dataesr/scientific-paragraphs-categorization huggingface dataset:
Each trial was recorded using a camera above the open field. The object exploration time was manually scored online using the homemade software 'Scorer32'. The experimenter was blinded for treatment and experimental conditions at the moment of scoring. Object locations and experimental conditions were counterbalanced across treatments, individuals, and sessions. For electrophysiological recordings, we run four cohorts of 2 animals each. Each cohort included one vehicle-and one RGS14414-treated animal, which performed the identical condition sequences with the same object-location patterns. Object locations and experimental conditions were counterbalanced across cohorts. Electrophysiological recordings took place during trials and rest periods (45 min before and 3 h after both training and test; and 45 min intertrial time during the training).
Behavioral data analysis Object Space Task. The total exploration time was calculated as the sum of the time spent exploring both object locations. The discrimination index (DI) was computed by subtracting the familiar object location exploration time to the novel object location and dividing it by the total exploration time. A DI > 0 means a preference for the new object location and consequently memory from the previous episode. A DI = 0 shows no preference for either the new object location or the fixed one. DI<0 means a preference for the stable object location.
Model The same computational model as in (Genzel et al. , 2019) was used (see article for more detailed methods). In short, the model learns place-object associations and then translates this memory into an exploratory behavior: the objects that were stably found at the same location have a very low uncertainty and are thus either less attractive or more attractive (depending on the individuals) during exploration than objects found at changing locations (high uncertainty in place-object association). The source code of the computational model and model simulation/fitting procedures is available here: https://github. com/MehdiKhamassi/ObjectSpaceExplorationModel The model employs two different parameters: a learning rate α, which determines the speed of memory accumulation; an inverse temperature β, which determines the strength and sign of memory expression during exploratory behavior.
Sleep architecture analysis Macroarchitecture. The total sleep time (TST), total wakefulness time, and total time for different sleep states (NonREM, REM, and Intermediate) were computed per session on MATLAB. The average across sessions was calculated per rat, and the mean and SEM were computed per treatment. Additionally, the % of TST of NonREM time, REM time, and intermediate time were calculated for the 3 h recording posttraining. Microarchitecture. The distribution of bout duration per stage was computed per treatment. The bouts number and duration were calculated per resting period and state. Only bouts longer than 4 seconds were considered for the analysis. The 1 s epoch state vectors of each session were concatenated with the interleaved trial wakefulness. NonREM episodes were defined as consecutive NonREM bouts without considering microarousals.
Sleep period was described as an event of sleep between wake events > 300 s. Each sleep period could include several NonREM periods defined as NonREM episodes without considering microarousals, or NonREM episodes followed by transitional stages and/or sleep cycles defined as NonREM episodes followed by transitional states and REM or NonREM followed by REM. In sleep, periods can include quiet wakefulness < 300 s. The number of sleep periods and the average duration per treatment were calculated on MATLAB. Additionally, the count and duration of NonREM periods and sleep cycles were also computed per treatment.
Theta coherence Theta coherence was computed as the magnitude squared coherence using the mscohere function in MATLAB and a custom-written script that collected the downsampled data from different animals and study days. The magnitude squared coherence was calculated as follows: 𝐶𝑥𝑦(𝑇) = |𝑃𝑥𝑦(𝑇)| 𝑃𝑥𝑥(𝑇)𝑃𝑦𝑦(𝑇) where Cxy is the magnitude coherence, Pxy is the cross spectral density of the hippocampal and prelimbic cortex signal, Pxx is the hippocampal signal, and Pyy is the prelimbic cortexsignal. The coherence analysis focused on two periods of interest namely, WAKE and REM periods. Since REM sleeping periods might occur several times over several sleeping cycles, REM periods from pre and posttrial sleep were first extracted and concatenated together before running the analysis. For both Wake and REM periods, the power and cross-power spectra were computed on overlapping time windows of 1 second with 80% overlap.
Ripple Detection The downsampled channels (1kHz) of the hippocampal pyramidal layer were loaded and the NonREM bouts were extracted. Using a 3 rd order Butterworth bandpass filter, the epochs of HPC signal were filtered to a frequency range of 100-300Hz. A custom MATLAB function was used for detecting the start, peak and end of the ripples by thresholding voltage peaks which lasted a minimum duration of 30 ms above the threshold. The start and end of the ripple were determined as half the value of the selected threshold. The standard deviations of concatenated NonREM bouts were computed individually for presleep and all post trials in a study day. The average of these standard deviations was calculated to find a single detection threshold per study day. An offset of 5 units was added to the threshold to reduce false positives.
Oscillations characteristics The traces of each event detected (ripples, spindles, delta waves) were extracted using the start and end timestamps obtained from the detectors. The traces of the events were filtered in their corresponding detection frequency band. Characteristics such as the amplitude and mean frequency were calculated for these filtered events using built-in and custom MATLAB functions. Namely, the amplitude of the events was calculated by computing the envelope of the filtered trace using a Hilbert transform. The absolute value of the result was taken and its maximum was found. The mean frequency of the filtered traces was computed using the meanfreq function of MATLAB.
Spectral analysis and Granger causality A two-second-long window centered on each ripple peak was extracted from the hippocampus and the prelimbic cortex channels respectively. All ripples across animals and conditions were combined per treatment and their amplitude was computed by finding the maximum of their envelope computed with a Hilbert transform. The median ripple amplitude was calculated and the corresponding two-second-long windows of the 2000 ripples which amplitude was the closest to the median amplitude were included in the following analysis. A notch filter at 50 Hz was applied to the ripple-centered windows using the ft_preprocessing function from the Matlab-based Fieldtrip toolbox  35. The Short-time Fourier transformation was calculated to detect the changes of spectral power in hippocampus and prelimbic cortex with respect to a time window of ± 1 second around each ripple.
Spike sorting An important step of our analysis was identifying the cortical neurons and their spiking activity. The sleep and trial recordings of each tetrode of the prelimbic cortex were extracted and concatenated chronologically to allow tracking the individual neuronal activity across the whole study day. The tetrode recordings were preprocessed by applying a bandpass filter between 300 and 3000 Hz. For each tetrode, multiple spike sorting algorithms were run using the SpikeInterface python-based framework  38. The spike sorters used were Tridesclous, SpikingCircus, Klusta, HerdingSpikes, Ironclust, and MountainSort4. The default parameters included in SpikeInterface per spike sorter were used. Agreement between the spike sorters was computed and only putative neurons that were detected by at least two spike sorters were considered.
Neuron classification The average neuron waveforms calculated from rats 1, 2, 6 and 9 were categorized as 'Vehicle' and stored in a 139 (t1) × 82 matrix, with 139 being the total number of putative neurons and 82 being the number of samples as mentioned before. Similarly, 'RGS14' data from rats 3, 4, 7, 8 was stored as a 353 (t2) × 82 matrix. Variables t1 and t2 were the total counts of neurons for the respective treatments. The bz_CellClassification. m script from Buzsaki lab's GitHub was used to compute the trough-to-peak delay time and the spike width for the spike waveform of each putative neuron. The function ClusterPointsBoundaryOutBW. m of the same repository was modified to incorporate visualization of interneuron and pyramidal data for both treatments. Neurons were then visualized in a 2D plane with x and y axes as trough-to-peak delay and spike width respectively.
Cortical activity during ripples Using the spike timestamps during NonREM sleep, the cortical pyramidal neuron response to hippocampal ripples was computed in a 2 second window defined around the peak of each ripple. For each ripple the activity of all pyramidal neurons detected during that day was extracted. The spikes timestamps in the 2 second window were normalized to vary from -1 to 1 seconds, where 0 was the ripple peak. After concatenating the normalized timestamps across all ripple-centered windows per neuron, they were binned in 10 ms bins and the number of spikes was determined for each bin. Hence, for each neuron, a [1 x 200] column vector was obtained. This vector was then z-normalized. The final z-scored vector was found by averaging the z-scored vector over all neurons. The data was smoothed twice using the MATLAB smooth function.
To visualize the activity of prelimbic cortex pyramidal neurons around the ripple, the firing activity of a neuron was determined from a 50 ms window around the peak of the ripple (-20 to +30 ms) by quantifying the number of spikes. After compiling the firing activity around the ripple peak for each neuron the values were sorted in an ascending order and was visualized using the MATLAB imagesc function. Increasing cortical plasticity leads to memory interference and enhanced hippocampal-cortical interactions -Supplementary Materials Irene Navarro-Lobato 1 *, Adrian Aleman-Zapata 1 , Anumita Samanta 1 , Milan Bogers 1 , Shekhar Narayanan 1 , Abdelrahman Rayan 1 , Alejandra Alonso 1 , Jacqueline van der Meij 1 , Mehdi Khamassi 2 , Zafar Khan.
Acknowledgments: We would like to thank  Pelin Özsezer ,  Barbora Roldanus  and  Kopal Agarwal  for data analysis,  Mònica Siuraneta ,  Vera Mascarenhas Pombeiro Duarte Silva ,  Jay Chen ,  Alysha Maurmair ,  Jet van der Stoep, Joris van Hout, Koen van den Berg  and  Rian Kraan  for performing behavioral experiments.
In the complex mosaic of ancestries that constitute a modern human genome any putative signatures of selection may therefore misrepresent the timing and magnitude of the actual event unless we can use ancient DNA to chart the individual ancestry components back into the evolutionary past. To investigate these formative processes in Eurasian prehistory, we conducted the largest ancient DNA study to date on human Stone Age skeletal material. We sequenced low-coverage genomes of 317 radiocarbon-dated (AMS) primarily Mesolithic and Neolithic individuals, covering major parts of Eurasia. We combined these with published shotgun-sequenced data to impute a dataset of >1600 diploid ancient genomes. Genomic data from 100 AMS-dated individuals from Denmark supported detailed analyses of the Stone Age population dynamics in Southern Scandinavia.
Samples and data In this study we present genomic data from 317 ancient individuals (Fig  1 , Extended data fig. 2 , Supplement Table  I ). A total of 272 were radiocarbon dated within the project, while 39 dates were derived from literature and 15 were dated by archaeological context. Dates were corrected for marine and freshwater reservoir effects (Supplementary Note 8) and ranged from the Upper Palaeolithic (UP) c. 25,700 calibrated years before present (cal. BP) to the mediaeval period (c. 1200 cal. BP). However, 97% of the individuals (N=309) span 11,000 cal. BP to 3,000 cal. BP, with a heavy focus on individuals associated with various Mesolithic and Neolithic cultures. Geographically, the sampled skeletons cover a vast territory across Eurasia, from Lake Baikal to the Atlantic coast, from Scandinavia to the Middle East, and they derive from a variety of contexts, including burial mounds, caves, bogs and the seafloor (Supplementary Notes 6-7).
5,000 BP. We extracted ancient DNA from tooth cementum or petrous bone and shotgun sequenced the 317 genomes to a depth of genomic coverage ranging from 0. 01X to 7. 1X (mean = 0. 75X, median = 0. 26X), with 81 individuals having >1X coverage. Using a new imputation method designed for low-coverage sequencing data  26  , we performed genotype imputation based on the 1,000 Genomes phased data as a reference panel. We also imputed >1,300 previously published shotgun-sequenced ancient genomes. This resulted in a "raw" dataset containing 8. 5 million common Single Nucleotide Polymorphisms (SNPs) (>1% MAF and imputation info score > 0. 5) from 1,664 imputed diploid ancient genomes. This number includes 42 high-coverage ancient genomes (Table  S2. 1, Supplementary Note 2) that were down-sampled to values between 0.
We filtered out samples with poor coverage or variant sites with low MAF in downstream analyses depending on the specific data quality requirements. For most analyses we use a subset of 1,492 imputed ancient genomes (213 sequenced in this study) after filtering individuals with very low coverages (<0. 1X) and/or low imputation quality (average genotype probability < 0. 8) and close relatives. This dataset allows us to characterise the ancient cross-continental gene pools and the demographic transitions with unprecedented resolution. We performed broad-scale characterization of this dataset using principal component analysis (PCA) and model-based clustering (ADMIXTURE), recapitulating and providing increased resolution into previously described ancestry clines in ancient Eurasian populations (Fig.
Deep population structure of western Eurasians Our study comprises the largest genomic dataset on European hunter-gatherers to date, including 113 imputed hunter-gatherer genomes of which 79 were sequenced in this study. Among them, we report a 0. 83X genome of an Upper Palaeolithic (UP) skeleton from Kotias Klde Cave in Georgia, Caucasus (NEO283), directly dated to 26,052 -25,323 cal BP (95%). In the PCA of all non-African individuals, it occupies a position distinct from other previously sequenced UP individuals, shifted towards west Eurasians along PC1 (Supplementary Note 3d). Using admixture graph modelling, we find that this Caucasus UP lineage derives from a mixture of predominantly West Eurasian UP hunter-gatherer ancestry (76%) with ~24% contribution from a "basal Eurasian" ghost population, first observed in West Asian Neolithic individuals  29  (Extended Data Fig.
BP (95%). Two temporal shifts in genomic admixture proportions confirm the major population genetic turnovers (Fig. 4 ) that was inferred from imputed data (Fig. 3 ). The multiproxy evidence, however, unveils the dramatic concomitant changes in all investigated phenotypic, environmental and dietary parameters (Fig. 4 ). Age skeletons sequenced in this study. The figure shows concomitant changes in several investigated parameters including (from the top) admixture proportions from non-imputed autosomal genome-wide data, Y-chromosomal and mitochondrial haplogroups, genetic phenotype predictions (based on imputed data) as well as  87  Sr/ 86 Sr and δ 13 C and δ 15 N isotope data as possible proxies for mobility and diet, respectively. Predicted height values represent differences (in cm) from the average height of the present-day Danish population, based on genotypes at 310 height-associated loci (Supplementary Note 4f).
We do not know how the Mesolithic Ertebølle population disappeared. Some may have been isolated in small geographical pockets of brief existence and/or adapted to a Neolithic lifestyle but without contributing much genetic ancestry to subsequent generations. The most recent individual in our Danish dataset with Mesolithic WHG ancestry is "Dragsholm Man" (NEO962), dated to 5,947-5,664 cal. BP (95%) and archaeologically assigned to the Neolithic Funnel Beaker farming culture based on his grave goods  81, 82. Our data confirms a typical Neolithic diet matching the cultural affinity but contrasting his WHG ancestry. Thus, Dragsholm Man represents a local person of Mesolithic ancestry who lived in the short Mesolithic-Neolithic transition period and adopted a Neolithic culture and diet. A similar case of very late Mesolithic WHG ancestry in Denmark was observed when analysing human DNA obtained from a piece of chewed birch pitch dated to 5,858-5,661 cal.
BP (95%)  83. The earliest example of Anatolian Neolithic ancestry in our Danish dataset is observed in a bog skeleton of a female from Viksø Mose (NEO601) dated to 5,896-5,718 cal. BP (95%) (and hence potentially contemporaneous with Dragsholm Man) whereas the most recent Danish individual showing Anatolian ancestry without any Steppe-related ancestry is NEO943 from Stenderup Hage, dated to 4,818-4,415 cal. BP (95%). Using Bayesian modelling we estimate the duration between the first appearance of Anatolian ancestry to the first appearance of Steppe-related ancestry in Denmark to be between 876 and 1100 years (95% probability interval, Supplementary Note 9) indicating that the typical Neolithic ancestry was dominant for less than 50 generations in Denmark. From this point onwards the steppe-ancestry was introduced, signalling the rise of the late Neolithic Corded Ware derived cultures in Denmark (i.
Sociocultural insights We used patterns of pairwise IBD sharing between individuals and runs of homozygosity (ROH) within individuals (measured as the fraction of the genome within a run of homozygosity f(ROH)) to examine our data for temporal shifts in relatedness within genetic clusters. Both measures show clear trends of a reduction of within-cluster relatedness over time, in both western and eastern Eurasia (Fig. 6 ). This pattern is consistent with a scenario of increasing effective population sizes during this period  110. Nevertheless, we observe notable differences in temporal relatedness patterns between western and eastern Eurasia, mirroring the wider difference in population dynamics discussed above. In the west, within-group relatedness changes substantially during the Neolithic transition (~9,000 to ~6,000 BP), where clusters of Neolithic farmer-associated individuals show overall reduced IBD sharing and f(ROH) compared to clusters of HG-associated individuals (Fig.
8D ). The fact that both H1d and H2d are found in apparently high frequencies in both early Anatolian Farmers and the earliest Yamnaya/Steppe-related ancestry groups suggests that any selective sweep acting on the H1d and H2d variants would probably have occurred in populations ancestral to both. We note that the strongest signal of selection observed in this locus is at MAPT (rs4792897; p=4. 65e-10; s=0. 03 (Fig. 8A ; Supplementary Note 4a), which codes for the tau protein  159  and is involved in a number of neurodegenerative disorders, including Alzheimer's disease and Parkinson's disease  [160] [161] [162] [163] [164]. However, the region is also enriched for evidence of reference bias in our imputed dataset-especially around the KANSL1 gene-due to complex structural polymorphisms (Supplementary Note 4i).
Acknowledgements This publication, the culmination of a research effort lasting over a decade, is dedicated to the memory of  Pia Bennike , who was part of a small core team that initiated the project. Sadly Pia passed away in 2017 but this study, and many others, testifies to her tremendous efforts and knowledge on Danish prehistoric skeletal material. We are deeply indebted to former and present staff members at the  National Museum, the Anthropological Laboratory , the regional museums and citizen scientists of  Denmark , who for many generations carefully collected, recorded and curated the prehistoric skeletal remains that form a key component of this study. We are equally thankful to curators of the many other institutions across major parts of Eurasia, who to the benefit of following generations curated human skeletal remains and gave us access and permission to sample this precious material.
This research has been conducted using the  UK Biobank Resource and the iPSYCH Initiative , funded by the  Lundbeck Foundation  ( R102-A9118  and  R155-2014-1724 ). This work was further supported by the  Swedish Foundation for Humanities and Social Sciences  grant (Riksbankens Jubileumsfond  M16-0455:1 ) to KK. M. E. A. was supported by  Marie Skłodowska-Curie Actions of the EU  (grant no. 300554 ), The  Villum Foundation  (grant no. 10120 ) and  Independent Research Fund Denmark  (grant no. 7027-00147B ). W. B. is supported by the  Hanne and Torkel Weis-Fogh Fund  ( Department of Zoology, University of Cambridge ); AP is funded by  Wellcome  grant  WT214300 , B. S. d. M and O. D. by the  Swiss National Science Foundation  ( SFNS PP00P3_176977 ) and  European Research Council  ( ERC 679330 ); M.
Data availability All collapsed and paired-end sequence data for novel samples sequenced in this study will be made publicly available on the European Nucleotide Archive, together with trimmed sequence alignment map files, aligned using human build GRCh37. Previously published ancient genomic data used in this study is detailed in Supplementary Table  VII , and are all already publicly available. Bioarchaeological data (including Accelerator Mass Spectrometry results) are included in the online supplementary materials of this submission.
Code availability The modified version of CLUES used in this study is available from https://github. com/standardaaron/clues. The pipeline and conda environment necessary to replicate the analysis of allele frequency trajectories of trait-associated variants in Supplementary Note 4a are available on Github at https://github. com/ekirving/mesoneo_paper. The pipeline to replicate the analyses for Supplementary Note 4c-4e can be found at https://github. com/albarema/neo. All other analyses relied upon available software which has been fully referenced in the manuscript and detailed in the relevant supplementary notes.
Acknowledgments. We thank  Jim Ledwell  for countless insightful discussions about this work and comments on previous versions of the manuscript. We acknowledge funding support from  National Science Foundation  Awards  6932401  and  6936732. This material is based upon work supported by the  National Science Foundation Graduate Research Fellowship Program  under Grant No. 174530. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the  National Science Foundation.
5 year-old adult zebrafish were quickly euthanized on ice prior to grinding using a 1-mL or 10-mL syringe in 0. 5 or 5 mL of E3 medium, respectively. Obtained samples were then filtered using the same 0. 22-µm filter and 1-mL syringe. Behavioral recordings. Zebrafish larvae were reared at density of 20 animals per 94 x 16 mm petri dish filled with 30 to 50 mL of control solution until 5 dpf. At 5 dpf, larvae were transferred to rectangular swim arenas (140 x 10 x 4 mm) filled with 4 mL of solution atop a white illumination panel where their spontaneous swimming was recorded for 5 or 10 minutes at 25 Hz using a ViewWorks camera (BASLER, acA2040-180km, https://www. baslerweb. com) controlled by the Hiris software (R&D Vision, https://www. rd-vision. com/r-d-vision-eng). Behavior of larvae following laser ablations of the posterior lateral line nerve were recorded in the same conditions but atop an infra-red illumination panel using an Edmund Optics camera (UI-3370SE USB 3.