Heavy Quark Radiative Energy Loss - Applications to RHIC

Heavy quark energy loss in a hot QCD plasma is computed taking into account the competing effects due to suppression of zeroth order gluon radiation bellow the plasma frequency and the enhancement of gluon radiation due to transition energy loss and medium induced Bremsstrahlung. Heavy quark medium induced radiative energy loss is derived to all orders in opacity, $(L/\lambda_g)^n$. Numerical evaluation of the energy loss suggest small suppression of high $p_\perp$ charm quarks, and therefore provide a possible explanation for the null effects observed by PHENIX in the prompt electron spectrum in $Au+Au$ as $\sqrt{s}=130$ and 200 AGeV.Comment: 4 pages, 4 figures, Contributed to 17th International Conference on Ultra Relativistic Nucleus-Nucleus Collisions (Quark Matter 2004), Oakland, California, 11-17 Jan 200

Drug resistance in non-B subtype HIV-1: Impact of HIV-1 reverse transcriptase inhibitors

Human immunodeficiency virus (HIV) causes approximately 2.5 million new infections every year, and nearly 1.6 million patients succumb to HIV each year. Several factors, including cross-species transmission and error-prone replication have resulted in extraordinary genetic diversity of HIV groups. One of these groups, known as group M (main) contains nine subtypes (A-D, F-H and J-K) and causes ∼95% of all HIV infections. Most reported data on susceptibility and resistance to anti-HIV therapies are from subtype B HIV infections, which are prevalent in developed countries but account for only ∼12% of all global HIV infections, whereas non-B subtype HIV infections that account for ∼88% of all HIV infections are prevalent primarily in low and middle-income countries. Although the treatments for subtype B infections are generally effective against non-B subtype infections, there are differences in response to therapies. Here, we review how polymorphisms, transmission efficiency of drug-resistant strains, and differences in genetic barrier for drug resistance can differentially alter the response to reverse transcriptase-targeting therapies in various subtypes

On the Steady Nature of Line-Driven Disk Winds: Application to Cataclysmic Variables

We apply the semi-analytical analysis of the steady nature of line-driven winds presented in two earlier papers to disk winds driven by the flux distribution of a standard Shakura & Sunyaev (1973) disk for typical cataclysmic variable (CV) parameters. We find that the wind critical point tends to be closer to the disk surface towards the inner disk regions. Our main conclusion, however, is that a line-driven wind, arising from a steady disk flux distribution of a standard Shakura-Sunyaev disk capable of locally supplying the corresponding mass flow, is steady. These results confirm the findings of an earlier paper that studied "simple" flux distributions that are more readily analyzable than those presented here. These results are consistent with the steady velocity nature of outflows observationally inferred for both CVs and quasi-stellar objects (QSOs). We find good agreement with the 2.5D CV disk wind models of Pereyra and collaborators. These results suggest that the likely scenario to account for the wind outflows commonly observed in CVs is the line-driven accretion disk wind scenario, as suggested early-on by Cordova & Mason (1982). For QSOs, these results show that the line-driven accretion disk wind continues to be a promising scenario to account for the outflows detected in broad absorption line (BAL) QSOs, as suggested early-on by Turnshek (1984), and analyzed in detail by Murray et al. (1995).Comment: 35 pages, 20 figure

Importance Sampling for Objetive Funtion Estimations in Neural Detector Traing Driven by Genetic Algorithms

To train Neural Networks (NNs) in a supervised way, estimations of an objective function must be carried out. The value of this function decreases as the training progresses and so, the number of test observations necessary for an accurate estimation has to be increased. Consequently, the training computational cost is unaffordable for very low objective function value estimations, and the use of Importance Sampling (IS) techniques becomes convenient. The study of three different objective functions is considered, which implies the proposal of estimators of the objective function using IS techniques: the Mean-Square error, the Cross Entropy error and the Misclassification error criteria. The values of these functions are estimated by IS techniques, and the results are used to train NNs by the application of Genetic Algorithms. Results for a binary detection in Gaussian noise are provided. These results show the evolution of the parameters during the training and the performances of the proposed detectors in terms of error probability and Receiver Operating Characteristics curves. At the end of the study, the obtained results justify the convenience of using IS in the training

Using the MitoB method to assess levels of reactive oxygen species in ecological studies of oxidative stress

In recent years evolutionary ecologists have become increasingly interested in the effects of reactive oxygen species (ROS) on the life-histories of animals. ROS levels have mostly been inferred indirectly due to the limitations of estimating ROS from in vitro methods. However, measuring ROS (hydrogen peroxide, H2O2) content in vivo is now possible using the MitoB probe. Here, we extend and refine the MitoB method to make it suitable for ecological studies of oxidative stress using the brown trout Salmo trutta as model. The MitoB method allows an evaluation of H2O2 levels in living organisms over a timescale from hours to days. The method is flexible with regard to the duration of exposure and initial concentration of the MitoB probe, and there is no transfer of the MitoB probe between fish. H2O2 levels were consistent across subsamples of the same liver but differed between muscle subsamples and between tissues of the same animal. The MitoB method provides a convenient method for measuring ROS levels in living animals over a significant period of time. Given its wide range of possible applications, it opens the opportunity to study the role of ROS in mediating life history trade-offs in ecological settings

Study of electron anti-neutrinos associated with gamma-ray bursts using KamLAND

We search for electron anti-neutrinos ($\overline{\nu}_e$) from long and short-duration gamma-ray bursts~(GRBs) using data taken by the KamLAND detector from August 2002 to June 2013. No statistically significant excess over the background level is found. We place the tightest upper limits on $\overline{\nu}_e$ fluence from GRBs below 7 MeV and place first constraints on the relation between $\overline{\nu}_e$ luminosity and effective temperature.Comment: 16 pages and 5 figure

APRIL: Active Preference-learning based Reinforcement Learning

This paper focuses on reinforcement learning (RL) with limited prior knowledge. In the domain of swarm robotics for instance, the expert can hardly design a reward function or demonstrate the target behavior, forbidding the use of both standard RL and inverse reinforcement learning. Although with a limited expertise, the human expert is still often able to emit preferences and rank the agent demonstrations. Earlier work has presented an iterative preference-based RL framework: expert preferences are exploited to learn an approximate policy return, thus enabling the agent to achieve direct policy search. Iteratively, the agent selects a new candidate policy and demonstrates it; the expert ranks the new demonstration comparatively to the previous best one; the expert's ranking feedback enables the agent to refine the approximate policy return, and the process is iterated. In this paper, preference-based reinforcement learning is combined with active ranking in order to decrease the number of ranking queries to the expert needed to yield a satisfactory policy. Experiments on the mountain car and the cancer treatment testbeds witness that a couple of dozen rankings enable to learn a competent policy

Arabidopsis Root-Type Ferredoxin: NADP(H) Oxidoreductase 2 is Involved in Detoxification of Nitrite in Roots

This work was supported by RIKEN [Special Postdoctoral Researchers (SPDR) fellowship to T.H.]