The Bolocam Galactic Plane Survey. XIII. Physical Properties and Mass Functions of Dense Molecular Cloud Structures

We use the distance probability density function (DPDF) formalism of Ellsworth-Bowers et al. (2013, 2015) to derive physical properties for the collection of 1,710 Bolocam Galactic Plane Survey (BGPS) version 2 sources with well-constrained distance estimates. To account for Malmquist bias, we estimate that the present sample of BGPS sources is 90% complete above 400 $M_\odot$ and 50% complete above 70 $M_\odot$. The mass distributions for the entire sample and astrophysically motivated subsets are generally fitted well by a lognormal function, with approximately power-law distributions at high mass. Power-law behavior emerges more clearly when the sample population is narrowed in heliocentric distance (power-law index $\alpha = 2.0\pm0.1$ for sources nearer than 6.5 kpc and $\alpha = 1.9\pm0.1$ for objects between 2 kpc and 10 kpc). The high-mass power-law indices are generally $1.85 \leq \alpha \leq 2.05$ for various subsamples of sources, intermediate between that of giant molecular clouds and the stellar initial mass function. The fit to the entire sample yields a high-mass power-law $\hat{\alpha} = 1.94_{-0.10}^{+0.34}$. Physical properties of BGPS sources are consistent with large molecular cloud clumps or small molecular clouds, but the fractal nature of the dense interstellar medium makes difficult the mapping of observational categories to the dominant physical processes driving the observed structure. The face-on map of the Galactic disk's mass surface density based on BGPS dense molecular cloud structures reveals the high-mass star-forming regions W43, W49, and W51 as prominent mass concentrations in the first quadrant. Furthermore, we present a 0.25-kpc resolution map of the dense gas mass fraction across the Galactic disk that peaks around 5%.Comment: Accepted for publication in ApJ; 32 pages, 21 figure

How structural adaptability exists alongside HLA-A2 bias in the human alphabeta TCR repertoire

How T-cell receptors (TCRs) can be intrinsically biased toward MHC proteins while simultaneously display the structural adaptability required to engage diverse ligands remains a controversial puzzle. We addressed this by examining alphabeta TCR sequences and structures for evidence of physicochemical compatibility with MHC proteins. We found that human TCRs are enriched in the capacity to engage a polymorphic, positively charged hot-spot region that is almost exclusive to the alpha1-helix of the common human class I MHC protein, HLA-A*0201 (HLA-A2). TCR binding necessitates hot-spot burial, yielding high energetic penalties that must be offset via complementary electrostatic interactions. Enrichment of negative charges in TCR binding loops, particularly the germ-line loops encoded by the TCR Valpha and Vbeta genes, provides this capacity and is correlated with restricted positioning of TCRs over HLA-A2. Notably, this enrichment is absent from antibody genes. The data suggest a built-in TCR compatibility with HLA-A2 that biases receptors toward, but does not compel, particular binding modes. Our findings provide an instructional example for how structurally pliant MHC biases can be encoded within TCRs

TUNGSTEN SHIELDS FOR CS-137 INLINE MONITORS IN THE CAUSTIC SIDE SOLVENT EXTRACTION PROCESS

The Department of Energy (DOE) selected Caustic-Side Solvent Extraction (CSSX) as the preferred technology for the removal of radioactive cesium from High-Level Waste (HLW) at the Savannah River Site (SRS). The CSSX process is a continuous process that uses a novel solvent to extract cesium from highly radioactive waste and concentrate it in dilute nitric acid. In-line analyses are performed with gamma-ray monitors to measure the C-137 concentration in the decontaminated salt solution (DSS) and in the strip effluent (SE). Sodium iodide (NaI) monitors are used to measure the Cs-137 concentration before the DSS Hold Tank, while Geiger-Mueller (GM) monitors are used for Cs-137 measurements before the SE hold tank. Tungsten shields were designed using Monte Carlo calculations and fabricated to provide the needed reduction of the process background radiation at the detector positions. A one-inch tungsten cylindrical shield reduced the background radiation by a factor of fifty that was adequate for the GM detectors, while a three-and-one-half-inch tungsten cylindrical shield was required for the NaI detectors. Testing of the NaI shield was performed at the SRS Instrument Calibration Facility. Based on this testing, the as-built shield is predicted to be able to detect the MCU DSS stream at concentrations above 0.003 Ci/gal under the ''worst case'' field conditions with a MCU feed solution of 1.1 Ci/gal and all of the process tanks completely full. This paper discusses the design, fabrication, testing and implementation of the tungsten shields in the MCU facility

The radio source count at 93.2 GHz from observations of 9C sources using AMI and CARMA

We present results from follow-up observations of a sample of 80 radio sources, originally detected as part of the 15.2-GHz Ninth Cambridge (9C) survey. The observations were carried out, close to simultaneously, at two frequencies: 15.7 GHz, using the Arcminute Microkelvin Imager (AMI) Large Array, and 93.2 GHz, using the Combined Array for Research in Millimeter-wave Astronomy (CARMA). There is currently little direct information on the 90-GHz-band source count for S ≲ 1 Jy. However, we have used the measured 15.7-to-93.2-GHz spectral-index distribution and 9C source count to predict the differential source count at 93.2 GHz as 26 ± 4(S/Jy)^(−2.15) Jy^(−1) sr^(−1); our projection is estimated to be most accurate for 10 ≲ S ≲ 100 mJy. Our estimated differential count is more than twice the 90-GHz prediction made by Waldram et al.; we believe that this discrepancy is because the measured 43-GHz flux densities used in making their prediction were too low. Similarly, our prediction is significantly higher than that of Sadler et al. at 95 GHz. Since our spectral-index distribution is similar to the 20-to-95-GHz distribution measured by Sadler et al. and used in making their prediction, we believe that the difference is almost entirely attributable to the dissimilarity in the lower frequency counts used in making the estimates

Low-Level Laser Therapy Activates NF-kB via Generation of Reactive Oxygen Species in Mouse Embryonic Fibroblasts

Background Despite over forty years of investigation on low-level light therapy (LLLT), the fundamental mechanisms underlying photobiomodulation at a cellular level remain unclear. Methodology/Principal Findings In this study, we isolated murine embryonic fibroblasts (MEF) from transgenic NF-kB luciferase reporter mice and studied their response to 810 nm laser radiation. Significant activation of NF-kB was observed at fluences higher than 0.003 J/cm2 and was confirmed by Western blot analysis. NF-kB was activated earlier (1 hour) by LLLT compared to conventional lipopolysaccharide treatment. We also observed that LLLT induced intracellular reactive oxygen species (ROS) production similar to mitochondrial inhibitors, such as antimycin A, rotenone and paraquat. Furthermore, we observed similar NF-kB activation with these mitochondrial inhibitors. These results, together with inhibition of laser induced NF-kB activation by antioxidants, suggests that ROS play an important role in the laser induced NF-kB signaling pathways. However, LLLT, unlike mitochondrial inhibitors, induced increased cellular ATP levels, which indicates that LLLT also upregulates mitochondrial respiration. Conclusion We conclude that LLLT not only enhances mitochondrial respiration, but also activates the redox-sensitive NFkB signaling via generation of ROS. Expression of anti-apoptosis and pro-survival genes responsive to NFkB could explain many clinical effects of LLLT.National Institutes of Health (U.S.) (grant R01AI050875)Center for Integration of Medicine and Innovative Technology (DAMD17-02-2-0006)United States. Dept. of Defense (CDMRP Program in TBI, W81XWH-09-1-0514)United States. Air Force Office of Scientific Research (FA9950-04-1-0079

Species Interactions Alter Evolutionary Responses to a Novel Environment

Adaptation to a novel environment is altered by the presence of co-occurring species. Species in diverse communities evolved complementary resource use, which altered the functioning of the experimental ecosystems

Chapter 12: Systematic Review of Prognostic Tests

A number of new biological markers are being studied as predictors of disease or adverse medical events among those who already have a disease. Systematic reviews of this growing literature can help determine whether the available evidence supports use of a new biomarker as a prognostic test that can more accurately place patients into different prognostic groups to improve treatment decisions and the accuracy of outcome predictions. Exemplary reviews of prognostic tests are not widely available, and the methods used to review diagnostic tests do not necessarily address the most important questions about prognostic tests that are used to predict the time-dependent likelihood of future patient outcomes. We provide suggestions for those interested in conducting systematic reviews of a prognostic test. The proposed use of the prognostic test should serve as the framework for a systematic review and to help define the key questions. The outcome probabilities or level of risk and other characteristics of prognostic groups are the most salient statistics for review and perhaps meta-analysis. Reclassification tables can help determine how a prognostic test affects the classification of patients into different prognostic groups, hence their treatment. Review of studies of the association between a potential prognostic test and patient outcomes would have little impact other than to determine whether further development as a prognostic test might be warranted

Oral Rabies Vaccination in North America: Opportunities, Complexities, and Challenges

Steps to facilitate inter-jurisdictional collaboration nationally and continentally have been critical for implementing and conducting coordinated wildlife rabies management programs that rely heavily on oral rabies vaccination (ORV). Formation of a national rabies management team has been pivotal for coordinated ORV programs in the United States of America. The signing of the North American Rabies Management Plan extended a collaborative framework for coordination of surveillance, control, and research in border areas among Canada, Mexico, and the US. Advances in enhanced surveillance have facilitated sampling of greater scope and intensity near ORV zones for improved rabies management decision-making in real time. The value of enhanced surveillance as a complement to public health surveillance was best illustrated in Ohio during 2007, where 19 rabies cases were detected that were critical for the formulation of focused contingency actions for controlling rabies in this strategically key area. Diverse complexities and challenges are commonplace when applying ORV to control rabies in wild meso-carnivores. Nevertheless, intervention has resulted in notable successes, including the elimination of an arctic fox (Vulpes lagopus) rabies virus variant in most of southern Ontario, Canada, with ancillary benefits of elimination extending into Quebec and the northeastern US. Progress continues with ORV toward preventing the spread and working toward elimination of a unique variant of gray fox (Urocyon cinereoargenteus) rabies in west central Texas. Elimination of rabies in coyotes (Canis latrans) through ORV contributed to the US being declared free of canine rabies in 2007. Raccoon (Procyon lotor) rabies control continues to present the greatest challenges among meso-carnivore rabies reservoirs, yet to date intervention has prevented this variant from gaining a broad geographic foothold beyond ORV zones designed to prevent its spread from the eastern US. Progress continues toward the development and testing of new bait-vaccine combinations that increase the chance for improved delivery and performance in the diverse meso-carnivore rabies reservoir complex in the US

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0):Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%–31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency ∼0.52–0.63 and 0.53). UpCH4 estimated annual global wetland CH4 emissions of 146 ± 43 TgCH4 y−1 for 2001–2018 which agrees closely with current bottom-up land surface models (102–181 TgCH4 y−1) and overlaps with top-down atmospheric inversion models (155–200 TgCH4 y−1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25° from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ORNLDAAC/2253).</p

Upscaling Wetland Methane Emissions From the FLUXNET-CH4 Eddy Covariance Network (UpCH4 v1.0): Model Development, Network Assessment, and Budget Comparison

Wetlands are responsible for 20%-31% of global methane (CH4) emissions and account for a large source of uncertainty in the global CH4 budget. Data-driven upscaling of CH4 fluxes from eddy covariance measurements can provide new and independent bottom-up estimates of wetland CH4 emissions. Here, we develop a six-predictor random forest upscaling model (UpCH4), trained on 119 site-years of eddy covariance CH4 flux data from 43 freshwater wetland sites in the FLUXNET-CH4 Community Product. Network patterns in site-level annual means and mean seasonal cycles of CH4 fluxes were reproduced accurately in tundra, boreal, and temperate regions (Nash-Sutcliffe Efficiency similar to 0.52-0.63 and 0.53). UpCH(4) estimated annual global wetland CH4 emissions of 146 +/- 43 TgCH4 y(-1) for 2001-2018 which agrees closely with current bottom-up land surface models (102-181 TgCH4 y(-1)) and overlaps with top-down atmospheric inversion models (155-200 TgCH4 y -1). However, UpCH4 diverged from both types of models in the spatial pattern and seasonal dynamics of tropical wetland emissions. We conclude that upscaling of eddy covariance CH4 fluxes has the potential to produce realistic extra-tropical wetland CH4 emissions estimates which will improve with more flux data. To reduce uncertainty in upscaled estimates, researchers could prioritize new wetland flux sites along humid-to-arid tropical climate gradients, from major rainforest basins (Congo, Amazon, and SE Asia), into monsoon (Bangladesh and India) and savannah regions (African Sahel) and be paired with improved knowledge of wetland extent seasonal dynamics in these regions. The monthly wetland methane products gridded at 0.25 degrees from UpCH4 are available via ORNL DAAC (https://doi.org/10.3334/ ORNLDAAC/2253).Plain Language Summary Wetlands account for a large share of global methane emissions to the atmosphere, but current estimates vary widely in magnitude (similar to 30% uncertainty on annual global emissions) and spatial distribution, with diverging predictions for tropical rice growing (e.g., Bengal basin), rainforest (e.g., Amazon basin), and floodplain savannah (e.g., Sudd) regions. Wetland methane model estimates could be improved by increased use of land surface methane flux data. Upscaling approaches use flux data collected across globally distributed measurement networks in a machine learning framework to extrapolate fluxes in space and time. Here, we train and evaluate a methane upscaling model (UpCH4) and use it to generate monthly, globally gridded wetland methane emissions estimates for 2001-2018. The UpCH4 model uses only six predictor variables among which temperature is dominant. Global annual methane emissions estimates and associated uncertainty ranges from upscaling fall within state-of-the-art model ensemble estimates from the Global Carbon Project (GCP) methane budget. In some tropical regions, the spatial pattern of UpCH4 emissions diverged from GCP predictions, however, inclusion of flux measurements from additional ground-based sites, together with refined maps of tropical wetlands extent, could reduce these prediction uncertainties