Institution
University of Massachusetts Amherst
Education•Amherst 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 published on a yearly basis
Papers
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Chalmers University of Technology1, Agrocampus Ouest2, Institut national de la recherche agronomique3, Aix-Marseille University4, University of Guelph5, Massey University6, Ege University7, Agro ParisTech8, Norwich Research Park9, Norwich University10, University of Massachusetts Amherst11, Spanish National Research Council12, Universidade Nova de Lisboa13, University of California, Davis14, Norwegian University of Life Sciences15, University of Greifswald16, Teagasc17
TL;DR: In this article, the authors proposed a general standardised and practical static digestion method based on physiologically relevant conditions that can be applied for various endpoints, which may be amended to accommodate further specific requirements.
Abstract: Simulated gastro-intestinal digestion is widely employed in many fields of food and nutritional sciences, as conducting human trials are often costly, resource intensive, and ethically disputable. As a consequence, in vitro alternatives that determine endpoints such as the bioaccessibility of nutrients and non-nutrients or the digestibility of macronutrients (e.g. lipids, proteins and carbohydrates) are used for screening and building new hypotheses. Various digestion models have been proposed, often impeding the possibility to compare results across research teams. For example, a large variety of enzymes from different sources such as of porcine, rabbit or human origin have been used, differing in their activity and characterization. Differences in pH, mineral type, ionic strength and digestion time, which alter enzyme activity and other phenomena, may also considerably alter results. Other parameters such as the presence of phospholipids, individual enzymes such as gastric lipase and digestive emulsifiers vs. their mixtures (e.g. pancreatin and bile salts), and the ratio of food bolus to digestive fluids, have also been discussed at length. In the present consensus paper, within the COST Infogest network, we propose a general standardised and practical static digestion method based on physiologically relevant conditions that can be applied for various endpoints, which may be amended to accommodate further specific requirements. A frameset of parameters including the oral, gastric and small intestinal digestion are outlined and their relevance discussed in relation to available in vivo data and enzymes. This consensus paper will give a detailed protocol and a line-by-line, guidance, recommendations and justifications but also limitation of the proposed model. This harmonised static, in vitro digestion method for food should aid the production of more comparable data in the future.
3,380 citations
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TL;DR: These data provide a template on which patterns of activity can be classified into intensity levels using the CSA accelerometer, and help to predict energy expenditure at any treadmill speed.
Abstract: Purpose:We established accelerometer count ranges for the Computer Science and Applications, Inc. (CSA) activity monitor corresponding to commonly employed MET categories.Methods:Data were obtained from 50 adults (25 males, 25 females) during treadmill exercise at three different speeds (4.8
3,267 citations
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01 Sep 1983TL;DR: In this article, a system consisting of two neuron-like adaptive elements can solve a difficult learning control problem, where the task is to balance a pole that is hinged to a movable cart by applying forces to the cart base.
Abstract: It is shown how a system consisting of two neuronlike adaptive elements can solve a difficult learning control problem. The task is to balance a pole that is hinged to a movable cart by applying forces to the cart's base. It is argued that the learning problems faced by adaptive elements that are components of adaptive networks are at least as difficult as this version of the pole-balancing problem. The learning system consists of a single associative search element (ASE) and a single adaptive critic element (ACE). In the course of learning to balance the pole, the ASE constructs associations between input and output by searching under the influence of reinforcement feedback, and the ACE constructs a more informative evaluation function than reinforcement feedback alone can provide. The differences between this approach and other attempts to solve problems using neurolike elements are discussed, as is the relation of this work to classical and instrumental conditioning in animal learning studies and its possible implications for research in the neurosciences.
3,240 citations
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TL;DR: It is shown that options enable temporally abstract knowledge and action to be included in the reinforcement learning frame- work in a natural and general way and may be used interchangeably with primitive actions in planning methods such as dynamic pro- gramming and in learning methodssuch as Q-learning.
3,233 citations
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23 Feb 2020
TL;DR: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper, where a brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
Abstract: The ATLAS detector as installed in its experimental cavern at point 1 at CERN is described in this paper. A brief overview of the expected performance of the detector when the Large Hadron Collider begins operation is also presented.
3,111 citations
Authors
Showing all 37601 results
Name | H-index | Papers | Citations |
---|---|---|---|
George M. Whitesides | 240 | 1739 | 269833 |
Joan Massagué | 189 | 408 | 149951 |
David H. Weinberg | 183 | 700 | 171424 |
David L. Kaplan | 177 | 1944 | 146082 |
Michael I. Jordan | 176 | 1016 | 216204 |
James F. Sallis | 169 | 825 | 144836 |
Bradley T. Hyman | 169 | 765 | 136098 |
Anton M. Koekemoer | 168 | 1127 | 106796 |
Derek R. Lovley | 168 | 582 | 95315 |
Michel C. Nussenzweig | 165 | 516 | 87665 |
Alfred L. Goldberg | 156 | 474 | 88296 |
Donna Spiegelman | 152 | 804 | 85428 |
Susan E. Hankinson | 151 | 789 | 88297 |
Bernard Moss | 147 | 830 | 76991 |
Roger J. Davis | 147 | 498 | 103478 |