Background: New therapeutic targets are needed to improve the outcomes for gastric cancer(GC) patients with advanced disease. Evasion of programmed cell death(apoptosis) is a hallmark of cancer cells and direct induct...Background: New therapeutic targets are needed to improve the outcomes for gastric cancer(GC) patients with advanced disease. Evasion of programmed cell death(apoptosis) is a hallmark of cancer cells and direct induction of apoptosis by targeting the pro-survival BCL2 family proteins represents a promising therapeutic strategy for cancer treatment. Therefore, understanding the molecular mechanisms underpinning cancer cell survival could provide a molecular basis for potential therapeutic interventions. Method: Here we explored the role of BCL2L1 and the encoded anti-apoptotic BCL-XL in GC. Using Droplet Digital PCR(ddPCR) technology to investigate the DNA amplification of BCL2L1 in GC samples and GC cell lines, the sensitivity of GC cell lines to selective BCL-XL inhibitors A1155463 and A1331852, pan-inhibitor ABT-263, and VHL-based PROTAC-BCL-XL was analyzed using(CellTiter-Glo) CTG assay in vitro. Western Blot(WB) was used to detect the protein expression of BCL2 family members in GC cell lines and the manner in which PROTAC-BCL-XL kills GC cells. Coimmunoprecipitation(Co-IP) was used to investigate the mechanism of A1331852 and ABT-263 kills GC cell lines. DDPCR, WB, and real-time PCR(RTPCR) were used to investigate the correlation between DNA, RNA, protein levels, and drug activity. Results: The functional assay showed that a subset of GC cell lines relies on BCL-XL for survival. In gastric cancer cell lines, BCL-XL inhibitors A1155463 and A1331852 are more sensitive than the pan BCL2 family inhibitor ABT-263, indicating that ABT-263 is not an optimal inhibitor of BCL-XL. VHL-based PROTAC-BCL-XL DT2216 appears to be active in GC cells. DT2216 induces apoptosis of gastric cancer cells in a time-and dose-dependent manner through the proteasome pathway. Statistical analysis showed that the BCL-XL protein level predicts the response of GC cells to BCL-XL targeting therapy and BCL2L1 gene CNVs do not reliably predict BCL-XL expression.Conclusion: We identified BCL-XL as a promising therapeutic target in a subset of GC cases with high levels of BCL-XL protein expression. Functionally, we demonstrated that both selective BCL-XL inhibitors and VHL-based PROTAC BCL-XL can potently kill GC cells that are reliant on BCL-XL for survival. However, we found that BCL2L1 copy number variations(CNVs) cannot reliably predict BCL-XL expression, but the BCL-XL protein level serves as a useful biomarker for predicting the sensitivity of GC cells to BCL-XL-targeting compounds. Taken together, our study pinpointed BCL-XL as potential druggable target for specific subsets of GC.展开更多
Seismology is a data-intensive and data-driven science.The rapid growth of seismometer density and data size calls for more efficient and effective processing tools.In recent years,artificial intelligence(AI)has been ...Seismology is a data-intensive and data-driven science.The rapid growth of seismometer density and data size calls for more efficient and effective processing tools.In recent years,artificial intelligence(AI)has been increasingly used in various areas of seismology.Among them,earthquake monitoring is likely the one most impacted(Kong QK et al.,2019;Mousavi and Beroza,2022).Popular seismic phase picking models and workflows like PhaseNet,EQTransformer,RISP,PALM,LOC-FLOW,QUAKE-FLOW(Zhu WQ and Beroza,2019;Mousavi et al.,2020;Liao SR et al.,2021;Zhou YJ et al.,2021;Zhang M et al.,2022;Zhu WQ et al.,2023)have been proposed and widely used.Also,AI algorithms for association(Ross et al.,2019;Yu ZY and Wang WT,2022),polarity determination and focal mechanism inversion(Ross et al.,2018;Zhang J et al.,2023;Li S et al.,2023),earthquake discrimination(Li ZF et al.,2018;Linville et al.,2019;Miao FJ et al.,2020)have emerged.展开更多
While TiFe alloy has recently attracted attention as the efficient catalyst to enhance de/hydrogenation rates of Mg/MgH_(2),the difficulty of its activation characteristics has hindered further improvement of reaction...While TiFe alloy has recently attracted attention as the efficient catalyst to enhance de/hydrogenation rates of Mg/MgH_(2),the difficulty of its activation characteristics has hindered further improvement of reaction kinetics.Herein,we report that the TiFe_(0.92)Mn_(0.04)Co_(0.04) catalyst can overcome the abovementioned challenges.The synthesized MgH_(2)-30 wt% TiFe_(0.92)Mn_(0.04)Co_(0.04) can release 4.5 wt%of hydrogen in 16 min at 250℃,three times as fast as MgH_(2).The activation energy of dehydrogenation was as low as 84.6 kJ mol^(-1),which is 46.8%reduced from pure MgH_(2).No clear degradation of reaction rates and hydrogen storage capacity was observed for at least 30 cycles.Structural studies reveal that TiFe_(0.92)Mn_(0.04)Co_(0.04) partially decomposes to in-situ generatedα-Fe particles dispersed on TiFe_(0.92)Mn_(0.04)Co_(0.04).The presence ofα-Fe reduces the formation of an oxide layer on TiFe_(0.92)Mn_(0.04)Co_(0.04),enabling the activation processes.At the same time,the hydrogen incorporation capabilities of TiFe_(0.92)Mn_(0.04)Co_(0.04) can provide more hydrogen diffusion paths,which promote hydrogen dissociation and diffusion.These discoveries demonstrate the advanced nature and importance of combining the in-situ generatedα-Fe with TiFe_(0.92)Mn_(0.04)Co_(0.04).It provides a new strategy for designing highly efficient and stable catalysts for Mg-based hydrogen storage materials.展开更多
Seismic data processing techniques,together with seismic instrumentation,determine our earthquake monitoring capability and the quality of resulting earthquake catalogs.This paper is intended to review the improvement...Seismic data processing techniques,together with seismic instrumentation,determine our earthquake monitoring capability and the quality of resulting earthquake catalogs.This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing.Over the past two decades,seismologists have made considerable advancements in seismic data processing,partly thanks to the significant development of computational power,signal processing,and machine learning techniques.In particular,wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data.Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution.These techniques are becoming standard,leading to emerging intelligent software systems for next-generation earthquake monitoring.Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms(for both permanent and temporary deployments)and in emergency real-time monitoring of ongoing sequences(e.g.,aftershock and induced seismicity sequences).We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks.展开更多
Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for differe...Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for different levels of use in China.We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet.This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China.Then,using different subsets of the DiTing data,we fine-tune the base picker to better adapt to different regions.In total,we provide 5 pickers for major tectonic blocks in China,33 pickers for provincial-level administrative regions,and 2 special pickers for the Capital area and the China Seismic Experimental Site.These pickers show improved performance in respective regions which they are customized for.They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets.We anticipate that this picker set will facilitate earthquake monitoring in China.展开更多
With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore...With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.展开更多
Seismic networks have significantly improved in the last decade in terms of coverage density,data quality,and instrumental diversity.Moreover,revolutionary advances in ultra-dense seismic instruments,such as nodes and...Seismic networks have significantly improved in the last decade in terms of coverage density,data quality,and instrumental diversity.Moreover,revolutionary advances in ultra-dense seismic instruments,such as nodes and fiber-optic sensing technologies,have recently provided unprecedented high-resolution data for regional and local earthquake monitoring.Nodal arrays have characteristics such as easy installation and flexible apertures,but are limited in power efficiency and data storage and thus most suitable as temporary networks.Fiber-optic sensing techniques,including distributed acoustic sensing,can be operated in real time with an in-house power supply and connected data storage,thereby exhibiting the potential of becoming next-generation permanent networks.Fiber-optic sensing techniques offer a powerful way of filling the observation gap particularly in submarine environments.Despite these technological advancements,various challenges remain.First,the data characteristics of fiber-optic sensing are still unclear.Second,it is challenging to construct software infrastructures to store,transfer,visualize,and process large amount of seismic data.Finally,innovative detection methods are required to exploit the potential of numerous channels.With improved knowledge about data characteristics,enhanced software infrastructures,and suitable data processing techniques,these innovations in seismic instrumentation could profoundly impact observational seismology.展开更多
文摘Background: New therapeutic targets are needed to improve the outcomes for gastric cancer(GC) patients with advanced disease. Evasion of programmed cell death(apoptosis) is a hallmark of cancer cells and direct induction of apoptosis by targeting the pro-survival BCL2 family proteins represents a promising therapeutic strategy for cancer treatment. Therefore, understanding the molecular mechanisms underpinning cancer cell survival could provide a molecular basis for potential therapeutic interventions. Method: Here we explored the role of BCL2L1 and the encoded anti-apoptotic BCL-XL in GC. Using Droplet Digital PCR(ddPCR) technology to investigate the DNA amplification of BCL2L1 in GC samples and GC cell lines, the sensitivity of GC cell lines to selective BCL-XL inhibitors A1155463 and A1331852, pan-inhibitor ABT-263, and VHL-based PROTAC-BCL-XL was analyzed using(CellTiter-Glo) CTG assay in vitro. Western Blot(WB) was used to detect the protein expression of BCL2 family members in GC cell lines and the manner in which PROTAC-BCL-XL kills GC cells. Coimmunoprecipitation(Co-IP) was used to investigate the mechanism of A1331852 and ABT-263 kills GC cell lines. DDPCR, WB, and real-time PCR(RTPCR) were used to investigate the correlation between DNA, RNA, protein levels, and drug activity. Results: The functional assay showed that a subset of GC cell lines relies on BCL-XL for survival. In gastric cancer cell lines, BCL-XL inhibitors A1155463 and A1331852 are more sensitive than the pan BCL2 family inhibitor ABT-263, indicating that ABT-263 is not an optimal inhibitor of BCL-XL. VHL-based PROTAC-BCL-XL DT2216 appears to be active in GC cells. DT2216 induces apoptosis of gastric cancer cells in a time-and dose-dependent manner through the proteasome pathway. Statistical analysis showed that the BCL-XL protein level predicts the response of GC cells to BCL-XL targeting therapy and BCL2L1 gene CNVs do not reliably predict BCL-XL expression.Conclusion: We identified BCL-XL as a promising therapeutic target in a subset of GC cases with high levels of BCL-XL protein expression. Functionally, we demonstrated that both selective BCL-XL inhibitors and VHL-based PROTAC BCL-XL can potently kill GC cells that are reliant on BCL-XL for survival. However, we found that BCL2L1 copy number variations(CNVs) cannot reliably predict BCL-XL expression, but the BCL-XL protein level serves as a useful biomarker for predicting the sensitivity of GC cells to BCL-XL-targeting compounds. Taken together, our study pinpointed BCL-XL as potential druggable target for specific subsets of GC.
文摘Seismology is a data-intensive and data-driven science.The rapid growth of seismometer density and data size calls for more efficient and effective processing tools.In recent years,artificial intelligence(AI)has been increasingly used in various areas of seismology.Among them,earthquake monitoring is likely the one most impacted(Kong QK et al.,2019;Mousavi and Beroza,2022).Popular seismic phase picking models and workflows like PhaseNet,EQTransformer,RISP,PALM,LOC-FLOW,QUAKE-FLOW(Zhu WQ and Beroza,2019;Mousavi et al.,2020;Liao SR et al.,2021;Zhou YJ et al.,2021;Zhang M et al.,2022;Zhu WQ et al.,2023)have been proposed and widely used.Also,AI algorithms for association(Ross et al.,2019;Yu ZY and Wang WT,2022),polarity determination and focal mechanism inversion(Ross et al.,2018;Zhang J et al.,2023;Li S et al.,2023),earthquake discrimination(Li ZF et al.,2018;Linville et al.,2019;Miao FJ et al.,2020)have emerged.
基金supported by The National Key Research and Development Program of China(2023YFB3809100)the National Natural Science Foundation of China(U23A200722)the Fundamental Research Funds for the Central Universities(2023CDJXY-016).
文摘While TiFe alloy has recently attracted attention as the efficient catalyst to enhance de/hydrogenation rates of Mg/MgH_(2),the difficulty of its activation characteristics has hindered further improvement of reaction kinetics.Herein,we report that the TiFe_(0.92)Mn_(0.04)Co_(0.04) catalyst can overcome the abovementioned challenges.The synthesized MgH_(2)-30 wt% TiFe_(0.92)Mn_(0.04)Co_(0.04) can release 4.5 wt%of hydrogen in 16 min at 250℃,three times as fast as MgH_(2).The activation energy of dehydrogenation was as low as 84.6 kJ mol^(-1),which is 46.8%reduced from pure MgH_(2).No clear degradation of reaction rates and hydrogen storage capacity was observed for at least 30 cycles.Structural studies reveal that TiFe_(0.92)Mn_(0.04)Co_(0.04) partially decomposes to in-situ generatedα-Fe particles dispersed on TiFe_(0.92)Mn_(0.04)Co_(0.04).The presence ofα-Fe reduces the formation of an oxide layer on TiFe_(0.92)Mn_(0.04)Co_(0.04),enabling the activation processes.At the same time,the hydrogen incorporation capabilities of TiFe_(0.92)Mn_(0.04)Co_(0.04) can provide more hydrogen diffusion paths,which promote hydrogen dissociation and diffusion.These discoveries demonstrate the advanced nature and importance of combining the in-situ generatedα-Fe with TiFe_(0.92)Mn_(0.04)Co_(0.04).It provides a new strategy for designing highly efficient and stable catalysts for Mg-based hydrogen storage materials.
基金supported by the USTC Research Funds of the Double First-Class Initiative(Grant No.YD2080002006)the Special Fund of the Institute of Geophysics,China Earthquake Administration(Grant No.DQJB21Z05).
文摘Seismic data processing techniques,together with seismic instrumentation,determine our earthquake monitoring capability and the quality of resulting earthquake catalogs.This paper is intended to review the improvement of earthquake monitoring capability from the perspective of data processing.Over the past two decades,seismologists have made considerable advancements in seismic data processing,partly thanks to the significant development of computational power,signal processing,and machine learning techniques.In particular,wide application of template matching and increasing use of deep learning significantly enhance our capability to extract signals of small earthquakes from noisy data.Relative location techniques provide a critical tool to elucidate fault geometries and seismicity migration patterns at unprecedented resolution.These techniques are becoming standard,leading to emerging intelligent software systems for next-generation earthquake monitoring.Prospective improvements in future research must consider the urgent needs in highly generalizable detection algorithms(for both permanent and temporary deployments)and in emergency real-time monitoring of ongoing sequences(e.g.,aftershock and induced seismicity sequences).We believe that the maturing of intelligent and high-resolution processing systems could transform traditional earthquake monitoring workflows and eventually liberate seismologists from laborious catalog construction tasks.
基金the National Key R&D Program of China(No.2021YFC3000700)the Special Fund of the Institute of Geophysics,China Earthquake Administration(Nos.DQJB22X08 and DQJB21Z05).
文摘Current popular deep learning seismic phase pickers like PhaseNet and EQTransformer suffer from performance drop in China.To mitigate this problem,we build a unified set of customized seismic phase pickers for different levels of use in China.We first train a base picker with the recently released DiTing dataset using the same U-Net architecture as PhaseNet.This base picker significantly outperforms the original PhaseNet and is generally suitable for entire China.Then,using different subsets of the DiTing data,we fine-tune the base picker to better adapt to different regions.In total,we provide 5 pickers for major tectonic blocks in China,33 pickers for provincial-level administrative regions,and 2 special pickers for the Capital area and the China Seismic Experimental Site.These pickers show improved performance in respective regions which they are customized for.They can be either directly integrated into national or regional seismic network operation or used as base models for further refinement for specific datasets.We anticipate that this picker set will facilitate earthquake monitoring in China.
基金supported by the joint NNSF&FDCT Project Number (0066/2019/AFJ)joint MOST&FDCT Project Number (0058/2019/AMJ),City University of Macao,Macao,China.
文摘With the rapid development of urban road traffic and the increasing number of vehicles,how to alleviate traffic congestion is one of the hot issues that need to be urgently addressed in building smart cities.Therefore,in this paper,a nonlinear multi-objective optimization model of urban intersection signal timing based on a Genetic Algorithm was constructed.Specifically,a typical urban intersection was selected as the research object,and drivers’acceleration habits were taken into account.What’s more,the shortest average delay time,the least average number of stops,and the maximum capacity of the intersection were regarded as the optimization objectives.The optimization results show that compared with the Webster method when the vehicle speed is 60 km/h and the acceleration is 2.5 m/s^(2),the signal intersection timing scheme based on the proposed Genetic Algorithm multi-objective optimization reduces the intersection signal cycle time by 14.6%,the average vehicle delay time by 12.9%,the capacity by 16.2%,and the average number of vehicles stop by 0.4%.To verify the simulation results,the authors imported the optimized timing scheme into the constructed Simulation of the Urban Mobility model.The experimental results show that the authors optimized timing scheme is superior to Webster’s in terms of vehicle average loss time reduction,carbon monoxide emission,particulate matter emission,and vehicle fuel consumption.The research in this paper provides a basis for Genetic algorithms in traffic signal control.
基金the USTC Research Funds of the Double First-Class Initiative(No.YD2080002006)。
文摘Seismic networks have significantly improved in the last decade in terms of coverage density,data quality,and instrumental diversity.Moreover,revolutionary advances in ultra-dense seismic instruments,such as nodes and fiber-optic sensing technologies,have recently provided unprecedented high-resolution data for regional and local earthquake monitoring.Nodal arrays have characteristics such as easy installation and flexible apertures,but are limited in power efficiency and data storage and thus most suitable as temporary networks.Fiber-optic sensing techniques,including distributed acoustic sensing,can be operated in real time with an in-house power supply and connected data storage,thereby exhibiting the potential of becoming next-generation permanent networks.Fiber-optic sensing techniques offer a powerful way of filling the observation gap particularly in submarine environments.Despite these technological advancements,various challenges remain.First,the data characteristics of fiber-optic sensing are still unclear.Second,it is challenging to construct software infrastructures to store,transfer,visualize,and process large amount of seismic data.Finally,innovative detection methods are required to exploit the potential of numerous channels.With improved knowledge about data characteristics,enhanced software infrastructures,and suitable data processing techniques,these innovations in seismic instrumentation could profoundly impact observational seismology.
