scispace - formally typeset
Institution

Temple University

EducationPhiladelphia, Pennsylvania, United States
About: Temple University is a(n) education organization based out in Philadelphia, Pennsylvania, United States. It is known for research contribution in the topic(s): Population & Poison control. The organization has 32154 authors who have published 64375 publication(s) receiving 2219828 citation(s).


Papers
More filters
Journal ArticleDOI
TL;DR: The latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine, has been optimized for use on 64-bit computing systems for analyzing larger datasets.
Abstract: We present the latest version of the Molecular Evolutionary Genetics Analysis (Mega) software, which contains many sophisticated methods and tools for phylogenomics and phylomedicine. In this major upgrade, Mega has been optimized for use on 64-bit computing systems for analyzing larger datasets. Researchers can now explore and analyze tens of thousands of sequences in Mega The new version also provides an advanced wizard for building timetrees and includes a new functionality to automatically predict gene duplication events in gene family trees. The 64-bit Mega is made available in two interfaces: graphical and command line. The graphical user interface (GUI) is a native Microsoft Windows application that can also be used on Mac OS X. The command line Mega is available as native applications for Windows, Linux, and Mac OS X. They are intended for use in high-throughput and scripted analysis. Both versions are available from www.megasoftware.net free of charge.

25,894 citations

Journal ArticleDOI
TL;DR: The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine and has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses.
Abstract: The Molecular Evolutionary Genetics Analysis (Mega) software implements many analytical methods and tools for phylogenomics and phylomedicine. Here, we report a transformation of Mega to enable cross-platform use on Microsoft Windows and Linux operating systems. Mega X does not require virtualization or emulation software and provides a uniform user experience across platforms. Mega X has additionally been upgraded to use multiple computing cores for many molecular evolutionary analyses. Mega X is available in two interfaces (graphical and command line) and can be downloaded from www.megasoftware.net free of charge.

11,718 citations

Journal ArticleDOI
01 Jan 1988
TL;DR: An ecological model for health promotion is proposed which focuses on both individual and social environmental factors as targets for health promotions and addresses the importance of interventions directed at changing interpersonal, organizational, community, and public policy factors which support and maintain unhealthy behaviors.
Abstract: During the past 20 years there has been a dramatic increase in societal interest in preventing disability and death in the United States by changing individual behaviors linked to the risk of contracting chronic diseases. This renewed interest in health promotion and disease prevention has not been without its critics. Some critics have accused proponents of life-style interventions of promoting a victim-blaming ideology by neglecting the importance of social influences on health and disease. This article proposes an ecological model for health promotion which focuses attention on both individual and social environmental factors as targets for health promotion interventions. It addresses the importance of interventions directed at changing interpersonal, organizational, community, and public policy, factors which support and maintain unhealthy behaviors. The model assumes that appropriate changes in the social environment will produce changes in individuals, and that the support of individuals in the population is essential for implementing environmental changes.

5,447 citations

Journal ArticleDOI
Daniel J. Klionsky1, Kotb Abdelmohsen2, Akihisa Abe3, Joynal Abedin4  +2519 moreInstitutions (695)
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.

4,756 citations

Journal ArticleDOI
TL;DR: Mechanisms that govern the processing of emotional information, particularly those involved in fear reduction, are proposed and applications to therapeutic practice and to the broader study of psychopathology are discussed.
Abstract: In this article we propose mechanisms that govern the processing of emotional information, particularly those involved in fear reduction. Emotions are viewed as represented by information structures in memory, and anxiety is thought to occur when an information structure that serves as program to escape or avoid danger is activated. Emotional processing is denned as the modification of memory structures that underlie emotions. It is argued that some form of exposure to feared situations is common to many psychotherapies for anxiety, and that confrontation with feared objects or situations is an effective treatment. Physiological activation and habituation within and across exposure sessions are cited as indicators of emotional processing, and variables that influence activation and habituation of fear responses are examined. These variables and the indicators are analyzed to yield an account of what information must be integrated for emotional processing of a fear structure. The elements of such a structure are viewed as cognitive representations of the stimulus characteristic of the fear situation, the individual's responses in it, and aspects of its meaning for the individual. Treatment failures are interpreted with respect to the interference of cognitive defenses, autonomic arousal, mood state, and erroneous ideation with reformation of targeted fear structures. Applications of the concepts advanced here to therapeutic practice and to the broader study of psychopathology are discussed.

4,366 citations


Authors

Showing all 32154 results

NameH-indexPapersCitations
Robert J. Lefkowitz214860147995
Rakesh K. Jain2001467177727
Virginia M.-Y. Lee194993148820
Yury Gogotsi171956144520
Timothy A. Springer167669122421
Ralph A. DeFronzo160759132993
James J. Collins15166989476
Robert J. Glynn14674888387
Edward G. Lakatta14685888637
Steven Williams144137586712
Peter Buchholz143118192101
David Goldstein1411301101955
Scott D. Solomon1371145103041
Donald B. Rubin132515262632
Jeffery D. Molkentin13148261594
Network Information
Related Institutions (5)
University of Pittsburgh
201K papers, 9.6M citations

97% related

University of North Carolina at Chapel Hill
185.3K papers, 9.9M citations

97% related

New York University
165.5K papers, 8.3M citations

96% related

University of Pennsylvania
257.6K papers, 14.1M citations

96% related

University of Southern California
169.9K papers, 7.8M citations

95% related

Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202249
20213,470
20203,280
20193,166
20183,019
20173,135