dataesr/scientific-paragraphs-categorization huggingface dataset:
Figure13displays the similarities and differences induced by the experimental setting in terms of word information density ranking. In each panel, the left column indicates the original ranks assessed using the whole parallel corpus (these ranks are thus identical to the leftmost ones reported in Figure9in the WID panel) while the right column shows the average rank over the twenty-fold sampling for WID_FP, WID_PP, and WID_NP in left, central, and right panel respectively. By construction, the left panel shows results identical to the WID panel shown in Figure9, with only three rank swaps between WID on the whole dataset and the 20-fold subset sampling. As expected, relaxing the parallelism constraints introduces larger differences: ranking is modified for 22 languages and 18 languages in the WID_PP and WID_NP configurations, respectively.
Yoon Mi Oh and François Pellegrino same meaning on average, despite fluctuations (but see Christodouloupoulos & Steedman, 2015 for a discussion on the translation process). [6] Figure 1. (a) Geographical distribution. (b) Distribution of the languages among WALS classical typological features and symbolic codes. Marker color and shape respectively encodes the fusion strategy and the exponence category. Marker size further indicates whether verbal inflection is limited (small size for Low values) or more extended (large size for Mid and High values). In each cell, the number of languages is displayed when different from zero. See text for details and Supplementary Information ( § 3) for the list of languages belonging to each category 2. Segmentation into words was performed by the authors with the Myan-word-breaker library (downloaded at https://github.
2014; Sun et al. 2017a; Yadav et al. 2018; Schotanus and Heitman 2020)  and are predicted to constitute functional centromeres in C. depauperatus (Figure  1A ). Gene prediction and annotation identified 6,342 and 6,329 protein-coding genes, respectively for CBS7841 and CBS7855. These numbers are in the lower range among the number of genes predicted for other Cryptococcus species with complete genomes publicly available (ranging from 6,405 in C. deuterogattii R254 to 8,248 in C. amylolentus) (D  'Souza et al. 2011; Janbon et al. 2014; Farrer et al. 2015; Sun et al. 2017a; Passer et al. 2019; Grohs Ferrareze et al. 2021). Analysis of Benchmarking Universal Single-Copy Orthologs (BUSCO) revealed a high level of completeness of gene sets, with over 90% of the full-length 4,284 BUSCO genes (tremellomycetes_odb10 dataset) being present in both isolates (Figure  1 figure supplement 1).
The remaining 10 queried genes were found scattered throughout the genome, including STE11, RUM1, and BSP1 on Chr 7, STE12 and ETF1 on Chr 1, and a single mating pheromone (MF) precursor gene on Chr 6 (Figure  2A  and Figure  2 source data 1) predicted to encode a 37-amino acid product with a C-terminal CAAX motif characteristic of fungal mating pheromones  (Kües et al. 2011; Coelho et al. 2017 ) (Figure  2B ). Among the genes missing in C. depauperatus, it was surprising to note the complete absence of homologs of the homeodomain transcription factors SXI1 (HD1) and SXI2 (HD2) as these genes invariably have central roles in mating type determination and regulation of the sexual cycle in other basidiomycetes [reviewed in  (Coelho et al. 2017) ]. To validate this further we performed  domain-based searches for proteins containing homeodomains (InterPro entries: IPR008422 and IPR001356).
Following PCR confirmation of gene deletion, mutants were analyzed by light microscopy and scanning electron microscopy for phenotypic defects. C. depauperatus mf and ste3a mutants displayed strikingly similar hyphal morphology, and defects in basidia maturation compared to wild-type CBS7841 (Figure  5 ). Notably, the two mutants showed a significant reduction in the frequency of basidia with spore chains (Figure  5B  and Figure  5 source data 1) and exhibited unsporulated basidia in both actively growing and older hyphae that were significantly smaller compared to wild-type unsporulated basidia (Figure  5C     In such cases, the basidia diameter was not significantly different from wild-type sporulating basidia (Figures  5B  and 5C ). This residual sporulation could be the result of various compensatory processes, to be explored in future studies, including: (i) constitutive activation of STE3a in the mf mutant; (ii) transgressive activation in the ste3a mutant of other Ste3-like receptors such as Cpr2, which is known in C.
Gene prediction and annotation To minimize the prediction of false positive gene structures in repetitive and low complexity regions, the genome assemblies were initially soft masked for repeats by RepeatMasker version open-4. 0. 7 using a custom repeat library consisting of known Cryptococcus neoformans transposable elements  (Goodwin and Poulter 2001)  and C. depauperatus elements identified de novo by RepeatModeler2 (https://github. com/Dfamconsortium/TETools; (Flynn et al. 2020). Gene models were predicted with the BRAKER2 pipeline v2. 1. 5 (https://github. com/Gaius-Augustus/BRAKER) (Bruna et al. 2021)  run in ETPmode with parameters "--etpmode --softmasking --fungus". In this running mode, BRAKER employs predicted protein sets of other species processed by ProtHint and RNA-seq data for training GeneMark-ETP  (Buchfink et al.
2015; Bruna et al. 2020). We used as input the available protein sets of Cryptococcus neoformans H99  (Janbon et al. 2014)  and Cryptococcus amylolentus CBS6039  (Sun et al. 2017a ) and RNA-seq data obtained from C. depauperatus grown in two different conditions (see below). RNA-seq data was preprocessed by Trim Galore v0. 6. 5 (https://github. com/FelixKrueger/TrimGalore) with default parameters except that reads shorter than 75 nt after quality or adapter trimming were discarded (parameters: "--paired --quality 20 --phred33 --length 75"). Splice read alignment was performed with STAR aligner v2. 7. 4a  (Dobin et al. 2013 ) (specific parameters for indexing: "--genomeSAindexNbases 11"; specific parameters for aligning: "--alignIntronMin 10 --alignIntronMax 2000 --outSAMtype BAM SortedByCoordinate") and the resulting BAM files were input to the BRAKER pipeline.
In this running mode of BRAKER, intron information present in both data sources was weighted as reliable evidence and enforced for gene prediction in both GeneMark-ETP and in AUGUSTUS. Gene predictions were further refined by removal of spurious gene calls identified within the predicted centromeric regions and by manually adding the short mating pheromone precursor gene. tRNAs were predicted using tRNAscan (Chan and Lowe 2019) and rRNAs predicted using RNAmmer  (Lagesen et al. 2007). Genes containing PFAM domains found in repetitive elements or overlapping tRNA/rRNA features were removed. Genes were named and numbered sequentially with "locus_tag" ids for CBS7841 and CBS7855 starting at L203_100001 and L204_100001, respectively, to distinguish from the gene calls previously deposited in GenBank for the two strains under the same "locus_tag" prefix.
For the protein coding gene name assignment, we combined names from HMMER PFAM/TIGRFAM, Swissprot and KEGG products. The completeness of the genome assemblies and gene predictions was evaluated with Benchmarking Universal Single-Copy Orthologs (BUSCO, v4. 0. 6) based on the presence/absence of a set of predefined single-copy orthologs of other Tremellomycetes (tremellomycetes_odb10 dataset)  (Kriventseva et al. 2019; Manni et al. 2021).
The best-scoring Maximum likelihood (ML) concatenation-based ML tree was inferred using the parameters "--seed 12345 -B 1000 -alrt 1000 -T 12". Support of each internal branch was assessed using 1,000 replicates of the Shimodaira-Hasegawa approximate likelihood ratio test (SH-aLRT) and ultrafast bootstrap (UFboot). For the second method, individual ML gene trees were inferred for the set of 4,074 single-copy orthologs with IQ-TREE but using instead the argument "-S", which instructs the program to perform model selection and tree inference separately for each alignment. A coalescent-based species phylogeny was then inferred with ASTRAL-MP v5. 15. 2  (Yin et al. 2019 ) using a file containing the set of 4,074 individual ML gene trees and the option "-t 2" to obtain on each branch a full set of different measurements, including quartet support for the main (q1) and alternative topologies (q2 and q3).
1. 2 (Minh et al. 2020a). For every branch of the species ML reference tree, gCF describes the fraction of single-gene trees that can recover a particular branch, and sCF is defined as the percentage of decisive sites of the alignment that support a given branch in the species tree. To evaluate this, the file containing all 4,074 single-gene trees was input to IQ-TREE using the specific options "--gcf -scf 1000 -seed 132465". For the phylogenetic analysis of selected genes found within the MAT locus of C. neoformans (Figure  2  -figure supplement 1), amino acid sequences were retrieved for each species, aligned, and trimmed as above, and subsequently used to infer ML phylogenies in IQ-TREE. All phylogenetic trees were visualized with iTOL v5. 6. 3  (Letunic and Bork 2021). (https://github.
com/samtools/bcftools/tree/develop/misc). A FASTA file was then generated in which bases with quality values lower than 20 (equivalent to 99% accuracy) were soft-masked to lowercase ("seqtk seq -q 20") (https://github. com/lh3/seqtk) and ambiguous bases were subsequently converted to an "N. " Levels of divergence per site (k, with Jukes-Cantor correction) between the two strains were estimated with VariScan v. 2. 0. 3  (Vilella et al. 2005)  using a nonoverlapping sliding-window of 5,000 sites. The resulting data was plotted with pyGenomeTracks (Lopez-Delisle et al. 2021). Because highly divergent regions (particularly the centromeres) are challenging to align to a reference genome using short-read data, all divergence estimates should be regarded as minimum estimates. RNA extraction and RNA-seq analysis RNA was extracted from C.
Statistical significance was determined with a one-way ANOVA and Tukey's post hoc test. *, significant at p<0. 05 ***, significant at p<0. 0001 (see Figure 6-source data 1). (C) Table summarizing genotypic analysis of basidiospores dissected from 12 independent basidia of the mf x ste3 cross. The germination rate of the dissected spores was 100%. Presence of wild-type MF and STE3 or mutant mf and ste3 alleles was determined by in-gene and junction PCRs. Each basidium was scored as parental ditype (PD), nonparental ditype (NPD), tetratype (TT) and uniparental (Uni. ) based upon the genotype of parental and recombinant spores they comprised. Phenotypic and genotypic analyses of each dissected spore are available in Figure 6-source data 2. Illustrations in panel A were produced with BioRender.
Acknowledgments We thank  Vikas Yadav  for critical reading of the manuscript,  Fred Dietrich  for computational resources,  Valerie Lapham of the Center for Electron Microscopy at North Carolina State University  for assistance with SEM, and the  Broad Institute Genomics Platform  for generating Illumina sequences. We are indebted to  Rytas Vilgalys  for inspiring us to study the unusual homothallic life cycle of Cryptococcus depauperatus. This study was supported by  NIH/NIAID   R01  Award  AI50113-17  and  NIH/NIAID   R01  Award  AI39115-24  awarded to J. H. , R01 grant  AI33654-04  awarded to J. H. ,  David Tobin , and  Paul Magwene , and  NIH/NHGRI  grant  U54HG003067  to the  Broad Institute. M. N. acknowledges support from  German Research Foundation ,  DFG  grant  NO407/7-2. J. H. is also Co-Director and Fellow of the CIFAR program Fungal Kingdom: Threats and Opportunities.
Study participants and clinical analysis Details of the study participants and their recruitment sites are summarized in Figure  1. Subjects were recruited from ADPKD clinical trials: HALT-PKD (n ¼ 49),  36, 37  TAME-PKD (n ¼ 83),  38  and DIPAK (n ¼ 12);  39  observational ADPKD studies: Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) (n ¼ 11),  40  Genkyst (n ¼ 10),  41  and DIPAK Observational Study (n ¼ 137);  42  genetic studies of ADPKD: ADKPD Modifier Study (n ¼ 49), the Mayo PKD Center (n ¼ 737), and the Irish Kidney Gene Project (IKGP; n ¼ 35);  43  and from other academic centers studying ADPKD. The relevant institutional review boards or ethics committees approved all studies, and participants gave informed consent. Clinical and imaging data were obtained by review of clinical records.
Genomics England 100K project All participants in the 100K Genomes Project (100kG) provided written consent to access their anonymized clinical and genomic data for research purposes. The project model and its informed consent process have been approved by the National Research Ethics Service, Research Ethics Committee for East of England (Cambridge South Research Ethics Committee). Whole-genome sequencing (WGS) was performed with the Illumina TruSeq DNA PCR-Free sample preparation kit (Illumina) and an Illumina HiSeq 2500 sequencer, generating a mean depth of 453 (range from 343 to 723) and greater than 153 for at least 95% of the reference human genome. WGS reads were aligned to the Genome Reference Consortium human genome build 37 (GRCh37) with Isaac Genome Alignment Software (version 01.
14; Illumina). Sequence data were analyzed with bcftools scripts designed to search vcf. gz files, and individual BAM files were viewed with IGV. Variant annotation was performed with Ensembl Variant Effect Predictor (VEP) with the following filter: canonical transcript (ENST00000426508. 7) IFT140 gene and high impact (see results for details). Phenotypes of identified carriers were manually reviewed in Genomics England Participant Explorer. We reviewed exit questionnaires, filled in by the clinicians at the NHS Genomics Medical Centres (GMCs) for each closed case, to detect subjects solved for other genes. Those recruited under the ''normalized specific disease'' term cystic kidney disease, included 1,550 individuals from 1,291 families.
UK Biobank UK Biobank is a large prospective study with over 500,000 participants aged 40-69 years when recruited in 2006-2010 and globally accessible to approved researchers who are undertaking healthrelated research that's in the public interest. 50 Exome data on $200,000 individuals have been made available. 51 Ethics approval for the UK Biobank study was obtained from the North West Centre for Research Ethics Committee (11/NW/0382). The exome data of 200,643 individuals were accessed for variants in IFT140 (GRCh38: chr16: 1,510,427-1,612,072) and filtered with Ensembl VEP for high or rare (gnomAD_AF % 0. 1%), low impact alleles (see results for details) predicted for the canonical transcript ENST00000426508. 7. IMPACT predictions were a subjective classification of the severity of the variant consequence based on agreement with SNPEff (see also results).
UK Biobank diagnoses and disease terms recorded in carriers of high and low impact variants were extracted, manually reviewed, and filtered for ICD-10 classifiers of kidney disease: Q61. x (cystic kidney disease), N28. 1 (cyst of kidney), N18. x (chronic kidney disease), N17. x (acute renal failure), I12. x (hypertensive renal disease), and N20. 0 (calculus of kidney); the term x indicates that all sub-classifications were taken into consideration (e. g. , N18. x includes all stages of CKD corresponding to N18. 1-N18. 5 and N18. 9 for unspecified CKD). A Fisher's exact two-sided test was used for enrichment of diagnoses in high impact variant carriers, and p % 0. 05 was considered statistically significant. The AstraZeneca PheWAS Portal is a repository of gene-phenotype associations for data derived from electronic health records, questionnaires, and continuous traits computed on exomes released by UK Biobank.
IFT140 is an ADPKD-spectrum candidate gene A gene with loss-of-function (LoF) variants identified in multiple ADPKD families from the tNGS analysis in part 1 of the study was IFT140 (MIM: 614620; Chr. 16p13. 3). IFT140 has 29 exons and a coding region of 4,386 bp (GenBank: NM_014714. 4) and encodes the IFT140 protein of 1462aa (GenBank: NP_055529. 2). IFT140, is a principal component of the IFT-A core complex (along with IFT122 and WDR19 [IFT144]), while IFT43, WDR35 (IFT121), and TTC21B (IFT139) form a peripheral subcomplex. 55 9] [60]  The SRTD9 phenotype includes retinal dystrophy, skeletal malformations (including small thorax, cone-shaped epiphyses, craniofacial abnormalities, and digit malformations), and chronic kidney disease (cysts and fibrosis). 58, 59 In addition, bi-allelic variants to IFT140 are associated with non-syndromic forms of retinal dystrophy (MIM: 617781), Leber congenital amaurosis, and retinitis pigmentosa.
Genomics England 100K Genomes Project analysis To determine the burden of likely pathogenic IFT140 variants more broadly, we analyzed genetic and clinical data from the 100K Genomes Project that includes National Health Service (NHS) subjects affected by a rare disease or cancer and relatives. IFT140 variants were extracted from WGS of 64,185 subjects and after annotation 26 distinct strongly predicted pathogenic variants (stop gain, start loss, and canonical splice acceptor and donor variants) were identified in a total of 152 individuals (89 probands and 63 relatives) from 111 different families. Among these 152 individuals, kidney cyst(s) were described in 40    3  and S3 ). Three families showed segregation of the IFT140 variant with the cystic phenotype in 3, 2, or 1 family member (Table  S3 ).
UK Biobank analysis Recently IFT140, or the recurrent IFT140 LoF variant c. 2399þ1G>T, was suggested to be associated with kidney cyst phenotypes in the UK Biobank population and the TOPMed Program. 52, 64 UK Biobank subjects were typically between 50 and 75 years old, and the study was not en-riched for monogenic disease. A total of 240,037 individuals with WES data were available for study. 52 Genes were screened for enrichment of protein-truncating The American Journal of Human Genetics 109, 136-156, January 6, 2022 145 variants (including nonsense, canonical splice, or frameshifts) in the ICD-10 code Q61 (cystic kidney disease; n ¼ 521) group compared to controls (without Q61; n ¼ 239,516). Individuals with monoallelic IFT140-truncating variants represented 2. 69% of Q61 cases compared to 0.
RefSeq transcripts ALG9, GenBank: NM_024740; BBS2, GenBank: NM_031885; CEP290, GenBank: NM_025114; COL4A1, GenBank: NM_001845; DYNC2H1, GenBank: NM_001080463; DZIP1L, GenBank: NM_173543; IFT43, GenBank: NM_052873; OFD1, GenBank: NM_003611; PKD1, GenBank: NM_001009944; PKD2, GenBank: NM_000297; PKHD1, GenBank: NM_138694; TMEM231, GenBank: NM_001077416; TMEM260, GenBank: NM_017799; TTC21B, GenBank: NM_024753; WDR35, GenBank: NM_001006657; WDR60, GenBank: NM_018051. b Higher scores indicate a higher probability of pathogenicity. c The ADPKD Mutation Database.
Acknowledgments We thank the families and coordinators for involvement in the study and  Saurabh Baheti  ( Mayo Clinic ),  Dana Miskulin  ( Tufts University ),  Susan L. Murray  ( Beaumont Hospital, Dublin ),  Aurore Despres  and  Christelle Guillerm  ( CHU Brest ),  Aude Promerat and Cecile Lemoine (Roubaix) ,  Anne-Laure Faucon  ( Corbeil-Essone ),  Emad Khazned (Bourges) ,  Alain Michel  ( Saint Malo ),  Charles J. Blijdorp  ( Erasmus Medical Center, Rotterdam ),  Rene M. M. van Aerts  ( University Medical Center Radboud, Nijmegen ), and  Shosha E. I. Dekker  ( Leiden University Medical Center ) for technical assistance or referring individuals. This research was conducted with data from  UK Biobank  (project  ID   43879 ) and data and findings from the 100,000  Genomes  Project. The study was supported by  NIDDK  grants  DK058816  and  DK059597  (P.
C. H. ); the  Mayo Translational PKD Center  ( DK090728 ; V. E. T. ); the  Zell Family Foundation ,  Robert  and  Billie Kelley Pirnie , an  Early Postdoc Mobility Stipendium ,  Swiss National Science Foundation  ( P2ZHP3_195181 ), and  Kidney Research UK  ( Paed_RP_001_ 20180925 ) (E. O. );  Kidney Research UK  and the  Northern Counties Kidney Research Fund  (J. A. S. ); a  Barbour Foundation Postgraduate Research Studentship  (R. P. ); and  Kuwait Foundation for the Advancement of Sciences (KFAS)  grant  PR17-13MM-07  (H. O. ). The  Irish Kidney Gene Project  was funded by  THE HEALTH RESEARCH BOARD, Irish Nephrology Society ,  Irish Kidney Association  under the  HRCI-HRB Joint Funding Scheme HRCI-HRB-2020-032 ; the  Baltimore PKD Research and Clinical Core Center  were supported by  P30DK090868  and  U54 DK126114.
a RefSeq transcripts ALG9, GenBank: NM_024740; BBS2, GenBank: NM_031885; CEP290, GenBank: NM_025114; COL4A1, GenBank: NM_001845; DYNC2H1, GenBank: NM_001080463; DZIP1L, GenBank: NM_173543; IFT43, GenBank: NM_052873; OFD1, GenBank: NM_003611; PKD1, GenBank: NM_001009944; PKD2, GenBank: NM_000297; PKHD1, GenBank: NM_138694; TMEM231, GenBank: NM_001077416; TMEM260, GenBank: NM_017799; TTC21B, GenBank: NM_024753; WDR35, GenBank: NM_001006657; WDR60, GenBank: NM_018051. b Higher scores indicate a higher probability of pathogenicity. c The ADPKD Mutation Database.
While the African continent has one of the largest renewable energy potentials in the world, it remains the most deprived region for access to electricity. Official development agencies can provide the funds and experience necessary for the sustainable development of this sector to those countries that suffer from limited domestic resources. We estimate the impact of renewable energy aid projects implemented in 19 sub-Saharan African countries on access to electricity for communities in the region. By combining georeferenced data from aid projects in renewable energy from AidData and survey data from the Afrobarometer, we find, using different propensity score matching methods, a positive and significant impact of these projects on electrification of communities. A comparison with the same sample on non-renewable energy projects shows a better efficiency of eco-friendly projects.
This study contributes to the literature on aid effectiveness in several important ways. First, we add to the recent and growing literature on local aid effectiveness at the sectoral level that combines micro-economic and georeferenced data with quasiexperimental identification strategies  (Isaksson & Kotsadam, 2018; Kotsadam et al. , 2018; Kotsadam & Tolonen, 2015; Martorano et al. , 2020). Indeed, we spatially link location of aid project from AidData and location of household community interviewed in the Afrobarometers survey in order to compare individuals close to aid projects with those far away. In order to identify the causal effect of these projects and to address concerns of selection bias, we use propensity score matching. Second, we focus on one particular sector of aid which has so far been little studied, the energy sector, and more precisely we focus on only one part of this sector, the renewable energy one, which, to the best of our knowledge, has never been studied empirically.
Thanks to the high-precision of information on the projects in the AidData database, we are able to restrict our analysis to the project specific to the renewable energy sector. Finally, to the best of our knowledge, we are the first to investigate the local effects of aid on electricity, a key development outcome in this region. Indeed, while most micro-economic studies have focused on health and education indicators, we retain as a dependent variable, the community access to electricity of the households interviewed in an Afrobarometer survey. Our result point first that, specific renewable energy aid projects can increase the level of electrification of nearby communities. Thus, while promoting clean technologies, these projects improve electrification. Second, by comparing this effect with those of non-renewable energy projects in the same sample and the same period, we find that eco-friendly energy projects have greater impact.