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Institution

University of Rochester

EducationRochester, New York, United States
About: University of Rochester is a education organization based out in Rochester, New York, United States. It is known for research contribution in the topics: Population & Laser. The organization has 63915 authors who have published 112762 publications receiving 5484122 citations. The organization is also known as: Rochester University.


Papers
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Journal ArticleDOI
TL;DR: In this article, the authors examined the relation between stock returns and stock market volatility and found that the expected market risk premium (the expected return on a stock portfolio minus the Treasury bill yield) is positively related to the predictable volatility of stock returns.

4,348 citations

Journal ArticleDOI
TL;DR: These guidelines are presented 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.
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. 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 vs. those that measure flux through the autophagy pathway (i.e., the complete process); thus, a block in macroautophagy that results in autophagosome accumulation needs to be differentiated from stimuli that result in increased 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. 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. 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 autophagy assays, we hope to encourage technical innovation in the field.

4,316 citations

Journal ArticleDOI
TL;DR: It is shown how the boundaries of an arbitrary non-analytic shape can be used to construct a mapping between image space and Hough transform space, which makes the generalized Houghtransform a kind of universal transform which can beused to find arbitrarily complex shapes.

4,310 citations

Journal ArticleDOI
TL;DR: In this article, the authors examine the specification and power of tests based on performance-matched discretionary accruals, and make comparisons with tests using traditional discretionary accumrual measures (e.g., Jones and modified-Jones models).

4,247 citations

Journal ArticleDOI
TL;DR: Self-Determination Theory (SDT) as mentioned in this paper is an empirically based theory of human motivation, development, and wellness, focusing on types, rather than just amount, of motivation, paying particular attention to autonomous motivation, controlled motivation, and amotivation as predictors of performance, relational, and well-being outcomes.
Abstract: Self-determination theory (SDT) is an empirically based theory of human motivation, development, and wellness. The theory focuses on types, rather than just amount, of motivation, paying particular attention to autonomous motivation, controlled motivation, and amotivation as predictors of performance, relational, and well-being outcomes. It also addresses the social conditions that enhance versus diminish these types of motivation, proposing and finding that the degrees to which basic psychological needs for autonomy, competence, and relatedness are supported versus thwarted affect both the type and strength of motivation. SDT also examines people’s life goals or aspirations, showing differential relations of intrinsic versus extrinsic life goals to performance and psychological health. In this introduction we also briefly discuss recent developments within SDT concerning mindfulness and vitality, and highlight the applicability of SDT within applied domains, including work, relationships, parenting, education, virtual environments, sport, sustainability, health care, and psychotherapy.

4,233 citations


Authors

Showing all 64186 results

NameH-indexPapersCitations
Eugene Braunwald2301711264576
Cyrus Cooper2041869206782
Eric J. Topol1931373151025
Dennis W. Dickson1911243148488
Scott M. Grundy187841231821
John C. Morris1831441168413
Ronald C. Petersen1781091153067
David R. Williams1782034138789
John Hardy1771178171694
Russel J. Reiter1691646121010
Michael Snyder169840130225
Jiawei Han1681233143427
Gang Chen1673372149819
Marc A. Pfeffer166765133043
Salvador Moncada164495138030
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023101
2022383
20213,841
20203,895
20193,699
20183,541