Institution
Oregon State University
Education•Corvallis, Oregon, United States•
About: Oregon State University is a education organization based out in Corvallis, Oregon, United States. It is known for research contribution in the topics: Population & Gene. The organization has 28192 authors who have published 64044 publications receiving 2634108 citations. The organization is also known as: Oregon Agricultural College & OSU.
Topics: Population, Gene, Context (language use), Climate change, Soil water
Papers published on a yearly basis
Papers
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TL;DR: The key decisions in evaluating collaborative filtering recommender systems are reviewed: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole.
Abstract: Recommender systems have been evaluated in many, often incomparable, ways. In this article, we review the key decisions in evaluating collaborative filtering recommender systems: the user tasks being evaluated, the types of analysis and datasets being used, the ways in which prediction quality is measured, the evaluation of prediction attributes other than quality, and the user-based evaluation of the system as a whole. In addition to reviewing the evaluation strategies used by prior researchers, we present empirical results from the analysis of various accuracy metrics on one content domain where all the tested metrics collapsed roughly into three equivalence classes. Metrics within each equivalency class were strongly correlated, while metrics from different equivalency classes were uncorrelated.
5,686 citations
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21 Jun 2000TL;DR: Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
Abstract: Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.
5,679 citations
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TL;DR: In this paper, the authors present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macro-autophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes.
Abstract: In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes.
For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure flux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defined as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (in most higher eukaryotes and some protists such as Dictyostelium) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the field understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy.
Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation, it is imperative to target by gene knockout or RNA interference more than one autophagy-related protein. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways implying that not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular assays, we hope to encourage technical innovation in the field.
5,187 citations
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TL;DR: This critical review provides a processing-structure-property perspective on recent advances in cellulose nanoparticles and composites produced from them, and summarizes cellulOSE nanoparticles in terms of particle morphology, crystal structure, and properties.
Abstract: This critical review provides a processing-structure-property perspective on recent advances in cellulose nanoparticles and composites produced from them. It summarizes cellulose nanoparticles in terms of particle morphology, crystal structure, and properties. Also described are the self-assembly and rheological properties of cellulose nanoparticle suspensions. The methodology of composite processing and resulting properties are fully covered, with an emphasis on neat and high fraction cellulose composites. Additionally, advances in predictive modeling from molecular dynamic simulations of crystalline cellulose to the continuum modeling of composites made with such particles are reviewed (392 references).
4,920 citations
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University of Zurich1, University of California, Berkeley2, Lawrence Berkeley National Laboratory3, University of Oldenburg4, Max Planck Society5, Leibniz University of Hanover6, University of Antwerp7, Oregon State University8, Technische Universität München9, Cornell University10, Newcastle University11, University of Florence12, Weizmann Institute of Science13
TL;DR: In this article, a new generation of experiments and soil carbon models were proposed to predict the SOM response to global warming, and they showed that molecular structure alone alone does not control SOM stability.
Abstract: Globally, soil organic matter (SOM) contains more than three times as much carbon as either the atmosphere or terrestrial vegetation. Yet it remains largely unknown why some SOM persists for millennia whereas other SOM decomposes readily—and this limits our ability to predict how soils will respond to climate change. Recent analytical and experimental advances have demonstrated that molecular structure alone does not control SOM stability: in fact, environmental and biological controls predominate. Here we propose ways to include this understanding in a new generation of experiments and soil carbon models, thereby improving predictions of the SOM response to global warming.
4,219 citations
Authors
Showing all 28447 results
Name | H-index | Papers | Citations |
---|---|---|---|
Robert Stone | 160 | 1756 | 167901 |
Menachem Elimelech | 157 | 547 | 95285 |
Thomas J. Smith | 140 | 1775 | 113919 |
Harold A. Mooney | 135 | 450 | 100404 |
Jerry M. Melillo | 134 | 383 | 68894 |
John F. Thompson | 132 | 1420 | 95894 |
Thomas N. Williams | 132 | 1145 | 95109 |
Peter M. Vitousek | 127 | 352 | 96184 |
Steven W. Running | 126 | 355 | 76265 |
Vincenzo Di Marzo | 126 | 659 | 60240 |
J. D. Hansen | 122 | 975 | 76198 |
Peter Molnar | 118 | 446 | 53480 |
Michael R. Hoffmann | 109 | 500 | 63474 |
David Pollard | 108 | 438 | 39550 |
David J. Hill | 107 | 1364 | 57746 |