Identification of Outlying Observations with Quantile Regression for Censored Data

Outlying observations, which significantly deviate from other measurements, may distort the conclusions of data analysis. Therefore, identifying outliers is one of the important problems that should be solved to obtain reliable results. While there are many statistical outlier detection algorithms and software programs for uncensored data, few are available for censored data. In this article, we propose three outlier detection algorithms based on censored quantile regression, two of which are modified versions of existing algorithms for uncensored or censored data, while the third is a newly developed algorithm to overcome the demerits of previous approaches. The performance of the three algorithms was investigated in simulation studies. In addition, real data from SEER database, which contains a variety of data sets related to various cancers, is illustrated to show the usefulness of our methodology. The algorithms are implemented into an R package OutlierDC which can be conveniently employed in the \proglang{R} environment and freely obtained from CRAN

Robust Likelihood-Based Survival Modeling with Microarray Data

Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently.

General, Strong Impurity-Strength Dependence of Quasiparticle Interference

Quasiparticle interference (QPI) patterns in momentum space are often assumed to be independent of the strength of the impurity potential when compared with other quantities, such as the joint density of states. Here, using the $T$-matrix theory, we show that this assumption breaks down completely even in the simplest case of a single-site impurity on the square lattice with an $s$ orbital per site. Then, we predict from first-principles, a very rich, impurity-strength-dependent structure in the QPI pattern of TaAs, an archetype Weyl semimetal. This study thus demonstrates that the consideration of the details of the scattering impurity including the impurity strength is essential for interpreting Fourier-transform scanning tunneling spectroscopy experiments in general.Comment: main manuscript: 8 pages, 6 figures, Supplementary Information: 3 pages, 6 figure

Robust Likelihood-Based Survival Modeling with Microarray Data

Gene expression data can be associated with various clinical outcomes. In particular, these data can be of importance in discovering survival-associated genes for medical applications. As alternatives to traditional statistical methods, sophisticated methods and software programs have been developed to overcome the high-dimensional difficulty of microarray data. Nevertheless, new algorithms and software programs are needed to include practical functions such as the discovery of multiple sets of survival-associated genes and the incorporation of risk factors, and to use in the R environment which many statisticians are familiar with. For survival modeling with microarray data, we have developed a software program (called rbsurv) which can be used conveniently and interactively in the R environment. This program selects survival-associated genes based on the partial likelihood of the Cox model and separates training and validation sets of samples for robustness. It can discover multiple sets of genes by iterative forward selection rather than one large set of genes. It can also allow adjustment for risk factors in microarray survival modeling. This software package, the rbsurv package, can be used to discover survival-associated genes with microarray data conveniently

Experimental observation of hidden Berry curvature in inversion-symmetric bulk 2H-WSe2

We investigate the hidden Berry curvature in bulk 2H-WSe2 by utilizing the surface sensitivity of angle resolved photoemission (ARPES). The symmetry in the electronic structure of transition metal dichalcogenides is used to uniquely determine the local orbital angular momentum (OAM) contribution to the circular dichroism (CD) in ARPES. The extracted CD signals for the K and K' valleys are almost identical but their signs, which should be determined by the valley index, are opposite. In addition, the sign is found to be the same for the two spin-split bands, indicating that it is independent of spin state. These observed CD behaviors are what are expected from Berry curvature of a monolayer of WSe2. In order to see if CD-ARPES is indeed representative of hidden Berry curvature within a layer, we use tight binding analysis as well as density functional calculation to calculate the Berry curvature and local OAM of a monolayer WSe2. We find that measured CD-ARPES is approximately proportional to the calculated Berry curvature as well as local OAM, further supporting our interpretation.Comment: 6 pages, 3 figure

Membranous expression of Her3 is associated with a decreased survival in head and neck squamous cell carcinoma

<p>Abstract</p> <p>Background</p> <p>Head and neck squamous cell carcinoma (HNSCC) still remains a lethal malignancy benefiting from the identification of the new target for early detection and/or development of new therapeutic regimens based on a better understanding of the biological mechanism for treatment. The overexpression of Her2 and Her3 receptors have been identified in various solid tumors, but its prognostic relevance in HNSCC remains controversial.</p> <p>Methods</p> <p>Three hundred eighty-seven primary HNSCCs, 20 matching metasis and 17 recurrent HNSCCs were arrayed into tissue microarrays. The relationships between Her2 and Her3 protein expression and clinicopathological parameters/survival of HNSCC patients were analyzed with immunohistochemistry.</p> <p>Results</p> <p>Her3 is detected as either a cytoplasmic or a membranous dominant expression pattern whereas Her2 expression showed uniform membranous form. In primary tumor tissues, high membranous Her2 expression level was found in 104 (26.9%) cases while positive membranous and cytoplasmic Her3 expression was observed in 34 (8.8%) and 300 (77.5%) samples, respectively. Membranous Her2 expression was significantly associated with histological grade (<it>P </it>= 0.021), as grade 2 tumors showed the highest positive expression. Membranous Her3 over-expression was significantly prevalent in metastatic tissues compared to primary tumors (<it>P </it>= 0.003). Survival analysis indicates that membranous Her3 expression is significantly associated with worse overall survival (<it>P </it>= 0.027) and is an independent prognostic factor in multivariate analysis (hazard ratio, 1.51; 95% confidence interval, 1.01-2.23; <it>P </it>= 0.040).</p> <p>Conclusions</p> <p>These results suggest that membranous Her3 expression is strongly associated with poor prognosis of patients with HNSCC and is a potential candidate molecule for targeted therapy.</p