dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Analysis Functional enrichment Gene Ontology (GO) analyses were performed using gProfiler  [70, 71]. Putative transmembrane domains were predicted using TMHMM v2. 0  [72]. Putative signal peptides without a transmembrane domain were predicted with SignalP-5. 0 [73]. The prediction of effectors and apoplast-targeted proteins were performed using the EffectorP and ApoplastP tools  [74, 75]. The prediction of cellular localization of the proteins was performed using the DeepLoc-1. 0 tool  [76]. The classification of CAZy was extracted from the Carbohydrate Active Enzymes database  [77]. Additional prediction tools and databases were KEGG, MEROPS, and InterPro, as well as manually curated B. cinerea protein lists, including plant-cell-wall-degrading enzymes (PCWDE), fungal cell wall enzymes (FCWE), transporters, purine metabolism, virulence factors, proteases, laccases  [2, 78, 79].
Additional prediction tools and databases, as well as manually curated B. cinerea protein lists, enabled the classification of 206 of these 270 proteins (76%) into eight functional categories (Table  S2 ): transport, metabolism, proteostasis, oxidoreduction, fungal-cell-wall-related enzymes (FCWE), vesicular trafficking and signaling (Figure  3D ). Sub-categorization allowed for a more detailed classification, revealing transporters of eight different classes, metabolic pathways linked to seven cellular requirements, components of the protein folding, modification and sorting machineries, or enzymes related to the biosynthesis of cell wall polysaccharides: chitin, glucan, and galactosaminogalactan. (Figure  3D ).
BcEVs therefore contain numerous cytosolic proteins deriving from various compartments of the fungal cell and involved in multiple biological processes. Only 17 of them contain a signal peptide. Investigation of putative interaction networks between the proteins of each sub-category was next investigated and revealed a high connectivity (60-100%) between the proteins classified in 'metabolism', 'DNA/RNA metabolism', 'proteostasis', 'vesicular trafficking', signaling and 'intracellular transport' (Figure  S3 ). Only one known virulence factor (BcSun1) and one cell-death-inducing protein (BcGs1) were found in the 673 BcEV protein of interest. No candidate effector protein could be identified in this dataset when the Effector-P prediction tool was used. However, BcGs1 is described as an effector of B.
This protocol has a very good reproducibility and aligns with the recommendations of the latest guidelines communicated by the International Society for Extracellular Vesicles MISEV2018  [18]. It yielded EVs of typical shapes and sizes, as attested by TEM and DLS analysis. In addition, the proteomic analysis of these BcEVs showed a satisfactory low (8. 8%) amount of predicted SP-containing proteins. In our hands, as in C. neoformans  [100] , the preparation of BcEVs benefits from harvesting mycelia from solid cultures. In addition, their density-gradient purification reduces the risk of contamination by co-pelleting non-EV molecules. A total of 2461 proteins were identified in BcEVs. According to the Fungal EV database 2021 (http://exve. icc. fiocruz. br, accessed on 15 December 2021). This is an intermediate situation between the 3312 and 3037 proteins found in C.
Third, their functional classification highlights typical categories found in EVs, such as transport, metabolism, proteostasis, oxidoreduction, cell wall remodeling, vesicular trafficking, translation and cytoskeleton. Fourth, the BcEVs proteins include acknowledged EV markers such as HSP, ESCRT, SNARE, Sec, and G proteins. Notably, they also include ten orthologs of established EV markers in C. albicans  [87]  and two markers found in mammalian and plant EVs that have not yet been reported in fungal EVs: the autophagyrelated protein Atg8 and one tetraspanin  [11]. Altogether, these data suggest that BcEVs, as a population of vesicles, carry a complex mixture of proteins (Figure  8 ). How this diversity of proteins is loaded into the BcEVs is unclear, but our dataset highlights several putative protein interaction networks; therefore, it is possible that protein complexes are taken up when the vesicles are formed.
Acknowledgments: The authors are grateful to  Isabelle Gonçalves ,  Luna Nadjare  and  Adrien Hamandjian  for their contribution to the analysis of the proteomics data, and to  Lucie Geay  and  Xavier Jaurand of the CTµ  for their contribution to the TEM work of infectious B. cinerea. The authors also thank  Bayer SAS  for access to specific equipment. This work used the platforms of the Grenoble Instruct-ERIC center (ISBG;  UAR 3518 CNRS-CEA-UGA-EMBL ) within the  Grenoble Partnership for Structural Biology  (PSB), supported by  FRISBI  ( ANR-10-INBS-0005-02 ) and GRAL, financed within the  University Grenoble-Alpes graduate school (Ecoles Universitaires de Recherche) CBH-EUR-GS  ( ANR-17-EURE-0003 ).
Statistical analysis Data analyses were performed using R version 4. 0. 3 software. The resistance status of the wild Cx. quinquefasciatus was assessed experimentally using the WHO cylinder test, and the results obtained were analysed according to the WHO criteria  [29] ; odds ratios (OR) were used to assess the effect of pre-exposure to PBO on pyrethroid mortality rates. For the cone test and the eave tube short contact assays, the proportions of mosquitoes killed were analysed with respect to the treatment and the exposure time (for the eave tube short contact assay only) as explanatory variables, using the Chi-square test followed by multiple comparisons using the fisher. multcomp function of the package RVAideMemoire. Residual activity of eave tube inserts was then monitored for 9 months using the eave tube bioassay, and the mosquito mortality data were fitted with a generalized linear model with binomial distribution (GLM), using the function 'glm' from the R base package.
Interactions between insecticide and persistence intervals (time since treatment) were also included in the GLMs. Pairwise comparisons were performed with the final model using the 'multcomp' package in R. For overnight release-recapture experiments, the number of Cx. quinquefasciatus entering huts through the eaves or windows was analysed using generalized linear mixed models (GLMMs) with a binomial distribution, and the treatment (open eaves or windows) was included as independent variable. The night of capture and sleepers were considered as random effects. Similarly, the number of Cx. quinquefasciatus entering huts with untreated or insecticide-treated inserts was analysed using a GLMM with a binomial distribution, considering the treatment (treated or untreated insert) as independent variable and the night of capture and sleepers as random effects.
The corresponding preprocessing pseudo-code is shown in Alg. 3. Note that if required, the interested reader can replace our handling of CFA images for alignment with the Bayer quad averaging strategy of  [13]  to be closer to the original implementation of Wronski et al. Validation of the pre-processing. Both BM and ICA algorithms can separately be run on either (i) FFT-filtered images [4, Chapter 10], (ii) demosaicked images  [20] , or (iii) the decimated grayscale images obtained by averaging the 2×2 Bayer quads. To compare these approaches we have generated a synthetic burst of images from which 2280 aligned patches were extracted, we then evaluate by comparing the quadratic error of the predicted flows. To take into account the potential difference of resolution between gray images, we have adjusted the size T of the patches so that the all gray images could be split into the same number of patches.
To avoid such scenarios, we switch from a multi-frame SR approach to a single-frame upscaling strategy in these areas by completely discarding values accumulated so far, and relying solely on the reference frame. This prevent blatant artifacts in the case of important displacements and deformations across frames, for instance. Other fusion strategies based on the value of R c may also be as correct since the details on this module are barely evoked in  [25, Sec 7. 4]. This alternative accumulation logic is detailed in Alg. 11, which is featured at the end of the main Algorithm 1. Note that one may use the same accumulation algorithm (Algorithm 4) as for the other frames, but with the risk of unacceptable visual artifacts in the final HR image. We have also compared this method with an alternate post-processing strategy applying a Gaussian or median filter on the final HR image I obtained from Eq.
Computation details To highlight the efficiency of the algorithm, we share in this section the run time and peak memory usage of our early "naive" implementation. Our python implementation uses Numba's just in time compilation (JIT), and relies on pyTorch's convolution and FFT functions. Our BM module does not use the fast ℓ 2 residual computation trick mentioned in  [13] ; we represent floating number with 32 bits, and do not use fast math approximation for operators such a square roots or exponentials. The memory deallocation is also naively left in the hands of the interpreter. The complexity of the algorithm can be split in two parts. During the initialization, the reference frame is processed, by computing the hessian matrices and the gradients necessary to the ICA algorithm. This represents a fixed temporal and memory cost.
Our goal is not to reproduce the performance but to provide an easy-to-read code useful to reproduce the visual results. To accelerate the code, one should implement Alg. 1 with a lower-level optimized language such as Vulkan, OpenCL, or pure CUDA to avoid the intermediate calls to the Python interpreter. Yet, even without a fully optimized implementation, our pipeline runs considerably faster than state-of-the-art approaches such as  [11]  or  [10]. Note that the memory usage of  [11]  is so high that processing a 12MP image is impossible, even for the higher-end consumer grade GPUs. Figure  12 : Illustration the additional frame-count aware spatial denoising for a ×2 zoom of a real 20 images burst. With the mere robustness logic, patches with a low accumulated robustness (black pixels in Figure  12d ) are mostly obtained using single-frame SR and are therefore exceptionally prone to demosaicing artifacts and noise (Figure  12b ).
Aknowledgements This work was partly financed by the  DGA  Astrid  Maturation  project  SURECAVI ANR-21-ASM3-0002 , the Office of Naval research grant  N00014-17-1-2552 , and the  ANR  project  IMPROVED   ANR-22-CE39-0006-04. This work was performed using HPC resources from  GENCI-IDRIS  (grant  2022-AD011012453R1 ). Centre Borelli  is also a member of  Université Paris Cité ,  SSA  and  INSERM. We thank  Thomas Romera ,  Thibaud Ehret  and  Jean-Michel Morel  for the insightful discussions throughout the project.
e. dorsolateral prefrontal cortex) in bvFTD  [19]. Until now, research into the BCFT in presymptomatic FTD has been lacking. The aim of the present study was therefore to: 1) investigate crosssectional differences in the BCFT (copy, recall and recognition) between presymptomatic FTD mutation carriers, symptomatic FTD mutation carriers and cognitively unimpaired controls; 2) explore associations between the BCFT and other neuropsychological tests, and 3) examine associations between the BCFT and grey matter (GM) volume. Additionally, we investigated normative data and relationships with age, sex and education from the cognitively unimpaired control group.
Participants We included baseline data of 758 participants from genetically confirmed FTD families with either a GRN or MAPT pathogenic variant, or C9orf72 repeat expansion, recruited within the GENFI 2 fifth data freeze between March 2015 and May 2019. We determined clinical status according to established diagnostic criteria  [1] [2] 21]  and a standardized clinical assessment, including medical and family history taking, extensive neuropsychological assessment covering the major cognitive domains (see Neuropsychological assessment below), and MR imaging of the brain  [14]. DNA genotyping was performed locally at each research site. >30 repeats in C9orf72 was considered to be pathogenic  [22]. The total sample consisted of 332 presymptomatic mutation carriers (GRN = 143; MAPT = 59; C9orf72 = 130), 136 symptomatic mutation carriers (GRN = 41; MAPT = 23; C9orf72 = 72), and 290 noncarriers that were used as reference group (GRN = 122; MAPT = 57; C9orf72 = 111).
MRI acquisition and (pre)processing Volumetric T1-weighted MR images were acquired on a 3 T scanner in 698 participants  (  15 participants were excluded from further analysis due to inadequate image quality. The DICOM images were subsequently corrected for gradient nonlinearity distortions and converted to NifTI format. These images were then analysed using the standard Voxel-Based Morphometry (VBM) pipeline in Statistical Parametric Mapping 12 (SPM12; Functional Imaging Laboratory, University College London, London, UK; www. fil. ion. ucl. ac. uk/spm) implemented in Matlab R2018a (Mathworks, USA). In the first pre-processing step, the T1-weighted images were normalized to a template space and segmented into GM, white matter (WM) and cerebrospinal fluid (CSF), after which they were rigidly aligned.
Statistical analysis We performed statistical analyses using SPSS Statistics 25. 0 (IBM Corp. , Armonk, NY, USA) and GraphPad Prism 5 (La Jolla, California, USA). Alpha was set at 0. 05 across all comparisons, unless otherwise specified, and two-tailed analyses were performed. We compared continuous demographic data between groups by means of one-way ANOVA with post hoc Bonferroni comparisons for normally distributed data, or Kruskal-Wallis tests with post hoc Mann-Whitney U tests in case of non-normally distributed data. Between-group differences in sex distribution were analysed using Pearson Х 2 tests. In our reference (healthy control) group, we calculated cumulative frequencies, percentile scores, and performance across age, sex and education for BCFT Copy, Recall and Recognition. We used Spearman rank correlations to explore the relationships between the BCFT Copy and Recall, and age and education.
Association for Frontotemporal Dementias Research Grant 2009, The Netherlands Organization for Scientific Research (NWO) (grant HCMI 056-13-018), ZonMw Memorabel (Deltaplan Dementie; project numbers 733050103 and 733050813), JPND PreFrontAls Consortium (project number 733051042) and Instituto de Salud Carlos III, Spain, and FEDER funds (grant number 20/00448). JBR is supported by the Wellcome Trust (103838), Medical Research Council (SUAG092 G116768) and the NIHR Cambridge Biomedical Research Centre (BRC-1215 -20014: the views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care). This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence Strategy within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy -ID 390857198).
Funding The Dementia Research Centre is supported by  Alzheimer's Research UK, Alzheimer's Society, Brain Research UK , and The  Wolfson Foundation. This work was supported by the  NIHR UCL/H Biomedical Research Centre , the  Leonard Wolfson Experimental Neurology Centre (LWENC) Clinical Research Facility , and the  UK Dementia Research Institute , which receives its funding from  UK DRI Ltd. , funded by the  UK Medical Research Council, Alzheimer's Society and Alzheimer's Research UK. JDR is supported by the  Miriam Marks Brain Research UK Senior  Fellowship and has received funding from an  MRC   Clinician Scientist Fellowship  ( MR/M008525/1 ) and the  NIHR Rare Disease Translational Research Collaboration  ( BRC149/NS/MH ). This work was also supported by the  MRC UK GENFI  grant ( MR/M023664/1 ), the Bluefield.
Ethical approval was granted by the French Comité de Protection des Personnes Ile de France X. All participants or their relatives provided written informed consent for participation. This study was prospectively registered (NCT04171024) and is reported according to the CONSORT guidelines. Randomisation was performed by a computer-generated random number sequence (http:// www. edgar web. org. uk/) with concealed allocation. Participants were allocated to either receive usual care and sham electrical stimulation (Sham Group) or usual care and a transcutaneous electrical diaphragm stimulation (TEDS Group).
Statistical analysis Based on the results of the studies with the most robust methodology  [23] [24] [25] , we estimated a rate of diaphragm dysfunction at weaning at 60% in the control group. Considering the response rate to diaphragm stimulation according to previous studies, we estimated a 60% response rate in the TEDS group. Therefore, a sample size of 56 participants was required for a statistical power of 80% and an alpha level at 0. 05. Anticipating a dropout rate of 20% we planned to include 66 individuals. Baseline data for both groups were summarised using means and standard deviations for continuous variables and absolute and relative frequencies for categorical data. We performed a chained equation multiple imputation with 20 imputed datasets for missing data. The between group differences were modelled using linear regression for quantitative variables and expressed as mean differences and 95% confidence intervals.
This data suggest a cumulative effect of carrying more than one P/LP variant in the desmosomal genes in ARVC  [10]. Previous studies indicate that 2-25% of patients harbour more than one P/LP variant in a desmosomal gene  [10] [11] [12] [13]. However, since the publication of these studies, variant adjudication criteria have become more strict and large databases like the Genome Aggregation Database (gnomAD) showed that variants previously associated with ARVC occur at a much higher frequency in the population than what would be expected based on disease prevalence. Several studies have been published with additional evidence for or against the pathogenicity of specific variants  [14]. Therefore, the true effect of multiple P/LP variants on clinical outcomes remains to be established. After thorough reclassification, we aimed to relate the presence of updated multiple P/LP desmosomal gene variants with clinical outcomes in patients.
We hypothesised that after reclassification, patients still having multiple P/LP variants in the five major desmosomal genes have a poorer outcome and prognosis than those with a single P/LP variant or with no P/LP. Therefore, we (a) collected data from published and unpublished patients with more than one desmosomal gene variant underlying ARVC, (b) reclassified these variants, and (c) updated clinical follow-up data to determine the outcome. We aim to contribute to more accurate risk stratification in ARVC patients with more than one P/LP variant.
Inclusion/Exclusion Criteria of the Systematic Literature and Database Search We searched PubMed (https:// www. ncbi. nlm. nih. gov/ pubmed/; 2018) and the ARVC Genetic Variants Database (http:// www. arvcd ataba se. info/)  [15]  to gather publications containing genetic and clinical information on patients carrying two or more desmosomal gene variants associated with ARVC, including patients having both compound and/or digenic heterozygous variants, as well as at least one homozygous variant. The PubMed search consisted of three combinations of keywords (Supplementary Table  1 ). We searched for selected terms in the titles and abstracts of the publications. In the ARVC Genetic Variants Database  [15] , we selected variants co-occurring with other desmosomal variants and collected the respective publications.
The search was restricted to English-language literature. To enrich relevant publications, we screened the full text of the pre-selected publications and collected data from publications in which carriers with more than one variant were mentioned. Furthermore, we checked the literature references in these selected manuscripts for carriers of more than one variant. While the term "variant carrier" historically refers to an individual who carries a heterozygous genetic variant without showing any symptoms of the associated recessive condition, here we use it for ARVC patients who carry a variant that can also be associated with an autosomal dominant inheritance.
ARVC Multiple Variant Database Compilation Using the selected publications (n = 67, Fig. 1A ), a database of patients with multiple ARVC variants was created. Moreover, unpublished patients with multiple ARVC variants were added, including those from the Dutch ARVC registry (n = 87)  [16]. Only patients carrying multiple variants (in the same gene or different genes) in DSC2, DSG2, DSP, JUP, and PKP2 genes were included. A predefined extraction sheet was used for data gathering, which included the PubMed identification number of the publication, the number and names of genes tested, and the name of the first author. Whenever available, the following information was extracted from each study: the gene(s) involved, the type of variant, its original classification, cDNA position and change, amino acid position and change, the composition of multiple variants (digenic/compound heterozygous or homozygous), and subject ID.
In case genomic coordinates were missing, the TransVar online tool was used to add this information  [17]. The following clinical information was collected: sex, the family status of a subject (proband or family member), the age at presentation or diagnosis of disease, the age of the first occurrence of the primary composite endpoint, which consisted of death of any cause, sudden cardiac death, death due to end-stage heart failure, heart transplant and/or left ventricular assist device (LVAD), sustained ventricular tachycardia, ventricular fibrillation, out of hospital cardiac arrest (OHCA), appropriate ICD therapy, and appropriate anti-tachycardia pacing (ATP). If data were missing in publications with five or more carriers of multiple variants, we asked the corresponding authors for follow-up data after the initial publication (Supplementary Table  2 ).
In addition to providing the missing and follow-up data, we Fig. 1  Study workflow and scheme of publication selection with information on patients with two or more ARVC-related desmosomal variants and subsequent patient selection for the database. *61 out of the 65 articles overlapped with the systematic PubMed search, ^number of patients after including information from contacted authors. Patient groups are based on the number of (likely) pathogenic ARVC variants after reclassification; P, pathogenic variant; LP, likely pathogenic variant; VUS, variant with uncertain significance; LB, likely Benign variant; B, benign variant asked the authors of the publications with five or more carriers of multiple variants to verify the data collected and to provide data of newly identified yet still unpublished multiple variant carriers when available.
Variant Reclassification The variants in patients carrying ostensible multiple P/LP variants were reclassified as pathogenic (P; class 5), likely pathogenic (LP; class 4), uncertain clinical significance (VUS; class 3), likely benign (LB; class 2), or benign (B; class 1)  [18]. We used the annotation and visualisation software, Alissa Interpret (Agilent, Santa Clara, CA) and Alamut (Interactive Biosoftware, Rouen, France), to reclassify the variants according to the American College of Medical Genetics (ACMG) and Genomics and the Association for Molecular Pathology criteria  [18]. The following summarised classification tree was used. Each variant was first checked if there was already information available in Alissa Interpret Managed Variant List (Agilent, Santa Clara, CA) and Alamut (Interactive Biosoftware, Rouen France), and other publicly available databases (e.