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Institution

University of Massachusetts Amherst

EducationAmherst Center, Massachusetts, United States
About: University of Massachusetts Amherst is a education organization based out in Amherst Center, Massachusetts, United States. It is known for research contribution in the topics: Population & Galaxy. The organization has 37274 authors who have published 83965 publications receiving 3834996 citations. The organization is also known as: UMass Amherst & Massachusetts State College.


Papers
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Journal ArticleDOI
TL;DR: It is hypothesized that mechanical, thermal and chemical effects of ultrasonication resulted in structural changes in BSA that altered the functional properties of the macromolecule which may be attributed to the formation of an ultrasonically induced state that differs from a thermally, mechanically or solvent induced state.

469 citations

Journal ArticleDOI
TL;DR: The SLEUTH model is a cellular automaton model with predefined growth rules applied spatially to gridded maps of the cities in a set of nested loops, and was designed to be both scaleable and universally applicable as discussed by the authors.

468 citations

Journal ArticleDOI
TL;DR: The method represents gene-expression dynamics as autoregressive equations and uses an agglomerative procedure to search for the most probable set of clusters given the available data and acquires an independent, principled measure to decide when two series are different enough to belong to different clusters.
Abstract: This article presents a Bayesian method for model-based clustering of gene expression dynamics. The method represents gene-expression dynamics as autoregressive equations and uses an agglomerative procedure to search for the most probable set of clusters given the available data. The main contributions of this approach are the ability to take into account the dynamic nature of gene expression time series during clustering and a principled way to identify the number of distinct clusters. As the number of possible clustering models grows exponentially with the number of observed time series, we have devised a distance-based heuristic search procedure able to render the search process feasible. In this way, the method retains the important visualization capability of traditional distance-based clustering and acquires an independent, principled measure to decide when two series are different enough to belong to different clusters. The reliance of this method on an explicit statistical representation of gene expression dynamics makes it possible to use standard statistical techniques to assess the goodness of fit of the resulting model and validate the underlying assumptions. A set of gene-expression time series, collected to study the response of human fibroblasts to serum, is used to identify the properties of the method.

468 citations

Journal ArticleDOI
TL;DR: In this article, the authors detect compact, star-forming galaxies (cSFGs) whose number densities, masses, sizes, and star formation rates qualify them as likely progenitors of compact, quiescent, massive galaxies.
Abstract: We combine high-resolution Hubble Space Telescope/WFC3 images with multi-wavelength photometry to track the evolution of structure and activity of massive (M_*> 10^10 M_☉) galaxies at redshifts z = 1.4-3 in two fields of the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey. We detect compact, star-forming galaxies (cSFGs) whose number densities, masses, sizes, and star formation rates (SFRs) qualify them as likely progenitors of compact, quiescent, massive galaxies (cQGs) at z = 1.5-3. At z≲2, cSFGs present SFR = 100-200 M_☉ yr^–1, yet their specific star formation rates (sSFR ~ 10^–9 yr^–1) are typically half that of other massive SFGs at the same epoch, and host X-ray luminous active galactic nuclei (AGNs) 30 times (~30%) more frequently. These properties suggest that cSFGs are formed by gas-rich processes (mergers or disk-instabilities) that induce a compact starburst and feed an AGN, which, in turn, quench the star formation on dynamical timescales (few 10^8 yr). The cSFGs are continuously being formed at z = 2-3 and fade to cQGs down to z ~ 1.5. After this epoch, cSFGs are rare, thereby truncating the formation of new cQGs. Meanwhile, down to z = 1, existing cQGs continue to enlarge to match local QGs in size, while less-gas-rich mergers and other secular mechanisms shepherd (larger) SFGs as later arrivals to the red sequence. In summary, we propose two evolutionary tracks of QG formation: an early (z≲2), formation path of rapidly quenched cSFGs fading into cQGs that later enlarge within the quiescent phase, and a late-arrival (z≳2) path in which larger SFGs form extended QGs without passing through a compact state.

467 citations

Journal ArticleDOI
TL;DR: It is concluded that the CSA and Caltrac accelerometers have similar validity and that either instrument can be used to estimate EE of groups.
Abstract: The validity of the Computer Science and Applications, Inc. (CSA) accelerometer in assessing physical activity was assessed during treadmill walking and running at three different grades. Energy expenditure (EE) served as the criterion measure. CSA data were compared to data collected with the Caltrac accelerometer. Both accelerometers were sensitive to changes in treadmill speed, but neither discriminated changes in treadmill grade. Caltrac and CSA activity counts were significantly and similarly correlated with EE (r = 0.66-0.82), relative VO2 (r = 0.77-0.89), heart rate (r = 0.66-0.80), treadmill speed (r = 0.82-0.92), and with each other (r = 0.77-0.82). CSA data were used to develop models to predict EE (kcal.min-1). Cross-validation resulted in a mean difference between actual and predicted EE of 0.02 kcal.min-1 (SEE = 0.85 kcal.min-1). The range of individual differences in the validation group was large for both the CSA model (-2.86 to +3.86 kcal.min-1) and Caltrac (-4.17 to +2.04 kcal.min-1). It is concluded that the CSA and Caltrac accelerometers have similar validity and that either instrument can be used to estimate EE of groups.

467 citations


Authors

Showing all 37601 results

NameH-indexPapersCitations
George M. Whitesides2401739269833
Joan Massagué189408149951
David H. Weinberg183700171424
David L. Kaplan1771944146082
Michael I. Jordan1761016216204
James F. Sallis169825144836
Bradley T. Hyman169765136098
Anton M. Koekemoer1681127106796
Derek R. Lovley16858295315
Michel C. Nussenzweig16551687665
Alfred L. Goldberg15647488296
Donna Spiegelman15280485428
Susan E. Hankinson15178988297
Bernard Moss14783076991
Roger J. Davis147498103478
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023103
2022535
20213,983
20203,858
20193,712
20183,385