X-ray Halos and Large Grains in the Diffuse Interstellar Medium

Recent observations with dust detectors on board the interplanetary spacecraft Ulysses and Galileo have recorded a substantial flux of large interstellar grains with radii between 0.25 and 2.0 mu entering the solar system from the local interstellar cloud. The most commonly used interstellar grain size distribution is characterized by a a^-3.5 power law in grain radii a, and extends to a maximum grain radius of 0.25 mu. The extension of the interstellar grain size distribution to such large radii will have a major effect on the median grain size, and on the amount of mass needed to be tied up in dust for a given visual optical depth. It is therefore important to investigate whether this population of larger dust particles prevails in the general interstellar medium, or if it is merely a local phenomenon. The presence of large interstellar grains can be mainly inferred from their effect on the intensity and radial profiles of scattering halos around X-ray sources. In this paper we examine the grain size distribution that gives rise to the X-ray halo around Nova Cygni 1992. The results of our study confirm the need to extend the interstellar grain size distribution in the direction of this source to and possibly beyond 2.0 mu. The model that gives the best fit to the halo data is characterized by: (1) a grain size distribution that follows an a^-3.5 power law up to 0.50 mu, followed by an a^-4.0 extension from 0.50 mu to 2.0 mu; and (2) silicate and graphite (carbon) dust-to-gas mass ratios of 0.0044 and 0.0022, respectively, consistent with solar abundances constraints. Additional observations of X-ray halos probing other spatial directions are badly needed to test the general validity of this result.Comment: 17 pages, incl. 1 figure, accepted for publ. by ApJ Letter

Metabolic and hormonal studies of Type 1 (insulin-dependent) diabetic patients after successful pancreas and kidney transplantation

Long-term normalization of glucose metabolism is necessary to prevent or ameliorate diabetic complications. Although pancreatic grafting is able to restore normal blood glucose and glycated haemoglobin, the degree of normalization of the deranged diabetic metabolism after pancreas transplantation is still questionable. Consequently glucose, insulin, C-peptide, glucagon, and pancreatic polypeptide responses to oral glucose and i.v. arginine were measured in 36 Type 1 (insulin-dependent) diabetic recipients of pancreas and kidney allografts and compared to ten healthy control subjects. Despite normal HbA1 (7.2±0.2%; normal <8%) glucose disposal was normal only in 44% and impaired in 56% of the graft recipients. Normalization of glucose tolerance was achieved at the expense of hyperinsulinaemia in 52% of the subjects. C-peptide and glucagon were normal, while pancreatic polypeptide was significantly higher in the graft recipients. Intravenous glucose tolerance (n=21) was normal in 67% and borderline in 23%. Biphasic insulin release was seen in patients with normal glucose tolerance. Glucose tolerance did not deteriorate up to 7 years post-transplant. In addition, stress hormone release (cortisol, growth hormone, prolactin, glucagon, catecholamines) to insulin-induced hypoglycaemia was examined in 20 graft recipients and compared to eight healthy subjects. Reduced blood glucose decline indicates insulin resistance, but glucose recovery was normal, despite markedly reduced catecholamine and glucagon release. These data demonstrate the effectiveness of pancreatic grafting in normalizing glucose metabolism, although hyperinsulinaemia and deranged counterregulatory hormone response are observed frequently

How to Blend a Robot within a Group of Zebrafish: Achieving Social Acceptance through Real-time Calibration of a Multi-level Behavioural Model

We have previously shown how to socially integrate a fish robot into a group of zebrafish thanks to biomimetic behavioural models. The models have to be calibrated on experimental data to present correct behavioural features. This calibration is essential to enhance the social integration of the robot into the group. When calibrated, the behavioural model of fish behaviour is implemented to drive a robot with closed-loop control of social interactions into a group of zebrafish. This approach can be useful to form mixed-groups, and study animal individual and collective behaviour by using biomimetic autonomous robots capable of responding to the animals in long-standing experiments. Here, we show a methodology for continuous real-time calibration and refinement of multi-level behavioural model. The real-time calibration, by an evolutionary algorithm, is based on simulation of the model to correspond to the observed fish behaviour in real-time. The calibrated model is updated on the robot and tested during the experiments. This method allows to cope with changes of dynamics in fish behaviour. Moreover, each fish presents individual behavioural differences. Thus, each trial is done with naive fish groups that display behavioural variability. This real-time calibration methodology can optimise the robot behaviours during the experiments. Our implementation of this methodology runs on three different computers that perform individual tracking, data-analysis, multi-objective evolutionary algorithms, simulation of the fish robot and adaptation of the robot behavioural models, all in real-time.Comment: 9 pages, 3 figure

Realizing a deep reinforcement learning agent discovering real-time feedback control strategies for a quantum system

To realize the full potential of quantum technologies, finding good strategies to control quantum information processing devices in real time becomes increasingly important. Usually these strategies require a precise understanding of the device itself, which is generally not available. Model-free reinforcement learning circumvents this need by discovering control strategies from scratch without relying on an accurate description of the quantum system. Furthermore, important tasks like state preparation, gate teleportation and error correction need feedback at time scales much shorter than the coherence time, which for superconducting circuits is in the microsecond range. Developing and training a deep reinforcement learning agent able to operate in this real-time feedback regime has been an open challenge. Here, we have implemented such an agent in the form of a latency-optimized deep neural network on a field-programmable gate array (FPGA). We demonstrate its use to efficiently initialize a superconducting qubit into a target state. To train the agent, we use model-free reinforcement learning that is based solely on measurement data. We study the agent’s performance for strong and weak measurements, and for three-level readout, and compare with simple strategies based on thresholding. This demonstration motivates further research towards adoption of reinforcement learning for real-time feedback control of quantum devices and more generally any physical system requiring learnable low-latency feedback control

Double Spin Asymmetries A_NN and A_SS at sqrt{s}=200 GeV in Polarized Proton-Proton Elastic Scattering at RHIC

We present the first measurements of the double spin asymmetries A_NN and A_SS at sqrt{s}=200 GeV, obtained by the pp2pp experiment using polarized proton beams at the Relativistic Heavy Ion Collider (RHIC). The data were collected in the four momentum transfer t range 0.01<|t|<0.03 (GeV/c)^2. The measured asymmetries, which are consistent with zero, allow us to estimate upper limits on the double helicity-flip amplitudes phi_2 and phi_4 at small t as well as on the difference Delta(sigma_T) between the total cross sections for transversely polarized protons with antiparallel or parallel spin orientations.Comment: 13 pages with 3 figures. Final version accepted by Phys. Lett.

Circulating microRNAs Reveal Time Course of Organ Injury in a Porcine Model of Acetaminophen-Induced Acute Liver Failure

Acute liver failure is a rare but catastrophic condition which can progress rapidly to multi-organ failure. Studies investigating the onset of individual organ injury such as the liver, kidneys and brain during the evolution of acute liver failure, are lacking. MicroRNAs are short, non-coding strands of RNA that are released into the circulation following tissue injury. In this study, we have characterised the release of both global microRNA and specific microRNA species into the plasma using a porcine model of acetaminophen-induced acute liver failure. Pigs were induced to acute liver failure with oral acetaminophen over 19h±2h and death occurred 13h±3h thereafter. Global microRNA concentrations increased 4h prior to acute liver failure in plasma (P<0.0001) but not in isolated exosomes, and were associated with increasing plasma levels of the damage-associated molecular pattern molecule, genomic DNA (P<0.0001). MiR122 increased around the time of onset of acute liver failure (P<0.0001) and was associated with increasing international normalised ratio (P<0.0001). MiR192 increased 8h after acute liver failure (P<0.0001) and was associated with increasing creatinine (P<0.0001). The increase in miR124-1 occurred concurrent with the pre-terminal increase in intracranial pressure (P<0.0001) and was associated with decreasing cerebral perfusion pressure (P<0.002)