dataesr/scientific-paragraphs-categorization huggingface dataset:
To date, the most convincing evidence of language and motor improvement with therapy has been achieved by prenatal LT4 treatment of n=1 affected male fetus (Table  2 ). 26  However, this approach is only feasible in index families and does not represent a solution for most patients. The ongoing TRIAC II trial (NCT02396459) is currently testing whether early initiation of Triac treatment before the age of 2. 5 years may have a beneficial effect. In this trial, the primary outcome measure is development as assessed by GMFM88 and Bayley III. Of note, many genetic disorders manifest in early childhood, and pediatric patients may continue to developmental milestones, albeit at a slower rate than healthy children. This apparent developmental progress may be misinterpreted as a treatment effect, if not compared to a natural history cohort or placebo group.
(ii) Species differences in TH transporter expression are known and led to the development of an MCT8-deficient mouse model that had a double knock-out of Mct8 and Oatp1c1 (organic anion transporter1 C1),  38  which is another TH transporter expressed in the murine but not in the human blood-brain barrier. These Mct8/Oatp1c1 double knock-out mice show neuropathological alterations (reduced gray matter, cerebellar thickness, impaired myelination and functional connectivity, fewer interneurons) and impaired locomotion (Rotarod, hanging wire performance),  38  both of which can be improved by Triac treatment. 39, 40  Whether the successful treatment of mice with respect to neurodevelopment would be transferable to human patients may be answered by the ongoing TRIAC II trial (NCT02396459). In general, it is a wellknown phenomenon that mouse models often do not resemble pediatric neurological diseases, leading to the development of mammalian models that are evolutionarily closer to humans, as has been done for Duchenne muscular dystrophy,  41  which could also be considered for MCT8 deficiency.
Nevertheless, power analysis with alpha = 0. 05, power = 0. 8, showed that 34 participants were needed for a moderate to large effect size with 6 predictors in a regression analysis, which was obtained in this study. Of the 133 children, 6 did not complete any of the memory measures required for this study. Their data was dismissed. An information sheet was handed to the parents with an option for their child(ren) to opt out from the study at any time. The study received ethical approval from the Ethics Committee of the School of Life Sciences, University of Hull. The anonymised data that support the findings of this study and the analysis script including power analysis have been made available on OSF and can be accessed at https://osf. io/jked2.
PRELIMINARY ANALYSES A limited amount of data was missing, with 2 participants missing data on the forwards Pathspan, 4 participants (including the 2 participants with missing data on the forwards Pathspan) had missing data on the backwards Pathspan and 4 different participants had missing data on the delayed Recall task. Little's test for data missing completely at random was not significant X 2 (23, N = 127) = 26,97, p =. 257, indicating that missing data were missing at random and were hence estimated using linear interpolation rather than deleted, using the package 'imputeTS'  (Moritz, 2018). Table  1  displays all tasks' mean, standard deviation and range per age group (Year 1: 5-6 years; Year 2: 6-7 years; Year 3: 7-8 years). Table  2  shows a Spearman (due to the use of ordinal Pathspan scores) correlation matrix for all tasks used in the study.
REGRESSION ANALYSES In order to replicate previous findings concerning a shift between visuospatial and verbal memory  (De Smedt et al. , 2009) , mixed effect regression models were carried out, using the 'nlme' package in R to explore the variance in overall mathematics scores accounted for by each memory type at different education levels (i. e. , per year group). Per age group, in the first step age, Pathspan forward, Pathspan backward, Recall and Delayed Recall were entered as fixed predictors of overall TEMA-3 scores and school was entered as random factor. In a second step, mean BAS scores were entered to control for general intelligence. In all year groups, the regression model controlled for mean BAS scores fitted the data better than the non-controlled model, although not significantly in Year 2, based on the AIC model selection (Year 1: AIC = 249.
PREDICTORS Note: * p <. 05, ** p <. 01, *** p <. 001, 1 p =. 085, 2 p =. 093, 3 p =. 062. Coolen and Castronovo Journal of Cognition DOI: 10. 5334/joc. 248 The second part of the analyses were mixed model regressions of all predictors with the complete data set (3 age groups together) to explore whether the shifts of predictive memory types between different years were also presented in the form of significant interactions. Furthermore, two regressions were also conducted to investigate whether the pattern of predicting memory types differ when predicting formal and informal TEMA-3 scores separately. Since the main predictor of interest in the following regressions was year group, age was not included in these regressions. Therefore, the age of the children was controlled for in the form of year group rather than age in months.
The database maintained by the Joint Committee for Traceability in Laboratory Medicine (JCTLM) currently lists the secondary commutable certified reference material (CRM) ERM DA-474/IFCC (DA-474) "C-Reactive Protein in Human Serum" and two generic immunoassay-based method principles (immunoturbidimetry and immunonephelometry) as the basis for implementing the metrological traceability of C-reactive protein (CRP) measurements. The DA-474 certification report states that its value was assigned by 6 commercial immunoassay measurement procedures that each used ERM DA-470 as calibrator  [1]. The value assigned to ERM-DA-470 is traceable to the 1st International Standard for CRP from the World Health Organization, WHO 85/506, as described in the certification report. The WHO 85/506 standard was prepared in 1985 and each ampoule contains the residue after freeze drying of a solution of 50 μg human CRP in 0.
A primary CRM must be pentameric when its intended use is to prepare secondary commutable calibrators for immunoassays. Otherwise, the primary CRM's assigned value will not correctly reflect the pentameric form. In addition, the pentamer structure must be preserved when primary calibrators are prepared from the primary CRM. Therefore, the full characterization of a primary CRM and its use to prepare primary calibrators for implementing traceability of CRP measurements must be unambiguously described for demonstrating that it is suitable and appropriate for its intended use. The JCTLM requires demonstration of extent-ofequivalence to an existing CRM or RMP when a new candidate CRM or RMP is nominated for listing in its database  [8, 9]. In the case of CRP, there is currently no primary CRM or RMP listed for the measurand.
Based on the data shown, results for clinical samples measured with procedures calibrated with these nominated CRMs may significantly differ from results when measured using currently available well harmonized end-user immunoassays calibrated to DA-474 or DA-472. This situation presented a challenge for the JCTLM review process as the potential bias may cause erroneous interpretation of patient results. The data shown at the workshop demonstrated the risk that introducing new candidate higher order references and therefore defining new calibration hierarchies for end-user immunoassays could cause disagreement for clinical sample results when compared to results based on the existing metrological traceability to WHO 85/506. Medical laboratory practice would be negatively impacted by JCTLM listing a new CRM or higher-order RMP that introduced a bias in an existing well established calibration hierarchy.
Affymetrix GeneChip Command Console software (AGCC) was used to acquire GeneChip images and generate CEL files for analysis. Arrays were imported to R applying RMA background correction and probe summarization. For each tissue, fold changes between conditions were computed after mean and variance normalization and centering using a generalized additive model (GAM). An empirical Bayesian partial density model was used to identify differentially expressed genes. Gene set enrichment analysis (GSEA) was performed using all genes in the array sorted by fold change computed in each tissue using GOSlim datasets  [20].
We further examined this using BioID-AP followed by western blotting. We observed ARID1A in Strep-purified lysates from both GEMC1 and MCIDAS proteins but enriched in GEMC1 samples, consistent with the BioID-MS data (Fig. 3A ). In contrast, we observed BRD9, a defining component of the ncBAF complex, highly enriched in MCIDAS samples, while the levels of core SWI/SNF components SMARCE1 (BAF57) and SMARCC1 (BAF155) were similar in each case and not identified in the BirA* controls (Fig. 3A ). To try and understand what region of the protein conferred the BRD9 proximal interaction, we swapped the C-termini of GEMC1 and MCIDAS directly after the CC domain to generate the hybrid proteins Fig. 2  The proximal interactomes of GEMC1 and MCIDAS. A Western blots from lysates of AD-293 cells transfected with FLAG-BirA*, FLAG-BirA*-GEMC1 or FLAG-BirA*-MCIDAS.
ACKNOWLEDGEMENTS We thank  F. Guillemot ,  C. Lynch ,  M. Serrano , and  S. Brody  for antibodies,  F. Supek  for cells and reagents,  A. Holland  and  C. Jewett  for DEUP1 antibody and expansion microscopy suggestions,  J. St-Germain  for data analysis,  J. Lüders  for help with expansion microscopy,  T. Dantas  for sharing unpublished data and support from the  IRB Functional Genomics and Biostatistics/Bioinformatics, Protein Expression and Mass Spectrometry Core Facilities. ML and BT were funded by  Severo Ochoa FPI  fellowships from the  Ministry of Science, Innovation and Universities (MCIU) ,  PK  by an  Advanced Postdoc Mobility fellowship  from the  Swiss National Science Foundation  and the  Kurt and Senta Herrmann Foundation  and I. G. C by an  AECC  fellowship. THS was funded by the  MCIU  ( PGC2018-095616-B-I00/GINDATA ) and by the  NIH   Intramural Research Program ,  National Cancer Institute, Center for Cancer Research.
X. S. was supported by  MINECO  ( PID2019-110198RB-I00 ) and the  European Research Council  (CONCERT, contract number  648201 ). IRB Barcelona is the recipient of institutional funding from  FEDER  and the  Centres of Excellence Severo Ochoa  award to  IRB Barcelona  from  MINECO (Government of Spain ). MRM was funded by the  National Heart, Lung and Blood Institute of the NIH  ( R01-HL128370 ). VC was funded by the  Associazione Italiana per la Ricerca sul Cancro (AIRC) , the  European Research Council (ERC)  grant  614541  and the  GiovanniArmenise foundation career development award  to VC. SR was funded by a  Singapore National Medical Research Council (NMRC) Open Fund- Individual Research Grant  ( OFIRG19nov-0037 ). HZ was supported by  National Cancer Institute  ( R01 CA220551 ).
For this, the use of mixed methods was justified by observational methodology, based on the categories of communications and the temporal structure of motor actions. An observational methodology is a methodological approach adapted to work on the ecological dimension in sport and physical education  (Pic et al. , 2018). More concretely, a quadrant III observational methodology was applied  (Anguera and Hernández-Mendo, 2016). A design was applied that was: (a) nomothetic, as data on different players were recorded, (b) punctual, because the observation were raised in a precise moment, and (c) multidimensional, since different dimensions (criteria) were taken into account.
Data analysis Therefore, we obtained 5 dependent variables (FLU, FLEX, CONV, SAEMP and IEMP) with two factors: type of activity (HB or SB) and group membership (G1 or G2). These are our 2 independent variables. First, we studied the differences between our two groups using a Mann-Witney test. Then, Pearsons correlation tests were performed between our 5 dependent variables. Finally, in order to investigate more precisely what could be the relationships between the sociomotor empathy and the motor creativity we carried out two different factor  10. 3389/fpsyg. 2023. 1185462 Frontiers in Psychology 07 frontiersin. org analysis separately for the two activities. The significance level was set at 0. 05. On this basis, we distinguish in our results different levels of significance: p < 0. 05, p < 0.
The data repository The data repository is saved in open csv format on the research. data. gouv. fr. site at the following DOI location: https://doi. org/10. 57745/1C8TIM. This repository consists of 11 columns and 46 lines. The results of each student are presented line by line (lines 2 to 46) according to the indicators of creativity (fluidity, flexibility and convergence) and empathy (instrumental and socio-affective) presented in column (column B to E for the Sitting ball and F to I for handball). The result of socio-affective empathy for each student, obtained through the sociometric questionnaire, is in column J. Remember that fluidity (indicator of praxical divergence) represents, for one collective game, the number of praxical communications realized by each player (60 min per game).
While there are various physical drivers that affect the behavior of wetlands and small lakes, the most critical among them, other than perhaps direct human management, is likely changing patterns of weather, hydrology and climate  [1]. In recent years, our ability to track the extent of small lakes and wetlands has increased manifold. Lehner and Döll  [4]  developed the Global Lakes and Wetlands Database (GLWD), which provides the maps and water surface area of the lake, wetlands, reservoirs and rivers. For lakes, GLWD was superseded by the HydroLAKES database  [5] , which mapped about 1. 4 million lakes larger than 0. 1 km2 (see Figure  1. 1 uncertainties of less than 2 cm  [11]. Such high performance in lake level estimation can be achieved only for very large lakes using satellite altimetry.
The satellite data is freely available on Google Earth Engine, a cloud-based computing platform ideally suited for a global study of lakes  [35]. The water elevation data were collected from the citizen scientists engaged or partnered via where the lake water height data is found to be a random anomaly from the underlying trend observed before and after. Such outliers were replaced with a 95% percentile threshold shown in Figure  3. 4 below. For the case of France, some photos sent may also be grainy and not readable. The presence of such outliers occurred in less than 0. 1% of the data. LOCSS gauges were installed in 2017 in the USA and France, 2019 in Bangladesh, 2021 in Nepal. The Landsat 8 OLI/TIRS (L8) sensor was used to estimate the water surface area through a variety of water classification techniques.
ENSEMBLE After estimating the water surface area and extracting the water surface elevation, volume of water stored above the minimum observed level was estimated for each of the techniques and sensors. Satellite water extent data were used for days that matched or were within 3 days of the measurement date of citizen scientists from LOCSS. To estimate the volume variation, we linearly interpolated the water surface area data, so that the timestamps of both water elevation and water surface area are the same, which makes it easy to calculate the volume change. The information of the exact bathymetry of the water bodies was not available, so we estimated the volume stored with respect to the lowest observed water surface elevation in the time series, similar to Ahmad et al. [29]  who had earlier applied citizen scientist height data for northeastern wetlands of Bangladesh.
BROADER IMPACTS Our lake height data were obtained from the citizen science program of LOCSS, which has the additional objective of validating and improving lake products anticipated from the planned Surface Water and Ocean Topography (SWOT) mission. The SWOT satellite mission is a joint mission of the National Aeronautics and Space Administration (NASA) and Centre National d'Etudes Spatiales (CNES) with contributions from the Canada Space Agency and the United Kingdom Space Agency. SWOT is planned for launch in November 2022  [45]. It will be the first satellite of its kind that will report water surface elevation and water surface area simultaneously with a revisit time of 21 days or less at a given location. The primary instrument on SWOT is Kaband Radar Interferometer (KaRIn), which uses radar interferometry and synthetic-aperture radar (SAR), which gives high-resolution water elevation and inundation extent  [45].
1. 2 RESEARCH QUESTION Our study explores uncertainty in volume estimation, which can provide valuable information to the decision-makers or stakeholders to make more robust decisions based on estimation uncertainty. There are various factors that affect the uncertainty in volume estimation. For ). A study by Sheng et al. [6] mapped 7. 7 million lakes that are larger than 0. 004km2. Meanwhile, Verpoorter et al. [3] used satellite imagery to identify Figure 1. 1: HydroLAKES Database Depiction of Global Lakes and Wetlands example, in South Asia, a key source of uncertainty is likely to be cloud cover during monsoon for the optical sensors and inundated vegetation for SAR microwave sensors. At higher latitudes or more than 117 million lakes globally. Hu et al. [7] studied the areal extent of the wetlands and Studying volumetric changes at a global scale is therefore not feasible using limited in-situ mountainous regions where lakes freeze, the area estimation may be more challenging due to the lakes by developing a new index (Precipitation Topographic Wetness Index).
PlioMIP2 models For this study, complete datasets for analysis are available from 15 out of 17 models participating in PlioMIP2. The models are listed in Table  1  along with their institute and reference to the individual model description. All models have performed a pre-industrial (E 280 ) simulation at a CO 2 concentration of 280 ppm (models participating in CMIP6 at 284. 3 ppm; see Table  1 ) and a mid-Pliocene (Eoi 400 ) simulation at a CO 2 concentration of 400 ppm and with boundary conditions implemented as described in  Haywood et al. (2016). The exception is HadGEM3, where the pre-industrial land-sea mask and bathymetry is also used in the mid-Pliocene simulation  (Williams et al. , 2021). For this reason, the model is excluded from any multi-model mean (MMM) and ensemble standard deviation calculations.
Proxy data To compare mid-Pliocene model results with reconstructed data, we use SST proxy data from a 30 kyr interval centered on the KM5c time slice by  Foley and Dowsett (2019)  and  McClymont et al. (2020a). The PRISM4 dataset (Foley and Dowsett, 2019) is a collection of U K' 37 proxies (calibrated using the  Müller et al. , 1998, method)   structions from six different sites between 30 and 70 • N in the North Atlantic have been used. For comparison to model anomalies, the NOAA ERSST5 dataset  (Huang et al. , 2017)  for the years 1870-1899 is used as the observational preindustrial SST dataset.
Data analysis All models except IPSL-CM5A and IPSL-CM5A2 have provided 100 years of annual model data for both E 280 and Eoi 400. IPSL-CM5A and IPSL-CM5A2 have provided 50year averages only, so they are excluded from analysis that requires annual data. Percentage differences between the mid-Pliocene and preindustrial are computed relative to the pre-industrial. An anomaly is defined as the difference between the mid-Pliocene and pre-industrial (mPWP-PI, Eoi 400 -E 280 ), unless stated otherwise. Standard deviations of individual model means are calculated using annual data. Standard deviations of MMM anomalies are calculated using the individual model Eoi 400 -E 280 anomalies. Data provided by individual modeling groups include the ocean potential temperature, ocean meridional velocity, ocean salinity, the Atlantic meridional streamfunction, the total Atlantic OHT, and the atmospheric zonal and meridional wind at 1000 hPa.
They also raise further questions, such as the degree to which the decrease in OHT by the gyre circulation is a response to the increase in OHT by the stronger Atlantic overturning circulation and how the ECS and ESS of individual models may influence modeled SST warming in the mid-Pliocene. Furthermore, given the influence of the stronger mid-Pliocene AMOC on the Atlantic OHT and enhanced SST warming, it raises the question as to what extent and in which context the mid-Pliocene is suitable as a future climate analog. The impact of a stronger AMOC on the regional and global climate is significant and must be taken into consideration when comparing the mid-Pliocene climate to future warming scenarios. The Jupyter Notebooks used for data processing and analysis are available through Zenodo: https://doi.
org/10. 5281/zenodo. 7288756 (Weiffenbach, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/cp-19-61-2023-supplement. Author contributions. JEW, MLJB, HAD and ASvdH designed the work. JEW performed the analysis and wrote the manuscript of the paper. The remaining authors provided the PlioMIP2 experiments and commented on the manuscript. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Special issue statement. This article is part of the special issue "PlioMIP Phase 2: experimental design, implementation and scientific results".
Code and data availability. PlioMIP2 data used for this paper are available upon request from Alan M. Haywood (a. m. haywood@leeds. ac. uk), with the exception of IPSL-CM6A, EC-Earth3-LR and GISS2. 1G. PlioMIP2 data from IPSL-CM6A, EC-Earth3-LR and GISS2. 1G can be obtained from the Earth System Grid Federation (ESGF) (https://esgf-node. llnl. gov/search/cmip6/ (ESGF, 2022). The U K.
and Mg/Ca SST reconstructions fromMcClymont et al. (2020a)  can be obtained through https://doi. org/10. 1594/PANGAEA. 911847(McClymont et al. , 2020b), and the U K 37 SST reconstructions from Foley and Dowsett (2019)  can be obtained through https://doi. org/10. 5066/P9YP3DTV. The observational preindustrial SSTs from the NOAA ERSST5 dataset(Huang et al. ,  2017)  can be downloaded from https://doi. org/10. 7289/V5T72FNM Clim. Past, 19, 61-85, 2023 https://doi. org/10. 5194/cp-19-61-2023.
As explained in the paper, the reference signal is selected to describe either tachycardia or bradycardia events. This paper is organised as follows: In Section 2, we provide an overview of sparse methods and the important role of the norm-based constraint. We connect the optimisation problem to that of sparse deconvolution using a Bernoulli-Gaussian model. This section begins with a quick summary of sparse decomposition methods (Section 2. 1); we introduce the probabilistic model of the observed signal (Section 2. 2) and the resulting apnoea detection model in Section 2. 3. The objective, the data used to implement the model, and additional data for testing are presented in Section 2. 4. Finally, the results of our data are presented in Section 3. 1; the application of this method to external data (PhysioNet Apnea ECG database) is presented in Section 3.