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| 1 | Application of Discrete Lumped Kinetic Modeling on Vacuum Gas Oil Hydrocracking显示文摘The kinetic model of vacuum gas oil (VGO) hydrocracking based on discrete lumped approach was investigated, and some improvement was put forward at the same time in this article. A parallel reaction scheme to describe the conversion of VGO into products (gases, gasoline, and diesel) proposed by Orochko was used. The different experimental data were analyzed statistically and then the product distribution and kinetic parameters were simulated by available data. Furthermore, the kinetic parameters were correlated based on the feed property, reaction temperature, and catalyst activity. An optimization code in Matlab 2011b was written to fine-tune these parameters. The model had a favorable ability to predict the product distribution and there was a good agreement between the model predictions and experiment data. Hence, the kinetic parameters indeed had something to do with feed properties, reaction temperature and catalyst activity. | Han Longnian Fang Xiangchen Peng Chong Zhao Tao | 2013 | China Petroleum Processing & Petrochemical Technology2013，15，2： | 8 |
| 2 | Ripple-associated high-firing interneurons in the hippocampal CA1 region显示文摘By simultaneously recording the activity of individual neurons and field potentials in freely behaving mice, we found two types of interneurons firing at high frequency in the hippocampal CA1 region, which had high correlations with characteristic sharp wave-associated ripple oscillations (100―250 Hz) during slow-wave sleep. The firing of these two types of interneurons highly synchronized with ripple oscillations during slow-wave sleep, with strongly increased firing rates corresponding to individual ripple episodes. Interneuron type I had at most one spike in each sub-ripple cycle of ripple episodes and the peak firing rate was 310±33.17 Hz. Interneuron type II had one or two spikes in each sub-ripple cycle and the peak firing rate was 410±47.61 Hz. During active exploration, their firing was phase locked to theta oscillations with the highest probability at the trough of theta wave. Both two types of interneurons increased transiently their firing rates responding to the startling shake stimuli. The results showed that these two types of high-frequency interneurons in the hippocampal CA1 region were involved in the modulation of the hippocampal neural network during different states. | WANG Ying, ZHANG Lu, PAN JingWei, XIE Kun, LI ShiQi, WANG ZhiRu & LIN LongNian Key Laboratory of Brain Functional Genomics of Ministry of Education, East China Normal University, Shanghai 200062, China | 2008 | Science China(Life Sciences)2008，51，2： | 3 |
| 3 | On brain activity mapping: Insights and lessons from Brain Decoding Project to map memory patterns in the hippocampus显示文摘The BRAIN project recently announced by the president Obama is the reflection of unrelenting human quest for cracking the brain code, the patterns of neuronal activity that define who we are and what we are. While the Brain Activity Mapping proposal has rightly emphasized on the need to develop new technologies for measuring every spike from every neuron, it might be helpful to consider both the theoretical and experimental aspects that would accelerate our search for the organizing principles of the brain code. Here we share several insights and lessons from the similar proposal, namely, Brain Decoding Project that we initiated since 2007. We provide a specific example in our initial mapping of real-time memory traces from one part of the memory circuit, namely, the CA1 region of the mouse hippocampus. We show how innovative behavioral tasks and appropriate mathematical analyses of large datasets can play equally, if not more, important roles in uncovering the specific-to-general feature-coding cell assembly mechanism by which episodic memory, semantic knowledge, and imagination are generated and organized. Our own experiences suggest that the bottleneck of the Brain Project is not only at merely developing additional new technologies, but also the lack of efficient avenues to disseminate cutting edge platforms and decoding expertise to neuroscience community. Therefore, we propose that in order to harness unique insights and extensive knowledge from various investigators working in diverse neuroscience subfields, ranging from perception and emotion to memory and social behaviors, the BRAIN project should create a set of International and National Brain Decoding Centers at which cutting-edge recording technologies and expertise on analyzing large datasets analyses can be made readily available to the entire community of neuroscientists who can apply and schedule to perform cutting-edge research. | TSIEN Joe Z. LI Meng OSAN Remus CHEN GuiFen LIN LongNian WANG Phillip Lei FREY Sabine FREY Julietta ZHU DaJiang LIU TianMing ZHAO Fang KUANG Hui | 2013 | Science China(Life Sciences)2013，56，9： | 1 |
| 4 | Identification of network-level coding units for real-time representation of episodic experiences in the hippocampus显示文摘 | Longnian Lin Remus Osaa Joe Z Tsien | 2005 | PNAS2005，102，17： | 1 |
| 5 | Distributed implantation of a flexible microelectrode array for neural recording显示文摘Flexible multichannel electrode arrays(fMEAs)with multiple flaments can be flexibly implanted in various patterns.It is necessary to develop a method for implanting the fMEA in different locations and at various depths based on the recording demands.This study proposed a strategy for reducing the microelectrode volume with integrated packaging.An implantation system was developed specifically for semiautomatic distributed implantation.The feasibility and convenience of the fMEA and implantation platform were verified in rodents.The acute and chronic recording results provied the effectiveness of the packaging and implantation methods.These methods could provide a novel strategy for developing fMEAs with more flaments and recording sites to measure functional interactions across multiple brain regions. | Chunrong Wei Yang Wang Weihua Pei Xinyong Han Longnian Lin Zhiduo Liu Gege Ming Ruru Chen Pingping Wu Xiaowei Yang Li Zheng Yijun Wang | 2022 | Microsystems & Nanoengineering2022，8，3： | 1 |
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