Smooth principal component analysis for high dimensional data

This paper considers smooth principle component analysis for high dimensional data with very large dimensional observations p and moderate number of individuals N. Our setting is similar to traditional PCA, but we assume the factors are smooth and design a new approach to estimate them. By connecting with Singular Value Decomposition subjected to penalized smoothing, our algorithm is linear in the dimensionality of the data, and it also favors block calculations and sequential access to memory. Different from most existing methods, we avoid extracting eignefunctions via smoothing a huge dimensional covariance operator. Under regularity assumptions, the results indicate that we may enjoy faster convergence rate by employing smoothness assumption. We also extend our methods when each subject is given multiple tasks by adopting the two way ANOVA approach to further demonstrate the advantages of our approach

Does Index Futures Trading Reduce Volatility in the Chinese Stock Market? A Panel Data Evaluation Approach

This paper investigates the effect of introducing index futures trading on the spot price volatility in the Chinese stock market. We employ a recently developed panel data policy evaluation approach (Hsiao et al. 2011) to construct counterfactuals of the spot market volatility, based mainly on cross-sectional correlations between the Chinese and international stock markets. This new method does not need to specify a particular regression or a time series model for the volatility process around the introduction date of index futures trading, and thus avoids the potential omitted variable bias caused by uncontrolled market factors in the existing literature. Our results provide empirical evidence that the introduction of index futures trading significantly reduces the volatility of the Chinese stock market, which is robust to different model selection criteria and various prediction approaches.

Coordinated voltage control for improved power system voltage stability by incorporating the reactive power reserve from wind farms

The absorption and output characteristics of reactive power of the doubly-fed induction generator (DFIG) greatly influence the voltage stability of PCC (Point of Common Coupling) where the wind farms are integrated into the bulk power grid. This study proposes a reactive power compensation strategy for coordinated voltage control (CVC) of PCC with large-scale wind farms to achieve the expected voltage quality of the power grid through a minimum amount of control actions in emergencies. To this end, the mechanism of reactive power and voltage control inside DFIG is first analyzed. Then, the concept of reactive power reserve (RPR) sensitivity concerning control actions is introduced and an index of voltage stability margin is proposed to evaluate and analyze the distance between the current operating point and the voltage collapse point by analyzing the relationship between reactive power reserve and voltage stability margin. In the event of an emergency, critical reactive power reserves are obtained to reduce the dimension and complexity of the control problem. The sensitivity of reactive power reserve and the control are formulated into a convex quadratic programming problem to optimize the control strategies for voltage stability. The proposed technology has been validated on the IEEE 39-bus system

Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness

Liquid-liquid phase separation is emerging as a crucial phenomenon in several fundamental cell processes. A range of eukaryotic systems exhibit liquid condensates. However, their function in bacteria, which in general lack membrane-bound compartments, remains less clear. Here, we used high-resolution optical microscopy to observe single bacterial aggresomes, nanostructured intracellular assemblies of proteins, to undercover their role in cell stress. We find that proteins inside aggresomes are mobile and undergo dynamic turnover, consistent with a liquid state. Our observations are in quantitative agreement with phase-separated liquid droplet formation driven by interacting proteins under thermal equilibrium that nucleate following diffusive collisions in the cytoplasm. We have discovered aggresomes in multiple species of bacteria, and show that these emergent, metastable liquid-structured protein assemblies increase bacterial fitness by enabling cells to tolerate environmental stresses

ATP-dependent dynamic protein aggregation regulates bacterial dormancy depth critical for antibiotic tolerance

Cell dormancy is a widespread mechanism used by bacteria to evade environmental threats including antibiotics. Here we monitored bacterial antibiotic tolerance and regrowth at the single-cell level and found that each individual survival cell shows different ‘dormancy depth’, which in return regulates the lag time for cell resuscitation after removal of antibiotic. We further established that protein aggresome - a collection of endogenous protein aggregates - is an important indicator of bacterial dormancy depth, whose formation is promoted by decreased cellular ATP level. For cells to leave the dormant state and resuscitate, clearance of protein aggresome and recovery of proteostasis are required. We revealed the ability to recruit functional DnaK-ClpB machineries, which facilitate protein disaggregation in an ATP-dependent manner, determines the lag time for bacterial regrowth. Better understanding of the key factors regulating bacterial regrowth after surviving antibiotic attack could lead to new therapeutic strategies for combating bacterial antibiotic tolerance

Membraneless organelles formed by liquid-liquid phase separation increase bacterial fitness

[Figure: see text]

Spatial Functional Principal Component Analysis with Applications to Brain Image Data

This paper considers a fast and effective algorithm for conducting functional principle component analysis with multivariate factors. Compared with the univariate case, our approach could be more powerful in revealing spatial connections or extracting important features in images. To facilitate fast computation, we connect Singular Value Decomposition with penalized smoothing and avoid estimating a huge dimensional covariance operator. Under regularity assumptions, the results indicate that we may enjoy the optimal convergence rate by employing the smoothness assumption inherent to functional objects. We apply our method on the analysis of brain image data. Our extracted factors provide excellent recovery of the risk related regions of interests in human brain and the estimated loadings are very informative in revealing the individual risk attitude

Développement de pseudoparticules rétrovirales dérivées du virus de la fièvre hémorragique de Crimée-Congo

LYON1-BU Santé (693882101) / SudocSudocFranceF

A Regime Shift Model with Nonparametric Switching Mechanism

In this paper, we propose a new class of regime shift models with flexible switching mechanism that relies on a nonparametric probability function of the observed threshold variables. The proposed models generally embrace traditional threshold models with contaminated threshold variables or heterogeneous threshold values, thus gaining more power in handling complicated data structure. We solve the identification issue by imposing either global shape restriction or boundary condition on the nonparametric probability function. We utilize the natural connection between penalized splines and hierarchical Bayes to conduct smoothing. By adopting different priors, our procedure could work well for estimations of smooth curve as well as discontinuous curves with occasionally structural breaks. Bayesian tests for the existence of threshold effects are also conducted based on the posterior samples from Markov chain Monte Carlo (MCMC) methods. Both simulation studies and an empirical application in predicting the U.S. stock market returns demonstrate the validity of our methods