Bose-Einstein Correlations in Multihadron Events at LEP

Bose-Einstein correlations in pairs of identical particles were analyzed in e+ e- multihadron annihilations at ~91.2 GeV at LEP. The first studies involved identical charged pions and the emitting source size was determined. Then the study of charged kaons suggested that the radius depends on the mass of the emitted particles. Subsequenty the dependence of the source radius on the event multiplicity was analyzed. The study of the correlations in neutral pions and neutral kaons extended these concepts to neutral particles. The shape of the source was analyzed in 3 dimensions and was found not to be spherically symmetric. In recent studies at LEP the correlations were analyzed in intervals of the average pair transverse momentum and of the pair rapidity to study the correlations between the pion production points and their momenta (position-momentum correlations). The latest e+ e- data are consistent with an expanding source.Comment: 8 pages, 10 eps figures. Invited paper at the ``Ninth Workshop on Non Perturbative QCD'', Institut d'Astrophysique de Paris, Paris, France, 4-8 June 200

Lower mass normalization of the stellar initial mass function for dense massive early-type galaxies at z ~ 1.4

This paper aims at understanding if the normalization of the stellar initial mass function (IMF) of massive early-type galaxies (ETGs) varies with cosmic time and/or with mean stellar mass density Sigma (M*/2\pi Re^2). For this purpose we collected a sample of 18 dense (Sigma>2500 M_sun/pc^2) ETGs at 1.2<z<1.6 with available velocity dispersion sigma_e. We have constrained their mass-normalization by comparing their true stellar masses (M_true) derived through virial theorem, hence IMF independent, with those inferred through the fit of the photometry assuming a reference IMF (M_ref). Adopting the virial estimator as proxy of the true stellar mass, we have assumed for these ETGs zero dark matter (DM). However, dynamical models and numerical simulations of galaxy evolution have shown that the DM fraction within Re in dense high-z ETGs is negligible. We have considered the possible bias of virial theorem in recovering the total masses and have shown that for dense ETGs the virial masses are in agreement with those derived through more sophisticated dynamical models. The variation of the parameter Gamma = M_true/M_ref with sigma_e shows that, on average, dense ETGs at = 1.4 follow the same IMF-sigma_e trend of typical local ETGs, but with a lower mass-normalization. Nonetheless, once the IMF-sigma_e trend we have found for high-z dense ETGs is compared with that of local ETGs with similar Sigma and sigma_e, they turn out to be consistent. The similarity between the IMF-sigma_e trends of dense high-z and low-z ETGs over 9 Gyr of evolution and their lower mass-normalization with respect to the mean value of local ETGs suggest that, independently on formation redshift, the physical conditions characterizing the formation of a dense spheroid lead to a mass spectrum of new formed stars with an higher ratio of high- to low-mass stars with respect to the IMF of normal local ETGs.Comment: 9 pages, 4 figures, accepted for pubblication in A&A, updated to match final journal versio

IORT with mobile linacs: the Italian experience

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The population of early-type galaxies: how it evolves with time and how it differs from passive and late-type galaxies

The aim of our analysis is twofold. On the one hand we are interested in addressing whether a sample of ETGs morphologically selected differs from a sample of passive galaxies in terms of galaxy statistics. On the other hand we study how the relative abundance of galaxies, the number density and the stellar mass density for different morphological types change over the redshift range 0.6<z<2.5. From the 1302 galaxies brighter than Ks=22 selected from the GOODS-MUSIC catalogue, we classified the ETGs on the basis of their morphology and the passive galaxies on the basis of their sSFR. We proved how the definition of passive galaxy depends on the IMF adopted in the models and on the assumed sSFR threshold. We find that ETGs cannot be distinguished from the other morphological classes on the basis of their low sSFR, irrespective of the IMF adopted in the models. Using the sample of 1302 galaxies morphologically classified into spheroidal galaxies (ETGs) and not spheroidal galaxies (LTGs), we find that their fractions are constant over the redshift range 0.6<z<2.5 (20-30% ETGs vs 70-80% LTGs). However, at z<1 these fractions change among the population of the most massive (M*>=10^(11) M_sol) galaxies, with the fraction of massive ETGs rising up to 40% and the fraction of massive LTGs decreasing down to 60%. Moreover, we find that the number density and the stellar mass density of the whole population of massive galaxies increase almost by a factor of ~10 between 0.6<z<2.5, with a faster increase of these densities for the ETGs than for the LTGs. Finally, we find that the number density of the highest-mass galaxies (M*>3-4x10^(11) M_sol) both ETGs and LTGs do not increase since z~2.5, contrary to the lower mass galaxies. This suggests that the population of the most massive galaxies formed at z>2.5-3 and that the assembly of such high-mass galaxies is not effective at lower redshift.Comment: 15 pages, 14 figures. Published in A&

The significance of GATA3 expression in breast cancer: a 10-year follow-up study.

GATA3 is a transcription factor closely associated with estrogen receptor alpha in breast carcinoma, with a potential prognostic utility. This study investigated the immunohistochemical expression of GATA3 in estrogen receptor alpha-positive and estrogen receptor alpha-negative breast carcinomas. One hundred sixty-six cases of invasive breast carcinomas with 10-year follow-up information were analyzed. Positive GATA3 and estrogen receptor alpha cases were defined as greater than 20% of cells staining. Time to cancer recurrence and time to death were analyzed with survival methods. Of 166 patients, 40 were estrogen receptor alpha negative and 121 estrogen receptor alpha positive. Thirty-eight (23%) recurrences and 51 (31%) deaths were observed. In final multivariable analyses, GATA3-positive tumors had about two thirds the recurrence risk of GATA3-negative tumors (hazard ratio = 0.65, P = .395) and comparable mortality risk (hazard ratio = 0.86, P = .730). In prespecified subgroup analyses, the protective effect of GATA3 expression was most pronounced among estrogen receptor alpha-positive patients who received tamoxifen (hazard ratio = 0.57 for recurrence and 0.68 for death). We found no statistically significant differences in recurrence or survival rates between GATA3-positive and GATA3-negative tumors. However, there was a suggestion of a modest-to-strong protective effect of GATA3 expression among estrogen receptor alpha-positive patients receiving hormone therapy

A fast - Monte Carlo toolkit on GPU for treatment plan dose recalculation in proton therapy

In the context of the particle therapy a crucial role is played by Treatment Planning Systems (TPSs), tools aimed to compute and optimize the tratment plan. Nowadays one of the major issues related to the TPS in particle therapy is the large CPU time needed. We developed a software toolkit (FRED) for reducing dose recalculation time by exploiting Graphics Processing Units (GPU) hardware. Thanks to their high parallelization capability, GPUs significantly reduce the computation time, up to factor 100 respect to a standard CPU running software. The transport of proton beams in the patient is accurately described through Monte Carlo methods. Physical processes reproduced are: Multiple Coulomb Scattering, energy straggling and nuclear interactions of protons with the main nuclei composing the biological tissues. FRED toolkit does not rely on the water equivalent translation of tissues, but exploits the Computed Tomography anatomical information by reconstructing and simulating the atomic composition of each crossed tissue. FRED can be used as an efficient tool for dose recalculation, on the day of the treatment. In fact it can provide in about one minute on standard hardware the dose map obtained combining the treatment plan, earlier computed by the TPS, and the current patient anatomic arrangement

Tremor in motor neuron disease may be central rather than peripheral in origin

BACKGROUND AND PURPOSE: Motor neuron disease (MND) refers to a spectrum of degenerative diseases affecting motor neurons. Recent clinical and post-mortem observations have revealed considerable variability in the phenotype. Rhythmic involuntary oscillations of the hands during action, resembling tremor, can occur in MND, but their pathophysiology has not yet been investigated. METHODS: A total of 120 consecutive patients with MND were screened for tremor. Twelve patients with action tremor and no other movement disorders were found. Ten took part in the study. Tremor was recorded bilaterally using surface electromyography (EMG) and triaxial accelerometer, with and without a variable weight load. Power spectra of rectified EMG and accelerometric signal were calculated. To investigate a possible cerebellar involvement, eyeblink classic conditioning was performed in five patients. RESULTS: Action tremor was present in about 10% of our population. All patients showed distal postural tremor of low amplitude and constant frequency, bilateral with a small degree of asymmetry. Two also showed simple kinetic tremor. A peak at the EMG and accelerometric recordings ranging from 4 to 12 Hz was found in all patients. Loading did not change peak frequency in either the electromyographic or accelerometric power spectra. Compared with healthy volunteers, patients had a smaller number of conditioned responses during eyeblink classic conditioning. CONCLUSIONS: Our data suggest that patients with MND can present with action tremor of a central origin, possibly due to a cerebellar dysfunction. This evidence supports the novel idea of MND as a multisystem neurodegenerative disease and that action tremor can be part of this condition

Ontology-Driven Food Category Classification in Images

The self-management of chronic diseases related to dietary habits includes the necessity of tracking what people eat. Most of the approaches proposed in the literature classify food pictures by labels describing the whole recipe. The main drawback of this kind of strategy is that a wrong prediction of the recipe leads to a wrong prediction of any ingredient of such a recipe. In this paper we present a multi-label food classification approach, exploiting deep neural networks, where each food picture is classified with labels describing the food categories of the ingredients in each recipe. The aim of our approach is to support the detection of food categories in order to detect which one might be dangerous for a user affected by chronic disease. Our approach relies on background knowledge where recipes, food categories, and their relatedness with chronic diseases are modeled within a state-of-the-art ontology. Experiments conducted on a new publicly released dataset demonstrated the effectiveness of the proposed approach with respect to state-of-the-art classification strategies