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Objective: To determine the critical roles of PU.1/cathepsin S activation in regulating inflammatory responses of macrophages during periodontitis. Background: Cathepsin S (CatS) is a cysteine protease and exerts important roles in the immune response. Elevated CatS has been found in the gingival tissues of periodontitis patients and is involved in alveolar bone destruction. However, the underlying mechanism of CatS‐driven IL‐6 production in periodontitis remains unclear. Methods: Western blot was applied to measure mature cathepsin S(mCatS) and IL‐6 expression in gingival tissues from periodontitis patients and RAW264.7 cells exposed to lipopolysaccharide from Porphyromonas gingivalis (P.g. LPS). Immunofluorescence was applied to confirm the localization of PU.1, and CatS in the gingival tissues of periodontitis patients. ELISA was performed to determine IL‐6 production by the P.g. LPS‐exposed RAW264.7 cells. Knockdown by shRNA was used to determine the effects of PU.1 on p38/ nuclear factor (NF)‐κB activation, mCatS expression and IL‐6 production in RAW264.7 cells. Results: The expressions mCatS and IL‐6 were significantly upregulated in gingival macrophages. In cultured RAW264.7 cells, increased mCatS and IL‐6 protein paralleled the activation of p38 and NF‐κB after exposure to P.g. LPS. CatS knockdown by shRNA significantly decreased P.g. LPS‐induced IL‐6 expression and p38/NF‐κB activation. PU.1 was significantly increased in P.g. LPS‐exposed RAW264.7 cells, and PU.1 knockdown dramatically abolished the P.g. LPS‐induced upregulation of mCatS and IL‐6 and the activation of p38 and NF‐κB. Furthermore, PU.1 and CatS colocalized in macrophages within the gingival tissues of periodontitis patients. Conclusion: PU.1‐dependent CatS drives IL‐6 production in macrophages by activating p38 and NF‐κB in periodontitis. [ABSTRACT FROM AUTHOR]

This article concerns robust modeling of the survival time for cancer patients. Accurate prediction of patient survival time is crucial to the development of effective therapeutic strategies. To this goal, we propose a unified Expectation‐Maximization approach combined with the L1‐norm penalty to perform variable selection and parameter estimation simultaneously in the accelerated failure time model with right‐censored survival data of moderate sizes. Our approach accommodates general loss functions, and reduces to the well‐known Buckley‐James method when the squared‐error loss is used without regularization. To mitigate the effects of outliers and heavy‐tailed noise in real applications, we recommend the use of robust loss functions under the general framework. Furthermore, our approach can be extended to incorporate group structure among covariates. We conduct extensive simulation studies to assess the performance of the proposed methods with different loss functions and apply them to an ovarian carcinoma study as an illustration. [ABSTRACT FROM AUTHOR]

Variable selection is of increasing importance to address the difficulties of high dimensionality in many scientific areas. In this paper, we demonstrate a property for distance covariance, which is incorporated in a novel feature screening procedure together with the use of distance correlation. The approach makes no distributional assumptions for the variables and does not require the specification of a regression model and hence is especially attractive in variable selection given an enormous number of candidate attributes without much information about the true model with the response. The method is applied to two genetic risk problems, where issues including uncertainty of variable selection via cross validation, subgroup of hard-to-classify cases, and the application of a reject option are discussed. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Multiple imputation (MI) is a commonly used technique for handling missing data in large-scale medical and public health studies. However, variable selection on multiply-imputed data remains an important and longstanding statistical problem. If a variable selection method is applied to each imputed dataset separately, it may select different variables for different imputed datasets, which makes it difficult to interpret the final model or draw scientific conclusions. In this paper, we propose a novel multiple imputation-least absolute shrinkage and selection operator (MI-LASSO) variable selection method as an extension of the least absolute shrinkage and selection operator (LASSO) method to multiply-imputed data. The MI-LASSO method treats the estimated regression coefficients of the same variable across all imputed datasets as a group and applies the group LASSO penalty to yield a consistent variable selection across multiple-imputed datasets. We use a simulation study to demonstrate the advantage of the MI-LASSO method compared with the alternatives. We also apply the MI-LASSO method to the University of Michigan Dioxin Exposure Study to identify important circumstances and exposure factors that are associated with human serum dioxin concentration in Midland, Michigan. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Statistical methods for variable selection, prediction, and classification have proven extremely useful in moving personalized genomics medicine forward, in particular, leading to a number of genomic-based assays now in clinical use for predicting cancer recurrence. Although invaluable in individual cases, the information provided by these assays is limited. Most often, a patient is classified into one of very few groups (e.g., recur or not), limiting the potential for truly personalized treatment. Furthermore, although these assays provide information on which individuals are at most risk (e.g., those for which recurrence is predicted), they provide no information on the aberrant biological pathways that give rise to the increased risk. We have developed an approach to address these limitations. The approach models a time-to-event outcome as a function of known biological pathways, identifies important genomic aberrations, and provides pathway-based patient-specific assessments of risk. As we demonstrate in a study of ovarian cancer from The Cancer Genome Atlas project, the patient-specific risk profiles are powerful and efficient characterizations useful in addressing a number of questions related to identifying informative patient subtypes and predicting survival. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we propose variable selection and estimation in generalized linear models using the seamless $L_0$ (SELO) penalized likelihood approach. The SELO penalty is a smooth function that very closely resembles the discontinuous $L_0$ penalty. We develop an efficient algorithm to fit the model, and show that the SELO-GLM procedure has the oracle property in the presence of a diverging number of variables. We propose a Bayesian information criterion (BIC) to select the tuning parameter. We show that under some regularity conditions, the proposed SELO-GLM/BIC procedure consistently selects the true model. We perform simulation studies to evaluate the finite sample performance of the proposed methods. Our simulation studies show that the proposed SELO-GLM procedure has a better finite sample performance than several existing methods, especially when the number of variables is large and the signals are weak. We apply the SELO-GLM to analyze a breast cancer genetic dataset to identify the SNPs that are associated with breast cancer risk. The Canadian Journal of Statistics 40: 745-769; 2012 © 2012 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Despite a complex cascade of cellular events to reconstruct the damaged extracellular matrix, ligament healing results in a mechanically inferior scarred ligament. During normal healing, granulation tissue expands into any residual normal ligamentous tissue (creeping substitution), resulting in a larger region of healing, greater mechanical compromise and an inefficient repair process. To control creeping substitution and possibly enhance the repair process, the antiinflammatory cytokine, interleukin-4 (IL-4), was administered to rats before and after rupture of their medial collateral ligaments. In vitro experiments showed a time-dependent effect on fibroblast proliferation after IL-4 treatment. In vivo treatments with IL-4 (100 ng/mL IV) for 5 days resulted in decreased wound size and type III collagen and increased type I procollagen, indicating a more regenerative early healing in response to the IL-4 treatment. However, continued treatment of IL-4 to day 11 antagonized this early benefit and slowed healing. Together, these results suggest that IL-4 not only influences the macrophages and T lymphocytes but also stimulates fibroblasts associated with the proliferative phase of healing in a dose-, cell-, and time-dependent manner. Although treatment significantly influenced healing in the first week after injury, IL-4 alone was unable to maintain this early regenerative response. [ABSTRACT FROM AUTHOR]