GluN2A NMDA Receptor Enhancement Improves Brain Oscillations, Synchrony, and Cognitive Functions in Dravet Syndrome and Alzheimer's Disease Models.

NMDA receptors (NMDARs) play subunit-specific roles in synaptic function and are implicated in neuropsychiatric and neurodegenerative disorders. However, the in vivo consequences and therapeutic potential of pharmacologically enhancing NMDAR function via allosteric modulation are largely unknown. We examine the in vivo effects of GNE-0723, a positive allosteric modulator of GluN2A-subunit-containing NMDARs, on brain network and cognitive functions in mouse models of Dravet syndrome (DS) and Alzheimer's disease (AD). GNE-0723 use dependently potentiates synaptic NMDA receptor currents and reduces brain oscillation power with a predominant effect on low-frequency (12-20 Hz) oscillations. Interestingly, DS and AD mouse models display aberrant low-frequency oscillatory power that is tightly correlated with network hypersynchrony. GNE-0723 treatment reduces aberrant low-frequency oscillations and epileptiform discharges and improves cognitive functions in DS and AD mouse models. GluN2A-subunit-containing NMDAR enhancers may have therapeutic benefits in brain disorders with network hypersynchrony and cognitive impairments

Spectator Effects during Leptogenesis in the Strong Washout Regime

By including spectator fields into the Boltzmann equations for Leptogenesis, we show that partially equilibrated spectator interactions can have a significant impact on the freeze-out value of the asymmetry in the strong washout regime. The final asymmetry is typically increased, since partially equilibrated spectators "hide" a part of the asymmetry from washout. We study examples with leptonic and non-leptonic spectator processes, assuming thermal initial conditions, and find up to 50% enhanced asymmetries compared to the limit of fully equilibrated spectators. Together with a comprehensive overview of the equilibration temperatures for various Standard Model processes, the numerical results indicate the ranges when the limiting cases of either fully equilibrated or negligible spectator fields are applicable and when they are not. Our findings also indicate an increased sensitivity to initial conditions and finite density corrections even in the strong washout regime.Comment: 23 pages, 4 figure

Exploring extra dimensions through inflationary tensor modes

Predictions of inflationary schemes can be influenced by the presence of extra dimensions. This could be of particular relevance for the spectrum of gravitational waves in models where the extra dimensions provide a brane-world solution to the hierarchy problem. Apart from models of large as well as exponentially warped extra dimensions, we analyze the size of tensor modes in the Linear Dilaton scheme recently revived in the discussion of the "clockwork mechanism". The results are model dependent, significantly enhanced tensor modes on one side and a suppression on the other. In some cases we are led to a scheme of "remote inflation", where the expansion is driven by energies at a hidden brane. In all cases where tensor modes are enhanced, the requirement of perturbativity of gravity leads to a stringent upper limit on the allowed Hubble rate during inflation.Comment: 29 pages, 7 figures; v2: added discussion on the emergence of curvature singularities and removed discussion on the NKKK case with horizon in the bulk, conclusions unaltered, matches the published versio

Charmed Hadrons from Strangeness-rich QGP

The yields of charmed hadrons emitted by strangeness rich QGP are evaluated within chemical non-equilibrium statistical hadronization model, conserving strangeness, charm, and entropy yields at hadronization.Comment: 6 pages, 2 figures SQM 2006; the same as J. Phys. G in pres

Improved bounds on Z3\mathbb{Z}_{3} singlet dark matter

We reconsider complex scalar singlet dark matter stabilised by a $\mathbb{Z}_{3}$ symmetry. We refine the stability bounds on the potential and use constraints from unitarity on scattering at finite energy to place a stronger lower limit on the direct detection cross section. In addition, we improve the treatment of the thermal freeze-out by including the evolution of the dark matter temperature and its feedback onto relic abundance. In the regions where the freeze-out is dominated by resonant or semi-annihilation, the dark matter decouples kinetically from the plasma very early, around the onset of the chemical decoupling. This results in a modification of the required coupling to the Higgs, which turns out to be at most few per cent in the semi-annihilation region, thus giving credence to the standard approach to the relic density calculation in this regime. In contrast, for dark matter mass just below the Higgs resonance, the modification of the Higgs invisible width and direct and indirect detection signals can be up to a factor $6.7$. The model is then currently allowed at $56.8$ GeV to $58.4$ GeV (depending on the details of early kinetic decoupling) $\lesssim M_{S} \lesssim 62.8$ GeV and at $M_{S} \gtrsim 122$ GeV if the freeze-out is dominated by semi-annihilation. We show that the whole large semi-annihilation region will be probed by the near-future measurements at the XENONnT experiment.Comment: 22 pages, 4 figure

Statistical J/psi production and open charm enhancement in Pb+Pb collisions at CERN SPS

Production of open and hidden charm hadrons in heavy ion collisions is considered within the statistical coalescence model. Charmed quarks and antiquarks are assumed to be created at the initial stage of the reaction and their number is conserved during the evolution of the system. They are distributed among open and hidden charm hadrons at the hadronization stage in accordance with laws of statistical mechanics. The model is in excellent agreement with the experimental data on J/psi production in lead-lead collisions at CERN SPS and predicts strong enhancement of the open charm multiplicity over the standard extrapolation from nucleon-nucleon to nucleus-nucleus collisions. A possible mechanism of the charm enhancement is proposed.Comment: Presented at 6th International Conference on Strange Quarks in Matter, Frankfurt am Main, 2001. 4 pages, LaTeX, 1 PS-figur

Mechanism and treatment for learning and memory deficits in mouse models of Noonan syndrome.

In Noonan syndrome (NS) 30-50% of subjects show cognitive deficits of unknown etiology and with no known treatment. Here, we report that knock-in mice expressing either of two NS-associated mutations in Ptpn11, which encodes the nonreceptor protein tyrosine phosphatase Shp2, show hippocampal-dependent impairments in spatial learning and deficits in hippocampal long-term potentiation (LTP). In addition, viral overexpression of an NS-associated allele PTPN11(D61G) in adult mouse hippocampus results in increased baseline excitatory synaptic function and deficits in LTP and spatial learning, which can be reversed by a mitogen-activated protein kinase kinase (MEK) inhibitor. Furthermore, brief treatment with lovastatin reduces activation of the GTPase Ras-extracellular signal-related kinase (Erk) pathway in the brain and normalizes deficits in LTP and learning in adult Ptpn11(D61G/+) mice. Our results demonstrate that increased basal Erk activity and corresponding baseline increases in excitatory synaptic function are responsible for the LTP impairments and, consequently, the learning deficits in mouse models of NS. These data also suggest that lovastatin or MEK inhibitors may be useful for treating the cognitive deficits in NS

Brain Tumor Segmentation with Deep Neural Networks

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test dataset reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster