Versatile Near-Infrared Super-Resolution Imaging of Amyloid Fibrils with the Fluorogenic Probe CRANAD-2

CRANAD-2 is a fluorogenic curcumin derivative used for near-infrared detection and imaging in vivo of amyloid aggregates, which are involved in neurodegenerative diseases. We explore the performance of CRANAD-2 in two super-resolution imaging techniques, namely stimulated emission depletion (STED) and single-molecule localization microscopy (SMLM), with markedly different fluorophore requirements. By conveniently adapting the concentration of CRANAD-2, which transiently binds to amyloid fibrils, we show that it performs well in both techniques, achieving a resolution in the range of 45–55 nm. Correlation of SMLM with atomic force microscopy (AFM) validates the resolution of fine features in the reconstructed super-resolved image. The good performance and versatility of CRANAD-2 provides a powerful tool for near-infrared nanoscopic imaging of amyloids in vitro and in vivo.     

一种新的超分辨记录点的读出技术

提出一种新的超分辨记录点的读出技术—超分辨反射膜技术，详细分析了其原理。用该技术，以Sb为超分辨反射膜，SiN为介电层，在激光波长为632．8nm和光学头的数值也径为0．40的读出光学系统中实现了直径为380nm的超分辨记录点的读出。同时研究了Sb薄膜厚度对读出信噪比的影响规律，发现最佳的Sb薄膜厚度为28～30nm，所得的信噪比为38～40dB。     

基于半像素错位的多幅图像重建高分辨率图像技术研究

介绍了一种基于半像素错位的多幅图像重建高分辨率图像技术。分析了半像素错位的多幅图像与高分辨率图像各像素灰度值的对应关系 ,并从CCD数字化采样的角度进行了论证。同时 ,结合实际摄像机CCD结构 ,求出了高分辨率图像重建的计算公式 ,并通过实验进行了验证和完善。重建的本质是以原高分辨率图像的 4邻域平均图像为基础 ,增加一定比例的边缘细节信息 ,去接近原高分辨率图像。CCD的动态范围越大 ,图像的灰度级越多 ,那么计算误差就越小 ,图像的边缘细节信息就可以利用更多 ,重建的图像就越接近原高分辨率图像。通过实验和分析表明 ,利用半像素错位的多幅低分辨率图像重建高分辨率图像的原理是正确的 ,方案是可行的     

Long-Term,Single-Molecule Imaging of Proteins in Live Cells with Photoregulated Fluxional Fluorophores

Single-molecule localization microscopy (SMLM) can reveal nanometric details of biological samples, but its high phototoxicity hampers long-term imaging in live specimens. A significant part of this phototoxicity stems from repeated irradiations that are necessary for controlled switching of fluorophores to maintain the sparse labeling of the sample. Lower phototoxicity can be obtained using fluorophores that blink spontaneously, but controlling the density of single-molecule emitters is challenging. We recently developed photoregulated fluxional fluorophores (PFFs) that combine the benefits of spontaneously blinking dyes with photocontrol of emitter density. These dyes, however, were limited to imaging acidic organelles in live cells. Herein, we report a systematic study of PFFs that culminates in probes that are functional at physiological pH and operate at longer wavelengths than their predecessors. Moreover, these probes are compatible with HaloTag labeling, thus enabling timelapse, single-molecule imaging of specific protein targets for exceptionally long times.     

Designing Sub‐2 nm Organosilica Nanohybrids for Far‐Field Super‐Resolution Imaging

Stimulated emission depletion (STED) microscopy enables ultrastructural imaging of biological samples with high spatiotemporal resolution. STED nanoprobes based on fluorescent organosilica nanohybrids featuring sub‐2 nm size and near‐unity quantum yield are presented. The spin–orbit coupling (SOC) of heavy‐atom‐rich organic fluorophores is mitigated through a silane‐molecule‐mediated condensation/dehalogenation process, resulting in bright fluorescent organosilica nanohybrids with multiple emitters in one hybrid nanodot. When harnessed as STED nanoprobes, these fluorescent nanohybrids show intense photoluminescence, high biocompatibility, and long‐term photostability. Taking advantage of the low‐power excitation (0.5 μW), prolonged singlet‐state lifetime, and negligible depletion‐induced re‐excitation, these STED nanohybrids present high depletion efficiency (>96 %), extremely low saturation intensity (0.54 mW, ca. 0.188 MW cm^?2), and ultra‐high lateral resolution (ca. λ_em/28).     

基于样本库的人脸图像处理方法

本文给出了一种基于样本库的人脸图像超分辨率方法,这一方法结合了人脸图像全局相似性和局部特征相似性的约束.局部结构的相似性将超分辨率解约束到人脸局部结构空间,不同尺度上的局部相似性指导搜索最优解,然后将局部结构重建的人脸图像投影到由人脸图像确定的特征人脸空间.实验结果表明该算法得到的人脸具有很好的准确性和视觉效果.     

Automatic Search Dense Connection Module for Super-Resolution

The development of display technology has continuously increased the requirements for image resolution. However, the imaging systems of many cameras are limited by their physical conditions, and the image resolution is often restrictive. Recently, several models based on deep convolutional neural network (CNN) have gained significant performance for image super-resolution (SR), while extensive memory consumption and computation overhead hinder practical applications. For this purpose, we present a lightweight network that automatically searches dense connection (ASDCN) for image super-resolution (SR), which effectively reduces redundancy in dense connection and focuses on more valuable features. We employ neural architecture search (NAS) to model the searching of dense connections. Qualitative and quantitative experiments on five public datasets show that our derived model achieves superior performance over the state-of-the-art models.     

超分辨近场结构技术及其应用

超分辨近场结构(Super RENS)技术是在传统的超分辨光盘技术和近场光学的基础上发展起来的新技术。介绍了Super RENS技术的基本原理:利用掩膜层的非线性效应或表面等离子体增强效应,在近场区域可以记录、读出超过衍射极限的信号。综述了该技术在纳米光信息存储和光刻方面应用研究的最新进展,提出了存在的问题,展望了它的发展前景。     

Theoretical Analysis of Super-Resolution Optical Disk Mastering Using a Photoreactive Dye Mask Layer

The super-resolution mastering of read-only optical disks using a photoreactive bleachable dye mask layer was theoretically analyzed. Equations have been derived which describe the photoreactions of the resist layer and mask layer. A numerical simulation based on these equations showed that the transmittance of the mask directly affects the quality of the formed pit shape. A high-quality pit shape is obtained by using a low transmittance mask.     

低分辨率医学图像的多图谱分割方法

基于多图谱的图像分割方法因其分割精度高和鲁棒性强,在医学图像分割领域被广泛研究,主要包含图像配准和标签融合两个步骤.目前对多图谱分割方法的研究通常都是在图谱图像和待分割目标图像具有相同分辨率的情况下展开的.然而,由于受图像采集时间,采集设备等影响,临床实践中采集的影像大多是低分辨率数据,使得目前在影像研究中广泛使用的方法无法有效应用于临床实践.因此,针对这一问题,我们结合图像超分辨率恢复方法,提出了精确鲁棒的低分辨率医学图像的多图谱分割方法,实验结果显示提出的方法显著地提高了多图谱分割方法的分割精度.