dataesr/scientific-paragraphs-categorization huggingface dataset:
76%), "g_Paracoccus" (mean abundance: 1. 77 and 1. 96%), and "g_Romboutsia" (mean abundance: 0. 58 and 0. 54%). A total of two genera were only identified by one pipeline (DADA2): "g_Defluviimonas" (mean abundance: 2. 05%) and "g_Agathobacter" (mean abundance: 1. 63%). For abundance less than 0. 5%, the 16S pipelines did not show concordance (using ICC  [31]  see Figure  S5 ). A total of four out of these ten genera ("g_Romboutsia," "g_Fusicatenibacter," "g_Defluviimonas," and "g_Defluviimonas") were not found in the mOTU-v2 database, which illustrates discrepancies between the 16S and WGS assignments. The six other genera belonged to the mOTU-v2 database but were not identified in the WGS datasets. Figure  3  presents the heatmap of abundances (log2 of-) that was estimated for each genus by the three 16S pipelines, including the 51 genera (most abundant according to WMS).
5%. Moreover, we have also shown that concordance between 16S RNA pipelines and WMS outputs is dependent on the abundance of the genus. Therefore, for low abundant bacteria (<0. 5%), for which no WMS reference is available, we advocate the use of a confirmation pipeline, such as WMS, or qPCR. At the genus level and using the same 16S rRNA database (SILVA, version 128)  [34] , we found that, for taxonomic assignments, 16S RNA pipelines were mostly concordant with WMS for abundant genera. We found that some genera recovered by the WMS datasets were not recovered by 16S rRNA taxonomic assignment. Moreover, for genera whose abundances were lower than 0. 5%, discrepancies were observed between WMS and 16S RNA pipelines. The lower the abundance of a genus (as identified by WMS), the lower the probability of it being correctly detected by any of the three 16S RNA pipelines.
High-density fermentation growth conditions Seed cultures were inoculated by adding a single colony from the streaked plates into 250 mL Erlenmeyer flasks containing 125 mL of selective LB liquid medium. The seed cultures were grown at 30 °C, shaking at 275 rpm for 14 h, and then immediately used to inoculate DASGIP® BioBlock fermenters containing 1 L complex medium (1. 2% soytone, 2. 4% yeast extract, 4 mM potassium hydroxide, 2% glycerol, 25 mM dibasic potassium phosphate, 0. 05% antifoam 204, 1× DeLissa Trace metals, and selective antibiotics). The fermenters were controlled by a computer running DASware software (Eppendorf, Hauppauge, NY). The pH was maintained at 7. 0 using 28% ammonium hydroxide and 10% phosphoric acid. The cultures were grown at 30 °C and agitated at an initial speed of 500 rpm.
PDI and GPx7 bioinformatic analysis Mouse GPx7 sequence (NP_077160. 1) without the ERretention signal was used as bait to search NCBI nr database restricted to eukaryotes via DELTA-BLAST  (Boratyn et al. 2013). Retrieved sequences were manually curated for domain topology, ER-retention signal, and length using SMART (http://smart. embl-heidelberg. de, (Letunic and Bork 2011)  and Signal P (http://www. cbs. dtu. dk/services/SignalP, (Petersen et al. 2011). Sequences were aligned with Clustal Omega (http://www. ebi. ac. uk/Tools/msa/clustalo,  (Sievers et al. 2011 ) and trees made with same program, using default parameter  (Blackshields et al. 2010). Dendrograms were plotted with iToL (http://itol. embl. de, (Letunic and Bork 2011) ) and manually edited for presentation. To clone the respective PDI homologs from each selected organism, the closest homolog to human protein disulfide isomerase precursor (PDI, NP_000909) were chosen and cloned 5′ to its respective GPx7.
Nucleotide sequences Nucleotide sequences of the E. coli codon optimized PDI-GPx7 fusions can be accessed via GenBank using the accession codes: For human (MT764745), mouse (MT764747), chicken (MT764744), zebrafish (MT764750), sponge (MT764749), pear (MT764748) and mollusk (MT764746) PDI-GPx7 fusions.
(continued)pACYC Duet-PDI-mmu GPx7-FLAG E. coli codon optimized PDI-GPx7 fusion from Mus musculus (Mouse PDI Accession Number NP_035162. 1 and GPx7 Accession Number NP_077160. 1) was synthesized and cloned into pACYC Duet (NcoI/SalI). 3' Flag tag was inserted using the primers NK1012-FLAG-r and NK1019-mmu-f. pACYC origin, CamR.
Genome analysis. We obtained a draft genome of 23 scaffolds representing 4,806,923 base pairs with a major one of 4,731,847 base pairs. Prodigal predicted 4,679 proteins, 4,594 of them being including into a major scaffold plus 48 tRNAs and 4,647 of them having a result in the nr database. The large majority of the best matches were obtained with Klenkia soli (2080 hits), and 958 with various Geodermatophilus spp. (Fig. 4 ). Regarding best hits' distribution, we observed that Klenkia 26. 3 genome isolated from germ-free mice is a close genome of another strain named Klenkia soli. 26. 3 bacterium deposited in CSUR (http://www. mediterranee-infection. com/article. php?laref=14&titre=collection-de-souches) culture collection under number CSURP6454. However, the complete 16S rRNA gene extracted from the complete genome of Klenkia 26.
3 is close to Klenkia terrae, for which no genome is currently available (Fig. 5 ). We decided to name this bacteria Klenkia terrae strain IHUMI-26. 3. Using Mauve aligner program, we visualised a close proximity, even if some blocks seem absent in Klenkia 26. 3 genome (data not shown). We determined 156,796 single-nucleotide polymorphisms between different blocks aligned between K. soli DSM 45843genome and K. terrae IHUMI-26. 3 genome. Analysis by OrthoANI confirmed the close relationship between genomes of K. soli strain DSM 45843 and K. terrae IHUMI-26. 3 (Fig. 6 ). qpcR on stool samples retrospectively used on stool sample. After the failed detection of the bacterium using 16S rRNA gene amplifications and the isolation of the bacterium. We designed 2 different specific primers against Rpob and gyrase subunit A genes of Klenkia spp.
and tested samples S1 and S2 used early for metagenomic. All qPCR are positive for the sample S2 (sample 26. 1) where both primers and sequencing confirmed the presence of K. terrae IHUMI-26. 3. qPCR with the same primer was tested against more stool samples from other isolators. Nine stools were positive in qPCR (data not shown), thus revealing a large contamination of An artificial bacterial mix was used to investigate this matter. We observed that the number of scaffolds obtained is 20 times higher in artificial mix compared to the metagenomic shotgun made on the 2 stools even if Fig. 5. Phylogenetic tree based on the 16S rRNA gene. Maximum likelihood tree was built using MEGA6. 0 package program with Jukes cantor method and 1,000 bootstrap replicates. The alignment was performed using MUSCLE and visualisation was done using iTol program.
Metagenomic sequencing on artificial mixture of bacteria. Strains of Staphylococcus aureus (CIP7625) Escherichia coli (CIP7624) and Clostridium butyricum NEC8 (accession number: GCA_001458815. 1) were used in association with the bacterium isolate 26. 3. After 7 days, when colonies were visible for the four bacteria, we picked one colony per each 4 bacteria and suspended it in 1 ml of PBS buffer to make an artificial mix and named it mix A. The same procedure of picking was reproduced 2 weeks later to simulate artificial aging of bacteria and was named mix B. Genomic DNA was sequenced on the MiSeq Technology (Illumina Inc, San Diego, CA, USA) with the paired-end application as described above. Briefly, DNA of mix A and B were quantified by a Qubit assay with the high sensitivity kit (Life technologies, Carlsbad, CA, USA) to 3.
5) for 90 seconds. After a 2-hour lysozyme incubation at 37 °C, DNA was extracted on the EZ1 biorobot (Qiagen) with the EZ1 DNA tissues kit. The elution volume was 50 µl. gDNA was quantified by a Qubit assay with the high sensitivity kit (Life technologies, Carlsbad, CA, USA) to 71. 5 ng/µl Genomic DNA was sequenced using the MiSeq (Illumina Inc, San Diego, CA, USA) with the same protocol that we previously used  28. As for the genomic assembly, we used paired-end reads (2,454,522 reads) after trimming the reads on the program CLCgenomics workbench 7. 5. The assembly was performed on CLC using standard parameter (a bubble size of 50 and a word size maximal at 64 and with a minimum length of contigs of 1,000 base pairs). Genes prediction was performed using Prodigal  29. Blastp against non-redundant database was done with an e-value cut-off at 10 -2.
tRNAs were predicted using bacteria tRNA-scan online parameter  30. Annotation was performed based on the blastp results and Interproscan v66. 0 https://www. ebi. ac. uk/interpro/search/sequence-search. The 23 scaffolds of the genome were available under the accession number: UEXK01000001-UEXK01000023 on the embl/EBI database. Mauve program was used to align genomes  31  and OrthoANI to perform genomic comparison  32. Alignment of 16S rRNA sequences. The 16S rRNA sequence obtained from the culture of Klenkia sp. was aligned with the primers cited on a previous section (536F and rp2) and with the sequence of our Klenkia terrae strain. For that, we used Muscle on MEGA 6. 06  33. We used this latter alignment on MVIEW  34  online https://www. ebi. ac. uk/Tools/msa/mview/. Detection of Klenkia sp.
by qpcR. Specific primers were designed against Rpob and Gyrase subunit A genes based on Klenkia spp available in NCBI database and with our Klenkia genome sequencing using Primer-blast  35. DNA extraction was performed using EZ1 DNA tissue kit (Qiagen) with Bacterial card and EZ1 automate. For the qPCR detection, we used the same primers. The DNA was amplified with the LightCycler 480 SYBR green I Master (Roche). The reaction mixture (20 µL) per sample was prepared as follows: DNA template (5 µL), forward primer (1 µL, 10 µM), reverse primer (1 µL, 10 µM), master mix (2×, 10 µL) and DEPC-treated water (3 µL). The amplification program includes an initial step of denaturation at 95 °C for 5 minutes followed by 45 cycles; each cycle consisted of denaturation at 95 °C for 10 seconds, annealing at 60 °C for 20 seconds and extension at 72 °C for 30 seconds, then single cycle of melting curve step followed by cooling.
Acknowledgements We sincerely thank  Takashi Irie ,  Kyoko Imai ,  Shigeki Matsubara ,  Taku Sakazume  and the  Hitachi team of Japan (Hitachi High-Technologies Corporation, Science & Medical Systems Business Group   24-14 , Nishi-shimbashi 1-chome, Minato-ku, Tokyo  105-8717 Japan ) for the collaborative study conducted together with the  IHU Méditerranée Infection , and for the installation of a TM4000 microscope at the  IHU Méditerranée Infection. This study was supported by  IHU Méditerranée Infection, Marseille, France  and by the  French Government  under the  «Investissements d'avenir» (Investments for the Future) program  managed by the  Agence Nationale de la Recherche  ( ANR , fr:  National Agency for Research) , (reference: Méditerranée Infection  10-IAHU-03 ). This work was also supported by  Region Provence-Alpes-Cote d' Azur and European funding FEDER PRIMI.
FACS data was acquired by FACS Diva v8. 0. 1 (BD Biosciences) and analyzed and visualized by Flowjo v10. 4. 2 (TreeStar). PCR analysis of V(D)J recombination products. Endogenous Vk 10-95 /Jk 4 coding joins (CJs) were amplified by nested-PCR as previously described for Vk 6-23 /Jk 1  35. A total of 500 ng of genomic DNA was amplified using JK4-pa and V10-95-pa primers and the following cycles: 1× (95 °C 5 min); 17× (94 °C 3 s, 62 °C 30 s, 72 °C 30 s); 1× (72 °C 5 min). Serial 4-fold dilutions of this reaction were amplified using JK4-pb and V10-95-pb primers and the following cycles: 1× (95 °C 5 min); 30× (94 °C 30 s, 62 °C 30 s, 72 °C 30 s); 1× (72 °C 5 min). Il2 gene was amplified using IMR42 and IMR43 primers and was used as loading control. The PCR gel images were acquired and analyzed using Image Lab v6.
Polq cDNA was amplified by semi-quantitative RT-PCR using Polq-F and Polq-R primers  66  (see Supplementary Table  2 ) and the following cycles: 1× (95 °C 5 min); 30× (95 °C 30 s, 60 °C 30 s, 72 °C 90 s); 1× (72 °C 5 min). β-actin was used as a control for transcript expression. The PCR gel images were acquired and analyzed using Image Lab v6. 0 (Biorad). v-Abl pro-B cells transplantation and Dasatinib treatment. Cells were cultured for 5 days and assessed for viability (at least 95% of viable cells) before transplantation. Cells were washed once in PBS and resuspended in PBS at 2. 5 millions/ ml before injection (200 μl, 0. 5 × 10 6 cells) in the tail vein of 7-8-week-old Rag2 -/- mice. 100 mM Dasatinib stock solution was prepared in DMSO and stored at -20 °C. 2 mM working Dasatinib solution was prepared in 80 mM citric acid (pH 3.
Igk locus capture-based target enrichment for sequencing. Coding and signal sequences of 140 mouse Igk V and 5 Igk J segments were downloaded from MGI database. 280 bps windows flanking recombination signal site (RSSs) of Igk V and J segments, including coding and signal sequences, were used to design Agilent SureSelect customized probes (Agilent Technologies). A total of 2348 120-mer probes (1168 for signal ends; 1180 for coding ends) were designed for Igk locus capture. SureSelect XT Target Enrichment System kit (Agilent Technologies) was used to prepare Igk locus capture sequencing libraries. Briefly, 3 μg gDNA was sonicated to 150 to 200 bps size. Sheared DNA was end repaired, dA tailed, ligated with provided paired-end adapter and amplified. The prepared DNA was hybridized to customized probes and captured by Dynabeads MyOne Streptavidin T1 (Life Technologies).
The pull-downed DNA was amplified with provided indexing primers of Illumina MiSeq for 250 bps paired-end sequencing. Data analysis and visualization. LAM-HTGTS (see also Supplementary Fig. 5 ). Sequencing reads in FASTQ files were decoded and adapter sequence trimmed using by ea-utils tool (https://expressionanalysis. github. io/ea-utils/). Paired-end reads with less than 20 nucleotides bait sequence in the 5' end of read1 were filtered out. Paired-end reads were processed by Samtools and mapped to the mouse mm9 reference genome using BWA mem  68, 69  with default settings. Paired-end reads with a mapping quality less than 10 and multiple mapped reads were filtered out. Mapped reads were then further analyzed using read 2. Among mapped read2, those that were not split mapped and mapped within the IgkJ4 bait to IgkJ2 regions were defined as germline.
Only the reads with split joins were further considered for microhomology and resection length analysis. For the calculation of resection length, the annotated Igk V and J segments were downloaded from MGI database. Only the segments with annotated recombination signal site were considered. To precisely calculate the resection length, V segments that are close to each other (less than 4 kbps distance) were filtered out and only 144 Igk V segments were kept. The resection toward both coding and signal end were calculated and the maximum resection length was set up to 4 kbps. For data analysis of Igk pull-down libraries, 2 × 250 bps paired-end reads were firstly assembled by FLASH tool  70  by at least 10 nts overlapping. Both assembled and un-assembled reads were mapped to mouse mm9 reference genome using BWA mem  68, 69  using default settings.
Acknowledgements We thank the  Institut Pasteur genomics  and cytometry platforms for help with sequencing and cell sorting,  Frederick Alt  and members of his lab for tremendous help with LAM-HTGTS experiments,  Barry Sleckman  for providing the  RAG2-Thy1. 1 complementation vector,  Joy Bianchi  for providing the Rag2 -/-p53 -/-v-Abl pro-B cell lines,  Carine Giovannangeli  and  Anne de Cian  for providing the Cas9 protein and  Thomas Mercher  for helpful discussions and suggestions. This project is funded by the  Institut Pasteur  (L. D. and C. D. labs), the  Institut National du Cancer  ( INCa Grant  #  PLBIO16-181  to L. D. and E. B. labs), the  Ligue Nationale Contre le Cancer  (Équipe Labellisée 2019 L. D. lab and Équipe Labellisée 2017 and 2020 E. B. lab) the  Cancéropôle IdF-INCa  ( Emergence 2016  grant to L.
The introduction of multivariate methods  (Haxby et al. , 2001)  that can detect value information encoded heterogeneously in brain activity patterns distributed across the brain, allowed to extend univariate results and answer questions related to the specific coding mechanisms the brain uses to encode value  (Kahnt, 2018). Although with these techniques scientists could decode various value signals in the human brain  (Clithero et al. , 2009; Kahnt et al. , 2010; Vickery et al. , 2011; Wisniewski et al. , 2015; Howard et al. , 2016; Yan et al. , 2016) , the exact neural code that it uses to represent value is seldom investigated. The only decoding study in humans that investigated how outcome value adaptation occurs in the brain  (Burke et al. , 2016)  considered only obtained outcomes. Moreover, despite the authors tested different possible types of adaptation in both univariate and multivariate signals, they employed methods that are hardly comparable.
Presentation order of the different pairs of symbols and the position of each symbol in the pair was pseudorandomized and unpredictable so that each symbol was displayed an equal number of times to either side of the screen. At the end of the four experimental runs and while the anatomic image was acquired, participants performed a post-learning evaluation of option value. We do not describe this task here because the data were not used for the analyses described in this paper. For details, please see the original article  (Palminteri et al. , 2015). acquisition parameters as follows: TR 2200 ms, TE 21 ms, FA 75°, FOV 192 mm, matrix size 64 Â 64, yielding an in-plane voxel resolution of 3 mm 3 with voxel size of 3 Â 3 Â 2. 3 mm. A T1-weighted anatomic sequence was recorded as well, with imaging parameters: TR 2700 ms, TE 4.
Statistical analyses fMRI data from this experiment has been previously analyzed with a model-based procedure to test the hypothesis that successful avoidance of punishment is reframed as a positive outcome and that neural activity in brain regions within the valuation system is better accounted by a relative model of value representation compared with an absolute model  (Palminteri et al. , 2015). An additional aim of the previous analyses was to assess neural encoding of choice and outcome in those different regions as a function of task context. The analyses described in this paper differ in either the method used (MVPA) or the question asked (whether and how outcome representation changes as a function of the available information). Behavioral analyses. For the sake of clarity and completeness, we report behavioral results (see Fig.
We ran LMM analyses using R 3. 2. 3 (RRID:SCR_ 001905) and the lme4 package  (Bates et al. , 2015 ; RRID:SCR_015654). We favored LMMs over repeated-measures ANOVA because they take into account subjects' sensitivity to different experimental conditions beyond individual variability in mean responses. Thus, LMMs are more powerful than repeated-measures ANOVA  (Barr et al. , 2013). Statistical significance of the tests was calculated with likelihood ratio tests of the model with the effect of interest against the model without that effect  (Barr et al. , 2013). Post hoc tests comparing different levels of the factors and interactions showing a significant effect were performed using the emmeans package  (Lenth, 2018). To maintain the alpha level at the intended value of 0. 05, we performed pairwise comparisons with the Tukey HSD test  (Tukey, 1949)  and chose the Holm's method  (Holm, 1979)  to correct for multiple comparisons and control the familywise error rate while maintaining high statistical power.
Comparing different value coding models The second goal of this study was to assess how the human brain encodes outcome value. Specifically, we aimed to test whether outcomes are represented using an absolute, a partially-adaptive, or a fully-adaptive code (see Fig. 4A ). To this purpose, we implemented different support vector regression (SVR) analyses (for a detailed description, see  Kahnt et al. , 2011)  that captured the characteristics of each of the theoretical models. Different from the classification approach described in Evaluating differences between output types, we used the local patterns of brain activity associated with multiple predictors (i. e. , the 4 possible outcomes: 10. 5e, 10. 0e, À0. 0e, and À0. 5e) to predict a continuous variable (i. e. , the outcome value as assumed by the theoretical model).
The three SVR analyses were performed on data from each pre-selected ROI (see Evaluating differences between output types), yielding to a prediction accuracy value for each participant, model, analysis, and ROI. We used LMM and likelihood ratio tests (see Evaluating differences between output types) to assess the effect of coding model, outcome type, and ROI on prediction accuracy (for details of each analysis, see Results, Revealing the neural code underlying outcome valence representation).
The second aim of the study was to investigate how outcome valence is represented in the brain. To this purpose, we compared the accuracy of three different coding models (i. e. , absolute, partially-adaptive, and fully-adaptive) in predicting valence encoding in the different outcome conditions. We found that outcome valence is represented with different neural codes when either complete or partial feedback is provided. In particular, although neural coding is better described by absolute coding when partial feedback is given, relative coding fits the fMRI data better when feedback is complete. This result is in line with previous research using computational modeling and showing a better fit of behavioral data for an absolute coding model when only partial feedback is used, whereas a relative model fits the data better when complete feedback trials are also introduced  (Bavard et al.
Thus, counterfactual outcomes are both represented and processed in these regions. Our finding that in the most ventral mPFC areas factual outcomes are encoded positively and counterfactual outcomes are encoded negatively is consistent with previous evidence  (Li and Daw, 2011; Klein et al. , 2017). We extend these results by showing that the pattern is opposite in the dorsal mPFC areas. Finally, we found that presenting information about the alternative choice outcome affects how the brain encodes outcome valence, moving from an absolute code when information is partial to a relative code when information is complete, thus confirming our original hypothesis. But how does counterfactual information affect behavior? As previous studies showed that learning is significantly higher when complete information is provided than when feedback is partial  (Palminteri et al.
c Pyramidal cell transfer function quantified with and without DBS. Depolarizing current steps (200 ms, 0-1. 2 nA in 0. 2 nA steps) were applied at 1 Hz before and during 100 s of DBS. DBS decreases evoked spiking activity. Left: raw data example and f-I relationship in a pyramidal neuron, showing a decreased evoked firing rate during DBS (DBS effect F 1,90 = 155. 72, p = 2. 5 × 10 -21 , 2-way ANOVA followed by Bonferroni-corrected post-hoc tests: all p < 10 -3 for 0. 4-1 nA injected current). Right: DBS increases the rheobase (p = 0. 0312, Wilcoxon signed-rank test, n = 17 neurons), without affecting the gain of the f-I curve (p = 0. 1746, paired t-test) (mean ± SEM and individual neurons). Rheobase and gain were respectively determined as the x-intercept and the slope of the f-I curve after a linear fit.