Holographic dark energy linearly interacting with dark matter

We investigate a spatially flat Friedmann-Robertson-Walker (FRW) cosmological model with cold dark matter coupled to a modified holographic Ricci dark energy through a general interaction term linear in the energy densities of dark matter and dark energy, the total energy density and its derivative. Using the statistical method of $\chi^2$-function for the Hubble data, we obtain $H_0=73.6$km/sMpc, $\omega_s=-0.842$ for the asymptotic equation of state and $z_{acc}= 0.89$. The estimated values of $\Omega_{c0}$ which fulfill the current observational bounds corresponds to a dark energy density varying in the range 0.25R < \ro_x < 0.27R.Comment: March 2012. 6 pp., 6 figures. Note: To appear in the proceedings of the CosmoSul conference, held in Rio de Janeiro, Brazil, 01-05 august of 201

Evaluation of epigenetic and radiomodifying effects during radiotherapy treatments in zebrafish

Radiotherapy is still a long way from personalizing cancer treatment plans, and its effective-ness depends on the radiosensitivity of tumor cells. Indeed, therapies that are efficient and successful for some patients may be relatively ineffective for others. Based on this, radiobiological research is focusing on the ability of some reagents to make cancer cells more responsive to ionizing radiation, as well as to protect the surrounding healthy tissues from possible side effects. In this scenario, zebrafish emerged as an effective model system to test for radiation modifiers that can potentially be used for radiotherapeutic purposes in humans. The adoption of this experimental organism is fully justified and supported by the high similarity between fish and humans in both their genome sequences and the effects provoked in them by ionizing radiation. This review aims to provide the literature state of the art of zebrafish in vivo model for radiobiological studies, particularly focusing on the epigenetic and radiomodifying effects produced during fish embryos’ and larvae’s exposure to radiotherapy treatments

Neural Network Parametrization of Deep-Inelastic Structure Functions

We construct a parametrization of deep-inelastic structure functions which retains information on experimental errors and correlations, and which does not introduce any theoretical bias while interpolating between existing data points. We generate a Monte Carlo sample of pseudo-data configurations and we train an ensemble of neural networks on them. This effectively provides us with a probability measure in the space of structure functions, within the whole kinematic region where data are available. This measure can then be used to determine the value of the structure function, its error, point-to-point correlations and generally the value and uncertainty of any function of the structure function itself. We apply this technique to the determination of the structure function F_2 of the proton and deuteron, and a precision determination of the isotriplet combination F_2[p-d]. We discuss in detail these results, check their stability and accuracy, and make them available in various formats for applications.Comment: Latex, 43 pages, 22 figures. (v2) Final version, published in JHEP; Sect.5.2 and Fig.9 improved, a few typos corrected and other minor improvements. (v3) Some inconsequential typos in Tab.1 and Tab 5 corrected. Neural parametrization available at http://sophia.ecm.ub.es/f2neura

Editorial: Fibrosis and inflammation in tissue pathophysiology

In adult mammals, tissue damage activates a wound healing response with acute inflammation followed by either complete repair (for low-grade damage or in highly regenerative tissues, such as the liver) or replacement fibrosis (for extensive damage or in poorly regenerative tissues, such as the myocardium). Persistent damage and repeated insults sustain continuous activation of repair pathways leading to chronic inflammation, progressive tissue fibrosis and sclerosis. Despite the evolutionary advantage conferred by scarring as a rapid repair mechanism, chronic fibrosis leads to tissue adverse remodeling and impaired function. Persistent low-level inflammation and fibrosis are observed in many pathological conditions (e.g. hypertension, obesity, diabetes, genetic diseases), and lead to further complications including atherosclerosis and ischemic events, organ failure, autoimmune diseases, cancer, aging, and reduced resilience to infectious diseases. Pathological fibrosis plays a major role in a wide range of diseases, accounting for an increasingly large fraction of mortality cases worldwide. While recent advances have unveiled many environmental and genetic causes of fibrotic disorders, a better understanding of both ubiquitous and tissue-specific regulatory pathways and cellular dynamics could help to design new targeted therapies, and to identify the etiology of idiopathic diseases. Within this Research Topic, we invite submission of articles (reviews, original research, or methodology articles) on the pathophysiological role of fibrosis and inflammation in different tissues. Areas to be covered include, but are not limited to: - genetic and environmental causes of persistent low-level inflammation and fibrosis (e.g. autoimmunity, hypertension, obesity, diabetes, genetic diseases, latent infections); - comorbidities including systemic sclerosis, neurological disorders, organ failure (heart, skeletal muscle, kidney, liver, lungs), cancer, and reduced resilience to infectious diseases; - in vivo (animal models) and in vitro (organoids, tissue culture) modelling of fibrotic diseases for the discovery of novel therapeutic targets and potential tissue-specific treatments; - vascular responses to inflammation and inflammation of vascular tissues; - system biology approaches to identify molecular and cellular networks leading to chronic inflammation and fibrosis

Progress in the Neural Network Determination of Polarized Parton Distributions

We review recent progress towards a determination of a set of polarized parton distributions from a global set of deep-inelastic scattering data based on the NNPDF methodology, in analogy with the unpolarized case. This method is designed to provide a faithful and statistically sound representation of parton distributions and their uncertainties. We show how the FastKernel method provides a fast and accurate method for solving the polarized DGLAP equations. We discuss the polarized PDF parametrizations and the physical constraints which can be imposed. Preliminary results suggest that the uncertainty on polarized PDFs, most notably the gluon, has been underestimated in previous studies.Comment: 5 pages, 2 figures; to appear in the proceedings of DIS 2010, Firenz

Intravitreal triamcinolone, bevacizumab and pegaptanib for occult choroidal neovascularization.

PURPOSE: To evaluate best-corrected visual acuity (BCVA) and foveal thickness (FT) changes in occult subfoveal choroidal neovascularization (CNV) from age-related macular degeneration (AMD) after intravitreal bevacizumab (IVB, 1.25 mg/0.05 ml), pegaptanib (IVP, 0.3 mg/0.09 ml) and triamcinolone acetonide (IVTA, 4 mg/0.1 ml) injected on an as needed basis. METHODS:   Retrospective, interventional, comparative study. BCVA (Early Treatment Diabetic Retinopathy Study LogMAR) and FT by optical coherence tomography (OCT) were evaluated during 12 months from first treatment. Patients were retreated if signs of neovascular activity were still present on angiography or OCT. RESULTS: Forty-eight eyes received IVB, 43 eyes received IVP, 52 eyes received IVTA. BCVA and FT at baseline were 1.22 ± 0.49 LogMAR and 410.2 ± 41.83 μm in the IVB group, 1.25 ± 0.43 LogMAR and 452.3 ± 44.83 μm in the IVP group and 1.31 ± 0.4 LogMAR and 456.6 ± 48.27 μm in the IVTA group. BCVA and FT improved in the three groups during follow-up. A significantly greater improvement of BCVA was present at month-3, month-6 and at month-12 in the IVB and IVP groups (p = 0.01). Improvement of FT was greater in the IVTA group at month-3 (p = 0.02), while it was greater in the anti-Vascular Endothelial Growth Factor (VEGF) groups at month-6 and month-12 (p = 0.01). A postoperative increase of intraocular pressure was detected in 9/52 (17.3%) eyes treated with IVTA, and in two cases it was resistant to topical therapy. CONCLUSION:   Intravitreal injection of anti-VEGF drugs administered on an as needed basis for AMD-related occult CNVs provided functional and anatomic improvement during 12 months of follow-up

Fitting Parton Distribution Data with Multiplicative Normalization Uncertainties

We consider the generic problem of performing a global fit to many independent data sets each with a different overall multiplicative normalization uncertainty. We show that the methods in common use to treat multiplicative uncertainties lead to systematic biases. We develop a method which is unbiased, based on a self--consistent iterative procedure. We demonstrate the use of this method by applying it to the determination of parton distribution functions with the NNPDF methodology, which uses a Monte Carlo method for uncertainty estimation.Comment: 33 pages, 5 figures: published versio

A multivariate approach to heavy flavour tagging with cascade training

This paper compares the performance of artificial neural networks and boosted decision trees, with and without cascade training, for tagging b-jets in a collider experiment. It is shown, using a Monte Carlo simulation of $WH \to l\nu q\bar{q}$ events, that for a b-tagging efficiency of 50%, the light jet rejection power given by boosted decision trees without cascade training is about 55% higher than that given by artificial neural networks. The cascade training technique can improve the performance of boosted decision trees and artificial neural networks at this b-tagging efficiency level by about 35% and 80% respectively. We conclude that the cascade trained boosted decision trees method is the most promising technique for tagging heavy flavours at collider experiments.Comment: 14 pages, 12 figures, revised versio