dataesr/scientific-paragraphs-categorization huggingface dataset:
| Participant enrolment The FACE-BD network in France consists in 12 centers from different regions in France 30 coordinated by Fondation FondaMental (www. fondation-fondamental. org). The first aim of this network is to support clinicians for the management of individuals with BD and give recommendation on personalized treatment strategies, which entirely guides the data collection in a computerized medical file to be further used for research through a web application (E-bipolar©). Anonymized data are stored in a national database FACE-BD that was approved by the French body overseeing the safety of computerized databases (Commission Nationale de l'Informatique et des Libertés, DR-2011-069). Secured access to this web-based system for research purposes is regulated by a scientific committee, who evaluates each project.
Individuals with BD are referred by a general practitioner or a psychiatrist to the expert center where they are assessed and followed annually for 3 years. Practitioners refer patients along with a referral letter that includes their main request and the related medical history. Patients are referred to the expert center for two main reasons: (1) Confirm or infirm diagnosis of BD; (2) provide personalized therapeutic guidance for BD. At inclusion and follow-up visits, the same evaluation package is used in all centers. The assessment protocol was approved by the relevant ethical review board and required only a letter of information to the patients to ascertain their non-opposition to the use of their data (CPP-IIe de France IX, 18 January 2010).
| RESULTS Data regarding smoking status was available for 3625 (93. 9% of all data extracted from the database) patients. There were 1529 (42. 2%) never-smokers, 1680 (46. 3%) current smokers and 416 (11. 5%) former smokers. The mean age of the sample was 40. 6 (SD = 12. 9) years and 61. 6% were women. Regarding BD type, 45. 7% were BD-1, 43. 5% BD-2 and 10. 8% BD-NOS, with a mean disorder duration of 16. 8 (SD = 11. 2) years. As previously published, participants from the FAC-BD cohort are mostly euthymic,  31  but often also show residual depressive symptoms, as shown by a mean MADRS = 10. 6 (SD = 9. 2) and median YMRS = 1 (IQR = 0-3).
ACKNOWLEDGMENTS The authors thank the  FondaMental Foundation  (wwwfondation-fondamental. org), private foundation supporting research in mental health that is developing a new model of translational research in psychiatry in France and supports the infrastructure of Bipolar Expert Centers. We express all our thanks to the nurses, and to the patients who were included in the present study. We thank  Hakim Laouamri , and his team ( Seif Ben Salem ,  Stephane Beaufort ,  Karmène Souyris ,  Victor Barteau  and  Mohamed Laaidi ) for the development of the FACE-BD computer interface, data management, quality control, and regulatory aspects.
Description of the experimental set-up The setup, to verify experimentally the proposed observer, is represented in Fig. 2 , and consists of the following parts. The localization system: comprising four fixed beacons (shown at the top-left of Fig. 2 ) and a mobile beacon mounted on the object to be tracked (top-right of Fig. 2 ). The beacon hardware is based on a DW1000 IC from Decawave, a single chip IEEE802. 15. 4-2011 UWB compliant device with internal high precision counter for ToF measurements. Each beacon is equipped with a micro-controller. The IMU: a LSM9DS1 ST Microelectronics chip that integrates 3 acceleration channels, 3 angular rate channels and 3 magnetic field channels with MEMS technology. The processing unit: comprising a Raspberry Pi3 core that runs compiled C code generated by the Matlab/Simulink coder, with a 100Hz sampling frequency.
The GRAPhEME (GeRmanium array for Actinides PrEcise MEasurements) experimental setup, developed by the IPHC laboratory from CNRS and installed at the EC-JRC-Geel GELINA facility is a powerful tool to answer this need  [1, 2]. Combining the prompt γ-ray spectroscopy and the time-of-flight methods, it measures partial (n, xnγ) reaction cross sections. This paper reports on the improvements made on the GRAPhEME setup and data analysis methodology to tackle the challenge of (n, xnγ) cross section measurements on high activity actinides. Results obtained so far on 233 U are presented compared to TALYS calculations.
This paper presents a methodology developed for (n, n'γ) reaction cross section determination for highly radioactive nuclei, * e-mail: francois. claeys@iphc. cnrs. fr based on the GRAPhEME experimental setup installed at the EC-JRC-Geel GELINA facility. Section 2 presents the experimental setup. Section 3 gives main features of the undertaken data analysis methodology. First preliminary results of 233 U(n, n'γ) will be shown and discussed in section 4. They will be compared with theoretical predictions from the nuclear reaction code TALYS. Finally, section 5 summarizes this work and gives an outlook on the next experiment with GRAPhEME.
One can see the good improvement in terms of energy resolution for the segmented detector. To measure the neutron flux, a Fission Chamber (FC) is used. It is made out of a highly enriched in ( 235 U > 99. 5%) UF 4 deposit of an arial density of 323. 757 (1740) µg. cm -2. A Cu-Cd-Pb shielding protects the target from background contamination. Finally, 13 TNT2 cards (100 MHz sampling frequency) are used for data acquisition  [8]. This experiment ran from 2014 to 2017, representing a total acquisition time of 4794 hours. Incident neutrons produced by GELINA may interact with the 233 U target and induce nuclear reactions ((n, f), (n, γ) or (n, xn) for instance), leaving the residual nucleus in an excited state. GRAPhEME detects γ-rays that are emitted after such reactions. For each event (in the FC or HPGe detectors), the acquisition system reads and stores the detection time of γ-rays and their energy.
This gives N233 U = 1. 327(60) × 10 21 nuclei. cm -  , which corresponds to a mass of m233 U = 7. 60(35) g. To extract the n DET γ (E n ), one constructs γ spectra from different energy windows in the neutron inelastic scattering energy range (i. e. E n ∈ [0. 1; 10] MeV). The dedicated software gf3  [10] , especially developed for γ spectroscopy with Ge detectors is then used to obtain the quantity of interest. In the framework of this data analysis, a specific care has been taken for the extraction of the γ transitions of interest. The radioactivity component has been subtracted from spectra and the meticulous work on peaks identification helped in the fitting procedure of remaining γ-rays of interest and from fission products.
Semi Monte Carlo method to determine integrated (n, n'γ) cross section From the differential cross section dσ dΩ , the Gaussian quadrature method is used to determine the integrated (n, n'γ) cross section as a finite and weighted sum of differential cross sections as the following: σ(E n ) = 2π 2 i=1 ω i dσ dΩ (θ i , E n ) , where θ 1 = 150 • , θ 2 = 110 • , ω 1 = 0. 69571 and ω 2 = 1. 30429. Up to now, the cross section and its uncertainty were computed with a deterministic approach. However, this method implies strong assumptions. In particular, it does not take into account correlations and covariances between parameters. To meet all these issues, we developed a semi Monte-Carlo (sMC) tool, implemented for the first time in the data analysis methodology with GRAPhEME data. It was developed in python3 along with G.
The authors thank the team of the  GELINA facility  for the preparation of the neutron beam and for their strong support day after day. This work was partly supported by PACEN/GEDEPEON then NEEDS and by the  European Commission  within the  Sixth Framework Programme through I3-EFNUDAT  (EURATOM contract no. 036434 ) and NUDAME (Contract  FP6-516487 ), and within the  Seventh Framework Programme EU-FRAT  ( EURATOM  contract no. FP7-211499 ), through  ANDES  ( EURATOM  contract no. FP7-249671 ) and through  CHANDA (EURATOM  contract no. FP7-605203 ). This project has received funding from the  Euratom  research and training programme  2014-2018  under grant agreement No  847552  and from the  ANR   DISPALEND-07-BLAN-0154-01.
I. INTRODUCTION The Internet of Things (IoT) has gained a great attention in the last decade. The world has been witnessing such a massive growth in the node deployment that the IoT survey reported on the Forbes website  [1]  forecasts more than 75 billion connected IoT devices by 2025. Massive IoT applications require energy-efficient and low-complexity nodes. To support such requirements, Low Power Wide Area Networks (LPWANs), that provide large coverage areas, low transmission data rates with small packet data sizes, low device complexity and long battery life have evolved  [2]. LPWANs include several technologies operating in the unlicensed industrial, scientific and medical (ISM) frequency band (868 MHz in Europe, 915 MHz in North America, and 433 MHz in Asia), and the Long Range Wide Area Network (LoRaWAN) developed by the LoRa alliance  [3]  is one of these massively deployed technologies for low power and long distance transmissions.
Detection of vigilance state-specific local microcircuitlevel assembly activity in sensorimotor cortex Calcium trace-derived activity raster plots for a total of 1,290 neurons from 6 mice (average = 215 ± 34 neurons/mouse) were extracted from the imaged movies (Figure  2A ). For use in all subsequent analysis, we isolated equal amounts of data for each of 4 vigilance states: active wakefulness (AW), quiet wakefulness (QW), NREMs (N), and REMs (R). Our first analysis focused on the activity of individual neurons. The proportion of cells active during periods of AW was significantly higher than for all other states, with the majority of all cells being active for at least 1 frame during the analysis period (AW = 84% ± 3%; QW = 36. 4% ± 10. 3%; N = 41. 5% ± 11. 7%; R = 43. 8% ± 11. 5%). Similarly, basic activity rates, measured as the number of active frames/second, were generally higher during periods of AW relative to all other states, with a significant difference in mean activity rate at the group level being found between AW and QW (AW = 0.
4 Hz; N = 3. 6 ± 0. 3 Hz), although transient spindle oscillation-associated (9-15 Hz) higher-frequency dynamics during select N data were sometimes observed (Figure  S6 ). In contrast, spectra for  (A) Experimental timeline. Left: an adeno-associated viral vector encoding the calcium indicator GCaMP6s driven under the hSyn promoter was injected into the left ventricle of neonatal mice (P0). Center: $3 months later, mice were implanted with an imaging window and EEG and EMG electrodes used to assess sleep activity. Right: following recovery from surgery, mice were trained to sleep on a non-motorized treadmill in order to image sensorimotor cortex across the full sleep-wake cycle. (B) Top: example hypnogram showing the general imaging procedure in habituated mice. GCaMP6s expression resulting from P0 injection was widespread and stable long term throughout the cortex, allowing for high-quality imaging data acquisition from sensorimotor cortex months after the initial injection.
Imaging data acquisition was completed during the second half of each experimental session when sleep activity was highest. Middle: example EEG and electromyogram (EMG) traces from major vigilance states analyzed in this study. Bottom: spectral analysis derived from 5 s time epochs centered around the example EEG traces above. Analysis of the EEG spectrum, EMG activity, and behavioral observation were used to determine vigilance states throughout the entire course of experiments (method details). Cell Reports 42, 112053, February 28, 2023 3 ii iii iv v n. s. (legend on next page) R data peaked in the theta (4-10 Hz) range (mean dominant frequency: R = 6. 3 ± 0. 2 Hz). Correlation analysis between microstate dynamics and each individual EEG channel indicated that no single EEG exerted disproportionate influence over microstate dynamics (Table  S3 ).
Finally, to assess the potential impact of head fixation on sleep quality, the day following completion of data acquisition, the treadmill apparatus was removed from the 2-photon imaging platform and replaced by an empty cage with woodchip bedding. The mouse was placed un-head fixed in the cage with the head stage pre-amplifier tether attached, and was subsequently recorded under otherwise identical experimental conditions (i. e. , at the same time, for the same duration, under the same lighting conditions, and with the microscope scanner active). Once this endpoint was reached, mice were utilized for experiments in an unrelated project.
Electrophysiological recording Electrophysiological recordings were done during all phases of the experiment (head-fixation habituation in order to track habituation progression as well as during imaging data acquisition experiments in fully habituated mice). For each recording, a head stage pre-amplifier (Neuralynx, Boseman, Montana, USA) tether was attached to the Mill-max connector pins at the rear of the head of the mouse immediately after being head fixed into the 2-photon imaging setup. Data from all electrodes were subsequently amplified by the head stage pre-amplifier before being digitized at 16,000 Hz using a digital acquisition system (Neuralynx, Boseman, Montana, USA) and saved to a hard disk. Tread movement was also captured through a video recording synchronized to the electrophysiology data.
2-Photon imaging of cortical neural activity To keep the size of datasets manageable and minimize the potential for photobleaching, imaging data acquisition was limited to the second half of the experiment ($2. 25 h in duration, hour 2. 25-4. 5 of experiment) when sleep activity was generally highest. At the start of this period, a 163 immersion objective (NIKON, NA 0. 8) connected to a GaSP PMT (H7422-40, Hamamatsu) was positioned over the imaging region of interest (field of view (FOV)) within the sensorimotor cortex at an imaging depth corresponding to $150-250 mm below the pial surface. A series of 25 movies (2500 frames per movie acquired at 8 Hz (thus each movie has a duration of $5 min 12 s with $12 s interval between consecutive movies), FOV size = 400 3 400 mm acquired at a resolution of 200 3 200 pixels) was obtained using a single beam multiphoton-pulsed laser scanning system coupled to a microscope (TriM Scope II, LaVision Biotech) and Ti: sapphire excitation laser (Chamelon Ultra II, Coherent) operated at 920 nm.
GCamp6s fluorescence was isolated using a bandpass filter of 510 nm/25nm. Images were acquired from the PMT signal using Imspector software (LaVision, Biotech) and subsequently saved to a hard disk. There was no evidence of photo-toxicity or significant bleaching as a result of the above imaging procedure. To synchronize imaging data with electrophysiological data, a TTL timestamp signal was sent from the imaging acquisition software to the digital electrophysiology acquisition system at the onset of each imaging frame scanning cycle.
Vigilance state architecture analysis For quantitative and qualitative analysis of sleep and wake activity of each experiment, raw EEG and electromyogram data files were first imported into MATLAB and downsampled to 1000 Hz. Data was then manually plotted and the major phases of the sleep-wake cycle occurring throughout the entire recording session were scored in 5 s epochs using the fast Fourier transform (FFT; 'fft' function in MATLAB) of the signal recorded from the right parietal EEG (AP -2. 67, ML +1. 54) in addition to the nuchal EMG signal. 18 The procedure was as follows: epochs of wakefulness were identified by a binned 'theta' (4-10 Hz, 'q') to 'delta' (1-4 Hz, 'd') power ratio greater than 1 and bursts of relatively high-amplitude movement-associated EMG activity (typically >0. 1 mV and >1 s in duration).
Once scoring was completed, the data was manually reviewed and crosschecked with video recordings to ensure a perfect correspondence between overt mouse behavior (e. g. , movement, grooming) and the scored vigilance state data (hypnogram). This step included removing large microarousals occurring during NREMs,  56  characterized by a transient and sudden reduction of high-amplitude slow wave activity concomitant with an increase in low-amplitude high-frequency activity and increased muscle tone. This was done as a safeguard to prevent the inadvertent inclusion of brief awakenings in NREMs data selected for later analysis. Total state proportions and average state durations for each experiment were calculated based on the hypnograms generated using the above procedure. For EEG microstate and imaging data analysis, periods of wakefulness were further segregated into 'active wake' or 'quiet wake' according to the presence or absence of movement-associated activity bursts in the EMG signal, respectively.
Thus, quiet wake periods were completely free of both running and other active behaviors, such as grooming. To ensure that periods of early transitional sleep were not accidentally identified as quiet wake, only periods of quiet wake completely free of any events reminiscent of large slow wave and spindle-like activity that were flanked by overt periods of active wakefulness were selected. As a final step, potential periods of active wake identified in the prior step were cross-checked with tread movement data to enable removal of all time points which did not correspond to periods of running behavior (e. g. , grooming behavior).
Spectral analysis of EEG data For analyses that prioritized frequency resolution (creation of basic EEG spectrograms, spectral analysis of EEG microstate dynamics), vigilance state-specific spectral analysis of EEG data was completed in MATLAB using the mtspecgramc function from the Chronux signal processing toolbox (parameters: window size = 5 s, step size = 5 s, tapers [3 5]). 57, 58 velet analysis of EEG data For analyses that prioritized temporal resolution (correlation of specified frequency bands with EEG microstate dynamics), vigilance state-specific Morlet wavelet analysis of EEG data was completed in MATLAB using the 'cwt' function. The absolute values of the resulting complex wavelet transforms were then calculated for each time point, and data were binned according to the following non-overlapping frequency definitions: 'delta' (1-4 Hz, d); 'theta' (4-10 Hz, q); 'alpha' (10-15 Hz, a); 'beta' (15-30 Hz, b); 'gamma' (30-50 Hz).
Report EEG microstate analysis To gain insight into global brain activity dynamics, we opted to employ EEG microstate analysis as this procedure could be incorporated into our cortical imaging protocol with relative ease as opposed to other potential techniques (e. g. , fMRI). For identification of EEG microstates, we used a protocol similar to a well-established procedure. 25, 27 EEG traces from each experiment were first bandpass filtered from 1-50 Hz using the 'filtfilt' function in MATLAB. The 'global standard deviation' (GSD), the standard deviation of all EEG signals, was then calculated for each data time point and the indices of peak locations in the GSD were subsequently found using the 'findpeaks' function in MATLAB. For each experiment, equal amounts of quiet wake, NREMs, and REMs data that were free of activity bursts in the EMG were then identified.
Periods of active wake were not analyzed due to the high potential for contamination of EEG signals with movement artifacts. The amount of data available for each state was therefore limited by the state with the least amount of data available (typically REMs). Valid data points for other states that had to be excluded as a result were removed at random. The EEG profile maps, the instantaneous potential of each EEG signal, associated with the indices of each identified peak in the GSD occurring during identified EMG noise-free quiet wake, NREMs, and REMs periods were then clustered separately as well as in combination (i. e. , select quiet wake, NREMs, and REMs periods combined) using the k means clustering 'kmeans' function in MATLAB (parameters: maximum number of clusters = 10, number of iterations per clustering step = 100), followed by silhouette analysis using the MATLAB 'silhouette' function.
Analysis of spindle activity in multi-EEG data Spindle analysis was completed on temporally continuous periods of select NREMs data (the same periods used for EEG microstate analysis during NREMs) using non-overlapping 100 ms windows; the following procedure was completed for each EEG channel individually. For each window, EEG data was first bandpass filtered (9-15 Hz) using the 'filtfilt' function in MATLAB. The data was then rectified and the sum of the absolute amplitude values was calculated. The mean and standard deviation (stdev) of absolute amplitude data values derived from all analysis windows was subsequently obtained and used to determine the threshold for spindle detection (mean +3 stdev). All time points associated with values exceeding this threshold were considered to be locations of significant spindle activity.
Determination of neurons (regions of interest) in imaging field of view The identification of regions of interest (ROIs) corresponding to neurons in the field of view (FOV) of motion corrected movies was completed using a semi-automated approach. Each motion corrected movie was manually played back frame-by-frame; during the playback, regions of interest (ROIs, i. e. , areas of the field of view with calcium signals characteristic of healthy cortical neurons) were manually identified. When a ROI was identified, a pixel in the center of the ROI was manually selected as a 'seed'. The calcium signal of this seed pixel throughout the course of the full movie was then z-scored, and the similarity of this data vector was compared to that for each pixel in the immediate vicinity (pixels within 10 mm of the seed pixel) via linear correlation using the 'corr' function in MATLAB.
Identification of transient activity in calcium traces Following extraction of calcium traces from identified ROIs, identification of calcium transients from this data was next completed for each experiment. For this, each raw calcium trace obtained from imaging data was first smoothed with a Gaussian filter using the 'smoothdata' function in MATLAB and a smoothing window size corresponding to 1 s of data (8 frames). The 'findpeaks' MATLAB function was then used to identify the location (time point), peak prominence, and width of peaks occurring in the smoothed trace. The onset timepoints for each peak were then identified by finding the closest preceding upwards deflection in the differentiated smoothed trace, calculated using the 'diff' function in MATLAB. Peaks corresponding to significant calcium transients were then automatically detected by determining those which had prominences greater than the set threshold value of 250 units.
This threshold value was determined for the current experiments as follows; first, the mean and standard deviation of the raw calcium signal for all ROIs was calculated during quiescent periods of the recording (periods where the animal was not moving and no cells were active) for each mouse. The value of 250 units was the nearest whole value (50 unit resolution required to best facilitate manual verification of detected peaks described below) that was greater than 3 standard deviations above the average standard deviation calculated for the noisiest experiment. The raw and corresponding smoothed traces, along with the calculated locations of significant calcium transients, were then verified by plotting the data in an interactive figure and scanning through the entire trace. Manual corrections were able to be made at this time; these were largely limited to the exclusion of transients detected during periods contaminated with uncorrectable movement artifacts, as well as the inclusion of clear transients occurring in quick succession.