Small optic suspensions for Advanced LIGO input optics and other precision optical experiments

We report on the design and performance of small optic suspensions developed to suppress seismic motion of out-of-cavity optics in the Input Optics subsystem of the Advanced LIGO interferometric gravitational wave detector. These compact single stage suspensions provide isolation in all six degrees of freedom of the optic, local sensing and actuation in three of them, and passive damping for the other three

Characterization of thermal effects in the Enhanced LIGO Input Optics

We present the design and performance of the LIGO Input Optics subsystem as implemented for the sixth science run of the LIGO interferometers. The Initial LIGO Input Optics experienced thermal side effects when operating with 7 W input power. We designed, built, and implemented improved versions of the Input Optics for Enhanced LIGO, an incremental upgrade to the Initial LIGO interferometers, designed to run with 30 W input power. At four times the power of Initial LIGO, the Enhanced LIGO Input Optics demonstrated improved performance including better optical isolation, less thermal drift, minimal thermal lensing and higher optical efficiency. The success of the Input Optics design fosters confidence for its ability to perform well in Advanced LIGO

Modeling Analysis of Primary Controls on Net Ecosystem Productivity of Seven Boreal and Temperate Coniferous Forests Across a Continental Transect

Process-based models are effective tools to synthesize and/or extrapolate measured carbon (C) exchanges from individual sites to large scales. In this study, we used a C- and nitrogen (N)-cycle coupled ecosystem model named CN-CLASS (Carbon Nitrogen-Canadian Land Surface Scheme) to study the role of primary climatic controls and site-specific C stocks on the net ecosystem productivity (NEP) of seven intermediate-aged to mature coniferous forest sites across an east–west continental transect in Canada. The model was parameterized using a common set of parameters, except for two used in empirical canopy conductance–assimilation, and leaf area–sapwood relationships, and then validated using observed eddy covariance flux data. Leaf Rubisco-N dynamics that are associated with soil–plant N cycling, and depend on canopy temperature, enabled the model to simulate site-specific gross ecosystem productivity (GEP) reasonably well for all seven sites. Overall GEP simulations had relatively smaller differences compared with observations vs. ecosystem respiration (RE), which was the sum of many plant and soil components with larger variability and/or uncertainty associated with them. Both observed and simulated data showed that, on an annual basis, boreal forest sites were either carbon-neutral or a weak C sink, ranging from 30 to 180 g C m−2 yr−1; while temperate forests were either a medium or strong C sink, ranging from 150 to 500 g C m−2 yr−1, depending on forest age and climatic regime. Model sensitivity tests illustrated that air temperature, among climate variables, and aboveground biomass, among major C stocks, were dominant factors impacting annual NEP. Vegetation biomass effects on annual GEP, RE and NEP showed similar patterns of variability at four boreal and three temperate forests. Air temperature showed different impacts on GEP and RE, and the response varied considerably from site to site. Higher solar radiation enhanced GEP, while precipitation differences had a minor effect. Magnitude of forest litter content and soil organic matter (SOM) affected RE. SOM also affected GEP, but only at low levels of SOM, because of low N mineralization that limited soil nutrient (N) availability. The results of this study will help to evaluate the impact of future climatic changes and/or forest C stock variations on C uptake and loss in forest ecosystems growing in diverse environments.Earth and Planetary Science

Search for Gravitational Waves from Low Mass Compact Binary Coalescence In 186 Days of LIGO\u27s Fifth Science Run

We report on a search for gravitational waves from coalescing compact binaries, of total mass between 2 and 35M, using LIGO observations between November 14, 2006 and May 18, 2007. No gravitational-wave signals were detected. We report upper limits on the rate of compact binary coalescence as a function of total mass. The LIGO cumulative 90%-confidence rate upper limits of the binary coalescence of neutron stars, black holes and black hole-neutron star systems are 1.4×10-2, 7.3×10-4 and 3.6×10-3yr-1L10-1, respectively, where L10 is 1010 times the blue solar luminosity

Chronic Hepatitis C Treatment in Patients with Drug Injection History: Findings of the INTEGRATE Prospective, Observational Study.

INTRODUCTION: People who inject drugs represent an under-treated chronic hepatitis C virus (HCV)-infected patient population. METHODS: INTEGRATE was a prospective, observational study investigating the effectiveness, safety, and adherence in routine clinical practice to telaprevir in combination with peg-interferon and ribavirin (Peg-IFN/RBV) in patients with history of injecting drug use chronically infected with genotype 1 HCV. RESULTS: A total of 46 patients were enrolled and included in the intent-to-treat (ITT) population. Among heroin and/or cocaine users (n = 37; 80%), 22% reported use in the past month; 74% (34/46) of patients were on opioid substitution therapy in the pre-treatment phase, and 43% (20/46) discontinued HCV treatment prematurely. Sustained virologic response rate was 54% (25/46) in the ITT population and 74% (25/34) in the per protocol (evaluable-for-effectiveness) population. The main reason for failure in the ITT analysis was loss to follow-up (n = 8; 17%). Adverse events occurred in 91% (42/46) of patients. Mean patient-reported adherence to study drugs was >89% at Week 4, Week 12 and end of treatment. CONCLUSION: Despite a high rate of treatment discontinuation (including loss to follow-up), self-reported adherence to treatment was good and virologic cure rates were similar to those reported in large real-world cohorts. Our findings suggest that people with a history of injecting drug use should be considered for treatment of chronic HCV infection, and highlight the need for improvements in patient support to boost retention in care and, in turn, help to prevent reinfection and transmission. CLINICAL TRIAL REGISTRATION: Clinicaltrials.gov identifier, NCT01980290. FUNDING: Janssen Pharmaceuticals

Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) grants nos. NNX12AP74G, NNX10AG01A, and NNX11AO08A. M. Altaf Arain thanks the support of Natural Sciences and Engineering Research Council (NSREC) of Canada. Penelope Serrano Ortiz was partially supported by the GEISpain project (CGL2014-52838-C2-1-R) funded by the Spanish Ministry of Economy and Competitiveness and the European Union ERDF funds. Sebastian Wolf acknowledges support from a Marie Curie International Outgoing Fellowship (European Commission, grant 300083). The FLUXCOM initiative is coordinated by Martin Jung, Max Planck Institute for Biogeochemistry (Jena, Germany). This work used eddy-covariance data acquired by the FLUXNET community and in particular by the following networks: AmeriFlux (US Department of Energy, Biological and Environmental Research, Terrestrial Carbon Program (DE-FG02-04ER63917 and DE-FG02-04ER63911)), AfriFlux, AsiaFlux, CarboAfrica, CarboEuropeIP, CarboItaly, CarboMont, ChinaFlux, FluxnetCanada (supported by CFCAS, NSERC, BIOCAP, Environment Canada, and NRCan), GreenGrass, KoFlux, LBA, NECC, OzFlux, TCOS-Siberia, USCCC. We acknowledge the financial support to the eddy-covariance data harmonization provided by CarboEuropeIP, FAO-GTOS-TCO, iLEAPS, the Max Planck Institute for Biogeochemistry, the National Science Foundation, the University of Tuscia and the US Department of Energy, and the databasing and technical support from Berkeley Water Center, Lawrence Berkeley National Laboratory, Microsoft Research eScience, Oak Ridge National Laboratory, the University of California - Berkeley, and the University of Virginia.Spatio-temporal fields of land–atmosphere fluxes derived from data-driven models can complement simulations by process-based land surface models. While a number of strategies for empirical models with eddy-covariance flux data have been applied, a systematic intercomparison of these methods has been missing so far. In this study, we performed a cross-validation experiment for predicting carbon dioxide, latent heat, sensible heat and net radiation fluxes across different ecosystem types with 11 machine learning (ML) methods from four different classes (kernel methods, neural networks, tree methods, and regression splines). We applied two complementary setups: (1) 8-day average fluxes based on remotely sensed data and (2) daily mean fluxes based on meteorological data and a mean seasonal cycle of remotely sensed variables. The patterns of predictions from different ML and experimental setups were highly consistent. There were systematic differences in performance among the fluxes, with the following ascending order: net ecosystem exchange (R2  0.6), gross primary production (R2> 0.7), latent heat (R2 > 0.7), sensible heat (R2 > 0.7), and net radiation (R2 > 0.8). The ML methods predicted the across-site variability and the mean seasonal cycle of the observed fluxes very well (R2 > 0.7), while the 8-day deviations from the mean seasonal cycle were not well predicted (R2 < 0.5). Fluxes were better predicted at forested and temperate climate sites than at sites in extreme climates or less represented by training data (e.g., the tropics). The evaluated large ensemble of ML-based models will be the basis of new global flux products.European Union (EU) GA 283080 283080 640176European Research Council (ERC) 647423Ministry of the Environment, Japan 2-1401JAXA Global Change Observation Mission (GCOM) project 115National Aeronautics & Space Administration (NASA) NNX12AP74G NNX10AG01A NNX11AO08ANatural Sciences and Engineering Research Council of CanadaGEISpain project - Spanish Ministry of Economy and Competitiveness CGL2014-52838-C2-1-REuropean Commission Joint Research Centre 300083United States Department of Energy (DOE) DE-FG02-04ER63917 DE-FG02-04ER63911FAO-GTOS-TCOiLEAPSMax Planck Institute for BiogeochemistryNational Science Foundation (NSF)University of Tusci

Hierarchical Hough all-sky search for periodic gravitational waves in LIGO S5 data

We describe a new pipeline used to analyze the data from the fifth science run (S5) of the LIGO detectors to search for continuous gravitational waves from isolated spinning neutron stars. The method employed is based on the Hough transform, which is a semi-coherent, computationally efficient, and robust pattern recognition technique. The Hough transform is used to find signals in the time-frequency plane of the data whose frequency evolution fits the pattern produced by the Doppler shift imposed on the signal by the Earth's motion and the pulsar's spin-down during the observation period. The main differences with respect to previous Hough all-sky searches are described. These differences include the use of a two-step hierarchical Hough search, analysis of coincidences among the candidates produced in the first and second year of S5, and veto strategies based on a $\chi^2$ test.Comment: 7 pages, 2 figures, Amaldi08 proceedings, submitted to JPC

Thermal adaptation of net ecosystem exchange

Thermal adaptation of gross primary production and ecosystem respiration has been well documented over broad thermal gradients. However, no study has examined their interaction as a function of temperature, i.e. the thermal responses of net ecosystem exchange of carbon (NEE). In this study, we constructed temperature response curves of NEE against temperature using 380 site-years of eddy covariance data at 72 forest, grassland and shrubland ecosystems located at latitudes ranging from ~29° N to 64° N. The response curves were used to define two critical temperatures: transition temperature (&lt;i&gt;T&lt;/i&gt;&lt;sub&gt;b&lt;/sub&gt;) at which ecosystem transfer from carbon source to sink and optimal temperature (&lt;i&gt;T&lt;/i&gt;&lt;sub&gt;o&lt;/sub&gt;) at which carbon uptake is maximized. &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;b&lt;/sub&gt; was strongly correlated with annual mean air temperature. &lt;i&gt;T&lt;/i&gt;&lt;sub&gt;o&lt;/sub&gt; was strongly correlated with mean temperature during the net carbon uptake period across the study ecosystems. Our results imply that the net ecosystem exchange of carbon adapts to the temperature across the geographical range due to intrinsic connections between vegetation primary production and ecosystem respiration