It is commonly held that increased risk of influenza in the elderly is due to a decline in the Ab response to influenza vaccination. This study prospectively evaluated the relationship between the development of influenza illness, and serum Ab titers and ex vivo cellular immune responses to influenza vaccination in community dwelling older adults including those with congestive heart failure (CHF). Adults age 60 years and older (90 subjects), and 10 healthy young adult controls received the 2003-04 trivalent inactivated influenza vaccine. Laboratory diagnosed influenza (LDI) was documented in 9 of 90 older adults. Pre- and postvaccination Ab titers did not distinguish between subjects who would subsequently develop influenza illness (LDI subjects) and those who would not (non-LDI subjects). In contrast, PBMC restimulated ex vivo with live influenza virus preparations showed statistically significant differences between LDI and non-LDI subjects. The mean IFN-γ:IL-10 ratio in influenza A/H3N2-stimulated PBMC was 10-fold lower in LDI vs non-LDI subjects. Pre-and postvaccination granzyme B levels were significantly lower in CHF subjects with LDI compared with subjects without LDI. In non-CHF subjects with LDI, granzyme B levels increased to high levels at the time of influenza infection. In conclusion, measures of the ex vivo cellular immune response to influenza are correlated with protection against influenza while serum Ab responses may be limited as a sole measure of vaccine efficacy in older people. Ex vivo measures of the cell-mediated immune response should be incorporated into evaluation of new vaccines for older adults.

In many social, economical, biological and medical studies, one objective is to classify a subject into one of several classes based on a set of variables observed from the subject. Because the probability distribution of the variables is usually unknown, the rule of classification is constructed using a training sample. The well-known linear discriminant analysis (LDA) works well for the situation where the number of variables used for classification is much smaller than the training sample size. Because of the advance in technologies, modern statistical studies often face classification problems with the number of variables much larger than the sample size, and the LDA may perform poorly. We explore when and why the LDA has poor performance and propose a sparse LDA that is asymptotically optimal under some sparsity conditions on the unknown parameters. For illustration of application, we discuss an example of classifying human cancer into two classes of leukemia based on a set of 7,129 genes and a training sample of size 72. A simulation is also conducted to check the performance of the proposed method.

Our results provided direct evidence that forest bathing has therapeutic effects on human hypertension and induces inhibition of the renin-angiotensin system and inflammation, and thus inspiring its preventive efficacy against cardiovascular disorders.

This study compared serum antibody titers and granzyme B (GrzB) levels in virus-stimulated peripheral blood mononuclear cells following influenza vaccination. Twelve of 239 older adults who subsequently developed laboratory-diagnosed influenza illness (LDI) had significantly lower GrzB levels compared to subjects without LDI (P=0.004). Eight subjects with LDI in the previous year showed an enhanced GrzB response to vaccination (P=0.02). Serum antibody titers following vaccination did not distinguish those older adults who developed LDI from those who did not. These results suggest that GrzB levels could be combined with antibody titers to more effectively predict vaccine efficacy in older adults.

High-frequency data observed on the prices of financial assets are commonly modeled by diffusion processes with micro-structure noise, and realized volatility-based methods are often used to estimate integrated volatility. For problems involving a large number of assets, the estimation objects we face are volatility matrices of large size. The existing volatility estimators work well for a small number of assets but perform poorly when the number of assets is very large. In fact, they are inconsistent when both the number, $p$, of the assets and the average sample size, $n$, of the price data on the $p$ assets go to infinity. This paper proposes a new type of estimators for the integrated volatility matrix and establishes asymptotic theory for the proposed estimators in the framework that allows both $n$ and $p$ to approach to infinity. The theory shows that the proposed estimators achieve high convergence rates under a sparsity assumption on the integrated volatility matrix. The numerical studies demonstrate that the proposed estimators perform well for large $p$ and complex price and volatility models. The proposed method is applied to real high-frequency financial data.Comment: Published in at http://dx.doi.org/10.1214/09-AOS730 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

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The wide availability of high-frequency data for many financial instruments stimulates an upsurge interest in statistical research on the estimation of volatility. Jump-diffusion processes observed with market microstructure noise are frequently used to model high-frequency financial data. Yet, existing methods are developed for either noisy data from a continuous diffusion price model or data from a jump-diffusion price model without noise. We propose methods to cope with both jumps in the price and market microstructure noise in the observed data. They allow us to estimate both integrated volatility and jump variation from the data sampled from jump-diffusion price processes, contaminated with the market microstructure noise. Our approach is to first remove

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