dataesr/scientific-paragraphs-categorization huggingface dataset:
The study conducted by Xiao et al showed that a metagenomic taxonomyguided database search strategy allows the construction of databases able to provide high sensitivity and precision in peptide identification in metaproteomic studies  [74]. This strategy merges both taxonomy-guided reference protein sequences from public databases and metagenome assembly. Zhang et al. have also developed a universal workflow (MetaPro-IQ) to expand the sensitivity of peptide identification and greatly increase proteins identified for each sample  [30]. A similar pipeline was used in the metaproteomics of saliva  [75]. In brief, the implementation of Unipept algorithm became essential in the field of metaproteomics to prepare custom databases and simplify data processing pipelines.
De novo sequencing search Otherwise, de novo peptide sequencing has become an alternative and complementary option for the assignment of peptide sequences to MS/MS spectra  [76]. Peptide de novo sequencing in the analytical process derives a peptide's amino acid sequence from its tandem mass spectrum without the assistance of sequence database. A clear advantage of de novo sequencing is that it works for both database and novel peptides. For example, a study combining protein databases search and peptide de novo sequencing, showed, respectively, the identification of 421 theoretical sequences and 333 new non-redundant proteins from faecal samples. The new peptides could not be mapped to the metagenomic sequence data  [44]. As such, search engines for taxonomic and functional analysis are challenged by the vast amount of unannotated sequences  [77].
De novo sequencing is often used to provide new unidentified sequences into databanks. This is possible thanks to a wide range of software tools. The most commonly used de novo peptide sequencing software programs are: PEAKS  [78] , PepNovo  [79] , Novor  [80] , NovoHMM  [81] , UniNovo  [82]  and MSNovo  [83]. De novo peptide sequences are searched against databases using the Basic Local Alignment Search Tool Protein (BLAST p) algorithm  [84]. However, during the process of de novo sequencing, some factors can cause difficulties including: incorrect assignment of ions, absence of ion fragments, existence of noise peaks in the spectrum, and post-translational modifications can contribute to the mass ambiguity and complicate the peptide fragmentation pattern. Moreover, the short length of tryptic peptides can impede MS-BLAST identification.
In summary, the use of customized databases with an iterative workflow should be encouraged in order to gain computational efficiency and focus on the protein content. This approach helps to reduce the rate of false identification associated with large databases and provides appropriate information. However, the processing of metagenomic data must be carried out carefully to ensure the best quality of the resulting metaproteomic databases. Besides, since databases generally do not cover all metaproteomes, de novo sequencing is highly useful for the detection of unknown peptide sequences directly from MS / MS spectra.
Exploration of the gut metaproteome A pioneering study of the human gut microbiota was conducted on two infants to investigate the functional role of gut microbiota during early growth  [36]. However, despite the relatively simple faecal protein profile, the analysis was limited in depth due to the absence of an appropriate reference database. A few years later, with the development of analytical techniques and the availability of protein data from metagenomes, a study on the fecal microbiota of a preterm infant was performed. It revealed a much more detailed profile of the intestinal metaproteome and host microbiota interactions  [85]. It will be interesting and valuable to collect more proteomic data that will allow a comparative study of microbial community functions between healthy preterm infants and those who develop diseases, such as neonatal necrotizing enterocolitis.
This study allowed the authors to describe the taxonomic composition and functional process of patients during the Enterobacteriaceae gut colonization. The analysis showed that public metagenome databases are incomplete and that sample-specific metagenomes improve results. This supports the idea that large database sizes come with several issues. Despite the symbiosis between the host and the gut microbiota, major changes can affect the functionality of the microbiome. From this dysbiosis, an inappropriate immune response of the host may result. It is now suggested that pathologies related to disorders of immunity or metabolism can be triggered or aggravated by the bacteria that we host. For instance, alteration of the gut microbiota has been implicated in metabolic diseases, such as obesity and diabetes  [103].
The latest of the omics approaches is culturomics, developed at our institute  [15]. This in-depth study tested 212 culture conditions to select 18 best conditions for the isolation of prokaryotes. This appraoch was effectively combined to rapid identification of bacterial colonies by MALDI TOF MS. Indeed, metaproteomics provides distinct and complementary microbial functional information to metagenomics and other approaches  [112]. Each "omics" dataset can be studied separately, but relevant information can certainly be extracted from a joint analysis of several of them. The power of these integrative tools on the descriptive level allows today a deep and large-scale characterization of biological systems. However, these technologies face some explanatory limitations. Among the most frequently mentioned difficulties, these must be emphasized: data management requires powerful bioinformatics tools, formulation of objective hypotheses is required to comprehend biological systems; large sets of data obtained under different experimental conditions are difficult to compare; relationship between a molecular signature and the biological interpretation of biomarkers is not always obvious.
Nevertheless, it is not widely used as a routine method because of technological drawbacks, such as the high number of clinical samples. These numerous samples require efficient tools, such as software that can handle very large amounts of data with a computational time that does not exceed 24 hours. In addition to this analytical aspect, identifying the proteins of the complex microbial consortium, comprising hundreds or thousands of species, has also proved a difficult task. The absence of complete genomic sequences, particularly of poorly characterized and uncultivated species, is a major challenge for researchers. The availability of a relevant database is one of the essential elements of metaproteomics for the complete analysis of the gut microbiota samples. Some researchers use protein databases from completed genomes, while others use sequences from different metagenomes, complete or not, and from diverse origins  [115].
Indeed, metaproteomics could help identify markers for clinical diagnosis and provide an overview of antigens, functions and taxa. However, not all the conditions are set at this time to allow routine metaproteomic analyses. Such a quantity of samples would require qualified personnel, standardization of sample preparation with a short processing time at low cost. One of the main challenges would also be the implementation of powerful and automated software. Moreover, software and the databases should conform to highquality standards and specific privacy regulations for the medical applications.
To date, major improvements and developments have made it possible to rigorously validate metaproteomic analyses, thanks to (i) optimised extraction, lysis and cell purification procedures, (ii) improved separation methods by liquid chromatography, and (iii) broader analysis of metaproteomes by rapid, accurate and sensitive mass spectrometry. Moreover, the availability of tailored sequence databases from high-quality metagenomics and development of bioinformatics tools, as well as efficient workflow pipelines, have improved the number of proteins identified. Furthermore, the contribution of metaproteomic data to other meta-omics datasets provides an exhaustive and complementary view of the functional state of the intestinal microbiome. Metaproteomics studies of the human gut (classified in a chronological order).
Bioinformatics Analysis Image analysis and base calling were performed using CASAVA v1. 8. 2 (Illumina) and the 109,658,782 reads were mapped to the reference human genome (GRCh37/hg19) using BWA v0. 7. 5a  (Li and Durbin, 2009)  leading to 95. 89% of the bases covered at least by 20x. GATK UG v3. 2-2 was used to call SNV and indel variations  (DePristo et al. , 2011). Annotation and ranking of SNV and indel were performed by VaRank  (Geoffroy et al. , 2015)  in combination with the Alamut Batch software (Interactive Biosoftware, Rouen, France). Very stringent filtering criteria were applied to filter out non-pathogenic variants (Supplementary Table  S1 ): (i) variants represented with an allele frequency of more than 1% in public variation databases including the 1000Genomes  (Genomes Project et al.
, 2015) , the gnomAD database  (Lek et al. , 2016) , the DGV database  (MacDonald et al. , 2014)  or our internal exome database, (ii) variants in 5 and 3 UTR, downstream, upstream or intronic locations and synonymous without pathogenic prediction of local splice effect, (iii) variants not in the ciliary genes  (Nevers et al. , 2017). Structural variants were predicted using CANOES  (Backenroth et al. , 2014)  and annotated by AnnotSV  (Geoffroy et al. , 2018). Our analysis was focused on compound heterozygous and homozygous variants consistent with a recessive mode of transmission. The IFT27 nomenclature is based on the RefSeq accession number  NM_006860. 4 (O'Leary et al. , 2016).
Finally, WES was performed on the index case and led to the identification of 2 already reported variations in the IFT27/BBS19 gene co-segregating in the family (Figure  1A ). The first variation, c. 104A > G (p. Tyr35Cys), is affecting a highly    (Bhogaraju et al. , 2011). The mutation Tyr35 is highlighted using a black " * " and the Switch I region is shown using a black line below the sequences. Residues are presented and colored according to the Jalview program  (Waterhouse et al. , 2009). Hs, Homo sapiens; Mm, Mus musculus; Xl, Xenopus laevis; Dr, Danio rerio; Cr, Chlamydomonas reinhardtii. conserved position in the protein localized in the switch I region of this Rab-like GTPase (Figures  2A, B ). The variation is absent from the variation databases (i. e. , 1000 genomes and gnomAD) and predicted as affecting the protein function (Supplementary Table  S3 ).
ACKNOWLEDGMENTS We would like to thank the patient's family for their participation. We would like also to acknowledge the members of the diagnostic laboratories (molecular and cytogenetic units, in particular  Manuela Antin ,  Anne-Sophie Leuvrey  and  Elsa Nourisson ) at the  University Hospital in Strasbourg  for technical assistance. The computing resources for this work were provided by the  BICS and BISTRO bioinformatics platforms in Strasbourg.
FUNDING CD was supported by a  FRM grant  ( ECO20170637509 ). Whole exome sequencing was performed by the  IGBMC Microarray and Sequencing platform , a member of the ' France Génomique' consortium  ( ANR-10-INBS-0009 ) and funded by " La Fondation Maladie Rare " through the programme  «High  throughput sequencing and rare diseases».
Simulated background samples. The main background to isolated-photon events arises from jets misidentified as photons. This background is subtracted using a data-driven technique, which is described in Section 5; thus, no MC sample is used to simulate this background. The background from electrons or positrons misidentified as photons is evaluated using MC samples generated with the program Sherpa 2. 2. 1  [21, [38] [39] [40] [41] [42]. The pp → Z/γ * → e + e -+ X and pp → W → eν + X processes were generated with matrix elements calculated for up to two additional partons at NLO and up to four partons at LO. The NNLO NNPDF3. 0 PDF set  [43]  was used in conjunction with a dedicated set of parton-shower-generator parameter values (tune) developed by the Sherpa authors. Simulation of pile-up.
Theoretical predictions The NLO pQCD predictions are computed using two programs, namely Jetphox 1. 3. 1_2 and Sherpa 2. 2. 2. The Jetphox program provides NLO QCD calculations of the direct and fragmentation contributions to the prompt-photon cross section. The number of massless quark flavours is set to five. The renormalisation (µ R ), factorisation (µ F ) and fragmentation (µ f ) scales are chosen to be µ R = µ F = µ f = E γ T. Variations of these scales are considered to get an estimate of the uncertainties due to missing higher-order terms in the perturbative expansion (see Section 8. 1). The nominal prediction is obtained using the MMHT2014  [57]  PDF set and the BFG set II of parton-to-photon fragmentation functions  [58] , both of which are determined at NLO. The strong coupling constant is set to α s (m Z ) = 0.
120 since it is the value assumed in the MMHT2014 PDF fit. For the electromagnetic coupling (α em ), the low-energy limit of 1/137. 036 is used. A parton-level isolation criterion is used which requires the total transverse energy from the partons inside a cone of size ∆R = 0. 4 around the photon direction to be below the same E iso T,cut applied at particle and detector level; this isolation prescription is referred to as 'fixed-cone isolation'. Predictions based on other PDF sets, namely CT14  [59] , ABMP16  [60] , HERAPDF2. 0 [61]  and NNPDF3. 0 [43] , are also compared with the data; in each case α s (m Z ) is set to the value assumed in the PDF fit. The Sherpa 2. 2. 2 program consistently combines parton-level calculations of γ + (1, 2)-jet events at NLO in pQCD and γ + (3, 4)-jet events at LO  [39, 40]  supplemented with a parton shower  [41]  while avoiding double-counting effects  [42].
Although the predictions are overall consistent with the measurement within the uncertainties, a detailed comparison is presented in terms of the ratios of theory predictions to data. The ratios of the predictions of Jetphox with µ R = µ F = µ f = E γ T using different PDF sets to the measured cross section as a function of E γ T are shown in Figures  8  and 9 ; the full theoretical uncertainty, including the variations of the scales, is shown for the prediction based on the MMHT2014 PDF set. The predictions based on the MMHT2014, CT14 and NNPDF3. 0 PDF sets are similar and closest to the data for |η γ | < 1. 37 for most of the range in E γ T. For 1. 56 < |η γ | < 2. 37, the prediction based on the HERAPDF2. 0 PDF set is closest to the data. The prediction based on the ABMP16 PDF set is further away from the data than that based on the MMHT2014 PDF set for E γ T 1 TeV in the region |η γ | < 1.
The symbols for the data are centred at unity and the inner (outer) error bars represent the relative data statistical uncertainties (statistical and systematic uncertainties added in quadrature). The hatched bands represent the full theoretical uncertainty associated to the prediction based on the MMHT2014 PDF set, including the variations of the scales. For comparison, the predictions using the CT14 (dashed lines) and NNPDF3. 0 (dotted lines) PDF sets are also included. µ R = µ F = µ f = E γ T using the MMHT2014 PDF set to the measured differential cross section for isolated-photon production (solid lines) as a function of E γ T in different regions of |η γ |. The symbols for the data are centred at unity and the inner (outer) error bars represent the relative data statistical uncertainties (statistical and systematic uncertainties added in quadrature).
The hatched bands represent the full theoretical uncertainty associated to the prediction based on the MMHT2014 PDF set, including the variations of the scales. For comparison, the predictions using the HERAPDF2. 0 (dashed lines) and ABMP16 (dotted lines) PDF sets are also included. T , the ratio of the ME+PS@NLO QCD prediction of Sherpa 2. 2. 2 and the ratio of the NNLO QCD prediction of Nnlojet to the measured differential cross section for isolated-photon production as a function of E γ T in different regions of |η γ |. The symbols for the data are centred at unity and the inner (outer) error bars represent the relative data statistical uncertainties (statistical and systematic uncertainties added in quadrature). The bands represent the theoretical uncertainty; in the case of Nnlojet, the uncertainties due to the PDFs and α s are estimated at NLO with Jetphox.
Summary and conclusions The cross section for inclusive isolated-photon production in pp collisions at √ s = 13 TeV is measured using data with an integrated luminosity of 36. 1 fb -1 collected by the ATLAS detector at the LHC. The differential cross section as a function of E Next-to-leading-order QCD predictions using several PDF sets are compared with the measurement and found to provide an adequate description of the data within the experimental and theoretical uncertainties. The comparison of data and theory is limited by the theoretical uncertainties due to terms beyond NLO in QCD. Experimental systematic uncertainties are smaller than the theoretical uncertainties over the full investigated phase space. The inclusion of higher-order matrix elements and parton showers, as provided by the Sherpa 2.
The forward primer (αFP90) and the probe (αMGB) were the same as those designed by  Lebron et al. (Lebron et al. , 2006). The reverse primer (αRP90) was designed in-house to get an amplified product of 90 bp (accession number GenBank: M26844. 1). The IC forward (FPMGB) and reverse primers (RPMGB) and the probe (MGBCI) are also presented in Table  S1. The qPCR assays were undertaken in 50 μL volumes in 96-well plates (Multiwell Plate 96). For the quantitation of Vero cell DNA, the qPCR mix contained: 10 μL of extracted sample; 25 μL of 2X QuantiTect Probe PCR Master Mix (Qiagen, Hilden, Germany); 0. 3 μL forward (30 μM) and 0. 3 μL reverse (30 μM) primer, 0. 4 μL αMGB probe (25 μM) and 14 μL nuclease-free water. For the IC the qPCR mix contained: 10 μL of extracted sample; 25 μL of 2X QuantiTect Probe PCR Master Mix; 0.
5 μL forward (30 μM) and 0. 5 μL reverse (30 μM) primer, 1 μL MGBCI probe (5 μM) and 13 μL nuclease-free water. Nuclease-free water was used as the negative control. The standard curve for the Vero DNA standard range consisted of duplicates of 10-fold serial dilutions of standard Vero DNA, from 1000 pg/reaction down to 0. 01 pg/reaction, prepared in nuclease-free water and 10 μL of each dilution tested as already described. The standard curve for IC was prepared similarly to obtain dilutions from 10 6 copies/reaction down to 10 2 copies/reaction, and tested as already described. qPCR was performed in the LightCycler ® 480 Real Time PCR system (Roche Molecular Diagnostics, California, USA). The thermocycler conditions were 95 °C for, followed by 40 cycles of 95 °C for and 60 °C for. The quantitation of residual Vero DNA by qPCR was based on the absolute quantification module of the LightCycler 480 software.
rcDNA size distribution assay The assessment of the size distribution of residual Vero DNA from extracted test samples was based upon the amplification by qPCR of five overlapping regions of increasing sizes targeting the β-actin gene. The target gene sequence (nearly 1138 bp) is of sufficient length to allow for a set of amplicons that span a range from 59 bp to 620 bp (Figure  S2 ). Primer pairs for the various sized and overlapping amplicons were designed using Primer Express 1. 0 (Applied Biosystems, Foster City, California, USA) and assessed for amplification of Vero sequences (accession number GenBank: AB004047. 1). Fig. 1  shows the β-actin gene sequence and the binding sites of the forward and reverse primers for each Vero DNA sizing fragment and the probe used. The sequences of the forward and reverse primers of the β-actin gene used for the five Vero DNA size fragments assessed and MGBOV probe are presented in Table  1.
By choosing the moment of addition of the ADH, it is possible to convert one or the other product in the form of alcohol. Indeed, by delaying the addition of ADH to the action of HPL, the (3Z)-aldehyde undergoes isomerization to the (2E) isomer before the start of the ADH reaction, resulting in a predominant production of (2E)-alcohol by reduction of the corresponding aldehyde. On the contrary, the ADH activity can be mixed simultaneously with that of HPL to primarily convert (3Z)-aldehyde to (3Z)-alcohol, avoiding isomerization of the double bond. Akacha et al. [215]  simulated (using the MATLAB program) the conversion of 13-HPOT to hexenal and hexenol in a two-enzyme system involving successive action of HPL from mint and yeast ADH. The aim of the model was to predict the steady-state performance of the kinetics in terms of conversion.
Bounded exhaustive testing (BET) is an elementary technique in automated unit testing. It consists in testing a function with all input data up to a given size bound. We implement BET to check logical and program properties, before attempting to prove them formally with the deductive verification tool Why3. We also present a library of enumeration programs for BET, certified by formal proofs of their properties with Why3. In order to make BET more efficient, we study and compare several strategies to optimize these programs.
g. , to Isabelle in Quickcheck  [4]  and more recently to Coq, in an extension of QuickChick  [14]  named CUT (Coq Unit Testing)  [7]. BET is also relevant to check properties produced by deductive verification, aka. verification conditions that a given program satisfies a given specification. We present a prototypical implementation of BET in the deductive verification tool Why3  [3]. Programs for Why3 are written in WhyML, a verification-oriented dialect of ML with some functional features, such as polymorphic algebraic types, but also imperative features, such as loops or records with mutable fields. The functional behavior of WhyML programs can be specified with formal annotations: preconditions, postconditions, invariants and loop variants, assertions, etc. , in a first-order logic with polymorphic types.
Why3 standard library defines theories or data structures for common types such as integers, lists or arrays. Why3 reduces programs and specifications to logical verification conditions whose satisfiability entails that the programs meet their specifications. Then, automated provers (e. g. , SMT solvers) or proof assistants (e. g. , Coq) can be used to prove these logical statements. Why3 also provides extraction to get correct-by-construction OCaml programs. Some BET tools implement techniques such as constraint solving or local choice with backtracking, either to enumerate data or to derive enumeration programs from data definitions (see  [6, Section 7]  for references). However, these techniques may fail or enumerate data too slowly. For effectiveness, we consider BET using a distinct handwritten enumeration program for each family of data of interest.
BET Function BET is implemented by the generic function small_check in Listing 1. 1, whose execution tests the property defined by the oracle function (first parameter) for all data of size n (second parameter). The first parameter of the module SmallCheck (on Line 2) is a characteristic predicate of the enumerated data. Note that the input type for the oracle function is a list rather than an array, because Why3 has limited support for function parameters that are functions working with mutable data. For the same reason, the generator functions cannot be input parameters for small_check function. Therefore we define them as module parameters (on Lines 3-4). They can be instantiated thanks to Why3's module cloning mechanism, as detailed in Section 2. 3. The return type verdict is composed of the field witness storing either a counterexample, if it exists, or the empty list (Nil) otherwise, and the field rank storing either the number of data tested when the witness is found, or the total number of tested data if there is no counterexample.