dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability Datasets for the sequence data file are available in SRA with accession numbers SRX9704326-SRX9704344; bioproject PRJNA686429. A Jupyter notebook reproducing bioinformatic analyses is available at https://github. com/Boussau/Notebooks/blob/ master/Notebooks/Viromics/FiguresAndAnalyses_NoTLR3. ipynb. Uncropped and unedited gel images for Supplementary Fig. 1  are provided as Supplementary Fig. 9.
Mice underwent conditioning for DRLM and REV tests, followed by stereotactic surgery (Fig. 2b ). Following recovery, mice were given a session at the nal conditioning stage for each behavioural test with the bre photometry patch cord attached, to adjust them to making operant and feeder responses under bre photometry conditions. Mice then underwent the CSS or CON procedure. After a 1-day interval, mice were studied in the DRLM test on 3 consecutive days. The behavioural ndings were similar to those obtained in the previous experiment, indicating that the integration of bre photometry did not have a confounding effect (Fig. 2c -f, supplementary Tables  S1, S2 ). Concerning BA-NAc neural pathway Ca 2+ activity in these behaving mice, the data are presented for DRLM tests 1 and 3 and are speci c to those trials in which mice made a DS feeder response and therefore obtained a pellet.
In DRLM test 4, mice were provided with a pellet of normal food and mice in each group consumed a low and similar amount indicating that they were all close to satiety with respect to low-salience food (supplementary Fig. 2h ). Mice were then studied in the REV test on 2 consecutive days and the data for test 2 were used to assess for BA-NAc neuron TeTxLC effects. Compared with control mice, TeTxLC mice tended to complete fewer responses (supplementary Fig. 2i ), they earned fewer rewards (Fig. 4g ) and tended to attain a lower nal progressive ratio (Fig. 4h ); there was no TeTxLC effect on pellet retrieval latency or post-reinforcement pause (supplementary Fig. 2j, k ). Therefore, expressing TeTxLC in BA-NAc neurons with the aim of chronically inhibiting their glutamate release replicates some speci c effects of CSS on reward-directed behaviour.
During the CSS/CON procedure (days 1-15), BW and food consumption per CSS mouse and CON mouse-pair were measured on days 1-12, mean values of BW and daily food intake were used as re-baseline values for these parameters. Starting on day 13 and continuing until the last day of behavioural testing, mice were food restricted to be at 95-100% re-baseline BW immediately prior to testing on each day. The overall mean duration of daily attack received by CSS mice was 50. 4±3. 8 s (range: 42. 1-54. 9 s). The baseline and re-baseline data for BW and food intake are given in Table  S1. Across CSS/CON days 1-15, the BW of CON and CSS mice remained close to baseline. As expected based on previous studies  23, [41] [42] [43] [44]  , absolute daily food intake was higher in CSS than CON mice during this period.
For conditioning and testing, a photometry conditioning chamber running IntelliMaze software (TSE Systems) was used, it had inner dimensions of 20. 5 × 26. 5 × 26. 5 cm (W × L × H) and was tted with a house light providing 10 lux, it was placed within an attenuation chamber. The chamber contained a feeder located in the centre of one side wall (Ø = 16 mm x depth = 16 mm) and extending 26 mm into the chamber, it was constructed in this way to enable mice tted with a cranial optic bre and patch cord to retrieve pellets. Each response into the feeder was detected via an infrared motion sensor. An enlarged operant stimulus (22 x 26 mm x depth = 32. 5 mm) activated by nose-poke (operant response) could be inserted to the left of the feeder on the same side wall, a white LED set into the rear of the operant stimulus was illuminated to indicate it was active, and operant responses were detected via an infrared motion sensor.
Fibre photometry and BA-NAc glutamate neuron activity Fibre photometry for optical recording of neural activity in freely moving mice was conducted as described previously  60. Brie y, a laser as excitation light source, a high-sensitivity photodetector, and customised software for signal processing, were used. To provide excitation, a 488 nm laser light was focused into a bre patch cord and delivered at the optic bre tip in the BA. Openings in the centre of the ceilings of the attenuation and operant chambers allowed for unrestricted movement of the patch cord. The latter was connected to the optic bre ferrule on the mouse cranium via a ceramic sheath. Backpropagated GCaMP6 uorescence was focused on a photomultiplier detector and custom-written software code was used for data acquisition (LABView, 2020).
Starting on day 13 and continuing until the last day of behavioural testing, mice were food restricted to yield 95-100% re-baseline BW immediately prior to each behavioural test session. On day 16 mice were placed in the conditioning chamber without any stimuli and connected to the patch cord: the GCaMP6 photometry signal of each mouse was recorded for 15 min to check for a su cient and stable signal, and mice with low signal relative to background were removed from the experiment. Mice underwent a DRLM-bre photometry test on each of days 17-19 and a REV-bre photometry test on each of days 20-22. The overall mean duration of daily attack received by CSS mice was 52. 8±7. 3 s (range: 32. 5-60. 0 s). The baseline and re-baseline data for BW and food intake are given in Table  S1. Across CSS/CON days 1-15, the BW of CON and CSS mice remained close to baseline.
5 s intervals. For each trial with a response in trials 1-20, during the DS-on phase, post-DS feeder phase, or ITI feeder phase, for each 0. 05 s time bin (t), the z-scored (normalised) signal intensity (F) was calculated using the formula (F(t) -F 0 )/SD 0 , where F 0 and SD 0 denote mean and standard deviation of baseline phase activity. The mean z-scored F(t) for trials with response in trials 1-20 was calculated for each t and each test and mouse. These mean z-scored signal F(t) values were then binned into time-normalized intervals or 0. 5 s intervals for statistical analysis. Six mice were excluded from the statistical analysis of the DRLM-bre photometry test data due to an insu cient GCaMP6 signal. For the REV-bre photometry test, as described above, the major behavioural measures of interest were total number of operant responses, number of chocolate pellets earned, and nal ratio attained.
Beginning 5 days post-surgery, BW and food intake were measured daily for 8 days, and the mean values were used as re-baseline values for these parameters. The baseline and re-baseline data for BW and food intake are given in Table  S1 , there were no group differences, and this was also the case during behavioural testing. Starting at 14 days post-surgery (designated as day 14) and continuing until the last day of behavioural testing, mice were food restricted to yield 95-100% re-baseline BW immediately prior to each test session. Behavioural testing commenced on day 15, and comprised 4 daily DRLM tests on days 15-18 (Table  S2 ) with a pellet of normal food provided on day 18, a 1 day interval, and then 2 daily REV tests on days 20-21. In the DRLM test on day 18, there was no difference in the amount of normal food eaten by mice in the 3 groups (0.
Slides/sections were placed on their edge in a covered dark box at RT for 10 min or until completely dried, and then in a capped 50 ml Falcon tube for either immediate cell collection or stored at -80°C for 7 days maximum. Fluorescent reporter protein-labelled tissue was collected from coronal sections at bregma AP -1. 6 to -2. 0 using a laser capture microdissection system (CellCut, MMI). Fluorescence settings were optimized for visualization of CTB-555 + tissue (channel TRITC) in the CSS -BA-NAc neuron transcriptome experiment, and for visualization of EGFP + tissue at wavelength 510 nm (channel FITC) or mCherry + tissue at wavelength 570 nm (channel TRITC) in the BA-NAc neuron tetanus toxin experiment. The membrane slide was positioned and using 40x magni cation, BA tissue areas that were CTB-555 + /EGFP + /mCherry + were each encircled at Ø = 35 µm using the MMI CellTools software.
Differential gene expression and pathway analysis Sequencing reads were mapped to the Mus musculus reference genome (mm10) using STAR v2. 5. 2b allowing for soft clipping of adapter sequences. An average of 20 million reads per sample was obtained, from which approximately 10 million reads were assigned to genomic features. Transcript quanti cation was conducted with RSEM v1. 3. 0 and featureCounts v1. 5. 1. QC, and downstream bioinformatic analyses were conducted with R v4. 1. 0 and Bioconductor v3. 12 tools. Brie y, we identi ed expressed genes based on the distribution of median log2 raw counts across samples, and this yielded a median of 13,637 expressed genes per sample in the CSS -BA-NAc neuron transcriptome experiment and a median of 13,419 expressed genes per sample in the BA-NAc neuron tetanus toxin experiment.
A Gaussian mixture model was tted to the distribution with mclust v5. 4. 7 to identify two clusters: genes with median expression values belonging to the cluster with the mean closest to 0 were ltered out from the expression matrix. Then, we normalized the expression matrix using the variance stabilizing transformation from package DESeq2 v1. 32. 0 and identi ed the 500 highest variable genes (HVGs). Principal component analysis (PCA) was conducted with these 500 HGVs using PCAtools 2. 4. 0. This allowed us to identify outlier samples (1 outlier (1011_0025) in the CSS -BA-NAc neuron transcriptome experiment and 1 outlier (1055_0002) in the BA-NAc neuron tetanus toxin experiment) which were removed prior to further analyses. Using brain cell type-speci c marker genes to identify the relative contribution of different cell types to the RNA sample (mouse visual cortex 65 ), the glutamate neuron gene marker Slca7a7, as well as the pan-neuronal gene marker Snap25, displayed consistent and markedly higher expression than marker genes for GABA (inter)neurons (Gad1, SSt, Pvalb) and each of the glial cell types (astrocyte: Aqp4, oligodendrocyte progenitor cell: Pdgfra, myelinating oligodendrocyte: Opalin, microglia: Ctss).
Acknowledgements This research was funded by the  Swiss National Science Foundation  ( 31003A_179381  to C. R. P. ) and by a  Boehringer-Ingelheim Innocentive  grant (Mouse models of apathy and helplessness, to C. R. P. ). We are grateful to  Björn Henz  and  Alex Osei  for animal caretaking, and to  Hannah Wyatt ,  Susanne Zach  and  Diana Santacruz  for technical support.
Data and code availability Raw sequencing data and gene expression matrices from the CSS -BA-NAc neuron transcriptome and BA-NAc neuron tetanus toxin transcriptome experiments were deposited in the Gene Expression Omnibus and can be accessed with accession codes GSE216587 and GSE216588, respectively. The code that was used to process and analyse the expression data is available on https://github. com/platraduzh/CSS_effects_on_BA-NAc_rnaseq.
Dataset We use the dataset from  [21] , containing P300 oddball EEG recordings of 21 healthy subjects since it is a high-quality dataset with a high number (32) of electrodes and concurrently recorded EOG responses for ocular artifact rejection. Nine targets were arranged on a monitor in front of the subject during an experimental session. The subject was asked to pay attention to a cued target for a block of stimulations. Within each block, the stimulations were organized in in 15 separate subsequent trials. A trial was defined as 9 stimulations in which each target is flashed precisely once per trial. Each target was cued four times, resulting in a dataset consisting of 36 blocks (4860 stimulations) per subject. Each stimulation corresponded to a single epoch in the preprocessed dataset.
Software and Preprocessing Data processing and classifier analysis were performed in Python using Scikit-Learn (version 1. 0. 1) [53]  and SciPy (version 1. 7. 1) [54]. The preprocessing pipeline was implemented using the MNE-Python toolbox (version 0. 24. 0) [55]. The dataset was converted to BIDS-EEG format  [56]  and managed and loaded with MNE-BIDS (version 0. 9)  [57]. The Riemannian classifier from Section 2. 6. 3 was implemented using pyRiemann (version 0. 2. 7). Statistical tests were performed in R (version 4. 1. 2). The EEG recorded at 2048 Hz was re-referenced off-line to the average of the mastoids. The reference electrodes were dropped from the analysis. Data were subsequently filtered between 0. 5Hz and 16Hz using forward-backward filtering with a fourth-order Butterworth IIR filter.
Methods A trenching analysis is performed to infer the geological slip rates of the AMF. To do so, we carried out geomorphological and structural mapping in the Lorca-Totana segment (Figure  2 ), and a paleoseismic investigation from eight paleoseismic trenches at four sites across four fault strands (out of five) in a NW-SE transect of the AMF (Figure  2a ). From NW to SE, the sites are referred to as La Tercia (T-2, T-6), El Roser (T-1), El Saltador (T-16) and La Hoya (T-0, T-3, T-4, T-5). At the El Saltador site, T-16 is an enlargement of a previous trench (TR-7;  Ferrater et al. , 2016)  (Figures  3d  and 3e ). At the La Hoya site, only T-3 provides useful paleoseismic data for the purpose of this study. To quantify the deformation, a vertical throw analysis is carried out from trench observations where uplift related to folding is detected (Figure  S1  in Supporting Information S1), and a paleochannel lateral offset analysis is carried out in the trenches with strike slip component.
Based on the information provided by the respective laboratories, radiocarbon concentrations (fraction of modern), radiocarbon age and correction for isotopic fractionation are carried out following  Stuiver and Polach (1977). Measurements are conducted on an AMS spectrometer. We calibrate the dates to years BP using the OxCal v. 4. 3. 2 software  (Ramsey, 2017)  and the IntCal20 atmospheric curve  (Reimer et al. , 2020). Finally, slip rates are computed combining the measured slips and numerical dates. For a better assessment of the uncertainties we use the MATLAB code by  Zechar and Frankel (2009). Detailed methods for the deformation quantification, sampling, dating procedures in each laboratory and trenching survey are available in the Supporting Information S1 of this paper. Uninterpreted trench images and MATLAB scripts are available in an online repository  (Gómez-Novell et al.
Another possibility is that stress changes occurred at a larger scale, related to static and/or dynamic interaction between the AMF and other EBSZ faults. The recent work by  Herrero-Barbero et al. (2021)  suggested that fault interaction is a possibility in the EBSZ by means of earthquake rupture propagation across fault sections. This study used the RSQSim code  (Dieterich & Richards-Dinger, 2010) , which is a physics-based earthquake simulator that allows to model earthquake occurrences in time and space, while also considering the physical characteristics of earthquake ruptures at the fault system scale. Specifically, for the AMF, the authors remark that the fault is capable of transferring slip to neighboring faults across the Guadalentín Depression, such as to the Los Tollos Fault. If correct, fault interaction in terms of stress transfer (static and/or dynamic) could be a plausible cause for the time-coincident slip rate increments in both the AMF and CAF, although the rupture compatibility analysis between them is pending.
Data Availability Statement The data used to interpret the trenches and compute slip rate distributions in the study are available at Figshare via https://doi. org/10. 6084/m9. figshare. 20057069. v1 (Gómez-Novell et al. , 2022)  with free access under the license CC-BY 4. 0. For the detailed geomorphological mapping we used a LiDAR-based high resolution digital terrain model (DTM) that was obtained in the frame of a previous project of the University of Barcelona (SHAKE project; CGL2011-30005-C02-01) funded by the former Spanish Ministry of Economy and Competitiveness. This DTM is available at Figshare via https://doi. org/10. 6084/m9. figshare. 19729597. v1 (SHAKE Working Group, 2022) with free access under the license CC-BY 4. 0. This article and its supplement contain part of the results of the doctoral thesis of Octavi Gómez-Novell (Gómez-Novell, 2021), which is available at the repository Dipòsit UB of the University of Barcelona: http://hdl.
Observations of ice type We use the Ocean and Sea Ice Satellite Application Facilities (OSI-SAF) Climate Data Record ice type product  (Aaboe et al. , 2019) , hereafter CDR, for comparison with the model, both to gain initial verification of our choice of how to define and evolve MYI, and then for an extensive analysis of simulated MYI extent (section 3). The daily dataset spans October to April from 1979-present, allowing for a comparison from autumn to spring over the time period that we run the model for. The dataset primarily uses brightness temperatures from passive microwave radiometers to classify ice types on a 25 km grid covering the Northern Hemisphere. It uses a Bayesian approach to obtain the probability of the signature being a given surface type (MYI, FYI or open water); any pixel that has more than 30% ice concentration and 75% probability of being MYI is classified as MYI.
Any grid cell that has less than 75% probability of being either MYI or FYI is classified as ambiguous. The total extent of MYI in the Arctic from the CDR data is shown as the black line in Figure  2a. Further details of the satellite data and classification https://doi. org/10. 5194/tc-2022-211 Preprint. Discussion started: 1 November 2022 c Author(s) 2022. CC BY 4. 0 License. method are provided in  Aaboe et al. (2019). The Arctic domain we use in our evaluation (Figure  2c ) is the region bounded by the Pacific, Canadian Arctic Archipelago, Fram Strait and Barents Sea gateways, similar to the region used in  Kwok (2018). The modelled MYI concentration is converted to a binary type classification for comparison with the CDR dataset using a threshold T : elements with MYI concentration exceeding T are considered as MYI.
The extent of modelled MYI (area of the elements detected as MYI) is sensitive to the value of T. Figure  2a  shows the evolution of MYI extent within the Arctic (Figure  2c ) computed with values of T ranging from 0. 30 (blue) to 0. 70 (red). The optimal value of T =0. 40 was found by minimization of the root mean square difference (RMSD) between the MYI extent from CDR and from the model (Figure  2b ). RMSD was computed from November to March due to large uncertainties in other periods; this affected the magnitude of the RMSD but not the value of T. To have an idea of the uncertainty of the CDR dataset, we use the "uncertainty" variable provided in the dataset and assign the lower bound of CDR MYI as only those grid cells with an uncertainty < 0. 02. We assign the upper bound of CDR MYI as all MYI cells plus ambiguous cells.
Observations of ice age For additional evaluation of our model, we use the dataset of Arctic ice age from the National Snow and Ice Data Center (NSIDC)  (Tschudi et al. , 2019) , hereafter NSIDC_age. The dataset is constructed by creating a 12. 5 × 12. 5 km grid of ice parcels for each age category, which are then advected and tracked as Lagrangian parcels according to weekly sea ice motion vectors estimated from satellite observations  (Maslanik et al. , 2011; Tschudi et al. , 2020)  and then interpolated back onto the original grid. If two parcels of different ages are advected into the same grid cell, the oldest age is taken for that grid cell, under the assumption that younger ice is easier to deform than older ice. At the summer minimum ice extent, if parcels remain, they are put into the next age category.
More information can be found in  Tschudi et al. (2016 Tschudi et al. ( , 2019 Tschudi et al. ( , 2020)). The approach taken to produce this dataset is rather different to that of the CDR ice type: it is based on the age of ice, rather than type, which makes a direct comparison of the two datasets difficult. Further, by choosing the oldest ice in the case formulated above, it may overestimate the age of the ice by prioritising small concentrations of old ice  (Korosov et al. , 2018). Despite differences between these methods, the dataset can still provide some information on how well the model is advecting the ice, particularly at lower MYI concentrations which the CDR data does not provide. Additionally, it provides weekly data during the summer season when satellites that differentiate between ice types based on surface properties fail.
While such an increase in MYI extent can occur mid-winter due to diverging ice drift, neither the NSIDC_age data nor the model show such an extensive increase, potentially indicating random errors in the CDR dataset. In general, the correspondence between the three datasets is reasonable. There are no obvious biases between them, beyond the fact that MYI extent derived from the NSIDC_age data is generally high compared to the others, which is to be expected given the assumptions used to create the product (e. g. Korosov et al. , 2018). There are also few years (for example, 2017) when we find persistent substantial difference in the MYI extent comparing the two data products and model. The rate of decline in MYI extent through winter simulated by the model generally agrees well with that of the CDR data, NSIDC_age data, or both.
Summary and conclusions We have implemented a novel way of tracing MYI area and volume in a coupled ice-ocean model. By utilising the Lagrangian framework of the sea ice model, we can track when each grid element experiences the end of summer and therefore upgrade the MYI based on physical conditions. This gives a more accurate estimate of the amount of FYI that replenishes the MYI in autumn. The model also allows us to track how the source and sink terms for MYI impact its evolution. The model generally agrees well with multiple datasets, both in integrated amount of MYI extent and its spatial distribution. The modelled ice drift is very good, which in turn gives a good spatial distribution of MYI, as long as the minimum ice extent in the fall is reasonably well captured. The main drawback of our approach is that as MYI is treated as a tracer, we must make simple assumptions about how MYI and FYI behave when melting and ridging.
Acknowledgements. This research has been funded by the  Norwegian Research Council  (FRASIL: grant no. 263044  and  Nansen Legacy : grant no. 27673 ) and  JPI Climate and JPI Oceans  ( MEDLEY  project, under agreement with the  Norwegian Research Council , grant no  316730 ). The computations were performed on resources provided by Sigma2 -the National Infrastructure for High Performance Computing and Data Storage in Norway.
Data availability. The ice type product from the OSI-SAF Climate Data Record is available at https://thredds. met. no/thredds/c3s/c3s. html (last visited June 2020). The ice age product from the National Snow and Ice Data Center is available at https://nsidc. org/data/nsidc-0611/ versions/4 (last visited September 2021). Author contributions. PR and EO obtained the funding. PR, EO and HR formulated the study. HR, EO and GB developed the new code for tracking MYI. HR carried out the analysis. GB produced the simulation and helped with analysis. AK assisted with evaluation against observations. HR wrote the manuscript with input from all authors.