运动调控自噬溶酶体通路改善阿尔茨海默病的机制

阿尔茨海默病(Alzheimer’s disease,AD)是一种常见的神经退行性疾病。自噬溶酶体功能异常阻碍了细胞对神经毒性物质的降解,是导致AD发生的关键因素。运动作为一种非药物治疗手段,可以通过激活PI3K/Akt、AMPK等相关信号通路上调自噬活性,并通过促进TFEB的核易位增强自噬溶酶体功能,提高对异常聚集蛋白和受损伤细胞器的降解,保护神经元,改善AD患者的认知功能障碍。本文阐述了自噬溶酶体功能障碍在AD发生发展中的作用,以及运动调控自噬溶酶体通路改善AD作用机制,旨在为AD的预防和治疗提供新策略。

利用TCGA数据库分析miR-196b在结直肠癌患者中的临床表达特征并探索miR-196b的体外抗5-FU作用

目的:研究微小RNA(miR)-196b在结直肠癌患者中的表达及其临床意义,探讨过表达miR-196b的结直肠癌细胞对5-氟尿嘧啶(5-FU)的敏感性。方法:在癌症基因组图谱(The Cancer Genome Atlas,TCGA)公共数据库下载结直肠癌患者miRNA测序数据及其对应的临床病理资料,运用SPSS 17.0分析miR-196b在结直肠癌患者中的表达水平以及临床特征;采用瞬时转染的方法在人结直肠癌HCT116细胞中过表达miR-196b,并用MTS法分析过表达miR-196b的细胞对5-FU药物敏感性的变化。结果:miR-196b高表达与结直肠癌患者淋巴结转移及TNM分期相关(P<0.05),与年龄和性别等无相关性。淋巴结转移和远处转移是直肠癌患者生存的独立影响因子(P<0.05),miR-196b的表达水平与患者生存状况无关。过表达miR-196b的HCT116细胞在不同浓度的5-FU作用下,细胞存活率均明显高于对照组(P<0.05)。结论:miR-196b可能是结直肠癌患者肿瘤分期和淋巴结转移的分子标志物,并可降低结直肠癌细胞对5-FU的敏感性。

精神分裂症断裂基因1启动子的高甲基化增加阿尔茨海默病的发病风险

目的基因的表观修饰与阿尔茨海默病(AD)的发生密切相关,精神分裂症断裂基因1(disrupted in schizophrenia1,DISC1)是AD的候选基因。然而DISC1启动子甲基化与AD发生的关系尚不清楚。方法采用亚硫酸氢盐转化后焦磷酸测序分析的方法检测中国汉族51例AD患者和63例健康对照者血液样本中DISC1的甲基化水平。采用标准方法检测血样中各生化指标。结果AD组DISC1的甲基化水平显著高于健康对照组(P=0.002)。载脂蛋白A(apolipoprotein A,ApoA)、血清脂蛋白(lipoprotein A,Lp(a))和DISC1 CpG3甲基化之间发现了显著的关联。其中,女性AD患者中DISC1甲基化与血浆ApoA水平呈正相关(P=0.010,P=0.003)。男性AD患者中DISC1甲基化与血浆Lp(a)水平呈正相关(P<0.0001)。DISC1启动子甲基化的曲线下面积(area under curve,AUC)为0.726(95%CI:0.626~0.827),灵敏度和特异度分别为0.569和0.869。结论外周血DISC1启动子高甲基化是AD发生的高风险因素,其可能是AD诊断的潜在生物标志物。

基于像素对齐和特征对齐的跨模态行人重识别

为了减少可见光-红外跨模态行人重识别中较大的跨模态差异,提出一种联合像素对齐和特征对齐的跨模态行人重识别方法。首先,从像素级角度出发,利用对齐生成对抗网络(alignment generative adversarial network,AlignGAN),将可见光图像转换为红外图像,减少可见光和红外图像之间的跨模态差距。其次,从特征级角度出发,通过交换可见光和红外图像的模态特定特征来生成跨模态配对图像,同时进行全局集合级对齐和细粒度实例级对齐。最后,运用基于非局部块的深度为50层的残差网络(50-layer residual nets,ResNet-50)捕获图像的长距离依赖关系。在SYSU-MM01数据集上进行了大量试验,我们的方法得到41.8%的识别准确率,在相比较的方法中准确率最高。可见,本方法可以有效地减少跨模态行人重识别中较大的跨模态差异。本研究结果可为跨模态行人重识别的研究提供参考。

自动驾驶场景下对交通路标对抗攻击的防御

针对现有单一对抗防御方法不能使自动驾驶视觉系统有效抵御交通路标对抗攻击的问题,提出一种多阶段对抗防御方法。首先,应用焦点损失消除正负样本数量不平衡的影响,提高对抗训练过程中模型分类的准确率;同时为使模型拥有更强的泛化能力,对数据集做混合数据增强,并在训练开始前预热学习率。然后,使用知识蒸馏算法将教师模型群的"知识信息"迁移到学生模型群。最后,以加权的方式平均学生模型群体的预测结果。经本防御方法训练后,学生模型对交通路标对抗样本的分类准确率由8%~19%提升到了69%~83%;同时与单一对抗防御方法相比,学生模型群体的预测准确率高达85%,优于现有防御模型。在轻量级条件下,利用本防御方法训练的深度学习模型能有效抵御交通路标的对抗攻击,可为自动驾驶视觉系统防御对抗攻击提供参考。

New normalized nonlocal hybrid level set method for image segmentation

This article introduces a new normalized nonlocal hybrid level set method for image segmentation.Due to intensity overlapping,blurred edges with complex backgrounds,simple intensity and texture information,such kind of image segmentation is still a challenging task.The proposed method uses both the region and boundary information to achieve accurate segmentation results.The region information can help to identify rough region of interest and prevent the boundary leakage problem.It makes use of normalized nonlocal comparisons between pairs of patches in each region,and a heuristic intensity model is proposed to suppress irrelevant strong edges and constrain the segmentation.The boundary information can help to detect the precise location of the target object,it makes use of the geodesic active contour model to obtain the target boundary.The corresponding variational segmentation problem is implemented by a level set formulation.We use an internal energy term for geometric active contours to penalize the deviation of the level set function from a signed distance function.At last,experimental results on synthetic images and real images are shown in the paper with promising results.

A variational formulation for physical noised image segmentation

Image segmentation is a hot topic in image science. In this paper we present a new variational segmentation model based on the theory of Mumford-Shah model. The aim of our model is to divide noised image, according to a certain criterion, into homogeneous and smooth regions that should correspond to structural units in the scene or objects of interest. The proposed region-based model uses total variation as a regularization term, and different fidelity term can be used for image segmentation in the cases of physical noise, such as Gaussian, Poisson and multiplicative speckle noise. Our model consists of five weighted terms, two of them are responsible for image denoising based on fidelity term and total variation term, the others assure that the three conditions of adherence to the data, smoothing, and discontinuity detection are met at once. We also develop a primal-dual hybrid gradient algorithm for our model. Numerical results on various synthetic and real images are provided to compare our method with others, these results show that our proposed model and algorithms are effective.

The Lifespan of Smooth Solutions to Semilinear Wave Equations in Schwarzschild Space-Time

This paper considers the Cauchy problem of the semilinear wave equations with small initial data in the Schwarzschild space-time,□_(g)u=|ut|^(P),where g denotes the Schwarzschild metric.When 1<p<2 and the initial data are supported far away from the black hole,we can prove that the lifespan of the spherically symmetric solu-tion obtains the same order as the semilinear wave equation evolving in the Minkowski space-time by introducing an auxiliary function.