Moduli and electromagnetic black brane holography

We investigate the thermodynamic and hydrodynamic properties of 4-dimensional gauge theories with finite electric charge density in the presence of a constant magnetic field. Their gravity duals are planar magnetically and electrically charged AdS black holes in theories that contain a gauge Chern-Simons term. We present a careful analysis of the near horizon geometry of these black branes at finite and zero temperature for the case of a scalar field non-minimally coupled to the electromagnetic field. With the knowledge of the near horizon data, we obtain analytic expressions for the shear viscosity coefficient and entropy density, and also study the effect of a generic set of four derivative interactions on their ratio. We also comment on the attractor flows of the extremal solutions.Comment: 39 pages, no figures; v2: minor changes, refs. added; v3: typo fixed; v4: a proof for decoupling of the viscosity mode added in appendix, matches the published versio

Highlighting priority areas for bovine viral diarrhea control in Italy : a phylogeographic approach

The prevalence and genetic diversity of bovine viral diarrhea virus (BVDV) in a geographic area are largely influenced by live animal trade and management practices. Despite control and eradication programs currently underway in several European countries, the risk of BVDV spread within and among countries is still present. BVDV-1 is the predominant type circulating in European cattle population. In this study, a phylogeographic analysis was applied to the BVDV-1 highest prevalent subtypes in Italy to reconstruct the origin and spatial-temporal distribution and to trace main viral flows between different locations to highlight priority areas for BVDV control. A comprehensive dataset of BVDV-1b (n\u202f=\u202f173) and 1e (n\u202f=\u202f172) 5' UTR sequences was analysed, including both novel and published sequences from Italy and from European countries bordering and/or with commercial cattle flows with Italy. A common phylogeographic pattern was observed for BVDV-1b and 1e subtypes: interspersion from multiple Italian areas and European countries was widespread until the end of the last century, whereas significant local clusters were observed starting from 2000. These findings support a continuous viral flow among different areas over long time scales with no evidence of significant geographical structure, while local transmission networks are limited to more recent years. Northern Italy has been confirmed as the area of origin of the main clades of both BVDV subtypes at national level, acting both as a crucial area for introduction and a maintenance source for other areas. Piedmont, Central and Southern Italian regions contributed to limited geographical distribution and local BVDV-1b and 1e persistence. On the whole, priority control measures for BVDV-1b and 1e in Italy should be focused on: i) implementation of BVDV systematic control in all Northern Italian regions to break the viral flow from larger to smaller animal populations; and ii) breaking the dynamics of infections in regions with self-maintenance of BVDV by voluntary control programs

Early-Time Energy Loss in a Strongly-Coupled SYM Plasma

We carry out an analytic study of the early-time motion of a quark in a strongly-coupled maximally-supersymmetric Yang-Mills plasma, using the AdS/CFT correspondence. Our approach extracts the first thermal effects as a small perturbation of the known quark dynamics in vacuum, using a double expansion that is valid for early times and for (moderately) ultrarelativistic quark velocities. The quark is found to lose energy at a rate that differs significantly from the previously derived stationary/late-time result: it scales like T^4 instead of T^2, and is associated with a friction coefficient that is not independent of the quark momentum. Under conditions representative of the quark-gluon plasma as obtained at RHIC, the early energy loss rate is a few times smaller than its late-time counterpart. Our analysis additionally leads to thermally-corrected expressions for the intrinsic energy and momentum of the quark, in which the previously discovered limiting velocity of the quark is found to appear naturally.Comment: 39 pages, no figures. v2: Minor corrections and clarifications. References added. Version to be published in JHE

Comparative studies on the pathogenicity and tissue distribution of three virulence variants of classical swine fever virus, two field isolates and one vaccine strain, with special regard to immunohistochemical investigations

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to compare the tissue distribution and pathogenicity of three virulence variants of classical swine fever virus (CSFV) and to investigate the applicability of various conventional diagnostic procedures.</p> <p>Methods</p> <p>64 pigs were divided into three groups and infected with the highly virulent isolate ISS/60, the moderately virulent isolate Wingene'93 and the live attenuated vaccine strain Riems, respectively. Clinical signs, gross and histopathological changes were compared in relation to time elapsed post infection. Virus spread in various organs was followed by virus isolation, by immunohistochemistry, applying monoclonal antibodies in a two-step method and by <it>in situ </it>hybridisation using a digoxigenin-labelled riboprobe.</p> <p>Results</p> <p>The tissue distribution data are discussed in details, analyzing the results of the various diagnostic approaches. The comparative studies revealed remarkable differences in the onset of clinical signs as well as in the development of the macro- and microscopical changes, and in the tissue distribution of CSFV in the three experimental groups.</p> <p>Conclusion</p> <p>The present study demonstrates that in the case of highly and moderately virulent virus variants the virulence does not affect the pattern of the viral spread, however, it influences the outcome, the duration and the intensity of the disease. Immunohistochemistry has the advantage to allow the rapid detection and localisation of the virus, especially in cases of early infection, when clinical signs are still absent. Compared to virus isolation, the advantage of this method is that no cell culture facilities are required. Thus, immunohistochemistry provides simple and sensitive tools for the prompt detection of newly emerging variants of CSFV, including the viruses of very mild virulence.</p

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users' training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research.DeepBacs guides users without expertise in machine learning methods to leverage state-of-the-art artificial neural networks to analyse bacterial microscopy images

DeepBacs for multi-task bacterial image analysis using open-source deep learning approaches

This work demonstrates and guides how to use a range of state-of-the-art artificial neural-networks to analyse bacterial microscopy images using the recently developed ZeroCostDL4Mic platform. We generated a database of image datasets used to train networks for various image analysis tasks and present strategies for data acquisition and curation, as well as model training. We showcase different deep learning (DL) approaches for segmenting bright field and fluorescence images of different bacterial species, use object detection to classify different growth stages in time-lapse imaging data, and carry out DL-assisted phenotypic profiling of antibiotic-treated cells. To also demonstrate the ability of DL to enhance low-phototoxicity live-cell microscopy, we showcase how image denoising can allow researchers to attain high-fidelity data in faster and longer imaging. Finally, artificial labelling of cell membranes and predictions of super-resolution images allow for accurate mapping of cell shape and intracellular targets. Our purposefully-built database of training and testing data aids in novice users’ training, enabling them to quickly explore how to analyse their data through DL. We hope this lays a fertile ground for the efficient application of DL in microbiology and fosters the creation of tools for bacterial cell biology and antibiotic research