RGMa and RGMb expression pattern during chicken development suggest unexpected roles for these repulsive guidance molecules in notochord formation, somitogenesis, and myogenesis

Background: Repulsive guidance molecules (RGM) are high-affinity ligands for the Netrin receptor Neogenin, and they are crucial for nervous system development including neural tube closure; neuronal and neural crest cell differentiation and axon guidance. Recent studies implicated RGM molecules in bone morphogenetic protein signaling, which regulates a variety of developmental processes. Moreover, a role for RGMc in iron metabolism has been established. This suggests that RGM molecules may play important roles in non-neural tissues. Results: To explore which tissues and processed may be regulated by RGM molecules, we systematically investigated the expression of RGMa and RGMb, the only RGM molecules currently known for avians, in the chicken embryo. Conclusions: Our study suggests so far unknown roles of RGM molecules in notochord, somite and skeletal muscle development. Developmental Dynamics, 2012. (C) 2012 Wiley Periodicals, Inc.AFMAFMCNPqCNPqFAPESPFAPES

On quasilinear parabolic evolution equations in weighted Lp-spaces II

Our study of abstract quasi-linear parabolic problems in time-weighted L_p-spaces, begun in [17], is extended in this paper to include singular lower order terms, while keeping low initial regularity. The results are applied to reaction-diffusion problems, including Maxwell-Stefan diffusion, and to geometric evolution equations like the surface-diffusion flow or the Willmore flow. The method presented here will be applicable to other parabolic systems, including free boundary problems.Comment: 21 page

Рекомендации по ограничению динамических перенапряжений в обмотке ротора асинхронизированного турбогенератора

In this paper, a Volume-of-Fluid (VOF)-based approach for the Direct Numerical Simulation (DNS) of reactive mass transfer in gas–liquid flows is described. At the interface, local thermodynamic equilibrium is assumed and modelled by Henry's law. First numerical simulation results are presented for non-reactive and reactive mass transfer from rising gas bubbles to a surrounding liquid. For the evaluation of reactive mass transfer simulations with a consecutive, competitive reaction system in the liquid, a local selectivity is employed

Evidence for a Kernel of Truth in Children’s Facial Impressions of Children’s Niceness, but not Shyness

Acknowledgements: We are grateful to the parents and children who helped make this research possible. We would like to thank Romina Palermo for providing us the opportunity to contact her sample of participants and to use some existing data. We also thank Lou Ewing for sharing the Zeb the Alien Scientist testing materials, and Saba Siddique for comments regarding a manuscript draft. Finally, we would like to thank Kaitlyn Turbett, Dielle Horne, Saba Siddique, Chloe Giffard, and Maira Vicente Braga, for help testing participants. JC, LJ, GR, and CS conceived the study and helped to draft and edit the manuscript. JC programmed the experiment, collected most participant data, performed the statistical analyses and drafted the first manuscript draft. EB coordinated image collection and testing schedules. All authors participated in the study design, and read, provided critical revisions and approved the final manuscript. The study methods, hypotheses and analyses were pre-registered(https://osf.io/kjtva/registrations). Funding: This research was supported by an APR Internship Academic Mentor Grant to CS, an Australian Research Council (ARC) Centre of Excellence Grant award to GR [CE110001021], ARC Discovery Early Career Research Award to CS [DE190101043], ARC Discovery Grant to GR and CS [DP170104602], ARC Discovery Grant to LJ [140101743], and a Research Training Program stipend to JC.Peer reviewedPostprin

The use of reinforcement learning algorithms to meet the challenges of an artificial pancreas

Blood glucose control, for example, in diabetes mellitus or severe illness, requires strict adherence to a protocol of food, insulin administration and exercise personalized to each patient. An artificial pancreas for automated treatment could boost quality of glucose control and patients' independence. The components required for an artificial pancreas are: i) continuous glucose monitoring (CGM), ii) smart controllers and iii) insulin pumps delivering the optimal amount of insulin. In recent years, medical devices for CGM and insulin administration have undergone rapid progression and are now commercially available. Yet, clinically available devices still require regular patients' or caregivers' attention as they operate in open-loop control with frequent user intervention. Dosage-calculating algorithms are currently being studied in intensive care patients [1] , for short overnight control to supplement conventional insulin delivery [2] , and for short periods where patients rest and follow a prescribed food regime [3] . Fully automated algorithms that can respond to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and which provide the necessary personalized control for individuals is currently beyond the state-of-the-art. Here, we review and discuss reinforcement learning algorithms, controlling insulin in a closed-loop to provide individual insulin dosing regimens that are reactive to the immediate needs of the patient

The effect of oxide precipitates on minority carrier lifetime in n-type silicon

Supersaturated levels of interstitial oxygen in Czochralski silicon can lead to the formation of oxide precipitates. Although beneficial from an internal gettering perspective, oxygen-related extended defects give rise to recombination which reduces minority carrier lifetime. The highest efficiency silicon solar cells are made from n-type substrates in which oxide precipitates can have a detrimental impact on cell efficiency. In order to quantify and to understand the mechanism of recombination in such materials, we correlate injection level-dependent minority carrier lifetime data measured with silicon nitride surface passivation with interstitial oxygen loss and precipitate concentration measurements in samples processed under substantially different conditions. We account for surface recombination, doping level, and precipitate morphology to present a generalised parameterisation of lifetime. The lifetime data are analysed in terms of recombination activity which is dependent on precipitate density or on the surface area of different morphologies of precipitates. Correlation of the lifetime data with interstitial oxygen loss data shows that the recombination activity is likely to be dependent on the precipitate surface area. We generalise our findings to estimate the impact of oxide precipitates with a given surface area on lifetime in both n-type and p-type silicon