dataesr/scientific-paragraphs-categorization huggingface dataset:
Single nucleotide polymorphisms were called independently for each individual with FreeBayes 24 using the following parameters:-standard-filters-no-population-priors -p 2-report-genotype-likelihood-max -C 3. A callability mask was constructed to identify regions of the reference genome were variants could not be confidentially called. Segments in the genome covered by less than four reads with a minimum base quality of 20 and mapping quality of 10 were identified in each individual. Any site outside the intersection of all such regions was excluded from downstream analyses. The total number of autosomal bases after this filtering was 2,386,325,518. Next, annotation data was added to the genotypes using SnpEff 4. 0e with the Macaca fascicularis built-in database in order to obtain a prediction of the effect on the encoded proteins  25.
Emulsion PCR (emPCR) was performed using the target barcoded-library with the Ion PGM Template OT2 200 Kit on an Ion OneTouch 2 automated system (Life Technologies/Thermo Fisher Scientific). After the emulsion was automatically broken with the OneTouch 2 instrument, the beads carrying the single-stranded DNA templates were enriched according to the manufacture's recommendation. Sequencing was performed using the Ion PGM Sequencing 200 Kit and Ion 318 Chip Kit v2. The raw data processing and base-calling, trimming and output of quality-filter sequence reads that were binned on the basis of the Ion Xpress Barcodes into 42 separate sequence fastq files, were all performed by the Torrent Suite 4. 2. 1 software (Life Technologies/Thermo Fisher Scientific) with full processing for shotgun analysis.
These files were further quality trimmed to remove poor sequence at the end of the reads with QVs of less than 10. The trimmed and barcode-binned sequence reads were used for automatically genotyping using Torrent Variant Caller (Life Technologies/Thermo Fisher Scientific). Finally, we carefully checked all variant sites by defining five and under read numbers as the not detected (ND) category. Calculations of odd ratio, probabilities of association. Animals were sorted as a function of the PVL at the set point and were separated into two groups of 21 animals each (a group with high PVL and the other with low PVL). All allele frequencies in the two groups were compared by Fisher's exact test and odds ratio with 95% confidence interval. All statistical analyses were performed with the use of R software, version R-3.
capture followed by multiplexed next generation sequencing and participated to the writing of the article. N. D. and R. L. G. , managed the experimental infection of the 42 macaques by S. I. V. and provided biological material and viral load data from these animals. N. C. J. and A. A. genotyped the M. H. C. of the 42 animals. H. J. G. built the multiple linear regression model and participate to the writing of the article. T. M. B. supervised the work of M. d. M. and participated to the writing of the article. A. B. designed the study, found the funds, analyzed all datasets and wrote the article and supplementary materials.
Acknowledgements Funds were from University Toulouse 3 and from the  Association Midi-Pyrénées Santé (AMPS). This work was supported by the " Investissements d' Avenir " programs managed by the  ANR  under reference  ANR-11-INBS-0008  funding the  Infectious Disease Models and Innovative Therapies  ( IDMIT, Fontenay-aux-Roses, France) infrastructure , and  ANR-10-EQPX-02-01  funding the  FlowCyTech facility (IDMIT, Fontenay-aux-Roses, France). We warmly thank all members of ASW and L2I core lab facility from IDMIT center. TMB is supported by  MINECO   BFU2014-55090-P (FEDER) ,  U01 MH106874 grant ,  Howard Hughes International Early Career, Obra Social "La Caixa " and  Secretaria d'Universitats i Recerca del Departament d'Economia i Coneixement de la Generalitat de Catalunya.
Allele calling Allele calling was performed using two core genome multilocus sequence typing (cgMLST) schemes: the scheme of Moura et al. [10] , as implemented in the BioNumerics (Applied Maths, Sint-Martens-Latem, Belgium) software (1,748 loci, available from http:// bigsdb. pasteur. fr/listeria) and the scheme of Ruppitsch et al. [23] , as implemented in the SeqSphere + (RIDOM, Münster, Germany) software (1,701 loci, available from http://www. cgmlst. org/ncs/schema/690488/). These schemes are further referred to as Moura CG and Ruppitsch CG. Both software applications were run with default parameters for allele calling, both based on either assemblies (assembly-based) or directly on reads (reads-based). In turn, each uses the basic local alignment search tool nucleotide (BLASTN) software to align assembled genomes to reference alleles: word size was 11, gap opening penalty 5 and gap extension penalty 2  [24].
Mismatch penalty was -3 and -1, and match reward 2 and 1 in Bionumerics 7. 6. 2 and SeqSphere +3. 4. 1, respectively. When more than one locus on the assembled genome matches a reference allele, such multiple allele calls are recorded, but no identifier is assigned. The direct reads-based allele calling in BioNumerics matches k-mers of size 35 from all known alleles against those of the reads, and considers the allele found if all its k-mers are present with the same number of occurrences. The SeqSphere + readsbased allele calling first maps the reads to reference genome NC_003210. 1 using the BWA-SW software, and then produces a consensus sequence  [21]. BLAST is subsequently used to align this sequence against the reference alleles for each locus, keeping only alignments that cover > 90% of the reference allele  [24].
Alignments were verified for presence of start and stop codons and for a minimum of 70% (Moura CG) or 90% (Ruppitsch CG) nt sequence identity to reference alleles. For IonTorrent sequences, allele calling was only done by mapping reads first to the reference alleles and then again to the resulting consensus allele sequence, each time removing indels of 1 and 2 nt to specifically address this type of sequencing error. To assess the impact of the different pipelines, i. e. combinations of input data (directly reads, SPAdes assembly or Velvet assembly) and scheme (Moura CG or Ruppitsch CG) on cgMLST analysis, two subsets of isolate pairs were used: pairs with AD ≤ 7 and pairs with AD ≤ 150. These correspond respectively to closely related isolates likely to share a common  Moura  [10]  , epi linked Moura  [10]  , no/unk link Ruppitsch  [16] , epi linked Ruppitsch  [16] , no/unk link 0 50 100 150 0 1,000 2,000 3,000 AD: allelic distance; CG: core genome; epi link: epidemiologically linked; no/unk link: unknown or not epidemiologically linked; PPV: positive predictive value.
Dataset Table  1  presents the sequenced isolates by country and the respective proportion of officially reported listeriosis cases by year, retrieved from the European Centre for Disease Prevention and Control (ECDC) Surveillance Atlas of Infectious Diseases  [4]. Overall in EU/EEA countries, 20-28% of all isolates from reported cases were sequenced for each year. This is a relatively uniform representativeness, though there was substantial variation between individual countries and also for some countries over the years.
In contrast, the corresponding curve for a stricter threshold of 97. 5% for acceptable CGC reaches this plateau only at around 80x coverage before trimming (data not shown). In summary, for our dataset, 22 isolates (0. 8%) failed at least one QC and were classified as 'Rejected'. No association was found between the sequencing platform used and failing the CGC QC that assesses sequence quality. A total of 40 isolates (1. 5%) passed at least one QC with a warning but failed none, and these were classified as 'AcceptedForOutbreak'. The remaining 2,664 (97. 7%) passed all three QCs and were classified as 'Accepted', and only these were used for subsequent analyses on analytical and epidemiological validation.
Epidemiological validation Our dataset contained 19 confirmed outbreaks from 10 EU/EEA countries with a median of 6. 5 cases (range: 3-19), plus two mother-child pairs, encompassing 152 isolates. These outbreaks were both microbiologically and epidemiologically confirmed by the respective national authorities, but confirmation was done using microbiological typing methods other than WGS. The outbreaks therefore represent an independent test set for evaluating epidemiological concordance of WGS. The number of corresponding epidemiologically linked pairs decreased with increasing AD (Figure  2a ), confirming that low cgMLST AD strongly correlates with epidemiological linkage. This pattern was similar between the two cgMLST schemes, as well as between allele calling approaches (data not shown).
The latter can be detected first either at national level or at EU/EEA level, depending on whether the second isolate of any involved country is also the second isolate overall. The average number of days that EU/EEA level detection for clusters of more than five isolates was earlier than national detection was at least 75 (AD ≤ 4) and at most 154 (AD ≤ 7). This indicates that, in terms of timeliness of detection, there can be a substantial gain by pooling data at the EU/EEA level. Finally, the 6-year time span of this dataset also allows examination of the evolution of clusters over time. Figure  2d  shows the aggregated number of isolates over a period of nearly 4 years belonging to the same cluster, with the first isolate in the cluster set at t = 0. These clusters were created using single linkage clustering and different AD cutoffs of 0, ≤ 4 and ≤ 7 to assess the impact of the cutoff on cluster duration and size.
A more stringent cluster cutoff of AD ≤ 4 in combination with single linkage clustering may be considered for identifying isolates with more compelling microbiological evidence of being part of the same outbreak. A second higher cutoff for weaker microbiological evidence could be applied as well, in order to define, for example, confirmed vs probable cases in an outbreak case definition. More confirmed outbreaks are needed to have a more accurate estimation of PPV and sensitivity as a function of cluster cutoff. Since confirmed outbreaks were delineated at national level only and using less discriminatory methods than cgMLST, there may be additional isolates in the dataset that are actually part of one of the confirmed outbreaks, leading to underestimation of PPV and possibly sensitivity. More confirmed outbreaks are also needed to understand why epidemiologically linked isolates could have substantially more allele differences than the cutoff, as described earlier  [9, 10].
This can be expected given the international nature of food trade, and indicates that there is potential for a substantial added public health value of introducing EU/EEA-wide, WGS-enhanced surveillance of listeriosis. Among other benefits, it may lead to earlier detection of clusters, which in our dataset was on average several months for multi-country clusters. At the same time, the molecular typing results must also be combined with epidemiological and food exposure investigations. Given the sometimes-long incubation period of listeriosis, the low number of cases and the severity of the disease, food exposure data should ideally be collected for all cases, without additional waiting for typing results, as presently done in, for example, Denmark, France and the US. It should also be complemented by WGS typing of officially sampled food isolates, and be combined with a joint analysis of the microbiological data to detect potential links between human cases and food items  [11, 31].
Acknowledgements We thank  Alfonsina Fiore  ( Istituto Superiore di Sanità, Italy ),  Jani Halkilahti  ( National Institute for Health and Welfare, Finland ) and  Judit Pászti  ( Országos Epidemiológia Központ, Hungary ) for technical support. Daniel Palm  ( ECDC, Sweden ),  Joana Revez  (ECDC),  Marc Struelens  (ECDC),  Max Heck  ( Rijksinstituut voor Volksgezondheid en Milieu, Netherlands ),  Mike Catchpole  (ECDC) and  Saara Kotila  ( ECDC ) for critical review. Marc Struelens  (ECDC) and  Sylvain Brisse  ( Institut Pasteur, France ) for advice and guidance.
Funding:  Timothy Dallman  ( Public Health England (PHE), United Kingdom ): The research in the United Kingdom was funded by the  National Institute for Health Research Health Protection Research Unit (NIHR HPRU ) in Gastrointestinal Infections at  University of Liverpool  in partnership with  Public Health England , in collaboration with  University of East Anglia, University of Oxford  and the  Institute of Food Research. The views expressed are those of the author(s) and not necessarily those of the  National Health Service (NHS) , the  NIHR , the  Department of Health or PHE. Alexandra Moura ,  Alexandre Leclercq  and  Marc Lecuit : The research in France was supported by  Institut Pasteur ,  Inserm and Santé Publique France.
gambiae s. s. and An. coluzzii (Culicidae)  [10]. This proteomic tool was successfully applied to the identification of mosquitoes at the adult  [11]  and juvenile  [12]  stages. Consequently, it is now conceivable to perform accurate live monitoring of mosquitoes before their emergence and to adjust control measures according to the mosquito detected  [13]. The standardization of protocols from sample homogenization method to MS spectra analysis, taking into account mode and duration of sample storage, has been performed  [14]. These optimized protocols will facilitate the creation and sharing of a mosquito MS reference database. The elucidation of Anopheles trophic preferences remains a key factor in the management of malaria transmission by incriminating vectors involved in pathogen transmission.
Recently the proof-of-concept use of MALDI-TOF MS profiling for the determination of Anopheles blood meal source was reported  [15, 16]. These studies underlined that correct identification of blood meal origin could be relevant for specimens freshly engorged, up to 24 hours after feeding. Moreover, it was demonstrated that the conventional field practice, consisting of crushing on Whatman paper filter the abdomen of bloodengorged mosquitoes, was compatible with future MS identification of the blood source  [17]. More recently, the detection of the Plasmodium infectious status of Anopheles was positively revealed by MALDI-TOF MS profiling  [18]. Taken together, these works highlight the potential of MALDI-TOF MS for elucidating host, Plasmodium and Anopheles interactions. Guidelines in the field should facilitate the creation and sharing of an open-source reference MS spectra database in the near future.
Additionally, a traveller database that can be used as a surveillance system to assess and monitor the emergence of drug resistance in endemic African areas where information is limited has been developed  [58]. Malaria resistance to most antimalarial drugs has developed in Southeast Asia and has spread to Africa. In this context, the identification of molecular resistance markers to these antimalarial drugs is urgently needed for monitoring the emergence and spread of resistance to antimalarial drugs. The IHU-Méditerranée Infection participates in identification, development and/or validation of new molecular resistance markers to doxycycline (pfmdt and pftetQ)  [59] [60] [61] [62] , quinine (pfnhe-1)  [63] , artemisinin (pfK13)  [64] [65] [66] [67] [68]  and quinolines (pfmdr1, pfmdr2, pfmdr5, pfmdr6, etc.
EXPERIMENTAL PROCEDURES Experimental Design and Statistical Rationale-Shotgun proteomics experiments on PC1/3 knockdown (KD) and nontarget (NT) cells treated with Paclitaxel were conducted in biological triplicate. Spheroids studies, viability tests, Western blotting analyses, calcium imaging experiments were also conducted in biological triplicate. Statistical Analysis-For the proteomics statistical analysis of extracted proteins or secreted media, only proteins presenting as sig- 1 The abbreviations used are: TAM, Tumor Associated Macrophages; ACN, Acetonitrile; Ca  nificant by the ANOVA test were used with FDR 5%. Normalization was achieved using a Z-score with a matrix access by rows. Obtained data from Western blotting were reported as mean Ϯ S. E. Mean values among different experimental groups were statistically compared by one-way ANOVA tests using Graph pad PRISM software or by student t test.
ImageJ software was used to quantify the bands. Experiments were done in triplicate (n ϭ 3). Filter-aided Sample Preparation (FASP)-Total protein extract (0. 1 mg) was used for FASP analysis as described previously. We performed FASP using Microcon devices YM-30 (Millipore, Burlington, MA) before adding trypsin (Promega) for protein digestion (40 g/ml in 0. 05 M NH 4 HCO 3 ). The samples were incubated overnight at 37 °C. The digests were collected by centrifugation, and the filter device was rinsed with 50 l of NaCl 0. 5 M. Next, 5%TFA was added to the digests, and the peptides were desalted with a Millipore ZipTip device before LC-MS/MS analysis  (13). The solution was then dried using the SpeedVac. Dried samples were solubilized in water/0. 1% formic acid before LC MS/MS analysis. Experiments were done in triplicate (n ϭ 3).
The LC eluent was electrosprayed directly from the analytical column, and a voltage of 1. 7 kV was applied via the liquid junction of the nanospray source. The chromatography system was coupled with a Thermo Scientific Q Exactive mass spectrometer programmed to acquire a data dependent Top 10 method. Survey scans were acquired at a resolution of 70,000 at m/z 400. Data Analyses-All the MS data were processed with MaxQuant (20) (version 1. 5. 1. 2) using the Andromeda (21) search engine. Proteins were identified by searching MS and MS/MS data against Decoy version of the complete proteome for Rattus norvegicus of the UniProt database  (22)  (Release June 2014, 33,675 entries) combined with 262 commonly detected contaminants. Trypsin specificity was used for the digestion mode with N-terminal acetylation and methionine oxidation selected as the variable.
Carbarmidomethylation of cysteines was set as a fixed modification, and we allowed up to two missed cleavages. For MS spectra, an initial mass accuracy of 6 ppm was selected, and the MS/MS tolerance was set to 20 ppm for HCD data. For identification, the FDR at the peptide spectrum matches (PSMs) and protein level was set to 0. 01. Relative, label-free quantification of proteins was performed using the MaxLFQ algorithm (  23 ) integrated into MaxQuant with the default parameters. Analysis of the proteins identified was performed using Perseus software (http://www. perseus-framework. org/) (version 1. 5. 0. 31). The file containing the information from identification was used with hits to the reverse database, and proteins only identified with modified peptides and potential contaminants were removed.
To quantify fold changes of proteins across samples, we used MaxLFQ. To visualize these fold changes in the context of individual protein abundances in the proteome, we projected them onto the summed peptide intensities normalized by the number of theoretically observable peptides. Functional annotation and characterization of identified proteins were obtained using PANTHER software (version 9. 0, http:// www. pantherdb. org) and STRING (version 9. 1, http://string-db. org). The GeneMANIA Cytoscape plugin  (24)  was used to generate 2 distinct coexpression networks from cell extract proteomics data: (1) a Ͻ Ͻ Paclitaxel-treated NT cells Ͼ Ͼ network composed of 99,556 recognized interactions, generated from the analysis of a data subset gathering control NT cells and Paclitaxel-treated NT cells and (2) a Ͻ Ͻ Paclitaxel-treated KD cells Ͼ Ͼ network composed of 86,780 recognized interactions, generated from the analysis of a data subset gathering control KD cells and Paclitaxel-treated KD cells.
10 objective. Image processing and quantification of spheroids and of invasion areas was performed using an in-house software. This in-house software considers cell density and not the (observer-dependent) limits of cell migration in the collagen matrix. The implemented algorithm uses local fluctuations of the image intensity for an automated estimation of the invasion magnitude. It is robust enough to handle micrographs of different generation methods and various qualities without the concept of an invasive front of the spheroids  (25). Invasion and spheroid areas are normalized for each day to the invasion and spheroid areas measured at day 0 and expressed in protocol defined units (PDU). These normalized data are reported as relative size to day zero. Relative size of day 0 thus equals 1.
NT and KD macrophages were treated with 30 M Paclitaxel for 24 h and supernatants were collected. Proteomics analyses of factors secreted by NT and KD cells after 24 h treatment with 30 M Paclitaxel or DMSO as control. A, MaxQuant and Perseus software were used for the statistical analysis, and a heat map was generated to show proteins that were significantly different between macrophages stimulated or not with Paclitaxel in the supernatants. B, String protein analyses of the unique proteins identified in PC1/3-KD (i) and NT cell supernatants (ii). C, Effect of PC1/3 downregulation on cytokine secretion under Paclitaxel and inhibitory conditions. The rat cytokine array assay was performed with NR8383 cell supernatants (NT/KD). The cells were untreated (control) or treated with IL-10 for 24 h without stimulation (IL-10) or with Paclitaxel for 24 h (IL-10ϩPaclitaxel).
We used the ANOVA test to perform a clustering of samples grouped on the basis of PC1/3 deficiency (NT versus PC1/3 KD cells) and Paclitaxel treatment (treated versus untreated cells) (Fig. 3A , supplemental Data S2 and S3). Eight specific clusters were identified. Cluster 1 corresponds to overexpressed proteins under Paclitaxel treatment in PC1/3 KD cells and cluster 3 to overexpressed proteins under Paclitaxel in both cell lines. Cluster 2 is specific for PC1/3 KD cells regardless the treatment. Cluster 6 represents proteins overexpressed in resting PC1/3 KD cells. We investigated using systems biology analysis, the interactome and network of the overexpressed proteins in PC1/3 KD cells after Paclitaxel treatment utilizing the GeoSoftware corresponding to clusters 1 and 2 (Fig. 3B ). Differential pathways were generated using the "direct interaction" algorithm to map the relationships of the identified proteins.
e. the concentration used to induce the proinflammatory supernatant) directly to the cocultures led to a complete growth and invasion arrest of the spheroids, irrespective of the presence or absence of the macrophages (data not shown). Thus, with this system we could not distinguish the effect of Paclitaxel alone from the effects of Paclitaxel stimulation on macrophages. By contrast, when the macrophages (NT or PC1/3 KD cells) were pre-activated by 30 M Paclitaxel before their embedding in collagen, thus avoiding the presence of Paclitaxel during the coculture, tumor growth and invasion were inhibited (Fig. 7Ba ). Quantification of the growth and invasion rate indicated that pre-activated NT and KD cells exerted a similar inhibitory effect (Fig. 7Bb ). We also analyzed the conditioned medium obtained after 6 days of coculture between C6 spheroids and Paclitaxel preactivated NT or PC1/3-KD cells to identify the factors specifically or more secreted.
Database This retrospective study is based on data from the Disease Analyzer database (QuintilesIMS), which compiles demographic, clinical, and pharmaceutical data obtained in an anonymous format from computer systems used in clinical practices  [15]. The quality and exactness of the data (e. g. , diagnoses and drug prescriptions) are regularly assessed by QuintilesIMS. Using prescription statistics for several drugs and age groups for several diagnoses, the Disease Analyzer database was found to be a representative database of clinical practices in Germany  [15]. Finally, several studies focusing on female cancers and using the same database have already been published  [16, 17].