dataesr/scientific-paragraphs-categorization huggingface dataset:
Cognitive models are obvious candidates to explore cardiovascular complexity since brain-heart interplay is generally more prompt to alter the complex dynamics of HRV rather than to produce a simple shift in the sympatho-vagal balance reflected in the power spectral density of HRV  [1, [29] [30] [31]. The main findings here, thanks to the application of the Chhabra and Jensen method to directly obtain the multifractal spectrum of HRV, were that: (i) during the Stroop task only, the width of the multifractal spectrum (MF) was greater than in baseline (resting) conditions (Figure  2b ); (ii) the subpart of the MF spectrum highlighting the large-sized fluctuations in HRV (MF-large) mainly distinguished specific HRV dynamics during the go/no-go task (Figure  2c ). It is worth noting that no such methodological sensitivity was reached in a previous analysis of this dataset  [2]  based on refined composite multiscale (sample) entropy, linear multiscale entropy (LMSE), conditional entropy computed with the binning model-free estimator (CEBi), conditional entropy computed with the kernel model-free estimator (CEKe), conditional entropy computed with the nearest neighbor model-free estimator (CENN), or the multifractal-multiscale detrended fluctuation analysis (MM-DFA).
The MF in our condition could be sensitive to changes in local interactivity during mental tasking, while DistEn might rather point to nonlinear behavior of the cardiovascular control when peripheral vascular beds have ineffective autonomic control. It could be added that MF in the present dataset analysis resembles RCMSE (sample entropy) in the previous exploration of the same dataset  [2]  since both estimates distinguish the HRV signature of cognitive interference during the Stroop test. On its side, MF-large, a specific aspect of MF where large-sized fluctuations are emphasized, is distinguished mainly from the go/no-go situation from other cognitive tasks. Large-sized fluctuations in a RR increments series rely dominantly on intense vagal outflow because only vagal arousals can have a rapid action on the sinus rhythm.
9 A stepwise procedure allowed the optimization of SCI identification, with distinction between SCI and non-clinically significant background noise or other white matter hyperintensities. Radiomics and AI will offer the opportunity to harness the potential of big data analysis to understand the natural history of SCD and optimize diagnostics for chronic complications such as SCI, while reducing variability across centers and in healthcare access. Socioeconomic, psychological, and, to some extent, environmental factors can also be systematically gathered by the implementation of validated "patient reported outcomes measures" (PROMs). ERICA Consortium 10 has recently released a PROMs repository as a first attempt to identify and centralize PROMs in RDs. PROMs are of special interest in SCD, especially following the COVID-19 pandemic, to assess disease burden (eg, pain not requiring emergency consultation) that is not captured in hospital clinical records.
Numerical validation After defining the stiffness estimation methods, the technique was validated according to a FE approach. Firstly, idealized synthetic geometries were defined. In particular, a cylinder, a 45 °and a 90 °elbow geometries were defined, to assess the influence of curvature (Figure  3 ). All the idealized geometries were designed with a diameter of 32 mm and a length of 100 mm. In addition, a patient-specific test case was selected from a segmented aortic geometry. For all the cases, a thickness of 2 mm was assumed. The numerical workflow for the validation on the patient-specific simulated case is summarized in Figure  4. The geometry of the ascending aorta was taken from a  contrast-enhanced CT dataset with ECG-gating, obtained with a 320detector scanner (Toshiba Aquilon One, Toshiba, Japan).
Patient-specific cases After evaluating the performances of the methods on FE validation cases, the estimation technique was imposed by using patient-specific data as input. The analysis procedure can be summarized in three main phases: image acquisition and processing i), systolic phase definition ii), stiffness estimation application iii). Image acquisition and processing-Three patient-specific aortic morphology were reconstructed from in vivo data. In particular, three datasets of 5-phase ECG-gated CT images were acquired. The following percentages of cardiac cycle phases are considered for the analysis: 0%, 20%, 40%, 60% and 80%. The cases selected were three males (25, 89 and 64 y. o. ) with tricuspid aortic valve conformation. The CT images were obtained with a 320-detector scanner (Toshiba Aquilon One, Toshiba, Japan) by adopting a iodinated contrast medium.
(2008) ;  Vignali et al. (2021b) ). Systolic peak phase definition-The resulting aortic surfaces from the image segmentation phase are adopted and used as input for the procedure described in Subsection 2. 1. In brief, the segmented surfaces from clinical data were mapped according to the already described morphing technique and the baseline centerline was calculated. Then, the corresponding circumferential strain maps at each phase recorded by the ECG-gating process were calculated. Each strain map was calculated considering the 0% phase as the Σ dia baseline reference. The Σ sys phase was then individuated. To choose the Σ sys phase among the different cardiac cycle phases form the ECG-gating, the different strain maps were analysed first. By assuming that the configuration revealing the maximum strain was the one associated with the most pressure difference, Σ sys was chosen by selecting the phase revealing the highest circumferential strain for each case.
3% and 16. 1% for the LH and IE methods, respectively. The results from the patient-specific cases analyses are then presented. Given the equivalent performances of the estimation methods from the first FE validations, the IE method was chosen for the patient-specific analysis. The values of SNR for each of the patient-specific CT datasets were the following: 35. 5 for case 1, 36. 1 for case 2 and 37. 0 for case 3. The circumferential strain maps at the  different cardiac phases from clinical data cases are presented in Figure  12  for all three cases. By inspecting the maximum strain value for each case, it was possible to select the systolic peak phase for each dataset: phase 20% for Case 1, phase 40% for Case 2 and Case 3. The only phase chosen as systolic peak was adopted for the stiffness calculation.
Using extant and prospectively collected population based clinical data across nine European sites, PRECISION-ALS provides a General Data Protection Regulation (GDPR) compliant framework that seamlessly collects, processes and analyses research-quality multimodal and multi-sourced clinical, patient and caregiver journey, digitally acquired data through remote monitoring, imaging, neuro-electric-signaling, genomic and biomarker datasets using machine learning and artificial intelligence. PRECISION-ALS represents a first-in-kind modular transferable pan-European ICT framework for ALS that can be easily adapted to other regions that face similar precision medicine related challenges in multimodal data collection and analysis.
Additional barriers in the clinical domain include the absence of robust clinical markers that can provide insights into heterogeneous pathogenic mechanisms, limitations in the ability of clinicians to fully characterize disease onset, an absence of robust quantitative markers of progression, and challenges in the design of informative clinical trials that can provide definitive conclusions  (9). To address these barriers, and to more fully understand and characterize human disease heterogeneity, large population-based datasets comprising multimodal clinical, imaging, genomic, transcriptomic and proteomic data are required. As ALS is a rare disease, a collaborative transnational approach is needed to generate sufficient scale, and to ensure that all aspects of the disease are captured. Moreover, given the limited capabilities of current clinical assessment, additional quantitative measures of disease onset and progression, and novel and appropriate data analytics that are grounded in meaningful clinical questions will be necessary to provide new knowledge that is applicable to clinical scenarios.
To fully harness the capabilities of AI and machine learning technologies, collaborative engagement between clinicians and data scientists will be required. The ultimate goal will be to provide data-driven subcohorts of patients with shared biological characteristics and attributes that cannot be discerned by clinical evaluation alone. To date, multiple challenges have impeded the ability to generate, manage and maintain such large and multimodal datasets, comprising clinical, genomic, transcriptomic, proteomic, imaging and neurophysiological data. Collection, integration and analysis of enriched multi sourced clinical datasets requires large-scale and long-term resourcing. Data privacy rights of subjects must be respected and protected. European general data protection regulations (GDPR) have demanded additional legally robust strategies to ensure transparent compliance with new and more stringent data governance requirements.
Data sources Strengths of PRECISION ALS include the existing infrastructure within Europe that can identify and collect well-phenotyped population-based patient cohorts with associated granular datasets for ALS The participating centres have established excellence in genomics, deep phenotyping, cognitive profiling, neuro-electrical signaling and imaging analysis. Clinical partners in PRECISION ALS are also expert in design-science, systems thinking ethnography, and in integrating data sets (phenotype, clinical, genomic) to support clinical care, continuous quality improvement, research, and innovation. Under GDPR compliant data sharing agreements, data collected from previously funded collaborations including FP7 (Euromotor https:// www. euromotorproject. eu/), JPND funded projects (SOPHIA, STRENGTH, ALSCARE, BRAINM END, (https://neurodegenerationresearch.
Extant data analyses PRECISION ALS has already collated data from 21,000 patients with ALS from 9 participating sites pertaining to incidence, prevalence, clinical phenotype, genomic signatures, engagement with health services and disease trajectories. Ongoing analysis of these datasets will provide a detailed perspective of the landscape of ALS in Europe, with particular emphasis on the prevalence of known gene variants, impact of specific gene variants on disease progression, the utilization of services and equipment, and cross-cultural differences that might exist, and will provide new predictive models of disease progression. These ongoing analyses will continue to inform the prospective data collection, which is scheduled to commence in quarter 2 of 2023. The collation and harmonization of these extant data have also informed and supported the development of a customized electronic data collection tool for prospective data capture and prototype bespoke ICT patient data platform.
Data collection instrument Existing infrastructure across the nine participating sites supports population-based capture of all patients. The customized electronic data collection instrument has been designed to capture all disease relevant data from each site, with the aim of including at least 50% of all incident patients providing an estimated inclusion of up to 2000 consenting ALS patients per annum. Additional data will be collected through telehealth devices and remote monitoring of consenting patients and caregivers. A digital application linked to a web-based administrative server platform and bespoke tablet devices have already been supplied to each participating site. Following extensive consultation, the application has been custom designed to collect demographic details, clinical and cognitive phenotype, co-morbidities, concomitant medications, family history, socioeconomic details, and details of service utilization.
Patient data platform The data collected will link directly with a customized design ICT Patient data platform (PDP). The platform will be structured to manage multi-source, multi-format, multi-modal and longitudinal data in a manner that is easily accessible to clinician scientists for research purposes. The ICT data platform is subject to appropriate data governance and metadata evolution. The platform will harness data from multiple sources, including imaging, neuro-electric-signaling, genomic and biomarker datasets. The underlying design of the Platform will enable a centralized store of structured patient data, while simultaneously indexing into bulky, specialist, sensitive or protected data that are collected locally at each site (e. g. , genomic or imaging data. ) As PRECISION ALS is an interdisciplinary project involving continuous discovery, identification, validation, and use of integrated multimodal P-ALS information, the ICT platform will be designed to provide an information pipeline that seeks to identify data sources and concludes with facilitation of well governed clinical research.
The programme will provide urgently needed clinical insights into disease heterogeneity by providing novel outcomes, refined subcohorts of patients generated by integrated multimodal analyses, better monitoring devices and new approaches to understanding and measuring clinic al meaningfulness. All of these outcomes will be invaluable to future clinical trial design and for health technology assessment. Once established, PRECISION ALS will be self-sustainable, as the combination of high-quality data and analytic capabilities continue to inform both clinical researchers and industry. The developed approaches intrinsic to PRECISION-ALS will also be suited to address extant barriers to collating and analyzing datasets from multiple sources across multiple jurisdictions, providing an invaluable resource to future multinational and cross-continent collaborative research, including harmonization with other cognate worldwide initiatives such as the Accelerating Medicine Partnership Programme, supported by FNIH in the US (https://fnih.
Crystallization and structure determination Expression of zebrafish VDR LBD (residues 156-453) and the following purification were done by the procedure reported previously  [18]. The protein was concentrated using Amicon ultra-30 (Millipore) to 3-7 mg/ml and incubated with a two-fold excess of ligand and a threefold excess of the coactivator SRC-1 NR2 (686-RHKILHRLLQEGSPS-700) peptide (IGBMC peptide synthesis common facility). Crystals were obtained in 50 mM Bis-Tris pH 6. 5, 1. 6 M lithium sulfate and 50 mM magnesium sulfate. Protein crystals were mounted in a fiber loop and flash-cooled under a nitrogen flux after cryo-protection with 20% glycerol. Data collection from a single frozen crystal was performed at 100 K on the PX2A beamline at SOLEIL (France). The raw data were processed with XDS  [25]  and scaled with AIMLESS  [26]  programs.
Samples were held on ice, protected from light and processed at 4 • C. Plasma was separated by centrifugation at 10 × g and stored at -20 • C. Calcium concentration was determined using Ca-Color Arsenazo III AA kit (Wiener Lab, Argentina), measuring the absorbance at 650 nM using a spectrophotometer. The calcium concentration was calculated from calcium standards provided by the manufacturer. To adjust for differences in hemolysis among samples, blanks were prepared and the absorbance reading was subtracted from the test reading. The data was analyzed with Prism 5. 0 (GraphPad Prism Software) and statistical significance was evaluated using the Student's t-test. A p value of<0. 05 was considered significant. Analysis of the results. One-way multiple comparison ANOVA test in concert with Uncorrected Fisher's LSD posttest was used to compare ligand-treated groups with the vehicle control using Prism V 7.
Acknowledgement This research was funded by  Agence Nationale de la Recherche  ( ANR-13-BSV8-0024-01  and  ANR-21-CE17-0009-01 ) and by  Fondation pour la Recherche Médicale  ( FRM-FDT20140930978 ), and supported by institutional funds from  Instruct-ERIC  for support and the use of resources of the  French Infrastructure for Integrated Structural Biology  ( ANR-10-LABX-0030-INRT  and  ANR-10-IDEX-0002-02 ). We thank the  IGBMC cell culture facility ,  P. Eberling  for peptide synthesis, and A. McEwen for help in X-ray data collections. We thank the staff of SOLEIL for assistance in using the beamlines.
The six-month follow up evaluation showed a strictly normal neurological exam and the patient shows complete recovery at the present time. This is an observational study on the effect of a drug administered within standard clinical procedures. An authorization number (N° EUDRACT: 2019 -A0 1613-54) of the local ethics committee of the French Public Hospitals in agreement with the principles of the Declaration of Helsinki of 1975 (as revised in 1983), was obtained to perform specific preoperative and postoperative (immediate and when symptoms regressed) MRI sequences as per protocol. Written informed consent was obtained from the parents for the pediatric patient.
In conclusion, we believe that our case study presenting brain imaging results helps to extend our understanding concerning the brain mechanism underlying the long-term effect of zolpidem in clinical improvement and highlights the importance of further brain functional studies. Finally, future multicenter and multinational clinical trials, including a large dataset, are still needed to further investigate the paradoxical effect of zolpidem in various clinical contexts (pCMS, aphasia, severe traumatic brain injuries, minimally conscious state) and provide further insight into the mechanisms involved in the effect of zolpidem in neurological recovery.
Blood pressure recording based on photoplethysmography Intraoperative arterial blood pressure was continuously monitored with the Clearsight ® device (Edwards Lifesciences, Irvine, CA) from the middle finger, starting before anaesthesia  (19). The analog signal was transmitted to the IntelliVue MP60 monitor (Philips, Eindhoven, The Netherlands) at a 125 Hz sampling frequency and recorded using the ixTrend software (ixellence, Wildau, Germany).
Cerebral blood flow velocity measured using transcranial Doppler Cerebral blood flow velocity was measured in the middle cerebral artery using a 1. 5 MHz ultrasound Doppler probe placed on the side of the head opposite to the intervention site by the same trained physician. The probe was positioned only during anaesthesia induction and for the subsequent 30 min, but not during the neuroradiology procedure. The probe settings were power at 100 mW, gate at 9 mm for a depth of 45-55 mm, and a gain ranging from 4 to 8. Maximal velocity envelopes were acquired at a sampling rate of 100 Hz, then visualised in real time using ADMS (Atys Data Management Software), and exported as. csv file.
Study design and participants This cross-sectional, multicentre study took place in Quebec City (Canada), Umeå (Sweden), and Diepenbeek (Belgium). The respective local ethics committee approved this research (Quebec City, Canada: CÉR 21785; Umea, Sweden: DNR 2015/426-31, 2020-06881; Diepenbeek, Belgium: B9115201941687), and all assessment procedures were standardized across the three centres. Healthy individuals aged between 40 and 89 years old were recruited for this study. Potential study participants were identified from respective local research databases and local advertisements. The following exclusion criteria were applied: i) known chronic diseases that could affect muscle function such as ischemic heart disease, heart failure, respiratory disorders, any cancer, diabetes, neurologic or orthopedic conditions, ii) use of statins, iii) use of systemic corticosteroids (oral or injection) within three months of study enrolment and iv) participation in a structured exercise programme aimed at reaching fitness-related goals.
ACKNOWLEDGEMENTS The authors thank  Serge Simard  ( Quebec City ) for conducting the statistical analysis. They also thank  Jany Harvey ,  Mickaël Martin ,  Cynthia Brouillard ,  Josée Picard ,  Marie-Claude Landcop ,  Brigitte Jean , and  Andrée-Anne Therrien  in  Quebec City , and  Eva Sunna  and  Per Liv  in  Umeå  for their help in various aspects of the study.
F. Uncertainties The results presented here are subject to considerable uncertainties. First, our analysis fully relies on the glacier outlines contained in the Randolph Glacier Inventory v6. 0 (RGI consortium, 2017). In some regions, ). With new data sets on ice thickness based on multi-model approaches and validated with comprehensive collections of local observations, the uncertainties of present-day glacier ice thickness, and correspondingly future bedrock topography has been strongly reduced  (Farinotti et al. , 2019; Welty et al. , 2020; Millan et al. , 2022). Nevertheless, we note that estimating the volume of potential future bedrock overdeepenings, and thus the depth of new glacial lakes or fjords remains to be highly uncertain as it importantly depends on local patterns of the inferred bedrock (Munoz et al.
Acknowledgments We would like to thank all the people in science and nature conservation who made this article possible through the Ice&Life project (www. iceandlife. com -online in early 2023), helped us to noticeably improve its content and especially  Manon Salerno ,  Chloé Charbert ,  Kenzo Héas  and So a  Lana. Ice&Life  received nancial support from  WWF-France  and  Mirova Forwards ,  Fondation Université Savoie Mont Blanc ,  Fédération de Recherche en Ecologie et Environnemnet Alpes ,  Fondation Eau Neige et Glace ,  Kilian Jornet Foundation ,  Millet Mountain Group ,  Crédit Agricole des Savoie ,  Quechua and Imepsa. This study was also carried out in collaboration with the project  Life Without Ice , funded by the  BNP Paribas Foundation. The funders were not involved in study design, data collection and analysis, decision to publish or preparation of the manuscript.
g Haeberli et al. , 2016; Compagno et al. , 2022; Lee et al. , 2022 for lakes, or Pitman et al. , 2022 for rivers) generates large interest in the academic, nature conservation, land planning or policymaking elds. Collecting in situ observations and modelling at nest scales to improve the anticipation of the transition between glaciers and the diverse postglacial ecosystems remain an important research challenge that will be the object of numerous research in the near future, as an extension of the pioneer global dataset presented here.
Data processing and analysis Data obtained from the NanoString GeoMx DSP platform were analyzed using a dedicated software (GeoMx Analysis Suite 2. 3). The quality control analysis showed that all ROIs had raw read counts above 1 million and good alignment rate and sequencing saturation. The local outliers were negative control probes and were removed from the ROIs. ROIs with deduplicated probe counts Q3 (upper quartile) < 2 were excluded. Furthermore, ROIs were removed if < 1% of their genes had read counts higher than the limit of quantification (LOQ). LOQ was defined as geomean (NegProbe) × geoSD(NegProbe)² for each ROI. Target filtering was applied to retain gene targets with read counts above LOQ in at least 10% of ROIs. Q3 normalization was applied on the filtered ROIs and gene targets.