Observation of the antimatter helium-4 nucleus at RHIC

We present the observation of the \Heebar nucleus, the heaviest antinucleus observed to date. In total, 18 \Heebar counts were detected at the STAR experiment at RHIC in 10$^{9}$ recorded Au+Au collisions at beam energies of $\sqrt{s_{NN}}$ = 200 GeV and 62 GeV. The background has been estimated, and the misidentification probability is found to be lower than 10$^{-11}$.Comment: 4 pages, 3 figures, to appear as proceedings of QM2011 conference at Annecy, Franc

A range extension for Haplomitrium mnioides (Lindb.) R.M.Schust.

Haplomitrium mnioides (Lindb.) R.M.Schust. is reported as new to Hainan Island. A continuous distribution of H. mnioides from west (Thailand) to east (Japan) is confirmed. Habitat pictures and a distribution map are provided

Cognitive Impairment Progression Analysis by Comparison of Clinical and Cognitive Scales, including Visualization using Agent-Based Simulation Approach

Alzheimer’s disease has been identified as the 6th leading cause of death in United States in 2015. One-third seniors die with Alzheimer’s and other dementia. Only 45% of AD patients report being told of their diagnosis. Staging the severity of AD is needed of no delay.The “preclinical” phase of AD is known to be a long-term progression process towards severe cognitive impairment. Clinical and Cognitive scaling methods, especially Clinical Dementia Rating (CDR) and Mini-mental State Examination (MMSE), are widely used in mapping the stage of cognitive impairment status. Many studies have also identified important clinical and physiological risk factors, such as age, smoking status, blood pressure, total serum cholesterol, current impairment status, etc. To discuss a better prediction method of AD for mild cognitive impairment patients, a longitudinal study of the impact of these influential factors is essential. The objective of this research is to firstly analyze the accuracy of both methods and secondly to propose a method that gives higher prediction accuracy. The main contribution of this paper is that we gave a thorough analysis on comparison of CDR and MMSE, commented on which method works better based upon personal demographic performance and brought up a pattern recognition model to predict the probability of patients reaching the unfavorable outcome, i.e. dementia, over time. We gained a method that achieves high accuracy by combining the regression model and MMSE cognitive scales. An agent-based simulation model was brought up to visualize the change of cognitive impairment status of patients over time, in various populations.Master of Science in EngineeringIndustrial and Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttp://deepblue.lib.umich.edu/bitstream/2027.42/136068/1/Cognitive Impairment Progression Analysis by Comparison of Clinical and Cognitive Scales, including Visualization using Agent-Based Simulation Approach.pd

Estimation and model selection in generalized additive partial linear models for correlated data with diverging number of covariates

We propose generalized additive partial linear models for complex data which allow one to capture nonlinear patterns of some covariates, in the presence of linear components. The proposed method improves estimation efficiency and increases statistical power for correlated data through incorporating the correlation information. A unique feature of the proposed method is its capability of handling model selection in cases where it is difficult to specify the likelihood function. We derive the quadratic inference function-based estimators for the linear coefficients and the nonparametric functions when the dimension of covariates diverges, and establish asymptotic normality for the linear coefficient estimators and the rates of convergence for the nonparametric functions estimators for both finite and high-dimensional cases. The proposed method and theoretical development are quite challenging since the numbers of linear covariates and nonlinear components both increase as the sample size increases. We also propose a doubly penalized procedure for variable selection which can simultaneously identify nonzero linear and nonparametric components, and which has an asymptotic oracle property. Extensive Monte Carlo studies have been conducted and show that the proposed procedure works effectively even with moderate sample sizes. A pharmacokinetics study on renal cancer data is illustrated using the proposed method.Comment: Published in at http://dx.doi.org/10.1214/13-AOS1194 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org