Iron homeostasis disturbance has been implicated in Alzheimer’s disease (AD), and excess iron exacerbates oxidative damage and cognitive defects. Ferroptosis is a nonapoptotic form of cell death dependent upon intracellular iron. However, the involvement of ferroptosis in the pathogenesis of AD remains elusive. Here, we report that ferroportin1 (Fpn), the only identified mammalian nonheme iron exporter, was downregulated in the brains of APPswe/PS1dE9 mice as an Alzheimer’s mouse model and Alzheimer’s patients. Genetic deletion of Fpn in principal neurons of the neocortex and hippocampus by breeding Fpnfl/fl mice with NEX-Cre mice led to AD-like hippocampal atrophy and memory deficits. Interestingly, the canonical morphological and molecular characteristics of ferroptosis were observed in both Fpnfl/fl/NEXcre and AD mice. Gene set enrichment analysis (GSEA) of ferroptosis-related RNA-seq data showed that the differentially expressed genes were highly enriched in gene sets associated with AD. Furthermore, administration of specific inhibitors of ferroptosis effectively reduced the neuronal death and memory impairments induced by Aβ aggregation in vitro and in vivo. In addition, restoring Fpn ameliorated ferroptosis and memory impairment in APPswe/PS1dE9 mice. Our study demonstrates the critical role of Fpn and ferroptosis in the progression of AD, thus provides promising therapeutic approaches for this disease.
One of the main challenges of visual object tracking comes from the arbitrary appearance of objects. Most existing algorithms try to resolve this problem as an object-specific task, i.e., the model is trained to regenerate or classify a specific object. As a result, the model need to be initialized and retrained for different objects. In this paper, we propose a more generic approach utilizing a novel two-flow convolutional neural network (named YCNN). The YCNN takes two inputs (one is object image patch, the other is search image patch), then outputs a response map which predicts how likely the object appears in a specific location. Unlike those object-specific approach, the YCNN is trained to measure the similarity between two image patches. Thus it will not be confined to any specific object. Furthermore the network can be endto-end trained to extract both shallow and deep convolutional features which are dedicated for visual tracking. And once properly trained, the YCNN can be applied to track all kinds of objects without further training and updating. Benefiting from the once-for-all model, our algorithm is able to run at a very high speed of 45 frames-per-second. The experiments on 51 sequences also show that our algorithm achieves an outstanding performance.
Alzheimer’s disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, accompanied by amyloid-β (Aβ) overload and hyperphosphorylated tau accumulation in the brain. Synaptic dysfunction, an important pathological hallmark in AD, is recognized as the main cause of the cognitive impairments. Accumulating evidence suggests that synaptic dysfunction could be an early pathological event in AD. Pathological tau, which is detached from axonal microtubules and mislocalized into pre- and postsynaptic neuronal compartments, is suggested to induce synaptic dysfunction in several ways, including reducing mobility and release of presynaptic vesicles, decreasing glutamatergic receptors, impairing the maturation of dendritic spines at postsynaptic terminals, disrupting mitochondrial transport and function in synapses, and promoting the phagocytosis of synapses by microglia. Here, we review the current understanding of how pathological tau mediates synaptic dysfunction and contributes to cognitive decline in AD. We propose that elucidating the mechanism by which pathological tau impairs synaptic function is essential for exploring novel therapeutic strategies for AD.
Halide perovskite semiconductors with extraordinary optoelectronic properties have been fascinatedly studied. Halide perovskite nanocrystals, single crystals, and thin films have been prepared for various fields, such as light emission, light detection, and light harvesting. High‐performance devices rely on high crystal quality determined by the nucleation and crystal growth process. Here, the fundamental understanding of the crystallization process driven by supersaturation of the solution is discussed and the methods for halide perovskite crystals are summarized. Supersaturation determines the proportion and the average Gibbs free energy changes for surface and volume molecular units involved in the spontaneous aggregation, which could be stable in the solution and induce homogeneous nucleation only when the solution exceeds a required minimum critical concentration (Cmin). Crystal growth and heterogeneous nucleation are thermodynamically easier than homogeneous nucleation due to the existent surfaces. Nanocrystals are mainly prepared via the nucleation‐dominated process by rapidly increasing the concentration over Cmin, single crystals are mainly prepared via the growth‐dominated process by keeping the concentration between solubility and Cmin, while thin films are mainly prepared by compromising the nucleation and growth processes to ensure compactness and grain sizes. Typical strategies for preparing these three forms of halide perovskites are also reviewed.
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