dataesr/scientific-paragraphs-categorization huggingface dataset:
8 ) created from the files distributed by Stanford (http://findlab. stanford. edu/functional_ROIs. html). This atlas has a voxel resolution of 3x3x3 mm and a volume shape of 53x63x52 voxels. We used the method get_willard_mask to resize the original atlas to match the shape of the BOLD images to be analyzed. In addition, this method saves the resampled mask in a specified path. For instance, Fig. 8  shows the Willard atlas resized to the shape of the functional image in Fig. 7. The following code illustrates how to resize the Willard atlas provided by PyHRF to match the shape of the image ~/data/bold. nii, and saves it in the folder ~/pyhrf. >>> willard = get_willard_mask('~/pyhrf', '~/data/bold. nii') /home/jariasal/pyhrf/mask_parcellation/willard_3mm. nii PyHRF also needs the experimental paradigm as input.
It must be given as a csv file following the convention described in the documentation (https://pyhrf. github. io/manual/paradigm. html). For the sake of convenience, we used the method convert_to_pyhrf_csv to read the paradigm file provided by the dataset (a tsv file) and rewrite it using the PyHRF format. Since each dataset has its own format for the paradigm, we give it as an input to our method. >>> columns_tsv =  ['onset', 'duration', 'weight', ' trial_type'] >>> paradigm = convert_to_pyhrf_csv( '~/data/paradigm. tsv', 0, columns_tsv) /tmp/tmpM3zBD5 Table  1  shows the experimental paradigm of the dataset ds000114 written using the PyHRF format. Note that it only contains motor stimuli since we are only interested in them for our BOLD analysis.
The reader can found more details about this command and its parameters in the PyHRF documentation. We show in Fig. 9  the active parcels (A), the PPMs (B), and the estimated HRFs (C) generated by PyHRF for the motor task Finger. Reading the description given in [GSB + 13], this task corresponds to finger tapping. Recall that PyHRF estimates a HRF for each parcel and generates a PPM for each condition. We compared the output of PyHRF with the thresholded statistical maps shared on the site Neurovault (http://www. neurovault. org/images/307/) for the same dataset and same task (see Fig. 9 ). While the experimental paradigm is not optimized for JDE (standard block paradigm is not ideal to estimate different points of the HRF course), we obtained similar results to standard statistical analysis additionally providing the form of the HRF.
Concluding Remarks In this paper we presented PyHRF, a software to analyze fMRI data using a Joint Detection-Estimation (JDE) approach of the cerebral activity. It jointly detects cortical activation and estimates the Hemodynamic Response Function (HRF), in contrast to existing tools, that consider the HRF as constant over the brain and over subjects, thus aiming to improve the reliability of the results. PyHRF is an open source software that has evolved rapidly over the last few years. As we showed, it allows to generate Posterior Probability Maps (PPMs) to describe the significance of the activation in each region of the brain. Moreover, PyHRF uses efficient estimation methods in order to provide a fast tool. Currently, the package does not provide finely tuned values for the parameters of the JDE model, leaving the user the cumbersome task of finding the best values for the estimation.
Since 2013, PyHRF has started to evolve to deal with functional Arterial Spin Labelling (fASL) [VWV + 13] data, including a physiological prior to make the perfusion estimation more robust [FPVS + 14]  [FPFC15b]. A fast solution for fASL based on VEM was proposed in  [FPFC15c] , with similar results to the solution based on stochastic simulation techniques  [FPFC15a]. Moreover, many efforts have been made in terms of image processing, userfriendliness and usability of the PyHRF tool to make it more easy to use by non experts and clinicians. In the years to come, we plan to develop a light viewer to explore the results of PyHRF interactively. Moreover, we aspire to make the package compatible with Python 3 and extend its use to the analysis of fMRI data on small animals.
The electrodes were fixed (2 cm apart center to center) over the muscle bellies for TA and lateral gastrocnemius (LG) muscles. The EMG signals were amplified and filtered using a bandwidth of 10-1000 Hz (Gould 6600), and then addressed to an analog-digital converter piloted by the Turbolab software (SM2I, France) for their digitization at a sampling rate of 1000 Hz.
The identification of these landmarks was conducted with an application of Matlab Program (The Mathworks, Inc. , Natick, MA, USA), under visual control. In order to characterize the COP displacement, we calculated the excursion separating two landmarks, the average velocity (sum of distance separating two points of acquisition related to time elapsed in the phase (Figure  1 ). For EMG data, we have analyzed the period corresponding to weight bearing transfer from STL to SWL during which an inhibition of postural muscle and activation of motor muscles were reported. From an algorithm developed with Matlab program, we determined phasic activation of LG and TA of the SWL under visual control. The Matlab application was developed using previous methodological studies  (Mickelborough et al. , 2004).
EMG data were smoothed with low-pass filter cut off frequency of 20 Hz. The threshold for EMG activation detection was defined as mean added with 3 standard deviations of reference phase. The muscle was considered as active after 25 ms of consecutive above the threshold. Then, we calculated relative time of coactivation of LG and TA muscles during weight bearing transfer from SWL to STL (ratio of time of coactivation with time elapse in the phase).
Statistics Statistical analysis was carried out with Statview software (SAS Institute, Cary, NC, USA). Given that the data were normally distributed (according to a Kolmogorov-Smirnov test) and the equality of variance Levene median test) was confirmed, an unpaired t-test was used to compare the physically active group and yoga group in terms of kinetic and EMG values. The threshold for statistical significance was set to p < 0. 05. We also assessed the relationship between kinetic COP displacement parameters and muscle coactivation by applying Pearson's correlation test.
Haemoglobinopathies are the commonest monogenic diseases, with millions of carriers and patients worldwide. Online resources for haemoglobinopathies are largely divided into specialised sites catering for patients, researchers and clinicians separately. However, the severity, ubiquity and surprising genetic complexity of the haemoglobinopathies call for an integrated website to serve as a free and comprehensive repository and tool for patients, scientists and health professionals alike. This paper presents the ITHANET community portal, an expanding resource for clinicians and researchers dealing with haemoglobinopathies. It integrates information on news, events, publications, clinical trials and haemoglobinopathyrelated organisations and experts and, most importantly, databases of variations, epidemiology and diagnostic and clinical data.
Recent advances in biotechnology, particularly the emergence of high-throughput sequencing technologies, as well as recent increased research interest in the field of rare diseases, which affect fewer that one in 2000 population-wide, have led to an explosion in the amount of genetic, clinical and diagnostic information. In the field of haemoglobinopathies, the commonest rare disease in the world, online resources are largely divided into specialised sites catering for patients, researchers and clinicians separately, while available information has been fragmented into different databases, mainly variation-centric and genotypephenotype databases. More specifically, relevant information can be found in genome-wide resources, such as dbSNP  [11] , OMIM  [12] , ClinVar  [13]  and HGMD  [14] , but the level of detailed annotation varies, particularly for rare, yet clinically significant, variations  [15].
In addition, locus-specific databases, such as HbVar  [16]  and several databases based on the Leiden Open Variation Database (LOVD) software  [17, 18] , have been successful in the past, but their curation has been a challenging task. Therefore, there is an urgent need for an integrated resource that will bring together all available haemoglobinopathy-related information and will set up a community-based framework for the continuous and consistent collection and integration of high-quality data. Herein, we present the ITHANET community portal, an expanding resource for clinicians and researchers dealing with haemoglobinopathies, that (a) integrates the latest haemoglobinopathy-related information, including news, events, publications and clinical trials, (b) provides information about the worldwide community of organisations and experts working on haemoglobinopathies, and (c) develops, and maintains databases of variations, epidemiological, diagnostic and clinical data.
The ITHANET portal was initially the result of a multinational Euro-Mediterranean project  [19]  and has progressed to becoming an expanding community resource in collaboration with the Global Globin 2020 (GG2020) Challenge by the Human Variome Project (HVP). GG2020 is now officially linked to the ITHANET portal after review of all known existing databases in the field  [20]  and with additional partnership through a shared ITHANET-GG2020 Expert Panel application for haemoglobinopathy-related variant classification under the Clinical Genome (ClinGen) Resource. The ITHANET portal welcomes contributions to its content and acknowledges all contributions in a specifically designed section. ITHANET is free and available at http://www. ithanet. eu.
thalassemia [tiab] OR thalassaemia [tiab] OR hemoglobin [tiab] OR haemoglobin [tiab] OR sickle-cell [tiab] OR hemoglobinopathies [tiab] OR haemoglobinopathies [tiab]". Subsequently, the references are manually filtered to find relevant information. Importantly, ITHANET is an active partner in and beneficiary of primary research data from several projects on haemoglobin disorders, while additional information is retrieved through subscriptions to scientific societies and organisations. In addition, ITHANET accepts and incorporates contributions to its content by local experts from any country in the world and acknowledges their contribution eponymously in affected content pages of databases IthaGenes and IthaMaps and on a specifically designed page ("Contributors and Curators").
Latest Updates Through its role as a community resource, the ITHANET portal collects and provides the most recent information about haemoglobinopathies and closely related topics from the fields such as human genetics and genomics. Specifically, recent news, such as new research grants, significant discoveries, press releases and clinical trials, are provided in the "News" section, while searchable lists of past and future scientific events, recent publications and clinical trials are provided in sections "Events", "Publications" and "Clinical Trials", respectively. Information on scientific events is retrieved through subscriptions to numerous scientific organisations/societies, patient organisations and research networks, while information on publications and clinical trials is retrieved on a weekly basis from NCBI PubMed and clinicaltrials.
IthaGenes IthaGenes is a database that organises genes and variations affecting haemoglobinopathies, including disease-causing and disease-modifying variations, as well as diagnostically relevant neutral polymorphisms  [2]. IthaGenes entries started out with globin gene causative mutations and annotations initially collated in the books "A Syllabus of Human Hemoglobin Variants (Second Edition)"  [21]  and "A Syllabus of Thalassemia Mutations"  [22]  and has since comprehensively incorporated any subsequently reported variations and annotations from recent articles and reports  [23] [24] [25] [26]  and from existing information on other public databases, such as HbVar  [16] , dbSNP  [11] , ClinVar  [13] , OMIM  [12]  and SwissVar  [27]. IthaGenes has six main sections covering the catalogue of genes and regions, the catalogue of mutations, advanced search, statistics, the catalogue of references, and, finally, frequently asked questions (FAQs).
Advanced search provides a powerful interface for queries and access to all IthaGenes database entries, with a wide range of search options as previously described  [2]. Moreover, IthaGenes integrates the NCBI sequence viewer for detailed graphical representation of each variation and provides phenotype, epidemiology and HPLC data, related publications and external links. Comprehensive coverage, search functionality, extensive annotation and visualisation, systematic inclusion of external links and interconnectedness internally and with other ITHANET portal sections make IthaGenes an exceptional resource for professionals in the field. Over the past few years, IthaGenes has been established as the largest database of variations related to haemoglobinopathies, with the annotation of around 2000 disease-causing or potentially disease-causing variations.
Notably, a joint ITHANET-GG2020 application is currently under review by ClinGen for the assignment of an Expert Panel status towards comprehensive annotation of all variants affecting haemoglobinopathies, based on the guidelines of the American College of Medical Genetics and Genomics (ACMG), and their submission to the NCBI ClinVar database  [13]. An ongoing pilot study evaluating the Panel's haemoglobinopathy-specific rules for a subset of variations will help fine-tune the classification criteria and represents the final step required for Panel approval by ClinGen. The Panel will begin the variant annotation with the genes located in the globin gene clusters, which carry the commonest and most highly penetrant variations, and will subsequently proceed with annotation of variations located in other genes that have a modifying effect on haemoglobinopathies.
The first IthaGenes release  [2]  was mainly focused on the annotation of variations on the globin gene clusters with a small number of additional modifier genes. However, the enormous and still expanding accumulation of data from genome-wide association studies aiming to identify novel modifiers of haemoglobinopathies has resulted in the accumulation of new genes and loci associated with haemoglobinopathy-related phenotypes. In fact, information on trans-acting modifiers is already considered as an important factor in the clinical management of patients with haemoglobinopathies  [28]. Thus and after incorporating a large number of genome-wide or targeted association studies over the past two years, the number of genes and loci annotated in IthaGenes has increased from 32 to a total of 232 and, consequently, the number of annotated variations from 1963 to a total of 2690, comprising around 600 disease-modifying variants.
In addition, most variants are assigned at least one of 41 clinical phenotypes included in IthaGenes that are now linked with established phenotypic annotations by the Human Phenotype Ontology  [29, 30]  and OMIM  [12]. Figure  2  shows the distribution of disease-modifying mutations by their associated phenotype. New variations can be directly reported by completing the IthaGenes submission form or by using the ITHANET contact form (http://www. ithanet. eu/contact). Data stored in IthaGenes are freely available for download in CSV and JSON format.
IthaMaps IthaMaps is a global epidemiology database of haemoglobinopathies, illustrating data on a dynamic global to regional map. For the first release of the IthaMaps database  [2] , information on relative mutation frequencies were extracted from 89 publications and were illustrated on an interactive map at the country level for a total of 56 countries. Herein, the database and interface have been revamped to allow integration of additional types of information and more detailed data. Specifically, the IthaMaps content can now be divided into three classes: (a) country-specific information on existing haemoglobinopathy-related healthcare policies, (b) country-specific information on the status of major haemoglobinopathies, such as the prevalence, incidence and overall burden of β-thalassaemia and SCD, and (c) relative allele frequencies of specific globin gene mutations at the national and regional level, dynamically linked to corresponding IthaGenes entries.
In addition and through synergies of ITHANET with the HVP GG2020 Challenge, IthaMaps has benefitted significantly from direct contributions to its content from local experts, with 21 scientists annotating or reviewing data for their countries of expertise. In combination with a high volume of extracted information from a total of 287 published reports, IthaMaps organises epidemiological data for 183 countries, thus becoming a valuable tool for stakeholders and policymakers aiming for evidence-based decision making. Table  1  shows the distribution of annotated countries for parameters that describe existing haemoglobinopathy-related healthcare policies, and Table  2  shows the number of annotated countries per continent for epidemiological parameters that describe the status and burden of major haemoglobinopathies.
DISCUSSION The ITHANET Portal is a growing public resource on haemoglobinopathies and, as an interactive community tool, it invites contributions to its content, including news, events, publications and clinical trials, and contact information for clinical, research and diagnostic centres and patient organisations for haemoglobinopathies as well as epidemiological, genetic and diagnostic data. Through these contributions and centralised development, the ITHANET portal aims at expanding its current content with useful tools for scientists and policymakers involved in researching, diagnosing, preventing and treating haemoglobinopathies. Specifically, future developments of the ITHANET portal include the annotation of IthaGenes entries according to the guidelines by the ACMG  [31] , as part of the ITHANET-GG2020 Expert Panel, the development of epidemiological tools to further facilitate evidence-based policymaking and the implementation of a complete genotype-phenotype database for haemoglobinopathies.
Notably, ITHANET is maintained by permanent funding, thus ensuring long-term development and update of the portal, and is an active partner in and beneficiary of primary research data from several projects on haemoglobinopathies. In addition, ITHANET is an active member of the European Reference Network EuroBloodNet for rare haematological diseases and is involved in the development of patient registries for the nonmalignant section of the network. Most importantly, through its partnership with the global HVP GG2020 Challenge, ITHANET has become a comprehensive resource for all information relating to the haemoglobinopathies and is evolving into an indispensable tool for the research, prevention and diagnosis of haemoglobinopathies.
However, their definitive diagnosis is becoming a challenging task, owing to the large and increasing number of newly identified Hb variants, with around 1360 Hb variants currently stored in IthaGenes. IthaChrom aims at supporting the diagnosis of structural haemoglobinopathies by providing digitised anonymised reports (as kindly provided by Bio-Rad Laboratories Inc. ) of standard diagnostic HPLC analyses. The interface allows database searches of key data, such as the retention time, specific globin genes, Hb variants, HPLC instruments and methodologies. To date, more than 600 HPLC diagnostic reports are available in IthaChrom and we aim at expanding this section by increasing the catalogue of HPLC methodologies and programmes and by allowing contributions from additional laboratories.
ACKNOWLEDGEMENTS We are grateful to the following scientists that contributed or curated data:  Aurelio Maggio ,  Bertha Ibarra ,  Casten W Lederer ,  Catherine Badens ,  Celeste Bento ,  Coralea Stephanou ,  Cornelis L Harteveld ,  Esri Voskaridou ,  Fahd Al-Mulla ,  Hafizur Rahman ,  Hassan Syahzuwan ,  Jacques Elion ,  Joanne Traeger-Synodinos ,  Joan-LLuis Vives Corrons ,  John Old ,  Leticia Ribeiro ,  Léon Tshilolo ,  Marina Kleanthous ,  Mas Rina Wati Abdoul Hamid ,  Michael Angastiniotis ,  Abiageli Nnodu ,  Paolo Moi ,  Pavlos Fanis ,  Petros Kountouris ,  Salah Al-Humood ,  Valeriya Kaleva ,  Xiangmin Xu ,  Yvonne Daniel , and  Bin Alwi Zilfalil. An up-todate list of ITHANET contributors and curators is provided at http://www. ithanet. eu/community/ithanet-contributors. The authors would like to thank the  Cyprus Institute of Neurology and Genetics  for funding and computer equipment and for hosting ITHANET.
Results Phylogeny, diversification analyses and biogeographic reconstructions. Divergence times estimates inferred with BEAST v. 1. 7. 5 on the concatenated partitioned plastid-nuclear dataset  25  (Fig. 2 ) showed two primary clades: Clade I, with only two small subclades: (a) and (b), and Clade II which includes several nested subclades: (c), (d), (e), and the core formed by 23 species divided into two groups, one formed by herbs (f) and the other by succulents (g). All subclades were recovered by both the nuclear and plastid datasets, except for the position of subclade (d) (M. deserticola and M. ignorata), which in the nuclear dataset appears as sister to subclades (a) and (b) (Figs  S2  and S3 ). Divergence of Monsonia from the genus Pelargonium was dated in the Late Eocene-Early Oligocene (c.
Species distribution models, niche breadth and overlap. To study the climate niche of Monsonia species, we obtained SDMs with high predictive accuracy, according to the AUC criterion (Area under the receiver operating curve). These distributions were consistent with their known distribution range of each of the 34 species analyzed (Fig. 4 ). Moreover, the Principal Component Analysis (PCA) of the complete Monsonia dataset (Fig. 4 ) captured c. 84. 5% of the variance in the first three components (PC1: 41. 1%, PC2: 28. 0%, PC3: 15. 4%) (Table  S1 ) and divided Monsonia species along the first axis (PC1) into two groups: those occurring in warmer habitats, such as the northern African savannas and deserts, and those inhabiting cooler ecotypes, such as the Drakensberg Mountains and the Namib coastal belt (Fig.
4 ). The second axis PC2 divided the desert species from those growing in wetter environments. In total, three major groups were recovered: a group of species distributed in the Sahara Desert (subclade c), the group of Namib-Karoo species (subclades b, d, e, g), and a heterogeneous group formed by species occurring in savannas, steppes and grasslands (subclades a, e, and f). Monsonia speciosa (Cape region) appears in both the second and third groups, while M. glauca, M. vanderietae and M. camdeboense appear in intermediate positions between these groups (Fig. 5 ). Niche breadth values were calculated with the variables selected for the complete dataset analysis (Fig. 3 ; Table  S2 ). Monsonia nivea exhibited the widest climatic niche (0. 690), followed by M. heliotropioides (0. 4933), M. senegalensis (0.
4529), M. burkeana (0. 4436), and M. angustifolia (0. 354). The remaining species showed narrower niche breadths. The lowest values were found in Namib-Karoo coastal species such as M. multifida (0. 0086), M. inermis (0. 0149), M. lheritieri (0. 0209), M. drudeana (0. 0238) or M. deserticola (0. 0299). In order to increase the power of niche ordination and permit a good selection of variables for species exclusive to South African biomes, we also performed an analysis using a reduced dataset which included the species restricted to this region (e. g. 27 species). The PCA of this dataset (Fig. 5 ) captured c. 80. 4% of the variance in the first three components (PC1: 33. 5%, PC2: 30. 3%, PC3: 16. 6%; Table  S3 ). This analysis also resulted in significant differentiation under the ANOSIM test (p < 0.