Coseismic Coulomb stress changes induced by a 2020-2021 M_(W)>7.0 Alaska earthquake sequence in and around the Shumagin gap and its influence on the Alaska-Aleutian subduction interface

Three M_(W)>7.0 earthquakes in 2020-2021 occurred in the Shumagin seismic gap and its adjacent area of the Alaska-Aleutian subduction zone,including the Mw7.8 Simeonof thrust earthquake on July 22,2020,the M_(W)7.6 Sand Point strike-slip earthquake on October 19,2020,and the M_(W)8.2 Chignik thrust earthquake on July 29,2021.The spatial and temporal proximity of these three earthquakes prompts us to probe stress-triggering effects among them.Here we examine the coseismic Coulomb stress change imparted by the three earthquakes and their influence on the subduction interface.Our results show that:(1)The Simeonof earthquake has strong loading effects on the subsequent Sand Point and Chignik earthquakes,with the Coulomb stress changes of 3.95 bars and 2.89 bars,respectively.The Coulomb stress change caused by the Sand Point earthquake at the hypocenter of the Chignik earthquake is merely around 0.01 bars,suggesting the negligible triggering effect on the latter earthquake;(2)The triggering effects of the Simeonof,Sand Point,and Chignik earthquakes on aftershocks within three months are not well pronounced because of the triggering rates of 38%,14%,and 43%respectively.Other factors may have played an important role in promoting the occurrence of these aftershocks,such as the roughness of the subduction interface,the complicated velocity structure of the lithosphere,and the heterogeneous prestress therein;(3)The three earthquakes caused remarkable coseismic Coulomb stress changes at the subduction interface nearby these mainshocks,with an average Coulomb stress change of 3.2 bars in the shallow region directly inwards the trench.

The miR-9-5p/CXCL11 pathway is a key target of hydrogen sulfide-mediated inhibition of neuroinflammation in hypoxic ischemic brain injury

We previously showed that hydrogen sulfide(H2S)has a neuroprotective effect in the context of hypoxic ischemic brain injury in neonatal mice.However,the precise mechanism underlying the role of H2S in this situation remains unclear.In this study,we used a neonatal mouse model of hypoxic ischemic brain injury and a lipopolysaccharide-stimulated BV2 cell model and found that treatment with L-cysteine,a H2S precursor,attenuated the cerebral infarction and cerebral atrophy induced by hypoxia and ischemia and increased the expression of miR-9-5p and cystathionineβsynthase(a major H2S synthetase in the brain)in the prefrontal cortex.We also found that an miR-9-5p inhibitor blocked the expression of cystathionineβsynthase in the prefrontal cortex in mice with brain injury caused by hypoxia and ischemia.Furthermore,miR-9-5p overexpression increased cystathionine-β-synthase and H2S expression in the injured prefrontal cortex of mice with hypoxic ischemic brain injury.L-cysteine decreased the expression of CXCL11,an miR-9-5p target gene,in the prefrontal cortex of the mouse model and in lipopolysaccharide-stimulated BV-2 cells and increased the levels of proinflammatory cytokines BNIP3,FSTL1,SOCS2 and SOCS5,while treatment with an miR-9-5p inhibitor reversed these changes.These findings suggest that H2S can reduce neuroinflammation in a neonatal mouse model of hypoxic ischemic brain injury through regulating the miR-9-5p/CXCL11 axis and restoringβ-synthase expression,thereby playing a role in reducing neuroinflammation in hypoxic ischemic brain injury.

Exosomes derived from microglia overexpressing miR-124-3p alleviate neuronal endoplasmic reticulum stress damage after repetitive mild traumatic brain injury

We previously reported that miR-124-3p is markedly upregulated in microglia-derived exosomes following repetitive mild traumatic brain injury.However,its impact on neuronal endoplasmic reticulum stress following repetitive mild traumatic brain injury remains unclear.In this study,we first used an HT22 scratch injury model to mimic traumatic brain injury,then co-cultured the HT22 cells with BV2 microglia expressing high levels of miR-124-3p.We found that exosomes containing high levels of miR-124-3p attenuated apoptosis and endoplasmic reticulum stress.Furthermore,luciferase reporter assay analysis confirmed that miR-124-3p bound specifically to the endoplasmic reticulum stress-related protein IRE1α,while an IRE1αfunctional salvage experiment confirmed that miR-124-3p targeted IRE1αand reduced its expression,thereby inhibiting endoplasmic reticulum stress in injured neurons.Finally,we delivered microglia-derived exosomes containing miR-124-3p intranasally to a mouse model of repetitive mild traumatic brain injury and found that endoplasmic reticulum stress and apoptosis levels in hippocampal neurons were significantly reduced.These findings suggest that,after repetitive mild traumatic brain injury,miR-124-3 can be transferred from microglia-derived exosomes to injured neurons,where it exerts a neuroprotective effect by inhibiting endoplasmic reticulum stress.Therefore,microglia-derived exosomes containing miR-124-3p may represent a novel therapeutic strategy for repetitive mild traumatic brain injury.

Common Spatial Pattern Ensemble Classifier and Its Application in Brain-Computer Interface

Abstract-Common spatial pattern （CSP） algorithm is a successful tool in feature estimate of brain-computer interface （BCI）. However, CSP is sensitive to outlier and may result in poor outcomes since it is based on pooling the covariance matrices of trials. In this paper, we propose a simple yet effective approach, named common spatial pattern ensemble （CSPE） classifier, to improve CSP performance. Through division of recording channels, multiple CSP filters are constructed. By projection, log-operation, and subtraction on the original signal, an ensemble classifier, majority voting, is achieved and outlier contaminations are alleviated. Experiment results demonstrate that the proposed CSPE classifier is robust to various artifacts and can achieve an average accuracy of 83.02%.

Real-Time Detection of Human Drowsiness via a Portable Brain-Computer Interface

In this paper, we proposed a new concept: depth of drowsiness, which can more precisely describe the drowsiness than existing binary description. A set of effective markers for drowsiness: normalized band norm was successfully developed. These markers are invariant from voltage amplitude of brain waves, eliminating the need for calibrating the voltage output of the brain-computer interface devices. A new polling algorithm was designed and implemented for computing the depth of drowsiness. The time cost of data acquisition and processing for each estimate is about one second, which is well suited for real-time applications. Test results with a portable brain-computer interface device show that the depth of drowsiness computed by the method in this paper is generally invariant from ages of test subjects and sensor channels (P3 and C4). The comparison between experiment and computing results indicate that the new method is noticeably better than one of the recent methods in terms of accuracy for predicting the drowsiness.

Real-Time Brain-Computer Interface System Based on Motor Imagery

Abstract-A brain-computer interface （BCI） real- time system based on motor imagery translates the user＇s motor intention into a real-time control signal for peripheral equipments. A key problem to be solved for practical applications is real-time data collection and processing. In this paper, a real-time BCI system is implemented on computer with electroencephalogram amplifier. In our implementation, the on-line voting method is adopted for feedback control strategy, and the voting results are used to control the cursor horizontal movement. Three subjects take part in the experiment. The results indicate that the best accuracy is 90%.

Neurological rehabilitation of stroke patients via motor imaginary-based brain-computer interface technology

The present study utilized motor imaginary-based brain-computer interface technology combined with rehabilitation training in 20 stroke patients. Results from the Berg Balance Scale and the Holden Walking Classification were significantly greater at 4 weeks after treatment （P 〈 0.01）, which suggested that motor imaginary-based brain-computer interface technology improved balance and walking in stroke patients.

Probabilistic Methods in Multi-Class Brain-Computer Interface

Abstract-Two probabilistic methods are extended to research multi-class motor imagery of brain-computer interface （BCI）： support vector machine （SVM） with posteriori probability （PSVM） and Bayesian linear discriminant analysis with probabilistic output （PBLDA）. A comparative evaluation of these two methods is conducted. The results shows that： 1） probabilistie information can improve the performance of BCI for subjects with high kappa coefficient, and 2） PSVM usually results in a stable kappa coefficient whereas PBLDA is more efficient in estimating the model parameters.

Possibility to Realize the Brain-Computer Interface from the Quantum Brain Model Based on Superluminal Particles

R. Penrose and S. Hameroff have proposed an idea that the brain can attain high efficient quantum computation by functioning of microtubular structure of neurons in the cytoskelton of biological cells, including neurons of the brain. But Tegmark estimated the duration of coherence of a quantum state in a warm wet brain to be on the order of 10>–13

Performance and Implementations of Vibrotactile Brain-Computer Interface with Ipsilateral and Bilateral Stimuli

The tactile P300 brain-computer interface( BCI) is related to the somatosensory perception and response of the human brain,and is different from visual or audio BCIs. Recently,several studies focused on the tactile stimuli delivered to different parts of the human body. Most of these stimuli were symmetrically bilateral.Only a fewstudies explored the influence of tactile stimuli laterality.In the current study,we extensively tested the performance of a vibrotactile BCI system using ipsilateral stimuli and bilateral stimuli.Two vibrotactile P300-based paradigms were tested. The target stimuli were located on the left and right forearms for the left forearm and right forearm( LFRF) paradigm,and on the left forearm and calf for the left forearm and left calf( LFLC)paradigm. Ten healthy subjects participated in this study. Our experiments and analysis showed that the bilateral paradigm( LFRF) elicited larger P300 amplitude and achieved significantly higher classification accuracy than the ipsilateral paradigm( LFLC). However, both paradigms achieved classification accuracies higher than 70% after the completion of several trials on average,which was usually regarded as the minimum accuracy level required for BCI system to be deemed useful.

Design of an EEG Preamplifier for Brain-Computer Interface

As a non-invasive neurophysiologieal index for brain-computer interface （BCI）, electroencephalogram （EEG） attracts much attention at present. In order to have a portable BCI, a simple and efficient pre-amplifier is crucial in practice. In this work, a preamplifier based on the characteristics of EEG signals is designed, which consists of a highly symmetrical input stage, low-pass filter, 50 Hz notch filter and a post amplifier. A prototype of this EEG module is fabricated and EEG data are obtained through an actual experiment. The results demonstrate that the EEG preamplifier will be a promising unit for BCI in the future.

脑机接口技术对缺血性脑卒中患者平衡功能及血清IL-6、TNF-α水平的影响

目的观察脑机接口控制脚踏训练系统对缺血性脑卒中患者平衡功能及血清白细胞介素-6(IL-6)、肿瘤坏死因子-α(TNF-α)水平的影响。方法选择2022年9月—2023年9月住院治疗的40例缺血性脑卒中患者作为观察对象。根据随机数字表法将患者均分为卒中对照组和卒中实验组。同时招募20例性别、年龄相当的健康受试者作为健康对照组,采集相关足底压力数据。卒中对照组患者接受包括上下肢主被动运动训练系统在内的常规康复训练,卒中实验组将卒中对照组中的上下肢主被动运动训练系统替换为脑机接口控制脚踏训练系统进行康复治疗,其他不变。治疗4周前后使用足底压力评估系统分别采集两组卒中患者睁闭眼状态下的双侧足底压力对称指数(SI)及身体压力中心(COP)摆动面积;采用Fugl-Meyer下肢运动功能评分(FMA-LE)、Berg平衡量表(BBS)对两组卒中患者进行评估;同时比较两组治疗前后血清IL-6、TNF-α水平。结果①两组卒中患者治疗后睁闭眼状态下的SI值及COP摆动面积均较入院前改善,且卒中实验组结果优于卒中对照组(P<0.05),但与健康对照组仍存在差距(P<0.05)。②两组卒中患者治疗后BBS、FMA-LE评分均较治疗前升高,且卒中实验组分数大于卒中对照组,差异有统计学意义(均P<0.05)。③两组卒中患者治疗后血清IL-6、TNF-α水平均较前下降,卒中实验组血清IL-6、TNF-α水平下降程度大于卒中对照组,差异有统计学意义(P<0.001,P<0.05)。结论脑机接口控制脚踏训练系统对于缺血性脑卒中偏瘫患者平衡功能的恢复具有促进作用,其机制可能与血清IL-6、TNF-α水平降低有关。

基于脑电的快速序列视觉呈现脑-机接口系统研究进展综述

脑-机接口(BCI)系统建立大脑与外部设备之间的直接交流通路,结合快速序列视觉呈现(RSVP)范式能够实现利用人类视觉系统进行高流通量图像目标检索。近些年来,RSVP-BCI系统在范式编码、脑电(EEG)解码和系统应用方面的研究取得了长足的进步。对范式编码的研究揭示不同范式参数对系统性能的影响,促进提升系统性能;脑电解码的研究在提升算法分类性能的同时推动少训练、零训练样本、多模态等场景下的应用;对RSVP-BCI系统应用的研究实现推动系统走向实际应用并拓宽了应用领域。同时,系统仍面临着迈向实际时可应用领域范围窄、脑电跨域解码难题以及计算机视觉飞速进步带来的挑战。该文对RSVP-BCI近年来的相关研究进展进行了回顾与总结,并对未来的发展方向进行了展望。

基于通道组合-数据对齐-多尺度全局CNN的MI-EEG分类

由于运动想象脑机接口(MI-BCI)范式不需要视觉刺激,应用MI-BCI范式在提高人机交互系统舒适度方面具有重要意义。为实现辅助设备的异步控制,提高模型的鲁棒性,减少通道使用数量以降低BCI系统输入的复杂性,提出一种基于通道组合(channel combination,CC)-数据对齐(euclidean space data alignment,EA)-多尺度全局卷积神经网络(multiscale global convolutional neural network,MGCNN)的运动想象脑电分类方法。通过引入大脑静息状态下的脑电信号,扩展MI-BCI输出指令集;利用CC将22通道脑电数据重构为左右对称通道加中间通道的3通道形式,重构后的数据经过EA方法规范后作为网络输入;构建多尺度卷积模块与全局卷积模块,并行提取脑电信号的局部特征和ERS/ERD全局特征;利用迁移学习提升模型的解码能力。结果表明:该方法在BCI Competition IV 2a数据集上达到了99.28%的平均准确率和0.99的Kappa值,提高了运动想象脑电分类精度,为在线异步运动想象脑机接口的应用与发展作出了贡献。

从脑-机接口到脑-器交互

脑-机接口(brain computer interface,BCI)建立了大脑和非生物设备之间直接的信息交互通道。在认可BCI技术独特应用价值的基础上,进一步整合了身心医学与脑-机接口,强调了“脑与身体”和“脑与环境”的相互支撑关系,提出了脑-器交互(brain-apparatus communication,BAC)框架。对脑-机接口到脑-器交互的发展历程进行了概述,并从脑-器官交互、脑-外部环境交互以及两者融合方面讨论了脑器交互对人类健康的影响。

Chlorogenic acid alleviates hypoxic-ischemic brain injury in neonatal mice

Recent studies have shown that chlorogenic acid(CGA),which is present in coffee,has protective effects on the nervous system.However,its role in neonatal hypoxic-ischemic brain injury remains unclear.In this study,we established a newborn mouse model of hypoxic-ischemic brain injury using a modified Rice-Vannucci method and performed intraperitoneal injection of CGA.We found that CGA intervention effectively reduced the volume of cerebral infarct,alleviated cerebral edema,restored brain tissue structure after injury,and promoted axon growth in injured brain tissue.Moreover,CGA pretreatment alleviated oxygen-glucose deprivation damage of primary neurons and promoted neuron survival.In addition,changes in ferroptosis-related proteins caused by hypoxic-ischemic brain injury were partially reversed by CGA.Furthermore,CGA intervention upregulated the expression of the key ferroptosis factor glutathione peroxidase 4 and its upstream glutamate/cystine antiporter related factors SLC7A11 and SLC3A2.In summary,our findings reveal that CGA alleviates hypoxic-ischemic brain injury in neonatal mice by reducing ferroptosis,providing new ideas for the treatment of neonatal hypoxic-ischemic brain injury.

Functional near-infrared spectroscopy in non-invasive neuromodulation

Non-invasive cerebral neuromodulation technologies are essential for the reorganization of cerebral neural networks,which have been widely applied in the field of central neurological diseases,such as stroke,Parkinson’s disease,and mental disorders.Although significant advances have been made in neuromodulation technologies,the identification of optimal neurostimulation paramete rs including the co rtical target,duration,and inhibition or excitation pattern is still limited due to the lack of guidance for neural circuits.Moreove r,the neural mechanism unde rlying neuromodulation for improved behavioral performance remains poorly understood.Recently,advancements in neuroimaging have provided insight into neuromodulation techniques.Functional near-infrared spectroscopy,as a novel non-invasive optical brain imaging method,can detect brain activity by measuring cerebral hemodynamics with the advantages of portability,high motion tole rance,and anti-electromagnetic interference.Coupling functional near-infra red spectroscopy with neuromodulation technologies offe rs an opportunity to monitor the cortical response,provide realtime feedbac k,and establish a closed-loop strategy integrating evaluation,feedbac k,and intervention for neurostimulation,which provides a theoretical basis for development of individualized precise neuro rehabilitation.We aimed to summarize the advantages of functional near-infra red spectroscopy and provide an ove rview of the current research on functional near-infrared spectroscopy in transcranial magnetic stimulation,transcranial electrical stimulation,neurofeedback,and braincomputer interfaces.Furthermore,the future perspectives and directions for the application of functional near-infrared spectroscopy in neuromodulation are summarized.In conclusion,functional near-infrared spectroscopy combined with neuromodulation may promote the optimization of central pellral reorganization to achieve better functional recovery form central nervous system diseases.

The diffusive fluxes of inorganic nitrogen across the intertidal sediment-water interface of the Changjiang Estuary in China

Ammonium and nitrate concentrations were analyzed in near-bottom water and pore water collected from ten stations of the intertidal flat of the Changjiang Estuary during April, July, November and February. The magnitudes of the benthic exchange fluxes were determined on the basis of concentration gradients of ammonium and nitrate at the near-bottom water and interstitial water interface in combination with calculations of a modified Fick＇ s first law. Ammonium fluxes varied from - 5.05 to 1.43 μg/（ cm^2·d） and were greatly regulated by the production of ammonium in surface sediments, while nitrate fluxes ranged from - 0. 38 to 1.36 μg/ （ cm^2·d） and were dominated by nitrate concentrations in the tidal water. It was found that ammonium was mainly released from sediments into water columns at most of stations whereas nitrate was mostly diffused from overlying waters to intertidal sediments. In total, 823.75 t/a ammonium-N was passed from intertidal sediments to water while about 521.90 t/a nitrate-N was removed from overlying waters to intertidal sediments. This suggests that intertidal sediments had the significant influence on modulating inorganic nitrogen in the tidal water.

嵌入式系统中运动想象脑-机接口编解码算法综述

脑-机接口技术通过在大脑与外部设备之间建立信息传输通路,使用户能够对外部设备进行直接控制。近年来,基于运动想象范式的脑-机接口编解码算法研究在医疗健康、教育娱乐及日常生活设备中的应用范围越来越广,这些算法通常需要嵌入到硬件设备中来满足实际应用的需求。介绍了近年来嵌入式系统中运动想象脑-机接口编解码算法研究现状,从传统机器学习算法和深度学习算法两个角度指出其对应的优缺点。重点介绍四类常用嵌入式平台的代表性设备及其优缺点,并针对不同的应用场景给出相应的硬件选型建议。归纳了更适用于嵌入式脑-机接口系统的评价指标并最终总结了领域内现存的挑战与未来发展方向。

脑机接口中脑电图-近红外光谱联合分析进展研究

脑机接口(BCI)能将受试者意图相关的大脑活动转化为外部设备控制指令,在神经疾病治疗、运动康复等方面具有较高应用潜力。BCI的实现需从人脑获取有意义的信号,而脑电图(EEG)可以反映神经电活动,主要用于对反映实时性要求较高的BCI系统;近红外光谱(NIRS)主要反映血流动力学水平,一般用于神经生理状态等需要精确定位脑活跃区域的研究。EEG和NIRS因其非侵入、方便穿戴、成本较低等优点,成为BCI的重要信号获取方法。相比于单模态BCI系统,基于EEG-NIRS联合分析的混合BCI系统由于具有更丰富的信号特征,在生理状态检测、运动想象等领域得到了越来越多的关注与研究。该文从EEG-NIRS联合分析在脑机接口中应用的研究现状出发,在数据和特征融合程度、层面上归纳最近的相关领域研究现状,并对EEG-NIRS信号处理手段的研究前景进行了展望。