dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013) , ν 0 , τ 0 , λ were set to 0. 050, 0. 046, and 0. 3, respectively, leading to a kernel function with a TF resolution of [∆t, ∆f ] = [10. 9 s, 0. 03 Hz]. Both Welch's approach and the CWT were computed with MATLAB subroutines, while the implementation details and software download for the SPWD are reported in  (Orini et al. , 2012). Based on the given methodologies, the instantaneous power in the β = [f 1 , f 2 ] band of interest can be obtained as P β (t) = f 2 f 1 S RR (t, f )df. (7) In particular, one can compute the absolute power in three bands V LF , LF and HF as the reported integral in (7). Besides, the ratios V LF LF and LF HF were also computed alongside two indices to represent the normalized LF power: LF V LF +LF and LF HF +LF. In case of Welch's algorithm, there is no dependency from the time variable t.
The multifractality parameters (H exp , C 2 ) were computed in the entire non-overlapping window according to three schemes discussed in Section 2. 2. Specifically, the multifractal features were derived using the Wavelet p-Leader and Bootstrap based MultiFractal analysis (PLBMF) MATLAB toolbox, described in  Wendt et al. (2007). This toolbox can be downloaded from https://www. irit. fr/~Herwig. Wendt/software. html. A fundamental design choice is the scale range [2 j 1 , 2 j 2 ] from which the exponent τ (q) is estimated from (9)  (Abry et al. , 2010) ,  (Wendt et al. , 2007). In case of HRV, the exponents [j 1 , j 2 ] are normally set equal to  [3, 12]. Given the fact that the scale can be written as a = 2 j = (f s /2)/f with f s as sampling frequency of the signal, it follows that the range LF HF = f I fm S RR (f )df f M f I S RR (f )df = (f 2-2Hexp I -f 2-2Hexp m ) (f 2-2Hexp M -f 2-2Hexp I ) (10) where [f m , f I ],[f I , f M ] represent the frequency bands of LF and HF.
In order to obtain one representative value for each recording in each windowing scheme, the median of this array of attributes over the different windows was computed for each recording, as highlighted by the grand-median block in Figure  2. After the features' extraction process and the grand-median step, three datasets were then obtained according to the three different windowing schemes. The number of features extracted for each dataset was then 27 in total: 21 for the spectral attributes, 2 for the temporal ones, 4 for fractal indices, as shown in Table  2. In order to investigate the ANS maturation, the HRV features were used to estimate the PMA of the patient, as shown by the last block of the diagram in Figure  2. Since the PMA is known for each recording, a linear mixed effects (LME) regression model was developed for each dataset with PMA.
Lavanga et al. Maturation of the autonomic nervous system in premature infants as response variable  (Lavanga et al. , 2018). The actual regression consisted of two steps. First, the features were selected via the least absolute shrinkage and selection operator (LASSO) due to the high number of features, after that the absolute power features were log-transformed. Specifically, the LASSO was repeated for 20 iterations on the entire dataset and the features which were selected in more than 40% of the total number of iterations (8 iterations out of 20) were included in the regression model  (Lavanga et al. , 2018). Second, a linear mixed-effect regression model was built with the selected subsets with multiple random splits of the data. In particular, the dataset was split introduces the random effect and the patient ID was taken as grouping variable since a set of one or more recordings belong to a patient (as discussed in a previous study  (Lavanga et al.
RESULTS The overview of the dataset is reported in Table  1 , which shows certain traits of the annotated bradycardias. The mean length is around 18 s. On average, there are 7 bradycardias per recording and the mean RR i during bradycardias is approximately 631 ms. The overview shows that the infants have an average PMA of 34 weeks and 35 recordings are collected in the range  (32) (33) (34) (35) (36)  weeks. A total of 22 recordings is collected in the first days of life, while a set of 17 recording has been included from the weeks close to discharge. A detailed overview of each recording is reported in Table  S1  and S2. Figure  3  and Table  3  report the trends in the three different windowing schemes for the following features: the mean µ RR and standard deviation of the HRV σ RR , the absolute power in the LF band (and its logarithmic transform), the relative LF LF +V LF power, the Hurst Exponent in the range [j 1 , j 2 ] = [5, 12] H exp [j 1 ,j 2 =5,12 ] and the width of the singularity spectrum in the same range C 2 [j 1 ,j 2 =5,12].
The three columns respectively represents bradycardia epochs (magenta circle data points), post-bradycardia epochs (light-blue squares data points) and between-bradycardia epochs (indigo diamonds data points). ρ is the correlation coefficient of the regression and p value represents the significance of the correlation. maturation models for fetal and preterm development by  (Hoyer et al. , 2013)   (De Wel et al. , 2017) ,  (Lavanga et al. , 2017) ,  (Lavanga et al. , 2018). Second, Figure  4  reports that the spectral ratio V LF LF and the Hurst exponent in the range [j 1 , j 2 ] =  [5, 12]  are more correlated than the LF HF and the Hurst exponent [j 1 , j 2 ] =  [3, 12]. This might indicate that neonates do not have sympathovagal balance that rely on the typical interplay between LF and HF  (Abry et al.
Figure2. The block diagram shows the main steps of the study. For each RR signal, artifact preprocessing is performed and associated resampling of the tachogram. The signal is split in different windows according to the scheme of Figure1. For each of these epochs, temporal, spectral and fractal features, which undergo a grand-median process if there is more than one epoch per scheme. The three datasets are then used to estimate the age of the recording in a linear mixed effects (LME) regression.
FUNDING Research supported by  Bijzonder Onderzoeksfonds KU Leuven (BOF)  :  C24/15/036  "The effect of perinatal stress on the later outcome in preterm babies",  EU :  H2020 MSCA-ITN-2018 : ' INtegrating Functional Assessment measures for Neonatal Safeguard (INFANS) ', funded by the  European Commission  under Grant Agreement # 813483. This research received funding from the  Flemish Government  ( AI Research Program ). SVH and ML are affiliated to  Leuven. AI -KU Leuven  institute for AI, B-3000, Leuven, Belgium. Funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.
DATA AVAILABILITY STATEMENT The datasets presented in this article are not readily available because the clinical metadata of patients (such as hospital ID, age, recording time-stamps, pain scales) are subject to the European dataprivacy policy. Requests to access the datasets should be directed to mlavanga@esat. kuleuven. be. The authors will try to provide an anonimized version of dataset in compliance with the privacy policy of the University Hospitals of Leuven, which is the owner of the data.
, 2017),(Koolen et al. , 2016). Inclusion criteria were: a normal neurodevelopmental outcome at 9 and 24 months corrected age (Bayley Scales of Infant Development-II, mental and motor score > 85), no severe brain lesions, assessed by ultrasound, and not taking any sedative or antiepileptic drugs during the EEG registration. The sampling ECG frequency was 250 or 500 Hz and the average length of the recording was 4h 44 min. An overview of the dataset is reported in Table1, while a complete description is reported in TableS1 and S2. The latter show the heterogenous interperiod sessions among recordings, which indicate that the measurements were not scheduled at.
Crude: crude LV sample; IP Des: immunoprecipitation of desmin; Sur IP: supernatant of desmin immunoprecipitation. Efficiency of Des IP was verified with desmin western blot. G Representative PLA immunofluorescence staining of desmin-Hsc70, desmin-LAMP2a, and desmin-CryAB in control and NCM treated with MG132 (10 µmol/L) for 18 h. Nuclei are stained in blue. Interestingly, ubiquitination of proteins associated with CMA activation are required to prevent desmin aggregates with clearance of both forms of desmin (soluble and insoluble) associated with a decrease of CryAB. This effect is independent of macroautophagy activation as the same effect was observed with combination of UPS and macroautophagy inhibition. Our data are in agreement with publications showing cleaved form of desmin in cytoplasmic aggregates  [34, 45].
MATERIALS AND METHODS Pathway network analysis Integrated pathway enrichment analysis was performed by using the knowledge-based canonical pathways and endogenous metabolic pathways MetaCore pathway analysis software (GeneGo). The differentially expressed  [50]  or phosphorylated  [3]  proteins were mapped into biological networks by using a manually curated proprietary database (MetaCore, GeneGo, St. Joseph, MI, USA), a pathway analysis tool. UniProt protein accession numbers provided from the UniProtKB database (http://www. uniprot. org) were uploaded into the database. For network analysis, expand by one interaction algorithm was used with phosphorylation/ dephosphorylation filters. We also used advanced options by selecting both directions, disconnected root nodes, and discarding low trust interactions, functional interactions, and binding interactions.
After washing with PBS, coverslips were mounted with glycerol/PBS 10X (90/10, v/v). Staining was visualized with the ×40 and ×63 objectives of LSM710 confocal microscope followed by Zen image acquisition and software analysis (Zeiss). Images were acquired with a resolution of at least 1024 × 1024 and analyzed with ImageJ software. At least 10 fields are photographed, corresponding to ~100 (×40 objective) -200 (×63 objective) cells depending on the magnification and the type of cells. Photographs shown are representative of the staining observed. Colocalization was quantified by Pearson's coefficient, describing the correlation between the intensities of 2 proteins with the JACoP plugins on ImageJ software. We performed proximity ligation assay (PLA) following manufacturer's instructions (Olink Bioscience, Uppsala, Sweden).
Briefly, after primary antibody incubation as described before, samples were incubated 1 h at 37 °C with secondary antibody coupled to oligonucleotidic probes (Duolink® In Situ PLA® Probe anti-mouse PLUS et anti-Rabbit MINUS, Sigma-Aldrich), diluted at 1/5 in BSA 1%. After two washes with PBS 1X, samples were incubated for 30 min at 37 °C with ligase (1/40 in ligation solution). After two washes with PBS 1X, samples were incubated for 1 h 40 min at 37 °C with polymerase (1/80 in amplification solution). After washing with PBS, coverslips were mounted with glycerol/PBS 10X (90/10, v/v). Staining was visualized with the ×40 objectives of LSM710 confocal microscope followed by Zen image acquisition. Images were acquired with a resolution of at least 1024 × 1024 and analyzed with ImageJ software.
Phos-tag gels. Soluble and insoluble proteins were analyzed on 10% gels containing 40 µmol/L of Phos-tag (Wako, Osaka, Japan) and 100 µmol/L of Mn 2+ for detection of the phosphorylated forms of desmin as previously described  [53]. At equivalent phosphorylation levels, the position of the phosphate group can also influence the apparent molecular weight of a protein in a Phos-tag gel. Blots imaging. The Chemidoc XRS+ camera (Biorad) and the Image Lab software were used for blots imaging and densitometry analysis. Membranes were incubated for 5 min with Clarity Western ECL Substrate (Biorad) before imaging. The signal was quantified from the image obtained just before saturation. The band corresponding to the protein of interest was framed within a defined area to express the signal intensity depending on the area.
FUNDING This work was supported by grants from the  E. U. FP7 HOMAGE (305507 ), F- CRIN INI-CRT , and the  "Agence Nationale de la Recherche "  ANR blanc-SVSE1  ( ANR-10-BLAN-1104 ). MB was supported by grants from " Groupe de Reflexion sur la Recherche Cardiovasculaire ", EDD was supported by grants from  "Region Hauts de France", "Institut Pasteur de Lille" and "Fondation Lefoulon Delalande ". AT was supported by grants from  "ERA-CVD  through " Agence Nationale de la Recherche " and " Féderation Française de Cardiologie ". FP has received a grant by the  French Government , managed by the  National Research Agency (ANR)  under the program " Investissements d'avenir " with the reference  ANR-16-RHUS-0003. EDD, AT, PM, VR, NL, and FP are members of  FHU-CARNAVAL. None of the funders had any role in study design, data collection, data analysis, interpretation, and writing the report.
We further tested whether using mean values for diameter and density of FWD, either per transect or per site, had an influence on the estimates. For LFWD, the position of each piece along the transect was also recorded. To test the influence of transect length on the accuracy of the estimate, we subdivided the 20 m transects into 1 m sub-segments. The data from the sub-segments could then be analyzed cumulatively to examine the effect of transect length and calculate optimal transect length and number.
For both FAS and the Reference Strips, the data are expressed in t ha -1. To compare results from the two methods, the estimates must be expressed in the same physical quantity. For all the woody debris (or all the debris in a subsample), we measured diameter, length and dry mass. Then, we used the mean density (Equation (2)) of the woody debris collected in the FAS design to transform the biomass measured from t ha -1 to volume in m 3 ha -1 (Equation (  3 )). The density can vary greatly according to tree species, piece diameter and decay rate. D = 1 n m i ∑ j = 1 M ij V ij (2) D: mean density of the woody debris collected M ij : mass of each piece, g V ij : volume of each piece, cm 3 n = number of pieces measured B = V × D (3) B: woody debris biomass, t ha -1 V: woody debris volume, m 3 ha -1 D: mean density of woody debris collected.
5 m sections (yellow small lines within the red transects). For biomass estimates, wood density is needed, so the pieces should be removed and taken to the laboratory. For pieces less than 4 cm in diameter, a subsample of around 100 pieces seems reasonable. Nevertheless, the best practice is to take a small portion of every piece ≥2 cm. If possible, the decay rate should be assessed for pieces >2 cm (and when possible, species should also be recorded), and a sufficient number of samples should be collected per diameter class and decay rate  [37]. Density will then be calculated per diameter class, decay rate and species. Sampling time can be reduced if data on the density of small woody pieces are available in the literature. For the broadleaf species (birch, hornbeam, chestnut, oak and poplar) studied in our stands, a density value of 0.
Density will then be calculated per diameter class, decay rate and species. Sampling time can be reduced if data on the density of small woody pieces are available in the literature. For the broadleaf species (birch, hornbeam, chestnut, oak and poplar) studied in our stands, a density value of 0. 61 and 0. 42 can be used for diameter class 0-0. 5 cm and 0. 5-4 cm respectively. For larger pieces, samples should be taken. In this procedure, VFWD (≤4 cm) are inventoried along 30 m/ha (15 transects ×2 ×1 m), and LFWD (4-7 cm) and CWD (>7 cm) along 300 m/ha (15 transects × 2 × 10 m). If more precision is required for CWD, a rarer type, inventory length should be increased; an easy way to do this is to measure CWD along the line with the transect starting points and/or along the edge of the inventory area.
In addition, a soft shoulder and elbow splint was used to support the forearm in a comfortable way during the 11-h immobilization (DONJOY model IMMO, "DGO GLOBAL"). To ensure that immobilization was effective, we quantitatively monitored physical activity of both arms during the three experimental conditions, using actimetry (Actigraph GT3X-BT, Pensacola, Florida, USA) on the participants' left and right forearms. Data were sampled at 30 Hz. Energy cost of physical activity was calculated in metabolic equivalent of task (MET), i. e. one MET equals the resting metabolic rate obtained during quiet sitting  35. We also verified that all participants maintained a regular sleep-wake schedule with a minimum of 7 h of sleep per night, at least three days before each experimental day. Compliance to the schedule was assessed using both sleep diaries and wrist actigraphy measures.
Electromyography recording. Electromyography (EMG) was recorded and monitored continuously on-line during each TMS session. EMG recordings used the following montage: active electrodes were attached to the skin overlying first dorsal interosseous muscle, reference electrodes were placed over the first metacarpophalangeal joints and ground electrodes were placed over the wrist bone. Acknowledge 4 software along with MP36 unit (Biopac systems, Goleta, CA, USA) were used to acquire EMG signal. The EMG signals were amplified and filtered online (20 Hz to 1 kHz), sampled at a rate of 5000 Hz and stored on a personal computer for display and later offline data analysis. Polysomnographic recording. Data acquisition. Polysomnography was acquired in all participants during the night immediately following NOIMMO, IMMO+MI and IMMO-MI conditions, with a V-Amp 16 system (Brain Products, Gilching, Germany), from 11 EEG electrodes (international 10-20 system: LM1, CZ, RM1, F3, FZ, F4, P3, PZ, P4, A1, A2), three electrooculogram electrodes, and two chin electromyogram electrodes (sampling rate: 250 Hz).
Sleep data analysis. Out of the 14 participants, three participants were excluded from spindles and spectral power analysis due to poor EEG signal quality (on one of the nights), while sleep stage scoring was still possible for these participants for all three experimental nights (n = 14). For sleep analyses, we used the PRANA software (version 10. 1. Strasbourg, France: Phitools) and custom MATLAB scripts. Two scorers blind to the experimental conditions determined the different sleep stages (NREM 1, 2, 3, REM sleep and wake) for each recorded night of sleep on 30-s epochs according to the AASM standards  43. From the sleep scoring, we computed the total time spent (min) in each sleep stage, as well as the percentage of each sleep stage relative to the total sleep period (TSP; from sleep onset to wake up time) and relative to the total sleep time (TST; TSP minus intra-wake epochs).
Sleep efficiency (TST/time in bed*100), and number of intraawakenings were also calculated. Epochs affected by artefacts were detected using a semi-automatic detection (PRANA software), and then verified visually by the two expert scorers. We performed two independent repeated measures analysis of variance (ANOVA RM ) with condition as within-subjects factor, which did not show any difference on artefact duration during the first cycle of sleep (F(2, 26) = 0. 68, p = 0. 51, η p 2 = 0. 05), and the whole night of sleep (F(2, 26) = 1. 68, p = 0. 20, η p 2 = 0. 11). The corresponding data points were removed from subsequent EEG analyses. The EEG spectrum power average was calculated with a 0. 2 Hz resolution, by applying a Fast Fourier Transform (FFT; 50% overlapping, 5 s windows, Hanning filter) on the left and right M1 channels.
Data analysis. For the hand laterality judgment task, we analyzed the percentage of correct responses and mean response time. Only data from correct responses were used to analyze response times. Results obtained on the hand laterality judgment for the three conditions were evaluated by means of repeated measures analysis of variance ANOVArm with condition (NOIMMO, IMMO+MI and IMMO-MI), laterality (left, right) and configuration (two or three dimensional) as within-subjects factors. Likewise, an ANOVArm was performed for the alphanumeric normal-mirror judgment task, with condition (NOIMMO, IMMO+MI and IMMO-MI), configuration (normal, mirror) and orientation (0°, 90°, 180°, or 270°) as within-subjects factors. To evaluate the effects of immobilization on cortical excitability, we compared the resting motor threshold (RMT) using an ANOVArm with laterality (left M1, right M1) and condition (NOIMMO, IMMO+MI and IMMO-MI) as within-subject factors.
Similar ANOVArms were performed with total sleep period (%), sleep latencies and efficiency (min and % respectively). We also analysed spindle features (number, density, duration, peak amplitude and mean frequency) by means of ANOVArms with electrode location (left M1, Right M1) and condition as withinsubjects factors. Similar analyses were performed on power spectrum measurements with electrode location and condition as within-subjects factors. All sleep analyses (sleep architecture, spindles features and spectral power data) were conducted separately during whole night of sleep and the first sleep cycle. We conducted correlation analyses by computing data from the multimodal assessment obtained in each condition to address the effect of MI treatment [IMMO+MI vs IMMO-MI] and the impact of immobilization [NOIMMO vs IMMO-MI].
Results Preliminary note. The mean score of the Stanford Sleepiness Scale assessed at 8 pm (i. e. before starting the multimodal assessment) did not differ as a function of conditions (mean ± SD; NOIMMO: 1. 92 ± 0. 64, IMMO+MI: 1. 83 ± 0. 55, IMMO-MI: 2. 23 ± 0. 89). Actigraphy data confirmed that all participants complied with the instructions regarding the right-arm immobilization procedure. A ANOVArm on the MET (i. e. metabolic equivalent of task) scores revealed main effects of condition (NOIMMO, IMMO+MI, IMMO-MI); F(2, 52) = 37. 99, p < 0. 001, η p 2 = 0. 59) and hand (Right, Left; F(1, 26) = 135. 29, p < 0. 001, η p 2 = 0. 83), as well as a significant condition x hand interaction (F(2, 52) = 70. 21, p < 0. 001, η p 2 = 0. 73) (Fig. S1 ). Bonferroni post-hoc tests showed that during the NOIMMO condition, physical activity with the right arm was higher relative to the left (p < 0.
Funding This study was supported by the  National Center of Competence in Research (NCCR) Affective Sciences  financed by the  Swiss National Science Foundation  (grant number:  51NF40-104897 ); the  Marie-Curie CoFund BRIDGE program  (AB) from the  European Union Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement  n°26717 , and hosted by the  University of Geneva.
Objective: The therapeutic strategies for hepatocellular carcinoma (HCC) have greatly expanded in recent years. However, the actual usage of each of these treatments in clinical routine remains unknown. Here, we analysed the distribution and changes of the main surgical and radiological therapeutic procedures nationwide during the last decade. Methods: Retrospectively, analysis of the data on all >18-year-old patients with a diagnosis of HCC identified in the French Program for the Medicalization of Information Systems database that contains all discharge summaries from all French hospitals. The number and percentage of the therapeutic procedures performed from January 2010 to December 2019 were extracted. Results: A total of 68,416 therapeutic procedures were performed in 34,000 HCC patients.
Moreover, Yttrium-90 selective internal radiation therapy (SIRT) is rapidly expanding, although its precise place in HCC management remains to be defined  [6]. Surgical techniques have also benefitted from many improvements, particularly the development of minimally invasive laparoscopic and/or robotic tumour resection  [7, 8]. The constant progress and refinement of HCC multidisciplinary management can be disturbing for non-specialised physicians and patients. Therefore, it is important to analyse the place and role of all these procedures in real life in large non-selected populations. In this study, we extracted data from an exhaustive national database that contains the discharge summaries from all French hospitals to analyse the changes over the last 10 years of the various technical procedures available for HCC management.