Hemiplegia after stroke has become a major cause of the world's high disabilities,and it is vital to enhance our understanding of post-stroke neuroplasticity to develop e±cient rehabilitation programs.This st...Hemiplegia after stroke has become a major cause of the world's high disabilities,and it is vital to enhance our understanding of post-stroke neuroplasticity to develop e±cient rehabilitation programs.This study aimed to explore the brain activation and network reorganization of the motor cortex(MC)with functional near-infrared spectroscopy(fNIRS).The MC hemodynamic signals were gained from 22 stroke patients and 14 healthy subjects during a shoulder-touching task with the right hand.The MC activation pattern and network attributes analyzed with the graph theory were compared between the two groups.The results revealed that healthy controls presented dominant activation in the left MC while stroke patients exhibited dominant activation in the bilateral hemispheres MC.The MC networks for the two groups had small-world properties.Compared with healthy controls,patients had higher transitivity and lower global e±ciency(GE),mean connectivity,and long connections(LCs)in the left MC.In addition,both MC activation and network attributes were correlated with patient's upper limb motor function.The results showed the stronger compensation of the unaffected motor area,the better recovery of the upper limb motor function for patients.Moreover,the MC network possessed high clustering and relatively sparse inter-regional connections during recovery for patients.Our results promote the understanding of MC reorganization during recovery and indicate that MC activation and network could provide clinical assessment signi¯cance in stroke patients.Given the advantages of fNIRS,it shows great application potential in the assessment and rehabilitation of motor function after stroke.展开更多
Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate a...Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.展开更多
目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的s...目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的shRNA(shRNA-METTL5)转染TNBC细胞后,用CCK-8、集落形成、伤口愈合以及Transwell实验分别检测细胞增殖活性、迁移与侵袭,Western blot检测Wnt/β-catenin信号关键蛋白的表达。构建异种移植瘤模型,验证敲降METTL5对TNBC细胞在体内生长以及Wnt/β-catenin信号活性的影响。结果METTL5在TNBC肿瘤组织和细胞系中表达上调(P<0.01)。敲降METTL5可抑制TNBC细胞的增殖、迁移和侵袭并降低了Wnt/β-catenin信号分子β-catenin、细胞周期蛋白(Cyclin)D1、基质金属蛋白酶(MMP)-2和MMP-7的表达(均P<0.01)。体内实验显示,敲降METTL5减缓了移植瘤生长和Wnt/β-catenin信号活性。结论敲降METTL5能抑制TNBC细胞的增殖、迁移与侵袭,其作用可能与抑制Wnt/β-catenin信号通路有关。展开更多
基金was supported by the National Key Research and Development Program of China(Nos.2020YFC2004300,2020YFC2004303 and 2020YFC2004302)the National Natural Science Foundation of China(Nos.32000980 and 82171533)+1 种基金the Guangdong Basic and Applied Basic Research Foundation(Nos.2022A1515140142,2019A1515110427 and 2020B1515120014)the Key Laboratory Program of Guangdong Higher Education Institutes(No.2020KSYS001)。
文摘Hemiplegia after stroke has become a major cause of the world's high disabilities,and it is vital to enhance our understanding of post-stroke neuroplasticity to develop e±cient rehabilitation programs.This study aimed to explore the brain activation and network reorganization of the motor cortex(MC)with functional near-infrared spectroscopy(fNIRS).The MC hemodynamic signals were gained from 22 stroke patients and 14 healthy subjects during a shoulder-touching task with the right hand.The MC activation pattern and network attributes analyzed with the graph theory were compared between the two groups.The results revealed that healthy controls presented dominant activation in the left MC while stroke patients exhibited dominant activation in the bilateral hemispheres MC.The MC networks for the two groups had small-world properties.Compared with healthy controls,patients had higher transitivity and lower global e±ciency(GE),mean connectivity,and long connections(LCs)in the left MC.In addition,both MC activation and network attributes were correlated with patient's upper limb motor function.The results showed the stronger compensation of the unaffected motor area,the better recovery of the upper limb motor function for patients.Moreover,the MC network possessed high clustering and relatively sparse inter-regional connections during recovery for patients.Our results promote the understanding of MC reorganization during recovery and indicate that MC activation and network could provide clinical assessment signi¯cance in stroke patients.Given the advantages of fNIRS,it shows great application potential in the assessment and rehabilitation of motor function after stroke.
基金financially supported by the Ministry of Science and Technology of China (2017YFA0204503 and 2018YFA0703200)the National Natural Science Foundation of China (52121002,U21A6002 and 22003046)+1 种基金the Tianjin Natural Science Foundation (20JCJQJC00300)“A Multi-Scale and High-Efficiency Computing Platform for Advanced Functional Materials”program,funded by Haihe Laboratory in Tianjin (22HHXCJC00007)。
文摘Reorganization energy(RE)is closely related to the charge transport properties and is one of the important parameters for screening novel organic semiconductors(OSCs).With the rise of data-driven technology,accurate and efficient machine learning(ML)models for high-throughput screening novel organic molecules play an important role in the boom of material science.Comparing different molecular descriptors and algorithms,we construct a reasonable algorithm framework with molecular graphs to describe the compositional structure,convolutional neural networks to extract material features,and subsequently embedded fully connected neural networks to establish the mapping between features and predicted properties.With our well-designed judicious training pattern about feature-guided stratified random sampling,we have obtained a high-precision and robust reorganization energy prediction model,which can be used as one of the important descriptors for rapid screening potential OSCs.The root-meansquare error(RMSE)and the squared Pearson correlation coefficient(R^(2))of this model are 2.6 me V and0.99,respectively.More importantly,we confirm and emphasize that training pattern plays a crucial role in constructing supreme ML models.We are calling for more attention to designing innovative judicious training patterns in addition to high-quality databases,efficient material feature engineering and algorithm framework construction.
