Astrocytic LRP1 mediates brain Aβ clearance and impacts amyloid deposition

Accumulation and deposition of amyloid-β (Aβ) in the brain represent an early and perhaps necessary step in the pathogenesis of Alzheimer's disease (AD). Aβ accumulation leads to the formation of Aβ aggregates, which may directly and indirectly lead to eventual neurodegeneration. While Aβ production is accelerated in many familial forms of early-onset AD, increasing evidence indicates that impaired clearance of Aβ is more evident in late-onset AD. To uncover the mechanisms underlying impaired Aβ clearance in AD, we examined the role of low-density lipoprotein receptor-related protein 1 (LRP1) in astrocytes. Although LRP1 has been shown to play critical roles in brain Aβ metabolism in neurons and vascular mural cells, its role in astrocytes, the most abundant cell type in the brain responsible for maintaining neuronal homeostasis, remains unclear. Here, we show that astrocytic LRP1 plays a critical role in brain Aβ clearance. LRP1 knockdown in primary astrocytes resulted in decreased cellular Aβ uptake and degradation. In addition, silencing of LRP1 in astrocytes led to downregulation of several major Aβ-degrading enzymes, including matrix metalloproteases MMP2, MMP9, and insulin-degrading enzyme. More important, conditional knock-out of theLrp1gene in astrocytes in the background of APP/PS1 mice impaired brain Aβ clearance, exacerbated Aβ accumulation, and accelerated amyloid plaque deposition without affecting its production. Together, our results demonstrate that astrocytic LRP1 plays an important role in Aβ metabolism and that restoring LRP1 expression and function in the brain could be an effective strategy to facilitate Aβ clearance and counter amyloid pathology in AD.SIGNIFICANCE STATEMENTAstrocytes represent a major cell type regulating brain homeostasis; however, their roles in brain clearance of amyloid-β (Aβ) and underlying mechanism are not clear. In this study, we used both cellular models and conditional knock-out mouse models to address the role of a critical Aβ receptor, the low-density lipoprotein receptor-related protein 1 (LRP1) in astrocytes. We found that LRP1 in astrocytes plays a critical role in brain Aβ clearance by modulating several Aβ-degrading enzymes and cellular degradation pathways. Our results establish a critical role of astrocytic LRP1 in brain Aβ clearance and shed light on specific Aβ clearance pathways that may help to establish new targets for AD prevention and therapy.</jats:p

Viability Discrimination of a Class of Control Systems on a Nonsmooth Region

The viability problem is an important field of study in control theory; the corresponding research has profound significance in both theory and practice. In this paper, we consider the viability for both an affine nonlinear hybrid system and a hybrid differential inclusion on a region with subdifferentiable boundary. Based on the nonsmooth analysis theory, we obtain a method to verify the viability condition at a point, when the boundary function of the region is subdifferentiable and its subdifferential is convex hull of many finite points

Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation

Purpose: Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries / veins classi cation are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases. Methods: We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A non-local total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins. Results: The proposed segmentation method yields competitive results on three pub- lic datasets (STARE, DRIVE, and IOSTAR), and it has superior performance when com- pared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to ve public databases 1 (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries / veins classi cation based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively. Conclusions: The experimental results show that the proposed framework has e ectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology recon- struction. The vascular topology information signi cantly improves the accuracy on arteries / veins classi cation

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2D/3D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on 8 publicly available datasets (six 2D datasets, one 3D dataset and one 3D synthetic dataset) demonstrate its superior performance to other state-ofthe- art methods

Face Recognition with Facial Occlusion Based on Local Cycle Graph Structure Operator

Facial occlusion is a difficulty in the field of face recognition. The lack of features caused by occlusion may reduce the face recognition rate greatly. How to extract the identified features from the occluded faces has a profound effect on face recognition. This chapter presents a Local Cycle Graph Structure (LCGS) operator, which makes full use of the information of the pixels around the target pixel with its neighborhood of 3 × 3. Thus, the recognition with the extracted features is more efficient. We apply the extreme learning machine (ELM) classifier to train and test the features extracted by LCGS algorithm. In the experiment, we use the olivetti research laboratory (ORL) database to simulate occlusion randomly and use the AR database for physical occlusion. Physical coverings include scarves and sunglasses. Experimental results demonstrate that our algorithm yields a state-of-the-art performance

GRXCR2 Regulates Taperin Localization Critical for Stereocilia Morphology and Hearing

Mutations in human GRXCR2, which encodes a protein of undetermined function, cause hearing loss by unknown mechanisms. We found that mouse GRXCR2 localizes to the base of the stereocilia, which are actin-based mechanosensing organelles in cochlear hair cells that convert sound-induced vibrations into electrical signals. The stereocilia base also contains taperin, another protein of unknown function required for human hearing. We show that taperin and GRXCR2 form a complex and that taperin is diffused throughout the stereocilia length in Grxcr2-deficient hair cells. Stereocilia lacking GRXCR2 are longer than normal and disorganized due to the mislocalization of taperin, which could modulate the actin cytoskeleton in stereocilia. Remarkably, reducing taperin expression levels could rescue the morphological defects of stereocilia and restore the hearing of Grxcr2-deficient mice. Thus, our findings suggest that GRXCR2 is critical for the morphogenesis of stereocilia and auditory perception by restricting taperin to the stereocilia base

Research on the structure function recognition of PLOS

PurposeThe present study explores and investigates the efficiency of deep learning models in identifying discourse structure and functional features and explores the potential application of natural language processing (NLP) techniques in text mining, information measurement, and scientific communication.MethodThe PLOS literature series has been utilized to obtain full-text data, and four deep learning models, including BERT, RoBERTa, SciBERT, and SsciBERT, have been employed for structure-function recognition.ResultThe experimental findings reveal that the SciBERT model performs outstandingly, surpassing the other models, with an F1 score. Additionally, the performance of different paragraph structures has been analyzed, and it has been found that the model performs well in paragraphs such as method and result.ConclusionThe study's outcomes suggest that deep learning models can recognize the structure and functional elements at the discourse level, particularly for scientific literature, where the SciBERT model performs remarkably. Moreover, the NLP techniques have extensive prospects in various fields, including text mining, information measurement, and scientific communication. By automatically parsing and identifying structural and functional information in text, the efficiency of literature management and retrieval can be improved, thereby expediting scientific research progress. Therefore, deep learning and NLP technologies hold significant value in scientific research