dataesr/scientific-paragraphs-categorization huggingface dataset:
85% had low to moderate impact (synonymous, missense, and non-coding variant). Phylogenomic trees were built based on two filtered datasets, one including all SNPs and the second without SNPs from introns (SNPs information see Supplementary Fig. 2 ), and both used A. sinodeliciosus, A. cf. subfloccosus, A. sinotetrasporus and A. qilianensis as outgroups. Both phylogenomic trees showed the same topology, and the tree based on all SNPs was shown in Fig. 2  and Supplementary Fig. 3. Seven different clades of A. bisporus were inferred: 1) Clade Eurotetrasporus, 2) Clade China, 3) Clade America I, 4) Clade America II, 5) Clade Europe I, 6) Clade Europe II, and 7) Clade Europe III. This is consistent with the result of PCA (Fig. 3 ). The strains information in each clade is shown in Table  1. The basal clade contained the two strains of A.
ACKNOWLEDGMENTS The research leading to these results has received funding from the  French government  under the management of  Agence Nationale de la Recherche  as part of the " Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ) and reference  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche -10- IA Institut Hospitalo-Universitaire -6 ). BCD is funded by  Inria  and  CJ Martin fellowship  funded by the  N HMRC  (app  1161356 ). The Imagen study received support from the following sources: the European Union-funded  FP6 Integrated Project IMAGEN (Reinforcement-related behaviour in normal brain function and psychopathology)  ( LSHM-CT-2007-037286 ), the  Horizon 2020  funded  ERC Advanced Grant  ' STRATIFY' (Brain network based stratification of reinforcement-related disorders)  ( 695313 ),  Human Brain Project  ( HBP SGA 2 ,  785907 , and  HBP SGA 3 ,  945539 ), the  Medical Research Council  Grant 'c-VEDA' ( Consortium on Vulnerability to Externalizing Disorders and Addictions)  ( MR/N000390/1 ), the  National Institute of Health (NIH)  ( R01DA049238 , A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and  its brain biomarkers ), the  National Institute for Health Research (NIHR) Biomedical Research Centre at South London  and  Maudsley NHS Foundation Trust  and  King's College London , the  Bundesministeriumfür Bildung und Forschung  ( BMBF  grants  01GS08152 ;  01EV0711 ; Forschungsnetz  AERIAL 01EE1406A ,  01EE1406B ; Forschungsnetz  IMAC-Mind 01GL1745B ), the  Deutsche Forschungsgemeinschaft  ( DFG  grants  SM 80/7-2 ,  SFB 940 ,  TRR 265 ,  NE 1383/14-1 ), the  Medical Research Foundation and Medical Research Council  (grants  MR/R00465X/1  and  MR/S020306/1 ), the  National Institutes of Health (NIH)  funded  ENIGMA  (grants  5U54EB020403-05  and  1R56AG058854-01 ),  NSFC  grant  82150710554  and  European Union  funded project ' environMENTAL ', grant no:  101057429.
Further support was provided by grants from: -the  ANR  ( ANR-12-SAMA-0004 ,  AAPG2019 -GeBra ), the  Eranet Neuron  ( AF12-NEUR0008-01 -WM2NA ; and  ANR-18-NEUR00002-01 -ADORe ), the  Fondation de France  ( 00081242 ), the  Fondation pour la Recherche Médicale  ( DPA20140629802 ), the  Mission Interministérielle de Lutte -contre-les- Drogues-et-les-Conduites-Addictives   (MILDECA) , the  Assistance-Publique-Hôpitaux-de-Paris  and  INSERM  (interface grant),  Paris Sud University IDEX   2012 , the  Fondation de l'Avenir  (grant  AP-RM-17-013  ), the  Fédération pour la Recherche sur le Cerveau ; the  National Institutes of Health, Science Foundation Ireland  ( 16/ERCD/3797 ), U. S. A. ( Axon ,  Testosterone   and Mental Health during Adolescence ;  RO1 MH085772-01A1 ) and by  NIH Consortium  grant  U54 EB020403 , supported by a cross -NIH alliance  that funds  Big Data  to  Knowledge Centres of Excellence.
Selection of the items for the trans-technology inventory based on a literature review We first constructed the trans-technology inventory thanks to a literature review on ICTs and NDD. Between October 29th, 2020 and November 2nd, 2020, we explored the PubMed Database with the following key-words and combinations ("design" OR "methodology" OR "framework" OR "protocol") AND ("neurodevelopmental disorder" OR "developmental disorder" OR "developmental disabilities" OR "autism" OR "developmental disorders") AND ("new technologies" OR "digital health" OR "eHealth" OR "technology based intervention" OR "technology" OR "technologies" OR "Information and communication technology" OR "ICT" OR "robot" OR "innovative technology"). We used the following criteria to select the studies: (i) they discussed how to design a tool when creating one; (ii) they reported on ICTs; (iii) they targeted individuals with NDD; and (iv) the papers were written in English.
Study Design The ATLAS TAVI registry (Anatomic Stenosis Severity as a Prognostic Marker in Patients With Low-Flow Low-Gradient Aortic Stenosis Undergoing Transcatheter Aortic Valve Implantation) is a retrospective, investigator-initiated, multicenter collaboration including patients with classical or paradoxical lowflow, LGAS undergoing transfemoral TAVR with available noncontrast multislice computed tomography data on aortic valve calcification (AVC). For the purpose of the present study, only data from patients with reduced LVEF (ie, LVEF <50%) were used. TAVR patients included in the study underwent the procedure between June 2012 and April 2021. The registry comprises patients from 10 international high-volume TAVR centers. A list of participating centers is given in the Supplemental Material (Table  S1 ).
The individual decision to perform TAVR was made in a patient-centered approach by experienced local heart teams on the basis of imaging data and symptomatic burden. As an external medical control group, patients with pseudosevere or moderate AS undergoing medical management WHAT THE STUDY ADDS were retrieved from the Institut Universitaire de Cardiologie et de Pneumologie registry (Québec, Canada). 7, 12 This registry included all consecutive adult patients with at least mild-tomoderate AS (aortic valve area ≤1. 5 cm 2 and peak aortic jet velocity ≥2. 5 m/s or mean gradient ≥25 mm Hg) who underwent comprehensive transthoracic Doppler echocardiography at rest at the Institut Universitaire de Cardiologie et de Pneumologie between 2005 and 2018 (Supplemental Methods). For the purpose of the present study, only patients with LVEF <50% were included.
Study End Point and Data Acquisition The primary study end points of interest were all-cause and cardiovascular mortality after 2 years. Survival follow-up of patients was obtained by each center, either directly from patients (ie, outpatient visit or telephone follow-up) or indirectly from inpatient records, general practitioners or population registers. Data were centrally collected in an anonymized fashion and all participating centers obtained local institutional review board approval for data collection and data transfer. The study was performed in accordance with the Declaration of Helsinki.
The proportional hazard assumption was verified in all Cox analyses based on inspection of the Schoenfeld residual temporal trends (all P>0. 12). The rate of missing data is comprised between 0 and 20% depending on the variable (20% for prior stroke and 10% for chronic obstructive pulmonary disease and <10% for all the other variables). Missing data were managed by multiple imputations. Clustering by center was taken into account by the use of robust variance estimates. To limit the baseline differences between patients with nonsevere AS (ie, PS-LGAS undergoing TAVR and Medical-Mod patients), a propensity score to receive TAVR was calculated for all patients with nonsevere AS. All demographic, clinical, and echocardiographic parameters were used to calculate the propensity score. Then patients with similar probability of TAVR (ie, propensity score) were matched using a greedy 5-to-1 digitmatching algorithm.
Study Limitations There are some important limitations inherent to this study that need to be acknowledged. First, the data were derived from a retrospective registry. Thus, no causal conclusions can be drawn from its results. Second, a substantial selection bias cannot be ruled out with regard to the reasons for treatment allocation (TAVR or medical management). Moreover, the group of patients with nonsevere AS undergoing medical management mostly included patients with moderate AS (AVA >1. 0 and <1. 5 cm 2 ) on resting echocardiography, whereas all TAVR patients had an AVA ≤1. 0 cm 2 on resting echocardiography. However, propensity score-matching resulted in an acceptable distribution of comorbidities and in an alignment of parameters indicating AS severity. Third, availability of dobutamine stress echocardiography data was incomplete for patients included in our study.
This delivered a frequency resolution of 26. 123 kHz, which is 6. 1 km s -1 at the center of the band. For the UHF-band observations, the total observable bandwidth of 544 MHz covering 544-1088 MHz was also split into 32 768 frequency channels, providing a channel resolution of 16. 602 kHz, or 6. 1 km s -1 at the center of the band, i. e. , 815. 992 MHz. For all observations, the correlator dump time was 8 s and the data were acquired for all four polarization products, labeled as XX, XY, YX, and YY. We also observed PKS 1934-638 and/or 3C 286 for flux density scale and bandpass calibrations. Because PKS 1830 is a bona fide VLA gain calibrator at this spatial resolution, a separate gain calibrator was not observed. The total on-source times on PKS 1830 are: 40 minutes (L-band Night1), 90 minutes (UHFband), and 90 minutes (L-band Night2).
All MALS data sets have been processed using ARTIP, the Automated Radio Telescope Imaging Pipeline  (Gupta et al. 2021 ), a Python-based pipeline using tasks and tools from the Common Astronomy Software Applications (CASA;  McMullin et al. 2007; The CASA Team et al. 2022 ). The specific details of the observations, calibration, and imaging of L-and UHFband data sets can be found in  Gupta et al. (2021)  and  Combes et al. (2021) , respectively. The Stokes-I continuum flux densities of PKS 1830 obtained from wideband radio continuum images in the L Night1 and UHF bands with robust = 0 weighting are 11. 245 ± 0. 001 Jy at 1270 MHz and 11. 40 ± 0. 01 Jy at 832 MHz, respectively. The radio emission is unresolved in these images, with spatial resolutions of 12 9 × 8 1 (position angle = -76°. 3) and 17 4 × 13 1 (position angle = +69°.
2021; Gupta et al. 2021 , for details of SPW boundaries). The pixel sizes for L-and UHF-band image cubes are 2 0 and 3 0, respectively. XX and YY spectra were extracted in all SPWs from a single pixel at the location of PKS 1830 determined from the continuum images. The residual flux density in each continuum-subtracted spectrum is on the order of 0. 5% and required additional spectral-baseline subtraction. Further details of RRL specific spectral line processing are provided in the next section. In passing, we note that we also made use of a MALS L-band data set of another radio source, PKS 1740-517 (hereafter PKS 1740), also known as J1744-5144, observed on 2020 September 20 (two days after Night2 observations of PKS 1830) with an on-source time of 56 minutes. This observation also used  PKS 1934-638 and 3C 286  for flux density and bandpass calibrations, and the unresolved radio source has a flux density of ∼7 Jy at 1270 MHz, comparable to PKS 1830.
We collected radio continuum measurements of PKS 1830 at ν  22 GHz (using the NASA/IPAC Extragalactic Database (NED) and Pramesh  Rao & Subrahmanyan 1988; Henkel et al. 2008; Intema et al. 2017)  and plot these in Figure  10. We fit the SED of PKS 1830 as a power-law index with an external screen of free-free absorption, using the functional form = In Figure  10 , we also plot how ionized gas that has physical properties constrained by the RRL models-using the same selection of models presented in Figure  7 -would attenuate a power-law continuum SED (normalized by S o and α of our fit). The emission from the RRLs we detect at z = 0. 89 would result in free-free absorption at lower frequencies than those observed in PKS 1830. For the z = 0. 89 galaxy to cause the free-free absorption, volume-averaged path lengths a factor of 5 larger are needed.
The reconstruction efficiency is calculated as the ratio of the number of reconstructed and generated (anti)deuterons in the simulation, after the proper re-weighting of the generated p T distribution according to the Levy-Tsallis function. The same track selection and PID criteria as those used in data are applied. The fraction of secondary deuterons is approximately 0. 5 in the lowest p T interval (0. 6 < p T < 0. 8 GeV/c) and decreases exponentially with increasing p T , becoming negligible for p T > 1. 6 GeV/c. The fraction of secondary deuterons is weakly dependent on the azimuthal region. The dominant contributions to the systematic uncertainties are related to the track selection, the particle identification, the limited knowledge of the ALICE material budget, the uncertainties on the hadronic interaction cross section of (anti)deuterons, and the procedure used to estimate the fraction of secondary nuclei.
Data & evaluation methods MAR has been often used over Greenland (e. g. ,  Fettweis et al. , 2020; Lambin et al. , 2022)  and Svalbard  (Lang et al. , 2015)  but less frequently over Canada, Iceland or the Russian Arctic. As our study deals with 3 new MAR domains, more attention is given to the evaluation of the results against field observations. First, the simulations are evaluated for their performance in reproducing real atmospheric conditions (in particular the 2m temperature, pressure and wind speed). Then, the reconstructed SMB is compared to the few observations available: satellite altimetry (from  Hugonnet et al. , 2021)  to compare the regionallyintegrated SMB from 2000 to 2020 over land-terminating glaciers, along with the SMB dataset from  Machguth et al. (2016)  over the Greenland Ice Sheet, available on the PROMICE website.
SMB evaluation As precipitation and snow surface processes are the most challenging variables to represent in climate models, large biases can arise between models and observations when simulating the SMB. It is crucial to evaluate the modeled SMB over the different regions, although direct observations remain scarce, especially over the Russian Arctic. estimates for all the land ice in Canada, Iceland, Svalbard, Russian Archipelagoes and the Greenland periphery. Because the MB is the difference between the SMB and the dynamical iceberg discharge (in the case of marine terminating glaciers), we selected data for only land-terminating glaciers, using their classification in the RGI 6. 0. Annual modeled SMB was then integrated over all the glaciers to be compared with MB estimates. This altimetry dataset is useful in order to evaluate the SMB over large remote regions of our study, where very sparse in situ observations are available.
We use the in situ SMB dataset from  Machguth et al. (2016)  to evaluate MAR as done in  Fettweis et al. (2020)  over Greenland, as the mass loss by iceberg discharge over the Greenland Ice Sheet is significant compared to smaller Arctic ice caps. This dataset contains historical SMB measurements from more than 3000 stakes over the ice sheet. It is quite different from the evaluation using the Hugonnet et al. (2021) MB product (annual spatially integrated data), so the results will not be intercomparable, but gives another estimate on the performance of MAR. 3 Evaluation.
Climate evaluation Table  1  presents the resulting mean bias, CRMSE, and r over all regions for the near-surface temperature, pressure, and wind speed. The results are computed annually and seasonally (JJA for summer and DJF for winter) and for each MAR domain. As the dataset for Svalbard and Russian Archipelagoes are inside the same domain, we separated them for the evaluation as the observational datasets are different. The height measurement for wind speed was not always available. We then use the 2m wind speed from MAR for the comparison, potentially leading to inherent biases. The correlation coefficient between MAR and observed 2-meters pressure (P2m) is mostly larger than 0. 9 over all regions. The high negative bias over Svalbard and Greenland is imputable to some of AWSs observations being corrected to sea level pressure, whilst model values are computed at the pixel elevation.
2 ). The latter is constant at approximately r = 0. 99 from 1950 to the present day. The analysis nevertheless reveals a slight increase of the correlation coefficient in the 2m temperature, from 0. 96 in 1955 to 0. 97 in 1985 as a results of satellite datasets assimilation (in particular sea ice cover (SIC) and sea surface temperature (SST)) after 1979: this strongly influences the reconstructions elsewhere, mostly where observational data was scarce  (Marshall et al. , 2022). However, the good amount of older observations in the Arctic (compared to the Southern Hemisphere) explains the good performance of MAR forced by ERA-5 before 1979  (Hersbach et al. , 2020) ; and even before 1957 the International Geophysical Year (see Table  S1 ).
The main bias of this evaluation comes with the integration of the 6-km MAR pixels over small glaciers (especially with small ice tongues) with a strong spatial SMB gradient, or very sensitive to site effects. Finally, while the RGI is generally precise in the classification of land/marine-terminating glaciers, it is sometimes less accurate (as in northern Svalbard for example), which could explain a slight positive bias of the simulated SMB values as some ice discharge would be included in the observation dataset. Over the Greenland Ice Sheet, the evaluation using the PROMICE dataset yields a correlation of 0. 93 between the model and the observations. The average bias is +0. 03 m w. e. yr-1 (for an average observational value of -0. 86 m. w. e yr-1) and the RMSE is 0. 43 m w. e yr-1.
Over the Greenland Ice Sheet, we used the IMBIE Mass Balance dataset  (Team, 2020). It consists of mass change measurements from satellite gravimetry and satellite altimetry from 1997 up to 2012. For the other regions, we used the altimetry dataset of  (Hugonnet et al. , 2021)  mentioned previously. By comparing the cumulative MB and the cumulative SMB (Fig. 9 ), we can estimate the calved volume over all the subregions of the study. Over the Canadian Arctic (Devon, Ellesmere and Baffin), the dynamic ice loss is relatively low (even close to zero in the case of Baffin), thus the ice mass loss can be considered as mainly driven by the surface mass balance and then the atmospherics conditions. This low dynamical ice loss can be explained because only a few glaciers are marine terminating in Baffin Island.
by the  Norwegian Meteorological Institute  (https://seklima. met. no/stations/) over Svalbard, by the the  Russian Meteorological Institute  for the Russian Arctic (available at https://www. ncei. noaa. gov/access/search/data-search/global-hourly). Over Iceland and Greenland, we used the compiled datasets of European Climate Assessment & Dataset (ECAD, https://www. ecad. eu/dailydata/customquery. php). We acknowledge the  PolarRES  european H2020 project (program call  H2020-LC-CLA-2018-2019-2020  under grant agreement no. 101003590 ) for funding this paper. Computational resources have been provided by the  Consortium des Équipements de Calcul Intensif (CÉCI) , funded by the  Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS)  under grant no. 2. 5020. 11 , and the  Tier-1 supercomputer (Zenobe)  of the  Fédération Wallonie Bruxelles  infrastructure, funded by the  Walloon Region  under grant agreement no.
, 2019; Gula et al. , 2019; Zhang Y. et al. , 2019; de Marez et al. , 2020a; Morvan et al. , 2020). A variety of methods have been utilized to study eddy dynamics at the submesoscale, including using higher resolution observational ocean color, radar, gliders, drifters, and sea surface temperature (SST) datasets  (Zamuda et al. , 2016; Drushka et al. , 2019; Zhang Y. et al. , 2019; Ni et al. , 2021). However, pending the first scientific data released to the public from the Surface Water and Ocean Topography mission, the main tool for studying submesoscale eddies on a regional scale remains the use of high resolution model simulations  (Durand et al. , 2010). Such models, coupled with observations where possible, have been used to study several elements of submesoscale eddies, including their likely generation mechanisms, interactions with topography, roles in the energy budget, influence on vertical transports, and more  (Morvan et al.
As before, these all correspond to M-CE hotspots, both in our simulation (Figure  2 ) and in observations  (Brokaw et al. , 2020). Internal SEVs of all types on the boundaries are comparatively rare. This may be due in part to the filtering process, since results towards the boundaries are less reliable where there is insufficient data to fill the full filter window and so there are less mesoscale eddy detections. The distribution, then, of internal SEVs closely follows the areas of large parent eddy formation, an unsurprising result. The same trends are seen in the amplitude and EKE of internal SEVs, though it is notable that the most intense M-CE, SEV-CEs are found not in the extended Loop Current but at the base of the Loop Current, itself not a hotspot of M-CE EKE (Figure  5T ). The properties of internal SEVs are further laid out over the duration of the dataset in Figure  6.
Funding Research reported in this manuscript was supported by the  Gulf Research Program of the National Academies of Sciences, Engineering, and Medicine  under award # 2000013149. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  Gulf Research Program  or the  National Academies of Sciences, Engineering, and Medicine. PE was funded by the  U. S. Department of Defense Science, Mathematics, and Research for Transformation (SMART) Scholarship  and the  University of South Carolina Presidential Fellowship.
V. RESULTS In this section, we present our analytical and simulated results for the LISA mission parameters. The main findings are summarized in Table  II , Figs. 8  and 10. We show the expected noise levels per interferometer, local commonmode suppression, and the propagation through TDI. FIG. 8. Simulated data for one local TMI ij -RFI ij subtraction with different balancing efficiencies, in the unlocked case. This shows the possible correlation properties of the RIN phase error, following the expected sinusoidal shape. For perfect 1f-RIN subtraction (b = 1), only 2f-RIN remains. The different noise floors are due to the unmatched power levels in the two interferometers. For these simulations, a slow sine injection from [-2π , 2π ] is injected to mimic TM motion for 30 000 s of data sampled at 1 kHz.
F. Propagation through TDI Having simulated all interferometers with and without locking, the phase outputs need to be propagated through TDI, similar to the real mission data. For this purpose, we use the software package PyTDI  [21]. In Fig. 10 , we show the results with different balancing efficiencies for the baseline locking configuration (N1-12), and compare these scenarios to the case of unlocked lasers. We also overlay the analytical expectations given below. As expected, we find that TDI suppresses the additional noises due to laser locking, and that the final noise resembles an uncorrelated readout noise, with the transfer function given in Eq. (40). The relevant noise level is given by φ = φISI ≈ (1 -b) • 8. 7 pm √ Hz , (42) with the condition that the ISIs dominate, and b < 1. The residual 2f-RIN terms of 2.