dataesr/scientific-paragraphs-categorization huggingface dataset:
Disease outcomes As a phenome-wide investigation, this study examined many binary disease outcomes. For outcomes of primary and sensitivity analyses, we used genome-wide association summary statistics from the European ancestry subset of UKB. The focus on European ancestry was needed to reduce heterogeneity and maximize statistical power. Pan-UKB performed 16,000 genome-wide association studies in ~500,000 UKB participants of various ancestries using genetic and phenotypic data  (PanUKBTeam, 2020). A total of 7,221 total phenotypes were categorized as "biomarker", "continuous", "categorical", "ICD-10 code", "phecode", or "prescription" (PanUKBTeam, 2020). We filtered for outcomes to retain categorical, ICD-10, and phecode types; non-null heritability in European ancestry as estimated by Pan-UKB; and relevance to disease, excluding medications.
This yielded 2,600 traits for primary analysis. For outcomes of replication analysis, we used genome-wide association summary statistics from FinnGen. To allow for compatibility between UKB and FinnGen datasets, phenotypes coded as FinnGen "endpoint IDs" were mapped to ICD-10 codes or phecodes corresponding to the coding convention used by UKB. Categorical traits in UKB could not be reliably mapped to FinnGen IDs, requiring us to omit categorical traits from replication analysis. Of 2,444 available FinnGen outcomes, we ultimately selected 221 outcomes for replication testing based on two conditions: (1) The outcome has an equivalent phenotype documented as an ICD-10 code or phecode in UKB, which we had included in the discovery analysis, and (2) the outcome was at least nominally significant (p < 0.
Data availability All data generated in this study are included in the manuscript and supplementary tables. All analyses used publicly available data (UKB, FinnGen), including previously published GWAS (GLGC) (Willer et al. , 2013). Obtaining access to UKB (PanUKBTeam, 2020) and FinnGen (FinnGen, 2020) GWAS summary statistics is detailed here (https://www. finngen. fi/en/access_results) and here (https://www. ukbiobank. ac. uk/enable-yourresearch/apply-for-access). Please note the summary statistics for FinnGen and Pan-UKB are made publicly available.
Two potential dividers were used as the joystick position sensors to measure the rotation angles of the joystick. The maximum rotation angles of this joystick are 20 o for each axis. The electronic board and two position sensors were connected to the Arduino Mega 2,560 board. 2. The Arduino board plays an intermediate role in this system. It reads the joystick positions using its Analog-Digital converters, converts the values to integer format, and then sends them to Simulink through the UDP connection thanks to its Ethernet shield. It receives the assistance torque values from Simulink through the UDP connection, converts them to the corresponding PWM values, and produces these torques by controlling two DC motors using its PWM modules. The source code for these Arduino functions was developed in Simulink thanks to the Arduino Support Package for Simulink.
The sample time for the Arduino program was set to 0. 01s. 3. The observer/controllers were implemented in Simulink Desktop Realtime to work in real-time conditions. The sample time was set to 0. 01s. Models 1), 7), 8) were used to simulate the movement of the wheelchair in the virtual environment. 4. A virtual-reality scenario simulated the environment so that the user could experience different situations to verify the functions of the proposed AS. It was designed using the Simulink 3D toolbox, as in Figure  6.
Acknowledgements This work is supported by the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  ( ERC-Advanced grant NECTAR , grant agreement No  884715 ). We thank the  80PRIME program  from the  CNRS  for funding the PhD thesis of MS. AH is grateful for funding from the  Knut and Alice Wallenberg Foundation  under  KAW 2020. 0076.
Context. Magnetic reconnection is a fundamental process in astrophysical plasmas that enables the dissipation of magnetic energy at kinetic scales. Detecting this process in situ is therefore key to furthering our understanding of energy conversion in space plasmas. However, reconnection jets typically scale from seconds to minutes in situ, and as such, finding them in the decades of data provided by solar wind missions since the beginning of the space era is an onerous task. Aims. In this work, we present a new approach for automatically identifying reconnection exhausts in situ in the solar wind. We apply the algorithm to Solar Orbiter data obtained while the spacecraft was positioned at between 0. 6 and 0. 8 AU and perform a statistical study on the jets we detect. Methods. The method for automatic detection is inspired by the visual identification process and strongly relies on the Walén relation.
It is enhanced through the use of Bayesian inference and physical considerations to detect reconnection jets with a consistent approach. Results. Applying the detection algorithm to one month of Solar Orbiter data near 0. 7 AU, we find an occurrence rate of seven jets per day, which is significantly higher than in previous studies performed at 1 AU. We show that they tend to cluster in the solar wind and are less likely to occur in the tenuous solar wind (<10 cm -3 near 0. 7 AU). We discuss why the source and the degree of Alfvénicity of the solar wind might have an impact on magnetic reconnection occurrence. Conclusions. By providing a tool to quickly identify potential magnetic reconnection exhausts in situ, we pave the way for broader statistical studies on magnetic reconnection in diverse plasma environments.
As a consequence, detecting exhausts in the decades of data provided by present and past solar wind missions such as Wind, ACE, Helios, PSP, and Solar Orbiter can be an onerous task. Tilquin et al. (2020)  attempted to detect reconnection jets automatically, and managed to drastically reduce the time needed to build a magnetic reconnection data set with Helios data by adopting a machine learning approach. More recently,  Eriksson et al. (2022)  also devised an automatic detection method and analysed ten years of WIND data based on typical reconnection signatures. Both of these studies represent significant progress towards analysing magnetic reconnection in the solar wind in a systematic way. In line with these works, here we present a new approach to automatically identify reconnection exhausts in situ using Bayesian considerations.
The objective of this work is to analyse the prevalence of magnetic reconnection in the solar wind using Solar Orbiter data. To this end, we present a new approach for automatically identifying reconnection exhausts in situ where we aim to obtain a low false-positive (FP) rate, an acceptable running time, and a reusable tool that can easily be applied by the scientific community. The method, described in Sect. 3, is inspired from the visual identification process and relies heavily on the Walén relation, as well as on Bayesian inference and physical considerations. In Sect. 4, we apply the algorithm to Solar Orbiter data from between 0. 6 and 0. 8 AU, where we detect 146 jets over a period of 20. 7 days. We perform a statistical study on the jets we detect and discuss our results in Sect. 5.
A condition on the electric current During a change of correlation coincidental with a current sheet, there always exists a magnetic shear and an associated current. In the limit of null magnetic shear and current, magnetic reconnection may not occur, but this constitutes a singularity. In practice, we should define a minimum current for our algorithm to detect an exhaust. Current measurements are not available as a data product on Solar Orbiter, and therefore we devise a 1D approximation for the electric current based on the Maxwell Ampere equation. In the lmn frame, the rotational of B writes: ∇ ∧ B =         ∂ m B n -∂ n B m ∂ n B l -∂ l B n ∂ l B m -∂ m B l        . ( 10 ) We assume no variation of B along m (∂ m = 0), and we neglect the terms ∂ n B m , ∂ l B m , and ∂ l B n compared to the B l variation.
Algorithm performance We apply the detection algorithm described in Sect. 3 to Solar Orbiter data running from July 14, 2020, at 05:15 to August 13, 2020, at 00:00. During this period of about a month, Solar Orbiter measured the solar wind properties from 0. 63 AU to 0. 82 AU and we display the solar wind properties in Fig. 4. The data gaps (Table  C. 1) in Fig. 4  are due to the absence of either magnetic field or plasma measurements and amount to 9 days of missing data; we therefore investigate a total of 20. 7 days of Solar Orbiter measurements. During this period, the algorithm detects 270 potential jets, and through visual inspection of these detections we are able to confirm 133 (49%) reconnection events and reject 137 (51%) false detections. Such false detections are typically due to deviations from a unique Alfvénicity sign produced at small scales compared to the window length, and we show an example in Fig.
5. There, even though the algorithm detects a change of correlation, a B l reversal, sufficient current, and sufficient V l variation, this event cannot be identified as a reconnection jet. By contrast with Fig. 3 , no obvious current sheet or ion jet is observed in panels a and b. In panels c to e, we see that ±∆V A shows better agreement with ∆V than -∆V A does, even though the anticorrelation is clear on the leading edge of the interval. This is due to a slight deviation 2 https://github. com/Nfargette/Magnetic_reconnection/ from Alfvénicity close to the centre of the window, which leads to large errors between ∆V and -∆V A. Another cause for false detection (not shown) is that, despite displaying a change of correlation and other required signatures (Sect. 3. 2), the presence of a jet is not sufficiently clear, for instance lacking evidence in terms of density, temperature, magnetic field magnitude, and so on.
In Fig. 6 , we display the observed distribution of the jet waiting time together with the expected Poisson distribution. While the exponential curve reproduces the overall trend of waiting times, it does not account for the amount of observed waiting times below 20 min (two first bars of the histogram). Long waiting times (>1200 min) are also unlikely for a Poisson process. We further confirmed this result by performing this analysis on a previous version of the magnetic field dataset (L2 V2, not shown), which contains fewer data gaps. By including 24. 3 days of data instead of 20. 7, the clustering of jets becomes even clearer, with waiting times 3 Time elapsed in between two consecutive jets. A98, page 7 of 15 The fact that reconnection jets seem to cluster in the solar wind has in fact been pointed out on a larger scale.
This also holds for lower wind temperatures (<3 eV) due to the correlation between solar wind speed and temperature (e. g. ,  Burlaga & Ogilvie 1970 , Lopez & Freeman 1986). Magnetic reconnections occur less frequently in conditions of higher solar wind speed (>435 km s -1 ), but such conditions represent a minority in the dataset. In addition, there exists a clear decrease in the occurrence of reconnections at low density (<10 cm -3 ). For the density, the mode of the standard distribution is not reproduced by the inflow condition distribution.
Eriksson et al. (2022)  acknowledge this and state in their work that the set they observe 'should be considered as the tip of the proverbial iceberg'. An application of our algorithm to WIND data for comparison is left for future work. In total, the reconnection exhausts we detect take up 0. 7% (3. 5 h) of our 20. 7 days of data, and the average jump in velocity in the l direction is 6 +13 -4 km s -1. Similarly to the conclusion of  Gosling et al. (2007) , these rather low values lead us to believe that these reconnection exhausts do not play a dominant role in heating and accelerating the solar wind near 0. 7 AU. In Sect. 4. 2, we show that, rather than occurring randomly, reconnection exhausts tend to cluster in the solar wind. The reason for this clustering remains unclear, but several explanations can be put forward.
Conclusions The key findings presented in this paper can be summarised as follows. -We present a new methodology to detect magnetic reconnection exhausts in the solar wind (Sect. 3). This automatic algorithm relies on the Walén relation, Bayesian inference, and additional physical considerations. The detection algorithm is publicly available 4. -We apply it to one month of Solar Orbiter data while the spacecraft was located near 0. 7 AU. We are able to confirm the observation of 146 reconnection exhausts over this period of observation, leading to an occurrence rate of 7 day -1 , which is higher that previously reported (Sect. 5). 4 https://github. com/Nfargette/Magnetic_reconnection/ -The reconnection exhausts we detect are mostly low shear and of short duration, consistent with the observations made at 1 AU  (Phan et al.
Acknowledgements. Solar Orbiter is a space mission of international collaboration between  ESA  and  NASA , operated by  ESA. Solar Orbiter Solar Wind Analyser (SWA)  data are derived from scientific sensors which have been designed and created, and are operated under funding provided in numerous contracts from the  UK Space Agency (UKSA) , the  UK Science and Technology Facilities Council (STFC) , the  Agenzia Spaziale Italiana (ASI) , the  Centre National d'Études Spatiales (CNES, France) , the  Centre National de la Recherche Scientifique (CNRS, France) , the  Czech  contribution to the  ESA   PRODEX programme  and  NASA. SWA work at  UCL/MSSL  was supported under  STFC  grants  ST/W001004/1  and  ST/X/002152/1. Solar Orbiter magnetometer data was provided by  Imperial College London  and supported by the  UK Space Agency.
Work at IRAP was supported by  CNRS ,  CNES  and  UPS. The work of  N. Fargette  and  J. P. Eastwood  was funded by the  STFC  grant  ST/W001071/1. The work of  A. P. Rouillard  was funded by the  ERC SLOW_SOURCE  project ( SLOW_SOURCE-DLV-819189 ). The work by  T. D. Phan  was supported by  NASA  grant  80NSSC20K1781  and  NASA  contract  NNG04EB99C. We visualize data using the CLWeb software (http://clweb. irap. omp. eu/) developed by  E. Penou ; as well as the AMDA science analysis system (http://amda. cdpp. eu/) provided by the  Centre de Données de la Physique des Plasmas (CDPP)  supported by  CNRS ,  CNES ,  Observatoire de Paris  and  Université Paul Sabatier (UPS) ,  Toulouse. Finally,  N. Fargette ,  B. Lavraud ,  T. D. Phan  and  C. J. Owen  wish to acknowledge the support of the  ISSI  team lead by  Denise Perrone  and  Sergio Toledo-Redondo , working on unravelling solar wind microphysics in the inner heliosphere.
Machine learning combined with large-scale neuroimaging databases has been proposed as a promising tool for improving our understanding of the behavioural emergence and early prediction of the neurodevelopmental outcome. A recent example of this strategy is a study by  Ouyang et al. (2020)  which suggested that cortical microstructure quantified by diffusion MRI through fractional anisotropy (FA) metric in preterm and full-term neonates can lead to effective prediction of language and cognitive outcomes at 2 years of corrected age as assessed by Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) composite scores. Given the important need for robust and generalisable tools which can reliably predict the neurodevelopmental outcome of preterm infants, we aimed to replicate the conclusions of this work using a larger independent dataset from the developing Human Connectome Project dataset (dHCP, third release) with early MRI and BSID-III evaluation at 18 months of corrected age.
Their results suggested that inter-individual variability of cortical microstructure at birth provided sufficient information for successful and robust prediction of later cognitive and language outcomes using linear support vector regression (SVR). Additionally, based on the evaluation of model feature importance, the authors suggested that the distinctive features were encoded in uniquely distributed patterns across the cerebral cortex. Despite these promising results, the authors of this original study remained cautious given the limitations of their strategy, highlighting the small dataset (46 infants) as one of the major problems affecting the potential generalisability of their proposed method to the wider population. Additionally, motor outcomes in their dataset could not be predicted, although they suggested that it was unclear whether these null results resulted from insufficient heterogeneity within the cohort (in terms of cortical inputs and/or outcome scores) or other factors such as low signal-tonoise ratio (SNR) of cortical FA in primary sensorimotor cortical regions associated with motor function.
Given these limitations and the important need for robust and generalisable tools which can reliably predict the neurodevelopmental outcome of preterm infants, this original study seemed to us an ideal target for conceptual replication. As opposed to direct replication, conceptual replication aims to test the generality of the original suggestion, i. e. that cortical FA close to birth is predictive of later neurodevelopmental outcome, with a different but similarly reliable method  (Zwaan et al. , 2017). In short, in the present study, we aimed to examine whether cortical microstructure close to birth -assessed with regional FA measures -is predictive of later neurodevelopmental outcomes in an independent dataset with early MRI acquired in neonates and behavioural follow-up as part of the "developing Human Connectome Project" (dHCP).
Data We included a sample of preterm and full-term neonates participating in the open-source developing Human Connectome Project (http:// www. developingconnectome. org/). Detailed subject demographic and clinical information can be found in the section Cohort Description. Data collection for the dHCP took place in London, UK from 2015 to 2020 with UK NHS research ethics committee approval (14/LO/1169, IRAS 138070), and written informed consent obtained from parents.
Cohort A Cohort A included all subjects available in the dHCP 3rd data release with complete diffusion and anatomical MRI which passed the quality control (QC)  (Edwards et al. , 2022) , and with available BSID-III composite scores assessed between 17 and 20 months of age. In cases of subjects with multiple sessions, we prioritised the session at the youngest age to approximate the original study that aimed to scan subjects as close to birth as possible. Additionally, we included only infants that were considered healthy and without major brain focal lesions (i. e. who did not present any visible abnormality of possible clinical significance on structural MRI evaluated by an expert Paediatric Neuroradiologist, recorded in dHCP databases as radiological score range 1-3). To approximate the exclusion criteria described in the original study from the clinical information available in the dHCP database as closely as possible, we additionally excluded 63 additional subjects who had a history of hypoxic-ischemic encephalopathy, lung disease or bronchopulmonary dysplasia, necrotizing enterocolitis requiring intestinal resection or complex feeding/nutritional disorders, maternal drug or alcohol abuse or smoking during pregnancy in their records.
Cohort B We additionally restricted the subject space by removing infants whose PMA at scan and GA at birth were outside the range of the original study ] GA at birth). We thus aimed to investigate whether prediction models can leverage wider age variability or whether the original results were limited to a narrow range of GA at birth and PMA at scan. Additionally, this allowed us to test whether low variability in motor outcome score in the original study may have been the reason for a lack of prediction by leveraging the larger size of the dHCP dataset through selecting for increased variability in the neurodevelopmental outcomes. With the GA at birth and PMA at scan matched as close as possible to the data in the original study,  Cohort B then included 196 subjects (57% male, median GA at birth 39.
Diffusion MRI MRI data was acquired using a Philips 3-T Achieva scanner (Philips Medical Systems, Best, The Netherlands). All infants were scanned during natural sleep in a scanner environment optimized for neonatal imaging including a dedicated transport system, positioning device and an optimally sized neonatal 32-channel receive coil with a custom-made acoustic hood as previously described  (Hughes et al. , 2017). The diffusion MRI data was used in its pre-processed state available from the dHCP 3rd data release. In sum, diffusion-weighted (DW) images were acquired following a multi-shell high angular resolution diffusion imaging (HARDI) protocol (with b = 0, 400, 1000, 2600 s/ mm2)  (Tournier et al. , 2019)  and pre-processed with correction for motion artefacts and slice-to-volume reconstruction using the SHARD approach, leading to a final isotropic voxel size of 1.
5mm  (Christiaens et al. , 2021; Hutter et al. , 2018). All included data passed SHARD QC provided in the dHCP  (Edwards et al. , 2022). Quantitative metric maps resulting from the diffusion tensor imaging (DTI) model, including fractional anisotropy (FA) maps, were computed based on b = 0 and 1000s/mm2 images using FSL's DTIFIT. For distribution of motion across the dataset as well as example FA maps for two subjects, see Supp. Fig. 1.
The *Pearson correlation between GA at birth and PMA at scan. A. Gondová et al. score medians and ranges were identical for the Cohort B. In case of the Cohort C, the median BSID-III composite scores were the following: cognitive score of  95, range [60-110] ; language score of 91, range ; and motor score of 98, range. Cohort D was a random subset of the Cohort C. Additionally, family socio-economic status (SES) was measured using the Index of multiple deprivation (IMD) which is a UK geographically defined composite social risk score comprising data on income, employment, health, education, living environment, and crime calculated at 18mCA assessment from the mother's home address at the time of birth.
Random parcellation. Additionally, cortical surfaces were parcellated into random bilateral parcels with similar area (k-means based on Dijkstra-path, optimized to balance the vertex numbers per region). The numbers were more than double the atlas parcels: 128, 256, 512, 1024. The random parcellation was performed in the dHCP surface template space after manual segmentation of the cingulate area, which was excluded from the final parcellations to avoid possible inclusion of non-cortical tissues (Fig. 1b ). The random parcellations were then projected to the subjects' anatomical space using the transformations available from the dHCP database.
During training, features were independently scaled between 0 and 1 using the training splits to determine the scaling factors which were then applied to the testing set. Predictive results were evaluated using a Pearson correlation coefficient (r) and mean absolute error (MAE) between the predicted and real composite scores. Additionally, we evaluated the coefficient of determination (R2), which is bounded between (-∞,1] with 0 suggesting the random prediction. In contrast to Pearson's r which is insensitive to scale with potential high correlations possible even for large differences between predicted and actual target value, R2 quantifies a portion of variation in the target predicted by the model and is thus a more indicative measure of predictive performance. However, as the outcome variance might differ between samples, R2 does not allow comparisons across different datasets: MAE measuring the predictive error in the units of the original target measure is then a useful metric.