Neurofibrillary tangles and plaques are not accompanied by white matter pathology in aged triple transgenic-Alzheimer disease mice

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that is the most common cause of dementia in aging populations. Although senile plaques and neurofibrillary tangles are well-established hallmarks of AD, changes in cerebral white matter correlate with cognitive decline and may increase the risk of the development of dementia. We used the triple transgenic (3xTg)-AD mouse model of AD, previously used to show that white matter changes precede plaque formation, to test the hypothesis that MRI detectable changes occur in the corpus callosum, external capsule and the fornix. T₂-weighted and diffusion tensor magnetic resonance imaging and histological stains were employed to assess white matter in older (11–17 months) 3xTg-AD mice and controls. We found no statistically significant changes in white matter between 3xTg-AD mice and controls, despite well-developed neurofibrillary tangles and beta amyloid immunoreactive plaques. Myelin staining was normal in affected mice. These data suggest that the 3xTg-AD mouse model does not develop MRI detectable white matter changes at the ages we examined.     

MALDI-MS of drugs: Profiling,imaging, and steps towards quantitative analysis

Matrix-assisted laser desorption/ionization (MALDI) is a soft ionization technique which can be used in mass spectrometry to produce ions from biomolecules without inducing the fragmentation associated with traditional methods of ionization. When used with small molecules, the lack of fragmentation allows identification of specific molecules against a background of alternative signals; thus, for example, the presence of drug molecules and metabolites can be distinguished from a range of alternative biomolecules present within a tissue sample. Using highly collimated lasers in matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) allows imaging of a tissue sample whereby the laser is rastered across the sample and individual mass spectra are collected in a serial manner. Thus, the distribution of the molecules within the tissue sample can be presented in the form of a 2D image. While the detection of specific drug molecules and metabolites within biological samples has its uses, quantification of those same molecules would be of greater benefit in a clinical setting. However, accurate quantification presents additional challenges. We present an overview of the MALDI-MS technique followed by recent progress in profiling drugs and their metabolites through imaging drug distributions within tissues and finish with recent developments in the quantification of drugs in tissues by MALDI-MSI.     

Synthesis and Characterization of CdTe@CdS Quantum Dots Layered on Silica Nanoparticles

CdTe@CdS quantum dots, cationic polyelectrolyte poly-diallyldimethylammonium chloride, and anionic polyelectrolyte polyacrylic acid were assembled on the surface of silica nanoparticles based on the electrostatic layer-by-layer self-assembly to prepare fluorescent composite nanoparticles. Transmission electron microscopy showed that the particles had a uniform size distribution (approximately 70 nm) and good monodispersity. The fluorescence shielding effect of the silica shell was reduced and the assembled quantum dots were well protected by the sandwich structure. The nanoparticles provided strong fluorescence, high stability for storage, and low photobleaching and leakage. Furthermore, they possessed high fluorescence stability and high-concentration staining for cytoplasm, which enabled them to be used for sensitive cellular imaging analysis. Because of the presence of numerous carboxyl groups, they have potential application for biolabeling and bioanalysis.     

基于COLLATE融合多图谱的心脏电影MRI右心室分割

右心室分割对肺动脉高压等疾病的心功能分析具有重要的临床意义.然而，右心室心肌薄、易变且不规则，其传统的医学图像分割方法仍然未能取得突破性进展.本文提出基于COLLATE（Consensus Level，Labeler Accuracy and Truth Estimation）的多图谱分割方法，首先以归一化互信息为相似测度对目标图像和图谱集进行B样条配准以获取粗分割结果；然后利用COLLATE对粗分割结果进行融合；最后采用基于形状约束的区域生长算法修正出现错误的数据.10例临床心脏磁共振短轴电影图像被用于算法验证.本文还将使用基于COLLATE的多图谱分割方法得到的结果与深度学习算法及手动分割进行了比较.结果显示与深度学习算法比较，使用本文算法得到的射血分数（Ejection Fraction，EF）与手动分割更加一致和相关，表明该算法的分割结果有望辅助临床心脏功能诊断.     

In Situ Proteome Profiling and Bioimaging Applications of Small‐Molecule Affinity‐Based Probes Derived From DOT1L Inhibitors

DOT1L is the sole protein methyltransferase that methylates histone H3 on lysine 79 (H3K79), and is a promising drug target against cancers. Small‐molecule inhibitors of DOT1L such as FED1 are potential anti‐cancer agents and useful tools to investigate the biological roles of DOT1L in human diseases. FED1 showed excellent in vitro inhibitory activity against DOT1L, but its cellular effect was relatively poor. In this study, we designed and synthesized photo‐reactive and “clickable” affinity‐based probes (AfBPs), P1 and P2 , which were cell‐permeable and structural mimics of FED1 . The binding and inhibitory effects of these two probes against DOT1L protein were extensively investigated in vitro and in live mammalian cells (in situ). The cellular uptake and sub‐cellular localization properties of the probes were subsequently studied in live‐cell imaging experiments, and our results revealed that, whereas both P1 and P2 readily entered mammalian cells, most of them were not able to reach the cell nucleus where functional DOT1L resides. This offers a plausible explanation for the poor cellular activity of FED1 . Finally with P1 / P2 , large‐scale cell‐based proteome profiling, followed by quantitative LC‐MS/MS, was carried out to identify potential cellular off‐targets of FED1 . Amongst the more than 100 candidate off‐targets identified, NOP2 (a putative ribosomal RNA methyltransferase) was further confirmed to be likely a genuine off‐target of FED1 by preliminary validation experiments including pull‐down/Western blotting (PD/WB) and cellular thermal shift assay (CETSA).     

LED微阵列投影系统设计

为满足便携式投影仪的市场需求,设计了一种基于LED微型阵列的投影系统。该系统由显示单元和投影物镜构成。采用尺寸为12 mm×9 mm的自发光LED微型阵列作为系统的显示单元,利用光学设计软件设计了投影物镜。投影物镜采用反远距光学结构,全视场角为80°,焦距为8 mm,属于强光、广角镜头。在空间频率20 lp/mm处,该物镜的调制传递函数大于0. 85,畸变小于2%,符合投影系统的设计要求。该投影系统具有体积小,结构简单,投影效果好,易加工等诸多优势,可为第三代投影技术的发展提供参考。     

Recent Progress on Manganese-Based Nanostructures as Responsive MRI Contrast Agents

Manganese-based nanostructured contrast agents (CAs) entered the field of medical diagnosis through magnetic resonance imaging (MRI) some years ago. Although some of these Mn-based CAs behave as classic T₁ contrast enhancers in the same way as clinical Gd-based molecules do, a new type of Mn nanomaterials have been developed to improve MRI sensitivity and potentially gather new functional information from tissues by using traditional T₁ contrast enhanced MRI. These nanomaterials have been designed to respond to biological environments, mainly to pH and redox potential variations. In many cases, the differences in signal generation in these responsive Mn-based nanostructures come from intrinsic changes in the magnetic properties of Mn cations depending on their oxidation state. In other cases, no changes in the nature of Mn take place, but rather the nanomaterial as a whole responds to the change in the environment through different mechanisms, including changes in integrity and hydration state. This review focusses on the chemistry and MR performance of these responsive Mn-based nanomaterials.     

NIFTy 3 – Numerical Information Field Theory: A Python Framework for Multicomponent Signal Inference on HPC Clusters

NIFTy , “Numerical Information Field Theory,” is a software framework designed to ease the development and implementation of field inference algorithms. Field equations are formulated independently of the underlying spatial geometry allowing the user to focus on the algorithmic design. Under the hood, NIFTy ensures that the discretization of the implemented equations is consistent. This enables the user to prototype an algorithm rapidly in 1D and then apply it to high‐dimensional real‐world problems. This paper introduces NIFTy  3, a major upgrade to the original NIFTy  framework. NIFTy  3 allows the user to run inference algorithms on massively parallel high performance computing clusters without changing the implementation of the field equations. It supports n‐dimensional Cartesian spaces, spherical spaces, power spaces, and product spaces as well as transforms to their harmonic counterparts. Furthermore, NIFTy  3 is able to handle non‐scalar fields, such as vector or tensor fields. The functionality and performance of the software package is demonstrated with example code, which implements a mock inference inspired by a real‐world algorithm from the realm of information field theory. NIFTy  3 is open‐source software available under the GNU General Public License v3 (GPL‐3) at https://gitlab.mpcdf.mpg.de/ift/NIFTy/tree/NIFTy_3 .