Institution
University of Siena
Education•Siena, Italy•
About: University of Siena is a education organization based out in Siena, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 12179 authors who have published 33334 publications receiving 1008287 citations. The organization is also known as: Università degli studi di Siena & Universita degli studi di Siena.
Topics: Population, Cancer, Large Hadron Collider, Sperm, Oxidative stress
Papers published on a yearly basis
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University of Göttingen1, City College of New York2, University of São Paulo3, University of Toronto4, University of Erlangen-Nuremberg5, Aalborg University6, Greifswald University Hospital7, Spaulding Rehabilitation Hospital8, Medical University of South Carolina9, University of Pennsylvania10, Technische Universität Ilmenau11, University of Oldenburg12, École Polytechnique Fédérale de Lausanne13, Paris 12 Val de Marne University14, University of New South Wales15, University of Aberdeen16, University of Trento17, University of Lisbon18, University of Kiel19, Technical University of Dortmund20, Ruhr University Bochum21, Ludwig Maximilian University of Munich22, Beth Israel Deaconess Medical Center23, Mannheim University of Applied Sciences24, University of Siena25, The Catholic University of America26, University College London27, University of Copenhagen28, Fukushima Medical University29, Massachusetts Institute of Technology30, University of Tübingen31
TL;DR: Structured interviews are provided and recommend their use in future controlled studies, in particular when trying to extend the parameters applied, to discuss recent regulatory issues, reporting practices and ethical issues.
699 citations
TL;DR: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community providing the theory and key practical aspects offlow cytometry enabling immunologists to avoid the common errors that often undermine immunological data.
Abstract: These guidelines are a consensus work of a considerable number of members of the immunology and flow cytometry community. They provide the theory and key practical aspects of flow cytometry enabling immunologists to avoid the common errors that often undermine immunological data. Notably, there are comprehensive sections of all major immune cell types with helpful Tables detailing phenotypes in murine and human cells. The latest flow cytometry techniques and applications are also described, featuring examples of the data that can be generated and, importantly, how the data can be analysed. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid, all written and peer-reviewed by leading experts in the field, making this an essential research companion.
698 citations
TL;DR: In this paper, the authors investigated the prevalence of inhaler mishandling in a large population of experienced patients referring to chest clinics; to analyze the variables associated with misuse and the relationship between inhaler handling and health-care resources use and disease control.
681 citations
University of Missouri1, Auburn University2, University of Cincinnati3, United States Environmental Protection Agency4, Maryville College5, University of Siena6, University of Florida7, Harvard University8, National Institutes of Health9, Washington State University10, National Institutes of Natural Sciences, Japan11, Brunel University London12, Case Western Reserve University13, University of Connecticut14, North Carolina State University15, Emory University16, Tulane University17, Universidad Miguel Hernández de Elche18, University of Granada19, University of Illinois at Chicago20, United States Geological Survey21, Tufts University22, Charité23, Carleton College24, University of Texas Medical Branch25, University of Massachusetts Amherst26
TL;DR: This document is a summary statement of the outcome from he meeting: “Bisphenol A: An Examination of the Relevance of cological, In vitro and Laboratory Animal Studies for Assessng Risks to Human Health” sponsored by both the NIEHS and IDCR at NIH/DHHS.
681 citations
Proceedings Article•
10 Sep 2000TL;DR: A focused crawling algorithm is presented that builds a model for the context within which topically relevant pages occur on the web that can capture typical link hierarchies within which valuable pages occur, as well as model content on documents that frequently cooccur with relevant pages.
Abstract: Maintaining currency of search engine indices by exhaustive crawling is rapidly becoming impossible due to the increasing size and dynamic content of the web. Focused crawlers aim to search only the subset of the web related to a specific category, and offer a potential solution to the currency problem. The major problem in focused crawling is performing appropriate credit assignment to different documents along a crawl path, such that short-term gains are not pursued at the expense of less-obvious crawl paths that ultimately yield larger sets of valuable pages. To address this problem we present a focused crawling algorithm that builds a model for the context within which topically relevant pages occur on the web. This context model can capture typical link hierarchies within which valuable pages occur, as well as model content on documents that frequently cooccur with relevant pages. Our algorithm further leverages the existing capability of large search engines to provide partial reverse crawling capabilities. Our algorithm shows significant performance improvements in crawling efficiency over standard focused crawling.
679 citations
Authors
Showing all 12352 results
| Name | H-index | Papers | Citations |
|---|---|---|---|
| Johan Auwerx | 158 | 653 | 95779 |
| I. V. Gorelov | 139 | 1916 | 103133 |
| Roberto Tenchini | 133 | 1390 | 94541 |
| Francesco Fabozzi | 133 | 1561 | 93364 |
| M. Davier | 132 | 1449 | 107642 |
| Roberto Dell'Orso | 132 | 1412 | 92792 |
| Rino Rappuoli | 132 | 816 | 64660 |
| Teimuraz Lomtadze | 129 | 893 | 80314 |
| Manas Maity | 129 | 1309 | 87465 |
| Dezso Horvath | 128 | 1283 | 88111 |
| Paolo Azzurri | 126 | 1058 | 81651 |
| Vincenzo Di Marzo | 126 | 659 | 60240 |
| Igor Katkov | 125 | 972 | 71845 |
| Ying Lu | 123 | 708 | 62645 |
| Thomas Schwarz | 123 | 701 | 54560 |