University of Wisconsin-Madison
Education•Madison, Wisconsin, United States•
About: University of Wisconsin-Madison is a(n) education organization based out in Madison, Wisconsin, United States. It is known for research contribution in the topic(s): Population & Poison control. The organization has 108707 authors who have published 237594 publication(s) receiving 11883575 citation(s).
Topics: Population, Poison control, Gene, Galaxy, Health care
Papers published on a yearly basis
01 Jul 1962-Physiologia Plantarum
TL;DR: In vivo redox biosensing resolves the spatiotemporal dynamics of compartmental responses to local ROS generation and provide a basis for understanding how compartment-specific redox dynamics may operate in retrograde signaling and stress 67 acclimation in plants.
Abstract: In experiments with tobacco tissue cultured on White's modified medium (basal meditmi hi Tnhles 1 and 2) supplemenk'd with kiticthi and hidoleacctic acid, a slrikin^' fourlo (ive-told intTease iu yield was ohtaitu-d within a three to Tour week j^rowth period on addition of an aqtteotis exlrarl of tobacco leaves (Fi^'ures 1 and 2). Subse(iueutly it was found Ihiit this jnoniotiou oi' f^rowih was due mainly though nol entirely to inorj^auic rather than organic con.stitttenls in the extract. In the isolation of Rrowth factors from plant tissues and other sources inorj '̂anic salts are fre(|uently carried along with fhe organic fraclioits. When tissue cultures are used for bioassays, therefore, il is necessary lo lake into account increases in growth which may result from nutrient elements or other known constituents of the medium which may he present in the te.st materials. To minimize interference trom rontaminaitis of this type, an altempt has heen made to de\\eh)p a nieditmi with such adequate supplies of all re(iuired tnineral nutrients and cotntnott orgattic cottslitueitls that no apprecial»le change in growth rate or yield will result from the inlroduclion of additional amounts in the range ordinarily expected to be present in tnaterials to be assayed. As a point of referetice for this work some of the culture media in mc)st common current use will he cotisidered briefly. For ease of comparis4)n Iheir mineral compositions are listed in Tables 1 and 2. White's nutrient .solution, designed originally for excised root cultures, was based on Uspeuski and Uspetiskaia's medium for algae and Trelease and Trelease's micronutrieni solution. This medium also was employed successfully in the original cttltivation of callus from the tobacco Iiybrid Nicotiana gtauca x A', tanijadorffii, atitl as further modified by White in 194̂ ^ and by others it has been used for the
01 Jul 2012-Nature Methods
TL;DR: The origins, challenges and solutions of NIH Image and ImageJ software are discussed, and how their history can serve to advise and inform other software projects.
Abstract: For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
01 Jul 2012-Nature Methods
TL;DR: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis that facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system.
Abstract: Fiji is a distribution of the popular open-source software ImageJ focused on biological-image analysis. Fiji uses modern software engineering practices to combine powerful software libraries with a broad range of scripting languages to enable rapid prototyping of image-processing algorithms. Fiji facilitates the transformation of new algorithms into ImageJ plugins that can be shared with end users through an integrated update system. We propose Fiji as a platform for productive collaboration between computer science and biology research communities.
•01 Nov 2008
TL;DR: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization, responding to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems.
Abstract: Numerical Optimization presents a comprehensive and up-to-date description of the most effective methods in continuous optimization. It responds to the growing interest in optimization in engineering, science, and business by focusing on the methods that are best suited to practical problems. For this new edition the book has been thoroughly updated throughout. There are new chapters on nonlinear interior methods and derivative-free methods for optimization, both of which are used widely in practice and the focus of much current research. Because of the emphasis on practical methods, as well as the extensive illustrations and exercises, the book is accessible to a wide audience. It can be used as a graduate text in engineering, operations research, mathematics, computer science, and business. It also serves as a handbook for researchers and practitioners in the field. The authors have strived to produce a text that is pleasant to read, informative, and rigorous - one that reveals both the beautiful nature of the discipline and its practical side.
TL;DR: This article gives an introduction to the subject of classification and regression trees by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.
Abstract: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 14-23 DOI: 10.1002/widm.8 This article is categorized under: Technologies > Classification Technologies > Machine Learning Technologies > Prediction Technologies > Statistical Fundamentals
Showing all 108707 results
|Eric S. Lander||301||826||525976|
|Ronald C. Kessler||274||1332||328983|
|Gordon H. Guyatt||231||1620||228631|
|Robert M. Califf||196||1561||167961|
|Jens K. Nørskov||184||706||146151|
|Terrie E. Moffitt||182||594||150609|
|H. S. Chen||179||2401||178529|
|Ramachandran S. Vasan||172||1100||138108|
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