622 research outputs found

    pi^0 pi^0 Scattering Amplitudes and Phase Shifts Obtained by the pi^- P Charge Exchange Process

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    The results of the analysis of the pi^0 pi^0 scattering amplitudes obtained with pi^- P charge exchange reaction, pi^- P --> pi^0 pi^0 n, data at 9 GeV/c are presented. The pi^0 pi^0 scattering amplitudes show clear f_0(1370) and f_2(1270) signals in the S and D waves, respectively. The pi^0 pi^0 scattering phase shifts have been obtained below Kbar K threshold and been analyzed by the Interfering Amplitude method with introduction of negative background phases. The results show a S wave resonance, sigma. Its Breit-Wigner parameters are in good agreement with those of our previous analysis on the pi^+ pi^- phase shift data.Comment: 4 pages, 4 figures. Proceedings of the int. conf. Hadron'99 at Beijing, Aug. 1999. Presented for the collaboration of A.M.Ma, K.Takamatsu, M.Y.Ishida, S.Ishida, T.Ishida, T. Tsuru and H. Shimizu, and the E135 collaboration. For our activities on sigma, visit http://amaterasu.kek.jp/sigm

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    From sequences to cognitive structures : neurocomputational mechanisms

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    Ph. D. Thesis.Understanding how the brain forms representations of structured information distributed in time is a challenging neuroscientific endeavour, necessitating computationally and neurobiologically informed study. Human neuroimaging evidence demonstrates engagement of a fronto-temporal network, including ventrolateral prefrontal cortex (vlPFC), during language comprehension. Corresponding regions are engaged when processing dependencies between word-like items in Artificial Grammar (AG) paradigms. However, the neurocomputations supporting dependency processing and sequential structure-building are poorly understood. This work aimed to clarify these processes in humans, integrating behavioural, electrophysiological and computational evidence. I devised a novel auditory AG task to assess simultaneous learning of dependencies between adjacent and non-adjacent items, incorporating learning aids including prosody, feedback, delineated sequence boundaries, staged pre-exposure, and variable intervening items. Behavioural data obtained in 50 healthy adults revealed strongly bimodal performance despite these cues. Notably, however, reaction times revealed sensitivity to the grammar even in low performers. Behavioural and intracranial electrode data was subsequently obtained in 12 neurosurgical patients performing this task. Despite chance behavioural performance, time- and time-frequency domain electrophysiological analysis revealed selective responsiveness to sequence grammaticality in regions including vlPFC. I developed a novel neurocomputational model (VS-BIND: “Vector-symbolic Sequencing of Binding INstantiating Dependencies”), triangulating evidence to clarify putative mechanisms in the fronto-temporal language network. I then undertook multivariate analyses on the AG task neural data, revealing responses compatible with the presence of ordinal codes in vlPFC, consistent with VS-BIND. I also developed a novel method of causal analysis on multivariate patterns, representational Granger causality, capable of detecting flow of distinct representations within the brain. This alluded to top-down transmission of syntactic predictions during the AG task, from vlPFC to auditory cortex, largely in the opposite direction to stimulus encodings, consistent with predictive coding accounts. It finally suggested roles for the temporoparietal junction and frontal operculum during grammaticality processing, congruent with prior literature. This work provides novel insights into the neurocomputational basis of cognitive structure-building, generating hypotheses for future study, and potentially contributing to AI and translational efforts.Wellcome Trust, European Research Counci

    Improving protein secondary structure prediction using a simple k-mer model

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    Motivation: Some first order methods for protein sequence analysis inherently treat each position as independent. We develop a general framework for introducing longer range interactions. We then demonstrate the power of our approach by applying it to secondary structure prediction; under the independence assumption, sequences produced by existing methods can produce features that are not protein like, an extreme example being a helix of length 1. Our goal was to make the predictions from state of the art methods more realistic, without loss of performance by other measures

    NAFLD and liver transplantation: Current burden and expected challenges

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    Because of global epidemics of obesity and type 2 diabetes, the prevalence of non-alcoholic fatty liver disease (NAFLD) is increasing both in Europe and the United States, becoming one of the most frequent causes of chronic liver disease and predictably, one of the leading causes of liver transplantation both for end-stage liver disease and hepatocellular carcinoma. For most transplant teams around the world this will raise many challenges in terms of preand post-transplant management. Here we review the multifaceted impact of NAFLD on liver transplantation and will discuss: (1) NAFLD as a frequent cause of cryptogenic cirrhosis, end-stage chronic liver disease, and hepatocellular carcinoma; (2) prevalence of NAFLD as an indication for liver transplantation both in Europe and the United States; (3) the impact of NAFLD on the donor pool; (4) the access of NAFLD patients to liver transplantation and their management on the waiting list in regard to metabolic, renal and vascular comorbidities; (5) the prevalence and consequences of post-transplant metabolic syndrome, recurrent and de novo NAFLD; (6) the alternative management and therapeutic options to improve the long-term outcomes with particular emphasis on the correction and control of metabolic comorbidities
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