The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-nois...The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.展开更多
Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating...Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.展开更多
The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods fo...The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors,especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval.In this paper,a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented.First,the distribution law of theoretical error is examined;adjacent traces exhibit variation characteristics in their waveforms.Second,a label cross-correlation superposition method for extracting highfrequency signals is presented to enhance the first-arrival picking precision.Results from synthetic and field data verify that the proposed approach is robust,successfully overcomes the limitations of low sampling frequency,and achieves precise outcomes that are comparable with those of high-sampling-frequency data.展开更多
Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline c...Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline chain furniture retailers, and the picking process is a key activity in distribution warehouse operations. To reduce the cost of distribution warehouse and alleviate the survival pressure of the offline chain furniture retailers, this paper focuses on optimizing the picking route of the IKEA Fuzhou distribution warehouse. It starts by creating a two-dimensional coordinate system for the storage location of the distribution warehouse using the traditional S-type picking strategy to calculate the distance and time of the sorting route. Then, the problem of optimizing the picking route is then transformed into the travelling salesman problem (TSP), and picking route optimization model is developed using a genetic algorithm to analyze the sorting efficiency and picking route optimization. Results show that the order-picking route using the genetic algorithm strategy is significantly better than the traditional S-type picking strategy, which can improve overall sorting efficiency and operations, reduce costs, and increase efficiency. Thus, this establishes an implementation process for the order-picking path based on genetic algorithm optimization to improve overall sorting efficiency and operations, reduce costs, increase efficiency, and alleviate the survival pressure of pandemic-affected enterprises.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
Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different...Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible.展开更多
Because of their unique mechanical and electrical properties,zinc oxide(ZnO)nanowires are used widely in microscopic and nanoscopic devices and structures,but characterizing them remains challenging.In this paper,two ...Because of their unique mechanical and electrical properties,zinc oxide(ZnO)nanowires are used widely in microscopic and nanoscopic devices and structures,but characterizing them remains challenging.In this paper,two pick-up strategies are proposed for characterizing the electrical properties of ZnO nanowires using SEM equipped with a nanomanipulator.To pick up nanowires efficiently,direct sampling is compared with electrification fusing,and experiments show that direct sampling is more stable while electrification fusing is more efficient.ZnO nanowires have cut-off properties,and good Schottky contact with the tungsten probes was established.In piezoelectric experiments,the maximum piezoelectric voltage generated by an individual ZnO nanowire was 0.07 V,and its impedance decreased with increasing input signal frequency until it became stable.This work offers a technical reference for the pick-up and construction of nanomaterials and nanogeneration technology.展开更多
On the basis of investigating picking and sightseeing resources in neighboring areas of Bayi Town,Linzhi City,this paper explored problems in the development of local leisure sightseeing and picking industry,and propo...On the basis of investigating picking and sightseeing resources in neighboring areas of Bayi Town,Linzhi City,this paper explored problems in the development of local leisure sightseeing and picking industry,and proposed scientific and reasonable countermeasures.展开更多
Objective The protein interacting with C kinase 1(PICK1)plays a critical role in vesicle trafficking,and its deficiency in sperm cells results in abnormal vesicle trafficking from Golgi to acrosome,which eventually di...Objective The protein interacting with C kinase 1(PICK1)plays a critical role in vesicle trafficking,and its deficiency in sperm cells results in abnormal vesicle trafficking from Golgi to acrosome,which eventually disrupts acrosome formation and leads to male infertility.Methods An azoospermia sample was filtered,and the laboratory detection and clinical phenotype indicated typical azoospermia in the patient.We sequenced all of the exons in the PICK1 gene and found that there was a novel homozygous variant in the PICK1 gene,c.364delA(p.Lys122SerfsX8),and this protein structure truncating variant seriously affected the biological function.Then we constructed a PICK1 knockout mouse model using clustered regularly interspaced short palindromic repeat cutting technology(CRISPRc).Results The sperm from PICK1 knockout mice showed acrosome and nucleus abnormalities,as well as dysfunctional mitochondrial sheath formation.Both the total sperm and motility sperm counts were decreased in the PICK1 knockout mice compared to wild-type mice.Moreover,the mitochondrial dysfunction was verified in the mice.These defects in the male PICK1 knockout mice may have eventually led to complete infertility.Conclusion The c.364delA novel variant in the PICK1 gene associated with clinical infertility,and pathogenic variants in the PICK1 may cause azoospermia or asthenospermia by impairing mitochondrial function in both mice and humans.展开更多
Niemann-Pick disease (NPD) refers to a group of patients who have varying degrees of lipid storage and foam cell infiltration in tissues, as well as overlapping clinical features, including hepatosplenomegaly, insuffi...Niemann-Pick disease (NPD) refers to a group of patients who have varying degrees of lipid storage and foam cell infiltration in tissues, as well as overlapping clinical features, including hepatosplenomegaly, insufficiency pulmonary and/or central nervous system (CNS). Thanks to the pioneering work of Roscoe Brady and colleagues, we now know that there are two distinct metabolic abnormalities that explain NPD. The first is due to the deficient activity of the acid sphingomyelinase enzyme (ASM;NPD “types A and B”), and the second is due to defective functioning in the transport of cholesterol (NPD “type C”). We report the case of a 13-year-old adolescent diagnosed with Niemann-Pick A/B disease.展开更多
PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成...PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成为极具前景的疾病治疗靶点。该文通过对近年来国内外发表的相关文献进行整理与分析,综述了PICK1蛋白的生理功能与其作为药物靶点的研究新进展,旨在为PICK1蛋白的深入研究提供理论支持。展开更多
蛋白质是生命功能的执行者.生命体中某些关键蛋白的功能异常往往是导致疾病发生的根本原因.这些疾病相关蛋白极有可能成为药物靶点,为新药研发和疾病治疗提供重要线索.PICK1蛋白(protein interacting with Cαkinase1)结合能力广泛、功...蛋白质是生命功能的执行者.生命体中某些关键蛋白的功能异常往往是导致疾病发生的根本原因.这些疾病相关蛋白极有可能成为药物靶点,为新药研发和疾病治疗提供重要线索.PICK1蛋白(protein interacting with Cαkinase1)结合能力广泛、功能多样以及在多种重要疾病(如:癌症、精神分裂症、疼痛、帕金森综合症等)的发生发展过程中发挥潜在的作用,使其成为一个可能的药靶蛋白.PICK1与绝大多数配体蛋白的相互作用是通过其PDZ结构域与配体C末端区域的结合介导的,使PICK1的PDZ结构域成为一个潜在的药物靶点.因此,可以利用生物小分子物质特异性地结合PICK1的PDZ结构域,干扰或阻断PICK1与配体蛋白的天然相互作用,最终达到治疗相关疾病的目的.展开更多
基金financially supported by the National Key R&D Program of China(2018YFA0702504)the National Natural Science Foundation of China(42174152)+1 种基金the Strategic Cooperation Technology Projects of China National Petroleum Corporation(CNPC)and China University of Petroleum-Beijing(CUPB)(ZLZX2020-03)the R&D Department of China National Petroleum Corporation(2022DQ0604-01)。
文摘The picking efficiency of seismic first breaks(FBs)has been greatly accelerated by deep learning(DL)technology.However,the picking accuracy and efficiency of DL methods still face huge challenges in low signal-to-noise ratio(SNR)situations.To address this issue,we propose a regression approach to pick FBs based on bidirectional long short-term memory(Bi LSTM)neural network by learning the implicit Eikonal equation of 3D inhomogeneous media with rugged topography in the target region.We employ a regressive model that represents the relationships among the elevation of shots,offset and the elevation of receivers with their seismic traveltime to predict the unknown FBs,from common-shot gathers with sparsely distributed traces.Different from image segmentation methods which automatically extract image features and classify FBs from seismic data,the proposed method can learn the inner relationship between field geometry and FBs.In addition,the predicted results by the regressive model are continuous values of FBs rather than the discrete ones of the binary distribution.The picking results of synthetic data shows that the proposed method has low dependence on label data,and can obtain reliable and similar predicted results using two types of label data with large differences.The picking results of9380 shots for 3D seismic data generated by vibroseis indicate that the proposed method can still accurately predict FBs in low SNR data.The subsequent stacked profiles further illustrate the reliability and effectiveness of the proposed method.The results of model data and field seismic data demonstrate that the proposed regression method is a robust first-break picker with high potential for field application.
基金supported in part by the National Key Research and Development Program of China under Grant 2018YFA0702501in part by NSFC under Grant 41974126,41674116 and 42004101。
文摘Picking velocities from semblances manually is laborious and necessitates experience. Although various methods for automatic velocity picking have been developed, there remains a challenge in efficiently incorporating information from nearby gathers to ensure picked velocity aligns with seismic horizons while also improving picking accuracy. The conventional method of velocity picking from a semblance volume is computationally demanding, highlighting a need for a more efficient strategy. In this study, we introduce a novel method for automatic velocity picking based on multi-object tracking. This dynamic tracking process across different semblance panels can integrate information from nearby gathers effectively while maintaining computational efficiency. First, we employ accelerated density clustering on the velocity spectrum to discern cluster centers without the requirement for prior knowledge regarding the number of clusters. These cluster centers embody the maximum likelihood velocities of the main subsurface structures. Second, our proposed method tracks key points within the semblance volume. Kalman filter is adopted to adjust the tracking process, followed by interpolation on these tracked points to construct the final velocity model. Our synthetic data example demonstrates that our proposed algorithm can effectively rectify the picking errors of the clustering algorithm. We further compare the performances of the clustering method(CM), the proposed tracking method(TM), and the variational method(VM) on a field dataset from the Gulf of Mexico. The results attest that our method offers superior accuracy than CM, achieves comparable accuracy with VM, and benefits from a reduced computational cost.
基金supported by the Major Research Plan on West-Pacific Earth System Multispheric Interactions (Nos.91858215,91958206)the National Natural Science Foundation of China (NSFC)Shiptime Sharing Project (No.41949581)the Key Research and Development Program of Shandong Province (No.2019GHY112019)。
文摘The precision and reliability of first-arrival picking are crucial for determining the accuracy of geological structure inversion using active source ocean bottom seismometer(OBS)refraction data.Traditional methods for first-arrival picking based on sample points are characterized by theoretical errors,especially in low-sampling-frequency OBS data because the travel time of seismic waves is not an integer multiple of the sampling interval.In this paper,a first-arrival picking method that utilizes the spatial waveform variation characteristics of active source OBS data is presented.First,the distribution law of theoretical error is examined;adjacent traces exhibit variation characteristics in their waveforms.Second,a label cross-correlation superposition method for extracting highfrequency signals is presented to enhance the first-arrival picking precision.Results from synthetic and field data verify that the proposed approach is robust,successfully overcomes the limitations of low sampling frequency,and achieves precise outcomes that are comparable with those of high-sampling-frequency data.
文摘Due to the effects of the COVID-19 pandemic and the rise of online shopping, the offline sales of IKEA Fuzhou have been declining since 2020. Because the cost of distribution warehouse is a major expense for offline chain furniture retailers, and the picking process is a key activity in distribution warehouse operations. To reduce the cost of distribution warehouse and alleviate the survival pressure of the offline chain furniture retailers, this paper focuses on optimizing the picking route of the IKEA Fuzhou distribution warehouse. It starts by creating a two-dimensional coordinate system for the storage location of the distribution warehouse using the traditional S-type picking strategy to calculate the distance and time of the sorting route. Then, the problem of optimizing the picking route is then transformed into the travelling salesman problem (TSP), and picking route optimization model is developed using a genetic algorithm to analyze the sorting efficiency and picking route optimization. Results show that the order-picking route using the genetic algorithm strategy is significantly better than the traditional S-type picking strategy, which can improve overall sorting efficiency and operations, reduce costs, and increase efficiency. Thus, this establishes an implementation process for the order-picking path based on genetic algorithm optimization to improve overall sorting efficiency and operations, reduce costs, increase efficiency, and alleviate the survival pressure of pandemic-affected enterprises.
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jointly supported by the National Natural Science Foundation of China (No. 42074060)the Special Fund, Institute of Geophysics, China Earthquake Administration (CEA-IGP) (Nos. DQJB19B29, DQJB20B15, and DQJB22Z01)supported by XingHuo Project, CEA (No. XH211103)
文摘Seismic phase pickers based on deep neural networks have been extensively used recently,demonstrating their advantages on both performance and efficiency.However,these pickers are trained with and applied to different data.A comprehensive benchmark based on a single dataset is therefore lacking.Here,using the recently released DiTing dataset,we analyzed performances of seven phase pickers with different network structures,the efficiencies are also evaluated using both CPU and GPU devices.Evaluations based on F1-scores reveal that the recurrent neural network(RNN)and EQTransformer exhibit the best performance,likely owing to their large receptive fields.Similar performances are observed among PhaseNet(UNet),UNet++,and the lightweight phase picking network(LPPN).However,the LPPN models are the most efficient.The RNN and EQTransformer have similar speeds,which are slower than those of the LPPN and PhaseNet.UNet++requires the most computational effort among the pickers.As all of the pickers perform well after being trained with a large-scale dataset,users may choose the one suitable for their applications.For beginners,we provide a tutorial on training and validating the pickers using the DiTing dataset.We also provide two sets of models trained using datasets with both 50 Hz and 100 Hz sampling rates for direct application by end-users.All of our models are open-source and publicly accessible.
基金supported by the Research Fund Program of the Guangdong Provincial Key Laboratory of Fuel Cell Technology。
文摘Because of their unique mechanical and electrical properties,zinc oxide(ZnO)nanowires are used widely in microscopic and nanoscopic devices and structures,but characterizing them remains challenging.In this paper,two pick-up strategies are proposed for characterizing the electrical properties of ZnO nanowires using SEM equipped with a nanomanipulator.To pick up nanowires efficiently,direct sampling is compared with electrification fusing,and experiments show that direct sampling is more stable while electrification fusing is more efficient.ZnO nanowires have cut-off properties,and good Schottky contact with the tungsten probes was established.In piezoelectric experiments,the maximum piezoelectric voltage generated by an individual ZnO nanowire was 0.07 V,and its impedance decreased with increasing input signal frequency until it became stable.This work offers a technical reference for the pick-up and construction of nanomaterials and nanogeneration technology.
文摘On the basis of investigating picking and sightseeing resources in neighboring areas of Bayi Town,Linzhi City,this paper explored problems in the development of local leisure sightseeing and picking industry,and proposed scientific and reasonable countermeasures.
基金supported by grants from Zhejiang Provincial Natural Science Foundation of China(No.LQ21H200007)National Natural Science Foundation of China(No.82202605,No.81772664 and No.82172363)+1 种基金Zhejiang Provincial People’s Hospital Excellent Scientific Research Start-up Fundation of China(No.ZRY2019C008)Hangzhou Medical College Fundamental Scientific Research Project of China(No.KYQN202116).
文摘Objective The protein interacting with C kinase 1(PICK1)plays a critical role in vesicle trafficking,and its deficiency in sperm cells results in abnormal vesicle trafficking from Golgi to acrosome,which eventually disrupts acrosome formation and leads to male infertility.Methods An azoospermia sample was filtered,and the laboratory detection and clinical phenotype indicated typical azoospermia in the patient.We sequenced all of the exons in the PICK1 gene and found that there was a novel homozygous variant in the PICK1 gene,c.364delA(p.Lys122SerfsX8),and this protein structure truncating variant seriously affected the biological function.Then we constructed a PICK1 knockout mouse model using clustered regularly interspaced short palindromic repeat cutting technology(CRISPRc).Results The sperm from PICK1 knockout mice showed acrosome and nucleus abnormalities,as well as dysfunctional mitochondrial sheath formation.Both the total sperm and motility sperm counts were decreased in the PICK1 knockout mice compared to wild-type mice.Moreover,the mitochondrial dysfunction was verified in the mice.These defects in the male PICK1 knockout mice may have eventually led to complete infertility.Conclusion The c.364delA novel variant in the PICK1 gene associated with clinical infertility,and pathogenic variants in the PICK1 may cause azoospermia or asthenospermia by impairing mitochondrial function in both mice and humans.
文摘Niemann-Pick disease (NPD) refers to a group of patients who have varying degrees of lipid storage and foam cell infiltration in tissues, as well as overlapping clinical features, including hepatosplenomegaly, insufficiency pulmonary and/or central nervous system (CNS). Thanks to the pioneering work of Roscoe Brady and colleagues, we now know that there are two distinct metabolic abnormalities that explain NPD. The first is due to the deficient activity of the acid sphingomyelinase enzyme (ASM;NPD “types A and B”), and the second is due to defective functioning in the transport of cholesterol (NPD “type C”). We report the case of a 13-year-old adolescent diagnosed with Niemann-Pick A/B disease.
文摘PICK1蛋白(protein interacting with C alpha kinase 1)是一种同时具有PDZ和BAR区域的支架蛋白,在哺乳动物体内与多种蛋白质相互作用,并被证明在多种生理过程中发挥重要的调节作用,同时参与了多种疾病病理过程。因此,PICK1蛋白可能成为极具前景的疾病治疗靶点。该文通过对近年来国内外发表的相关文献进行整理与分析,综述了PICK1蛋白的生理功能与其作为药物靶点的研究新进展,旨在为PICK1蛋白的深入研究提供理论支持。
文摘蛋白质是生命功能的执行者.生命体中某些关键蛋白的功能异常往往是导致疾病发生的根本原因.这些疾病相关蛋白极有可能成为药物靶点,为新药研发和疾病治疗提供重要线索.PICK1蛋白(protein interacting with Cαkinase1)结合能力广泛、功能多样以及在多种重要疾病(如:癌症、精神分裂症、疼痛、帕金森综合症等)的发生发展过程中发挥潜在的作用,使其成为一个可能的药靶蛋白.PICK1与绝大多数配体蛋白的相互作用是通过其PDZ结构域与配体C末端区域的结合介导的,使PICK1的PDZ结构域成为一个潜在的药物靶点.因此,可以利用生物小分子物质特异性地结合PICK1的PDZ结构域,干扰或阻断PICK1与配体蛋白的天然相互作用,最终达到治疗相关疾病的目的.
