dataesr/scientific-paragraphs-categorization huggingface dataset:
3. The tadpole equations were modified to be solved for im(Aκ) and im(A λ ) instead of im(Tκ) and im(T λ ) at tree-level: NMSSMCALC solves the tree-level tadpole equations to calculate im(Aκ) and im(A λ ), but calculates the radiative shifts to im(Tκ) and im(T λ ), i. e. solves the loop-corrected tadpole equations with respect to other parameters than the tree-level ones. The SPheno code produced by SARAH always solvs the tadpole equations at tree-and loop-level for the same parameters. This would have already given some difference at the one-loop level for specific complex phases, in particular for complex κ. 4. The complex phases in the Yukawa couplings, which for instance appear via thresholds in the case of complex M 3 , were always put to zero becuase NMSSMCALC supports only real Yukawa couplings.
CP even fraction Fig. 14  Impact of arg(κ) for a scenario with two Higgs states close to 125 GeV as proposed in Ref. [57]. Top, left: The mass of the second and third physical scalar at one-loop (dashed), and including two-loop corrections O(αsαt) (dotted), as well as all two-loop corrections as calculated by SARAH/SPheno (full line). Top, right: difference between the scalars when using O(αsαt) only (red), and the complete corrections (blue). Bottom, left: the singlet fraction of second (red) and third (blue) scalar using 2-loop corrections O(αsαt) only (dashed), and the complete 2-loop corrections in the gaugeless limit (full). Bottom, right: the CP-even fraction of the two states. The colour code is the same as on the left. dependence on the phases of M 3 and A t is included to a large extent in the corrections involving the strong coupling.
Acknowlegements We thank  Dao Thi Nhung  for clarifying discussions concerning NMSSMCALC and  Alexander Voigt  for double checking one-loop results for the complex MSSM. MDG acknowledges support from  Agence Nationale de Recherche  grant  ANR-15-CE31-0002  "HiggsAutomator," and would like to thank  Pietro Slavich  for interesting discussions.
Aptoptosis/Cell Division Boolean network The model focuses on the regulation of cell division and apoptosis by the EGFR signalling pathway and a BRCA1/TP53 DNA damage response module. These genes have been identified as central in the process of tumor formation in breast cancer  [23, 36]. The model incorporates the positive and negative interactions between nuclear TP53 and MDM2 described by  [8] , the main messengers of the PI3K/AKT and MAPK signalling following EGFR activation described by  [52]  and adds BRCA1 and PARP1 regulation of DNA damage. These pathways are gathered into a unique Boolean network through the lens of their role in the regulation of the G1/S transition and the triggering of apoptosis in case of DNA damage. The corresponding Boolean network  6  , constructed from published litterature and signalling pathways databases (KEGG  [21]  and Signor  [39] ), is shown in Figure  3  and the molecular mechanism for each interaction is detailed and referenced in Appendix (see Table  2 ).
The rate at which acetate is secreted is constant (i. e. , it does not depend on glucose consumption, which could affect the generality of the conclusions). Thus, none of the previous models take into account a realistic random mutational process, and none of them explicitly models the genomic level. Indeed, it is difficult to include a competition process as well as realistic mutational dynamics in a single mathematical model. Another approach consists in simulating evolution with individual-based models. Computational models of in silico experimental evolution have already been used to explore the evolution of cross-feeding interactions. Johnson and Wilke  [26]  used the Avida software  [27]  to study the evolution of resource competition between two digital species coexisting via mutualistic cross-feeding in a closed environment, with only two possible metabolites.
Model Evo 2 Sim is a multi-scale and individual-based computational model. Digital bacterial-like organisms own a coarse-grained genome that contains genomic units encoding a simplified metabolic network. The organisms evolve on a two-dimensional toroidal grid (the environment), uptaking, transforming and releasing metabolites, and dividing in the presence of empty spots or dying. Extracellular metabolites diffuse across the grid spots. In this model, metabolites are implicit molecules identified by a tag 2 N Ã. The model is described in more details below, and summarized in Fig 1. The source code is written in C++. All the material necessary to replay experiments (software, parameter files, strain backups,. ) is freely available at http://www. evoevo. eu/adaptive-diversification-simulations/.
The latest version of Evo 2 Sim is available at http://www. evoevo. eu/evo2sim-software/. Genome structure. The genome is a circular single-stranded sequence of genomic units, inspired from  [35, 36]. Genomic units belong to three different types: non-coding units (NC), promoter units (P), and enzyme coding units (E). The order of the units in the genome determines the existence of functional regions, meaning that not all sequences of units are functional. The functional regions of a genome are those that have the following pattern: a promoter (P) followed by one or more enzyme coding units (E). A promoter can thus control several coding units, as bacterial operons. The first genomic unit that is not enzyme coding interrupts transcription and marks the end of the functional region. Non-coding units (NC) have no particular function.
Plant material Three genotypes of P. nigra L. , originating from individual trees of natural populations in France (Drôme 6; FR-6), Italy (La Zelata; IT1) and Spain (Ebro 2; SP-2; see  DeWoody et al. 2015)  were studied. Cuttings from these genotypes were purchased from INRA Orleans (Orléans, France), where the materials are available for research purposes under the accession numbers N-38 ('Italy'), RIN2-new ('Spain') and 6-J29 ('France'). The cuttings were planted in 10 l plastic pots filled with a 1:1 (v/v) mixture of peat and sand, amended with a slow-release fertilizer (4 g l -1 of Nutricote T100, 13:13:13 NPK and micronutrients; FERTIL S. A. S. , Boulogne Billancourt, France) and 1 g l -1 CaMg(CO 3 ) 2. Plants were grown in two chambers of a fully automated glasshouse for phenotyping located at Champenoux, France (48°45′09.
The absorbance of each assay mixture was determined two to three times at 280 nm using a plate reader (mQuant; Bio-Tek Instruments, Winooski, VT, USA) and KC4 software  (v. 3. 3;. Included in each plate was a standard cell wall preparation of poplar, which was a large mixed sample of cell wall extract that had been assayed 20 times to provide an accurate assay value to test for consistency in subsequent assays; also included was one negative control as the absorbance baseline, which contained all reactants but lacked cell wall material. Acetyl bromide-soluble lignin percentage content (ABSL%) was calculated using absorption reading at 280 nm (A280), path length determined for the 96-well microplates with a volume of 200 μl per well used during the analysis (0. 556 cm) (PL), reaction volume in liters (VR) the sample weight in grams (WS), and the specific absorption coefficient (SAC, 17.
Statistical analysis of growth and wood anatomical data Statistical analyses were performed using R v3. 1. 1  (R Development Core Team 2015). Two-factorial mixed linear models with 'genotype' and 'treatment' included as main and interaction effect as well as a random effect for greenhouse chamber were fitted to the data using the function 'lme', package 'nlme'  (Pinheiro et al. 2015). Normality and homogeneity of variance were tested visually by plotting the residuals. The data were transformed logarithmically (log2) if needed to achieve normality and homogeneity of variance of the residuals. In case of a significant (P < 0. 05) genotype main effect, post-hoc tests were computed using the function 'lsm' (package 'lsmeans'; Lenth 2015) called within the function 'ghlt' (package 'multcomp';  Hothorn et al.
Successively, libraries were loaded on Illumina cBot for cluster generation on the flow cell, following the manufacturer's instructions and sequenced in 50 bp single-end mode at sixfold multiplex on the Illumina HiSeq2000 (Illumina). The CASAVA 1. 8. 2 version of the Illumina pipeline was used to process raw data for both format conversion and de-multiplexing. In average, 26. 91 million reads were produced per sample (min 21. 12). Short reads have been deposited into NCBI Short Read Archive under BioProject accession PRJNA359401.
Bioinformatic analyses Raw sequence reads were processed with the Python package Cutadapt v1. 4. 2  (Martin 2011)  to remove any residual adapter contamination. Reads were subsequently trimmed to remove lowquality reads (option -trim_qual_left/right = 25) and reads shorter than 40 nucleotides, using the PRINSEQ software v. lite-0. 20. 4  (Schmieder and Edwards 2011). Trimmed reads were aligned to the P. trichocarpa v3. 0 transcriptome database  (Tuskan et al. 2006 ) using TopHat2 v2. 0. 10 (Kim et al. 2013). A count table was generated using the Python package HTSeq v0. 6. 1  (Anders et al. 2015). Further inferential analyses of the count data were done with the R package DESeq2, version 1. 6. 3 (Love et al. 2014 ) unless otherwise stated. Normalization of count tables was done based on the 'median ratio method' (Anders and Huber 2010) implemented in the function 'estimateSizeFactors'.
Genes with a significant genotype main effect (FDR = 0. 05) in transcript abundance were denominated as genotype-related differentially expressed genes (GDEGs). Pairwise genotype contrasts were computed for the set of GDEGs by specifying the contrasts in the function 'results'. Tree Physiology Volume 38, 2018 Gene co-expression analysis and correlation to wood traits The GDEGs and DDEGs, which had been obtained by the bioinformatics analyses described above, were used to re-construct transcriptional networks using the R package WGCNA version 1. 47  (Langfelder and Horvath 2008, 2012). Gene co-expressions were summarized as adjacency matrices using soft-thresholding powers of 20. The soft-thresholding powers were selected based on low mean connectivity and a >90% model fit to scale-free topology.
Adjacency matrices were transformed into topological overlap matrices, which were used to determine modules of coexpressed genes, with a cut height of 0. 99 and the minimal module size set to 20 and 30 for the network of DDEGs and GDEGs, respectively. Modules whose correlation of eigengene values was >0. 8 were merged. Eigengene values (corresponding to the first principal component of the modules' gene expression data) of modules of DDEGs or GDEGs were then correlated with wood traits. Enrichment of gene ontology (GO) categories  (Ashburner et al. 2000)  within modules was tested using 'The Ontologizer'  (Bauer et al. 2008). Genes assigned to individual modules were used as study sets, and the sets of all genes showing a droughtor genotype-main effect, respectively, as the reference sets ('gene population').
The transcript abundances of the individual genes in modules related to the saccharification potential were tested for significant correlation with the saccharification potential. The resulting set was mapped onto MapMan categories (MapMan v3. 5. 1R2; Thimm et al. 2004 ) based on their A. thaliana best hits. MapMan's built-in Wilcoxon rank sum test with Benjamini-Yekutieli correction was used to identify bins enriched in genes with high correlations to saccharification potential. Arabidopsis best hits assigned to the MapMan bin 'cell wall' (bin 10) were used as baits in the coexpression database CressExpress  (Srinivasasaianagendra et al. 2008 ; http://cressexpress. org/) to identify genes commonly co-regulated with these bait genes across multiple experiments. CressExpress v3. 0 was used with default quality-control statistic D to search for co-regulated genes in the microarray data from experiments NASC_137 ('AtGenExpress Stress Treatments, (control plants); GSE5620;  Kilian et al.
2007 ), NASC_141 ('AtGenExpress Stress Treatments' (drought stress); GSE5624;  Kilian et al. 2007 ), NASC_153 ('AtGenExpress Developmental Series shoots and stems'; GSE5633), NASC_14 ('control of lignification';  Rogers et al. 2005 ) and NASC_27 ('Assembly of the cell wall pectic matrix', GSE6181). Genes were considered coexpressed with the saccharification-related bait genes when the gene-expression data across the selected microarray experiments were correlated with r 2 > 0. 8 and P < 0. 05. Co-expressed genes annotated as transcription factors were identified according to the list of A. thaliana transcription factors available at the Plant Transcription Factor Database v4. 0  (Jin et al. 2016 ; http:// planttfdb. cbi. pku. edu. cn/). The identified transcription factors were compared with the transcription factors in the list of DEGs and genes present in both lists were extracted as candidate genes.
Quantitative real time polymerase chain reaction (qRT-PCR) of selected genes RNAseq data were validated by quantitative real time polymerase chain reaction (qRT-PCR) on a Light Cycler 480 system (Roche Diagnostics, Mannheim, Germany). Primers were designed with Oligo Explorer (Gene Link, Hawthorne, NY, USA, http://www. genelink. com/). We used three genes without significant changes across our conditions as the references (Potri. 012G141400, Potri. 001G006400, Potri. 005G074900; primer sequences (5′-3′, forward and reverse): AAATTGAGGTTGGGGAAGGC, ACAACATC TGGCATGCATCC for Potri. 012G141400; GATCCTCATGATGCTGC TGA, GCAACTTGTACGCTCCCTTG for Potri. 001G006400; ACCAA CGAGACAAGGTGCTT, CTTTTGGGCTTCTTGCAAAC for Potri. 00 5G074900), and four genes as targets (Potri. 014G035500, Potri. 012G097900, Potri.
010G083600, Potri. 004G038700; primer sequences (5′-3′, forward and reverse): TTCGCAGCCC AACATTACAAAG, GATAGTAGGTGGGGATGGCATG for Potri. 01 4G035500; AATCTCCCTCATTGCCATCAC, ATGATGATCAGAC GCCGCTGG for Potri. 012G097900; GGTTGCAGGAATTAGGAAC GC, ACGTTTCCCAGTTCCATGTTG for Potri. 010G083600; CGGTTC ACTCCTCTTCCTTATG, TGGCAAGAGTGAGAAGGATCTG for Potri. 00 4G038700). Primer efficiencies and Cq-values were obtained using LinRegPCR v2016. 2. 0. 0  (Ruijter et al. 2009 ). The expression of target genes was related to the expression of the reference genes as the inverse of the normalized relative quantity defined in  Hellemans et al. (2007). The correlation of the relative expression of target genes determined by qRT-PCR with normalized transcript abundance obtained by RNAseq was analyzed. The following correlations were obtained: Potri.
The transcriptome of the developing xylem varied substantially among genotypes and was moderately affected by drought In order to identify genes and clusters of co-expressed genes underlying genotype or drought-induced variation in saccharification potential and wood traits, whole-transcriptome gene expression profiling of the developing xylem was done by RNA-sequencing. After trimming to remove adaptor contamination and low-quality reads we obtained 23. 1 ± 0. 95 M reads (mean ± SE) per sample. After alignment to the P. trichocarpa reference and removal of genes with low expression levels, a count table was obtained for 23,393 unique gene models. Principal component analysis of the count data revealed a prominent separation of genotypes along both of the first two principal components, which together explained 60% of the variance, while clustering according to treatment was less apparent (Figure  S2  available as Supplementary Data at Tree Physiology Online).
Gene co-expression modules showed distinct correlations with wood anatomical and chemical traits In order to relate the sets of GDEGs and DDEGs to genotyperelated or drought-induced variation in wood traits, we first reconstructed transcriptional networks of GDEGs and DDEGs by WGCNA and then correlated modules of co-expressed genes with wood traits (Figure  3 ). The GDEGs clustered in 18 co-expression modules (DX-G1 to DX-G18; Figure  3A ). Wood traits that showed no genotyperelated variation (number of cambial cell layers, thickness of fiber and vessel walls, and the fraction of cell wall area; Table  1 ) showed no or weak correlations with the co-expression modules of GDEGs (Figure  3A ). Furthermore, we identified three modules DX-G1, DX-G12 and DX-G7 without any significant correlation with wood traits (Figure  3A ).
Numbers above the diagonal denote Pearson's correlation coefficient r, asterisks denote significance of correlation (***P < 0. 001; **P < 0. 01; *P < 0. 05). Lignin, lignin content in dry cell wall; vfreq, vessel frequency; vlumen, average vessel lumen area; vwall, vessel wall thickness; ffreq, fiber frequency; flumen, fiber lumen area; f_dwall, fiber double wall thickness; cwa, percentage of cell wall area of vessels and fibers; wdx, relative radial width of the developing xylem; ray, percentage of ray area; sacc, saccharification potential expressed as glucose yield per dry cell wall residue; caml, number of cambium cell layers. All trait data had the same units as in Table  1. Tree Physiology Volume 38, 2018 significantly negatively correlated with saccharification potential (P = 0. 017, Figure  3B ).
Metabolic processes in the developing xylem underlying variation in saccharification potential Since variation in saccharification potential was largely independent from wood anatomical traits (Figure  2 ), we sought to get additional insights into the metabolic control of saccharification potential by mapping all GDEGs that were assigned to any of the saccharification-correlated modules and whose transcript abundance was significantly correlated with the saccharification potential onto MapMan functional categories ('bins'). An over-representation of GDEGs with strong positive or negative correlations to saccharification potential was detected in five bins, namely 'cell'/cell vesicle transport', 'cell wall', 'misc/cytochrome P450' and 'protein targeting secretory pathway' (Table  2 ). A closer inspection of the bin 'cell wall' revealed that transcript abundances of GDEGs involved in 'cell wall modification' (bin 10.
7), 'hemicellulose synthesis' (bin 10. 3), 'pectin synthesis' (bin 10. 4), 'cellulose synthesis' (bin 10. 2) and 'cell wall degradation' (bins 10. 6. 2 and 10. 6. 3) were mostly negatively correlated with saccharification potential (Figure  5A , Table  S5  available as Supplementary Data at Tree Physiology Online). In contrast, bins 'pectin esterases' (bin 10. 8) and 'cell wall/precursor synthesis' (bin 10. 1) included specific processes that were positively correlated with saccharification  UDP-L-rhamnose synthase) were positively correlated with saccharification potential (Figure  5B ; Table  S5  available as Supplementary Data at Tree Physiology Online). In order to identify regulatory genes commonly co-expressed with saccharification-related GDEGs assigned to the MapMan bin 'cell wall' we used the latter genes as baits in an in silico coexpression analysis.
Acknowledgments We thank  C. Kettner  ( Forest Botany and Tree Physiology, Göttingen, Germany ) for plant maintenance and plant production and  T. Klein  ( Laboratory for Radio-Isotopes, Göttingen, Germany ) for RNA isolation. We acknowledge the providers of the original P. nigra genotypes 'France  6J-29 ' (INRA, Paris, France represented by  G. Pilate ) and ' Spain RIN2-new ' (CITA, Zaragosa, Spain, represented by  JV Lacasa Azlor)  and  C. Bastien  ( INRA, Orleans, France ) for providing the stock cuttings.
The methodology is evaluated for a former mine site in Queensland, Australia involving multiple reactive contaminant species undergoing complex geochemical reactions. PHREEQE  [3] , a reactive, chemical transport model, is a widely used computer code using the two-step procedure for solution. Tebes-Stevensa, Valocchia  [4]  developed a reactive transport simulation code (FEREACT) to investigate the chemical reaction-based transport for cobalt and EDTA (Ethylene Diamine Tetra-acetic Acid) through an iron oxide-coated sand column. Prommer, Barry  [5]  presented numerical computation results for both reactive and non-reactive chemical species transport in an aquifer site contaminated by hydrocarbons. They used the PHT3D  [6]  model, coupling the non-reactive part of MT3DMS  [7]  model with the reactive geochemical model PHREEQC-2  [8].
Moreover, MT3DMS is widely used in various chemical reactive transport simulators such as RT3D  [9] , SEAM3D  [10] , BioRedox  [11] , DART  [12] , PHAST  [13]  and PH-WAT  [14]. These models have been coupled with saturated media flow simulation models. On the other hand, one-dimensional movement simulators such as HP1  [15]  and RICH-PHREEQ  [16]  have been used in recent years as unsaturated porous media flow models incorporating detailed geochemistry. The numerical model HYDROGEOCHEM 5. 0  [17]   [18]  for simulating transport of reactive chemical species in an aquifers, has also gained popularity in recent years  [19]   [20]  [21]  [22]. Wissmeier and Barry  [23]  presented a software tool for groundwater flow and transport movement in combination with geochemistry in two and three-dimensional systems.
The multiple component multiple species reactive transport model PHT3D  [48]  is utilized in this study or linking with the optimal source characterization model. PHT3D combines MT3DMS and PHREEQC  [49]. The PHREEQC component allows for a variety of low temperature aqueous geochemical reactions. The PHT3D is used in this study to simulate both physical and chemical transport processes in the contaminant aquifer. In PHT3D several species can be integrated. The number of species and their initial concentrations on basic transport package need to be changed compared the MT3DMS module. It is also required to add the concentrations for each new species in the source/sink package. The chemical equations  [48]  which are solved by PHREEQC are: reac c r t ∂ = ∂ (2) 1, s i i i n C c Y s = = + ∑ (3) where r reac is a source/sink chemical reaction rate term; c is the concentration (molar) of the uncomplexed aqueous component, s i is the concentration (molar) of the i th complexed species, n is the number of dissolved chemical species t complexed with the aqueous component, s i Y is the stoichiometric coefficient of the aqueous component in the i th complexed species.
In the search process of the SA algorithm, a random nearby solution is replaced utilizing the probability depending on the difference between the corresponding function values and algorithm control parameters (initial temperature, temperature reduction rate, etc. ). Here, SIMANN a FORTRAN code developed by  [51]  is utilized as the optimization algorithm. A new linked simulation optimization model linking SIMANN, the SA based optimization algorithm with reactive transport simulation model (PHT3D) is developed for this study. In order to achieve computational efficiency, the SA algorithm parameters need to be suitably specified. The SA parameters needed to be specified include the following: The initial temperature (T); The error tolerance for termination (called EPS); The temperature reduction factor (RT).
In the developed linked simulation optimization model developed incorporating PHT3D, the chemical reactions need to be specified in a database file, which will be used by the model during the simulation process. There are certain norms for creation of the reactions input data base for PHT3D. This reaction database enables consideration of the specified reactions. Cu SO CuSO + - + = 2. 36 In this study, reaction databases were utilized to simulate copper related contamination. Once the database is created, it has to be linked to PHT3D in order to launch the simulation. In the Basic Transport Package for PHT3D, defining species also implies choosing a reactions database. This is where the database can be linked to the simulation program. The different active species existing in the databases is then considered in the simulation (Table  2 ).