The simulation analysis of contact characteristics of biomimetic flexible surfaces

Based on the foot structure of the climbing biology and multivariate coupling bionic technology, the bionic flexible convex surface was designed and a 3D model was created using the digital modeling software. Finite Element Analysis software was used for contacting analysis to the bionic flexible convex foot structure in the state of dry friction and wet adhesion, and then studied frictional contact performance. The results of Finite Element Analysis shows that the contact stress of the convex is much larger than the stress of the area around it in the dry friction state and the deformation is mainly concentrated in the convex’s top. The friction between the hemispherical convex surface and the contact surface is the maximum and the cylindrical convex surface is the minimum. The friction between the bionic flexible convex structure and the solid contact surface in wet adhesion state is larger than dry state.Keywords: Bionic, flexible, contact, finite element, wet adhesio

Dark solitons manipulation using optical event horizon

We demonstrate that the optical event horizon can provide an effective technique to actively control the propagation properties of a dark soliton with another weak probe wave. Careful power adjustment of the probe wave enables the black soliton converted into a gray one with varying grayness through the nonlinear interaction, corresponding to a nearly adiabatic variation of the soliton’s speed. The sign of the phase angle for the newly formed gray soliton at optical event horizon is significantly dependent on the frequency of the launched probe wave. Linear-stability analysis of dark solitons under the perturbation of a weak probe wave is performed to clarify the intrinsic mechanism of the nonlinear interaction. The probe wave manipulated collisional dynamics between both dark solitons are investigated as an analogue of the combined white-hole and black-hole horizons which provides some insights into exploring the transition between integrable and non-integrable systems

Active control of adiabatic soliton fission by external dispersive wave at optical event horizon

We show that the group-velocity-led optical event horizon (OEH) in optical fibers provides a convenient way to actively control the propagation property of higher-order solitons by a comparatively weak dispersive wave (DW) pulse. It has been found numerically that clean soliton breakup, a process by which a second-order soliton completely splits into a pair of constituent solitons with vastly different power proportions after interacting with the weak DW pulse, will occur while external DWs become polychromatic. The temporal separation between both constituent solitons can be controlled by adjusting the power of the external DW. The more energetic main soliton is advanced/trailed in time depending on the selected frequency of input DW pulse. We have developed an analytic formalism describing the external acting-force (AF) perturbation. These results provide a fundamental explanation and physical scaling of optical pulse evolution in optical fibers and can find applications in improved supercontinuum sources

DeepMerge: Deep-Learning-Based Region-Merging for Image Segmentation

Image segmentation aims to partition an image according to the objects in the scene and is a fundamental step in analysing very high spatial-resolution (VHR) remote sensing imagery. Current methods struggle to effectively consider land objects with diverse shapes and sizes. Additionally, the determination of segmentation scale parameters frequently adheres to a static and empirical doctrine, posing limitations on the segmentation of large-scale remote sensing images and yielding algorithms with limited interpretability. To address the above challenges, we propose a deep-learning-based region merging method dubbed DeepMerge to handle the segmentation of complete objects in large VHR images by integrating deep learning and region adjacency graph (RAG). This is the first method to use deep learning to learn the similarity and merge similar adjacent super-pixels in RAG. We propose a modified binary tree sampling method to generate shift-scale data, serving as inputs for transformer-based deep learning networks, a shift-scale attention with 3-Dimension relative position embedding to learn features across scales, and an embedding to fuse learned features with hand-crafted features. DeepMerge can achieve high segmentation accuracy in a supervised manner from large-scale remotely sensed images and provides an interpretable optimal scale parameter, which is validated using a remote sensing image of 0.55 m resolution covering an area of 5,660 km^2. The experimental results show that DeepMerge achieves the highest F value (0.9550) and the lowest total error TE (0.0895), correctly segmenting objects of different sizes and outperforming all competing segmentation methods

CDC42—A promising immune-related target in glioma

Glioma is the worst prognostic neoplasm in the central nervous system. A polarity-regulating GTPase in cells, known as cell division cycle 42 (CdC42), has been proven to have its overactivation tightly connected to high tumor malignancy. The RNA-seq and protein expression of CDC42 in tumor and comparison tissues were analyzed based on the online tools; CDC42 was remarkably boosted in tumor tissues compared to normal controls. A total of 600 patients in the analysis set from The Cancer Genome Atlas (TCGA) database and 657 patients in the validation set from the Chinese Glioma Genome Atlas (CGGA) database were adopted. The expression of CDC42 in clinical features and biological functions of glioma was analyzed, including differential expression analysis, survival analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and immune infiltration analysis. The enrichment of CDC42 was shown to be strongly associated with poor prognosis and terrible clinical indexes of glioma, including higher World Health Organization scale grade, wild-type isocitrate dehydrogenase 1 expression, O6-methylguanine-DNA methyltransferase non-methylated status, and 1p19q non-codeletion status (p < 0.0001). Functional enrichment analysis showed that CDC42 was highly correlated with immune and inflammatory responses in glioma. Additionally, the concentration extent of CDC42 was closely related to immune infiltration, immune checkpoints, and regulatory T (Treg) cell markers (CD4, CD25, and CD127). All evidence suggested that CDC42 may be a potential target for glioma immunotherapy

Multi-omics analyses of glucose metabolic reprogramming in colorectal cancer

BackgroundGlucose metabolic reprogramming (GMR) is a cardinal feature of carcinogenesis and metastasis. However, the underlying mechanisms have not been fully elucidated. The aim of this study was to profile the metabolic signature of primary tumor and circulating tumor cells from metastatic colorectal cancer (mCRC) patients using integrated omics analysis.MethodsPET-CT imaging, serum metabolomics, genomics and proteomics data of 325 high 18F-fluorinated deoxyglucose (FDGhigh) mCRC patients were analyzed. The para-tumor, primary tumor and liver metastatic tissues of mCRC patients were used for proteomics analysis.ResultsThe glucose uptake in tumor tissues as per the PET/CT images was correlated to serum levels of glutamic-pyruvic transaminase (ALT), total bilirubin (TBIL), creatinine (CRE). Proteomics analysis indicated that several differentially expressed proteins were enriched in both GMR and epithelial-mesenchymal transition (EMT)-related pathways. Using a tissue-optimized proteomic workflow, we identified novel proteomic markers (e.g. CCND1, EPCAM, RPS6), a novel PCK1-CDK6-INSR protein axis, and a potential role for FOLR (FR) in GMR/EMT of CRC cells. Finally, CEA/blood glucose (CSR) was defined as a new index, which can be used to jointly diagnose liver metastasis of colorectal cancer.ConclusionsGMR in CRC cells is closely associated with the EMT pathway, and this network is a promising source of potential therapeutic targets

LncRNA SENCR suppresses abdominal aortic aneurysm formation by inhibiting smooth muscle cells apoptosis and extracellular matrix degradation

Abdominal aortic aneurysm (AAA) is a progressive chronic dilatation of the abdominal aorta without effective medical treatment. This study aims to clarify the potential of long non-coding RNA SENCR as a treatment target in AAA. Angiotensin II (Ang-II) was used to establish AAA model in vitro and in vivo. Reverse transcription quantitative PCR and western blot were performed to measure the expression of SENCR and proteins, respectively. Annexin V-FITC/PI double staining was carried out to detect the apoptotic rate in vascular smooth muscle cells (VSMCs), and cell apoptosis in aortic tissues was determined by TUNEL staining. Besides, hematoxylin and eosin and Elastica van Gieson staining were performed for histological analysis of aortic tissues. SENCR was downregulated in AAA tissues and Ang-II-stimulated VSMCs. Overexpression of SENCR could inhibit Ang-II-induced VSMC apoptosis, while inhibition of SENCR facilitated Ang-II-induced VSMC apoptosis. Moreover, the expression of matrix metalloproteinase (MMP)-2 and MMP-9 in Ang-II-induced VSMCs was reduced following SENCR overexpression, while tissue inhibitor of metalloproteinases 1 (TIMP-1) expression was increased. In vivo, overexpression of SENCR improved the pathological change in aortic tissues and the damage in arterial wall elastic fibers induced by Ang-II, as well as suppressed Ang-II-induced cell apoptosis and extracellular matrix degradation. Overall, SENCR was decreased in AAA. Overexpression of SENCR inhibited AAA formation via inhibition of VSMC apoptosis and extracellular matrix degradation. We provided a reliable evidence for SENCR acting as a potential target for AAA treatment

Mapping blue-ice areas in Antarctica using ETM+ and MODIS data

AbstractBlue-ice areas (BIAs) and their geographical distribution in Antarctica were mapped using Landsat-7 ETM+ images with 15 m spatial resolution obtained during the 1999–2003 austral summers and covering the area north of 82.5° S, and a snow grain-size image of the MODIS-based Mosaic of Antarctica (MOA) dataset with 125 m grid spacing acquired during the 2003/04 austral summer from 82.5°S to the South Pole. A map of BIAs was created with algorithms of thresholds based on band ratio and reflectance for ETM+ data and thresholds based on snow grain size for the MOA dataset. The underlying principle is that blue ice can be separated from snow or rock by their spectral discrepancies and by different grain sizes of snow and ice. We estimate the total area of BIAs in Antarctica during the data acquisition period is 234 549 km2, or 1.67% of the area of the continent. Blue ice is scattered widely over the continent but is generally located in coastal or mountainous regions. The BIA dataset presented in this study is the first map covering the entire Antarctic continent sourced solely from ETM+ and MODIS data. This dataset can potentially benefit other studies in glaciology, meteorology, climatology and paleoclimate, meteorite collection and airstrip site selection.</jats:p