dataesr/scientific-paragraphs-categorization huggingface dataset:
All CLSM stacks were processed using IMARIS (Bitplane) to produce projection images of the biofilms. Quantitative analysis of each Z-stack to quantify the green biovolumes corresponding to the L. monocytogenes subpopulations of the biofilms was performed using the ICY image analysis software (64) using a homemade script previously described, with some adaptations  (65). The biomass software algorithm did not reliably score low cell numbers correctly; therefore, the biovolume of scans where Յ5 cells were observed in the green channel of the image projection was set to 0, when the scored biovolume was above a threshold of 800 m 3. The biovolume of the red channel was not extracted, since red fluorescent background interfered with the Syto 61-specific signal. Accession number(s). Data from this whole-genome shotgun project have been deposited at DDBJ/ENA/GenBank under the accession numbers MVOJ00000000, MVOK00000000, MVOL00000000, MVOM00000000, and MVON00000000.
ACKNOWLEDGMENTS We thank  Bjørn C. T. Schirmer  for isolation of microbiota strains from meat processing plants and  Merete Rusås Jensen ,  Anette Wold Åsli ,  Tove Maugesten ,  Signe M. Drømtorp , and  Julien Deschamps  for excellent technical assistance. This work was supported by  Norwegian Research Funding for Agriculture and Food Industry  (grant no. 234355 ). The collection of strains used in this work was funded by.
Reads were extracted from BaseSpace (Illumina) with an automatic trimming of the adaptor plus removal of Ns. The sequencing yield ranged from 0. 13 to 4. 37 Gb per sample. Quality control was performed with FastQC (http://www. bioinformatics. babraham. ac. uk/projects/fastqc). Bioinformatics analysis and operational taxonomic unit identification were performed with the metagenome classifiers "one Codex" (6), Kaiju  (7) , and the MG-RAST pipeline  (8). Lactobacillus kunkeei was found dominant in honey, as observed by Asama (3), and in other royal jelly and pollen samples, too. Accession number(s). Metagenome raw sequencing data have been made publicly available through the Sequence Read Archive (SRA) (9) of the National Center for Biotechnology Information under the SRA accession numbers given in Table  1.
Interpretation of the terms of reference In FGE. 205 Revision 1, EFSA evaluated the additional data on genotoxicity submitted for the substances, oct-1-en-3-one. 081] and pent-1-en-3-one ], by the flavour industry. For these substances, being the representative of four of the flavouring substances in FGE. 07, the Panel concluded that the concern for a genotoxic potential could be ruled out. As a follow up to this conclusion, the substances, but- ], non-1-en-e-ol ], hex-1en-3-one ] and. 210], were evaluated by the Panel in accordance with the Procedure described in Commission Regulation (EC) No 1565/2000 3 , in line with the background and terms of references as provided by the European Commission. The present revision of FGE. 07, FGE. 07Rev5 includes the assessment of four additional candidate substances  02.
Estimated daily per capita intake (MSDI approach) The intake estimation is based on the MSDI approach, which involves the acquisition of data on the amounts used in food as flavourings  (SCF, 1999). These data are derived from surveys on annual production volumes in Europe. These surveys were conducted in 1995 by the International Organization of the Flavour Industry (IOFI), in which flavour manufacturers reported the total amount of each flavouring substance incorporated into food sold in the EU during the previous year  (IOFI, 1995). The intake approach does not consider the possible natural occurrence in food. Average per capita intake (MSDI) is estimated on the assumption that the amount added to food is consumed by 10% of the population 4 (Eurostat, 1998). This is derived for candidate substances from estimates of annual volume of production provided by industry and incorporates a correction factor of 0.
206 (EFSA CEF Panel, 2011) where the Panel concluded that the data available ruled out the concern for genotoxicity. Pseudo-ionone was tested in Salmonella Typhimurium strains TA98, TA100, TA1535, TA1537 and TA102 in the presence or absence of S9 and it is concluded that under the test conditions applied pseudoionone is not mutagenic in bacteria. Pseudo-ionone was also evaluated in an in vitro micronucleus assay in human peripheral blood lymphocytes for its ability to induce chromosomal damage or aneuploidy in the presence and absence of rat S9 fraction as an in vitro metabolising system. Under the conditions of this study, pseudo-ionone was not clastogenic and/or aneugenic in cultured human lymphocytes. As pseudo-ionone ] is a representative with respect to genotoxicity evaluation for  02.
206  (EFSA CEF Panel, 2011)  where the Panel concluded that the data available ruled out the concern for genotoxicity. 6-Methylhepta-3,5-dien-2-one was tested in S. Typhimurium strains TA98, TA100, TA1535, TA1537 and TA102 in the presence or absence of S9 and it was concluded that under the test conditions applied 6-methylhepta-3,5-dien-2-one is not mutagenic in bacteria. 6-Methylhepta-3,5-dien-2-one was also evaluated in an in vitro micronucleus assay in human peripheral blood lymphocytes for its ability to induce chromosomal damage or aneuploidy in the presence and absence of rat S9 fraction as an in vitro metabolising system. Under the conditions of this study, 6-methylhepta-3, 5-dien-2-one was not clastogenic and/or aneugenic in cultured human lymphocytes. As 6-methylhepta-3,5-dien-2-one [FL-no: 07.
Candidate substances with alpha-beta-unsaturated carbonyl structural alert Oct-1-en-3-one [FL-no: 07. 081] and pent-1-en-3-one [FL-no: 07. 102] were evaluated with respect to genotoxicity in FGE. 205 (EFSA CEF Panel, 2012a) and FGE. 205Rev1 (EFSA CEF Panel, 2016). Due to positive effects in the bacterial mutagenicity assays of the two representative substances pent-1-en-3one [FL-no: 07. 102] and oct-1-en-3-one [FL-no: 07. 081], an in vivo Comet assay on the first site of contact (e. g. the stomach or duodenum) and on the liver was requested on the most potent substance, pent-1-en-3-one (EFSA CEF Panel, 2012a). In response to the data request in FGE. 205, the industry submitted in vivo data on both pent-1-en-3-one and oct-1-en-3-one. Pent-1-en-3-one [FL-no: 07. 102] was tested for its potential to induce micronuclei in the polychromatic erythrocytes of the bone marrow of treated rats and to induce DNA damage in the liver and duodenum of the same animals (Keig-Shevlin, 2015b,c).
Reversal learning was, however, impaired  (Fig EV1C and D) , which supports the view that loss of Fmn2 in young mice does not affect the consolidation of new memories but is required for changing responses to existing memories. Taking together, these data support the view that the analysis of FMN2 would be a good starting point to investigate the mechanisms by which neuropsychiatric diseases at young age contribute to an increased risk for age-associative memory decline. Before continuing our analysis, however, we sought to provide further evidence for this hypothesis. To this end, we tested Fmn2 expression in blood samples from PTSD patients via qPCR. We observed a significant reduction in Fmn2 expression in PTSD patients when compared to age-matched control individuals  (Zieker et al, 2007; Fig 1G).
These data confirm that loss of Fmn2 accelerates age-associative memory impairment. Next Synaptophysin was used to confirm enrichment of synaptosomes. Scale bar: 20 lm. F LTP was similar in Fmn2 À/À (n = 12) and wild-type control mice (n = 8). G The absolute level of LTD was similar amongst Fmn2 À/À and wild-type mice (n = 10). H Depotentiation was significantly impaired in Fmn2 À/À mice (n = 12) when compared to control (n = 11; *P < 0. 05, t-test). Data information: For all experiments, parasagittal brain slices from 14to 21-day-old mice were used. Cere, cerebellum; Hip, hippocampus, mf; mossy fiber, str. The EMBO Journal we decided to investigate the impact of FMN2 on cognitive decline in the presence of another risk factor for AD, namely amyloid deposition. To this end, we employed APPPS1-21 mice.
GO-term and pathway analysis revealed that the genes deregulated were linked to ribosome function, RNA splicing, Alzheimer's disease, and oxidative phosphorylation  (Fig 5D and F). While genes linked to oxidative phosphorylation were increased in 3-month-old mice, genes of the same complexes and especially genes encoding subunits of the NADH dehydrogenase, ATPase, and Cytochrome C reductase were all downregulated in 8-month-old mice  (Fig 5E, Dataset EV1). These data suggest that the susceptibility to develop PTSD-like phenotypes observed in 3-month-old Fmn2 À/À mice is not accompanied by massive changes in gene expression, while memory impairment in 8-month-old Fmn2 À/À mice correlates with substantial deregulation of transcriptome plasticity. The fact that the spliceosome appeared to be deregulated in 8-month-old Fmn2 À/À mice is in line with recent data suggesting that during aging, synaptic plasticity genes are deregulated at the level of differential exon usage  (Stilling et al, 2014b; Benito et al, 2015).
Thus, we also analyzed differential exon usage. In 3-month-old Fmn2 À/À mice, we detected 46 genes that showed altered differential exon usage when compared to 3-monthold control littermates  (Fig 5G, Dataset EV1). No significant pathways were identified. The same comparison was performed in 8-month-old Fmn2 À/À and age-matched control mice and revealed 286 differentially expressed exons  (Fig 5G). Further GO-term and pathways analysis showed that processes affected by differential splicing in 8-month-old Fmn2 À/À mice are linked to synapse function  (Fig 5H). In line with the gene expression data, we also observed that processes linked to energy metabolism and oxidative phosphorylation were deregulated  (Fig 5H). In conclusion, these data indicate that the PTSD-like phenotypes observed in Fmn2 À/À mice are not linked to substantial changes in gene expression, while aberrant gene expression is accelerated in the context of aging as an additional AD risk factor.
Acknowledgements This work was supported by the following third party funds to AF: DFG research group  KFO241/PsyCourse Fi981-4  and  Fi981 11-1 ,  DFG  project  179/1-1/2013 , an  ERC consolidator grant  ( DEPICODE 648898 ), and the  BMBF  project  Integrament ; PF was supported by the  KFO241 / PsyCourse  Project  FA 241/16-1  (PF); FS was supported by the  DFG  grant  SA1005/2-1. RCAB was supported by a  Ramón y Cajal grant  ( RYC-2014-15246 ) and by  Galicia Innovation Agency  ( IN607D-2016/003 ). We thank  George Lu  ( University of Michigan ) for proofreading the manuscript.
For functional assays in vivo, gnotobiotic larvae were created by adding a single bacterial isolate to sterile flasks containing axenic larvae. Adult mosquitoes that had undergone different gnotobiotic treatments as larvae were used to test for variation in life-history and antimicrobial phenotypes. were assigned to Salmonella (Ssp_ivi) and Rhizobium (Rsp_ivi) genera. The third isolate (Esp_ivi) was isolated from a sylvatic breeding site and belongs to the Enterobacteriaceae family; however, classification at the genus level was inconsistent among databases (alternatively Salmonella, Escherichia, or Shigella). In the 16S data set, the Enterobacteriaceae and Rhizobium taxonomical groups were present in both domestic and sylvatic breeding sites, whereas the Salmonella taxonomical group was only found in domestic breeding sites.
Targeted bacterial metagenomics analysis Read filtering, OTU clustering, and annotation were performed with the MASQUE pipeline (https://github. com/aghozlane/masque), as described by Quereda et al. (63). A total of 2851 OTUs were obtained at 97% sequence identity threshold. The statistical analyses were performed with SHAMAN (shaman. c3bi. pasteur. fr) based on R software (v3. 1. 1) and bioconductor packages (v2. 14). Because bacterial communities were expected to differ substantially between mosquito midguts and water samples, the normalization of OTU counts was performed at the OTU level by sample type (midgut or water) using the DESeq2 normalization method. All samples including the negative controls and technical replicates were included in the normalization step. The technical replicates were removed from the data set before analysis.
To account for possible contamination at various steps in the sampleprocessing pipeline, the OTU counts were corrected with the reads from the negative controls (see above). All OTUs found in the negative control samples were removed from the normalized OTU table unless the count in a real sample was >10 times higher than the mean OTU count in the negative controls. This operation was performed with a homemade script in R  (64). This normalized OTU count table with the OTUs found in the negative controls removed (file S8) was used for the richness, Shannon index, Venn diagrams, abundance heat map, and NMDS analysis. Observed richness, Shannon index, and Bray-Curtis distances were calculated with the vegan package in R  (65). The effects of sample types and ecotypes on the bacterial richness were tested by fitting a generalized linear model (GLM) with a Poisson distribution.
The Spearman correlation with real distances and stress value was estimated with the vegan R package  (65). Effects of habitat and sample type on b diversity were tested with the betadisper and adonis permutational multivariate ANOVA methods from the vegan R package with 999 permutations of the Bray-Curtis distance matrix derived from OTU counts. In SHAMAN, a GLM was fitted and vectors of contrasts were defined to determine the significance in abundance variation between sample types. The GLM included the main effect of habitat (sylvatic or domestic), the main effect of sample type (midgut or water), and their interaction. The resulting P values were adjusted for multiple testing according to the Benjamini and Hochberg procedure. All OTUs that were present in the negative controls were excluded from the final list of differentially abundant OTUs.
To confirm the OTU-based results with an OTU-independent method, a dissimilarity matrix was generated with the SIMKA software  (66). Reads with a positive match against the sequences assembled from the negative controls were removed using Bowtie v2. 2. 9  (67). Then, k-mers of size 32 and occurring at least greater than two times were identified with SIMKA. Bray-Curtis dissimilarity was estimated between each sample.
The same colony was also put into 20 ml of sterile water and exposed to two rounds at 95°C for 2 min and resting on ice for 2 min. The samples were then centrifuged for 5 min at maximum speed to remove cell debris, and the supernatant was used to amplify the entire 16S region by PCR [5′-AGAGTTTGATCCTGGCTCAG-3′ (forward) and 5′-AAGGAGGTGATCCAGCCGCA-3′ (reverse)] using Expand High-Fidelity Polymerase (Sigma-Aldrich). The PCR products were purified using the QIAquick PCR Purification kit (Qiagen), quantified by NanoDrop (NanoDrop Technologies Inc. ), and sequenced by Sanger sequencing. The sequences were aligned and classified at the genus level using the SILVA database (www. arb-silva. de/). The raw sequence data are available at the European Nucleotide Archive under accession number PRJEB16334.
To test the pupation rate of each of the 37 initial test isolates, individual colonies were inoculated into triplicate flasks of axenic larvae, as described above. The number of pupae was counted in each flask every day for 17 days. The list of 37 isolates was further narrowed down to 16 candidate isolates based on those that reached 60% pupation. Of the 16 candidate isolates, three isolates were chosen on the basis of differences in pupation rate (file S9) and differences in the cultivable bacterial composition found in adult midguts after 4 to 6 days in the insectary (file S10). On the basis of their full-length 16S sequence, two of the three isolates were assigned to Salmonella (Ssp_ivi) and Rhizobium (Rsp_ivi) genera. The third isolate (Esp_ivi) was assigned to the Enterobacteriaceae family, but classification at the genus level was inconsistent among databases (alternatively Salmonella, Escherichia, or Shigella).
Acknowledgments: We thank two anonymous reviewers for the helpful comments on an earlier version of the manuscript,  N. Rahola  and  M. F. Ngangue  for the assistance and expertise during the fieldwork,  A. Fontaine  for the help with the statistical analysis,  C. Lallemand  for the assistance with mosquito rearing, and all members of the  Lambrechts laboratory  for the insightful discussions. We also thank  L. Ma  for the technical assistance on the  MiSeq and Centre d'Informatique pour la Biologie of Institut Pasteur  for providing the computational resources. We are grateful to  A. Ponlawat  and  T. Fansiri  for the initial sampling of the mosquito population from Thailand. Funding: This study was supported by the  French government 's  Investissement d'Avenir program ,  Laboratoire d'Excellence Integrative Biology of Emerging Infectious Diseases  (grant  ANR-10-LABX-62-IBEID ), the  French Agence Nationale de la Recherche  ( MOSQUIBIOTA project , grant  ANR-16-CE35-0004 ), the  City of Paris Emergence(s) program in Biomedical Research , and the  French Centre National de la Recherche Scientifique, Ecology of Health (ECOSAN) program  (grant AFRICAEDES).
The Genomics Facility is a member of the " France Génomique "  consortium  (grant  ANR-10-INBS-09-08 ). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Author contributions: L. B. D. , C. V. M. , and L. L. conceived the study. L. B. D. , D. J. , D. A. , and C. P. carried out the fieldwork. L. B. D. , D. J. , and I. M. -C. performed the experiments. C. B. supervised the high-throughput sequencing. L. B. D. , G. M. , S. V. , A. G. , and L. L. analyzed the data. L. B. D. and L. L. wrote the manuscript. All authors edited and commented on the manuscript.
Simulated sequence data for validation A fragment of chromosome 7 (1Mb) of Oryza sativa ssp japonica cv NipponBare IRGSP1. 0 was extracted from position 1,000,000 to 1,999,999 using the extractseq tool from EMBOSS  [19]. Three duplications (namely duplications 1-3) were artificially created within this sequence using home-made Perl script (available on demand), respectively at positions 300-1500; 350,000-390,000 (containing another initial duplication) and 800,000 to 810,000. A total of nine virtual 'clones' were constructed. Clone1, without duplicated sequences, was considered as identical to the reference. Clone2 has the three duplicated sequences without mutation. Clones 4-6 have the duplicated sequences and mutations with 3% of supplementary divergence, while clones 7-9 have the duplicated sequences with the same mutations and 6% of divergence.
Sequence data for Oryza glaberrima and O. barthii and initial quality control Eight accessions of African cultivated rice Oryza glaberrima (TOG5307, TOG5307f, TOG5321, TOG5666, TOG5887, TOG7291, UB06, UG26), and six wild relatives Oryza barthii (B88, IG05, IRGC106302, MB323, TB41, TG57) were used in this study (see  [22]  for more informations about those accessions). Asian cultivated rice O. sativa IR64 (ssp indica) and Azucena (ssp japonica) were also included as control. All samples were sequenced at Genoscope (France), in the frame of the IRIGIN project (http://irigin. org), as follows: Sequencing: Libraries were prepared using the NEBNext DNA Modules Products (New England Biolabs, MA, USA) with a 'on beads' protocol developed at the Genoscope, thus reducing the costs and increasing the yields.
respectively. About 50 billion useful paired-end reads were obtained per run. QC and initial treatments: Low quality clusters were filtered during the sequencing run by Real Time Analysis (RTA) software. Filtering steps were performed on whole paired FASTQ files: Illumina adapters and primers were removed, nucleotides with quality value lower than 20 were trimmed from both ends and sequences between the second unknown nucleotide (N) and the end of the read were trimmed. Reads shorter than 30 nucleotides after trimming were discarded. These trimming steps were achieved using fastxtend (http://www. genoscope. cns. fr/fastxtend/), a software based on the FASTX library  [23]. The filtered reads and their mates that mapped onto run quality control sequences (PhiX genome) were removed using SOAP aligner  [24].
Results on experimental data We then test our tool on an experimental set of African rice individuals sequenced at relatively high-depth (see Materials and methods). African cultivated rice Oryza glaberrima and its wild relative O. barthii diverged from Asian cultivated rice O. sativa ancestor almost 1 million year ago  [33, 34] , and may harbor duplicated regions compared to Asian rice. We thus tested 8 cultivated and 6 wild samples to evaluate our tool on real data. To avoid individual effect due to the reference genome (i. e. identification of AEH loci only because of a loss of duplication in Nipponbare), we add as control two other O. sativa sample, IR64 (indica subspecies) and Azucena (japonica subspecies), sequenced as described in 2. 2. 1. Those two individuals must be homozygous and similar to the reference for an AEH locus on the African samples to be validated as such.
Acknowledgments GD was supported by an  IRD  grant ( 2013-2017 BEST FellowShip ). CM was supported by  ANR  ( AfriCrop  project # ANR-13-BSV7-0017 ) and NUMEV labex (LandPanToggle # 2015-1-030-LARMANDE ). Authors want to thanks Genoscope members for the sequencing of all rice data. This work was supported by  France Génomique French National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  ANR  (# ANR-10-INBS-09 ), in the frame of the  IRIGIN  project (http://irigin. org).
The primers and amplicons that were used for the identification of the 4A161L transcripts are indicated. The position of the KpnI restriction site, DUX4 transcriptional and translation start sites (TSS and ATG) and polyadenylation signal (PAS) are indicated as well as the intron-exon composition for DUX4La and DUX4Lb hampered by the high GC-content in D4Z4 and specifically the high Cytosine content (70%) in the region 599-737 bp proximal to the DUX4 polyadenylation signal. These problems are encountered for PCR and Sanger sequencing of DNA and RNA, and also for 3′ RACE reaction. The identified 4A161L DUX4 transcripts have been submitted to Genbank under accession numbers MF422078 (DUX4La) and MF422079 (DUX4Lb). 4A161L sequences with intronexon structure and exon numbering of the DUX4La and DUX4Lb transcript can be found in Genbank under accession numbers MF693913 and KQ983258.
In silico alignment DUX4 reads Reads were first analysed using FastQC (version 0. 10. 1). If any known adapters were detected to be overrepresented by FastQC, the full adapter sequence was then used for clipping using the cutadapt tool (version 1. 5), setting the minimum read length to 20. In any case, low quality bases in all reads were trimmed using the sickle tool (version 1. 33) with default options. If cutadapt was performed before running sickle, the paired reads are processed using a custom script beforehand to ensure the read pairs were still insync. Mapping was done to the respective 4A161 allele sequence using the GSNAP tool (version 2014-12-23) with the following flags: -batch 4, -nthreads 8, -novelsplicing 1, and -format sam. The resulting alignments were then compressed, sorted, and indexed, before viewed in the IGV tool.