Loss of vesicular dopamine release precedes tauopathy in degenerative dopaminergic neurons in a Drosophila model expressing human tau.

While a number of genome-wide association studies have identified microtubule-associated protein tau as a strong risk factor for Parkinson's disease (PD), little is known about the mechanism through which human tau can predispose an individual to this disease. Here, we demonstrate that expression of human wild-type tau is sufficient to disrupt the survival of dopaminergic neurons in a Drosophila model. Tau triggers a synaptic pathology visualized by vesicular monoamine transporter-pHGFP that precedes both the age-dependent formation of tau-containing neurofibrillary tangle-like pathology and the progressive loss of DA neurons, thereby recapitulating the pathological hallmarks of PD. Flies overexpressing tau also exhibit progressive impairments of both motor and learning behaviors. Surprisingly, contrary to common belief that hyperphosphorylated tau could aggravate toxicity, DA neuron degeneration is alleviated by expressing the modified, hyperphosphorylated tau(E14). Together, these results show that impairment of VMAT-containing synaptic vesicle, released to synapses before overt tauopathy may be the underlying mechanism of tau-associated PD and suggest that correction or prevention of this deficit may be appropriate targets for early therapeutic intervention

A Two-Phase Maximum-Likelihood Sequence Estimation for Receivers with Partial CSI

The optimality of the conventional maximum likelihood sequence estimation (MLSE), also known as the Viterbi Algorithm (VA), relies on the assumption that the receiver has perfect knowledge of the channel coefficients or channel state information (CSI). However, in practical situations that fail the assumption, the MLSE method becomes suboptimal and then exhaustive checking is the only way to obtain the ML sequence. At this background, considering directly the ML criterion for partial CSI, we propose a two-phase low-complexity MLSE algorithm, in which the first phase performs the conventional MLSE algorithm in order to retain necessary information for the backward VA performed in the second phase. Simulations show that when the training sequence is moderately long in comparison with the entire data block such as 1/3 of the block, the proposed two-phase MLSE can approach the performance of the optimal exhaustive checking. In a normal case, where the training sequence consumes only 0.14 of the bandwidth, our proposed method still outperforms evidently the conventional MLSE.Comment: 5 pages and 4 figure

Domain Specific Approximation for Object Detection

There is growing interest in object detection in advanced driver assistance systems and autonomous robots and vehicles. To enable such innovative systems, we need faster object detection. In this work, we investigate the trade-off between accuracy and speed with domain-specific approximations, i.e. category-aware image size scaling and proposals scaling, for two state-of-the-art deep learning-based object detection meta-architectures. We study the effectiveness of applying approximation both statically and dynamically to understand the potential and the applicability of them. By conducting experiments on the ImageNet VID dataset, we show that domain-specific approximation has great potential to improve the speed of the system without deteriorating the accuracy of object detectors, i.e. up to 7.5x speedup for dynamic domain-specific approximation. To this end, we present our insights toward harvesting domain-specific approximation as well as devise a proof-of-concept runtime, AutoFocus, that exploits dynamic domain-specific approximation.Comment: 6 pages, 6 figures. Published in IEEE Micro, vol. 38, no. 1, pp. 31-40, January/February 201

Distributed Training Large-Scale Deep Architectures

Scale of data and scale of computation infrastructures together enable the current deep learning renaissance. However, training large-scale deep architectures demands both algorithmic improvement and careful system configuration. In this paper, we focus on employing the system approach to speed up large-scale training. Via lessons learned from our routine benchmarking effort, we first identify bottlenecks and overheads that hinter data parallelism. We then devise guidelines that help practitioners to configure an effective system and fine-tune parameters to achieve desired speedup. Specifically, we develop a procedure for setting minibatch size and choosing computation algorithms. We also derive lemmas for determining the quantity of key components such as the number of GPUs and parameter servers. Experiments and examples show that these guidelines help effectively speed up large-scale deep learning training

Simplified ZrTiOx-based RRAM cell structure with rectifying characteristics by integrating Ni/n + -Si diode

A simplified one-diode one-resistor (1D1R) resistive switching memory cell that uses only four layers of TaN/ZrTiO( x )/Ni/n(+)-Si was proposed to suppress sneak current where TaN/ZrTiO( x )/Ni can be regarded as a resistive-switching random access memory (RRAM) device while Ni/n(+)-Si acts as an Schottky diode. This is the first RRAM cell structure that employs metal/semiconductor Schottky diode for current rectifying. The 1D1R cell exhibits bipolar switching behavior with SET/RESET voltage close to 1 V without requiring a forming process. More importantly, the cell shows tight resistance distribution for different states, significantly rectifying characteristics with forward/reverse current ratio higher than 10(3) and a resistance ratio larger than 10(3) between two states. Furthermore, the cell also displays desirable reliability performance in terms of long data retention time of up to 10(4) s and robust endurance of 10(5) cycles. Based on the promising characteristics, the four-layer 1D1R structure holds the great potential for next-generation nonvolatile memory technology

Repulsive Guidance Molecule (RGM) Family Proteins Exhibit Differential Binding Kinetics for Bone Morphogenetic Proteins (BMPs)

Bone morphogenetic proteins (BMPs) are members of the transforming growth factor beta superfamily that exert their effects via type I and type II serine threonine kinase receptors and the SMAD intracellular signaling pathway to regulate diverse biologic processes. Recently, we discovered that the repulsive guidance molecule (RGM) family, including RGMA, RGMB, and RGMC/hemojuvelin (HJV), function as co-receptors that enhance cellular responses to BMP ligands. Here, we use surface plasmon resonance to quantitate the binding kinetics of RGM proteins for BMP ligands. We show that among the RGMs, HJV exhibits the highest affinity for BMP6, BMP5, and BMP7 with KD 8.1, 17, and 20 nM respectively, versus 28, 33, and 166 nM for RGMB, and 55, 83, and 63 nM for RGMA. Conversely, RGMB exhibits preferential binding to BMP4 and BMP2 with KD 2.6 and 5.5 nM respectively, versus 4.5 and 9.4 nM for HJV, and 14 and 22 nM for RGMA, while RGMA exhibits the lowest binding affinity for most BMPs tested. Among the BMP ligands, RGMs exhibit the highest relative affinity for BMP4 and the lowest relative affinity for BMP7, while none of the RGMs bind to BMP9. Thus, RGMs exhibit preferential binding for distinct subsets of BMP ligands. The preferential binding of HJV for BMP6 is consistent with the functional role of HJV and BMP6 in regulating systemic iron homeostasis. Our data may help explain the mechanism by which BMPs exert cell-context specific effects via a limited number of type I and type II receptors

Potassium {4-[(3S,6S,9S)-3,6-dibenzyl-9-isopropyl-4,7,10-trioxo-11–oxa-2,5,8-triazadodecyl]phenyl}trifluoroborate

[[abstract]]The reported compound 4 was synthesized and fully characterized by 1H NMR, 13C NMR, 11B NMR, 19F NMR, and high resolution mass spectrometry.[[booktype]]電子版[[countrycodes]]CH