dataesr/scientific-paragraphs-categorization huggingface dataset:
RMSD and bias vary with water class, with typically lower RMSD in water classes representing clearer open-ocean waters  (Jackson et al. , 2017). OC CCI v3. 1 data have been compared with the GCOS requirements for uncertainty (Table  1 ) by computing absolute per-pixel relative error (∈ = | 100 [1-(1/10 m )] |) from bias (m) e. g. , for chl-a, m is in units of log 10 (chl). In general, OC CCI products meet the GCOS requirements for uncertainty (see Figures  3D, E ) though long-term stability is still under investigation. Although the time series is too short to unambiguously detect climate change impacts  (Henson et al. , 2010) , the CDR can be compared to climate indicators. 2017 ) described the characteristics of algorithms and methods needed for an ocean colour CDR. Amongst the 20 requirements are included: use of retrieval algorithms robust to changes in the environment; methods or algorithms should perform routinely and globally and should minimise gaps in data; inter-sensor bias needs to be corrected before multi-sensor merging; and algorithms should retrieve products with minimal uncertainties.
It is important that the Sentinel-3 OLCI-like series be continued well beyond the planned 20 years, to provide a baseline for long-term climate data records. A further development will be the integration of the oceancolour CDR with in situ data from Argo and BGC-Argo programmes, as well as other EO data in synergy, to characterise the 3D structure of the ocean. In particular, the BGC-Argo  fleet is expected to expand, albeit not to the density of Argo, but with backscattering sensors  (Dall'Olmo and Mork, 2014; Dall'Olmo et al. , 2016; Organelli et al. , 2017)  and radiance sensors  (Leymarie et al. , 2018; Wojtasiewicz et al. , 2018) , which will enable greater understanding of the biogeochemical structure and possibly additional data for sensor validation. Furthermore, additional sensors could provide observations on biological diversity  (Boss et al.
• There is a continuing need to improve retrieval algorithms, as well as the methods to establish the quality of their performance: hence, it is recommended that strict protocols for in situ data gathering be followed, with full system uncertainty characterisation, to obtain Fiducial Reference Measurements. This will help ensure that combined datasets used for algorithm construction and satellite product validation and product evaluation have minimal inter-investigator differences. • As hyperspectral satellite sensors are about to become a reality, it is important to explore novel algorithms to reduce uncertainties in products by exploiting the new capability. • Continuity in campaigns to obtain Fiducial Reference Measurements supporting system vicarious calibration and validation of sensors is key to the success of oceancolour missions, both when they join the ocean-colour constellation and as they extend the duration of their operation in space.
• Given the value and cost of in situ data it is important that international or de facto standards are followed that allow linking or interoperability when data are held in multiple databases. • Ocean colour data will be increasingly used for initialisation and updating of short-term numerical forecast models as well as validating hindcast climate model runs. • Mobile communication is expected to grow worldwide, especially in developing countries, supporting citizen science and communication of EO-based information to citizens: this two-way communication will both aid the ocean-colour field and increase scientific awareness. • Capacity building and training will remain important to fully utilise satellite observation and integrated observing systems for societal benefit worldwide, but notably in developing countries.
Study planning and clinical evaluation All participants enrolled in the symptomatic cohort were evaluated on Study-visit (SV) days 0, 3, 7 and 28. During each SV, real-time RT-PCR was run on serum and urine samples. Serologies (IgM and IgG) for ZIKV, DENV and Chikungunya Virus (CHIKV)were also run on serum samples at each SV. Plaque Reduction Neutralization Test (PRNT) was not performed on the positive serologic samples. At SV0 and 28, cell blood count and other blood chemistries were also performed. The baseline clinical evaluation collected data on demographic characteristics, medical history and exposure history. All SV collected: extensive clinical data, symptoms from a checklist, potential pregnancy, neurocognitive and hearing function assessment, disability using WHODAS 12 (WHO Disability Assessment Schedule), any complications, and assessment of Guillain-Barre syndrome, with dedicated tests if present  [18].
Statistical analyses Numerous classification methods were considered as candidates to model the relationship between serial serology and disease category. We chose to work with a decision tree paradigm due to its relative simplicity of interpretation and its ability to easily handle a response variable with more than two categories. We used the R statistical software  [25]  with the rpart package  [26]  to build a classification tree model using serial serology measurements to predict disease status. The classification tree procedure picks important variables and cutoffs to use to predict disease status. We examined the following variables in the classification tree procedure: ZIKV-sIgG, ZIKV-sIgM, DENV-sIgG, and DENV-sIgM at SV0, SV28, and change in those variables from SV0 to SV28. To maintain interpretability and parsimony, we constrained the classification tree to not exceed a depth of 4 (maximum number of decision criteria that can be used to assign categories) and required a minimum node size of 5 subjects (at least 5 subjects must be assigned to a category based on each decision criterion).
"Comité de Ética en Investigación del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán" -Registration number: CONBIOETICA -09CEI-011-20160627 and "Comité de Investigación del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán" -Registration number: 18 CI 09 012 005). The study was recorded on Clinical. Trials. gov (NCT02831699). It was supported by the Mexico Ministry of Health and US National Institute of Allergy and Infectious Diseases (NIAID) and sponsored by the Division of Clinical Research of NIAID. Signed informed consent was obtained from all subjects or from a legal representative if the subject was not able to sign the consent form.
Acknowledgments Dr. Francisco Camas-Durán ,  Dr. Zoila Priego-Smith  ( Clínica Hospital Dr. Roberto Nettel Flores ,  Instituto de Seguridad y Servicios Sociales de los Trabajadores del Estado) ;  Dr. Karla   Reyna Navarro Fuentes (Unidad de Medicina Familiar  No. 11 ,  Instituto Mexicano del Seguro Social );  Dr. Paola Alcalá ,  Dr. Alfredo Vera-Maloof ,  Dr. Luis Rojas ,  Dr. Cielo Mayorga ,  Dr. Benjamin Hidalgo ,  NP Erika Estudillo(LaRED)  participated in data collection. Alexander López  ( Hospital Regional de.
( "Comité de Ética en Investigación del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán" -Registration number: CONBIOETICA -09CEI-011-20160627 and "Comité de Investigación del Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubirán" -Registration number: 18 CI 09 012 005). The study was recorded on Clinical. Trials. gov (NCT02831699). It was supported by the Mexico Ministry of Health and US National Institute of Allergy and Infectious Diseases (NIAID) and sponsored by the Division of Clinical Research of NIAID. Signed informed consent was obtained from all subjects or from a legal representative if the subject was not able to sign the consent form.
Acknowledgements This work was supported by the  French National Research Agency  under the  STORISK  project (No. ANR-15-CE03-0003 ). Virginie Duvat  was also funded by the  INSeaPTION  project, which is part of  ERA4CS , an  ERA-NET  initiated by  JPI Climate , and funded by  FORMAS (SE) ,  BMBF (DE) ,  BMWFW (AT) ,  IFD (DK) ,  MINECO  ( ES ),  ANR (FR)  with co-funding by the  European Union  (Grant.
Internet of things sensor node requires to process data at the source. These systems need highly energy efficient microprocessor units (MCUs) using embedded nonvolatile memories (eNVM). MCU often uses a combination of flash and RAM memories. However, flash technology suffers from a limited endurance, low write speed, and scalability issues. They need high voltage during the writing step, which implies the use of charge pumps. An alternative fast, low power, and high endurance eNVM could greatly enhance energy efficiency. At the beginning, FRAM was built with ferroelectric perovskite insulators. However, perovskites show scalability issues,  5, 6  while ferroelectric HfO 2 has never stopped being improved to fit with industrial requirements since its discovery in 2011. 7 At the present time, it appears as a promising candidate to realize new efficient FRAM: HfO 2 processes are already integrated in the Si CMOS industry, and their ferroelectric properties are adequate for using oxide layer thinner than 10 nm.
ACKNOWLEDGMENTS This work was realized on the NanoLyon technology platform and has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under Grant Agreement No. 780302. The authors thank  GDR OxyFun  for its financial support. The authors also thank  Ruben Vera  ( Centre de Diffractométrie Henri Longchambon, Université de Lyon ) and  Ingrid Cañero Infanto (Institut des Nanotechnologies de Lyon)  for their help in the XRD data collection,  Jérémy Stevens  ( Ecole Normale Supérieure de Lyon ) for his help in the EDX data collection,  Geneviève Grenet  ( Institut des Nanotechnologies de Lyon ) and  Claude Botella  ( Institut des Nanotechnologies de Lyon ) for their help in the XPS data collection, and  Aziz Benamrouche  ( Institut des Nanotechnologies de Lyon ) for his help in the AFM data collection.
Clinical and radiological data Clinical data were collected from Gustave Roussy Institute, Curie Institute, and Necker-Enfants Malades Hospital. Complete clinical history was retrieved when possible: gender, age at diagnosis, family history, known tumor predisposition syndrome, symptoms at diagnosis, extent of resection, neoadjuvant or adjuvant treatments (chemotherapy, radiation), progression-free survival (PFS) defined as the date of the first radiologically confirmed relapse, long-term follow-up, and overall survival (OS). Extent of resection was noted as gross total resection (GTR), subtotal resection (STR), defined as the resection of more than 90% of the tumor volume, or partial resection (PR) (less than 90% of tumor volume). All cases were subject to local radiological review by an experienced pediatric neuroradiologist (VDR).
RNA sequencing and analysis RNAseq was performed on two cases on the Integragen platform (Evry, France). Libraries were prepared with TruSeq Stranded mRNA kit according to the supplier recommendations. Briefly, the key stages of this protocol were, successively, the purification of PolyA containing mRNA molecules using poly-T oligo attached magnetic beads from 1 µg total RNA, a fragmentation using divalent cations under elevated temperature to obtain approximately 300 bp fragments, double-stranded cDNA synthesis and finally Illumina adapters ligation and cDNA library amplification by PCR for sequencing. Sequencing was then carried out on paired-end 75 bb using Illumina NextSeq500. The software used for RNA alignment was TopHat2. 1. 0, including Bowtie2. A quantitative expression profile is generated using Cufflinks 2.
2. 0  [31]. Single nucleotide variants (SNV) and small insertions and deletions (Indels) were detected with Samtools/BcfTools (Broad Institute). Functional consequences of variants on genes, transcripts, and protein sequence, as well as regulatory regions, were predicted by Variant Effect Predictor (VEP release 83) (stop, splicing, missense, synonymous), as well as by location of the variants (e. g. , upstream of a transcript, in coding sequence, in non-coding RNA, in regulatory regions). Regarding missense changes, two bioinformatics predictions for pathogenicity were available SIFT (sift5. 2. 2), PolyPhen (2. 2. 2). Other information like quality score, homozygote/heterozygote status, count of variant allele reads, and the presence of the variant in the COSMIC database (version 71) was reported.
DNA methylation profiling and t-SNE analysis DNA extraction from FFPE was performed using the QIAamp DNA FFPE Tissue Kit and the Qiacube (QIAGEN, Hilden, Germany) according to the manufacturer's instructions. 250-500 ng of DNA was extracted from each tissue sample. Kits used for bisulfite conversion and reparation were the Zymo EZ DNA methylation kit and ZR-96 DNA Clean and Concentrator-5 (Zymo Research, Irvine, CA, USA) and bisulfite DNA was processed using the Illumina Infinium HD FFPE DNA Restore kit and Infinium FFPE QC kit (Illumina, San Diego, CA, USA). The DNA was then processed using the Illumina Infinium HumanMethylation EPIC Bead-Chip array (Illumina, San Diego, CA, USA) according to the manufacturer's instructions. The iScan control software was used to generate raw data files from the BeadChip in IDAT format and analyzed using GenomeStudio version 2.
0 (Illumina, San Diego, CA, USA). The following filtering criteria were applied: removal of probes targeting the X and Y chromosomes, removal of probes containing single-nucleotide polymorphisms (dbSNP132 Common) within five base pairs of and including the targeted CpG site, and removal of probes not mapping uniquely to the human reference genome (hg19), allowing for one mismatch  [3]. The raw IDAT files were uploaded to https ://www. molec ularn europ athol ogy. org for supervised analysis using the Random Forest methylation class prediction algorithm, as previously described  [3]. Copy number profiles were calculated as previously described  [3]. Raw signal intensities were obtained from iDat files using the minfi Bioconductor package v1. 28. 4. Background correction and dye-bias correction were performed on each sample.
After a correction for the type of material tissue (FFPE or frozen) was performed with the removeBatchEffect function (limma package v3. 38. 3). Filtering criteria of probes were removal of probes targeting X or Y chromosomes, removal of probes containing single nucleotide polymorphisms, and probes not included in the EPIC array. We selected the most variable probes for t-SNE (t-Distributed Stochastic Neighbor Embedding) (SD > 0. 30) with parameter theta = 0, pca = TRUE, max_iter = 2500, perplexity = 30, based on the method by Capper et al. [3]. t-SNE was performed using the Rtsne package, version 0. 15. The following glioma reference classes were included: MC-AAP (here annotated as ANA_PA; 21 cases); diffuse leptomeningeal glioneuronal tumor (DLGNT; 8 cases); GBM, G34 mutant (GBM_G34; 41 cases); GBM, mesenchymal subtype (GBM_MES; 56 cases); GBM of the midline (GBM_MID; 24 cases); GBM, MYCN mutant (GBM_MYCN; 16 cases); GBM, RTK I subtype (GBM_RTK_I; 64 cases); GBM, RTK II subtype (GBM_RTK_II; 143 cases); GBM, RTK III subtype (GBM_RTK_III; 13 cases); ganglioglioma (LGG_ GG; 21 cases); PA or ganglioglioma of the supratentorial hemispheres (LGG_PA_GG_ST; 24 cases); PA of the midline (LGG_PA_MID; 38 cases); PA of the posterior fossa (LGG_PA_FP; 114 cases).
Genomic DNA methylation 850 K DNA methylation profiling was performed on all 31 PAAFs. Three cases did not correspond to a specific DNA methylation class and were subsequently designated as "no match cases" (patients 3, 10, and 12); since they yielded unreliably low classifier scores below 0. 18 (supplementary Table  1 , online resource). They did, however, cluster as PA in t-SNE analysis (case 10 to LGG_PA_MID, cases 3 and 12 to LGG_PA_PF). Twenty cases received a calibrated score superior to 0. 90. Seven tumors yielded a classifier diagnosis below the established threshold of 0. 90 (0. 89-0. 46); however, in all these cases the highest calibrated score was consistent with clinical, radiological, histomolecular data and t-SNE cluster. In one medullar case, the highest calibrated score obtained was 0.
Numerical procedures The sound absorption coefficient of the resonator systems presented in the previous subsection is calculated based on pressure and velocity fields numeri-  For the FLNS model, we also computed the acoustical variables (pressure, velocity etc. ) using the Comsol "Thermoacoustics, Frequency Domain" interface (FLNS) also available in the "Acoustics" module of the software. The resonator and a small part of the waveguide (in the connection region) are meshed with tetrahedral elements (Fig. 6b ), the remaining part of the waveguide is meshed by sweeping elements at the interface surface with the already meshed part. In order to compute losses, boundary layers meshing are applied to the resonators boundaries and to the waveguide top surface as indicated in Fig. 3b. As we can see on Fig.
Acknowledgements This work was supported by the  Labex CeLyA of Université de Lyon , operated by the  French National Research Agency  ( ANR-10-LABX-0060/ ANR-11-IDEX-0007 ). This work was also supported by  Région Rhône Alpes Auvergne (via european FEDER)  in the  FUI (21st) project  " Actidalle ".
Nevertheless, our interpretation departs from this picture and is supported by the combination of MIR data with previous analysis of neutron diffraction experiments,  55  with the present FIR data, and with previous evidence that deviations of the diffusion coefficien of water from the Stokes-Einstein equation are not necessarily due to the crossing of a transition line. 77 s a matter of fact, we have seen a new band showing up in the FIR spectra. At each pressure, this occurs at a temperature very close to the temperature of crossover of spectral components Nos. 2 and 3 of the MIR spectra. Thus, the two finding must be correlated. In particular, the crossover of bands Nos. 2 and 3 has been interpreted as due to a predominantly icelike dynamics at the lower temperatures; accordingly, the appearance of the new band in the FIR spectra can be regarded as the signature of the formation of a solid phase within the pore volume.
Real-time Fourier Transform-InfraRed spectroscopy Real time-FTIR was employed for measuring the photopolymerization kinetics on a Thermo Scientific Nicolet 6700 FTIR (Waltham, USA) and were analyzed using an OMNIC 32 software. The crosslinking reaction proceeded under UV irradiation (OmniCure S2000 Spot UV curing system, 200 W, 250-450 nm, Mercury lamp) calibrated with OmniCure R2000 UV Radiometer. The real intensity imparted to the samples was 10 mW•cm -2 measured using radiometer (Solatell). One drop of the above mixture was deposited on the Attenuated Total Reflectance (ATR) unit of the infrared spectrometer where it was exposed to UV irradiation for 300 s for crosslinking. The kinetics was monitored by real-time FTIR, following the disappearance of the bands at 1615 and 1635 cm -1 corresponding to the vinyl ether.
Acknowledgements Open access funding provided by  University of Innsbruck and Medical University of Innsbruck. We thank the  Neurocritical Care Society , and also thank the iCSD meeting sponsors: Moberg  ICU Solutions, Raumedic, Hemedex, Integra, Natus, Nihon Kohden, Philips, Ad-Tech, Neurovisio, Perimed, Moor Instruments, PMT Corporation ,  Glia Media , and  UC Gardner Neuroscience Institute. Authors' work on this topic was supported by the  Austrian National Bank  (OeNB-Nr. :  14082 , R. H. );  Department of Defense :  W81XWH-BAA-15-1 ; W81XWH-18-DMRDP-PTCRA, Moberg ICU Solutions subcontract; Office of the Assistant Secretary of Defense for Health Affairs, through the  Defense Medical Research and Development Program  under Award No. W81XWH-16-2-0020  (J. A. H. );  Medical Research Council, UK  (S.
Therefore, understanding the relationship between network formation and properties of such systems has remained undiscovered. The advent and successful implementation of 'Cure Index', CI as a simple yet powerful criterion for analyzing cure state  [16, 17]  has opened a promising way for taking molecular image from crosslinking in thermoset nanocomposites. The cure state in epoxy nanocomposites containing different sorts of 0D and then 1D nanoparticles was accordingly patterned as Poor, Good, or Excellent Cure. However, the possibility of prediction of ultimate properties by the CI remains critical questions to be answered by the investigators. We do know that taking the first steps towards a convincing experimental framework relying on CI requires a huge dataset, but attempts are to speed up the interpretation process.
Dysferlinopathy is a rare, recessively inherited muscular dystrophy caused by mutations in the DYSF  [1] [2] [3] [4]  gene. Potential therapies are in development, and some of them have entered human clinical trials (NCT02579239, NCT01863004, NCT02710500). As the development of newly licensed and emerging treatments for Duchenne muscular dystrophy  5, 6  and spinal muscular atrophy type 1  7  has shown, proving efficacy and satisfying regulators can be difficult in slowly progressive and varied conditions. 8 propriate powering of a clinical trial requires understanding of disease progression and responsiveness of various outcome measures over time. 9 Selection of outcome measures for clinical trials in dysferlinopathy is particularly challenging. Dysferlinopathy is characterized by a range of ages at onset and patterns of weakness and severity,  4, 10  variable rate of progression,  4  modifying factors that are not clearly elucidated,  [10] [11] [12] [13]  and no appropriate measures for monitoring progression.
Statistical analysis Statistical analysis was completed with SAS 9. 3 (SAS Institute Inc, Cary, NC). Mean and median change scores were calculated for each test between baseline and 6 months, 6 months and year 1, and baseline and year 1. Data from some visits were excluded if the visit occurred ±2 months outside the scheduled appointment date. A majority of functional outcome measures were not normally distributed; thus, comparisons between baseline and follow-up time points were conducted with the Wilcoxon signed rank-sum test and standardized response mean (SRM). 17 A comparison was made between the performance of patients with the most common clinical diagnoses of limb girdle muscular dystrophy 2B (LGMD2B) and Miyoshi myopathy with Kruskal-Wallis test with Steel-Dwass-Critchlow-Fligner correction for multiple comparisons.
18 nambulant patients (unable to walk 10 meters with usual orthotics and walking aids) were compared to 3 categories of ambulant patients: ambulation affected severely, moderately, or mildly. Using patients' a-NSAA scores at baseline, we defined ambulant severity categories based on a clinical impression of disease severity and binomial distribution of a-NSAA scores, with cut points of 0 to 10 (severe disease severity), 11 to 40 (moderate disease severity), and 41+ (mild disease severity). The lower cut point was based on the end of the first a-NSAA peak, and the high cut point was placed at the start of the second peak, with the wide spread in between representing a plateau between peaks. Significance was defined as p ≤ 0. 05. Results are expressed as median change (range) and p value. Power analyses to support a hypothetical future clinical trial were conducted to estimate the sample size required to detect significant statistical differences in progression over the course of 1 year with PASS 14 software.
Data availability statement Deidentified cohort level data can be requested. All protocol assessments are in the public domain apart from the a-NSAA, which can be requested from the steering group (publication in draft). The statistical plan is detailed above. All data for this natural history study will be stored for at least 5 years after the end of the study. Data can be requested by all interested stakeholders for clinical research and trial planning. Data requests are reviewed by the Jain COS Steering Committee, who can be contacted via the corresponding author.