Stability and inviscid limit of the 3D anisotropic MHD system near a background magnetic field with mixed fractional partial dissipation

A main result of this paper establishes the global stability of the three-dimensional MHD equations near a background magnetic field with mixed fractional partial dissipation with $\alpha, \beta\in(\frac{1}{2}, 1]$. Namely, the velocity equations involve dissipation $(\Lambda_1^{2\alpha} + \Lambda_2^{2\alpha}+\sigma \Lambda_3^{2\alpha})u$ with the case $\sigma=1$ and $\sigma=0$. The magnetic equations without partial magnetic diffusion $\Lambda_i^{2\beta} b_i$ but with the diffusion $(-\Delta)^\beta b$, where $\Lambda_i^{s} (s>0)$ with $i=1, 2, 3$ are the directional fractional operators. Then we focus on the vanishing vertical kinematic viscosity coefficient limit of the MHD system with the case $\sigma=1$ to the case $\sigma=0$. The convergent result is obtained in the sense of $H^1$-norm

Statistics local fisher discriminant analysis for industrial process fault classification

In order to effectively identify industrial process faults, an improved Fisher discriminant analysis (FDA) method, referred to as the statistics local Fisher discriminant analysis (SLFDA), is proposed for fault classification. For mining statistics information hidden in process data, statistics pattern analysis is firstly applied to transform the original measured variables into the corresponding statistics, including second-order and higher-order ones. Furthermore, considering the local structure characteristics of fault data, local FDA (LFDA) is performed which computes the discriminant vectors by modifying the optimization objective with local weighting factor. Simulation results on the benchmark Tennessee Eastman process show that the proposed SLFDA has a better fault classification performance than the FDA and LFDA methods

A Unified Blockchain-Semantic Framework for Wireless Edge Intelligence Enabled Web 3.0

Web 3.0 enables user-generated contents and user-selected authorities. With decentralized wireless edge computing architectures, Web 3.0 allows users to read, write, and own contents. A core technology that enables Web 3.0 goals is blockchain, which provides security services by recording content in a decentralized and transparent manner. However, the explosion of on-chain recorded contents and the fast-growing number of users cause increasingly unaffordable computing and storage resource consumption. A promising paradigm is to analyze the semantic information of contents that can convey precisely the desired meanings without consuming many resources. In this article, we propose a unified blockchain-semantic ecosystems framework for wireless edge intelligence-enabled Web 3.0. Our framework consists of six key components to exchange semantic demands. We then introduce an Oracle-based proof of semantic mechanism to implement on-chain and off-chain interactions of Web 3.0 ecosystems on semantic verification algorithms while maintaining service security. An adaptive Deep Reinforcement Learning-based sharding mechanism on Oracle is designed to improve interaction efficiency, which can facilitate Web 3.0 ecosystems to deal with varied semantic demands. Finally, a case study is presented to show that the proposed framework can dynamically adjust Oracle settings according to varied semantic demands.Comment: 8 pages, 5 figures, 1 tabl

Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

Thanks to the augmented convenience, safety advantages, and potential commercial value, Intelligent vehicles (IVs) have attracted wide attention throughout the world. Although a few autonomous driving unicorns assert that IVs will be commercially deployable by 2025, their implementation is still restricted to small-scale validation due to various issues, among which precise computation of control commands or trajectories by planning methods remains a prerequisite for IVs. This paper aims to review state-of-the-art planning methods, including pipeline planning and end-to-end planning methods. In terms of pipeline methods, a survey of selecting algorithms is provided along with a discussion of the expansion and optimization mechanisms, whereas in end-to-end methods, the training approaches and verification scenarios of driving tasks are points of concern. Experimental platforms are reviewed to facilitate readers in selecting suitable training and validation methods. Finally, the current challenges and future directions are discussed. The side-by-side comparison presented in this survey not only helps to gain insights into the strengths and limitations of the reviewed methods but also assists with system-level design choices.Comment: 20 pages, 14 figures and 5 table

An In Vivo Screen Identifies PYGO2 as a Driver for Metastatic Prostate Cancer

Advanced prostate cancer displays conspicuous chromosomal instability and rampant copy number aberrations, yet the identity of functional drivers resident in many amplicons remain elusive. Here, we implemented a functional genomics approach to identify new oncogenes involved in prostate cancer progression. Through integrated analyses of focal amplicons in large prostate cancer genomic and transcriptomic datasets as well as genes upregulated in metastasis, 276 putative oncogenes were enlisted into an in vivo gain-of-function tumorigenesis screen. Among the top positive hits, we conducted an in-depth functional analysis on Pygopus family PHD finger 2 (PYGO2), located in the amplicon at 1q21.3. PYGO2 overexpression enhances primary tumor growth and local invasion to draining lymph nodes. Conversely, PYGO2 depletion inhibits prostate cancer cell invasion in vitro and progression of primary tumor and metastasis in vivo In clinical samples, PYGO2 upregulation associated with higher Gleason score and metastasis to lymph nodes and bone. Silencing PYGO2 expression in patient-derived xenograft models impairs tumor progression. Finally, PYGO2 is necessary to enhance the transcriptional activation in response to ligand-induced Wnt/β-catenin signaling. Together, our results indicate that PYGO2 functions as a driver oncogene in the 1q21.3 amplicon and may serve as a potential prognostic biomarker and therapeutic target for metastatic prostate cancer.Significance: Amplification/overexpression of PYGO2 may serve as a biomarker for prostate cancer progression and metastasis. Cancer Res; 78(14); 3823-33. ©2018 AACR

Opposing roles of TGFβ and BMP signaling in prostate cancer development

SMAD4 constrains progression of Pten-null prostate cancer and serves as a common downstream node of transforming growth factor β (TGFβ) and bone morphogenetic protein (BMP) pathways. Here, we dissected the roles of TGFβ receptor II (TGFBR2) and BMP receptor II (BMPR2) using a Pten-null prostate cancer model. These studies demonstrated that the molecular actions of TGFBR2 result in both SMAD4-dependent constraint of proliferation and SMAD4-independent activation of apoptosis. In contrast, BMPR2 deletion extended survival relative to Pten deletion alone, establishing its promoting role in BMP6-driven prostate cancer progression. These analyses reveal the complexity of TGFβ-BMP signaling and illuminate potential therapeutic targets for prostate cancer

Myeloid-derived suppressor cells inhibit T cell activation through nitrating LCK in mouse cancers

Potent immunosuppressive mechanisms within the tumor microenvironment contribute to the resistance of aggressive human cancers to immune checkpoint blockade (ICB) therapy. One of the main mechanisms for myeloid-derived suppressor cells (MDSCs) to induce T cell tolerance is through secretion of reactive nitrogen species (RNS), which nitrates tyrosine residues in proteins involved in T cell function. However, so far very few nitrated proteins have been identified. Here, using a transgenic mouse model of prostate cancer and a syngeneic cell line model of lung cancer, we applied a nitroproteomic approach based on chemical derivation of 3-nitrotyrosine and identified that lymphocyte-specific protein tyrosine kinase (LCK), an initiating tyrosine kinase in the T cell receptor signaling cascade, is nitrated at Tyr394 by MDSCs. LCK nitration inhibits T cell activation, leading to reduced interleukin 2 (IL2) production and proliferation. In human T cells with defective endogenous LCK, wild type, but not nitrated LCK, rescues IL2 production. In the mouse model of castration-resistant prostate cancer (CRPC) by prostate-specific deletion of Pten, p53, and Smad4, CRPC is resistant to an ICB therapy composed of antiprogrammed cell death 1 (PD1) and anticytotoxic-T lymphocyte-associated protein 4 (CTLA4) antibodies. However, we showed that ICB elicits strong anti-CRPC efficacy when combined with an RNS neutralizing agent. Together, these data identify a previously unknown mechanism of T cell inactivation by MDSC-induced protein nitration and illuminate a clinical path hypothesis for combining ICB with RNS-reducing agents in the treatment of CRPC