Heat-evoked activation of TRPV4 channels in an HEK-293 cell expression system and in native mouse aorta endothelial cells

Peer reviewe

Open-independent, Open-locating-dominating Sets

A distinguishing set for a graph G = (V, E) is a dominating set D, each vertex $v \in D$ being the location of some form of a locating device, from which one can detect and precisely identify any given "intruder" vertex in V(G). As with many applications of dominating sets, the set $D$ might be required to have a certain property for <D>, the subgraph induced by D (such as independence, paired, or connected). Recently the study of independent locating-dominating sets and independent identifying codes was initiated. Here we introduce the property of open-independence for open-locating-dominating sets

Attitude Control System Design & Verification for CNUSAIL-1 with Solar/Drag Sail

CNUSAIL-1, to be launched into low-earth orbit, is a cubesat-class satellite equipped with a 2 m × 2 m solar sail. One of CNUSAIL’s missions is to deploy its solar sail system, thereby deorbiting the satellite, at the end of the satellite’s life. This paper presents the design results of the attitude control system for CNUSAIL-1, which maintains the normal vector of the sail by a 3-axis active attitude stabilization approach. The normal vector can be aligned in two orientations: i) along the anti-nadir direction, which minimizes the aerodynamic drag during the nadir-pointing mode, or ii) along the satellite velocity vector, which maximizes the drag during the deorbiting mode. The attitude control system also includes a B-dot controller for detumbling and an eigen-axis maneuver algorithm. The actuators for the attitude control are magnetic torquers and reaction wheels. The feasibility and performance of the design are verified in high-fidelity nonlinear simulations

Myricetin: A Naturally Occurring Regulator of Metal-Induced Amyloid-β Aggregation and Neurotoxicity

No AbstractPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/84385/1/1198_ftp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/84385/2/cbic_201000790_sm_miscellaneous_information.pd

Recent Development of Bifunctional Small Molecules to Study Metal-Amyloid-β Species in Alzheimer's Disease

Alzheimer's disease (AD) is a multifactorial neurodegenerative disease related to the deposition of aggregated amyloid-β (Aβ) peptides in the brain. It has been proposed that metal ion dyshomeostasis and miscompartmentalization contribute to AD progression, especially as metal ions (e.g., Cu(II) and Zn(II)) found in Aβ plaques of the diseased brain can bind to Aβ and be linked to aggregation and neurotoxicity. The role of metal ions in AD pathogenesis, however, is uncertain. To accelerate understanding in this area and contribute to therapeutic development, recent efforts to devise suitable chemical reagents that can target metal ions associated with Aβ have been made using rational structure-based design that combines two functions (metal chelation and Aβ interaction) in the same molecule. This paper presents bifunctional compounds developed by two different design strategies (linkage or incorporation) and discusses progress in their applications as chemical tools and/or potential therapeutics

Learning Optimal Deep Projection of 18^{18}F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes

Several diseases of parkinsonian syndromes present similar symptoms at early stage and no objective widely used diagnostic methods have been approved until now. Positron emission tomography (PET) with $^{18}$F-FDG was shown to be able to assess early neuronal dysfunction of synucleinopathies and tauopathies. Tensor factorization (TF) based approaches have been applied to identify characteristic metabolic patterns for differential diagnosis. However, these conventional dimension-reduction strategies assume linear or multi-linear relationships inside data, and are therefore insufficient to distinguish nonlinear metabolic differences between various parkinsonian syndromes. In this paper, we propose a Deep Projection Neural Network (DPNN) to identify characteristic metabolic pattern for early differential diagnosis of parkinsonian syndromes. We draw our inspiration from the existing TF methods. The network consists of a (i) compression part: which uses a deep network to learn optimal 2D projections of 3D scans, and a (ii) classification part: which maps the 2D projections to labels. The compression part can be pre-trained using surplus unlabelled datasets. Also, as the classification part operates on these 2D projections, it can be trained end-to-end effectively with limited labelled data, in contrast to 3D approaches. We show that DPNN is more effective in comparison to existing state-of-the-art and plausible baselines.Comment: 8 pages, 3 figures, conference, MICCAI DLMIA, 201