Regularized estimation of linear functionals of precision matrices for high-dimensional time series

This paper studies a Dantzig-selector type regularized estimator for linear functionals of high-dimensional linear processes. Explicit rates of convergence of the proposed estimator are obtained and they cover the broad regime from i.i.d. samples to long-range dependent time series and from sub-Gaussian innovations to those with mild polynomial moments. It is shown that the convergence rates depend on the degree of temporal dependence and the moment conditions of the underlying linear processes. The Dantzig-selector estimator is applied to the sparse Markowitz portfolio allocation and the optimal linear prediction for time series, in which the ratio consistency when compared with an oracle estimator is established. The effect of dependence and innovation moment conditions is further illustrated in the simulation study. Finally, the regularized estimator is applied to classify the cognitive states on a real fMRI dataset and to portfolio optimization on a financial dataset.Comment: 44 pages, 4 figure

L2L^2 Asymptotics for High-Dimensional Data

We develop an asymptotic theory for $L^2$ norms of sample mean vectors of high-dimensional data. An invariance principle for the $L^2$ norms is derived under conditions that involve a delicate interplay between the dimension $p$, the sample size $n$ and the moment condition. Under proper normalization, central and non-central limit theorems are obtained. To facilitate the related statistical inference, we propose a plug-in calibration method and a re-sampling procedure to approximate the distributions of the $L^2$ norms. Our results are applied to multiple tests and inference of covariance matrix structures.Comment: 3

Estimation of dynamic networks for high-dimensional nonstationary time series

This paper is concerned with the estimation of time-varying networks for high-dimensional nonstationary time series. Two types of dynamic behaviors are considered: structural breaks (i.e., abrupt change points) and smooth changes. To simultaneously handle these two types of time-varying features, a two-step approach is proposed: multiple change point locations are first identified based on comparing the difference between the localized averages on sample covariance matrices, and then graph supports are recovered based on a kernelized time-varying constrained $L_1$-minimization for inverse matrix estimation (CLIME) estimator on each segment. We derive the rates of convergence for estimating the change points and precision matrices under mild moment and dependence conditions. In particular, we show that this two-step approach is consistent in estimating the change points and the piecewise smooth precision matrix function, under certain high-dimensional scaling limit. The method is applied to the analysis of network structure of the S\&P 500 index between 2003 and 2008

The distribution variation of pathogens and virulence factors in different geographical populations of giant pandas

Intestinal diseases caused by opportunistic pathogens seriously threaten the health and survival of giant pandas. However, our understanding of gut pathogens in different populations of giant pandas, especially in the wild populations, is still limited. Here, we conducted a study based on 52 giant panda metagenomes to investigate the composition and distribution of gut pathogens and virulence factors (VFs) in five geographic populations (captive: GPCD and GPYA; wild: GPQIN, GPQIO, and GPXXL). The results of the beta-diversity analyzes revealed a close relationship and high similarity in pathogen and VF compositions within the two captive groups. Among all groups, Proteobacteria, Firmicutes, and Bacteroidetes emerged as the top three abundant phyla. By using the linear discriminant analysis effect size method, we identified pathogenic bacteria unique to different populations, such as Klebsiella in GPCD, Salmonella in GPYA, Hafnia in GPQIO, Pedobacter in GPXXL, and Lactococcus in GPQIN. In addition, we identified 12 VFs that play a role in the intestinal diseases of giant pandas, including flagella, CsrA, enterobactin, type IV pili, alginate, AcrAB, capsule, T6SS, urease, type 1 fimbriae, polar flagella, allantoin utilization, and ClpP. These VFs influence pathogen motility, adhesion, iron uptake, acid resistance, and protein regulation, thereby contributing to pathogen infection and pathogenicity. Notably, we also found a difference in virulence of Pseudomonas aeruginosa between GPQIN and non-GPQIN wild populations, in which the relative abundance of VFs (0.42%) of P. aeruginosa was the lowest in GPQIN and the highest in non-GPQIN wild populations (GPXXL: 23.55% and GPQIO: 10.47%). In addition to enhancing our understanding of gut pathogens and VFs in different geographic populations of giant pandas, the results of this study provide a specific theoretical basis and data support for the development of effective conservation measures for giant pandas

Macrophage MicroRNAs as Therapeutic Targets for Atherosclerosis, Metabolic Syndrome, and Cancer

Macrophages play a crucial role in the innate immune system and contribute to a broad spectrum of pathologies in chronic inflammatory diseases. MicroRNAs (miRNAs) have been demonstrated to play important roles in macrophage functions by regulating macrophage polarization, lipid metabolism and so on. Thus, miRNAs represent promising diagnostic and therapeutic targets in immune disorders. In this review, we will summarize the role of miRNAs in atherosclerosis, metabolic syndrome, and cancer by modulating macrophage phenotypes, which has been supported by in vivo evidence

Research on the development of graphic design from the perspective of new media

This article discusses the significance of the importance of diversifying graphic design techniques in the context of the emergence of new virtual reality technologies in the field of mass media. The authors examine the influence of interactive technologies in various areas of mass media design, which form new requirements for the presentation of modern graphic design, in particular, the use of traditions and innovations, active integration of new trends and challenges of the time into graphic design. It is noted that transformations in the presentation of information in modern society have brought new opportunities and ideas for the development of graphic design in the future

Research Progress on Intervention of Natural Products from Plants in Neurotoxicity of Acrylamide

Acrylamide (ACR) is a common toxic substance in foods, which can cause serious damage to human organs and systems, especially the nervous system. At present, there are no appropriate measures to prevent and treat ACR neurotoxicity. In recent years, it has been reported that some natural plant products with high safety for consumption, strong antioxidant activity and low cost can intervene in ACR-induced neurotoxicity. This paper mainly introduces the neurotoxicity of ACR, natural plant products that can intervene in ACR neurotoxicity, and the underlying mechanism of action in order to provide a theoretical reference and research ideas for the treatment of ACR neurotoxicity in multiple ways and though multiple targets, as well as the development and application of natural products against ACR neurotoxicity