2,653 research outputs found
Isotope effects in the Hubbard-Holstein model within dynamical mean-field theory
We study the isotope effects arising from the coupling of correlated
electrons with dispersionless phonons by considering the Hubbard-Holstein model
at half-filling within the dynamical mean-field theory. In particular we
calculate the isotope effects on the quasi-particle spectral weight , the
renormalized phonon frequency, and the static charge and spin susceptibilities.
In the weakly correlated regime , where is the Hubbard
repulsion and is the bare electron half-bandwidth, the physical properties
are qualitatively similar to those characterizing the Holstein model in the
absence of Coulomb repulsion, where the bipolaronic binding takes place at
large electron-phonon coupling, and it reflects in divergent isotope responses.
On the contrary in the strongly correlated regime , where the
bipolaronic metal-insulator transition becomes of first order, the isotope
effects are bounded, suggesting that the first order transition is likely
driven by an electronic mechanism, rather then by a lattice instability. These
results point out how the isotope responses are extremely sensitive to phase
boundaries and they may be used to characterize the competition between the
electron-phonon coupling and the Hubbard repulsion.Comment: 10 pages, 8 figures. The paper has been already accepted on Phys.
Rev.
Polaronic and nonadiabatic phase diagram from anomalous isotope effects
Isotope effects (IEs) are powerful tool to probe directly the dependence of
many physical properties on the lattice dynamics. In this paper we invenstigate
the onset of anomalous IEs in the spinless Holstein model by employing the
dynamical mean field theory. We show that the isotope coefficients of the
electron effective mass and of the dressed phonon frequency are sizeable also
far away from the strong coupling polaronic crossover and mark the importance
of nonadiabatic lattice fluctuations in the weak to moderate coupling region.
We characterize the polaronic regime by the appearence of huge IEs. We draw a
nonadiabatic phase diagram in which we identify a novel crossover, not related
to polaronic features, where the IEs attain their largest anomalies.Comment: 5 pages, 4 figure
Allyl sulfur compounds and cellular detoxification system: effects and perspectives in cancer therapy
Natural organosulfur compounds (OSCs) have been shown to have chemopreventive effects and to suppress the proliferation of tumor cells in vitro through the induction of apoptosis. The biochemical mechanisms underlying the antitumorigenic and anti-proliferative effects of garlic-derived OSCs are not fully understood. Several modes of action of these compounds have been proposed, and it seems likely that the rate of clearance of allyl sulfur groups from cells is a determinant of the overall response. The aim of this review is to focus attention on the effects of natural allyl sulfur compounds on the cell detoxification system in normal and tumor cells. It has been already reported that several natural allyl sulfur compounds induce chemopreventive effects by affecting xenobiotic metabolizing enzymes and inducing their down-activation. Moreover, different effects of water- and oil-soluble allyl sulfur compounds on enzymes involved in the detoxification system of rat tissues have been observed. A direct interaction of the garlic allyl sulfur compounds with proteins involved in the detoxification system was studied in order to support the hypothesis that proteins possessing reactive thiol groups and that are involved in the detoxification system and in the cellular redox homeostasis, are likely the preferential targets of these compounds. The biochemical transformation of the OSCs in the cell and their adducts with thiol functional groups of these proteins, could be considered relevant events to uncover the anticancer properties of the allyl sulfur compounds. Although additional studies, using proteomic approaches and transgenic models, are needed to identify the molecular targets and modes of action of these natural compounds, the allyl sulfur compounds can represent potential ideal agents in anticancer therapy, either alone or in association with other antitumor drugs
Stress evaluation in hares (Lepus europaeus Pallas) captured for traslocation
With the aim to evaluate the capturing techniques some haematic and physiological parameters were studied to discrim- inate stressed hares from non stressed hares. A total of 66 wild hares (experimental group) were sampled in 14 different non-hunting areas, where hares are usually captured for later release in low-density areas. In the same season a total of 30 hares (about 1 year old), reared in cages and thus showing a reduced fear of man, were sampled (control group). In each area the hares were captured by cours- ing with 3-4 dogs (greyhounds or lurches). The dogs were released by the different hunter teams to find and drive into trammel nets any hare that was seen running. After capture, the hares remained inside darkened, wooden capture-boxes for a variable period of time before blood drawing. For blood sample collection all the hares were physically restrained and their eyes immediately covered. Blood, always collected within 1-2 minutes, was drawn from the auricular vein. Blood samples (plasma) were analysed for glucose, AST, ALT, CPK and cortisol concentrations. Body temperature, heart and respiratory rate, sex, and age were evaluated in each hare. The effect of origin, sex and age on haematic and physiolog- ical parameters was analysed by ANOVA. Every measured parameter of the hares bearing to the capture group or the control group (reared) was then subjected to stepwise and to discriminant analysis, in order to select the groups of stressed (discriminated by the controls) and non-stressed hares. CPK, AST and glucose were found to be the best parameters for distinguishing stressed from non-stressed hares. The intensive exercise suffered by the wild hares induced a depletion of energetic reserves, so that most of the captured hares showed lower glucose and higher CPK activity in the plasma, probably due to muscle damage (P< 0.05). After reclassi- fying the hares in the two groups of stressed and non stressed hares, the reference values (means ± SE) resulted as fol- lows: estimated non-stressed hares, glucose 234 ± 9 .4 mg/dl, AST 112 ± 22.2 U/l, CPK 1334 ± 734 U/l; estimated stressed hares, glucose 128 ± 7 mg/dl, AST 164 ± 13 U/l, CPK 4658 ± 454 U/l. These three cheap and quickly analysable analytes can be useful to the game manager in detecting stressed and non stressed hares, in order to improve the capturing techniques by the evaluation of the following relationship: (number of stressed hares + number of the dead hares during the capture)/number of total captured hares
Cosmological birefringence constraints from CMB and astrophysical polarization data
Cosmological birefringence is a rotation of the polarization plane of photons
coming from sources of astrophysical and cosmological origin. The rotation can
also depend on the energy of the photons and not only on the distance of the
source and on the cosmological evolution of the underlying theoretical model.
In this work, we constrain few selected models for cosmological birefringence,
combining CMB and astrophysical data at radio, optical, X and gamma
wavelengths, taking into account the specific energy and distance dependences.Comment: 12 pages, 2 figure
Feature transforms for image data augmentation
A problem with convolutional neural networks (CNNs) is that they require large datasets to obtain adequate robustness; on small datasets, they are prone to overfitting. Many methods have been proposed to overcome this shortcoming with CNNs. In cases where additional samples cannot easily be collected, a common approach is to generate more data points from existing data using an augmentation technique. In image classification, many augmentation approaches utilize simple image manipulation algorithms. In this work, we propose some new methods for data augmentation based on several image transformations: the Fourier transform (FT), the Radon transform (RT), and the discrete cosine transform (DCT). These and other data augmentation methods are considered in order to quantify their effectiveness in creating ensembles of neural networks. The novelty of this research is to consider different strategies for data augmentation to generate training sets from which to train several classifiers which are combined into an ensemble. Specifically, the idea is to create an ensemble based on a kind of bagging of the training set, where each model is trained on a different training set obtained by augmenting the original training set with different approaches. We build ensembles on the data level by adding images generated by combining fourteen augmentation approaches, with three based on FT, RT, and DCT, proposed here for the first time. Pretrained ResNet50 networks are finetuned on training sets that include images derived from each augmentation method. These networks and several fusions are evaluated and compared across eleven benchmarks. Results show that building ensembles on the data level by combining different data augmentation methods produce classifiers that not only compete competitively against the state-of-the-art but often surpass the best approaches reported in the literature
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