dataesr/scientific-paragraphs-categorization huggingface dataset:
Our gating strategy builds on the characteristics of the new generation of cytometers that allow precise standardization of the fluorescent signals (voltage-dependent settings). The FSC/SSC gates that are commonly used as a first step in gating strategy were omitted due to variance that could not be controlled. This approach facilitated the rational setting of gates and permitted batch-analysis using FlowJo. As a result of these efforts, only minimal gate positions adjustment was required for a small number of cell populations. Additional standardization of post-analytic gating procedures is addressed in an accompanying manuscript [Chen et al. , co-submission]. In conclusion, our efforts are in line with several international consortia, with high coherence in the staining protocols reported by the EuroCell Consortium  [7].
Acknowledgments We thank  Olivier Mary  for organizing sample transport and  Christophe Rousseau  for purchasing. We thank  BD, eBio, Miltenyi  for providing the antibodies for testing,  Traci Zell  for her advice in the choice of fluorochromes in panel design and  Cedric Ait Mansour  for his help in the cytometer set-up. This work benefited from support of the  French government's Invest in the Future Program , managed by the  Agence Nationale de la Recherche  ( ANR , reference  10-LABX-69-01 ).
micro-manager. org/ wiki/). When calcium imaging was combined with photostimulation of Vglut2::ChR2-YFP preparations, the calcium imaging back-illumination was set at a minimal level using neutral density filters in the light-path, which caused no noticeable changes in the rhythmic activity of the RTN. Relative fluorescence changes (ΔF/F) in response to photostimulation in Figures 1E-G, 5H, 8E,F show the ΔF/F image calculated by averaging a 1 s sequence acquisition (10 frames) starting at the offset of the light stimulus. Whole-cell patch-clamp recordings were performed under visual control using differential interference contrast and infrared video microscopy, a MultiClamp 700B amplifier, a digitizing interface Digidata 1550 and the software program pClamp10 (all from Molecular Devices, Sunnyvale, CA).
Acknowledgements We thank  V éronique Dubreuil  for early data on periV cells,  Marie-Rose Hirsch  and  Zoubida Chettouh  for technical assistance,  Guillaume Dab  ée,  Christophe Auger  and  Carmen Le Moal  for expert animal husbandry and  T M üller  and  C Birchmeier  for antibodies.
, 2006a)  and reanalysis of the flowering seasons back to 1997  (Siljamo et al. , 2008c; Veriankaitė et al. , 2010). The COSMO-ART birch module was developed at the University of Karlsruhe  (Helbig et al. , 2004; Vogel et al. , 2008)  and MeteoSwiss  (Pauling et al. , 2012; Zink et al. , 2013)  and is currently used for pollen forecasting for central and south-western Europe. Development is also going on in Denmark with the regional ENVIRO-HIRLAM system  (Mahura et al. , 2009)  applied for forecasting over northern Europe. Finally, combining the COSMO-ART and SILAM source terms,  Efstathiou et al. (   2011 ) developed a regional-scale model for the USA and applied it to birch and ragweed. MACC (Monitoring of Atmospheric Composition and Climate, http://www. gmes-atmosphere. eu) pollen simulations are based on the SILAM source term.
Acknowledgements. The work was performed within the scope of the FP-7  MACC-II  project. The birch source term is developed within the scope of the  Academy of Finland  POLLEN and APTA projects. Observational data were provided by national pollen monitoring of  Austria, Estonia, Germany, Finland, France, Latvia, Lithuania, Russia, Spain, Switzerland , and  Ukraine , members of the  European Aeroallergen Network. Edited by:  S. Galmarini.
Identifying key microRNAs (miRNAs) contributing to the genesis and development of a particular disease is a focus of many recent studies. We introduce here a rank-based algorithm to detect miRNA regulatory activity in cancerderived tissue samples which combines measurements of gene and miRNA expression levels and sequence-based target predictions. The method is designed to detect modest but coordinated changes in the expression of sequence-based predicted target genes. We applied our algorithm to a cohort of 129 tumour and healthy breast tissues and showed its effectiveness in identifying functional miRNAs possibly involved in the disease. These observations have been validated using an independent publicly available breast cancer dataset from The Cancer Genome Atlas. We focused on the triple negative breast cancer subtype to highlight potentially relevant miRNAs in this tumour subtype.
For those miRNAs identified as potential regulators, we characterize the function of affected target genes by enrichment analysis. In the two independent datasets, the affected targets are not necessarily the same, but display similar enriched categories, including breast cancer related processes like cell substrate adherens junction, regulation of cell migration, nuclear pore complex and integrin pathway. The R script implementing our method together with the datasets used in the study can be downloaded here (http://bioinfo-out. curie. fr/projects/ targetrunningsum).
The defined enrichment score for a given miRNA is expected to be high if most of its predicted targets are at the top or at the bottom of the ranked list. The significance of the enrichment score is evaluated by a permutation procedure. As final result we obtain miRNAs showing a statistically significant enrichment score, which we consider as potential regulators in the analyzed conditions. The analysis pipeline is summarized in Figure  1. It has been implemented as a freely available R script (code available at http:// bioinfo-out. curie. fr/projects/targetrunningsum). We applied our method to elucidate the regulatory effect of miRNAs on the breast cancer transcriptome. Breast cancer is classified into various subtypes mainly based on the immunohistochemical staining of estrogen (ER), progesterone (PR) and HER-2 (ERBB2) receptors.
The complex nature and heterogeneity of this disease, particularly with regard to gene expression profiles, make it difficult to detect the shaping effect of miRNAs on the transcriptome. We applied our algorithm to a breast carcinoma dataset including gene and miRNA expression from normal and breast tumour samples (which we refer to as Maire dataset  [22, 23] ) and we show that it is able to identify miRNAs with statistically significant enrichment score. These results are then compared with those obtained on an independent dataset of normal and breast tumour samples from The Cancer Genome Atlas (TCGA)  [24]. We further focus on the triple negative breast cancer (TNBC) to highlight miRNAs potentially relevant in this particular tumour subgroup. This subtype is intensively studied due to the lack of effective targeted therapies.
We ran our algorithm including only samples characterized as triplenegative breast tumours and identified a set of miRNAs showing statistically significant signal in both Maire and TCGA datasets. Finally, we investigated miRNAs identified as potential regulators to characterize the function of their targets. In the proposed algorithm, hundreds of genes account for the enrichment signal of a single miRNA and we expect a subset of them to participate in common cellular functions. We use multiple annotation databases to infer biological processes affected by the identified miRNAs. For those identified in both datasets, the corresponding sets of target genes were analyzed separately. We observe that even if the specific genes accounting for the enrichment of biological categories among miRNA target genes detected in the Maire dataset and in the TCGA datasets are not necessarily the same, they are associated to common cancer-related pathways.
Functional miRNAs in breast cancer The analyzed dataset includes 129 tumour and healthy breast tissues for which both miRNA and mRNA expression profiles are available (see Material and Methods). In this study, we used sequence-based target sets obtained from TargetScan version 6. 2  [8, 25] , a widely used algorithm which takes into account sequences that match the seed region of each miRNA and evaluate their conservation in several species. The confidence of target predictions is calculated as described in Methods. We include in our study 394 miRNAs for which both expression data and TargetScan predictions are available. We identified 136 miRNAs as potential regulators with FDR < 0. 1 (results are reported in Additional File 1, Table  S1 ). Among the top significant miRNAs, we found several ones that are known to function as oncomirs, such as the members of miR 17-92 cluster and its paralog cluster miR-106b-25, miR-15 and miR-16  [26].
To validate these results, we applied our algorithm to an independent dataset of 521 healthy and cancerous breast tissue samples from the TCGA project (see Material and Methods). This study includes 260 miRNA for which both expression data and TargetScan predictions are available. Of these, 142 miRNAs are identified as regulators with FDR < 0. 1. The intersection between results obtained in both datasets contains 44 miRNAs (see Figure  2a  and Table  1 ) which corresponds to a statistically significant overlap (hypergeometric p -value < 0. 05). For a limited subset of 7 commonly identified miRNAs, the enrichment score obtained for the Maire dataset has opposite sign compared to that obtained for the TCGA dataset. In these cases, predicted targets are enriched among genes whose expression is positively correlated with the expression of the miRNA.
Triple negative breast cancer specific study Of all breast tumours, TNBC is a very malignant subtype with poorly characterized molecular pathogenesis  [31]. To elucidate the role of miRNA regulation in this specific cancer subtype, we applied our algorithm to the Maire dataset including only TNBC samples (n = 37). Similarly, we performed the analysis on the TCGA dataset selecting only TNBC samples (n = 82). Tumors were assigned to this subgroup according to ER, PR and HER2 negative status. Using TargetScan predictions, the algorithm identified 166 significant miRNAs in the Maire dataset (89 of them included in the common universe of miRNAs analyzed in both datasets) and 75 (64) in the TCGA dataset with FDR < 0. 1. The agreement between results obtained in the two datasets is highly significant (41 commonly identified miRNAs, hypergeometric p -value < 10 -4 ).
Identification of biological processes targeted by miRNAs To assess the biological relevance of miRNAs identified as potential regulators, we investigated whether the genes that account for the enrichment signal of a given miRNA participate in the same cellular process or signalling pathway. The assumption that some miRNAs downregulate a group of genes participating in the same pathway is supported by multiple experimental studies  [36] [37] [38]. Based on this hypothesis, for each significant miRNA we tested the leading-edge subset of genes for functional enrichment using curated annotation databases, such as Gene Ontology  [39] , KEGG  [40] , BioCarta  [41] , Reactome  [42]  and ACSN  [43]. For miRNAs identified in both Maire and TCGA datasets, the corresponding subsets of leading-edge targets were analyzed separately.
Conclusions High-throughput mRNA and miRNA expression data from large cohorts of normal and pathological tissue samples can be exploited to detect miRNA regulatory activity. The rankbased algorithm introduced here is able to detect miRNAmediated target destabilization from normal and breast cancer expression profiles. Reproducible results were obtained in two independent datasets, providing a list of miRNAs potentially relevant in breast cancer. Moreover, the association of these miRNAs to cancer related processes is supported by functional enrichment of affected target genes. The fact that a better overlap was obtained between Maire cohort and TCGA when restricting the analysis to the TNBC subtype compared to when using all samples may at first seem counterintuitive since using more samples should allow better power to detect correlations.
Tissue collection Healthy samples from mammary plastic surgery and tumor samples were obtained from patients treated at the hospital of Institut Curie (Biological Resource Center, Paris, France) as described previously  [22, 23]. Experiments were performed in agreement with the Bioethic Law No. 2004-800 and the Ethic Charter from the French National Institute of Cancer (INCa), and after approval of the ethics committee of our Institution. From the initial dataset, we retained only the samples for which both mRNA and miRNA data were available, numbering to 129, including 11 healthy breast tissue samples, 37 TNBC, 28 ER-/HER2+, 24 Luminal A and 29 Luminal B breast tumour samples as characterized by immunohistochemical staining.
MiRNA expression data Samples were hybridized on the Agilent miRNA microarray kit (V3). One hundred ng of total RNAs was hybridized to the microarrays according to the manufacturer's instructions. Hybridized microarrays were scanned with a DNA microarray scanner (Agilent G2565BA) and features were extracted using the Agilent Feature Extraction image analysis tool with default protocols and settings. Data were first transformed using the reverse hyperbolic sine function and quantile normalized. The data were then corrected for a hybridization batch effect using a linear model including the hybridization series as a fixed effect. Next, probes with negative intensity values in 95% or more of the arrays were discarded, leaving 516 miRNAs for analysis. When technical replicates for a sample were present, they were subsequently averaged.
MiRNA and gene expression data from TCGA We conducted our study on the publicly available data of tumour and healthy breast tissues from TCGA described in  [24]. We selected 500 tumors and 21 tumor-adjacent normal breast tissue samples for which both mRNA and miRNA data were available. Of the 500 tumors, 82 were assigned to the TNBC subtype according to ER, PR and HER2 negative status. In this dataset mRNA expression levels were determined by Agilent custom 244K whole genome microarrays and miRNA abundance was measured by Illumina sequencing technology. Level 3 released data contain quality controlled and processed data done by Broad Institute's TCGA workgroup with expression call for genes per samples. Gene level expression data were normalized by using Robust Multi-array Average (RMA) and expression values were gene centered.
Acknowledgements The authors would like to thank  Dr. Guillem Rigaill  for suggesting the experimental design. The research leading to these results and the publication of this article have received funding from the  European Union Seventh Framework Programme  ( FP7/2007-2013 )  ASSET  project under grant agreement number  FP7-HEALTH-2010-259348. This article has been published as part of  BMC Genomics Volume 16 Supplement 6 , 2015:  Proceedings of the Italian Society of Bioinformatics  (BITS): Annual Meeting 2014: Genomics. The full contents of the supplement are available online at http://www. biomedcentral. com/bmcgenomics/supplements/ 16/S6.
Behavioral apparatus The treadmill (Dattica) measured 80 cm long by 14 cm wide and was surrounded by 50 cm high plexiglas walls. There was no resting platform and the only walkable area was the treadmill belt, which was driven by a brushless digital motor (BG 44 SI, Dunkermotoren) controlled by a custom made program (LabVIEW, National Instruments) and a multifunction computer board (PXI-6254, National Instruments). A 60 W light bulb placed behind the camera illuminated the whole apparatus. One wall of the treadmill was equipped with a liquid well to deliver drops of sucrose solution (black triangle, Fig. 1a ). A photodetector positioned at 10 cm from the front wall signaled the first entrance of the animal in the so-called stop area in each trial (black circle, Fig. 1a ). A warning sound (1.
Data acquisition and processing Wide band (0. 1-8000 Hz) neurophysiological signals from the tetrode arrays were amplified 1000 times via a Plexon VLSI headstage and a PBX2 amplifier and continuously acquired at 20 kHz on two synchronized National Instruments A/D cards (PCI 6254, 16 bit resolution). Spike sorting was performed semi-automatically  48  using the clustering software KlustaKwik (http://klustakwik. sourceforge. net) and the graphical spike sorting application Klusters (http://klusters. sourceforge. net)  49.
Moment-to-moment correlation between firing rate and main task variables Correlation plots shown in Figure  4  (middle panels) were computed from all the trials, binned in 250 ms long windows, from treadmill onset to treadmill offset. In some cases (Fig. 3 , Fig. 6  and Supplementary Fig. 12 ), we restricted our analysis to trials or portions of trials displaying the archetypical front-rear-front sequence. To avoid biasing our correlation analysis with a minority of data points at the extreme range of the behavioral variables (e. g. lowest and highest speeds), we calculated the distributions of speeds and positions during each session and restricted the analysis to windows for which the value of the variable of interest (speed and position) fell inside the 90 th percentile of its distribution.
0-1. 63; accepted values < 5), the "detectiontolerance index" (median 0. 89, range 0. 61-0. 99; accepted values > 0. 2) and the "conditional index" of the Belsley collinearity test (median 2. 03, range 1-5. 67; accepted values < 10; Matlab codes from Brian Lau http://www. subcortex. net/research/code/collinearitydiagnostics-matlab-code). To test if striatal firing rates were generally influenced by more than one variable we compared regression models that took into account a single predictor with models that took into account several (2, 3 or 4) predictors. This was done by computing the Akaike information criterion values for each model. To test if changes in firing rate preceded running speed changes, for each trial, spike trains were systematically shifted by 100, 250, 500, 1000 or 2000 ms, backward or forward relative to the behavioral data.
General statements on data collection and statistics No statistical methods were used to pre-determine sample sizes but our sample sizes (number of animals and total number of recorded units) are similar to those reported in previous publications  4, 7, 31, 38, 39. Data collection could not be performed blindly however in most of the cases, the experimenter was not present in the experimental room during data collection. Moreover, analysis of the spiking activity and behavior was performed offline and Matlab programs were run in batch mode on all the data, independently of experimental conditions. The statistical tests used do not assumed normality of data distributions (which were always shown). The behavioral and neuronal data along with the Matlab codes used to generate the figures are available upon request to the corresponding author.
supported by the Spanish Ministerio de Ciencia e Innovación (Ramon-Y-Cajal program, D. R. ) EU-fp7 (International Reintegration Grant, IRG230976, D. R. ; Marie Curie International Incoming Fellowship, IIF253873, P. R. -O), INSERM (Avenir Program, D. R. ), European Research Council (ERC-2013-CoG -615699_NeuroKinematics, D. R. ) and the Mexican Consejo Nacional de Ciencia y Tecnología (P. R. -O).
Acknowledgments We thank  Drs John Krakauer ,  Kamran Diba ,  Jerome Epsztein ,  Olivier Manzoni  and  Henry Martin  for excellent discussions and critical reading of this and/or earlier version of the manuscript;  Ines Cordon Morillas ,  Carola Sales-Carbonell  and  Laetita Lalla  for help with training of the animals;  Jesus Perez-Ortega  for help with LabView Vision;  Dr Andrea Brovelli  for advice with multiple regression analysis;  Robert Martinez  for technical help. This work was.
In this report we examined this question by tracking the eye movements of human observers while they were presented a series of trivia questions that created high or low epistemic curiosity states. Because curiosity can covary with other epistemic factors such as confidence and surprise, we asked subjects to provide independent ratings of the 3 subjective states. We tested the hypothesis that curiosity will influence eye movement control, and that these influences would be sufficiently specific to allow curiosity to be ''read out" from eye movements using data mining algorithms. The results confirmed both predictions. We show that curiosity enhanced anticipatory eye movements toward the expected location of the answer and the dwell time on the answer after it was presented, without affecting other metrics of saccades or fixations.
Procedure During the experiments subjects were comfortably seated in a dimly lit room with their head stabilized by a chin-rest at a distance of 54 cm from a computer screen. Eye position and pupil size were measured at a sampling rate of 500 Hz using an Eye-link 1000 eye-tracking system configured for binocular tracking. Before data collection began, the subjects received a task description and performed a few practice trials that were not included in the data set. In the first part of a session the subjects were required to perform a series of 120 trials in which they read and rated trivia questions and were subsequently shown the answer. The trials were evenly divided between 60 one-question trials in which the subjects received a single question, and 60 two-question trials, in which they saw two sequentially presented questions and could select the one for which they wished to see the answer.
Data analysis To study the impact of epistemic curiosity on eye movement patterns we measured eye position as a function of time during the answer period, as well as the number, amplitudes and peak velocities of individual saccades and the number and durations   4 ) anticipation of answer, (  5 ) presentation of answer, (5) surprise rating. of individual fixations. Saccades were detected offline as displacements with amplitudes larger than 1 degrees of visual angle and velocity exceeding 0. 1 deg/s. A fixation was counted when eyeposition was stable for a minimum of 100 ms. Because subjects had a tendency to emphasize ratings of 1 and 5 for all measures (Fig. 2A-C ), we focused on comparing trials with these ratings, which we refer to as, respectively, ''low" or ''high" curiosity, confidence or surprise.
Implementation We parameterized the Random Forests algorithm using 250 random trees, each using 8 features, and a 10-fold crossvalidation method that selects each 10th of data as a test set, while using the remaining data for training. We implemented the analysis in the open-source software ''Weka"  (Witten & Frank, 2005) , which is freely available at http://www. cs. waikato. ac. nz/ ~ml/weka, and make the full annotated eye movement data set publicly available at: www. gottlieblab. com. Readers can reproduce our analysis by using Weka with parameters -I 250 -K 8 -S 1.
Curiosity We conducted the mining analyses separately for predicting ratings of curiosity, confidence and surprise. In a first population analysis we used all trials with ratings of 1 or 5, pooled across all observers. Because any imbalance in the frequency of high and low ratings can artificially inflate prediction accuracy, we resampled the pooled data set to provide equal number of trials with each type of rating, thereby ensuring that chance accuracy was 50%. After resampling we were left with 990 trials for the analysis of curiosity, 1144 trials for confidence and 1042 trials for surprise. In this remaining data set, the number of trials per subject was too low to provide a meaningful evaluation of how accurate a machine learning algorithm is for individual subjects. Therefore, to verify the consistency of the algorithm, we used a standard crosssubject validation procedure, whereby we trained the algorithm on 19 of the 20 subjects and tested prediction accuracy on the remaining subject, iterating until each subject served as a test.