Texas A&M University

About: Texas A&M University is a education organization based out in College Station, Texas, United States. It is known for research contribution in the topics: Population & Gene. The organization has 72169 authors who have published 164372 publications receiving 5764236 citations.

Ratio-based decisions and the quantitative analysis of cDNA micro-array images

Abstract: Gene expression can be quantitatively analyzed by hybridizing fluor-tagged mRNA to targets on a cDNA micro-array. Comparison of gene expression levels arising from co-hybridized samples is achieved by taking ratios of average expression levels for individual genes. In an image-processing phase, a method of image segmentation identifies cDNA target sites in a cDNA micro-array image. The resulting cDNA target sites are analyzed based on a hypothesis test and confidence interval to quantify the significance of observed differences in expression ratios. In particular, the probability density of the ratio and the maximum-likelihood estimator for the distribution are derived, and an iterative procedure for signal calibration is developed.

Estimation of number of spectrally distinct signal sources in hyperspectral imagery

Abstract: With very high spectral resolution, hyperspectral sensors can now uncover many unknown signal sources which cannot be identified by visual inspection or a priori. In order to account for such unknown signal sources, we introduce a new definition, referred to as virtual dimensionality (VD) in this paper. It is defined as the minimum number of spectrally distinct signal sources that characterize the hyperspectral data from the perspective view of target detection and classification. It is different from the commonly used intrinsic dimensionality (ID) in the sense that the signal sources are determined by the proposed VD based only on their distinct spectral properties. These signal sources may include unknown interfering sources, which cannot be identified by prior knowledge. With this new definition, three Neyman-Pearson detection theory-based thresholding methods are developed to determine the VD of hyperspectral imagery, where eigenvalues are used to measure signal energies in a detection model. In order to evaluate the performance of the proposed methods, two information criteria, an information criterion (AIC) and minimum description length (MDL), and the factor analysis-based method proposed by Malinowski, are considered for comparative analysis. As demonstrated in computer simulations, all the methods and criteria studied in this paper may work effectively when noise is independent identically distributed. This is, unfortunately, not true when some of them are applied to real image data. Experiments show that all the three eigenthresholding based methods (i.e., the Harsanyi-Farrand-Chang (HFC), the noise-whitened HFC (NWHFC), and the noise subspace projection (NSP) methods) produce more reliable estimates of VD compared to the AIC, MDL, and Malinowski's empirical indicator function, which generally overestimate VD significantly. In summary, three contributions are made in this paper, 1) an introduction of the new definition of VD, 2) three Neyman-Pearson detection theory-based thresholding methods, HFC, NWHFC, and NSP derived for VD estimation, and 3) experiments that show the AIC and MDL commonly used in passive array processing and the second-order statistic-based Malinowski's method are not effective measures in VD estimation.

An Empirical Investigation of Customer Satisfaction after Service Failure and Recovery

Abstract: Relatively little research has addressed the nature and determinants of customer satisfaction following service failure and recovery. Two studies using scenario-based experiments reveal the impact of failure expectations, recovery expectations, recovery performance, and justice on customers’ postrecovery satisfaction. Customer satisfaction was found to be lower after service failure and recovery (even given high-recovery performance) than in the case of error-free service. The research shows that, in general, companies fare better in the eyes of consumers by avoiding service failure than by responding to failure with superior recovery.

Metal Oxide Surfaces and Their Interactions with Aqueous Solutions and Microbial Organisms.

Abstract: Reference LPI-ARTICLE-1999-017View record in Web of Science Record created on 2006-02-21, modified on 2017-05-12

Identifying airborne transmission as the dominant route for the spread of COVID-19.

Abstract: Various mitigation measures have been implemented to fight the coronavirus disease 2019 (COVID-19) pandemic, including widely adopted social distancing and mandated face covering. However, assessing the effectiveness of those intervention practices hinges on the understanding of virus transmission, which remains uncertain. Here we show that airborne transmission is highly virulent and represents the dominant route to spread the disease. By analyzing the trend and mitigation measures in Wuhan, China, Italy, and New York City, from January 23 to May 9, 2020, we illustrate that the impacts of mitigation measures are discernable from the trends of the pandemic. Our analysis reveals that the difference with and without mandated face covering represents the determinant in shaping the pandemic trends in the three epicenters. This protective measure alone significantly reduced the number of infections, that is, by over 78,000 in Italy from April 6 to May 9 and over 66,000 in New York City from April 17 to May 9. Other mitigation measures, such as social distancing implemented in the United States, are insufficient by themselves in protecting the public. We conclude that wearing of face masks in public corresponds to the most effective means to prevent interhuman transmission, and this inexpensive practice, in conjunction with simultaneous social distancing, quarantine, and contact tracing, represents the most likely fighting opportunity to stop the COVID-19 pandemic. Our work also highlights the fact that sound science is essential in decision-making for the current and future public health pandemics.