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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: A theory of planned behavior, an extension of Ajzen and Fishbein's (1980, Understanding attitudes and predicting social behavior Englewood-Cliffs, NJ: Prentice-Hall) theory of reasoned action, was tested in two experiments.

5,298 citations

Journal ArticleDOI
TL;DR: The results support current proposals for the length of quarantine or active monitoring of persons potentially exposed to SARS-CoV-2, although longer monitoring periods might be justified in extreme cases.
Abstract: Using news reports and press releases from provinces, regions, and countries outside Wuhan, Hubei province, China, this analysis estimates the length of the incubation period of COVID-19 and its pu...

5,215 citations

Journal ArticleDOI
TL;DR: Transitions are proposed for species data tables which allow ecologists to use ordination methods such as PCA and RDA for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases.
Abstract: This paper examines how to obtain species biplots in unconstrained or constrained ordination without resorting to the Euclidean distance [used in principal-component analysis (PCA) and redundancy analysis (RDA)] or the chi-square distance [preserved in correspondence analysis (CA) and canonical correspondence analysis (CCA)] which are not always appropriate for the analysis of community composition data. To achieve this goal, transformations are proposed for species data tables. They allow ecologists to use ordination methods such as PCA and RDA, which are Euclidean-based, for the analysis of community data, while circumventing the problems associated with the Euclidean distance, and avoiding CA and CCA which present problems of their own in some cases. This allows the use of the original (transformed) species data in RDA carried out to test for relationships with explanatory variables (i.e. environmental variables, or factors of a multifactorial analysis-of-variance model); ecologists can then draw biplots displaying the relationships of the species to the explanatory variables. Another application allows the use of species data in other methods of multivariate data analysis which optimize a least-squares loss function; an example is K-means partitioning.

4,194 citations

Journal ArticleDOI
TL;DR: The advent of AuNP as a sensory element provided a broad spectrum of innovative approaches for the detection of metal ions, small molecules, proteins, nucleic acids, malignant cells, etc. in a rapid and efficient manner.
Abstract: Detection of chemical and biological agents plays a fundamental role in biomedical, forensic and environmental sciences1–4 as well as in anti bioterrorism applications.5–7 The development of highly sensitive, cost effective, miniature sensors is therefore in high demand which requires advanced technology coupled with fundamental knowledge in chemistry, biology and material sciences.8–13 In general, sensors feature two functional components: a recognition element to provide selective/specific binding with the target analytes and a transducer component for signaling the binding event. An efficient sensor relies heavily on these two essential components for the recognition process in terms of response time, signal to noise (S/N) ratio, selectivity and limits of detection (LOD).14,15 Therefore, designing sensors with higher efficacy depends on the development of novel materials to improve both the recognition and transduction processes. Nanomaterials feature unique physicochemical properties that can be of great utility in creating new recognition and transduction processes for chemical and biological sensors15–27 as well as improving the S/N ratio by miniaturization of the sensor elements.28 Gold nanoparticles (AuNPs) possess distinct physical and chemical attributes that make them excellent scaffolds for the fabrication of novel chemical and biological sensors (Figure 1).29–36 First, AuNPs can be synthesized in a straightforward manner and can be made highly stable. Second, they possess unique optoelectronic properties. Third, they provide high surface-to-volume ratio with excellent biocompatibility using appropriate ligands.30 Fourth, these properties of AuNPs can be readily tuned varying their size, shape and the surrounding chemical environment. For example, the binding event between recognition element and the analyte can alter physicochemical properties of transducer AuNPs, such as plasmon resonance absorption, conductivity, redox behavior, etc. that in turn can generate a detectable response signal. Finally, AuNPs offer a suitable platform for multi-functionalization with a wide range of organic or biological ligands for the selective binding and detection of small molecules and biological targets.30–32,36 Each of these attributes of AuNPs has allowed researchers to develop novel sensing strategies with improved sensitivity, stability and selectivity. In the last decade of research, the advent of AuNP as a sensory element provided us a broad spectrum of innovative approaches for the detection of metal ions, small molecules, proteins, nucleic acids, malignant cells, etc. in a rapid and efficient manner.37 Figure 1 Physical properties of AuNPs and schematic illustration of an AuNP-based detection system. In this current review, we have highlighted the several synthetic routes and properties of AuNPs that make them excellent probes for different sensing strategies. Furthermore, we will discuss various sensing strategies and major advances in the last two decades of research utilizing AuNPs in the detection of variety of target analytes including metal ions, organic molecules, proteins, nucleic acids, and microorganisms.

3,879 citations

Journal ArticleDOI
TL;DR: The nature of perceived behavioral control, the relative importance of attitudes and subjective norms, the utility of adding more predictors, and the roles of prior behavior and habit are highlighted.
Abstract: This survey of attitude theory and research published between 1996 and 1999 covers the conceptualization of attitude, attitude formation and activation, attitude structure and function, and the attitude-behavior relation. Research regarding the expectancy-value model of attitude is considered, as are the roles of accessible beliefs and affective versus cognitive processes in the formation of attitudes. The survey reviews research on attitude strength and its antecedents and consequences, and covers progress made on the assessment of attitudinal ambivalence and its effects. Also considered is research on automatic attitude activation, attitude functions, and the relation of attitudes to broader values. A large number of studies dealt with the relation between attitudes and behavior. Research revealing additional moderators of this relation is reviewed, as are theory and research on the link between intentions and actions. Most work in this context was devoted to issues raised by the theories of reasoned action and planned behavior. The present review highlights the nature of perceived behavioral control, the relative importance of attitudes and subjective norms, the utility of adding more predictors, and the roles of prior behavior and habit.

3,813 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
2022536
20213,983
20203,858
20193,712
20183,385