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Institution

University of California, Davis

EducationDavis, California, United States
About: University of California, Davis is a education organization based out in Davis, California, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 78770 authors who have published 180033 publications receiving 8064158 citations. The organization is also known as: UC Davis & UCD.
Topics: Population, Poison control, Gene, Galaxy, Genome


Papers
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Journal ArticleDOI
TL;DR: In this paper, the ASTM E96 standard method for determining water vapor permeability (WVP) was modified for hydrophilic edible films, and the WVP Correction Method was developed to account for the water vapor partial pressure gradient in stagnant air layer of the test cup.
Abstract: The ASTM E96 Standard Method for determining water vapor permeability (WVP) was modified for hydrophilic edible films. Accurate measurement of relative humidity conditions and maintenance of 152 m/min air speeds were essential outside the test cups. The WVP Correction Method was developed to account for the water vapor partial pressure gradient in stagnant air layer of the test cup. Errors were as high as 35% without this correction. Applying these guidelines explained commonly observed thickness effects on WVP values of hydrophilic films. Relative humidity was the cause of observed thickness effects.

849 citations

Journal ArticleDOI
TL;DR: A novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions is proposed and PLS proves superior to the well known dimension reduction method of Principal Components Analysis (PCA).
Abstract: Motivation: One important application of gene expression microarray data is classification of samples into categories, such as the type of tumor. The use of microarrays allows simultaneous monitoring of thousands of genes expressions per sample. This ability to measure gene expression en masse has resulted in data with the number of variables p (genes) far exceeding the number of samples N . Standard statistical methodologies in classification and prediction do not work well or even at all when N < p. Modification of existing statistical methodologies or development of new methodologies is needed for the analysis of microarray data. Results: We propose a novel analysis procedure for classifying (predicting) human tumor samples based on microarray gene expressions. This procedure involves dimension reduction using Partial Least Squares (PLS) and classification using Logistic Discrimination (LD) and Quadratic Discriminant Analysis (QDA). We compare PLS to the well known dimension reduction method of Principal Components Analysis (PCA). Under many circumstances PLS proves superior; we illustrate a condition when PCA particularly fails to predict well relative to PLS. The proposed methods were applied to five different microarray data sets involving various human tumor samples: (1) normal versus ovarian tumor; (2) Acute Myeloid Leukemia (AML) versus Acute Lymphoblastic Leukemia (ALL); (3) Diffuse Large B-cell Lymphoma (DLBCLL) versus B-cell Chronic Lymphocytic Leukemia (BCLL); (4) normal versus colon tumor; and (5) Non-SmallCell-Lung-Carcinoma (NSCLC) versus renal samples. Stability of classification results and methods were further assessed by re-randomization studies. Availability: The methodology can be implemented using a combination of standard statistical methods, available, for example, in SAS. Illustrative SAS code is available from the first author.

847 citations

Journal ArticleDOI
TL;DR: The difficulties of defining mindfulness are discussed, the proper scope of research into mindfulness practices is delineated, and crucial methodological issues for interpreting results from investigations of mindfulness are explained.
Abstract: During the past two decades, mindfulness meditation has gone from being a fringe topic of scientific investigation to being an occasional replacement for psychotherapy, tool of corporate well-being, widely implemented educational practice, and "key to building more resilient soldiers." Yet the mindfulness movement and empirical evidence supporting it have not gone without criticism. Misinformation and poor methodology associated with past studies of mindfulness may lead public consumers to be harmed, misled, and disappointed. Addressing such concerns, the present article discusses the difficulties of defining mindfulness, delineates the proper scope of research into mindfulness practices, and explicates crucial methodological issues for interpreting results from investigations of mindfulness. For doing so, the authors draw on their diverse areas of expertise to review the present state of mindfulness research, comprehensively summarizing what we do and do not know, while providing a prescriptive agenda for contemplative science, with a particular focus on assessment, mindfulness training, possible adverse effects, and intersection with brain imaging. Our goals are to inform interested scientists, the news media, and the public, to minimize harm, curb poor research practices, and staunch the flow of misinformation about the benefits, costs, and future prospects of mindfulness meditation.

847 citations

Book
30 Oct 2009
TL;DR: The design of LDPC codes based on combinatorial designs, graphs, and superposition and their application to LDPC code applications and advanced topics are described.
Abstract: Channel coding lies at the heart of digital communication and data storage, and this detailed introduction describes the core theory as well as decoding algorithms, implementation details, and performance analyses. In this book, Professors Ryan and Lin provide clear information on modern channel codes, including turbo and low-density parity-check (LDPC) codes. They also present detailed coverage of BCH codes, Reed-Solomon codes, convolutional codes, finite geometry codes, and product codes, providing a one-stop resource for both classical and modern coding techniques. Assuming no prior knowledge in the field of channel coding, the opening chapters begin with basic theory to introduce newcomers to the subject. Later chapters then extend to advanced topics such as code ensemble performance analyses and algebraic code design. 250 varied and stimulating end-of-chapter problems are also included to test and enhance learning, making this an essential resource for students and practitioners alike.

846 citations

Journal ArticleDOI
TL;DR: The physiological evidence extends early selection theories by providing neurophysiologically precise information about the stages of visual processing affected by attention and provides physiological evidence for early precategorical selection during visual attention.
Abstract: Visual selective attention improves our perception and performance by modifying sensory inputs at an early stage of processing. Spatial attention produces the most consistent early modulations of visual processing, which can be observed when attention is voluntarily allocated to locations. These effects of spatial attention are similar when attention is cued in a trial-by-trial, or sustained, fashion and are manifest as changes in the amplitudes, but not the latencies, of evoked neural activity recorded from the intact human scalp. This modulation of sensory processing first occurs within the extrastriate visual cortex and not within the striate or earlier subcortical processing stages. These relatively early spatial filters alter the inputs to higher stages of visual analysis that are responsible for feature extraction and ultimately object perception and recognition, and thus provide physiological evidence for early precategorical selection during visual attention. Moreover, the physiological evidence extends early selection theories by providing neurophysiologically precise information about the stages of visual processing affected by attention.

845 citations


Authors

Showing all 79538 results

NameH-indexPapersCitations
Eric S. Lander301826525976
Ronald C. Kessler2741332328983
George M. Whitesides2401739269833
Ronald M. Evans199708166722
Virginia M.-Y. Lee194993148820
Scott M. Grundy187841231821
Julie E. Buring186950132967
Patrick O. Brown183755200985
Anil K. Jain1831016192151
John C. Morris1831441168413
Douglas R. Green182661145944
John R. Yates1771036129029
Barry Halliwell173662159518
Roderick T. Bronson169679107702
Hongfang Liu1662356156290
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023262
20221,122
20218,398
20208,661
20198,165
20187,556