dataesr/scientific-paragraphs-categorization huggingface dataset:
2019). Furthermore, the description of initial stand characteristics and detailed temporal dendrometric data provided here should allow new users to simulate ONF silvicultural scenarios to the closest, freely calculate unavailable dendrometric variables in the database (e. g. mean total height, relative density index) or access available Capsis extensions to offer additional features. Besides, to face global environmental changes, the diversification of silvicultural scenarios is a key component of future forest management. Therefore, potential model users may experiment, through model simulation, new silvicultural scenarios to address various issues and unexplored silvicultural contexts. ONF silvicultural scenarios of the present database can serve as references in comparison to these newly simulated alternatives.
Carbon accounting: an example of further valorisation The French carbon offset certifications program called Label Bas Carbone (LBC) has devoted 3 methodologies designed for forest activities at the moment (i. e. afforestation activities, reconstitutions of degraded forest stands by plantation, coppice conversion into high forests). The first two methodologies require an assessment of biomass carbon storage at 30 years old and the mean carbon storage on the complete revolution length. These two values have been added in the database to answer these two LBC's specific requirements. If the simulation data did not provide any value at 30 years old, a linear approximation was made between the values of the two closest ages available. Furthermore, because the volumes of the database are all calculated based on the same equations, they ensure consistency and comparability in calculation.
On the other hand, because one of the provided volumes of the database is the whole stem volume, biomass calculations do not need a volume expansion factor  (Loustau 2004; Longuetaud et al. 2013)  to convert merchantable stem volumes to whole stem volumes like it is presently done in the LBC. As described in the potential uses of the database, graphical comparisons (Fig. 3 ) for different species or silvicultural contexts are valuable illustrations for forest managers to understand temporal dynamics of forest carbon storage. The present version of the database does not yet provide any data for storage in harvested wood products nor for the carbon emissions avoided by their use. Some of the silvicultural scenarios from the database have been used in the Carbon Accounting Tool (CAT:  Pichancourt et al.
2018)  to assess these two carbon pools in Capsis. However, the generalisation of this method to every silvicultural scenarios of the database is currently impossible; as CAT operates with volume equations specific to each growth and yield model, carbon outputs do not match the ones provided in our database using standardized volume equations. Moreover, only a few species-specific processing chains and harvested wood products greenhouse gas emissions assessments were formalised in research projects for the French territory  (Cornillier et al. 2017; Vallet et al. 2019 ) and with different degrees of precision. Thus, further work would be required for being able to complete the database in this respect.
Limits of silvicultural simulations in a changing climate One of the most important weaknesses of current growth and yield models is their limited ability to predict stand dynamics in the face of global productivity changes. Current forest management may thus experience difficulties to respond to climate-related disturbances that are actually not represented in silvicultural scenarios like those presented in the database (postponement of thinnings, troubles of regeneration). Nevertheless, forest managers will continue to rely, at least in part, on current silvicultural scenarios (among other management tools) to adapt their decisions to a changing environment. These scenarios are indeed solid enough to be applied in low Table  3  Synthesis of people involved in performing models for simulation on the one hand and in expertise for validation of simulation on the other hand, for each studied species.
Authors of simulation models contributed to add silvicultural scenarios (SSCs) of the ONF guides in the database a Corresponds to the original author of simulations, so no additional experts were involved Fig. 2  Temporal evolution of simulated MAI in whole stem volume for each silvicultural scenario of the database. Silvicultural scenarios are discriminated by fertility index (specific for a species and silvicultural context, a same index does not correspond to equivalent environmental conditions for two different silvicultural guides), the different silvicultural guides and alternatives, or stand origins are not discriminated Fig. 3  Temporal evolution of total carbon storage (above and underground carbon) for silvicultural scenarios in stands of contrasted fertility indexes for four common French species  Fournier et al.
Annals of Forest Science (2022) 79:48  vulnerable stands to guarantee the production of quality roundwood in the future, and flexible enough to allow building alternatives pathways in case of significant changes. Moreover, these silvicultural scenarios still represent good references for the evaluation of climate-sensitive growth and yield models to assess their consistency with usual dendrometric relationships in a constant climate. For instance, comparisons of different variables (density, basal area, DBH) have been made for oak stands between simulations from the GO+ model  (Moreaux et al. 2020)  and two scenarios from the database using the Fagacées model under RCP 2. 6 scenario as baseline silvicultural scenarios in the Forets21 project (https:// foret s21. inra. fr/ pelic an3. 1/ itk-querc us.
Even though each growth and yield model supplies their own stand volume outputs based on different volume tables, in the database, we computed every volume at tree-level based on volume tables from Deleuze et al. (2014) for standardisation purposes. During this process, two merchantable volumes following Gschwantner et al. (2019) definitions and a whole stem volume were calculated: • Merchantable stem volume (over-bark volume of the bole, diameter > 7 cm) • Merchantable stem and branches volume (overbark volume of the bole and big branches, diameter > 7 cm) • Whole stem volume (over-bark volume of every aerial biomass compartment of the tree).
Acknowledgements We thank all writers who have taken part in any guide of ONF collection and more precisely :  Jean Ladier ,  Freddie Rey ,  David Tresmontant ,  Thierry Quesney ,  Ludovic Chabaud ,  Loïc Nicolas ,  Pascal Jarret ,  Stéphane Claude ,  Pascal Debailleul ,  Guillaume Declochez ,  Olivier Lanter ,  Jean-Pierre Polmard ,  Eric Toussaint ,  James Wright ,  Bruno Chopard ,  Jean-François Cerf ,  Pauline Delord ,  Lilian Pauziès ,  Didier Canteloup  who have contributed to the silvicultural guides found in the database. We also thank  Pierre Duplat ,  Mien Tran-Ha ,  Jean-Hugues Bartet ,  Jérôme Piat , and  François Conrard  who initiated and greatly contributed to the writing of numerous growth tables and dendrometric improvements at ONF. We also thank everyone who contributed in collecting dendrometric data on field experiments or in forest management which helped build these silvicultural scenarios, and  Antoine Fergeau  who designed the silvicultural guides map.
Dendrometric variables are described in Table2and each variable definition is available in the metadata file of the database as well. Each row of the database represents the age of a forest stand within a silvicultural scenario. content, mean carbon storage during the silvicultural scenario, carbon capture flow. carb_stor_mean = 1 N. ∫ N 0 carb_stor stand_age. dstand_age (5) The second table records the information needed to replicate the simulations for a database user: • General information on the model used for simula- tion : name of the model, version, option of initialisa- tion, ONF agents involved in the simulation and pro- cessing chain; • A characterisation of the initial stand: type of stand (real or virtual), origin, possible modification of the original variables, dendrometric characteristics (age, dominant height, N, BA, diameter class distribution), model parameters to calculate whole stem volume for juvenile stands prior to simulation.
ha -1 harv_ba Harvested basal area in the stand m2. ha -1 harv_dbh Mean quadratic diameter of the harvested stems cm harv_vstem_c7 Harvested merchantable stem volume in the stand m3. ha -1 harv_vtot_c0 Harvested whole stem volume in the stand m3. ha -1 harv_vstem_c7_unit Mean harvested merchantable stem volume per tree m3. stem -1 harv_kg Thinning qualifier coefficient Carbon accounting variables biom_dens_Dupouey Basic specific gravity of the species according to the Dupouey database carb_aer Carbon storage in aerial biomass of the stand tCO 2. ha -1 carb_root Carbon storage in root biomass of the stand tCO 2. ha -1 carb_stor Carbon storage in total biomass of the stand tCO 2. ha -1 harv_carb_aer Exported carbon content from harvested biomass tCO 2. ha -1 carb_stor_mean Mean carbon storage on a rotation length tCO 2.
Introduction: [ F]fluorodeoxyglucose ([ F]FDG) brain PET is used clinically to detect small areas of decreased uptake associated with epileptogenic lesions, e. g. , Focal Cortical Dysplasias (FCD) but its performance is limited due to spatial resolution and low contrast. We aimed to develop a deep learningbased PET image enhancement method using simulated PET to improve lesion visualization. Methods: We created numerical brain phantoms (MRI segmented into regions) and assigned di erent plausible activity values (e. g. , GM/WM ratios) resulting in ground truth high quality (GT-HQ) PET phantoms. With a validated Monte-Carlo PET simulator, we then created simulated standard quality (S-SQ) [ F]FDG scans. We trained a ResNet on % of this dataset ( % used for validation) to learn the mapping between S-SQ and GT-HQ PET, outputting a predicted HQ (P-HQ) PET.
The 3D network trained to learn the mapping between the simulated SQ PET (S-SQ PET) and the corresponding ground-truth HQ (GT-HQ) PET did not require anatomical input. We assessed the quality of the network-predicted HQ (P-HQ) PET. We repeated the process for simulated lesional brain PET data with cortical focal hypometabolism to simulate difficult-to-detect small EZ. Lastly, we used real PET data to illustrate the proof-of-concept that a model trained on Monte-Carlo simulated PET data is applicable on real data.
Medical image data We used an open, multi-vendor [General Electrics, Philips and Siemens 3T magnetic resonance imaging (MRI) scanners] brain MRI database, Calgary-Campinas (32), using 173 T1weighted (T1w) 3D volumes (1 mm 3 voxels) from subjects with an average age of 53. 4 ± 7. 3 years (range 29-80, 50% women). Additionally, we used the publicly available database CERMEP-IDB-MRXFDG  (33).
Generation of ground truth high quality [ F]FDG PET We created GT-HQ [ 18 F]FDG PET by assigning activities to the nine labels of the numerical brain phantoms. Activities were derived from the distribution of normal [ 18 F]FDG PET values from the CERMEP-iDB-MRXFDG database  (33)  after partial volume correction according to the Geometric Transfer Matrix (GTM) method  (21). We first simulated a series of normal brain SQ [ 18 F]FDG PET scans. A total of 10 different brain activity distribution were generated for each anatomical brain model, resulting in 2100 (10 × 210) GT-HQ PETs. As a first step, WM activity was randomly chosen according to the observed distribution in  (33). Activity ratios between cerebral GM and WM were then selected as 1. 2, 1. 8, 2. 4, 3. 0, 3. 6, 4. 2, 4. 8, 5. 4, 6. 0, 6.
Tomographic reconstruction Corrected simulated sinograms were reconstructed with e7 reconstruction tool TM (Siemens Healthineers) using a 3D ordinary poisson-ordered subsets expectation maximization algorithm, incorporating the system point spread function, using 3 iterations and 21 subsets. Reconstructions were performed with a matrix size of 172 × 172 × 127 and a zoom factor of 2, yielding a voxel size of 2. 04 × 2. 04 × 2. 03 mm 3. The attenuation correction used a pseudo-CT synthetized with MaxProb multi-atlas attenuation correction method from the T1w MRI (38). Gaussian post-reconstruction 3D filtering (FWHM 4 mm isotropic) was applied to all PET images. In the end, we have a database of 2100 pairs of GT-HQ and S-SQ PET images, with various anatomies and activity contrasts between brain structures.
Network implementation and optimization The simulated images were split into training, validation, and testing datasets, with a ratio of 80/10/10%. Due to limitations of GPU memory during training, the network was trained with 32 × 32 × 32 voxel patches. Twenty patches per volume were randomly chosen for the training and validation set. Mean absolute error was used as the loss function during training and the optimizer was AdamW  (46). The learning rate was set to 10 -4 and reduced by a factor of 0. 1 when the validation loss stagnated for more than 10 epochs. The batch size was set to 50 and the maximum number of epochs to 200 using early stopping (validation loss not improving during more than 60 epochs). We trained our model on a GPU server on 1 NVIDIA V100 GPU (32GB) running Python 3. 9.
Non-lesioned simulated brains The model was successfully trained to learn the mapping from the S-SQ PET to the GT-HQ PET after 105 epochs. Figure  2  showcases the result for one subject from the test dataset in transverse, coronal, and sagittal slices for the GT-HQ PET, its corresponding S-SQ PET, and the P-HQ PET. The performance metrics computed on the test set for the P-HQ PET are shown in Table  1  and are plotted in Figure  3. The values of those metrics on the S-SQ PET were also included for comparison. P-HQ PET showed improved image quality compared to the S-SQ PET (p < 0. 0001 for all comparisons). We computed the recovery coefficient of the GM and the WM in the test set for the S-SQ PET and the P-HQ PET. Recovery coefficients were significantly improved in the P-HQ PET for the WM and the GM compared to the S-SQ PET (p ≤ 0.
GT-HQ PET RLA lesion ranged from 0. 99 to 1. 04 and standard deviation ranged from 0. 024 to 0. 047. Post-hoc Anova analysis showed a significative difference for GM and WM RC, with a better RC for the lowest ratio (1. 2). The mean COV across all test datasets in the GM ROI was 38. 9 ± 2. 0 in the S-SQ PET and minimally higher at 39. 3 ± 2. 0 in the P-HQ PET (difference not significant, p = 0. 051). The mean COV in the WM ROI was very similar at 4. 90 ± 0. 89 for S-SQ PET and 4. 91 ± 0. 89 for P-HQ PET (p = 0. 97).
Lesioned simulated brain At the group level, the visual detection rate was 38% in the S-SQ PET increasing to 75% in the P-HQ PET (p < 0. 05) with a similar overall diagnostic confidence score of 3. 3 ± 1. 6 vs. 3. 5 ± 1. 5 (p > 0. 05). Kappa coefficients for inter-reader concordance were 0. 77 for all images, 0. 88 for P-HQ PET and 0. 72 for S-SQ PET. Overall mean visual detection rates (44 vs. 42% in the S-SQ PET and 75 vs. 72% in the P-HQ PET) and diagnostic confidence scores (3. 2 ± 1. 7 vs. 3. 1 ± 1. 5 in the S-SQ PET and 3. 4 ± 1. 5 vs. 3. 5 ± 1. 3 in the P-HQ PET) were not statistically different between readers. Figure  4  shows an example of one subject with a right frontal hypometabolic lesion of 327 mm 3 from the test dataset for the GT-HQ PET, the S-SQ PET and the P-HQ PET. Through visual inspection, the hypometabolic lesion was easier to detect and with more confidence on the P-HQ PET.
Discussion In this work, we trained a network to map Monte-Carlo S-SQ PET to their GT-HQ PET. In an independent test set, the P-HQ PET showed improved image quality compared to S-SQ PET across several objective quantitative metrics. In an independent dataset with small, simulated epilepsy lesions, the P-HQ PET significantly improved the relative lesion activity and visual detectability. Lastly, we have shown that the model was able to generalize to clinical data, illustrating the proof-ofconcept that a model trained on Monte-Carlo simulated PET data is applicable on real data. To train our model we had to overcome the limited availability of high quality training data, a common challenge for the deblurring problem (53) and so we chose to use simulation. We developed a pipeline based on a Monte-Carlo based PET simulator as it can accurately model the PET acquisition process including physical effects resulting in realistic sinograms  (29)  that have the same data distribution as the real PET.
Next, we used the same pipeline to generate data with a simulated epileptogenic lesion. This pipeline now enables creation of a whole range of realistic datasets for training if needed. Our model has several particularities. We went beyond previous published PET deblurring methods with AI which used 2D models  (27, 28, (54) (55) (56). We developed a 3D model, following results in the PET denoising field where 3D models tend to outperform 2D or 2. 5D models  (39, 57)  because of additional features in 3D space. To prevent the impact of regional homogeneity on GT-HQ PET on the model parameters, we trained using small brain patches (32 × 32 × 32 mm 3 ) from PET data simulated with of large number of GM/WM ratios. Thus, at the end of the training, the network weights were defined to respond to a wide range of voxel values (including hypometabolism) and patterns.
Conclusion In this work, we trained a deep learning model to map S-SQ PET to their GT-HQ PET using a new large realistic Monte-Carlo simulated database. In an independent test set, the P-HQ PET showed improved image quality compared to S-SQ PET across several quantitative objective metrics. Moreover, in the context of epilepsy simulated lesions, the P-HQ PET improved the relative lesion activity and their visual detection. Following this validation on simulated lesion data and the successful clinical application to illustrate the proof-of-concept that a model trained on Monte-Carlo simulated PET data is applicable on real data, next steps are to perform a generalization study and to assess the performance of the P-HQ PET in a cohort of epilepsy patients with well-characterized lesions and/or normal standard-quality PET.
FIGUREResults from one subject belonging to the test dataset. The first column depicts the Ground Truth High Quality (HQ) PET, the second column the corresponding simulated Standard Quality (SQ) PET and the third column the Predicted HQ PET, i. e. , the output from the proposed network. For each set, from top to bottom, transverse, coronal, and sagittal slices are shown. Images are displayed using radiological conventions (subject's left on the right). Bq, Becquerel.
FIGUREResults from one subject belonging to the test dataset with a simulated right frontal hypometabolic lesion with a volume of. cm. First column, enlarged view of the lesion in transverse view; second column, transverse view, third column, coronal view, fourth column, sagittal view. The relative lesion activity was. in the ground-truth high-quality-PET,. in the simulated standard-quality PET, and decreased to. in the predicted HQ-PET. Arrowheads indicate the location of the simulated lesion. Images are displayed using radiological conventions (subject's left on the right). Bq, Becquerel cm : centimetres cubed.
FIGUREResults from one subject belonging to the test dataset with a simulated right frontal hypometabolic lesion with a volume of mm. First column, transverse view, second column, coronal view, third column, sagittal view centered on the lesion. The relative lesion activity was. in the ground-truth high-quality (HQ) PET,. in the simulated standard-quality (SQ) PET, and decreased to. in the predicted HQ PET. Arrowheads indicate the location of the simulated lesion. Images are displayed using radiological conventions (subject's left on the right). Bq, Becquerel cm : centimetres cubed.
Acknowledgments The School of Biomedical Engineering and Imaging Sciences is supported by the  Wellcome EPSRC Centre for Medical Engineering at King's College London  [ WT 203148/Z/16/Z ] and the  Department of Health  via the  National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy's & St Thomas '  NHS Foundation Trust  in partnership with  King's College London and King's College Hospital NHS Foundation Trust. This work was supported by the  LABEX PRIMES  ( ANR-11-LABX-0063 ) of  Université de Lyon , within the program " Investissements d'Avenir " operated by the  French National Research Agency (ANR).
Data availability statement MRI data used are available at https://sites. google. com/ view/calgary-campinas-dataset/download?authuser=0. The MRxFDG data used are available from the corresponding author at https://doi. org/10. 1186/s13550-021-00830-6. A sample of standard and high quality pet data are available at https://osf. io/4j6xu and further access can be requested by contacting the corresponding author. Comparable deep learning model code are available at https://github. com/Project-MONAI/MONAI.
which includes T1w MRI (Siemens 1. 5T MRI) 3D volumes from 37 subjects (average age 38. 11 ± 11. 36 years; range 23-65, 54% women). It also includes 37 PET and computed tomography (CT) images from a Siemens Biograph mCT64, which we used to estimate a range of realistic FDG uptake values in brain PET as explained below. FDG PET data consisted in a static 10-min PET acquisition started 50 min after the injection of 122. 3 ± 21. 3 MBq of [ 18 F]FDG. PET sinograms were reconstructed with Siemens' iterative ordered subset expectation maximization (OSEM) "High Definition" reconstruction, incorporating the spatially varying point spread function, with CT-based attenuation correction. To illustrate the capability of the developed AI deblurring method on clinical PET data, we used 10 datasets from epilepsy subjects with an average age of 23.
| METHODS The PRISM study was a phase III, international multicenter, singlearm, open-label, prospective trial of the safety and efficacy of a 10% Ulis, France) for patients with CIDP [2]. This study, approved by local independent ethics committees, was registered at ClinicalTrials. gov (NCT02293460) and EudraCT (2013-005557-73). Details of patient selection and recruitment as well as product administration, were described in detail in earlier work. 2  In summary, IVIg was administered at an initial dose of 2 g/kg over 2 to 5 days during the first course, then maintenance doses of 1 g/kg over 1 to 2 days repeated every 3 weeks (±7 days) during the seven subsequent courses. PRISM study evaluations with the primary outcome measure, adjusted INCAT, were performed at 3-weekly intervals between the pretreatment assessment and the end of study (EOS).
ACKNOWLEDGEMENTS None. DISCLOSURE STATEMENT Y. A. R. has received speaker/consultancy honoraria from LFB, Polyneuron, and  Argenx ; has received educational support from LFB; and has received research grants from  LFB  and  CSL Behring. R. O. , F. K. , and S. P. are employees of LFB. E. N. -O. has received speaker/consultancy honoraria from  Argenx ,  Belgium, Baxalta/Takeda (Italy and USA) ,  CSL-Behring (Italy and USA), Hoffmann-La Roche (Switzerland) ,  Janssen & Janssen (USA) ,  Kedrion (Italy) , and  LFB (France). LFB had no role in determining the final content and conclusions of this article.
Type II DNA topoisomerases of the family A (Topo IIAs) are present in all Bacteria (DNA gyrase) and eukaryotes. In eukaryotes, they play a major role in transcription, DNA replication, chromosome segregation, and modulation of chromosome architecture. The origin of eukaryotic Topo IIA remains mysterious since they are very divergent from their bacterial homologs and have no orthologs in Archaea. Interestingly, eukaryotic Topo IIAs have close homologs in viruses of the phylum Nucleocytoviricota, an expansive assemblage of large and giant viruses formerly known as the nucleocytoplasmic large DNA viruses. Topo IIAs are also encoded by some bacterioviruses of the class Caudoviricetes (tailed bacteriophages). To elucidate the origin of the eukaryotic Topo IIA, we performed in-depth phylogenetic analyses on a dataset combining viral and cellular Topo IIA homologs.