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Institution

Sapienza University of Rome

EducationRome, Lazio, Italy
About: Sapienza University of Rome is a education organization based out in Rome, Lazio, Italy. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 62002 authors who have published 155468 publications receiving 4397244 citations. The organization is also known as: La Sapienza & Università La Sapienza di Roma.


Papers
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Journal ArticleDOI
TL;DR: In this paper, a second-order accurate, highly efficient method is developed for simulating unsteady three-dimensional incompressible flows in complex geometries, which is achieved by using boundary body forces that allow the imposition of the boundary conditions on a given surface not coinciding with the computational grid.

1,643 citations

Journal ArticleDOI
David Capper1, David Capper2, David Capper3, David T.W. Jones2  +168 moreInstitutions (54)
22 Mar 2018-Nature
TL;DR: This work presents a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and shows that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods.
Abstract: Accurate pathological diagnosis is crucial for optimal management of patients with cancer. For the approximately 100 known tumour types of the central nervous system, standardization of the diagnostic process has been shown to be particularly challenging-with substantial inter-observer variability in the histopathological diagnosis of many tumour types. Here we present a comprehensive approach for the DNA methylation-based classification of central nervous system tumours across all entities and age groups, and demonstrate its application in a routine diagnostic setting. We show that the availability of this method may have a substantial impact on diagnostic precision compared to standard methods, resulting in a change of diagnosis in up to 12% of prospective cases. For broader accessibility, we have designed a free online classifier tool, the use of which does not require any additional onsite data processing. Our results provide a blueprint for the generation of machine-learning-based tumour classifiers across other cancer entities, with the potential to fundamentally transform tumour pathology.

1,620 citations

Journal ArticleDOI
TL;DR: Traces as mentioned in this paper is a graph isomorphism algorithm based on the refinement-individualization paradigm, and it is implemented in several of the key implementations of the program nauty.

1,602 citations

Journal ArticleDOI
B. P. Abbott1, Richard J. Abbott1, T. D. Abbott2, Fausto Acernese3  +1235 moreInstitutions (132)
TL;DR: This analysis expands upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars.
Abstract: On 17 August 2017, the LIGO and Virgo observatories made the first direct detection of gravitational waves from the coalescence of a neutron star binary system. The detection of this gravitational-wave signal, GW170817, offers a novel opportunity to directly probe the properties of matter at the extreme conditions found in the interior of these stars. The initial, minimal-assumption analysis of the LIGO and Virgo data placed constraints on the tidal effects of the coalescing bodies, which were then translated to constraints on neutron star radii. Here, we expand upon previous analyses by working under the hypothesis that both bodies were neutron stars that are described by the same equation of state and have spins within the range observed in Galactic binary neutron stars. Our analysis employs two methods: the use of equation-of-state-insensitive relations between various macroscopic properties of the neutron stars and the use of an efficient parametrization of the defining function pðρÞ of the equation of state itself. From the LIGO and Virgo data alone and the first method, we measure the two neutron star radii as R1 ¼ 10.8 þ2.0 −1.7 km for the heavier star and R2 ¼ 10.7 þ2.1 −1.5 km for the lighter star at the 90% credible level. If we additionally require that the equation of state supports neutron stars with masses larger than 1.97 M⊙ as required from electromagnetic observations and employ the equation-of-state parametrization, we further constrain R1 ¼ 11.9 þ1.4 −1.4 km and R2 ¼ 11.9 þ1.4 −1.4 km at the 90% credible level. Finally, we obtain constraints on pðρÞ at supranuclear densities, with pressure at twice nuclear saturation density measured at 3.5 þ2.7 −1.7 × 1034 dyn cm−2 at the 90% level.

1,595 citations

Journal ArticleDOI
TL;DR: This presents the first test of the Ruelle principle on a many particle system far from equilibrium, and a specific prediction, obtained without the need to construct explicitly the SRB itself, is shown to be in agreement with a recent computer experiment on a strongly sheared fluid.
Abstract: Ruelle`s principle for turbulence leading to what is usually called the Sinai-Ruelle-Bowen (SRB) distribution is applied to the statistical mechanics of many particle systems in nonequilibrium stationary states. A specific prediction, obtained without the need to construct explicitly the SRB itself, is shown to be in agreement with a recent computer experiment on a strongly sheared fluid. This presents the first test of the principle on a many particle system far from equilibrium. A possible application to fluid mechanics is also discussed.

1,587 citations


Authors

Showing all 62745 results

NameH-indexPapersCitations
Charles A. Dinarello1901058139668
Gregory Y.H. Lip1693159171742
Peter A. R. Ade1621387138051
H. Eugene Stanley1541190122321
Suvadeep Bose154960129071
P. de Bernardis152680117804
Bart Staels15282486638
Alessandro Melchiorri151674116384
Andrew H. Jaffe149518110033
F. Piacentini149531108493
Subir Sarkar1491542144614
Albert Bandura148255276143
Carlo Rovelli1461502103550
Robert C. Gallo14582568212
R. Kowalewski1431815135517
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023405
20221,106
20219,796
20209,753
20198,332
20187,615