bamboo is of great importance to its conservation and uses. However, identify bamboo manually is complicated, expensive, and time-consuming. Here, we analyze the most evident and characteristic anatomical elements of cross section images, that’s a particularly vital breakthrough point. Meanwhile, we present a novel approach with respect to the automatic identification of bamboo on the basis of the cross-sectional images through computer vision.Two diverse transfer learning strategies were applied for the learning process, namely fine-tuning with fully connected layers and all layers, the results indicated that fine-tuning with all layers being trained with the dataset consisting of cross-sectional images of bamboo is an effective tool to identify and recognize intergenericbamboo, 100% accuracy on the training dataset was achieved while 98.7% accuracy was output on the testing dataset, suggesting the proposed method is quite effective and feasible, it’s beneficial to identify bamboo and protect bamboo in coutilization. More collection of bamboo species in the dataset in the near futuremight make EfficientNet more promising for identifying bamboo.  

Fiber Characteristics and Mechanical Properties of <i>Oxytenanthera abyssinica</i>

Unlike the culm hollow structure of most bamboo species, Oxytenanthera abyssinica has a unique solid or semi-solid culm, which may endow it with superior mechanical performance. In this study, the variation in fiber morphology and micro-mechanical properties across the radial regions of bamboo culm was examined by optical microscopy, scanning electron microscopy, X-ray diffraction, and nanoindentation. Results showed that the mean values of vascular bundle frequency and fiber tissue proportion were 1.76 pcs/mm2 and 21.04%, respectively, both of which increased gradually from inner to outer. The mean length, diameter, and length-diameter ratio of the fiber were 2.10 mm, 21.54 μm, and 101.41 respectively. The mean indentation modulus of elasticity (IMOE) and hardness were 21.34 GPa and 545.88 MPa. The IMOE exhibited a significant increase from the inner to the middle region, and little change was observed from the middle to the outer region. There were slight fluctuations in hardness along the radial direction. The mean crystallinity and microfibril angle(MFA) of the fibers was 68.12% and 11.26 degrees, respectively. There is a positive correlation between cellulose crystallinity and the IMOE and hardness, while there is a negative correlation between the MFA and the IMOE and the hardness

Bamboo Structure and Its Impact on Mechanical Properties: A Case Study of <i>Bambusa arundinaceae</i>

Bamboo is a naturally occurring composite material, which exhibits a decomposable structure with varying composition. The distinct structural features of bamboo contribute to its exceptional strength and flexibility, making it an excellent choice for construction purposes. However, only a limited portion of bamboo species has been studied for its mechanical properties, and research on Bambusa arundinaceae has primarily focused on its pharmaceutical values. Therefore, we investigated the relationship between the structural characteristics of B. arundinaceae and its mechanical properties using axial compression experiments and tangential bending experiments. The results showed that the distribution density of vascular bundles (VBs) of B. arundinaceae ranged from 1.98 to 4.34 pcs/mm2,while the volume fraction of fiber sheaths (FSs) ranged from 35.82 to 42.58%. The average compressive strength, flexural strength, and flexural elasticity modulus were 113.99 MPa, 239.07 MPa, and 17.39 GPa, which were 97.56%, 64.07%, and 66.09% higher than those of moso bamboo (Phyllostachys edulis), respectively. The compressive strength, flexural strengths, and elasticity modulus of B. arundinaceae were positively correlated with both the distribution density of VBs and the volume fraction of FSs. These insights are crucial for the advancement of durable and efficient materials in diverse sectors including construction and manufacturing

Transcriptomic characterization revealed that METTL7A inhibits melanoma progression via the p53 signaling pathway and immunomodulatory pathway

METTL7A is a protein-coding gene expected to be associated with methylation, and its expression disorder is associated with a range of diseases. However, few research have been carried out to explore the relationship between METTL7A and tumor malignant phenotype as well as the involvement potential mechanism. We conducted our research via a combination of silico analysis and molecular biology techniques to investigate the biological function of METTL7A in the progression of cancer. Gene expression and clinical information were extracted from the TCGA database to explore expression variation and prognostic value of METTL7A. In vitro, CCK8, transwell, wound healing and colony formation assays were conducted to explore the biological functions of METT7A in cancer cell. GSEA was performed to explore the signaling pathway involved in METTL7A and validated via western blotting. In conclusion, METTL7A was downregulated in most cancer tissues and its low expression was associated with shorter overall survival. In melanoma, METTL7A downregulation was associated with poorer clinical staging, lower levels of TIL infiltration, higher IC50 levels of chemotherapeutic agents, and poorer immunotherapy outcomes. QPCR results confirm that METTL7A is down-regulated in melanoma cells. Cell function assays showed that METTL7A knockdown promoted proliferation, invasion, migration and clone formation of melanoma cells. Mechanistic studies showed that METTL7A inhibits tumorigenicity through the p53 signaling pathway. Meanwhile, METTL7A is also a potential immune regulatory factor

Table_1_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.docx

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p

Table_2_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.docx

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p

Image_2_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.tif

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p

Image_1_Vemurafenib inhibits immune escape biomarker BCL2A1 by targeting PI3K/AKT signaling pathway to suppress breast cancer.tif

ObjectivesTo investigate the role of immune escape encoding genes on the prognosis of BC, and to predict the novel targeting agents.MethodsHuman immune genes and immune escape encoding genes were obtained from the IMMPORT database and the previous study. Sample information and clinical data on BC were obtained from the TCGA and GTEX databases. Obtaining differentially expressed protein data from cBioportal database. To construct a risk score model by lasso analysis, and nomogram was used to predict score core. GSCA, TIMER and CELLMINER databases were used for immune and drug susceptibility correlation analyses. Cell experiments were verified by MTT, Western blotting, and RT-qPCR.ResultsWe found prognostic models consisting of eleven immune escape related protein-coding genes with ROC curves that performed well in the ontology data (AUC for TCGA is 0.672) and the external data (AUC for GSE20685 is 0.663 and for GES42568 is 0.706). Five core prognostic models are related to survival (EIF4EBP1, BCL2A1, NDRG1, ERRFI1 and BRD4) were summarized, and a nomogram was constructed to validate a C-index of 0.695, which was superior to other prognostic models. Relevant drugs targeting core genes were identified based on drug sensitivity analysis, and found that Vemurafenib downregulates the PI3K-AKT pathway and BCL2A1 protein in BC, as confirmed by external data and cellular assays.ConclusionsBriefly, our work establishes and validates an 11-immune escape risk model, and five core prognostic factors that are mined deeply from this model, and elucidates in detail that Vemurafenib suppresses breast cancer by targeting the PI3K/AKT signaling pathway to inhibit the immune escape biomarker BCL2A1, confirms the validity of the prognostic model, and provides corresponding targeted agents to guide individualized treatment of BC patients.</p