dataesr/scientific-paragraphs-categorization huggingface dataset:
Changes in Whole Blood Gene Expression in Individuals Immunized With ex vivo Generated DC HIV Vaccine All 19 included patients received the four vaccinations as planned (Figure  1 ) with no changes in vaccine dose. Among them, 16 had all data available for the integrative analysis. Their characteristics are reported in Table  1. First, we analyzed changes in transcriptomic whole blood gene expression throughout the study; i. e. , every 4 weeks from baseline, including time points before each vaccination at weeks 0, 4, 8, and 12 and at week 22, i. e. , 2 weeks before ATI. For this, we developed a specific statistical method, the time course gene set analysis, to detect significant change of gene abundance in groups of genes over time taking into account the heterogeneity of the dynamics The composition of M3.
e. , gene set) level were needed to detect the signals in this study during the vaccination period  (23)  because this type of analysis is much more powerful than a gene-by-gene analysis  (38). By not measuring changes of gene abundance at early days after vaccination, we may have missed the dynamics of the innate response detectable at day 1 and day 3 after vaccination  (10, 11, 14, 39). Nevertheless, it is interesting to note that subtle changes are still detectable later on (i. e. , after day 7) when using geneset/module approaches with appropriate statistical methods  (23). This is confirmed also by our re-analysis of public datasets of transcriptomic changes after Pneumococcus or Influenzae vaccination in humans  (11). Modules including genes associated with inflammation (M3. 2, M4. 2, M5.
ACKNOWLEDGMENTS The authors wish to thank all participants of the ANRS/VRI DALIA 1 trial and the members of the trial committees and of the ANRS/VRI DALIA 1 study team. DSMB members:  William Duncan ,  Roy M. Gulick ,  Daniel R. Kuritzkes ,  James Neaton ,  Richard Pollard  (Chair). Event Validation Committee members:  Joseph Fay ,  Ronald Mitsuyasu ,  Jean-Paul Viard. Trial management:  Derek Blankenship ,  Carson Harrod ,  David Jutras ,  Bryan King ,  Christine Lacabaratz ,  Sophie P'erusat ,  Elisa Priest ,  Charlie Quinn ,  Anna Laura Ross ,  Louis Sloan. Apheresis Team  at  Baylor University Medical Center (BUMC)  led by  Dr Luis Pineiro. Vaccine/GMP team:  Susan Burkeholder ,  Amanda Cobb ,  Charles McWilliams ,  Jennifer Finholt-Perry ,  Lee Roberts ,  David Schackmann.
FUNDING This study was supported by a grant from the  Agence Nationale de Recherches sur le SIDA  et les hépatites virales (ANRS). LR received a Ph. D. scholarship financed by  Sidaction. This work was supported by the  Investissements d'Avenir program  managed by the  ANR  under reference  ANR-10-LABX-77-01  funding the  Vaccine Research Institute (VRI).
Patients Demographics Patients eligible for pancreatic surgery with suspicion of pancreatic cancer or IPMN (intraductal papillary and mucinous neoplasm) with worrisome features as determined by CT-scan (computerized tomography scanner) and or MRI (magnetic resonance imaging) without metastasis were enrolled at the department of hepatobiliary surgery of Bordeaux university hospital between February and November 2017. This prospective study was conducted according to the Declaration of Helsinki, the French rules (Law for Bioethics November 2016) and the recommendations of CNIL (Comit e National Informatique et Libert e), and was approved by an Institutional Review Board. The biological collection was declared to and approved by the "comit e de protection des personnes sud ouest outremer" under the number 2016-A00431-50 and the database was registered in Clinical Trials under the number: NCT03032913.
Anti-CD9-PE antibody (Biolegend) and anti-GPC1 primary antibody (PIPA528055, Thermofisher) revealed with Alexa Fluor 647 donkey anti rabbit IgG (Biolegend) were used together. All staining steps and washes were carried out in PBS1X/ BSA 0. 1%. Samples were examined on a BD FACS CANTO II apparatus using unstained beads and beads stained with isotype controls (PE mouse IgG1 Kappa isotype control clone MOPC-21 (Biolegend) and rabbit igG isotype control Alexa Fluor 647 conjugate (OZYME, Montigny-le-Bretonneux, France)) to setup quantification areas on dot plots. Data were collected on FSC and SSC linear parameters (645 V and 520 V, respectively) and on a logarithmic scale for Alexa 647 and PE (455 V and 400 V, respectively). Data were analyzed with BD FACS Diva software (BD Biosciences, Le Pont de Claix, France).
Statistics Qualitative and quantitative data were analyzed with usual statistic tools (including Student's-test, Chi-Square and Fisher exact tests) using GraphPad-Instat and GraphPad-Prism 8. 0 software (GraphPad Software Inc. , San Diego, CA, USA). The Spearman correlation coefficient was used to assess the correlation between different variables. Group differences were tested using the nonparametric Wilcoxon-ManneWhitney test for two groups. Receiver operating curves (ROC) were obtained to describe the accuracy of detecting cancer. The cutoff points were selected using Youden's index, which maximizes the sum of sensitivity and specificity. ROCs and Youden's index calculation were performed using SAS software (version 9. 4, SAS institute, Cary, NC, USA).
Correlation of Exosome Levels to Clinicohistopathologic Risk Factors and Prognostic Values The median follow-up was 18 months (range 2-23). All data were included until December 2018. A low positive correlation between the percentage of GPC1-positive exosomes in peripheral blood (Figure  5A ), but not in portal blood (Figure  5B ) and the tumor size was found (Pearson's test r ¼ 0. 39, Figure  5A ), with a p value close to statistical significance (P ¼. 07). Exosome rate did not correlate with other histolopathological parameters, including tumor stage, the number of invaded lymph nodes and carcinoma differentiation. Despite the short follow-up period (median 554 days; mean 532 days; range 74-718 days) and the small size of the cohort, we analyzed the exosome results in the context of the clinical follow-up.
g. [8, 18, 37] ) and tropical species (e. g. [17, [38] [39] [40] ). Using automated sensors, we collected for the first time acoustic and environmental data to monitor simultaneously and regularly explosive breeding events in tropical anuran communities, at five temporary ponds located along the Kaw Mountain in French Guiana. This systematic passive acoustic monitoring allowed us to tackle key ecological questions related to the patterns, causes and consequences of such a striking phenomenon. We specifically addressed four questions:  (1)  What are the main meteorological factors that trigger the emergence of explosive breeders? (2) Which species co-occur before and during explosive breeding events? (3) What is the variation in the acoustic community composition within and between sites? (4) What are the main acoustic patterns, spectral characteristics and diversity before and during explosive breeding events? Answering these questions may shed light on the potential selective pressures shaping these complex acoustic communities.
Sampling protocol We monitored anuran calling activity and weather conditions simultaneously in each pond using automated sensors with a regular sampling schedule. To record the acoustic communities, we placed on the edge of each pond at breast height an automated sound recorder equipped with an omnidirectional microphone (SM2, Wildlife Acoustics, Inc. , Concord, MA, USA). The device was set up to record data 1 min every 29 min, at 44. 1 kHz and 16 bit resolution, so that we obtained 5616 recordings for each pond. To register local abiotic environmental data, we placed next to the sound recorder a data logger (H21-002, Onset) equipped with sensors to measure three weather variables: rainfall (Onset, S-RGB-M002), temperature, and relative humidity (Onset, S-THB-M008). In addition, we retrieved two global environmental variables, atmospheric pressure (PTB220, Vaisala) and solar radiation (CMP6, Kipp and Zonen), from the nearest weather station at the Félix Eboué airport (4°50′N; 52°22′W), 19 km from the study site.
The final predictor matrix included 48 variables with 466 observations. We measured prediction accuracy and variable importance on classification using the random forest statistical classifier  [41]. We assessed the importance of the predictor variables by comparing the difference in misclassification error (mean decrease accuracy) between the original data and a permuted set of data. The modified data for each predictor variable consisted in randomly permuted observations that are passed down the random forest. The higher the decrease in accuracy between the original and the modified data, the higher the importance of the predictor variable  [42].
Community diversity analysis We investigated temporal and spatial variation on the diversity and composition of the acoustic communities of explosive breeding events. We define a community as the set of species heard at a given time interval on a given pond. For each event, we systematically discretized the temporal gradient of 48 h into four temporal periods of 12 h. A first period (t1) ranged from 24 to 12 h before the explosive breeding event, a second period (t2) ranged from 12 h before to the onset of the event, a third period (t3) enclosed the first 12 h of the event, and a fourth period (t4) ranged from 12 to 24 h after the onset of the event. We then sub-sampled our database by choosing one recording every 2 h, for a total of 240 recordings of 60 s. Three of us (EC, AF and PG), who are highly trained in aural identification of anuran species of French Guiana, scrutinized each recording and annotated the occurrence of calling species.
For each diversity index, we tested the differences among periods of the explosive breeding event (i. e. t1, t2, t3 and t4) and between events (i. e. the first and second event per site), as well as the interaction between both factors, with repeated-measures ANOVA. Shapiro-Wilk and Mauchly tests revealed no violation of the assumptions of normality and sphericity, respectively, when using ANOVA tests (in all cases: W > 0. 76, df = 5, p > 0. 05; X 2 < 0. 02, df = 5, p > 0. 05). Tukey test with Bonferroni correction was finally performed as post hoc procedure to examine pairwise comparisons between time periods. The type I error was set at a nominal level of 5%. Acoustic and statistical analyses were computed using the R software  [49]. In particular, spectral audio features and dissimilarity matrices were computed using the seewave R-package  [50] , community and diversity ordination analyses were calculated with the adiv R-package  [51] , and statistical classification was computed with the random forest R-package  [52].
Acknowledgements The authors would like to thank  Dorothée Deslignes  from the  CNRS Cayenne  for the field-work logistics operations. In addition, authors would like to warmly thank  Jeannot and Odette Morvan  from the  Gîte Patawa  for their help during field work in French Guiana. We acknowledge support from an  'Investissement d' Avenir ' Grant managed by  Agence Nationale de la Recherche  ( CEBA, Ref. ANR-10-LABX-25-01 ). Support from the  Marais de Kaw Reserve.
Funding This research was supported by the  Labex CEBA  ( Centre d'Étude de la Biodiversité Amazonienne ), which provided financial and logistic support for the data collection. JSU was supported by  COLCIENCIAS  ( Doctoral Scholarship  of the Colombian government, 2014 call # 646 ). DLL was supported by a  Global Marie S. Curie fellowship  ( European Commission , program  H2020 ,  EAVESTROP-661408 ), a postdoctoral grant  Atracción de Talento Investigador (Comunidad de Madrid, CAM, Spain ,  2016-T2/AMB-1722 ), and acknowledges funding provided by the  Ministerio de Economía, Industria y Competitividad  ( CGL2017-88764-R ,  MINECO/AEI/FEDER, Spain). None of the funders had any role in the design, analysis, interpretation of results or writing the manuscript.
Runx3 High group was characterized by the expression of Hmx1, Fyxd7, Fam19a4 and an enrichment in Ntrk3 while the Runx3 Low group expressed Sst, Pou2f2 and Tbx3 (Fig. 4d, h ). The correlation between Runx3 mRNA level and TRKC protein, and between TRKC protein and Ntrk3 mRNA, was then validated in vivo (Fig. 4f, g ), as well as the existence of the two PSNs subgroups (Fig. 4h-k ). Our results thus confirmed the existence of two PSNs subgroups based on their gene expression and that may correspond to the two PSNs groups with distinct survival outcome (Fig. 4l ). We explored further our dataset and performed a gene ontology analysis to reveal benchmark biological categories behind the genetic blueprint of the Runx3 High and Runx3 Low. The top significant categories shared by both subgroups were biological categories associated with neuron development, axogenesis and neuron projection (Fig.
c t-SNE plot showing two distinct populations of Ntrk3 + /Runx3 + PSNs at E11. 5, the Clusters 1 and 2 in b. eTRKA: early TRKA neurons; MR: mechanoreceptive neurons  18  ; both populations transiently express TRKC at an earlier time point  38. d List of Cluster 1-(top panel) and Cluster 2-enriched (bottom) genes. e The two clusters express similar levels of classic neuronal markers but differ in their level of Runx3 and Ntrk3 transcripts and are named Runx3 High and Runx3 Low. Box plots visualize the summary of the dataset (minimum, lower quartile, median, upper quartile and maximum). Wilcoxon rank sum test (# indicates significant difference, see Methods section for details). f, g In vivo validation of the correlation between TRKC protein and Runx3 mRNA (f) and between TRKC protein and Ntrk3 mRNA (g).
d Examples of differentially expressed genes found in Runx3 High or Runx3 Low subpopulations of PSNs. e Expression of select genes that encode proteins relevant to neuronal physiology (asterisks depict genes important for the generation of action potentials). Note that this category of genes is uniquely over-represented in Runx3 High PSNs. f Expression of genes important for the generation a mature action potentials (Scn3b/7a/9a) and for mechanotransduction in PSNs (see text for details). Boxplots visualize the summary of the dataset (minimum, lower quartile, median, upper quartile and maximum). g Summary of the differential expression profile of the two subpopulations of PSNs, with a majority of genes (17,352 genes) showing similar patterns; genes enriched in Runx3 High PSNs encode proteins associated with synaptic communication.
h UMAP plot of PSNs single cells RNA-seq data from E11. 5 and E12. 5 (before and after cell death period, respectively), revealing two clusters at E11. 5 (blue and red, corresponding to the clusters in a) and only one cluster at E12. 5 (green). i Expression pattern using UMAP plots with specific Runx3 High and Runx3 Low marker genes shows that marker genes of E11. 5 Runx3 High remain expressed at E12. 5. j Unspliced-spliced phase portraits (left, cells colored according to a) and u residuals (right) are shown for each gene in i. k Velocity estimates projected onto the two-dimensional UMAP plot of the dataset shown in h, and with a similar color code. Velocity vectors indicate positive velocity (strong directional flow) from one cell state to another and overall can be interpreted as a maturation directionality from Cluster 1 to Cluster 3.
Staining was documented by confocal microscopy (Zeiss LSM700) using identical settings between control and experimental images. Optical sections were 2 μm in 20× overview pictures unless specified. Quantifications. For neuronal counting, soma area and intensity quantifications ImageJ software was used. Only neurons with a visible nucleus were used. Quantification of molecular markers in the dorsa root ganglia was carried out on a minimum of five DRGs sections or more randomly selected sections per animal, per condition (see figure legends for n's and genotypes). In Fig. 1a , for accurate quantification and comparison of the number of PSNs across stages, we quantified the total number of PSNs in every third sections of C5 and C7 DRGs. Also, note that for the βIII-tubulin quantification, the two outer lateral rows of cells in the different DRGs sections were not included in the analysis due to saturation of signal caused by axonal track passage that are heavily stained.
Mapk6 and Gapdh were included as reference genes. No template and no reverse transcriptase controls did not amplify, or were detected at least five cycles after the least strong biological sample. In addition, amplicons were sequenced to confirm specificity. Data were analyzed using qBase+ (Biogazelle, Belgium). The truncated isoforms of Ntrk3 cannot be uniquely measured by qPCR method. Hence, the quantity of Ntrk3 T transcripts is deduced by subtracting the value of Ntrk3 FL from the value of pan Ntrk3. smFISH. Ten-micrometer-thick sections were mounted on cover glasses. Fresh sections selected for the experiment were post-fixed with 4% PFA (in PBS) for 10 min at room temperature, rinsed with PBS and permeabilized with methanol. After permeabilization, the sections were incubated for 10 min at 70 °C in Tris-EDTA pH 8.
0. After washing twice with SSC 2×, the sections were incubated with hybridization buffer containing 250 nM fluorescent label probes (LGC Biosearch Technologies; see Supplementary Table  1 ) for 4 h at 38. 5 °C. After four 20% formamide-SSC 2× washes, the slides were counterstained with Hoechst, washed with SSC 2× and mounted with Prolong Gold mounting medium (ThermoFisher Scientific). Image stacks (0. 3 µm distance) were acquired using a Nikon Ti-E with motorized stage (Nikon). The images were analyzed with a custom python script using the numpy, scipy. ndimage (http://www. scipy. org/) and scikit-image libraries  68. Briefly, after background removal using a large kernel Gaussian filter, a Laplacian-of-Gaussian was used to enhance the RNA dots in the maximum projected image. Background objects significantly larger than the smFISH dots were removed after image thresholding and the remaining RNA dots counted in user-defined ROIs.
Single Tomato-positive cells were sorted by FACS into individual wells of 384-well plates containing lysis buffer. The plates containing single cells were frozen immediately on dry ice and stored at -80 °C. We used six animals (ten DRGs per animal) per experiment. Single-cell RNA sequencing. • Smart-Seq2 protocol was performed on single isolated cells by Eukaryotic Single Cell Genomics Facility at SciLifeLab, Stockholm  69. Raw data processing: the raw data of single-cell RNA sequencing was processed with standard pipeline by Eukaryotic Single Cell Genomics Facility at SciLifeLab, Stockholm. Most of the downstream analysis was done using R software package Seurat v2. 0. • Pre-processing (Initial filtration, normalization): In our initial analysis of the data, we noticed contamination of nonsensory neurons in each dataset.
Furthermore, we removed genes that were not reliably detected, our condition for retention was: a gene needs to have at least 38 read counts across the whole dataset and at 18 counts in at least one cell. To select the most informative genes for downstream analysis, we applied a feature selection procedure  70. Briefly, a noise model was fit on the data and genes are ranked by the residuals to the fit, the top 1400 genes were selected. We log-transformed the data matrix and we generated a 20 nearest-neighbors graph using Euclidean distance in the space spanned by the first six principal components. This graph was, then, imputed to the Louvain community detection algorithm to cluster the cells. For the core analysis on the Runx3 + /Ntrk3 + populations (n = 274), we used a QC and filtering procedures that identical to the one described above with the following differences: that we selected more genes (1900 genes), more PCA components (65)  and 25 nearest neighbors to construct the graph and perform Louvain clustering.
These choices were aimed at extracting the most accurate representation of the dataset. To visualize the data, we used UMAP with parameters n_neighbors = 40, learning_rate = 0. 6, min_dist = 0. 5 and spread = 0. 55. RNA velocity analysis was then performed using our tool velocyto. py  43. We used the default parameters that we used in the original publication  43  with three differences: • We pre-processed the data matrix only minimally (since the data is deeply sequenced and high quality), by calling velocyto. knn_imputation with parameters k = 5, b_sight = 50 and b_maxl = 20 • Only the most mature cell population (Cluster 3 in Fig. 5h ) consisting entirely of cells sampled at the time point E12. 5 was used to fit the degradation rate parameter (i. e. gamma). This procedure corresponds to the assumption that these cells are less rapidly changing and closer to a steady state.
Acknowledgements We thank  D. Ginty  for the TrkC CreER mice;  Y. Groner  and  D. Levanon  for the Runx3 mice;  C. Ibanez  for the R26 tdTOM mice;  P. Ernfors ,  H. Abdo  and  L. Calvo Henrique  for the R26 CreERT2 ;R26 tdTOM mice;  T. Jessell  for the RUNX3 antibody;  N. Ghyselinck  and  P. Dollé  ( IGBMC, Strasbourg ) for the Raldh floxed mice; the  CLICK imaging Facility  supported by the  Knut and Alice Wallenberg Foundation. We are also grateful to  Prof. A. El Manira  and  P. Ernfors  for critical reading of the manuscript. This work was supported by the  Swedish Research Council (VR) , the  Ragnar Söderberg Foundation ,  Knut and Alice Wallenberg Foundation ,  Swedish Brain Foundation ,  Karolinska Institutet , the  Karolinska Institutet Strategic Research program in Neuroscience (StratNeuro) , the  Ming Wai Lau Foundation and Åke Wiberg Foundation  (F.
L. ); by  RARENET  V No. 1. 7  EU Regional Development Fund, University of Strasbourg Institute of Advanced Studies (USIAS)  (K. N. ); and by  ERC  (BRAINCELL grant  261063 ), VR, the  Wellcome Trust  (grant  108726/Z/15/Z ), and the  EU  ( FP7/DDPDGENES ) (S. L. ). F. L. is a Ragnar Söderberg fellow in Medicine, a Wallenberg Academy Fellow in Medicine and a MWLC investigator. Open access funding provided by  Karolinska Institute.
General All data for this study were analysed in R (R Core Team 2017). We used base R and the 'lme4' package  (Bates et al. 2015)  to run all generalised linear models (GLMs) and the generalised linear mixed model (GLMM), respectively. All GLM(M)s included experimental treatment group (aviary/laboratory) as a fixed effect and were fitted using a binomial distribution and logit-link function; the GLMM included subject ID as a random effect (to account for data non-independence). The goodness-of-fit of all models was confirmed (all p > 0. 05) using the 'testUniformity' function in the 'DHARMa' package  (Hartig 2017) , and Type II likelihood-ratio chi-square tests (from the 'car' package; Fox and Weisberg 2011) were used to assess the significance of main effects  (Langsrud 2003).
Experimental Design For the optimization of efficacy of different coagulant into the production of waragashi, the experiments were conducted according to a central composite design with two variables. The central composite design predicts uniformly at all constant distances from the center points. The design was generated by Design Expert statistical software (version 6. 0, Stat-Ease, Inc. , Minneapolis, MN), (Table  1 ). The variables were process temperature (40°C-70°C) and coagulant quantity (5mL-25mL).
Manufacture of Waragashi Fresh cow milk is lightly heated at SM-8B Water Bath Microfield instrument England and coagulates with latex of ones of coagulant for 10 minutes. The obtained coagulum was straining after 10 other minutes of heat at 100°C on the heating plate (CAMAG TLC PLATE HEATER III). The waragashi was left at drainage for one hour before it weighing. Coagulants were added to heated milk according to the experimental design with two factors (temperature, amount of coagulant) generated by Design Expert statistical software (version 6. 0, Stat-Ease, Inc. , Minneapolis, MN).