Effects of a Dual-Task Paradigm and Gait Velocity on Dynamic Gait Stability during Stair Descent

Falls during stair negotiation have become one of the leading causes of accidental death. The effects of a concurrent cognitive or manual dual-task paradigm on dynamic gait stability remain uncertain. How much dynamic gait stability is influenced by gait velocity is also not clear. A total of 16 healthy young females descended a staircase under three different walking conditions: descend stairs only (single task), descend stairs while performing subtraction (cognitive dual-task), and descend stairs while carrying a glass of water (manual dual-task). An eight-camera Vicon motion analysis system and a Kistler force plate embedded into the third step of the staircase were used synchronously to collect kinematic and kinetic data. Gait velocity decreased and dynamic gait stability increased with both cognitive and manual dual-task conditions. The center of mass–center of pressure inclination angle increased with gait velocity but decreased with the manual dual-task condition compared to the single-task condition. Changes in gait velocity caused by the dual-task paradigm can partially explain the effects of dual-task dynamic gait stability. The influence of gait velocity should be considered in the assessment of dual-task effects

Classical vs Quantum Advice and Proofs under Classically-Accessible Oracle

It is a long-standing open question to construct a classical oracle relative to which BQP/qpoly $\neq$ BQP/poly or QMA $\neq$ QCMA. In this paper, we construct classically-accessible classical oracles relative to which BQP/qpoly $\neq$ BQP/poly and QMA $\neq$ QCMA. Here, classically-accessible classical oracles are oracles that can be accessed only classically even for quantum algorithms. Based on a similar technique, we also show an alternative proof for the separation of QMA and QCMA relative to a distributional quantumly-accessible classical oracle, which was recently shown by Natarajan and Nirkhe.Comment: 31 pages. Added classically-accessible classical oracle separation of QMA and QCMA and updated the abstrac

Vector Quantized Diffusion Model with CodeUnet for Text-to-Sign Pose Sequences Generation

Sign Language Production (SLP) aims to translate spoken languages into sign sequences automatically. The core process of SLP is to transform sign gloss sequences into their corresponding sign pose sequences (G2P). Most existing G2P models usually perform this conditional long-range generation in an autoregressive manner, which inevitably leads to an accumulation of errors. To address this issue, we propose a vector quantized diffusion method for conditional pose sequences generation, called PoseVQ-Diffusion, which is an iterative non-autoregressive method. Specifically, we first introduce a vector quantized variational autoencoder (Pose-VQVAE) model to represent a pose sequence as a sequence of latent codes. Then we model the latent discrete space by an extension of the recently developed diffusion architecture. To better leverage the spatial-temporal information, we introduce a novel architecture, namely CodeUnet, to generate higher quality pose sequence in the discrete space. Moreover, taking advantage of the learned codes, we develop a novel sequential k-nearest-neighbours method to predict the variable lengths of pose sequences for corresponding gloss sequences. Consequently, compared with the autoregressive G2P models, our model has a faster sampling speed and produces significantly better results. Compared with previous non-autoregressive G2P methods, PoseVQ-Diffusion improves the predicted results with iterative refinements, thus achieving state-of-the-art results on the SLP evaluation benchmark

3D-SeqMOS: A Novel Sequential 3D Moving Object Segmentation in Autonomous Driving

For the SLAM system in robotics and autonomous driving, the accuracy of front-end odometry and back-end loop-closure detection determine the whole intelligent system performance. But the LiDAR-SLAM could be disturbed by current scene moving objects, resulting in drift errors and even loop-closure failure. Thus, the ability to detect and segment moving objects is essential for high-precision positioning and building a consistent map. In this paper, we address the problem of moving object segmentation from 3D LiDAR scans to improve the odometry and loop-closure accuracy of SLAM. We propose a novel 3D Sequential Moving-Object-Segmentation (3D-SeqMOS) method that can accurately segment the scene into moving and static objects, such as moving and static cars. Different from the existing projected-image method, we process the raw 3D point cloud and build a 3D convolution neural network for MOS task. In addition, to make full use of the spatio-temporal information of point cloud, we propose a point cloud residual mechanism using the spatial features of current scan and the temporal features of previous residual scans. Besides, we build a complete SLAM framework to verify the effectiveness and accuracy of 3D-SeqMOS. Experiments on SemanticKITTI dataset show that our proposed 3D-SeqMOS method can effectively detect moving objects and improve the accuracy of LiDAR odometry and loop-closure detection. The test results show our 3D-SeqMOS outperforms the state-of-the-art method by 12.4%. We extend the proposed method to the SemanticKITTI: Moving Object Segmentation competition and achieve the 2nd in the leaderboard, showing its effectiveness

Joint Parsing and Generation for Abstractive Summarization

Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to remedy this problem by jointly generating a sentence and its syntactic dependency parse while performing abstraction. If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged. The proposed method thus holds promise for producing grammatical sentences and encouraging the summary to stay true-to-original. Our contributions of this work are twofold. First, we present a novel neural architecture for abstractive summarization that combines a sequential decoder with a tree-based decoder in a synchronized manner to generate a summary sentence and its syntactic parse. Secondly, we describe a novel human evaluation protocol to assess if, and to what extent, a summary remains true to its original meanings. We evaluate our method on a number of summarization datasets and demonstrate competitive results against strong baselines.Comment: AAAI 2020 (Main Technical Track

A Novel ZIP4-HDAC4-VEGFA Axis in High-Grade Serous Ovarian Cancer

We have recently identified ZIP4 as a novel cancer stem cell (CSC) marker in high-grade serous ovarian cancer (HGSOC). While it converts drug-resistance to cisplatin (CDDP), we unexpectedly found that ZIP4 induced sensitization of HGSOC cells to histone deacetylase inhibitors (HDACis). Mechanistically, ZIP4 selectively upregulated HDAC IIa HDACs, with little or no effect on HDACs in other classes. HDAC4 knockdown (KD) and LMK-235 inhibited spheroid formation in vitro and tumorigenesis in vivo, with hypoxia inducible factor-1 alpha (HIF1α) and endothelial growth factor A (VEGFA) as functional downstream mediators of HDAC4. Moreover, we found that ZIP4, HDAC4, and HIF1α were involved in regulating secreted VEGFA in HGSOC cells. Furthermore, we tested our hypothesis that co-targeting CSC via the ZIP4-HDAC4 axis and non-CSC using CDDP is necessary and highly effective by comparing the effects of ZIP4-knockout/KD, HDAC4-KD, and HDACis, in the presence or absence of CDDP on tumorigenesis in mouse models. Our results showed that the co-targeting strategy was highly effective. Finally, data from human HGSOC tissues showed that ZIP4 and HDAC4 were upregulated in a subset of recurrent tumors, justifying the clinical relevance of the study. In summary, our study provides a new mechanistic-based targeting strategy for HGSOC

Exploring the effects of peripheral sensibility on visuospatial and postural capacities during goal-directed movements in long-term Tai Chi practitioners

BackgroundFalls are directly related to visuospatial ability and postural stability. Perturbations of upper body movements pose a challenge to older adults and may cause falls. This study investigated visuospatial ability and postural stability during goal-directed upper body movements between the Tai Chi and control groups and tried to connect them with their sensations.Materials and methodsThirty-seven older adults were recruited to perform the touch (TT) and blind touch (BTT) tasks. The target positioning error (TPE), ankle proprioception, tactile sensation, time to stabilization (TTS), and maximum displacement (Dmax) of the center of pressure trajectory were compared between the groups during the tasks. The relationships of visuospatial ability and postural stability to proprioception and tactile sensation were investigated.ResultsDmax in the mediolateral (DmaxML) direction decreased during BTT compared to TT among the Tai Chi group but not the control group. Compared to the control group, less Dmax in the anterio-posterior (DmaxAP) direction, and shorter TTS in AP/ML (TTSAP/TTSML) directions were observed among the Tai Chi group. Compared to TT, DmaxAP decreased during the BTT. The Tai Chi group had less TPE in the vertical (TPEV) direction and in three-dimensional space. Among the Tai Chi group, TPEV, TTSML, and DmaxAP were correlated to their proprioception during plantarflexion; TTSAP was correlated to tactile sensation at the great toe during the TT and BTT; DmaxAP was correlated to tactile sensation at the great toe during the TT. Among the control group, TTSML was correlated to ankle proprioception during dorsiflexion and plantarflexion during the BTT.ConclusionLong-term Tai Chi practitioners exhibited superior visuospatial ability and postural stability during goal-directed upper body movements, which was associated with sensitive proprioception and tactile sensation

The novel ZIP4 regulation and its role in ovarian cancer

Our RNAseq analyses revealed that ZIP4 is a top gene up-regulated in more aggressive ovarian cancer cells. ZIP4's role in cancer stem cells has not been reported in any type of cancer. In addition, the role and regulation of ZIP4, a zinc transporter, have been studied in the context of extracellular zinc transporting. Factors other than zinc with ZIP4 regulatory effects are essentially unknown. ZIP4 expression and its regulation in epithelial ovarian cancer cells was assessed by immunoblotting, quantitative PCR, or immunohistochemistry staining in human ovarian tissues. Cancer stem cell-related activities were examined to evaluate the role of ZIP4 in human high-grade serous ovarian cancer cells in vitro and in vivo. RNAi and CRISPR techniques were used to knockdown or knockout ZIP4 and related genes. Ovarian cancer tissues overexpressed ZIP4 when compared with normal and benign tissues. ZIP4 knockout significantly reduced several cancer stem cell-related activities in EOC cells, including proliferation, anoikis-resistance, colony-formation, spheroid-formation, drug-resistance, and side-population in vitro. ZIP4-expressing side-population highly expressed known CSC markers ALDH1 and OCT4. ZIP4 knockout dramatically reduced tumorigenesis and ZIP4 overexpression increased tumorigenesis in vivo. In addition, the ZIP4-expressing side-population had the tumor initiating activity. Moreover, the oncolipid lysophosphatic acid effectively up-regulated ZIP4 expression via the nuclear receptor peroxisome proliferator-activated receptor gamma and lysophosphatic acid 's promoting effects in cancer stem cell-related activities in HGSOC cells was at least partially mediated by ZIP4 in an extracellular zinc-independent manner. Our critical data imply that ZIP4 is a new and important cancer stem cell regulator in ovarian cancer. Our data also provide an innovative interpretation for the apparent disconnection between low levels of zinc and up-regulation of ZIP4 in ovarian cancer tissues

Highly thermostable mixed lanthanide organic frameworks with high quantum yield for warm white light-emitting diodes

A mixed lanthanide organic framework was prepared via hydrothermal methods using m-phthalic acid (m-H2BDC), 1,10-phenanthroline (1,10-Phen), and Ln3+ ions, formulated as [HNMe2][Eu0.095Tb1.905(m-BDC)3(phen)2] (ZTU-6). The structure and stability of ZTU-6 were characterised by X-ray diffraction (XRD) and thermogravimetric analysis (TGA), which revealed a three-dimensional pcu topology with high thermal stability. Fluorescence tests showed that ZTU-6 emitted orange light with a high quantum yield of 79.15%, and it can be effectively encapsulated in a light-emitting diode (LED) device emitting orange light. In addition, ZTU-6 was found to be compatible with BaMgAl10O17:Eu2+ (BAM) blue powder and [(Sr,Ba)2SiO4:Eu2+] silicate yellow and green powder to create a warm white LED with a high colour rendering index (CRI) of 93.4, a correlated colour temperature (CCT) of 3908 K, and CIE coordinates of (0.38, 036)