dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion The PEACE MENA registry was the first registry to include a majority of Arab countries in the MENA regions. It has provided data on the current management of AMI and AHF, described with standardized definitions and data variables. The present study assessed-for the first timethe socioeconomic and educational status of these patient populations. The main findings of this pilot-phase study of the PEACE MENA registry were that patients in the MENA region were relatively young at clinical presentation and had a high prevalence  of cardiovascular risk factors, compared to patients in Western countries. Similarly, previous registry studies conducted in the region reported a high prevalence of cardiovascular risk factors  [9] [10] [11] [12] [13] [14] [15]. This high risk profile highlighted the urgent need of preventive measures, screening for early detection, and adequate control of cardiovascular risk factors.
Acknowledgments This study was conducted under the auspices of the  Saudi Heart Association. We would like to thank the following individuals for their contributions to data collection:  Noor AlKamel ,  Monther Abdulrahman ,  Rehab Nuri ,  Salma Messaous ,  Abdelrahman Qawasmeh ,  Azzouz AbdulMalek ,  Djermani Dahlia ,  Osama Amin ,  Mirjana Radovic ,  Ray Moshi ,  Noora Alsagheir ,  AbdulKareem AlRabaie ,  Rafik Refaat ,  Eva Casiechetty ,  Hazim Hilmi ,  Hosameldin Ahmed , Ismail,  Kenza El-mouhabbis ,  Dania Srifi ,  Basma Murad ,  Kadhim Sulaiman , and  Prashanth Panduranga. Prof. Khalid F. AlHabib  (Principal Investigator) had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
The vibration response of the composite ABH is studied by means of the transfer matrix method (TMM)  [27]. In particular, the eigenvalue of the scattering matrix S for the propagating waves are analysed in the complex frequency plane  [13, 14] , providing insightful interpretations on the absorption efficiency of the ABH. All the analytical results are validated with full wave numerical simulations by Finite Element Models, which have been implemented using 2D models of solid mechanics in COMSOL Multiphysics software. The work is organised as follows. In Section 2, the theoretical model used to analyse the reflection of flexural waves by a profiled termination is presented for a 1D reflection problem. In Section 3, the physical interpretations of the ABH effect of a truncated ABH with a coating layer are presented by using the complex frequency plane in the lossless and lossy cases.
Acknowledgements This work has been funded by the  RFI Le Mans Acoustic  ( Région Pays de la Loire ) within the framework of the  Metaplaque  project. This article is based on work from  COST action DENORMS CA 15125 , supported by  COST (European Cooperation in Science and Technology). This work was partly supported by the  Spanish Ministry of Economy and Innovation (MINECO) , by the  European Union FEDER  through project  FIS2015-65998-C2-2 , by the project  AICO/2016/060  by  Consellería de Educación, Investigación ,  Cultura y Deporte de la Generalitat Valenciana  and by the project eTNAA  ANR-17-CE08-0035-01  (projet  ANR   2017-2010 ).
Metabolic fingerprinting experiments aim to determine relative differences between two or more systems elucidating a biological relationship. Therefore, statistical strategies are typically used in a chemometrics style. Univariate and multivariate statistics can be used as standard approaches to extract relevant information from complex datasets  [20] [21] [22] [23]. This study aims to investigate the effect of DCF, CBZ and their transformation products on P. australis metabolites using mass spectrometric untargeted screening analysis. By studying the fingerprint of P. australis leaves, rhizomes and roots with/without incubation at two different concentrations, 10 and 100 µM and 10 and 50 µM of DCF, as well as CBZ, respectively. Also, a statistical workflow was used to discriminate against the changes in P.
Phragmites australis Leaf, Rhizome and Root Metabolic Fingerprints In this section, the 11,442 variables were differentiated according to the plant part that they originated from. The variables were in the loading score plot (Figure  2d ). This step was performed to improve the statistical significance of the dataset through additional cross-validation. Also, the OPLS-DA analysis was conducted to investigate the structure of the data. Another 18% of the data was investigated through the variation between X (metabolites) and Y (plant parts) given by R 2 X(cum) and explained approximately 94% of the variations in the various samples (R 2 Y(cum)). We found the predictive value of the model was (Q 2 (cum) = 74%), which was created by OPLS-DA. The cross-validation performance was confirmed by analysis of variance (ANOVA).
Untargeted Metabolomics Analysis of Phragmites australis Incubated with DCF or CBZ The untargeted metabolomics analysis of P. australis incubated with/without DCF or CBZ, respectively, was performed using OPLS-DA to assign the changes in its metabolic fingerprint. The P. australis metabolic fingerprints of different extraction solvents with various plant parts were investigated in Sections 2. 2 and 2. 3, respectively, to test the organization and reliability of the data. Then, the large data set was used to perform the untargeted analysis and assign a list of metabolites that determined the distance between different groups. Also, the metabolic markers of P. australis incubated with/without DCF or CBZ were plotted each by the OPLS-DA, which represented the variability in metabolic patterns due to the different incubation (Figure  3 , that is, the loading plot in Figure  3a  and the OPLS-DA score plot in 3B).
The carbamazepine-10,11-epoxide was statistically significant variable important (VIP) > 1 and p < 0. 05. However, in this case quercetin (compound 1 in Figure  4d ) intensity was found to decrease significantly. 2. 5. Metabolism of Diclofenac in Phragmites australis P. australis was exposed for 96 h to diclofenac in the two concentrations of 10 and 100 µM, respectively. Thereafter, the data was processed with MassHunter Workstation Software Profinder B. 06. 00 (Agilent Technologies, Waldbronn, Germany) to detect the DCF molecule and its metabolites (including background subtraction). Diclofenac and its hydroxylated metabolites were detected in the roots and rhizomes of P. australis. Diclofenac was identified at 295. 0173 Da with 2 ppm deviation from the monoisotopic mass, which eluted at 26 min (logD (pH7) > 0).
Metabolism of Carbamazepine in Phragmites australis P. australis was exposed for 96 h to carbamazepine in two CBZ concentrations of 10 and 50 µM, respectively. Thereafter, the data was processed with MassHunter Workstation Software Profinder B. 06. 00 to detect the compound and its metabolites (including background subtraction). According to Sauvêtre and co-workers (2018), four different pathways for carbamazepine metabolism (PCM) were investigated in the plant  [28]. The different transformation products were identified in different P. australis samples using reference standards. The mean monoisotopic mass of standards and different samples and their absolute variation were tabulated, as well as the retention time in both standards and samples and the variation between them (Table  S3 , Figure  S4 ).
Impacts of DCF and CBZ on Phragmites australis Metabolic Pathways Metabolic pathway analysis was performed to identify the pathways that were induced upon the incubation of P. australis with DCF or CBZ via MetaboAnalyst 4. 0 software (Montreal, QC, Canada) based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. The results are established on OPLS-DA and S-plot analysis of different metabolites with/without DCF or CBZ incubation depending on the p (corr) vector in the OPLS-DA module. Consequently, these metabolites represented the metabolic profile (DMF) which differentiate between control and incubated samples. For the investigation, the mummichog algorithm was the first implementation of this concept to infer pathway activities from a ranked list of MS peaks identified by untargeted metabolomics.
The DMF was applied to test their participation in pathways using a combination of network analysis and functional enrichment analysis and listed in Tables  S4  and S5. The pathways exhibiting p < 0. 05 were considered a statistically significant metabolic pathway, meaning that they were affected with DCF or CBZ or their transformation products via MetaboAnalyst 4. 0 software based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. KEGG pathway analysis of the analyzed metabolites using the pathway data set of A. thaliana matched 27 pathways in P. australis incubated with DCF. Further, 11 were significantly biologically active and are listed in the table in (Figure  5a ). They were identified as glycolysis/gluconeogenesis, ascorbate and aldarate metabolism, fructose and mannose metabolism, galactose metabolism, the pentose phosphate pathway, arginine biosynthesis, alanine, aspartate and glutamate metabolism, purine metabolism, pyrimidine metabolism, glutathione metabolism and phenylalanine metabolism.
A large t2 range value was observed mostly in acidic 90% MeOH extracts, that is, a value far above the critical limits in the score space. Hence, this is likely to be an outlying observation. The elimination of acidic 90% MeOH extracts might improve the model. Also, most of the metabolites extracted with acidic 90% MeOH have been detected in three other solvents. The same observation was made for Lemna minor extracts and can be described with the different occurrence of charged molecules caused by different pH values  [26]. The statistical software allowed cross-validation between the datasets. Furthermore, an additional step of cross-validation of the data was done through the differentiation between the x (metabolites) and the plant parts. The results reveal the significance of the data organization.
Moreover, the relative standard deviation (%RSD) ranged from 0. 5% to 3. 6%. Thus, the results indicate the accuracy, repeatability and reproducibility of the LC system as previously reported in the investigation of plant metabolites in Lemna minor samples  [18]. 4. 6. Data Evaluation 4. 6. 1. Spectrometric Data Evaluation Data acquired with MassHunter Workstation LC/MS Data Acquisition software B 05. 00, (Agilent Technologies, Waldbronn, Germany) was subsequently analyzed with Profinder B. 06. 00 (Agilent Technologies, Waldbronn, Germany) to extract the so-called "features" by their retention times (RT), molecular mass and their peak intensity in various P. australis extracts. This was performed in a combination of the 3-fold injections of each sample after removing the features found in the corresponding blank samples.
DCF and CBZ Transformation Products Detection DCF and CBZ transformation product standards were analyzed using Agilent Profinder B. 06. 00 (Agilent Technologies). They were detected in the P. australis extracts, with masses estimated at ±10 ppm and RT ±0. 3 min of the exact mass and RT of the standards, respectively. The metabolites were identified and suspected (when analytical standards were not available) after RPLC-HILIC-ESI-TOF-MS separation in the suspect analysis. (Suspects screening typically is performed with accurate and high-resolution mass spectrometers to observe the empirical formula of each molecule present and/or with tandem-mass spectrometry to observe specific fragment spectra). A local database was built using MassHunter PCDL Manager B. 04. 00 (Agilent Technologies, Waldbronn, Germany).
Statistical Data Analysis Data statistical analyses were conducted with SIMCA 16 software (Malmö, Sweden). Further analysis and data evaluation were performed with Microsoft Access and Excel 2016 (Redmond, Washington, WA, USA) and OriginPro 2019, Origin Lab cooperation, Northampton, MA, USA. The preprocessed data is a matrix. The rows are the exact masses, retention times (RTs) and abundances of each sample, which were listed in a Microsoft Excel Sheet. For statistical analysis, it is common to handle the data matrices with rows as observations and columns as compounds  [36]. Therefore, the data was organized in the Microsoft Access Database file (DBF), which was exclusively built to be suitable for SIMCA 16. The main advantage of the data matrix is the inherent support to align quantitative data (plant part, plant number, extraction solvent and drug incubation) along with related metadata (i.
e. , feature annotations/abundance as columns and sample annotations as rows). In DBF, the RTs, masses and abundances were connected to the corresponding plant part (i. e. , leaf, rhizome and root), plant number (i. e. , plants 1 and 2), extraction solvent and drug incubation. Once the matrix was built, comprehensive statistical analyses could be performed by using the vast range of functions provided by the software. The matrix consisted of 432 observations (i. e. , the incubation with/without DCF or CBZ) and 11,442 variables (features). Furthermore, the plant part, plant number and extraction solvent were used as secondary observations. The data was not transformed and centered; however, it was scaled. The data was analyzed according to the following two strategies, considering the statistical analyses in untargeted metabolomics.
Further information about the data setup can be found in the supplementary material. By default, SIMCA provides an algorithm called "cross-validation" to get the most valid model by calculating the adequate number of principal components to prevent overfitting of the data in the model. Furthermore, the software provides a large number of visual diagrams (score plots (with Hotelling's ellipse), DModX (Distant to Model) and statistic tables to assess the quality of the model in addition to the R 2 and Q 2.
1. Metabolite fingerprinting was used to capture metabolite patterns across metabolite profiles. They are characterized without further identification steps (i. e. , without need for standard reference material). Partial Least Squares (PLS) and Orthogonal Partial Least Squares regression-Discriminant Analysis (OPLS-DA) were used to relate sets of X-variables (such as plant part, plant number, extraction solvent and drug incubation) to the metabolites matrix. SIMCA 16 has a tool called Multiblock Orthogonal Component Analysis (MOCA). MOCA's concept is used to accomplish a fast and accurate analysis of multiple blocks of data (variables) registered for the same set of observations. MOCA aims at extracting the information in complex multiblock data analytics. Furthermore, it will extract two sets of components: the joint and the unique components.
2. Metabolite profiling which uses sets of predefined metabolites were studied in different samples of P. australis and differences in metabolites were usually related to the incubation with DCF or CBZ. Metabolite/variable selection was conducted to observe only the most significant metabolite candidates that explain the differences between the samples using S-and contribution-plots. The statistical models were built with confidence limits at 95%. Also, the differentiating metabolic profile (DMF) was chosen based on their contribution to the variation and correlation within the data sets. The related metabolic pathways were analyzed using MetaboAnalyst 4. 0. Moreover, their contributions and biological clarifications were described based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) database.
Conclusions Metabolites of P. australis influenced by pharmaceuticals were investigated using RPLC-HILIC-ESI-TOF-MS. The experimental data and the statistical analysis revealed a change in the metabolites' fingerprint between the different extracts, different plant parts and upon incubation with DCF and CBZ. The PLS and OPLS-DA identified the statistically significant clusters between the different groups. Further, significant DMF was determined in P. australis after the incubation with DCF or CBZ, individually. Different metabolic pathways were predicted from the statically identified DMF. These pathways were related mainly to the defense of the plant against stressful environmental conditions. P. australis adapted to each drug differently. P. australis could putatively use the glutathione metabolism pathway and unsaturated fatty acid pathway to protect itself during the incubation with DCF and CBZ, respectively.
Measurements We determined the stage of development using Gosner stages  (Gosner, 1960). Embryos were checked twice a day. Hatching occurred at Gosner stage 22 (hereafter GS 22) after 16. 10 ±0. 02 days. At GS 22, hatching success, mortality and deformation rates were assessed as follows: undeveloped eggs and embryos that did not hatch were counted, and hatchlings (GS 22) were qualified as normal, dead or deformed. All live and undeformed tadpoles were put in a petri dish with water from their own tank and photographed above graph paper in order to measure their length (body; tail and total length, N = 1639). Morphological measurements were performed with the software ImageJ  (Schneider et al, 2012). All measurements were performed by the same person throughout the experiment. All experiments took place in a thermally controlled room with the temperature set at 17°C (both air and water) and under natural 12:12 hr day/night photoperiod.
We did not use tank as the replication unit in our analytical design because each tank represented a single treatment (either control, low, medium or high) and contained only the eggs of one clutch. Balanced sample sizes across clutches and treatments (e. g. , each clutch was represented by 30 eggs in each treatment) and the use of clutch identity as a random factor allow to avoid significant pseudoreplication issues. All statistical analyses were conducted with R. Studio v1. 2. 1335 (R Core  Team, 2019). First, we used Generalized Linear Models (GLMERs, (package lme4)) with a log-link function for variables following binomial distribution to test whether there were correlations between treatment and embryonic mortality. Second, we fitted two mixed-effect linear models (LMERs, package nlme ,  (Pinheiro et al, 2017) ) to analyze differences in development duration and total length with "Clutch" as a random factor to control for the non-independence of individuals coming from the same clutches.
Acknowledgments Xavier Bonnet and Héloïse Guillot helped during preliminary experiments. Olivier Chastel  and  Frédéric Angelier  commented an earlier version of this MS. Funding was provided by the  CNRS , the  Agence de l'Eau Loire-Bretagne , the  Agence de l'Eau Adour-Garonne , the  Région Nouvelle-Aquitaine  ( MULTISTRESS & AQUASTRESS  projects), the  ANSES  ( BiodiTox  project #  2019/1/031 ) and the  CPER Econat.
Viewing the Micro-CT images. Most imaging of the partial mastectomies, including assessment of margin status, was carried out with the Bruker Data-Viewer image visualization tool, which presents trans-axial, coronal, and sagittal views in two dimensions, allowing for smooth scrolling and viewing the entire specimen at any angle. Three-dimensional content of the Micro-CT images was also made by Bruker image reconstruction software (See attached Video File).
The dataset Features of the 173 partial mastectomies examined are summarized in TABLE  1. Of these 173 cases, 28 (16%) were DCIS and 145 (84%) were invasive cancers. Of the 129 invasive carcinomas, 14 were lobular carcinomas (10%), 129 were ductal carcinomas (90%), 112 (87%) of the invasive cancers were noted by the pathologist to contain DCIS, while 17 (13%) contained no DCIS, 108 (84%) were judged by the pathologist to have only a single focus of cancer, while 21 (16%) were judged multifocal, 10 of which (8%) were judged to have exactly 2 foci. The pathologist judged 114 of the 173 partial mastectomy specimens to be margin positive (66%, "ink on tumor") and 59 as margin negative (34%). A very large fraction of breast cancer patients at our hospital are afforded shaved margins, and these shaved margins excise residual cancer in a large number of cases.
Reads for margin status Micro-CT images of all 173 specimens were read to assess margin status, as defined by the image of cancer touching the edge of the specimen (TABLE  1 ). Reads were carried out with the Bruker Data-Viewer image visualization tool, scrolling through the entire specimen in the Z-dimension. Most reads took about half an hour, and all were carried out blind to the margin assessment of the pathologist to avoid bias Among the 173 specimens, the pathologists judged 114 (66%) to be margin positive. As noted above, most patients at our hospital are rendered margin free by shaved margin, thus making available an unusually large number of specimens for which the pathologists reported cancer at the edge. Among the 173 specimens in our dataset, Micro-CT identified an area where the image of the cancer touched the edge in 134 (77%) of the cases.
The readers scrolled through the entire specimen in the Z-dimension, imaging one computer generated section (20-70 microns) at a time. Using a sample X-Ray guide image (called SPR) each dataset was explored around key areas marked by the biopsy clips and the Kopans' loops that had been placed in the patient before surgery; these localization features were almost always found to coincide with the region of invasive and in situ carcinomas. The extent in each direction (x, y, z) of the suspected malignant features was established and the margins of the specimen were carefully viewed by orienting the dataset along the tumor/margin axis. Any suspected spot of positivity was investigated further to confirm that it was continuous with the tumor region and not a deceptively located benign density.
ACKNOWLEDGEMENTS: Many thanks for equipment provided by Bruker Skyscan and Nikon Metrology, and funding provided by Nikon Metrology. Micro-CT imaging was also provided at the MIT Center for Bits and Atoms, 20 Ames Street. Cambridge, MA 02139. Many thanks to Drs. Barbara Smith and Michelle Gadd, and especially to Dr. Thomas Gudewicz for his gracious help wit the pathological aspects of this work.
Total RNA was extracted from young actively growing leaves using the Qiagen RNeasy Plant Mini kit (Qiagen, Manchester, UK) with the optional DNase digestions step, following the manufacturer's instructions. RNA was poly-A enriched and sequenced on Illumina HiSeq 2000 at the WTCHG genomics facility in Oxford (UK). The newly generated sequence data is available from NCBI under bioproject number PRJNA629313 (biosamples accessions SAMN14776665 and SAMN14776666). Raw sequence reads were aligned against the reference transcriptome  [47]  with RSEM v. 1. 2. 31  [54] , bam files processed with Samtools v. 1. 2. 1  [55]  and single nucleotide polymorphism (SNP) calling carried out with HaplotypeCaller from GATK v. 4. 1. 2. 0  [56]. Then, resulting VCF files for separate samples were merged together with bcftools v1.
Integration of Genetic Map and Genome Sequence for the PAR Boundary Region In order to estimate physical size of the PAR and the fuzzy PAR boundary region we used the sequences of the sequenced transcripts encoded by genes adjacent to the PAR boundary to find the corresponding genomic scaffolds in the partial S. latifolia genome assembly published previously  [47]. BLAST searches with stringent parameters (see methods) identified corresponding genomic scaffolds for 76 out of 86 genes in the proximity of the PAR boundary (Table  S2 ). In total we identified 72 genomic scaffolds with the total length 2. 84 Mb. Most genes analyzed corresponded to separate scaffolds, reflecting a highly fragmented state of the genome assembly. The only two exceptions to this were scaffolds QBIE01000100. 1 and QBIE01001489.