Simultaneous calibration: a joint optimization approach for multiple Kinect and external cameras

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Statistically and Computationally Efficient Estimating Equations for Large Spatial Datasets

<div><p>For Gaussian process models, likelihood based methods are often difficult to use with large irregularly spaced spatial datasets, because exact calculations of the likelihood for <i>n</i> observations require <i>O</i>(<i>n</i>³) operations and <i>O</i>(<i>n</i>²) memory. Various approximation methods have been developed to address the computational difficulties. In this paper, we propose new unbiased estimating equations based on score equation approximations that are both computationally and statistically efficient. We replace the inverse covariance matrix that appears in the score equations by a sparse matrix to approximate the quadratic forms, then set the resulting quadratic forms equal to their expected values to obtain unbiased estimating equations. The sparse matrix is constructed by a sparse inverse Cholesky approach to approximate the inverse covariance matrix. The statistical efficiency of the resulting unbiased estimating equations are evaluated both in theory and by numerical studies. Our methods are applied to nearly 90,000 satellite-based measurements of water vapor levels over a region in the Southeast Pacific Ocean.</p></div

Fused Adaptive Lasso for Spatial and Temporal Quantile Function Estimation

<div><p></p><p>Quantile functions are important in characterizing the entire probability distribution of a random variable, especially when the tail of a skewed distribution is of interest. This article introduces new quantile function estimators for spatial and temporal data with a fused adaptive Lasso penalty to accommodate the dependence in space and time. This method penalizes the difference among neighboring quantiles, hence it is desirable for applications with features ordered in time or space without replicated observations. The theoretical properties are investigated and the performance of the proposed methods are evaluated by simulations. The proposed method is applied to particulate matter (PM) data from the Community Multiscale Air Quality (CMAQ) model to characterize the upper quantiles, which are crucial for studying spatial association between PM concentrations and adverse human health effects.</p></div

Impact of Childhood Abuse on the Risk of Non-Suicidal Self-Injury in Mainland Chinese Adolescents

<div><p>Background</p><p>Childhood abuse has been associated with significant increases in non-suicidal self-injury (NSSI) behaviors in adolescents; however, only general definitions of this risk indicator have been examined. This study identified relationships between specific forms of childhood abuse and NSSI in mainland Chinese adolescents.</p><p>Method</p><p>A total of 14,221 cases were retained from an epidemiological study involving adolescents from junior and senior middle schools. Information relating to the perpetrator, perceived harm, timing of exposure to different types of childhood abuse, and NSSI were obtained. Logistic regression was used to analyze relationships between each form of childhood abuse and NSSI.</p><p>Results</p><p>Approximately 51.0% of the students reported at least one abusive childhood experience. Nearly one in four students (24.9%) reported that they had engaged in NSSI in the past 12 months. Each type of childhood abuse, occurring at any time within the first 16 years of life, especially in situations of continuous exposure, was significantly associated with NSSI. A significant graded relationship was found between number of abusive childhood experiences and NSSI. Students maltreated by parents or others were at high risk of engaging in NSSI, the risk was greater in students maltreated by both; students who had been exposed to childhood abuse with no perceived harm still demonstrated an elevated risk for NSSI. The pattern of associations did not vary by gender.</p><p>Conclusions</p><p>These findings suggest that experiencing any of various forms of childhood abuse should be considered a risk factor for NSSI during adolescence. Further research should focus upon psychosocial, neural, and genetic factors that might moderate or mediate the onset of NSSI in adolescents who have experienced childhood abuse.</p></div

Expressions of mPGES-1 protein and mRNA in diabetic mice.

<p>(A) mPGES-1 protein analysis by Western blotting (n = 4–5 in each group). (B) Densitometry analysis of mPGES-1 Western blotting. (C) mPGES-1 mRNA expression by qRT-PCR (N = 6–9 per group). Data are mean ± SE.</p

Protein expressions of mPGES-2, cPGES and 15-PGDH in diabetic mice.

<p>(A) mPGES-2 protein analysis by Western blotting (N = 4 per group). (B) cPGES protein analysis by Western blotting (N = 4–5 per group). (C) 15-PGDH protein analysis by Western blotting (N = 3–4 per group). (D) Densitometry analysis of mPGES-2 Western blotting. (E) Densitometry analysis of cPGES Western blotting. (F) Densitometry analysis of 15-PGDH Western blotting. Data are mean ± SE.</p

Urinary PGE2 excretion and renal PGE2 content in diabetic mice.

<p>(A) 24-hour urinary PGE2 excretion after 6 weeks of diabetes. (B) Increase of 24-hour urinary PGE2 excretion after 6 weeks of diabetes. (C) Kidney PGE2 content after 6 weeks of diabetes. (D) Increase of kidney PGE2 content after 6 weeks of diabetes. N = 6–9 in each group. Data are mean ± SE.</p

Forms of NSSI behaviors by gender, n(%).

<p>* anyone of NSSI</p><p>Forms of NSSI behaviors by gender, n(%).</p

Distribution of childhood abuse by gender, n(%).

<p>Distribution of childhood abuse by gender, n(%).</p

mRNA expressions of mPGES-2, cPGES and 15-PGDH in diabetic mice.

<p>(A) mPGES-2 mRNA expression by qRT-PCR (N = 6–9 per group). (B) cPGES mRNA expression by qRT-PCR (N = 6–9 per group). (C) 15-PGDH mRNA expression by qRT-PCR (N = 6–9 per group). Data are mean ± SE.</p