Institution
University of Wisconsin-Madison
Education•Madison, Wisconsin, United States•
About: University of Wisconsin-Madison is a education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 108707 authors who have published 237594 publications receiving 11883575 citations.
Topics: Population, Poison control, Gene, Health care, Galaxy
Papers published on a yearly basis
Papers
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TL;DR: Diffusion tensor imaging (DTI) is a promising method for characterizing microstructural changes or differences with neuropathology and treatment and the biological mechanisms, acquisition, and analysis of DTI measurements are addressed.
2,315 citations
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TL;DR: A computer program, DOTUR, is developed, which assigns sequences to OTUs by using either the furthest, average, or nearest neighbor algorithm for each distance level, which addresses the challenge of assigning sequences to operational taxonomic units (OTUs) based on the genetic distances between sequences.
Abstract: Although copious qualitative information describes the members of the diverse microbial communities on Earth, statistical approaches for quantifying and comparing the numbers and compositions of lineages in communities are lacking. We present a method that addresses the challenge of assigning sequences to operational taxonomic units (OTUs) based on the genetic distances between sequences. We developed a computer program, DOTUR, which assigns sequences to OTUs by using either the furthest, average, or nearest neighbor algorithm for each distance level. DOTUR uses the frequency at which each OTU is observed to construct rarefaction and collector's curves for various measures of richness and diversity. We analyzed 16S rRNA gene libraries derived from Scottish and Amazonian soils and the Sargasso Sea with DOTUR, which assigned sequences to OTUs rapidly and reliably based on the genetic distances between sequences and identified previous inconsistencies and errors in assigning sequences to OTUs. An analysis of the two 16S rRNA gene libraries from soil demonstrated that they do not contain enough sequences to support a claim that they contain different numbers of bacterial lineages with statistical confidence (P > 0.05), nor do they contain enough sequences to provide a robust estimate of species richness when an OTU is defined as containing sequences that are no more than 3% different from each other. In contrast, the richness of OTUs at the 3% level in the Sargasso Sea collection began to plateau after the sampling of 690 sequences. We anticipate that an equivalent extent of sampling for soil would require sampling more than 10,000 sequences, almost 100 times the size of typical sequence collections obtained from soil.
2,314 citations
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TL;DR: This paper presents a development and analysis of the spatial filtering method for localizing sources of brain electrical activity from surface recordings and explores its sensitivity to deviations between actual and assumed data models.
Abstract: A spatial filtering method for localizing sources of brain electrical activity from surface recordings is described and analyzed. The spatial filters are implemented as a weighted sum of the data recorded at different sites. The weights are chosen to minimize the filter output power subject to a linear constraint. The linear constraint forces the filter to pass brain electrical activity from a specified location, while the power minimization attenuates activity originating at other locations. The estimated output power as a function of location is normalized by the estimated noise power as a function of location to obtain a neural activity index map. Locations of source activity correspond to maxima in the neural activity index map. The method does not require any prior assumptions about the number of active sources of their geometry because it exploits the spatial covariance of the source electrical activity. This paper presents a development and analysis of the method and explores its sensitivity to deviations between actual and assumed data models. The effect on the algorithm of covariance matrix estimation, correlation between sources, and choice of reference is discussed. Simulated and measured data is used to illustrate the efficacy of the approach.
2,313 citations
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2,296 citations
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University of Barcelona1, University of the Basque Country2, Technical University of Denmark3, Malmö University4, University of Copenhagen5, SINTEF6, Aarhus University7, Brown University8, University of Wisconsin-Madison9, University of Warwick10, Carnegie Mellon University11, Purdue University12, Karlsruhe Institute of Technology13, ETH Zurich14, University of Freiburg15
TL;DR: The atomic simulation environment (ASE) provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
Abstract: The Atomic Simulation Environment (ASE) is a software package written in the Python programming language with the aim of setting up, steering, and analyzing atomistic simula- tions. In ASE, tasks are fully scripted in Python. The powerful syntax of Python combined with the NumPy array library make it possible to perform very complex simulation tasks. For example, a sequence of calculations may be performed with the use of a simple "for-loop" construction. Calculations of energy, forces, stresses and other quantities are performed through interfaces to many external electronic structure codes or force fields using a uniform interface. On top of this calculator interface, ASE provides modules for performing many standard simulation tasks such as structure optimization, molecular dynamics, handling of constraints and performing nudged elastic band calculations.
2,282 citations
Authors
Showing all 109671 results
Name | H-index | Papers | Citations |
---|---|---|---|
Eric S. Lander | 301 | 826 | 525976 |
Ronald C. Kessler | 274 | 1332 | 328983 |
Gordon H. Guyatt | 231 | 1620 | 228631 |
Yi Chen | 217 | 4342 | 293080 |
David Miller | 203 | 2573 | 204840 |
Robert M. Califf | 196 | 1561 | 167961 |
Ronald Klein | 194 | 1305 | 149140 |
Joan Massagué | 189 | 408 | 149951 |
Jens K. Nørskov | 184 | 706 | 146151 |
Terrie E. Moffitt | 182 | 594 | 150609 |
H. S. Chen | 179 | 2401 | 178529 |
Ramachandran S. Vasan | 172 | 1100 | 138108 |
Masayuki Yamamoto | 171 | 1576 | 123028 |
Avshalom Caspi | 170 | 524 | 113583 |
Jiawei Han | 168 | 1233 | 143427 |