dataesr/scientific-paragraphs-categorization huggingface dataset:
Multicenter epilepsy data We evaluate the generalizability of our approach to data from several institutions: Milan (n = 20), Paris (n = 19), Strasbourg (n = 33), and EPISURG (n = 133). We curated the EPISURG dataset from patients with refractory focal epilepsy who underwent resective surgery between 1990 and 2018 at the National Hospital for Neurology and Neurosurgery (NHNN), London, United Kingdom. All images in EPISURG were defaced using a predefined face mask in the Montreal Neurological Institute (MNI) space to preserve patient identity. In total, there were 430 patients with postoperative T 1 w MRI, 268 of which had a corresponding preoperative MRI. EPISURG is available online and can be freely downloaded  [21]. The same human rater (F. P. G. ) annotated all images semi-automatically using 3D Slicer 4.
Brain tumor datasets The Brain Images of Tumors for Evaluation (BITE) dataset  [19]  consists of ultrasound and MRI of patients with brain tumors. We use 13 postoperative T 1 w MRI with gadolinium contrast enhancement (T 1 wCE) to perform a qualitative assessment of our model's generalization to images from a substantially different domain (contrast-enhanced images) and different pathology, where different surgical techniques may affect RC appearance.
We used adaptive moment estimation (AdamW) to adjust the learning rate, with weight decay of 10 -2 , and a learning scheduler that divides the learning rate by ten every 20 epochs. We optimized our network to minimize the mean soft Dice loss of each mini-batch. For training, a mini-batch size of ten images (five per GPU) was used. Self-supervised training took approximately 27 hours. Fine-tuning on a small annotated dataset took approximately seven hours.
Self-supervised learning: training with simulated resections only In our first experiment, we assess the relation between the resection simulation complexity and the segmentation performance of the model. We train our model with simulated resections on the publicly available dataset D pre = {X preop i } npre i=1 , where n pre = 1813 (Section 3. 1). We use 90% of the images in D pre for the training set D pre,train and 10% for the validation set. At each training iteration, b images from D pre,train are loaded, resected, preprocessed and augmented to obtain a mini-batch of b training instances {(X simi , Y simi )} b i=1. Note that the resection simulation is performed on the fly, which ensures that the network never sees the same resection during training. Models were trained for 60 epochs, using an initial learning rate of 10 -3.
We use the model weights from the epoch with the lowest mean validation loss obtained during training for evaluation. Models were tested on the 133 annotated images in EPISURG. To investigate the effect of the simulated cavity shape on model performance, we modify φ sim to generate cuboid-(Fig. 3a ) or ellipsoid-shaped (Fig. 3b ) resections, and compare with the baseline "noisy" ellipsoid (Fig. 3c ). The cuboids and ellipsoid meshes are not perturbed using simplex noise, and cuboids are not rotated. Best results were obtained by the baseline model (80. 5 (18. 7)), trained using ellipsoids perturbed with procedural noise. Models trained with cuboids and rotated ellipsoids performed significantly (57. 9 (73. 1), p < 10 -8 ) and marginally (79. 0 (20. 0), p = 0. 123) worse. Milan  (20)  Paris (  19 ) Strasbourg (33) EPISURG (  133 ) EPISURG (worst)  (20)  Dataset for fine-tuning.
Fine-tuning on small clinical datasets We assessed the generalizability of our baseline model by fine-tuning it on small datasets from four institutions that may use different surgical approaches and acquisition protocols (including contrast enhancement and anisotropic spacing in Strasbourg) (Section 3. 1. 2). Additionally, we fine-tuned the model on 20 cases from EPISURG with the lowest DSC in Section 3. 4. 1. For each dataset, we load the pretrained baseline model, initialize the optimizer with an initial learning rate of 5×10 -4 , initialize the learning rate scheduler and fine-tune all layers simultaneously for 40 epochs using 5-fold crossvalidation. We use model weights from the epoch with the lowest mean validation loss for evaluation. To minimize data leakage, we determined the above hyperparameters using the validation set of one fold in the Milan dataset.
4. 1). Our model generalizes well to clinical data from different institutions and pathologies, including epilepsy and glioma. Models may be easily fine-tuned using small annotated clinical datasets to improve performance. Moreover, our resection simulation and learning strategy may be extended to train with arbitrary modalities, or synthetic modalities generated from brain parcellations  [1]. Therefore, our strategy can be adopted by institutions with a large amount of unlabeled data, while fine-tuning and testing on a smaller labeled dataset. Poor segmentation performance is often due to very small cavities, where the cavity was not detected, and large brain shift or subdural edema, where regions were incorrectly segmented. The former issue may be overcome by training with a distribution of cavity volumes which oversamples small resections.
As part of this work, we curated and released EPISURG, an MRI dataset with annotations from three independent raters. EPISURG could serve as a benchmark dataset for quantitative analysis of pre-and postoperative imaging of open resection for epilepsy treatment. To the best of our knowledge, this is the first open annotated database of post-resection MRI for epilepsy patients. Acknowledgements Some of the data used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at http://adni. loni. usc. edu/. IXI can be found at https://brain-development.
org/ixi-dataset/. Data were also provided in part by the Open Access Series of Imaging Studies (OASIS) (https://www. oasis-brains. org/). We thank Philip Noonan and David Drobny for the fruitful discussions and feedback. Availability of data and material EPISURG can be freely downloaded from the UCL Research Data Repository  [21]. Code availability The code for resection simulation, training and inference is available at https://github. com/fepegar/resseg-ijcars. A tool to segment RCs using our best model (Section 3.
4. 1) can be installed from the Python Package Index (PyPI): pip install resseg. Authors' contributions Conceptualization: F. P. G. , R. S. , J. S. D. and S. O. ; Methodology: F. P. G. and R. S. ; Software, Formal Analysis, Investigation and Visualization: F. P. G. ; Resources: F. P. G. , M. R. , F. C. , V. F. , V. N. , C. E. , I. O. and J. S. D. ; Data Curation: F. P. G. and J. S. D. ; Writing -Original Draft: F. P. G. ; Writing -Review & Editing: F. P. G. , R. D. , R. S. , J. S. D. and S. O. ; Supervision: T. V. , R. S. , J. S. D. and S. O. ; Project Administration: J. S. D. and S. O. ; Funding Acquisition: R. S. , J. S. D. and S. O. Funding This publication represents, in part, independent research commissioned by the Wellcome Innovator Award (218380/Z/19/Z/). Computing infrastructure at the Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS) (UCL) (203145Z/16/Z) was used for this study.
Human brain white matter undergoes a protracted maturation that continues well into adulthood. Recent advances in diffusion-weighted imaging (DWI) methods allow detailed characterizations of the microstructural architecture of white matter, and they are increasingly utilized to study white matter changes during development and aging. However, relatively little is known about the late maturational changes in the microstructural architecture of white matter during post-adolescence. Here we report on regional changes in white matter volume and microstructure in young adults undergoing university-level education. As part of the MRi-Share multi-modal brain MRI database, multi-shell, high angular resolution DWI data were acquired in a unique sample of 1,713 university students aged 18-26. We assessed the age and sex dependence of diffusion metrics derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) in the white matter regions as defined in the John Hopkins University (JHU) white matter labels atlas.
, 2015; Dean et al. , 2017) , childhood to adolescence  (Genc et al. , 2017; Mah et al. , 2017; Dimond et al. , 2020; Lynch et al. , 2020). These studies have indicated an age-related increase in NDI, with very little change observed in ODI in the first two decades of life  (Mah et al. , 2017; Dimond et al. , 2020; Lynch et al. , 2020) , although studies covering a wider age range indicate that ODI in many WM tracts starts to increase in early adulthood  (Chang et al. , 2015; Slater et al. , 2019). Nevertheless, a large-scale study focusing on the period of early adulthood to detail the late maturational changes in regional WM properties is still lacking. In the present study, we characterize variations in WMrelated metrics, including regional volumes and microstructural properties measured using both DTI and NODDI, in the MRiShare database, a large cross-sectional cohort of young adults undergoing university-level education  (Tsuchida et al.
MRI Acquisition The complete MRi-Share brain imaging acquisition and analysis protocols of the MRi-Share study have been detailed in  Tsuchida et al. (2020). Briefly, all MRI data were acquired on the same Siemens 3T Prisma scanner with a 64-channels head coil (gradients: 80 mT/m-200 T/m/s) in the 2 years between November 2015 and November 2017. The MRi-Share acquisition protocol closely emulated that of the UKB MR brain imaging study  (Alfaro-Almagro et al. , 2018) , in terms of both modalities and scanning parameters, with the exception of task-related functional MRI that was not acquired in MRi-Share participants. Here, we will focus on the MRi-Share structural (T1 and T2-FLAIR) and DWI brain imaging protocol. The key acquisition parameters for these scans were as follows; For the DWI we acquired 8, 32, and 64 directions each for b-values 300, 1,000, and 2,000 s/mm 2  , respectively, and acquired eight pairs of b = 0 images acquired in Anterior-Posterior (AP) and the reverse PA phase encoding, interleaved during the b > 0 acquisition.
, 2012). For fitting DTI, volumes with the highest b-value (b = 2,000 s/mm 2 ) were stripped from the data, as the accuracy of the fit starts to decrease above b = 1,000 s/mm 2  (Jensen and Helpern, 2010). Note that it still used multi-shell data, using volumes with both b = 300 and 1,000 s/mm 2 in addition to b = 0 images. The diffusivity maps were further cleaned by removing diffusivity value outliers using Random Sample Consensus (RANSAC) approach  (Choi et al. , 2009) , as implemented in the scikit-learn package (0. 19. 1). 6 The denoising, DTI computation, and the RANSAC outlier removal were performed by wrapping Scipy scripts, developed by Sherbrooke Connectivity Imaging Lab. 7  For NODDI, the full set of multishell data was used for the fitting. We also used the empirical values of cohort-specific isotropic and parallel diffusivity as the dPar and dIso parameters for fitting NODDI (set to 1.
Quality Control A detailed description of the quality control (QC) procedure for image analysis is provided in  Tsuchida et al. (2020). Briefly, all structural scans were reviewed by one of the three experienced MD investigators of the MRiShare study to check for major artifacts or structural abnormalities before processing. During image processing of the structural or DWI pipelines, pipelinespecific QC images were generated for each subject. For the structural pipeline that generated reference T1 images for other modalities, a trained rater (N. B. ) reviewed individual subjectspecific QC images for each step of the processing for all subjects and verified that the quality of the SPM-based tissue segmentation and spatial normalization were satisfactory. For the DWI pipeline, a number of subject-specific QC images and quantifiable QC metrics mainly related to the quality of DWI data were generated (see Supplementary Material).
Additional QC metrics for the spatial normalization were extracted by computing the image similarity of individual WM tissue probability map and DTI and NODDI scalar maps to the cohort-average maps, using Fisher z-transformed Pearson's correlation r between the two images. Two investigators (A. T. and L. P. ) identified and reviewed the subject-specific QC images Similarly, we checked the group-level distributions at the level of individual phenotypes for any missing values and the extreme outliers. Four subjects did not have any volumetric or DTI/NODDI values for fornix (FX), as the WM probability map did not overlap with this small ROI in the standard space. For the same reason, one subject was missing data for the tapetum (TAP). In addition, for corticospinal tract (CST; n = 4) and inferior cerebellar peduncle (ICP; n = 6) ROIs, mean DTI/NODDI values were not computed in the pipeline since these ROIs extended beyond the bounding box of the DWI-derived images in the standard space.
, 2021) , including all terms in the model as the "observed" variables. The specification of the observed variables (as opposed to manipulated variables in other research designs) allows the correction of the effect size estimate, which makes this measure less dependent on specific research design features  (Olejnik and Algina, 2003). Visualizations of statistical summaries were created with ggplot2  (Wickham, 2016) , and tables were created with the gt package  (Iannone et al. , 2020)  in R. Linear fitting of age effects for each sex was performed by predicting the given WM property in each sex using the emmeans package  (Lenth, 2021). For evaluating the interrelations between the age-related variations in the regional WM volumes and diffusion metrics, we first computed the β * values for the respective terms in each metric using the robust standardization through refitting, implemented with the effectsize package  (Ben-Shachar et al.
DISCUSSION The primary objective of the present study was to characterize the late maturational changes in the regional WM properties during post-adolescence in the large and unique sample from the MRi-Share database. We also examined sex differences in the WM of this sample and assessed whether the age-related changes differed between the two sexes. Below we discuss our main findings in relation to the existing literature, comment on the specific features of our dataset, and methodological strengths and limitations of the present study.
Potential Limitations As we describe more in detail in  Tsuchida et al. (2020) , our sample from the MRi-Share database is drawn from students undergoing university-level education in Bordeaux, and as such, not necessarily a representative sample of healthy young adults. As a consequence, our sample is dominated by female participants, for example, and likely have different sociodemographic backgrounds and levels of education than the rest of the population of the same age range. They are also not guaranteed to be perfectly "healthy, " as the i-Share study, from which the MRi-Share participants were drawn, was designed to investigate the physical and mental health of students, and did not exclude those with a past or current history of mental illness, alcohol intake, smoking habits, and/or use of any recreational drugs and psychotropic medications.
While this undoubtedly increases the variance unaccounted for in our data, it also makes our data more representative of the sampled population. The MRi-Share database is also currently cross-sectional, limiting our inference of maturational trajectory from the agerelated variations in the data. The analysis of age effects based on cross-sectional data has been shown to lead to spurious findings unsupported from longitudinal analysis, especially when using quadratic models to describe non-linear patterns of age-related changes  (Fjell et al. , 2010; Pfefferbaum and Sullivan, 2015). In our sample with a relatively limited age range of 18-26 years, we found that linear age trends were sufficient for characterizing agerelated variations in the data, thus avoiding some of the pitfalls of fitting quadratic age models.
0. 10) before the option to correct for within-volume movement  (Andersson et al. , 2017)  and interactions between susceptibility and motion  (Andersson et al. , 2018)  implemented in the latest version of the tool. Future investigations with this dataset may benefit from the updated preprocessing pipeline that incorporates these steps and examine the reliability of the findings from the current study. Regarding the specific methodology for characterizing the regional WM properties, we used the ROIs based on the JHU ICBM-DTI-81 white matter labels atlas, computing the mean DTI/NODDI values within regions with high WM probability based on the multi-channel tissue segmentation with T1 and FLAIR scans. The ROIs in this atlas represent the WM regions with relatively well-organized structures that are clearly visible in the color-coded map of the tensor fields and should not be conflated with tracts obtained through tractography-based methods: The naming of these ROIs is based on the primary WM fiber population passing through the region, but these ROIs often represent a limited portion of a given tract, with arbitrary boundaries, and also may contain different fiber populations.
DATA AVAILABILITY STATEMENT The datasets presented in this article are not readily available because to access i-Share and MRi-Share de-identified data, a request can be submitted to the i-Share Scientific Collaborations Coordinator (ilaria. montagni@u-bordeaux. fr) with a letter of intent (explaining the rationale and objectives of the research proposal), and a brief summary of the planned means and options for funding. The i-Share Steering Committee will assess this request, and provide a response (principle agreement, request to reformulate the application or for further information, refusal with reasons). If positive, applicants will have to complete and return an application package which will be reviewed by the principal investigator, the Steering Committee, and the operational staff.
Reviews will be based on criteria such as the regulatory framework and adherence to regulations (access to data, confidentiality), the scientific and methodological quality of the project, the relevance of the project in relation to the overall consistency of the cohort in the long term, the complementarity/competition with projects planned or currently underway, ethical aspects. Deidentified data (and data dictionaries) will be shared after (i) final approval of the application, and (ii) formalization of the specifics of the collaboration. The JHU WM phenotypes and associated QC metrics presented in this study are available in the Dryad repository (https://doi. org/10. 5061/dryad. cvdncjt4m), and source codes for the statistical analysis presented are available on GitHub (https://github. com/atsuch/ MRiShare_regionalWM_Age_analysis) or Zenodo (https://doi.
, 2011). The quantifiable QC metrics represented their numerical summary. It included 1) the mean relative total RMS and "restricted" RMS, 2) the mean number of Eddy-based outlier slices and AFNI-based percent voxels per volume, 3) the mean CNR (for each b > 0 image) and tSNR (for each b-value) inside the brain, and 4) the mean DTI residuals and fraction of implausible voxel inside brain mask (for their distribution, see Supplemental Material in  Tsuchida et al. , (2020) ). We additionally computed the image similarity between the spatially normalised individual image and the corresponding cohort average map (the WM tissue map, and DTI and NODDI maps) using Pearson's correlation to spot any subjects whose spatially normalised image deviated significantly from the cohort average. Although the reviewing of individual QC images for subjects who had an outlier value in one or more quantitative DWI QC metrics revealed some indication of within-volume motion in those with multiple outlier QC values, the number of volumes affected was relatively small even in the worst case, and it was not immediately clear if the image quality rendered the data unusable.
FUNDING The i-Share cohort has been funded by a grant ANR-10-COHO-05-01 as part of the Programme Investissements d'Avenir. Supplementary funding was received from the Conseil Régional of Nouvelle-Aquitaine, reference 4370420. The MRi-Share cohort and the ABACI software development have been supported by ANR-10-LABX-57 (TRAIL) and ANR-16-LCV2-0006 (GINESISLAB for the software) grants. Some regulatory and ethical aspects of MRi-Share have been supported by the European Research Council (ERC) under the European Union's Horizon 2020 Research and Innovation Programme under grant agreement no. 640643. AT, NB, and AL have been supported by a grant from the Fondation pour la Recherche Médicale (DIC202161236446) and AP by a grant ANR-15-HBPR-0001-03 (as part of the EU FLAG-ERA MULTI-LATERAL consortium).
The current study aims to build on existing research by making use of an emerging open-source computational fluid dynamics (CFD) fire simulation code known as FireFOAM, to predict fire behaviour in a mechanically ventilated nuclear compartment. An existing in-house modified version of FireFOAM developed by the authors' research group, is further modified in the present work to include a Conjugate Heat Transfer (CHT) model to account for the heat transfer between combustion gases and solid boundaries. The CHT is validated using the experimental wall temperatures and heat fluxes. Furthermore, a mechanical ventilation model has been developed and implemented into FireFOAM. This newly modified version of FireFOAM is employed to predict the pressure variations in a nuclear compartment and the flow rates in the ventilation network.
Considering only CFD approaches, mainly three simulation codes have been compared to the PRISME database: Fire Dynamic simulator (FDS), the in-house ISIS code of IRSN, and FireFOAM. Regarding FDS,  Wahlqvist and Van Hees (2013)  made use of a heating ventilation and air conditioning (HVAC) model to simulate PRISME Source, PRISME Door, and PRISME Leak experiments. Pressure inside the compartment and volume flow rate in the ventilation system were predicted with reasonable accuracy. The authors also proposed to predict the burning rate of pool fires and the effect of oxygen vitiation with an acceptable agreement  (Wahlqvist and Van Hees, 2016). Sikanen and Hostikka (2017)  proposed a full prediction of the burning rate including the modelling of the liquid fuel. FDS has also been used to model the ventilation system of non-PRISME tests containing mechanical ventilation.
D Species laminar diffusion coefficient ( m 2 s ) g i Gravity in the ith direction ( m s 2 ) h Enthalpy (J/kg) p Pressure (Pa) Pr t Prandtl number Q Heat source (W) q''' Heat release rate per unit volume ( W m 3 ) q'' ( W mK ) σ Stefan-Boltzmann constant (5. 67x10 -8 Wm -2 K -4 ) ∇ nabla-operator ( ∂ ∂x , ∂ ∂y , ∂ ∂z ) Subscripts f Liquid s Solid y Local coordinate normal to the solid The main goal of the present study is to assess the capability of a modified in-house version of FireFOAM that includes CHT and a ventilation model, to predict pressure variations in a typical nuclear mechanically ventilated room during fire. The current study also aims to validate a CHT approach and highlights the significance of using the CHT model which is a novel approach to the study of heat transfer in nuclear compartments.