Cryptopleurine Targets NF-κB Pathway, Leading to Inhibition of Gene Products Associated with Cell Survival, Proliferation, Invasion, and Angiogenesis

Cryptopleurine, a phenanthroquinolizidine alkaloid, was known to exhibit anticancer activity; however, the underlying mechanism is poorly understood. Because the nuclear factor-κB (NF-κB) transcription factors control many physiological processes including inflammation, immunity, and development and progression of cancer, we investigated the effects of cryptopleurine on tumor necrosis factor alpha (TNF-α)-induced NF-κB activation pathway and on the expression of NF-κB-regulated gene products associated with many pathophysiological processes.MDA-MB231, MDA-MB435, MCF-7, HEK293, RAW264.7 and Hep3B cells were used to examine cryptopleurine's effect on the NF-κB activation pathway. Major assays were promoter-reporter gene assay, electrophoretic mobility shift assay (EMSA), in vitro immune complex kinase assay, real-time PCR, Western blot analysis, and Matrigel invasion assay. Experiments documenting cell proliferation and apoptosis were analyzed by MTT method and flow cytometry, respectively. The results indicated that cryptopleurine suppressed the NF-κB activation through the inhibition of IκB kinase (IKK) activation, thereby blocking the phosphorylation and degradation of the inhibitor of NF-κB alpha (IκBα) and the nuclear translocation and DNA-binding activity of p65. The suppression of NF-κB by cryptopleurine led to the down-regulation of gene products involved in inflammation, cell survival, proliferation, invasion, and angiogenesis.Our results show that cryptopleurine inhibited NF-κB activation pathway, which leads to inhibition of inflammation, proliferation, and invasion, as well as potentiation of apoptosis. Our findings provide a new insight into the molecular mechanisms and a potential application of cryptopleurine for inflammatory diseases as well as certain cancers associated with abnormal NF-κB activation

A Unified Framework for 3D Point Cloud Visual Grounding

Thanks to its precise spatial referencing, 3D point cloud visual grounding is essential for deep understanding and dynamic interaction in 3D environments, encompassing 3D Referring Expression Comprehension (3DREC) and Segmentation (3DRES). We argue that 3DREC and 3DRES should be unified in one framework, which is also a natural progression in the community. To explain, 3DREC help 3DRES locate the referent, while 3DRES also facilitate 3DREC via more fine-grained language-visual alignment. To achieve this, this paper takes the initiative step to integrate 3DREC and 3DRES into a unified framework, termed 3D Referring Transformer (3DRefTR). Its key idea is to build upon a mature 3DREC model and leverage ready query embeddings and visual tokens from the 3DREC model to construct a dedicated mask branch. Specially, we propose Superpoint Mask Branch, which serves a dual purpose: i) By harnessing on the inherent association between the superpoints and point cloud, it eliminates the heavy computational overhead on the high-resolution visual features for upsampling; ii) By leveraging the heterogeneous CPU-GPU parallelism, while the GPU is occupied generating visual and language tokens, the CPU concurrently produces superpoints, equivalently accomplishing the upsampling computation. This elaborate design enables 3DRefTR to achieve both well-performing 3DRES and 3DREC capacities with only a 6% additional latency compared to the original 3DREC model. Empirical evaluations affirm the superiority of 3DRefTR. Specifically, on the ScanRefer dataset, 3DRefTR surpasses the state-of-the-art 3DRES method by 12.43% in mIoU and improves upon the SOTA 3DREC method by 0.6% [email protected]. The codes and models will be released soon

Destructive effect of train load on rust-cracked sleepers at mesoscopic scale

Under the coupling effect of environment and stray current, concrete railway sleepers will have rust and cracks. The dynamic load of trains exacerbates the crack propagation in the sleepers, thereby reducing their service life. To address this problem, this paper employed the Mesoscale Concrete Model with Real Aggregate Geometry to construct a three-dimensional micro-model of the concrete sleeper, examined the distribution of corrosion current density within the sleepers under various corrosion durations, and investigated the effect of rust accumulation on the steel surface on the sleeper cracking. Moreover, the damage evolution of corroded sleepers under train load was studied. The research results revealed that: (1) The corrosion current density increased rapidly in the early stage of corrosion. After 30 years of corrosion, 10 % of the steel reinforcement protective layer cracked, while after 50 years of corrosion, the cracked area reached 40 %. (2) The maximum thickness of rust accumulation in the middle of the sleeper rebar was 0.4 mm after 30 years of corrosion, and 0.6 mm after 50 years of corrosion. (3) Under the train load, the maximum instantaneous damage increase of the sleepers was 0.314 %, and the maximum damage increase within the 30-year corroded sleepers was 0.05 %

Application prospect analysis of augmented reality technology in mine ventilation system

Basic concept and features of augmented reality technology were introduced, idea of applying augmented reality technology to mine ventilation system was proposed combining with development trend of smart mines in China. Application prospect of augmented reality technology in mine ventilation system was analyzed from three aspects, namely 3D visualization of data model, orientation recognition and guidance and realistic enhancement of ventilation tunnel. The analysis results show that using augmented reality technology can realize visualization of the ventilation tunnel data, and obtain real-time information of roadway environment, orientation recognition and guidance are more precise and humanized, which provides a new technical means for intelligent management of mine ventilation system

Fabrication and Formability of Continuous Carbon Fiber Reinforced Resin Matrix Composites Using Additive Manufacturing

In the current process for additive manufacturing of continuous carbon fiber reinforced resin matrix composites, the fiber and resin matrix are fed into the molten chamber, and then impregnated and compounded in the original position, and finally extruded and deposited on the substrate. It is difficult to control the ratio of fiber and resin, and to achieve good interface fusion, which results in an unsatisfactory enhancement effect. Therefore, an additive manufacturing process based on continuous carbon fiber reinforced polylactic acid composite prepreg filament was explored in this study. The effects of various process parameters on the formability of composites were studied through systematic process experiments. The results showed that the process parameters of additive manufacturing have a systematic influence on the forming quality, accuracy and efficiency, and on the mechanical properties of CFRP. Through the experimental optimization of various process parameters, a continuous and stable forming process was achieved when the nozzle aperture was 0.8 mm, the nozzle printing temperature was 240 °C, the substrate temperature was 60 °C, the wire feeding speed was 5 mm/s, the nozzle moving speed was 5 mm/s, the path bonding rate was 40%, and the printing layer thickness was 0.7 mm. Based on the optimized process parameters, direct additive manufacturing of a lightweight and high-strength composite cellular load-bearing structure could be realized. Its volume fraction of carbon fiber was approximately 7.7%, and the tensile strength was up to 224.3 MPa

Research on Workshop-Based Positioning Technology Based on Internet of Things in Big Data Background

This paper first analyzes the data collection and data management of the workshop, obtains the data of the workshop changes with time, and accumulates the data. There are bottleneck problems such as big data being difficult to be fully used. Then, the concept of the Internet of Things was introduced into the workshop positioning to realize the comprehensive use of the big data in the workshop. Finally, aiming at the positioning problem of manufacturing workshop items, the ZigBee positioning algorithm, the received signal strength indication algorithm RSSI and the trilateration algorithm, is applied, and the trilateral positioning algorithm is applied to the CC2430 wireless MCU, and the positioning node is designed and implemented. The three-sided localization algorithm was used to locate and simulate the horizontal and vertical comparisons of six groups of workshop terminals. The results showed that the difference between the simulated position and the actual position did not exceed 1m, which was in line with the positioning requirements of the workshop

Identification of 14-3-3 Family in Common Bean and Their Response to Abiotic Stress.

14-3-3s are a class of conserved regulatory proteins ubiquitously found in eukaryotes, which play important roles in a variety of cellular processes including response to diverse stresses. Although much has been learned about 14-3-3s in several plant species, it remains unknown in common bean. In this study, 9 common bean 14-3-3s (PvGF14s) were identified by exhaustive data mining against the publicly available common bean genomic database. A phylogenetic analysis revealed that each predicted PvGF14 was clustered with two GmSGF14 paralogs from soybean. Both epsilon-like and non-epsilon classes of PvGF14s were found in common bean, and the PvGF14s belonging to each class exhibited similar gene structure. Among 9 PvGF14s, only 8 are transcribed in common bean. Expression patterns of PvGF14s varied depending on tissue type, developmental stage and exposure of plants to stress. A protein-protein interaction study revealed that PvGF14a forms dimer with itself and with other PvGF14 isoforms. This study provides a first comprehensive look at common bean 14-3-3 proteins, a family of proteins with diverse functions in many cellular processes, especially in response to stresses