dataesr/scientific-paragraphs-categorization huggingface dataset:
Candidates were identified mostly by searching for homologues of well-characterized plant genes. This was carried out with the help of MassBlast, a software developed within the BacHBerry project (https:// github. com/averissimo/mass-blast; Verı ´ssimo et al. 2017). We annotated all major enzyme families, including cytochromes P450 (CYP), 2-oxoglutaratedependent dioxygenases (2-OGDs), UDP-glycosyltransferases (UGTs), BAHD acyltransferases, and Omethyltransferases (OMTs), in all 13 selected berry germplasms. Average counts per transcriptome are presented in Table  4. We also detected candidate transcript(s) for 60-80% of the known biochemical steps of the phenylpropanoid pathway, representing between 109 and 180 candidates per transcriptome. Approximately 40% of these candidates identified from the transcriptomes were full-length sequences.
Furthermore, biochemical activities of some of enzymes encoded by the candidate genes were tested. We also looked for transcripts encoding regulators of phenolic biosynthesis. Since the R2R3-MYB gene family operates extensively in controlling phenylpropanoid metabolism in plants  (Stracke et al. 2001; Hartmann et al. 2005) , initial searches were focused on identifying MYB-genes that were strongly expressed in berries. However, members of the bHLH and bZIP families of transcription factors were also identified in the berry RNA-seq databases since they may also play roles in regulating accumulation of phenolic bioactives  (Hartmann et al. 2005). These data could be particularly useful in breeding programs for selection of plants with improved bioactive contents. The presence of genes encoding putative transporters of phenolic compounds was also examined.
Bioinformatics and modelling There were multiple activities within BacHBerry, both within the discovery and the microorganism engineering sectors, which required the use of bioinformatics tools. Hence, the mathematical modeling and computational parts of the project aimed at addressing three major problems. The first two were related to gene expression, either for performing functional genomics analysis using the RNA-seq data from berries or to design sequences for heterologous expression of plant genes in microbes. The third problem concerned metabolic network optimization for both a single population and for a community of microbes. We detail below the main results obtained on each of the above activities. Other subjects that were addressed as a part of this project were metabolic communication between the different species in a community  (Milreu et al.
Sequencing data analysis and plant functional genomics Understanding which genes are differentially expressed during phenolic production by plants requires the capacity to undertake a rapid analysis of RNA-seq data. One of the main problems to be addressed in this case is the search for coding sequences that are homologous to those already present in sequence databases, which involves identification, selection and annotation of the best matches. Currently, such analyses rely heavily on manual curation as well as on the manual assembly of results obtained using different computational procedures and sequence databases. Automated processes become crucial in the light of the ever-increasing amounts of generated transcriptome data. Furthermore, automation of processes also allows improvement of the quality of the analyses, as the likelihood of human error grows with the amount of manual processing and verifications necessary.
Moreover, most of the existing tools are able to annotate the transcriptome but lack the curation functionalities that are as important as the primary homology-based identification of candidate genes. As mentioned previously we developed a pipeline called MassBlast  (Verı ´ssimo et al. 2017) , to improve and accelerate the annotation procedure of RNA-seq data. Besides allowing for operators to query multiple databases for performing homology searches, the tool also provides a set of parameters that can be defined in a user-friendly way. MassBlast seeks the best alignment between the sequences in a dataset consisting of contigs obtained from RNA-seq assemblies and a database of known sequences. In the context of BacHBerry and as a general validation of the method, MassBlast was used for the analysis of the blackberry transcriptome, and preliminary results showed a significant improvement in annotation speed (5 min vs.
These results were then further filtered and experimentally validated, resulting in several promising mutant strains where production was increased by up to three-fold (unpublished results). Implementation of this pipeline is possible via a software package called OptPipe, available at https:// github. com/AndrasHartmann/OptPipe. We then explored the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables were involved in the model. In particular, we proposed multi-objective models to suggest reaction deletion strategies and to deal with problems where several functions must be optimized simultaneously, such as maximization of bioproducts while minimizing toxicity. We thus introduced Multi-Objective Mixed integer Optimization for metabolic engineering, a computational framework that aims to model and solve optimization problems, generated for predicting reaction knockout strategies by means of multiobjective programming.
Conclusions During 3 years of the project, the BacHBerry partners successfully established a complete pipeline for screening, bioprospecting, identification, and microbial production of plant phenolic compounds in Grampositive bacteria. A summary of the most important results is presented in Table  5. The major highlights are construction of the extensive germplasm collection database, establishment of a method for high-throughput screening of bioactive extracts using the yeastbased human disease models, generation of a vast berry transcriptome database, construction of microbial cell factories in L. lactis and C. glutamicum for the production of polyphenols with yields comparable to those currently available from the literature, release of several software packages for transcriptome analysis and in silico-based metabolic engineering, as well as optimization of fermentation conditions and downstream processing for the selected organisms aimed at increasing the yield and the purify of the final products.
Acknowledgements The authors would like to thank the  European Union 's  Seventh Framework Programme  ( BacHBerry,  Project No. FP7-613793 , and  FP7-PEOPLE-2013-COFUND , Project No. FP7-609405 ) for the financial support. AD, RF, MHHN, PG, SGS, and JF would also like to acknowledge the  Novo Nordisk Foundation. We express our gratitude to  Dr. Martha Cyert  ( Stanford School of Medicine, EUA ),  Dr. Hitoshi Shimoi  ( National Research Institute of Brewing, Japan ) and  Dr. Yoshio Araki  ( Graduate School of Biosphere Science, Hiroshima University, Japan ) for providing the yeast strain YAA5. We also thank  Dr. Ian Macraedie ,  RMIT University, Australia ) for providing the plasmid p416_GPDpr-GFP-A42.
lactis preparation, phenolics families and seven and pterostilbene) and and C. glutamicum through content assessment and orders were generated anthocyanin production optimization of fractionation Metabolomics data from berry species originating from UK, mainland Europe, Russia, Chile, and China and analyzed New algorithms for functional genomics and improved computational methods for pathway Production of various stilbenes (trans-resveratrol, pinosylvin, and piceatannol), methylated stilbenes (pinostilbene and fermentation parameters (batch vs. fed-batch operation, substrate and dissolved oxygen concentration, etc. ) was obtained and made identification were pterostilbene), flavanones Whey was evaluated as an available in form of an established (naringenin, pinocembrin, alternative carbon source online database Implementation of robust assays based on the SMART platform for discovery of bioactivity and functionality Obtained berry extracts were assayed for bioactivity against several human diseases, and additionally evaluated for other functional uses (eq.
Equal amounts based on trace-labeling of the RNA were analyzed by SDS-PAGE and silver staining. Arrowheads indicate bands enriched in the SRE + RNP that might correspond to Smaug (109 kDa), Trailer Hitch (69 kDa), and Me31B (52 kDa). (D) Specific association of proteins with the SRE + RNA was confirmed by Western analysis in an independent pull-down assay. Smg, Cup, Me31B, and PABPC served as controls for Bel. (E) Proteins in the purified RNP fractions were analyzed by mass spectrometry and label-free quantification. Apparent protein abundance in SRE + versus SRE -RNP was plotted on a log 2 scale. Proteins enriched in the SRE + RNP beyond P = 0. 05 and NOT11 are labeled. The complete list of proteins represented in E is found in Supplemental Table  S2. Different sets of proteins in the same data are highlighted in Supplemental Figure  S1 , and additional enriched proteins are listed in Supplemental Table  S3.
All RNAs were allowed to assemble repressor complexes before affinity purifications were carried out. The quantities of immobilized RNAs were determined from their specific radioactivities, and amounts of associated proteins were estimated by Western blotting and comparison to standard curves of purified recombinant material. Representative data are shown in Figure  5A  and Supplemental Figure  S2 , and a summary of the average stoichiometries is presented in Figure  5B. RNA association of Smg was SREdependent, but independent of RNA length: The stoichiometry was between 1 and 2 for all three RNAs. Within the accuracy of the experiment, this was equimolar with the SREs (see legend to Supplemental Fig. S2 ). Binding of Cup was also approximately stoichiometric with the SREs. Bel bound independently of RNA length, but tended to be less abundant; with the longest RNA, specific binding was no longer distinguishable from background.
The high concentration of Me31B, exceeding that of mRNA, is consistent with data in other organisms (Ernoult-Lange et al. 2012 and references cited therein). The MS data suggest that the CCR4-NOT complex is not part of the core repressor complex. Association of the CCR4-NOT complex with the 630 nt RNA was examined by quantitative Western blotting. In agreement with the MS analysis, CCR4, Caf1, and Not2 bound the RNA in an SRE-dependent manner, but were clearly substoichiometric (Fig. 5E  and data not shown).
1% formic acid). Peptides were separated with a linear gradient: 0%-35% B (100% acetonitrile, 0. 08% formic acid) for 90 min at 40°C and a flow rate of 300 nL/min. Full MS data were acquired in the orbitrap (R = 60,000), MS/MS spectra (HCD, 30% normalized collision energy) were recorded in the linear trap for 5 sec (most intense signals). MS data were analyzed with MaxQuant 1. 5. 2. 8 (Cox and Mann 2008) (RRID: SCR:014485). For protein identification, data were searched against the Uniprot proteome (www. uniprot. org) of D. melanogaster  (20,042 protein entries; accessed January 19, 2015). The inverted sequences of all proteins were used for decoy analysis. Mass accuracy was set to 20 ppm and 0. 5 Da for precursor and fragment ions, respectively. Carbamidomethylation was set as fixed modification, and methionine oxidation and N-terminal acetylation were set as variable modifications.
The distances were fitted against the normal distribution to obtain σ². To estimate P-values for each protein enrichment, the σ²-values were fitted with the equation y = a × b -c• (bin-45). The squared differences to the model were multiplied with the number of proteins per bin to weight the data in the nonlinear least squares fit. Data are available via ProteomeXchange with identifier PXD006596. Analysis of RNA associated with the repressor complex Repressor complex was isolated as described above from 400 μL of reaction mixture without gradient centrifugation, and no yeast RNA was used during the pull-down and washing procedures. RNA was eluted with TRIzol for 10 min at 80°C. Five hundred nanograms of total RNA was used in the small RNA protocol with the TruSeq Small RNA Sample Prepkit v2 (Illumina) according to the manufacturer's instructions.
8 M urea and digested with trypsin overnight at 37°C. LC/MS/MS analysis was performed as described above except that MS/MS spectra (HCD, 30% normalized collision energy) were acquired in the Orbitrap analyzer (R = 60,000) for 5 sec (most intense signals). Data were evaluated as described above. Cross-links were analyzed by means of the MeroX software  (Götze et al. 2015)  with the following parameters: Trypsin was set as the protease, the BuUrBu crosslinker was selected, methionine oxidation was considered as variable modification, cysteine alkylation was set as static modification, the signal-to-noise ratio was set to 1. 5, precursor precision was set to 3 ppm, and fragment ion precision was set to 20 ppm. The analysis was performed with activated RISE-mode, and data were searched against a sequence database containing the set of enriched proteins from the RNA-pull-down experiment as well as sequences of 200 standard protein contaminants (cRAP-database http://www.
Microscopy and image processing Fluorescent images were acquired using a Carl Zeiss LSM 780 LASER scanning confocal microscope (Montpellier RIO Imaging facility) and a 40× PLAN Apochromatic 1. 3 oil immersion objective. The acquisition software was Zen. Contrast and relative intensities were processed with ImageJ software. Light microscope images were acquired using Leica Leitz DMRB Fluorescence Microscope with Nomarsky lens. Colocalization was quantified by FIJI as follows: Background was subtracted with a rolling ball radius of 30 μm, and the Pearson correlation coefficient (PCC) was calculated using the colloc2 plugin with auto-thresholding. Mean PCC was calculated from three images.
ACKNOWLEDGMENTS We thank  F. Kluge  and  D. Taenzler  for excellent assistance with experiments;  P. Enke  for generating baculovirus clones;  K. Krohn ,  IZKF, University of Leipzig , for performing Illumina sequencing;  M. Hentze ,  E. Izaurralde ,  P. Lasko ,  A. Nakamura ,  C. Smibert ,  N. Sonenberg ,  R. Wharton , and  J. Wilhelm  for reagents;  H. Ashe  for fly stocks;  F. Falvo  for the BuUrBu cross-linker; and  M. Jeske  and  C. Eckmann  for comments on earlier versions of the manuscript. This work was supported by grants from the  Deutsche Forschungsgemeinschaft  ( WA 548/13-2  within  FOR 855 ;  WA 548/ 16-1; GRK 1591 ) to E. W. and by  CNRS-University of Montpellier   UMR9002 ,  Association Nationale de la Recherche et de la Technologie  ( ANR-15-CE12-0019-01 ),  Fondation pour la Recherche Médicale  (Equipe FRM 2013  DEQ20130326534 ), and  Fondation ARC pour la Recherche sur le Cancer  ( ARC Libre 2009 , no.
Molecular assessment Fifty-two primary clear-cell primary tumor samples corresponding to 39 LR (75%) and 13 PR patients (25%) were analyzed. Among the different genes analyzed, only HES1, HEY and HEYL, putative effectors of Notch signaling, showed increased expression in the LR group compared to PR group (Figure  1A , 1B, and 1C, respectively; Figure  2 ). A significant positive internal correlation between the expression of these 3 genes was also observed (Figure  1D , and data not shown). After the ROC analyses, only HEYL levels discriminated the studied populations with significant sensitivity and specificity (AUC=0. 704). These analyses also provided a possible cut-off value of 34. 85 (reference: GUSB). Of note, when the patients were classified according to their HEYL levels using this cut-off, an increased disease-specific survival in the high expression group was observed (Figure  1E ).
Study procedures Immunohistochemistry and genetic analyses were carried out in available archived formalin-fixed paraffin embedded (FFPE) clear-cell primary tumor samples. Hematoxylin and eosin stained sections of the tumor samples were examined by a pathologist to confirm the diagnosis and estimated tumor content. These analyses were performed at the Molecular Laboratory of Hospital 12 de Octubre (Madrid, Spain). The following patient characteristics and outcome data were collected using uniform data collection templates: Date of birth, gender, age at CRC diagnosis, date of first diagnosis of tumor disease, baseline TNM, baseline cancer stage, histological subtype, Furhman grade, comorbidities, nephrectomy (yes/no), nephrectomy type (total/partial), percentage of tumor necrosis, Karnofsky/ECOG performance status, baseline lactate dehydrogenase level, baseline corrected calcium level, baseline hemoglobin level, left ventricular ejection fraction, presence of thrombocytosis at baseline, presence of neutrophilia at baseline, baseline Heng risk factors, previous radiotherapy received (yes/no), number of metastatic locations, recurrence (metastatic) location and date, and treatments received (dates, type, doses, associated toxicity and response [PFS, time to progression and OS]).
Quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR) Expression levels of 13 selected miRNAs (miR-942, miR-628-5p, miR-133a, miR-484, miR-141, miR-23a, miR-23b, miR-27b, miR-21, miR-200a, miR-200c, miR-141and miR-155) and mRNAs (MYC, VHL, HIF1A, HIF2, GLI1, GLI2, BMP4, PTCH1, HES1, HEY, HEYL, AXIN2, SUFU and JAG1 were monitored by RT-qPCR in both groups. The miRNA were selected on the basis of their involvement in angiogenesis, modulation of the Notch, Hedgehog and/or Wnt signaling pathways, or by being previously reported as biomarkers of Sunitinb response in renal cancer samples  [23] [24] [25] [26]. For the mRNA normalization six previously reported genes were assayed (ACTB, TBP, GusB, GAPDH, PPIA1 and RPS13) and evaluated using three normalizer evaluation software (GeNorm, NormFinder and BestKeeper).
Data and Monte Carlo samples The data used in this analysis were collected by ATLAS during 2015 and 2016, with a mean number of additional pp interactions per bunch crossing (pile-up) of approximately 14 in 2015 and 21 in 2016, and a centre-of-mass collision energy of 13 TeV. Following requirements based on beam and detector conditions and data quality, the data set corresponds to an integrated luminosity of 14. 7 fb -1. The uncertainty in the combined 2015 and 2016 integrated luminosity is ±2. 9%. It is derived, following a methodology similar to that detailed in Refs. [30]  and  [31] , from a preliminary calibration of the luminosity scale using x-y beam-separation scans performed in August 2015 and May 2016. Data events are collected using a combination of single-lepton and dilepton triggers  [32] , in order to maximise the signal acceptance.
428  [40]  is used to simulate the parton shower, hadronisation, and the underlying event. The CTEQ6L1 PDF set is used with the corresponding Perugia2012  [41]  tune. In the case of both the MG5_aMC@NLO and Powheg samples, the EvtGen v1. 2. 0 program  [42]  is used for properties of the bottom and charm hadron decays. Sherpa 2. 1. 1  [43]  simulated samples use the CT10 PDF set with Sherpa's own internal parton shower  [44]  and hadronisation methods, as well as the Sherpa default underlying-event tune. Diboson processes with four charged leptons, three charged leptons and a neutrino or two charged leptons and two neutrinos are simulated using the Sherpa 2. 1. 1 generator. Matrix elements contain all diagrams with four electroweak vertices. They are calculated for up to one (4 , 2 + 2ν) or zero (3 + 1ν) partons at next-to-leading order in α S (NLO) and up to three partons at LO using the Comix  [45]  and OpenLoops  [46]  matrix element generators and merged with the Sherpa parton shower using the ME+PS@NLO prescription  [47].
The real-lepton efficiency, real , is measured in Z → data events using a tag-and-probe method in CR-real, defined in Table  4. In this region the p T of the leading lepton is required to be > 40 GeV, and only events with exactly two SFOS leptons are selected. The fake-lepton efficiency, fake , is measured in CR-fake, a region enriched with fake leptons by requiring same-sign lepton pairs. The lepton p T requirements are the same as those in CR-real, with the leading lepton being tagged as the "real" lepton and the fake efficiency being evaluated based on the sub-leading lepton in the event. An E miss T requirement of < 125 GeV is used to reduce possible contamination from Beyond Standard Model processes. In this spectrum when the method is applied to data in VRZ. Here the flavour-symmetric background is estimated using the data-driven flavour-symmetry method, and the fake-lepton background is estimated using the data-driven method explained in Section 7.
Interpretation In this section, exclusion limits are shown for the SUSY models detailed in Section 3. The asymptotic CL S prescription  [89, 97] , implemented in the HistFitter program  [96] , is used to determine cross-section upper limits at 95% confidence level (CL) for the on-Z search. For the edge search, pseudo-experiments are used to evaluate the cross-section upper limits. A Gaussian model for nuisance parameters is used for all signal and background uncertainties. Exceptions are the statistical uncertainties of the flavoursymmetry method, γ + jets method and MC-based backgrounds, all of which are treated as Poissonian nuisance parameters. The different experimental uncertainties are treated as correlated between signal and background events. The theoretical uncertainty of the signal cross section is not accounted for in the limit-setting procedure.
In Niger, moisture content ranged from 12. 7 to 13. 3%. Dosso and Maradi markets are often supplied with maize from Benin and the grain moisture contents observed there are similar to those recorded in Malanville. Overall, the mean grain moisture content varied between 12. 7 and 13. 7% for wholesalers and retailers. The moisture levels we observed paralleled those reported by  Scott et al. (2014)  who have shown that hermetic storage of maize conditioned at 12 or 15% moisture does not allow the development of aflatoxin in laboratory at 26 C after one and two months storage. The data we collected here on grain moisture in both countries suggests that maize could be safely stored in hermetic containers such as PICS (Purdue Improve Crop Storage) bags. The impurity levels in wholesalers' grain averaged 2.
Macrophages were incubated in D10 in technical triplicates with opsonised beads at a multiplicity of 5 beads/cell. Phagocytosis was allowed to proceed for the indicated time in D10 at 37˚C. Cells were fixed with 2% PFA for 15 min at RT. Chamber slides were mounted with an aqueous-based mounting medium (Fluoromount, Sigma-Aldrich, UK) for CLSM analysis. Fluorescence images were obtained with a Zeiss LSM 510 confocal microscope using the 543nm laser line with a 63X 1. 3 NA objective set at pinhole diameter equivalent to 1-1. 5 Airey units. Images were analysed using the Zeiss LSM software. Images were taken at 20x magnification. Following removal of the fluorescent background, percentages of beads co-localizing with lysosomes were determined by manually counting beads positive for dexTR staining in at least 50 macrophages per replicate.
Acknowledgments We thank  Otto Holst ,  Research Center Borstel, Germany , for kindly providing TDM from M. tuberculosis. This work was supported by grants to U. S. from the  Wellcome Trust UK  ( WT082825 ) and the  German 'Bundesministerium fu ¨r Bildung und Forschung' (BMBF) program  ' Medical Infection Genomics ' ( 0315834C-D ), and to R. L. DFG SFB  796 ,  TB B6.