The catalytic activities of nine neutral nickel and palladium a-acetylide complexes [M=(C=CR)2(PR'3)2)M=Ni,Pd; R=Ph,CH2OH,CH2OOCH3,CH2OOCPh,CH2OOCPhOH-o; R'=Ph,Bu] are compared.Among them,Ni(Cs=CPh)2-(PBuj)2 s...The catalytic activities of nine neutral nickel and palladium a-acetylide complexes [M=(C=CR)2(PR'3)2)M=Ni,Pd; R=Ph,CH2OH,CH2OOCH3,CH2OOCPh,CH2OOCPhOH-o; R'=Ph,Bu] are compared.Among them,Ni(Cs=CPh)2-(PBuj)2 shows the highest catalytic activity and gives the polystyrene with high molecular weight(Mw=188800)and a syndio-rich microstructure.The catalytic behavior of transition metal acetylides is related to metal,phosphine,and alkynyl ligands bonded to the metal atoms.展开更多
Background Naturalistic stimuli,such as videos,can elicit complex brain activations.However,the intricate nature of these stimuli makes it challenging to attribute specific brain functions to the resulting activations...Background Naturalistic stimuli,such as videos,can elicit complex brain activations.However,the intricate nature of these stimuli makes it challenging to attribute specific brain functions to the resulting activations,particularly for higher-level processes such as social interactions.Objective We hypothesized that activations in different layers of a convolutional neural network(VGG-16)would correspond to varying levels of brain activation,reflecting the brain's visual processing hierarchy.Additionally,we aimed to explore which brain regions would be linked to the deeper layers of the network.Methods This study analyzed functional MRI data from participants watching a cartoon video.Using a pre-trained VGG-16 convolutional neural network,we mapped hierarchical features of the video to different levels of brain activation.Activation maps from various kernels and layers were extracted from video frames,and the time series of average activation patterns for each kernel were used in a voxel-wise model to examine brain responses.Results Lower layers of the network were primarily associated with activations in lower visual regions,although some kernels also unexpectedly showed associations with the posterior cingulate cortex.Deeper layers were linked to more anterior and lateral regions of the visual cortex,as well as the supramarginal gyrus.Conclusions This analysis demonstrated both the potential and limitations of using convolutional neural networks to connect video content with brain functions,providing valuable insights into how different brain regions respond to varying levels of visual processing.展开更多
文摘The catalytic activities of nine neutral nickel and palladium a-acetylide complexes [M=(C=CR)2(PR'3)2)M=Ni,Pd; R=Ph,CH2OH,CH2OOCH3,CH2OOCPh,CH2OOCPhOH-o; R'=Ph,Bu] are compared.Among them,Ni(Cs=CPh)2-(PBuj)2 shows the highest catalytic activity and gives the polystyrene with high molecular weight(Mw=188800)and a syndio-rich microstructure.The catalytic behavior of transition metal acetylides is related to metal,phosphine,and alkynyl ligands bonded to the metal atoms.
基金supported by(US)National Institute of Mental Health grants to Xin Di(R15MH125332)Bharat B.Biswal(R01MH131335).
文摘Background Naturalistic stimuli,such as videos,can elicit complex brain activations.However,the intricate nature of these stimuli makes it challenging to attribute specific brain functions to the resulting activations,particularly for higher-level processes such as social interactions.Objective We hypothesized that activations in different layers of a convolutional neural network(VGG-16)would correspond to varying levels of brain activation,reflecting the brain's visual processing hierarchy.Additionally,we aimed to explore which brain regions would be linked to the deeper layers of the network.Methods This study analyzed functional MRI data from participants watching a cartoon video.Using a pre-trained VGG-16 convolutional neural network,we mapped hierarchical features of the video to different levels of brain activation.Activation maps from various kernels and layers were extracted from video frames,and the time series of average activation patterns for each kernel were used in a voxel-wise model to examine brain responses.Results Lower layers of the network were primarily associated with activations in lower visual regions,although some kernels also unexpectedly showed associations with the posterior cingulate cortex.Deeper layers were linked to more anterior and lateral regions of the visual cortex,as well as the supramarginal gyrus.Conclusions This analysis demonstrated both the potential and limitations of using convolutional neural networks to connect video content with brain functions,providing valuable insights into how different brain regions respond to varying levels of visual processing.
