dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical Analyses Data analyses were conducted using IBM SPSS Statistics 26 (IBM Ltd. , Portsmouth, United Kingdom). All data were assessed for normality using probability-probability plots and the Kolmogorov-Smirnov test, and for homogeneity of variance using the Levene's test. For data that did not conform to normality and/or homoscedasticity nonparametric statistical tests were applied. To evaluate differences between DAPI, Ki67, C33, and Ki67/CC3, a two-way analysis of variance (ANOVA) with a Bonferroni post hoc correction was applied. To evaluate differences in SOX2, Nestin, and Nestin/SOX2 a series of Kruskal-Wallis tests with Dunn's post hoc corrections were applied. All tests carried out were two-sided and the alpha criterion used was p < 0. 05. Data are represented as the mean (M) and standard deviation (SD), or the median (Mdn) and interquartile range (IQR).
FUNDING This project was part of the  EU consortium DCogPlast "Diet Cognition and Plasticity " funded by  JPI-HDHL (Medical Research Council UK :  MR/N030087/1 ;  French National Research Agency   ANR-15-HDHL-0002-05 ;  PCIN-2015-229-MINECO ;  CiberFES-Cofund by FEDER Program  from  EU ,  2017SGR1546  and  ICREA   2018 Academia Award  from the  Generalitat de Catalunya) , the  BMWFW  under  BMWFW-10. 420/0009-WF/V/3c/2015. PL was supported by the  Urban Mental Health  RPA from the  University of Amsterdam.
Stress-Induced Changes in GM Are Transferrable to Host Mice In light of these results, we examined the GM profile by 16S rRNA sequencing. We first confirmed that abx significantly decreased microbial diversity as expected (Figures  S3A-S3D ). Taxonomic analysis of bacterial families revealed several UCMS-related changes in GM composition, most of which were transferred to UCMS-tr mice (Figures  2K-2N ; Figure  S1 ). Principal-coordinate analysis (PCoA) showed that the different groups clustered differently (Figure  1L ). It is to be noted that the relative dispersion between the points corresponding to UCMS and UCMS-tr was not significant (Bray-Curtis index; data not shown). These changes were also detectable in phylum level, namely, in Firmicutes, Tenericutes, and candidate division TM7  (Figures S1B, S1G, and S1H).
16S sequencing data analysis Sequences were clustered into operational taxonomic units (OTU) and annotated with the MASQUE pipeline (https://github. com/ aghozlane/masque) as previously described  (Cole et al. , 2009). The final list of 16S rRNA targeted amplicons were converted into the negative logarithm of the expressions of the bacterial taxa (phylum and family). Final data were expressed as the average (represented in a heatmap) and sum (represented in barplots) of the negative logarithm of OTU expression across experimental conditions. All data indexing, segregation and heatmap plotting were performed using custom-made scripts in MATLAB (Mathworks Inc. , 2015 release). Further statistical tests were conducted in Prism software (GraphPad). The microbiota profiles obtained were further characterized using the following methods: a.
A scale-free topology model is generated by raising the co-expression similarity to a power: aij = sijß with b R 1. Aij is a weighted adjacency between two genes is proportional to their similarity on a logarithmic scale, log (aij) = b 3 log (sij). PickSoftThreshold package in R was used (our data had b = 3). The adjacency matrix is then used to generate a Topological Overlap Measure (TOM), describing the relative co-occurrence between two nodes and placing it in the framework of the entire network. The whole network connectivity distribution is shown as a network heatmap, and the branches in the hierarchical clustering dendrograms correspond to modules. The modules were color-coded and data was segregated from each module to classify them into major bacterial families. c. Autocorrelation among modules: The significant modules (or clusters) were determined using an ANOVA test on the cluster z-scores across the experimental conditions: CT, CT-tr, UCMS and UCMS-tr.
For GC/MS, samples were derivatized under dried nitrogen using bistrimethyl-silyl-trifluoroacetamide and analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer using electron impact ionization. Chemical entities were identified by comparison to metabolomic library entries of purified standards. Data were analyzed following log transformation and NA values were replaced with minimum observed values when necessary. Principal component analysis (PCA) was applied on the 710 assessed metabolites. The six first principal components (PC) explained 63. 78% of the variance of the data, and PC1 and PC2 accounted for 19. 6% and 17. 63% of the variance respectively. To get an insight of the metabolic pathways represented within each PC, we looked at the correlation coefficients between the PC and the metabolites.
ACKNOWLEDGMENTS We are grateful to the members of the  Centre for Gnotobiology of the Institut Pasteur  for technical support with the mice with a modified/controlled microbiota. We thank all of the  Lledo lab members ,  Prof. Chantal Henry  and  Dr. Maud Pascal  in particular, and  Dr. Nicolas Kuperwasser  for helpful comments. This work was funded by the  Investissements d'Avenir program  (grants  ANR-11-IDEX-0004-02  and  ANR-10-LABX-73 ), the  Agence Nationale de la Recherche  (grant  ANR-15-CE37-0004-01 ), AG2R  La Mondiale , and  Mutuelle de la Region Lyonnaise. We also thank the  Fondation des Gueules Cassees, E ´cole des Neurosciences de Paris , and  Labex Revive  for fellowships (to E. S. and S. S. ).
, 2005) , even though we take them as representative of all CRYs and RORs. Fig. 2  A) shows a solution of the model for the calibrated parameters (see Table  1 ). The model fits well to the high temporal resolution experimental data for relative fluorescence intensities of VENUS-tagged REV-ERB protein obtained from  (Feillet et al. , 2014)  (Fig. 2 B ). We collected data from Fig. 1 , Panel A of  (Feillet et al. , 2014) , applied a filter to remove irregularities and normalized these data as percentage of the REV-ERB mean value (% ). This normalization allows to better compare our results with the experimental data obtained in single-live-cell imaging of REV-ERB ::VENUS, that measures relative levels of protein expression  (Feillet et al. , 2014). The fit is obtained via an optimization based on non-linear cost minimization, with the use of matlab's fminsearch command.
2 and the osmolality to 280 mOsmol l -1. With this solution, GABA-IPSCs reversed at -70 mV. GABA-IPSCs and AMPA-EPSCs were simultaneously recorded at a holding potential of -45 mV. The signals were digitized using a Digidata1440A converter (Axon Instruments, San Jose, CA, USA). Axoscope software version 8. 1 (Axon Instruments, San Jose, CA, USA) or pCLAMP1 0. 0. 1. 10 (Axon Instruments, San Jose, CA, USA) and MiniAnalysis 6. 03 (Synaptosoft, Decatur, CA, USA) programs were used for acquisition and analysis. Series resistance (R s ), membrane capacitance (Cm) and input resistance (R i ) were determined by an online fitting analysis of the transient currents in response to a 5-mV pulse with Acquis 4. 0 software (Bio-logic, Orsay, France). Criteria for accepting a recording included a resting potential < -55 mV, R i > 400M , R s < 25M and cells exhibiting more than 20% change in series resistance were excluded from the analysis.
Electrophysiological Recordings in Neuronal Cell Culture Electrophysiological recordings from neurons were performed at 14 DIV. Neurons were continuously perfused with an extracellular solution containing the following (in mM): 150 NaCl, 2. 5 KCl, 2. 5 HEPES, 20 D-glucose, 2. 0 CaCl 2 , 2. 0 MgCl 2 , 0. 01 CNQX, 0. 4 D-APV pH 7. 4, at 1 ml/min. Recording electrodes (4-6 M ) were filled with a solution containing the following (in mM): 140 CsCl, 10 HEPES, 2. 5 MgCl 2 , 4 Na-ATP, 0. 4 Na-GTP, 10 sodium phosphocreatine, 0. 6 EGTA, pH 7. 2, and with calcium-chelator BAPTA for indicated experiment. Smo-agonist SAG was applied in the bath at 100 nM for 10 min. Recordings were made using an Axopatch-200A amplifier and pCLAMP acquisition software (Molecular Devices). Series resistance was compensated electronically.
After fixation, cultures were permeabilized and co-incubated overnight with mouse anti-MAP2 (1:2,000). Cells were exposed to a saturating concentration (10 µg/ml) of FITC-conjugated anti-rabbit IgG (Invitrogen) and Cy5-conjugated anti-mouse IgG (Millipore) for 1. 5 h. Quantification was performed using ImageJ software (NIH, Bethesda, MD, USA 1  ). The ratio of surface-bound BDNF-mCherry-FITC to total BDNF-mCherry was estimated as the ratio of the area of co-localized FITC and BDNF-mCherry/total area of BDNF-mCherry and expressed as the ratio of co-localized signals. Images were acquired using a laser scanning confocal microscope (Zeiss LSM 510 Meta) with a 40× oil-immersion objective.
All procedures were performed in a phosphate-free solution containing 140 mM NaCl, 5 mM KCl and 10 mM HEPES-Na, pH 7. 4. The acquisition (Zeiss LSM 510) of A488 (pCREB), Cy3 (CREB) and then Cy5 (MAP2) was sequential to avoid overlap of excitation and emission of fluorescence. The optical sections were digitized (1,024 × 1,024 pixels) and processed using ImageJ software.
Real-Time qRT-PCR RNA was isolated and quantified by reading the absorbance at 260 nm (NanoPhotometer, IMPLEN) using an RNeasy kit (Qiagen), then converted to cDNA using 1 µg RNA and a QuantiTect Reverse Transcription kit (Qiagen) according to the manufacturer's instructions. PCR was carried out with the LightCycler 480 SYBR Green I Master (Roche Applied Science) with 1 µl cDNA using the following oligonucleotides (QuantiTect Primer Assay, Qiagen): Smo: QT00190183; Ptch1: QT01579669; Gli1: QT01290324 and glyceraldehyde-3phosphate dehydrogenase (GAPDH): QT001199633. Relative mRNA values were calculated using the LC480 software and GAPDH as the housekeeping gene. The minimal sample size for each reported group analyzed using qRT-PCR is three biological replicates.
Canonical Shh-Smo signaling leads to an increased expression of its downstream target gene Gli1 (Ruiz i Altaba, 1998;  Jacob and Lum, 2007) , whereas non-canonical pathway inhibits Gli1 expression in the developing spinal cord  (Belgacem and Borodinsky, 2015). To further investigate whether the acute response to Smo activation triggers a non-canonical signaling pathway, Gli1 expression was measured using qRT-PCR after incubating the hippocampal slices for 45-min or 6 h with 100 nM SAG. The results show that 45-min SAG treatment does not change Gli1 transcription factor mRNA levels, while 6 h incubation induces an increase in Gli1 mRNA transcripts (Figure  4C ). Overall, these data show that SAG-induced-increase in sGABA A -IPSCs frequency is dependent on postsynaptic Ca 2+ signaling probably through non-canonical Shh-Smo signaling pathway.
FUNDING This work was supported by The  National Institute of Health and Medical Research (INSERM) , the  National Center for Scientific Research (CNRS) , the  National Agency for Research (ANR , grant number  R07066AS 2008-2011 ) to QD, MH, DD, YB, MK, AM,  J-LG , and CP and the A* MIDEX  project (n •  ANR-11-IDEX-0001-02 ) funded by the  «Investissements d'Avenir» French Government program , managed by the  French National Research Agency (ANR). IMERA/INSERM Grant, Marie Curie Prestige/ERC Grant  ( PRESTIGE-2016-3-0025 ) and  Brain & Behavior Research Foundation (NARSAD)  Young investigator grant (# 25356 ) to YHB.
Electrophysiology Whole cell patch-clamp of visualized layer five pyramidal medial PFC or medium spiny neurons and field potential recordings were made in coronal slices as previously described  (Bara et al. , 2018; Borsoi et al. , 2019). Neurons were visualized using an upright microscope with infrared illumination. The intracellular solution was based on K+ gluconate (in mM: 145 K + gluconate, 3 NaCl, 1 MgCl 2 , 1 EGTA, 0. 3 CaCl 2 , 2 Na 2+ ATP, and 0. 3 Na + GTP, 0. 2 cAMP, buffered with 10 HEPES). The pH was adjusted to 7. 2 and osmolarity to 290-300 mOsm. Electrode resistance was 4-6 MOhms. Recordings were performed with an Axopatch-200B amplifier as previously described  (Bara et al. , 2018; Borsoi et al. , 2019)   (Scheyer et al. , 2020b). Data were low pass filtered at 2kHz, digitized (10 kHz, DigiData 1440A, Axon Instrument), collected using Clampex 10.
No other parameters of cell excitability were found modified comparing MSNs in slices obtained from Sham-, as compared to WIN-exposed rats (Figure  5c -e; Table  8 ). The NAc plays an important role in reward-associated behavior, and recent data indicate a relationship between LTD in the NAc and reward-seeking behavior including sucrose consumption  (Bobadilla et al. , 2017; Bilbao et al. , 2020). Thus, we examined the magnitude of sucrose preference in a two-bottle choice paradigm in the adult offspring of Sham-and WIN-treated dams. Here, we found that while both groups exhibited a preference for a 5% sucrose solution (as compared to plain water) and consumed similar total quantities of liquid during the test (Figure  6a ; Table  9 ), the ratio of sucrose/water consumption was significantly higher in WIN-as compared to Sham-treated adult offspring (Figure  6b ; Table  9 ).
Quantification of Co-localizing GFP or mCherry and ChAT Quantitative analysis of double-labeled neurons for ChAT and GFP or mCherry was conducted to evaluate the extent of GFP expression in cholinergic neurons in the striatum. This analysis was performed in the rat and monkey P injected with CAV-pChAT-GFP and CAV-mCherry. The numbers of single-and double-labeled neurons were determined in the striatum for each animal, from five sections (400 µm apart from each other) surrounding the injection sites previously localized on cresyl violet staining adjacent sections. For each section, an image of the entire striatal region was obtained from a single confocal slice with 20× objective and sequential acquisition of the different wavelength channels (LSM 510 Zen, Zeiss). The analysis was then performed with Neurolucida software (version 7, mbfBioscience) as followed: for each confocal image, all GFP-labeled neurons were identified on the green channels and examined for co-localization with ChAT in the blue channels and/or mCherry in the red one.
FUNDING This work was supported by the  Centre National de la Recherche Scientifique (PA) , the  Institut National de la Santé et de la Recherche Médicale (ME) ,  Aix-Marseille University  (PA, ME),  University of Montpellier (EK) , the  Association France Parkinson (PA, EK) , the  Fondation pour la Recherche Médicale (EK) , and the  LabEx EpiGenMed (EK). A-CM was supported by a doctoral fellowship from the  French Ministry of Education and Research, Fondation pour la Recherche Médicale , and  Fondation des Treilles. HE was supported by a doctoral fellowship from  Agence Universitaire Francophone  and the  Egyptian government. Partenariats Hubert Curien IMHOTEP (ME, HE).
, 2009; Margrie et al. , 2002). Access resistance and membrane capacitance of the cells were monitored on-line with the membrane test feature of the acquisition software and analyzed off-line. The mean access resistance was 52. 4 ± 2. 5 MΩ. Membrane potential was not corrected for liquid junction potential which was estimated to be approximatively 11 mV (LJP calculator, Clampex). Recordings were discarded when the access resistance exceeded 100 MΩ or when the action potential peak dropped below 0 mV.
Electrophysiology All recordings were made in the current-clamp configuration. Recordings were obtained with a Multiclamp 700B amplifier connected to the Digidata 1440A system. Data were acquired with pClamp 10 (Molecular Devices), digitized at 10 kHz, filtered at 3 kHz, and analyzed off-line with Clampfit 10. 4 (Molecular Devices) and Python. To measure input resistance, we injected trains of current pulses (-100 pA, 100 ms) in a separate set of recordings (n = 13).
Acknowledgements We are grateful to  Ania Gonçalves  for help in confocal imaging and  Mario Carta ,  Lisa Roux ,  Xavier Leinekugel ,  Ruth Betterton , and  Catherine Marneffe  for useful comments on the manuscript and fruitful discussions. This study was supported by the  Centre National de la Recherche Scientifique , the  European Commission (EIF Fellowship  awarded to A. K. ; 702037), the  ANR  (grant Hippencode # 14-CE13-0015 ), and the  Fondation pour la Recherche Médicale  (to M. M. ; FDT20170437372).
A software tool for synchronization of video with signals would be of broad general use to behavioral neuroscientists. A new program, called neurotic (NEUROscience Tool for Interactive Characterization), allows users to review and annotate signal data synchronized with video, performs simple initial analyses including signal filtering and spike detection, is easy to use, and supports a variety of file formats. The program also facilitates collaborations by using a portable specification for loading and processing data and retrieving data files from online sources. Two examples are shown in which the software is used to explore experimental datasets with extracellular nerve or muscle recordings and simultaneous video of behavior. The configuration specification for controlling how data are located, loaded, processed, and plotted is also summarized.
Introduction In behavioral neuroscience, to interpret data after it is collected, it is essential that behavioral video be synchronized with electrophysiology. Although some commercial data acquisition systems may provide this functionality using proprietary hardware, software, and data formats [e. g. , Plexon CinePlex Software  (Jacobson et al. , 2014) , Blackrock Microsystems NeuroMotive], and although specialized tools exist for separately analyzing signals or behavioral videos using licensed analysis programs like MATLAB (http://www. mathworks. com/solutions/neuroscience), there are few free and open-source software tools with the capability to synchronize video and neural signals. Although there are many open source neuroscience tools and libraries for analysis of neural signals alone [e.
g. , Open Ephys (http://open-ephys. org) and NeuralEnsemble (http://neuralensemble. org) are communities focused on developing open source tools for neuroscientists], they often have steep learning curves and require that users program their own scripts. Especially appealing would be an open source application that facilitated the review of electrophysiology data with synchronized video, was simple to install on any computer, easy for nonexperts to use, and powerful enough for experienced data scientists to use in their data pipelines. We have developed neurotic (NEUROscience Tool for Interactive Characterization), a Python tool with these capabilities (for important links, see Table  1 ). A graphical user interface (GUI) provides interactive plots of raw electrophysiology data, simultaneous video, and simple annotation tools.
The program also performs basic data processing, like filtering and spike detection. Since it is built on the Neo library  (Garcia et al. , 2014) , it can read many common electrophysiology file formats. The application is easily installed using common Python tools (pip or conda) or standalone installers, with built-in examples running quickly. After creating human-readable configuration files, users can explore data with the GUI. Portable configuration files and a data-fetching feature facilitate collaboration. Programmers can bypass the GUI and use the application programming interface (API) in their analysis pipelines in scripts and Jupyter notebooks  (Kluyver et al. , 2016).
Software overview neurotic relies on the Python library Neo  (Garcia et al. , 2014)  for reading electrophysiology files, including formats like Axon, AxoGraph, Blackrock, Intan, and Open Ephys files; it also includes some generic formats like MATLAB MAT-files, HDF5, plain text ASCII, and generic binary data files, provided they are structured properly. Neo manages units (e. g. , ensuring signals measured in millivolts and microvolts are summed correctly) using the quantities package (http://pypi. org/project/quantities), and both rely on the ubiquitous Numpy library (van der  Walt et al. , 2011). neurotic can take advantage of a feature of many of Neo's file readers, which is to read slices of large files as needed, rather than loading entire files into memory at once; this "fast loading" feature greatly reduces load times and memory consumption.
neurotic reads spreadsheet files (CSV format) using the Python library pandas  (McKinney, 2010)  to import epochs and events. Epochs are labeled periods of time with a beginning and an end which are used to annotate data by labeling periods of interest. Events are labeled points in time that serve as "bookmarks" into the experiment, allowing users to jump to them. The neurotic interface allows users to create and save epochs to a spreadsheet. neurotic can read many video file formats using the PyAV library (http://pypi. org/project/av). Sound stored in video files is currently ignored by neurotic. neurotic can perform processing and analysis procedures on the data, including applying filters, generating spike trains from signal peaks, and identifying bursts (periods of high activity in the spike trains).
These procedures are aided by the Python library elephant (http://pypi. org/ project/elephant), which provides advanced algorithms for analyzing Neo objects. Many of these algorithms are applications of signal processing procedures implemented in the SciPy package  (Virtanen et al. , 2020). Finally, neurotic uses the Python library ephyviewer (http://pypi. org/project/ephyviewer) for plotting and interacting with signals, epochs, events, spike trains, and video frames. ephyviewer is built on the pyqtgraph library (http://www. pyqtgraph. org), which creates a Qt application interface (www. qt. io). Figure  1A  shows the neurotic user interface for an example dataset in which neural and force data were collected as an animal fed  (Gill and Chiel, 2020). Electrophysiology signals are plotted after filtering, and points mark peaks identified by window discriminators.
Software configuration neurotic requires configuration before use with a dataset. For example, the paths to the electrophysiology data file and behavioral video file must be specified. Configurations are saved in the human-readable YAML file format which can be used to load and process data repeatedly. Figure  2  shows an example of a detailed configuration written in the YAML format. A minimal configuration could be much simpler, including, for instance, a single file path. The example is divided into eight sections: (1) a unique dataset name and brief description; (2) file paths may be listed relative to a primary directory; (3) video synchronization parameters; (4) signal plotting parameters; (5) signal filtering; settings for (6) the epoch encoder, (7) peak detection, and (8) burst detection.
Multiple configurations like that shown in Figure  2  may be contained in a single YAML file. This allows multiple datasets, or alternate processing and display configurations for the same dataset, to be contained within one file. When neurotic is first opened and the configuration file is selected, a list of dataset configurations is displayed (Fig. 1B ). The user can choose which configuration to load and can adjust options for how data are loaded and displayed. For downloadable datasets, including those on private password-protected servers or public data repositories like GIN (http://gin. g-node. org), the configuration may include URLs for the files, and neurotic can download them. In combination with relative file paths, this makes configuration files fully portable, such that users may share them with collaborators and load datasets from other computers.