Functions of Extracellular Pyruvate Kinase M2 in Tissue Repair and Regeneration

Pyruvate kinase M2 (PKM2) is a glycolytic enzyme expressed in highly proliferating cells. Studies of PKM2 have been focused on its function of promoting cell proliferation in cancer cells. Our laboratory previously discovered that extracellular PKM2 released from cancer cells promoted angiogenesis by activating endothelial cell proliferation and migration. PKM2 activated endothelial cells through integrin αvβ3. Angiogenesis and myofibroblast differentiation are key processes during wound healing. In this dissertation, I demonstrate that extracellular PKM2 released from activated neutrophils promotes angiogenesis and myofibroblast differentiation during wound healing. PKM2 activates dermal fibroblasts through integrin αvβ3 and PI3K signaling pathway. I also claim that extracellular PKM2 plays a role during liver fibrosis. PKM2 protects hepatic stellate cells from apoptosis by activating the survival signaling pathway

Metallic Icosahedron Phase of Sodium at Terapascal Pressures

Alkali metals exhibit unexpected structures and electronic behavior at high pressures. Compression of metallic sodium (Na) to 200 GPa leads to the stability of a wide-band-gap insulator with the double hexagonal hP4 structure. Post-hP4 structures remain unexplored, but they are important for addressing the question of the pressure at which Na reverts to a metal. Here we report the reentrant metallicity of Na at the very high pressure of 15.5 terapascal (TPa), predicted using first-principles structure searching simulations. Na is therefore insulating over the large pressure range of 0.2-15.5 TPa. Unusually, Na adopts an oP8 structure at pressures of 117-125 GPa, and the same oP8 structure at 1.75-15.5 TPa. Metallization of Na occurs on formation of a stable and striking body-centered cubic cI24 electride structure consisting of Na12 icosahedra, each housing at its center about one electron which is not associated with any Na ions.Comment: 5 pages, 4 figures, PRL (2015

Non-separable 2D wavelets with two-row filters

In the literature 2D (or bivariate) wavelets are usually constructed as a tensor product of 1D wavelets. Such wavelets are called separable. However, there are various applications, e.g. in image processing, for which non-separable 2D wavelets are preferable. In this paper, we investigate the class of compactly supported orthonormal 2D wavelets that was introduced by Belogay and Wang [2]. A characteristic feature of this class of wavelets is that the support of the corresponding filter comprises only two rows. We are concerned with the biorthogonal extension of this kind of wavelets. It turns out that the 2D wavelets in this class are intimately related to some underlying 1D wavelet. We explore this relation in detail, and we explain how the 2D wavelet transforms can be realized by means of a lifting scheme, thus allowing an efficient implementation. We also describe an easy way to construct wavelets with more rows and shorter columns

A THz Video SAR Imaging Algorithm Based on Chirp Scaling

In video synthetic aperture radar (SAR) imaging mode, the polar format algorithm (PFA) is more computational effective than the backprojection algorithm (BPA). However, the two-dimensional (2-D) interpolation in PFA greatly affects its computational speed, which is detrimental to the real-time imaging of video SAR. In this paper, a terahertz (THz) video SAR imaging algorithm based on chirp scaling is proposed, which utilizes the small synthetic angular feature of THz SAR and the inherent property of linear frequency modulation. Then, two-step chirp scaling is used to replace the 2-D interpolation in the PFA to obtain a similar focusing effect, but with a faster operation. Point target simulation is used to verify the effectiveness of the proposed method.Comment: 5 pages, 7 figure

Dual Preference Distribution Learning for Item Recommendation

Recommender systems can automatically recommend users with items that they probably like. The goal of them is to model the user-item interaction by effectively representing the users and items. Existing methods have primarily learned the user's preferences and item's features with vectorized embeddings, and modeled the user's general preferences to items by the interaction of them. In fact, users have their specific preferences to item attributes and different preferences are usually related. Therefore, exploring the fine-grained preferences as well as modeling the relationships among user's different preferences could improve the recommendation performance. Toward this end, we propose a dual preference distribution learning framework (DUPLE), which aims to jointly learn a general preference distribution and a specific preference distribution for a given user, where the former corresponds to the user's general preference to items and the latter refers to the user's specific preference to item attributes. Notably, the mean vector of each Gaussian distribution can capture the user's preferences, and the covariance matrix can learn their relationship. Moreover, we can summarize a preferred attribute profile for each user, depicting his/her preferred item attributes. We then can provide the explanation for each recommended item by checking the overlap between its attributes and the user's preferred attribute profile. Extensive quantitative and qualitative experiments on six public datasets demonstrate the effectiveness and explainability of the DUPLE method.Comment: 23 pages, 7 figures. This manuscript has been accepted by ACM Transactions on Information System

MRTNet: Multi-Resolution Temporal Network for Video Sentence Grounding

Given an untrimmed video and natural language query, video sentence grounding aims to localize the target temporal moment in the video. Existing methods mainly tackle this task by matching and aligning semantics of the descriptive sentence and video segments on a single temporal resolution, while neglecting the temporal consistency of video content in different resolutions. In this work, we propose a novel multi-resolution temporal video sentence grounding network: MRTNet, which consists of a multi-modal feature encoder, a Multi-Resolution Temporal (MRT) module, and a predictor module. MRT module is an encoder-decoder network, and output features in the decoder part are in conjunction with Transformers to predict the final start and end timestamps. Particularly, our MRT module is hot-pluggable, which means it can be seamlessly incorporated into any anchor-free models. Besides, we utilize a hybrid loss to supervise cross-modal features in MRT module for more accurate grounding in three scales: frame-level, clip-level and sequence-level. Extensive experiments on three prevalent datasets have shown the effectiveness of MRTNet.Comment: work in progres

Unsteady Unidirectional Flow of Voigt Fluid through the Parallel Microgap Plates with Wall Slip and Given Inlet Volume Flow Rate Variations

In order to solve the velocity profile and pressure gradient of the unsteady unidirectional slip flow of Voigt fluid, Laplace transform method is adopted in this research. Between the parallel microgap plates, the flow motion is induced by a prescribed arbitrary inlet volume flow rate which varies with time. The velocity slip condition on the wall and the flow conditions are known. In this paper, two basic flow situations are solved, which are a suddenly started and a constant acceleration flow respectively. Based on the above solutions, linear acceleration and oscillatory flow are also considered