XMM-Newton observation of the highly magnetised accreting pulsar Swift J045106.8-694803: evidence of a hot thermal excess

Several persistent, low luminosity (LX ~ 1034 erg s-1), long spin period (P > 100 s) High Mass X-ray Binaries have been reported with blackbody components with temperatures > 1 keV. These hot thermal excesses have correspondingly small emitting regions (< 1 km2) and are attributed to the neutron star polar caps. We present a recent XMM-Newton target of opportunity observation of the newest member of this class, Swift J045106.8-694803. The period was determined to be 168.5 ± 0.2 s as of 17 July 2012 (MJD = 56125.0). At LX ~ 1036 erg s-1, Swift J045106.8-694803 is the brightest member of this new class, as well as the one with the shortest period. The spectral analysis reveals for the first time the presence of a blackbody with temperature kTBB = 1.8 +0.2-0.3 keV and radius RBB = 0.5 ± 0.2 km. The pulsed fraction decreases with increasing energy and the ratio between the hard (> 2 keV) and soft (< 2 keV) light curves is anticorrelated with the pulse profile. Simulations of the spectrum suggest that this is caused by the pulsations of the blackbody being ~ π out of phase with those of the power law component. Using a simple model for emission from hot spots on the neutron star surface, we fit the pulse profile of the blackbody component to obtain an indication of the geometry of the system

Effect of Nicotine Exposure on Fatigue Mechanics of Murine Arteries

Nicotine is an addictive substance found in electronic cigarettes (e-cigarettes) and cigarettes, and the use of such products has growing concern because of the prevalent use by young adults. Nicotine exposure degrades arterial tissue and results in increased arterial stiffness, which is linked to cardiovascular events, such as stroke and myocardial infarction. The hypothesis of this research is that fatigue loading reveals changes to the mechanical behavior of nicotine-treated arteries that is more informatively discriminating than stiffness alone. Ten murine arteries, five untreated and five nicotine-treated, were subjected to cyclic fatigue loading in an open-circumferential configuration by a custom tensometer with a motorized actuator and inline load cell. The specimens were subjected to an alternating strain of ±50%, superimposed on a mean strain of 150%. A power law was fit to the experimental data to extract parameters indicative of peak stress, loss of tension, degradation slope, and oscillation band. Compared to untreated specimens, nicotine-treated specimens exhibited a 108% higher peak stress, 118% greater loss of tension, and 107% larger width of the oscillation band. The tension loss and oscillation band width provided additional discriminating evidence of different mechanical behavior, supporting the hypothesis that fatigue testing can reveal distinctions in mechanical behavior that are not evident in static testing alone

MatchIt: Nonparametric Preprocessing for Parametric Causal Inference

MatchIt implements the suggestions of Ho, Imai, King, and Stuart (2007) for improving parametric statistical models by preprocessing data with nonparametric matching methods. MatchIt implements a wide range of sophisticated matching methods, making it possible to greatly reduce the dependence of causal inferences on hard-to-justify, but commonly made, statistical modeling assumptions. The software also easily fits into existing research practices since, after preprocessing data with MatchIt, researchers can use whatever parametric model they would have used without MatchIt, but produce inferences with substantially more robustness and less sensitivity to modeling assumptions. MatchIt is an R program, and also works seamlessly with Zelig.

Numerical algebraic geometry for model selection and its application to the life sciences

Researchers working with mathematical models are often confronted by the related problems of parameter estimation, model validation, and model selection. These are all optimization problems, well-known to be challenging due to non-linearity, non-convexity and multiple local optima. Furthermore, the challenges are compounded when only partial data is available. Here, we consider polynomial models (e.g., mass-action chemical reaction networks at steady state) and describe a framework for their analysis based on optimization using numerical algebraic geometry. Specifically, we use probability-one polynomial homotopy continuation methods to compute all critical points of the objective function, then filter to recover the global optima. Our approach exploits the geometric structures relating models and data, and we demonstrate its utility on examples from cell signaling, synthetic biology, and epidemiology.Comment: References added, additional clarification

How volume of water effects in morphological growth and number of endomycorrhizas on P. delicatum [abstract]

Abstract only availableIn alpine tundra plants, water is the most important environmental factor of plant growth. And the endomycorrhiza is the greatest symbiotic organism. Because, it provides Nitrogen and Phosphorus to the plants and it obtains the a Carbohydrate from the plants. Based on the experimental result, I thought about that if the plants had reached the limit, would they effect to the mycorrhiza? For that question, I made a experiment plan.Through controling of water volume, I could get the data of P.delicatum (called Jacob's ladder) about the morphological growths and the increment of the endomycorrhizas for 3 weeks. To control the environment, I built a weather port which can avoid rain, in addition opened the side of the weather port to make sure other variations as equal. After that, I made 4 treatments, which 10 plants have randomly assigned. And using ANOVA-test, I got 0.8615 as probablity. It means they are randomly assigned well. Each treatment has different water volume from 50ml every day to none of water. And then, I collected pre-data and post-data of morphological feature and number of endomycorrhizas. Unfortunately, I can't make a conclusion now. Because, I finished counting og the endomycorrhizas yesterday. So I need time to apply statistical approachs to my data. But I presuppose the result as the more watered plants would be had more endomycorrhizas

A Role for Nogo Receptor in Macrophage Clearance from Injured Peripheral Nerve

SummaryWe report a role for Nogo receptors (NgRs) in macrophage efflux from sites of inflammation in peripheral nerve. Increasing numbers of macrophages in crushed rat sciatic nerves express NgR1 and NgR2 on the cell surface in the first week after injury. These macrophages show reduced binding to myelin and MAG in vitro, which is reversed by NgR siRNA knockdown and by inhibiting Rho-associated kinase. Fourteen days after sciatic nerve crush, regenerating nerves with newly synthesized myelin have fewer macrophages than cut/ligated nerves that lack axons and myelin. Almost all macrophages in the cut/ligated nerves lie within the Schwann cell basal lamina, while in the crushed regenerating nerves the majority migrate out. Furthermore, crush-injured nerves of NgR1- and MAG-deficient mice and Y-27632-treated rats show impaired macrophage efflux from Schwann cell basal lamina containing myelinated axons. These data have implications for the resolution of inflammation in peripheral nerve and CNS pathologies