Objective: The aim of our study was to investigate the effect of diallyl trisulfide (DATS) combining radiation on DNA injury-repair of Esophageal cancer EC109 cells. Methods: Using 10 and 20 μg/mL DATS on EC109 cells...Objective: The aim of our study was to investigate the effect of diallyl trisulfide (DATS) combining radiation on DNA injury-repair of Esophageal cancer EC109 cells. Methods: Using 10 and 20 μg/mL DATS on EC109 cells, and taking X-ray radiation 24 h later. Investigate the radiosensitization effect of DATS on EC109 cells by clone formation, and the mechanism of DNA injury-repair by Comet Assay. Results: The clone formation resulted that DATS had radiosensitization effect on EC109 cells. Radiosensitization enhancement ratios of 10 and 20 μg/mL DATS in combination with radiation were 1.55, 1.64 (Do) and 1.43, 1.75 (Dq) respectively. In the comet assay, the TM (tail moments) of 20 μg/mL DATS combining radiation group lines at 0 h, 2 h, 6 h and 24 h were 7.16 ± 2.61, 3.65 ± 2.06, 2.09 ± 0.83, 1.45 ± 1.37 respectively. They were slightly increased than radiation group (0.95 ± 0.65, 0.11 ± 0.07, 0.1 ± 0.05, 0.11 ± 0.08) and DATS group (1.81 ± 1.23, 1.58 ± 1.40, 0.45 ± 0.25, 0.60 ± 0.40) (P < 0.01). The result showed that DATS combining radiation had the effect of increasing DNA damage and inhibiting DNA repair on EC109 cells. Conclusion: DATS has radiosensitization effect on Esophageal cancer EC109 cells. And the effect is probably related with DNA injury-repair.展开更多
Background:Although there is growing evidence of the use of artificial intelligence(AI)techniques in sports,ethical issues surrounding AI use are being discussed at a minimal level.Thus,this systematic scoping review ...Background:Although there is growing evidence of the use of artificial intelligence(AI)techniques in sports,ethical issues surrounding AI use are being discussed at a minimal level.Thus,this systematic scoping review aimed to summarize the current ethical implications associated with using AI in sports.Methods:In this study,a total of 9 databases-MEDLINE/PubMed,Embase,Cochrane Library,ProQuest,EBSCOhost,IEEE Xplore,Web of Science,Scopus,and Google Scholar--were searched.The review protocol was registered(https://osfio/42a8q)before extracting data.The search yielded 397 studies,and 25 studies met the inclusion and exclusion criteria.Results:The 25 studies were categorized into 4 primary ethical concerns:fairness and bias,transparency and explainability,privacy and data ethics,and accountability in AI's application in sports.These categorizations were derived based on the systematic review ofethical issues highlighted across the selected studies.Fifteen studies delved into fairness and bias,focusing on how AI can perpetuate existing inequalities in sports.Thirteen studies addressed the lack of transparency,emphasizing the challenges in interpretability and trust in AI-driven decisions.Privacy and data ethics emerged as significant in22 studies,highlighting risks related to the misuse of athletes’sensitive data.Finally,account-ability was examined in 8 studies,stressing the ethical obligations of AI developers and users in sports contexts.The thematic analysis revealed overlapping concerns,as some studies addressed multiple issues simultaneously.Conclusion:Future research should focus on developing ethical frameworks tailored to underrepresented sports contexts and creating global standards for AI regulation in sports.This includes investigating the implications of AI applications in amateur sports,enhancing diversity in AI training datasets,and exploring the integration of ethical AI practices across various sports governance structures.展开更多
On May 19,2025,the groundbreaking ceremony for the Phase I of the nylon fiber project of Colorful Nylon Fiber Co.,Ltd.(a subsidiary of Eversun Corporation),was held in the Dat Do Industrial Park in Vung Tau Province,V...On May 19,2025,the groundbreaking ceremony for the Phase I of the nylon fiber project of Colorful Nylon Fiber Co.,Ltd.(a subsidiary of Eversun Corporation),was held in the Dat Do Industrial Park in Vung Tau Province,Vietnam.Representatives from the Eversun Corporation,the local government of Vietnam,the Vung Tau Industrial Park,and cooperative enterprises jointly witnessed this important moment.Jiangen Wang,General Manager of Eversun Corporation,Yuxin Chen,General Manager of Resultant Construction Co.,Ltd.,and Youtong Chen,Deputy Director of Dat Do Industrial Park Management Committee attended the ceremony.展开更多
This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise co...This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising dur-ing measurements, thereby enhancing the robustness and stabi-lity of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference sig-nals utilizing local information and communication with neigh-bors. Subsequently, a fixed-time sliding mode controller is intro-duced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve dis-tributed average tracking of reference signals, and rigorous ana-lytical methods are employed to substantiate the fixed-time sta-bility. Finally, numerical simulation results are provided to vali-date the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.展开更多
Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles.While artificial intelligence(AI)offers promise,its typical need for vast datasets to learn from hinde...Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles.While artificial intelligence(AI)offers promise,its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers.We introduce learning at criticality(LaC),a reinforcement learning scheme that tunes large language models(LLMs)to a sharp learning transition,addressing this information scarcity.At this transition,LLMs achieve peak generalization from minimal data,exemplified by 7-digit base-7 addition-a test of nontrivial arithmetic reasoning.To elucidate this peak,we analyze a minimal concept-network model designed to capture the essence of how LLMs might link tokens.Trained on a single exemplar,this model also undergoes a sharp learning transition.This transition exhibits hallmarks of a second-order phase transition,notably power-law distributed solution path lengths.At this critical point,the system maximizes a“critical thinking pattern”crucial for generalization,enabled by the underlying scale-free exploration.This suggests LLMs reach peak performance by operating at criticality,where such explorative dynamics enable the extraction of underlying operational rules.We demonstrate LaC in quantum field theory:an 8B-parameter LLM,tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums,solves unseen,higher-order problems,significantly outperforming far larger models.LaC thus leverages critical phenomena,a physical principle,to empower AI for complex,data-sparse challenges in fundamental physics.展开更多
文摘Objective: The aim of our study was to investigate the effect of diallyl trisulfide (DATS) combining radiation on DNA injury-repair of Esophageal cancer EC109 cells. Methods: Using 10 and 20 μg/mL DATS on EC109 cells, and taking X-ray radiation 24 h later. Investigate the radiosensitization effect of DATS on EC109 cells by clone formation, and the mechanism of DNA injury-repair by Comet Assay. Results: The clone formation resulted that DATS had radiosensitization effect on EC109 cells. Radiosensitization enhancement ratios of 10 and 20 μg/mL DATS in combination with radiation were 1.55, 1.64 (Do) and 1.43, 1.75 (Dq) respectively. In the comet assay, the TM (tail moments) of 20 μg/mL DATS combining radiation group lines at 0 h, 2 h, 6 h and 24 h were 7.16 ± 2.61, 3.65 ± 2.06, 2.09 ± 0.83, 1.45 ± 1.37 respectively. They were slightly increased than radiation group (0.95 ± 0.65, 0.11 ± 0.07, 0.1 ± 0.05, 0.11 ± 0.08) and DATS group (1.81 ± 1.23, 1.58 ± 1.40, 0.45 ± 0.25, 0.60 ± 0.40) (P < 0.01). The result showed that DATS combining radiation had the effect of increasing DNA damage and inhibiting DNA repair on EC109 cells. Conclusion: DATS has radiosensitization effect on Esophageal cancer EC109 cells. And the effect is probably related with DNA injury-repair.
文摘Background:Although there is growing evidence of the use of artificial intelligence(AI)techniques in sports,ethical issues surrounding AI use are being discussed at a minimal level.Thus,this systematic scoping review aimed to summarize the current ethical implications associated with using AI in sports.Methods:In this study,a total of 9 databases-MEDLINE/PubMed,Embase,Cochrane Library,ProQuest,EBSCOhost,IEEE Xplore,Web of Science,Scopus,and Google Scholar--were searched.The review protocol was registered(https://osfio/42a8q)before extracting data.The search yielded 397 studies,and 25 studies met the inclusion and exclusion criteria.Results:The 25 studies were categorized into 4 primary ethical concerns:fairness and bias,transparency and explainability,privacy and data ethics,and accountability in AI's application in sports.These categorizations were derived based on the systematic review ofethical issues highlighted across the selected studies.Fifteen studies delved into fairness and bias,focusing on how AI can perpetuate existing inequalities in sports.Thirteen studies addressed the lack of transparency,emphasizing the challenges in interpretability and trust in AI-driven decisions.Privacy and data ethics emerged as significant in22 studies,highlighting risks related to the misuse of athletes’sensitive data.Finally,account-ability was examined in 8 studies,stressing the ethical obligations of AI developers and users in sports contexts.The thematic analysis revealed overlapping concerns,as some studies addressed multiple issues simultaneously.Conclusion:Future research should focus on developing ethical frameworks tailored to underrepresented sports contexts and creating global standards for AI regulation in sports.This includes investigating the implications of AI applications in amateur sports,enhancing diversity in AI training datasets,and exploring the integration of ethical AI practices across various sports governance structures.
文摘On May 19,2025,the groundbreaking ceremony for the Phase I of the nylon fiber project of Colorful Nylon Fiber Co.,Ltd.(a subsidiary of Eversun Corporation),was held in the Dat Do Industrial Park in Vung Tau Province,Vietnam.Representatives from the Eversun Corporation,the local government of Vietnam,the Vung Tau Industrial Park,and cooperative enterprises jointly witnessed this important moment.Jiangen Wang,General Manager of Eversun Corporation,Yuxin Chen,General Manager of Resultant Construction Co.,Ltd.,and Youtong Chen,Deputy Director of Dat Do Industrial Park Management Committee attended the ceremony.
基金supported by the National Natural Science Foundation of China(61673130).
文摘This paper investigates the sliding-mode-based fixed-time distributed average tracking (DAT) problem for multiple Euler-Lagrange systems in the presence of external distur-bances. The primary objective is to devise controllers for each agent, enabling them to precisely track the average of multiple time-varying reference signals. By averaging these signals, we can mitigate the influence of errors and uncertainties arising dur-ing measurements, thereby enhancing the robustness and stabi-lity of the system. A distributed fixed-time average estimator is proposed to estimate the average value of global reference sig-nals utilizing local information and communication with neigh-bors. Subsequently, a fixed-time sliding mode controller is intro-duced incorporating a state-dependent sliding mode function coupled with a variable exponent coefficient to achieve dis-tributed average tracking of reference signals, and rigorous ana-lytical methods are employed to substantiate the fixed-time sta-bility. Finally, numerical simulation results are provided to vali-date the effectiveness of the proposed methodology, offering insights into its practical application and robust performance.
基金supported by the National Key Research and Development Program of China(Grant No.2024YFA1408604 for K.C.and X.C.)the National Natural Science Foundation of China(Grant Nos.12047503,12447103 for K.C.and X.C.,12325501 for P.Z.,and 12275263 for Y.D.and S.H.)+1 种基金the Innovation Program for Quantum Science and Technology(Grant No.2021ZD0301900 for Y.D.and S.H.)the Natural Science Foundation of Fujian Province of China(Grant No.2023J02032 for Y.D.and S.H.)。
文摘Fundamental physics often confronts complex symbolic problems with few guiding exemplars or established principles.While artificial intelligence(AI)offers promise,its typical need for vast datasets to learn from hinders its use in these information-scarce frontiers.We introduce learning at criticality(LaC),a reinforcement learning scheme that tunes large language models(LLMs)to a sharp learning transition,addressing this information scarcity.At this transition,LLMs achieve peak generalization from minimal data,exemplified by 7-digit base-7 addition-a test of nontrivial arithmetic reasoning.To elucidate this peak,we analyze a minimal concept-network model designed to capture the essence of how LLMs might link tokens.Trained on a single exemplar,this model also undergoes a sharp learning transition.This transition exhibits hallmarks of a second-order phase transition,notably power-law distributed solution path lengths.At this critical point,the system maximizes a“critical thinking pattern”crucial for generalization,enabled by the underlying scale-free exploration.This suggests LLMs reach peak performance by operating at criticality,where such explorative dynamics enable the extraction of underlying operational rules.We demonstrate LaC in quantum field theory:an 8B-parameter LLM,tuned to its critical point by LaC using a few exemplars of symbolic Matsubara sums,solves unseen,higher-order problems,significantly outperforming far larger models.LaC thus leverages critical phenomena,a physical principle,to empower AI for complex,data-sparse challenges in fundamental physics.
