Fluorescence lifetimes of diphenylhexatriene-containing probes reflect local probe concentrations: Application to the measurement of membrane fusion

An important process in the life of a cell is fusion between cellular membranes. This is the process by which two cellular compartments surrounded by different membranes join to become a single compartment surrounded by a single membrane, without significant loss of compartment contents. To demonstrate fusion, the cell biophysicist must demonstrate all three critical aspects of the process: (1) mixing of membrane components, (2) mixing of compartment contents; and (3) retention of compartment contents. Most commonly, accomplishing this involves the use of fluorescence probes. The general theme to the methods described involves some form of concentration-dependent quenching. An unique method developed in our laboratory utilizes the concentration dependence of the fluorescence lifetime of a phosphatidylcholine containing carboxyethyl diphenylhexatriene at position 2 and palmitic acid at position 1 of glycerol (DPHpPC). The fluorescence lifetime of this molecule and that of its parent fluorophore diphenylhexatriene (DPH) shorten dramatically as their two-dimensional concentrations in a membrane increase. This lifetime quenching can be described by dimer formation that reduces the symmetry of the DPH excited state. This phenomenon allows one to use the fluorescence lifetime to gain insight into the local concentration of probe in microscopic regions of a membrane. One application of this is in distinguishing lipid transfer between the outer leaflets of two contacting membrane bilayers from fusion between these membranes that leads to mixing of lipids in both the inner and outer leaflets of the membrane bilayers. This allows a single measurement to demonstrate fusion between membrane pairs.Abbreviations PEG poly(ethylene glycol) - Na₂EDTA ethyiene-diamine-tetraacedic acid, disodium salt - LUV large, unilamellar vesicles made by rapid extrusion technique - DPH 1,6-diphenyl-trans-1,3,5-hexatriene - DPHpPC 1-palmitoyl-2-[[[2-[4- (phenyl-trans-1,3,5-hexatrienyl)phenyl]ethyl]oxy]carbonyl]-3-sn-phosphatidylcholine - DPPC 1,2-dipalmitoyl-3-sn-phosphatidylcholine - PA palmitic acid - NBD-PE N-(7-nitro-2,1,3-benzoxadiazol-4-yl)-PE - Rh-PE N-(lissamine Rhodamine B sulfoyl)-PE - R₁₈ octadecyl Rhodamine B chloride - ANTS 1-aminonaphthalene-3,6,8-trisulfonic acid - DPX N,N-p-xylylene-bis(pyradinium bromide)     

基于sMLD特征的远红外航空图像配准算法

航空场景下的远红外图像迫切需要准确、鲁棒、快速的特征描述与自动配准方法。由于已有的多重线型描述符MLD特征存在“特征孤岛”和“尺度变换受限”的问题,因此针对远红外航空图像的配准问题,文章提出了一种聚合特征点以及线型描述符分段统计的特征描述方式,即sMLD特征。结合sMLD特征相互连接构成网状拓扑结构的特性,还提出了一种由粗到细的分支加速匹配算法RF-BA。其中,RF-BA粗匹配充分利用拓扑图的结构,通过局部寻优算法提高匹配的效率。RF-BA精匹配利用最小外接凸四边形原则和GMS校验原则,提升配准精度。实验结果表明,与已有的几种代表性配准方法相比,所提方法在配准精度和时间开销方面均具有更好的性能。     

基于transformer自适应特征向量融合的图像分类

针对目前基于transformer的图像分类模型直接应用在小数据集上性能较差的问题,本文提出了transformer自适应特征向量融合网络,该网络在特征提取器中将不同阶段的特征进行融合,减少特征信息丢失的同时获得更多不同感受野下的信息,同时利用最大池化来去除特征中的冗余信息,从而使提取的特征更具有判别性。此外,为了充分利用图像的各级特征信息来进行分类预测,本文将网络各阶段产生的特征向量进行融合,使融合后的特征向量更具有表征能力,从而减少网络对大数据集的依赖,使网络在小数据集中也能获得很好的性能。实验表明,本文提出的 算法在数据集Mini-ImageNet-100、CIFAR-100和ImageNet-1k上的TOP-1准确率分别达到了74.22%、85.86%和81.4%。在没有增加计算量的情况下,在baseline上分别提高了6.0%、3.0%和0.1%,且参数量减少了18.3%。本文代码开源在“https://github.com/xhutongxue/afvf”。     

基于改进DeepLabV3+的铣床碎屑图像分割方法

针对铣床碎屑形状不规则导致图像分割中碎屑轮廓不清晰、分割精度低的问题,本文提出一种改进的DeepLabV3+铣床碎屑分割算法。首先在DeepLabV3+的Xcepetion模块中嵌入通道与空间注意力机制(convolutional block attention module, CBAM)模型,优化通道的权重和位置信息,加强碎屑图像区域的特征学习;其次将DeepLabV3+的空洞空间卷积池化金字塔(atrous spatial pyramid pooling, ASPP)模块改为密集连接(dense conolutional network, DenseNet)方式,增大碎屑图像特征点的感受野,提升铣床碎屑图像特征的复用效率;最后在解码过程中采用多尺度自适应特征融合方法,聚合多尺度特征作为解码器的输入特征,提高碎屑图像分割的精度与鲁棒性。实验结果表明,本文算法优于其他分割算法,改进后算法相比DeepLabV3+,像素准确率提高0.026,平均交并比(mean intersection over union,MIOU)提高0.020,F₁值提高了0.013。     

基于改进3D UNet的前列腺MR图像分割

针对前列腺磁共振 (magnetic resonance, MR)图像边缘模糊、对比度较低,灰度值分布不均衡而导致分割精度较差的问题,提出了一种结合双路径注意力(dual path attention,DPA) 和多尺度特征聚合(multi-scale feature aggregation,MFA) 模块的改进3D UNet网络模型。首先,对数据集进行重采样和裁剪处理以适应模型输入。然后,在3D UNet网络的编码器各层引入DPA 并添加残差连接,加强特征的 编码能力。同时,在网络解码器中加入MFA模块,以充分利用空间上下文信息,增强语义信息。最后,在公开数据集PROMISE12上进行验证,所提出的模型的Dice系数为89.90%,Hausdorff 距离为9.37 mm。相比较于其他模型,所提出模型的分割结果更优,且参数量和运算量更少。     

基于提取双选紧密特征的RGB-D显著性检测网络

针对现有算法对不同来源特征之间的交互选择关注度欠缺以及对跨模态特征提取不充分的问题,提出了一种基于提取双选紧密特征的RGB-D显著性检测网络。首先,为了筛选出能够同时增强RGB图像显著区域和深度图像显著区域的特征,引入双向选择模块(bi-directional selection module, BSM);为了解决跨模态特征提取不充分,导致算法计算冗余且精度低的问题,引入紧密提取模块(dense extraction module, DEM);最后,通过特征聚合模块(feature aggregation module, FAM)对密集特征进行级联融合,并将循环残差优化模块(recurrent residual refinement aggregation module, RAM)配合深度监督实现粗显著图的持续优化,最终得到精确的显著图。在4个广泛使用的数据集上进行的综合实验表明,本文提出的算法在4个关键指标方面优于7种现有方法。     

Blind omnidirectional image quality assessment with representative features and viewport oriented statistical features

With the development of information technologies, various types of streaming images are generated, such as videos, graphics, Virtual Reality (VR)/omnidirectional images (OIs), etc. Among them, the OIs usually have a broader view and a higher resolution, which provides human an immersive visual experience in a head-mounted display. However, the current image quality assessment works cannot achieve good performance without considering representative human visual features and visual viewing characteristics of OIs, which limited OIs’ further development. Motivated by the above problem, this work proposes a blind omnidirectional image quality assessment (BOIQA) model based on representative features and viewport oriented statistical features. Specifically, we apply the local binary pattern operator to encoder the cross-channel color information, and apply the weighted LBP to extract the structural features. Then the local natural scene statistics (NSS) features are extracted by using the viewport sampling to boost the performance. Finally, we apply support vector regression to predict the OIs’ quality score, and experimental results on CVIQD2018 and OIQA2018 Databases prove that the proposed model achieves better performance than state-of-the-art OIQA models.     

Transformer-based global–local feature learning model for occluded person re-identification

Most recent occluded person re-identification (re-ID) methods usually learn global features directly from pedestrian images, or use additional pose estimation and semantic analysis model to learn local features, while ignoring the relationship between global and local features, thus incorrectly retrieving different pedestrians with similar attributes as the same pedestrian. Moreover, learning local features using auxiliary models brings additional computational cost. In this work, we propose a Transformer-based dual-branch feature learning model for occluded person re-ID. Firstly, we propose a global–local feature interaction module to learn the relationship between global and local features, thus enhancing the richness of information in pedestrian features. Secondly, we randomly erase local areas in the input image to simulate the real occlusion situation, thereby improving the model’s adaptability to the occlusion scene. Finally, a spilt group module is introduced to explore the local distinguishing features of pedestrian. Numerous experiments validate the effectiveness of our proposed method.     

Optimized deep learning-based intrusion detection for wireless sensor networks

The technological innovations and wide use of Wireless Sensor Network (WSN) applications need to handle diverse data. These huge data possess network security issues as intrusions that cannot be neglected or ignored. An effective strategy to counteract security issues in WSN can be achieved through the Intrusion Detection System (IDS). IDS ensures network integrity, availability, and confidentiality by detecting different attacks. Regardless of efforts by various researchers, the domain is still open to obtain an IDS with improved detection accuracy with minimum false alarms to detect intrusions. Machine learning models are deployed as IDS, but their potential solutions need to be improved in terms of detection accuracy. The neural network performance depends on feature selection, and hence, it is essential to bring an efficient feature selection model for better performance. An optimized deep learning model has been presented to detect different types of attacks in WSN. Instead of the conventional parameter selection procedure for Convolutional Neural Network (CNN) architecture, a nature-inspired whale optimization algorithm is included to optimize the CNN parameters such as kernel size, feature map count, padding, and pooling type. These optimized features greatly improved the intrusion detection accuracy compared to Deep Neural network (DNN), Random Forest (RF), and Decision Tree (DT) models.     

HSP-MFL: A High-level Semantic Property driven Multi-task Feature Learning Network for unsupervised person Re-ID

Unsupervised person re-identification aims to distinguish different pedestrians from discriminative representations on the basis of unlabeled data. Currently, most unsupervised Re-ID approaches explore visual representations to generate pseudo-labels for model’s training, which may suffer from background noise and semantic loss. To tackle this problem, this paper proposes a High-level Semantic Property driven Multi-task Feature Learning Network (HSP-MFL) to firstly introduce three high-level semantic properties for unsupervised person Re-ID. Technically, we design a novel Multiple Feature Fusion Module (MFFM) to deeply explore the complex correlation among multiple semantic and visual features to capture the discriminative feature cues, as well as a multi-task training scheme to generate robust fusion features. The architecture is quite simple and does not consume extra labeling costs. Extensive experiments on three datasets demonstrate that both high-level semantic properties and multi-task learning are effective in performance improvement, yielding SOTA mAPs for unsupervised person Re-ID.