HD139614: the interferometric case for a group-Ib pre-transitional young disk

The Herbig Ae star HD 139614 is a group-Ib object, which featureless SED indicates disk flaring and a possible pre-transitional evolutionary stage. We present mid- and near-IR interferometric results collected with MIDI, AMBER and PIONIER with the aim of constraining the spatial structure of the 0.1-10 AU disk region and assess its possible multi-component structure. A two-component disk model composed of an optically thin 2-AU wide inner disk and an outer temperature-gradient disk starting at 5.6 AU reproduces well the observations. This is an additional argument to the idea that group-I HAeBe inner disks could be already in the disk-clearing transient stage. HD 139614 will become a prime target for mid-IR interferometric imaging with the second-generation instrument MATISSE of the VLTI.Comment: SPIE Astronomical Telescopes and Instrumentation conference, June 2014, 11 pages, 7 Figure

Use of Short Tandem Repeat Sequences to Study Mycobacterium leprae in Leprosy Patients in Malawi and India

Molecular typing has provided an important tool for studies of many pathogens. Such methods could be particularly useful in studies of leprosy, given the many outstanding questions about the pathogenesis and epidemiology of this disease. The approach is particularly difficult with leprosy, however, because of the genetic homogeneity of M. leprae and our inability to culture it. This paper describes molecular epidemiological studies carried out on leprosy patients in Malawi and in India, using short tandem repeat sequences (STRS) as markers of M. leprae strains. It reveals evidence for continuous changes in these markers within individual patients over time, and for selection of different STRS-defined strains between different tissues (skin and nerve) in the same patient. Comparisons between patients collected under different circumstances reveal the uses and limitations of the approach—STRS analysis may in some circumstances provide a means to trace short transmission chains, but it does not provide a robust tool for distinguishing between relapse and reinfection. This encourages further work to identify genetic markers with different stability characteristics for incorporation into epidemiological studies of leprosy

The Genome of the Epsilonproteobacterial Chemolithoautotroph Sulfurimonas dentrificans

Sulfur-oxidizing epsilonproteobacteria are common in a variety of sulfidogenic environments. These autotrophic and mixotrophic sulfur-oxidizing bacteria are believed to contribute substantially to the oxidative portion of the global sulfur cycle. In order to better understand the ecology and roles of sulfur-oxidizing epsilonproteobacteria, in particular those of the widespread genus Sulfurimonas, in biogeochemical cycles, the genome of Sulfurimonas denitrificans DSM1251 was sequenced. This genome has many features, including a larger size (2.2 Mbp), that suggest a greater degree of metabolic versatility or responsiveness to the environment than seen for most of the other sequenced epsilonproteobacteria. A branched electron transport chain is apparent, with genes encoding complexes for the oxidation of hydrogen, reduced sulfur compounds, and formate and the reduction of nitrate and oxygen. Genes are present for a complete, autotrophic reductive citric acid cycle. Many genes are present that could facilitate growth in the spatially and temporally heterogeneous sediment habitat from where Sulfurimonas denitrificans was originally isolated. Many resistance-nodulation-development family transporter genes (10 total) are present; of these, several are predicted to encode heavy metal efflux transporters. An elaborate arsenal of sensory and regulatory protein-encoding genes is in place, as are genes necessary to prevent and respond to oxidative stress

Aboveground biomass density models for NASA's Global Ecosystem Dynamics Investigation (GEDI) lidar mission

NASA's Global Ecosystem Dynamics Investigation (GEDI) is collecting spaceborne full waveform lidar data with a primary science goal of producing accurate estimates of forest aboveground biomass density (AGBD). This paper presents the development of the models used to create GEDI's footprint-level (similar to 25 m) AGBD (GEDI04_A) product, including a description of the datasets used and the procedure for final model selection. The data used to fit our models are from a compilation of globally distributed spatially and temporally coincident field and airborne lidar datasets, whereby we simulated GEDI-like waveforms from airborne lidar to build a calibration database. We used this database to expand the geographic extent of past waveform lidar studies, and divided the globe into four broad strata by Plant Functional Type (PFT) and six geographic regions. GEDI's waveform-to-biomass models take the form of parametric Ordinary Least Squares (OLS) models with simulated Relative Height (RH) metrics as predictor variables. From an exhaustive set of candidate models, we selected the best input predictor variables, and data transformations for each geographic stratum in the GEDI domain to produce a set of comprehensive predictive footprint-level models. We found that model selection frequently favored combinations of RH metrics at the 98th, 90th, 50th, and 10th height above ground-level percentiles (RH98, RH90, RH50, and RH10, respectively), but that inclusion of lower RH metrics (e.g. RH10) did not markedly improve model performance. Second, forced inclusion of RH98 in all models was important and did not degrade model performance, and the best performing models were parsimonious, typically having only 1-3 predictors. Third, stratification by geographic domain (PFT, geographic region) improved model performance in comparison to global models without stratification. Fourth, for the vast majority of strata, the best performing models were fit using square root transformation of field AGBD and/or height metrics. There was considerable variability in model performance across geographic strata, and areas with sparse training data and/or high AGBD values had the poorest performance. These models are used to produce global predictions of AGBD, but will be improved in the future as more and better training data become available

Solar neutrino detection sensitivity in DARWIN via electron scattering

We detail the sensitivity of the proposed liquid xenon DARWIN observatory to solar neutrinos via elastic electron scattering. We find that DARWIN will have the potential to measure the fluxes of five solar neutrino components: pp, 7Be, 13N, 15O and pep. The precision of the 13N, 15O and pep components is hindered by the double-beta decay of 136Xe and, thus, would benefit from a depleted target. A high-statistics observation of pp neutrinos would allow us to infer the values of the electroweak mixing angle, sin 2θw, and the electron-type neutrino survival probability, Pee, in the electron recoil energy region from a few keV up to 200 keV for the first time, with relative precision of 5% and 4%, respectively, with 10 live years of data and a 30 tonne fiducial volume. An observation of pp and 7Be neutrinos would constrain the neutrino-inferred solar luminosity down to 0.2%. A combination of all flux measurements would distinguish between the high- (GS98) and low-metallicity (AGS09) solar models with 2.1–2.5σ significance, independent of external measurements from other experiments or a measurement of 8B neutrinos through coherent elastic neutrino-nucleus scattering in DARWIN. Finally, we demonstrate that with a depleted target DARWIN may be sensitive to the neutrino capture process of 131Xe

Pancreatic cancer risk in relation to lifetime smoking patterns, tobacco type, and dose-response relationships.

BACKGROUND: Despite smoking being a well-established risk factor for pancreatic cancer (PC), there is a need to further characterize PC risk according to lifespan smoking patterns and other smoking features. Our aim was to deeply investigate them within a large European case-control study. METHODS: Tobacco smoking habits and other relevant information was obtained from 2,009 cases and 1,532 controls recruited in the PanGenEU study using standardized tools. Multivariate logistic regression analysis was performed to evaluate PC risk by smoking characteristics and interactions with other PC risk factors. Fractional polynomials and restricted cubic splines were used to test for non-linearity of the dose-response relationships and to analyse their shape. RESULTS: Relative to never-smokers, current smokers (OR=1.72, 95%CI: 1.39-2.12), those inhaling into the throat (OR=1.48, 95%CI: 1.11-1.99), chest (OR=1.33, 95%CI: 1.12-1.58), or using non-filtered cigarettes (OR=1.69, 95%CI: 1.10-2.61), were all at an increased PC risk. PC risk was highest in current black tobacco smokers (OR=2.09, 95%CI: 1.31-3.41), followed by blond tobacco smokers (OR=1.43, 95%CI: 1.01-2.04). Childhood exposure to tobacco smoke relative to parental smoking was also associated with increased PC risk (OR=1.24, 95%CI: 1.03-1.49). Dose-response relationships for smoking duration, intensity, cumulative dose, and smoking cessation were non-linear and showed different shapes by tobacco type. Effect modification by family history of PC and diabetes was likely. CONCLUSIONS: This study reveals differences in PC risk by tobacco type and other habit characteristics, as well as non-linear risk associations. IMPACT: This characterization of smoking-related PC risk profiles may help in defining PC high-risk populations