Comparison between cannabidiol and sertra⁃line in modulation of post-traumatic stress dis⁃order-like behaviors and fear memory in mice

OBJECTIVE Post-traumatic stress disorder(PTSD)is characterized by poor adapta⁃tion to a traumatic experience and disturbances in fear memory regulation,and currently lacks effective medication.Cannabidiol(CBD)is the primary component of the Cannabis sativa plant;it does not have any psychoactive effects and has been implicated in modulating fear learning in mammals.The present study investigated the effect of CBD on PTSD-like behaviors in a mouse pre-shock model,the effect of CBD in the modulation of trauma-related fear memory,a crucial process leading to core symptoms of PTSD.METHODS Pre-shock model was applied in which mice were submitted to training with two days of 0.8 mA×12 times of foot-shock,and PTSD-like behaviors was evaluated during 3 and 26 d,including freezing time to the conditioned context,open field test,elevated plus maze test and social interaction test.RESULTS CBD(10 mg·kg^(-1))administration alleviated main PTSD-like symptoms in the mouse pre-shock model by attenuating trauma-related fear memory,decreasing anxiety-like behavior,and increasing social interaction behavior.However,sertraline(15 mg·kg^(-1))was only effective when adminis⁃tered throughout the test period.Furthermore,CBD reduced the formation,retrieval,and recon⁃solidation of trauma-related fear memory,whereas sertraline only reduced fear-memory retrieval.Neither CBD nor sertraline influenced the acquisi⁃tion of trauma-related fear memory.CONCLU⁃SION CBD produced anti-PTSD-like actions in mice,and could disrupt trauma-related fear mem⁃ory by interfering with multiple aspects of fear memory processing in mice.These findings indi⁃cate that CBD may be a promising candidate for treating PTSD.

Development of Ubiquitous Simulation Service Structure Based on High Performance Computing Technologies

The simulation field became essential in designing or developing new casting products and in improving manufacturing processes within limited time, because it can help us to simulate the nature of processing, so that developers can make ideal casting designs. To take the prior occupation at commercial simulation market, so many development groups in the world are doing their every effort. They already reported successful stories in manufacturing fields by developing and providing the high performance simulation technologies for multipurpose. But they all run at powerful desk-side computers by well-trained experts mainly, so that it is hard to diffuse the scientific designing concept to newcomers in casting field. To overcome upcoming problems in scientific casting designs, we utilized information technologies and full-matured hardware backbones to spread out the effective and scientific casting design mind, and they all were integrated into Simulation Portal on the web. It professes scientific casting design on the NET including ubiquitous access way represented by ＂Anyone, Anytime, Anywhere＂ concept for casting designs.

STRONG APPROXIMATION FOR MOVING AVERAGE PROCESSES UNDER DEPENDENCE ASSUMPTIONS

Let {Xt,t ≥ 1} be a moving average process defined by Xt = ∑^∞ k=0 αkξt-k, where {αk,k ≥ 0} is a sequence of real numbers and {ξt,-∞ 〈 t 〈 ∞} is a doubly infinite sequence of strictly stationary dependent random variables. Under the conditions of {αk, k ≥ 0} which entail that {Xt, t ≥ 1} is either a long memory process or a linear process, the strong approximation of {Xt, t ≥ 1} to a Gaussian process is studied. Finally, the results are applied to obtain the strong approximation of a long memory process to a fractional Brownian motion and the laws of the iterated logarithm for moving average processes.

Hidden Process Offline Forensic Based on Memory Analysis in Windows

Malicious software programs usually bypass the detection of anti-virus software by hiding themselves among apparently legitimate programs.In this work,we propose Windows Virtual Machine Introspection（WVMI）to accurately detect those hidden processes by analyzing memory data.WVMI dumps in-memory data of the target Windows operating systems from hypervisor and retrieves EPROCESS structures’address of process linked list first,and then generates Data Type Confidence Table（DTCT）.Next,it traverses the memory and identifies the similarities between the nodes in process linked list and the corresponding segments in the memory by utilizing DTCT.Finally,it locates the segments of Windows’EPROCESS and identifies the hidden processes by further comparison.Through extensive experiments,our experiment shows that the WVMI detects the hidden process with high identification rate,and it is independent of different versions of Windows operating system.

XB-SIM*:A Simulation Framework for Modeling and Exploration of ReRAM-Based CNN Acceleration Design

Resistive Random Access Memory(ReRAM)-based neural network accelerators have potential to surpass their digital counterparts in computational efficiency and performance.However,design of these accelerators faces a number of challenges including imperfections of the Re RAM device and a large amount of calculations required to accurately simulate the former.We present XB-SIM,a simulation framework for Re RAM-crossbar-based Convolutional Neural Network(CNN)accelerators.XB-SIM can be flexibly configured to simulate the accelerator’s structure and clock-driven behaviors at the architecture level.This framework also includes an Re RAM-aware Neural Network(NN)training algorithm and a CNN-oriented mapper to train an NN and map it onto the simulated design efficiently.Behavior of the simulator has been verified by the corresponding circuit simulation of a real chip.Furthermore,a batch processing mode of the massive calculations that are required to mimic the behavior of Re RAM-crossbar circuits is proposed to fully apply the computational concurrency of the mapping strategy.On CPU/GPGPU,this batch processing mode can improve the simulation speed by up to 5.02 or 34.29.Within this framework,comprehensive architectural exploration and end-to-end evaluation have been achieved,which provide some insights for systemic optimization.