dataesr/scientific-paragraphs-categorization huggingface dataset:
Equality does not hold in general, but a direct computation finds that [K : F ] must divide 6 (resp. 2) when j(E) = 0 (resp. 1728), and this occurs when k = Q. We now fix E as the elliptic curve y 2 = x 3 + 6x + 7 with Cremona label 144b1. Note that ̺ E,7 is surjective; this can be seen in the entry for this curve in the L-functions and Modular Forms Database  [LMFDB]  and was determined by the algorithm in  [Sut16]. It follows that Gal(Q(E[7])/Q) ≃ GL 2 (F 7 ), and Proposition 3. 34 implies that H := Gal(K/Q) ≃ PGL 2 (F 7 ). For our chosen curve E we have a = 6 and b = 7. Plugging these values into equation (3. 22) and applying the algorithm of  [Sut12]  to simplify the result yields the curve listed in entry #14 of Table  5  for H = G C 0 7. To determine the field K, we apply Proposition 3.
Fité received financial support from the German Research Council (CRC 701), the Excellence Program María de Maeztu MDM-2014-0445, and MTM2015-63829-P. Sutherland was supported by NSF grants DMS-1115455 and DMS-1522526. This project has received funding from the European Research Council (ERC) under the European Unions Horizon 2020 research and innovation programme (grant agreement No 682152), and from the Simons Foundation (grant #550033).
The role of soil microorganisms in regulating soil organic matter (SOM) decomposition is of primary importance in the carbon cycle, in particular in the context of global change. Modeling soil microbial community dynamics to simulate its impact on soil gaseous carbon (C) emissions and nitrogen (N) mineralization at large spatial scales is a recent research field with the potential to improve predictions of SOM responses to global climate change. In this study we present a SOM model called ORCHIMIC, which utilizes input data that are consistent with those of global vegetation models. ORCHIMIC simulates the decomposition of SOM by explicitly accounting for enzyme production and distinguishing three different microbial functional groups: fresh organic matter (FOM) specialists, SOM specialists, and generalists, while also implicitly accounting for microbes that do not produce extracellular enzymes, i.
, 2005) , although its generic input data would allow it to be embedded in any other global land surface model for grid-based simulations. The ORCHIMIC model is described in Sect. 2 and the two conceptually simpler models -a first-order kinetics model called CENTURY, which was derived from  Parton et al. (1987)  and constitutes the SOC decomposition module of the ORCHIDEE model  (Krinner et al. , 2005) ; and a firstorder kinetics model called PRIM, which is a variant of the CENTURY model modified to include interactions between pools to enable the representation of priming of decomposition rates  (Guenet et al. , 2016)  -are described in Sect. 3. The model parameters were calibrated against soil incubation data from  Blagodatskaya et al. (2014) (Sect. 4). Different idealized tests of the ORCHIMIC model response including doubling FOM input and/or a stepwise increase in temperature were performed (Sect.
CENTURY and PRIM soil carbon models Here we give a brief summary of CENTURY and PRIM, the two benchmark models with which we compare OR-CHIMIC for simulating incubation experiments. The CEN-TURY model is the SOM module of the ORCHIDEE global land biosphere model  (Krinner et al. , 2005). It is a simplification of the original CENTURY model  (Parton et al. , 1987 (Parton et al. , , 1988)) , as it does not consider nitrogen interactions. The PRIM variant of CENTURY was developed to capture the magnitude of the priming of SOM decomposition induced by varying litter inputs  (Guenet et al. , 2016). Both are C-only models and have the same structure with similar pools and fluxes as shown in Fig. 2. The effects of soil moisture, temperature, pH, lignin and clay content on the decomposition of each substrate pool are also the same as those used in ORCHIMIC.
3d ). The negative cumulative priming effect as simulated by the various ORCHIMIC variants lasted between 6 and 8 days. Similar to the observations, the modeled cumulative priming effect by the ORCHIMIC variants increased very quickly from day 8 onwards, and subsequently slowed down after 13-17 days. At the end of the experiment (day 103), the modeled cumulative priming effect values from the six ORCHIMIC variants were between 170 and 183 mg C (kg soil) -1 , only 2. 5-11 % higher than that observed. It can be argued that ORCHIMIC only does a better job at fitting the incubation data because it has more degrees of freedom than the two other models (Table  5 ). The Akaike information criterion (AIC) takes this into account  (Bozdogan, 1987)  by considering the optimized model performance and its number of adjustable parameters.
Code and data availability. The ORCHIMIC v1. 0 is programmed in the Python programming language. In order to run the model the basic Python packages numpy, os and sys must be preinstalled. The source code, optimized parameter values and script used to reproduce the results (shown in Sect. 6. 4) are available online (https://github. com/huangysmile/ORCHIMIC/releases/ tag/v1. 0; DOI: https://doi. org/10. 5281/zenodo. 1164740, Huangysmile, 2018).
More recently, the EDEN project (European Commission Project on "Emerging Diseases in a Changing European Environment: http://www. eden-fp6project. net, 2004-2007)  included an investigation of West Nile virus ecology leading to bird trapping and tick collection in the Djoudj National Park of Birds (16°25'N, 16°18'W, Saint Louis Department, Saint Louis Region) and Barkedji (15°17'N, 14°52'W, Linguere Department, Louga Region). Study sites and tick collections have been presented in detail elsewhere  (Sylla et al. , 2004; 2007; 2008). Tick collections were mapped at the scale of the squared degree including the locality where each tick sample was collected. Maps were generated using SavGIS software (IRD, www. savgis. org). Geographical position of each locality was given by the gazetteer of Senegal  (Board on Geographic Names, 1965 , Dpt.
RF/microwave power amplifier (PA) is one of the components that has a large effect on the overall performance of communication system especially in transmitter system and their design is decided by the parameters of transistor selected. This letter presents a new concept of a wide-band microwave amplifier using scattering parameters that is often used in the radio frequency communication systemas an application of the active integrated antenna  [1] [2]. This power amplifier operates from 1. 75 GHz to 2. 15GHz frequency and it is based on AT-41410 NPN transistor that has a high transition frequency of 10GHz. The proposed Single Stage PA is designed by microstrip technology and simulated with Advanced Design System (ADS) software. The simulation results indicate good performances; the small power gain (S21) is changed between 11.
Simulation Results and Discussion The proposed wide-band power amplifier was characterized by small and large-signal simulations. The large-signal simulation were obtained by Harmonic Balanced simulator for achieve variation of output power with corresponding input power. Where scattering parameters simulations were utilized to get a small signal (Small-Signal Gain, Return loss. ). Both small and large signal simulations have been executed by the advanced Design System (ADS) software. The simulation S-parameter result illustrated in figures below. As depicted figure 4 presents the variation of the power gain S21 and the unilateral transmission (S12) between 1. 75GHz and 2. 15GHz. it can be observed that this power gain changes between 11. 7dB and 10dB. Thus, these values indicate a good amplification of the microwave PA over a wideband and for the unilateral transmission; we notice that this parameter changes between -18.
Methods Samples were collected during the 45-day OUTPACE (Oliotrophic to UlTra oligotrophic PACific Experiment) cruise (DOI: https://doi. org/10. 17600/15000900) onboard the R/V L'Atalante in February-March 2015 (austral summer). The west to east zonal transect along ∼ 19 • S started in Noumea (New Caledonia) and ended in Papeete (French Polynesia) (Fig. 1 ). It covered a trophic gradient from oligotrophy (deep chlorophyll maximum (DCM) located at ∼ 100 m) in MA waters around New Caledonia, Vanuatu, Fiji and Tonga, to ultra-oligotrophy (DCM located at 115-150 m) in GY waters located at the western boundary of the South Pacific Gyre  (Moutin et al. , 2017 , for details on this cruise). Data were collected at 17 stations including 14 short-duration (SD; 8 h) stations (SD1 to SD15; note that SD13 was not sampled) and 3 long-duration (LD; 7 days) stations (LDA, LDB and LDC).
nanoSIMS analyses, data processing and group-specific rate calculations NanoSIMS analyses were performed using an N50 nanoSIMS instrument (Cameca, Gennevilliers, France) at the French National Ion MicroProbe Facility according to  Bonnet et al. (2016a, b)  and  Berthelot et al. (2016). Briefly, a ∼ 1. 3 pA cesium (16 KeV) primary beam focused onto a ∼ 100 nm spot diameter was scanned across a 256 × 256 or 512 × 512 pixel raster (depending on the image size) with a counting time of 1 ms per pixel. Samples were pre-sputtered prior to analyses with a current of ∼ 10 pA for at least 2 min to achieve sputtering equilibrium and ensure a consistent implantation and analysis of the cell interior by removing the cell surface. Negative secondary ions ( 12 C 14 N -, 12 C 15 N -) were collected by electron multiplier detectors, and secondary electrons were also imaged simultaneously.
A total of 10-50 serial quantitative secondary ion images were generated that were combined to create the final image. Mass resolving power was ∼ 8000 in order to resolve isobaric interferences; 20 to 100 planes were generated for each cell analyzed. NanoSIMS runs are time-intensive and not designed for routine analysis, but a minimum of 250 cells of UCYN-B per station and 30 Trichodesmium filament portions were analyzed to take into account the variability of activity among the population. Data were processed using the LIMAGE software. Briefly, all scans were corrected for any drift of the beam and sample stage during acquisition. Isotope ratio images were created by adding the secondary ion counts for each recorded secondary ion for each pixel over all recorded planes and dividing the total counts by the total counts of a selected reference mass.
Drivers of high N 2 fixation rates in the WTSP? N 2 fixation rates measured in MA waters (average 631 ± 286 µmol N m -2 d -1 ) are 3 to 4 times higher than model predictions for this area (150-200 µmol N m -2 d -1 ,  Gruber, 2016). They are in the upper range of the higher category (100-1000 µmol N m -2 d -1 ) of rates defined by  Luo et al. (2012)  in the N 2 fixation MAREDAT database for the global ocean and thus identify the WTSP as an area for high N 2 fixation in the global ocean. Recent studies performed in the western part of the WTSP, i. e. , in the Solomon, Bismarck  (Berthelot et al. , 2017; Bonnet et al. , 2009 Bonnet et al. , , 2015) )  and Arafura  (Messer et al. , 2015; Montoya et al. , 2004)  seas, also reveal extremely high rates (> 600 µmol N m -2 d -1 ), indicating that this high N 2 fixation activity area extends geographically west-east from Australia to Tonga and north-south from the Equator to 25-30 • S, covering a vast ocean area of ∼ 13 × 106 km 2 (i.
The seafloor of the WTSP hosts the Tonga-Kermadec subduc-tion zone which stretches 2500 km from New Zealand to the Tonga archipelago (Fig. 1 ). It has among the highest densities of submarine volcanoes associated with hydrothermal vents recorded in the ocean (2. 6 vents / 100 km,  Massoth et al. , 2007) , which discharge large quantities of material into the water column, including biogeochemically relevant elements such as Fe and Mn. Guieu et al. (2018)  used hydrological data recorded by Argo float in situ measurements, atlas data and simulations from a general ocean circulation model to argue that the high dissolved Fe concentrations may be sustained by a submarine source. They show that such Fe inputs could spread throughout the WTSP through mesoscale activity mainly westward through the South Equatorial Current, SEC (Fig.
Acknowledgements. This research is a contribution of the  OUT-PACE (Oligotrophy from Ultra-oligoTrophy PACific Experiment ) project (https://outpace. mio. univ-amu. fr/,last access: June 2018) funded by the  Agence Nationale de la Recherche  (grant  ANR-14-CE01-0007-01 ), the  LEFE-CyBER program  ( CNRS-INSU) , the  Institut de Recherche pour le Développement (IRD) , the  GOPS program (IRD)  and the  CNES  ( BC T23 ,  ZBC 4500048836 ). The OUTPACE cruise (http://dx. doi. org/10. 17600/15000900,last access: June 2018) was managed by the  MIO (OSU Institut Pytheas, AMU)  from  Marseilles (France). The authors thank the crew of the  R/V L'Atalante  for outstanding shipboard operations. Gilles Rougier  and  Marc Picheral  are warmly thanked for their efficient help in CTD rosette management and data processing, as well as  Catherine Schmechtig  for the LEFE-CyBER database management and  Thibaut Wagener  for providing the map of bathymetry.
Aurelia Lozingot  is acknowledged for the administrative work. Mar Benavides  was funded by the  People Programme  ( Marie Skłodowska-Curie Actions ) of the European Union's  Seventh Framework Programme  ( FP7/2007-2013)  under  REA  grant agreement number  625185. The participation, nucleic acid sampling and analysis were provided to  Rachel A. Foster  by the  Knut and Alice Wallenberg Foundation. Rachel A. Foster  also acknowledges the assistance by  Lotta Berntzon ,  Marcus Stenegren  and  Andrea Caputo. The satellite products are provided by  CLS  with the support of  CNES. Edited by: Douglas G. Capone Reviewed by:  Carolin Löscher  and three anonymous referees.
Procedure Two investigators (trained physiotherapists) will collect the demographic data (name, date of birth and gender), basic anthropometric measurements (weight, height, body mass index), history of CLBP and clinical symptoms (factors that alleviate or aggravate symptoms) prior to randomisation. Before randomisation, participants will be matched for the maximal voluntary isometric contraction (MVIC) of back muscles, measured by electromyography (EMG). Eligible participants will be blinded and randomised in equal proportions between the two groups: Group-1 (G1, n=16) LMSE and Group-2 (G2, n=16) BERE. The participants of the two groups will participate in an 8-week intervention programme of treatment for two non-consecutive days each week. The two groups will be monitored by experienced physiotherapists (>5 years of experience) who will supervise the exercise programmes, placing each patient into the appropriate position to achieve the correct posture and muscle contraction.
19 rce platform A BIOMEC 400, EMG System, SP, Brazil will be used. The vertical ground reaction force data from the force platform is adjusted at 100 Hz to perform the task. All force signals are filtered with a 35 Hz low-pass secondorder Butterworth filter and converted into centre of pressure (COP) data using MATLAB-based routines (Mathworks, Natick, Massachusetts, USA). COP data will be used for calculations:  21 22  (1) 95% confidence elliptical area of COP (A-COP in cm 2 ), (2) mean velocity (VEL in cm/s) and (3) mean frequency (in Hz) of COP sway for both anteroposterior (A/P) and mediolateral (M/L). The reliability of COP parameters has been supported in the literature for young adults both with and without CLBP (based on the intraclass coefficient correlation: ICC>0. 80 and SEM<1. 30).
sample power calculation We used the best data available at this time to estimate the sample size, because no study has investigated these effects on back endurance outcome. These data were from a study that shows what effects back stabilisation and endurance exercises training have on back maximal strength measure comparable to what was used in the present study (using a similar principle back effort test), namely, MVIC (details in the succeeding paragraphs). 13 re-post results were considered as independent (from two groups) to comply with the present statistical analyses (between-group factor). From the mean clinical differences after intervention across 0°-12° of trunk flexion during back effort test, the values were 26. 5 ft-lb for endurance group versus 46. 5 ft-lb for stabilisation group (with similar SD=20 ft-lb).
26 27 MF will be calculated in successive time windows (50% overlapped) of 250 ms for the total contraction time in the fatigue protocol condition. A least squares linear regression analysis will then be applied to the MF time series to calculate the rate of decline in MF over time (MF/time slope). The slope from this relationship will then be divided by the corresponding intercept value (obtained from linear regression analysis) and multiplied by 100 to yield the normalised EMG index of muscle fatigue (NMFslp); this procedure has been shown to control for subcutaneous tissue thickness differences between participants. 26 The EMG data processing will be performed using EMG work analysis from the Delsys system (V. 4. 0, Delsys, Massachusetts, USA) and MATLAB subroutines (V. 8. 0; The MathWorks, Natick, Massachusetts, USA, release 14).
Open access Analysis of effect of treatment A two-way analysis of variance with repeated measures will be performed to compare the differences between G1 and G2 and times (baseline vs end of 8 weeks measurement) and the effects of interaction (Groups × Times). A posthoc Tukey test will be used to locate the differences between the groups, if necessary. The ES, using Cohen's d for main outcomes, will also be computed if significant differences are present as well as the 95% CI. Analyses will be conducted as intention to treat. The IBM SPSS 19 statistical package (IBM , Armonk, New York, USA) will be used. ethics All participants will be informed about the study procedures, risks, benefits of the investigation and will sign an informed consent form before participating in the trial. The study has been registered on ClinicalTrials.
AHN, RAdS, AFA, GL, BMOA, CFA. Funding RAdS was funded by  CNPq  (PQ level 2) and  FUNADESP  for research support. GL was supported by FRSQ researcher funding (chercheur boursier Junior 2) in the Sherbrooke University. Competing interests None declared. Patient consent Obtained. ethics approval Ethics Committee of  UNOPAR  (technical advice 1860811),  Brazil  ( 56009316. 0.
| MRY bulls population characterization and changes through time To gain more insight into the structure of the data and its changes over time, we analysed genetic merit and genetic diversity. Therefore the population was described on the one hand by measuring its average genetic merit G ¼ NVI and how it changed through time with the rate of genetic merit (ΔG) per year, as the slope of the linear regression of average NVI per year of birth. On the other hand, Principal Component Analysis (PCA) based on genomic relationships between individuals was used to allow visual characterization of the population genetic diversity. The inertia of the PCA cloud representing each population was calculated as the sum of the eigenvalues of the individuals included in the population of interest. Moreover, the individual observed heterozygosity was measured as the proportion of heterozygous markers per individual and populational observed heterozygosity was measured as the average heterozygosity of the population.
ACKNOWLEDGEMENTS The authors want to thank the  Centre for Genetic Resources of the Netherlands (CGN) of Wageningen University & Research and the Cooperative Cattle Improvement organization CRV BV (Arnhem, The Netherlands)  for given access to the data. The authors would also like to thank  H. Sulkers  for providing valuable information about the history of the Meuse-Rhine-Issel cattle breed and the two anonymous reviewers for their comments and suggestions that contributed to the improvement of this manuscript. SE Eynard benefited from a grant from the  European Commission , within the framework of the  Erasmus Mundus joint doctorate "EGS-ABG ," co-funded by the  Dutch Ministry of Economic Affairs  ( KB-21-004-003 ).
(2016) , is a NCTC8325 derivative repaired solely for rsbU, whereas HG003, the strain we focus on in this analysis and that is now used as a model strain for S. aureus regulation studies  (Herbert et al. , 2010) , is repaired for both rsbU and tcaR genes. According to the compilation of data from several publications, S. aureus N315, NCTC8325, and Newman strains each could have over 500 putative regulatory RNAs  (Sassi et al. , 2015) , the precise figure changing according to strains and sources  (Carroll et al. , 2016; Mäder et al. , 2016). Most sRNAs were rediscovered in each independent analysis often under a different name. In order to compile an accurate list of bona fide sRNAs, we visually analyzed high-density coverage published data plus our own RNA-seq data (deep sequencing of pooled RNA extracts from cultures of HG003 strain grown in 16 different growth conditions; GEO GSE104971) as reported in Methods, and performed in-depth curation according to the rules defined in the previous chapter.
RNA-seq for sRNA Detection Experiments were performed with the S. aureus HG003 strain grown in different conditions: (i) eight samples in rich medium (BHI) at OD 600nm  0. 6, 1. 8, 3. 3, 4. 5, 7. 2, 9. 8, and 12. 8 , and stationary phase (24 h), (ii) seven samples under stress conditions (cold shock, heat shock, oxygen limitation, alkaline stress, oxidative stress, disulfide stress, iron-depleted condition and (iii) one sample from colonies on BHI-agar plates (also see GSE10497 in GEO database). Total RNAs were extracted from these 16 growth conditions, pooled together and processed using the MICROBExpress kit (Ambion, AM1905) as recommended by the suppliers, to remove rRNAs. They were then sequenced using an Illumina Genome Analyzer IIx generating single-end 40-nt reads. After a FastQC (v0.
Literature and Experimental Data Integration S. aureus global studies available in literature are summed up in Table  S1. sRNA annotations (coordinates and strand) were collected from the following whole transcriptome analyses: 255 "indep" (transcripts with a promoter determined independently of annotated features) or "inter" (between two annotated regions transcribed from independent promoters)  (Mäder et al. , 2016) , 286 sRNAs  (Carroll et al. , 2016) , 352 NCTC8325 automatically annotated sRNAs  (Sassi et al. , 2015)  using HG003 RNA-seq data (this work; GEO GSE104971), and 53 sRNAs  (Beaume et al. , 2010). These sRNA annotations were pooled together as a GFF file for the present expert analysis. sRNA expression profiles and reported annotations from different strains were compared with HG001 transcription profiles of S.
, 2015). Results are presented in Data Sheet 2. sRNA Conservation sRNA sequence similarities were searched against a nucleotide database (see Table  S2  for the list of strains). Complete genomic sequences were downloaded from the NCBI database. Similarity search parameters (blastall 2. 2. 26) were defined to report a maximum of hits (-e 1000 -W7) with specific scoring criteria (-r2 -G5 -E2) designed for sRNA identification  (Ott et al. , 2012). For each genome, only the blast hit with the best score was kept and divided by the score obtained in S. aureus NCTC8325. The resulting score ratios are represented by a color scale: the more the sequence of the hit is similar to the sRNA sequence, the darker the pixel is (R script). A 50% similarity ratio threshold was applied to define conserved sRNA genes.