l-connectivity, l-edge-connectivity and spectral radius of graphs

Let G be a connected graph. The toughness of G is defined as t(G)=min{\frac{|S|}{c(G-S)}}, in which the minimum is taken over all proper subsets S\subset V(G) such that c(G-S)\geq 2 where c(G-S) denotes the number of components of G-S. Confirming a conjecture of Brouwer, Gu [SIAM J. Discrete Math. 35 (2021) 948--952] proved a tight lower bound on toughness of regular graphs in terms of the second largest absolute eigenvalue. Fan, Lin and Lu [European J. Combin. 110 (2023) 103701] then studied the toughness of simple graphs from the spectral radius perspective. While the toughness is an important concept in graph theory, it is also very interesting to study |S| for which c(G-S)\geq l for a given integer l\geq 2. This leads to the concept of the l-connectivity, which is defined to be the minimum number of vertices of G whose removal produces a disconnected graph with at least l components or a graph with fewer than l vertices. Gu [European J. Combin. 92 (2021) 103255] discovered a lower bound on the l-connectivity of regular graphs via the second largest absolute eigenvalue. As a counterpart, we discover the connection between the l-connectivity of simple graphs and the spectral radius. We also study similar problems for digraphs and an edge version

STUDY ON MECHANICAL BEHAVIOR OF CABLE - STAYED BRIDGE SUPPORT SYSTEM IN MULTI - FULCRUM UNBALANCED ROTATION

With the maturity and wide application of the bridge rotation construction technology, the single-fulcrum spherical hinge balance rotation can not meet the need of crossing over the high-speed railway catenary and other obstacles, so the unbalanced rotation construction is often needed. In order to ensure the stability and safety of the unbalanced rotation process, a multi-pivot rotation method is proposed. In this paper, the railway cable-stayed bridge over Harbin West Avenue is taken as the research object, and the multi-fulcrum rotating construction method over the metal contact network is adopted. The Abaqus finite element model is established, the influence of different rotation angular velocity, friction coefficient of slideway and position of support foot on the force of support system in the course of rotation is studied. The results show that, compared with the traditional single-pivot rotation, the force on the multi-pivot rotation support foot becomes the main force component, and the force on the spherical hinge decreases. The rotation angular velocity is positively correlated with Mises stress of the support foot and the spherical hinge. The friction coefficient of the slideway has a great influence on the force of the support foot. When the friction coefficient of the slideway changes in order of 0.02,0.04,0.06,0.08 and 0.1, the friction stress of the outer edge of the support foot increases linearly. Considering the force of spherical hinge and support foot, the best position of supporting foot is 7.3 m from the center of spherical hinge. The research in this paper can be used for reference in the future multi-pivot unbalanced rotation construction

The Functions and Composition of Board of Directors : From the Perspectives of Agency Theory and Resource Dependence Theory

departmental bulletin pape

Defect identification in adhesive structures using multi-Feature fusion convolutional neural network

The interface-debonding defects of adhesive bonding structures may cause a reduction in bonding strength, which in turn affects the bonding quality of adhesive bonding samples. Hence, defect recognition in adhesive bonding structures is particularly important. In this study, a terahertz (THz) wave was used to analyze bonded structure samples, and a multi-feature fusion convolutional neural network (CNN) was used to identify the defect waveforms. The pooling method of the squeeze-and-excitation (SE) attention mechanism was optimized, defect feature weights were adaptively assigned, and feature fusion was conducted using automatic label net-works to segment the THz waveforms in the adhesive bonding area with fine granularity waveforms as an input to the multi-channel CNN. The results revealed that the speed of the THz waveform labeling with the automatic labeling network was 10 times higher than that with traditional methods, and the defect-recognition accuracy of the defect-recognition network constructed in this study was up to 99.28%. The F1-score was 99.73%, and the lowest pre-embedded defect recognition error rate of the generalization experiment samples was 0.27%

Two simple assays for assessing the seeding activity of proteopathic tau

The regional distribution of neurofibrillary tangles of hyperphosphorylated tau aggregates is associated with the progression of Alzheimer’s disease (AD). Misfolded proteopathic tau recruits naïve tau and templates its misfolding and aggregation in a prion-like fashion, which is believed to be the molecular basis of propagation of tau pathology. A practical way to assess tau seeding activity is to measure its ability to recruit/bind other tau molecules and to induce tau aggregation. Based on the properties of proteopathic tau, here we report the development of two simple assays to assess tau seeding activity ----- capture assay in vitro and seeded-tau aggregation assay in cultured cells. In the capture assay, proteopathic tau was applied onto a nitrocellulose membrane and the membrane was incubated with cell lysate containing HA-tagged tau151-391 (HA-tau151-391). The captured tau on the membrane was determined by immuno-blots developed with anti-HA. For the seeded-tau aggregation assay, HEK-293FT cells transiently expressing HA-tau151-391 were treated with proteopathic tau in the presence of Lipofectamine 2000 and then lysed with RIPA buffer. RIPA-insoluble fraction containing aggregated tau was obtained by ultracentrifugation and analyzed by immuno-blot developed with anti-HA. To validate these two assays, we assessed the seeding activity of tau in the middle frontal gyrus, middle temporal gyrus and basal forebrain of AD and control brains and found that AD, but not control, brain extracts effectively captured and seeded tau151-391 aggregation. Basal forebrain contained less phospho-tau and tau seeding activity. The levels of captured tau or seeded-tau aggregates were positively correlated to the levels of phospho-tau, Braak stages and tangle sores. These two assays are specific and sensitive and can be carried out in a regular biomedical laboratory setting by using routine biochemical techniques

The lncRNA ADAMTS9-AS2 Regulates RPL22 to Modulate TNBC Progression via Controlling the TGF-β Signaling Pathway

BackgroundLong non-coding RNAs (lncRNAs) are key regulators of triple-negative breast cancer (TNBC) progression, but further work is needed to fully understand the functional relevance of these non-coding RNAs in this cancer type. Herein, we explored the functional role of the lncRNA ADAMTS9-AS2 in TNBC.MethodsNext-generation sequencing was conducted to compare the expression of different lncRNAs in TNBC tumor and paracancerous tissues, after which ADAMTS9-AS2differential expression in these tumor tissues was evaluated via qPCR. The functional role of this lncRNA was assessed by overexpressing it in vitro and in vivo. FISH and PCR were used to assess the localization of ADAMTS9-AS2within cells. Downstream targets of ADAMTS9-AS2 signaling were identified via RNA pulldown assays and transcriptomic sequencing.ResultsThe expression ofADAMTS9-AS2 was decreased in TNBC tumor samples (P < 0.05), with such downregulation being correlated with TNM stage, age, and tumor size. Overexpressing ADAMTS9-AS2 promoted the apoptotic death and cell cycle arrest of tumor cells in vitro and inhibited tumor growth in vivo. From a mechanistic perspective, ADAMTS9-AS2 was found to control the expression of RPL22 and to thereby modulate TGF-β signaling to control TNBC progression.ConclusionADAMTS9-AS2 controls the expression of RPL22 and thereby regulates TNBC malignancy via the TGF-β signaling pathway