dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and data collection Data were collected using a Case Report Form (CRF) and a patient diary. Together with information on resources consumption used to conduct the economic evaluation  [13]  we collected data on socio-demographic and clinical characteristics, pain intensity, physical ability and HRQoL. Data were collected on the 12 months Fig. 1  Schematic definition and duration of the observational period. Schematic representation of the observational period. All the patients were enrolled at T -1 , completed the questionnaires about the previous 12 months, and underwent a simulation test period (STP), named preSCS period. At T 0 , those who responded positively to the STP were implanted with implantable pulse generator (postSCS period) and completed again the questionnaires at that time and at T 6, T 12, T 18 and T 24 before enrolment (preSCS period) and on the postSCS period at each scheduled 6-month follow-up visit.
Socio-demographic data included age, gender, marital status, working status, and education collected by the physician at enrolment. Pain intensity was recorded using the Numerical Rating Scale (NRS), which is scored from 0 (no pain) to 10 (the most intense pain imaginable)  [19]. In particular, each patient was asked to score on the NRS the mean and the maximum level of pain perceived at the low back and one or both the legs, in the previous 12 months (at enrolment) and in the previous 6 months (during follow-up). The level of disability was measured with the Oswestry Disability Questionnaire, which is a back-specific questionnaire covering 10 dimensions of functional ability: intensity of pain, lifting, ability to care for oneself, ability to walk, ability to sit, sexual function, ability to stand, social life, sleep quality, and ability to travel.
In particular, for each patient that did not continue the study until the scheduled end of observational period, we carried forward to 24 months the NRS, ODI, SF-36 and EQ-5D information available from the last data available. On the database managed with the LOCF approach we applied all the following analyses. Using the EQ-5D descriptive system we calculated utility scores by means of an algorithm that uses population-based (social) values estimated in Italy  [27]. To reach objective 1, i. e. to compare HRQoL of the patients with that of the corresponding general population, we used the reference EQ-5D-3 L data for the Italian general population  [28]. Since the HRQoL data available from the general population were not longitudinal but cross sectional, to compare them with the longitudinal data from the patients we assumed that HRQoL in the general population is overall constant.
We adopted multiple regression analyses, including either the EQ-5D VAS or utility index as dependent continuous variables, and age, sex and education as potential confounders (independent variables) widely recognized in the scientific literature (e. g. [28] ). In particular, we applied 5 regression models for each dependent variable, one per time period, each one including the same HRQoL data from the general population. The predictor of interest, to distinguish between general population (reference) and patients, was introduced as a binary explanatory variable. The other independent variables were included to adjust the results for age (linear variable), sex and education (categorical variables). According to the distribution of the data, we applied the linear regression analysis to the VAS, while to the utility index we applied the Tobit model, which accounts for ceiling effect  [29, 30].
To estimate the relationship between pain intensity, functional disability, and HRQoL during the overall observational period (objective 2) we adopted multilevel random intercept regression linear analyses, known also as "hierarchical model". With these models we estimated the adjusted association between HRQoL with NRS and ODI across the time, i. e. taking into account for the repeated measures per participant, with level one of the hierarchy being observations over time within a patient and level two being the patient. Multilevel models have been used to analyse longitudinal HRQoL data  [31] [32] [33]. In particular we performed 4 regression models. In each model we included one among the following as dependent continuous variables: EQ-VAS, utility index, SF-36 PCS and MCS scores. In every model we included mean NRS and ODI index as independent continuous variables of interest to find the association with HRQoL, while age baseline HRQoL, time of data collection (continuous variables), sex, education, and previous surgery (categorical variables) were all included as potential confounders.
Acknowledgements The present study was conducted also with the contribute of  P. Cisotto, MD , from the  Ospedale "S. Maria di Cà Foncello ," Treviso, during the design, data collection and results interpretation, who has passed some years ago. Furthermore, we would like to thank  G. Beccagutti ,  M. Grifi ,  E. Tartaglione ,  C. Campo  and  D. D'Ostilio  from  Medtronic Italy ,  T. De Santo  from  Medtronic EMEA Regional Clinical Center  and  R. Ciampichini  for their technical support. For the support provided during data collection, the authors would like to thank:  C. Bonezzi ,  MD-Unità di Medicina del Dolore IRCCS Fondazione Salvatore Maugeri, Pavia ;  B. Cioni ,  MD-U. O. Neurochirurgia Funzionale e Spinale Policlinico Gemelli, Roma ;  G. De Carolis ,  MD-U. O. Terapia del Dolore Azienda Ospedaliero Universitaria Pisana ,  Presidio Opsedaliero "S.
Immunoblotting Cell lysates were prepared using a protocol described previously  (Setty et al. , 2007)  and γ-tubulin was used as a loading control in all experiments. Immunoblots were developed with the Clarity Western ECL substrate (Bio-Rad) and imaged in a Bio-Rad Molecular Imager ChemiDoc XRS+ imaging system equipped with a Supercooled (-30°C) CCD camera (Bio-Rad) using Image Lab 4. 1 software. Protein band densities were measured, normalized to that of γ-tubulin, and the fold change with respect to control quantified and then indicated in the figure. The percentage Mβ formation was calculated from the total PMEL (sum of P1, P2 and Mβ band densities) after γ-tubulin normalization. In certain experiments, the percentage knockdown was also quantified after γ-tubulin normalization.
Immunofluorescence microscopy and image analysis For steady state localization studies, cells on coverslips were fixed with 2% formaldehyde (in PBS) and then stained with primary antibodies followed by the respective secondary antibodies as described previously  (Setty et al. , 2007). Bright field (BF) and immunofluorescence (IF) microscopy of cells was performed on an Olympus IX81 motorized inverted fluorescence microscope equipped with a CoolSNAP HQ2 (Photometrics) CCD camera using 60× (oil) U Plan super apochromat objective. Acquired images were deconvolved and analyzed using cellSens Dimension software (Olympus). Pigmentation (normal or hypopigmentation) in cells was quantified from BF images visually by counting ∼100 cells in each experiment. The average pigmentation in cells was calculated and then plotted.
Similarly, a reduced fluorescence staining of TYRP1 or TYR in shRNA-depleted cells was quantified visually and then plotted as percentage of cells that had lost the staining of respective proteins (Fig. S1A ). In Fig. S1F , melanocytes with ∼50 or fewer melanosomes/cell were considered as hypopigmented cells, counted visually from the randomly taken BF images of each and then plotted. The colocalization between two colors was measured by selecting equal square areas in the entire cell excluding the perinuclear area and then the Pearson's correlation coefficient (r) value was estimated using cellSens Dimension software. The average r value per each cell was calculated and then plotted or represented as mean value along with the s. e. m. Note that the maximum intensity projection of non-deconvolved Z-stack images were used for estimating the r values.
The mean fluorescence intensity (MFI) of immunostained melanocytes was measured using ImageJ software and corrected total cell fluorescence (CTCF) was calculated using the below formula. CTCF (in arbitrary units, A. U. or AU)=area of the cell (MFI of cell-MFI of background). The averaged CTCF values from 10-25 cells/condition were calculated and indicated in the figure. The analyzed images were assembled using Adobe Photoshop.
Live-cell imaging Cells were plated on 2-cm glass-bottomed dishes (Mat Tek Corporation) and then transfected with respective constructs. After 24 h, cells were visualized under an Olympus IX81 fluorescence microscope equipped with an environmental chamber maintained at 37°C with 5% CO 2 and analyzed by cellSens Dimension software. Time lapse microscopy of both GFP and RFP was performed by capturing image streams over 3-5 min using a CoolSNAP HQ2 (Photometrics) CCD camera. Similarly, the time lapse imaging was performed on a Zeiss LSM880 laser scanning microscope with Airyscan mode using Zen lite 2. 0 software to obtain the movies equivalent to the super resolution. Images were analyzed and converted into either TIFF or avi format for visualization.
For quantification, melanosome stages were defined by morphology  (Raposo et al. , 2001)  and vacuoles were defined as empty organelles. The number of melanosomes and vacuoles per µm 2 cytosol were counted using ImageJ software. We counted ten cells from each control sh and Rab4A sh condition. Furthermore, we estimated the melanosome stages from 883 total melanosomes of control sh and 300 total melanosomes of Rab4A sh cells.
Determining cell surface expression levels Cells were harvested, washed with 1× PBS and then suspended in growth medium (supplemented with 25 mM HEPES pH 7. 4) containing saturating concentrations of unconjugated primary antibodies on ice for 30-45 min. Cells were washed and incubated with respective Alexa Fluor 488conjugated secondary antibody for 30-45 min on ice. Finally, cells were washed, suspended in ice-cold FACS buffer (5% FBS, 1 mM EDTA and 0. 02% sodium azide in PBS) and the fluorescence intensity was measured using FACS Canto (BD biosciences). Data was analyzed using FlowJo (Tree Star) software and plotted as the mean fluorescence intensity (MFI) as described previously  (Setty et al. , 2007).
Statistical analysis Statistical significance was determined with an unpaired Student's t-test or via ANOVA using the GraphPad software. All values are given as the mean±s. e. m. DBT-RA program (to P. A. )  and Medical Research Council (MRC) core support to the MRC/UCL Laboratory for Molecular Cell Biology University Unit (MC_UU_1201814 to A. S. and C. A). Deposited in PMC for immediate release.
Acknowledgements We thank  M. S. Marks ,  J. S. Bonifacino  and  A. Peden  for generous gifts of reagents, and  E. V. Sviderskaya  and  D. C. Bennett  for mouse melanocytes. We also thank  D. Dey ,  R. Keerthana ,  C. Praneeth  and  A. B. Sneha  for their technical help. We acknowledge the shRNA resource center, Bioimaging, flow cytometry and PICT-IBiSA EM facilities.
Funding This work was supported by a  Indo-French Centre for the Promotion of Advanced Research (CEFIPRA)  Project (4903-1 to S. R. G. S. and G. R. );  Wellcome Trust-DBT   India Alliance Senior Fellowship  ( 500122/Z/09/Z  to S. R. G. S. );  Department of Biotechnology, Ministry of Science and Technology (DBT)-RNAi task-force  ( BT/PR4982/AGR/36/718/2012  to S. R. G. S. );  IISc-DBT partnership program  (to S. R. G. S. );  Institut Curie ,  CNRS ,  Fondation ARC pour la Recherche sur le Cancer  ( SL220100601359  to G. R. );  Labex   CelTisPhyBio. Post-doctoral fellowship  (to C. B. );.
Human ECGs Twenty-four-hour Holter recordings (2/3-lead configuration, sampling rate: 200 Hz) were obtained from the Telemetric and Holter ECG Warehouse (THEW, hosted at the University of Rochester Medical Center, NY, USA), an initiative hosting a warehouse of digital Holter ECGs open to the scientific community. The E-HOL-03-0202-003 database of 200 healthy subjects [age > 15: 39 ± 16 (mean ± SD), 100 women, 100 men] was used to study HF oscillations in the healthy population. This database was also used to study influence of smoking on cardiac autonomic modulation (non-smoking, n = 145 vs. smoking, n = 55). Subjects were considered as eligible for enrolment and defined as healthy based on a battery of tests and information including no history of cardiovascular disease or disorders (stroke, TIAs and peripheral vascular disease), no history of high blood pressure (>150/90 mmHg), no medication, no chronic illness, normal physical examination, normal echocardiography, normal exercise testing and no pregnancy  (Couderc, 2012).
The E-HOL-03-0202-003 database refers to one Holter ECG recording per subject. The E-HOL-03-0480-013 database of genotyped LQTS patients was used to study HF oscillations under β-blocker treatment conditions in humans. The LQTS data were accumulated over 25 years and donated to the THEW by the Hospital Lariboisière (Paris, France). In the present work, 181 Holter recordings were analysed from 127 LQT1 patients [age > 15: 37 ± 15 (mean ± SD)]. All LQT1 patients were genotyped with mutations of the KCNQ1 subunit. Twenty-eight out of these 127 patients were recorded at successive ages of their life during on or off β-blocker treatment periods. From the 181 Holter recordings, 110 were collected from patients off β-blocker treatment and 71 from patients on β-blocker treatment.
Animal ECGs Translational investigations were conducted in cynomolgus monkeys and beagle dogs to check the validity of the model across species. Twenty-four-hour telemetric ECG recordings were obtained in both species. All animal care and experimental procedures were subjected to ethical review (ethics committee no CEEA-111) according to European directive 2010/63/UE on animal welfare. Animal studies are reported in compliance with the ARRIVE guidelines  (Kilkenny et al. , 2010; McGrath and Lilley, 2015). In accordance with the 3Rs encouraging the reduction in the number of animals used for experimental research, no animal experiments were specifically conducted for this study: only experiments recorded in the internal CERB database were re-analysed for this work. All animal experiments were conducted in the conditions described below.
The HFAM ratio We observed that changes in HFHR oscillations were more sensitive to sympathetic blockade by atenolol than HFRR oscillations (Figure  1 ). We hypothesized that the relative influence of the two autonomic systems could be derived from the HFHR/HFRR ratio. Because the two variables (HFHR and HFRR) have different units, both parameters need to be the normalized. For this purpose, reference values for HF oscillations of the HR and RR interval, named HFHR ref and HFRR ref , were determined in humans from 200 healthy subjects, cynomolgus monkeys (n = 77) and beagle dogs (n = 82). For monkeys and dogs, 'n' values refer to independent animals recorded in baseline conditions (treatment-free recording period) for 24 h. Data were collected over a period of 6 years  The normalized oscillations ratio HFHRN/HFRRN was named the HF autonomic modulation (HFAM) ratio.
(2010 to 2015). This database does not include any replicates of treatment-free recording period for any animals. HFHR ref and HFRR ref values are rounded values from mean values which are calculated over the entire circadian period in each species, under baseline conditions (Table1). More details about calculations of HFHR ref and HFRR ref values from distribution histograms of HFRR and HFHR oscillations in each species are given in Supporting Information FigureS1.
(2010 to 2015). This database does not include any replicates of treatment-free recording period for any animals. HFHR ref and HFRR ref values are rounded values from mean values which are calculated over the entire circadian period in each species, under baseline conditions (Table1). More details about calculations of HFHR ref and HFRR ref values from distribution histograms of HFRR and HFHR oscillations in each species are given in Supporting Information FigureS1. Then, HFHR and HFRR oscillations were normalized (N) as follows: HFHRN HFHR/HFHR ref in normalized units HFRRN HFRR/HFRR ref in normalized units.
Nevertheless, when accounting for all three quantitative markers, as displayed in the 3D scatter plot of Fig. 6a , one could easily classify brain tissues. These results underline the necessity of developing an endomicroscope with multimodality capabilities for robust in vivo tissue interrogation. All in all, these results based on tissue autofluorescence signals coming from brain tumors are taking part in the construction of an optical database that will be implemented in a two-photon multimodal endomicroscope. A first prototype of this intraoperative surgical tool is under development in our laboratory and has shown great performances when it comes to collecting signals  30, 31. Compared to the different diagnostic techniques such as frozen sections or formaldehyde fixed H&E stains performed by neuropathologists today, this method does not require any tissue resection.
Otorinolaringologia carcinoma 1 Table  1. Origin of the metastasis tissues used in this study. file of the superimposed image of TPM and SHG response) to two senior neuropathologists six months after the initial H&E based diagnosis pertaining to 25 randomly selected patients (control, n = 7, GBM, n = 8, metastasis, n = 10). They were asked to classify the sampled tissue in four categories: (1) GBM; (2) metastasis; (3) healthy tissue; or (4) unclassified. The neuropathologist had access to clinical data typically available along with the TPM images. This includes patients' age, gender, clinical presentation, tumor location, and the pre-operative MR images. The neuropathological blind analysis of TPM images and its corresponding ability to discriminate between control, GBM, and metastasis tissues was evaluated using the classification properties defined as the sensitity, specificty and the accuracy of a diagnostic test, respectively following Eqs 1-3.
Fluorescence and SHG image acquisition. This study was conducted on the multimodal two-photon microscope at the PIMPA (multiphotonic imaging platform for small animals) platform at the IMNC laboratory, Orsay, France. A Mai Tai DeepSee Ti:Sapphire laser source with automated dispersion compensation was used for two-photon excitation. The source's average power was 2. 4 W at 800 nm excitation and was tunable from 690 nm to 1040 nm. The repetition rate of the laser source was 80 MHz and the output pulse duration was set to 70 fs. The laser was combined to a confocal and multiphoton microscope, the TCS SP8 MP (Leica Microsystems, Germany) and was controlled through the Leica software, Symphotime x64. Different visible excitation diodes were also included in the setup, including the 405 nm excitation wavelength.
Large regions of interest were selected with Leica's acquisition software. Spectral imaging and analysis. The spectrally-resolved fluorescence intensities were detected by a hybrid detector (HyD, Leica, Germany) placed in the confocal head of the microscope piloting the grating and mirror in front of the detector. The spectral resolution was 10 nm, covering the range from 380 nm to 780 nm. A spectral mosaic was acquired on a 3 × 3 image area i. e. a spectral measurement was made for each image of the mosaic then the software merged the information to give its mean fluorescence spectrum. The spectral excitation-emission matrix was acquired by varying the excitation wavelength and detecting the fluorescence across the whole emission band. The power at the output of the microscope objective was measured using a power meter (Nova II, Ophir, USA) so that the fluorescence spectra were adjusted according to the corresponding excitation power.
The acquired spectra were treated with Matlab scripts developed at the IMNC laboratory  30  , where the fluorescence and SHG signal of five endogenous molecules were spectrally decomposed (NADH, FAD, lipopigments, porphyrins I and II). Five ROIs, each corresponding to 200 μm in diameter were chosen for spectral analysis. The SHG, NADH and FAD peak intensities were extracted at 890 nm excitation wavelength to calculate two quantitative markers, the SHG peak intensity and the redox ratio (ROx)  25. The ROx is defined in Eq. 4 as: = ROx NADH FAD ( 4 ) FLIM acquisition. The microscope integrated a FLIM module from PicoQuant (GmbH, Berlin, Germany), in order to acquire fluorescence lifetime imaging. Each ROI (512 × 512 pixels) was also imaged in FLIM mode at a repetition rate of 100 Hz, where the final image was the result of averaging twenty single frames.
For each pixel, the fluorescence decay profiles were fit to a mono-or bi-exponential function using the Symphotime software (Symphotime x64 bit, PicoQuant, GmbH, Berlin, Germany) to recover the lifetime values. Ten to fifteen ROIs were selected from different structures observed in the FLIM image. The goodness of fit was assessed by calculating χ2-value as defined in Eq. 5: ∑ χ µ σ µ σ = - x mean variance ( ) : , : (5) i i i i i i 2 2 2 2 The criterion for an acceptable fit was having χ 2 -values of around 1. 0 (χ 2 range 0. 8 to 1. 6). Additionally, the residuals had to be randomly distributed around zero within the intervals 4 and -4. The average lifetime (τ avg ) for each ROI was measured using Eq. 6. τ τ = ∑ ∑ a a ( 6 ) avg i i i i i Scoring system. Three quantitative indicators were used: (1) Redox ratio: NADH/FAD, (2) fitted SHG intensity and, (3) the average lifetime, all under an 890 nm excitation wavelength.
Statistical analysis. Statistical analyses of the SHG and fluorescence intensities, along with the spectral widths were generated using Matlab (R 2013a), using an ANOVA test for the three tissue groups. This was followed by applying the Bonferroni method to determine which mean values are significantly different within a 95% confidence interval. The statistical values found in Fig. 6(b-d ) were computed using the software R (x64 3. 2. 0), where the computed p-values < 0. 05 were considered to be statistically significant.
Acknowledgements This work is financially supported by  ITMO Cancer AVIESAN (Alliance Nationale pour les Sciences de la Vie et de la Santé, National Alliance for Life Sciences & Health)  within the framework of the Cancer Plan for MEVO & IMOP projects, by  CNRS  with  "Defi instrumental" grant , and the  Institut National de Physique Nucleáire et de Physique des Particules  ( IN2P3 ) and by the "Ligue contre le cancer". This work was done in the PIMPA Platform partly funded by the  French program  " Investissement d' Avenir " run by the  "Agence Nationale pour la Recherche"  (grant " Infrastructure d'avenir en Biologie Santé - ANR -11-INBS-0006 ").
C. Iteration Stopping Criterion Based on Pixel Re-estimation Image quality will be improved after each restoration operation of Eq. (  8 ). So, if we replace the input data x in Eqs. (  6 )-(  8 ) by the updated image  , a better restoration performance can be expected because the corrected intensities allow a more accurate calculation of the similarity metric s. However, over iterations, the benefits brought by this pixel correction will become more and more out-weighted by the negative effects caused by the inclusion of irrelevant pixels and the restored image quality will deteriorate if the iteration continues further. The ground truth image is not available to control the iteration number required to reach the optimal restoration. Therefore, the restoration deterioration is evaluated via a re-estimation strategy based on the assumption that the best restoration is reached when the surrounding intensities are able to provide the best estimation of the non-corrupted image information.