dataesr/scientific-paragraphs-categorization huggingface dataset:
Gaia-TESS synergy for exoplanets The 34 months of Gaia data used for DR3 have also been used to analyse the transiting exoplanet of the TESS mission  (Ricker et al. 2015). The TESS mission is surveying bright stars in search of exoplanets using the transit method. TESS issues a monthly list of newly discovered candidates of transiting exoplanets. However, the TESS point spread function is large (about 1 arcmin), and therefore the light of each target star blends with the light from nearby sources. Therefore, follow-up photometric observations are required in order to exclude false detections of transits that are actually caused by blending with a nearby eclipsing binary. Thanks to its high spatial resolution and multi-epoch photometry, Gaia can help determine the true nature of the candidate variability.
A collaboration between Gaia and TESS was A13, page 12 of 19 established, and TESS exoplanet candidates are regularly analysed within the Gaia consortium using unpublished Gaia data, and the results are shared with the TESS collaboration. With a rate of about 5%, Gaia can identify false-positive candidates, and at a rate of about 5%, Gaia can even confirm true detections of transiting planets. Panahi et al. (2022b)  present the details of this ongoing cooperation. As soon as the 66 months of calibrated data are available internally to the consortium, these new data will be used by this Gaia-TESS collaboration to improve the above rates.
2, 1:1000) at 4 °C. Upon washing, sections were incubated for 2 h RT with Alexa-fluorophore-conjugated secondary antibodies (Invitrogen). Confocal microscopy was performed with a Stellaris 5 (Leica) Laser Scanning System and images were processed and analyzed by FIJI/ImageJ Software. Confocal z-stack projections of confocal micrographs for GCaMP6f-GFP signal and for EAAC1 or GAD67 immunofluorescence were thresholded and used to manually outline positive cells. Cell masks produced for GCaMP6f and EAAC1 or GAD67 were subsequently multiplied, to generate the counts of double positive cells.
The behavioral apparatus was controlled via Arduino custom code. Behavioral task events were transferred onto Excel with PLX-DAQ interface. Facial videography was recorded with a frame rate of 10 Hz. Before behavior started, mice were handled (1-2 days) and acquainted to head fixation (1-2 days). Behaviour started with 2 sessions of habituation where mice were presented with 30 CS+ (16 kHz trilling sound, 75 dB, duration: 1 s), 30 CS-(10 kHz pure tone, 75 dB, duration: 1 s) and 5 unpredicted US (airpuff, pressure: 4-5 psi, duration: 0. 5 s), delivered in random order with an inter-trial interval between 30 and 45 seconds. During conditioning sessions (2-3 days), mice received a total of 30 CStrials and 30 CS+ trials where US was followed after a delay of 0. 5 s from the end of CS. For two-photon recordings, mice were submitted to extra sessions of habituation and conditioning (1 to 5) to maximize data collection.
For in vitro recordings, the paired group (P) was trained as described while the unpaired group (U) received the same number of stimuli with the same characteristics, but CS+ and US were never contingent (30 CS+, 30 CS-and 30 unpredicted US trials). For Jawsmediated optogenetic silencing, 638 nm light (3 mW) was delivered by a laser (Matchbox Integrated Optics) to the LHb through a fiber cable (M83L1, Thorlabs) connected to a fiber optic implanted in the head of the mouse (CMFLC12L05, Thorlabs). Light was delivered during CS+ trials for 5 s starting from cue presentation. Behavioral assessment was recorded for a total of 15 s each trial with a sample rate of 10 fps starting 5 s before CS or US delivery. Data were analyzed using a custom-made Python script by thresholding the images to get only the eye pixel values.
The average power of the beam measured at the front of the objective was between 80 and 125 mW. At each trial, recordings started 5 s before cue delivery, and a 15s-long video was collected. Images were collected using a computer equipped with Prairie view (Bruker). Prior experiment started, 1 up to 3 FOV per mouse was chosen to maximize the number of neurons recorded each session by adjusting the imaging plane (z-axis), and each FOV was spaced at least 60 μm from one another to prevent visualization of the same cells across multiple FOVs. For imaging sessions with multiple fields of view, the conditioning session was split, allowing an equal number of trials per FOV. Sessions in which mice did not display any behavioral learning phenotype, were excluded from the analysis. After data collection, videos were motion-corrected with a planar hidden Markov model (SIMA v1.
3. 2), and regions of interest (ROIs) were manually drawn around each cell using the standard deviation projection of the motion-corrected video using ImageJ. 19. Neurons that were not tracked for at least one habituation and one conditioning session were not kept in the analysis. Next, fluorescence time series data were extracted with ImageJ BAR plug-in and analyzed using custom Python data analysis pipelines. % ΔF/F0 was calculated on a trial-by-trial basis as: ((F -F0)/F0) *100, where F0 is the average of the three seconds preceding the start of the CS presentation. To classify neurons based on their responses to CS and US (inhibited, not-responding or excited), we compared the area under the curve (AUC) during 1. 5 s baseline versus the AUC during the 1. 5 s anticipatory period (CS and delay) or US presentation (Wilcoxon Rank Sum Test, using a significance threshold of p ≤ 0.
05). χ2 test was then used to compare the distribution of responses in CS+ and CS-trials, and across sessions. We performed decoding analyses to determine if neuronal activity could be used to predict the general mouse eyeblink during cue presentation on any given trial. To do so, we used a binary decoder for each neuron where its activity (computed as AUC) during cue presentation (i. e. from 0 to 1. 5 s from cue starting) was used to predict whether the eye blinking (as CR) was lower than the 60th percentile or higher than the 40th percentile of that particular session. To complete the decoding analysis, we used the Python module Scikitlearn with GridSearchCV and a support vector classification (SVC) estimator  [24]. This included a radial basis function kernel. Quantification of performance was done using 100 folds validation for each neuron, the average accuracy score across these parameters was used as the metric of accuracy.
Fiber photometry recordings Fiber photometry recordings were performed using the ChiSquare X2-200 system (ChiSquare Biomaging, Brookline, MA). Briefly, blue light from a 473 nm picosecond-pulsed laser (at 50 MHz; pulse width ∼80 ps FWHM) was delivered via a single mode fiber. Fluorescence emission was collected by a multimode fiber with a sampling frequency of 100 Hz. The single mode and multimode fibers were arranged side by side in a ferrule that is connected to a detachable multimode fiber implant. The emitted photons were bandpass-filtered (FF01-550/88, Semrock) to a single photon detector. Photons were recorded by the time-correlated single photon counting (TCSPC) module (SPC-130EM, Becker and Hickl, GmbH, Berlin, Germany) in the ChiSquare X2-200 system. Photometric recordings were analyzed using a custom made python code where the signal was smoothed (running average: 10) and % ΔF/F0 was calculated on a trial-bytrial basis as: ((F -F0)/F0)*100, where F0 is the average of the three seconds preceding the start of the CS (or US presentation for unpredicted US trials).
In vivo electrophysiological recordings Juxtacellular recordings of single LHb neurons (dataset in Fig. 1 ) were performed as previously described  [14, 25, 26]. A subset of recordings (n neurons =149) was obtained in animals anesthetized with a mix of ketamine/xylazine according to previously published procedures  [26]. Briefly, craniotomies were performed at stereotaxic coordinates (1. 5 mm posterior, 0. 5 mm lateral to bregma) above the LHb. Glass electrodes (impedance: 7-9 MOhm) were filled with a tracer-containing solution (1. 5-2% Neurobiotin, SP-1120, Vector Laboratories). Juxtacellular voltage signals were acquired (ELC-03XS, NPI Electronic, Tamm, Germany) and digitized at 25 kHz (POWER1401-3 and Spike2 v. 8. 02e, CED, Cambridge, UK). Spontaneous spiking was monitored for at least 5 min, followed by foot shock stimulation (-1 mA, 0.
5 s, every 5 s, number of trials=20-197 trials) to the contralateral hind limb (A365R Stimulus Isolator, WPI). Juxtacellular labeling was performed using standard procedures. Recordings that were histologically confirmed to be outside of the LHb and recordings containing more than one unit or indications of cell damage were excluded from the analysis. Spikes from juxtacellular signals were isolated through threshold-based peak detection and visual inspection assisted by PCA, essentially as previously described  [27]. After recording, animals were euthanized with an overdose of pentobarbital, transcardially perfused and their brains processed for histological analysis as previously described  [23]  (see also 'histology and immunohistochemsitry' above). Part of the juxtacellular dataset in Fig.
The signal was filtered (band-pass 500-5000 Hz), pre-amplified (DAM80, WPI, Germany), amplified (Neurolog System, Digitimer, UK), and displayed on a digital storage oscilloscope (OX 530, Metrix, USA). Experiments were sampled on-and offline by a computer connected to CED Power 1401 laboratory interface (Cambridge Electronic Design, Cambridge, UK) running the Spike2 software (Cambridge Electronic Design). Single units were isolated, and the spontaneous activity was recorded for a minimum of 3 min before assessing their response to foot shock delivery (duration: 0. 5 s, intensity: 1 mA, ITI: 5 s). At the end of each experiment, mice were euthanized (overdose of isoflurane prior to killing) and the electrode placement was determined with an iontophoretic deposit of pontamine sky blue dye (1 mA, continuous current for 5 min).
7-4 MΩ; Phymep, France) under an Olympus-BX51 microscope (Olympus, France). The signal was amplified, filtered at 5 kHz and digitized at 10 kHz (Multiclamp 200B; Molecular Devices, USA). Data were acquired using Igor Pro with NIDAQ tools (Wavemetrics, USA). Access resistance was continuously monitored with a -4 mV step delivered at 0. 1 Hz. Extracellular stimulation from AMPI ISO-Flex stimulator was delivered through glass electrodes placed in the LHb. All recordings were made in voltage-clamp configuration. For AMPA/GABA evoked excitatory currents were recorded at -60mV, and evoked inhibitory currents were recorded at +5 mV. AMPA/NMDA ratios were obtained recording compound AMPA + NMDA EPSCs at +40 mV, and subtraction of the APV-insensitive component (APV, 100 μM). The paired-pulse ratio was obtained by recording the neurons at -60 mV and by delivering two pulses at 50 msec interpulse intervals.
Spontaneous excitatory currents were pharmacologically isolated by bath application of picrotoxin (PTX, GABAAR antagonist; 100 μM). The internal solution contained (in mM) CsMeSO3 120, CsCl 10, HEPES 10, EGTA 10, creatine phosphate 5; Na2ATP 4; Na3GTP 0. 4, QX-314 5. All drugs were purchased from HelloBio. Real time place aversion/preference test C57BL/6 J wild-type male mice 8-10 weeks old were tested in a custommade behavioral arena with two compartments with different visual cues and wall texture for 15 min. The time spent on each compartment was recorded via a digital camera interfaced with Ethovision software (Noldus). Paired-side for the CS+ auditory tone was randomly assigned and balanced among experimental subjects and groups. Mice were habituated for one session to the tone to avoid novelty-induced effects.
Quantification and statistical analysis All statistical analyses were conducted using Prism (v. 9, GraphPad) or the Python scikit-learn library. Statistical tests used in this study include paired and unpaired t-test, Wilcoxon matched-pairs tests, Chi-Square test, one-way and two-way analysis of variance (ANOVA). When parametric tests were used, data normality was confirmed using the Shapiro-Walk normality test. P values were corrected for multiple comparisons when necessary. In the box plots, the center lines indicate the median, and the box limits indicate the upper and lower quantiles (95%); box plots include single observations. The significance threshold was held at α = 0. 05 and tests were two-sided. Sample sizes were not predetermined using statistical methods but based on experimental settings within the laboratory.
e. anticipatory behaviours). The LHb responds to aversive external stimuli with a phasic increase in neuronal activity in both awake and anesthetized animals  [5, 10, 14] , a functional feature independent of the nature of the negative stimulus. Accordingly, foot shock, air puff, and radiant heat promote excitation of the LHb  [7, 42, 43]. Thus, excitation in the LHb is defined to encode negative valence, and, if persistent, to underlie negative affect in psychiatric states  [12]. In the present work, we systematically characterized neuronal responses in the LHb by using in-vivo juxtacellular recordings during foot shock stimulation in anesthetized animals. Our dataset of ~200 neurons extends previous observations  [14, 25]  and shows that neuronal responses to foot shock are diverse, and represented by both excitation and inhibition  [25].
ACKNOWLEDGEMENTS We thank  F. J. Meye ,  M. Carta ,  J. Clerke , and all the members of the  Mameli laboratory  for their comments on the manuscript. We thank  D. Congiu  and  B. Girard  for their support in the analysis of calcium imaging data. The authors thank the  GENIE Project at   HHMI Janelia Research Campus  for the kind gift of the iGABASnFR2 construct and the  International Max Planck Research School for Intelligent Systems (IMPRS-IS)  for supporting  Lisa Schmors.
Climatic changes in southern Europe during the Holocene are characterised by a strong spatial and temporal heterogeneity whose patterns are still poorly understood, notably the presence or not of a Holocene thermal maximum (HTM; 10,000-6,000 cal. BP). The reconstructed climatic patterns also differ according to the proxies used (e. g. , pollen, chironomid) and the latitude of the data. Here, a multi-proxy approach combining pollen and lipid biomarkers (branched Glycerol Dialkyl Glycerol Tetraethers, brGDGTs) is applied to the Canroute sedimentological sequence to reconstruct the climatic variation over the last 15,000 years in southern Massif Central, France. This area is poorly documented in terms of vegetation and climate. To provide reliable climate reconstructions, we have (1) performed a multi-method comparison based on transfer functions applied to pollen (MAT, WA-PLS, BRT) and molecular biomarkers (brGDGTs), (2) investigated the role of modern databases/calibrations in climate reconstructions.
Three different calibration databases were tested for pollen data: one global based on a Eurasian Pollen Database, and two regional databases corresponding to Mediterranean/Temperate Europe and Temperate Europe/Scandinavian databases respectively. Nine global calibrations were tested for lipid biomarkers including eight for soil and one for peat. The use of different modern databases highlights the importance of considering environmental and ecological constraints when using transfer functions on pollen sequences. Pollen and brGDGT-inferred climate trends are consistent, notably for the Lateglacial, the Early and Late Holocene. However, the reconstructions notably differ concerning the presence of a Holocene thermal maximum with the Modern Analogue Technique (MAT) pollen-based method but not apparent with the BRT pollen method nor brGDGT.
, 2020). These calibrations were developed from global databases that group together surface samples, for which the current climatic conditions are known, from distinct types of substrates: soils  (Peterse et al. , 2012; De Jonge et al. , 2014a; Naafs et al. , 2017a; Dearing Crampton-Flood et al. , 2020) , lake sediments  (Dearing Crampton-Flood et al. , 2020)  and peat  (Naafs et al. , 2017b). The use of brGDGTs to reconstruct annual temperatures in Europe during the Lateglacial and Holocene is still rare (e. g. ,  Martin et al. , 2020; Robles et al. , 2022b; Rodrigo-Gámiz et al. , 2022; Ramos-Roman et al. , 2022). Due to the complexity and number of interactions throughout the ecosystem  (Birks and Birks, 2006)  and the specificity of the brGDGT proxy itself, it is advisable to use several independent proxies to obtain reliable temperature reconstructions.
Age-depth model Radiocarbon dating of the CAN02 sequence was carried out by the Poznan Radiocarbon Laboratory (Poland) on 16 peat samples (bulk). The calibration and the age-depth model (Table  2 , Fig. 2 ) were performed with the R language (R Core  Team, 2022)  and the R Studio software (RStudio Team, 2020) with the Clam package  (Blaauw et al. , 2022)  using the IntCal20 calibration  (Reimer et al. , 2020).
Pollen analysis The pollen analysis of the CAN02 sequence was carried out at the Institut des Sciences de l'Evolution de Montpellier (ISEM). A total of 65 samples have been analysed (sampling interval of 1 cm for 159-138 cm, 2 cm for 138-128 cm, and 4 cm for 128-0 cm) and the pollen was identified and counted under an optical microscope. Pollen sums are at least 500 grains per sample and exclude spores. 148 taxa were identified, but here, a simplified pollen diagram was made to represent the vegetation variability over the last 15,000 years. The pollen data are presented as a function of age in years cal. BP. (Fig. 6 ).
Quantitative reconstruction of climate parameters Various methods have been developed to quantify climatic parameters from the pollen signal (see the review of  Chevalier et al. , 2020)  and multi-method approaches have been developed to increase the reliability of palaeoclimatic reconstructions  (Peyron et al. , 2005 (Peyron et al. , , 2011 (Peyron et al. , , 2013 (Peyron et al. , , 2017;; Salonen et al. , 2019; Robles et al. , 2022a, b). These methods were initially developed to calibrate the relationship between modern pollen data (soils, mosses) and current climate parameters. Our multi-method approaches include (1) transfer functions, based on linear regressions (Weighted Averaging Partial-Least Squares regression) between pollen taxa and climate parameters; (2) assemblage approaches, based on the analogy principle, between fossil and modern assemblages (Modern Analogue Technique), and (3) recent machine-learning technics with regression trees (Random Forest and Boosted Regression Trees) to quantify the climate parameters on the CAN02 sequence.
, 2020). The other two methods (RF and BRT) have been developed more recently and are based on Machine Learning  (Salonen et al. , 2019). These methods use regression trees to divide pollen data by successive separations of samples according to their abundance in the pollen spectrum. Random Forest is based on the estimation and combination of many regression trees, each tree is estimated from a set of pollen samples by bootstrapping  (Chevalier et al. , 2020). The bootstrapping approach consists, for a database of size n, of sampling with replacement n samples of the modern database to create a new database of the same size as the original database. Boosted Regression Trees differ from RF in the definition of the modern database. For RF, each sample has the same probability of being selected, for BRT the under-represented samples in the previous tree have a higher probability of being selected.
This approach is called "boosting" and increases the model's performance concerning the least well-predicted elements  (Salonen et al. , 2019). Due to the regression tree signal variation, the BRT method's final signal is an average of 15 independent runs of the BRT algorithm. For these four methods, locals and hydrophytes were not used in the reconstruction of climate parameters as they could not be strictly related to the regional climate. The role of the modern pollen database in the reliability of climate reconstructions is also investigated here. sub-sampled databases are compared to the temperature reconstructed from the EAPDB (Fig. 7 ). Here, we reconstruct the mean annual temperature (MAAT) from the Canroute pollen record as the MAAT is the only comparable parameter between pollen and brGDGT reconstructions.
Statistical treatments A principal component analysis (PCA) on correlation matrix has been conducted on XRF and brGDGTs, with the FactoMineR package  (Le et al. , 2008). The PCA aims to illustrate the components that explain the most variations across samples and thus permits to explore relationships between the variables. For both PCA, a k-means clustering allowed the highlight of subgroups in the data, with a chosen value of a cluster number (k) determined with a within-cluster sum of squares method. The clustering analysis has been performed with the Factoextra package  (Kassambara and Mundt, 2017). For XRF data, due to the low Transfer functions and reliability tests were performed with the rioja  (Juggins and Juggins, 2020) , randomForest  (Breiman, 2001)  and dismo  (Hijmans et al. , 2017)  packages.
Pollen-inferred mean annual temperature To provide reliable climate reconstruction, we applied to the Canroute pollen assemblages a multi-method approach tested with three different modern pollen databases. The results based on the global EAPDB and regional TEMPSCAND databases indicate broadly the same temperature trends, but with smaller amplitudes for the TEMPSCAND calibration. The MEDTEMP regional calibration shows opposite trends compared to the other two databases, particularly during the Lateglacial (Fig. 7 ). The EAPDB and TEMPSCAND databases are associated to higher values of R² than the MEDTEMP ones (Table  3 ). For the EAPDB and TEMPSCAND, three of the methods (MAT, WA-PLS and BRT) show a similar climatic signal (Fig. 7a , b, d) characterised by (1) cold temperatures during the Lateglacial, followed by (2) a warming through the Early Holocene, resulting in a thermic optimum accentuated depending on the methods used, then (3) a sudden cooling around 6,000 cal.
Consistency of brGDGTs relative abundances with peat and soils databases The Canroute fossil samples and the surface samples from Massif Central are consistent with the global peat and soil databases that are globally dominated by tetra-and pentamethylated brGDGTs (Fig. 8a ,  Yang et al. , 2014; Naafs et al. , 2017a, b; Dearing Crampton-Flood et al. , 2020). The use of both types of calibrations (peat and soil) therefore appears to be consistent with the brGDGT assemblages observed in the CAN02 sequence. , 2014a)  show more reliable reconstructed MAAT anomalies (Fig. 8b ). For the samples from the Caroux site (CAR-1-15 and CAR-C2), which is also a soligenous peatland and is geographically the closest site to Canroute (Fig. 1a ), the calibrations based on Index1, and multi-regression appear to be the most reliable (Fig.
Climate signal from pollen Different studies underline the importance of the modern pollen database used for the reconstruction of climate parameters  (Turner et al. , 2021)  and point out the advantage of regional calibration databases  (Dugerdil et al. , 2021a, b). The calibrations performed with the Eurasian pollen database (EAPDB) and regional Temperate and Scandinavian pollen database (TEMPSCAND) broadly indicate similar temperature trends at Canroute, while the Mediterranean and Temperate pollen database (MEDTEMP) calibration shows opposite trends compared to the other two calibrations (Fig. 7 ). These discrepancies can be explained by the dominance, at this period, of taxa such as Artemisia, Amaranthaceae and Poaceae (Fig. 6 ), able to constitute physiognomically and floristically similar arid steppes, both in the southern Mediterranean and Irano-Turanian warm context (e.