dataesr/scientific-paragraphs-categorization huggingface dataset:
The simulated forest structure and the remote sensing NIR values from the windthrow and clear-cut have similar return times to those of old-growth forest conditions. Future studies applying ELM-FATES should focus on improving stem density predictions, which were underestimated, and on enhancing the capacity to compare with remote sensing observations through representation of canopy-spectralreflectance characteristics. Code and data availability. The Landsat data used in this study are freely available through the Google Earth Engine platform. ELM-FATES is available at https://github. com/NGEET/fates. Observational data used to compare remote sensing and modeling results have been previously published and references provided (e. g. Mesquita et al. , 2015; Jakovac et al. , 2014). Supplement.
Acknowledgements. This research was supported as part of the  Next-Generation Ecosystem Experiments-Tropics (NGEE-Tropics ) of the  TES Program  and the  RUBISCO  project under the Regional and  Global Climate Modeling Program , funded by the  US Department of Energy, Office of Science, Office of Biological and Environmental Research , under contract  DE-AC02-05CH11231. Among others, the  NGEE-Tropics Program  supports a large part of the past and current model development for the ELM Functionally Assembled Terrestrial Ecosystem Simulator (FATES). Daniel Magnabosco-Marra  was supported as part of the  ATTO  Project,  Max Planck Society (MPG) , and  German Federal Ministry of Education and Research (BMBF). Rosie A. Fisher  was supported by the  National Center for Atmospheric Research , which is sponsored by the  National Science Foundation  under cooperative agreement no.
Landsat NIR band and ELM-Financial support. This research has been supported as part of the  Next Generation Ecosystem Experiments-Tropics , funded by the  US Department of Energy, Office of Science, Office of Biological and Environmental Research  under contract no. DE-AC02-05CH11231. This research was also supported through the  Reducing Uncertainties in Biogeochemical Interactions through Synthesis and Computation Scientific Focus Area (RUBISCO SFA ) under contract to LBNL, which is sponsored by the  Regional and Global Climate Modeling (RGCM) Program in the Climate and Environmental Sciences Division (CESD) of the Office of Biological and Environmental Research (BER) in the US Department of Energy Office of Science. Review statement. This paper was edited by  Christopher Still  and reviewed by two anonymous referees.
Study design This study is an ancillary analysis from the French and euRopean Outcome reGistry in Intensive Care Units (FROG-ICU) cohort. The original study was a prospective, observational, multicenter cohort study of 2087 patients and was designed to assess the long-term survival rates of patients admitted to ICU. The study was approved by the local ethics committees (Comite ́ de Protection des Personnes, Ile de France IV, IRB n°00003835 and Commission d'e thique biome dicale de l'hôpital de Louvain, IRB n° B403201213352) and registered on https://clinicaltrials. gov/ct2/show/NCT01367093. The methodology and results of the original study have previously been published  14, 15.
Data collection Data regarding CA patients at ICU discharge were retrospectively collected from medical reports according to Utstein-style guidelines  10. Briefly, these recommendations provide to researchers a structured framework to uniformly report CA. Details regarding general data collection at ICU discharge have been reported in the original study  15. Causes of ICU admission were also recorded and classified in 19 categories. Beside CA, most prevalent causes of ICU admission were individualized: respiratory failure, septic shock, neurological causes, cardiogenic shock, immediate post-operative room (urgent or scheduled surgery). The remaining 13 causes were coded as "other causes of ICU admission". The time point of baseline characteristics was defined at ICU discharge. Health-related quality of life parameters.
Statistical analyses Data are expressed as median [interquartile range, IQR] or number (%) as appropriate. After visual assumption of normality and variance homogeneity by the F-test, Student's, Welch or Wilcoxon rank tests were applied for univariate analysis as appropriate. In order to balance CA with non-CA populations at ICU discharge, an analysis derived from the propensity score was performed. Propensity score is used in observational studies to estimate the effect of an intervention by creating a pseudo-randomization in order to reduce selection bias. With the matching propensity score method chosen, we expected a very good matching at the cost of a strong reduction in final populations. In the propensity score model herein, exposure was, in a population of patients discharged alive from ICU, CA as cause of initial admission.
Model variables included in the propensity score were variables associated with one-year outcome at 0. 05 level and with less than 20% of missing data  15. These variables, included according to both the biological plausibility and the statistical analysis, were already validated and published in the original study  15  : age, Charlson score, prior severe valvular disease, prior severe artery disease, chronic renal disease, recent active malignant tumor, prior physical or mental disabilities, red blood pack transfusion during ICU stay and length of stay. The following variables at ICU discharge were also included: systolic arterial pressure, temperature, platelets, white blood count, and total protein. CA and non-CA patients were matched using a 1:1 nearest neighbor matching algorithm with no replacement, along with a caliper of 0.
1 of the standard deviation of the propensity score. Imbalance between the two groups before and after propensity score matching was assessed using a standardized difference, with a value < 10% considered as acceptable for defining the study patients' characteristics as being balanced with respect to the previously described features. Thereafter, one-year survival and HRQOL parameters were assessed in the matched dataset. A Holm post-hoc test was applied for multiple comparisons. Kaplan-Meier curves were drawn and overall one-year survival was estimated by using the log-rank test in non-matched and matched CA and non-CA populations. Secondary, a multivariate analysis using a logistic regression model including the variables described above was also performed to assess if CA was independently associated with one-year mortality.
Fifth, roughly only 1/3 of non-CAs and only 2/3 of CAs provided quality-of-life data. Even considering the number of deaths during the one-year follow-up, these missing data could be an important source of bias. Finally, we included both IHCA and OHCA. IHCA usually has a better prognosis, and thus the inclusion of IHCA would have biased the analysis towards a "healthier" CA group  33.
Acknowledgements. Authors' contributions. AK, AM, EG, ND: study concept and design. AM, EG: Data acquisition. AC, AK, AM, BM, EF, EG, ND: Analysis and interpretation of the data. AK, ND: Drafting of the manuscript. Critical revision of the manuscript: all declared authors. AK, EF, EG: Statistical analysis. EG, AM: Administrative, technical, or material support. EG, AM: Study supervision. All authors read and approved the final manuscript.
To avoid these drawbacks, the present work was designed as a case-control study based on confirmed phenotypes. Only confirmed positive or negative individuals were included, based on the criteria previously defined from a longitudinal study of MAP fecal shedding and serological patterns in dairy cattle  [5]. To avoid a putative bias of non-exposure, the negative animals were born in the same herd and in the same period as the positive animals. In total, 1644 Holstein and 649 Normande cows with relevant and accurate contrasted phenotypic profiles (control and non-clinical/clinical cases) were genotyped with a medium-density SNP chip (50 K SNP); whole-genome sequences (WGS) were subsequently imputed using the 1000 Bull Genomes reference population (run6)  [19]. Here, we present the results obtained from GWAS analyses conducted in both Holstein and Normande cows on the imputed WGS datasets.
A two-step approach was applied in order to improve the accuracy of imputed genotypes of the WGS variants [23]: from 50 to 777 K high-density (HD) SNPs using FImpute software  [24] , and then from imputed HD SNPs to WGS, using the Minimac software  [25]. In spite of a longer computing time, Minimac was preferred to FImpute for the imputation of WGS because it infers allele dosages in addition to the best-guess genotypes. Compared to best-guess genotypes, allele dosages are expected to be more correlated to the true genotypes  [26]  and to lead to better targeting of causative mutations in GWAS analyses  [27]. Imputations from 50 K to the HD SNP level were performed using within-breed reference sets of 776 Holstein and 546 Normande bulls that were genotyped with the Illumina BovineHD BeadChip (Illumina Inc.
, San Diego, CA)  [28]. WGS variants were imputed from HD SNP genotypes using WGS of 2333 Bos taurus animals, from the 6th run of the 1000 Bull Genomes Project  [26, 29]. These animals represent 51 cattle breeds and include 544 Holstein and 44 Normande individuals. In the Normande breed, the number of animals with wholegenome sequences was rather limited but we identified most of them as influential ancestor bulls with a high cumulated contribution to the breed (74%). We applied the protocol described in  [30]  and produced 23,781,173 and 23,610,986 autosomal variants for the Holstein and Normande cows, respectively. The precision of imputation from HD SNP to sequence level was assessed using the coefficient of determination (R 2 ) calculated with the Minimac software  [25]. In order to remove variants with low imputation accuracies, only variants with an R 2 value higher than 30% and a MAF higher than 1% were retained for further association analyses, i.
Whole-genome sequence association analyses We performed single-trait association analyses between all variants and the MAP resistance/susceptibility phenotypes (0/1/2 and 0/1). All association analyses were performed using the mlma option of the GCTA software (version 1. 24), which applies a mixed linear model that includes the variant to be tested  [31]. The categorical nature of the phenotype (2 or 3 classes) was not accounted for in this model assuming a normal distribution. where y is the vector of individual phenotypes; µ is the overall mean; b is the additive fixed effect of the variant to be tested for association; x is the vector of predicted allele dosages, varying between 0 and 2; u ∼ N 0, Gσ 2 u is the vector of random polygenic effects, with G the genomic relationship matrix (GRM) that is calculated by using the HD SNP genotypes  [32] , and σ 2 u the polygenic variance that is estimated based on the null model y = 1µ + u + e and then fixed while testing for the association between each variant and the trait of interest; and e ∼ N 0, Iσ 2  e is (1) the vector of random residual effects, with I the identity matrix and σ 2 e the residual variance.
None of these intergenic variants completely removed the signal for either of the two phenotypes. Of the ten variants included in conditional analyses of BTA23, five completely removed the peak for both phenotypes. Three were located in genes, at 25,181,661 bp (rs209183236) in ELOVL5 (intron), 26,733,104 bp (rs470365293) in ENSBTAG00000023541 (intron), and 28,085,410 bp (rs109539043) in IER3 (upstream region). The two others were located in intergenic regions at 25,554,248 bp (rs210655104) and 28,012,299 bp (rs209284762).
We were then able to take advantage of the work of the 1000 Bull Genomes project to impute genotypes of the cows at the WGS level to directly identify candidate variants for resistance to paratuberculosis in Holstein and Normande cows. Our original design was intended to be the same in both Holstein and Normande breeds, with a full dataset for approximately 1500 cows in each breed. However, Holstein and Normande cows represent 64% and 9% of French herds, respectively, and it was much more difficult to recruit Normande than Holstein cows. For this reason, the final dataset was much smaller in the Normande (649) than in the Holstein (1644) breed, which clearly affected detection power; with a predefined significance threshold (-log 10 (P) ≥ 6), we identified more QTL regions with more significant effects in Holstein than in Normande cows.
However, additional data are needed in the Normande breed to increase the size of the population examined and improve the balance between numbers of case and control phenotypes. By arbitrarily defining QTL regions in 1-Mbp-intervals, we identified several QTL on BTA12 and 23. However, for each of these chromosomes, conditional GWAS led to the identification of variants that each explained all the effects detected on BTA12 (rs41667085 in the ABCC4 gene) and on BTA23 (rs209183236 in ELOVL5, rs4703655293 in ENSBTAG00000023541, rs109539043 in IER3, and rs210655104 and rs209284762 in intergenic regions). These results suggest that the identified variants could be the causative variants, or at least in strong LD with the causative variants. On both BTA12 and 23, we identified a large region that contains variants with significant effects.
In each of the QTL regions detected here, we were able to identify genes of interest, some of which have been previously associated with traits related to the intestine or with responses to infection in mice, rats, or humans. Both ABCC4 and CBFA2T2 have been associated with intestinal inflammation and abnormal intestinal morphology (mucosa, goblet cell, enteroendocrine cell, or epithelium). Of all the genes located in the vicinity of the MHC on BTA23, C4A, ENSBTAG00000006864, and IER3 have been linked to abnormal intestinal morphology or physiology, whereas PKHD1, ENSBTAG00000013919, ENSBTAG00000038397, and SKIV2L are involved in bowel diseases (Crohn's disease, ulcerative colitis, syndromic diarrhea, gastrointestinal ulcer, or tricho-hepatoenteric-syndrome). Some of these genes have also been associated with an abnormal response to infection (C4A, ENSBTAG00000006864, and IER3), increased/ decreased susceptibility to bacterial infection (C4A and ENSBTAG00000006864), or induced colitis (ABCC4 and IER3).
, 2007; Tessier et al. , 2020). While many studies have already described day-time to night-time hydroacoustic survey results  (Vondracek and Degan, 1995; Guillard and Vergés, 2007; Ye et al. , 2013) , this study is the first, to the best of our knowledge, to compare hydroacoustic day-time and night-time data from 14 different temperate natural lakes. In this study, we compared a proxy of fish density, mean acoustic scattering strength (sA with units m 2 ha -1 )  (MacLennan et al. , 2002; Yule et al. , 2013) , and a proxy of fish size, mean target strength (TS with units dB)  (MacLennan et al. , 2002) , between day and night samplings. Biomass estimates computed from these two metrics were analysed to identify differences and verify if night-time is the most appropriate sampling period, as described in the literature and standards.
Data were georeferenced using a global positioning system (GPS). The survey design, a series of equally spaced parallel transects, covering areas with depths >5 m, was similar between day-time and night-time, but due to logistic reasons, the tracks were not strictly identical between the two periods. The sampling effort was computed by calculating the degree of coverage, which is defined as the ratio between the sampling distance travelled (km) and the square root of the lake surface area (km 2 )  (Aglen et al. , 1983). As a general recommendation, the degree of coverage should be at least 3. 0 and preferably near or above 6. 0. (Emmrich et al. , 2012). All studied lakes had a minimum degree of coverage greater than 4. 9, being mainly above 6 (Tab. 1). The acoustic data recording was limited to the first 100 m because fish are scarce at greater depths in these type of lakes  (Yule et al.
, 2012; Dra stík et al. , 2017; Morrissey-McCaffrey et al. , 2018). Fish density (r a ) per ha was estimated using Forbes and Nakken's (1972) equation: r a ¼ sA 4p Ã 10 TS=10 : ð2Þ Average individual fish weight w (kg) was calculated from individual sizes based on  Carlander's (1969)  equation (w = TL 3 ). Then, fish biomass B (kg ha -1 ) was calculated by multiplying mean fish density by average fish weight: B ¼ r w: ð3Þ 2. 4 Data analysis Data were analysed using the Sonar 5-Pro software (version 6. 0. 4)  (Balk and Lindem, 2011). Lake bottoms were automatically detected by the software and then manually corrected. An exclusion zone of 0. 3 m above the bottom was used to avoid false echoes from the bottom. Non-fish echoes from air bubbles, macrophytes, secondary bottom echo detections, and similar were manually removed.
, 2006) , which is the case for acoustic metrics. The SMA evaluates whether the major-axis regression results for the comparison of day-time and night-time periods follow a 1:1 line, which would indicate no difference. In other words, it tests the null hypothesis of intercept zero and slope 1 of a linear relationship. All analyses were carried out using software R, version 3. 4. 3 (R Core Team, 2014) and the Smatr package (version 3. 4-8;  Warton et al. , 2012)  for the SMA test.
Acknowledgements. This work was supported by  Pole ECLA (ECologie LAcustre ) from the  OFB (Office Français de la Biodiversité) ,  AnaEE-France , and the  OLA Observatory  (DOI: 10. 4081/jlimnol. 2020. 1944)provided boat and technical facilities. Part of the data came from the " Projet Lac " led by  Prof. Ole Seehausen  ( EAWAG  and the  University of Berne) , as well as cantonal authorities and FOEN for funding. We want to thank the anonymous referees for their very helpful comments, which greatly improved the manuscript.
The NASA New Frontiers asteroid sample return mission Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) has provided a large amount of data on the asteroid (101955) Bennu, including highquality spectra obtained by the OSIRIS-REx Visible and InfraRed Spectrometer (OVIRS). Aims. To better constrain the surface properties and compositional variations of Bennu, we studied the visible and near-infrared spectral behavior across the asteroid surface by means of a statistical analysis aiming to distinguish spectrally distinct groups, if present. Methods. We applied the G-mode multivariate statistical analysis to the near-infrared OVIRS spectra to obtain an automatic statistical clustering at different confidence levels. Results. The statistical analysis highlights spectral variations on the surface of Bennu.
Introduction The NASA asteroid sample return mission Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer (OSIRIS-REx) was launched on September 8, 2016, and arrived at the near-Earth asteroid (101955) Bennu on December 3, 2018. The primary objective of the mission is to collect and return to Earth a pristine sample from Bennu. For asteroid characterization and sample site selection, the OSIRIS-REx spacecraft carries two camera suites (OCAMS and TAGCAMS), a laser altimeter (OLA), and two hyperspectral point spectrometers (OVIRS and OTES), along with a studentled X-ray imaging spectrometer (REXIS)  (Lauretta et al. 2017). The initial observations of Bennu confirmed its spinning-top shape (i. e. , a spheroid with an equatorial bulge), as expected from the inversion of Earth-based radar data  (Nolan et al.
74 ± 0. 01 µm typical of CI and CM meteorites  (Hamilton et al. 2019 ). The analogy with these meteorites is also confirmed by OTES, which operates from 5. 7 to 100 µm at lower spatial resolution  (Hamilton et al. 2019). OSIRIS-REx has performed several mission phases with different observation strategies to best characterize Bennu and the prospective sampling sites. The Detailed Survey (DS) phase started with the Baseball Diamond subphase of flybys from March to April 2019, which was followed by the Equatorial Stations subphase from April to June, during which the asteroid was observed at multiple local solar times. Starting in October 2019 and extending into spring 2020, Reconnaissance (Recon A, B, and C) observations obtained the highest spatial resolution data of the candidate sampling sites identified from the DS data  (Lauretta et al.
2017 ). Here we use a multivariate statistical method to analyze the OVIRS spectra (level L3c) obtained at DS equatorial station 3 (EQ3). We selected EQ3 for analysis because it is the best-quality OVIRS dataset (at maximum solar illumination) that provides near-global coverage. We investigate the possible spectroscopic heterogeneity on the surface of Bennu, as we have done previously for asteroid (162173) Ryugu using data obtained by the JAXA Hayabusa2 mission  (Barucci et al. 2019).
OVIRS data The OVIRS instrument is a hyperspectral point spectrometer working in the spectral region from 0. 4 to 4. 3 µm with a circular 4 mrad field of view (FOV;  Reuter et al. 2018, and Simon et al. 2018). During EQ3, the spacecraft was located approximately above the equator, scanning north-south at several local solar times of the day. EQ3 occurred on May 9, 2019, at local time 12:30 pm, which allowed us to obtain the best available homogeneous observations for a complete map of the surface of Bennu. The spacecraft was at a distance of 4. 8 km from the surface of Bennu, covering phase angles ranging from 7 •. 75 to 10 •. 37. During this station, 17 790 spots were observed with a spatial resolution per spot of about 20 m cross-track and 30m along-track. We selected all the spectra with a fill factor =1, i ≤ 70 • and e ≤ 70 • for a total of 7151 spectra.
Conclusions From the results of the multivariate statistical analysis of OVIRS spectral (visible and near-infrared) data of Bennu, we confirm small spectral variations evident in the detection of statistically different spectral groups, whose number increases as the confidence level decreases. The statistical analysis of the spectral data at 3σ confidence level indicates a homogeneous surface at the spectral and spatial resolution (20 × 30 m/spot) of the data. Slightly different areas comprising only about 2% of the analyzed data are separated into their own group at a confidence level of 2. 7σ. When the confidence level is lowered to 2. 0σ, four groups are detected that comprise about 11% of the dataset and are well separated from the predominant group, the background. This shows that in general, Bennu has a global homogeneous surface at the resolution of the data we used, although some areas are characterized by clear variations on the spectral slopes.
Acknowledgements. We are grateful to the entire OSIRIS- REx Team  for making the encounter with Bennu possible. This material is based on work supported by  NASA  under Contract  NNM10AA11C  issued through the  New Frontiers Program. MAB, PHH, AP, MF, JDPD, SF, FM and PM acknowledge funding support from  CNES. PHH  acknowledges funding support by the  DIM ACAV+ program  of the  Region Ile de France. JRB and GP were supported by  Italian Space Agency  grant agreement n. 2017-37-H. 0. PM acknowledges funding support from the  European Union   Horizon 2020 research and innovation programme  under grant agreement No  870377  (project  NEO-MAPP), as well as Academies of Excellence: Complex systems and Space , environment, risk, and resilience, part of the IDEX JEDI of the  Université Côte d'Azur.