Paucatalinone A from Paulownia Catalpifolia Gong Tong Elicits mitochondrial-mediated cancer cell death to combat osteosarcoma

As the global cancer burden escalates, the search for alternative therapies becomes increasingly vital. Natural products, particularly plant-derived compounds, have emerged as promising alternatives to conventional cancer treatments due to their diverse bioactivities and favorable biosafety profiles. Here, we investigate Paucatalinone A, a newly discovered geranylated flavanone derived from the fruit of Paulownia Catalpifolia Gong Tong, notable for its significant anti-cancer properties. We revealed the capability of Paucatalinone A to induce apoptosis in osteosarcoma cells and deciphered its underlying mechanisms. Our findings demonstrate that Paucatalinone A substantially augments apoptosis, inhibits cell proliferation, and demonstrates a pronounced anti-tumor effect in a murine model of osteosarcoma. Mechanistically, Paucatalinone A disrupts calcium homeostasis and exacerbates intracellular reactive oxygen species accumulation, leading to mitochondrial impairment, cytoskeletal collapse, and caspase-dependent apoptotic cell death. This study underscores the potential of Paucatalinone A in initiating apoptosis in cancer cells and highlights the therapeutic efficacy of plant-derived agents in treating osteosarcoma, offering a viable approach for managing other intractable cancers

Research on Prediction of Wear Amount in the Gear Transmission Process Under Complex Working Conditions

Wear is the main failure form in the gear transmission process. The increased wear will increase the non-linear backlash of the gear teeth, reduce the transmission accuracy and increase the impact force of the tooth surface, which will lead to aggravated vibration of the gear transmission system, and impose great impact on the gear transmission performance and the stable operation of the equipment. In order to solve the above problems, a method for predicting the wear amount in the gear transmission process under complex working conditions is proposed. The gear wear state is identified and determined based on the formal wear index. Through in-depth analysis of the gear wear mechanism, a typical gear wear process curve is drawn based on this, the gear transmission friction torque value is calculated, and a mathematical model of the gear wear amount is built; then the known gear state values are input into the construction model, the predicted gear wear amount is obtained, and the prediction of gear wear amount is realized. The experimental results show that under three complex working conditions, the prediction simulation data proposed in this study is closer to the actual parameters, which fully verifies that the prediction accuracy of the wear amount is higher

Road Network Capacity Reliability Considering Travel Time Reliability

AbstractIn order to investigate the interaction between travel time reliability and road network capacity reliability, a bi-level programming model based on travel time reliability is set up for the evaluation of capacity reliability in this paper. In the model, the object is to maximize the basic OD traffic demand multiplier in the upper level. For this purpose stochastic user equilibrium traffic assignment is used to describe travelers’ route choice behaviors in the lower programming level, and a prescribed travel time threshold is set in the upper level problem as a constraint to travel time. By assuming that link capacity is continuous truncated normal random variable, using Monte Carlo simulation technique in conjunction with the sensitivity analysis method of road network equilibrium flow, a heuristic algorithm is established to estimate road network capacity reliability. Numerical study on a small road network is presented to demonstrate the validity of the proposed model and algorithm. At the same time, the impact of the travel time threshold, the traffic demand threshold, the traveler's perception error, the variance of the link capacity and the level of OD traffic demand on the road network capacity reliability are fully examined

Learning Static-Adaptive Graphs for RGB-T Image Saliency Detection

Many works have been proposed on image saliency detection to handle challenging issues including low illumination, cluttered background, low contrast, and so on. Although good performance has been achieved by these algorithms, detection results are still poor based on RGB modality. Inspired by the recent progress of multi-modality fusion, we propose a novel RGB-thermal saliency detection algorithm through learning static-adaptive graphs. Specifically, we first extract superpixels from the two modalities and calculate their affinity matrix. Then, we learn the affinity matrix dynamically and construct a static-adaptive graph. Finally, the saliency maps can be obtained by a two-stage ranking algorithm. Our method is evaluated on RGBT-Saliency Dataset with eleven kinds of challenging subsets. Experimental results show that the proposed method has better generalization performance. The complementary benefits of RGB and thermal images and the more robust feature expression of learning static-adaptive graphs create an effective way to improve the detection effectiveness of image saliency in complex scenes

Learning Static-Adaptive Graphs for RGB-T Image Saliency Detection

Many works have been proposed on image saliency detection to handle challenging issues including low illumination, cluttered background, low contrast, and so on. Although good performance has been achieved by these algorithms, detection results are still poor based on RGB modality. Inspired by the recent progress of multi-modality fusion, we propose a novel RGB-thermal saliency detection algorithm through learning static-adaptive graphs. Specifically, we first extract superpixels from the two modalities and calculate their affinity matrix. Then, we learn the affinity matrix dynamically and construct a static-adaptive graph. Finally, the saliency maps can be obtained by a two-stage ranking algorithm. Our method is evaluated on RGBT-Saliency Dataset with eleven kinds of challenging subsets. Experimental results show that the proposed method has better generalization performance. The complementary benefits of RGB and thermal images and the more robust feature expression of learning static-adaptive graphs create an effective way to improve the detection effectiveness of image saliency in complex scenes

Learning Static-Adaptive Graphs for RGB-T Image Saliency Detection

Many works have been proposed on image saliency detection to handle challenging issues including low illumination, cluttered background, low contrast, and so on. Although good performance has been achieved by these algorithms, detection results are still poor based on RGB modality. Inspired by the recent progress of multi-modality fusion, we propose a novel RGB-thermal saliency detection algorithm through learning static-adaptive graphs. Specifically, we first extract superpixels from the two modalities and calculate their affinity matrix. Then, we learn the affinity matrix dynamically and construct a static-adaptive graph. Finally, the saliency maps can be obtained by a two-stage ranking algorithm. Our method is evaluated on RGBT-Saliency Dataset with eleven kinds of challenging subsets. Experimental results show that the proposed method has better generalization performance. The complementary benefits of RGB and thermal images and the more robust feature expression of learning static-adaptive graphs create an effective way to improve the detection effectiveness of image saliency in complex scenes.</jats:p

Numerical simulation and experimental investigation of multiphase mass transfer process for industrial applications in China

This paper presents a comprehensive review of the remarkable achievements by Chinese scientists and engineers who have contributed to the multiscale process design, with emphasis on the transport mechanisms in stirred reactors, extractors, and rectification columns. After a brief review of the classical theory of transport phenomena, this paper summarizes the domestic developments regarding the relevant experiments and numerical techniques for the interphase mass transfer on the drop/bubble scale and the micromixing in the single-phase or multiphase stirred tanks in China. To improve the design and scale-up of liquid-liquid extraction columns, new measurement techniques with the combination of both particle image velocimetry and computational fluid dynamics have been developed and advanced modeling methods have been used to determine the axial mixing and mass transfer performance in extraction columns. Detailed investigations on the mass transfer process in distillation columns are also summarized. The numerical and experimental approaches modeling transport phenomena at the vicinity of the vapor-liquid interface, the point efficiency for trays/packings regarding the mixing behavior of fluids, and the computational mass transfer approach for the simulation of distillation columns are thoroughly analyzed. Recent industrial applications of mathematical models, numerical simulation, and experimental methods for the design and analysis of multiphase stirred reactors/crystallizers, extractors, and distillation columns are seen to garnish economic benefits. The current problems and future prospects are pinpointed at last