dataesr/scientific-paragraphs-categorization huggingface dataset:
In hydrogeological modelling, two approaches are commonly used for model calibration: zonation and the pilot-points method. Zonation assumes an abrupt spatial change in parameter values, which could be unrealistic in field applications. The pilot-points method produces smoothly distributed parameters compared to the zonation approach; however, the number and placement of pilot-points can be challenging. The main goal of this paper is to explore the effect of pilot-points number and locations on the calibrated parameters. A 3D groundwater flow model was built for the northern karst aquifer of Qatar. A conceptual model of this aquifer was developed based on MODFLOW software (United States Geological Survey). The model was calibrated using the parameter estimation and uncertainty analysis (PEST) package employing historical data of groundwater levels.
Top and bottom of the layers were identified based on structural contours of the geology  [28]  and borelogs  [20, 21]. The east, north, and west boundaries are the sea, which was considered as a constant head boundary, whereas the southern boundary was considered as a head-dependent flow boundary. The General Head Boundary GHB module in MODFLOW was used to simulate the flow along the southern boundary. Flow values were estimated based on contour maps and previous modelling studies  [24]. During the calibration process, the conductance value of the GHB was adjusted to decrease the error in the water budget  [4]. The aquifer is highly heterogeneous as hydraulic conductivity varies significantly from one location to another. In general, hydraulic conductivity may vary between 0. 01 m/day up to more than 100 m/day, due to the existence of fractures and conduits in the limestone  [24].
The hydraulic conductivity values were later calibrated using the parameter estimation and uncertainty analysis software (PEST) with MODFLOW 2000  [14]. Groundwater recharge comes essentially from rainfall, which is too little. Despite the lower amount of rainfall, recharge occurs when heavy storms create enough runoff that accumulates in land depressions. Several studies have estimated rainfall recharge in Qatar  [20] [21] [22] 29, 30]. From the previously-mentioned studies, it was found that rainfall recharge varies between 5 and 68 million m 3 per year. In this study, the recharge spatial distribution is based on Baalousha et al. [22].
The model comprises one layer representing Rus and Um Al-Radhuma Formation. Top and bottom of the layers were identified based on structural contours of the geology  [28]  and bore-logs  [20, 21]. The east, north, and west boundaries are the sea, which was considered as a constant head boundary, whereas the southern boundary was considered as a head-dependent flow boundary. The General Head Boundary GHB module in MODFLOW was used to simulate the flow along the southern boundary. Flow values were estimated based on contour maps and previous modelling studies  [24]. During the calibration process, the conductance value of the GHB was adjusted to decrease the error in the water budget  [4]. The aquifer is highly heterogeneous as hydraulic conductivity varies significantly from one location to another.
In general, hydraulic conductivity may vary between 0. 01 m/day up to more than 100 m/day, due to the existence of fractures and conduits in the limestone  [24]. The hydraulic conductivity values were later calibrated using the parameter estimation and uncertainty analysis software (PEST) with MODFLOW 2000  [14]. Groundwater recharge comes essentially from rainfall, which is too little. Despite the lower amount of rainfall, recharge occurs when heavy storms create enough runoff that accumulates in land depressions. Several studies have estimated rainfall recharge in Qatar  [20] [21] [22] 29, 30]. From the previously-mentioned studies, it was found that rainfall recharge varies between 5 and 68 million m 3 per year. In this study, the recharge spatial distribution is based on Baalousha et al.
In this case, the threshold is the absolute mean residual head at any point that is less than 0. 5. This value is considered adequate for model calibration given the uncertainty in the historical target heads values. It is assumed that the aquifer was in steady state conditions (or near steady state) because groundwater abstraction at that time was very low, and groundwater abstraction was little  [24]. In the presumed steady state conditions in 1958, groundwater levels varied between 0. 0 near the coastline to more than 13 m above the mean sea level in the central part of the aquifer. For calibration, groundwater level data from 1958 was used as observation points covering the entire model domain. As the uncertainty of this data is similar, all points had an equal weight in calibration.
Methods The ASAS study is a longitudinal cohort in 29 centres worldwide and has included patients with a suspicion of axSpA (>3 months' back pain, onset <45 years, with or without peripheral symptoms) or peripheral SpA (current peripheral arthritis and/or dactylitis and/or enthesitis but without current chronic back pain)  [5, 9, 10]. Only patients suspected of axSpA were included in the analysis for this study. The DESIR study (NCT01648907, datalock: April 2015) is a longitudinal cohort study in 25 French centres that included patients aged 1850 years with inflammatory back pain for 53 months and <3 years  [11]. The SPACE study is an ongoing inception cohort and includes patients aged 516 years with chronic back pain (53 months, 42 years, onset 445 years) from rheumatology outpatient clinics in the Netherlands, Italy, Norway and Sweden  [12].
All three studies were approved by local ethical committees and informed consent was obtained before inclusion from all patients. A detailed description of all cohorts is provided elsewhere  [5, 912]. All patients underwent a full diagnostic work-up in which clinical, laboratory and imaging data were collected at baseline, including HLA-B27 testing and radiography of the SI joints (X-SI). MRI of the SI (MRI-SI) was performed in all DESIR and SPACE patients, but in the ASAS cohort the MRI-SI was considered obligatory only for the first 20 patients for each centre. For each patient the clinical diagnosis of axSpA was established by the treating rheumatologist based on the information obtained from the full diagnostic work-up. The ASAS expert definition of PFH is the presence of AS, AAU, psoriasis, IBD, or ReA in first-or second-degree relatives.
Data analysis Baseline data of patients suspected of axSpA in the ASAS, DESIR and SPACE cohorts were analysed. Descriptive statistics were used to present demographic and clinical characteristics for each cohort. In each cohort, univariable logistic regression models were performed with HLA-B27 status and ASAS-PFH as determinants and a clinical axSpA diagnosis as outcome. The analyses were repeated in multivariable models with both determinants. All analyses were repeated with a PFH of AS and a PFH of AAU. Interactions were tested between HLA-B27 status and each PFH. Interaction terms with P < 0. 10 were considered to be statistically significantly. Age and gender were tested for confounding. Results were stratified for age, gender and ethnicity in order to test whether results differed between subgroups.
However, these patients were typed using currently obsolete methods with higher risks of mistypings  [16, 17]. Given the potential implications for clinical practice it is important to stress several limitations. The DESIR cohort had inflammatory back pain as an inclusion criterion, while the ASAS and SPACE cohorts included patients with chronic back pain. Nevertheless, similar results were found in all three cohorts. Although data of three cohorts were analysed, there was an under-representation of patients with for instance an African or South American ethnicity. The ASAS cohort included predominantly patients with a Caucasian or Asian ethnicity and the DESIR and SPACE cohorts included predominantly Caucasian patients, which corresponds to the two largest populations of axSpA patients worldwide  [18].
Acknowledgements We thank all the investigators and teams involved in the ASAS, SPACE and DESIR cohort. Funding: This work has been supported by unrestricted grants from  French Society of Rheumatology and Pfizer Ltd France  for the DESIR study. The DESIR study is conducted as a  Programme Hospitalier de Recherche Clinique (PHRC) , which is sponsored by the  Assistance PubliqueHopitaux de Paris.
The sequence is available in the GenBank TM , EMBL, and DDJB databases under the accession number LT599044. For emfr2 cloning we first identified by BLAST analyses on the published E. multilocularis genome sequence  [14]  a reading frame encoding a FGFR-like TKD annotated as EmuJ_000196200. Transcriptome analyses  [14]  and 5'_RACE experiments, however, indicated that there is actually read-through transcription between gene models EmuJ_000196300 and EmuJ_000196200. We thus designed primers 5'-ATG TGT CTC CGA GCT CTC TG-3', binding to the 5' end regions of gene model EmuJ_000196300, and primer 5'-TTA CTC GCT CGA TCG TGG GG-3', binding to the reading frame 3' end of gene model EmuJ_000196200, to PCR amplify the entire reading frame from metacestode cDNA. The resulting PCR fragment was subsequently cloned using the TOPO XL cloning kit (Invitrogen) and fully sequenced.
The sequence is available in the GenBank TM , EMBL, and DDJB databases under the accession number LT599045. For emfr3 cloning and sequencing we used primers directed against the CDS 5' end (5'-ATG GCA CCT AAG GTT GTG TCA GGA-3') and 3' end (5'-GCA GAT GAG TAA GAA ACC CTC-3') of gene model EmuJ_000893600  [14]  for direct PCR amplification of the reading frame from metacestode cDNA. The resulting PCR fragment was subsequently cloned using the TOPO XL cloning kit (Invitrogen) and sequenced. The sequence is available in the GenBank TM , EMBL, and DDJB databases under the accession number LT599046.
As a control gene for normalization, the constitutively expressed gene elp was used with primers as described previously  [34]. qRT-PCR conditions and primer amplification efficiency control experiments were performed essentially as previously described  [34]. The relative expression of emfr genes in each sample was estimated using the delt-delta-Ct method  [36] , normalising with elp expression levels using the StepOne software (Thermo Fisher). All experiments were carried out as technical triplicates.
Computer analyses and statistics Amino acid comparisons were performed using BLAST on the nr-aa and swissprot database collections available under (https://www. genome. jp/). Genomic analyses and BLAST searches against the E. multilocularis genome  [14]  were done using resources at (https://parasite. wormbase. org/index. html). CLUSTAL W alignments were generated using MegAlign software (DNASTAR Version 12. 0. 0) applying the BLOSUM62 matrix. Domain predictions were carried out using the simple modular architecture research tool (SMART) available under (http:// smart. embl-heidelberg. de/) as well as PROSITE scans available under (https://prosite. expasy. org/scanprosite/). Two-tailed, unpaired student's T-tests were performed for statistical analyses (GraphPad Prism, version 4). Error bars represent standard error of the mean.
As shown in Fig 7A  , FGF1  treatment had a clear effect of EmMPK1 phosphorylation already after 30 sec of exposure. In the case of FGF2, the effect was still measurable, but clearly less pronounced than in the case of FGF1 (Fig  7A ). We then also measured the effects of BIBF 1120 treatment on EmMPK1 phosphorylation. To this end, metacestode vesicles were incubated in hepatocyte-conditioned medium and were then subjected to BIBF 1120 treatment (5 μM, 10 μM) for 30 min. As shown in Fig  7B , this led to diminished phosphorylation of EmMPK1 when 10 μM BIBF 1120 was used. Taken together, these data indicated that, like in mammals and other invertebrates, the E. multilocularis Erk-like MAPK cascade can be activated through FGF signaling, initiated by exogenously added, host-derived FGF ligands.
Motivation: In metabolomics, the detection of new biomarkers from NMR spectra is a promising approach. However, this analysis remains difficult due to the lack of a whole workflow that handles spectra pre-processing, automatic identification and quantification of metabolites and statistical analyses, in a reproducible way. Results: We present ASICS, an R package that contains a complete workflow to analyse spectra from NMR experiments. It contains an automatic approach to identify and quantify metabolites in a complex mixture spectrum and uses the results of the quantification in untargeted and targeted statistical analyses. ASICS was shown to improve the precision of quantification in comparison to existing methods on two independent datasets. In addition, ASICS successfully recovered most metabolites that were found important to explain a two level condition describing the samples by a manual and expert analysis based on bucketting.
The only exception seems to be the W4M e-infrastructure  (Guitton et al. (2017) , available through the Galaxy platform 1 ), whose automatic identification and quantification step is based on an earlier version of ASICS but the environment only allows one-by-one spectrum analysis. Furthermore, none of the existing workflow is as flexible, easily installed and embedded with other tools than an R package can be. The R package ASICS (Automatic Statistical Identification in Complex Spectra) was designed to fill this gap. The identification and quantification method is partially based on  Tardivel et al. (2017)  but has been strongly revisited and improved to provide a fine tuning of all the parameters. Changes on the identification step (library distortion) and on the quantification step (model fitting) have also been implemented to improve the results and to reduce the computational cost.
Material and methods ASICS is an R package available on Bioconductor  (Gentleman et al. (2004) , http://bioconductor. org/packages/ASICS/) that combines all the steps of the analysis of 1 H NMR spectra (library of pure spectra management, preprocessing, quantification, post-quantification statistical analyses). The package also includes functions to directly perform statistical analyses on buckets and diagnosis tools to assess the quality of the quantification. All functionalities of the ASICS package are summarized in Figure  1  and described in the next sections.
Preprocessing the complex mixture spectrum After the data are imported from raw 1D Bruker spectral data files or other types of files, several preprocessing steps are recommended in order to remove technical biases. Free Induction Decay ou décroissance de l'induction libre Baseline correction Most of 1 H NMR spectra have baseline distortions coming from various sources like instrument instability. These distortions can induce an increase or a decrease in peak intensities and skew the results of quantification. Wang et al. (2013)  developed a method to estimate the baseline for a spectrum by classifying each point as a signal 1 https://usegalaxy. org/ or a noise point and by using a linear interpolation between noise points to construct the baseline. Then, the baseline is subtracted from its spectrum.
Peak alignment Due to pH or temperature variations between the acquisition of multiple spectra, peak positions of the same metabolite can change between spectra. It is better to align all peaks before analyses, especially if a binning algorithm is used. Vu et al. (2011)  developed an algorithm, implemented in the R package speaq, to carry out this alignment. It is based on continuous wavelet transform to detect peaks and hierarchical clustering to align all spectra on a reference one. Removal of unwanted regions It is also frequent to exclude a part of the spectra from the analysis. For instance, the part corresponding to water (4. 5-5. 1 ppm) is of no interest for most biological analyses and thus frequently removed prior to statistical analyses. Urea region (5. 5-6. 5 ppm) is also frequently excluded in case of urine samples.
Data importation Creation of a library of pure spectra (one spectrum by metabolite) Factor of interest with at least two levels (find differences between two or more conditions) threshold is sm = 0. 02. These values have been calibrated on various real datasets with the help of NMR experts. However, both values can be changed by the user, depending on his spectrometer and experimental conditions. Translation and distortion The alignment algorithm described in Section 2. 1 can not be used to align reference spectra with the complex mixture spectrum. The reason is that this method is not adapted to spectra with a low number of peaks as those of the pure metabolite contained in the reference library. Compared to  Tardivel et al. (2017) , this step is now split into two parts: a first step was added to globally shift the spectrum before a local peak distortion is performed in a second step (Figure  2  scaled grid and a ∈ - m w 2 -w 1 0.
Individual and variable plots are available to ease the visualisation and interpretation of PCA results (Supplementary Figure  S2 ). Discriminant analysis When the samples correspond to two experimental conditions, Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA,  Trygg and Wold (2002) ) can be performed to find the metabolites with the highest discriminant power between these two conditions with a dedicated function based on the implementation available in the ropls package  (Thévenot et al. , 2015). Prediction error and variable importance in projection (VIP) are computed by a 10-fold cross-validation procedure, with a stability index for the VIP based on the results of the folds. Individual and variable plots are also available (Supplementary Figure  S3 ).
Plasma metabolome at the end of gestation in piglets Genetic selection performed during the last decades has been associated with an increase in perinatal mortality in domestic pig, Sus scrofa  (Canario et al. , 2006 (Canario et al. , , 2007)). One main factor related to neonate survival is the maturation of fetal tissues and organs in late gestation  (Voillet et al. , 2014; Yao et al. , 2017; Voillet et al. , 2018; Gondret et al. , 2018). In order to explore the development of the metabolic status in late gestation, an experiment was performed on pig fetuses. Metabolomic data were acquired on plasma samples collected on n = 155 Large White (LW) fetuses at 90 days of gestation and on n = 128 fetuses at 110 days of gestation (birth is around 114 days; ANR PORCINET project). All The R package ASICS for 1 H NMR analysis chosen as indicators of carbohydrate metabolism).
More details about the experimental design and data acquisition can be found in Supplementary Section S2. Similar analyses were performed on buckets and on relative quantifications computed with ASICS to assess the performance of the method. The aim of these analyses was to find metabolites best explaining the differences between the two groups: fetuses at 90 and 110 days of gestation. Lists of metabolites obtained with both approaches were compared as well as the direction of change between groups, based on two OPLS-DA, one on buckets and the other on quantifications obtained with ASICS. VIP thresholds for both OPLS-DA were set to 1. Metabolites that were quantified were used to make a quantitative assessment of ASICS by comparing the obtained (estimated) quantifications with the dosages.
Pearson correlation between quantifications and dosages were computed for every metabolite directly measured by dosage. These correlations were also compared with the correlations obtained for other quantification methods: Autofit, batman, Bayesil and rDolphin. Contrary to ASICS, these methods were too slow or not automated to allow the quantification for the 283 spectra. Therefore, quantifications were performed on a subsample of the original dataset that corresponded to the deciles of the lactate, fructose and glucose dosage to ensure representativity (32 spectra). Computational times were also recorded. For ASICS quantifications, water and urea regions were excluded and the maximum shift, M , was set to 0. 01. In order to perform all quantifications with batman in a reasonable time, its library was reduced to the 160 common metabolites between batman and ASICS reference libraries and the number of iterations was set to 10,000.
04-ppm width. The original study used a combination of PLS-DA and statistical tests (t-test, F -test, Kruskal-Wallis test and Kolmogorov-Smirnov test) on buckets (with a manual expert identification) to find differences between the healthy and ill individuals. This dataset allowed us to test the performance of ASICS on a different fluid (urine) in a different species (human). Contrary to  Salek et al. (2007) , we kept glucose region for a quantification with ASICS because the glucose spectrum was available in the library. However, regions of water and urea were excluded. The other parameters of the different methods were set to their default values except for ASICS threshold that was set to sm = 0. 05, because we had observed that this dataset was noisier than the previous one. In addition, to control differences that could originate from the analysis method itself, we performed the comparison between the buckets and the ASICS quantifications with the same method, OPLS-DA, as for the study about perinatal survival (VIP thresholds set to 1.
(2015)  (results also given in Supplementary Table  S5 ). Those comparisons highlighted the relevance of ASICS quantification that showed results consistent with previous studies and prior knowledge on Type 2 diabete: some of the metabolites were extracted by ASICS and by bucket quantification, like alanine or acetoacetate (Supplementary Table  S5 ), and have also been identified in  Salek et al. (2007). We were also able to extract other metabolites, like the glucose (D-Glucose), the guanidinoacetic acid or the glycerol, that were not previously described because the glucose region was excluded from the study in  Salek et al. (2007). The glycerol was identified both by ASICS and by  Salek et al. (2007)  in experiments on rats and mice (the glucose region was only excluded in the human dataset and not in the rat and mouse datasets).
Another metabolite rarely identified in T2DM, arabitol (L-Arabitol), was quantified as decreasing only with ASICS and firstly described by  Yousri et al. (2015)   The R package ASICS for 1 H NMR analysis of patients. Together with glucose-6-phosphate, also only identified with ASICS, these metabolites reflect the pentose pathway activity in diabetics. Metabolites associated to this pathway were also previously identified in urine as strongly associated with T2DM development in a diabetic rat model  (Sun et al. , 2014). Finally, only ASICS allowed the identification of GABA (γ-aminobutyric acid), a neuromediator recently identified to be increased in T2DM and related to a lower cognitive functioning observed in some diabetic patients  (Van Bussel et al. , 2016). In conclusion, not only was ASICS able to automatically recover the main findings of the bucket and expert analysis, it was also able to extract a number of metabolites that are relevant and confirmed by other independent studies but not found by the bucket and expert analysis (glycerol, guanidinoacetic acid, acetone, arabitol, glucose-6-phosphate and GABA).