dataesr/scientific-paragraphs-categorization huggingface dataset:
11, 12 ere, we propose to engineer novel bispecic receptors able to bridge glycoconjugates or protocell surfaces carrying two different types of glycoconjugates for the creation of more complex molecular or nano-objects in an ordered fashion. Such a glycan receptor, coined Janus lectin aer the homonymous roman god, can be designed starting from glycan binding proteins (GBPs) that could be either carbohydrate binding modules (CBMs) or lectins. CBMs are small peptide domains, usually associated with carbohydrate-active enzymes. 13 Their variety of specicity, described in the CAZY database,  14  and their ability to fold as separated domains make them useful tools in protein fusion technology. 15 Lectins have ne specicity towards complex oligosaccharides and can bind to glycoconjugates in a multivalent manner resulting in strong avidity that compensates the weaker affinity at each site.
Affinity data SPR experiments were performed using a Biacore X100 biosensor instrument (GE Healthcare) at 25 C. Biotinylated Lfucose-polyacrilamide (PAA), 3 0 -SL and 6 0 -SL (Lectinity) were immobilized on CM5 chips (GE Healthcare) that were precoated with streptavidin, following a protocol described in literature. 20 Biotinylated 3 0 -SL and 6 0 -SL were diluted to 1 mg mL À1 in HEPES buffer with 0. 05% Tween 20 (HBS-T) before being injected in one of the ow cells. Low immobilization levels of 94 and 64 response units were obtained for 3 0 -SL and 6 0 -SL respectively. Biotinylated L-fucose-PAA wax mixed with biotinylated D-Gal-PAA (90/10 at 200 mg mL À1 ) before immobilisation to a level of 450 RU. D-Gal-PAA reference surface was always present in ow cell 1, thus allowing for the subtraction of bulk effects and non-specic interactions with streptavidin.
Acknowledgements The authors would like to thank  ICMG FR 2607  for providing QCM-D and mass spectrometry facilities ( Characterization of Interactions Platform of the Nanobio Program ). This work was supported by  Agence Nationale pour la Recherche: SynGlycTis  ( ANR-14-SYNB-0002-02 ;  BMBF 031A464 ),  Labex ARCANE  ( ANR-11-LABX-0003-01 ),  Glyco@Alps  ( ANR-15-IDEX-02 ), the  Excellence Initiative of the German Research Foundation  ( EXC 294 ) and the  German Research Foundation  grant " RTG 2202 -Transport across and into membranes ". O. R. acknowledges the  European Research Council   Consolidator Grant "LEGO" (647938 ). The help of  Oriane Machon  for SPR was much appreciated.
Sequence Retrieval, Probe Design, and Microarray Features For all rumen microorganisms known to degrade cellulose, hemicelluloses, and oligosaccharides, nucleic sequences encoding the CAZyme families GH5, GH9, GH10, GH11, GH43, GH48, CE1, and CE6 were extracted from the CAZy database (RRID:SCR_012909 1  ;  Lombard et al. , 2014)  when available (Supplementary Table  S1 ). Sequences from environmental anaerobic microorganisms not yet known to be part of the rumen microbiota were also included. Probes were designed specifically in the coding sequence of the catalytic domain of each gene. Consensus sequences were determined for sequences with more than 96% of identity that should not be distinguished with the microarray. In this particular case, probes were designed in conserved regions and targeted a set of genes instead of a unique gene.
Sense oligonucleotide probes were designed using the HiSpOD software (RRID:SCR_014403 2  ;  Dugat-Bony et al. , 2011). Parameters used were: probe length = 25-mers, Tm min = 46 • C, Tm max = 64 • C, and Complexity = 10. Specificity of deduced probes was tested against the EnvExBase database implemented in the HiSpOD software, and probes with more than 85% of identity and more than 15 consecutive bases (similarity stretch) with a non-targeted sequence were discarded by following the criteria of  Kane et al. (2000)  and  He et al. (2005). Probes presenting any potential cross-hybridization with coding sequences from gut or anaerobic microorganisms were also discarded. Remaining probes were further evaluated by a BLASTN (RRID:SCR_001598;  Camacho et al. , 2008; BLAST+ 2. 2. 23)  against rDNA sequences of the NCBI non-redundant database (release 176; RRID:SCR_004860) to ensure specificity of probes (e-value <1e-05).
Imaging, Data Preprocessing, and Analysis Immediately after washing steps, microarray slides were scanned using an Agilent G2505B microarray scanner (Agilent Technologies, Santa Clara, CA, United States) with a resolution of 5 µm and the XDR mode (Extended Dynamic Range) set up at 10 and 100% PhotoMultiplier Tubes (PMT) gain. Fluorescence intensities of oligonucleotide spots were extracted from the scanned images using the Feature Extraction software (V11. 0, Agilent Technologies, Santa Clara, CA, United States). Data are available under NCBI GEO (accession number GSE107550). Data were processed with dedicated scripts based on C++ and Delphi languages. For each probe, median intensity value of the three replicates was conserved and used as the probe signal value. The signal-to-noise ratio (SNR), similar to the detection threshold response and corresponding to the probe signal value divided by the local background intensity value, was calculated for each probe.
Isolation of a GH11 Transcript of Piromyces communis from Total Rumen Content Messenger RNA enriched RNA (180 ng) from total rumen content was reverse-transcribed in cDNA as described above and using a mix of primers deduced from probes targeted the gene encoding a GH11 hemicellulase from Piromyces communis (protein accession number AAG18439). Primers were eliminated by column-based purification using the QIAquick PCR Purification Kit (Qiagen, Hilden, Germany). One-tenth of the reverse transcription reaction was used to perform a primary PCR. Then a secondary nested PCR was achieved with 1. 5 µl of the first PCR reaction used as PCR template. PCR was performed using the polymerase GoTaq G2 DNA (Promega, Madison, WI, United States) following supplier recommendations. Amplification steps were: initial denaturation (94 • C, 2 min), 35 cycles comprising denaturation (95 • C, 30 s), hybridization (50 • C, 30 s), elongation (72 • C, 60 s), and final elongation (72 • C, 5 min).
RNA-Seq Data Microarray data from rumen RNA hybridization were compared with RNA-seq data (available on Sequence Read Archive, SRP070140) obtained in a previous work  (Comtet-Marre et al. , 2017). Briefly, translated non-rRNA were submitted to a local version of dbCAN (RRID:SCR_013208)  (Yin et al. , 2012)  to detect and annotate sequences for CAZymes (e-value < 1e-05). dbCAN positive sequences were subjected to sequence similarity searches using BLASTX (BLAST+ 2. 5. 0; RRID:SCR_001653)  (Camacho et al. , 2008)  against the NCBI NR database protein (RRID:SCR_003257) (e-value < 1e-05). BLAST results (25 best hits for each read) were used for taxonomic mining of putative CAZyme transcripts using the MEGAN software version 6 (RRID:SCR_011942)  (Huson et al. , 2011). The taxonomic level was determined using the lowest common ancestor-based algorithm (LCA) implemented in the software.
GoArrays probes presented a better sensitivity than short probes, with a SNR of 5-to 17-fold higher for PCR products (not shown for gDNA), confirming the high sensitivity. The genomes of rumen fungi harbor multicopies of GH48 genes of which nucleic sequences could reach 99% of identity  (Youssef et al. , 2013). This observation was confirmed for the strain Piromyces sp. M4 for which several GH48 genes were found (Forano et al. , Unpublished data). Thereby, detection of sequence variants of genes used for probe design can be particularly useful  (Dugat-Bony et al. , 2012b). The capacity of FibroChip probes to detect these sequences was evaluated by hybridizing a GH48 clone from Piromyces sp. M4 (GenBank accession number MF773967), containing a sequence with 95% identity to the ones from other strains of Piromyces sp.
Application of the FibroChip to Targeted Analysis of the Transcriptome of Fibrobacter succinogenes S85 Transcriptomic and proteomic data have shown that gut fibrolytic microorganisms are able to adapt the expression and a Number of probes that gave a positive signal (positive probes). b SNR: signal of hybridization defined as a signal-to-noise ratio. production of CAZymes according to the substrate available  (Béra-Maillet et al. , 2000; Berg-Miller et al. , 2009; Despres et al. , 2016). To test the ability of the FibroChip to detect the differential expression of genes, the microarray was used to analyze the transcriptome of the rumen bacterium F. succinogenes S85 cultivated in two media differing by their carbon source, i. e. , the simple sugar Clb or the complex substrate WS. Indeed, F.
They belonged to all the targeted CAZyme families, except GH10. The most induced genes (log2 ratio > 2. 5) encoded a GH11 endo-β-1,4-xylanase (protein accession number ACX76028), the GH5 cellodextrinase CedA (ACX75179), a GH5 not yet characterized (ACX74338), and a GH9 endo-β-1,4glucanase (EGB, Cel9G; ACX73672). The genes encoding EGB and CedA were also previously found as the most expressed genes by F. succinogenes S85 grown on cellulose in vitro, and were also among the most F. succinogenes expressed genes in the rumen of sheep  (Béra-Maillet et al. , 2000 , 2009). The transcripts of a GH9 endoglucanase (EGE or CelE; ACX75451) were the only ones with a negative log2 ratio value. The expression of celE was then assessed by RT-qPCR. Expression of this gene was quantified at 1. 4 × 10 5  ± 2.
The contribution of eukaryotes was also analyzed further (Figure  3 ). Fungi produced a diversified array of GH transcripts, covering all but one targeted GH families (Figure  3A ), while protozoa produced mainly GH5 and to a lesser extent GH10 and GH11 transcripts (Figure  3C ). The genus Epidinium appeared   as a major contributor to the production of transcripts from the targeted CAZyme families. However, it should be noted that sequences from rumen fungi and protozoa are still very scarce in databases, and the FibroChip was designed according to the sequences known at the time of the design. The snapshot given by the FibroChip may be biased, particularly for the rumen eukaryotes. In order to further assess the specificity of the GoArrays probes and confirm the FibroChip data for the rumen sample, transcripts of a P.
Comparison of Microarray and RNA-Seq Data The suitability of the FibroChip microarray was then evaluated against RNA-seq analysis performed on the same rumen sample  (Comtet-Marre et al. , 2017). The distribution of transcripts into the eight CAZyme families as assessed by the two highthroughput approaches was compared (Figure  4 ). The two  methods gave quite similar results for transcripts assigned to families CE1, CE6, GH9, and GH10. However, a discrepancy was observed for the other four families GH11, GH43, GH48, and GH5, which were either under-estimated (GH11, GH48) or overestimated (GH43, GH5) by the DNA microarray. In the RNA-seq dataset, there was a high proportion (15%) of GH11 sequences affiliated to "uncultured bovine ciliate from environmental samples, " which were probably not trapped by the FibroChip, possibly explaining the divergences between the two results.
Concerning GH48, there was a clear under-estimation of bacterial GH48 transcripts by the microarray. Indeed, bacteria accounted for 89. 4 and 36. 4% of total GH48 transcripts in the RNA-seq and FibroChip data, respectively. These results indicated that the FibroChip must be completed by integrating new sets of probes targeting new sequences from GH11 and GH48 families which are now available in the databases. Regarding GH5 the differences between FibroChip and RNA-seq data were mainly due to sequences affiliated to fungal GH5. Indeed, the fungal GH5 transcripts represented 12. 7 and 0. 5% of the total GH5 sequences as measured by the FibroChip and RNA-seq analysis, respectively. For GH43, the differences between the two approaches were distributed on several taxons. Finally, the over-estimation of transcripts affiliated to families GH5 and GH43 by the FibroChip might result from the under-estimation of other families with the microarray (GH11 and GH48), which would thus lead to different proportions of CAZyme families.
In addition, the methodologies used for RNA-seq annotation and taxonomic affiliation of CAZymes transcripts (described in  Comtet-Marre et al. , 2017)  may also introduce some bias in the results and thereby in the comparison. The FibroChip data highlighted the important, probably still under-estimated, role of eukaryotes in rumen fiber degradation which was in line with the RNA-seq metatranscriptomic analysis. The RNA-seq data indicated that rumen fungi sequences accounted for 6% of the sequences from families targeted by the FibroChip which is close to what was found with the microarray (8%). Qi et al. (2011)  showed a higher discovery rate of fungal CAZymes in the rumen of the muskox by metatranscriptomics compared to previous metagenomic studies; however, in other studies, a substantial number of fungal transcripts was not always identified  (Edwards et al.
CONCLUSION The new FGA, FibroChip, represents a high-throughput tool that would enable monitoring the gene expression of key rumen prokaryotic and eukaryotic microorganisms involved in cellulolysis and hemicellulolysis. It would be applicable to study temporal variations in CAZyme gene expression profiles in the rumen according to diet composition, environmental changes, supplementation of feed additives, etc. This monitoring could be applied in nutritional experiments involving a large number of animals, and would allow to better understand the mechanisms of control or modulation of the fiber-degrading activity of rumen microbiota. With the constant increase in available information in the genomic, metagenomic, and specific databases such as CAZy, it will be possible to update and enrich the current version of the FibroChip with new target genes, in particular for rumen eukaryotes.
ACKNOWLEDGMENTS The authors thank  Faouzi Jaziri ,  Pascale Lepercq ,  Aurélie Ameilbonne , and  Réjane Beugnot  for their technical contribution to this work and  Christel Béra-Maillet  and  Caroline Mirande  for the gift of plasmids with cloned genes. They acknowledge the contribution of late  Kevin J. Shingfield  (deceased September 11, 2016) in planning and performing the animal experimentation.
All microscopy images were blinded before collection and were not unblinded until analysis was complete. After setting image collection gain levels from control slide before blinding, all images were collected with only EGFP visible to the experimenter. Images collected before blinding were discarded and not included in analysis. In rescue and overexpression experiments (see  Figs. 2, 3, 9) , neurons that did not match predefined expression levels (e. g. , rescue construct did not express or expressed too much, overexpression levels were too low or too high) were collected due to blinding but were then excluded from the data pool. Unless otherwise noted, all cells were transfected with EGFP to fill and mark cells that had been successfully transfected. For images with multiple channels, EGFP signal was used as a mask to exclude staining from nontransfected cells.
Neurons were then treated with 600 M active or scrambled BAI1 Stachel peptide in ACSF for 1 h as described above. The same neurons were then imaged using the epifluorescent microscope (Carl Zeiss) to collect final values after treatment. Cross-sectional surface area of spines was also analyzed for each time point for each neuron. Data analysis. All measurements of spines in 3D confocal z-stack images were analyzed using Imaris (Bitplane Scientific). EGFP-expressing neurons were used for all analyses. Spines were modeled by the AutoPath tracing function in Imaris for all parameters. Spine and synapse data were collected in a blinded fashion from only secondary or tertiary segments of dendrites (Ͼ100 m per neuron). The colocalization plugin with the Pearson colocalization coefficient was used to analyze all overlap between fluorescent signals.
Background: Thanks to a reasonable cost and simple sample preparation procedure, linear MALDI-ToF spectrometry is a growing technology for clinical microbiology. With appropriate spectrum databases, this technology can be used for early identification of pathogens in body fluids. However, due to the low resolution of linear MALDI-ToF instruments, robust and accurate peak picking remains a challenging task. In this context we propose a new peak extraction algorithm from raw spectrum. With this method the spectrum baseline and spectrum peaks are processed jointly. The approach relies on an additive model constituted by a smooth baseline part plus a sparse peak list convolved with a known peak shape. The model is then fitted under a Gaussian noise model. The proposed method is well suited to process low resolution spectra with important baseline and unresolved peaks.
In essence, identifications are performed in minutes by simply acquiring an experimental spectrum of the whole microorganism cells and comparing the resulting peak list with a database  [2, 3]. In this context we propose a new method for peak extraction especially adapted to linear MALDI-ToF spectra. The usual approach for MALDI mass spectra processing generally consists of chaining several procedures. Most of the times we have a smoothing step, a baseline correction step and only then the final the peak extraction  [4]. The main idea of our new method is to jointly perform these steps with the aim of reducing the potential unrecoverable artifacts introduced by a sequential processing. In the next sections we briefly present the three main steps of the usual approaches. We then describe our method and its detailed derivation.
Algorithm implementation We provide a reference implementation 1 that can be used to reproduce the results of the following sections. This implementation is coded in C++ and runs under Linux. The main page of the project details all the steps to reproduce the results of the "Joint baseline computation-peak deconvolution", "Comparison with the usual sequential approach" and "Real data" sections. Typical run-times are one second for the synthetic data and three seconds for the examples using real spectra data. To keep this implementation as simple as possible a basic projected gradient descent is used instead of the more effective Algorithm 2. Also note that this implementation requires CSV input files. A more versatile version of our algorithm, using Algorithm 2, is used in  [39]. However, this implementation is not yet publicly available.
Real data We present some results obtained by our algorithm on real MALDI-ToF mass spectra. The Fig. 11  shows the peaks obtained when the low resolution spectrum is deconvolved with a tight peak shape. We have used a Gaussian peak shape with σ peak = 0. 4 m/z. The goal is to evaluate the behavior of the algorithm when we try to deconvolve isotopic motifs. The notable result here is to see that the returned peak centers are approximately spaced by 1 m/z which is the expected value. This result is encouraging Fig. 9  Details of the Savitzky-Golay smoothing + SNIP baseline, σ noise = 1, m SNIP = 28. The baseline removal can introduce serious artifacts. In a sequential approach these artifacts directly affect the peak picking algorithm Fig. 10  Computed baselines comparison. The blue curve is the baseline obtained by our method whereas the black one is the baseline computed by the SNIP algorithm.
Acknowledgment We thank  Bruno Lacroix  (bioMérieux) and  Pierre Mahe  (bioMérieux) for their contribution during the  ANR  Grant submission and execution. We thank  Laurent Gerfault  ( CEA ) for his advises for the development of the  BHI-PRO MALDI  model, data processing and data analysis. We thank  Amna Klich ,  Delphine Maucort-Boulch ,  Pascal Roy (Biostatistique, Hospices Civils de Lyon ) and  Patrick Ducoroy (CLIPP)  for their advises and comments during the development and tests of the method.
Live cell imaging Embryos were imaged using a spinning disc confocal unit (CSU-10; Yokogawa Electric Corporation) with Borealis (Spectral Applied Research) on an inverted microscope (Ti; Nikon) and a charge-coupled device camera (Orca-R2; Hamamatsu Photonics). Z-sectioning was done with a Piezo-driven motorized stage (Applied Scientific Instrumentation), and focus was maintained using Perfect Focus (Nikon) before each Z-series acquisition. An acousto-optic tunable filter was used to select the excitation light of two 100 mW lasers for excitation at 491 and 561 nm for GFP and mCherry, respectively (Spectral Applied Research), and a filter wheel was used for emission wavelength selection (Sutter Instruments). The system was controlled by MetaMorph software (Molecular Devices). For most experiments, a 20 Â 0.
Image analysis MetaMorph (Molecular Devices) and ImageJ (National Institutes of Health  [Schneider et al. , 2012] ) software were used for all data analyses. Cytokinetic phenotypes were scored using maximum projections of the Z-sections collected over the course of cell division. Cells were only scored if imaging began before anaphase onset and continued until the next cell cycle, when the daughter cell nuclei underwent anaphase and/or division failure. For the phenotypic analysis in Figures  1  and 2 , cells were binned into five cytokinetic phenotypes; 'completes'=cell under observation divided successfully and the contractile ring remained closed when the daughter cell nuclei entered the next division cycle; 'fails, full constriction'=contractile ring constriction continues until no observable gap is seen with the PH::GFP membrane marker, but then regresses and cytokinesis fails; 'fails, partial constriction'=the cytokinetic furrow tip ingresses such that a double membrane forms  (Lewellyn et al.
Acknowledgements We thank all members of the  Canman, Dumont , and  Shirasu-Hiza  labs for their support;  Isaiah Thomas ,  Carrie Walsh ,  Amanda Smith ,  Nancy Quinn ,  Kania Rimu ,  Eva Sophia Blake , and  Fallon Jung  for lab assistance. We thank  Diana Klompstra  and  Jeremy Nance  ( NYU ) for invaluable guidance on C. elegans embryo micro-dissection;  Iva Greenwald  and  Justin Benavidez  (Columbia),  Bob Goldstein  (UNC),  Daniel Dickinson  ( UT Austin );  Morris Maduro  ( UC Riverside ),  Bruce Bowerman  ( U of Oregon ), and  Joseph  and  Jean Sanger  ( SUNY Upstate Med U ) for advice and helpful discussions;  Ronen Zaidel-Bar  ( Tel-Aviv U Med School ) for worm strains;  Shawn Jordan  for assistance with ImageJ;  Caroline Connors  for assistance with cell fate analysis; and  Sriram Sundaramoorthy ,  Yelena Zhuravlev , and (especially)  Sophia Hirsch  for critical comments on this manuscript.
This work was funded by: a  Charles H Revson Senior Fellowship in Biomedical Science (TD) , ( ANR-16-CE13-0020-01 ) (JD); FRM DEQ20160334869 (JD);  NIH R01GM105775  (MSH);  NIH R01AG045842  (MSH);  NIH R01GM117407  (JCC); and  NIH DP2OD008773 (JCC).
Recent improvements to the pipeline include the adoption of more sensitive bioinformatic tools for sourcing search models, enhanced model-preparation techniques including better ensembling of homologues, and the use of phase improvement and model building on the resulting solution. The pipeline has also been deployed as an online service through CCP4 online, which allows its users to exploit large bioinformatic databases and coarse-grained parallelism to speed up the determination of a possible solution. Finally, the molecular-graphics application CCP4mg has been combined with MrBUMP to provide an interactive visual aid to the user during the process of selecting and manipulating search models for use in MR. Here, these developments in MrBUMP are described with a case study to explore how some of the enhancements to the pipeline and to CCP4mg can help to solve a difficult case.