dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials Materials for the described computational protocols consist basically of datasets obtained from public databases and processing software. -A metabolic database of reference covering chemical structures and reactions. Metanetx  [8]  is a consensus database that reconciliates multiple databases. -Models of metabolism for chassis organisms. Biomodels  [9]  and BiGG  [2] , among others, are databases containing genome-scale models for most commonly used organisms. -Software to compute molecular signatures, which are a specialized type of topological chemical descriptors. MolsigMolSig  [4] , among others, is an open-source package that provides such capabilities. -Matrix manipulation software such as octave, matlabMATLAB, scipy, R, etc. -Computation of elementary modes. Efmtool  [10]  provides both a Java and matlabMATLABbased efficient implementations.
Computation of Molecular Signatures The first step to generate an extended metabolic space is to encode all compounds of a metabolic database in a format that will allow the subsequent encoding of enzymatic reactions. We propose here to showcase the important steps that should be kept in mind through the use of one of the available encoding methods, the molecular signature  [4]  (see Note 1). 2. Check compounds for incomplete structural data. Some compounds can be defined with incomplete Markush structure or wildcard atoms. Those compounds typically stand to define classes of compounds (e. g. , "an alcohol") and should be removed since they cannot be interpreted through the molecular signatures algorithm used in this protocol. 3. Standardize compounds. Molecular signatures encode directly molecular graphs from a MDL MolFile input.
Users must ensure that compounds (resp. , chemical groups) that should be considered identical have the same molecular graph (resp. , subgraph) (see Note 3). (a) Neutralize or remove charges. As much as possible, chemical groups should be represented with the same protonation state to prevent different tautomeric forms. One can either use heuristics to add or remove hydrogen when necessary or simply remove all charges from the compound dataset. (b) Choose one conjugated form by compound. This is particularly important for aromatic compounds, which could appear under different kekulé forms in the database. A good solution is to explicitly use aromatic bonds in the molecular graph description. (c) Use a consistent hydrogen representation, either implicit or explicit. 4. To compute the signature of a chemical compound, we need initially to consider its molecular graph.
7. Based on previous definitions, the computation of the molecular signature involves two steps: (a) Choose a diameter to encode enzymatic promiscuity. To some extent, enzymes have the ability to process additional reactants that are structurally similar to the known ones. In a context where it is important to maximize the number of reactions to get more leads, modeling promiscuity can reveal itself to be a critical feature (see Note 4). We recommend starting with a diameter of 12 and going lower (down to 4) if no satisfying solution can be found. (b) Compute molecular signatures. The MolSig software  [4]   2. Thus, σ(R) is the difference in terms of atomic signatures (i. e. , molecular fragments) occurring during a reaction; created (resp. consumed or needed) fragments being positives (resp.
1. The extended metabolic space of diameter d, denoted by M , represents all the possible compounds C and allowed transformations (reactions) R between compound as spanned by the enumerated reactions computed by following the method described. 2. A chassis is a subset of the extended metabolic space O M that corresponds to the extended metabolic network of an organism at signature diameter d. A chassis is defined by the set of nominal reactions annotated for the enzymes present in the organism. 3. The list of nominal metabolic reactions for a given organism can be compiled from databases such as KEGG  [14] , MetaCyc  [15] , BiGG  [2] , BRENDA  [16] , etc. The choice of one database over the others depends on several factors: (c) The way the model is going to be analyzed, i. e. , network analysis, steady-state 4.
Reactions are often defined in a database-specific flat-file where reactants are referenced by their compound identifier. Most of the time, you will find a file in MDL SDF or MOL format binding the compound identifiers to their respective structures. Other interchangeable formats are usually available such as SMILES and InChI. Inter-conversion between formats using standard software such as Open Babel  [24]  yields to equivalent representations of the compound. A sanity check can help to ensure that they all refer to the same compound. This will eventually filter out wrong annotations. 3. Before being converted into molecular signatures, molecular graphs do not need to represent chemically valid compounds in terms of valence, charges, etc. The important point is that compounds (moieties) that should be considered identical according to the final application share the same molecular graph (subgraph).
Of course, those simplifications introduced at the compound encoding step must be kept in mind while interpreting the results. 4. Putative enzymes promiscuity can be modeled through molecular signatures given an appropriate diameter. Obviously, as we lower the diameter, the stronger is the promiscuity hypothesis and the riskier are the predictions. 5. Molecular signatures can take into account stereo-chemistry, which is particularly appealing when working with enzymes. Nonetheless, if stereo information is considered, it is important to ensure that it is available (and valid) for most of the compounds; otherwise, compounds with and without stereo information will be perceived differently through signatures. 6. Metabolic databases contain generally a substantial portion of reactions that are not stoichiometrically balanced.
Reaction signatures can be computed for reactions that do not need strictly balanced input reaction. Nonetheless, working with balanced reactions is always recommended and is a sign of a well-curated database. 7. This mathematical expression simply states that the reaction signature is the set formed by the difference between product signatures and reactions signatures. Intuitively, it can be understood as the chemical groups that are transferred or transformed through the reaction. 8. Multi-substrate reactions are difficult to handle with the proposed equation. Indeed, testing all compounds with a reaction would take N tries, where N is the total number of compounds in the database and m the number of substrates anticipated for that reaction. A more practical option is to allow promiscuity for only one substrate at a time, therefore limiting the number of trials toto N m.
/eproofing. springer. com/books/printpage. php?token=5n9Uu. 1. Let DB be a database binding compounds signatures to their respective molecular graphs. 2. Let σ(R) be the molecular signature associated with a reaction R, and {S , i [1, n]} a set of candidate substrates potentially reacting together. 3. Under the enzymatic promiscuity hypothesis determined by d, we predict that R can process any candidate substrate {S , i [1, n]} if:.
Acknowledgment The authors acknowledge funding from the  BBSRC  under grant  BB/M017702/1 , " Centre for synthetic biology of fine and speciality chemicals. "J. L. F. acknowledges funding provided by the  ANR  under grant  ANR-15-CE21-008. B. D. is affiliated to the  Doctoral School Structure et Dynamique des Systèmes Vivant ,  Université Paris-Saclay.
Computation of Reaction SignatureThe step following the encoding of compounds is the encoding of reactions into reaction signatures. Reaction signatures should be understood as an exchange of fragments. Unlike other reaction models, reaction signatures do not need any atom-atom mapping to be computed, nor do they need reaction to be balanced (see Note 6). e. Proofing | Springer http://eproofing. springer. com/books/printpage. php?token=5n9Uu. 3. 2. d d 5 of 14 8/5/17, 6:32 PM computes molecular signatures starting with compounds in MDL MolFiles format, which can be easily retrieved from metabolic and chemical databases or converted from other equivalent formats (see Note 5). 1. Let R be a reaction for which all substrates {S , i [1, n]} and products {P , i [1, m]} are encoded in molecular signatures, respectively { σ(S ), i [1, n]} and { σ(P ), j [1, m]}.
/eproofing. springer. com/books/printpage. php?token=5n9Uu. 1. Let DB be a database binding compounds signatures to their respective molecular graphs. 2. Let σ(R) be the molecular signature associated with a reaction R, and {S , i [1, n]} a set of candidate substrates potentially reacting together. 3. Under the enzymatic promiscuity hypothesis determined by d, we predict that R can process any candidate substrate {S , i [1, n]} if: e. Proofing | Springer m j=1 d http:/(a) , i. e. , if the signatures of candidate substrates P j ∪ n d S i i=1 include all fragments consumed by R, d (b) and the predicted product(s) signature(s) σ(P ) correspond to some previously known compound(s) in DB, with (see Note 8). ( ) d ∪.
They identified a set of 408 genes specifically upregulated in the mouse gut. We compared the list of genes that are significantly upregulated by P TET -CRZ2 to those identified in Rosenbach et al. (Figure  4d ). Among the 408 genes that are induced in the gut, 45 were also upregulated by P TET -CRZ2 at time points 2 or 4 hr (out of 269 genes, Figure  4d ), yielding a ~2. 6-fold enrichment of mouse gutinduced genes in our dataset (P = 1. 1 × 10 -9 using a hypergeometric test, Figure  4d ). Interestingly, the GO terms "glycolytic process" (ADH1, ENO1, FBA1, and TDH3; P = 1. 11 × 10 -3 ), "cell surface" (ADH1, ENO1, FBA1, HWP1, IHD1, PGA54, SAP9, SUN41, TDH3, and TOS1; P = 5. 25 × 10 -5 ), and "symbiosis, encompassing mutualism through parasitism" (ADH1, ENO1, FBA1, HWP1, SAP9, SUN41, and TDH3; P = 6.
, 2012). One could speculate that CRZ2 contributes to early gut colonisation by transiently increasing C. albicans adherence to components of the GI environment. Many putative mannoprotein-encoding genes are direct targets of Crz2p, including PGA6, PGA54, PGA38, IHD1, PHR2, PLB1, PLB4. 5, ECM331, SAP9, UTR2, SUN41, and INT1 (Figure  6 ) and carry the N-glycosylation signature Asn-X-Ser/Thr in their predicted protein sequence (predicted using NetNGlyc version 1. 0). Some of them encode putative adhesins such as PGA6, PGA38, and FGR41 or were shown to affect C. albicans adhesion to host cells (SAP9, UTR2, and SUN41;  Alberti-Segui et al. , 2004; Albrecht et al. , 2006; Hiller, Heine, Brunner, & Rupp, 2007). INT1 encodes an integrin-like surface protein required for efficient caecal colonisation in mice  (Bendel et al.
, 2013) , and the catabolism of fatty acids/N-acetylglucosamine or phosphate uptake  (Pande et al. , 2013; Pierce et al. , 2013). It is also linked to the ability of preventing bile-salt toxicity, particularly in the upper section of the gut. CRZ2 confers increased resistance to bile salts (Figure  9 ), which is considered a major selective pressure that shapes the structure of the microbial communities residing in the gut  (Ridlon, Harris, Bhowmik, Kang, & Hylemon, 2016). Many bacterial species that colonise the GI tract play a role in bile salt metabolism and are able to make bile salts available as substrates for further modifications by the intestinal microbiota, mainly through the activity of bile salt hydrolases  (Urdaneta & Casadesus, 2017). Our inspection of the fungal genomes suggests that the genome of only one Candida species, Candida kefyr, encodes a putative bile salt hydrolase (GenBank accession # AFC60678.
Labelled DNA was resuspended in 50 μl DigEasy Hyb solution (Roche), incubated at 95°C for 5 min, snapcooled on ice, and directly deposited on a barcode microarray that we previously described (Agilent Technologies, GEO platform # GPL17420;  Cabral et al. , 2014)  containing (a) ~12 on-chip replicates of both sense and antisense DNA sequences complementary to 657 tags (representing 572 strain tags +78 unused tags) and (b) different negative control spots (Agilent reference). Hybridization was performed overnight at 25°C, followed by washing and scanning of the arrays using GenePix 4200 AL scanner (Molecular Devices). This experiment was performed 3 times independently. Microarray data were analysed using ArrayPipe v2. 0  (Hokamp et al. , 2004). Z-score (i. e. , number of standard deviations from the population mean) calculations were performed using ArrayPipe v2.
0, and thresholds for considering significant deviation from the population were set at absolute Z-score values ≥2 and P values <0. 05. Only strains with tags that met thresholds for both sense-and antisense-barcode fluorescence signals were considered as altered in cell fitness. Microarray data have been deposited at GEO under accession number GSE67215, and Z-scores and fold-change data are available in Table  S2.
| Confirmation of the in vitro fitness-profiling data by liquid growth assay or microscopic examination Strains were individually grown 3 times independently in 96-well plates at a starting optical density (OD 600 ) of 0. 1 in 100 μl of YPD supplemented or not with 40 μg ml -1 doxycycline. The OD 600 was measured every 5 min using a TECAN Infinite M200 reader. The temperature was set at 30°C. TECAN OD 600 readings were converted into "flask OD 600 " reading using the following formula: OD Flask = OD Tecan × 12. 2716 -1. 0543  (Ericson, Hoon, St Onge, Giaever, & Nislow, 2010) , and doubling times were calculated within the exponential growth interval as previously described  (St Onge et al. , 2007). Strains displaying altered morphology or cell-cell aggregation phenotypes were microscopically examined with a Leica DM RXA microscope (Leica Microsystems) at a 40× magnification.
Images were captured with a Hamamatsu ORCA II-ER cooled CCD camera, using the Openlab software version 3. 5. 1 (Improvision Inc. ). The protocol was approved by Institut Pasteur Health Center Animal Care Committee (protocol number 10. 455). Nine-to 12-weeks-old female BALB/c (Charles River, France) were given drinking water containing 5% sucrose, 0. 1 mg ml -1 gentamycin, and 2 mg ml -1 streptomycin and was supplemented or not with 2 mg ml -1 doxycycline, during the course of the whole experiment (14 days). Mice were CFUs per 1 g of stool on Day 10. Barcodes were directly amplified from dox-treated (Cy5-labelled), dox-untreated (Cy5-labelled) mouse stool-derived, and inoculum-derived (Cy3-labelled) genomic DNA preps using primers CipSAC2-UP-2 and CipSAC2-DWN-2 and processed for hybridization to barcode microarrays as described above (Section 4.
Reactions were processed in a MicroAmp Optical 96-Well Reaction Plate (Applied Biosystems) using an Eppendorf realplex 4 Mastercycler real-time PCR instrument (Eppendorf) with 1 cycle at 50°C for 2 min, 1 cycle at 95°C for 10 min, and 50 cycles at 95°C for 15 s and 58°C for 1 min, followed by melting-curve generation to rule out amplification of unspecific products. Data were analysed using the realplex software version 2. 2 (Eppendorf). For each experiment, threshold cycle (C T ) values were determined using the realplex software. The relative abundance (n-fold) of each strain from dox-supplemented or dox-free cages as compared with the inoculum was calculated using the 2 -ΔΔCT method, as follows: ΔC T = C T (selected strain) -C T (TEF3 reference for calibration) in each condition and ΔΔC T = ΔC T (doxtreated or doxuntreated sample) -ΔC T (inoculum).
The TEF3 gene was used for calibration, and the ACT1 gene was used as a control for normalisation of C. albicans DNA abundance (primers TEF3-F/R and ACT1-F/R, Table  S12 ). Up to seven independent qPCR experiments were performed on different days using two technical replicates each time. A two-tailed Student's t test was applied by comparing the relative abundance of each strain from doxycycline-treated cages to the untreated controls. Statistical significance was set at P < 0. 05. Data from only one cage are shown in Figure  3b  and are representative of those from the second cage. To independently validate the CRZ2 overexpression phenotype and test the effect of CRZ2 deletion on GI colonisation, 5 × 10 7 cells from a 1:1 mixture of CEC4439 (P TET -CRZ2, GFP):CEC4442 (P TETempty vector, BFP), CEC4267 (wild-type, GFP):CEC4425 (wild-type control, BFP), CEC4263 (crz2Δ/crz2Δ mutant #1, GFP):CEC4425 (wild-type control, BFP), or CEC4265 (crz2Δ/crz2Δ mutant #2, GFP): CEC4425 (wild-type control, BFP) in 200 μl 1X PBS were used to inoculate BALB/c or BALB/cByJ mice as described above.
, 2013). As an additional control experiment, we also compared the transcriptome of strain C90 (Cy5-labelled) to that in crz2Δ/Δ (CEC369, Cy3-labelled) under normoxia 30°C and under hypoxia 37°C (Table  S6 ). Hybridization was performed as described elsewhere  (Znaidi et al. , 2013). Images of the Cy5 and Cy3 fluorescence were generated by scanning the expression arrays using an Axon Autoloader 4200AL scanner (Molecular Devices, Downingtown, PA, USA). Images were analysed with the GenePix Pro 6. 1. 0. 2 software (Molecular Devices). GenePix Results files were imported into the ArrayPipe 2. 0 software for spot filtering, background subtraction (limma normexp BG correction), and Lowess global normalisation of signal intensities  (Hokamp et al. , 2004). Replicate arrays (n = 3) were combined, and fold-change and P values (standard Student's t test within group) were calculated.
| Chromatin immunoprecipitation and ChIP-chip Strains C251, C252 (TAP epitope-tagged), and C255 and C257 (untagged control; Table  S11 ) were grown overnight in 2 ml YPD at 30°C, diluted to an OD 600 of 0. 3 in 50 ml YPD medium supplemented with 40 μg ml -1 doxycycline and grown during 4 hr at 30°C. The subsequent steps of DNA cross-linking, DNA shearing, and chromatin immunoprecipitation (ChIP) were conducted as described in  Liu et al. (2007) , with some modifications. Briefly, cultures were treated with 1% formaldehyde (cross-linking) and snap-frozen in liquid nitrogen. scanner and analysed with the GenePix software version 7. 0 (Molecular Devices). Data normalisation (Quantile) and peak finding were conducted using CisGenome  (Ji et al. , 2008). The Integrated Genomics Viewer software was used for visualisation of the ChIP-chip results  (Thorvaldsdottir, Robinson, & Mesirov, 2012 ; Figure  5a ).
g2. bx. psu. edu/), and DNA sequences encompassing ±250 bp around peak summits in Crz2p datasets were extracted using the Extract Genomic DNA tool version 2. 2. 2. The resulting sequences together with an equivalent set of randomly chosen sequences from the C. albicans genome (used as a negative control) were used as input for motif discovery with the Regulatory Sequence Analysis Tools (http://rsat. ulb. ac. be/rsat/) peak-motifs algorithm  (Thomas-Chollier et al. , 2012). Significance coefficients of overrepresented motifs (sig scores =log 10 [Evalue]) are calculated using a binomial test.
d'E. ), and the Wellcome Trust (The Candida albicans ORFeome project, WT088858MA to C. d'E. and C. M. ). C. M. acknowledges support from the Medical Research Council, UK (New Investigator Award, G0400284), the MRC Centre for Medical Mycology (MR/N006364/1), and the University of Aberdeen. S. Z. is an Institut Pasteur International Network Affiliate Program Fellow. S. Z. , L. v. W. , and A. H. C. were the recipients of post-doctoral fellowships from the European Commission (FINSysB, PITN-GA-2008-214004 to S. Z. ), the Agence Nationale de la Recherche (KANJI, ANR-08-MIE-033-01 to S. Z. ; ERA-Net Infect-ERA, FUNCOMPATH, ANR-14-IFEC-0004 to A. H. C. ; CANDIHUB, ANR-14-CE-0018 to L. v. W), and the French Government's Investissement d'Avenir program (Institut de Recherche Technologique BIOASTER, ANR-10-AIRT-03 to S.
ACKNOWLEDGEMENTS We are grateful to members of the genomics core facility ( PF2 ,  Génopole ) for the availability of the microarray scanner and the  Alain Jacquier's lab  for making the GenePix software available. We are grateful to  Drs Suzanne Noble  and  Aaron Mitchell  for providing Candida albicans mutant collections. We thank all members of the  Fungal Biology & Pathogenicity Unit , particularly  Drs Anne Neville  and  Adeline Feri , for their numerous insights during the course of this project. This work has been supported by grants from the  Agence Nationale de la Recherche  ( KANJI ,  ANR-08-MIE-033-01  to C. d'E. and F. D. ;  ERA-Net Infect-ERA ,  FUNCOMPATH ,  ANR-14-IFEC-0004 ; and  CANDIHUB ,  ANR-14-CE-0018  to C. d'E. ), the  French Government 's  Investissement d'Avenir program  ( Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases ,  ANR-10-LABX-62-IBEID  to C.
Funding information Grant/ Award Number:  KANJI ,  ANR-08-MIE-033-01 ;  ERA ANR-14-CE-0018 ,  ANR-14-IFEC-0004  and  ANR-08-MIE-033-01 ;  European Commission , Grant/Award Number: FinSysB PITN-GA-2008-214004;  French Government's Investissement d'Avenir program , Grant/ Award Numbers:  ANR-10-AIRT-03  and  ANR-10-LABX-62-IBEID ;  University of Aberdeen ;  MRC Centre for Medical Mycology , Grant/ Award Number: MR/N006364/1;  Medical Research Council, UK , Grant/Award Number:  G0400284 ;  Wellcome Trust , Grant/Award Number: WT088858MA --------------------------------------------.
C. d'E. ), and the Wellcome Trust (The Candida albicans ORFeome project, WT088858MA to C. d'E. and C. M. ). C. M. acknowledges support from the Medical Research Council, UK (New Investigator Award, G0400284), the MRC Centre for Medical Mycology (MR/N006364/1), and the University of Aberdeen. S. Z. is an Institut Pasteur International Network Affiliate Program Fellow. S. Z. , L. v. W. , and A. H. C. were the recipients of post-doctoral fellowships from the European Commission (FINSysB, PITN-GA-2008-214004 to S. Z. ), the Agence Nationale de la Recherche (KANJI, ANR-08-MIE-033-01 to S. Z. ; ERA-Net Infect-ERA, FUNCOMPATH, ANR-14-IFEC-0004 to A. H. C. ; CANDIHUB, ANR-14-CE-0018 to L. v. W), and the French Government's Investissement d'Avenir program (Institut de Recherche Technologique BIOASTER, ANR-10-AIRT-03 to S.
The divergence of the beam (and therefore available beam size) at the detector position is thus also determined by the coating of the neutron guide. Most facilities possess two measuring positions at varying distances to the source and hence varying collimator lengths. Kardjilov et al. [19]  studied by simulation the impact of the neutron guide geometry on the image quality. Two focusing neutron guides (linearly tapered or cone-beam and elliptic) and a straight neutron guide with circular pinhole (for comparison) each of total length 3m were simulated using Monte Carlo simulation code MCSTAS. The standard (straight guide with circular pinhole) configuration provides a beam that is just 1/9 the area of the beams produced by the focusing guides. Both experiments and simulations demonstrate superior spectral and geometry homogeneity of the cone-beam setup compared to the conventional pinhole geometry.
qPCR analyses Samples were immediately frozen in liquid nitrogen and ground with a Retsch machine and 3 mm metal beads. Subsequently, RNA was extracted with TriZol (Invitrogen) and further purified with the RNeasy plant mini kit (Qiagen). For cDNA synthesis, the iScript cDNASynthesis Kit (Bio-Rad) was used with 1 µg of RNA as starting material. Quantitative real-time PCR (qRT-PCR) was performed with the LightCycler 480 Real-Time SYBR Green PCR System (Roche). The data were normalized against the average of housekeeping genes AT1G13320 and AT2G28390  (Czechowski et al. , 2005) , as follows: dCt=Ct (gene)-Ct [average (housekeeping genes)] and ddCt=dCt (Control)-dCt (treatment). Ct represents the number of cycles at which the SYBR Green fluorescence reached a threshold during the exponential phase of amplification.