dataesr/scientific-paragraphs-categorization huggingface dataset:
05) when a Linear Mixed-effects Model was fitted (LMM; R package lme4). The bacterial abundances were not averaged over the time because of a significant increase over the time (LMM, p-values > 0. 05 for FL and PA bacteria of each phytoplankton species). LMM was fitted to the FL and PA bacterial abundances separately using all time points and was followed by a Dunnett post-hoc test to determine the differences between each treatment and the control. The differences between FL and PA bacterial abundances in each treatment were analyzed using an LMM and a Tukey post-hoc test using the R package emmeans.
2017). The FIRSTLIGHT database 1 includes several properties for all snapshots of the main galaxy progenitor of the 289 zoom-in simulations, such as the virial, stellar and gas masses, and its SFR, in steps of 10 Myr. The database starts when the galaxy reaches the halo mass of M vir = 10 9 M and ends in the last available snapshot at z ≥ 5. In general, these galaxies show nonuniform star formation histories, spending most of their time (70%) in bursts of star formation  (Ceverino et al. 2018) , consistent with cosmological gas accretion events. In this work, we use all snapshots within the redshift range 6. 5 ≤ z ≤ 10. 5. This sample is composed of 10,064 snapshots, and covers a wide range of stellar masses (∼ 10 5-9 M ), SFRs (∼ 0 -30 M yr -1 ), and metallicities (Z = 3 × 10 -5 -8 × 10 -3 ).
Calibration of MRS observations The MACS1149-JD1 and B14-65666 MIRISim simulated MRS observations are calibrated with the JWST calibration pipeline (release 0. 9. 6). 9  The pipeline is divided into three different processing stages. The first stage performs a detector-level correction, where the MRS observations are corrected for saturation, linearity, and dark current. It also applies the jump detection and ramp-fitting modules to transform the raw MRS ramps observations to slope detector products. We use a rejection threshold of 1. 75σ to identify the jumps between adjacent frames and correct the cosmic ray events. The selected rejection threshold is optimized to produce the best S/N on the final calibrated spectrum. The modification of the rejection threshold from 4σ to 1. 75σ produces variations of S/N in factors of ∼1.
Acknowledgements. The authors gratefully thank the Referee for the constructive comments and recommendations that helped to improve the quality of the paper, and the  EC MIRI  test team and  MIRISim developers  for providing a great and useful tool, the  MIRI  instrument simulator (MIRISim). The authors also acknowledge the  STScI  and the developer team of the official JWST calibration pipeline. This work was supported by the  Spanish Ministry for Science, Innovation  and  Universities  project number  ESP2017-83197. D. C. acknowledges the  Gauss Center for Supercomputing  for funding this project by providing computing time on the  GCS Supercomputer SuperMUC at Leibniz Supercomputing Centre  (Project ID: pr92za). D. C. is supported by the state of  Baden-Württemberg  through bwHPC. D. C.
is a DAWN fellow. A. L. acknowledges funding from the  Comunidad de Madrid, Spain , under  Atracción de Talento Investigador Grant   2017-T1/TIC-5213. J. P. P. and T. V. T. acknowledge financial support from  UK Space Agency grants. A. A. -H. acknowledges support from the  Spanish Ministry of Science, Innovation and Universities  through grants  AYA2015-64346-C2-1-P  and  PGC2018-094671-B-I00 , which were party funded by the  FEDER program  and from  CSIC  grant  PIE201650E36. K. I. C. acknowledges funding from the  European Research Council  through the award of the Consolidator Grant ID 681627-BUILDUP.
Institutional review board approval was granted by the Comité de Protection des Personnes Nord-Ouest IV, the ethical committee of the university hospital of Lille. Statistical analysis. GraphPad Prism (version 6) software was used to compare the distribution of biomarkers in the different groups with the Mann-Whitney two-tailed test and to generate receiver operating characteristic (ROC) curves, derive cutoffs, and construct graphs. A P value of Ͻ0. 05 was considered statistically significant.
ACKNOWLEDGMENTS We thank  Val Hopwood  for editing assistance. This work was supported by internal funding from the  Fonds d'Aide à l'Émergence et à l'Excellence du CHRU de Lille-Bonus  H and by the  Program Hospitalier de Recherche Clinique du Ministère des Affaires Sociales, de la Santé et de la Ville   PHRC 1918 ,  2011 ,  Candigène. B. S. has received travel grants from  Pfizer  and  MSD  and a research grant from bioMérieux. M. M. has received speaker fees from  Gilead ,  Pfizer ,  Biotest ,  Janssen , and  MSD  and a grant from  Gilead , all of which are outside the present work. A. A. has received untied travel grants from  MSD ,  Gilead , and  Astellas  and honoraria from Pathoquest and  Gilead. C. V. has received research support to his institution from  Pfizer  and  MSD  and speaker and advisory board fees from  Gilead ,  Pfizer , and  MSD , all of which are outside the present work.
Study area and sample collection From December 2011 to March 2013, paired samples of blood and heart (n = 194) were collected randomly from backyard pigs. The samples were obtained in 67 different backyard holdings, from 11 counties in central, northwestern and western Romania (Transylvania) (Fig. 1 ). The backyard pigs were reared in small extensive farms, 1-3 pigs per unit, were slaughtered at home and were used for familial consumption. The pig owners voluntarily participated in the study. A questionnaire was prepared for collection of data regarding the age, sex and region of origin of the animals. From each pig, a blood sample was collected from the jugular vein during the slaughtering. Heart samples were collected individually. The samples were transported under refrigerated conditions to the laboratory.
Cycling conditions were: initial denaturation 15 min at 95 °C, followed by 35 cycles of 94 °C for 30 s, 61 °C for 3 min, 72 °C for 30 s, and 30 min at 60 °C. PCR products were diluted 1:10 with deionized formamide (Applied Biosystems, Life Technologies, Carlsbad, California). One microliter of each diluted PCR product was mixed with 0. 5 µl of a dye-labelled size standard (ROX 500, Applied Biosystems) and 23. 5 µl of deionized formamide (Applied Biosystems). This mixture was denatured at 95 °C for 5 min. The PCR products were electrophoresed using an automatic sequencer (ABI PRISM 3130xl, Applied Biosystems). The size of the alleles in bp was estimated using GeneMapper analysis software (version 4. 0, Applied Biosystems). An unrooted neighbour-joining dendrogram was produced from the microsatellite data using Populations 1.
Statistical analyses Frequency, apparent prevalence and its 95% confidence interval (95% CI) were calculated for the anti-T. gondii antibodies and T. gondii DNA. These parameters were determined overall and by age group (young, < 1 year old; adults, ≥ 1 year old), sex (males, females), and for each region (central, north-western, western). The difference in prevalence among groups was statistically analyzed using a Chi-square test of independence. A P-value of < 0. 05 was considered statistically significant. Data were processed using EpiInfo 2000 software (CDC, Atlanta, GA, USA). In addition, the level of agreement between T. gondii detection methods (IFAT and PCR) was calculated using the overall agreement measure and Cohen's Kappa statistic with the Win Episcope 2. 0 program. Positive and negative percent agreement was also calculated.
Acknowledgements This article is based upon research within the framework of  COST Action FA 1408 -A European Network for Foodborne Parasites in Europe (EURO-FBP ), supported by  COST (European Cooperation in Science and Technology ; http:// www. cost. eu)and  PN-III-P1-1. 1-MC-2018-1950 ,No. 537/17. 10. 2018 ( UEFISCDI ). The authors thank  Cristian Domşa  for support in the preparation of the figures.
Study design and patients In this observational, multicentric, retrospective study, we identified patients with advanced ALK-positive NSCLC from whom tumor samples had been obtained at progression on an ALK TKI as part of the routine procedure. Both tissue biopsies and circulating tumor DNA samples were considered. Clinical and molecular data were collected retrospectively at each center. The investigators used RECIST1. 1 to evaluate responses to ALK TKIs. The objective response rate was defined as the rate of complete or partial response. The disease control rate was defined as the rate of complete or partial response or stable disease. Overall survival was calculated from the start of the first ALK TKI treatment received. All the data reported in this study were extracted from a database approved by the French National Data Protection Authority (CNIL).
AmpliSeq libraries were prepared with the Ion AmpliSeq Library Kit 2. 0 Ion Xpress barcode adapters kit, primers (Ion AmpliSeq custom panel or Colon and Lung V2) (Thermofisher Scientific), and 10 ng or less of each DNA sample according to the manufacturer's instructions (Table  S1 ). Emulsion PCR, enrichment, and chip loading were performed on an Ion Chef instrument with the Ion PI™ Hi-Q™ Chef Kit or the Ion 540™ Kit-Chef. Samples were sequenced with Ion P1 chips on Ion Proton System or Ion 540 chips on the Ion S5XL system (Thermofisher Scientific). Data were analyzed with the Torrent suite software v. 5. 2. 2 (Thermofisher Scientific). For the ALK, EGFR, and KRAS genes, manual inspection of sequences was done on an integrative genomic viewer (IGV).
Statistical analysis Quantitative variables are expressed as means (standard deviation, SD) in the case of a normal distribution or as medians (interquartile ranges) otherwise. Categorical variables are expressed as numbers (percentages). The normality of distributions was assessed with histograms and the Shapiro-Wilk test. Survival rates were calculated from the date of introduction of the first ALK TKI until the date of death or last news and derived from Kaplan-Meier survival curves. Medians of survival were calculated with confidence intervals. Statistical testing was conducted at a two-tailed α-level of 0. 05. Data were analyzed with the SAS software version 9. 4 (SAS Institute, Cary, NC).
Discussion The present study has focused on targeted NGS, a technique now widely used in daily molecular testing. We show that its use in routine practice allowed identifying an ALK TKI resistance mechanism in 9 out of 23 patients (39%) with advanced ALK-positive NSCLC. Gainor et al. report a similar rate of ALK mutations for other NGS panels  [11]. Thus, targeted NGS appears suitable for detecting ALK mutations in routine practice. It is anticipated that ALK mutations will be detected more frequently in the near future, Gainor et al. having shown that they arise more frequently following second-or third-generation TKI treatment (54-71%) than after crizotinib (20% of patients)  [11]. Meanwhile two secondgeneration TKIs, ceritinib and alectinib, have demonstrated efficacy as first-line treatment,  [4, 25]  and ongoing clinical trials are currently evaluating next-generation TKIs (NCT03052608, NCT02737501, NCT02767804) in this context.
The SNOWPACK, GEOTOP, HTESSEL, UTOPIA, SMASH and S3M snow models are forced, first, with high-quality measurements performed at the experimental site of Torgnon, located at 2160 m a. s. l. in the Italian Alps (control run). Then, the models are forced by data at gradually lower temporal and/or spatial resolutions, obtained i) by sampling the original Torgnon 30-minute time series at 3, 6, and 12 hours, ii) by spatially interpolating neighboring in-situ station measurements and iii) by extracting information from GLDAS, ERA5, ERA-Interim reanalyses at the gridpoint closest to the Torgnon station. Since the selected models are characterized by different degrees of complexity, from highly sophisticated multi-layer snow models to simple, empirical, single-layer snow schemes, we also discuss the results of these experiments in relation to the model complexity.
Results show that when forced by accurate 30-min resolution weather station data the single-layer, intermediate-complexity snow models HTESSEL and UTOPIA provide similar skills as the more sophisticated multi-layer model SNOWPACK, and these three models show better agreement with observations and more robust performances over different seasons compared to the lower complexity models SMASH and S3M. All models forced by 3-hourly data provide similar skills as the control run while with 6-and 12-hourly temporal resolution forcings we generally observe a reduction in model performances, except for the SMASH model which shows low sensitivity to the temporal degradation of the input data. Spatially interpolated data from.
PILPS2d evaluated twentyone land surface schemes forced by 18 years of observed meteorological data from a grassland catchment in Russia, with the aim to investigate the reasons for models scatter in the output snowpack variables. Weaknesses in reproducing mid-season ablation were shown to produce systematic scatter among the models. Albedo and fractional snow cover were both key variables for an accurate representation of the amount of energy absorbed by the snowpack. Indeed, the ablation during the early snow season is another major source of divergence among models: in early winter a thin snow cover is highly sensitive to changes in the forcings and the resulting differences in snowpack conditions tend to persist throughout the whole snow season owing to internal feedback processes. PILPS2e showed the difficulty of reproducing spring melting.
The set of multimodel, multi-forcing simulations finally allows to discuss the relationship between model complexity and model sensitivity to errors/inconsistencies in the meteorological forcings. This paper is structured as follows: Sect. 2 presents the snow models employed in the study while Sect. 3 describes the station of Torgnon and the datasets employed for the experiments. Section 4 describes in detail the set of 12 devised experiments and, for each experiment, the method employed to derive the forcing. Section 5 focuses on the evaluation of snow model outputs against observations, finally Sections 6 and 7 discuss the results and draw the conclusions.
, 2013). Wind-induced phenomena are limited in this site, since it experiences low winds, with an average half-hourly wind speed of 1. 6 ± 1. 3 m/s over the 2012-2014 period. The station measures all the input variables needed to force the snow models, including air temperature, total precipitation, shortwave (SWIN) and longwave (LWIN) incoming radiation, wind direction and speed, relative humidity, surface pressure, surface temperature. These variables are measured at high frequency, and then aggregated at 30-minute temporal resolution. nique to measure both liquid and solid fractions. This is a consolidated technique that provides higher confidence on the reliability of precipitation data than standard rain gauges  (Kochendorfer et al. , 2017). As the OTT pluviometer has been operational since mid-2012, in our analysis we consider the dataset spanning the period from October 1 st , 2012 to June 30 th , 2017, covering five complete snow seasons.
Spatial interpolation of meteorological forcings from neighboring stations The spatial interpolation of ground meteorological observations represents one of the most commonly used practices in the operational applications of hydrological models. In order to test the performances of the models in this condition, an interpolated dataset has been generated for the Torgnon monitoring site by using the MeteoIO library  (Bavay and Egger, 2014). Meteorological data from six neighboring stations have been interpolated over a squared digital elevation model of 16 km 2 with a grid resolution of 50 meters centered on the coordinates of Torgnon (Fig. A1  and Tab. A1). The algorithm used for the interpolation is the inverse distance weight (IDW) as first choice for all the meteorological variables. The interpolation accounts also for vertical gradients of both temperature and precipitation.
GLDAS (Global Land Data Assimilation System ) is a global dataset exploiting satellite and ground-based observational data combined with advanced modelling and data assimilation techniques in order to generate optimal fields of surface variables  (Rodell et al. , 2004). In particular, the GLDAS-2. 1 archive used in this study contains 36 land surface fields from January 2000 to present time at 0. 25 • (lon/lat) spatial and 3-hour temporal resolutions  (Rui and Beaudoing, 2018). ERA-Interim  (Dee et al. , 2011)  is a global reanalysis including a variety of 3-hourly surface parameters describing atmospheric and land-surface conditions, and 6-hourly upper-air parameters covering the troposphere and stratosphere. ERA-Interim has spatial resolution of 0. 75 • , at the latitude of Torgnon corresponding to about 59 km in the zonal and 83 km in the merid- ERA5  (Hersbach and Dee, 2016)  is the latest ECMWF global reanalysis product, providing data at higher resolution than ERA-Interim, both in space (30 km) and in time (1 hour).
ERA-5 uses one of the most recent versions of the Earth system model and data assimilation methods applied at ECMWF and modern parameterizations of Earth processes compared to older versions used in ERA-Interim. With respect to ERA-Interim, ERA5 has also an improved global hydrological and mass balance, reduced biases in precipitation, and refinements of the variability and trends of surface air temperature  (Hersbach and Dee, 2016).
Therefore, in the experiment RAD-ERAI we take into account the shading of the radiation sensor in case of snowfall by replacing radiometer measurements with ERA-Interim reanalysis data. In the third experiment, SWIN-CLS, we employ external SWIN data resulting from the clear sky radiation  (Yang et al. , 2001 (Yang et al. , , 2006) )  attenuated through the cloud masks from the Meteosat Second Generation satellite in the following way. For each of the 34 radiometers in the Aosta Valley an averaged attenuation factor F is computed as: F = 1 N N i=1 R i st SW IN i (1) where N is the number of cloud-covered stations, R i st is the measured radiation at the i th station and SW IN i is the corresponding modeled radiation in clear sky condition. The incident solar radiation in cloudy conditions at the location j is given by: SW IN j inc = R j F (2) Experiments TIME-3h, TIME-6h and TIME-12h investigate the sensitivity of the models to the temporal resolution of the meteorological forcing, since the temporal resolution of many available datasets is coarser than that employed in the CTL run.
We basically employ Torgnon data every 3, 6 and 12 hours since October 1st, 2012 time 00:00 UTC, and linearly interpolate them at the 30 minute time step for all variables except for total precipitation. teoIO and reanalysis data required to be rearranged and interpolated to 30-minute resolution in order to be used as forcing for snow models. In the case of ERA-Interim, for example, forecasts are initialized only twice a day at 00:00 UTC and 12:00 UTC and accumulated fluxes of total precipitation, surface solar and thermal downward radiation are available as forecasts at 3-hour intervals for the following 12 hours. From these forecasts we derive the average fluxes over 3-hour intervals and we assume the fluxes to be constant during each interval. For the other ERA-Interim parameters, namely 2-meter temperature, dew-point temperature, surface pressure, 10 metre U and V wind components, we consider the analyses at 00:00, 06:00, 12:00, 18:00 UTC and the forecasts at +3 hours.
These data are linearly interpolated in time to the integration time step (30 minutes) of the snow models. Some calculations are necessary to obtain all the variables required by the models. For example, ERA-Interim does not directly provide relative humidity, which we derive using the Magnus formula from the dewpoint temperature and the 2-meter air temperature  (Lawrence, 2005). The last two experiments, ERAI-LR and ERAI-BIAS, investigate if it is possible to improve the performances of snow models when they are forced by reanalyses, for instance ERA-Interim, by applying two simple bias-correction methods to adjust air temperature and hence the amount of solid precipitation with respect to the total one. In ERAI-LR we take into account the fact that ERA-Interim has a smoothed topography and the altitude of the gridpoint closest to the Torgnon station is 680 m lower than the actual elevation of the station.
In ERAI-LR experiment we adjust the temperature data assuming a fixed A desirable feature of each experiment is that the differences in the model outputs are mainly due to the internal model characteristics rather than to the different parameterizations used by the models to derive the solid and liquid precipitation from the total precipitation input. To this end, we calculate externally the rainfall and the snowfall amounts using a fixed threshold on wet-bulb temperature. Specifically, precipitation is considered as snowfall when wet-bulb temperature is lower than or equal to 1 • C and as rainfall otherwise. A slightly different approach was used for GEOTOP which requires precipitation totals (rather than solid and liquid precipitation separately) and then it separates rainfall and snowfall through an internal parameterization based on a fixed threshold on dew-point temperature.
RAD-ERAI, SWIN-CLS -model sensitivity to the radiation input A typical problem occurring in case of snowfall is that when the radiation sensors get covered by snow they record inaccurate or even wrong data. To take into account this issue and test how it affects snow simulations, in the experiment RAD-ERAI we use incoming longwave and shortwave radiation data from the Torgnon station except in case of snowfall, when we employ external LWIN and SWIN data derived from ERA-Interim. In the other experiment, SWIN-CLS, we replace observed incoming shortwave radiation data with the external data described in Sect. 4. The results of these simulations are reported in Table  4. Although the difference between external data and Torgnon data can be high at the time step of the model (not shown), their overall impact on snow simulations is low.
In our experiment the lower-complexity models reveal a lower sensitivity to errors in the meteorological forcing, and when these models are driven with 6-hourly and 12-hourly input data they provide similar performances as selected intermediate-complexity models. So, with low temporal resolution data the performances of intermediate and low complexity models get comparable. However, from these experiments an added value of the most sophisticated model SNOWPACK emerges. SNOWPACK forced by the 12-hour resolution forcing still provides lower errors than the simplest model S3M forced by the best available forcing at 30 minute temporal resolution (Table  4 ).
MeteoIO, GLDAS, ERA5 and ERA-Interim -model sensitivity to the spatial resolution and bias in the forcing We consider a rather standard case for which no station measurements are available for the area of interest and one has to rely on gridded datasets, which are generally characterized by lower resolution and lower accuracy with respect to station measurements. To explore a representative range of possible alternatives we employ datasets with different characteristics: the MeteoIO dataset, based on the interpolation of data from neighboring stations, GLDAS, ERA5 and ERA-Interim reanalyses at 25, 31 and 80 km respectively. An overview of the comparison between the meteorological forcing provided by these datasets and the observations in Torgnon is shown in Fig. 3. MeteoIO forcing is in fairly good agreement with observations.