dataesr/scientific-paragraphs-categorization huggingface dataset:
We included Old World (C. dromedarius, C. bactrianus, C. ferus) and New World camels (Lama glama, L. guanicoe, Vicugna pacos, V. vicugna), Bos taurus, Pantholops hodgsonii and Homo sapiens; all accession numbers are given in Table  S1. While in the background model a single value of the dN/dS ratio (ω) is considered among all sites and branches, the branch-specific model allows a varying ω among the branches in the phylogeny  91. We used a LRT to determine whether or not the branch-specific model was a significant improvement over the background model. Branch lengths are scaled as the number of base pair changes per site, with the exception of the human outgroup sequence which is rescaled for aesthetic purposes. The figures depicted on the phylogenetic tree are adopted from https://openclipart.
For each model we retrieved the log likelihood values from which we computed the likelihood ratio tests (LRT) for the significance (P-value) of the sites detected under positive selection. Finally, we investigated the effect of amino acid changes on the protein function, using TreeSAAP  52. This program uses statistical models of molecular evolution and phylogenetic trees to predict positive selection based on amino acid physiochemical properties. TreeSAAP takes into account the magnitude of the impact of the amino acid replacements on local physiochemical properties and determines whether the observed degree of amino acid changes deviates from the neutral expectation. Radical magnitudes of changes ≥6, with P ≤ 0. 001, are considered as an evidence for positive directional selection for a given physiochemical property  92.
Divergence time estimation. We estimated the divergence times among Camelidae with MULTIDIVTIME  53  , which calculates posterior credibility intervals (CIs) of divergence times using a Bayesian approach and assuming a relaxed molecular clock with autocorrelation among rates. This method has been shown to produce posterior CIs that contain the true divergence time of simulated datasets ≥95% of the time as long as the assumption of autocorrelation among rates was not violated  93. We followed the protocol outlined by Rutschmann  94  for the 13 protein-coding DNA sequences. The analysis can be summarized as follows: for each gene's alignment we determined the best-fit nucleotide substitution model and generated a ML tree topology in MEGA6 according to the procedure above. We calculated model parameters, including nucleotide frequencies, the transition/transversion rate ratio, and rate heterogeneity among sites for each gene using 'baseml' in PAML v4.
8  51. Next, branch lengths and a variance-covariance matrix were estimated using the 'estbranches' function in MULTIDIVTIME against the ML tree topology for the 10-species dataset described above. We then ran five independent chains of MULTIDIVTIME to estimate divergence times, with each chain containing 10 5 burn-in steps followed by 10 6 steps sampled every 100 th step (10 4 samples in total). We parameterized the MULTIDIVTIME runs using rttm = 6. 5, rtrate = 0. 07, and brownmean = 0. 3 and with equivalent standard deviations, respectively. The time between the ingroup root and median of the tips (rttm) corresponded to 1 unit ≈ 10 million years, and was retrieved from the TimeTree database  95  as the mean time of the most recent common ancestor between the families Bovidae and Camelidae (65 mya).
The rate (rtrate) was calculated as rttm divided by the median branch length between the ingroup root and tips. We set brownmean to approximately 2/rttm. We included a prior to constrain the node connecting the Camelini and Lamini tribes to 8-30 mya based upon both fossil  96, 97  , and molecular  98, 99  evidence. Prior to estimating posterior probabilities, we assessed convergence of the independent MULTIDIVTIME chains using the R package 'boa' v1. 1. 8  100 to ensure the runs reached a stationary distribution. We examined each chain and its parameters (divergence time and rate for each node) independently for convergence using the corrected scale reduction factors (CSRF) from  54, 55. A chain is suggested to have converged when the CSRF is 1, or more specifically, when the 97. 5% upper confidence limit of the estimate is ≤1.
2  54. Furthermore, we also examined for convergence across all parameters and chains simultaneously using the multivariate potential scale reduction factor (MPSRF)  54, 55. The MPSRF converges to 1 upon reaching stationarity when the number of steps is reasonably large. After convergence was verified, all chains were combined and the posterior density mean and 95% CIs were estimated for the parameters. We repeated the above analysis in MULTDIVTIME using only the first and second codon positions with a modified rtrate = 0. 012. Because mtDNA evolves rapidly, it is often subject to the effects of saturation (multiple mutations at a single site) -especially at deep divergence times  101, 102. As a result, divergence estimates need to take into account the possible effects of saturation. To determine the potential for saturation effects in our dataset, we used the DNA saturation analysis suggested in ref.
Acknowledgements We are very grateful to the  Wild Camel Protection Foundation  for the support in sample collection, to  W. Asaad  and  R. Saleh  for facilitating sample collection in Syria and Jordan and to  G. Gassner ,  First Austrian Camel Riding School , and  H. Burgstaller ,  Graefliche Kamelheimat , for reference samples from Austria. We acknowledge general support from  C. Schlötterer ,  Institute of Population Genetics, Vetmeduni Vienna, Austria. EM and RRF were supported by the  Austrian Science Foundation (FWF)  grant  P24706-B25  to PAB, recipient of an  APART fellowship  ( 11506 ) of the  Austrian Academy of Sciences. JC was partially funded by the  COIN Centre of Excellence ,  Academy of Finland. The authors wish to acknowledge  CSC -IT Center for Science, Finland , for computational resources.
Availability of data. The complete camel mitochondrial genomes obtained in this study are deposited in Genbank with accession numbers listed below: Drom439 (Qatar): KU605072, Drom795 (Saudi Arabia): KU605073, Drom796 (Saudi Arabia): KU605074, Drom797 (Saudi Arabia): KU605075, Drom801A (Austria): KU605076, Drom802 (UAE, Dubai): KU605077, Drom806 (Kenya): KU605078, Drom816 (Sudan): KU605079, Drom820 (Pakistan): KU605080, DC158 (Austria): KU666460, DC269 (Kazakhstan): KU666461, DC399 (Mongolia): KU666462, DC400 (Mongolia):KU666463, DC402 (Mongolia): KU666464, DC408 (Mongolia): KU666465, WC214 (Mongolia): KU666451, WC216 (Mongolia): KU666452, WC218 (Mongolia): KU666453, WC219 (Mongolia): KU666454, WC220 (Mongolia): KU666455, WC247 (Mongolia): KU666456, WC303 (Mongolia): KU666457, WC304 (Mongolia): KU666458, WC305 (Mongolia): KU666459.
Objective: The objective of this study was to evaluate the diagnostic efficacy of Pixonbased reconstruction method on planar somatostatin receptor scintigraphy (SRS). Methods: All patients with neuroendocrine tumors (NETs) disease who were referred for SRS to our department during 1-year period from January to December 2015 were consecutively included. Three nuclear physicians independently reviewed all the data sets of images which included conventional images (CI; 15 min/view) and processed images (PI) obtained by reconstructing the first 450 s extracted data using Oncoflash ® software package. Image analysis using a 3-point rating scale for abnormal uptake of 111 Indium-DTPA-Phe-octreotide in any lesion or organ was interpreted as positive, uncertain, or negative for the evidence of NET disease.
Due to an unusual appearance of pure Pixon images that could disturb the nuclear physician interpretation  (20) , the OncoFlash ® reconstruction software package (Siemens Medical Solutions ® , Erlangen, Germany) has recently been developed. This reconstruction processing consists to linearly blend the pure Pixon image with the unprocessed image while maintaining an equivalent SNR. This type of blending can reduce planar scintigraphy duration or injected dose by 50% in different types of planar scintigraphy without impact on the clinical value as shown by previous studies  (22) (23) (24). To the best of our knowledge, no study investigating the diagnostic utility of Pixon-based method for reconstruction of 111-In-SRS planar images has been reported. Therefore, the objective of the present study was to validate the clinical utility of the Oncoflash ® software package for reconstruction of planar SRS images.
Conventional Images Conventional images (CI) (Figure  1 ) were acquired as raw image data on Syngo ® MI application as above without processing. Processed Images (PI) Processed images (Figure  1 ) were obtained by reconstructing the first 450-s extracted data using Oncoflash ® software package with following parameters: denoising level = 1. 7, maximum kernel radius = 10, number of kernel = 12, maximum iterations = 20, and a 30% blending of the pure Pixon image (Figure  1 ) with the unprocessed image, as suggested by Mawlawi et al. (22).
Planar scintigraphy Data analysis Three experienced nuclear medicine physicians reviewed all the images independently, and they were kept blinded to the details of patients and images. All the images (CI and PI) were anonymized with the award of a number between 1,000 and 9,999. The images were read in two sessions, e. g. , if the CI of a patient was read in first session, the corresponding PI of the patient was evaluated in the next session. The two scan reading sessions were spaced by at least a month. The duration of each session to finish reading of all the images was restricted to 1 week. To assess the intra-observer variability, another scan reading session (at 4 months later) was conducted by randomly picking up 15 (a sample size of approximately 20. 0% of the total study population) images each from CI and PI series.
Significance level was set 0. 05. Data were analyzed using SPSS statistical software v20 (IBM). Clinical indications of SRS were: staging of histologically proven-WD-NETs in 17(24%) cases, localization of disease in situation of functional syndrome in 16(23%) cases, follow-up of patients with known disease to detect recurrence in 27(38%) cases, therapeutic response assessment in 10(14%) cases, selection of patients for PRRT in 1(1%) cases.
Moreover, the mean κ value observed did not differ (0. 596 vs. 0. 567) between CI and PI data. However, our data are consistent with another publication assessing the inter-observer agreement for both SRS planar images and SPECT-CT acquisitions performed in 35 patients with suspected or histologically proven NET. Apostolova et al. also found also, kappa values of 0. 593 and 0. 860 in planar images and SPECT-CT interpretation respectively  (30). Furthermore, in considering abdominal area analysis, which is most likely site of primary disease, we found a higher average agreement between readers (GA vs. MA) in PI analysis. Otherwise, other mean κ values showed identical global agreement in per-organ inter-observer analysis. As we previously hypothesized, these results suggested the non-inferiority of PI interpretation by different physicians (in comparison with CI) to target areas where have to be performed an additional SPECT-CT acquisition, which improve diagnostic performance of exam.
Interestingly, image interpretation by experienced readers showed disagreement only in one image data (PI and CI) set. However, detailed results did not highlight any significant discrepancies (≥2 grades) in the assessment of the maximal lesion uptake according to Krenning scale. These different results in per-exam, per-organ and per-lesion intra-observer analysis also suggested a good reproducibility in planar PI interpretation, at least equivalent with CI study. Bones, cervical, and mediastinal nodes areas have been considered as positively involved by at least 1 of the 3 different observers in only less than 5. 0% of the cases. These cases were thus taken into consideration for κ statistical analysis. It has been reported previously that disease prevalence could affect Cohen κ statistics and extremely low κ values could be observed  (27).
6a  and b. The dependence of the exponential index γ on the amplitude of surface waves a L is found by processing corresponding time series similar to those shown in Fig. 2a. For this, we select time intervals where the edge wave amplitude grows and calculate γ by exponential approximation of the time-dependent amplitude. Parameters of the turbulence are measured by ADV in the middle of the experimental flume, 0. 04 m below the free surface (0. 14 m from the bottom), at a distance of x = 0. 9 m from the shoreline. At this point, it is possible to neglect the turbulence caused by the near-bottom oscillating boundary layer and detect the wave breaking turbulence. Here, we should specify some difficulties related to the characteristic features of ADV signals. The recorded ADV signals contain the so-called spikes, which are filtered using the MATLAB algorithm  (Nikora and Goring, 1998; Goring and Nikora, 2002).
Acknowledgements. This work is dedicated to  Alexander Ezersky , who was the key author and the main driver of this study. Last summer, he sadly passed away after a long-lasting fight with cancer, leaving the manuscript unfinished. Until his last days, he tried to dedicate his time to work, including the results presented here. Therefore, it is important for us to conclude his work in memory of a dear friend and colleague. The present study was supported by  Russian Presidential grants   MD-6373. 2016. 5  and  NS-6637. 2016. 5. Ira Didenkulova  and  Efim Pelinovsky  also thank the visitor programme of the  University of Caen Normandy , which allowed this fruitful collaboration. Edited by: R. Grimshaw Reviewed by: two anonymous referees.
Electron Backscattered Diffraction (EBSD) Crystal-preferred orientation (CPO) of olivine, orthopyroxene, clinopyroxene, and amphibole were obtained by EBSD technique. Diffraction patterns were generated with a JEOL-5600 scanning electron microscope equipped with an Oxford-HKL Technology EBSD system and CHANNEL+ software at Géosciences Montpellier. Kikuchi patterns were obtained using W filament at 17 kV acceleration voltage with a working distance of 23 mm. Complementary EBSD analyses were carried out on a Zeiss EVO MA 15 SEM-EBSD facility of the IACT (Armilla, Spain) using LaB 6 filament with 20 kV acceleration voltage and 24 mm working distance. In both cases the data collection was done using the AZtec 2. 4 data acquisition software (Oxford/HKL) with 4 × 4 binning and low gain settings.
The fabric strength is quantified using the J-index of olivine, which is the volume-averaged integral of the squared orientation densities  (Bunge, 1982). The J-index is dimensionless; it is 1 for a random orientation distribution and infinity for a single crystal orientation. The J-index should represent fabric strength correctly if at least 100-150 olivine grains are indexed in per thin sections  (Ben Ismail & Mainprice, 1998). Mantle peridotites display J-indexes between 2 and 20 with a peak around 5  (Tommasi & Vauchez, 2015). The J-index of olivine CPO was calculated using the MTEX toolbox in MATLAB (http://mtex-toolbox. github. io/; Bachmann et al. , 2010 , Hielscher & Schaeben, 2008; Mainprice et al. , 2011)  with orientation distribution functions calculated using a "de la Vallée Poussin" kernel with a half width of 10°.
Seismic Properties The three-dimensional distribution of seismic properties was calculated based on the CPO and modal abundance of olivine, orthopyroxene, and clinopyroxene of the studied xenoliths  (Mainprice, 1990). The average seismic properties of the SCLM beneath the Styrian Basin were estimated by averaging the calculated elastic tensors of each xenolith. The foliation and lineation were assumed to have a constant orientation in all xenoliths; therefore, we used the rotated CPO data sets (maximum of  [100]  to the east-west, maximum of [010] to the north-south). Consequently, the average value is a maximum estimate of the seismic properties of the studied xenoliths. For olivine, orthopyroxene, and clinopyroxene, the single crystal elastic tensors of  Abramson et al. (1997 ), Jackson et al.
Acknowledgments We acknowledge the constructive criticism and helpful comments of  Q-K. Xia , an anonymous reviewer, and the Editor,  John Geissman. We are grateful to  F. Barou  for his assistance during EBSD-SEM analyses. L. E. Aradi  is grateful to  Bernardo Cesare ,  Levente Patkó , and  Raúl Carampin  for their help during the EPMA measurements. The FTIR analyses were carried out with the help of  Judith Mihály  and  Csaba Németh. This research was partially granted by the  Hungarian Science Foundation  (OTKA,  78425  to  Cs. Szabó ). K. H. 's work was funded by the  European Union Seventh Framework Programme Marie Curie postdoctoral fellowship  (grant  PIEFGA-2012-327226 ) and by the  Juan de la Cierva Postdoctoral Fellowship  (grant  FPDI-2013-16253 ) of the  Spanish Ministry of Economic and Competitiveness (MINECO).
This project has been implemented with the support provided to  I. J. Kovács  from the  National Research, Development and Innovation Fund of Hungary , financed under the K119740 funding scheme. The data used in this paper are listed in the references, tables, and supporting information. The raw EBSD and geochemical data are available from the corresponding author upon request. The FTIR spectra are available at the PULI (Pannonian Uniform Lithospheric Infrared spectral database) website (http://puli. mfgi. hu/). This is the 86 publication of the  Lithosphere Fluid Research Lab (LRG).
Methods We did a multinational population based register study, including all women and their singleton infants born between 1 January 2008 and 31 December 2010 in the Scandinavian countries (Denmark, Norway, and Sweden) and those recorded in the EFEMERIS database in France. 15 tting and data sources The Scandinavian countries have population based national registers, which include prospectively collected information for all inhabitants on demographic and health indicators such as births, dispensed drugs, and hospital contacts. The total population in the Scandinavian countries is approximately 20 million inhabitants, and the national parliaments have given informed consent to be included in the registers on behalf of their population. 16 All registers include the civil registration number of each resident, a unique number assigned at birth or immigration, which allows linkage of individual data between registers.
From the Scandinavian countries, we obtained data from the medical birth registers, the registers for prescribed drugs, and the patient registers. The Scandinavian medical birth registers include data on maternal characteristics, pregnancy, delivery, and the neonatal period, including information on maternal body mass index and on smoking during pregnancy. For congenital malformations, the data came from either the medical birth registers or the patient registers. All diagnoses and congenital malformations were recorded according to ICD-10 (international classification of diseases, 10th revision). 17 The prescribed drug registers include data on dispensed substances, dispensed dose, package sizes, and formulations according to the Anatomical Therapeutic Chemical (ATC) classification of drugs together with the date of dispensing.
18 rom France, we included women recorded in the EFEMERIS database, which is described in detail elsewhere. 14 15 EFEMERIS is a population based database that holds health related data in the Haute-Garonne district, which has 1 million inhabitants. The register contains individual based information on prescribed drugs, pregnancy outcome, and neonatal health from national health insurance system, Mother and Child Protection Service, hospitals, and neonatal clinics. The data are anonymised before transmission to the pharmacology service. From the various data sources, we constructed two separate databases: one including Scandinavian data (pooling data from the three countries into one database) and one with French data. Figure  1  describes the flow of data into the analysis.
Participants All women together with their infants born during the study period and included in the Scandinavian birth registers or the EFEMERIS database were eligible for inclusion. Only singleton infants born at 154 days (22 weeks) of gestation or later were included. We excluded all infants (and their mothers) with an ICD-10 code suggesting chromosomal aberrations (Q90-99) or for whom the information on gestational age at delivery was missing. Women who had been admitted to hospital because of influenza or pneumonia (ICD-10 code J09-18) during pregnancy were excluded from the Scandinavian database. In the EFEMERIS database, information on hospital admission during pregnancy was not available so.
exposure to neuraminidase inhibitors during embryo-fetal life We defined exposure as having filled a prescription after the date of assumed conception (that is, from 14 days after the first day of the last mentrual period), for either of the two neuraminidase inhibitors-oseltamivir (ATC code J05AH02) or zanamivir (ATC code JO5AH01)as recorded in the prescribed drug registers or the EFEMERIS database. Last menstrual period was based on gestational age calculated at an obstetric ultrasound examination that most of the included women had in the first or second trimester. We defined first trimester as gestational day 0-90, second trimester as gestational day 91-180, and third trimester as gestational day 181 to delivery. For congenital malformations, we evaluated only exposures during the first trimester in the study.
Outcome measures We assessed the following neonatal outcomes: low birth weight, low Apgar score, preterm birth, small for gestational age birth, stillbirth, neonatal mortality, neonatal morbidity, and congenital malformations. We defined low birth weight as weighing less than 2500 g at birth regardless of gestational age, low Apgar as having an Apgar score of 0-6 at 5 minutes of life, preterm birth as birth before 259 gestational days (37 weeks), and small for gestational age birth as weighing 2 standard deviations or less below the corresponding mean birth weight by gestational length and infant sex in Sweden (Scandinavian data) or France (French data). 19 Stillbirth was defined as recorded in the respective national registers or EFEMERIS database, neonatal mortality as death of a liveborn infant within the first 28 days of life, and neonatal morbidity as having one or more recorded ICD-10 codes P00-P99 within the first 28 days of life, grouped according to the affected organ system (supplementary table B).
statistical analysis The data in each database (Scandinavian countries and France) included information at the individual level, and in a first step we analysed the two databases separately. The Scandinavian database thus encompassed individual data from all three countries. We used logistic regression to estimate crude and adjusted odds ratios together with 95% confidence intervals for low birth weight, low Apgar score, small for gestational age birth, stillbirth, neonatal mortality, and neonatal morbidity. We used exact logistic regression for sparse data-that is, when no cases were reported among the exposed population. We used Cox proportional hazards regression models to estimate the hazards ratios together with 95% confidence intervals for preterm birth, with the gestational length in days as the underlying time variable.