Foreword

The spectrum of a platinum hollow-cathode lamp containing neon carrier gas was recorded photographically and photoelectrically with a 10.7 m normal-incidence vacuum spectrograph. Wavelengths and intensities were determined for about 5600 lines in the region 1130–4330 Å. An atlas of the spectrum is given, with the spectral lines marked and their intensities, wavelengths, and classifications listed. Lines of impurity species are also identified. The uncertainty of the photographically measured wavelengths is estimated to be ± 0.0020 Å. The uncertainty of lines measured in the photoelectric scans is 0.01 Å for wavelengths shorter than 2030 Å and 0.02 Å for longer wavelengths. Ritz-type wavelengths are given for many of the classified lines of Pt II with uncertainties varying from ±0.0004 to ± 0.0025 Å. The uncertainty of the relative intensities is estimated to be about 20%.     

Coding of far and near space during walking in neglect patients.

Previous studies have shown that far space can be remapped as near when reached by a stick that artificially prolongs the participants' personal space. In the present study, the authors asked whether a similar remapping occurs when far space is reached not by using a toot but by locomotion. Neglect patients showed more severe neglect in far than in near space in bisection tasks executed from different distances either by pointing to a target line with a projection light pen or by walking across the line. A kinematic study of the walking performance of one of those neglect patients showed that, contrary to the prediction of remapping during locomotion, the walking trajectories were rectilinear. The authors interpreted these results as evidence that in their patients-at least for short, linear trajectories-no remapping of space took place during locomotion. The location of far objects was coded at the beginning of the movement, and the error in the bisection computation was generated within the 1st representation that was activated. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Neuropsychology and Computerized Axial Tomography: Further Comments.

Comments on the original article "Implications of computerized axial tomography for clinical neuropsychology" by Danny Wedding and Howard Gudeman (see record 1981-08364-001), which reviewed the implications of computerized axial tomography (CAT) for clinical neuropsychology. A major change anticipated by Wedding and Gudeman is that the role in neuropsychology of localizing central nervous system lesions will become obsolete, as this function will be supplanted by the CAT scan technique. This suggests that the emergence of CAT may have a deleterious effect on the utilization of neuropsychologic (NP) techniques, particularly the utilization of NP evaluation in the diagnostic process. The current authors feel that this issue merits further discussion, and that some clarification and extension of the Wedding and Gudeman article is in order. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Review of Laterality: Functional asymmetry in the intact brain.

Reviews the book, Laterality: Functional asymmetry in the intact brain by M. P. Bryden (1982). Bryden's book is, first of all, an impressive review of the literature on laterality research with normal subjects, giving a well-organized and lucid expositon of a very large body of information (the citation list has close to 800 entries). In the process of reviewing the literature, however, the book does much more. It provides (a) a critical challenge to many of the practices and interpretations common in this area of research, together with guidelines for improving methodology in the future; (b) a thoughtful discussion of most of the major theoretical issues in the field, and (c) an argument for some of Bryden's own controversial proposals. The book is an impressive, important, and provocative contribution to the field; it is both a major sourcebook, filled with data, and a manifesto filled with challenging proposals. One may take issue with specific details, but this does not diminish one's excitement at finding such a rich and stimulating collection of ideas gathered in one place. The book is a "must read" for those involved in this area of research. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

基于3D U-Net的轻量级脑肿瘤分割网络北大核心CSCD

针对现有脑肿瘤核磁共振成像(magnetic resonance imaging, MRI)分割神经网络的参数量和计算量较大且对肿瘤区域小目标分割精度不高的问题,提出一种改进的轻量级脑肿瘤分割网络MF-RES2Net(multiple fiber residual-like networks)。该网络以3D U-Net为基础架构,将多纤模块(multi-fiber, MF)和类残差模块(RES2)相结合代替传统卷积模块。MF将特征图像的通道进行混合,增加了通道间信息的交流融合;RES2将通道均分,单通道的卷积结果相加到相邻通道,在扩大图像感受野的同时保留了细节特征,同时降低网络参数量。此外,为改善数据不平衡问题,提出一种改进的加权损失函数,提高了网络对小目标的分割精度。将MF-RES2Net在BRATS 2019数据集进行验证,完整肿瘤、核心肿瘤和增强肿瘤分割的平均Dice系数分别为89.98%、84.02%、77.62%,参数量和浮点数分别为3.16 M和16.24 G,结果表明:该网络在降低参数量和计算量的同时进一步提升了分割性能,有效地降低了网络运行时的设备要求。     

The application of hybrid feature based on local mean decomposition for motor imagery electroencephalogram signal classification

This paper proposed a hybrid feature extraction algorithm based on local mean decomposition (LMD), which has better solved the existing problems of low classification performance and adaptability limitation. LMD is employed to decompose the electroencephalogram (EEG) signal into multiple components, and then, the hybrid features based on instantaneous energy, fuzzy entropy, and mathematical morphological features are extracted on specific components, and the optimal feature combination is selected by analysis of variance (ANOVA). Finally, the classification result is output by the linear discriminant analysis (LDA) classifier. The results show that the maximum accuracy of the subjects in Data Set III of BCI-II by the method in this paper is 92.14%, and the maximum mutual information value is 0.8. The number of novel features used in this paper is small, and the complexity of the algorithm is reduced. It can adaptively select effective features according to individual differences and has good robustness.     

基于脑电网络图特征的情绪识别研究

针对情绪脑电信号提出一种网络图特征学习与情绪识别算法。首先,利用情绪脑电数据构建对应的情绪脑电网络;其次,在由情绪脑电网络尺度定义的高维空间构建脑电网络样本间的局部邻接关系图以挖掘样本集的分布特性,进而得到样本集的图拉普拉斯矩阵;在此基础上,进一步利用谱图理论对情绪脑电网络的最优低维空间映射进行求解,在保留原始样本局部邻接关系的前提下实现对情绪脑电网络的降维与重新表达,并将每个情绪脑电网络样本表示成1组脑电网络特征集;最后利用提取到的情绪脑电网络特征集,结合支持向量机分类学习算法,针对情绪识别任务进行识别模型的训练和学习,实现对情绪状态的准确解码与识别。在国际公开情绪脑电数据集的实验结果表明：相较于传统情绪识别算法,本文所提方法能有效提升情绪识别准确率,在基于公开数据集的多类情绪识别任务中分别达到91.85%（SEED数据集, 3类）、79.36%（MAHNOB-HCI数据集,3类）和79%（DEAP数据集,4类）的稳健识别效果。     

Identification of differential brain regions in MCI progression via clustering-evolutionary weighted SVM ensemble algorithm

Mild cognitive impairment (MCI) as the potential sign of serious cognitive decline could be divided into two stages, i.e., late MCI (LMCI) and early MCI (EMCI). Although the different cognitive states in the MCI progression have been clinically defined, effective and accurate identification of differences in neuroimaging data between these stages still needs to be further studied. In this paper, a new method of clustering-evolutionary weighted support vector machine ensemble (CEWSVME) is presented to investigate the alterations from cognitively normal (CN) to EMCI to LMCI. The CEWSVME mainly includes two steps. The first step is to build multiple SVM classifiers by randomly selecting samples and features. The second step is to introduce the idea of clustering evolution to eliminate inefficient and highly similar SVMs, thereby improving the final classification performances. Additionally, we extracted the optimal features to detect the differential brain regions in MCI progression, and confirmed that these differential brain regions changed dynamically with the development of MCI. More exactly, this study found that some brain regions only have durative effects on MCI progression, such as parahippocampal gyrus, posterior cingulate gyrus and amygdala, while the superior temporal gyrus and the middle temporal gyrus have periodic effects on the progression. Our work contributes to understanding the pathogenesis of MCI and provide the guidance for its timely diagnosis.     

基于Markov随机场的Student's t混合模型的脑MR图像分割

为解决在使用期望最大化(EM)算法求解混合模型前需要额外的计算问题,提出一种新的基于Markov随机场的Student's t混合模型,该模型能直接利用简单有效的EM算法求解。试验结果表明,该方法能有效克服噪声对图像分割的影响,获得较好的分割结果。     

Neuroproteomics in neurotrauma

Neurotrauma in the form of traumatic brain injury (TBI) afflicts more Americans annually than Alzheimer's and Parkinson's disease combined, yet few researchers have used neuroproteomics to investigate the underlying complex molecular events that exacerbate TBI. Discussed in this review is the methodology needed to explore the neurotrauma proteome-from the types of samples used to the mass spectrometry identification and quantification techniques available. This neuroproteomics survey presents a framework for large-scale protein research in neurotrauma, as applied for immediate TBI biomarker discovery and the far-reaching systems biology understanding of how the brain responds to trauma. Ultimately, knowledge attained through neuroproteomics could lead to clinical diagnostics and therapeutics to lessen the burden of neurotrauma on society.