dataesr/scientific-paragraphs-categorization huggingface dataset:
Saturation limits A necessary test is to evaluate at what magnitude saturation sets in (because of the fixed exposure time over the whole sky), and therefore to place a safe upper brightness limit to the use of APASS data to model properties of RAVE stars. The test is carried out in Figure  5 , where the tabular value of 245 Landolt primary standards is compared to that derived by APASS (which obviously uses only non-saturated Landolt standards to perform its calibration). Note that APASS uses the same 14-arcsec diameter digital aperture that matched the Landolt 14-arcsec photoelectric photometer aperture, so any neighboring star included in Landolt's aperture is also included in the APASS photometry. Figure  5  shows that most of the B,V observations are unsaturated up to 9. 5 mag, but excellent seeing can cause this limit to shift down to 10.
Perhaps not incidentally, data for members of open cluster appear more dispersed in Figure  9  than data for field stars in Figure  8 ), and (c) the many different variants over which independent authors -whose data were entered into Cat-UBV -tried to replicate the Johnson's BV bands, Landolt's BV bands being just one of such variants (a comprehensive discussion of the realisation of the Johnson's system and the difficulties of reproducing it with instrumentation that changed with time, is given for example in  Bessell (1990) , in the books by  Golay (1974)  and  Straižys (1995) , and the two volumes of the Asiago Database on Photometric Systems by  Moro & Munari (2000) , and  Fiorucci & Munari (2003). The comparison between the CMC-15 and APASS r ′ magnitudes for the 228 805 RAVE DR4 stars in common is presented in Figure  10.
A first sample comes from  Ruchti et al. (2010 Ruchti et al. ( , 2011a,b),b) , and a second sample is composed of stars from the PASTEL database  Soubiran et al. (2010). Distances to these stars were obtained from isochrone fitting as described by  Zwitter et al. (2010). These distances are homogeneous with those to RAVE stars computed in the same way (see below). The sam-ple of Ruchti+PASTEL stars with unsaturated APASS+2MASS magnitudes is composed by 242 stars. They are distributed similarly to RAVE stars in terms of temperature and surface gravity: those hotter than 5300 K are generally main sequence objects, while giants dominates at lower temperatures. In both the RAVE survey and the Ruchti+PASTEL sample the number of giants is larger than the main sequence stars. They however differ significantly in metallicity: the median metallicity of Ruchti+PASTEL main sequence stars is -0.
Alaskan pollution and meteorological conditions in April 2008 Satellite measurements provide complementary information on the atmospheric abundance of various gas species. In this study, the ability of the ultraviolet and visible spectrometer OMI (Ozone Monitoring Instrument), on board the NASA's Earth Observing System (EOS)-Aura satellite, was used to detect sulfur dioxide. Aura is a polar orbiting satellite with a nadir ground footprint of 13 × 24 km 2 and a 2600 km cross track swath providing daily and contiguous global mapping of the trace gas species. SO 2 column data are derived using the band residual difference (BRD) algorithm  (Krotkov et al. , 2006)  and the linear fit (LF) algorithm  (Yang et al. , 2007). These algorithms use the SO 2 absorption bands between 310 and 360 nm to retrieve the column density of SO 2.
For this study, the OMI level 2G daily gridded (0. 125 × 0. 125 • ) for the mid-tropospheric (between 5 to 10 km) SO 2 products (TRM OMI L2G SO 2 ) are used to assess qualitatively the temporal variability of SO 2 over the study area of Alaska. OMI L2G SO 2 products were acquired using the GES-DISC Interactive Online Visualization ANd aNalysis Infrastructure (GIOVANNI) developed by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). OMI L2G SO 2 products were filtered to remove data with high radiative cloud fraction (> 0. 3), large solar zenith angle (> 70 • ) and anomalous scenes. Data include only those for which the GIOVANNI quality flags bit 0 was not set, and thus defined as "good data" (GIOVANNI website: http://disc. sci. gsfc. nasa. gov/giovanni/ overview/index.
2 is due to unfavorable weather conditions for the transport of pollution into Alaska. The OMI SO 2 L2G data were used to assess qualitatively the transport of SO 2 in the free troposphere from anthropogenic and natural sources. Figure  6  shows the evolution and transport of the OMI total column density of SO 2 in the mid-troposphere (5-10 km) for ISDAC periods A. 1, A. 2, B. 1 and B. 2 (Fig. 2 ), over the eastern part of Asia/Russia to Alaska. At first glance, a low concentration of SO 2 is recorded by OMI in the mid-troposphere above the Kamchatka/Aleutians during period A. 1 compared to the other three periods (Fig. 6a ). More surprisingly, there is even a higher concentration of SO 2 above the VE domain during period B. 2 when a lower pollution rate is measured above the AE/BBE domains (Fig.
During the ISDAC field experiment, the National Research Council of Canada (NRC) Convair-580 was equipped with 41 instruments, including aerosol sensors. The aerosol number concentration (Na) was measured with CPC-3775 (> 0. 004 µm) and PCASP (0. 12-3 µm) probes. Note that data from the CPC-3775 were valid for lower altitudes only (< 3. 5 km above mean sea level) due to instrument limitation. Data from the PCASP were missing for the studied flight F21. A single mass spectrometer (SPLAT-II) measuring the concentration and chemical composition of aerosols (Zelenyuk et al. , 2010) and a continuous flow diffusion chamber (CFDC) measuring the IN concentration were also available on the aircraft. Unfortunately, the CFDC was not functional for flights prior to 8 April 2008 (including flights 8-15) and aerosol chemical composition from SPLAT-II for studied flights F9 and F21 were not available at the beginning of the analysis (A.
icare. fr) in France. More details on DARDAR can be found in Delanoë and    4. The op-erational CALIOP aerosol data (version 3. 01) were also used along the selected CALIPSO track. These products provided the optical properties of aerosol layers averaged every 5 km horizontally  (Liu et al. , 2009). Two additional criteria are considered: the layers detected only at a horizontal resolution of 80 km and an optical thickness at 532 nm over 0. 03. These two conditions are needed to identify aerosol layers with low optical depths (de Villiers, 2010). All aerosol layers discussed in this section have a cloud-aerosol discrimination (CAD) greater than 90 %, which ensures that the detected layer by CALIOP contained aerosols  (Eguchi et al. , 2009).
Particles in box B F9 1 more importantly rise (about 4 km) from 31 March to the observation time on 1 April at the top high. CL F9 1 seems to belong to the category of TIC-1/2A with a high concentration of small ice crystals. The CALIPSO/DARDAR mask independently reveals some aerosol layers near the B F9 1 and B F9 2 boxes between 65-74 • N (identified AL F9 1 in Fig. 9a ) in the midtroposphere on 31 March and a wide aerosol layer (identified AL F9 2 in Fig. 9b ) over southern Aleutians volcanoes between 41-52 • N from 1 to 10 km in altitude on 30 March 2008. The mean attenuated backscatter coefficient at 532 nm (β att ), the mean volume depolarization ratio at 532 nm (δ v ) and the mean attenuated total color ratio (CR att ) for each aerosol layer are shown in Table  5. CALIPSO level 2 aerosol layer data at 5 km resolution were used.
(2009)  and  Giannakaki et al. (2012)  approaches were used to separate mineral dust and non-dust (considered anthropogenic particles) contributions based on CALIOP lidar observations. CALIPSO level 2 aerosol profile data at 5 km resolution were used. Assuming that aerosols can be treated as two aerosol types externally mixed, the particulate backscatter contribution of the first aerosol type (β p,1 ) is obtained from the measured total particulate backscatter coefficient (β p,t ) by the following equation: β p,1 = β p,t δ p,t -δ p,2 1 + δ p,1 δ p,1 -δ p,2 1 + δ p,t , (1) where δ p,t , δ p,1 and δ p,2 are the observed total particulate depolarization ratio and the assumed particulate depolarization ratios of the two pure aerosol types, respectively. The backscatter coefficient of the second aerosol type is given by the following equation: β p,2 = β p,t -β p,1 (2).
6a  and c ). The level of acidity of the aerosols forming the TIC-2B-F21 ice cloud can also be determined using in situ measurements taken during the ARCPAC/ARCTAS Campaign and by examining the thermodynamic properties of the aerosol layer. The ARCPAC/ARCTAS projects overlapped in space and time with the Convair 580 aircraft from the ISDAC Campaign. The NASA ARCTAS P-3B and the NOAA ARCPAC WP-3D aircrafts sampled the aerosol acidity near Barrow on 15 April 2008, respectively, between 22:21:00-01:17:00 UTC and 00:25:00-02:12:00 UTC. An aerosol mass spectrometer (AMS)  (Dunlea et al. , 2009)  measuring submicron aerosol mass and composition as a function of their size was used. These data are also within the precision of the ARCPAC AMS measurement (35 %). The observed aerosol  acidity was defined by the mean neutralized fraction as f = (NH + 4 ) / (2(SO 2- 4 ) + (NO - 3 )) with all concentrations in molar units  (Fisher et al.
Acknowledgements. This work was funded by the  Natural Sciences and Engineering Research Council of Canada (NSERC) , the  Fonds Québécois de la Recherche sur la Nature et la Technologie (FQRNT)  and the  Centre National d'Etudes Spatiales (CNES). A. Stohl (NILU)  and  ECMWF  are acknowledged for providing the FLEXPART model and the meteorological analyses. We also acknowledge the OMI mission scientists and associated  NASA  personnel for the production of the data used in this research effort. We thank the  ICARE Data and Services Center  (http://www. icare. univ-lille1. fr/drupal/)for providing access to the space-borne data used in this study. G. Ancellet  and  R. A. de Villiers  are acknowledged for their work on FLEXPART modeling. We thank  S. Howell  for providing acces to the ARCTAS AMS data.
Costs data All costs are expressed in Canadian dollar 2011 value. All costs estimated before 2011 were adjusted to June 2011 levels based on the health component of the Canadian Consumer Price Index  [40]. The costs included in the analysis from a Ministry of health (MoH) perspective were those associated with medication, EPS management and healthcare resources used in the management of metabolic complications (Table  2 ). The cost of Saphris® was provided by Lundbeck Canada Inc. , and the costs of the other antipsychotics were taken from the Liste des médicaments (list of medications) provided by the Régie de l' assurance maladie du Québec (RAMQ, Quebec's health insurance board)  [41]. For olanzapine, costs differ across dosages as well as manufacturers. Therefore, the RAMQ database was used to estimate the mean cost of olanzapine, based on its use in a real-life setting.
The costs associated with metabolic complications were those covered by the MoH, including medical costs (physician, outpatient care, emergency visits, hospitalizations, and intensive care unit) and medications. Costs associated with a complication were estimated using data from pharmaceutical and medical services retrieved from Quebec's Provincial Health Plan database. The RAMQ data are anonymized and are publicly available, at a cost, for research purposes. The RAMQ Direction for analysis and management of information manage the distribution of RAMQ data to external parties. More specifically, for each complication, the difference between the median annual costs incurred by patients aged from 40 to 44 years who had the complication from January 1, 2003 to December 31, 2009 and the median annual costs incurred by patients in the same age range who did not have the complication was calculated.
In addition, the analysis accounted for adverse events associated with treatment, including EPS and weight gain as well as treatment switches due to EPS. Furthermore, because weight gain is a progressive adverse effect, the choice of a one-year period for weight gain development better reflects the reality. Pharmaceutical and medical services were taken from a RAMQ database to estimate the costs of metabolic complications based on real-life settings. The RAMQ database was also used to accurately estimate the cost of antipsychotics used by BPD patients in real-life settings. Because different doses of antipsychotics are used for different indications, estimates of treatment costs in real-life settings, and specifically in a BPD population, constituted the most appropriate method. However, this economic evaluation has several limitations.
First, despite the limited clinical data on atypical antipsychotics in the treatment of BPD, there was sufficient evidence to develop an economic evaluation comparing asenapine to olanzapine specifically for this indication. However, there was insufficient evidence to support an economic evaluation comparing asenapine to other atypical antipsychotics. In addition, because certain data on BPD patients were unavailable, some model parameters, such as disutility associated with EPS and risks of developing complications, were obtained in SCZ patients and general population respectively. Moreover, given the absence of direct comparison between asenapine and olanzapine in clinical trials of antipsychotics used as combination therapy, this economic evaluation focused solely on a comparison of these agents as monotherapy for BPD.
Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0. 93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.
The use of two separate, relatively narrow, thermal bands has been shown to minimize the error in the retrieval of LST  [11]. The spatial resolution of TIRS data is 100 m with a revisit time of 16 days, and as a result, applications are different than those of other sensors with coarser spatial resolutions and shorter revisit times. While Landsat-8 images are already freely distributed through the U. S. Geological Survey (USGS), to the best of our knowledge, no SWA for LST retrieval from TIRS has been published. Although several SWAs have been developed for use with other sensors  [12] [13] [14] [15] , some adaptations are required in order to implement them for the TIRS spectral bands. Therefore, the objective of this letter is to develop a SWA, adapted for use with Landsat-8 TIRS data, along with its accuracy assessment.
While this is not always the case, this first-order estimation of the atmospheric transmittance for the whole scene, which relies on measured water vapor, is preferable to not accounting for it at all  [25]. To roughly meet this assumption, cloud pixels must be masked. The atmospheric transmittance can then be simulated for the entire scene, based on the point measurement of the total content of water vapor in the column and standard atmospheric profiles using radiative transfer models, such as Modtran. The use of sun photometers for estimating water vapor has some limitations. As mentioned earlier, they do not provide information when the sky is cloudy or at night. Furthermore, due to the lack of global coverage by ground-based instruments, AERONET data might not be available for all TIRS users for estimating the atmospheric water vapor content, especially in the northern latitudes, Asia, Australia, and central Africa.
In these areas, other means are suggested, such as the use of a total column water vapor product provided by European Centre for Medium-Range Weather Forecasts (ECMWF) or the Moderate Resolution Imaging Spectroradiometer (MODIS) Precipitable Water product (MOD05_L2, MYD05_L2). While these products' spatial resolutions are significantly coarser than TIRS, they capture some spatial variability in water vapor distribution. Accordingly, ECMWF and MODIS data can be used when the users wish to refrain from assuming constant water vapor throughout the scene. The disadvantage of these data products is that in many cases, they do not accurately represent the state of the atmosphere at the time of TIRS overpass. Unlike the sun photometer that can perform measurements instantaneously or at a close temporal proximity to TIRS, the water vapor estimations derived from these products may be produced within several hours of the TIRS acquisition, and thus do not capture the exact atmospheric conditions at the acquisition time.
This is indeed a limitation, and for this reason, these products should be used with caution. As a rule, users should select the data source according to its accessibility. When multiple sources of data are available, temporal proximity to the TIRS image acquisition and spatial proximity of the water vapor measurement site to the study area should be preferred. When facing a dilemma between temporal and spatial precision, the users have to consider this trade-off and make an optimal decision based on their experience. The results of Modtran 4. 0 simulations, conducted for a mid-latitude summer and for a 1976 standard US atmospheric profile to determine the relation between water vapor and atmospheric transmittance, are presented in Table  2. Throughout this paper, the model developed for mid-latitude summer is used as an example.
Determination of LSE The emissivity of land, in contrast to that of the ocean, is significantly different than unity, and varies with the heterogeneity of vegetation, surface moisture, roughness, and viewing angle  [26]. Since LSE can change substantially over short distances, it is important to estimate its value for every pixel prior to applying the SWA. Several methods have been suggested to estimate the emissivity for other sensors and can also be applied to TIRS. Techniques for emissivity estimations from infrared and visible data are reviewed and discussed in detail elsewhere  [27, 28]. Adapting some of these techniques for use with the Landsat-8 requires the use of OLI bands to indirectly estimate the LSE in the TIRS bands. For instance, the LSE could be obtained from a land-cover classification, in which the emissivity values for each class are assumed  [29].
Summary We presented a SWA for Landsat-8 TIRS data. The ability to derive LST temperatures accurately has immediate impacts for potential Landsat-8 users and applications. As data from the new Landsat-8 are distributed freely, its high resolution thermal abilities can allow for new scientific advances in earth science. Although the spatial resolution of TIRS is degraded to 100 m, in comparison with the 60 m resolution of the Landsat-7 ETM+ thermal band, the added value of having two bands is more accurate LST estimations with TIRS than with its predecessor. In addition, the 100 m resolution is sufficient for water consumption measurements over fields irrigated by center pivot systems  [9] , as well as for other uses over relatively homogeneous areas. In this paper, we have outlined possible strategies for evaluating the atmospheric water vapor content and LSE.
Acknowledgments This work was supported by the  Israeli Ministry of Science, Technology and Space , and the  Pratt Foundation. The authors wish to thank the two anonymous reviewers and  Marie Weiss  of the  French National Institute for Agricultural Research (INRA)  for their valuable comments. Gyanesh Chander  and  Eugene A. Fosnight  from the  US Geological Survey  are thanked for providing Landsat-8 data.
Recordings were acquired, analysed and processed in accordance with the workflow proposed by  Pennekamp & Schtickzelle (2013) , using a stereomicroscope (Leica M205 C; Leica Mikrosysteme Vertrieb GmbH, Ernst-Leitz-Strasse 17-37, 35578 Wetzlar, Germany) and mounted digital CMOS camera (Hamamatsu C11440) in combination with the software programs IMAGEJ  (Abramoff et al. 2004 ) and R (R Development Core Team 2008). Paramecium cells were transferred via micropipette into 1 mL of growth medium spread across a glass Sedgewick-Rafter counting cell. Videos were recorded for 5 s with a 40-ms field delay and 10-ms exposure (giving 25 frames per second) at low (7Á89) magnification. To minimize blur and achieve highest optical resolution and contrast, the image was set to grey scale, and highintensity external illumination was placed around the stage plate (Schott VisiLED MC 1500; SCHOTT AG, Hattenbergstrasse 10, 55122 Mainz, Germany).
So that the software could visually sep-arate Paramecium from artefacts, the video recordings were converted to 8-bit format, and a size threshold of 10-255 pixel lengths was specified for Paramecium. Videos in which Paramecium ceased swimming, swam vertically or exited the field of view were discarded. ImageJ's Particle Analyzer and Particle Tracker returned x, y coordinates (pixel locations within the field of view) for cells within each frame of each videoalong with estimates of length and width in pixels, aspect ratio (dimensionless ratio of length to width), and cross-sectional area in square pixels. Mobility was quantified in terms of the frequency of turns made by Paramecium and the average swimming speed of Paramecium in each video. Turns were defined as movements that caused Paramecium to deviate at least 45 degrees from its original trajectory.
Acknowledgements Support for this work came from the  University of Zurich Institute of Evolutionary Biology and Environmental Studies. We thank  David Montagnes ,  Dennis Hansen ,  Yves Choffat ,  Dan Flynn ,  Thomas Massie ,  Marco Plebani ,  Gian Marco Palamara ,  Mahtab Mohebbi  and the members of the Princeton Disease Modeling Group for helpful comments and technical assistance.
RNA isolation and microarray experiments Trizol (Invitrogen) was used to extract RNA from treated or control worms (combined sexes in both cases) followed by a DNAse digestion (RNAeasy kit; Qiagen). The quality of RNA was checked by microfluidic electrophoresis (Bioanalyzer; Agilent Technologies). For microarray experiments a S. mansoni customdesigned oligonucleotide platform (60-mers) was used containing 44,000 probes representing nearly the complete S. mansoni and S. japonicum transcriptome (Agilent Technologies;  [46, 47] ). All associated information (probes, annotation) is available at Gene Expression Omnibus (GEO) under the accession number GPL8606. For the microarray experiments RNA from treated and control males and females (300 ng each for biological replicates) was used for cDNA amplification followed by Cy3 and Cy5 labelling during in vitro transcription (Quick Amp Labelling Kit, two colors; Agilent Technologies).
Dye-swap approaches were done as internal technical replications for each sample. Three microarray hybridizations were performed for each sample (inhibitor treatment for 24 and 48 h as well as control), which included two technical replicates for each of the three biological replicates. As probe for hybridization, 825 ng cRNA of each labelled inhibitor sample was used and combined with a control sample labelled with the opposite dye. Hybridization was done at 65uC for 17 h with rotation followed by slide washing (according to the Agilent manual) and scanning (Gene Pix 4000B Scanner; Molecular Devices). Raw data were acquired using the Feature Extraction software (Agilent Technologies). They are available under GEO study number GSE53154. For subsequent data analyses, genes were considered as transcribed only if the corresponding probe had a signal significantly higher than background (using default parameters from the Feature Extraction software and considering the ''IsPosAndSig'' result from the output).