Imaging Neural Activity in the Primary Somatosensory Cortex Using Thy1-GCaMP6s Transgenic Mice

The mammalian brain exhibits marked symmetry across the sagittal plane. However, detailed description of neural dynamics in symmetric brain regions in adult mammalian animals remains elusive. In this study, we describe an experimental procedure for measuring calcium dynamics through dual optical windows above bilateral primary somatosensory corticies (S1) in Thy1-GCaMP6s transgenic mice using 2-photon (2P) microscopy. This method enables recordings and quantifications of neural activity in bilateral mouse brain regions one at a time in the same experiment for a prolonged period in vivo. Key aspects of this method, which can be completed within an hour, include minimally invasive surgery procedures for creating dual optical windows, and the use of 2P imaging. Although we only demonstrate the technique in the S1 area, the method can be applied to other regions of the living brain facilitating the elucidation of structural and functional complexities of brain neural networks

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

Confirming the 115.5-day periodicity in the X-ray light curve of ULX NGC 5408 X-1

The Swift/XRT light curve of the ultraluminous X-ray (ULX) source NGC 5408 X-1 was re-analyzed with two new numerical approaches, Weighted Wavelet $Z$-transform (WWZ) and CLEANest, that are different from previous studies. Both techniques detected a prominent periodicity with a time scale of $115.5\pm1.5$ days, in excellent agreement with the detection of the same periodicity first reported by Strohmayer (2009). Monte Carlo simulation was employed to test the statisiticak confidence of the 115.5-day periodicity, yielding a statistical significance of $> 99.98$ (or $>3.8\sigma$). The robust detection of the 115.5-day quasi-periodic oscillations (QPOs), if it is due to the orbital motion of the binary, would infer a mass of a few thousand $M_\odot$ for the central black hole, implying an intermediate-mass black hole in NGC 5408 X-1.Comment: 6 pages, 2 figures, submitted to Research in Astronomy and Astrophysics (RAA

M2GRL: A Multi-task Multi-view Graph Representation Learning Framework for Web-scale Recommender Systems

Combining graph representation learning with multi-view data (side information) for recommendation is a trend in industry. Most existing methods can be categorized as \emph{multi-view representation fusion}; they first build one graph and then integrate multi-view data into a single compact representation for each node in the graph. However, these methods are raising concerns in both engineering and algorithm aspects: 1) multi-view data are abundant and informative in industry and may exceed the capacity of one single vector, and 2) inductive bias may be introduced as multi-view data are often from different distributions. In this paper, we use a \emph{multi-view representation alignment} approach to address this issue. Particularly, we propose a multi-task multi-view graph representation learning framework (M2GRL) to learn node representations from multi-view graphs for web-scale recommender systems. M2GRL constructs one graph for each single-view data, learns multiple separate representations from multiple graphs, and performs alignment to model cross-view relations. M2GRL chooses a multi-task learning paradigm to learn intra-view representations and cross-view relations jointly. Besides, M2GRL applies homoscedastic uncertainty to adaptively tune the loss weights of tasks during training. We deploy M2GRL at Taobao and train it on 57 billion examples. According to offline metrics and online A/B tests, M2GRL significantly outperforms other state-of-the-art algorithms. Further exploration on diversity recommendation in Taobao shows the effectiveness of utilizing multiple representations produced by \method{}, which we argue is a promising direction for various industrial recommendation tasks of different focus.Comment: Accepted by KDD 2020 ads track as an oral paper. Code address:https://github.com/99731/M2GR

Autophagy in Alcoholic Liver Disease, Self-eating Triggered by Drinking

Macroautophagy (autophagy) is an evolutionarily conserved mechanism. It is important for normal cellular function and also plays critical roles in the etiology and pathogenesis of a number of human diseases. In alcohol-induced liver disease, autophagy is a protective mechanism against the liver injury caused by alcohol. Autophagy is activated in acute ethanol treatment but could be suppressed in chronic and/or high dose treatment of alcohol. The selective removal of lipid droplets and/or damaged mitochondria is likely the major mode of autophagy in reducing liver injury. Understanding the dynamics of the autophagy process and the approach to modulate autophagy could help finding new ways to battle against alcohol-induced liver injury