dataesr/scientific-paragraphs-categorization huggingface dataset:
Samples were blocked with 0. 05% saponin and 5% fetal calf serum in PBS for 30 min at RT and stained with anti-p4E-BP1 antibodies for 2 hours at RT. Cells were then washed with PBS and incubated with diluted primary antibodies in PBS for 1 hour at RT. The coverslips were washed three times with PBS, followed by incubation with fluorescently labeled conjugated secondary antibodies for 1 hour at RT protected from light. Coverslips were washed three times with PBS and mounted on glass slides with Prolong-DAPI (4 0 ,6-diamidino-2-phenylindole) mounting medium. Images were acquired using a 100× Plan Apo VC 1. 4 oil objective with a wide-field microscope (Nikon Eclipse 90i upright) using a cooled interlined charge-coupled device camera (CoolSNAP HQ2, Roper Scientific) or with an inverted Eclipse Ti-E (Nikon) spinning disk confocal microscope (Yokogawa CSU-X1 confocal scanner unit interfaced with Metamorph software, Molecular Devices by Gataca Systems, Massy, France).
A z stack of images was taken every 0. 2 μm by mean of a piezoelectric motor (Physik Instrumente). For Airyscan high-resolution imaging, MDA-MB-231 cells were fixed and immunostained as described above. Images were acquired using Zeiss LSM 900 confocal microscope equipped with Airyscan detectors using an oil immersion 63× Plan Apochromat, numerical aperture 1. 4 objective. Z stacks were acquired with optimal Airyscan settings for each individual channel. After acquisition, stacks were processed by three dimensional Airyscan processing features in Zeiss Zen software.
For cell center-to-periphery distribution analysis, the line joining the cell centroid (0) and vesicle center was prolonged to the cell contour (1) and the vesicle position was computed on the 0-to-1 axis. The macro is available (https://github. com/Anne-SophieMACE/ EndosomalProteinDetection). The mean intensity of p4E-BP1 per cell was quantified using a homemade ImageJ macro. Briefly, cells were manually delimited using the phalloidin signal and the p4E-BP1 mean intensity per cell was measured using the "Measure" command of ImageJ.
For identification, data were searched against the Homo sapiens UP000005640 database using Sequest HT through Proteome Discoverer (v. 2. 4). The enzyme specificity was set to trypsin, and a maximum of two missed cleavage sites were allowed. Oxidized methionine, N-terminal acetylation, methionine loss, and methionine acetylation loss were set as variable modifications. Maximum allowed mass deviation was set to 10 parts per million for monoisotopic precursor ions and 0. 6 Da for MS/MS peaks. The resulting files were further processed using myProMS v. 3. 9. 2 (https://github. com/bioinfo-pf-curie/myproms)  (58). False discovery rate (FDR) was calculated using a percolator  (59)  and was set to 1% at peptide level for the whole study. Label-free quantification was performed using peptide extracted ion chromatograms (XICs) computed with MassChroQ v.
Statistics and reproducibility All data are presented as means ± SEM from at least three independent experiments except indicated otherwise. GraphPad Prism software was used for statistical analysis. Data were tested for normal distribution using the parametric and nonparametric tests. One-way analysis of variance (ANOVA), Kruskal-Wallis, or Mann-Whitney tests were applied as summarized in table  S5. Statistical significance was defined as *P < 0. 05, **P < 0. 01, ***P < 0. 001, and ****P < 0. 0001; ns, nonsignificant.
Behavioral task and stimuli. Stimuli were presented on a 21" CRT monitor (Dell Trinitron) with a refresh rate of 85 Hz and a resolution of 1024x768 pixels, viewed at a distance of 87 cm. All stimuli were created using the COGENT graphics toolbox (developed by John Romaya at the LON at the Wellcome Department of Imaging Neuroscience) running in MATLAB (Mathworks Inc. ) with custom experimental control software  (109). The monkeys were trained to perform a visual search task. A trial started when the monkey acquired fixation on a 0. 3° red (26. 2 cd/m 2 ) fixation dot in the center of the screen. After 200 ms of fixation within a 1. 2° diameter window, 6 stimuli appeared, arranged in a circle around the fixation point, at 5. 3°e ccentricity. Simultaneously, the fixation dot became green (98.
Acknowledgements We thank  Kor Brandsma ,  Anneke Ditewig , and  Lex Beekman  for animal care and biotechnical assistance; Bram van Vugt and  Pia Jentgens  for assistance with data acquisition and animal training;  Matthew Self  for graciously allowing us to photograph his 'alternatively organized' desk for the scene depicted in Figure 1; and  Jan Theeuwes  and  Daniël Schreij  for early discussions of the work. The in-house experimental control software was originally developed by  Chris van der Togt. This work was supported by  NWO  ( Crossover Program 17619 "INTENSE" ;  VENI 451. 13. 023 ), the  European Union   FP7 (ERC 339490  "Cortic_al_gorithms"), the  Human Brain Project  (Agreement No. 945539 , " Human Brain  Project  SGA3 "), and the  Friends Foundation of the Netherlands Institute for Neuroscience.
With these participants, all groups were matched for years of education (F(2,134) = 0. 72; p =. 49) and handedness (χ 2 = 3. 43, df = 4, p =. 49). Pre-HD participants and controls were matched for sex (χ 2 = 1. 14, df = 1, p =. 71). There were more men in the HD patients than in controls (χ 2 = 6. 15, df = 1, p =. 01) and in pre-HD (χ 2 = 10. 52, df = 2, p =. 005). HD patients and controls were matched for age (t(110. 95) = 0. 58, p =. 56). Pre-HD participants were younger than controls (t(56. 28) = -3. 32, p =. 002) and HD (t(62. 45) = -3. 74, p <. 001). Among these participants, HD patients and 30 Controls also underwent brain MRI at M12. VBM data preprocessing and analysis were performed with CAT12 (www. neuro. uni-jena. de/cat/), an SPM12 toolbox (http://www. fil. ion. ucl. ac. uk/spm) running on Matlab (https://www.
We evaluated the correlation between performances for the temporal production task, the results of clinical assessment tests, and demographic data, with Pearson's correlation. Finally, exploratory factor analysis, using oblimin rotation, was conducted using the 'fa' function from the R package psych  (Revelle, 2021)  and the "EFA' function from the R package EFAtools  (Steiner et al. , 2021). Scree plot was produced using the parallel function in R, and we considered the standard fit indices: Root Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Bayesian Information Criteria (BIC).
MRI data analysis VBM analyses investigated regional differences between gene carrier participants (HD and Pre-HD) and Controls at baseline, in terms of smoothed, modulated and normalized grey matter volume and delineated grey matter atrophy. Our approach was similar to previous studies looking at correlations between VBM and cognitive performance in HD patient  (Giavazzi et al. , 2018; Lemoine et al. , 2021). We first assessed group differences (Controls vs. gene carriers) in grey matter volume with a full factorial design model, with group as the main factor and age, sex, total intracranial volume (TIV), and the type of MRI sequence used as covariates. An atrophy mask was created for subsequent analyses, which included the clusters showing a significant effect of group in this analysis (FWE-corrected, p <.
05 with at least 50 contiguous voxels). Significant clusters were anatomically labeled by superimposing the statistical parametric maps on the AAL atlas implemented in MRIcron software (htt p://www. mricro. org). Using the data at baseline (M0), we then investigated the relation between grey matter volume and performance in the temporal production task in the gene carrier participants (N = 85) within this atrophy mask. To do so, we conducted a linear regression across participants where age, sex, TIV and MRI type were included as covariates. Again, statistical outcomes were based on FWE-corrected p <. 05 and a minimum of 50 voxels per cluster. For each gene carrier participant, we used FSL (https://fsl. fmrib. ox. ac. uk/) to extract the mean probability of GM values within the significant clusters, for illustration purposes.
Funding Repair-HD  is funded from the  European Union 's  Seventh Framework Programme  for research, technological development, and demonstration under grant agreement n • 602245 and for the French part by grants from the  National centre of reference for Huntington's Disease , grants. The team is supported by -NeurATRIS   ANR-11-INBS-0011  /  Agence Nationale de la Recherche  ( French National Research Agency)  and  ANR-17-EURE-0017  Frontcog. This work is also funded in part from the  Agence Nationale pour la Recherche ,  ANR-10-IDEX-0001-02 PSL*. The Centre for Trials Research and  The Brain Repair and Intracranial Neurotherapeutics (BRAIN) Unit , receives funding from  Welsh Government through Health and Care Research Wales.
The mean of Fst values across genes comparing 1000 Genomes to extinct lineages (11%) is higher than those for 1000 Genomes population comparisons (4%), which suggests that there are structural differences between them and us that reflect both explanations-drift and conservation. Denisovans have been previously noted to exhibit geographical and temporal population structure. 16, 17 Based on our data the last common ancestor shared by Homo (contemporary humans, Neanderthals, Denisova, and others) and Pan (chimpanzees, bonobos) had a conserved set of ORs.
1 The VCF reference genome for genomes analyzed was hg19/GRCh37. Only variants with a minimum genotype quality of 20 (GC20) were used for downstream analysis. The choice of restricting ancient genome samples to those generated by Max Planck was to reduce the noise introduction by different methods of variant calling and handling damaged genomes. Although the draft genomes of Neandertals and Denisovans were reported as having more nonfunctional ORs than contemporary humans,  50  these genomes have since been revised. 28, 51 The bioinformatic identification of olfactory pseudogenes has been experimentally challenged  52  by data showing the receptors containing coding sequence regions split into separate exons are conserved across mammals and expressed at the same level as protein-coding receptors with a single exon.
53  also catalogued variants from contemporary using 1000 Genomes dataset  54  consisting of over 2,500 individuals from 26 populations in Africa, the Americas, Europe, and Asia (Table  S3 ). The 26 groups in 1000 Genomes were comprised of individuals with at least 3 out of 4 grandparents identifying membership in the group. While there are some groups engaging in pastoral or traditional horticulture/farming lifestyles, most practice mixed subsistence economies and lead western lifestyles.
Variant calling Max Planck and 1000 Genomes Project both used the reference genome, hg19/GRCh37, to call variants using VCFtools. 55 GRCh37 is built from sequences from different individuals and serves as the reference. If the reference allele is C at a specific genomic position, a variant is called if it is not a C. We excluded insertions and deletions from analysis. BCFtools 56 was used to slice VCFs into chromosomes (ancient DNA) and genes of interest. For 1000 Genomes, BCF tools was also used to generate population specific VCF files. BCF tools was used to create consensus sequences for each gene for the entire 1000 Genomes dataset and for each population. We cataloged variants using two sets of VCF data published by Max Planck Institute for Evolutionary Anthropology Leipzig in 2013 and 2016.
Both datasets are based on the same original sequence data but the 2016 VCFs were generated using snpAD, an ancient DNA damage-aware genotyper, and are more conservative estimates mutations. 51 e 2016 ancient VCFs (Tables  S2  and S6 ) include high quality data from three Neandertals (Altai, Chagyrskaya, Vindija), lower quality data from one Neandertal (Mezmaiskaya), high quality from one Denisova (Denisovan 3), and one ancient human contemporary to Altai Neandertal and Denisova in Denisova Cave (Ust'-Ishim). The 2013 VCF data analysis included high quality data-but not subject to the damage-aware genotyperfor Altai Neandertal, Denisova 3, and Ust'-Ishim. Variants were called using a custom bioinformations pipeline (Figure  S5 ). Data are found at http://cdna. eva. mpg. de/ust-ishim/VCF and http://cdna.
Homology model The protocol followed a previously published method  57  and is described here. 391 human OR sequences were aligned to pre-aligned sequences of 11 GPCRs including bovine rhodopsin (PDB: 1U19), human chemokine receptors CXCR4 (PDB: 3ODU) and CXCR1 (PDB: 2LNL), and human adenosine a2A receptor (PDB: 2YDV) using Jalview. 59 The four experimental GPCR structures (1U19, 3ODU, 2YDV and 2LNL) were used as templates to build the human consensus OR by homology modeling with Modeller. The human consensus amino acid sequence was determined by aligning 391 human OR sequences and by selecting the most conserved amino acid for each position. Five models were obtained and the one fulfilling several constraints (binding cavity sufficiently large, no large folded structure in extra-cellular loops, all TMs folded as a-helices, a small a-helix structure between TM3 and TM4) was retained for further structural analysis.
ACKNOWLEDGMENTS The high-performance computing and data storage resources operated by the  Research Computing Systems Group at the University of Alaska Fairbanks, Geophysical Institute. US National Science Foundation  Award # 1550409  (KCH),  US National Institutes of Health  grant  K99DC018333  (CADM),  US National Science Foundation  Award  1556207  (HM),  US National Institutes of Health  grant  DC014423  (HM),  US National Institutes of Health  grant  DC016224  (HM).
REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies mouse antiRho4D2 Millipore Sigma Catalog#MABN15, RRID:AB_10807045 (PE)-donkey anti-mouse F(ab')2 Fragment antibody Jackson Immunologicals Catalog#715-116-150, RRID:AB_2340798 Critical commercial assays Dual-Glo Luciferase Assay System Promega Catalog# E2940 Deposited data Functional Data (raw) This paper https://github. com/kchoover14/ OldNoseshttps://doi. org/ 10. 6084/m9. figshare. 21542943 Protein Models. pdb This paper https://github. com/kchoover14/ OldNoseshttps://doi. org/10. 6084/ m9. figshare. 21542943 Functional data (2013 all and active) This paper https://github. com/kchoover14/ OldNoseshttps://doi. org/10. 6084/ m9. figshare. 21542943 Functional data (2016 all and active) This paper https://github. com/kchoover14/ OldNoseshttps://doi.
org/10. 6084/ m9. figshare. 21542943 Materials availability This study did not generate new unique reagents (see key resources table for materials used) All protein models are made available in https://github. com/kchoover14/OldNoses. DOI is listed in the key resources table. Date code and availability Data d All raw in vitro data are made available in https://github. com/kchoover14/OldNoses. DOI is listed in the key resources table. d All raw in vitro data analysis is reported in the paper. d This paper analyzes existing, publicly available genomic data. These accession numbers for the datasets are listed in the key resources table. d Data generated from analysis of the shared genomic data are made available in https://github. com/ kchoover14/OldNoses. DOI is listed in the key resources table.
ACKNOWLEDGMENTS We thank the ICM zebrafish, histology, and imaging facilities. This work was supported by  INSERM , the  French MS Foundation ARSEP and Investissements d'Avenir   ANR-10-IAIHU-06  ( IHU-A-ICM ) and  ANR-11-INBS-0011  (NeurATRIS). Mary-Amélie Masson  is recipient of a PhD fellowship from the  Ministry of Research and Education attributed by Sorbonne University  (Doctoral School ED3C, ED 158). Figures were created with Biorender. com.
Mean-field (MF) models are computational formalism used to summarize in a few statistical parameters the salient biophysical properties of an inter-wired neuronal network. Their formalism normally incorporates different types of neurons and synapses along with their topological organization. MFs are crucial to efficiently implement the computational modules of large-scale models of brain function, maintaining the specificity of local cortical microcircuits. While MFs have been generated for the isocortex, they are still missing for other parts of the brain. Here we have designed and simulated a multi-layer MF of the cerebellar microcircuit (including Granule Cells, Golgi Cells, Molecular Layer Interneurons, and Purkinje Cells) and validated it against experimental data and the corresponding spiking neural network (SNN) microcircuit model.
SNN model This cerebellar cortex model was built using the Brain Scaffold Builder (BSB) (https://bsb. readthedocs. io/en/latest/), a neuroinformatic framework allowing a detailed microcircuit reconstruction based on neuron morphologies and orientations and the incorporation of active neuronal and synaptic properties  [56]. The model was simulated as a SNN with ~3x10 4 extended-Generalised Leaky Integrate and Fire (E-GLIF) neurons  [52, 54]  and ~1. 5x10 6 alphashaped conductance-based synapses  [62]. The SNN simulations were performed using NEST version 2. 18 (https://zenodo. org/record/2605422)  [4, 63]. 2. 1. 1 Neuron model. The E-GLIF formalism describes the time evolution of membrane potential (V m ) depending on two intrinsic currents to generate slow adaptation (I adap ) and fast depolarisation (I dep ), using a the system of three Ordinary Differential Equations  [54]  dV m ðtÞ dt ¼ 1 C m ð C m t m ðV m ðtÞ À E rev Þ À I adap ðtÞ þ I dep ðtÞ þ I e þ I syn dI adap ðtÞ dt ¼ k adap ðV m ðtÞ À E rev Þ À k 2 I dep ðtÞ dI dep ðtÞ dt ¼ k 1 I dep ðtÞ ð1Þ 8 > > > > > > > < > > > > > > > : where I syn = synaptic current (it models the synaptic stimulus, see section  V m ðt spk þ Þ ¼ V r I adap ðt spk þ Þ ¼ I adap t spk þ þ A 2 I dep ðt spk þ Þ ¼ A 1 ð2Þ where t þ spk = time instant immediately following the spike time t spk ; V r = reset potential; A 2 , A 1 = model currents update constants.
Timing optimisation. The MF time-constant T was optimised by comparing the model prediction with experimental data of the cerebellar Granular layer (Fig  4 ). The simulated average activity was interpolated with the experimental Local Field Potential (LFP) measured with high-density microelectrode arrays (HD-MEA) in the granular layer of acute mouse cerebellar slices  [67]. LFP data were recorded at 37˚C. The external stimulus consisted in a pulse train of 5 stimuli of 50 Hz amplitude. This stimulation protocol was repeated nine times changing the HD-MEA recording channels across each experiment (Fig  4A ). The LFP signals recorded were averaged across the nine experiments resulting in five values that represented the average of each pulse of the input trains. These average records were normalised on the amplitude of the signal recorded after the first stimulus  [68].
Hardware and software The SNN was built with the BSB release 3. 0 (https://bsb. readthedocs. io/en/v3-last) and the numerical simulations were performed with NEST version 2. 18 (https://zenodo. org/record/ 2605422). The MF design, the timing optimization, the MF validation, and the MF predictive simulations were implemented in Python 3. 8. Functions packages written for the present work are available on https://github. com/RobertaMLo/CRBL_MF and a DEMO is released on EBRAINS platform at https://wiki. ebrains. eu/bin/view/Collabs/mean-field-cerebellar/. All optimisation procedures and simulations were run on a Desktop PC provided with AMD Ryzen 7 2700X CPU @ 2. 16GHz with 32 GB RAM in Ubuntu 16. 04. 7 LTS (OS).
This research has received funding from the  European Union 's  Horizon 2020 Framework Program for Research and Innovation  under the  Specific Grant  Agreement No. 945539  ( Human Brain Project SGA3 ) to ED, CGWK, FP and AD, and under the  Marie Sklodowska-Curie  grant agreement No. 892175  to YZ. CGWK received funding from  BRC  (# BRC704/CAP/CGW ),  MRC  (# MR/S026088/1 ),  Ataxia UK, MS Society  (# 77 ),  Wings for Life  (# 169111 ). This research has also received funding from  Centro Fermi project "Local Neuronal Microcircuits"  to ED. Special acknowledgement to EBRAINS and FENIX for informatic support and infrastructure. RL, and AG have been supported by  Human Brain Project SGA3. This work was also supported by # NEXTGENERATIONEU (NGEU)  and funded by the  Ministry of University and Research (MUR) ,  National Recovery and Resilience Plan (NRRP) , project  MNESYS  ( PE0000006 ) - A Multiscale integrated approach to the study of the nervous system in health and disease  ( DN.
3 × 150 mm, 120 Å, particle size 3 µm) using a variable gradient of the mobile phase composed of 0. 1% (v/v) formic acid in 2% ACN 98% water (phase A) and 0. 1% (v/v) formic acid in 98% ACN 2% water (phase B) with a flow rate of 5 µL/min. The following gradient details are given for the percentages of phase B in phase A only: from 3 to 25% for 68 min, from 25 to 35% for 5 min, 35 to 80% for 2 min, 80% for 3 min, 80 to 3% for 1 min, 3% for 6 min. The temperature of the autosampler and column were maintained at 8 • C and 35 • C, respectively. The column eluent was directed into the AB Sciex TripleTOF 5600+ system, then was operated in positive ion mode by electrospray ionization using the DuoSpray ® and Turbo V ® ionization sources and controlled by the Analyst software (version 1. 7. 1). Ionization parameters were as follows: ISVF = 5500, GS1 = 15, GS2 = 15, CUR = 25, TEM = 150.
MS spectra were acquired in high sensitivity (HS) mode for 250ms from 400 to 1250 m/z and MS/MS scan from 100 to 1500 m/z (60 ms accumulation time, 20 ppm mass tolerance, rolling collision energy). The generation of protein libraries was performed using the ProteinPilot software (AB Sciex, version 5. 0. 2) with a UniProt human-filtered proteome database (February 2021), specifying iodoacetamide and methionine oxidation as variable modifications. The detected protein threshold was set to 10% (unused ProtScore > 0. 05). Identification data for the two independent biological samples were merged to generate the so-called hBP-solo and hBP-coc protein libraries. 2. 4. Data-Independent Analysis (DIA)/Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH) SWATH-MS was done using the same material and instrumental setup as described for the DDA-MS analysis with some modifications.
Briefly, a 100-variable-window setup was generated using the SWATH ® Variable Window Calculator 1. 1 (AB Sciex) with a 1 m/z window overlap on the lower side of the window. The MS1 survey scan was acquired from 400-1250 m/z for 50 ms and MS2 spectra were acquired in high-sensitivity mode from 400-1500 m/z for 30 ms. The total cycle time was ~3. 1 s. The collision energy used in SWATH-MS was that applied to a doubly charged precursor centered in the middle of the isolation window calculated with the same collision energy equation for DDA, and with a CES of 10 eV. For the analyses conducted in the capillary flow and microflow rate, the SWATH-MS data were recorded as described for the DDA-MS. The SWATH-MS data analysis was performed using PeakView software (version 2. 2, AB Sciex) for a local SWATH-MS processing workflow.
Principal component analysis (Pareto mode for scaling) was performed on samples based on expression to visualize sample clustering, and group comparisons were performed with a Student's t-test to point out fold change (FC) and p values. Data were filtered for p value < 0. 01 and FC > 2 (log(FoldChange) > 0. 3) or FC < -2 (log(FoldChange) < 0. 3). All the quantification results for proteins with less than 2 peptides were automatically excluded, and the accession number of the proteins that met both conditions were analyzed using MetaScape  [34]  and the STRING database  [35]. The interaction maps from the STRING database were obtained for an accuracy of 0. 4 and customized by Cytoscape (version 3. 9. 1) [36, 37]  to point out the proteins of interest.