232 research outputs found

    A C-Function For Non-Supersymmetric Attractors

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    We present a c-function for spherically symmetric, static and asymptotically flat solutions in theories of four-dimensional gravity coupled to gauge fields and moduli. The c-function is valid for both extremal and non-extremal black holes. It monotonically decreases from infinity and in the static region acquires its minimum value at the horizon, where it equals the entropy of the black hole. Higher dimensional cases, involving pp-form gauge fields, and other generalisations are also discussed.Comment: References adde

    Centrality dependence of charged particle production at large transverse momentum in Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm{NN}}} = 2.76 TeV

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    The inclusive transverse momentum (pTp_{\rm T}) distributions of primary charged particles are measured in the pseudo-rapidity range η<0.8|\eta|<0.8 as a function of event centrality in Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm{NN}}}=2.76 TeV with ALICE at the LHC. The data are presented in the pTp_{\rm T} range 0.15<pT<500.15<p_{\rm T}<50 GeV/cc for nine centrality intervals from 70-80% to 0-5%. The Pb-Pb spectra are presented in terms of the nuclear modification factor RAAR_{\rm{AA}} using a pp reference spectrum measured at the same collision energy. We observe that the suppression of high-pTp_{\rm T} particles strongly depends on event centrality. In central collisions (0-5%) the yield is most suppressed with RAA0.13R_{\rm{AA}}\approx0.13 at pT=6p_{\rm T}=6-7 GeV/cc. Above pT=7p_{\rm T}=7 GeV/cc, there is a significant rise in the nuclear modification factor, which reaches RAA0.4R_{\rm{AA}} \approx0.4 for pT>30p_{\rm T}>30 GeV/cc. In peripheral collisions (70-80%), the suppression is weaker with RAA0.7R_{\rm{AA}} \approx 0.7 almost independently of pTp_{\rm T}. The measured nuclear modification factors are compared to other measurements and model calculations.Comment: 17 pages, 4 captioned figures, 2 tables, authors from page 12, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/284

    Measurement of charm production at central rapidity in proton-proton collisions at s=2.76\sqrt{s} = 2.76 TeV

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    The pTp_{\rm T}-differential production cross sections of the prompt (B feed-down subtracted) charmed mesons D0^0, D+^+, and D+^{*+} in the rapidity range y<0.5|y|<0.5, and for transverse momentum 1<pT<121< p_{\rm T} <12 GeV/cc, were measured in proton-proton collisions at s=2.76\sqrt{s} = 2.76 TeV with the ALICE detector at the Large Hadron Collider. The analysis exploited the hadronic decays D0^0 \rightarrow Kπ\pi, D+^+ \rightarrow Kππ\pi\pi, D+^{*+} \rightarrow D0π^0\pi, and their charge conjugates, and was performed on a Lint=1.1L_{\rm int} = 1.1 nb1^{-1} event sample collected in 2011 with a minimum-bias trigger. The total charm production cross section at s=2.76\sqrt{s} = 2.76 TeV and at 7 TeV was evaluated by extrapolating to the full phase space the pTp_{\rm T}-differential production cross sections at s=2.76\sqrt{s} = 2.76 TeV and our previous measurements at s=7\sqrt{s} = 7 TeV. The results were compared to existing measurements and to perturbative-QCD calculations. The fraction of cdbar D mesons produced in a vector state was also determined.Comment: 20 pages, 5 captioned figures, 4 tables, authors from page 15, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/307

    Anisotropic flow of charged hadrons, pions and (anti-)protons measured at high transverse momentum in Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm NN}}=2.76 TeV

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    The elliptic, v2v_2, triangular, v3v_3, and quadrangular, v4v_4, azimuthal anisotropic flow coefficients are measured for unidentified charged particles, pions and (anti-)protons in Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm NN}} = 2.76 TeV with the ALICE detector at the Large Hadron Collider. Results obtained with the event plane and four-particle cumulant methods are reported for the pseudo-rapidity range η<0.8|\eta|<0.8 at different collision centralities and as a function of transverse momentum, pTp_{\rm T}, out to pT=20p_{\rm T}=20 GeV/cc. The observed non-zero elliptic and triangular flow depends only weakly on transverse momentum for pT>8p_{\rm T}>8 GeV/cc. The small pTp_{\rm T} dependence of the difference between elliptic flow results obtained from the event plane and four-particle cumulant methods suggests a common origin of flow fluctuations up to pT=8p_{\rm T}=8 GeV/cc. The magnitude of the (anti-)proton elliptic and triangular flow is larger than that of pions out to at least pT=8p_{\rm T}=8 GeV/cc indicating that the particle type dependence persists out to high pTp_{\rm T}.Comment: 16 pages, 5 captioned figures, authors from page 11, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/186

    Particle-yield modification in jet-like azimuthal di-hadron correlations in Pb-Pb collisions at sNN\sqrt{s_{\rm NN}} = 2.76 TeV

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    The yield of charged particles associated with high-pTp_{\rm T} trigger particles (8<pT<158 < p_{\rm T} < 15 GeV/cc) is measured with the ALICE detector in Pb-Pb collisions at sNN\sqrt{s_{\rm NN}} = 2.76 TeV relative to proton-proton collisions at the same energy. The conditional per-trigger yields are extracted from the narrow jet-like correlation peaks in azimuthal di-hadron correlations. In the 5% most central collisions, we observe that the yield of associated charged particles with transverse momenta pT>3p_{\rm T}> 3 GeV/cc on the away-side drops to about 60% of that observed in pp collisions, while on the near-side a moderate enhancement of 20-30% is found.Comment: 15 pages, 2 captioned figures, 1 table, authors from page 10, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/350

    Suppression of charged particle production at large transverse momentum in central Pb-Pb collisions at sNN=2.76\sqrt{s_{\rm NN}} = 2.76 TeV

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    Inclusive transverse momentum spectra of primary charged particles in Pb-Pb collisions at sNN\sqrt{s_{_{\rm NN}}} = 2.76 TeV have been measured by the ALICE Collaboration at the LHC. The data are presented for central and peripheral collisions, corresponding to 0-5% and 70-80% of the hadronic Pb-Pb cross section. The measured charged particle spectra in η<0.8|\eta|<0.8 and 0.3<pT<200.3 < p_T < 20 GeV/cc are compared to the expectation in pp collisions at the same sNN\sqrt{s_{\rm NN}}, scaled by the number of underlying nucleon-nucleon collisions. The comparison is expressed in terms of the nuclear modification factor RAAR_{\rm AA}. The result indicates only weak medium effects (RAAR_{\rm AA} \approx 0.7) in peripheral collisions. In central collisions, RAAR_{\rm AA} reaches a minimum of about 0.14 at pT=6p_{\rm T}=6-7GeV/cc and increases significantly at larger pTp_{\rm T}. The measured suppression of high-pTp_{\rm T} particles is stronger than that observed at lower collision energies, indicating that a very dense medium is formed in central Pb-Pb collisions at the LHC.Comment: 15 pages, 5 captioned figures, 3 tables, authors from page 10, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/98

    Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review

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    [EN] Purpose: To systematically review evidence regarding the association of multi-parametric biomarkers with clinical outcomes and their capacity to explain relevant subcompartments of gliomas. Materials and Methods: Scopus database was searched for original journal papers from January 1st, 2007 to February 20th , 2017 according to PRISMA. Four hundred forty-nine abstracts of papers were reviewed and scored independently by two out of six authors. Based on those papers we analyzed associations between biomarkers, subcompartments within the tumor lesion, and clinical outcomes. From all the articles analyzed, the twenty-seven papers with the highest scores were highlighted to represent the evidence about MR imaging biomarkers associated with clinical outcomes. Similarly, eighteen studies defining subcompartments within the tumor region were also highlighted to represent the evidence of MR imaging biomarkers. Their reports were critically appraised according to the QUADAS-2 criteria. Results: It has been demonstrated that multi-parametric biomarkers are prepared for surrogating diagnosis, grading, segmentation, overall survival, progression-free survival, recurrence, molecular profiling and response to treatment in gliomas. Quantifications and radiomics features obtained from morphological exams (T1, T2, FLAIR, T1c), PWI (including DSC and DCE), diffusion (DWI, DTI) and chemical shift imaging (CSI) are the preferred MR biomarkers associated to clinical outcomes. Subcompartments relative to the peritumoral region, invasion, infiltration, proliferation, mass effect and pseudo flush, relapse compartments, gross tumor volumes, and high-risk regions have been defined to characterize the heterogeneity. For the majority of pairwise cooccurrences, we found no evidence to assert that observed co-occurrences were significantly different from their expected co-occurrences (Binomial test with False Discovery Rate correction, alpha=0.05). The co-occurrence among terms in the studied papers was found to be driven by their individual prevalence and trends in the literature. Conclusion: Combinations of MR imaging biomarkers from morphological, PWI, DWI and CSI exams have demonstrated their capability to predict clinical outcomes in different management moments of gliomas. Whereas morphologic-derived compartments have been mostly studied during the last ten years, new multi-parametric MRI approaches have also been proposed to discover specific subcompartments of the tumors. MR biomarkers from those subcompartments show the local behavior within the heterogeneous tumor and may quantify the prognosis and response to treatment of gliomas.This work was supported by the Spanish Ministry for Investigation, Development and Innovation project with identification number DPI2016-80054-R.Oltra-Sastre, M.; Fuster García, E.; Juan -Albarracín, J.; Sáez Silvestre, C.; Perez-Girbes, A.; Sanz-Requena, R.; Revert-Ventura, A.... (2019). Multi-parametric MR Imaging Biomarkers Associated to Clinical Outcomes in Gliomas: A Systematic Review. Current Medical Imaging Reviews. 15(10):933-947. https://doi.org/10.2174/1573405615666190109100503S9339471510Louis D.N.; Perry A.; Reifenberger G.; The 2016 world health organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 2016,131(6),803-820Ostrom Q.T.; Gittleman H.; Fulop J.; CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008-2012. Neuro-oncol 2015,17(Suppl. 4),iv1-iv62Yachida S.; Jones S.; Bozic I.; Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010,467(7319),1114-1117Gerlinger M.; Rowan A.J.; Horswell S.; Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med 2012,366(10),883-892Sottoriva A.; Spiteri I.; Piccirillo S.G.M.; Intratumor heterogeneityin human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 2013,110(10),4009-4014Whiting P.F.; Rutjes A.W.; Westwood M.E.; QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med 2011,155(8),529-536Stupp R.; Mason W.P.; van den Bent M.J.; Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 2005,352(10),987-996Ponte K.F.; Berro D.H.; Collet S.; In vivo relationship between hypoxia and angiogenesis in human glioblastoma: a multimodal imaging study. J Nucl Med 2017,58(10),1574-1579Pope W.B.; Kim H.J.; Huo J.; Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009,252(1),182-189Mörén L.; Bergenheim A.T.; Ghasimi S.; Brännström T.; Johansson M.; Antti H.; Metabolomic screening of tumor tissue and serum in glioma patients reveals diagnostic and prognostic information. Metabolites 2015,5(3),502-520Prager A.J.; Martinez N.; Beal K.; Omuro A.; Zhang Z.; Young R.J.; Diffusion and perfusion MRI to differentiate treatment-related changes including pseudoprogression from recurrent tumors in high-grade gliomas with histopathologic evidence. AJNR Am J Neuroradiol 2015,36(5),877-885Kickingereder P.; Burth S.; Wick A.; Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016,280(3),880-889Yoo R-E.; Choi S.H.; Cho H.R.; Tumor blood flow from arterial spin labeling perfusion MRI: a key parameter in distinguishing high-grade gliomas from primary cerebral lymphomas, and in predicting genetic biomarkers in high-grade gliomas. J Magn Reson Imaging 2013,38(4),852-860Liberman G.; Louzoun Y.; Aizenstein O.; Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma. Eur J Radiol 2013,82(2),e87-e94Ramadan S.; Andronesi O.C.; Stanwell P.; Lin A.P.; Sorensen A.G.; Mountford C.E.; Use of in vivo two-dimensional MR spectroscopy to compare the biochemistry of the human brain to that of glioblastoma. Radiology 2011,259(2),540-549Xintao H.; Wong K.K.; Young G.S.; Guo L.; Wong S.T.; Support vector machine multi-parametric MRI identification of pseudoprogression from tumor recurrence in patients with resected glioblastoma. J Magn Reson Imaging 2011,33(2),296Ingrisch M.; Schneider M.J.; Nörenberg D.; Radiomic Analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Invest Radiol 2017,52(6),360-366Ulyte A.; Katsaros V.K.; Liouta E.; Prognostic value of preoperative dynamic contrast-enhanced MRI perfusion parameters for high-grade glioma patients. Neuroradiology 2016,58(12),1197-1208O’Neill A.F.; Qin L.; Wen P.Y.; de Groot J.F.; Van den Abbeele A.D.; Yap J.T.; Demonstration of DCE-MRI as an early pharmacodynamic biomarker of response to VEGF Trap in glioblastoma. J Neurooncol 2016,130(3),495-503Kickingereder P.; Bonekamp D.; Nowosielski M.; Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional mr imaging features. Radiology 2016,281(3),907-918Roberto S-R.; Antonio R-V.; Luis M-B.; Angel A-B.; Gracián G-M.; Quantitative mr perfusion parameters related to survival time in high-grade gliomas. European Radiology 2013,23(12),3456-3465Jain R.; Poisson L.; Narang J.; Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 2013,267(1),212-220Fathi K.A.; Mohseni M.; Rezaei S.; Bakhshandehpour G.; Saligheh R.H.; Multi-parametric (ADC/PWI/T2-W) image fusion approach for accurate semi-automatic segmentation of tumorous regions in glioblastoma multiforme. MAGMA 2015,28(1),13-22Caulo M.; Panara V.; Tortora D.; Data-driven grading of brain gliomas: a multiparametric MR imaging study. Radiology 2014,272(2),494-503Alexiou G.A.; Zikou A.; Tsiouris S.; Comparison of diffusion tensor, dynamic susceptibility contrast MRI and (99m)Tc-Tetrofosmin brain SPECT for the detection of recurrent high-grade glioma. Magn Reson Imaging 2014,32(7),854-859Van Cauter S.; De Keyzer F.; Sima D.M.; Integrating diffusion kurtosis imaging, dynamic susceptibility-weighted contrast-enhanced MRI, and short echo time chemical shift imaging for grading gliomas. Neuro-oncol 2014,16(7),1010-1021Seeger A.; Braun C.; Skardelly M.; Comparison of three different MR perfusion techniques and MR spectroscopy for multiparametric assessment in distinguishing recurrent high-grade gliomas from stable disease. Acad Radiol 2013,20(12),1557-1565Chawalparit O.; Sangruchi T.; Witthiwej T.; Diagnostic performance of advanced mri in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 2013,96(10),1365-1373Li Y.; Lupo J.M.; Parvataneni R.; Survival analysis in patients with newly diagnosed glioblastoma using pre- and postradiotherapy MR spectroscopic imaging. Neuro-oncol 2013,15(5),607-617Shankar J.J.S.; Woulfe J.; Silva V.D.; Nguyen T.B.; Evaluation of perfusion CT in grading and prognostication of high-grade gliomas at diagnosis: a pilot study. AJR Am J Roentgenol 2013,200(5)Zinn P.O.; Mahajan B.; Sathyan P.; Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS One 2011,6(10)Matsusue E.; Fink J.R.; Rockhill J.K.; Ogawa T.; Maravilla K.R.; Distinction between glioma progression and post-radiation change by combined physiologic MR imaging. Neuroradiology 2010,52(4),297-306Juan-Albarracín J.; Fuster-Garcia E.; Manjón J.V.; Automated glioblastoma segmentation based on a multiparametric structured unsupervised classification. PLoS One 2015,10(5)Itakura H.; Achrol A.S.; Mitchell L.A.; Magnetic resonance image features identify glioblastoma phenotypic subtypes with distinct molecular pathway activities. Sci Transl Med 2015,7(303)Ion-Margineanu A.; Van Cauter S.; Sima D.M.; Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients. BioMed Res Int 2015,2015Durst C.R.; Raghavan P.; Shaffrey M.E.; Multimodal MR imaging model to predict tumor infiltration in patients with gliomas. Neuroradiology 2014,56(2),107-115Yoon J.H.; Kim J.H.; Kang W.J.; Grading of cerebral glioma with multi-parametric MR Imaging and 18F-FDG-PET: concordance and accuracy. European Radiol 2014,24(2),380-389Demerath T.; Simon-Gabriel C.P.; Kellner E.; Mesoscopic imaging of glioblastomas: are diffusion, perfusion and spectroscopic measures influenced by the radiogenetic phenotype? Neuroradiol J 2017,30(1),36-47Qin L.; Li X.; Stroiney A.; Advanced MRI assessment to predict benefit of anti-programmed cell death 1 protein immunotherapy response in patients with recurrent glioblastoma. Neuroradiology 2017,59(2),135-145Boult J.K.R.; Borri M.; Jury A.; Investigating intracranial tumour growth patterns with multiparametric MRI incorporating Gd-DTPA and USPIO-enhanced imaging. NMR Biomed 2016,29(11),1608-1617Server A.; Kulle B.; Gadmar Ø.B.; Josefsen R.; Kumar T.; Nakstad P.H.; Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 2011,80(2),462-470Chang P.D.; Chow D.S.; Yang P.H.; Filippi C.G.; Lignelli A.; Predicting glioblastoma recurrence by early changes in the apparent diffusion coefficient value and signal intensity on FLAIR images. AJR Am J Roentgenol 2017,208(1),57-65Yi C.; Shangjie R.; Volume of high-risk intratumoralsubregions at multi-parametric MR imaging predicts overall survival and complements molecular analysis of glioblastoma. Eur Radiol 2017,27,3583-3592Khalifa J.; Tensaouti F.; Chaltiel L.; Identification of a candidate biomarker from perfusion MRI to anticipate glioblastoma progression after chemoradiation. Eur Radiol 2016,26(11),4194-4203Prateek P.; Jay P.; Partovi S.; Madabhushi A.; Tiwari P.; Radiomic features from the peritumoral brain parenchyma on treatment-naïve multi-parametric MR imaging predict long versus short-term survival in glioblastomamultiforme: preliminary findings. Eur Radiol 2017,27(10),4188-4197Lemasson B.; Chenevert T.L.; Lawrence T.S.; Impact of perfusion map analysis on early survival prediction accuracy in glioma patients. Transl Oncol 2013,6(6),766-774Inano R.; Oishi N.; Kunieda T.; Visualization of heterogeneity and regional grading of gliomas by multiple features using magnetic resonance-based clustered images. Sci Rep 2016,6,30344Delgado-Goñi T.; Ortega-Martorell S.; Ciezka M.; MRSI-based molecular imaging of therapy response to temozolomide in preclinical glioblastoma using source analysis. NMR Biomed 2016,29(6),732-743Cui Y.; Tha K.K.; Terasaka S.; Prognostic imaging biomarkers in glioblastoma: development and independent validation on the basis of multiregion and quantitative analysis of MR images. Radiology 2016,278(2),546-553Price S.J.; Young A.M.H.; Scotton W.J.; Multimodal MRI can identify perfusion and metabolic changes in the invasive margin of glioblastomas. J Magn Reson Imaging 2016,43(2),487-494Sauwen N.; Acou M.; Van Cauter S.; Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI. Neuroimage Clin 2016,12,753-764Jena A.; Taneja S.; Gambhir A.; Glioma recurrence versus radiation necrosis: single-session multiparametric approach using simultaneous O-(2-18F-Fluoroethyl)-L-Tyrosine PET/MRI. Clin Nucl Med 2016,41(5),e228-e236Kim H.S.; Goh M.J.; Kim N.; Choi C.G.; Kim S.J.; Kim J.H.; Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 2014,273(3),831-843Christoforidis G.A.; Yang M.; Abduljalil A.; “Tumoral pseudoblush” identified within gliomas at high-spatial-resolution ultrahigh-field-strength gradient-echo MR imaging corresponds to microvascularity at stereotactic biopsy. Radiology 2012,264(1),210-217Wang S.; Kim S.; Chawla S.; Differentiation between glioblastomas, solitary brain metastases, and primary cerebral lymphomas using diffusion tensor and dynamic susceptibility contrast-enhanced MR imaging. AJNR Am J Neuroradiol 2011,32(3),507-514Hanahan D.; Weinberg R.A.; Hallmarks of cancer: the next generation. Cell 2011,144(5),646-674Macdonald D.R.; Cascino T.L.; Schold S.C.; Cairncross J.G.; Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990,8(7),1277-1280Wen P.Y.; Macdonald D.R.; Reardon D.A.; Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010,28(11),1963-1972Sorensen A.G.; Batchelor T.T.; Wen P.Y.; Zhang W-T.; Jain R.K.; Response criteria for glioma. Nat Clin Pract Oncol 2008,5(11),634-644Rosenkrantz A.B.; Friedman K.; Chandarana H.; Current status of hybrid PET/MRI in oncologic imaging. AJR Am J Roentgenol 2016,206(1),162-172Castiglioni I.; Gallivanone F.; Canevari C.; Hybrid PET/MRI for In vivo imaging of cancer: current clinical experiences and recent advances. Curr Med Imaging 2016,12,106Mainta I.C.; Perani D.; Delattre B.M.A.; FDG PET/MR imaging in major neurocognitive disorders. Curr Alzheimer Res 2017,14,186-197Marner L.; Henriksen O.M.; Lundemann M.; Larsen V.A.; Law I.; Clinical PET/MRI in neurooncology: opportunities and challenges from a single-institution perspective. Clin Transl Imaging 2017,5(2),135-149R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria; 2015. Available from: https://www.R-project.org

    Transverse momentum spectra of charged particles in proton-proton collisions at s=900\sqrt{s} = 900 GeV with ALICE at the LHC

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    The inclusive charged particle transverse momentum distribution is measured in proton-proton collisions at s=900\sqrt{s} = 900 GeV at the LHC using the ALICE detector. The measurement is performed in the central pseudorapidity region (η<0.8)(|\eta|<0.8) over the transverse momentum range 0.15<pT<100.15<p_{\rm T}<10 GeV/cc. The correlation between transverse momentum and particle multiplicity is also studied. Results are presented for inelastic (INEL) and non-single-diffractive (NSD) events. The average transverse momentum for η<0.8|\eta|<0.8 is <pT>INEL=0.483±0.001\left<p_{\rm T}\right>_{\rm INEL}=0.483\pm0.001 (stat.) ±0.007\pm0.007 (syst.) GeV/cc and \left_{\rm NSD}=0.489\pm0.001 (stat.) ±0.007\pm0.007 (syst.) GeV/cc, respectively. The data exhibit a slightly larger <pT>\left<p_{\rm T}\right> than measurements in wider pseudorapidity intervals. The results are compared to simulations with the Monte Carlo event generators PYTHIA and PHOJET.Comment: 20 pages, 8 figures, 2 tables, published version, figures at http://aliceinfo.cern.ch/ArtSubmission/node/390

    Centrality Dependence Of The Pseudorapidity Density Distribution For Charged Particles In Pb-pb Collisions At √snn=2.76tev

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