dataesr/scientific-paragraphs-categorization huggingface dataset:
It can also be used to predict the occurrence of structural motifs in protein sequences. Indeed, the alpha version of the server which was made available on-line earlier, has already been used by some research groups for the structural characterization of RNA binding sites in protein structures and predicting proteins sequences that contain RNA binding sites  34, 35  and also in predicting β-turns and their types  36. The web-based tool currently does not feature the hybrid method with noise filtering scheme because it would require running an instance of BLASTClust on every query. We plan to implement this functionality in a future improvement to the tool. known homologues are featured. The hybrid method with noise filtering scheme for querying the database was applied for the prediction whereby the conditions were identical to experiment B1 as featured in Figure  3  and Table  6.
56million protein chains and their corresponding local structure represented as one of the 16 PBs were obtained and stored in PENTAdb. Of these 68. 84 million pentapeptides, 2. 26 million are unique which represents 70. 9% of the total number theoretically possible 3. 2 million (205) pentapeptides. The content of the database accessible to PB-kPRED at these threshold values is given in Table1. There is a 32-fold.
Shown are the accuracies for the PDB30 dataset using both the majority rule method and hybrid method. For each of the 15,544 query protein sequences, the portion of PENTAdb accessible for prediction was dynamically determined using MySQL queries: only pentapeptides coming from protein chains in PDB that shared sequence identities below the indicated cut-off values were accessible to PB-kPRED for prediction of local structures in terms of PBs.
Acknowledgements Authors thank  Tristan Rialland  and  Sara Bachiri  for providing technical support in the development of the PB-kPRED web server. This work was supported by the  Région Réunion  and the  Fond Social Européen  [grant no. 20131528 ] to IV. This work was in part supported by  Conseil Régional des Pays de la Loire  in the framework of  GRIOTE  project. AdB and FC acknowledge grants from the  Ministry of Research (France) ,  National Institute for Blood Transfusion (INTS, France) ,  National Institute for Health and Medical Research (INSERM, France)  and labex GR-Ex. The labex  GR-Ex , reference  ANR-11-LABX-0051  is funded by the program " Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02. AdB acknowledge supports by  University Paris Diderot, Sorbonne, Paris Cité (France) , FC acknowledge supports by  Université de La Réunion, Faculty of Sciences and Technology.
In 2005, Etchebest and colleagues 26 used a combination of statistical optimization procedure and improved sequence family data to bump up the accuracy to 48. 7%. Incorporating secondary structure predictions from PSI-PRED into the PB prediction process did not contribute much to improve the accuracy i. e only 1% gain resulting in 49. 9%. Machine learning techniques have also been used to predict protein local structure in terms of PBs. Support vector machine based methods like LOCUSTRA  20  , svmPRAT  22  and SVM-PB-Pred 21 achieve mean accuracies of 61. 0%, 67. 0% and 53. 0% respectively. A dual layer neural network based prediction method achieved 58. 5% accuracy  24. The most refined version of the PB-kPRED method proposed here, i. e hybrid method with noise filtering scheme, outperformed most of the previously developed methods for PB prediction except for svmPRAT where it performed equivalently.
56million protein chains and their corresponding local structure represented as one of the 16 PBs were obtained and stored in PENTAdb. Of these 68. 84 million pentapeptides, 2. 26 million are unique which represents 70. 9% of the total number theoretically possible 3. 2 million (205) pentapeptides. The content of the database accessible to PB-kPRED at these threshold values is given in Table1. There is a 32-fold decrease (from 68. 62 to 5. 13 million) in the number of pentapeptides in PENTAdb when PDB chains not sharing more than 30% sequence identity are considered. Nevertheless, there is only a 1. 3 fold decrease in the number of unique pentapeptides present in PENTAdb at this threshold. [Table1abouthere]Notallpossibletri-PBcombinationsare observed in known protein structuresOut of all the theoretically possible 4,096(16 3) tri-PBs, a total of 1,375 (i.
Bianco and Halleck  [9]  experimentally evaluated the behavior, morphology, and stability of sand arches near the wellbore in two-phase saturated sand samples. They analyzed the effects of changes in fluid flow velocity and water saturation during the sand production process. With the growing use of artificial intelligence modeling in petroleum engineering, Kanj and Abousleiman  [15]  developed an artificial neural network (ANN) modeling to predict important sanding indication parameters for gas wells of Northern Adriatic Basin. Later, another ANN model was developed by Azad et al. [16]  to predict critical bottom hole flowing pressure inhibiting sand production using 38 datasets collected from three different oilfields wells located in the southeast of Iran. The results showed that the developed model provides better prediction than other analytical models.
Database collection A data set of 31 wells of Northern Adriatic Basin reported by Moricca et al. [10]  was used for modeling. Adriatic Sea is geographically bounded by Italy, Albania, and the former Yugoslavian republics. The northern and central portions of this area are known to form one geological unit commonly referred to as the Northern Adriatic Basin  [10]. The Northern Adriatic Basin is considered Italy's main supplier in natural gas. Based on extensive experimental tests on core samples as well as drilled cuttings in different fields of the Northern Adriatic Basin, the following data were obtained  [10] : 1. The producing formation porosity varies between 10 and 40% while it has very low permeability ranging from 10 to 100 mD. 2. The lithology of vertical column is composed of sand shale alterations.
While the producing formation contains uncemented clays. The cohesive strength averages to 19 kg/cm 2. According to AGIP's research group  [10] , out of all studied wells, 23 wells considered as problematic due to sand production, and the remaining 8 wells considered as sand free. Reported main factors affecting sand production were: total vertical depth (TVD), transmit time (TT), cohesive strength of the formation (COH), water and gas flow rates (Q w & Q g ), bottom hole flowing pressure (BHFP), drawdown pressure (DD), effective overburden stress (EOVS), shut per foot (SPF), perforation interval (H perf ). This dataset was examined for incomplete data and as the result two sets were removed. The finalized data set consisting of 29 data points is reported in Table  1. As previously mentioned, our objective is to develop a mathematical non-linear relationship between the available data which considered as inputs (i.
Results and discussion In order to develop the LSSVM classification model, the MATLAB programming workspace was used. Initially, the dataset was divided into "training" and "validating" sets. The optimum values of the parameters of LSSVM algorithm namely s 2 and g were evaluated by simulated annealing (SA)  [46]. Determined optimized values are: s 2 ¼ 444. 420282248436 and g ¼ 10475. 3147940118. It is worth it to point out that the numbers of the reported digits of these parameters are generally determined by sensitivity analysis of the total errors of the optimization procedure with respect to the corresponding values. The most common method for evaluation of the quality of a classification model is to compute a confusion matrix of the actual class versus predicted class  [47]. From this matrix some parameters including true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) can be extracted and used to evaluate a binary classification model.
amylioliquefaciens is made possible by the recent sequencing of multiple strains of the species. Similar to other bacterial groups the conserved "core" genome is defined as the shared genetic material among nearly all the strains of the species. The core genome contains majority of housekeeping genes and is interspersed with "accessory" genomic parts. It is believed that accessory genome is present in some strains while being absent in the rest of the species strains  (Ozer et al. , 2014). In the current study, genomes of 48 strains of B. amylioliquefaciens available in GenBank (genomes submitted until December, 2016) have been mined for genes contributing to plant-beneficial functions and therefore, plant growth promotion potential and secondary metabolite arsenal. The contribution of core and accessory genome to plant growth promotion and secondary metabolite biosynthesis are also discussed.
Selection of Genomes and Genome Phylogeny Genomes of B. amyloliquefaciens used in the study were selected among those submitted until December, 2016 in GenBank DNA database. They all have been deposited under the nomination B. amyloliquefaciens. The genomes and their corresponding strains have been described in Table  1. Nucleotide as well as the amino acid sequences of the whole genomes and the deduced coding sequences were retrieved from the GenBank DNA database for all strains (Table  1 ). Whole genome alignments have been conducted using REALPHY (The Reference sequence alignment based phylogeny builder, available at http://realphy. unibas. ch; Bertels et al. , 2014). A Maximum Likelihood (ML) algorithm  (Felsenstein, 1981)  as implemented in MEGA v. 6  (Tamura et al. , 2013)  with evolutionary distances computed using the Kimura 2-parameter model  (Kimura, 1980)  was used to build the phylogenetic tree.
Validity of branches in the resulting tree was evaluated by bootstrap re-sampling support of the data sets with 1,000 replications. Average nucleotide identity (ANI) values of B. amyloliquefaciens strains were estimated using the algorithm developed by  Goris et al. (2007)  combined with the 95∼96% cut-off for species boundary proposed by  Richter and Rosselló-Móra (2009) , as implemented in the server EzBioCloud available at http://www. ezbiocloud. net/tools/ani (Yoon et al. , 2017). In silico genome-to-genome distance values were calculated using the web-based DSMZ service available at http://ggdc. dsmz. de.
, 2007). Secondary Metabolite Clusters Identification using Antismash, Prism, Napdos, NP. Search, and Bagel3 The annotated draft genome sequence files, which included information for both contigs and ORFs (Table  1 ) were subjected to secondary metabolite gene cluster analysis using antiSMASH 3. 0  (Weber et al. , 2015) , prediction informatics for secondary metabolomes (PRISM)  (Skinnider et al. , 2015) , NapDos  (Ziemert et al. , 2012) , NP. search  (Li et al. , 2009) , and the bacteriocin specific software BAGEL3  (Van Heel et al. , 2013).
Secondary Metabolites from B. amyloliquefaciens Secondary metabolite clusters present in the genome of the B. amyloliquefaciens collection have been evaluated using antiSMASH 3. 0  (Weber et al. , 2015) , prediction informatics for secondary metabolomes (PRISM)  (Skinnider et al. , 2015) , NapDos  (Ziemert et al. , 2012) , NP. search  (Li et al. , 2009) , and the bacteriocin specific software BAGEL3  (Van Heel et al. , 2013). As shown in Figure  3  and Supplementary Table  S1 , different strains showed high levels of diverse secondary metabolite clusters using all implied programs. Rarefaction analysis of secondary metabolite clusters from the results of genome sequencing progress clearly attested that saturation could not be reached using all genome collection analyzed (Figure  3B ). A very clear correlation between genome size and number of gene clusters known to be involved in secondary metabolite biosynthesis and mined by antiSMASH was found.
, 2007; Richter and Rosselló-Móra, 2009; Meier-Kolthoff et al. , 2013). Using these accurate tools, several later heterotypic synonyms were documented in this group such as B. methyltrophicus, B. amyloliquefaciens subsp. plantarum, and B. oryzicola that have been shown, using phylogenomics, later heterotypic synonyms of B. velezensis  (Dunlap et al. , 2016). Therefore, phylogenomic approaches are urgently required to resolve outstanding problems in the phylogenetic systematics of the B. subtilis group  (Dunlap et al. , 2016). Phylogenomic analysis of all sequenced genomes of B. amyloliquefaciens strains available in GenBank, the National Centre for Biotechnology Information (NCBI) database (Table  1 ), allowed us to check taxonomic validity of these isolates, determine the extent of inter-species genome variability within B.
, 2015). In our study using recently described bioinformatic tools designed for the identification of clusters involved in secondary metabolism such as PRISM  (Skinnider et al. , 2015) , antiSMASH 3. 0  (Weber et al. , 2015) , NapDos  (Ziemert et al. , 2012) , NP. searcher  (Li et al. , 2009) , and the bacteriocin specific software BAGEL3  (Van Heel et al. , 2013)  and the B. amyloliquefaciens genomes available in databases, we documented high structural and functional diversity of secondary products in the species and their underlying gene clusters. Our data clearly showed high variety of secondary metabolites suggested by the high number of matches using five different programs for their prediction. Rarefaction analysis of secondary metabolite clusters from the results of genome sequencing progress demonstrated clearly that saturation could not be reached using all genomes available and more sequencing effort of new strains is necessary to tackle the wide diversity of secondary metabolites potentially harbored by the species.
ETHICS STATEMENT This research did not involve any work with human participants or animals by any of the authors. to the Ministry of Education, Science, Research and Sport of the Slovak Republic for financial support in the frame of the project "VEGA 1/0046/16. " Part of the study was financially supported by the Life Plus project HESOFF, Life 11 ENV/PL/459 financed by the European Union and the National Fund for Environmental Protection and Water Management in Warsaw (Grant to TO).
FUNDING This project was funded by the  Government of Kuwait  (to FA) and the  European Union Seventh Framework Programme  under grant agreement  245268  (ISEFOR; to LB). Further support came from the  SwissBOL  project, financed by the  Swiss Federal Office for the Environment  (grant holder LB) and the  Sciex-Scientific Exchange Program  (https://www. swissuniversities. ch/en/topics/sciex/) (NMS. CH; to LL and LB). LL and EP are indebted.
With this staining, extracellular matrix is stained in green and the cytoplasm of the cells is stained in purple. Taking advantage of the contrast provided by this staining between the granuloma and the extracellular matrix, the granulomatous lesions of FBR can be easily outlined on the numerical microphotography taken with a camera-equipped light microscope and the area was quantified using the calibrated NIS-Elements Imaging software (Nikon France). 2. 5. Liver Enzyme Measurements. Blood samples were collected from each animal by vein puncture before sacrifice. Sera were stored at -80 °C until measurements of albumin, bilirubin, alkaline phosphatase (ALP), gammaglutamyltransferase (γ-GT), aspartate aminotransferase (ASAT), and alanine aminotransferase (ALAT) on an automated analyzer in the Biochemistry Department of Pellegrin Hospital, Bordeaux, France.
Tartrate-Resistant Acid Phosphatase Activity Detection. Acid phosphatase activity in the presence of 50 mM tartrate was assayed with naphtol AS-MX phosphate (Sigma) as a substrate and freshly prepared Fast Violet B (Sigma). Liver sections were incubated overnight at 4 °C in the substrate solution and counterstained with von Kossa. Cells were considered positive when evidencing strong pink staining with numerous red granules spread throughout the cytoplasm. 2. 11. Statistics. Data are presented as means ± SD. Statistical comparisons between groups were performed using the Wilcoxon signed rank test. p < 0 05 was taken to imply statistical significance.
Genotyping The genotyping data used in this study was from a high-density rice array (HDRA) composed of 700,000 single-nucleotide polymorphisms (SNPs). The HDRA was developed as an Affymetrix Custom GeneChip Array from a SNP discovery dataset consisting of ~16 millions SNPs (generated by re-sequencing 128 rice samples at ~7X genome coverage). Methods for the development of the HDRA, including SNP discovery and selection, probe design, genotype calling and quality control were described by McCouch  [24]. The SNP with minor allele frequency (MAF) less than 0. 05 were removed, and finally 395,553 SNP markers were selected for GWAS.
Data analysis and QTL mapping The Spearman's rank correlation coefficients between each trait pair were calculated by the SAS8. 1 PROC CORR (SAS Institute, 1996). To estimate the effects of replication, genotype and conditions, analysis of variance was carried out by SAS8. 1 PROC GLM. We performed a genome wide association study (GWAS) to detect the trait-SNP associations for all measured traits using 395,553 SNPs and the mean trait values of the 211 accessions from each of the environments. Marker-trait associations were conducted by compressed mixed linear model implemented in the Genome Association and Prediction Integrated Tool (GAPIT), a package of R software  [25]. The critical p-value for declaring significant marker-trait association was set to 1. 0 × 10 -4. Adjacent significant SNP with distances less than 200 kb were merged into single QTL.
In our study, Fe and Zn tolerance-related morphological traits (SH, RL, SFW, SDW, RDW, SWC) in stress and control conditions and physiological traits (SFe and SZn) in stress condition were measured, and their derived traits such as ratio of stress to control were also calculated and all used for input data to detect QTL. Definitely, QTL affecting ratio traits were directly related to stress tolerance  [30]. However, ratio traits may reduce trait differences or variations in the population, thus probably resulting in that some QTL were undetectable. So besides comparing QTL results using ratio traits, we also compared QTL detections between stress and control conditions as we dissected drought tolerance QTL in our previous reports  [31, 32]. Most of QTL at seedling stage associated with SH, RL, SFW, SDW, RDW, SWC, SFe and SZn detected in this study were specific to either Fe or Zn stress condition.
Funding Introduction of rice germplasms was supported by the "948" Project to JX ( 2016-X16 ) from the  Chinese Ministry of Agriculture  (http:// www. moa. gov. cn/) and the  CAAS Innovative Team Awards  to JX (http:// www. caas. net. cn/),design of the study, data collection and experimental supplies were funded the "863"  Key Project  to JX ( 2014AA10A601 ) from the  Chinese Ministry of Science and Technology  (http://www. 863. gov. cn/ ), data analysis and interpretation were funded by the  Shenzhen Peacock Plan  to ZL (http://www. szsti. gov. cn/)(#: 20 , 130 , 415 , 095 , 710 , 361 ), and the publication costs of this article was provided by  Bill and Melinda Gates Foundation  project to ZL (# OPP1130530 ).
Statistical analysis All experiments were carried out at least in triplicates. The results are shown as arithmetic means (± standard deviation). The normality of distribution was assessed by Shapiro-Wilk test and significance of differences between means was assessed by one-way variance analysis (ANOVA) with post hoc Tukey test. The Principal Component Analysis (PCA) with varimax rotation was applied to assess correlations among variables. All statistical analyses were carried out using R: A language and environment for statistical computing, version 3. 1. 3 (Foundation for Statistical Computing, Vienna, Austria, 2015). ANOVA was carried out using "lm" function and Tukey test was done using "HSD. test" function in the "agricolae" package. The PCA was carried out in "psych" package  (Beaumont 2012).
In each period, respiratory and haemodynamic data were continuously recorded, and renal data was recorded at the end of each period. The following treatment groups were defined for this experiment: • Group K, n = 6, received ketoprofen (Ketofen, 100 mg. mL -1 , Merial, Lyon, France) (2 mg. kg -1 completed with saline; total volume of 0. 04 mL. kg -1 ), • Group KiNO, n = 6, received ketoprofen (2 mg. kg -1 completed with saline; total volume of 0. 04 mL. kg -1 ) and 40 ppm iNO added to O 2 throughout the experiment, • Group C, n = 6, received a placebo-control injection (saline solution, 0. 04 mL. kg -1 ) and inhaled 100% O 2.
Inulin concentration was measured in urine and plasma with an enzymatic assay  [17] ; GFR was considered equal to inulin clearance, and was calculated with the standard clearance formula: GFR ¼ Clu inulin ¼ UO x ðCu inulin =Cp inulin Þ Where Clu inulin and UO are the renal inulin clearance and the urine output (mL. min -1 ) respectively, and Cu inulin and Cp inulin are the inulin urine and arterial plasma concentration, respectively. Right RBF measured by ultrasonic flowmetry (RBFul) was continuously registered. Data was treated with the AcqKnowledge 1 acquisition software. In Experiment I, in order to evaluate the influence of the flow probe on renal function, RBF was also monitored with the PAH clearance method (RBF PAH ): 2. 0 g of PAH were added to the 50. 0 mL inulin i. v. bolus and 0.
Data collection Continuous recording of systolic, mean and diastolic SAPs and PAPs, heart rate, CO, ETCO 2 , respiratory rate, and SpO 2 was divided in 20 minute periods and averaged separately. Thirty minutes after inulin/PAH CRI initiation, following steady state achievement, urine produced by each kidney was collected and discarded. Blood and urine were then collected for the determination of inulin/PAH concentration. Starting at time = T0-10 min, arterial blood was collected at 20 minute intervals on 3 (Experiment I) or 7 (Experiment II) occasions. Starting at time = T0, urine was collected individually from both ureters at 20 minute intervals on 3 (Experiment I) or 7 (Experiment II) occasions. Total UO was calculated at each interval. All samples were stored at -80˚C pending analysis.
Statistical analysis Data was analysed with SAS 9. 1 for Windows (SAS Institute Inc, Cary, North-Carolina, U. S. A. ). To evaluate the stability of the model (or within-time variation), a linear mixed-effect model for repeated measures with maximum estimation was used to assess the influence of time on cardiovascular, respiratory and renal parameters in both Experimental phases. Exploratory data analysis was secondly performed to identify potential relationships between variables. Afterwards, a linear mixed-effect model for repeated measures with maximum estimation was used to assess determinants of the time-course of UO percent change with respect to baseline value. In this model, measurement time, treatment group, CO, RBF and GFR were included as fixed factors, and pig within treatment group was a random variable.
Moreover, we obtained similar tendencies with data obtained by both methods, even though RBF PAH values appeared higher. This inconvenient has already been described as a limitation of RBF estimation using a colorimetric PAH dosage method  [26]. Our data are in accordance with data reported by Mo ¨ckel et al. [27] , who concluded that the ultrasonic method is a reliable technique to estimate RBF in pigs. Based on these data, this last method was used to assess RBF in Phase II. Although it is an invasive procedure, the ultrasonic method has the advantage to allow a continuous measurement of RBF and an early detection of variations. NSAIDs-induced AKI is a well-documented complication, with both haemodynamic impairment and inflammation playing a role, but in an anaesthetic context, decreased renal perfusion appears as the principal cause  [28].