dataesr/scientific-paragraphs-categorization huggingface dataset:
Materials and Methods A comprehensive review of the English-language literature on UA surgery for OSA treatment was performed. A selection of the references was carried out in PubMed, EM-BASE, Cochrane, and CENTRAL electronic database up to January 2021 using the following search keywords: "sleep apnea" OR "OSA" OR "OSAS" OR "phenotyping" OR "physiological Traits" combined with the use of the AND function with " OSAS surgery" OR "palatal surgery" OR "uvulopharingopalatoplasty" OR "expansion sphincter-pharyngoplasty" OR "lateral pharyngoplasty" OR "relocation pharyngoplasty" OR "anterior palatoplasty" OR "hyoid suspension" OR "maxillo-mandibular advancement" OR "hypoglossal nerve stimulation" OR "transoral robotic tongue reduction" OR "TORS" OR "barbed palatoplasty" to better select the research.
UA Imaging Examination Imaging examination can lead clinicians towards a specific diagnostic framing. Cephalometric measurements on lateral teleradiography include several anatomical landmarks, such as the posterior airway space, UA length, and hyoid-mandibular plane perpendicular distance  [41] [42] [43]. Furthermore, literature data reported that the UA length, measured as the vertical distance between the superior-posterior hyoid and the inferior-posterior hard palate, parallel to the long axis of the airway, was associated with the presence and severity of OSA and pharyngeal collapsibility. Therefore, UA length seems to have a high sensitivity and a high specificity for predicting the presence of OSA  [41] [42] [43]. The role of cephalometry in predicting treatment outcomes still needs to be ascertained.
This research demonstrated that specific sensorimotor conflicts, including bodily signals from the arm and trunk, are sufficient to induce mild to moderate PH in healthy participants, linking PH to the misperception of the source and identity of sensorimotor signals of one's own body. Here, we adapted our robotic procedure to PD patients and elicited riPH, allowing us to characterize a subgroup of patients that is highly sensitive to the sensorimotor procedure, and to identify their aberrant sensorimotor processes (study 1). We next determined the common PH-network in frontal and temporal cortex, by combining MR-compatible robotics in healthy participants with brain network analysis in neurological non-PD patients with PH (study 2). Finally, we recorded resting-state fMRI data in a new and independent sample of PD patients and identified pathological functional connectivity patterns within the common PH-network, which were predictive for the occurrence of PD-related PH (study 3).
1A ). Based on previous data  26, 28, 31  , tactile feedback was delivered either synchronously with patients' movements (synchronous control condition, a spatial conflict is present between movement in front and touch on the back) or with a 500ms delay (asynchronous condition) associated with an additional spatio-temporal sensorimotor conflict shown previously to induce PH  26, 36  (Supplementary S3). The robotic procedure was able to induced PHs in patients with PD. Importantly, PD-PH patients rated the intensity of riPH higher than PD-nPH patients (main effect of Group: permutation p-value=0. 01) (Fig. 1B ). Confirming the general importance of conflicting asynchronous sensorimotor stimulation  26  for riPH, both sub-groups gave higher PH ratings in the asynchronous versus synchronous condition (main effect of Synchrony: permutation p-value=0.
Common PH-network for sPH and riPH (study2. 2) To determine neural similarities between riPH and sPH and confirm the sensorimotor contribution to sPH, we first applied lesion network mapping (Supplementary S21) and identified network connectivity mapping in neurological non-parkinsonian patients, in whom sPH were caused by focal brain damage (study2. 2), and then determined the common network (cPH-network) between the riPH and sPH. Lesion network mapping  50  extends classical lesion symptom mapping by considering each lesion as a seed (region of interest, ROI) and computing its connectivity map (in normative resting state fMRI data, publicly available database, 126 healthy participants 51 ) (Fig. S5 ). This analysis revealed that all lesions had functional connectivity with bilateral posterior superior temporal gyrus/temporo-parietal junction (pSTG/TPJ), bilateral middle cingulate cortex (MCC), bilateral insula, and right IFG, constituting the sPH-network (Fig.
Experimental procedure (study2. 1) The experimental procedure was based on a pilot study performed in a mock scanner (Supplementary S16). Participants were blindfolded during the task and received auditory cues through earphones to start (1 beep) and to stop (2 beeps) the movement. The paradigm was implemented using an in-house software (ExpyVR, http://lnco. epfl. ch/expyvr) and Visual studio 2013 interface (Microsoft) was used to control the robotic system. Participants underwent two runs of 12 min each, during which they repeatedly had to move the front robot for 30s with their right hand followed by 20s of rest for a total of 16 repetitions per condition (8 repetitions for the motor and touch control tasks) (Supplementary S15-S19 and Fig. S3 ). Synchronous and the asynchronous conditions were randomized across runs.
fMRI experiment fMRI data acquisition The imaging data was acquired with a 3T Siemens Magnetom Prisma MR scanner at Campus Biotech MR Platform (Geneva). The functional data were acquired using an Echo Planar Imaging (EPI) sequence with a full brain coverage (43 continuous slices, FOV=230mm, TR=2. 5s, TE=30ms, flip angle=90°, in-plane resolution=2. 5x2. 5mm2, slice thickness=2. 5mm using a 64-channel head-coil) containing 320 volumes for the experimental runs and 160 volumes for the localizer runs. For each participant, an anatomical image was recorded using a T1-weighted MPRAGE sequence (TR=2. 3s, TE=2. 32 ms, Inversion time=900ms, flip angle=8°, 0. 9mm isotropic voxels, 192 slices per slab and FOV=240mm).
fMRI data analysis All the fMRI data analysis reported were pre-processed using SPM12 toolbox (Wellcome Departement of Cognitive Neurology, Institute of Neurology, UCL, London, UK) in Matlab (R2016b, Mathworks). Slice timing correction and spatial realignment was applied to individual functional images. The anatomical image was then co-registered with the mean functional image and segmented into grey matter, white matter and cerebro-spinal fluid (CSF) tissue. Finally, the anatomical and the functional images were normalized to the Montreal Neurological Institute (MNI) brain template. Functional images were then smoothed with a Gaussian kernel with full-width half-maximum of 6mm. Head motion was assessed based on framewise displacement (FD) calculation  69. All participants had a mean FD value inferior to 0.
Resting state fMRI analysis For the pre-processing steps see above and Supplementary S21. The anatomical T1-weighted image was segmented into grey and white matter and CSF. Spatial realignment was applied to individual functional images. The six realignment parameters and their first-degree derivatives were added in addition to the averaged signals of the white matter and cerebrospinal fluid. Subjects with the excessive motion were excluded from the analysis, this comprised 25 subjects which had a mean FD higher than 0. 5mm and where more than 15% of scans were affected by movement. In total, 126 subjects were included for the analysis. Then, fMRI data was bandpass-filtered in the range of 0. 008-0. 09Hz. The resting state data was analyzed using the CONN-fMRI Functional Connectivity toolbox  73  (v.
Study 3 Participants (study3. 1) Data from thirty PD patients were analyzed in this study. All patients were prospectively recruited from a sample of outpatients regularly attending to the Movement Disorders Clinic at Hospital de la Santa Creu i Sant Pau (Barcelona) based on the fulfilling of MDS new criteria for PD. Informed consent to participate in the study was obtained from all participants. The study was approved by the local Ethics Committee. Patients were diagnosed by a neurologist with expertise in movement disorders. Each patient was interviewed regarding years of formal education, disease onset, medication history, current medications, and dosage (levodopa daily dose). Motor status and stage of illness were assessed by the MDS-UPDRS-III. All participants were on stable doses of dopaminergic drugs during the 4 weeks before inclusion.
Masking protocol It is not possible to blind local investigators to allocation as it is obvious which patients are receiving inhaled sedation: AnaConDa is connected to the endotracheal tube and requires the use of exhaled isoflurane monitor and a syringe driver. As the INASED study uses a nurse driven protocol, withdrawing of sedation is not initiated by the medical investigator but by the nurse in charge of the patient, based on this prespecified protocol. This is similar to what is used for spontaneous breathing trial (SBT), which are triggered daily by the nurse without medical consent if all the prespecified criteria are met. 24  SBT fails, patient is not extubated. If it succeeds, patient is extubated. Blinding of outcome data assessment is ensured as the cognitive function is evaluated by a research assistant that will not be aware of patient assignment group.
Recruitment The initial duration of patient enrolment expected is 2 years, starting in July 2020. The 2020: approval by an independent Ethics Committee; 2020-2022: recruitment period; 2022: end of recruitment, monitoring of participating centres and queries to investigators; blind review to determine protocol violation, to define intention-totreat and per-protocol analysis populations; new queries to investigators, cleaning and closure of the database; 2023: data analysis, writing of the manuscript and submission for publication.
METHODS: ASSIGNMENT OF INTERVENTION, DATA COLLECTION, MANAGEMENT AND ANALYSIS Allocation, data collection and monitoring will be carried out according to the same methods described in the HIGH-WEAN protocol. 26 location and sequence intervention A computer-generated, centre stratified randomisation is performed in a 1:1 ratio, using a centralised web-based management system (Cleannfile). The strategy assigned to the patient (intravenous or inhaled sedation) will be initiated immediately after randomisation.
Data collection and management Data will be collected on an electronic case report form (e-CRF) by a trained investigator or research assistant at each centre. A blank copy of the e-CRF can be printed from the e-CRF. This enables the investigator or research assistant to fill it out with the data of the included patients, which will be captured. Once data collection has been completed, the investigator or research assistant shall sign and date the copy. This document will constitute an integral part of the patient's medical records; as such, it shall be retained permanently. Data recorded in the e-CRF that originate in source documents must be consistent with each other; if they are not, the differences have got to be justified and documented. Blinded and patient identifiable data are stored separately in secure databases.
All patient identifiable data are stored by the coordinating centre. Site staff will be available to facilitate the monitoring visits and ensure that all required documentation is available for review. At time of inclusion, the following data will be collected: Patient characteristics, severity scores (Acute Physiology and Chronic Health Evaluation score, SOFA score), haemodynamics, vasoactive drug support, ventilation mechanics, laboratory findings, clinical ICU complications, length of stay and mortality will be recorded. Delirium will be assessed twice daily using the CAM-ICU. 27 ll these parameters will be collected each day from day 1 to ICU discharge. Cognitive function and functional outcome will be evaluated at discharge, 3 and 12 months using two kinds of scores: ► CANTAB test, combining six cognitive evaluations with an iPad during a 45-60 min medical consultation (those tests were also used in the Spice functional and neuro-psychological outcomes SPICEFANS substudy.
STATISTICAL METHODS All the analyses will be performed by an independent statistician, following a predefined statistical analysis plan. The analysis will be performed on an intention-to-treat basis, after a blind review of the data and final database lock. All the analyses will be conducted using SAS V. 9. 3 statistical software (SAS Institute). A two-tailed p value equal or less than 0. 05 will be considered as statistically significant. All tests, except for the primary outcome, will be exploratory.
[36] [37] [38]  Randomised clinical trials (RCTs) examining volatile anesthesics effects and safety aspects in ICU are currently recruiting (NCT01983800) or have been published demonstrating the safety and acceptability in ICUs with limited experience of using volatile anesthesics-based sedation. 39 Inhaled sedation has shown decrease of epithelial injury and inflammation in ARDS. 14 Those results should however be confirmed in an RCT (NCT04235608). Safety use for the staff in charge of the patient has been established. 17 18 ecommendations for use have been issued. 40 Inhaled volatile anaesthetics to conserve intravenous sedatives agents have proven to be effective during the COVID-19 pandemic (NCT04383730). 41 42  Schoen et al report that sevoflurane improved short-term postoperative cognitive ability in patients undergoing circulatory assisted heart surgery compared with propofol.
21 It is not possible to blind local investigators to allocation treatment. However, withdrawing of sedation, SBT, extubation will follow a nurse-driven protocol. Blinding of outcome data assessment is ensured as the cognitive function is evaluated by a research assistant that will not be aware of patient assignment group. Given the current data and potential of isoflurane sedation to improve patient outcomes, INASED is a well designed, adequately powered RCT within a homogeneous population to truly understand the potential clinical effects of this sedation modality. Contributors PB and EL designed the study and wrote the manuscript together. EN provided substantial contributions to the conception and design of the study, wrote the statistical analysis plan and estimated the sample size. P-YE, SE, AWT, CG, GG, FR, OH and SJ contributed for drafting the work, revising it critically for important intellectual content and approved the final version of the manuscript.
Competing interests PB reports financial support (travel expanse coverage to attend scientific meetings) from Sedana Medical. SE declares receiving consulting fees, unrestricted research grants and equipment research support from Aerogen, unrestricted research grant from Fisher & Paykel, unrestricted research grant form Hamilton medical, consulting fees from La Diffusion Technique Française. AWT reports financial support (payment for lectures and travel expanse coverage to attend scientific meetings) from Fisher & Paykel, Covidien, Maquet-Getinge and GE Healthcare. SJ reports receiving consulting fees from Drager, Medtronic, Baxter,Fresenius Medical and Fisher & Paykel. EL is cofounder and shareholder of Oxynov, a R and D Canadian company dedicated to automated oxygen administration. He is also a consultant for Sedana Medical, GE Healthcare and Smiths Medical.
Statistical Analysis SPSS statistical software, version 20 (IBM Corp. , Armonk, NY, USA) was used for statistical analysis. No imputation was made for missing data. Values are presented as means ± standard deviation (SD). Auditory outcomes and anatomic measurements for the two sides were analyzed using a paired-samples t-test Wilcoxon rank-sum test. A P-value < 0. 05 was considered statistically significant.
An ideal knowledge-free method should limit type 1 error inflation -induced by an increased number of hypotheses to testand should deal with multicollinearity. This is of significant relevance since neuroimaging studies revealed significant pairwise correlations of AD hallmarks, like amyloid-β and also tau proteinopathies, between different regional values  ( Lockhart et al. , 2017 ; Veronese et al. , 2019 ). In the present study, we investigated a non-a-priori design (i. e. , hypothesis-free) based on Partial Least Square (PLS) analysis, a statistical method that allows an exploratory whole-brain approach. PLS estimates principal components, maximizing the covariance within and between 2 tables of the same set of observations to find shared information  ( Abdi and Williams, 2013 ). PLS is a component-based tool with several advantages compared to univariate methods, including higher suitability for modeling of datasets with considerable collinearity among variables.
At the point of the study inclusion, several data were collected, namely demographic and clinical data, and Apolipoprotein E (APOE) genotype (see Supplementary materials for more details). Exclusion criteria were a history of neurological or psychiatric diseases, including depressive disorders. Medical conditions, potential causing cognitive decline of non-AD biological nature, were ruled out at baseline (see  Dubois et al. , 2018  for more details). The study was conducted following the tenets of the Declaration of Helsinki of 1975 and approved by the local Institutional Review Board at the participating center. All participants or their representatives gave written informed consent to use their clinical data for research purposes.
Preprocessing strategy A robust approach for variable selection, widely used in data science  ( Hastie et al. , 2009 ; Xu and Goodacre, 2018 ) , consists of dividing the dataset into a training set, used for selecting the variables, and a testing set, used to evaluate the selection. This latter provides an unbiased assessment of the variable selection procedure and, thus, enhances the confidence in the findings. To this end, we randomly split the dataset into a training and a testing dataset with a ratio of 1:1 stratifying by age, sex, APOE ε4 allele, education level, and total intracranial volume (TIV). We reported descriptive statistics for the strata variables in the split datasets using the mean and standard deviation (SD) for quantitative variables and the frequency counts and percentages for categorical variables.
Correlation between ROIs First, we conducted correlation analyses on the training dataset to explore how imaging measurements were related across the different brain sub-regions according to the two PET techniques. To this end, for each pair of the 84 A β-PET (as well as FDG-PET) ROIs, we calculate the Pearson's correlations of the SUVR. Histograms and boxplots were build up to represent distributions of the pairwise correlation coefficients obtained with A β-PET and FDG-PET data.
Data modeling and feature selection Associations between plasma A β42/A β40 ratio and either regional A β-PET SUVR or FDG-PET were determined through PLS models  ( Wold et al. , 1993 ) , on the training dataset (one model was fitted for each PET technique). Based on the NIPALS (Non-Linear iterative Partial Least Square) algorithm, PLS is a dimension reduction method, which is well-suited for managing high levels of correlations like, in the present study, between regional PET measures. In PLS, the X-matrix of imaging data can be reduced to a subset of orthogonal latent variables (or components), where each latent variable is constructed as a weighted sum of the X-variables, maximizing the covariance with the variable Y of A β42/A β40 ratios. For feature selection, the method combining PLS and the variable importance in projection (VIP) scores (also called PLS-VIP method) have been introduced to detect the most influential predictors in explaining the variation of the Y-variable  ( Chong and Jun, 2005 ).
Based on the PLS components, a VIP score can be calculated for each sub-region to quantify its contribution to the variance explained by the component  ( Mehmood et al. , 2012 ; Wold et al. , 1993 ). Predictors with large VIP, larger than 1, are commonly the most relevant for explaining Y  ( Chong and Jun, 2005 ). To achieve a stable selection, we used a bootstrap procedure to compute the VIP scores. Average VIP scores were thus calculated for each sub-region based on 50 0 0 models obtained from bootstrap versions of the original dataset. Finally, the averaged VIP scores, sorted by descending values, were used to select and rank the most associated regions. For each PET technique, the PLS-VIP method was performed using the R package mixOmics  ( Chong and Jun, 2005 ; Rohart et al. , 2017 )  specifying the "canonical mode" with 1 PLS component for the PLS model.
Internal reproducibility As no independent cohort was available, we assessed the reproducibility of our method on data collected at months 12 (M12) and M24. We expected to find the same regions as previously identified. Besides, since some associations may vanish over time, it inquires sustainability of the identified associations during the subjects' follow-up period. Thus, we replicated the same procedure described above for selecting and validating the regions at the longitudinal level with data at M12 and M24 with the same split samples used at M0. We tested A β accumulation and metabolism over a 1-year followup using plasma A β42/A β40 ratio measure at M12 and PET SU-VRs at M24. Similarly, we tested A β accumulation and neuronal metabolism over a 2-year follow-up using plasma A β42/A β40 ratio measured at baseline and PET measured at M24.
Confounding analysis As regional A β-PET SUVR and plasma A β42/A β40 can be influenced by the total amount of A β accumulation in patients, we conducted a confounding analysis to test whether the association between regional A β-PET SUVR and plasma A β42/A β40 can be explained by the brain overall amount of A β accumulation. This question was addressed at a cross-sectional level by using structural equation modeling (SEM) on the testing dataset. SEM is a convenient approach to model at the same time the relationships between regional A β and plasma A β42/A β40, between global A β and plasma A β42/A β40 and between regional and global A β. All models (one model by sub-region), including the covariates age, sex and APOE ε4 genotype, were built with the R package lavaan  ( Rosseel, 2012 ). We calculated p-values on all plasma A β42/A β40, global and regional A β-PET SUVR based on models fitted with 50 0 0 bootstrap replicates of the original testing set.
Datasets description Analyses were performed on the 261 participants from the INSIGHT-preAD cohort who completed structural MRI, A β-PET, and FDG-PET acquisition at study inclusion. Demographic characteristics, APOE ε4 genotype subgroups, and TIV are summarized for the training and the test datasets in Table  1. We did not find any significant difference between the two datasets regarding sex, APOE ε4 allele, age, education level, or TIV ( p -values > 0. 39). The distributions of the stratifying variables in the training and test sets are provided in the supplementary material ( Figure  S1  ). Because of the good accordance of data, we considered both datasets were homogeneous for the subject sampling. A β-PET SUVR showed significant pairwise correlations with cortical regions ( Fig. 1 A ).
Indeed, the regional pairwise correlation ranged from 0. 58 to 0. 99 with a median of 0. 87. Out of the 84 cortical structures investigated with bootstrapped PLS-VIP, the averaged VIP scores highlighted a subset of 34 regions with a score above 1 ( Table  2  ). Most of these regions were located in the frontal cortex, the cingulate cortex, the bilateral precuneus, and the right insula. We observed that the selected regions are mainly located in the left hemisphere (see Supplementary material for more details). From the testing dataset, we observed a significant negative association between A β42/A β40 ratio and all the regions previously selected ( Table  2  ). These associations remained valid after corrections for multiple testing. The values of Cohen's f 2 ranging from 0. 04 to 0. 15 indicated a small to medium effect size of plasma A β42/A β40.