Astrocytic expression of Parkinson's disease-related A53T α-synuclein causes neurodegeneration in mice

<p>Abstract</p> <p>Background</p> <p>Parkinson's disease (PD) is the most common movement disorder. While neuronal deposition of α-synuclein serves as a pathological hallmark of PD and Dementia with Lewy Bodies, α-synuclein-positive protein aggregates are also present in astrocytes. The pathological consequence of astrocytic accumulation of α-synuclein, however, is unclear.</p> <p>Results</p> <p>Here we show that PD-related A53T mutant α-synuclein, when selectively expressed in astrocytes, induced rapidly progressed paralysis in mice. Increasing accumulation of α-synuclein aggregates was found in presymptomatic and symptomatic mouse brains and correlated with the expansion of reactive astrogliosis. The normal function of astrocytes was compromised as evidenced by cerebral microhemorrhage and down-regulation of astrocytic glutamate transporters, which also led to increased inflammatory responses and microglial activation. Interestingly, the activation of microglia was mainly detected in the midbrain, brainstem and spinal cord, where a significant loss of dopaminergic and motor neurons was observed. Consistent with the activation of microglia, the expression level of cyclooxygenase 1 (COX-1) was significantly up-regulated in the brain of symptomatic mice and in cultured microglia treated with conditioned medium derived from astrocytes over-expressing A53T α-synuclein. Consequently, the suppression of COX-1 activities extended the survival of mutant mice, suggesting that excess inflammatory responses elicited by reactive astrocytes may contribute to the degeneration of neurons.</p> <p>Conclusions</p> <p>Our findings demonstrate a critical involvement of astrocytic α-synuclein in initiating the non-cell autonomous killing of neurons, suggesting the viability of reactive astrocytes and microglia as potential therapeutic targets for PD and other neurodegenerative diseases.</p

Zero-shot learning via discriminative representation extraction

Zero-shot learning (ZSL) aims to recognize classes whose samples did not appear during training. Existing research focuses on mapping deep visual feature to semantic embedding space explicitly or implicitly. However, ZSL improvements led by discriminative feature transformation is not well studied. In this paper, we propose a ZSL framework that maps semantic embeddings to a discriminative representation space, which are learned in two different ways: Kernelized Linear Discriminant Analysis (KLDA) and Central-loss based Network (CLN). KLDA and CLN can both force samples to be intra-class aggregation and inter-class separation. With the learned discriminative representations, we map class embeddings to representation space using Kernelized Ridge Regression (KRR). Our experiments show that both KLDA+KRR and CLN+KRR surpass state-of-art approaches in both recognition and retrieval task

A turn-off fluorescent probe for the detection of Cu2+ based on a tetraphenylethylene-functionalized salicylaldehyde Schiff-base

A non-planar tetraphenylethylene-functionalized salicylaldehyde Schiff-base fluorescent probe (TPE-An-Py) with aggregation-induced enhanced emission (AIEE) characteristics was synthesized via a classical Knoevenagel condensation reaction, and exhibited a high sensitivity towards copper ions in aqueous media with a "turn-off" fluorescence mechanism; the limit of detection is 2.36 × 10-7 mol L-1. Importantly, the coordination mode of the probe towards copper was further evaluated by UV-vis and NMR spectroscopy and a 1:2 stoichiometry was identified. A single crystal X-ray diffraction study confirmed the binding mode. In addition, the AIEE fluorescent probe can be applied to the detection of Cu2+ in practical samples with satisfactory recoveries in a range of 106-111% in lake water and 97-108% in tap water

Smart hydrogels with wide visible color tunability

Pigmentary coloration can produce viewing angle-independent uniform colors via light absorption by chromophores. However, due to the limited diversity in the changes of the molecular configuration of chromophores to undergo color change, the existing materials cannot produce a wide range of visible colors with tunable color saturation and transmittance. Herein, we propose a novel strategy to create materials with a wide visible color range and highly tunable color saturation and transmittance. We fabricated a hydrogel with poly (acrylamide-co-dopamine acrylamide) networks swollen with Fe3+-containing glycerol/water in which the covalently crosslinked polyacrylamide backbone with pendant catechols can ensure that the hydrogel maintains a very stable shape. Hydrogels containing adjustable catechol-Fe3+ coordination bonds with flexible light-interacting configuration changes can display a wide range of visible colors based on the complementary color principle. The catechol-Fe3+ complexes can dynamically switch between noncoordinated and mono-, bis- and tris-coordinated states to harvest light energy from a specific wavelength across the whole visible spectrum. Therefore, these hydrogels can be yellow, green, blue, and red, covering the three primary colors. Moreover, color saturation and transmittance can be flexibly manipulated by simply adjusting the Fe3+ content in the hydrogel networks. The versatility of these smart hydrogels has been demonstrated through diverse applications, including optical filters for color regulation and colorimetric sensors for detecting UV light and chemical vapors. This proposed smart hydrogel provides a universal color-switchable platform for the development of multifunctional optical systems such as optical filters, sensors, and detectors

Observation of Quantum Griffiths Singularity and Ferromagnetism at Superconducting LaAlO3/SrTiO3(110) Interface

Diverse phenomena emerge at the interface between band insulators LaAlO3 and SrTiO3, such as superconductivity and ferromagnetism, showing an opportunity for potential applications as well as bringing fundamental research interests. Particularly, the two-dimensional electron gas formed at LaAlO3/SrTiO3 interface offers an appealing platform for quantum phase transition from a superconductor to a weakly localized metal. Here we report the superconductor-metal transition in superconducting two-dimensional electron gas formed at LaAlO3/SrTiO3(110) interface driven by a perpendicular magnetic field. Interestingly, when approaching the quantum critical point, the dynamic critical exponent is not a constant but a diverging value, which is a direct evidence of quantum Griffiths singularity raised from quenched disorder at ultralow temperatures. Furthermore, the hysteretic property of magnetoresistance was firstly observed at LaAlO3/SrTiO3(110) interfaces, which suggests potential coexistence of superconductivity and ferromagnetism

Genome-Wide Association Analyses Highlight the Potential for Different Genetic Mechanisms for Litter Size Among Sheep Breeds

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Effects of mGluR5 Antagonists on Parkinson's Patients With L-Dopa-Induced Dyskinesia: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

Background: Modulation of Metabotropic glutamate receptor 5 (mGluR5) may be a novel therapeutic approach to manage Parkinson's disease (PD) Patients with L-dopa-induced dyskinesia (LID).Objectives: The objective of this meta-analysis was to evaluate the effects of mGluR5 antagonists for the treatment of LID patients.Methods: Several electronic databases were consulted up to July 30, 2017. Randomized clinical trials (RCTs) that compared mGluR5 antagonists vs. placebo in LID patients were included. Pooled weighted mean difference (WMD) with 95% confidence intervals (CIs) were calculated using random-effects models.Results: Nine trials including 776 patients met all inclusion criteria. We pooled the whole data and found apparent difference between mGluR5 antagonists and placebo in terms of mAIMS (p = 0.010). However, there was no significant improvements on antidyskinetic in terms of LFADLDS (p = 0.42) and UPDRS Part IV (p = 0.20). Meanwhile, the effect size of UPDRS part III was similar in mGluR5 antagonist groups with in placebo groups (p = 0.25). Adverse events incidence was higher with mGluR5 antagonists than with placebo, especially at the expense of increased dizziness (16.3 vs. 4.3%), visual hallucination (10.1 vs. 1.1%), or fatigue (10.1 vs. 4.8%).Conclusions: mGluR5 antagonists had a greater treatment effect on the mAIMS in LID patients, however, there was no improvements on antidyskinetic in terms of LFADLDS and UPDRS Part IV compared with placebo. According to these results, we unable to recommend mGluR5 antagonists for the routine treatment of LID patients right now

Reversible Engineering of Topological Insulator Surface State Conductivity through Optical Excitation

Despite the broadband response, limited optical absorption at a particular wavelength hinders the development of optoelectronics based on Dirac fermions. Heterostructures of graphene and various semiconductors have been explored for this purpose, while non-ideal interfaces often limit the performance. The topological insulator is a natural hybrid system, with the surface states hosting high-mobility Dirac fermions and the small-bandgap semiconducting bulk state strongly absorbing light. In this work, we show a large photocurrent response from a field effect transistor device based on intrinsic topological insulator Sn-Bi1.1Sb0.9Te2S. The photocurrent response is non-volatile and sensitively depends on the initial Fermi energy of the surface state, and it can be erased by controlling the gate voltage. Our observations can be explained with a remote photo-doping mechanism, in which the light excites the defects in the bulk and frees the localized carriers to the surface state. This photodoping modulates the surface state conductivity without compromising the mobility, and it also significantly modify the quantum Hall effect of the surface state. Our work thus illustrates a route to reversibly manipulate the surface states through optical excitation, shedding light into utilizing topological surface states for quantum optoelectronics

A Novel Inhibitor of Homodimerization Targeting MyD88 Ameliorates Renal Interstitial Fibrosis by Counteracting TGF-β1-Induced EMT in Vivo and in Vitro

Background/Aims: The TLR/MyD88/NF-κB signaling pathway has been successfully used to treat renal interstitial fibrosis (RIF). However, the exact therapeutic mechanism is still unknown. Here, we assessed the therapeutic efficacy of TJ-M2010-2, a small molecular compound that inhibits MyD88 homodimerization, in RIF induced by ischemia reperfusion injury (IRI). Methods: In vivo, RIF was induced in mice by IRI, and the mice were prophylactically treated with TJ-M2010-2. In vitro, HK-2 cells were incubated with TGF-β1 to induce EMT, and the cells were pretreated with TJ-M2010-2. Results: We found that, compared with the IRI group, the TJ-M2010-2 group showed marked attenuation of RIF and renal function injury; decreased expression of TGF-β1, α-SMA, vimentin, MMP2 and MMP9; and increased E-cadherin expression. Furthermore, TGF-β1-induced EMT was blocked by TJ-M2010-2 in HK-2 cells, as evidenced by blocked morphologic transformation, restored E-cadherin expression and inhibited α-SMA expression. In addition, compared to the TGF-β1 group, the TJ-M2010-2 group showed profound inhibition of the expression of TRAF6, p65 and Snail and upregulation of the expression of IκBα. Conclusion: This MyD88 inhibitor may be a potential therapeutic agent to ameliorate RIF

Methods for segmentation and classification of digital microscopy tissue images

High-resolution microscopy images of tissue specimens provide detailed information about the morphology of normal and diseased tissue. Image analysis of tissue morphology can help cancer researchers develop a better understanding of cancer biology. Segmentation of nuclei and classification of tissue images are two common tasks in tissue image analysis. Development of accurate and efficient algorithms for these tasks is a challenging problem because of the complexity of tissue morphology and tumor heterogeneity. In this paper we present two computer algorithms; one designed for segmentation of nuclei and the other for classification of whole slide tissue images. The segmentation algorithm implements a multiscale deep residual aggregation network to accurately segment nuclear material and then separate clumped nuclei into individual nuclei. The classification algorithm initially carries out patch-level classification via a deep learning method, then patch-level statistical and morphological features are used as input to a random forest regression model for whole slide image classification. The segmentation and classification algorithms were evaluated in the MICCAI 2017 Digital Pathology challenge. The segmentation algorithm achieved an accuracy score of 0.78. The classification algorithm achieved an accuracy score of 0.81. These scores were the highest in the challenge