dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement: OCAMS images used for particle counting are available via the Planetary Data System (PDS)  [71]. Data are delivered to the PDS according to the OSIRIS-REx Data Management Plan available in the OSIRIS-REx PDS archive  [72]. Shape models of Bennu are available via the Small Body Mapping Tool at http://sbmt. jhuapl. edu/ (accessed on 26 March 2021). The particle measurement data are available upon request (knburke@email. arizona. edu).
These pieces were digested for 45 min at 37°C and then mechanically dissociated and sifted in a 100 µm cell-strainer. After centrifugation, cells were resuspended in PBS supplemented with 2 mg/mL collagenase D, 0. 34 mg/mL Hyaluronidase (Sigma #H3506), 0. 04 mg/mL DNase, 1% FBS and 1% P/S. Cells were incubated for 20 min at 37°C. Finally, cells were seed on rat-tail type-I collagen (Corning) coated Petri dishes or coverslips with DMEM/F-12 medium (Gibco #31331-028) supplemented with 3. 6% heat-inactivated FBS, 1% P/S and 1‰ gentamicin (Gibco #15750-037) and incubated at 37°C in a CO2-equilibrated incubator. A small subset of cells, negative for CK19 and albumin, was detected in IBDU cultures using phalloidin staining. They had elongated cell body with bundles of F-actin terminating at the cell surface typical of fibroblasts (data not shown).
Fura-2AM was alternately excited at 340 and 380 nm, and ratio of the resulting images (340nm/380nm) were produced every 1 s. The source of excitation light was a xenon arc lamp, and excitation wavelength was selected by a fast excitation filter wheel (Illumination Systems MT20, Olympus). Digital images were sampled at 12-bit resolution by a fast-scan, cooled charge-coupled device (B/W CCD) digital camera (Orca-ER, Hamamatsu). All the images were background subtracted and controlled by CellˆR software (Olympus). A positive response was defined as a signal at least twice larger than the ∆ratio deviation determined from 3 min recording of the standard baseline. The peak ratio value was determined as the difference (∆ratio) between the maximum peak value and the baseline, during hypotonic shock or drug application.
Genes Images were later exported into Image J software for final processing. Negative controls (i. e. secondary antibodies alone, no primary antibodies) were performed in all experiments and showed no staining. Immunoprecipitation from transfected HEK. P1KO cells. Proteins from HEK. P1KO cells transiently transfected with a given construct (mPiezo1-GFP, mPannexin1-flag, GFPsparck) were extracted using a buffer containing 25 mM Hepes, 150 mM NaCl, 15 mM EDTA, 20 mM N-Ethylmaleimide (Sigma #E3876), 1. 2% TritonX-100 and a cocktail of protease inhibitors (Roche #04 693 116 001). Cell lysates were then rotated for 30 min and centrifuged at 10. 000 g for 20 min at 4°C.
Xcellence RT Olympus https://xcellencert. software. informer. co m/download/ ZEN Zeiss https://www. zeiss. fr/mi croscopie/produits/mic roscope-software/zenlite. html GraphPad; prism 7. 00 GraphPad software Inc. https://www. graphpad. com/ MASCOT software Matrix science https://www. matrixscie nce. com/.
Results And Discussion Strain identi cation MALDI-TOF MS failed to identify strain Marseille-P3249. Therefore, 16S rRNA gene sequencing was performed and using a blast comparison against the NCBI nucleotide database, strain Marseille-P3249 exhibited a 98. 3% sequence similarity with Gemella bergeri strain 617 - 93, being the phylogenetically closest species with standing in nomenclature (Fig. 1 ) (18). Thus, and according to Kim et al. , this strain may be classi ed within a new bacterial species within the Gemella genus as it exhibits more than 1. 35% sequence divergence with its phylogenetically closest species with a validly published name  (19). A gel view performed to compare the mass spectra of strain Marseille-P3249 and its phylogenetically-close species con rmed the novelty of this strain with its unique peak pro le (Fig.
Figures Figure  1  16S rRNA gene sequence phylogenetic analysis highlighting the position of strain Marseille-P3249 relative to other species. Sequence alignment and phylogenetic inferences were obtained using the maximum likelihood method within MEGA 7 software. The scale bar represents a 2% sequence divergence using 1000 replicates. GenBank accession numbers are indicated in parenthesis. Graphical circular map of the chromosome. From outside to the center: Genes on the forward strand colored by COG categories (only genes assigned to COG), genes on the reverse strand colored by COG categories (only gene assigned to COG), RNA genes (tRNAs green, rRNAs red), GC content and GC skew. Figure 2.
05; Table 2). Further, these results were unchanged when testing severe cases of COVID-19 (n ¼ 1,304) or when restricting the burden tests to include variants with an MAF < 1% or singleton variants (Table  S7 ). Therefore, in alignment with a similar report,  23  we also found no evidence for an association between RVs in these 13 interferon-signaling genes. Lastly, we performed the same analysis for an additional 32 genes that are involved in the etiology of SARS-CoV-2 infection (ACE2, TMPRSS2), encode therapeutic targets for COVID-19 obtained through the Clin-icalTrials database (see web resources) (e. g. , IL6R, JAK1), or have been implicated in other immune or infectious diseases through GWASs (e. g. , IL33). After correcting for 1,600 burden tests performed (32 genes 3 five traits 3 five allele frequency thresholds 3 two burden tests; Bonferroni significance threshold p < 3.
a b s t r a c t The ability to extract cues from faces is fundamental for social animals, including humans. An individual's profile of functional connectivity across a face network can be shaped by common organizing principles, stable individual traits, and time-varying mental states. In the present study, we used data obtained with functional magnetic resonance imaging in two cohorts, IMAGEN ( N = 534) and ALSPAC ( N = 465), to investigate -both at group and individual levels -the consistency of the regional profile of functional connectivity across populations (IMAGEN,.
Based on previous research described above, we expected to find a canonical profile of functional connectivity in the face network that presents consistently across populations and ages at group level. When diving into the individual level, we first assessed the extent to which each participant deviates from the canonical profile. Then, we asked whether such deviations relate to the degree of (global) psychopathology reported by each individual. Lastly, we used three sets of fMRI data acquired at 14, 19, and 22 years of age in IMAGEN to investigate to what extent individual connectivity profiles are stable from adolescence to adulthood.
MRI acquisition and processing In IMAGEN, high-resolution T1-weighted images and functional images were obtained at eight European sites with 3-Tesla MRI systems by different manufacturers (Siemens: five sites, Philips: two sites, General Electric: one site). The MR protocol and cross-site standardization of the IMAGEN study are described in Schumann et al. ( Schumann et al. , 2010 )  and IMAGEN Github ( https://github. com/ imagen2 ). Briefly, T1-weighted anatomical images were acquired using 3D MPRAGE sequences, (slice thickness = 1. 1 mm; TR = 2300 ms; TE = 2. 8 ms), and functional images were acquired with GE-EPI sequences (resolution = 3. 4 × 3. 4 mm; slice thickness = 2. 4 mm; TR = 2200 ms; TE = 30 ms). In ALSPAC, scanning was performed on a General Electric 3-Tesla MRI systems, with T1-weighted anatomical images acquired using 3D FSPGR sequences (slice thickness = 1 mm; TR = 7.
9 ms, TE = 3. 0 ms), and functional images acquired using GE-EPI sequences (resolution = 3. 4 × 3. 4 mm; slice thickness = 2. 4 mm; TR = 3000 ms, TE = 35 ms). Quality control, preprocessing of anatomical and functional data were performed, respectively, using MRIQC 0. 15. 0  ( Esteban et al. , 2017 ) , fMRIPrep 1. 3. 2  ( Esteban et al. , 2019 )  and FSL_regfilt 5. 0. 9. The same preprocessing procedures were applied to the IMAGEN dataset and the ALSPAC dataset. Details can be found in Supplements (Supplementary Methods) and our previous study  ( Liao et al. , 2021 ). During the fMRI session, participants viewed passively short videoclips displaying ambiguous facial expressions (with face movements such as nose twitching, opening mouth, blinking eyes), angry facial expression or non-biological control stimuli (consisting of black-and white-concentric circles of various contrasts, expanding and contracting at various speeds; details can be found in the initial report describing this paradigm  ( Grosbras and Paus, 2006 ) ).
Regions of interest and network definition Regions of interest (ROIs) that are relevant to face processing and constitute the face network in this study were defined by a probabilistic map computed in a subsample of adolescents ( n = 1,110) of the IMA-GEN dataset, as reported in  Tahmasebi et al. (2012). In this probabilistic map, 25 ROIs included the following brain regions: amygdala (Amyg-dalaL, AmygdalaR), cerebellum (CerebellumL, CerebellumR), putamen (PutamenL, PutamenR), rhinal sulcus (RhinalSulcusL, RhinalSulcusR), anterior superior temporal sulcus (AntSTSL, AntSTSR), posterior temporal sulcus (PostSTSL, PostSTSR), fusiform face area (FFAL, FFAR), lateral occipital cortex (LOCL, LOCR), V2-V3 (V2V3L, V2V3R), premotor cortex (PMCL, PMCR), mid ventrolateral frontal cortex (MVLFCL, MVLFCR), mid dorsolateral frontal cortex (MDLFCL, MDLFCR) in both left and right hemisphere, and presupplementary motor area (PreSMAR) (see detailed information in Supplementary Table  S2 ).
Statistics analysis We hypothesized that there is a canonical profile of functional connectivity in the face network that is similar across populations and ages. To test this hypothesis, for each of the two face conditions, we first calculate group-averaged face networks for each cohort and each visit (namely, ALSPAC, IMAGEN Visit 1, IMAGEN Visit 2 and IMAGEN Visit 3). Pearson correlation coefficients were then calculated between a given pair of face networks, with high correlation values indicating a high similarity between the two networks. The similarity across populations was evaluated using the ALSPAC dataset and the Visit-2 IMA-GEN dataset, which were acquired in participants of similar ages ( i. e. , 19 years). The similarity across time was evaluated by comparing the group-averaged face networks acquired in IMAGEN at Visit 1, Visit 2 and Visit 3.
In IMA-GEN dataset, the group-averaged profiles of the face network acquired at Visit 1 (14 years of age), Visit 2 (19 years of age) and Visit 3 (22 years of age) were highly similar to each other in both face conditions (Angry: Visit 1-Visit 2, r = 0. 991, p < 0. 001; Visit 1-Visit 3, r = 0. 983, p < 0. 001; Visit 2-Visit 3, r = 0. 991, p < 0. 001, Fig. 1. Ambiguous: Visit 1-Visit 2, r = 0. 989, p < 0. 001; Visit 1-Visit 3, r = 0. 976, p < 0. 001; Visit 2-Visit 3, r = 0. 988, p < 0. 001, Supplementary Fig. S3 ). Thus, on the group level, the canonical profile of functional connectivity of the face network appears highly stable between adolescence and young adulthood. In addition, we observed that the group-averaged profile of the whole-brain network was invariant across populations and ages (Supplementary Fig.
1  ) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article. ). we expected relatively low (compare with group level) intra-individual stability of connectivity profiles of face network over years. Data obtained in IMAGEN participants who completed all three visits were used to investigate to what extend the individual connectivity profile of face network change over years. We found the individual stability of connectivity profiles ranged from -0. 11 to 0. 77 ( Fig. 3 A ). For the angry face condition, mean ( ± SD) stability (correlation coefficients) for Visit 1-Visit 2, Visit 1-Visit 3 and Visit 2-Visit 3 were 0. 294 ± 0. 131, 0. 288 ± 0. 127, 0. 285 ± 0. 127, respectively. For the ambiguous face, mean ( ± SD) stability for Visit 1-Visit 2, Visit 1-Visit 3 and Visit 2-Visit 3 were 0.
Acknowledgments We are extremely grateful to all the families who took part in this study the whole ALSPAC team, which includes midwives, interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists and nurses. The  UK Medical Research Council and Wellcome  (Grant ref:  102215/2/13/2 ) and the  University of Bristol  provide core support for ALSPAC. A comprehensive list of grants funding is available on the ALSPAC website ( http://www. bristol. ac. uk/alspac/external/documents/grantacknowledgements. pdf ). This research was specifically funded by  Wellcome Trust  and  MRC  ( 076467/Z/05/Z ). IMAGEN received support from the following sources: the  European Union -funded  FP6  Integrated Project IMAGEN (Reinforcement-related behavior in normal brain function and psychopathology) ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant 'STRATIFY' (Brain network  based stratification of reinforcement-related disorders) (695313),  Human Brain Project  ( HBP SGA 2 ,  785907 , and  HBP SGA 3, 945539 ), the  Medical Research Council Grant 'c-VEDA' (Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute of Health (NIH)  ( R01DA049238 , A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers), the  National Institute for Health Research (NIHR) Biomedical Research center at South London and Maudsley NHS Foundation Trust and King's College London , the  Bundesministeriumfür Bildung und Forschung (BMBF  grants  01GS08152 ;  01EV0711 ;  Forschungsnetz AERIAL 01EE1406A ,  01EE1406B ), the  Deutsche Forschungsgemeinschaft (DFG  grants  SM 80/7-2 ,  SFB 940 ,  TRR 265 ,  NE 1383/14-1 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ), the  National Institutes of Health (NIH)  funded ENIGMA (grants  5U54EB020403-05  and  1R56AG058854-01 ).
Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004 ,  AAPG2019 -GeBra ), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe ), the  Fondation de France  ( 00081242 ), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ), the  Mission Interministérielle de Lutte -contre-les-Drogues-et-les-Conduites-Addictives (MILDECA), the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX 2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013 ), the  Fédération pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ),  U. S. A. (Axon, Testosterone and Mental Health during Adolescence ;  RO1 MH085772-01A1 ), and by  NIH Consortium  grant  U54 EB020403 , supported by a cross- NIH  alliance that funds Big Data to Knowledge Centres of Excellence.
The virtual reality setup was implemented as described previously (Schmidt-Hieber and Häusser, 2013). Briefly, motion on the treadmill was read out as described above and linearly converted to one-dimensional movement along the virtual reality corridor. The virtual environment was projected onto a spherical dome screen (120 cm diameter), covering nearly the entire field of view of the animal, using a quarter-sphere mirror (45 cm diameter) and a projector (Casio XJ-A256) located below the mouse. The virtual linear corridor was 1. 2 m long, enriched with objects placed along the linear track and vertical or oblique grating textures on the walls. A reward zone was located at the end (1. 0-1. 1 m) of the corridor. The Blender Game Engine (http://www. blender. org) was used in conjunction with the Blender Python API to drive the virtual reality system.
Behavioral performance of the training group was comparable between different sessions (Figure  1A-C ). Locomotor behavior was comparable between the trained group and the spontaneously running group (Figure  3B-D ). To detect whisker and body motion (Figure  S4 ), we filmed the animal using an infrared-sensitive camera (Point Grey CM3-U3-13Y3M-CS) operating at a frame rate of 200 Hz. The animal was illuminated by an array of infrared LEDs. Whisker movements were analyzed with the markerless pose estimation software DeepLabCut  (Mathis et al. , 2018). The bases and tips of 4 whiskers, i. e. a total of 8 labels, were identified across all captured frames. To quantify whisker movement, across all captured frames we computed the sum of the Euclidean distances, in units of pixels, that were covered by each of the 8 whisker labels between adjacent frames.
In vivo extracellular electrophysiology Extracellular recordings of population activity were made using Neuropixels probes  (Jun et al. , 2017). Probes were lowered into the dorsal part of the MOs region to a depth of ~2. 0mm measured from the brain surface via a small craniotomy (~500 µm) at an angle of 15°from vertical using a micromanipulator (Sensapex uMp-4). A silver/silver chloride wire, which was soldered to the external reference of the probes and connected to ground, was positioned in an additional small craniotomy. Recordings were performed from head-fixed mice placed on the treadmill as described above. Electrophysiological data were recorded with SpikeGLX (https://billkarsh. github. io/SpikeGLX/) and signals from the AP channels (0. 3-10 kHz bandwidth, sampling rate of 30 kHz) were used for further processing.
Spike sorting for unit identification was performed with Kilosort 2 (https://github. com/MouseLand/Kilosort). Identified units were manually curated using Phy (https://github. com/cortex-lab/phy). Putative cell types were assessed based on peak-to-valley ratio and half-valley width of the spike waveforms for each neuron by fitting a Gaussian mixture model  (Kim et al. , 2016b; Stark et al. , 2013). Units with low classification confidence (P < 0. 95) were unassigned. The interneuron population was further split based on average firing rate: interneurons with a mean firing rate > 10 Hz were classified as fast-spiking interneurons.
In vivo whole-cell electrophysiology data analysis Input resistance was calculated from the steady-state voltage response to a small hyperpolarizing 500-ms current pulse from baseline membrane potential (Figure  2G ). Only data from mice that were resting during this period were used (speed < 0. 5 cm/s). Baseline membrane potential was measured before current pulse injections at the beginning of the recording. Spontaneous firing rate and membrane potential were measured across recordings with durations exceeding 60s. To analyze subthreshold membrane potential and its variance, traces were digitally low-pass filtered at 5 kHz and resampled at 10 kHz. Action potentials were then removed by thresholding to determine action potential times and then masking values 2 ms before and 10-20 ms after the action potential peak.
Membrane potential oscillations were analyzed by bandpass-filtering membrane potential traces after removal of action potentials. Power spectra were computed from subthreshold membrane potential traces using Hanning windowing over data windows of 215 sampling points (∼328 ms). Membrane potential variance time series were computed in rolling time windows with a width of 1000 ms. Changes in subthreshold membrane potential (ΔVm) were computed by subtracting the mean of subthreshold membrane potential traces after spike removal (see above). To compute movement-aligned mean traces of ΔVm (Figures 4, 6 and S6-7), we aligned traces to movement periods spanning 15 seconds before movement onset until 5 seconds after movement onset. After alignment, we computed the mean of each trace between 15 and 10 seconds before the onset of movement, and subtracted this baseline from each trace.
Acknowledgments We thank  Ian Duguid  and  Josh Dacre  for comments on the manuscript. We thank  Lucile Le Chevalier-Sontag  and  Claire Lecestre  for their technical support. This work was supported by grants from the  ERC (StG 678790  NEWRON to C. S. -H. , MSCA 800027 FindMEMO to M. A. , and  Human Brain  Project  SGA2 785907  to J. -P. C. and F. K. ), the  Pasteur Weizmann Council , and a  Pasteur-Roux  fellowship to M. A.
mio. osupytheas. fr, last access: 10 February 2021). Various satellite datasets were used during PROTEVSMED-SWOT. Sea surface temperature (SST, levels 3 and 4, 1 km resolution, not shown in this study) and chlorophyll a concentrations ([Chla], level 3, 1 km resolution) were provided by CMEMS (Copernicus Marine Environment Monitoring Service, https://marine. copernicus. eu, last access: 10 February 2021). Moreover ocean color composite maps were provided by CLS with the support from CNES. They are constructed with a simple weighted average over the five previous days of the data issued from the Suomi/NPP/VIIRS sensor. The altimetry-derived geostrophic velocities from AVISO (Archiving, Validation and Interpretation of Satellite Oceanographic) database were exploited to extract near realtime daily maps.
The conductivity and temperature data were lag-corrected to reduce salinity spiking with the methodology developed by  Lueck and Picklo (1990) ,  Morison et al. (1994)  and  Mensah et al. (2009) , before conversion to absolute salinity S A and conservative temperature Θ, in accordance with TEOS-10 standards  (McDougall et al. , 2012). In the following, temperature and salinity refer to absolute salinity and conservative temperature. Horizontal velocities between 19 m and 253 m have been measured with a VMADCP operating at 150 kHz. VMADCP data treatment was performed with the Matlab software Cascade V. 7 (https://www. umr-lops. fr/en/ Technology/Software/CASCADE-7. 2, last access: 10 February 2021). The sea surface temperature and salinity have been measured continuously along the ship route by the underway TSG.
Two signals of fluorescence induced by the light excitation were also recorded, a red fluorescence (FLR) induced by chlorophyll a and an orange fluorescence (FLO) induced by the phycoerythrin pigment. Two distinct protocols have been run sequentially every 30 min, in order to process the 1164 samples. The first protocol (FLR6) has a FLR trigger threshold fixed at 6 mV and is able to analyze a volume of 1. 5 cm 3. It was dedicated to the analysis of the smaller phytoplankton. For instance, Synechococcus were optimally resolved and counted with this protocol. The second protocol (FLR25), targeted nanophytoplankton and microphytoplankton with FLR trigger level fixed at 25 mV and an analyzed volume of 4 cm 3. The data were acquired thanks to the USB software (Cytobuoy b. v. ) but analyzed with the CytoClus software (Cytobuoy b.
v. ). The combination of the various variables recorded by the flow cytometer exhibits various clusters of particles (cells), which abundance (cells per cubic centimetre) and average variable intensities are provided by the CytoCLUS software. The later generates several two-dimensional cytograms (e. g. Fig. 12 , see section 3. 3 for explanation of the group identification) of retrieved information from the 4 pulse shapes curves (FWS, SWS, FLO, FLR) obtained for every single cell. On 5 May 2018, a SeaExplorer glider, manufactured by Alseamar (codename: SEA003), was deployed at sea. After a short transit, it performed a route approximately parallel to the NS hippodrome (red track on Fig. 1 ). This glider, set to dive down to 650 m depth, was equipped with a pumped conductivity-temperature-depth sensor (Seabird's GPCTD) from which the conservative temperature (Θ), the absolute salinity (S A ) and the density anomaly referenced to the surface (σ 0 ) were derived using TEOS-10 toolbox  (McDougall and Barker, 2011).