dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Standards, Quality Assessment, and Analytic Softwares The Adoption of Data Standards and Open Analytic Solutions Are Readily Attainable Data Standards. There is a clear need for metadata standards in NHP data acquisition. The Brain Imaging Data Structure (BIDS) framework  (Gorgolewski et al. , 2016) , used in the initial PRIME-DE data release, is recommended given its rapid maturation and widespread adoption in human neuroimaging, including EEG and MEG. However, the BIDS format will require revision to capture the range of metadata unique to NHPs. Minimally, species and scanning position (upright, sphinx) require specification. Metadata could also include details regarding anesthesia protocol, contrast agents, coil type (surface versus volume), head-fixation information, subspecies, age, sex, universal specimen identifiers, body weight, available genomic information, and animal origin.
The NIfTI (Neuroimaging Informatics Technology Initiative) and GIfTI (geometry format under NIfTI) formats, for volumetric and surface datasets respectively, provide a standard for porting data between software packages. The CIFTI (Connectivity Informatics Technology Initiative) format appears to be well positioned as a framework for connectivity analyses that span surface-based representations and subcortical regions. Quality Assessment. NHP imagers have yet to reach a consensus on quality assessment or assurance. Some datasets might be of higher quality, even if these are from fewer animals. There are also concerns that implementing high QC standards at this initial stage will stall data sharing, and analytic methods may be developed to rescue lower quality data. In the human literature, steps toward universal approaches to quantify data quality are being made (e.
g. , MRIQC) and could be adapted for NHP imaging. However, most existing tools are optimized for human heads, which have very different tissue profiles and are imaged at lower resolution. Investigators are leveraging technical advances (e. g. , multichannel segmentation, deep learning, improved templates) to break through this barrier and avoid manual correction. However, at present, visual inspection and ratings remain key steps for quality assessment and analytical validation. Given these realities, the PRIME-DE consortium has recommended sharing all data regardless of data quality and to share QC ratings for the datasets. Finally, real-time quality assessments have been recently automated in the human literature (e. g. , the Framewise Integrated Real-time MRI Monitoring [FIRMM]) and could be adapted for NHP imaging for motion monitoring, feedback, and to assess when sufficient data have been collected.
Coordinated Paradigm Design Common Ground in Functional Imaging Creates Opportunities for Globally Coordinated Activity Functional localizers are commonly used in human and NHP imaging, spanning retinotopy, tonotopy, object perception, somatotopy, eye movements, social cognition, and more. To date, labs have tended to use customized approaches by creating and using their own localizer stimuli, typically in a relatively limited number of subjects. Commonalities in focus areas across laboratories create opportunities for coordinated paradigm design and data sharing. First, the simple sharing of final statistical maps (e. g. , via NeuroVault, Open Science Framework, or the Brain Analysis Library of Spatial Maps and Atlases [BALSA]) would generously allow applying meta-analytic techniques and aggregating across site information.
Equally important, the sharing of functional localizer stimuli would allow harmonizing efforts and, as a result, improve the likelihood of reproducible findings, dramatically enhancing the value of shared datasets. Complementing lower-level functional localizers are naturalistic stimuli (e. g. , films), which can be used to probe a range of systems, including higher-order association areas. Unfortunately, there is great variation in naturalistic stimuli across laboratories and in custom analyses that are needed to extract meaningful information from these localizers. As a first step, the community agreed that small groups will work together on obtaining coordinated localizer data for different modalities in 30 individuals as a basis for creating template-based probabilistic maps. These data will be invaluable to the broader community that often requires information on where functional fiducials reside in specific individuals.
Ambitions for the Next Five Years Over the course of the next five to ten years, the PRIME-DE GCW attendees agreed that it will be possible to collect and share structural scans from 1,000 NHPs in various species with further grassroots sharing efforts of higher quality and more extensive datasets in 200 animals. With financial support, the coor-dination of activities centered around localizers could yield data from 30 animals for a given localizer, as the community works toward a multi-faceted primate functional localizer. More substantial investment would allow the generation of a large-scale, multimodal resource for NHPs similar to the Human Connectome Project, possibly including developmental samples (pediatric, fetal) and metadata (genotyping and phenotyping information, etc. ). The integration of digitized neuronal tract tracing data, neurophysiology (high density recordings, laminar, etc.
Conclusion We have synthesized a perspective put forward by the GCW meeting on the challenges and opportunities for NHP imaging and the ambitions of the community. Given the grassroots nature of the effort, the community recognized the need to meet regularly to strengthen communication and facilitate progress. Following the lead of its human counterpart, NHP imaging is unquestionably evolving toward reproducible and scalable science. To accelerate the pace of its evolution through increased collaboration, sharing, and investment, large-scale global neuroscience ventures (e. g. , the BRAIN Initiative, Human Brain Project) and other funding schemes will need to support the community objectives for the next five to ten years of data generation and sharing. If the PRIME-DE GCW serves as a litmus test, exciting advances, and discoveries will become evident by global collaboration and support.
ACKNOWLEDGMENTS Facilities and event support for the The PRIMateE (PRIME-DE) Data Exchange Global Collaboration Workshop were provided by the  Wellcome Trust. Administrative and logistical support for the GCW and the Brainhack immediately following the event were provided by the  Child Mind Institute , as well as the  National Institute of Mental Health  ( P50MH109429 ). Travel stipends for early career investigators were made possible through the generous support of the  BRAIN Initiative  ( R24MH114806 ),  Kavli Foundation , and  Wellcome Trust. The views expressed in this article do not necessarily represent the views of the  National Institutes of Health , the  Department of Health and Human Services , or the  United States Government.
Atlas computation All patients underwent volumetric T1-weighted MRI with gadolinium 3 months after surgery. These sequences were normalized spatially to the Montreal Neurologic Institute (MNI) 152 template brain space, at 1 mm 3 voxel spatial resolution  (Evans et al. , 1992). Non-brain structures were removed from the T1-images with the brain extraction tool (BET) of the FMRIB software library (FSL; http://www. fmrib. ox. ac. uk/fsl). Registration of resulting images was performed by applying 12 parameter affine transformations using FSL's linear image registration tool (FLIRT). Of note, this method proved more reliable than non-linear registration methods that were explored due to factors specific to patients harboring brain tumors (brain shift, local mass effect, sizeable resection cavity, ventricular changes).
, 2006). We computed a detailed three-dimensional probability distribution atlas for critical human brain functions with the largest stimulation-based functional dataset of the human brain to date (1187 functional responses in 256 patients). This "heat map" has, as an unprecedented feature, the capacity to integrate direct functional responses at both the cortical and the subcortical level obtained by stimulation. As a consequence, it may serve as a minimal and highly-specific anatomic-functional background for integration with different neuroimaging data as well as other functional (fMRI, TMS, EEG, etc. ) and structural (i. e. tractography) datasets, with the goal of resolving the "true" governing principles behind physiologic brain processing, as well as patterns of re-organization following injury, i.
Limitations Our dataset, although unique given the different aspects highlighted above, does have some limitations. First, we propose data coming from a pathological model which assumes limited plasticity despite tumor involvement. Although tumor-induced plasticity may occur, LGGs have been shown to represent a valid model for exploring normal brain functions, and in fact, DES has been consistently utilized to confirm previous hypotheses of human brain processing  (Duffau, 2015). As discussed above, the overall distribution of the functional responses is topographically congruent with the atlases used for matching analyses and the current literature, including large meta-analysis involving healthy human subjects. Finally, the large number of responses in this study (1821 responses in 256 patients) of patients with widely distributed tumor locations should minimize the contribution of plasticity effects in individual patients to our overall statistically-derived functional maps.
A second limitation relates to the collection of only positive functional responses in our series, in spite of a homogenous distribution of these responses over the brain at both cortical and subcortical levels. To minimize this possible issue, we adopted a solid multinomial statistical analysis and provided the confidence intervals plots of the positive critical nodes across our large functional datasets for both number of stimulations and subjects tested. The exploration with DES of the entire exposed cortex and subcortical WM in each mapping session, including beyond the margins of the tumor, contributed to provide reliability to this data set. Third, we utilized a linear transformation methods for normalizing patients brains to the MNI-125 atlas. While this may introduce some degree of error during normalization, we found that the linear approach worked best in our dataset, due to a combination of factors including brain shift, local mass effect, resection cavity distortion, and ventricular changes.
2 mM EGTA, and 0. 3 mM Na 2 GTP). Cells were identified under gradient contrast illumination and examined for transduction by epifluorescence illumination. Blue light [473 nm, Doric light-emitting diode (LED), 4 mW] was pulsed onto slices from an optical fiber (400-m diameter) placed above the LC. Whole-cell voltage and current-clamp recordings were obtained with a Multiclamp 700A amplifier (Axon Instruments). All membrane potentials were corrected for a junction potential of 13 mV. The reversal potential for chloride ions using this combination of internal and external solutions was calculated as -68. 7 mV. Data were acquired using a Power1401 A-D converter (CED) and displayed and analyzed with Spike2 software (CED, Cambridge Electronic Design).
Auditory stimulation All experiments were conducted in a double-wall sound-attenuating acoustic chamber (Industrial Acoustics Company, Winchester, UK). All sounds were stereo signals programmed in MATLAB, where one channel (containing a 1-s 4-kHz tone pip) was routed to the mono speaker in the chamber and the other channel was routed to the electrophysiology acquisition system. The sounds were transduced into voltage signals by a high-sampling rate (192 kHz) sound card (LynxTWO, Lynx, USA), amplified (SA1, TDT), and played free field through a magnetic speaker (MF1, TDT) mounted 35 cm above the animal. For optogenetic experiments, where precise synchronization between auditory and laser stimulation was required, sounds were digitally loaded from a PC and routed to the mono speaker via a TDT RZ2 system (TDT, USA).
Pupillometry Rats were anesthetized with 1. 5% isoflurane and placed in a stereotaxic frame. An optic fiber connected the implanted optic fiber ferrule to the 473-nm laser. The eye was illuminated with an infrared light and monitored continuously with a vision color camera (VGAC; TDT) synchronized to a dedicated RV2 video processor (TDT), allowing frame-by-frame synchronization between video data (30 frames/s) and laser stimulation. We examined the effects of different pulse durations (10 to 90 ms) on pupil size. For LC activation (Fig. 3 , J to L), we applied unilateral laser stimulation to the LC while recording from the contralateral pupil. For LC silencing (Fig. 5 , E and F), we applied bilateral laser stimulation while recording from the left eye followed by the right eye, and then averaged the pupil size change of the two eyes.
Fluorescence microscopy Images were acquired by a Leica STED high-resolution laser scanning confocal microscope (Leica, Wetzlar, Germany) and a 20 oil/1. 4 NA objective. Quantification of extent of colocalization was carried out by Imaris software (Bitplane, Zurich, Switzerland). For histology of rats used in acute silencing experiments, images were acquired by Zeiss Axisoskop2 with pE2 LED excitation system (CoolLED, UK) and analyzed using a Zeiss Axiocam and AxioVision software. For tissue from acute optogenetic silencing experiments, images were acquired using a Leica DMI6000 inverted epifluorescence microscope equipped with Leica DFC365FX monochrome digital camera and Leica LAS-X acquisition software.
Data analysis Sleep staging and awakenings Wakefulness was defined by low-amplitude, high-frequency EEG activity, co-occurring with high tonic EMG activity with occasional phasic EMG bursts. In chronic LC recording experiments, we divided the wakefulness epochs between active wake (grooming, eating, and exploration) and quiet wake based on video recording. NREM sleep was defined by high-amplitude, low-frequency EEG, co-occurring with reduced EMG tone. REM sleep was defined by low-amplitude, high-frequency EEG dominated by posterior theta activity co-occurring with flat EMG, as in  (47). In arousal threshold experiments, each experimental trial was visually inspected, and those occurring during sleep were categorized as either eliciting behavioral awakening or maintained sleep. Behavioral awakening was declared if wake-like EEG flattening (without dominant theta) was present within 3 s from sound onset and lasted for at least 3 s.
Locomotor activity analysis Rat position and movement were analyzed with a custom video software written in MATLAB that determines the animal's center of mass and movement based on subtraction of consecutive images. After initial input from the user [who marks the initial animal location via a dedicated graphical user interface (GUI)], the software subtracts consecutive frames and, based on the number of pixels that were changed, estimates the degree of movement. Position and movements were compared between saline and yohimbine experiments via paired t tests (fig. S1D ). We then calculated and compared the increase in locomotion time and distance traveled. Pupillometry To extract pupil area, video images were first cropped around the eye. A mask based on the median values was applied, and the best centrally fitted circle was selected using the "regionprops" function in MATLAB.
Unit identification and spike sorting Neuronal clusters were identified using the "wave_clus" software package (52) as described previously. For acute activation recordings, extracellular recordings were high-pass filtered above 300 Hz, and then a threshold at 5 SD above the median noise level was computed (for chronic recordings, a similar threshold was set manually). Then, detected events were clustered using superparamagnetic clustering and were categorized as noise or neuronal clusters. For acute inhibition recordings, units were identified using the KiloSort clustering package  (53). The Python-based clustering package Phy (https://github. com/kwikteam/phy-contrib) was then used to manually curate Kilosort output to select only well-isolated single units with clear refractory periods and to remove artifacts.
Acknowledgments: We thank  I. Slutsky  and members of  Nir laboratory  for discussions and suggestions,  C. Cirelli  and  G. Tononi  for comments on an earlier draft,  Y. Sela  for help with spindle detection, and  M. Segal  and  S. Sara  for the helpful discussions on LC experiments. We thank the  Sackler Cellular & Molecular Imaging Center (SCMIC)  and  L. Mittelman  for dedicated assistance. Funding: This work was supported by the  Israel Science Foundation (ISF)  grants  1326/15  and  51/11  (I-CORE cognitive sciences) and the  Adelis Foundation  (to Y. N. ). E. J. K. is an INSERM fellow. O. Y. is supported by the  European Research Council  ( ERC-2013-StG OptoNeuromod 337637 ) and the  Adelis Foundation. CAV2 vector production was supported by  CNRS BioCampus (Montpellier). A. S. is a  Wellcome Trust -funded PhD student on the Neural.
Non-diabetic STEMI patients receiving metformin with the onset of reperfusion for 4 months had no improvement of LVEF  (Lexis et al. , 2014). Although metformin has been shown to inhibit mitochondrial permeability transition pore opening in vitro  (Guigas et al. , 2004) , it is also unclear whether metformin has interactions that augment or inhibit the effects of other ischaemic or pharmacological conditioning strategies. Two clinical trials have shown that exenatide, a GLP-1 analogue, limits infarct size in patients with STEMI undergoing primary PCI  (Lonborg et al. , 2012b; Woo et al. , 2013) , whereas two other trials failed to show reduction of cardiovascular events with long-term DPP-4 inhibitor treatment  (Scirica et al. , 2013; White et al. , 2013). The outcome of combination therapies is unknown, but an ongoing trial is assessing whether RIC and intravenous exenatide immediately before primary PCI reduce infarct size measured by cardiac MRI performed 3-7 days after primary PCI (NCT02404376).
and ANR-16-RHUS-0009; TrygFonden, Grant/ Award Number: 109624; OPeRa, Grant/Award Number: ANR-10-IBHU-0004 OPeRa; Singapore Ministry of Education Academic Research Fund Tier 2, Grant/Award Number: MOE2016-T2-2-021; Singapore Ministry of Health's National Medical Research Council, Grant/Award Numbers: NMRC/ CGAug16C006 and NMRC/CSA-SI/0011/ 2017; Duke-National University Singapore Medical School; British Heart Foundation, Grant/Award Number: CS/14/3/31002; Danish Council for Strategic Research, Grant/ Award Number: 11-108354; German Research Foundation, Grant/Award Number: SFB 1116 B08 1 | INTRODUCTION.
ACKNOWLEDGEMENTS P. K. and G. H. were supported by the  German Research Foundation  ( SFB 1116 B08 ). H. E. B. was supported by the  Danish Council for Strategic Research  Grant  11-108354 , the  Novo Nordisk Foundation  ( NNF14OC0013337  and  NNF15OC0016674 ), and  Trygfonden  ( 109624 ). D. J. H. was supported by the  British Heart Foundation  ( CS/14/3/31002 ), the  National Institute for Health Research University College London Hospitals Biomedical Research Centre, Duke-National University Singapore Medical School, Singapore Ministry of Health's National Medical Research Council  under its  Clinician Scientist-Senior Investigator scheme  ( NMRC/CSA-SI/0011/2017 ) and  Collaborative Centre Grant scheme  ( NMRC/CGAug16C006 ), and the  Singapore Ministry of Education Academic Research Fund Tier 2  ( MOE2016-T2-2-021 ).
Funding information Research Council  under its  Clinician Scientist-Senior Investigator scheme, Grant/Award Number :  NMRC/CSA-SI/0011/ 2017 ;  Duke-National University Singapore Medical School, Singapore Ministry of Health's National Medical Research Council  under its  Collaborative Centre Grant scheme, Grant/ Award Number :  NMRC/CGAug16C006 ;  National Institute for Health Research University College London Hospitals Biomedical Research Centre , Grant/Award Number:  CS/ 14/3/31002 ;  Novo Nordisk Foundation , Grant/Award Numbers:  Conditioning Based Intervention Strategies ,  NNF15OC0016674  and  NNF14OC0013337 ;  RHU MARVELOUS, Grant/Award Numbers :  ANR-16-RHUS-0009 ---------------------------------------------------------------------.
Behavioral experiments Upon a full week of recovery, implanted animals were connected with custom light-weight cables to an AC amplifier (BMA-400, CWE Inc. ) and neurograms were filtered (high-pass: 100 Hz, low-pass: 10 KHz), collected at 10kHz using a National Acquisition card (USB 6211) and live-integrated using the LabScribe NI software (iWorxs).
Plethysmography recordings Four unrestrained implanted animals were connected to the AC amplifier and placed inside a hermetic whole-body plethysmography chamber  (Ruffault et al. , 2015) , customized to allow the passage of the EMG cable. Respiratory volume and diaphragm EMG activity were recorded simultaneously over a period of 10 minutes using the LabScribe NI software. Respiratory volume was derived to obtain the respiratory flow (Figure  1C, D ).
5000-C-USB). The open-source software ToxTrac (  (Rodriguez et al. , 2017) , https://sourceforge. net/projects/toxtrac) was used for automated tracking of the animal's position over time. Following geometric calibration, the following parameters were extracted for each mice (Figure  1H ): the mobility rate defined as the percent of time that the animal spent moving above 7,5 cm/s, the average mobility speed defined as the average speed of the animal during mobility (i. e. above 7,5 cm/s) and the total distance travelled per 10-minute recording. Within each category (i. e. control or EMG-implanted) a grand-mean ± SD across mice was then calculated to produce histograms.
At the end of the running experiment, a breathing recovery period was recorded. We also recorded breathing changes throughout a continuous 10 min exercise at 40 cm/s on the motorized treadmill for n=2 untrained and n=2 trained animals. Finally, we evaluated breathing changes during gallop. For this animals were placed in a linear corridor (80 x 10 cm) and brief puffs of air were applied to the back of the animals to trigger fast escape locomotion which comprises a few steps of gallop, as previously described  (Talpalar et al. , 2013). The test was repeated, with several minutes of rest between trials, until enough gallop bouts (20 to 30 bouts) were acquired. During running sessions, animals were filmed from the side at 200 fps and 0. 5 ms exposure time using a CMOS camera (Jai GO-2400-USB) and images were streamed to a hard disk using the 2 nd LOOK software (IO Industries).
ACKNOWLEDGEMENTS This work was funded by an  ANR  grant to  JB  ( ANR-17-CE16-0027 ) and supported by  NeuroPSI, University Paris-Saclay  and  CNRS. CH holds doctoral fellowships from  Région Ile-de-France  and  Fondation pour la Recherche Médicale. We thank the animal facility housing animals,  Aurélie Heuzé  for lab management, and  Edwin Gatier  and  Anthony Renard  for help with DeepLabCut.
RNA extraction and quantification of gene expression by real-time qPCR Total RNA from mouse hippocampi was extracted with the RNeasy mini kit according to the manufacturer's instructions  (Qiagen, Courtaboeuf, France). RNA quality and concentration were assessed by spectrophotometry with the NanodropTM apparatus (Thermoscientific, Wilmington, DE, USA). Total RNA (1 µg) was reverse transcribed using the iScriptTM cDNA synthesis kit (Bio-Rad). RT-qPCR was performed in duplicate for each sample using SYBR Green Super-mix (Bio-Rad) for 40 cycles with a 2-step program (5 s of denaturation at 96 °C and 10 s of annealing at 60 °C). Amplification specificity was assessed with a melting curve analysis. Primers were designed using Primer3 software. The relative expression of genes of interest were expressed relative to expression of the reference gene, Glyceraldehyde 3-phosphate dehydrogenase (GAPDH), which an earlier study found was unaffected in this model  (Van Steenwinckel et al.