scispace - formally typeset
Search or ask a question

Miami University

EducationOxford, Ohio, United States
About: Miami University is a education organization based out in Oxford, Ohio, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 9949 authors who have published 19598 publications receiving 568410 citations. The organization is also known as: Miami of Ohio & Miami-Ohio.

More filters
Journal ArticleDOI
TL;DR: The current status of botanical screening efforts, as well as in vivo studies of their effectiveness and toxicity, are summarized and the structure and antimicrobial properties of phytochemicals are addressed.
Abstract: The use of and search for drugs and dietary supplements derived from plants have accelerated in recent years. Ethnopharmacologists, botanists, microbiologists, and natural-products chemists are combing the Earth for phytochemicals and “leads” which could be developed for treatment of infectious diseases. While 25 to 50% of current pharmaceuticals are derived from plants, none are used as antimicrobials. Traditional healers have long used plants to prevent or cure infectious conditions; Western medicine is trying to duplicate their successes. Plants are rich in a wide variety of secondary metabolites, such as tannins, terpenoids, alkaloids, and flavonoids, which have been found in vitro to have antimicrobial properties. This review attempts to summarize the current status of botanical screening efforts, as well as in vivo studies of their effectiveness and toxicity. The structure and antimicrobial properties of phytochemicals are also addressed. Since many of these compounds are currently available as unregulated botanical preparations and their use by the public is increasing rapidly, clinicians need to consider the consequences of patients self-medicating with these preparations.

7,486 citations

Journal ArticleDOI
TL;DR: The use (and misuse) of GLMMs in ecology and evolution are reviewed, estimation and inference are discussed, and 'best-practice' data analysis procedures for scientists facing this challenge are summarized.
Abstract: How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.

7,207 citations

Journal ArticleDOI
Asru K. Sinha1
TL;DR: A simple colorimetric assay for catalase activity has been described using K2Cr2O7/acetic acid reagent to determine values of different enzyme sources and compared with the values obtained by titrimetric methods.

4,827 citations

Journal ArticleDOI
TL;DR: In this paper, the mean absolute scaled error (MESEME) was proposed as the standard measure for comparing forecast accuracy across multiple time series across different time series types, and was used in the M-competition as well as the M3competition.

3,870 citations

Journal ArticleDOI
TL;DR: In this paper, a six-component framework is proposed for characterizing a business model, regardless of venture type, and the framework is illustrated using a successful mainstream company, demonstrating the manner in which business models might emerge and evolve over time.

2,372 citations


Showing all 10040 results

Krzysztof Matyjaszewski1691431128585
James H. Brown12542372040
Mark D. Griffiths124123861335
Hong-Cai Zhou11448966320
Donald E. Canfield10529843270
Michael L. Klein10474578805
Heikki V. Huikuri10362045404
Jun Liu100116573692
Joseph M. Prospero9822937172
Camillo Ricordi9484540848
Thomas A. Widiger9342030003
James C. Coyne9337838775
Henry A. Giroux9051636191
Martin Wikelski8942025821
Robert J. Myerburg8761432765
Network Information
Related Institutions (5)
Arizona State University
109.6K papers, 4.4M citations

94% related

University of Georgia
93.6K papers, 3.7M citations

93% related

Pennsylvania State University
196.8K papers, 8.3M citations

93% related

Michigan State University
137K papers, 5.6M citations

93% related

Virginia Tech
95.2K papers, 2.9M citations

92% related

No. of papers from the Institution in previous years