dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, we used volume to calculate the Lateralization Indices (LIs) for each pair of the SLF branches, according to the formula: (Right volume-Left volume)/(Right volume + Left volume). Positive values indicate a rightward asymmetry, whereas negative values reflect a leftward asymmetry. We selected this metric because there is evidence that the pattern of lateralization of the SLF branches is associated with attention performance in neurotypical adults  [25]. In line with the Human Brain Mapping Committee on Best Practice in Data Analysis and Sharing (COBIDAS) guidelines on data sharing (https://www. humanbrainmapping. org/files/2016/COBIDASreport. pdf), deidentified raw diffusion imaging data will be made publicly available at http://www. bcblab. com upon publication.
Statistical analysis Categorical approach. We used SPSS (v26, IBM) to confirm the normality of tract metrics and conduct the statistical analyses. Taking a categorical approach, the association between tract metrics and treatment response was tested using logistic regressions, in which the independent variable was one tract metric (volume, LI, and HMOA for the ten selected tracts) and the dependent variable was treatment response (as classified by either BAARS-IV score or k-mean clustering). As a descriptive measure of the association with treatment response, we calculated the area under the curve (AUC) from the corresponding receiver operating characteristic (ROC) curves. We also performed 10-fold cross-validation to estimate the strength of the identified factors associated with treatment response in new data.
Machine learning Finally, we explored whether combining our strongest imaging metric with clinical and neuropsychological data through machine learning provided a more comprehensive profile of factors associated with treatment response (classification based on k-mean clustering). We included clinicodemographic and neuropsychological variables, as previously suggested  [14, 15]. The former included age, ADHD presentation, baseline severity, medication-naïve status, MPH dose at follow-up, treatment adherence, and use of illicit drugs. Neuropsychological variables included handedness, total IQ, and measures of inattention, impulsivity, and hyperactivity as extracted from the CPT during the Qb test, both under placebo and under an acute dose of MPH. Please see Supplementary Methods (p. 3) for the complete list of variables extracted.
We created different models using Least Absolute Shrinkage and Selection Operator (LASSO) regressions. These are conceptually very similar to standard multiple logistic regressions, except that they can accept more independent variables, even if collinear, because they automatically select a limited set of them. The lasso regression has a regularization parameter to select more or fewer independent variables. To find the optimal regularization parameter, we conducted 10-fold cross-validation and chose the parameter with the minimum cross-validation error. Due to our relatively small sample size, we used the whole sample to both fit the lasso regression and assess its accuracy. Potential limitations of this approach are presented in the discussion. Before conducting the lasso regression, we used multiple imputations to estimate the missing values  [38] , creating several imputed datasets.
Specifically, we imputed 20 datasets per run  [39] , and we ran the process 10 times, so we obtained 200 models. Among these models, those including a higher number of variables may achieve a higher balanced accuracy (BAC) but are also more likely to overfit. Hence, to limit overfitting while achieving optimal accuracy, we selected the model with the least number of variables that produced a BAC above 80%, as previously recommended  [40].
RESULTS Sample 60 male adults with ADHD completed the study (see Fig. S1  for Flow Diagram). They were mostly White British (71%), had a mean (±SD) age of 28 (±7) years and a full-scale IQ of 109 (±12); the majority was right-handed (78%) and medication-naïve (77%). Data on individuals previously exposed to medication is reported in Table  S2. Controls were matched for sex, age, and IQ, and were mostly right-handed (90%). Concerta XL was titrated up to 54 mg in most participants, but the dose had to be reduced to 36 mg in 24% of cases and to 18 mg in 7% of cases due to side effects. Only two participants increased the dose. We considered dose at follow-up among the potential confounding factors in our statistical analysis (see the "Methods" section). At follow-up, 42 participants were identified as responders and 18 as nonresponders based on their BAARS-IV score.
ACKNOWLEDGEMENTS We thank all the participants who took part in the study, the  Adult ADHD Clinic and Pharmacy Team at the Maudsley Hospital ; and appreciate the contribution of the  QbTech Company  (https://www. qbtech. com), who provided the materials to carry out the Qb test. We would like to thank the  NATBRAINLAB  (http:// www. natbrainlab. co. uk) and the  Department of Forensic and Neurodevelopmental Sciences, King's College London , for helpful discussion.
FUNDING This research project was funded by Shire (project IST-ALB-000217) and included support from the NIHR Maudsley Biomedical Research Centre. Further, the results leading to this publication have received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No 777394 for the project AIMS-2-TRIALS. This Joint Undertaking receives support from the European Union's Horizon 2020 research and innovation programme and EFPIA and AUTISM SPEAKS, Autistica, SFARI. Dr. Michel Thiebaut de Schotten was supported by the European Union's Horizon 2020 research and innovation program under the European Research Council (ERC) Consolidator Grant "DISCONNECTOME" (ERC-2018-COG 818521), and the University of Bordeaux's IdEx "Investments for the Future" program RRI "IMPACT," which received financial support from the French government.
Study design and participants We retrospectively collected data for patients admitted to any of 15 ICUs in western France between 1 and 2020 and 31 December 2021. The data for each patient were entered into an electronic case-report form (Castor EDC, Amsterdam, The Netherlands) by the investigators in each participating centre. Patients older than 18 years were eligible for inclusion if they required ICU admission due to severe lung disease with a positive reverse transcriptase-polymerase chain reaction test for SARS-CoV-2 in one or more upper and/or lower respiratory tract samples. Non-inclusion criteria were pregnancy, guardianship, therapeuticdose anticoagulation for comorbidities or prevention of COVID-19-related VTE, and PE diagnosed by computed tomography pulmonary angiography (CTPA) before or within 24 h after ICU admission.
CTPA is now widely performed to detect PE in patients admitted for severe COVID-19. Data suggesting that CTPA may lead to PE overdiagnosis were reported several years before the COVID-19 pandemic  [47]. In our cohort, 28% of PE cases involved sub-segmental arteries. Recent guidelines suggest withholding anticoagulation in patients with sub-segmental PE who have no risk factors for thrombosis recurrence, no evidence of DVT on serial imaging, and good cardiorespiratory reserve  [48]. However, these criteria are unlikely to be met by patients with critical illness due to COVID-19. More research is needed to determine the optimal anticoagulation strategies for preventing and treating PE in patients with COVID-19. The retrospective design is a major limitation of our study. Another is that our patients were managed over the first two years of the pandemic, during which the treatment of severe COVID-19 underwent considerable changes driven by robust scientific evidence.
2021 ; Gaidos et al. 2022 ). V1298 Tau also offers the opportunity to study atmospheres of young planets, by measuring or constraining the atmospheric e v aporation of the innermost planets resulting from the high activity of the host star  (Poppenhaeger, Ketzer & Mallonn 2021 ; Vissapragada et al. 2021 ; Maggio et al. 2022 ). V1298 Tau has been monitored between late 2019 and early 2023 with the near-infrared (NIR) spectropolarimeter SPIRou in the framework of the SPIRou Le gac y Surv e y (SLS), a large programme allocated 310 nights at the Canada-France-Hawaii Telescope ( CFHT ), within a PI programme, and most recently, within the SPICE large programme (174 nights allocated between late 2022 and mid 2024) at CFHT , aiming at consolidating and enhancing the results provided by the SLS. We start this paper with a detailed description of our spectropolarimetric data set (Section 2 ).
O B S E RVAT I O N S We e xtensiv ely observ ed V1298 Tau with SPIRou between late 2019 and early 2023, collecting 181 high-resolution spectra ranging from 950 to 2500 nm at a spectral resolving power of ∼70 000  (Donati et al. 2020 ). Altogether, 97 of these observations were collected in the framework of the SLS (2019 October 2-2021 January 8, 2022 February 1), more specifically within the WP3 package focusing on the magnetic topology of PMS stars, 52 within the PI Programme of Benjamin Finociety (run ID 21BF21, 2021 September 17-2022 January 30) and the 32 remaining ones within the SPICE large programme (2022 No v ember 2-2023 February 9). More specifically, the observations are spread o v er four observing seasons: (i) ∼140 d from 2019 October to 2020 February, (ii) ∼135 d from 2020 August to 2021 January, (iii) ∼137 d from 2021 September to 2022 February, and (iv) ∼99 d from 2022 No v ember to 2023 February.
( 2006 ). 2 In particular, the poloidal component is characterized by sets of complex coefficients noted α , m and β , m while the toroidal component is fully described by a set of complex coefficients γ , m , where and m denote the degree and order of the spherical harmonic mode in the expansion. We limited our study to the spherical harmonic modes up to a degree max = 10 as adding higher degree modes does not significantly impro v e the reconstruction. ZDI also allows one to reconstruct the distribution of dark and bright features at the surface of active stars from distorsions of the Stokes I LSD profiles. Ho we ver, we find that, for V1298 Tau, brightness inhomogeneities have only a minor impact on our data (and our reconstructed magnetic maps) as only very weak variations, due to spots and plages, are observed in our Stokes I LSD profiles.
We usually assume that the star rotates a solid body in a first step, then include surface differential rotation (DR) in the reconstruction as described in Section 3. 3. All results shown below were obtained taking into account the mean DR we estimated for V1298 Tau, following the process detailed in Section 3. 3. Before applying this method to our data set, we first computed the rotation cycles c of our observations using the following ephemeris:    A1  for the definition of the subsets 3  ). We then applied ZDI on each of these subsets, assuming a line model featuring a mean wavelength, Doppler width and Land é factor of 1750 nm, 3. 4 km s -1 , and 1. 2, respectively. Assuming that the orbital axis of the transiting planets coincides with the stellar rotation axis i , we should have i close to 90 •.
g. limited data sampling, we carried out the following experiment. Using the ZDI map of 2022 No v ember, we simulated a data set with the same phase co v erage and SNR as that of our 2023 January data, and applied ZDI to these simulated data. As the reconstructed large-scale magnetic topology is close to the input field, we can conclude that the strong tilt of the magnetic topology that we report from out 2023 January data is likely real. The large-scale magnetic topology of V1298 Tau also features a significant toroidal field, enclosing between 10 per cent (2020 August-September) and 35 per cent (2021 October) of the reconstructed magnetic energy, and found to be mainly axisymmetric and dipolar. We also find that the quadratically averaged large-scale magnetic flux o v er the stellar surface, noted < B V > , varies by ∼80 per cent between 2020 August and 2022 No v ember (from 100 to 180 G).
4  ). In addition, for both observing seasons, the confidence contours are slightly distorted, reflecting that the χ 2 r maps are not perfect 2D paraboloids, especially when moving away from the minimum of the maps, which likely increases the size of the error bars. The higher level of DR measured from 2020 August-2021 January data is consistent with the evolution of the longitudinal field. We indeed estimate a lower decay time-scale of B during this observing season, indicating a faster evolution of the large-scale field that may partly reflect a stronger DR. We chose a unique set of parameters to describe the DR at the surface of V1298 Tau, computed as the weighted means of Downloaded from https://academic. oup. com/mnras/article/526/3/4627/7291934 by guest on 26 October 2023.
Time-dependent Imaging of Magnetic Stars As our data are spread o v er sev eral years, we also applied the new tomographic method named TIMeS outlined in  Finociety & Donati ( 2022 ). As for ZDI, this method aims at finding the simplest large-scale magnetic topology consistent with the data (i. e. Stokes V LSD profiles) but this time allowing it to evolve with time. TIMeS uses sparse approximations to identify as few spherical harmonic modes (associated with α , m , β , m , and γ , m coefficients) as possible to reconstruct the magnetic topology and GPs to model the timedependence of each identified coefficients. This method can only be applied in the cases where the magnetic field is not too strong, typically with a magnetic flux lower than ∼3 kG for a v sin i = 25 km s -1. Given the vsin i of V1298 Tau and the values of < B V > and of the local field inferred from the ZDI reconstructions in the previous sections ( 3 kG), we therefore assume that TIMeS can be applied to our data.
, in preparation) , fully consistent with our estimate of M b = 0. 07 ± 0. 17 M. Our estimate of planet c's mass ( M c = 0. 05 ± 0. 07 M ) is similar to the estimate of 19. 8 + 9. 3 -8. 9 M ⊕ (i. e. 0. 062 + 0. 029 -0. 028 M ) derived by  Sikora et al. ( 2023 ) , but with error bars 2. 3 × larger, both results being fully consistent within 1 σ. Our RV data does not allow us to bring stronger constraints on the mass of planet d with respect to the previous studies, our upper limit being ∼1. 6 × larger than that of  Sikora et al. ( 2023 )  but still ∼1. 6 × lower than that of Su árez  Mascare ˜ no et al. ( 2021 ). The derived upper mass limits yield upper limits for the planet densities of ρ b < 0. 89 g cm -3 , ρ c < 2. 94 g cm -3 , and ρ d < 1. 89 g cm -3. Future observations of V1298 Tau will help to refine these densities and suggest what their internal structures could be.
Formation and evolution of the V1298 Tau system According to the orbital periods of the four planets, the V1298 Tau system is likely not in a resonant chain with only planet b, c, and d having respective period ratios close to 3:2 (for c versus d) and 2:1 (for d versus b). A resonant chain would indeed fa v our an orbital period of planet e close to 48 or 49 d rather than 53 d (or even 46 d), which seems unlikely given our data (see Tables  D1  and D2  ). Our finding is consistent with previous studies focussing on the formation of this planetary system (Tejada Are v alo, Tamayo & Cranmer 2022 ;  Turrini et al. 2023 ). This implies that either the V1298 Tau system did not form in a resonant chain, or if it did, it must have undergone a rapid phase of dynamical instabilities that broke the resonant chain after the disc dispersal (i.
The first one is to reconstruct the largescale magnetic topology of the star using ZDI and TIMeS from new data to better characterize the dynamo process at the origin of the observed field and its evolution (e. g. potential magnetic cycle subject to α processes). The second goal is obviously to refine estimates of the mass of the four planets, but also to firmly ascertain the orbital period of planet e, a quite challenging task given the amplitude of the stellar activity jitter more than 2. 6 × larger than the semi-amplitude of the presumably most massive planet (i. e. V1298 Tau e). Obtaining accurate measurements of the mass of the four planets of this system is an essential step to further constrain the mass-radius relation at early stages of evolution, as only few other planets aged of less than 30 Myr have precise measured radius and mass (AU Mic b and c, HD 114 082 b, and HIP 67 522 b; see e.
AC K N OW L E D G E M E N T S This work includes data collected in the framework of the SPIRou Le gac y Surv e y (SLS), an international large programme that was allocated on the Canada-France-Hawaii Telescope ( CFHT ) at the summit of Maunakea by the Institut National des Sciences de l'Univers of the Centre National de la Recherche Scientifique of France, the National Research Council of Canada, and the University of Hawaii. We acknowledge funding by the European Research Council (ERC) under the H2020 research & innovation programme (grant agreement #740651 NewWorlds). SHPA acknowledges financial support from CNPq, CAPES, and FAPEMIG. AC acknowledges funding from the French ANR under contract number ANR-18-CE31-0019 (SPlaSH). This work is supported by the French National Research Agency in the framework of the Inv estissements d'Av enir programme (ANR-15-IDEX-02), through the funding of the 'Origin of Life' project of the Grenoble-Alpes University.
MNRAS 526, 4627-4672 (2023) Figure B3. Stokes V LSD profiles associated with the spectra collected between 2019 and 2023. The observed data are in black while the reconstructed profiles derived with ZDI are shown in red. On the right of each profile, we show the rotation cycles while on their left we depict the 3 σ error bar. Downloaded from https://academic. oup. com/mnras/article/526/3/4627/7291934 by guest on 26 October 2023.
We review and highlight recent progress in: a. ) Detectors such as high-speed complementary metal-oxide semiconductor (CMOS) cameras, hybrid pixelated array detectors integrated with Timepix4 and other application-specific integrated circuits (ASICs), and digital photon detectors; b. ) U-RadIT modalities such as dynamic phase contrast imaging, dynamic diffractive imaging, and four-dimensional (4D) particle tracking; c. ) U-RadIT data and algorithms such as neural networks and machine learning, and d. ) Applications in ultrafast dynamic material science using XFELs, synchrotrons and laser-driven sources. Hardwarecentric approaches to U-RadIT optimization are constrained by detector material properties, low signal-to-noise ratio, high cost and long development cycles of critical hardware components such as ASICs.
CONTENTS I. Introduction II. Open problems in U-RadIT optimization A. Physics principles and computation B. U-RadIT hardware metrics C. Open problems and opportunities III. Instruments A. Sensors for ionizing radiation B. Timepix ASICs C. Hybrid pixelated array detectors (PADs) D. Ultrafast CMOS cameras E. 3D photon-to-digital converters F. Beyond silicon sensors and CMOS integration IV. Methods and U-RadIT modalities A. Dynamic X-ray Phase Constrast Imaging B. Dynamic Diffractive imaging C. Move contrast X-ray imaging D. 4D particle tracking V. Data and Algorithms A. Data sets: large and small B.
For U-RadIT experimental data interpretation, the simulations described above need to couple with radiation transport and radiation interactions with matter. The detector models are also needed to account for detector responses such as noise, the point spread function and other effects in the experimental data. There are now a growing number of multi-physics codes available for detector modeling, e. g. Allpix squared multi-physics simulation framework  [31] , which has been used to generate synthetic data for a silicon detector  [32]. One simplification to detector modeling is that the material composition and structures, such as charge collection, defects, and storage capacitors, may be assumed to be constant and do not change with time. However, as the ionizing radiation sources such as X-rays and charged particles continue to become brighter and operate at higher repetition rate, time-dependent detector responses may need to be accounted for data interpretation.
On the other hand, indirect sensors use a 2-step process where scintillators convert ionizing radiation into visible light, which is then detected by visible light cameras. These setups offer more flexibility and radiation hardness, but may offer lower energy or spatial resolution. Frame rate and record length The frame rate measures how frequently multiple images can be taken in sequential imaging or "movie mode". Record length measures how many frames of images can be taken and recorded, limited by temporally storage memory (commonly used in burst mode imaging) or data transmission bandwidth (in continuous mode imaging). In U-RadIT, the frame rate of an image sensor is dictated by the repetition rate of radiation source, , as well as by the detector. Synchrotrons and XFELs can now or will soon (as in APS-U) deliver bright sub-ns pulses at a rate above 10 MHz, which exceeds the highest frame rates of existing cameras, e.
QE and sensitivity of indirect detection depend on scintillator light yield and photodetectors, with more details given in, e. g. [58]. Gain and noise For improved sensitivity for individual quantum detection, charge amplifiers are used within pixels to multiply the raw charge collected. Direct image sensor such as AGIPD 1. 0 has a noise around 300 e -  [37]. A balance between the amount of gain and the dynamic range is sometimes needed. The state-of-the-art CMOS image sensors now have a noise per pixel below 1 e -at room temperature, such sensors are often used for indirect imaging in RadIT applications. Data bit depth and dynamic range 8 to 16 data bits are common in digital image sensors. Logarithm of the dynamic range is proportional to the bit depth. Lowering the noise floor potentially makes the full data bits available for dynamic range or maximizing the number of quanta detectable per pixel.
The High Energy Physics community has led the way in understanding and mitigating the effects of radiation in CMOS circuits  [59]  and has designed ASICs capable of withstanding 100's of MRads  [60]. In-situ data storage In ultrafast imaging, in-situ data storage is used to improve the frame rate by removing the time burden of data transfer. For example, HPV-X2 has 128 storage cells per pixel. AGIPD has 352 storage cells because each pixel is 200 µm. The Keck-PAD can store 8 frames of data at up to 10 MHz frame-rate. The insitu or temporary storage capacity limits the maximum number of frames that can be taken before a pause is required to transfer the stored images off the detector.
Higher information yield can be obtained through, e. g. on-board machine learning (ML). Not all '10H' features or requirements can be met simultaneously in a single experiment or a detector, and tradeoffs are often adopted in practice. For example, a tradeoff between efficiency and temporal resolution (frame-rate) may be necessary for ultrafast X-ray measurements. Another example of tradeoff is the spatial resolution and data yield due to the real estate constraint on a wafer. Cost reduction, including CMOS prototyping cost, is yet another important driver for tradeoffs in hardware optimization. For example, even though the production costs per wafer are more-or-less constant with time, the masking costs have exploded recently.