dataesr/scientific-paragraphs-categorization huggingface dataset:
Rise times were measured between 10% and 90% of peak amplitude (Supplementary Table  1 ). Decay time constants were derived by fitting the sum of two exponentials and the first fast tau component was extracted (Supplementary Table  1 ). Data were acquired using Elphy software (Gerard Sadoc, Centre National de la Recherche Scientifique; Gif-sur-Yvette, France) and analyzed with Elphy and IgorPro (Neuromatic by Jason Rothman, www. neuromatic. thinkrandom. com). In acute slices, none of the recorded GC exhibited ChR2-mediated inward currents (0/64) and IPSC kinetics in MCs and TCs were not consistent with GC-mediated inhibition  49.
1 mW), thereby avoiding cross-excitation of ChRimsonR  27. Sessions with significant averaged changes in the reflected blue light (>1% ΔF/F) were discarded from the analysis. Signals from both photodetectors were digitized by a digital-to-analog converter (DAC; Power 1401, CED) at 5 kHz and recorded using Spike2 software. Mice were placed in a small, ventilated cage (~0. 5 L). Using a custom-built air-dilution olfactometer controlled by the CED card, pure monomolecular odorants were diluted in mineral oil and saturated odorized air was further mixed with the air stream (1/10 dilution) before being delivered into the ventilated cage (flow rate of 4 L/ min), thanks to solenoid pinch valves. Odors were presented for 5 s every 60 s and dynamics of odor introduction and exhaust in the cage were constantly monitored using a mini photoionizer detector (miniPID, Aurora) positioned at the ceiling of the cage.
Calcium imaging using two-photon microscopy Acquisition parameters and imaging. After viral injections, a cranial window (3. 0 × 1. 4 mm glass) was placed over both OB and a stainlesssteel head bar (L-shaped) was cemented to the skull. Mice were then allowed to recover for a month. During this period, the animals were progressively habituated to the head fixed position while staying quiet in the 50-ml open-ended support tube. Calcium activity was imaged using a two-photon system (950 nm, Spectra Physics) with a Prairie Investigator microscope (Prairie View Software 5. 4. 64. 100, Bruker) and equipped with GaAsP photomultiplier tubes (PMTs). Ca 2+ transients were imaged using a 16X, 1. 05 NA microscope objective (Nikon) with a 2X digital zoom. The field of view was 512 ×512 pixels (423. 7 ×423.
7 μm), imaged at 15 Hz using a resonant galvanometer. Imaging planes (MCL, EPL or GL) were determined using anatomical landmarks and layers depth profiles as in refs 53,56. Mean recording depth ± s. d (relative to the GL): MCL, 201. 8 ± 29. 9 μm; EPL, 60. 1 ± 20. 7 μm. MCs were further distinguished from TCs by their denser packing and larger soma size  32  , less dense neuropil  55. Stimulation protocols. Trials consisted in 8 s baseline, 2 s stimulation (odor, light, or odor+light) and 10 s inter-trial interval. Trials were grouped in blocks of 20 trials. 2-3 blocks were acquired per stimulus type. Data was acquired from 6 OBs of 4 animals. Light activation of GABAergic cortical axons. The LED illumination for full-field photo-activation feature of the Investigator series (Bruker) was used to photo-stimulate the GABAergic axons in the OB.
Selected sections were counterstained with DAPI and mounted for image acquisition (Axiovert 200 with Apotome system, Zeiss Zen 2. 6 Blue, Zeiss). Image analysis Motion correction. A full field of view motion correction was performed using a custom-made program in MATLAB 2018a. A twodimensional cross-correlation of every frame with the average projection of the entire image set was used to identify the out of frame z-movements (Pearson's r > 0. 65 in the 2D cross-correlation). For lateral motion correction, the established ImageJ plugin MoCo 95 was used. In brief, it uses a Fourier-transform to improve the efficacy for identifying translational motion. Principal component analysis (PCA) assisted reconstruction. We employed PCA on the raw motion-corrected datasets, after concatenating all the trials for each experiment into a 3-dimensional matrix.
It leads to the possibility to express the original data in a lower dimension, capturing the largest variability in the dataset. The original image set is reconstructed from the most variable eigenvectors (nonvarying (i. e. , inactive) pixels and shot noise in the dataset do not have high variability across time and can be excluded). The reconstruction was done by a linear combination of the PC scores and the PC coefficients for the first 10 PCs of the dataset (Supplementary Fig. 10a, b ). Identification of regions-of-interest (ROIs). The PCA-reconstructed images were used for the identification of ROIs. ROIs were manually drawn on the cell bodies using ImageJ 1. 53t and were imported in MATLAB 2018a (Supplementary Fig. 10c ). As explained above, in order to remove the contribution of neuropil and background fluctuation, we performed a second PCA inside each ROI (Supplementary Fig.
10d-f ). We used PC1-3 to redefine the outer bounds of each ROI for activity quantification. Note that PCA reconstructions did not modify the fluorescence data inside the ROIs. This process eliminated noisy signals (Supplementary Fig. 10g-m ) and improved signal-to-noise ratio by 25% in a similar two-photon calcium imaging dataset  96. Data analysis Z-score calculation. For each ROI, the pixel intensities were smoothed across 5 frames and z-score was calculated for each cell as follow: z = μresp À μbaseline ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi σresp 2 =n À σbaseline 2 =n q ð1Þ With µ and σ being the mean and standard deviation; resp, response (1 s after shutter reopening) and baseline is 1 s before shutter closes.
Population network model The firing rate of the excitatory (MCs or TCs, MC/TCs) and inhibitory subnetworks (GCs) were described by the following differential equations (MATLAB 2018a): where I is the odor input, w mc!gc is the strength of the connection between MCs (or TCs) and GCs and Àw mc!gc is the reciprocal connection from GCs to MCs (or TCs). w sac!gc is the strength of the inhibitory connection from dSACs to GCs. w f b!mc is the strength of the cortical GABAergic feedback (FB) connection on MCs (or TCs). According to our slice electrophysiology results, the strength of the GABAergic feedback is roughly twice stronger on GCs, therefore it was set as 2*w f b!mc. The strength of the feedback on dSACs is even stronger and because, for the sake of simplicity, dSACs are not modeled, we modeled the inhibitory input strength to GCs in presence of feedback decreasing with the inverse of the strength of the feedback to MCs (or TCs).
Acknowledgements We thank  Dr. William Podlaski  for help with the model. We thank  Sara Moberg  for comments on the manuscript. We also thank  Carine Moigneu  for her help with some viral injections,  Julien Grimaud  and  Lucie Dixsaut  for early works. We also wish to thank  Uwe Maskos ,  Christoph Schmidt-Hieber  and  David DiGregorio  from the  Institut Pasteur  for the gift of SST-Cre, PV-Cre and VIP-Cre mice. We also thank the  Genetically-Encoded Neuronal Indicator and Effector (GENIE)  Project and the  Janelia Farm Research Campus  of the  Howard Hughes Medical Institute  for sharing GCaMP6f constructs. We are greatful to  Alexandru A. Hennrich  and  Karl-Klaus Conzelmann  ( Max Von Pettenkofer Institute Virology and Gene Center, Medical Faculty, Ludwig-Maximilians-University Munich, Ger-many ) for the generous gift of (EnvA)SAD-ΔG-mCherry virus.
We also thank the  Vector Core of the Laboratory for Translational Research in Gene Therapy  ( INSERM UMR 1089 ,  Université de Nantes, France ) for AAV vector production and the  Viral Core Facility  of the  McGovern Institute (MIT)  for HSV production. This work was supported by the life insurance company " AG2R-La-Mondiale ", the  Agence Nationale de la Recherche  ( ANR-15-CE37-0004  " SmellBrain ",  ANR-16-CE37-0010  " ORUPS ",  ANR-15-NEUC-0004  " Circuit-OPL ") and the  Laboratoire d'Excellence Revive (Investissement d'Avenir ,  ANR-10-LABX-73 ). Our laboratory is part of the  Ecole des Neurosciences de Paris (ENP) Ile-de-France  network and is affiliated with the  Bio-Psy Laboratory of Excellence. C. M. is a recipient of a fellowship from the  French Ministère de l'Education Supérieure et de la Recherche  and was also supported by the  Fondation de la Recherche Médicale  ( FDT20160435483 ).
Data availability Processed imaging data is available online at: https://zenodo. org/ record/7050088#. YxXaSHbMJD8 (https://doi. org/10. 5281/zenodo. 7050088). Data generated in this study are available in the Source data file. Correspondence and requests for all other materials and information should be addressed to C. M. and G. L. Source data are provided with this paper.
Experimental section All manipulations involving α-aminophosphonates were carried out under dry argon. Solvents were dried by conventional methods and were distilled immediately before use. Routine 1 H, 13 C{ 1 H}, 31 P{ 1 H} and 19 F{ 1 H} spectra were recorded with Bruker FT instruments (AC300 and 500). 1 H NMR spectra were referenced to residual protonated solvents (δ = 2. 50 ppm for DMSO-d6 and δ = 7. 26 ppm for CDCl 3 ). 13 C NMR chemical shifts are reported relative to deuterated solvents (δ = 39. 52 ppm for DMSO-d6 and δ = 77. 16 ppm for CDCl 3 ). 31 P and 19 F NMR spectroscopic data are given relative to external H 3 PO 4 and CCl 3 F, respectively. Chemical shifts and coupling constants are reported in ppm and Hz, respectively. Infrared spectra were recorded with a Bruker FT-IR Alpha-P spectrometer.
RESULTS Initially, a megasatellite was defined as a direct and contiguous DNA repeat of three motifs or more, each of an individual length of at least 90 bases  (Tekaia et al. , 2013). We initially planned to search whole-genome sequences with two programs: Tandem Repeat Finder (TRF)  (Benson 1999)  and MREPS  (Kolpakov et al. , 2003). Both were run on 61 vertebrate species genomes and on the well-described S. cerevisiae genome. These 61 species were chosen because, when this work started, their whole-genome sequences were available and annotated in the Ensembl database (release 101)  (Yates et al. , 2020). They are detailed in Table  S1. Danio rerio (zebrafish), Macropus eugenii (wallaby), and Tarsius syrichta (tarsier) genomes contained an abnormally high number of tandem repeats compared with all other vertebrate genomes, suggesting that the sequence quality and/or its assembly were not acceptable, and they were excluded from subsequent studies.
We therefore decided to include these ZFPs in the present analysis. The 24,407 bona fide TRs exhibit base motif lengths ranging from 30-285 amino acids  (Figure 2, inset). Because repeated motifs of each TR can be very divergent from each other, containing many insertions or deletions, it was not possible to rely on their sequence to build families. Therefore, multiple sequence alignments were performed on TR motifs, and hidden Markov models (HMMs) were determined for each TR alignment (Figure  1 , step 2). The HMM profiles were subsequently compared ''all against all'' using the HMMSEARCH software (HH-suite). To build the resulting graph, the log10 of the e-value (maximum 200) provided by HMMSEARCH was used as a distance. This value, although not a mathematical distance, was used to draw a complete graph between HMMs (Figure  1 , step 3).
Thus constructed, the connected components of this graph constitute a set of TR orthologous families without duplicates. This method has the advantage of not being sensitive to TR phasing because the merging of families only depends on orthologous relationships based on similarity distributed over the whole protein sequence and not only on the TR. Mergings of identical families led to a final number of 257 families made of orthologous proteins in which at least one TR was detected (ORTHO FAM; Figure 1, step 7). Because, in the first place, we were interested in identifying megasatellites and not protein TRs, for each protein in which a TR was identified, the corresponding gene and transcript annotations were recovered from the Ensembl genome database. From these annotations, exon and intron coordinates were extracted, and DNA self-matrices were run, for each megasatellite-containing gene (Figure  1 , step 8).
These self-matrices were designed to manually check megasatellites to classify or discard them. Each of these 5,834 individual DNA matrices was visually inspected and validated or discarded from the database when no megasatellite was visible (Figure  1 , step 9). At this stage, megasatellites were manually annotated as being contained in only one exon (MONOMEGA), more than one exon (MULTIMEGA), or overlapping at least one intron-exon junction (OVERMEGA). In some cases, the megasatellite was spread over several small exons separated by large intronic regions. These were called HIDDEN MULTIMEGA in the database but were considered MULTIMEGA in all subsequent analyses. Altogether, 3,982 megasatellites were identified, distributed among 142 families, including at least one member in one species.
Quality control of the pipeline To determine whether our approach was exhaustive and discriminative, ORTHO FAM families were annotated using the Pfam database. Four well-described protein families known to possess large tandem repeat domains were examined: WD40  (Smith et al. , 1999) , leucine-rich repeats (LRRs; Kobe and Deisenhofer 1994), Ankyrin  (Mosavi et al. , 2004) , and Kelch  (Adams et al. , 2000). In our analysis, WD40 and Kelch were each clustered into one single family (ORTHO FAM 189 and ORTHO FAM 156, respectively), proving that our pipeline correctly clusters members of these protein families that were detected by T-REKS. LRRs and Ankyrin were, respectively, found in three and five ORTHO FAMs, with HMM describing these ORTHO FAMs as significantly different because of the complexity and sequence variability of these repeats.
In Pfam, Ankyrin repeats are also described by five different domains  (Gasparini et al. , 2017). Therefore, although our approach does not claim to be exhaustive, it seems sensitive and discriminative enough to identify well-known large tandem repeat families and describes 19 The T-REKS software was run on protein sequences extracted from the 58 vertebrate and S. cerevisiae proteomes. T-REKS identified 1,542,346 protein TRs whose base motif ranged from 1-285 amino acids (step 1). Multiple sequence alignments were performed on TR motifs, and hidden Markov models (HMMs) were determined for each TR alignment (step 2). HMM profiles were subsequently compared ''all against all'' using the HMMSEARCH software. To build the resulting graph, the log10 of the e-value (maximum 200) provided by HMMSEARCH was used as a distance.
Each of these 5,834 individual DNA matrices was visually inspected and validated or discarded from the database when no megasatellite was visible (step 9). This manual curation led to a final number of 3,982 megasatellites belonging to 142 megasatellite families. that were not previously annotated as such (see ''Distribution and function of megasatellites among species'' below). As a final validation of family homogeneity, all proteins in each family were annotated with a Panther identifier (STAR Methods). The result shows that 90% of the families contain only one single Panther ID, 8% contain two Panther IDs, and only 2% contain 3-5 identifiers (Figure  S2 ). This proves the validity and consistency of our families. Megasatellites are more frequent in subtelomeric regions Minisatellites are unevenly distributed along eukaryotic chromosomes.
Distribution and function of megasatellites among species Altogether, 3,982 megasatellites were detected in the 59 yeast and vertebrate families, not including ZFP-encoding genes (see below). Because paralogous genes were discarded during the analysis, only one member of each ORTHO FAM family was represented in each species; hence, the number of megasatellites per species is equal to the number of families present in that species. The species with the fewest repeats is Petromyzon marinus (19 megasatellites), whereas M. musculus has the highest number of megasatellites (133; Table  S1 ). For each megasatellite, the encoded protein was compared with the Pfam protein motif database, and the corresponding annotation (if any) was retrieved. Of 3,982 megasatellites, 205 did not match a protein motif in Pfam and therefore correspond to undescribed repeats.
They were clustered in 19 different ORTHO FAMs. For each of these, an independent search in the Panther database (STAR Methods) was performed, and significant homology was found in 17 cases of 19 (Table  S5 ). The most frequent functions encoded by megasatellites were linked to cell membrane metabolism (15%); intracellular trafficking, including interactions with actin, myosin, and tubulin (11%); and RNA metabolism (10%). A significant fraction of them (8%) could not be associated with any known function based on sequence homology or protein motifs (Figure  5A ). To assess whether these functions were significantly overrepresented compared with all cellular functions, we used the GO classification. GO terms (molecular functions) of megasatelliteencoded proteins were extracted and compared with all GO terms of the annotated vertebrate genomes using g:Profiler  (Raudvere et al.
Third-generation sequencing using Nanopore or PacBio long reads should help overcome this problem when sequence quality of such reads will be improved to the level of alternative technologies. Another issue is allelic polymorphism because all vertebrate genomes are diploid but only one set of chromosome sequence was available in the database. This issue is, of course, not specific to tandem repeat analyses and will be improved in the coming years with more efficient and thorough sequence analyses and database storage. Finally, tandem repeat detection is another limitation because it is known that no algorithm is able to correctly detect all tandem repeats of a given genome  (Leclercq et al. , 2007). For this reason, we focused the present work on megasatellites found in proteincoding genes, but non-coding regions also contain large tandem repeats, like CNVs  (Sudmant et al.
TR clustering Distances between MSA were calculated with HH-suite (So ¨ding 2005). In order to group related TR (e. g. belonging to orthologous proteins), we instantiated a TR similarity graph from which clusters of strongly similar TR were extracted using the MCL method (van Dongen and Abreu-Goodger 2012). Practically, we first transformed each MSA into an HMM using the hhmake tool, using default parameters. For each megasatellite cluster, only one HMM was generated. Then we concatenated all produced HMM profiles into a giant HMM dataset. In order to generate similarity matrix of all TR, each profile was compared to all others, running a HHsearch against the full dataset. All results were then transcoded from hhr formatted results to ''abc'' format (https://micans. org/mcl/man/clmprotocols.
Database and dotplots These new ORTHO FAM were deposited in a SQLite database. For each protein component of these families, genomic sequence and gene model corresponding to each protein were retrieved. Using this information and a modified version of Gepard  (Krumsiek et al. , 2007) , dotplots for all the genes encoding these proteins were generated. Each of the 5,834 dot plots was visually inspected and manually annotated, using the following criteria. If the megasatellite was detected in only one exon, it was annotated as MONOMEGA  (Figure 3, top). If it was overlapping two or more exons, it was annotated as MULTIMEGA (Figure  3 , middle). If the megasatellite was overlapping at least one exon-intron junction, it was annotated as OVERMEGA (Figure  3 , bottom). These cases were the simple ones and with some experience can be quickly annotated.
More complex cases include megasatellites that are spread over several small exons separated by large intronic regions (for example ORTHO FAM 1746, Supplemental Material S1). In such rare cases, the self-matrix was insufficient to visually detect the megasatellite. In these cases, the ambiguity came from the resolution of the self-matrix, which did not allow to clearly visualize diagonals. This was due to several unusually long introns cutting the megasatellite in several undetectable small pieces. These were called HIDDEN MULTIMEGA in the database, but were considered as MULTIMEGA in all subsequent analyses. Finally, when no megasatellite could be detected by eye on the matrix, the protein self-matrix was checked. In all cases, a TR was found on the matrix, showing that T-REKS correctly identified protein TR but the megasatellite was erased by subsequent mutations following its formation.
Megasatellite distribution along chromosomes In order to study the distribution of megasatellites along the chromosomes, we first extracted the identifiers of all megasatellite-containing genes in our database. The chromosomal position of all extracted genes was then obtained by querying the Ensembl REST API using the "lookup/id" resource (https://rest. ensembl. org/). In parallel, chromosome lengths were retrieved from Ensembl using the REST API by querying the information related to the assembly through the "info/assembly" resource. At this stage, were kept only chromosomes properly identified with numbers (contigs were therefore excluded). With this information, all chromosomes were cut into 10 bins of equal lengths. Finally, the center of each megasatellite-containing gene was assigned to a chromosomal bin according to its location on the chromosome.
Function of megasatellite-encoded proteins Protein sequences were extracted from Ensembl  (Yates et al. , 2020)  and compared to protein motifs listed in the Pfam database, a widely used resource for classifying protein sequences into families and domains  (Mistry et al. , 2021). When no hit was found, the classification retrieved from the PANTHER database was used instead (when available). The PANTHER classification system (http://www. pantherdb. org) is a comprehensive system that combines genomes, gene function classifications, pathways and statistical analysis tools  (Mi et al. , 2019).
These expected numbers were compared to the observed numbers of ZFP in each clade, using a Chi2 test (Figure  7B ). For GO terms, the significance threshold was set at 0. 05 and calculated using the g:SCS tailor-made algorithm of the g:Profiler database, using a correction for multiple testing (biit. cs. ut. ee/gprofiler/gost). All statistical tests were performed using 'R'  (Millot 2011) , and all of the statistical details can be found in STAR Methods.
METHODS 5. 1. Virtual Screening. The NCI, Specs, antimicrobial chemotherapeutics (ACD), and Maybridge databases were screened in silico for a total of over 500,000 compounds filtering for hitlike and leadlike properties (M w < 500; c log P < 5; H-Bond donors < 5; H-Bond acceptors < 10; polar surface area < 150 Å 2 ) and ADME/Tox predictions. Compounds passing filters were docked using DOCK 4. 0 software to published crystal structures of ALK kinase domain (PDB IDs: 3L9P, 2XB7, 3LCT, 3LCS, 2XP2, 5J7H), all in the DFG-in conformation, to account for slight differences observed in the position of the Ploop (Supporting Figure  S5 ). Top 1000 scoring compounds were then re-docked with FlexX and GOLD, using flexible docking procedures, to validate the screening results and to confirm the docking poses.