Effect of AFM nanoindentation loading rate on the characterization of mechanical properties of vascular endothelial cell

Vascular endothelial cells form a barrier that blocks the delivery of drugs entering into brain tissue for central nervous system disease treatment. The mechanical responses of vascular endothelial cells play a key role in the progress of drugs passing through the blood–brain barrier. Although nanoindentation experiment by using AFM (Atomic Force Microscopy) has been widely used to investigate the mechanical properties of cells, the particular mechanism that determines the mechanical response of vascular endothelial cells is still poorly understood. In order to overcome this limitation, nanoindentation experiments were performed at different loading rates during the ramp stage to investigate the loading rate effect on the characterization of the mechanical properties of bEnd.3 cells (mouse brain endothelial cell line). Inverse finite element analysis was implemented to determine the mechanical properties of bEnd.3 cells. The loading rate effect appears to be more significant in short-term peak force than that in long-term force. A higher loading rate results in a larger value of elastic modulus of bEnd.3 cells, while some mechanical parameters show ambiguous regulation to the variation of indentation rate. This study provides new insights into the mechanical responses of vascular endothelial cells, which is important for a deeper understanding of the cell mechanobiological mechanism in the blood–brain barrier

Bioinformatic Analysis of Potential Key Genes in Castration-resistant Prostate Cancer Development

BackgroundCastration-resistant prostate cancer (CRPC) is one of the most prevalent cancers in males with a high fatality rate. Its molecular mechanism is still unclear, and there is no effective treatment.ObjectiveTo explore the key genes involved in CRPC development using bioinformatic analysis, offering new ideas for the diagnosis and treatment of CRPC.MethodsThe data set GSE32269 which contains human primary prostate cancer and CRPC was downloaded from the Gene Expression Omnibus database for further bioinformatic analysis. R language was used to identify differentially expressed genes (DEGs) in CRPC. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were further performed by using DAVID. A protein-protein interaction (PPI) network of DEGs was constructed by using STRING database for screening potential key genes. And the identified potential key genes were further analyzed by survival analysis and receiver operating characteristic (ROC) curve analysis.Results279 DEGs were identified in microarray dataset GSE32269. GO enrichment analysis and KEGG pathway analysis revealed that cell division, mitosis and cell cycle signaling pathways may play an important role in the development of CRPC. PPI network screening revealed that there were 15 potential key genes, among which CDC20, MAD2L1 and NUSAP1 expressed differentially in CRPC patients: those with highly expressed CDC20, MAD2L1 and NUSAP1 had statistically lower overall survival rate and disease-free survival rate than did those with low expressed CDC20, MAD2L1 and NUSAP1 (P<0.05) . The area under the ROC curve of CDC20, MAD2L1 and NUSAP1 to predict the occurrence of CRPC were 0.933, 0.762, and 0.950, respectively, indicating that each of them may have a high diagnostic value for CRPC.ConclusionCDC20, MAD2L1 and NUSAP1 may be key candidate genes associated with the development of CRPC

HIV-1 can infect northern pig-tailed macaques (Macaca leonina) and form viral reservoirs in vivo

Viral reservoirs of HIV-1 are a major obstacle for curing AIDS. The novel animal models that can be directly infected with HIV-1 will contribute to develop effective strategies for eradicating infections. Here, we inoculated 4 northern pig-tailed macaques (NPM) with the HIV-1 strain HIV-1NL4.3 and monitored the infection for approximately 3 years (150 weeks). The HIV-1-infected NPMs showed transient viremia for about 10 weeks after infection. However, cell-associated proviral DNA and viral RNA persisted in the peripheral blood and lymphoid organs for about 3 years. Moreover, replication-competent HIV-1 could be successfully recovered from peripheral blood mononuclear cells (PBMCs) during long-term infection. The numbers of resting CD4+ T cells in HIV-1 infected NPMs harboring proviruses fell within a range of 2- to 3-log10 per million cells, and these proviruses could be reactivated both ex vivo and in vivo in response to co-stimulation with the latency-reversing agents JQ1 and prostratin. Our results suggested that NPMs can be infected with HIV-1 and a long-term viral reservoir was formed in NPMs, which might serve as a potential model for HIV-1 reservoir research

Gate-tunable Topological Valley Transport in Bilayer Graphene

Valley pseudospin, the quantum degree of freedom characterizing the degenerate valleys in energy bands, is a distinct feature of two-dimensional Dirac materials. Similar to spin, the valley pseudospin is spanned by a time reversal pair of states, though the two valley pseudospin states transform to each other under spatial inversion. The breaking of inversion symmetry induces various valley-contrasted physical properties; for instance, valley-dependent topological transport is of both scientific and technological interests. Bilayer graphene (BLG) is a unique system whose intrinsic inversion symmetry can be controllably broken by a perpendicular electric field, offering a rare possibility for continuously tunable valley-topological transport. Here, we used a perpendicular gate electric field to break the inversion symmetry in BLG, and a giant nonlocal response was observed as a result of the topological transport of the valley pseudospin. We further showed that the valley transport is fully tunable by external gates, and that the nonlocal signal persists up to room temperature and over long distances. These observations challenge contemporary understanding of topological transport in a gapped system, and the robust topological transport may lead to future valleytronic applications

Zachowania we współpracy chińskich MŚP z perspektywy dostawców

During the economic downturn, the cooperative behavior of the supply chain will change due to the increase of risk level. The purpose of this paper is to measure cooperative behavior more effectively. Therefore, a model of supply chain cooperation is constructed from three dimensions: risk perception, inter-organizational trust and decision preference. Taking the processing trade enterprises in Guangdong Province as a sample and using the structural equation model for analysis, the research found that trust and decision preferences significantly impact cooperative behavior, and decision-making preference partially mediates the relationship between inter-organizational trust and cooperative behavior. However, risk perception has no direct impact on cooperation behavior but has an indirect impact through the mediation of inter-organizational trust and decision-making preferences. Interorganizational trust is partially intervened between risk perception and decision preference. Further, the mediating effect of the inter-organizational trust alone is greater than the dual mediating effect of inter-organizational trust and decision preferences.W okresie spowolnienia gospodarczego zachowanie kooperacyjne łańcucha dostaw ulegnie zmianie ze względu na wzrost poziomu ryzyka. Celem tego artykułu jest skuteczniejszy pomiar zachowań kooperacyjnych. Dlatego model współpracy w łańcuchu dostaw zbudowany jest z trzech wymiarów: percepcji ryzyka, zaufania między organizacjami i preferencji decyzyjnych. Biorąc za próbę przedsiębiorstwa zajmujące się handlem przetwórstwem w prowincji Guangdong i wykorzystując do analizy model równań strukturalnych, badania wykazały, że zaufanie i preferencje decyzyjne mają istotny wpływ na zachowanie kooperacyjne, a preferencje decyzyjne częściowo pośredniczą w związku między zaufanie i współpraca. Jednak postrzeganie ryzyka nie ma bezpośredniego wpływu na zachowanie w zakresie współpracy, ale pośrednio, poprzez pośrednictwo zaufania między organizacjami i preferencji decyzyjnych. Zaufanie między organizacjami jest częściowo interweniowane między percepcją ryzyka a preferencjami decyzyjnymi. Co więcej, efekt mediacyjny samego zaufania międzyorganizacyjnego jest większy niż podwójny efekt mediacyjny zaufania międzyorganizacyjnego i preferencji decyzyjnych

Tourist Satisfaction Enhancement Using Mobile QR Code Payment: An Empirical Investigation

Innovative technologies have greatly changed people’s lives, including their travel experiences. This study investigates the antecedents and outcomes of the quick response (QR) code payment technology used in tourism to provide empirical evidence that mobile technologies can be used to enhance tourist satisfaction. An empirical analysis using 247 field survey responses reveals that relative advantage, compatibility, and observability innovation attributes significantly affect tourists’ attitudes positively toward QR code payment services, which results in their use of the technology while traveling. However, image—the subjective norm in innovation diffusion—has no effect on such use. Furthermore, the study confirms that the use of the QR code payment technology in tourism influences an individual’s transaction satisfaction and travel satisfaction, suggesting that this technology can be used to advance the tourism industry. Theoretical and practical implications of the findings and future research directions are also discussed

Research on Classification of Water Stress State of Plant Electrical Signals Based on PSO-SVM

Plant electrical signals are physiological signals within the plant body that respond to both external and internal stimuli. Using plant electrical signals as an effective indicator for evaluating the plant growth status is a new theory and method to study the relationship between environmental factors affecting plant growth and plant growth responses. This novel proposed approach is advantageous in terms of response sensitivity and accuracy. Therefore, automation and intelligence of agricultural plant cultivation can be realized and implemented by monitoring the changes in the patterns of plant electrical signals. This paper investigated rapeseed plants in three groups and evaluated the effect of soil water content as the controlled environmental variable. The plant electrical signals under different soil water contents were collected for wavelet packet noise reduction processing. The plant electrical signals were analyzed from three aspects: time domain, frequency domain, and wavelet packet decomposition. The mean, root mean square, standard deviation in the time domain, the power spectral entropy (PSE) of the centroid frequency (SCF) in the frequency domain, and the electrical signal energy in the wavelet packet decomposition were used as the eigenvectors required for classification. The external plant morphological data and the rapeseed growth under different soil water contents were collected to establish plant water stress evaluation indexes. By this, the optimal water demand gradient for rapeseed growth was obtained. The plant water stress evaluation index was used to verify the classification effect of electrical signals, combined with the plants’ electrical signal changes under different water stress conditions to comprehensively evaluate the growth status of plants under different soil water contents. Finally, a support vector machine (SVM) and a particle swarm optimized support vector machine (PSO-SVM) were used to classify the water stress status of plants and establish prediction model the relationship between plant growth status and plant electrical signals under different soil water contents. The results showed that the plant water stress state classification model accuracy based on SVM was 90.83%, and the mean square error MSE was 0.175, while the accuracy of the plant water stress state classification model based on PSO-SVM was 94.3167%, and the mean square error was 0.1646. The classification experiments results show that the water stress of plants and the classification of plant growth status can be realized utilizing electrical signal analysis, with the support vector machine classification model after particle swarm optimization being more accurate. This method lays the foundation for the realization of automation in agricultural plant cultivation and monitoring through plant electrical signals