Exponential Error Convergence in Data Classification with Optimized Random Features: Acceleration by Quantum Machine Learning

Random features are a central technique for scalable learning algorithms based on kernel methods. A recent work has shown that an algorithm for machine learning by quantum computer, quantum machine learning (QML), can exponentially speed up sampling of optimized random features, even without imposing restrictive assumptions on sparsity and low-rankness of matrices that had limited applicability of conventional QML algorithms; this QML algorithm makes it possible to significantly reduce and provably minimize the required number of features for regression tasks. However, a major interest in the field of QML is how widely the advantages of quantum computation can be exploited, not only in the regression tasks. We here construct a QML algorithm for a classification task accelerated by the optimized random features. We prove that the QML algorithm for sampling optimized random features, combined with stochastic gradient descent (SGD), can achieve state-of-the-art exponential convergence speed of reducing classification error in a classification task under a low-noise condition; at the same time, our algorithm with optimized random features can take advantage of the significant reduction of the required number of features so as to accelerate each iteration in the SGD and evaluation of the classifier obtained from our algorithm. These results discover a promising application of QML to significant acceleration of the leading classification algorithm based on kernel methods, without ruining its applicability to a practical class of data sets and the exponential error-convergence speed.Comment: 28 pages, no figur

Quantum Ridgelet Transform: Winning Lottery Ticket of Neural Networks with Quantum Computation

A significant challenge in the field of quantum machine learning (QML) is to establish applications of quantum computation to accelerate common tasks in machine learning such as those for neural networks. Ridgelet transform has been a fundamental mathematical tool in the theoretical studies of neural networks, but the practical applicability of ridgelet transform to conducting learning tasks was limited since its numerical implementation by conventional classical computation requires an exponential runtime $\exp(O(D))$ as data dimension $D$ increases. To address this problem, we develop a quantum ridgelet transform (QRT), which implements the ridgelet transform of a quantum state within a linear runtime $O(D)$ of quantum computation. As an application, we also show that one can use QRT as a fundamental subroutine for QML to efficiently find a sparse trainable subnetwork of large shallow wide neural networks without conducting large-scale optimization of the original network. This application discovers an efficient way in this regime to demonstrate the lottery ticket hypothesis on finding such a sparse trainable neural network. These results open an avenue of QML for accelerating learning tasks with commonly used classical neural networks.Comment: 27 pages, 4 figure

Isolation and functional characterization of a β-eudesmol synthase, a new sesquiterpene synthase from Zingiber zerumbet Smith

AbstractIn this paper, we have identified a new sesquiterpene synthase gene (ZSS2) from Zingiber zerumbet Smith. Functional expression of ZSS2 in Escherichia coli and in vitro enzyme assay showed that the encoded enzyme catalyzed the formation of β-eudesmol and five additional by-products. Quantitative RT-PCR analysis revealed that ZSS2 transcript accumulation in rhizomes has strong seasonal variations. To further confirm the enzyme activity of ZSS2 and to assess the potential for metabolic engineering of β-eudesmol production, we introduced a gene cluster encoding six enzymes of the mevalonate pathway into E. coli and coexpressed it with ZSS2. When supplemented with mevalonate, the engineered E. coli produced a similar sesquiterpene profile to that produced in the in vitro enzyme assay, and the yield of β-eudesmol reached 100mg/L

Signalling through MyD88 drives surface expression of the mycobacterial receptors MCL (Clecsf8, Clec4d) and Mincle (Clec4e) following microbial stimulation

Acknowledgements We would like to thank the staff of the animal facility for their support and care for our animals. Funding was provided by the Wellcome Trust (102705) and Medical Research Council (UK) (MR/J004820/1) and a University of Aberdeen Studentship to BK.Peer reviewedPostprintPublisher PD

Necrotic Cell Sensor Clec4e Promotes a Proatherogenic Macrophage Phenotype Through Activation of the Unfolded Protein Response.

BACKGROUND: Atherosclerotic lesion expansion is characterized by the development of a lipid-rich necrotic core known to be associated with the occurrence of complications. Abnormal lipid handling, inflammation, and alteration of cell survival or proliferation contribute to necrotic core formation, but the molecular mechanisms involved in this process are not properly understood. C-type lectin receptor 4e (Clec4e) recognizes the cord factor of Mycobacterium tuberculosis but also senses molecular patterns released by necrotic cells and drives inflammation. METHODS: We hypothesized that activation of Clec4e signaling by necrosis is causally involved in atherogenesis. We addressed the impact of Clec4e activation on macrophage functions in vitro and on the development of atherosclerosis using low-density lipoprotein receptor-deficient (Ldlr-/-) mice in vivo. RESULTS: We show that Clec4e is expressed within human and mouse atherosclerotic lesions and is activated by necrotic lesion extracts. Clec4e signaling in macrophages inhibits cholesterol efflux and induces a Syk-mediated endoplasmic reticulum stress response, leading to the induction of proinflammatory mediators and growth factors. Chop and Ire1a deficiencies significantly limit Clec4e-dependent effects, whereas Atf3 deficiency aggravates Clec4e-mediated inflammation and alteration of cholesterol efflux. Repopulation of Ldlr-/- mice with Clec4e-/- bone marrow reduces lipid accumulation, endoplasmic reticulum stress, and macrophage inflammation and proliferation within the developing arterial lesions and significantly limits atherosclerosis. CONCLUSIONS: Our results identify a nonredundant role for Clec4e in coordinating major biological pathways involved in atherosclerosis and suggest that it may play similar roles in other chronic inflammatory diseases.This work was supported by a European Research Council grant (to Z.M.), and by the British Heart Foundation (Z. M.).This is the author accepted manuscript. The final version is available from the American Heart Association via https://doi.org/10.1161/CIRCULATIONAHA.116.02266

Necrotic Cell Sensor Clec4e Promotes a Proatherogenic Macrophage Phenotype Through Activation of the Unfolded Protein Response

$\textbf{Background}$: Atherosclerotic lesion expansion is characterized by the development of a lipid-rich necrotic core known to be associated with the occurrence of complications. Abnormal lipid handling, inflammation, and alteration of cell survival or proliferation contribute to necrotic core formation, but the molecular mechanisms involved in this process are not properly understood. C-type lectin receptor 4e (Clec4e) recognizes the cord factor of Mycobacterium $\textit{tuberculosis}$ but also senses molecular patterns released by necrotic cells and drives inflammation. $\textbf{Methods}$: We hypothesized that activation of Clec4e signaling by necrosis is causally involved in atherogenesis. We addressed the impact of Clec4e activation on macrophage functions in vitro and on the development of atherosclerosis using low-density lipoprotein receptor–deficient ($\textit{Ldlr}$$^{−/−}$) mice in vivo. $\textbf{Results}$: We show that Clec4e is expressed within human and mouse atherosclerotic lesions and is activated by necrotic lesion extracts. Clec4e signaling in macrophages inhibits cholesterol efflux and induces a Syk-mediated endoplasmic reticulum stress response, leading to the induction of proinflammatory mediators and growth factors. $\textit{Chop }$and $\textit{Ire1a}$ deficiencies significantly limit Clec4e-dependent effects, whereas $\textit{Atf}$3 deficiency aggravates Clec4e-mediated inflammation and alteration of cholesterol efflux. Repopulation of $\textit{Ldlr}$$^{−/−}$ mice with $\textit{Clec4e}$$^{−/−}$ bone marrow reduces lipid accumulation, endoplasmic reticulum stress, and macrophage inflammation and proliferation within the developing arterial lesions and significantly limits atherosclerosis. $\textbf{Conclusions}$: Our results identify a nonredundant role for Clec4e in coordinating major biological pathways involved in atherosclerosis and suggest that it may play similar roles in other chronic inflammatory diseases.This work was supported by a European Research Council grant (to Z.M.), and by the British Heart Foundation (Z. M.).This is the author accepted manuscript. The final version is available from the American Heart Association via https://doi.org/10.1161/CIRCULATIONAHA.116.02266

Notch controls the number of intraepithelial TCRαβ+CD8αα+ T cells

Intestinal intraepithelial lymphocytes (IELs) expressing CD8αα on αβ T cells (TCRαβ+CD8αα+ IELs) have suppressive capabilities in enterocolitis, but the mechanism that maintains homeostasis and cell number is not fully understood. Here, we demonstrated that the number of TCRαβ+CD8αα+ IELs was severely reduced in mice lacking recombination signal binding protein for immunoglobulin kappa J region (Rbpj) or Notch1 and Notch2 in T cells. Rbpj-deficient TCRαβ+CD8αα+ IELs expressed low levels of Atp8a2, which encodes a protein with flippase activity that regulates phospholipid asymmetry of plasma membrane such as flipping phosphatidylserine in the inner leaflet of plasma membrane. Rbpj-deficient TCRαβ+CD8αα+ IELs cannot maintain phosphatidylserine in the inner leaflet of the plasma membrane. Furthermore, depletion of intestinal macrophages restored TCRαβ+CD8αα+ IELs in Rbpj-deficient mice, suggesting that exposure of phosphatidylserine on the plasma membrane in Rbpj-deficient TCRαβ+CD8αα+ IELs acts as an “eat-me” signal. Together, these results revealed that Notch–Atp8a2 is a fundamental regulator for IELs and highlighted that membrane phospholipid asymmetry controlled by Notch-mediated flippase expression is a critical determinant in setting or balancing the number of TCRαβ+CD8αα+ IELs

Air-Stable and Reusable Cobalt Phosphide Nanoalloy Catalyst for Selective Hydrogenation of Furfural Derivatives

While metal phosphides have begun to attract attention as electrocatalysts, they remain underutilized in the field of liquid-phase molecular transformations. Herein, we describe a supported cobalt phosphide nanoalloy (nano-Co₂P) that functions as a highly efficient, reusable heterogeneous catalyst for the selective hydrogenation of furfural derivatives. The carbonyl moieties of several furfural derivatives were selectively hydrogenated to produce the desired products in high yields. In contrast to conventional nonprecious metal catalysts, nano-Co₂P uniquely exhibited air stability, which enabled easy and safe handling and precluded the need for H₂ pretreatment. Infrared and density functional theory studies revealed that the highly efficient hydrogenation is due to the favorable activation of the carbonyl moiety of furfural derivatives through the backdonation to its π* orbital from the Co d-electrons.Hiroya Ishikawa, Min Sheng, Ayako Nakata, Kiyotaka Nakajima, Seiji Yamazoe, Jun Yamasaki, Sho Yamaguchi, Tomoo Mizugaki, and Takato Mitsudome. Air-Stable and Reusable Cobalt Phosphide Nanoalloy Catalyst for Selective Hydrogenation of Furfural Derivatives. ACS Catalysis 2021, 11, 750-757, DOI: 10.1021/acscatal.0c03300.This document is the unedited Author’s version of a Submitted Work that was subsequently accepted for publication in ACS Catalysis, copyright © American Chemical Society after peer review. To access the final edited and published work see https://doi.org/10.1021/acscatal.0c03300