Effects of post-fire logging on forest surface air temperatures in the Siskiyou Mountains, Oregon, USA

Following stand-replacing wildfire, post-fire (salvage) logging of fire-killed trees is a widely implemented management practice in many forest types. A common hypothesis is that removal of fire-killed trees increases surface temperatures due to loss of shade and increased solar radiation, thereby influencing vegetation establishment and possibly stand development. Six years after a wildfire in a Mediterranean-climate mixed-conifer forest in southwest Oregon, USA, we measured the effects of post-fire logging (> 90 per cent dead tree (snag) removal) on growing season surface air temperatures. Compared with unlogged severely burned forest, post-fire logging did not lead to increased maximum daily surface air temperature. However, dead tree removal was associated with lower nightly minimum temperatures (similar to 1 degrees C) and earlier daytime heating, leading to a 1-2 degrees C difference during the warming portion of the day. Effects varied predictably by aspect. The patterns reported here represent a similar but muted pattern as previously reported for microclimatic changes following clear-cutting of green trees. Effects of microsites such as tree bases on fine-scale temperature regimes require further investigation

Dark Matter Constraints from the Sagittarius Dwarf and Tail System

2MASS has provided a three-dimensional map of the >360 degree, wrapped tidal tails of the Sagittarius (Sgr) dwarf spheroidal galaxy, as traced by M giant stars. With the inclusion of radial velocity data for stars along these tails, strong constraints exist for dynamical models of the Milky Way-Sgr interaction. N-body simulations of Sgr disruption with model parameters spanning a range of initial conditions (e.g., Sgr mass and orbit, Galactic rotation curve, halo flattening) are used to find parameterizations that match almost every extant observational constraint of the Sgr system. We discuss the implications of the Sgr data and models for the orbit, mass and M/L of the Sgr bound core as well as the strength, flattening, and lumpiness of the Milky Way potential.Comment: 6 pages, 0 figures. Contribution to proceedings of ``IAU Symposium 220: Dark Matter in Galaxies'', eds. S. Ryder, D.J. Pisano, M. Walker, and K. Freema

The RRAT Trap: Interferometric Localization of Radio Pulses from J0628+0909

We present the first blind interferometric detection and imaging of a millisecond radio transient with an observation of transient pulsar J0628+0909. We developed a special observing mode of the Karl G. Jansky Very Large Array (VLA) to produce correlated data products (i.e., visibilities and images) on a time scale of 10 ms. Correlated data effectively produce thousands of beams on the sky that can localize sources anywhere over a wide field of view. We used this new observing mode to find and image pulses from the rotating radio transient (RRAT) J0628+0909, improving its localization by two orders of magnitude. Since the location of the RRAT was only approximately known when first observed, we searched for transients using a wide-field detection algorithm based on the bispectrum, an interferometric closure quantity. Over 16 minutes of observing, this algorithm detected one transient offset roughly 1' from its nominal location; this allowed us to image the RRAT to localize it with an accuracy of 1.6". With a priori knowledge of the RRAT location, a traditional beamforming search of the same data found two, lower significance pulses. The refined RRAT position excludes all potential multiwavelength counterparts, limiting its optical luminosity to L_i'<1.1x10^31 erg/s and excluding its association with a young, luminous neutron star.Comment: Submitted to ApJ, 7 pages, 5 figure

A Multiwavelength View of a Mass Outflow from the Galactic Center

The Galactic center (GC) lobe is a degree-tall shell of gas that spans the central degree of our Galaxy. It has been cited as evidence for a mass outflow from our GC region, which has inspired diverse models for its origin. However, most work has focused on the morphology of the GC lobe, which has made it difficult to draw strong conclusions about its nature. Here, I present a coherent, multiwavelength analysis of new and archival observations of the GC lobe. Radio continuum emission shows that the GC lobe has a magnetized layer with a diameter of 110 pc and an equipartition field strength ranging from 40 to 100 $\mu$G. Recombination line emission traces an ionized shell nested within the radio continuum with diameter of 80 pc and height 165 pc. Mid-infrared maps at 8 and 15 $\mu$m show that the GC lobe has a third layer of warm dust and PAH-emission that surrounds the radio continuum shell with a diameter of 130 pc. Assuming adiabatic expansion of the gas in the GC lobe, its formation required an energy input of about $5\times10^{52}$ ergs. I compare the physical conditions of the GC lobe to several models and find best agreement with the canonical starburst outflow model. The formation of the GC lobe is consistent with the currently observed pressure and star formation rate in the central tens of parsecs of our Galaxy. Outflows of this scale are more typical of dwarf galaxies and would not be easily detected in nearby spiral galaxies. Thus, the existence of such an outflow in our own Galaxy may indicate that it is relatively common phenomenon in the nuclei of spiral galaxies. (Abridged)Comment: Accepted to ApJ. 15 pages, 8 (compressed) figure

Season of the year influences infection rates following total hip arthroplasty

To research the influence of season of the year on periprosthetic joint infections. METHODS We conducted a retrospective review of the entire Medicare files from 2005 to 2014. Seasons were classified as spring, summer, fall or winter. Regional variations were accounted for by dividing patients into four geographic regions as per the United States Census Bureau (Northeast, Midwest, West and South). Acute postoperative infection and deep periprosthetic infections within 90 d after surgery were tracked. RESULTS In all regions, winter had the highest incidence of periprosthetic infections (mean 0.98%, SD 0.1%) and was significantly higher than other seasons in the Midwest, South and West (P \u3c 0.05 for all) but not the Northeast (P = 0.358). Acute postoperative infection rates were more frequent in the summer and were significantly affected by season of the year in the West. CONCLUSION Season of the year is a risk factor for periprosthetic joint infection following total hip arthroplasty (THA). Understanding the influence of season on outcomes following THA is essential when risk-stratifying patients to optimize outcomes and reduce episode of care costs. © The Author(s) 2017

Infants and iCubs:Applying Developmental Psychology to Robot Shaping

Despite the abundance of autonomous system in the natural world we have not managed to even approximate the competences and skills seen in humans and ani-mals. One key reason is that robots, like humans but unlike other computational systems, are embedded in the real world and have to experience noisy, chaotic, un-structured environments. Even for robots with limited sensing and actuating capa-bilities the complexity arising from interactions with the real world makes uncon-strained learning unreliable and computationally demanding. Infants use scaffolding, or shaping, to overcome this sensory overload. Scaffolding, which can be produced by internal or external influences, guides the development of the infant to learn complex abilities from primitive beginnings through a se-quence of staged development [1]. At each stage the infant is restricted by a series of constraints, which limit interaction and reduce the perceived complexity of the environment [2]. These constraints are eased over time as the infant learns to mas-ter its abilities, leading to further exploration and learning. We are using these ideas to create robots that learn developmentally

The R Package bgmm : Mixture Modeling with Uncertain Knowledge

Classical supervised learning enjoys the luxury of accessing the true known labels for the observations in a modeled dataset. Real life, however, poses an abundance of problems, where the labels are only partially defined, i.e., are uncertain and given only for a subsetof observations. Such partial labels can occur regardless of the knowledge source. For example, an experimental assessment of labels may have limited capacity and is prone to measurement errors. Also expert knowledge is often restricted to a specialized area and is thus unlikely to provide trustworthy labels for all observations in the dataset. Partially supervised mixture modeling is able to process such sparse and imprecise input. Here, we present an R package calledbgmm, which implements two partially supervised mixture modeling methods: soft-label and belief-based modeling. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. On real data we present the usage of bgmm for basic model-fitting in all modeling variants. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. This functionality is presented on an artificial dataset, which can be simulated in bgmm from a distribution defined by a given model

The Nature of Nonthermal X-ray Filaments Near the Galactic Center

Recent Chandra and XMM-{\it Newton} observations reported evidence of two X-ray filaments G359.88-0.08 (SgrA-E) and G359.54+0.18 (the ripple filament) near the Galactic center. The X-ray emission from these filaments has a nonthermal spectrum and coincides with synchrotron emitting radio sources. Here, we report the detection of a new X-ray feature coincident with a radio filament G359.90-0.06 (SgrA-F) and show more detailed VLA, Chandra and BIMA observations of the radio and X-ray filaments. In particular, we show that radio emission from the nonthermal filaments G359.90-0.06 (SgrA-F) and G359.54+0.18 (the ripple) has a steep spectrum whereas G359.88-0.08 (SgrA-E) has a flat spectrum. The X-ray emission from both these sources could be due to synchrotron radiation. However, given that the 20 \kms molecular cloud, with its intense 1.2mm dust emission, lies in the vicinity of SgrA-F, it is possible that the X-rays could be produced by inverse Compton scattering of far-infrared photons from dust by the relativistic electrons responsible for the radio synchrotron emission. The production of X-ray emission from ICS allows an estimate of the magnetic field strength of ~0.08 mG within the nonthermal filament. This should be an important parameter for any models of the Galactic center nonthermal filaments.Comment: 14 pages, 9 figures, in Cospar 2004 session E1.4; editors: Cara Rakowski and Shami Chatterjee; "Young Neutron Stars and Supernova Remnants", publication: Advances in Space Research (in press

Two new methods to fit models for network meta-analysis with random inconsistency effects.

BACKGROUND: Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. METHODS: The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. RESULTS: We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. CONCLUSIONS: The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported