dataesr/scientific-paragraphs-categorization huggingface dataset:
RNA quality was evaluated in the Nanodrop 2000c spectrophotometer (Thermo Scientifi c). cDNA synthesis was carried out in the LifePro thermal cycler (BIOER) using the RT2 First strand kit (Qiagen, 330401). Reverse transcription was made in the Rotorgene-Q (Qiagen) thermo cycler using the RT2 SYBRGreen ROX FAST Mastermix (Qiagen, 330620). Data were normalized against the housekeeping gene β2 microglobulin and analyzed using the RT 2 Profi ler PCR Array analysis software using an arithmetic mean (Sabiosciences). Five genes of interest were further analyzed using the same chemistry as for the PCR array and primers (Table  1 , Supporting Information). In-Cell Western : Protein expression was estimated by in-cell Western. The experiments was carried out for 7 days with time points at day 3 and 7 for VEGFR-2 and caveolin-1 and at day 1, 3, and 7 for β-catenin, Wnt2, bFGF, and BMP-2.
In this protocol, anti-Wnt2 primary antibody (Table  2 , Supporting Information) and antirabbit secondary antibody Alexa Fluor 594 (Life Technologies, A21442) were used following manufacturer's instructions. After removing the secondary antibody, cells were washed, mounted with the antifade agent ProLong Gold Antifade Reagent with DAPI (Molecular Probes P36935), and observed under the inverted microscope (Olympus, IX51). Images were obtained at day 3 using the same time of exposure for all materials. Statistical Analysis : Data are presented as the mean ± standard error of the mean (SEM). The statistical signifi cance between experimental groups was assessed through the unpaired Student's t-test using GraphPad Prism software with 95% of confi dence level. * p < 0. 05, ** p < 0. 01, and *** p < 0.
Acknowledgements L. Santos was supported by  Marie Curie  actions  FP7  through the  Intra-European Marie Curie Fellowship Vascular Bone. G. Fuhrmann acknowledges fi nancial support through the  FP7 Marie Curie actions  ( Intra-European Marie Curie Fellowship ) and the  German Academic Exchange Service  ( Postdoctoral Fellowship ). The authors thank  Benjamin Pierce  and  Panagiotis Papathanasiou  for critically reading the manuscript,  Silva Goldoni  and  Vincent Leonardo  for the helpful discussions, and  Prof. Adam Engler  and  Jessica Wen  at  UCSD  for measuring the Young's modulus of the PAAm hydrogels utilized in this study.
Search strategy The literature search was performed in August 2014 in five electronic databases (Pubmed, Embase, CINAHL with full text, PsycINFO and Web of Science). The search strategy was based on search terms within four key elements: (a) sedentary behaviour and its synonyms (e. g. sedentariness); (b) determinants and its synonyms (e. g. correlates, factors); (c) types of sedentary behaviour (e. g. TV viewing, gaming) and (d) possible determinants of sedentary behaviour (e. g. environmental, behavioural). Terms referring to these four elements were used as MESH-headings and title or abstract words in all databases. The complete list of search terms is shown in Additional file 1: Table  S1. In addition, the reference lists of all included articles were scanned for articles that met the inclusion criteria.
Data extraction A standardized template was used to extract data from the included studies using the following heading; General Informationtitle of article, main author and publication year, sample characteristics, study characteristics, measurement of sedentary behaviour and determinants, statistical methods and main results. The four reviewers involved in article selection, extracted data independently (SC,EF and JMO,CB). A quality assurance process enabled cross checking of the data extraction. Discrepancies were resolved through discussion.
Results Searches of the five databases (Pubmed, Embase, CINAHL with full text, PsycINFO and Web of Science) yielded 4472 records. After duplicates were removed 4050 titles and abstracts were screened against the inclusion criteria. 3877 were excluded for the following reasons; relevance (2780) exercise interventions (108) did not include older adults (327) measured inactivity rather than sedentary behaviour (341) were conducted in special populations (318) or were incorrect records  (3). 171 full-text articles were assessed for eligibility with 22 studies meeting the inclusion criteria. Figure  1  illustrates selection of studies from search to inclusion. Table  1  provides an overview of the main characteristics of the studies included. Three of the studies were conducted in North America, 12 in Europe (6 in the UK), five in Asia and two in Australia.
Helium was used as a carrier gas and maintained at a constant flow of 4. 6 ml min -1. Data acquisition and processing were carried out using the HP Chemstation soft ware (Rev. B. 03. 02 (341), Agilent Technologies). HTGC/MS analyses of trimethylsilylated aliquots were performed using a Thermo Scientific Trace 1300 gas chromatograph coupled with an ISQ single quadrupole mass spectrometer. Diluted samples were introduced using a PTV injector in split mode (split flow of 30 ml min -1 , split ratio of 6. 0) onto a 0. 53 mm fused silica precolumn connected to a 15 m × 0. 32 mm i. d. fusedsilica capil lary column coated with dimethyl polysiloxane stationary phase (Rxi1HT; film thickness, 0. 1 μ m; Restek). The initial injection port temperature was 50 °C with an evaporation phase of 0. 05 min, followed by a transfer phase from 50 °C to 380 °C at 0.
2 °C min -1. The oven temperature was held isothermally for 2 min at 50 °C, increased at a rate of 10 °C min -1 to 280 °C, then at a rate of 25 °C min -1 to 380 °C and finally held at 380 °C for 5 min. Helium was used as a carrier gas and maintained at a constant flow 5 ml min -1. The mass spectrometer was operated in the electron ionization (EI) mode (70 eV) with a GC interface temperature of 380 °C and a source temperature of 340 °C. The emission current was 50 μ A and the mass spectrometry set to acquire in the range of m/z 50-950 Daltons at two scans per second. Data acquisition and processing were carried out using the Thermo XCalibur software (version 3. 0. 63). Peaks were identified on the basis of their mass spectra, gas chromatography (GC) retention times, by comparison with the NIST mass spectral library (version 2.
Interpolation, kriging and plotting were all performed in R version 2. 15. 1 (ref. 34). Interpolation was performed using the function 'interp' from the package 'akima' (CRAN repository, http://cran. rproject. org/web/packages/akima/akima. pdf). Kriging was performed using the function 'krige. conv' from the package 'geoR'' (CRAN repository, http://cran. rproject. org/web/packages/geoR/geoR. pdf, further information on the package 'geoR' can be found at http://www. leg. ufpr. br/geoR). R code available upon request to P. G.
Acknowledgements We thank the  UK Natural Environment Research Council  for partial funding of the mass spectrometry facilities at  Bristol  (contract no. R8/H10/63 ; http://www. lsmsf. co. uk) and  English Heritage, European Research Council ,  Leverhulme Trust, Ministère de la Culture et de la Communication, Ministère de l'Enseignement Supérieur et de la Recherche  ( ACI Jeunes Chercheurs ),  Natural Environment Research Council ,  Région PACA ,  Royal Society and Wellcome Trust  for funding.
A joint Swiss-Belgian team proposed to build an airborne imaging spectrometer termed 'Airborne Prism Experiment' (APEX), under the scientific lead of Klaus Itten at the University of Zurich. He served as APEX principal investigator from 1995 to 2009 and Michael Schaepman from 2009 onwards. A potential APEX system was for the first time presented to a wider public in 1997  (Itten et al. , 1997). The scientific, industrial and operational consortium of APEX was subsequently established as follows. The science lead is with the University of Zurich, tasked to perform model simulations, establish system specifications and validate instrument performance, develop a science grade processing facility, and perform the project management. The institutional partner and co-investigator VITO is responsible for the operational implementation of the APEX processor, APEX operations and data distribution.
The industrial consortium is composed of RUAG Aerospace, Switzerland (integration, mechanical and electrical subsystems, navigation and control), OIP Sensor Systems, Belgium (optical subsystem), and Netcetera AG, Switzerland (readout electronics, software). In addition, ESA as overall project responsible established two further contracts, one with Sofradir, France (short-wave infrared (SWIR) detector) and the German Aerospace Center (DLR), Germany (calibration home base). APEX went into operations in 2009 and acquires science grade spectroscopy data since 2010. APEX is on lease by ESA to the University of Zurich and VITO until 2015 and thereafter under ownership of the latter two institutions. In this contribution, we discuss the evolution of the APEX instrument starting with simulating its key performance indicators, and definition of specifications, its optical, electronic and mechanical design.
APEX specifications and performance modeling The APEX system was specified to allow simulating spaceborne imaging spectrometers, supporting mission calibration and validation efforts. The following specifications are outlined as boundary conditions  (Schaepman, De Vos, & Itten, 1998 ): • Pushbroom imaging with ≤1000 imaging pixels across track, covering a swath width of 2. 5-5 km, depending on flight altitude, • Spectral wavelength range covering 450-2500 nm, • At least 200 programmable or 300 predefined spectral bands, adaptable to specific application requirements, • Spectral sampling interval b15 nm and a spectral sampling width of b 1. 5 the sampling interval, and • Ability to provide calibrated data and products to geocoded and calibrated data. Further on, the dispersive system of APEX had to be based on prisms, given a requirement from European Space Agency.
The electrical subsystem of APEX is composed of the frontend electronics, supporting frame rates of up to 43. 3 Hz. Following an analogdigital conversion and multiplexing the two detector channels, data are processed in a Field Programmable Gate Array (FPGA) to stream 16 bit words in a serialized fashion through an optical high-speed link at 700 Mbit/s to the control rack. Ancillary information is transmitted in parallel over a serial RS422 link to the control and storage rack. The final APEX instrument component contains a built-in in-flight calibration facility (IFC). Before and after ground data acquisition, a mirror mechanism allows imaging an internal stabilized Quartz Tungsten Halogen lamp. The lamp is located near the baffle of the instrument, therefore it's light is transmitted through a fiber-bundle and a diffusor, followed by a set of spectral calibration filters fully illuminating diffusely the ground imager in the optical path of APEX  (Schläpfer, Schaepman, Bojinski, & Börner, 2000).
A moveable and calibrated ground mirror is the only optical element not seen by APEX during in-flight calibration. A filter wheel with six filter positions in this path holds four spectral  ). IFC data analysis relies on the use of a feature-fitting algorithm comparing laboratory and in-flight shifts of particular filter absorption lines. In addition, the same method is used on atmospheric absorption lines during normal imaging, allowing monitoring the instrument's spectral stability (D'Odorico,  Guanter, Schaepman, & Schläpfer, 2011). Remaining spectral and radiometric variations are mostly due to changing pressure/humidity/temperature affecting the prisms refraction properties, and can be estimated using ancillary data recorded by the APEX instrument  (Jehle et al. , in press ).
Control and storage unit The control and storage unit (CSU) hosts instrumentation to operate APEX (Fig. 2 ). This includes the Inertial Navigation System (INS) with dGPS data processing capabilities (Applanix POS/AV 410 IMU/GPS). APEX optical and positional data are linked using GPS based timestamps during processing by forming a smoothed, best-estimate trajectory for each acquired imaging frame. Further components are the flight management system with interfaces to the operator and pilot (TRACK'AIR XTrack), the APEX computer with its storage unit (Solid State Disks) and the power supply units. While largely everything in the CSU is available as commercial-off-the-shelf, only a custom-made PCI card is needed to be developed in order to connect the optical link from the instrument to the host system.
Data originating from the PCI card use a multi-threaded shared memory architecture to ensure sufficient data throughput to the solid-state-memory disks. All of the CSU is driven by a tiered software approach. Low-level interfaces control disk read/ write operations, while a middle tier level handles all logging and alerting. High-level software controls and enables configuring the system, its status and displays a waterfall image in real-time on screen for the operator. Finally, the power distribution unit connects the aircraft power supply with all APEX instruments requiring power.
, 2009; Plaza et al. , 2009; Schaepman et al. , 2009b; Ustin et al. , 2009). We concentrate here on describing a few key applications, which are either unique to APEX or significantly advance the use of APEX-like data. APEX data itself are available as a general purpose APEX data set, which can be downloaded (http://www. apex-esa. org) (Hueni et al. , 2012). On the same site, APEX quick-looks are also listed. Many of those datasets can be obtained free of charge for scientific use by contacting the responsible person. Although representative at regional scale, advanced APEX products have also implications for environmental research at larger scales. Process models, for example dynamic global vegetation models (LPG-GUESS  (Smith et al. , 2001) ), are unique tools to quantify the impact of environmental change on ecosystem functioning.
Operational and science grade processing APEX operational grade data processing is performed within the Central Data Processing Centre (CDPC) at VITO. First, spectral misregistration is performed using a spectrum-matching technique  (Gao, Montes, & Davis, 2004). Geometric processing is performed using direct georeferencing, including the use of standard or user-provided Digital Elevation Models (DEM). Subsequently, a smile-aware atmospheric correction is performed to retrieve hemispherical-conical reflectance factors (HCRF) in combination with MODTRAN5. Finally coordinate projection is performed using the General Cartographic Transformation Package (GCTP)  (USGS, 1998). For all reprojections, a seven parameter Helmert transformation is used. In a final step, HCRF data are resampled to the central wavelengths as calibrated in the CHB for the actual acquisition  (Biesemans et al.
, 2007). APEX science grade data processing is corrected to minimize the impact of atmospheric and topographic effects  (Richter & Schläpfer, 2002; Schläpfer & Richter, 2002). The subsequent automated atmospheric compensation process accounts for irradiance properties in complex terrain, atmospheric scattering and absorbers, as well as instrument smile effects  (Richter, Schlapfer, & Muller, 2011). This combined geoatmospheric processing is used for all situations, where users wish to obtain uniform geo-locatable bottom of atmosphere reflectance values. In addition, APEX data can be normalized to contain nadir-viewing geometries by applying a Li-Ross BRDF correction based on a continuous land cover classification  (Weyermann, Damm, Kneubuhler, & Schaepman, 2014). Using a spectral unmixing based approach, land cover types with substantial BRDFs are extracted from the APEX scene and expressed as linear combinations of three kernels (isotropic, geometric (Li-kernel), volumetric (Ross-kernel)).
This approach allows seamless mosaicking of several APEX flight lines, while minimizing angular effects dominating flight patterns that are chosen to comply with minimal operational constraints and not optimized for minimal directional illumination influences  (Laurent, Schaepman, Verhoef, Weyermann, & Chavez, 2014). While operational APEX data processing is available to everyone browsing data in the CDPC archive of VITO, science grade data processing is highly experimental and only applied on request. However, the APEX PAF is upgraded to include always latest developments of the science grade processing.
Validation of this process is performed using a radiance based vicarious calibration procedure, ensuring consistency when comparing in-situ and airborne measurements (see Fig. 4B ). We finally apply simultaneous fluorescence line depth (FLD) and pigment retrieval approaches to APEX data (Fig. 6 ). Vegetation fluorescence (Fs) is derived using the constrained 3FLD approach  (Damm et al. , 2014). Because of a very low Fs signal, residual along-track striping is still visible in the APEX data. Fs values range between 0 … 10 mW m -2 nm -1 sr -1. Simultaneous field validation using a field spectroradiometer (ASD FieldSpec FR Pro) is carried out. We use a spectral deconvolution approach to minimize Fs retrieval differences between APEX and the field spectroradiometer measurements  (Zhao, Jia, & Li, 2010).
In situ data were collected using a stratified random sampling approach while measuring reflected and emitted radiances at each calibration point (n = 16), resulting in a good agreement between airborne and in situ Fs (R 2 = 0. 87, relative RMSE = 27. 76%). Spatial distribution of Fs, such as measured with APEX, can serve as proxy for instantaneous plant photosynthesis. Pigments are derived in relative units and visual validation suggests feasibility of the method. Measurements of pigments in leaves are well understood  (Gitelson, Chivkunova, & Merzlyak, 2009) , however their validation using leaf pigment concentrations is ongoing, and leaf models including separate representation of these pigments and plant structure are sparse  (Townsend, Serbin, Kruger, & Gamon, 2013)  and not applied in an inverse fashion to remotely sensed data as of yet.
Conclusions and outlook We report on the latest status of APEX with focus on advanced radiometry measurements and Earth science applications. APEX has undergone a complete development cycle, ranging from modeling system specifications to analyzing APEX data in a coherent fashion. Following the start of the operational activities of APEX in 2010, small improvements were made to the hardware (more reliable electronics boards, update of storage capacity, etc. ), and major improvements to sensor modeling, refinement of the PAF and development of science grade retrieval algorithms. APEX has very high compliance with its initial specifications (though not in all parts), and currently allows operational data acquisition of traceable radiometric measurements and production of Earth science applications.
The APEX instrument has demonstrated to deliver traceable spectroradiometric measurements. The PAF processes data using the latest calibration information, individually optimized for each single scene. This results in scene-specific band positions. While this is no threat to individually analyzing scenes, most of the commercially available software does not support convolution techniques 'on-the-fly', putting a de-convolution/convolution effort on those users using multiple scenes for their analysis. Controversial discussions are ongoing, if APEX band settings should be resampled to a 'common band set', or leaving the original measurements unaltered. Currently, the latter is the case. APEX was designed using a robust approach to sensor modeling. Even though the instrument model was updated on a regular basis, a key challenge remains to develop a sensor independent model, allowing for forward and inverse modeling approaches.
This will further foster the development of new models or retrieval algorithms-both empirical and physical-allowing a new generation of spectroscopy instruments and science professionals to be trained and developing new ideas. With APEX we still explore only a tiny fraction of what could actually be explored with Earth related imaging spectroscopy. Key to the successful application of spectroscopy is still acquisition of a coherent set of independent and simultaneous retrievals of the Earth system spheres. We encourage everyone to make the best use of these data to further tackle these plentiful opportunities much better in the near future!.
Acknowledgments We thank the  University of Zurich  for continued funding and support to APEX. APEX is further supported through  ESA ,  ESA PRODEX (16298/02/NL/US  and  15449/01/NL/Sfe ), the  Swiss National Science Foundation (SNSF) , the  Belgian Science Policy Office (BELSPO) , the  Swiss University Conference (SUK, KIP-SEON) , the  University of Zurich Research Priority Program on 'Global Change and Biodiversity' (URPP GCB) , the  Swiss Space Office (SSO) , the  European Metrology Research Programme  (EMRP, which is jointly funded by the EMRP participating countries within EURAMET and the  European Union) , and the  German Aerospace Center (DLR). We thank former  UZH  APEX project managers  Jens Nieke  and  Edoardo Alberti  and former  ESA   APEX  project managers  Gerd Ulbrich ,  Jose Gaviras , and  Hilde Schroeven-Deceuninck  for their support.
Despite the recent improvement of airlines, French Polynesians remain relatively isolated from each other and a particular structuring of the population could not be excluded, with putative genetic consequences. However, this deviance was not observed with the others four investigated polymorphisms. Among all of the successfully genotyped participants of the study, we observed MAFs equal to 0. 32 for cases and 0. 27 for controls for the SNP rs944289. Compared to the frequency reported in the NCBI dbSNP database (MAF NCBI = 0. 43) and to those reported in other studies  [25] [26] , the frequency in Polynesian individuals was clearly lower. The thyroid specific transcription factor FOXE1 plays a major role in the morphogenesis of the thyroid gland and in the regulation of thyroid differentiated state maintenance  [22, 38].
e. of Asiatic, European or other ethnic origin). All study participants were interviewed. Epidemiological and anthropological information were collected, including height, weight, ethnic origin of parents, and dietary habits. A detailed estimation of radiation doses received at different ages during atmospheric nuclear tests has been performed  [13] , based on the radioactive fallout data published by France at the end of each year of tests, on 131 I and 137 Cs counts in fresh milk and of total g-activity and 137 Cs in vegetables and fish, and on meteorological data 21 days before and seven days after each test. Estimation of the water level in cisterns before each test, size of the cisterns and information on wind and rain were also used. For each study participant, the total thyroid dose was the sum of the thyroid doses reconstructed for each test, taking into account age, source of drinking water and data collected in the diet questionnaire.
Genotyping For SNP rs965516 and rs1867277, 10 ng gDNA were analysed using a specific TaqMan Predesigned SNP Genotyping Assay (Applied Biosystems, Foster City, CA, USA). Fluorescence readings and data analyses were performed with the ABI PRISM 7900HT Sequence detection system. For rs944289 and rs1801516, 25ng gDNA were analysed using High-Resolution Melting curve (HRM), with a specific probe. Some representative samples were re-sequenced by dye-terminator to confirm the genotype  [42]. Fluorescence readings and data analyses were done with the Idaho Technology LightScanner Inc. Hi-Res Melting System. The length polymorphism rs71369530 in FOXE1 is due to a variable number of alanine repeats. Thirty nanograms of gDNA was amplified by PCR with fluorescently end-labelled forward primers (5'-6-FAM or 5'-HEX), by using KAPA 2G Fast HotStart ReadyMix (KAPA Biosystems, Woburn, MA, US) in a 10 μl-final reaction volume (0.