dataesr/scientific-paragraphs-categorization huggingface dataset:
We included participants with congenital upper limb anomalies, as summarized in Tables  S2  and S3  in the Supplemental Material. Information on limb differences was self-reported, but verified for a subset of participants (88% and 55% in children and adults, respectively). We recruited children and adults without limb differences to match the education level and age of the special population over the same period. We recruited a larger sample of children without limb differences to give us a greater understanding of the range of typical performance in a two-handed population. Two-handed children were recruited through an existing university volunteer families database and affiliated Facebook page. For all participants, participation was approved by university ethics boards.
S5 Attempt clustering procedure To measure exploratory behavior, in the Results we looked at how likely participants were to "switch" strategies between attempts. Since strategies are hard to quantify, we used a nonparametric clustering package implemented in Python's SciPy package  (Virtanen et al. , 2020)  to discover strategy clusters from participant data. For each different level, we aggregated all attempts across all participants, taking the [x, y] spatial positions of each attempt as variables, but ignoring the specific choice of tool. We applied clusters separately for each participant group (children vs. adults, limb differences vs. no limb differences), but aggregated data across all participants from each group. Applying the Dirichlet Process mixture modeling package then gave us clusters for each level and group, as well as the probabilities that each attempt belonged to each cluster.
Acknowledgements We thank  Mathew Kollamkulam  for collecting the data from the adult samples,  Opcare, Limbo and Reach  for assistance with participant recruitment, and all participating families and volunteers. The study was supported by a  Wellcome Trust   Senior Research Fellowship  ( 215575/Z/19/Z ), awarded to TRM. KRA, KAS, and JBT were supported by  National Science Foundation Science Technology Center Award   CCF-1231216 ;  Office of Naval Research Multidisciplinary University Research Initiative  ( ONR MURI) N00014-13-1-0333 ; and research grants from  ONR ,  Honda , and  Mitsubishi Electric. For the purpose of Open Access , the author has applied a CC BY public copyright licence to any Author Accepted Manuscript (AAM) version arising from this submission.
ACC was listed as present in Madagascar (EPPO global database, CABI disease map 011), but there is some doubt about disease occurrence, which has not been confirmed by laboratory analyses during grove inspections. In the present study, we built on the high discriminatory power of microsatellite markers to assess the genetic structure of X. citri pv. citri in the SWIO region and to estimate the importance of strain circulation between the islands. A subset of strains representative of the genetic diversity was assigned to X. citri pv. citri lineages using minisatellite data. More specifically, we set out to answer the following questions: (i) Do all islands in the SWIO region host X. citri pv. citri genetic lineage 1, previously reported in Réunion, and to what extent are they related? (ii) Does this region host previously unreported lineages, and how prevalent are they? (iii) Is copper resistance present on SWIO islands other than Réunion? To answer these questions, we assembled and characterized a comprehensive strain collection, sampled from citrus in the Comoros (Anjouan, Grande Comore, Moheli and Mayotte), the Mascarenes (Mauritius, Rodrigues) and the Seychelles (Mahé).
The number of tandem repeats at each microsatellite locus was computed from fragment length for all assayed strains using GeneMapper 4. 0 (Applied Biosystems, Courtaboeuf, France) and used as input data. Nei's unbiased estimates of genetic diversity for MLVA-14 data were calculated using the poppr 2. 8. 3 package  [31]  in R version 3. 6. 1 (R Core Team, 2019. A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www. R-project. org/ (accessed on 27 April 2021)). Allelic richness (A) was calculated using the rarefaction procedure for unequal sample sizes (subsample size n = 21) using the hierfstat 0. 04-22 R package  [32]. Private alleles were identified using poppr. Genetic clusters (GCs) were identified as networks of haplotypes that differed by more than five microsatellite loci, using the algorithm recommended for MLVA data, combining global optimal eBURST and Euclidean distances in PHYLOViZ v1.
2  [33]. Relaxed clonal complexes (RCCs) were delineated as networks of MLVA-14 haplotypes linked by up to triple-locus variations. The population structure of X. citri pv. citri was further assessed using discriminant analysis of principal components (DAPC)  [34]. Here, we used the adegenet V. 2. 1. 1. R package  [35] , which involved optimizing the retained number of principal components from cross-validation analysis. DAPC was used, as it is free of any assumption linked to a population genetic model (e. g. , Hardy-Weinberg equilibrium or linkage equilibrium), and, thus, it is suited for analysis of datasets produced from predominantly clonal bacteria.
Minisatellite Typing (MLVA-31) The MLVA-31 scheme was used to analyse the genetic relatedness (assessed from the genotyping of 31 minisatellites) between a subcollection of SWIO strains selected on the basis of MLVA-14 data (Table  S1 ) and a worldwide strain collection of X. citri pv. citri  [15, 16, 21]  (http://bioinfo-web. mpl. ird. fr/MLVA_bank/Genotyping/view. php (accessed on 27 April 2021)). Primer pairs targeting single-locus alleles were used in a multiplex PCR format (Clontech Terra PCR Direct Polymerase Mix). One of each primer in the PCR mix was 5 -labelled with one of the following fluorescent dyes: 6-FAM, NED, PET and VIC (Applied Biosystems). PCRs and capillary electrophoresis were performed with an initial injection of 23 s, as described earlier  [17]. X. citri pv. citri strain IAPAR 306  [30]  was used as a control in each experiment.
Input data were obtained as described above (Section 2. 2). The MLVA-31 dataset generated in this study was deposited in the Xanthomonas citri genotyping database (http://bioinfo-web. mpl. ird. fr/MLVA_bank/Genotyping/view. php (accessed on 27 April 2021))  [15]. PHYLOViZ v1. 2 was used to build a minimum-spanning tree (MST) from the MLVA-31 dataset  [33]. We assigned SWIO strains to genetic lineages using discriminant analysis of principal components (DAPC), as previously reported  [16, 34]. Subclusters were identified as groups of strains linked by up to triple-locus variations using PHYLOViZ  [15].
citri pv. citri. We expected to see partial resistance on older leaves. When we spray-inoculated very young leaves, we expected a delayed lesion development until the stomata became susceptible. Areas under disease progression stairs (AUDPS) were computed from disease severity data using the agricolae 1. 3. 3 R package  [39]. AUDPS were converted into a ratio by dividing by the maximal area recorded and corrected to within ]0; 1[. A mixed generalized linear model with beta distribution was built for each host species with the strain or GC as the fixed effect and the plant as random effect using glmmTMB 1. 0. 2. 1 R package  [40]. The strain effect was analysed by deviance test using car 3. 0. 6 R package  [41] , and multiple comparisons, using emmeans 1. 5. 3 R package. In order to limit the heterogeneity in leaf susceptibility, the amount of censored data was estimated by maximizing the log-likelihood of the beta regression model for each host species.
Binary disease status datasets were also produced from disease severity scores and were analysed using survival analysis  [42]  with the survival 3. 1. 8 [43] and survminer 0. 4. 6 R packages. The analysis estimates the probability for a leaf to remain healthy until a given point in time. Comparisons between strains or GCs were performed using chi-square-based test statistics  [42]. Twenty-five days after inoculation (DAI), we enumerated X. citri pv. citri population sizes from single canker lesions. For each strain-cultivar combination, nine single canker lesions were collected using a sterile 8 mm punch. Each lesion was placed in a sterile microtube containing 1 mL of sterile buffer and gently agitated for 15 min. (50 rpm) at room temperature, mimicking the natural release of the bacterium from canker lesions in the presence of free water.
Each lesion was then transferred to a fresh tube and homogenized for 30 s in 1 mL of sterile buffer with sterile ceramic beads in a FastPrep24 device (MP Biomedicals). Appropriate dilutions of washates and grindates were plated on semiselective KC agar plates using a Spiral plater (Interscience). Plates were incubated for 3-4 days at 28 • C, and Xanthomonas-like colonies were enumerated, as recommended by the manufacturer. Exuded X. citri pv. citri population sizes, expressed as log(ufc. lesion -  ) were calculated from washate enumerations. Total X. citri pv. citri population sizes were calculated from washate + grindate enumerations. For each plant species, the strain effect on bacterial population size was analysed by ANOVA with Box-Cox power data transformation (residual variance homogenisation) using car and MASS 7.
The minimum spanning tree (not shown) suggested four main genetic clusters (GC) in the dataset. This structure was further supported by DAPC (Figure  2 ). Ninety-three percent of haplotypes identified in the Comoros (Anjouan, Grande Comore, Moheli and Mayotte) and the Mascarenes (Mauritius, Réunion and Rodrigues) clustered as GC1. The remaining haplotypes from these locations were structured as minor clusters (i. e. , networks grouping ≤3 haplotypes) or singletons (i. e. , haplotypes whose multilocus allelic profile differed from its closest relative at ≥6 microsatellite loci) (Table  2 ). Interestingly, all strains originating from the Seychelles were grouped into three distinct GCs, GC2 (n = 44), GC3 (n = 25) and GC4 (n = 13), which did not contain any haplotypes from any other island. The structure of GC1 from the Comoros and Mascarene Archipelagos was further examined by delineating RCCs, which grouped together closely related strains, in order to identify putative epidemiological links between strains.
Minisatellite-Based Assignation of SWIO Strains to X. citri pv. citri Genetic Lineages A subset of 215 strains from all GCs, minor clusters and singletons was submitted to minisatellite genotyping in order to assign them to the genetic lineages identified in X. citri pv. citri (Table  2 )  [15]. This complemented a dataset previously obtained from Réunion  [23] , yielding a total of 388 MLVA-31 genotyped SWIO strains. Based on DAPC, most strains from the SWIO region were assigned to genetic lineage 1 (pathotype A). A network of 73 MLVA haplotypes differing by single-or double-locus variations was produced among these strains (Figure  S1 ). Sixteen of these haplotypes were identified from at least two distinct SWIO islands. Haplotype #2, which was detected in five islands (Anjouan, Mayotte, Moheli, Mauritius and Réunion) and previously identified in different regions (Asia, Middle East, Oceania and South America)  [16] , was identified as the most frequent MLVA-31 haplotype in the SWIO region and as the putative founder haplotype.
Discussion We conducted a comprehensive molecular and pathological characterization of 568 X. citri pv. citri strains from the SWIO region. The present study confirmed a high prevalence of ACC in the SWIO region (the Comoros, the Mascarenes and the Seychelles), where X. citri pv. citri causes severe repeated disease outbreaks in citrus groves. X. citri pv. citri populations in the SWIO region were genetically diverse. Importantly, no copper resistance was detected in islands other than Réunion. Consistent with its worldwide prevalence  [15] , the present study highlighted the marked predominance of pathotype A genetic lineage 1 strains in the SWIO region. Very recently, SNP data showed that genetic lineage 1 strains causing ACC in the Seychelles are genetically diverse and clearly differ from strains originating from the Comoros and the Mascarenes  [49].
Although microsatellite data have recognized limitations for accurately assessing deep genetic relatedness among groups of individuals  [50, 51] , the present study revealed large genetic differences between strains from the Seychelles vs. the Comoros and the Mascarenes. These results suggest that several distinct introduction events of X. citri pv. citri have occurred at both regional and island scale (e. g. , Mahé in the Seychelles where strains isolated the same year from the same citrus species can be assigned to different GCs). Here, we provide the first report of the presence of genetic lineage 4 pathotype A* strains in three islands in the SWIO region, namely Moheli, Mauritius and Réunion. These strains were found to be genetically diverse based on minisatellite and microsatellite data.
The Seychelles Host Genetically Diverse X. citri pv. citri Pathotype A Strains Differing in Pathogenicity In the present study, we used a spray-inoculation technique to assess the pathogenicity of strains representative of the four GCs, which were identified in the SWIO region on three citrus species. A recent study evidenced that the structural and chemical properties of leaf surface components impact the susceptibility to X. citri pv. citri, i. e. , pathogenicity differs among citrus lines. This was observed after spray inoculation but not after infiltration inoculation  [52]. Interestingly, strains assigned to one of the unique groups detected in the Seychelles (GC3) differed to strains assigned to other GCs in terms of the dynamics of canker lesion development, i. e. , disease incidence and AUDPS data on citron, an ancestral Citrus species from which some modern lines originated [7].
Funding: The  European Union  (ERDF contract  GURDT I2016-1731-0006632 ,  ERDF INTERREG IV and INTERREG V) ,  Conseil Régional de La Réunion , the  French Agropolis Foundation  ( Labex Agro-Montpellier, E-SPACE  project number  1504-004 ),  ANSES  and  CIRAD  provided financial support. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Nested qualitative study A qualitative sub-study is conducted alongside the trial, using semi-structured interviews with both the lay counsellors who delivered the intervention and with a sample of participants from each study arm. The sub-study aims to investigate the acceptability, challenges, enablers and potential benefits of the combined intervention compared to ESoC. The sub-study also investigates the acceptability and effectiveness of the therapist training and therapy supervision for this intervention. The data will be analysed using grounded theory principles and thematic content analysis  [85, 86].
Data management {19} Study data are collected and managed using a secure, web-based data collection platform on encrypted tablet computers which are securely uploaded to a central server hosted at AHRI. The platform is a secure, web-based software platform designed to support data capture for research studies, providing (1) an intuitive interface for validated data capture; (2) audit trials for tracking data manipulation and export procedures; (3) automated export procedures for seamless data downloads to common statistical packages and (4) procedures for data integration and interoperability with external sources. The tablets are encrypted, and all documents are stored safely under confidential conditions. On all trial-specific documents, other than the signed consent forms, the participant is referred to by the trial participant number, not by name.
Confidentiality {27} The trial staff ensure that participants' anonymity is protected as far possible. The trial complies with GCP and established practice which requires data to be anonymised as soon as it is practical to do so. Audio and video recordings are stored on a secure server with very limited access (to senior research team members and researchers working directly with a particular participant or directly involved in coding data). Participants are informed that audio-visual recordings will not be used outside of the team of researchers, and this is outlined in the consent form. Following the completion of the trial, the data will be downloaded and de-identified. Plans for collection, laboratory evaluation and storage of biological specimens for genetic or molecular analysis in this trial/future use {33} Not applicable, no biological samples are collected.
Methods in analysis to handle protocol non-adherence and any statistical methods to handle missing data {20c} Women with outcome data will be analysed in the groups to which they are randomly assigned, regardless of deviation from the protocol or treatment received (modified ITT population). Women whose infant died during the trial will also be included unless they withdrew consent to participate further in the trial. The number and percentage of individuals missing data for outcome measurements that are based on summing items to give overall scores (e. g. EPDS, GAD-7, and subscales of the PSI/SF and CBCL) will be described. Missing items will be imputed if ≤ 20% are missing using the median score of completed scale items unless alternative guidance is provided in the scoring manual. A multiple imputation analysis will be performed for the primary infant and maternal outcome if attrition exceeds 5%.
Plans to give access to the full protocol, participant leveldata and statistical code {31c} Data will be available beginning 9 months and ending 36 months following main article publication to researchers who provide a methodologically sound proposal purposes to achieve aims in the proposal and /or for individual participant data metaanalysis. Data is documented and stored on the AHRI Data Repository (https://data. ahri. org) with a digital object identifier (doi) and can be accessed with permission and in line with AHRI policies and procedures. Data requestors will need to sign a data access agreement before any data can be shared. In addition, Study Protocol and Statistical Analysis Plan documents will be available. Data sets associated with publications will be made available in line with Wellcome Trust data policies and journal requirements.
The operational sub-group includes the four trial components (recruitment, assessment, therapy, ESoC) and is managed by the trial coordinator. The operational teams meet at least once a week, with individual meetings and contact points as needed. The four teams are operated separately to ensure blinding. The data management subgroup meets weekly and includes the AHRI data manager, trial manager and trial coordinator. The investigators' group consist of the trial management group and experts (investigators) in maternal health and early child development. The investigators are consulted regularly dependent on their specific roles and small sub-group meetings are organised as needs be. The whole group meets formally every 3 months. Trial Progress Reports containing updates, summary statistics and graphs and discussion points are submitted for these meetings.
Frequency and plans for auditing trial conduct {23} The trial is being conducted in accordance with the currently approved protocol, GCP, relevant regulations and SOPs. The risk management protocol and operational SOPs are reviewed as necessary throughout the trial to reflect significant changes to the protocol or outcomes of monitoring activities. The AHRI research data management team conducts frequent checks of the trial, including recruitment patterns and the quality and completeness of data using relevant software as well as manual checks. Lists of missing data will be generated automatically for regular checks. Furthermore, the data collection software is designed to optimise correct data capture by specifying the data and values required. Plans for communicating important protocol amendments to relevant parties (e.
Availability of data and materials {29} Data will be available beginning 9 months and ending 36 months following main article publication to researchers who provide a methodologically sound proposal that purposes to achieve aims in the approved proposal and /or for individual participant data meta-analysis. Data is documented and stored on the Africa Health Research Institute (AHRI) Data Repository (https:// data. ahri. org) with a digital object identifier (doi) and can be accessed with permission and in line with AHRI policies and procedures. Data requestors will need to sign a data access agreement before any data can be shared. In addition, Study Protocol and Statistical Analysis Plan documents will be available. Data sets associated with publications will be made available in line with Wellcome Trust data policies and journal requirements.
Such advances would enable accurate, physics-based inputs from the Sun to global climate models. Assessing how solar activity models perform requires their testing with historical datasets. Reconstruction of past solar activity and long-term climate variations (across centuries) also opens up the possibility of separating natural and anhttps://doi. org/10. 5194/angeo-39-1013-2021 Ann. Geophys. , 39, 1013-1035, 2021 thropogenic causes of climate change. In the industrial and post-industrial era, anthropogenic forcing clearly dominates over natural climate drivers, and thus going back to the preindustrial era to establish the role of natural drivers is crucial. However, large uncertainties remain. There are information gaps, for example, for past solar spectral irradiance variations over millennial timescales and the floor of activity during the Maunder Minimum.
Acknowledgements. The authors acknowledge the  International Space Science Institute , which provided support for two fora, one at  ISSI Beijing  and one at  ISSI Bern. Financial support. Loren C. Chang  was supported by grant no. 109-2636-M-008-004  from the  Taiwan Ministry of Science and Technology. Nat Gopalswamy is supported by  NASA 's Living with a  Star programme. Olga V. Khabarova  was partially supported by  RFBR  grant no. 19-02-00957. Emilia Kilpua acknowledges the  Sol-MAG  project ( ERC-COG 724391 ), funded by the  European Research Council (ERC)  in the framework of the  Horizon 2020 Research and Innovation Programme , and the  Finnish Centre of Excellence in Research of Sustainable Space (Academy of Finland , grant no. 312390 ). Review statement. This paper was edited by  Dalia Buresova  and reviewed by two anonymous referees.
| Data analysis Paired-end sequence data obtained from probe libraries were filtered and trimmed using cutadapt version 2. 10 (Martin, 2011). We only kept those read pairs showing a fragment of the P1 adapter (consisting of the T7 promoter and the restriction cut-site) at the beginning of the first read, and the second cut-site at the beginning of the second read. For the P1 adapter, no more than three errors were allowed, including at best one base indel at read start. For the second adapter, full sequence match to the second cut-site and full overlap thereof were enforced. The resulting trimmed read pairs were then aligned against the horse EquCab3 reference genome  (Kalbfleisch et al. , 2018)  using paleomix version 1. 3. 2 (Schubert et al. , 2014)  and both bwa version 0. 7. 17 backtrack algorithm  (Li et al.