Current methods of epitope identification for cancer vaccine design

The importance of the immune system in tumor development and progression has been emerging in many cancers. Previous cancer vaccines have not shown long-term clinical benefit possibly because were not designed to avoid eliciting regulatory T-cell responses that inhibit the anti-tumor immune response. This review will examine different methods of identifying epitopes derived from tumor associated antigens suitable for immunization and the steps used to design and validate peptide epitopes to improve efficacy of anti-tumor peptide-based vaccines. Focusing on in silico prediction algorithms, we survey the advantages and disadvantages of current cancer vaccine prediction tools.     

基于融合多网络深层卷积特征和稀疏双关系正则化方法的乳腺癌图像分类研究

乳腺癌是全球女性癌症死亡的主要原因之一。现有诊断方法主要是医生通过乳腺癌观察组织病理学图像进行判断,不仅费时费力,而且依赖医生的专业知识和经验,使得诊断效率无法令人满意。针对以上问题,设计基于组织学图像的深度学习框架,以提高乳腺癌诊断准确性,同时减少医生的工作量。开发一个基于多网络特征融合和稀疏双关系正则化学习的分类模型:首先,通过子图像裁剪和颜色增强进行乳腺癌图像预处理;其次,使用深度学习模型中典型的3种深度卷积神经网络(InceptionV3、ResNet-50和VGG-16),提取乳腺癌病理图像的多网络深层卷积特征并进行特征融合;最后,通过利用两种关系(“样本-样本”和“特征-特征”关系)和l_F正则化,提出一种有监督的双关系正则化学习方法进行特征降维,并使用支持向量机将乳腺癌病理图像区分为4类—正常、良性、原位癌和浸润性癌。实验中,通过使用ICIAR2018公共数据集中的400张乳腺癌病理图像进行验证,获得93%的分类准确性。融合多网络深层卷积特征可以有效地捕捉丰富的图像信息,而稀疏双关系正则化学习可以有效降低特征冗余并减少噪声干扰,有效地提高模型的分类性能。     

成人烟雾病脑血流重建手术中不同亚型患者皮层脑电信号特征差异分析

目的 研究不同类型、不同难度的认知任务组合情况下,脑力负荷变化情况的精细表征。方法 设计一种基于逻辑运算、工作记忆和运动执行的脑力负荷诱发范式,利用该范式开展24名男性参试者参与的实验,采集参试者主观量表评分、任务绩效和脑电图（EEG）信号,并计算EEG信号多个频带的功率特征。结果 主观量表和任务绩效分析表明,计算难度、N-back 等级均能诱发出不同等级的脑力负荷;EEG信号分析表明,脑力负荷的增加伴随着前额叶theta 波增强和 alpha 波的减弱;利用支持向量机（SVM）构建脑力负荷分类模型,能实现平均75%单因素三分类正确率和81.7%的脑力负荷三分类正确率;利用逐步回归模型可实现对脑力负荷的预测。结论 EEG信号的频域特征能够反映多因素认知任务的脑力负荷变化情况,可以对认知因素水平和脑力负荷进行分类和连续预测。     

表面肌电的支持向量机分类

支持向量机(SVM)是一种新的机器学习机制.研究了基于支持向量机的控制假手表面肌电识别方法和性能,并与反向传播(BP)神经网络分类器进行了比较.分类的六种手腕部动作分别是腕内旋、腕外旋、展拳、握拳、肘部外旋、肘部内旋.利用"一对一"的分类策略和二叉树构造多类SVM分类器.核函数分别采用多项式和径向基函数.实验结果表明SVM可以有效地对表面动作肌电进行分类,SVM分类准确率普遍优于传统的BP神经网络,且具有良好的泛化推广能力.不同的核函数对分类准确率影响较小.     

Joint application of rough set-based feature reduction and Fuzzy LS-SVM classifier in motion classification

This paper presents an effective classification scheme consisting of the rough set theory (RST)-based feature selection and the fuzzy least squares support vector machine (LS-SVM) classifier for the surface electromyographic (sEMG)-based motion classification. The wavelet packet transform (WPT) is exploited to decompose the four-class motion EMG signals to the non-overlapped sub-bands and the energy characteristic of each sub-band is adopted to form the original feature set. In order to reduce the computation complexity, the RST is utilized to get the reduction feature set without compromising classification accuracy. In the feature reduction phase, cluster separation index (CSI) is introduced to evaluate the performance of the proposed algorithm. In the sequel, the Fuzzy LS-SVM is constructed for the multi-class classification task. The RST-based feature selection is independent of the classifier design. Consequently the classification performance will vary with different classifiers. We make the comparison between the proposed classification scheme and the commonly used classification scheme, such as the combination of the principal component analysis (PCA)-based feature selection and the neural network (NN) classifier. The results of comparative experiments show that the diverse motions can be identified with high accuracy by the proposed scheme. Compared with other feature extraction and selection algorithms and classifiers, superior performance of the proposed classification scheme illustrates the potential of the SVM techniques combined with WPT and RST in EMG motion classification.     

基于游离脂成分的20种中药寒热药性支持向量机分析

目的:探讨中药寒热药性与游离脂的相关性,进一步阐明中药四性与物质基础的关系。方法:选取10种寒性中药和10种热性中药,提取粗脂,2%硫酸-甲醇于70℃水浴进行甲酯化反应,GC-MS法测定,并运用支持向量机进行统计分析。结果:运用支持向量机法建立了区分20种中药寒热药性的数学判别模型,其判别结果与传统中药药性理论一致。结论:20种中药游离脂成分与其寒热药性存在明显的相关性,支持向量机可以作为区分寒热药性的统计学工具,游离脂是中药寒热药性的物质基础之一。     

基于红外光谱数据的中药药性识别研究

目的 探讨中药红外光谱与药性的相关性.方法 以红外光谱数据为分类指标,用主成分分析提取主成分,然后采用交叉验证法对不同药性中药进行交叉训练,建立中药药性的预测模型.结果 该模型对预测集中平性药的识别正确率为83.33%,对非平性药的识别正确率为82.5%,总正确率82.89%,结论中药红外光谱与药性具有一定的相关性.     

Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics

Background

Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus.

Aims

We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC).

Methods

We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification.

Results

By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances.

Conclusions

The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.