dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements: For the purpose of  Open Access , the author has applied a CC BY public copyright licence to any Author Accepted Manuscript version arising from this submission. We thank the  Newcastle University Flow Cytometry Core Facility ;  Sanger Institute Cellular Genetics IT; Newcastle upon Tyne NHS Trust NovoPath ;  Tamilvendhan Dhanaseelan  of  HDBR  for assistance with human fetal tissue processing and cell freezing;  Natalina Elliott  for contributions toward CITE-seq panel design. CAl is funded by the  Open Targets consortium  ( OTAR026  project) and  Wellcome Sanger core funding  ( WT206194 ). MI is supported by the  Wellcome Trust  ( 215116/Z/18/Z ) and thanks the PhD program FIRE and the  Graduate School EURIP of Université Paris Cité  for their financial support. JPal is funded by  NIH   NHLBI R01 (HL151777 ).
Funding: We acknowledge funding from the  Wellcome Human Cell Atlas Strategic Science Support  ( WT211276/Z/18/Z ),  MRC Human Cell Atlas award ,  Wellcome Human Developmental Biology Initiative , and  HDBR  ( MRC/Wellcome   MR/R006237/1 ). MH is funded by  Wellcome  ( WT107931/Z/15/Z ), The  Lister Institute for Preventive Medicine and NIHR  and  Newcastle Biomedical Research Centre. SAT is funded by  Wellcome  ( WT206194 ) and the  ERC Consolidator Grant ThDEFINE. Relevant research in the BG group was funded by  Wellcome  ( 206328/Z/17/Z ) and the  MRC  ( MR/M008975/1  and  MR/S036113/1 ). IR is funded by  Blood Cancer UK  and by the  NIHR Oxford Biomedical Centre Research Fund. EL is funded by a  Sir Henry Dale  Fellowship jointly funded by the  Wellcome Trust  and the  Royal Society  ( 107630/Z/15/A ).
Drafted the manuscript: LJ, SW, RB, IG, MH Designed the manuscript figures: JE, RB, CAd Data and materials availability: All novel raw sequencing data from this study are made publicly available at ArrayExpress as FASTQs and count matrices as follows: -i) Human embryonic liver and yolk sac 10x scRNA-seq (E-MTAB-10552) -ii) Human embryonic yolk sac 10x scRNA-seq (E-MTAB-11673) -iii) Human embryonic yolk sac Smart-seq2 scRNA-seq (E-MTAB-10888) -iv) Human embryonic yolk sac CITE-seq (E-MTAB-11549) -v) Human embryonic liver CITE-seq (E-MTAB-11618) -vi) Human fetal liver CITE-seq (E-MTAB-11613) Accessions for published data reused in this study are detailed comprehensively in Table  S6. Processed single cell datasets and supplementary tables are available for interactive exploration and download as well as corresponding trained scVI and logistic regression models via our interactive web portal (https://developmental.
( H ) H FDG overlaid with PAGA showing monocyte-independent trajectory from iPSC scRNAseq (21) AEC to macrophage prior to D21 (n=5; k=779; left) and monocyte-dependent trajectory to macrophage after D21 (n=7; k=8,553; right). Arrows indicate the trend of trajectory. Coloured by cell state (Table S7, S5). (I) Matrix heatmap showing the pySCENIC-derived regulons associated with the iPSC macrophage trajectories as shown in i for the iPSC scRNA-seq dataset (21).
based on non-invasive data (MRI, PET, EEG, MEG) and, frequently, on the basis of intracerebral recordings, in particular stereoelectroencephalography (SEEG). Numerous works have shown that epileptogenic regions are more often organized in a network than originating from a single epileptic focus  1, 2. SEEG data indicate that epileptogenic networks follow a hierarchical organization with maximal epileptogenicity in the epileptogenic zone network (EZN)  1. Many methods have been proposed to quantify these EZNs. The epileptogenicity index (EI) quantifies the EZN based on both the spectral and temporal properties of SEEG signals  3. Subsequently, connectivity EI (cEI)  4  combines connectivity features with EI for better prediction, especially of seizures with slow onset patterns. A statistical parametric map method was proposed to map EI from SEEG signals anatomically onto the patient's MRI  5.
The source-to-sensor map, the so-called gain matrix, is a function of the distance between the sources and sensors  24. Brain regions, the connectivity matrix, and the gain matrix are all defined in the patient-specific brain space, which are determined by the T1-MRI images. Note that, in the VEP pipeline the source space covers the whole brain, specifically, 162 brain regions defined by the VEP atlas in the NMM, whereas the sensors only sample part of the brain, depending on the patient-specific SEEG implantation scheme. The model inversion module allows us to infer the unknown model parameters by fitting the SEEG data features. The VEP workflow includes two independent model inversion methods based on Bayesian inference, namely the optimization and sampling pipelines, which complement each other.
The optimization pipeline uses the LBFGS algorithm  25  , given prior information and the likelihood function of the data, to obtain a (local) maximum of the posterior distribution estimate of the model parameters, so-called a maximum a posteriori (MAP). The low computational costs and high robustness of this MAP method allow us to measure the sensitivity of the estimated results in terms of the sampled SEEG sensor space. To do this, 100 MAP estimates are obtained on datasets with a random sensor removed, resulting in a distribution of epileptogenicity values (EVs) for each region. In the sampling pipeline, full Bayesian inference uses a self-tuning variant of the Hamiltonian Monte Carlo (HMC) algorithm to estimate the potential multimodal posterior distributions of the parameters of the personalized NMMs  26, 27.
Multiple chains start from different random initial conditions to sample the model parameters from their respective posterior distributions. While HMC handles large parameter space well, its implementations still struggle with the highdimensional and unfavorable posterior geometries, so a reparameterization of the regional parameters is used to facilitate an efficient exploration of the posterior distribution over the reparameterized configuration space in terms of computational time and convergence diagnostics. The inputs of the model inversion module are the data feature and a prior. The envelope of signal power (high-pass filtered signal followed by envelope smoothing) from empirical SEEG recordings serves as the data feature. We illustrate the data features from the two patients with the most common seizure onset patterns in Fig.
2a ,b as well as three patients with other patterns in Supplementary Fig. 2. The prior can be estimated or defined by added knowledge, such as the MRI-visible lesions or the clinical EZ hypothesis based on a combination of clinical evidences or extended data analysis from the SEEG signal. We also designed prior estimate algorithms based on delay information from filtered SEEG signals in multiple frequency bands during seizure onset. The VEP workflow can use the different priors to provide a complete picture as the outcome from the model-based inference. The output of the model inversion module results in a distribution of EVs, which is determined by the key parameters from the model inversion and the patient's structural connectivity. We will explain this important definition in more detail in the next subsection.
We validated the VEP using synthetic data generated by the NFMs (see Fig. 3 ). We used 6D Epileptor on each of the 260,000 vertices representing the cortical mesh. The EZN was set to cover 3 brain regions in the left hemisphere. Fig. 3a  illustrates the time series in the selected source signals and the signal power, which is mapped based on the brain high-resolution meshes. We observed complex spatial patterns within a brain region in which the seizure onset and offset times were not necessarily synchronized. We generated synthetic SEEG sensor data using the gain matrix while considering both the orientation and distance of the dipoles with respect to the sensors (Fig. 3b ). The envelope function of the synthetic SEEG data provided the data feature for the VEP model inversion. Here the VEP inversion module was based on the NMM, with a lower dimensional model (2D Epileptor), which captures the envelope function, and a low-resolution gain matrix that considers only the distance between the nodes and sensors (Fig.
3c ). Based on the distribution of EVs, the EZN was declared. Both the optimization and sampling pipelines identified the ground-truth EZN, as shown in Fig. 3d  and Fig. 3e , respectively. The optimization pipeline used the informative prior from our designed VEP prior algorithm. The clinical table based on the distribution of EVs shows the optimized results from datasets with different SEEG spatial samplings (Fig. 3d ). The sampling pipeline used a noninformative prior in which all the brain regions followed the same prior distribution. The posterior distribution of EVs from the sampling pipeline was based on 16 chains starting from 8 different optimized initial conditions. Each HMC chain created 500 samples (Fig. 3e ). The heatmap projected on the patient's T1-MRI shows the spatial mapping of the EZN (sagittal, axial and coronal view images shown in Fig.
The posterior distribution of 8 brain regions (Fig. 4d ) confirmed the 3 brain regions identified by the optimization pipeline. Another four brain regions, seizing after these first 3 brain regions in the EZN, showed multimodal posterior probability distributions. The multimodality comes from the non-identifiability of the observable date feature, i. e. , different parameter sets can lead to different patterns of latent source activity but still have a similar observable SEEG data in the sensor space. We analyzed four seizures of this patient and integrated the results of the four seizures to obtain the three brain regions as EZNs (Supplementary Fig. 9 , and Methods). The EZN from the clinical hypothesis (L-IGB and L-OFC) and VEP atlas (L-IGB, L-OFC, and L-IFS) were overlaid onto the pre-surgical T1-MRI (Fig.
In this paper, we have provided a clear definition of the EZN from a modeling perspective and the corresponding calculations. Both the full sampling of the HMC in the Bayesian inference and the neural field high resolution simulation are state of the art. The current VEP has been extensively tested using a cohort of 53 retrospective patients and is now being evaluated prospectively in a large French clinical trial (EPINOV NCT03643016) recruiting 350 patients from 11 epilepsy centers. The main objective of EPINOV is to evaluate the capacity of the Virtual Epileptic Patient (VEP) algorithms to improve clinical outcomes in patients suffering from focal drug-resistant epilepsy undergoing SEEG surgery. Other studies have shown that the EZN is highly correlated with the brain structural lesions observed in MRI  35.
Patient data We obtained the dataset from 53 patients with drug resistant focal epilepsy who underwent a standard presurgical protocol at La Timone hospital in Marseille. Informed written consent was obtained for all patients in compliance with the ethical requirements of the Declaration of Helsinki and the study protocol was approved by the local Ethics Committee (Comité de Protection des Personnes sud Méditerranée 1). The non-invasive evaluation included assessing the patients' clinical records, neurological examinations, neuropsychological testing, and EEG recordings. The subjects' clinical data are given in Supplementary Table  1. 16 bits/sample) using no digital filter. Two hardware filters were present in the acquisition procedure: a high-pass filter (cut-off frequency equal to 0. 16 Hz at -3 dB), and an anti-aliasing low-pass filter (cut-off at one third of the respective sampling frequency).
Data processing To construct the individual brain network models, we first preprocessed the T1-MRI and DW-MRI data. Volumetric segmentation and cortical surface reconstruction were from the patient-specific T1-MRI data using the recon-all pipeline of the FreeSurfer software package (http://surfer. nmr. mgh. harvard. edu). The cortical surface was parcellated according to the VEP atlas  22  , and the code is available at https://github. com/HuifangWang/VEP_atlas_shared. git. We used the MRtrix software package to process the DW-MRI 47 , using the iterative algorithm described in  48  to estimate the response functions and subsequently used constrained spherical deconvolution  49  to derive the fiber orientation distribution functions. The iFOD2 algorithm  50  was used to sample 15 million tracts.
The structural connectome was constructed by assigning and counting the streamlines to and from each VEP brain region. The diagonal entries of the connectome matrix were set to 0 to exclude self-connections within areas and the matrix was normalized so that the maximum value was equal to one. We obtained the location of the SEEG contacts from post-implantation CT scans using GARDEL (Graphical user interface for Automatic Registration and Depth Electrodes Localization), as part of the EpiTools software package  51. Then we coregistered the contact positions from the CT scan space to the T1-MRI scan space of each patient.
We specified the prior probabilities for the epileptogenicity parameter ‫ݔ‬ , for brain region ݅, the time scale of the slow variable where ߭ and ߭ are the empirical and estimated SEEG data features. In our case, we considered the data feature to be both the seizure envelopes and the simulated SEEG channel power. Two algorithmic diagnostic metrices are goodness of convergence and goodness of fit. Goodness of convergence represents the number of runs that terminated properly, i. e. , the varying of the likelihood converges to the given threshold. Goodness of fit is defined by 1 െ ∑ሺ‫ݎܽݒ‬ሺ߭ െ ߭ ሻሻ/ ∑ሺ‫ݎܽݒ‬ሺ߭ሻ, where the sum is across all the SEEG recorded channels.
Sensor sensitivity and clinical reports In the optimization pipeline, we performed bootstrapping to derive a measure of confidence for our estimated EVs, in terms of the robustness of the identified brain regions with respect to sensor location sensitivity. In each bootstrapping sample, a randomly chosen bipolar SEEG contact is removed from the full set and the remaining data is used to run the optimization algorithm in Stan. This means that the empirical data, which needs to be fitted in each run, contains one less SEEG envelope timeseries, one less SEEG estimated power, and one less row in the sourcesensor mapping matrix. This procedure is repeated 100 times, selecting SEEG contacts randomly with replacement. All EVs are normalized by subtracting the minimum median EV and dividing by the difference between the maximum and minimum median EVs.
3, 4 5] [6] [7]  A large homozygous deletion on chromosome 2q12-q13 including the entire NPHP1 gene is the most frequent disease-causing variant accounting for 20% to 40% of all cases. ] [10]  Three clinical subtypes have been established based on age at onset of ESKD as follows: Infantile, juvenile, and adolescent NPH. 11, 12 The so called "classical juvenile NPH" is the most common entity associated with ESKD at a median age of 13 years. 13 Conversely, in infantile NPH, ESKD is reached before the age of 5 years (mean age 8 months) whereas in adolescent forms kidney function is usually preserved until adulthood (mean age 19 years). 11, 14, 15 Variants in NPHP2 and NPHP3 rather predispose for infantile NPH, whereas other genes are linked to late onset forms (e. g. , NPHP4). 5, 16 evertheless, the more widespread use of modern sequencing techniques in combination with clinical information gained from large-scale databases opened up new perspectives to define more precisely the age spectrum for the onset of ESKD associated with distinct gene variants.
Patient Recruitment Phenotypic data of 383 genetically characterized individuals was obtained from 3 independent sources, namely the Network of Early Onset Cystic Kidney Diseases (NEOCYST) clinical registry (n ¼ 105),  27  an online survey sent out to the members of the European Reference Network for Rare Kidney Diseases (ERKNet; n ¼ 60) and a complementary literature search (n ¼ 218) (Figure  1 ). In addition, clinical information of 44 genetically unsolved individuals was available from the NEOCYST registry. Patients were enrolled in the registry from February 1, 2010, through December 31, 2020. The ERKNet-based online survey asked for a limited data set containing information on sex, age, genetic background, GFR decline, hypertension, proteinuria, and a medical history of ACEI treatment.
Patient baseline characteristics at study entry are outlined in Supplementary Table  S1. All patients and/or their parents gave written informed consent. The study was approved by the Ethics Committee of the University of Münster (2016-284-f-S) in accordance with the Declaration of Helsinki. For the literature search, the MEDLINE library was screened for original articles including case reports up to March 2020, providing data on kidney survival or onset of ESKD for the following genotypes: NPHP3, NPHP4, and NPHP11/TMEM67. In total, data from 218 individuals (86 NPHP3, 64 NPHP4, and 68 NPHP11/TMEM67) were identified and extracted from the literature containing information on the age at ESKD and the underlying disease-causing variants (Supplementary Table  S2 ). No literature-based data was generated for the NPHP1 cohort.
ACKNOWLEDGMENTS The authors contributing their patients:  Ulrike Walden ,  Katharina Hohenfellner ,  Ludwig Patzer ,  Henry Fehrenbach ,  Hagen Staude ,  Martin Koemhoff  and  Heiko Billing. NEO-CYST (Network of Early Onset Cystic Kidney Diseases ; www. neocyst. de) 27is funded by the  German Federal Ministry of Education and Research -grant code  01GM1515A. This study was also supported by the  European Reference Network for Rare Kidney Diseases , which is partly co-funded by the  European Union  within the framework of the  Third Health Program  " ERN-2016-Framework Partnership Agreement 2017-2021. ".
Participants and Procedures We surveyed participants enrolled in the SPIN Cohort. Eligible SPIN Cohort participants must be classified as having SSc according to the 2013 ACR/EULAR criteria  (14) ; ≥ 18 years of age; fluent in English, French, or Spanish; and able to respond to questionnaires via the Internet. Eligible individuals are invited by their attending physician or supervised nurse coordinator to participate in the SPIN Cohort, and written informed consent is obtained. The local SPIN physician or supervised nurse coordinator completes a medical data form that is submitted online to initiate participant registration. After completion of online registration, an automated welcoming email is sent to participants with instructions for activating their SPIN account and completing SPIN Cohort measures online.
The email invitation and announcements provided a link to the survey on Qualtrics (15). In Qualtrics, participants entered their SPIN username (email address) in order to access and complete the survey questions. The survey was closed in October 2019. We excluded participants who only partially completed the survey. SPIN Cohort assessment data were obtained from the most recently completed assessments prior to completing the SPIN Physical Activity Survey for participants and prior to the initial survey invitation for non-participants, without time restriction. The SPIN Cohort was approved by the Research Ethics Committee of the Centre intégré universitaire de santé et de services sociaux du Centre-Ouest-de-l'Île-de-Montréal (#MP-05-2013-150) and by the research ethics committees of each participating centre.
Sociodemographic and Medical Characteristics Medical data were provided by SPIN physicians upon enrollment in the SPIN Cohort, and included time since first non-Raynaud's phenomenon symptoms, time since SSc diagnosis, SSc subtype, degree of joint contractures for small and large joints, tendon friction rubs status, interstitial lung disease status, pulmonary arterial hypertension status, Raynaud's phenomenon status, digital ulcer status (digital pulp and anywhere else on the finger), and gastrointestinal tract involvement status (esophageal, stomach, and intestinal). For each participant, we calculated the time from when sociodemographic and medical characteristics were obtained at entry into the SPIN Cohort to survey completion.
BnMLO2_2 is associated with SSR resistance To detect if loci for SSR resistance were existed in natural populations, a GWAS was conducted in a panel of natural population consisting of 222 accessions. Phenotypic measurements were de ned as stem lesion length at 17 days post-inoculation (dpi) with S. sclerotiorum in the natural environment. Phenotypic data showed the extreme variations as it ranges from 1. 5 cm to 22. 8 cm. A total of 2,779,265 SNPs were used to perform GWAS analysis using general linear model. Our results showed that, one signi cant block from 17. 35-17. 45 million base pairs (Mbs) on chromosome A08 was identi ed targeting SSR resistance (Fig. 1A  and Table  S1 ). Among the 0. 1 Mb block, most of the signi cant SNPs were within the region of a MLO family gene, named BnMLO2_2 (BnaA08g25340D), and the signi cant SNPs showed strong LD (Fig.
BnMLO2 positively regulates SSR resistance in B. napus Generally, promoter regions played a huge role in the regulation of gene expression at mRNA levels. As shown in Fig. 1 , the BnMLO2_2 promoter regions contain eight types of variations distributed across our natural population, and among, eight SNPs were highly associated with SSR resistance. Therefore, the cis-acting regulatory elements in the promoter regions of the three haplotypes were further investigated using related data retrieved from the "PlantCARE" database. Speci cally, 1. 5 kb upstream coding region of BnMLO2_2 was analyzed, and a total of 30 cis-acting regulatory elements were obtained, and of these, 28 were found in three haplotypes (Fig. 2A , Table  S3 ), except GA-motif and ERE, which was only detected in accessions of haplotype 1 and 3, respectively (Fig.
sclerotiorum Many genes expressed in different times and spaces and performed speci c functions together to regulate the growth and development of plant organisms. There are seven Arabidopsis orthologous genes of AtMLO2 reported in B. napus  [51]. To understand their functions during the entire growth period, we used transcriptome data of 35 different tissues and stages of B. napus cultivar ZS11 retrieved from B. napus transcriptome information resource (BnTIR), a recently available database  [52]. The fragments per kilobase of transcript per million mapped reads (FPKM) were used to evaluate the expression level of seven genes belongs to BnMLO gene family. Results showed that, BnMLO2_1 and BnMLO2_7 were relatively expressed lower in all tissues, and BnMLO2_2 was relatively higher expressed in leaf and silique tissues (Fig.
4G ). Phylogenetic and Syntenic Relationship of MLO Gene Families in Brassica species B. napus was formed by the event of allopolyploidy between the recent ancestors of B. rapa and B. oleracea, which shared the ancient ancestor of Arabidopsis  [56, 57]. During the brassicas genome duplications and genome mergers, MLO genes were embedded from Arabidopsis into the brassicas including B. napus (Fig. 5A ). Using 15 Arabidopsis MLO protein sequences as query, 23, 28, and 57 genes were identi ed in B. rapa (BaMLO), B. oleracea (BoMLO) and B. napus (BnMLO), respectively through the reciprocal BLASTP between the protein sequences retrieved from their respective genome databases (Table  S8 ). The 57 BnMLO genes were distributed across all the chromosomes of B. napus, and their gene length and physio-chemical properties were also different (Figure  S2 , Table  S9 ).
SNP and GWAS The resequencing data were based on published data from our laboratory  [76]. GWAS was performed in R script under the general linear model (GLM) using 2779265 SNPs  [77]. The minor allele frequency (MAF) ≥ 0. 05. The signi cance threshold for GWAS was set to P < 1. 799×10 -8 (0. 05/SNP number). The Manhattan plot was displayed through qqman software  [78].