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Institution

University of Turin

EducationTurin, Piemonte, Italy
About: University of Turin is a education organization based out in Turin, Piemonte, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 29607 authors who have published 77952 publications receiving 2480900 citations. The organization is also known as: Universita degli Studi di Torino & Università degli Studi di Torino.


Papers
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Journal ArticleDOI
TL;DR: It is shown from extensive numerical simulations of the NLS equation how freak waves in a random sea state are more likely to occur for large values of the Phillips parameter alpha and the enhancement coefficient gamma.
Abstract: Freak waves are very large, rare events in a random ocean wave train. Here we study their generation in a random sea state characterized by the Joint North Sea Wave Project spectrum. We assume, to cubic order in nonlinearity, that the wave dynamics are governed by the nonlinear Schrodinger (NLS) equation. We show from extensive numerical simulations of the NLS equation how freak waves in a random sea state are more likely to occur for large values of the Phillips parameter alpha and the enhancement coefficient gamma. Comparison with linear simulations is also reported.

487 citations

Journal ArticleDOI
TL;DR: It is argued that mobile phone data, when used properly and carefully, represents a critical arsenal of tools for supporting public health actions across early-, middle-, and late-stage phases of the COVID-19 pandemic.
Abstract: The coronavirus 2019–2020 pandemic (COVID-19) poses unprecedented challenges for governments and societies around the world ( 1 ). Nonpharmaceutical interventions have proven to be critical for delaying and containing the COVID-19 pandemic ( 2 – 6 ). These include testing and tracing, bans on large gatherings, nonessential business and school and university closures, international and domestic mobility restrictions and physical isolation, and total lockdowns of regions and countries. Decision-making and evaluation or such interventions during all stages of the pandemic life cycle require specific, reliable, and timely data not only about infections but also about human behavior, especially mobility and physical copresence. We argue that mobile phone data, when used properly and carefully, represents a critical arsenal of tools for supporting public health actions across early-, middle-, and late-stage phases of the COVID-19 pandemic. Seminal work on human mobility has shown that aggregate and (pseudo-)anonymized mobile phone data can assist the modeling of the geographical spread of epidemics ( 7 – 11 ). Thus, researchers and governments have started to collaborate with private companies, most notably mobile network operators and location intelligence companies, to estimate the effectiveness of control measures in a number of countries, including Austria, Belgium, Chile, China, Germany, France, Italy, Spain, United Kingdom, and the United States ( 12 – 21 ). There is, however, little coordination or information exchange between these national or even regional initiatives ( 22 ). Although ad hoc mechanisms leveraging mobile phone data can be effectively (but not easily) developed at the local or national level, regional or even global collaborations seem to be much more difficult given the number of actors, the range of interests and priorities, the variety of legislations concerned, and the need to protect civil liberties. The global scale and spread of the COVID-19 pandemic highlight the need for a more harmonized or coordinated approach. In the …

487 citations

Journal ArticleDOI
TL;DR: Evidence is provided for a major early onset FAD locus on the long arm of chromosome 14 near the markers D14S43 and D 14S53 and it is suggested that the inheritance of FAD may be more complex than had initially been suspected.
Abstract: Familial Alzheimer's disease (FAD) has been shown to be genetically heterogeneous, with a very small proportion of early onset pedigrees being associated with mutations in the amyloid precursor protein (APP) gene on chromosome 21, and some late onset pedigrees showing associations with markers on chromosome 19. We now provide evidence for a major early onset FAD locus on the long arm of chromosome 14 near the markers D14S43 and D14S53 (multipoint lod score z = 23.4) and suggest that the inheritance of FAD may be more complex than had initially been suspected.

485 citations

Journal ArticleDOI
11 May 1989-Nature
TL;DR: It is shown that p190 is indistinguishable from the protein encoded by the c-met protooncogene and that the αβ-subunit structure is conserved in other human cell lines, and the high level of p190 found in the GTL-16 cell line is accompanied by amplification and overexpression of c- Met.
Abstract: Growth factor receptors with protein tyrosine kinase activity are central to the control of proliferation of both normal and malignant cells. Using anti-phosphotyrosine antibodies, we have previously identified a transmembrane glycoprotein with abnormally high protein tyrosine kinase activity in a human gastric tumour cell line (GTL-16). Electrophoresis under non-reducing conditions revealed that this kinase (relative molecular mass 145,000 (145 K)) is disulphide-linked to a 50K chain in an alpha beta-complex of 190K (p190). From its novel two-chain structure, we deduced that p190 was the prototype of a new class of tyrosine kinase receptors. We now show that p190 is indistinguishable from the protein encoded by the c-met proto-oncogene and that the alpha beta-subunit structure is conserved in other human cell lines. We also show that the high level of p190 found in the GTL-16 cell line is accompanied by amplification and overexpression of c-met. This provides the first example of a functional alteration of c-met in a human tumour cell line.

485 citations


Authors

Showing all 30045 results

NameH-indexPapersCitations
Michael Grätzel2481423303599
Lewis C. Cantley196748169037
Kenneth C. Anderson1781138126072
Elio Riboli1581136110499
Giacomo Bruno1581687124368
Silvia Franceschi1551340112504
Thomas E. Starzl150162591704
Paolo Boffetta148145593876
Marco Costa1461458105096
Pier Paolo Pandolfi14652988334
Andrew Ivanov142181297390
Chiara Mariotti141142698157
Tomas Ganz14148073316
Jean-Pierre Changeux13867276462
Dong-Chul Son138137098686
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023202
2022623
20215,733
20205,428
20194,544
20184,233