Bootstrapping in a high dimensional but very low sample size problem

High Dimension, Low Sample Size (HDLSS) problems have received much attention recently in many areas of science. Analysis of microarray experiments is one such area. Numerous studies are on-going to investigate the behavior of genes by measuring the abundance of mRNA (messenger RiboNucleic Acid), gene expression. HDLSS data investigated in this dissertation consist of a large number of data sets each of which has only a few observations. We assume a statistical model in which measurements from the same subject have the same expected value and variance. All subjects have the same distribution up to location and scale. Information from all subjects is shared in estimating this common distribution. Our interest is in testing the hypothesis that the mean of measurements from a given subject is 0. Commonly used tests of this hypothesis, the t-test, sign test and traditional bootstrapping, do not necessarily provide reliable results since there are only a few observations for each data set. We motivate a mixture model having C clusters and 3C parameters to overcome the small sample size problem. Standardized data are pooled after assigning each data set to one of the mixture components. To get reasonable initial parameter estimates when density estimation methods are applied, we apply clustering methods including agglomerative and K-means. Bayes Information Criterion (BIC) and a new criterion, WMCV (Weighted Mean of within Cluster Variance estimates), are used to choose an optimal number of clusters. Density estimation methods including a maximum likelihood unimodal density estimator and kernel density estimation are used to estimate the unknown density. Once the density is estimated, a bootstrapping algorithm that selects samples from the estimated density is used to approximate the distribution of test statistics. The t-statistic and an empirical likelihood ratio statistic are used, since their distributions are completely determined by the distribution common to all subject. A method to control the false discovery rate is used to perform simultaneous tests on all small data sets. Simulated data sets and a set of cDNA (complimentary DeoxyriboNucleic Acid) microarray experiment data are analyzed by the proposed methods

Investigating the possibility of using an AR mask to support online psychological counseling

Mental health problems are prevalent issues in modern society. While psychological counseling has been a traditional solution, it faces such barriers as negative societal perceptions and limited accessibility. To overcome these, researchers have explored remote counseling via video/voice calls. Despite its advantages of accessibility, convenience, and anonymity, the lack of personal interaction continues to be a drawback. This study investigates the feasibility of using an augmented reality (AR) mask in remote counseling and whether it could increase the level of self-disclosure-which is often used to measure the effectiveness of counseling-while maintaining anonymity. We conducted a controlled experiment and a qualitative user study to assess the effects of AR masks on counselees' level of self-disclosure during remote counseling. The results suggest that AR masks can increase this level more effectively for people with a low disposition for self-disclosure

Diquarks and the production of charmed baryons

Utilizing a quark model characterized by parameters that effectively replicate the masses of ground state hadrons, we illustrate that $(us)$ or $(ds)$ diquarks exhibit greater compactness in comparison to $(ud)$ diquarks. Concretely, the binding energy of the $(us)$ diquark - defined as the diquark's mass minus the combined masses of its individual quarks - is found to be more attractive than that of the $(ud)$ diquark. This heightened attraction present in $(us)$ diquarks could lead to enhanced production of $\Xi_c/D$ particles in high-energy pp or ultrarelativistic heavy-ion collisions.Comment: 9 pages, 5 figure

Outcomes of Cancer Patients Undergoing Percutaneous Pericardiocentesis for Pericardial Effusion

AbstractBackgroundPericardial effusion (PE) is common in cancer patients, but the optimal therapeutic approach is not well defined. Percutaneous pericardiocentesis is less invasive than surgery, but its long-term effectiveness and safety have not been well documented.ObjectivesThe goal of this study was to evaluate outcomes of cancer patients undergoing percutaneous pericardiocentesis for PE and assess the procedure’s safety in patients with thrombocytopenia.MethodsCancer patients who underwent percutaneous pericardiocentesis for PE between November 2009 and October 2014 at the MD Anderson Cancer Center were included. Procedure-related complications, effusion recurrence rate, and overall survival were analyzed.ResultsOf 1,645 cancer patients referred for PE, 212 (13%) underwent percutaneous pericardiocentesis. The procedure was successful in 99% of the cases, and there were no procedure-related deaths. Four patients had major procedure-related bleeding that did not vary by platelet count <50,000/μl or ≥50,000/μl (p = 0.1281). Patients with catheter drainage for 3 to 5 days had the lowest recurrence rate (10%). Median overall survival was 143 days; older age (i.e., >65 years), lung cancer, platelet count <20,000/μl, and malignant pericardial fluid were independently associated with poor prognosis. Lung cancer patients with proven malignant effusions had a significantly shorter median 1-year survival compared with those with nonmalignant effusions (16.2% vs. 49.0%, respectively; log-rank test p = 0.0101). A similar difference in 1-year survival was not observed in patients with breast cancer (40.2% vs. 40.0%; log-rank test p = 0.4170).ConclusionsPercutaneous pericardiocentesis with extended catheter drainage was safe and effective as the primary treatment for PE in cancer patients, including in those with thrombocytopenia. Malignant PE significantly shortened the survival outcome of patients with lung cancer but not those with breast cancer

Lipocalin 2 promotes inflammatory breast cancer tumorigenesis and skin invasion

Inflammatory breast cancer (IBC) is an aggressive form of primary breast cancer characterized by rapid onset and high risk of metastasis and poor clinical outcomes. The biological basis for the aggressiveness of IBC is still not well understood and no IBC-specific targeted therapies exist. In this study, we report that lipocalin 2 (LCN2), a small secreted glycoprotein belonging to the lipocalin superfamily, is expressed at significantly higher levels in IBC vs non-IBC tumors, independently of molecular subtype. LCN2 levels were also significantly higher in IBC cell lines and in their culture media than in non-IBC cell lines. High expression was associated with poor-prognosis features and shorter overall survival in IBC patients. Depletion of LCN2 in IBC cell lines reduced colony formation, migration, and cancer stem cell populations in vitro and inhibited tumor growth, skin invasion, and brain metastasis in mouse models of IBC. Analysis of our proteomics data showed reduced expression of proteins involved in cell cycle and DNA repair in LCN2-silenced IBC cells. Our findings support that LCN2 promotes IBC tumor aggressiveness and offer a new potential therapeutic target for IBC