scispace - formally typeset
Search or ask a question
Institution

University of Wisconsin-Madison

EducationMadison, 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
More filters
Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

Journal ArticleDOI
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

NameH-indexPapersCitations
Eric S. Lander301826525976
Ronald C. Kessler2741332328983
Gordon H. Guyatt2311620228631
Yi Chen2174342293080
David Miller2032573204840
Robert M. Califf1961561167961
Ronald Klein1941305149140
Joan Massagué189408149951
Jens K. Nørskov184706146151
Terrie E. Moffitt182594150609
H. S. Chen1792401178529
Ramachandran S. Vasan1721100138108
Masayuki Yamamoto1711576123028
Avshalom Caspi170524113583
Jiawei Han1681233143427
Network Information
Related Institutions (5)
University of Washington
305.5K papers, 17.7M citations

96% related

University of Pennsylvania
257.6K papers, 14.1M citations

96% related

University of California, San Diego
204.5K papers, 12.3M citations

95% related

University of Michigan
342.3K papers, 17.6M citations

95% related

Stanford University
320.3K papers, 21.8M citations

95% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023333
20221,390
202110,148
20209,483
20199,278
20188,546