SPITZER observations of dust destruction in the Puppis A supernova remnant

The interaction of the Puppis A supernova remnant (SNR) with a neighboring molecular cloud provides a unique opportunity to measure the amount of grain destruction in an SNR shock. Spitzer Space Telescope MIPS imaging of the entire SNR at 24, 70, and 160 μm shows an extremely good correlation with X-ray emission, indicating that the SNR’s IR radiation is dominated by the thermal emission of swept-up interstellar dust, collisionally heated by the hot shocked gas. Spitzer IRS spectral observations targeted both the Bright Eastern Knot (BEK) of the SNR where a small cloud has been engulfed by the supernova blast wave and outlying portions of the associated molecular cloud that are yet to be hit by the shock front. Modeling the spectra from both regions reveals the composition and the grain size distribution of the interstellar dust, both in front of and behind the SNR shock front. The comparison shows that the ubiquitous polycyclic aromatic hydrocarbons of the interstellar medium are destroyed within the BEK, along with nearly 25% of the mass of graphite and silicate dust grains

A hidden Markov model for reconstructing animal paths from solar geolocation loggers using templates for light intensity

Background Solar archival tags (henceforth called geolocators) are tracking devices deployed on animals to reconstruct their long-distance movements on the basis of locations inferred post hoc with reference to the geographical and seasonal variations in the timing and speeds of sunrise and sunset. The increased use of geolocators has created a need for analytical tools to produce accurate and objective estimates of migration routes that are explicit in their uncertainty about the position estimates. Results We developed a hidden Markov chain model for the analysis of geolocator data. This model estimates tracks for animals with complex migratory behaviour by combining: (1) a shading-insensitive, template-fit physical model, (2) an uncorrelated random walk movement model that includes migratory and sedentary behavioural states, and (3) spatially explicit behavioural masks. The model is implemented in a specially developed open source R package FLightR. We used the particle filter (PF) algorithm to provide relatively fast model posterior computation. We illustrate our modelling approach with analysis of simulated data for stationary tags and of real tracks of both a tree swallow Tachycineta bicolor migrating along the east and a golden-crowned sparrow Zonotrichia atricapilla migrating along the west coast of North America. Conclusions We provide a model that increases accuracy in analyses of noisy data and movements of animals with complicated migration behaviour. It provides posterior distributions for the positions of animals, their behavioural states (e.g., migrating or sedentary), and distance and direction of movement. Our approach allows biologists to estimate locations of animals with complex migratory behaviour based on raw light data. This model advances the current methods for estimating migration tracks from solar geolocation, and will benefit a fast-growing number of tracking studies with this technology

Brain Functional Connectivity and Anatomical Features as Predictors of Cognitive Behavioral Therapy Outcome for Anxiety in Youths

Background Because pediatric anxiety disorders precede the onset of many other problems, successful prediction of response to the first-line treatment, cognitive-behavioral therapy (CBT), could have major impact. However, existing clinical models are weakly predictive. The current study evaluates whether structural and resting-state functional magnetic resonance imaging can predict post-CBT anxiety symptoms. Methods Two datasets were studied: (A) one consisted of n=54 subjects with an anxiety diagnosis, who received 12 weeks of CBT, and (B) one consisted of n=15 subjects treated for 8 weeks. Connectome Predictive Modeling (CPM) was used to predict treatment response, as assessed with the PARS; additionally we investigated models using anatomical features, instead of functional connectivity. The main analysis included network edges positively correlated with treatment outcome, and age, sex, and baseline anxiety severity as predictors. Results from alternative models and analyses also are presented. Model assessments utilized 1000 bootstraps, resulting in a 95% CI for R2, r and mean absolute error (MAE). Outcomes The main model showed a mean absolute error of approximately 3.5 (95%CI: [3.1-3.8]) points a R2 of 0.08 [−0.14 - 0.26] and r of 0.38 [0.24 – 0.511]. When testing this model in the left-out sample (B) the results were similar, with a MAE of 3.4 [2.8 – 4.7], R2 −0.65 [−2.29 – 0.16] and r of 0.4 [0.24 – 0.54]. The anatomical metrics showed a similar pattern, where models rendered overall low R2. Interpretation The analysis showed that models based on earlier promising results failed to predict clinical outcomes. Despite the small sample size, the current study does not support extensive use of CPM to predict outcome in pediatric anxiety. Clinical Trial NCT0001805

Talbot Effect for Exciton Polaritons

e demonstrate, experimentally and theoretically, a Talbot effect for hybrid light-matter waves—an exciton-polariton condensate formed in a semiconductor microcavity with embedded quantum wells. The characteristic “Talbot carpet” is produced by loading the exciton-polariton condensate into a microstructured one-dimensional periodic array of mesa traps, which creates an array of phase-locked sources for coherent polariton flow in the plane of the quantum wells. The spatial distribution of the Talbot fringes outside the mesas mimics the near-field diffraction of a monochromatic wave on a periodic amplitude and phase grating with the grating period comparable to the wavelength. Despite the lossy nature of the polariton system, the Talbot pattern persists for distances exceeding the size of the mesas by an order of magnitude. Thus, our experiment demonstrates efficient shaping of the two-dimensional flow of coherent exciton polaritons by a one-dimensional “flat lens.

Inherent limits of light-level geolocation may lead to over-interpretation

In their 2015 Current Biology paper, Streby et al. [1] reported that Golden-winged Warblers (Vermivora chrysoptera), which had just migrated to their breeding location in eastern Tennessee, performed a facultative and up to “>1,500 km roundtrip” to the Gulf of Mexico to avoid a severe tornadic storm. From light-level geolocator data, wherein geographical locations are estimated via the timing of sunrise and sunset, Streby et al. [1] concluded that the warblers had evacuated their breeding area approximately 24 hours before the storm and returned about five days later. The authors presented this finding as evidence that migratory birds avoid severe storms by temporarily moving long-distances. However, the tracking method employed by Streby et al. [1] is prone to considerable error and uncertainty. Here, we argue that this interpretation of the data oversteps the limits of the used tracking technique. By calculating the expected geographical error range for the tracked birds, we demonstrate that the hypothesized movements fell well within the geolocators’ inherent error range for this species and that such deviations in latitude occur frequently even if individuals remain stationary

Constructing and evaluating a continent‐wide migratory songbird network across the annual cycle

Determining how migratory animals are spatially connected between breeding and non‐breeding periods is essential for predicting the effects of environmental change and for developing optimal conservation strategies. Yet, despite recent advances in tracking technology, we lack comprehensive information on the spatial structure of migratory networks across a species’ range, particularly for small‐bodied, long‐distance migratory animals. We constructed a migratory network for a songbird and used network‐based metrics to characterize the spatial structure and prioritize regions for conservation. The network was constructed using year‐round movements derived from 133 archival light‐level geolocators attached to Tree Swallows (Tachycineta bicolor) originating from 12 breeding sites across their North American breeding range. From these breeding sites, we identified 10 autumn stopover nodes (regions) in North America, 13 non‐breeding nodes located around the Gulf of Mexico, Mexico, Florida, and the Caribbean, and 136 unique edges (migratory routes) connecting nodes. We found strong migratory connectivity between breeding and autumn stopover sites and moderate migratory connectivity between the breeding and non‐breeding sites. We identified three distinct “communities” of nodes that corresponded to western, central, and eastern North American flyways. Several regions were important for maintaining network connectivity, with South Florida and Louisiana as the top ranked non‐breeding nodes and the Midwest as the top ranked stopover node. We show that migratory songbird networks can have both a high degree of mixing between seasons yet still show regionally distinct migratory flyways. Such information will be crucial for accurately predicting factors that limit and regulate migratory songbirds throughout the annual cycle. Our study highlights how network‐based metrics can be valuable for identifying overall network structure and prioritizing specific regions within a network for conserving a wide variety of migratory animals

Catching Element Formation In The Act

Gamma-ray astronomy explores the most energetic photons in nature to address some of the most pressing puzzles in contemporary astrophysics. It encompasses a wide range of objects and phenomena: stars, supernovae, novae, neutron stars, stellar-mass black holes, nucleosynthesis, the interstellar medium, cosmic rays and relativistic-particle acceleration, and the evolution of galaxies. MeV gamma-rays provide a unique probe of nuclear processes in astronomy, directly measuring radioactive decay, nuclear de-excitation, and positron annihilation. The substantial information carried by gamma-ray photons allows us to see deeper into these objects, the bulk of the power is often emitted at gamma-ray energies, and radioactivity provides a natural physical clock that adds unique information. New science will be driven by time-domain population studies at gamma-ray energies. This science is enabled by next-generation gamma-ray instruments with one to two orders of magnitude better sensitivity, larger sky coverage, and faster cadence than all previous gamma-ray instruments. This transformative capability permits: (a) the accurate identification of the gamma-ray emitting objects and correlations with observations taken at other wavelengths and with other messengers; (b) construction of new gamma-ray maps of the Milky Way and other nearby galaxies where extended regions are distinguished from point sources; and (c) considerable serendipitous science of scarce events -- nearby neutron star mergers, for example. Advances in technology push the performance of new gamma-ray instruments to address a wide set of astrophysical questions.Comment: 14 pages including 3 figure

Genome-Wide Association and Trans-ethnic Meta-Analysis for Advanced Diabetic Kidney Disease: Family Investigation of Nephropathy and Diabetes (FIND)

Diabetic kidney disease (DKD) is the most common etiology of chronic kidney disease (CKD) in the industrialized world and accounts for much of the excess mortality in patients with diabetes mellitus. Approximately 45% of U.S. patients with incident end-stage kidney disease (ESKD) have DKD. Independent of glycemic control, DKD aggregates in families and has higher incidence rates in African, Mexican, and American Indian ancestral groups relative to European populations. The Family Investigation of Nephropathy and Diabetes (FIND) performed a genome-wide association study (GWAS) contrasting 6,197 unrelated individuals with advanced DKD with healthy and diabetic individuals lacking nephropathy of European American, African American, Mexican American, or American Indian ancestry. A large-scale replication and trans-ethnic meta-analysis included 7,539 additional European American, African American and American Indian DKD cases and non-nephropathy controls. Within ethnic group meta-analysis of discovery GWAS and replication set results identified genome-wide significant evidence for association between DKD and rs12523822 on chromosome 6q25.2 in American Indians (P = 5.74x10-9). The strongest signal of association in the trans-ethnic meta-analysis was with a SNP in strong linkage disequilibrium with rs12523822 (rs955333; P = 1.31x10-8), with directionally consistent results across ethnic groups. These 6q25.2 SNPs are located between the SCAF8 and CNKSR3 genes, a region with DKD relevant changes in gene expression and an eQTL with IPCEF1, a gene co-translated with CNKSR3. Several other SNPs demonstrated suggestive evidence of association with DKD, within and across populations. These data identify a novel DKD susceptibility locus with consistent directions of effect across diverse ancestral groups and provide insight into the genetic architecture of DKD

Emotion regulation in heavy smokers: experiential, expressive and physiological consequences of cognitive reappraisal

Emotion regulation dysfunctions are assumed to contribute to the development of tobacco addiction and relapses among smokers attempting to quit. To further examine this hypothesis, the present study compared heavy smokers with nonsmokers in a reappraisal task. Specifically, we investigated whether nondeprived smokers and deprived smokers differ from nonsmokers in cognitive emotion regulation and whether there is an association between the outcome of emotion regulation and the cigarette craving. Sixty-five participants (23 nonsmokers, 22 nondeprived smokers, and 20 deprived smokers) were instructed to down-regulate emotions by reappraising negative or positive pictorial scenarios. Self-ratings of valence, arousal and cigarette craving as well as facial electromyography and electroencephalograph activities were measured. Ratings, facial EMG, and EEG data indicated that both nondeprived smokers and deprived smokers performed comparably to nonsmokers in regulating emotional responses via reappraisal, irrespective of the valence of pictorial stimuli. Interestingly, changes in cigarette craving were positively associated with regulation of emotional arousal irrespective of emotional valence. These results suggest that heavy smokers are capable to regulate emotion via deliberate reappraisal and smokers’ cigarette craving is associated with emotional arousal rather than emotional valence. This study provides preliminary support for the therapeutic use of reappraisal to replace maladaptive emotion-regulation strategies in nicotine addicts