dataesr/scientific-paragraphs-categorization huggingface dataset:
Additional details on French forestry are available in the supplementary materials. To estimate aboveground PFRs, the CamBEE method requires five main input data, viz. , the forest cover raster; forest statistics on gross annual increment (GAI) and natural losses (NL) if net annual increment (NAI) is not available per region; factor of basic wood density (D), and biomass expansion factor (BEF). Other ancillary data required for mapping are the administrative shapefile (e. g. , country or regional boundary). The source of data used in the present study is provided in Table  2. Used for identifying the publicly owned and the privately owned forests of France in the exemplification section [66] -1 A French administrative regional unit that groups several communes based on some of their common competences.
Contrarily, low spatial resolution data contain lower level of details which might not be suitable for supporting local bioeconomy decisions. As there are no standard procedures to decide what spatial resolution would be ideal for spatial quantification of PFRs at the country scale, a metric based on computational complexity is here proposed (in terms of size of data and processing time). To test the deviation of estimated results at different spatial resolutions, the theoretical potential of aboveground PFRs of conifer forests in the Occitanie region was evaluated. The conifer forest of the Occitanie region was sampled at the spatial resolution of 100-meter, 30-meter, 20-meter, 10-meter, 5-meter, and 1-meter (step 2). Figure  4  shows the generated forest raster at different spatial resolutions overlaid on a base map.
96 gigabytes) compared to the 10-meter one (0. 012 gigabytes). The final uncompressed size of the processed raster containing information on the location and quantity of PFRs for the conifer forest of the Occitanie region was 490 gigabytes for the 1-meter resolution data (Supplementary Table  2 ). The values highlighted in bold in Figure  5  show the increase in the complexity in terms of time per pixel area and file size per pixel area. There is a substantial increase from 10-meter spatial resolution data to 5-meter data for both processing time and file size. This test was performed only for one forest type of one region of France; thus, the estimation of PFRs for the entire country at a very high spatial resolution would be rather tedious.
The final estimated figure for France thus deviates by ca. 38% from the national statistical data; here it is not claimed as a proof of validation, but it builds confidence around the range found with CamBEE. In contrast, the spatially-explicit study of Hamelin et al. [4] , where estimates are reported at the European NUTS level 3, reveal a theoretical potential of 455 PJ year -1 for PFR in France (results extrapolated from the raw dataset of the authors). This represents ca. 24 Million Mg DM year -1 , based on a lower heating value (LHV) of 19. 19 GJ Mg -1 DM  [8] , which is nearly 3-fold the potential found in the present study. The higher value reported in  [4]  is explained by (i) the inclusion of stumps as a residual component of the stream, and (ii) the methodological framework itself, where country-level data on forest statistics were simply (and coarsely) scaled down to different administrative regions.
Conversely, the present framework aggregates pixel-level data to different administrative regions following a bottom-up approach, thus avoiding inconsistencies in the resolution of the dataset used, and providing more reliable estimates. This indicates that the results of  [4]  may be overestimated, and accordingly that the residual biomass potential generated in Europe might be lower than previously anticipated. The results obtained from the CamBEE framework are only as good as the input data supplied. Although the uncertainty assessment tries to capture the deviation as ranges, uncertain data will make this range large. In the exemplification section, the forest increment statistics that are derived from  [63]  uses the statistical data of the French intercommunal plans for energy and climate (PCAET;  [68] ), which in turn cross-reference the NFI survey data with the 11 French Biome Regions (GRECO;  [77] ) and the French Forestry Regions (SER;  [77] ) data.
Acknowledgments This work was carried out within the framework of the research project Cambioscop (https://cambioscop. cnrs. fr),partly financed by the  French National Research Agency ,  Programme Investissement d'Avenir  ( ANR-17-MGPA-0006 ) and  Region Occitanie  ( 18015981 ). The authors gratefully acknowledge  Dr. Antoine Collin  of  IGN  for insightful discussions, advices, and the access to BD Forêt version 2 data and to  Dr. Laurent Polidori  and  Dr. Jordi Inglada  of  CESBIO  for sharing the land cover data of Metropolitan France. The authors further acknowledge  Miriam Buitrago  of  ADEME  for guidance on the ALDO tool. Finally, sincere thanks to  Natascha Raue  for her diligent proofreading of this paper.
Clinical Longitudinal Dataset Our clinical dataset consists of a longitudinal collection of 18 MS patients (12 women, mean age 31. 4 years, sd 5. 6) which were clinically assessed and scored using the expanded disability status scale (EDSS)  [45]  and the multiple sclerosis severity score (MSSS)  [46] , and 10 age-matched healthy volunteers (8 women, mean age 29. 4, sd 6. 3). All the patients first underwent MRI and PET scan at baseline (t 0 ). Then they all repeated the whole protocol after either 1-2 months or 3-4 months (t 1 ) to explore the best time interval for dynamic remyelination and demyelination quantification. The healthy volunteers only underwent one scan. All participants signed written informed consent to participate in the study, which was approved by the local ethics committee of the Pitié-Salpêtrière hospital.
The clinical and demographic information is detailed in  [1]. At t 1 , because of missing MR images, 3 patients were excluded from the t 1 dataset. Finally, for each participant, we used the following data: a) PET IMAGES: PET examinations were performed on a high-resolution research tomograph (HRRT; CPS Innovations, Knoxville, TN) which achieves an intraslice spatial resolution of 2. 5mm, with 25-cm axial and 31. 2-cm transaxial fields of view. The 90-minute emission scan was initiated with a 1-minute intravenous bolus injection of [ 11 C]PIB (mean = 358 ± 34 MBq). The Logan graphical reference method  [47]  was applied at the voxel level on PET scans in native space to obtain ). The three ROIs (lesions, NAWM and "other") used in Eq. 4 were delineated as follows. WM lesions of MS patients were manually contoured by an expert rater on T2-w scans with reference to FLAIR images.
Implementation and Training Details We used the clinical longitudinal dataset described in the Section 2. 4 for the prediction of dynamic myelin changes. The model is trained, validated and tested on baseline (t 0 ) dataset by using 3-fold cross validation. For each cross-validation split, the dataset is divided into a training set with 2/3 subjects (including 3 subjects for validation), and a testing set consisting of 1/3 subjects. The fixed model is then directly applied to the images of these 1/3 subjects from t 1 dataset. This can also be considered as a way to evaluate model generalization. For both time points (t 0 and t 1 ), the preprocessing steps mainly consist of brain extraction  [52] , intensity inhomogeneity correction  [53]  and affine intra-subject registration of MR data onto [ 11 C]PIB PET DVR image space using FLIRT algorithm in the FSL package  [48].
Clinical Correlation It is concluded in the recent clinical research work  [1]  that there is no significant association between the index of dynamic demyelination and EDSS (p = 0. 72), but the index of remyelination is a significant explanatory factor for EDSS with -0. 67 as beta-coefficient (p = 0. 006). We thus calculate the correlation between the synthesized-PET-derived indices of myelin content change and the clinical score EDSS. In our work, because all the mandatory MRI data were not available for the initial patients list, the clinical correlation have been recomputed for both the synthesized-PET-derived and true-PET-derived indices based on a subset of subject to check the consistency of our method. As no significant change was found in EDSS during follow-up, the EDSS measured at baseline was used for clinical correlation which is calculated using multiple linear regression with EDSS as response variable and age, gender and T2-w lesion load as additional covariates.
In practice, these masks cannot be always available. In the future work, instead of using manually predefined attention, a self-learned attention could be helpful to solve this problem. Second, multisequence MR images are used as inputs to provide as much information as possible to the network. However, the subjects with incomplete MR images cannot be used and have to be excluded from the dataset. This is a great loss especially for small medical image datasets. It would be helpful for future work to discover the way to deal with multisequence images independently, so that every incomplete MR series can be used for training. Furthermore, the intra-subject registration used in our work as a preprocessing step for multisequence MR images may also influence the synthesized image quality because of image noise and different selections of parameters in the regis-tration step.
Acknowledgments The first author is funded by an  Inria  fellowship. This work has been supported by the  French government , through the  3IA Côte d'Azur Investments in the Future  project managed by the  National Research Agency (ANR)  with the reference number  ANR-19-P3IA-0002. The research leading to these results has received funding from the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute)  and reference  ANR-10-IAIHU-06  ( Agence Nationale de la Recherche - 10-IA Institut Hospitalo-Universitaire-6 ) and and from the  "Contrat d'Interface Local" program  (to  Dr Colliot ) from  Assistance Publique-Hôpitaux de Paris  ( AP-HP ). The clinical study was funded by specific grants from  ELA  ( European Leukodystrophy Association , grant  2007-0481 ), and  INSERM-DHOS  (grant 2008-recherche clinique et translationnelle) and has been sponsored by  APHP (Assistance Publique des Hôpitaux de Paris).
Surgical procedure The routine surgical procedure at the University Hospital in Clermont-Ferrand began with a meticulous pre-surgical planning. A brief description of the procedure is given here while a complete description can be found elsewhere  [46]. A stereotactic CT (0. 59 × 0. 59 × 1. 25 mm), stereotactic T1 MRI (0. 63 × 0. 63 × 1. 30 mm), and white-matter attenuation inversion recovery (WAIR, 0. 54 × 0. 53 × 2. 0 mm) sequence were acquired (Sonata 1. 5T, Siemens, Germany) to be used for the planning. Based on the spontaneous contrast observed on the WAIR sequence and an in-house developed high field (4. 7 Tesla) brain atlas, the neurosurgeon carefully outlined various thalamic nuclei and basal ganglia structures using a commercial planning software iPlan stereotaxy 3. 0 (Brainlab). After delineating the desired target structure, i.
Accelerometric tremor evaluation The changes in tremor during surgery were evaluated applying a previously published method using an accelerometer  [42]. In short, a 3-axis acceleration sensor was attached to the patient's wrist and data were recorded and stored for offline analysis using an in-house developed computer application. Data recording was synchronized with the MicroGuide Pro electrophysiology system (Alpha Omega Eng. , Nazareth, Israel) used for MER and stimulation tests. For every stimulation test, changes in tremor were determined compared with the data recorded immediately before the start of the test (baseline state). By using such a protocol, changes in tremor due to brain tissue damage caused by introducing the electrode would not influence the quantitative assessment. Post-operative analysis of the data using Matlab consisted of calculating and filtering the magnitude of acceleration.
Patient-specific stimulation data For each implantation, the planned trajectory to reach the target structure and the stimulation test positions along the trajectory were extracted from the planning software (iPlan stereotaxy) and converted to the co-ordinate system of the brain model using Matlab. The target co-ordinates and the trajectory angles were used to calculate and to place the stimulating contact and the parallel electrode in the brain model for the different stimulation test positions. The stimulation current amplitude applied intraoperatively was used to simulate the EF. In order to keep the simulation time reasonable, we decided to simulate for each stimulation test position the EF distribution for the current amplitudes with (1) the first appearance of highest change in tremor (between 0.
Surgical planning data In addition to the planned trajectory and target information, the manually outlined anatomical structures were extracted from the iPlan software via a specifically designed interface based on VVLink and VTK (VTK 5. 2. 0, Kitware Inc. New York, USA). In order to reduce error sources, only the CT data set was used as reference for the outlined anatomical structures and the target co-ordinates. In consequence, the structures initially outlined on the WAIR-weighted sequence were resampled in iPlan to the stereotactic CT data set providing a higher resolution and minimal distortion of the stereotactic reference system.
Clinical application A clinical study was undertaken at the University Hospital in Clermont-Ferrand, France (Ref: 2011-A00774-37/AU905, Comité de Protection des Personnes Sud-Est 6, Clermont-Ferrand, France), and 5 patients who were treated for ET using DBS were included for the current work (Table  1 ) after obtaining informed written consent. The number of stimulation tests varied from patient to patient based on the size of the region of interest and the occurrence of adverse effects during the stimulation tests. Three types of adverse effects were observed for different patients, i. e. , pyramidal effects, paresthesia, and dysarthria. Accelerometer data were recorded during the surgery following the above presented protocol to evaluate the changes in tremor induced by varying stimulation current amplitudes.
Implementation From the EF simulations, the improvement map is generated using Matlab scripts as presented in Fig. 3. All the EF simulation files from COMSOL (refer Section 2. 3. 4) are imported in Matlab, and the extent of the stimulation test region in a given hemisphere is calculated (Fig. 3 , Step 1). A 3D mesh-grid with a resolution 4 times the resolution of the CT images is created (Fig. 3 , Step 2). Using the Delaunay Triangulation (Boris Delaunay 1934) and identifying points in the triangulation, the position of each EF in this mesh-grid is identified and a mask is created. The mask is multiplied with the improvement associated with the EF, i. e. , the voxels within the EF hold the improvement value and the rest are set to zero. All such improvement masks have the same dimensions and are therefore appended together to create a 4D matrix (Fig.
3 , Step 3). By using a many to one function (e. g. , minimum, mean, maximum, etc. ) on the 4th dimension, a 3D matrix is obtained having the same dimensions as the mesh-grid, and each voxel holding only one improvement out of all the EF simulations that encompassed it (Fig. 3 , Step 4). After identifying the different levels of improvement in this 3D matrix and using the location-query function, the X, Y, and Z coordinates of each improvement level are obtained and exported. For the EF of adverse effect thresholds, their coordinates in the mesh-grid are identified and exported as csv files without any additional processing. For visual analysis of the stimulation map, data were imported in Paraview (Kitware, Clifton Park, NY, USA) using Python (Python. org) scripts in the following order: (1) the T1 MR and the WAIR MR data sets were imported and cropped to the region of interest.
Data analysis The stimulation maps were analyzed individually by simultaneously moving the axial, coronal, and sagittal sections across all the visualized data in the Paraview's orthographic slice view. For each map, the location of the region showing the highest improvement was identified and carefully studied with respect to the outlined anatomical structures and the adverse effect threshold outline. The trajectory and optimal depth that would have been selected based on the stimulation maps to implant the lowest contact of the permanent lead was determined such that the lower border of this contact would align with the lower edge of the highest improvement region. This would allow stimulation of the highest improvement region through a mono-polar or bi-polar setup of the lead. In addition, in each map, the interaction of various anatomical structures with the induced clinical effects was meticulously examined.
Results Through the clinical study in the University Hospital in Clermont-Ferrand, accelerometer data was recorded for a total of 129 test stimulation positions from the 5 DBS surgeries. Using the FEM-based technique, 148 simulations (129 therapeutic + 19 adverse effects) were performed based on these stimulation tests and summarized in 9 stimulation maps. These maps vary from patient to patient depending on the planned trajectory and the test stimulation positions along it. Figure  4  shows a typical stimulation map of the right hemisphere of patient 5. The inferolateral part of the thalamus was explored during the stimulation tests (Fig. 4a-c ) with the planned target at the inferior border of the VIM (Fig. 4d-f ). The highest improvement region (95%) can be seen further along the trajectory from + 1 mm to + 3 mm in front of the target (Fig.
Related work Visualization of intraoperative DBS data has been previously proposed. D'Albis et al. [13]  developed PyDBS to automate tasks like image registration, image segmentation, and visualization. They designed it to assist clinicians during preoperative planning and post-operative electrode validation, but not during intraoperative electrode placement. Guo et al. [20]  used non-linear image registration to visualize digitized brain atlas, segmented deep brain nuclei, and final surgical targets as well as electrophysiological information from their own database on the patient images. Although their software could be used during the surgery, it did not have provisions to visualize therapeutic or adverse effects of stimulation at a given position. The group of D'Haese  [14]  developed a system consisting of a central repository (cranial vault), modules to interact with the repository (CRanial VAult Explorer, CRAVE), and algorithms to automate certain tasks in the DBS treatment (pre-operative, intraoperative, and postoperative phases).
Their system is able to visualize therapeutic and adverse effects of stimulation, which the clinical team would manually enter during the surgery. However, the use of their intraoperative module during surgeries showed that the manual task of providing information to their system was stressful for the surgical team. In addition, none of the software described above estimates the spatial effect of stimulation. Miocinovic et al. [32]  proposed a software called Cicerone which visualizes patient images with brain atlas, MER, DBS leads, and the volume of tissue activated (VTA) in 3 dimensions and can be used intraoperatively. However, the estimation of VTA is not patient-specific and is based only on the DBS lead. In contrast to the existing literature, the approach described in this study was specifically designed for intraoperative use.
Transferability In its current form described in this study, the procedure of creating the stimulation maps is very specific to the type of data available from the University Hospital in Clermont-Ferrand. In general, however, the technique is very adaptable to the type of data available. In the absence of pre-operative anatomical outline, the patient images can be co-registered to digitized atlases using open-source software like 3D Slicer  [17]  and segmented to outline the relevant anatomical structures. Regarding the spatial estimation of stimulation, the EF simulation procedure can be adapted to the type of MR data acquired, the type of exploration electrode used, and the stimulation parameters in a given surgical center. Concerning the use of accelerometers to evaluate tremor, our previous study has shown its advantages over the existing visual method and the relative ease with which it can be added to the surgical procedure without major alterations or loss of patient comfort.
Limitations The primary focus of this paper is to describe the method of generating the stimulation maps and to apply it to 9 implantations. Nevertheless, to draw statistically significant conclusions, the approach has to be applied to a larger number of patients. Moreover, in order to establish that the implant position of the lead based on the stimulation maps is optimal, a larger control group study must be performed comparing lead implant position choices made using conventional methods with those made based on the stimulation maps. In terms of drawbacks of individual datasets, the reconstruction of the outline of anatomical structures is limited by the voxel dimension, which can only be improved by using imaging systems with better resolution. Any caveats associated with the quantitative tremor evaluation and EF simulation techniques also affect the stimulation maps.
Conclusion This paper describes a new technique to generate stimulation maps to summarize and visually analyze data from intraoperative stimulation tests for deep brain stimulation surgery. Data collected from 9 implantations were analyzed with the aim to identify the optimal site to implant the chronic DBS lead. Clinicians found the visualizations intuitive and easy to interpret and to identify the region resulting in highest improvement in tremor. In 7 of the 9 stimulation maps, the highest improvement region was found to be in the PSA in agreement with the scientific consensus. This method has the potential to simplify the surgical team's task in identifying the ideal implant location of the chronic DBS lead and to facilitate and expedite the use of directional leads in DBS.
e. , encrypted in transit, at rest, and, most importantly, in analysis, and can be decrypted only by the data owner. We compared secure imputation with three other state-of-the-art non-secure methods under different settings. We found that HE-based methods provide full genetic data security with comparable or slightly lower accuracy. In addition, HE-based methods have time and memory requirements that are comparable and even lower than the non-secure methods. We provide five different implementations and workflows that make use of three cutting-edge HE schemes (BFV, CKKS, TFHE) developed by the top contestants of the iDASH19 Genome Privacy Challenge. Our results provide strong evidence that HE-based methods can practically perform resource-intensive computations for high throughput genetic data analysis.
Introduction Whole-genome sequencing (WGS)  1, 2  has become the standard technique in clinical settings for tailoring personalized treatments  3  and in research settings for building reference genetic databases  [4] [5] [6]. Technological advances in the last decade enabled a massive increase in the throughput of WGS methods  7  , which provided the opportunity for population-scale sequencing  8  , where a large sample from a population is sequenced, for studying ancestry, complex phenotypes  9, 10  , as well as rare  [11] [12] [13]  and chronic diseases  14. While the price of sequencing has been decreasing, the sample sizes are increasing to accommodate the power necessary for new studies. It is anticipated that tens of millions of individuals will have access to their personal genomes in the next few years.