dataesr/scientific-paragraphs-categorization huggingface dataset:
Tractography Reconstruction We applied a combination of diffusion MRI analysis tools that arose from the specific experience of our team. We used a multi-fiber ball-and-stick modeling to estimate the orientation of white matter fibers at each voxel from the diffusion-weighted MRI data. The ball-and-stick model is a multi-compartment approach constrained to include an isotropic "ball" compartment and multiple anisotropic "sticks" compartments  (51). The model was fitted to the diffusion-weighted MRI data using a Bayesian estimation procedure to robustly estimate fiber orientations and volume fractions, as well as their total count  (52). Parameter settings included the continuous exponential approach for multishell data and a maximum of three fiber compartments per voxel. To reconstruct the hyperdirect pathway fibers using the ROIs described above, we used a multi-fiber streamline tractography algorithm with a model-based interpolation framework  (53, 54)  from the Quantitative Imaging Toolkit (QIT)  (55).
Although we did not investigate the somatotopy of the fibers inside the STN due to the presence of multiple fiber crossings that pose technical limitations to tractography algorithms, our results in the zona incerta immediately superior to the STN show a predominant overlap of hyperdirect fascicles projecting from different motor regions without any clear segregation of the fibers. These discrepancies between MERs findings and tractography results might arise from the difference in scale between the two techniques: the 10 µm diameter of the tip of a microelectrode enables single neuronal response recordings whereas the 1. 25 mm voxel size of a diffusionweighted MRI dataset contains over 100,000 axons. In addition, hyperdirect pathway fibers projecting from large regions of the primary motor cortex homunculus are compacted into a relatively small space at the level of the internal capsule and zona incerta.
We conducted our analysis using HCP diffusion MRI data which are the best neuroimaging data currently available to study the connectivity of the human brain. The 1. 25 mm-voxels of the diffusion-weighted images nevertheless contain a large number of axons belonging to different white matter bundles. Future work with a higher number of compartments will enable us to refine the tractography reconstruction of complex fiber crossings of the hyperdirect pathway with the superior longitudinal fasciculus. Further developments also include the exploration of other advanced tractography methods such as multi-fiber unscented Kalman Filter tractography  (47). Finally, a comparison of histological data with our tractography findings would help investigate the topography of hyperdirect pathway fibers in the internal capsule and in the zona incerta.
Confirmatory factor analysis (CFA) CFA of all ADSD and ANSD items loading onto a single underlying factor was performed on the basis of data collected at Day 3 using polychoric correlations, the asymptotic covariance matrix, and robust weighted least squares estimation (ie, diagonally weighted least squares or weighted least squares with a mean and variance correction). Root mean square error of approximation < 0. 06, comparative fit index > 0. 95, non-normed fit index > 0. 95,  30  weighted root mean square residual 1. 0,  31  and factor loadings > 0. 40  32  were considered indicative of good model fit.
Item response theory (IRT) IRT (a latent variable modeling approach) was used to gain further insight into the appropriateness of the response categories and item discrimination. Data on the ADSD and ANSD items from Days 3 and 10 (separately) were analyzed using the graded response model in FlexMIRT. 33 Item-person maps were used to evaluate the range along the underlying domain of asthma severity that the set of ADSD and ANSD items is able to reliably measure. If the ADSD and ANSD provide reliable measurement both for severe asthma and for mild or controlled asthma, the itemperson map should show that the set of item thresholds produces ample information across the range of the x-axis. A curve showing the expected reliability of scores produced at any point along the x-axis was included in the plot.
Form-level missing data To inform the number of days across which scores should be averaged to produce a reliable score, the Spearman-Brown prophecy formula was used. 42 nformed consent (parental/guardian permission and assent for participants 12 -17 years) was obtained for all participants before entry into the study. All eligible participants attended an initial visit with site staff (within 5 working days of screening) during which they received training on how to operate a handheld device (HTC H2 smartphone) on which the study outcome measures were completed and to explain study requirements. A short interview was conducted with each participant to ensure they understood the training. During the study, participants were expected to complete the 8 ADSD items every evening and the 8 ANSD items every morning for 10 days.
The analyses used to evaluate item performance and cross-sectional measurement properties of the ADSD/ANSD were conducted using classical and modern psychometric methods. An overview of analyses involved in each stage is provided in Table  I. Unless specified otherwise, all analyses were conducted on the cross-sectional analysis sample (ie, participants with available data at Day 3). Day 3 was selected to allow participants the first 2 days to familiarize themselves with the ePRO device and study measures, also providing an opportunity for any issues with completion (should they arise) to be resolved. No imputations were performed for item-level missing data. All statistical analyses were performed using SAS version 9. 4 (SAS Institute Inc. , Cary, NC),  43  Mplus (Muthen & Muthen, Los Angeles, CA),  44  and FlexMIRT (Vector Psychometric Group, Chapel Hill, NC).
DISCUSSION Patient-reported evaluations of asthma symptom severity are important outcomes in asthma treatment research. Study sponsors have used various PRO measures to evaluate symptom severity. However, the limitations of PRO measures historically implemented in clinical trials have precluded the selection of a standardized measure that can be used by study sponsors across clinical programs, limiting the interpretation of symptom-based endpoints and the ability to compare data across studies. 19 The ADSD/ANSD were developed as part of a multi-sponsor collaboration, alongside clinical experts and in discussion with FDA to fill that measurement gap. Findings from this quantitative study complement existing qualitative evidence supporting the content validity of the ADSD/ANSD. 22, 45 Strong support was found for the reliability and validity of ADSD/ANSD items and scores derived from both measures.
Data collected in these interventional settings will also be used to evaluate the ability of ADSD/ANSD scores to detect changes in symptom severity and to determine the amount of within-patient change that is clinically meaningful. Furthermore, the implementation of "washout periods" in clinical studies provides the opportunity to explore measurement properties among participants generally expected to be more severe (ie, greater proportion on step 5 medications) and to have greater variability in asthma severity than those typically recruited into a non-interventional study. As the ADSD/ANSD are used in clinical studies it will be necessary to consider how they may be used to derive efficacy endpoints. The ADSD/ANSD could be used to derive symptomfree days, a concept commonly used in clinical trials but for which there are concerns regarding lack of standardization and of sensitivity to change (due to floor/ceiling effects) in participants with severe and mild asthma.
Funding for this research was provided by the following Patient-Reported Outcome Consortium members: Allergan; AstraZeneca AB, United States;  Boehringer Critical Path Institute  is supported by the  Food and Drug Administration (FDA) ,  United States of the US Department of Health and Human Services (HHS)  and is 54. 2% funded by  FDA/HHS  totaling  $13 , 239 , 950 , and  45. 8%  funded by nongovernment source(s) totaling $11,196,634. The contents are those of the authors and do not necessarily represent the official views of, nor an endorsement by, the  FDA/HHS , or the US government. For more information, please visit FDA. gov. Conflicts of interest:  A. Gater ,  N. Bonner , and  R. Hall  report being employees of  Adelphi Values Ltd  who received payment from the. S. Eremenco  has nothing to disclose.
Construction of Catalogs We selected 381 earthquakes that occurred during the temporary network deployment by searching the local ND catalog, which is automatically picked using SeisComP3  (Weber et al. , 2007). We then manually picked P and S phases and determined locations using the 1D global velocity model IASP91  (Kennett & Engdahl, 1991)  and the earthquake analysis package SEISAN  (Havskov & Ottemoller, 1999). In total, 5542 P-and 3241 S-arrivals were picked manually. This catalog is hereafter referred to as the reference catalog. We applied automatic methods to pick phases and then constructed an auto-detected earthquake catalog, which we compared to the reference catalog. EQTransformer  (Mousavi et al. , 2020)  was used to detect P-and S-arrivals on continuous waveform data. This deep-learning framework is composed of three decoders that accomplish different tasks: earthquake detection, P-phase picking, and S-phase picking.
These auto and manual picks are generally consistent for P-phases (Figure  3a ) but, for S-phases, some of the auto-detections appear to be later than manual picks (Figure  3b ). While the probabilities of P-phases were distributed evenly from 0. 1 to 0. 9, those of S-phases are mostly lower than 0. 5 (right panel in Figure  3 ). The bias for S-phases is likely a result of the lower probability and reflects the difficulty in identifying an S-phase arrival within a complex waveform. To establish distance versus travel time relations, we generated a 1D velocity model (Figure  4a ) using arrivals in the reference catalog and the inversion program VELEST  (Kissling et al. , 1994). Five hundred input models were generated from a resampled IASP91 velocity model  (Kennett & Engdahl, 1991)  with random perturbations within ±15% of the original values at each layer to evaluate the stability of the output velocity model.
The solution was found using QR decomposition with MATLAB (The MathWorks, Inc. , 2019). Except for distance-related terms, each row of matrix G has two non-zero elements indicating which event was recorded by which component of which station. To avoid the trade-off between the two constants M L and S i in matrix G, we adopted a constraint that the sum of all station corrections equals to zero. This constraint assumes bias on magnitude estimates in a network can be averaged out  (Hutton & Boore, 1987) , although it is known that the estimates will keep a systematic drift for regions where M L is over-or underestimated  (Ristau et al. , 2016).
5). Although the M w 7. 5 generates greater displacement records (Figure  9a ), its ground accelerations derived from velocity records are smaller than those caused by the M L 3. 8 earthquake (Figure  9b ). Because high-frequency energy attenuates over the longer propagation distance (∼280 km), energy of the M w 7. 5 earthquake is mostly at <5 Hz (Figure  9c ), whereas that of the M L 3. 8 is concentrated at high frequencies up to 50 Hz (limited by sampling rate, Figure  9d ). We compare residuals of ground accelerations (observed from seismic stations minus predicted from GMPEs in SNC (Figure  10 ). Observed accelerations are either recorded by local accelerometers that are part of the G/ND network or differentiated from broadband velocity data in 2000-2020. The two types of data are found consistent when comparing them at sites where both data types are available.
(c) The crust of New Caledonia is composed of sub-horizontal layers, with a high-velocity ultramafic layer near the surface that could result in atypical energy propagation. The local geology is not similar to locations used to construct the GMPEs that were used. Further work is required to test all these hypotheses. New Caledonia is in an unusual tectonic setting, because most seismic sites near subduction zones are on over-riding plates, due to uneven land distribution. Hence, data used to construct GMPEs for subduction zones are typically collected on subduction hanging walls. It remains unclear whether NC is representative of other subduction zone footwalls, but our analysis provides a hypothesis that GMPEs appropriate for subduction footwalls may be quite different to subduction hanging walls and that shaking may be much less than otherwise expected.
Summary We used 14 months of seismic data from a local broadband seismometer network to generate the first published earthquake catalog for New Caledonia. Earthquakes in the NHV dominate the catalog but there is also seismicity in SNC. The hypocenter distribution near Vanuatu follows the dipping geometry of the subduction zone, while earthquakes in SNC are scattered at depths <20 km, revealing two nearby but distinct seismic zones. The proposed best-fit magnitude function and station corrections provide M L estimates that are consistently lower than M USGS for NHV earthquakes. The underestimate of M w by M L is due to smaller-than-expected amplitudes of seismic shaking from earthquakes in the subduction zone recorded at seismic stations in New Caledonia. Due to insufficient data, it is not clear if this underestimate of M w by M L also affects local earthquakes in SNC.
Data Availability Statement The waveform data collected by the temporary ZI network  (Townend & Savage, 2018)  are available on the IRIS Data Management Center (http://ds. iris. edu/ds/nodes/dmc/). The data from ND network (Centre IRD de Nouméa, Nouvelle-Calédonie, 2010) and G network (IPGP and EOST, 1982)  are available on Réseau sismologique et géodésique français (https://www. resif. fr/). The earthquake catalogue, codes, and 1D velocity model are archived and accessible at Zenodo (https://doi. org/10. 5281/zenodo. 7008015, Chin & Sutherland, 2022).
Causespecific hazard regression models were then introduced to study the potential impact of factors on each transition hazard. Proportional hazards assumption for the covariate was evaluated, and a time-dependent correction coefficient was applied wherever appropriate  (8). In the sensitivity analysis, weaning start was defined as the switch to pressure support ventilation mode with FiO 2 ≤50% and positive end expiratory pressure <8 cmH 2 O. Stacked prediction probabilities of transitions were plotted. Multi-state analysis was performed using R 3. 1. 2 package mstate (9) (R Foundation for Statistical Computing, Vienna, Austria). The other analyses were conducted on SPSS Base 21. 0 statistics software package (SPSS Inc. , Chicago, IL). Two-sided p-values <0. 05 were considered significant.
Data Analysis NIRS data were acquired at 10 Hz, and the signal was smoothed using a 10th-order low-pass zero-phase Butterworth filter (cut-off frequency 0. 8 Hz) using Pandas software library functions for Python (Python 3. 8. 8, Python Software Foundation, The device was adhered to the limb with double-sided auto-adhesive tape (Coheban, 3M, Cergy-Pontoise, France) and wrapped in black cloth and an elastic bandage to prevent any disturbance due to light interference or unintentional movement. Sensors 2022, 22, 5165 5 of 23.
Data Analysis NIRS data were acquired at 10 Hz, and the signal was smoothed using a 10th-order lowpass zero-phase Butterworth filter (cut-off frequency 0. 8 Hz) using Pandas software library functions for Python (Python 3. 8. 8, Python Software Foundation, https://www. python. org, accessed on 19 October 2021)  [32]. To avoid any disturbance linked to the beginning of the protocol, TSI baseline , HHB baseline and O 2 HB baseline were calculated as the average of the last minute before the first occlusion. The difference (∆TSI, ∆HHB and ∆O 2 HB) between the maximum reached corresponding to the hyperemia spike during the reoxygenation (for TSI max and O 2 HB max ) and the minimum reached during the occlusion (TSI min and O 2 HB min ) and inversely for HHB max and HHB min were calculated (Figure  3 ).
Hemoglobin difference (HBdiff) was calculated as the difference between oxygenated hemoglobin (O2Hb) and deoxygenated hemoglobin (HHb). Reoxygenation rate was calculated for each parameter (TSIreoxy_rate, HHBreoxy_rate, O2HBreoxy_rate and HBdiff_reoxy_rate) as the upslope (r² = 0. 968 ± 0. 016) of TSI, [HHb], [O2HB] and HBdiff between the start and the end of the reoxygenation curve, as well as the intercept and the coefficient of determination (r²) (Figure  3 ). The start, the end, and the highest velocity value (Vpeak) of the reoxygenation curve were calculated using automatic peak detection Python routine, with a 5% threshold for both start and end, applied to speed values. Speed values were obtained by derivative collected data and smoothed using a 10th-order low-pass zero-phase Butterworth filter (cut-off frequency 0.
Statistical Analysis The three reperfusions per session are named R1, R2 and R3 for reperfusion 1, 2 and 3, respectively. Sessions 1 and 2 are named S1 and S2, respectively. Hemoglobin difference (HB diff ) was calculated as the difference between oxygenated hemoglobin (O 2 Hb) and deoxygenated hemoglobin (HHb). Reoxygenation rate was calculated for each parameter (TSI reoxy_rate , HHB reoxy_rate , O 2 HB reoxy_rate and HB diff_reoxy_rate ) as the upslope (r 2 = 0. 968 ± 0. 016) of TSI, [HHb], [O 2 HB] and HB diff between the start and the end of the reoxygenation curve, as well as the intercept and the coefficient of determination (r 2 ) (Figure  3 ). The start, the end, and the highest velocity value (Vpeak) of the reoxygenation curve were calculated using automatic peak detection Python routine, with a 5% threshold for both start and end, applied to speed values.
Statistical Analysis The three reperfusions per session are named R1, R2 and R3 for reperfusion 1, 2 and 3, respectively. Sessions 1 and 2 are named S1 and S2, respectively. Student's t-tests were performed for the participants' characteristics and environmental conditions using JASP (Version 0. 16. 1. 0, www. jasp-stats. org, accessed on 24 March 2022) after checking normality (Shapiro-Wilk test) and the equality of variances (Levene's test). The effect sizes (ES) are reported using Cohen's d and interpreted as small (0. 2 ≥ d > 0. 5), medium (0. 5 ≥ d > 0. 8), and large (d ≥ 0. 8)  [33]. Intraclass correlation coefficient (ICC) and 95% confidence intervals (CI-95%) were calculated with the Pingouin package for Python using a two-way mixed-effects model with absolute agreement to compare repeated measures pairwise (Type 2, 1)  [25].
Data collection and definitions Data on demographics, comorbidities, medication use and investigations were collected at study enrolment. Estimated glomerular filtration rate (eGFR) was assessed using the Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equation. AF classification was based on recommendations from the European Society of Cardiology  [14]. Severity of AF-related symptoms was established with the European Heart Rhythm Association (EHRA) classification  [15]. Thromboembolic risk was defined according to the CHA 2 DS 2 -VASc score and bleeding risk according to the HAS-BLED score.
| Sequence processing and transcriptome assembly To   S15 ). To address potential contaminates from the sequence reads we removed potential archaea, bacteria, fungi, and viral reads with Kraken v2. 0. 7-beta  (Wood & Salzberg, 2014)  (--confidence 0. 7), which filtered out around 5 million reads (Table  S15 ). For our six sampled species, only Ac. polyacanthus has an available reference genome (ENSEMBL ASM210954v1). We used this reference to map the high-quality reads, whereas for the other five species (Am. curacao, O. compressus, D. aruanus, P. adelus, P. moluccensis) de novo transcriptomes needed to be assembled. The de novo assemblies were performed using DRAP v. 1. 92 (Cabau et al. , 2017) , a pipeline that includes many software packages (e. g. , Trinity, Oases, CD-HIT and TransDecoder) to produce corrected and nonredundant sets of transcripts per species.
On average 603 million high-quality reads were used to create species-specific transcriptome references. et al. , 2016)  were applied to assess their completeness and quality scores, which indicated that they include almost single copy orthologues in the Actinopterygii database (Table  S16 ), and good contig ratio in orthologous genes were much higher (82. 9%-93. 8%) than de novo assemblies and ORFs (Table  S17 ).
| Orthologous gene detection and annotation To be able to compare the molecular response across the six species, we detected orthologous genes based on the ORF protein sequences using the default parameters in OrthoFinder v2. 3. 3  (Emms & Kelly, 2015). 116,503 initial orthologous groups among the six species were then filtered to contain at least one transcript for each species (remaining 17,564 orthogroups). We further filtered out groups based on alignment quality and number of mapped reads. Firstly, nucleotide sequences of the ORFs in each species were concatenated as the raw references to estimate the initial number of mapped reads for each transcript by RSEM v1. 3. 2  (Li & Dewey, 2011). Secondly, all transcript protein sequences in each group were then mapped to the Swiss-Prot database (560,823 proteins, release-2019-11) in UniProt (release-2019-11) by BLASTP (BLAST v2.
6. 0+) with the default parameters. Secondly, for each transcript, only the top 10 hits (based on E-value) with E-value <1E-25 were selected. If a transcript was blasted to different positions of the same protein, the transcript with the smallest E-value was kept. Thirdly, For BLAST hits per species that were assigned to the same protein, we selected the one with highest number of mapped reads for each species. Finally, we removed the orthogroups with blast hits to the same protein in all six species if their average E-value of the six hits was larger than 1E-30. When more than one protein was detected in the same orthogroup, the one with largest total bit score was retained. As a result, 14,635 orthologous groups (Table  S18 ) that include only one representative transcript per species were chosen as the final set of orthologous genes for the further analysis.
| Gene expression analyses To identify gene expression differences between samples collected at CO 2 seep and control reefs, the high-quality reads were mapped against the reference transcript for each species with Bowtie 2 v2. 3. 5. 1  (Langmead et al. , 2009)   compressus with name including "B cell," "T cell," "T-helper" (Table  S6 ) as immune-related functions, all DEGs of each species underlying these functions were extracted as the DEGs related to immune functions. And the genes with name including "calcium", "sodium", "potassium", and "chloride" were regarded as the preliminary ion transport-related genes. To understand gene expression shifts in the six species through a phylogenetic perspective, the analysis of EVE model  (Rohlfs & Nielsen, 2015) , which models gene expression as a quantitative trait across a phylogeny to estimate expression variance within versus among species, was performed based on a maximum likelihood (ML) tree of the six species.