The Irr and RirA proteins participate in a complex regulatory circuit and act in concert to modulate bacterioferritin expression in Ensifer meliloti 1021

In this work we found that the bfr gene of the rhizobial species Ensifer meliloti, encoding a bacterioferritin iron storage protein, is involved in iron homeostasis and the oxidative stress response. This gene is located downstream of and overlapping the smc03787 open reading frame (ORF). No well-predicted RirA or Irr boxes were found in the region immediately upstream of the bfr gene although two presumptive RirA boxes and one presumptive Irr box were present in the putative promoter of smc03787. We demonstrate that bfr gene expression is enhanced under iron-sufficient conditions and that Irr and RirA modulate this expression. The pattern of bfr gene expression as well as the response to Irr and RirA is inversely correlated to that of smc03787. Moreover, our results suggest that the small RNA SmelC759 participates in RirA- and Irr-mediated regulation of bfr expression and that additional unknown factors are involved in iron-dependent regulation.Fil: Costa, Daniela. Instituto de Investigaciones Biológicas "Clemente Estable"; UruguayFil: Amarelle, Vanesa. Instituto de Investigaciones Biológicas "Clemente Estable"; UruguayFil: Valverde, Claudio Fabián. Universidad Nacional de Quilmes; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: O`Brian, Mark R.. State University of New York; Estados UnidosFil: Fabiano, Elena. Instituto de Investigaciones Biológicas "Clemente Estable"; Urugua

Controlling Excitations Inversion of a Cooper Pair Box Interacting with a Nanomechanical Resonator

We investigate the action of time dependent detunings upon the excitation inversion of a Cooper pair box interacting with a nanomechanical resonator. The method employs the Jaynes-Cummings model with damping, assuming different decay rates of the Cooper pair box and various fixed and t-dependent detunings. It is shown that while the presence of damping plus constant detunings destroy the collapse/revival effects, convenient choices of time dependent detunings allow one to reconstruct such events in a perfect way. It is also shown that the mean excitation of the nanomechanical resonator is more robust against damping of the Cooper pair box for convenient values of t-dependent detunings.Comment: 11 pages, 5 figure

Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with congenital heart disease (CHD). Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge. A dilated CNN with a receptive field of 131x131 voxels was trained for myocardium and blood pool segmentation in axial, sagittal and coronal image slices. Performance was evaluated within the HVSMR challenge. Automatic segmentation of the test scans resulted in Dice indices of 0.80$\pm$0.06 and 0.93$\pm$0.02, average distances to boundaries of 0.96$\pm$0.31 and 0.89$\pm$0.24 mm, and Hausdorff distances of 6.13$\pm$3.76 and 7.07$\pm$3.01 mm for the myocardium and blood pool, respectively. Segmentation took 41.5$\pm$14.7 s per scan. In conclusion, dilated CNNs trained on a small set of CMR images of CHD patients showing large anatomical variability provide accurate myocardium and blood pool segmentations

N-Delta(1232) axial form factors from weak pion production

The N-Delta axial form factors are determined from neutrino induced pion production ANL & BNL data by using a state of the art theoretical model, which accounts both for background mechanisms and deuteron effects. We find violations of the off diagonal Goldberger-Treiman relation at the level of 2 sigma which might have an impact in background calculations for T2K and MiniBooNE low energy neutrino oscillation precision experiments.Comment: 4 pages, 1 figur

Neutrino induced threshold production of two pions and N^*(1440) electroweak form factors

We study the threshold production of two pions induced by neutrinos in nucleon targets. The contribution of nucleon, pion and contact terms are calculated using a chiral Lagrangian. The contribution of the Roper resonance, neglected in earlier studies, has also been taken into account. The numerical results for the cross sections are presented and compared with the available experimental data. It has been found that in the two pion channels with $\pi^+\pi^-$ and $\pi^0\pi^0$ in the final state, the contribution of the $N^*(1440)$ is quite important and could be used to determine the $N^*(1440)$ electroweak transition form factors if experimental data with better statistics become available in the future.Comment: This version corrects a mistake on the helicity amplitudes sign. Additional comments on resonance-background relative sign are added. Other minor corrections. Matches published version. 17 pages, 7 figure

The Mars Climate Database

The Mars Climate Database (MCD) [1] is a database of statistics describing the climate and environment of the Martian atmosphere. It was constructed directly on the basis of output from mulitannual integrations of two general circulation models (GCMs)developed by Laboratoire de Météorologie Dynamique du CNRS, France, the University of Oxford, UK, and Instituto de Astrofisica de Andalucia, Spain, with support from the European Space Agency (ESA) and Centre National d–Etudes Spatiales (CNES). A description of the MCD is given along with a comparison between spacecraft observations of Mars and results predicted at similar locations and times in the MCD. The MCD can be used as a tool for mission planning and has been applied to prepare for several missions in Europe and the USA. It also provides information for mission design specialists on the mean state and variability of the Martian environment from the surface to above 120km. The GCMs on which the database is founded, include a set of physical parameterizations (radiative transfer in the visible and thermal infrared ranges, turbulent mixing, condensation-sublimation of CO2, thermal conduction in the soil and representation of gravity waves) and two different codes for the representation of large scale dynamics: a spectral code for the AOPP version and a grid-point code for the LMD version. The GCMs correctly reproduce the main meteorological features of Mars, as observed by the Mariner 9 and Viking orbiters, the Viking landers, and Mars Global Surveyor (MGS). As well as the standard statistical measures for mission design studies, the MCD includes a novel representation of large-scale variability, using empirical eigenfunctions derived from an analysis of the full simulations, and small-scale variability based on parameterizations of processes such as gravity wave propagation. The database allows the user to choose from 5 dust storm scenarios including a best guess, default scenario, deduced from recent MGS observations, an upper boundary for an atmosphere without dust storms, as observed by Viking the landers, and a clear, cold, lower boundary scenario, as observed by Phobos 2 and from Earth. The full version of the MCD is available on CDROM (for UNIX systems and PCs) and is also accessible through an interactive WWW interface at http://www-mars.lmd.jussieu.fr/

Tversky loss function for image segmentation using 3D fully convolutional deep networks

Fully convolutional deep neural networks carry out excellent potential for fast and accurate image segmentation. One of the main challenges in training these networks is data imbalance, which is particularly problematic in medical imaging applications such as lesion segmentation where the number of lesion voxels is often much lower than the number of non-lesion voxels. Training with unbalanced data can lead to predictions that are severely biased towards high precision but low recall (sensitivity), which is undesired especially in medical applications where false negatives are much less tolerable than false positives. Several methods have been proposed to deal with this problem including balanced sampling, two step training, sample re-weighting, and similarity loss functions. In this paper, we propose a generalized loss function based on the Tversky index to address the issue of data imbalance and achieve much better trade-off between precision and recall in training 3D fully convolutional deep neural networks. Experimental results in multiple sclerosis lesion segmentation on magnetic resonance images show improved F2 score, Dice coefficient, and the area under the precision-recall curve in test data. Based on these results we suggest Tversky loss function as a generalized framework to effectively train deep neural networks

Spatial chaos of an extensible conducting rod in a uniform magnetic field

The equilibrium equations for the isotropic Kirchhoff rod are known to form an integrable system. It is also known that the effects of extensibility and shearability of the rod do not break the integrable structure. Nor, as we have shown in a previous paper does the effect of a magnetic field on a conducting rod. Here we show, by means of Mel'nikov analysis, that, remarkably, the combined effects do destroy integrability; that is, the governing equations for an extensible current-carrying rod in a uniform magnetic field are nonintegrable. This result has implications for possible configurations of electrodynamic space tethers and may be relevant for electromechanical devices

Automated joint skull-stripping and segmentation with Multi-Task U-Net in large mouse brain MRI databases

Skull-stripping and region segmentation are fundamental steps in preclinical magnetic resonance imaging (MRI) studies, and these common procedures are usually performed manually. We present Multi-task U-Net (MU-Net), a convolutional neural network designed to accomplish both tasks simultaneously. MU-Net achieved higher segmentation accuracy than state-of-the-art multi-atlas segmentation methods with an inference time of 0.35 s and no pre-processing requirements. We trained and validated MU-Net on 128 T2-weighted mouse MRI volumes as well as on the publicly available MRM NeAT dataset of 10 MRI volumes. We tested MU-Net with an unusually large dataset combining several independent studies consisting of 1782 mouse brain MRI volumes of both healthy and Huntington animals, and measured average Dice scores of 0.906 (striati), 0.937 (cortex), and 0.978 (brain mask). Further, we explored the effectiveness of our network in the presence of different architectural features, including skip connections and recently proposed framing connections, and the effects of the age range of the training set animals. These high evaluation scores demonstrate that MU-Net is a powerful tool for segmentation and skull-stripping, decreasing inter and intra-rater variability of manual segmentation. The MU-Net code and the trained model are publicly available at https://github.com/Hierakonpolis/MU-Net