SN 2005hj: Evidence for Two Classes of Normal-Bright SNe Ia and Implications for Cosmology

HET Optical spectra covering the evolution from about 6 days before to about 5 weeks after maximum light and the ROTSE-IIIb unfiltered light curve of the "Branch-normal" Type Ia Supernova SN 2005hj are presented. The host galaxy shows HII region lines at redshift of z=0.0574, which puts the peak unfiltered absolute magnitude at a somewhat over-luminous -19.6. The spectra show weak and narrow SiII lines, and for a period of at least 10 days beginning around maximum light these profiles do not change in width or depth and they indicate a constant expansion velocity of ~10,600 km/s. We analyzed the observations based on detailed radiation dynamical models in the literature. Whereas delayed detonation and deflagration models have been used to explain the majority of SNe Ia, they do not predict a long velocity plateau in the SiII minimum with an unvarying line profile. Pulsating delayed detonations and merger scenarios form shell-like density structures with properties mostly related to the mass of the shell, M_shell, and we discuss how these models may explain the observed SiII line evolution; however, these models are based on spherical calculations and other possibilities may exist. SN 2005hj is consistent with respect to the onset, duration, and velocity of the plateau, the peak luminosity and, within the uncertainties, with the intrinsic colors for models with M_shell=0.2 M_sun. Our analysis suggests a distinct class of events hidden within the Branch-normal SNe Ia. If the predicted relations between observables are confirmed, they may provide a way to separate these two groups. We discuss the implications of two distinct progenitor classes on cosmological studies employing SNe Ia, including possible differences in the peak luminosity to light curve width relation.Comment: ApJ accepted, 31 page

Phase 1, placebo-controlled, dose escalation trial of chicory root extract in patients with osteoarthritis of the hip or knee

<p>Abstract</p> <p>Background</p> <p>Extracts of chicory root have anti-inflammatory properties <it>in vitro </it>and in animal models of arthritis. The primary objective of this investigator-initiated, Phase 1, placebo-controlled, double blind, dose-escalating trial was to determine the safety and tolerability of a proprietary bioactive extract of chicory root in patients with osteoarthritis (OA). Secondary objectives were to assess effects on the signs and symptoms of this disorder.</p> <p>Methods</p> <p>Individuals greater than 50 years of age with OA of the hip or knee were eligible for trial entry. A total of 40 patients were enrolled in 3 cohorts and were treated with escalating chicory doses of 600 mg/day, 1200 mg/day and 1800 mg/day for 1 month. The ratio of active treatment to placebo was 5:3 in cohorts 1 and 2 (8 patients) each and 16:8 in cohort 3 (24 patients). Safety evaluations included measurement of vital signs and routine lab tests at baseline and the end of the treatment period. Efficacy evaluations at baseline and final visits included self-assessment questionnaires and measurement of the 25-foot walking time.</p> <p>Results</p> <p>In the highest dose cohort, 18 patients who completed treatment per protocol were analyzed for efficacy. In this group, 13 patients showed at least 20% improvement in the defined response domains of pain, stiffness and global assessment: 9 of 10 (90%) patients randomized to active treatment with chicory and 4 of 8 (50%) patients randomized to placebo (P = 0.06). In general, the treatment was well-tolerated. Only one patient who was treated with the highest dose of chicory had to discontinue treatment due to an adverse event.</p> <p>Conclusions</p> <p>The results of this pilot study suggest that a proprietary bioactive extract of chicory root has a potential role in the management of OA and merits further investigation. Clinicaltrials.gov identifier: NCT 01010919.</p

Unemployment expectations in an agent-based model with education

Why are unemployment expectations of the “man in the street” markedly different from professional forecasts? We present an agent-based model to explain this deep disconnection using boundedly rational agents with different levels of education. A good fit of empirical data is obtained under the assumptions that there is staggered update of information, agents update episodically their estimate and there is a fraction of households who always and stubbornly forecast that the unemployment is going to raise. The model also sheds light on the role of education and suggests that more educated agents update their information more often and less obstinately fixate on the worst possible forecast

Is time-variant information stickiness state-dependent?

This paper estimates information stickiness with regard to inflation expectations in the United States and the Eurozone for the 1981/06–2015/12 and 1998/Q4–2015/Q2 periods, respectively, and further investigates whether such information stickiness is state- dependent. Based on a bootstrap sub-sample rolling-window estimation, we find that information stickiness varies over time, which contradicts the strict time dependency implied under sticky-information theory. We provide evidence that information stickiness depends on inflation volatility, which indicates that information stickiness is state-dependent and that it has a time trend. Using a threshold model, we estimate structural changes in the state- dependence and time-trend of information stickiness. The results show that information stickiness has been more dependent on inflation volatility and has had a higher time-trend in both regions following the 2008 financial crisis.info:eu-repo/semantics/publishedVersio

Disability and the Immigrant Health Paradox: Gender and Timing of Migration

Although research has documented better health and longer life expectancy among the foreign-born relative to their U.S.-born counterparts, the U.S. Mexican-origin immigrant population is diverse and the healthy immigrant effect likely varies by key structural and demographic factors such as gender, migration history, and duration in the United States. Using a life course framework, we use data from the Hispanic Established Populations for the Epidemiologic Study of the Elderly (H-EPESE 1993–2013) which includes Mexican-American individuals aged 65 and older to assess the heterogeneity in the immigrant health advantage by age of migration and gender. We find that age of migration is an important delineating factor for disability among both men and women. The healthy immigrant hypothesis is only observable among mid- and late-life migrant men for ADL disability. While among immigrant women, late-life migrants are more likely to have an IADL disability putting them at a health disadvantage. These findings illustrate that Mexican immigrants are not a homogeneous group and migrant health selectivity depends on both gender and when migrants arrived in the United States

Causal Measures of Structure and Plasticity in Simulated and Living Neural Networks

A major goal of neuroscience is to understand the relationship between neural structures and their function. Recording of neural activity with arrays of electrodes is a primary tool employed toward this goal. However, the relationships among the neural activity recorded by these arrays are often highly complex making it problematic to accurately quantify a network's structural information and then relate that structure to its function. Current statistical methods including cross correlation and coherence have achieved only modest success in characterizing the structural connectivity. Over the last decade an alternative technique known as Granger causality is emerging within neuroscience. This technique, borrowed from the field of economics, provides a strong mathematical foundation based on linear auto-regression to detect and quantify “causal” relationships among different time series. This paper presents a combination of three Granger based analytical methods that can quickly provide a relatively complete representation of the causal structure within a neural network. These are a simple pairwise Granger causality metric, a conditional metric, and a little known computationally inexpensive subtractive conditional method. Each causal metric is first described and evaluated in a series of biologically plausible neural simulations. We then demonstrate how Granger causality can detect and quantify changes in the strength of those relationships during plasticity using 60 channel spike train data from an in vitro cortical network measured on a microelectrode array. We show that these metrics can not only detect the presence of causal relationships, they also provide crucial information about the strength and direction of that relationship, particularly when that relationship maybe changing during plasticity. Although we focus on the analysis of multichannel spike train data the metrics we describe are applicable to any stationary time series in which causal relationships among multiple measures is desired. These techniques can be especially useful when the interactions among those measures are highly complex, difficult to untangle, and maybe changing over time