dataesr/scientific-paragraphs-categorization huggingface dataset:
Consistent with this is our previous demonstration that very few viral particles are transmitted from single aphids (5), likely resulting in frequent segment loss sometimes resulting in infection failure. Unfortunately, this previous study quantifying the bottleneck that aphid transmission imposes to FBNSV populations did not specifically monitor the transmission of segments C and U4, and thus we have no data allowing direct comparison. If 'missing segments' were lost after transmission, we see no obvious reason explaining why segment U4 should be lost more often than segment C, as it is more frequent than segment C both in plants  (8)  and aphids  (25). Our results on the transmission from plants with the reconstituted genomes indicate that segment loss is much rarer when donor plants have all eight segments, suggesting that transmission from plants with incomplete infections may be particular in this respect.
Forty qPCR cycles of 95°C for 10 s, 60°C for 10 s and 72°C for 10 s were applied to the samples. Post-PCR data analyses were carried as described elsewhere  (38). Because the goal of this study is to investigate whether complete infections can be reconstituted from incomplete infections, we need to define values of Ct, i. e. the number of cycles needed to reach the fluorescence threshold, beyond which a segment can be considered as absent. Indeed, it is possible in principle to obtain some amplification with the primers of a segment even when it is absent if the primers can hybridize at a low rate with other segments or plant DNA sequences. In order to establish threshold Ct beyond which each of segments C, N or U4 could be considered with confidence as absent, we performed qPCRs on samples of plants which were agro-inoculated with all FBNSV segments but the focal one.
Acknowledgments This work was funded by  ANR  Reassort  ANR-20-CE02-0016  and  ANR  Nanovirus  ANR-18-CE92-0028. MY and SB acknowledge support from  INRAE  dpt. SPE, YM from  CNRS  and  IRD , and JLZ from  IRD. The authors acknowledge valuable technical help provided by  Ms Sophie Le Blaye. Many thanks to  Bernard Francq  and  Thomas Mathew  for their insights on tolerance intervals.
Bioclimatic and Population Predictors We extracted climatic and elevation covariates, such as monthly maximum and minimum temperatures, rainfall, and elevation, from global Bioclim data  [17]  at a spatial resolution of 2. 5 min or about 4. 5 km at the equator. We used Moderate Resolution Imaging Spectroradiometer (MODIS) Land Cover Type (MCD12Q1) data  [18]  and land-use changes included in human activities from the Global Human Modification of Terrestrial Systems dataset  [19]. Finally, we used the Gridded Population of the World, (GPWv4) for the human population density raster  [20]. The raster layers were resampled to a fixed resolution of 4. 5 km and stacked to a raster brick. We obtained the geographical distribution and spatial extent of the primary hosts and reservoir mammals from the IUCN red list  [21] ; the list of mammals is included in Supplementary Materials.
Model Fitting and Model Prediction The models were fitted using the R package "hsdm", which uses a hierarchical Bayesian approach incorporating spatial dependency into the analysis by accounting for geographical clumping, which can be explained by biological (reservoir and host movement) or bioclimatic variables. In our study, we analyzed the data using hierarchical binomial and ZIB hierarchal SDM models with and without spatial autoregression. The ZIB models combine a binomial process for observability and a Bernoulli process for habitat suitability  [22, 23]. To model the spatial autocorrelation, we used a SDM with an intrinsic conditional autoregressive model (iCAR)  [24]. The model is fitted using a Bayesian framework that allows the use of pre-validated predictors and the generation of parameter uncertainties.
Conclusions Landscape (deforestation, urbanization, elevation, flooded areas, etc. ), climatic (rainfall, temperature, etc. ), and epidemic data were used in combination to estimate the potential role of human-induced landscape and climate changes on the emergence of Filoviridae, Coronaviridae, and Henipaviruses at a global scale. Such predictive approach for identifying regions at high risk of disease emergences should be considered for monitoring local and global pathogen emergences and for the identifying potential future epidemics. By recognizing the influence of predictive environmental factors on EIDs and adopting a predictive approach to disease emergence, unprecedented EID outbreaks could be made predictable.
They are implicated in many important biological processes such as cell cycle regulation, DNA methylation, DNA repair, damage response, chromatin remodeling transcription, and translational processes  (Dulac, 2010; Bourgeron, 2015; Vissers et al. , 2016; Deciphering Developmental Disorders, 2017). The advent of next-generation sequencing (NGS) technologies, comprising whole exome and genome sequencing along with the use of different bioinformatics databases that promote sharing of information on genotype-phenotype correlation, has been a significant factor in the remarkable progress in unraveling the genetics of ID  (Boycott et al. , 2017; Khan et al. , 2020; Umair et al. , 2020). The spectrin beta, non-erythrocytic 5 (SPTBN5) gene, alternatively called beta V spectrin, BSPECV, HUSPECV, and HUBSPECV is located on chromosome 15q15.
Consent Approval and Patient Recruitment Family elders (parents) provided written informed consent for molecular analysis and publication of the clinical data for research and diagnosis purposes. The families reported in the present study originated from Pakistan and Italy. Family A was diagnosed at Khalifa Gul Nawaz Teaching Hospital, and the district headquarter hospital in Bannu Khyber Pakhtunkhwa, Pakistan. Families (B-D) were diagnosed at the Medical Genetics department of the University of Siena, Italy (Figure  1A ). Genetic counseling was performed to evaluate each patient's personal and familial history.
Whole Exome Sequencing and Data Analysis Trio whole-exome sequencing (WES) was performed, including the proband and both parents. The caring clinicians gathered clinical and mutation details of the patients harboring the SPTBN5 variants by getting in touch through Gene Matcher (http://www. genematcher. org). Exome sequencing and variant interpretation and analysis were as previously described  (Monies et al. , 2019; Shao et al. , 2021). All the variants were screened according to the location, frequency, and type of variation. Variants were filtered with a minor allele frequency (MAF) cutoff of 1% in the Exome Variant Server (http://evs. gs. washington. edu/ EVS/), GnomAD (https://gnomad. broadinstitute. org), and 1000 Genomes (http://www. 1000genomes. org/). Sanger sequencing was performed using stranded methods  (Khan et al.
Protein Modeling AlphaFold  (Jumper et al. , 2021)  was used to produce highquality structural models of three 1,000 residue segments of SPTBN5, containing the residues of interest. The average pLDDT score of the three models was higher than 80, and the residue pLDDT of His89, Asn2937, and Glu3262 and their local region was higher than 90, indicating a very confident 3D configuration. A configuration of four V100 GPUs and 16 CPU cores (provided by the KAUST IBEX cluster) was used for modeling. Models were manually inspected, and mutations were evaluated using the Pymol program (pymol. org). The American College of Medical Genetics and Genomics (ACMG) 2015 guidelines were used for the interpretation of variants  (Li et al. , 2020).
Primer Designing and Mutation Confirmation Gene Runner (version 5. 0. 69 Beta, Hastings, NY, USA) software was used for Primers design. Sanger sequencing was performed using an ABI3730 Automated Sequencer to verify co-segregation of the identified variants with the disease phenotype (Thermo Fisher Scientific, Waltham, MA, USA). The Sanger sequencing results were examined and compared with the help of visual software such.
FUNDING This research by KP-G, FG-V, and SA were supported by the  King Abdullah University of Science and Technology (KAUST)  through the baseline fund and the Award No. FCC/1/1976-25  and  REI/1/4446-01  from the  Office of Sponsored Research (OSR). For computer time, this research used the resources of the  Supercomputing Laboratory at KAUST. We are grateful to our patients for their cooperation. This work was generated within the ERN ITHACA (European Reference Network for Intellectual Disability, Telehealth, Autism, and  Congenital Anomalies). The Cell lines and DNA bank of  Rett Syndrome , X-linked mental retardation, and other genetic diseases, member of the  Telethon Network of Genetic Biobanks  (project nos. GTB12001  and  GFB18001 ), funded by  Telethon Italy , and of the EuroBioBank network provided us with specimens.
Methods Participants. Seventy-three non-exposed and one hundred and two exposed subjects participated in this study  10. Exposed participants were recruited through a transdisciplinary and longitudinal research "Program 13-Novembre" (http://www. memoire13novembre. fr/), a nationwide funded program in partnership with victims' associations. In the current study, the data of two participants (one nonexposed and one exposed) were excluded from the final analyses, as they had an unusually low number of remaining pairs, making it impossible for us to calculate an item-specific belief computational model (see below). The final sample consists of 101 exposed and 72 nonexposed participants. Non-exposed participants were not present in Paris on 13 November 2015 and were recruited from local panel of volunteers.
All the participants completed the study between 13 June 2016 and 7 June 2017. Participants were financially compensated for their participation in the study. The study was approved by the regional research ethics committee ("Comité de Protection des Personnes Nord-Ouest III", sponsor ID: C16-13, RCB ID: 2016-A00661-50, clinicaltrial. gov registration number: NCT02810197). All the participants gave written informed consent before participation, in agreement with French ethical guidelines. Participants were asked not to consume psychostimulants, drugs, or alcohol prior to or during the experimental period. Materials. The stimuli were three series of lists of 72 word-object pairs composed of neutral abstract French words  54  and objects selected from the Bank Of Standardized Stimuli (BOSS)  55.
Three series of four lists of 18 pairs assigned to four conditions (think, no-think, baseline, and unprimed for the final priming test task after the Think/No-think phase) were created, plus 8 fillers used for practice. The lists of pairs were presented in counterbalanced order across the three series, the four conditions and the three groups of participants and matched on different properties that may influence performance to the task. The lists of words were matched on average naming latency, number of letters and lexical frequency  54. The lists of objects were matched relative to the naming latency, familiarity and visual complexity levels, viewpoint, name and object agreement and manipulability  55. Stimuli were presented using the Psychophysics Toolbox implemented in MATLAB (MathWorks).
Participants engaged significantly less in other strategies than in direct suppression to control awareness of the No-think items (Wilcoxon's signed Rank test: z > 140, p < 0. 001). Moreover, Kruskal-Wallis tests did not evidence any difference between the groups for any kind of strategies used (H(2) < 2. 73, ps > 0. 26). MRI acquisition parameters. MRI data were acquired on a 3 T Achieva scanner (Philips). All participants first underwent a high-resolution T1-weighted anatomical volume imaging using a 3D fast field echo (FFE) sequence (3D-T1-FFE sagittal; TR = 20 ms, TE = 4. 6 ms, flip angle = 10°, SENSE factor = 2, 180 slices, 1 × 1 × 1 mm 3 voxels, no gap, FoV = 256 × 256 × 180 mm 3 , matrix = 256 × 130 × 180). This acquisition was followed by the TNT functional sessions which were acquired using an ascending T2-star EPI sequence (MS-T2-star-FFE-EPI axial; TR = 2050 ms, TE = 30 ms, flip angle = 78°, 32 slices, slice thickness = 3 mm, 0.
As our aim was to model participants' beliefs about their own intrusion ratings during the TNT, input u at time t was outcome y at time t-1: u t ð Þ ¼ y tÀ1 ð Þ ð2Þ To model individual time series of internal beliefs, we used the HGF, KF, and RW models implemented in the TAPAS toolbox (available at https://www. tnu. ethz. ch/ de/software/tapas), which applies variational Bayesian inversion to infer hidden states maximizing the log-model evidence (LME). Perceptual models Two-level hierarchical Gaussian filter: We used a two-level HGF as a perceptual model. Developed by Mathys et al. 23  , the HGF assumes that agents form internal beliefs in a hierarchical fashion. Implementing a variational approximation approach, the HGF allowed us to estimate trial-by-trial trajectories of internal beliefs at multiple levels.
Free perceptual parameters and corresponding belief trajectories were estimated using a quasi-Newtonian optimization algorithm  23. For state, item, combined trajectories of belief, we computed model accuracy using the sum of the negative log-likelihood of the choice probability. Validation of computational modeling Model falsification. A common issue in computational modeling is how to assess the performance of a set of different models in generating plausible data, given that generative and predictive performances of a model can sometimes be dramatically different  24. This is an important step that allows the models that best generate plausible data to be identified and those with poor generative performances to be rejected. This procedure is known as model falsification  24. The goal of these simulations was to establish whether the models were able to generate the behavioral reduction in intrusion proportion that we observed across the four blocks of the TNT task (see Fig.
e. , Pðm s jY g Þ): P θ s2g j Y g ; m 2 f D ¼ ∑ m2f D P θ s2g j y s2g ; m P m s2g j Y g ð16Þ where Y g is the dataset of the whole group g, containing data for each participant in the group, y s2g. Importantly, a separate analysis was performed for each group, to ensure that the participant's posterior probabilities Pðm s jY g Þ were derived from his or her group's distribution. It should be noted that this was possible because the computational top-down modulation family outperformed the other families in all three groups, and no statistical differences were detected between groups with respect to the preferred model architecture (see the "Results" section). Statistical analyses were performed on BMA coupling parameters using one-tailed t tests according to a priori hypotheses, in the three target memory regions (rHIP, cHIP, PC), as well as the wHIP (i.
The mean angle θ cannot be computed from the arithmetic mean of all sampled angles. We used the Circular Statistics toolbox in MATLAB  70  to compute the mean angle θ across participants in each group. Confidence intervals were also computed by bootstrapping the estimation of this group mean 2000 times. Group comparisons were performed using Watson's two-sample tests, a nonparametric version of the two-sample t test for circular data. For all group comparisons, alpha was set at 0. 05.
Acknowledgements We thank all the people who volunteered to take part in this study and the victim associations that supported this project. We thank the medical doctors (especially  M. Mialon  and  E. Duprey ) and the staff at the Cyceron biomedical imaging platform in Caen. We also thank the researchers; psychologists  M. Deschamps ,  P. Billard ,  B. Marteau ,  R. Coppalle , and  C. Becquet ; technicians; and administrative staff at  U1077 (Caen) , at " Program 13-Novembre" in Paris, at INSERM "Délégation Régionale Nord-Ouest" (Lille)  and  "Pôle Recherche Clinique"(Paris). We thank  Jean-François Démonet  for comments and feedbacks on this manuscript. We thank  Elizabeth Portier  for final English editing of the manuscript. This study was funded by the  French Commissariat-General for Investment (CGI)  via the  National Research Agency (ANR)  and the  "Program d'investissement pour l'Avenir (PIA).
"The study was realized within the  framework of "Program 13-Novembre" (EQUIPEX Matrice ) headed by D. P. and F. E. This program is sponsored by the  CNRS  and  INSERM  and supported administratively by  HESAM Université , bringing together 35 partners (see www. memoire13novembre. fr). G. L. is funded by a  Ph. D. fellowship from the  Normandy Region and Normandy University.
Data availability All the raw behavioral and imaging data are archived at the GIP Cyceron center in Caen and are part of an ongoing longitudinal research project. Raw behavioral and brain imaging data are available under restricted access due to legal and sponsorship regulation for the current research project. These data can be shared with researchers upon reasonable request, via data request to Dr. Pierre Gagnepain (pierre. gagnepain@inserm. fr). The clinical data and the subject-specific DCM coupling parameters data necessary for the statistical analyses of the current research article have been deposited on the GitHub repository (https://github. com/PierreGagnepain/ predictive_control) and are also available on Zenodo (https://doi. org/10. 5281/zenodo. 6362400)  71.
Code availability Computational models were implemented in the TAPAS toolbox (https://www. tnu. ethz. ch/de/software/tapas). Preprocessing of fMRI data and first-level DCM analysis were performed with SPM12 (https://www. fil. ion. ucl. ac. uk/spm/; version DCM12. 5 revision 7479). The log-family evidence was computed using the MACS toolbox (https://github. com/JoramSoch/MACS/releases/tag/v1. 3), and Bayesian model comparisons were performed with the VBA toolbox (https://mbb-team. github. io/VBA-toolbox/). Codes for implementing model falsification, parameter and model recovery, as well as computational DCM to study predictive control, is available on GitHub (https://github. com/PierreGagnepain/predictive_control).
ISSA aims to (1) provide a generic, reusable and extensible pipeline for the analysis and processing of an open scientific archive, (2) translate the results into a semantic index in the form of an RDF knowledge graph (KG); (3) develop innovative search and visualization services exploiting the index, aimed at researchers, decision makers, or STI professionals. Geared towards genericity and reusability, the proposed solution adheres to the FAIR principles  [35]  and the open science guidelines. Furthermore, ISSA adopts a pragmatic approach that strives to rely on robust, industry-proven, scalable solutions, and integrate them into a coherent, easily deployable pipeline. The processing pipeline, depicted in Fig. 1 , involves various artificial intelligence techniques: natural language processing, knowledge engineering, semantic web and linked data.
Publications' metadata and full text are processed in order to extract thematic descriptors  1  and named entities (NE). To allow services to reason upon the extracted knowledge while leveraging terminological references such as ontologies or thesauri, thematic descriptors and NEs are linked with resources such as Wikidata, DBpedia and GeoNames. The resulting KG serves as a keystone able to support the development of services such as search and visualization. In particular, the Arviz  [24]  and MGExplorer  [25]  visualization tools make it possible to explore and visualize thematic association rules, networks of co-publications, or of articles with co-occurring topics, in order to concretely answer competency questions. These visualization tools are highly configurable and can be tailored to a wide range of scenarios.
To demonstrate the effectiveness of the proposed solution, we deployed it for the needs of a real-world use case, Agritrop  [1] , CIRAD 2  's open archive of 110,000+ resources (i. e. , book, book chapter, article, thesis, etc. ). By drawing on the outcome of interviews conducted with CIRAD researchers and documentalists, we show the ability of these services to meet user needs and competency questions with relevant answers. In the rest of this paper, section 2 provides an overview and a comparison with related work. Section 3 describes the pipeline spanning metadata retrieval, extraction and linking of thematic descriptors and NEs, and construction of the KG. Then, section 4 presents the exploitation and visualization tools and how they were configured in the Agritrop use-case. Section 5 provides further information about the accessibility of the pipeline and the KG generated in the case of Agritrop.
Related Works For over twenty years, the open science movement has aimed at making scientific research results freely accessible, considerably transforming the landscape of scientific production. Initiatives such as Research Data Alliance  [6]  (RDA) that federates working groups on FAIR principles, metadata standards, and semantic resources (ontologies, thesauri, etc. ); or Go Fair [2] and European Open Science Cloud (EOSC)  [11] , have laid the ground work for the implementation of the FAIR principles for open science. In this context, the role of open archives and of how to exploit them are central questions: many projects, including the ISSA project, have taken up this dimension, covering complementary aspects. The OpenMinted  [5]  project aimed at creating a generic Software As A Service EU infrastructure for text mining, based on a modular architecture, that researchers could use by contributing their use-cases.
After 5 years of development, the project fell short of delivering a fully functional prototype, merely laying the foundational components of the infrastructure. The related Visa TM project  [7]  was to be the core knowledge extraction component, integrating thesauri and ontologies from many domains, but only achieved a very preliminary integration  [20]. In contrast, the ISSA project adopts a more modest but focused and pragmatic approach, proposing a generic pipeline adaptable to multiple domains, based on the integration of robust, industry-proven and scalable existing tools, and deployable by each community. ISSA also has a strong focus on using Linked Open Data and FAIR principles, which are absent from OpenMinted. The ISTEX infrastructure, which was meant to be the corpus provider for OpenMinted  [20] , has goals related to ISSA in that it aims at constituting corpora of scientific publications and providing research communities with tools to explore relevant subsets of the curated corpora.
Annif does not propose any new methods per se, but provides a framework and API to integrate existing machine learning models and tools to index corpora of scientific publications. In addition to the integration of multiple supervised and unsupervised models (TensorFlow deep net, Omikuji, fastText and Gensim), Annif supports multiple vocabulary formats, comes with standardized evaluation protocols and metrics, and supports multiple languages. In the ISSA pipeline, a corpus is extracted per language and split into training, validation and testing sets, in order to train the Annif model. The recreation of new models can be triggered independently from the pipeline, either manually or automatically at fixed intervals. The trained models are used in the pipeline to classify each article. For articles already manually indexed we end up with two sets of descriptors, one set corresponding to manual annotation and one set corresponding to automatic annotation.
To train Annif to annotate new articles with AGROVOC terms, we extracted a corpus of approximately 12,000 English and French open-access articles. Descriptors manually annotated by curators were retrieved from Agritrop. For each language, separate training sets were created based on automatic language detection 4. We experimented with different available models and chose the best performing one, namely an ensemble of lexical matching (MLLM)  [32]  and a tree-based machine learning algorithm  [30].