dataesr/scientific-paragraphs-categorization huggingface dataset:
Theoretical solubility prediction of caffeine A conductor-like screening model for real solvents (COSMO-RS) was used to theoretically calculate the thermodynamic properties of solvation without any experimental data. The solute is treated as if embedded in a dielectric medium via a molecular surface constructed around the molecule. This is a macroscopic approach using a dielectric constant of the solvent. Parameters such as s-surface, s-profile and s-potential (Figs. 2a-2c ) aid in estimating the solubility of the solute in the solvent system. In this case, the theoretical solubility of caffeine in the solvent systems was determined. The data was calculated in COSMOthermX'20 program (BIOVIA, Dassault systems). The standard quantum chemical method triple-valence polarized basis set (TZVP) was employed in this study and the results are expressed as log10(x_solub).
Ortholog clustering All proteomes from the SPAdes assemblies and from 5 reference genomes (Alcoy, NC_014125; Corby, NC_009494. 2; Philadelphia, NC_002942. 5; Lens, NC_006369; Lorraine, NC_018139. 1) were clustered into ortholog protein families using OrthoMCL  [84]. Variants were then matched to their respective proteome to identify the consequences of the mutations, and to which protein family they belonged. Annotation of the protein families in which multiple variants were found between independent samples was done by searching for the protein accession numbers in the first reference genome and using each region as a query for a BLAST search.
Simulation of non-selective environment To estimate the distribution of mutations per gene to expect in a non-selective environment, 123 genes (which corresponds to the observed number of SNPs in this study) were sampled randomly, with replacement, from the 3033 genes harbored by L. pneumophila Paris. The number of genes sampled once or more times was recorded. The sampling was repeated 1000 times. The probability that the observed number of genes mutated twice and three times, respectively, would be greater than in a non-selective environment was assessed by comparing the actual number of genes to distribution obtained from the 1000 simulations, using one-sided Mann-Whitney tests, wilcox. test in the stats package of R  [85]. The results were visualized with ggplot2  [86].
Functional annotation An amino acid sequence search in the RCSB Protein Data Bank  [91]  was used to identify homologous structures, protein sequences were then aligned using MAFFT-LINSI and the structural effect of the mutations were predicted via NetSurfP-2. 0  [92]. Mutations were visualized in crystal structures of a Pseudomonas aeruginosa homologs (PDB accessions: 3DWO for the OmpP1/FadL homolog and 4RNH for the EAL-containing protein) via YASARA View  [93].
Acknowledgments The authors would like to thank  Matilda Morin ,  Elisabeth Hallin  and  Daniela Klingenberg  ( Public Health Agency of Sweden) , as well as  Carmen Pelaz  ( Legionella Reference Laboratory in Spain ) for providing strains from their collection;  Ulrika Lustig  for her patient technical assistance; and  Elisabeth Kay  for providing the pXDC101 plasmid. The authors would also like to acknowledge support from  Science for Life Laboratory , the  National Genomics Infrastructure, NGI, and Uppmax  for providing assistance in DNA sequencing and computational infrastructure.
We also analyzed the effects of the mutation of cellulose related genes FEI2. 1 and CESA5  [41, 57]. Germination of cesa5 seeds showed a slight delay in germination in both freshly harvested and vernalized seeds (8% and 31% before 24 h, respectively). On the other hand, the fei2. 1 mutant was earlier compared to WT (21% before 24 h in freshly harvested seeds compared to 12% WT). The germination rate of fei2. 1 vernalized seeds was 72% at 24 h, compared to 45% for WT (Table  1 ). Overall, these data show that disruption of CW structure in cesa5 and fei2. 1 influences seed germination processes, with CESA5 acting as positive regulator and FEI2 as negative regulator. As for the CW pectin regulators, two mutant alleles of FLY1 were analyzed. fly1. 1 freshly harvested seeds were able to germinate faster than fly1.
Seed and Silique Morphological Characterization For seed measurements, images were taken using a Leica MZ6 stereomicroscope with 5 biological replicates and 50 seeds each. The seeds were collected from a pool of 5 independent plants. The images were analyzed using the SmartGrain software  [106] , given seed size (area size-AS) (refers to the total contents within the border), length (L) (longitudinal length), and width (W) (transverse to longitudinal length). For silique length, photos were taken using a stereomicroscope with 3 biological replicates of 10 siliques each. The siliques were collected from a pool of 5 plants. The photos were analyzed with ImageJ software  [107]. Statistical analyses were performed using ANOVA, followed by Tukey's honestly significant difference (HSD) test (p < 0.
Chromatographic data were integrated with OpenLab software (Agilent, Santa Clara, CA, USA, https://www. agilent. com, accessed on  C m -1 ; 3 m at 280 • C) optimized for the separation of the most common CW monosaccharides chemically derivatized, such as arabinose (Ara), fucose (Fuc), galactose (Gal), galacturonic acid (GalA), glucose (Glc), glucuronic acid (GlcA), mannose (Man), rhamnose (Rha), and xylose (Xyl). Ara/Rha and Gal/Rha ratios can reflect the relative importance of Ara or Gal in side-chains of RG-I, while the GalA/Rha ratio reflects the proportion between RG-I and HG, as these two monosaccharides are unique monomers of these respective pectin backbones  [113] [114] [115]. The last sugar ratio reported here described the pectic backbone sugar GalA with respect to the neutral pectic sugars involved in side chains (GalA/FRAGX), and this inferred a measure for the linearity of pectin  [62].
Acknowledgments: We greatly appreciate the labs of  T. Western ,  C. Somerville ,  J. Kieber ,  G. Haughn ,  I. Hara-Nishimura ,  H. North  as well as the  NASC collection  for providing some mutant lines used in this work. We thank  R. Petrella  for technical support and  E. Kiegle  for review of the manuscript.
Funding: This research was funded the  Research Projects of National Interest (PRIN)   PRIN201719LCOLO_01 ,  H2020-MSCA-RISE EvoFruland (101007738 ),  Coordination for the Improvement of Higher Education Personnel (CAPES) ,  Brazilian Council for Research and Development (CNPq) , and the  European Union   FP7  project  EVOCODE  ( 247587 ). Work in the S. D. F. laboratory is financed by the  Mexican National Council of Science and Technology (CONACyT) , grant  CB-2017-2018-A1-S-10126. The  H2020-MSCA-RISE  " POLYPLOID " project ( 101007438 ), the FOSC-COFUND-2019 Project  ID-236  " CropsForChange ," and the  CARIPLO SEED WAKE APT  ( 2016-0723 ) are gratefully acknowledged.
Metabolomics underutilised crops banana cassava sweet potato yam potato a b s t r a c t Biochemical characterisation of germplasm collections and crop wild relatives (CWRs) facilitates the assessment of biological potential and the selection of breeding lines for crop improvement. Data from the biochemical characterisation of staple root, tuber and banana (RTB) crops, i. e. banana ( Musa spp. ), cassava ( Manihot esculenta ), potato ( Solanum tuberosum ), sweet potato ( Ipomoea batatas ) and yam ( Dioscorea spp. ), using a metabolomics approach is presented. The data support the previously published research article "Metabolite database for root, tuber, and banana crops to facilitate modern breeding in understudied crops"  (Price et al. , 2020)   [1]. Diversity panels for each crop, which included a variety of species, accessions, landraces and CWRs, were characterised.
Anal ysis of these crops was performed on different plant tissues (e. g. leaf, tuber and root) and for banana and cassava, on plants under two different cultivation conditions: in vitro propagation and open field. The respective species/accessions were subjected to a standard methanol-water-chloroform extraction, followed by different metabolomics techniques. LC-MS and GC-MS analysis was performed for untargeted profiling of polar and non-polar extracts. Analysis of non-polar extracts by LC-PDA was performed for a more targeted screening of isoprenoids (e. g. carotenoids and chlorophylls). Compounds in the samples were compared to retention time and UV/Vis spectrum of authentic standards ( Fig. 1  ). Molecular features detected in the different analysis techniques, were compared to authentic standards and spectral features in databases (e.
g. NIST) for metabolite identification. For GC-MS, Automated mass spectral deconvolution and identification system (AMDIS) was used and settings were modified specific to certain crops ( Table  1  ). For LC-MS analysis, the R package metaMS was used and a script for molecular feature extraction and library comparison was modified for samples analysed with maXis Ultra-High Resolution QTOF (Bruker, Germany). The outputs from both analysis techniques are available, as unprocessed Excel tables listing the areas of individual molecular features/metabolites in the respective samples, in Mendeley Data repository  [2] [3] [4] [5] [6] [7] [8]. The identified metabolites were quantified relative to internal standards. A database was compiled for the present RTB crops and includes the quantitative range of each metabolite present in the individual tissues of each genus  [1].
Metabolite extraction Lyophilised tissue was ground and homogenised to a fine powder. Aliquots (10 mg) were weighed for each sample and extracted with a methanol/water/chloroform extraction method. Due to the size of the individual sample sets, sample batch of 22 sample were created. Each sample batch included an extraction blank and a quality control, which represented a pool of a samples in the respective sample set. Extraction methods were optimised for specific chemical classes and for each crop  [10] [11] [12] [13] [14] [15] [16] [17] [18]  , e. g. carotenoid extraction for yam with 200 mg/sample. The yam dataset was created with GC-MS analysis of aqueous and organic phase and HPLC-DAD analysis of the organic phase. Datasets for all other crops (sweet potato, potato, banana and cassava) were created with GC-MS and LC-MS analysis of the aqueous phase and UPLC-DAD analysis of the organic phase.
Liquid chromatography-mass spectrometry (LC-MS) analysis Aqueous extracts were dried down and resuspended in methanol/water (1:1, 100 μL). Internal standard (homogentisic acid, 5 μg, or genistein, 10 μg) was added to each sample, the extraction blank and the quality control. Samples were filtered (nylon, 0. 45 μm) and analysed with Dionex Ultimate 30 0 0 UHPLC (Thermo Scientific) coupled to maXis Ultra-High Resolution QTOF (Bruker, Germany) in negative electrospray ionisation mode (Vi, 5 μl). Aliquots of samples (10 μL) were separated with Acquity BEH C18 column and a solvent gradient including 0. 1% formic acid in water and acetonitrile  [11]. Extraction of chemical features from raw data files and search chemical database was compiled with R package metaMS  [ 19 , 20 ]  ( Fig. 2  ) including an in-house library with authentic standards.
Data processing Data output from the respective data analysis software was tabulated using IdAlign (Centre for Computational Systems Biology, University of Western Australia, http://www. softsea. com/ review/IdAlign. html ) and Microsoft Excel 2016. Metabolite features present in extraction blanks were subtracted or excluded from the data sets. The identified metabolites and molecular features were quantified relative to the respective internal standard and the datasets were normalised to the individual sample weights ( μg/g dry weight). Some compounds were detected as multiple derivatives (e. g. glutamic acid and pyroglutamic acid) and their areas were merged before normalisation. In some cases, the data needed to be normalised with the quality controls to correct for batch effects (e. g.
Acknowledgments The authors would like to thank  International Potato Center (CIP, Peru) ,  International Institute of Tropical Agriculture  ( IITA, Nigeria ),  International Center for Tropical Agriculture  ( CIAT, Colombia), Bioversity International Musa Germplasm Transit Centre (ITC, Belgium), Agricultural Research Centre for International Development (CIRAD, France ) and  Royal Botanical Gardens, Kew  ( United Kingdom ), for providing in vitro plants and germplasm. This work was funded by the  CGIAR Research Program on Roots, Tubers and Bananas (RTB)  with support by  CGIAR Fund Donors  ( www. cgiar. org/funders ) and the  African Cassava Whitefly Project  ( www. cassavawhitefly. org ) through  Natural Resources Institute (NRI) ,  University of Greenwich , from a grant provided by the  Bill and Melinda Gates Foundation  (Grant  OPP1058938 ).
3 min. The resulting data matrix containing integrated peak areas of both unidentified chemical features and annotated metabolites was exported as Microsoft Excel Open XML Spreadsheet (. xlsx) format. JID: DIB ARTICLE IN PRESS [mUS1Ga; March 13, 2022;13:54 ] Please cite this article as: M. Drapal, L. Perez-Fons and E. J. Price et al. , Datasets from harmonised metabolic phenotyping of root, tuber and banana crop, Data in Brief, https://doi. org/10. 1016/j. dib. 2022. 108041.
Study population and sampling procedure Between May 2014 and June 2016, 181 pig farms and 181 broiler farms were visited in nine European countries (Belgium, Bulgaria, Germany, Denmark, Spain, France, Italy, the Netherlands and Poland). Countries were anonymized to letters 'A-I' in line with previous EFFORT publications. 6, 7, 9 In each country, 25 fresh faecal samples were randomly collected on each of 20 conventional farms (or 21 pig farms and 19 broiler farms in Country E or 21 broiler farms in Country A and Country B) complying with the previously described inclusion criteria (e. g. nonmixed, as close to slaughter as possible). 6, 7, 10 Data from 179 pig farms and 180 broiler farms remained for the present analysis, excluding 3 farms that cannot be linked to AMU data. In agreement with local farming organizations, farms were selected based on inclusion criteria and partly based on convenience (e.
g. distance to the farm). Also considering the limited sample numbers per country, the selected farms cannot be regarded as representing the livestock sector of the participating countries. Faecal samples were collected without floor contact (sterile spoons were used). Within 24 h, all individual faecal samples were transported to the laboratory at 4°C and stored at -80°C until DNA extraction. 6, 7 estionnaire, AMU and biosecurity measurement General herd characteristics, AMU (group-treatment and purchased) and biosecurity information were retrieved from a standardized questionnaire (Table  S1 , available as Supplementary data at JAC Online) completed by farmers in each participating farm together with the visiting researchers. 6, 7 Group-treatment AMU was defined as any treatment simultaneously applied to all animals present in, at least, the smallest housing unit (i.
12, 13 NA of pooled faecal samples from pigs and broilers was extracted at the Technical University of Denmark (DTU) and shipped on dry ice to the Oklahoma Medical Research Foundation (OMRF; Oklahoma City, OK, USA) for shotgun metagenomic sequencing. In total, faecal DNA from 181 pigs and 181 broilers was sequenced on the HiSeq 3000 platform (Illumina), yielding. 18 billion paired-end reads. More details about the subsequent processing of metagenomic data were described in our previous research. 6, 7, 10 ng et al.
6, 7, 13, 19, 20  linear mixed model with both country and farm as random effects was applied to take the between-country and between-farm variation into account. Changes in estimates and significance of associations with or without AMU in the model were determined. Firstly, we ran the mixed model for AMR and selected farm characteristics other than AMU. Associations were selected by univariable analysis (P , 0. 2) and subsequently an automatic backward analysis was conducted using the 'step' function in the 'R' package lmerTest. 21 The multivariable model without AMU was adjusted after the fixed parts were eliminated step by step (P. 0. 05). Considering the high level and limited variance of biosecurity score in Country I, we performed a sensitivity analysis between ARG abundances and farm biosecurity without Country I.
Secondly, the same procedure was applied, but with adjustment for AMU in all mixed models. Due to the right-skewed distribution, AMU was log 10 transformed after adding a pseudocount of 1. Considering the number of broiler farms without AMU, a sensitivity analysis was performed between ARG abundances and binary AMU (1 or 0 meaning using antimicrobials or not, respectively). After checking the distribution of datasets, Spearman's rank correlation was used to evaluate the correlation of relative ARG abundances between individual qPCR data and previously published pooled metagenomic data for pigs and broilers. 6, 7, 10 The median of 5-7 individual qPCR results per farm was calculated before correlation analysis. To match the ARG targets of qPCR, all downstream gene abundances of aph(3 ′ )-III, erm(B), sul2 and tet(W) were collected from the metagenomic data (FPKM) and summed per gene target.
Discussion To find potential risk factors that contributed to AMR abundance in pigs and broilers, we assessed the relationship between onfarm AMR levels and AMU and other farm characteristics using a linear mixed model. The results showed that in addition to AMU, risk factors such as age and weight of animals, and biosecurity measures, were also significantly associated with AMR levels in pig and broiler faeces. A moderate correlation was observed between median individual qPCR data and previously published pooled metagenomic data. The between-country and between-farm variation could partially be explained by AMU. Different ARG targets seem to have different sample size requirements to accurately represent their overall farm-level abundance. It has been well documented that many farm factors other than AMU can affect the AMR levels in farm animals.
In the correlation analysis, we only found a moderate correlation of AMR between the median of 5-7 individual qPCR readouts and the previously published metagenomic data assessed in pooled samples. 10 In addition to the incompletely reproduced risk factor analysis results of these two datasets, we speculate that collecting 5 or 7 individual faecal samples per farm probably does not represent the farm-level AMR as accurately as pooling 25 individual faecal samples together per farm. Meanwhile, we found consistent results with previous metagenomic data in risk factor analysis of erm(B), and we observed a high correlation of erm(B) abundance between median individual qPCR data and pooled metagenomic data in both pigs and broilers. This may indicate that ARGs have different sample size requirements per farm to accurately represent their overall farm level.
(1) Ripley's K(r) has values larger than πr 2 for clustered processes, but smaller values for regular processes. The statistical estimation of K(r) follows its definition. Here, we describe a naive estimator without edge-correction (thus with some bias). Assuming n points c i in a window W of area A(W), S(r) can be estimated as Ŝ𝑆(𝑟𝑟) = 1 𝑛𝑛 𝑛𝑛 ∑ 𝑖𝑖=1 𝑁𝑁𝑖𝑖(𝑟𝑟), ( 2 ) where N i (r) is the number of points within a circle of radius r and centered at c i (not counting the circle's center c i ). An estimate of K(r) is then obtained by dividing  Ŝ() by an estimate of P 20 , namely n/A(W). Better estimators of K(r) are described in the literature on point process statistics (e. g. ,  Baddeley et al. , 2016; Illian et al. , 2008)  and are implemented in the SpatStat package of the R software. The sum in  Ŝ() is the same as n p (r), the number of point pairs within W whose interpoint distances are smaller than r (as used in fractal statistics;  Hentschel & Procaccia, 1983).
Analysis of Two Natural Fracture Networks We here analyze two natural fracture patterns. In the first, large-scale fractures were observed in a satellite image covering 120,000 m 2 of the southern flank of the Jabal Akhdar dome in the Oman Mountains. This network has been widely studied because it is typical of those in large fractured carbonate reservoirs of the Middle East  (Gomez-Rivas et al. , 2014; Hilgers et al. , 2006; Holland, Saxena, et al. , 2009; Holland, Urai, et al. , 2009). The map used in this paper (Figure  7a ) is from  Zeeb et al. (2013). The other fracture pattern is from the Hornelen basin in Norway. The original data set consists of seven maps digitalized at several scales by  Odling (1997) ; we focus our investigation on the one they mapped by hand from an observation height of 4 m.
First-Order Characteristics The Oman pattern was previously analyzed by  Zeeb et al. (2013). Nevertheless, we studied the data again and did not obtain the exact same results; they originally identified 650 fracture traces ranging from 3 to 179 m long, whereas we identified 696 fractures with ranging from 1 to 134 m long. Furthermore, whereas Zeeb et al. (2013)  reported that approximately 5% of the fractures appear to be cut by the boundary of the observation window, in our digitalization it is closer to 7%. This illustrates some impact of subjective interpretation; there is some probability that other researchers would produce distinct maps from the same data. For an unbiased estimation of the number of fractures, we counted 663 southern endpoints of fractures in the 120,000 m 2 window. This yields an estimated fracture density of P 20 = 5.
18 m -2 , almost twice the fracture density. The network consists of three main fracture sets oriented ∼50°, ∼130°, and ∼170°. We note that  Odling (1997)  reported the orientation of the third fracture set to be 0° (=180°) based on consideration of a larger area. The rose diagram of branch strikes is, as expected, similar to that for the fractures (Figure  13b ). Here, we use the same statistical analysis as for the Oman data set, but for three sets of fractures (N50, N130, N170). As for the Oman data set, a small part of the total length of the network (9. 6%) is not represented by these three subsets. Stronger deviations from stationarity appear when considering these three subsets (Figure  14a ). Indeed, the spatial densities of sets N130 and N170 are greater in the center and southeast of the exposure, respectively (green and red, respectively, in Figure  14a ).
Mark-Correlation Functions and Variograms The empirical isotropic and anisotropic mark-correlation functions and variograms for the branches of the Hornelen data set (Figure  17b ) reveal a similar form to the spatial correlation as in the Oman data set (Figure  12b ), but at quite different length scales and ranges of correlation. In contrast, for fractures, the results from the Oman data set show more random behavior, whereas the Hornelen data set (Figure  17a ) shows some inhibition of fracture lengths at short distances (up to 0. 6 m), and the mark-variogram indicates a tendency toward similar lengths up to 0. 5 m. Combined, this information indicates that nearby fractures and branches in the Hornelen data set tend to be short and of similar length. More precisely, we here consider a randomly chosen pair of fractures with lengths l 1 and l 2 and an intercenter distance r.
However, branches have some topological meaning and capture information about connectivity. Therefore, the use of branches may sometimes be a good compromise for variability and correlation analyses. In concrete applications, it probably makes sense to study fractures and branches in parallel, at least until some tendency in the data indicates one to be more useful. For instance, in well-developed and highly connected fracture networks, branches may be considered the main constitutive geometrical elements and thus offer more interesting information. If, in contrast, a network contains many isolated (e. g. , en echelon) fractures, information on connectivity may be of little interest, and an analysis of fractures may be preferable. The Hornelen data set includes both long, interconnected fractures and small, isolated fractures.