dataesr/scientific-paragraphs-categorization huggingface dataset:
2 ). Next, we calculated MAPS for uAUG-creating variants arising upstream of 1,659 genes known to cause developmental disorders (DD; confirmed or probable genes from the Developmental Disease Gene to Phenotype (DDG2P) database). While uAUGcreating variants upstream of all DD genes do not show a signal of selection above all observed uAUG-creating variants, the MAPS score is significantly inflated when limiting to 279 DD genes with a known dominant LoF mechanism (permuted P = 0. 0012; Fig. 1c ). Variants that disrupt uORF stop codons are selected against. As uAUG-creating variants that form oORFs have a significantly higher MAPS score than those with an in-frame UTR stop codon, we hypothesised that variants that disrupt the stop site of existing uORFs should also be under selection (Fig. 2a ).
2c ), suggesting that removing these stop sites is likely to be deleterious. Specific genes are sensitive to uAUG-perturbing variants. We searched the Human Gene Mutation Database (HGMD)  27  and ClinVar  28  for uORF-creating or uORF-disrupting variants, identifying 39 uAUG-creating and four stop-removing (likely) pathogenic/disease mutations in 37 different genes. All four stopremoving variants disrupt uORFs with uAUGs in a strong or moderate Kozak consensus and result in an oORF overlapping the CDS (Supplementary Table  2 ). Compared to all possible uAUG-creating variants in these 37 genes, the 39 reported disease-causing uAUG-creating variants (Supplementary Table  1 ) are significantly more likely to be created into a moderate or strong Kozak consensus (binomial P = 3. 5 × 10 -4 ), create an outof-frame oORF (binomial P = 1.
We predict these to be a fruitful set to search for additional diseasecausing uORF-perturbing variants (Supplementary Data 1; Supplementary Fig. 5b ). To aid in the identification of uORFperturbing variants we have created plugin for the Ensembl Variant Effect Predictor (VEP)  32  which annotates variants for predicted effects on translational regulation (available at https:// github. com/ImperialCardioGenetics/uORFs). A novel uORF frameshift causes neurofibromatosisN type 2. We analysed targeted sequencing data from a cohort of 1134 unrelated individuals diagnosed with neurofibromatosis type 2, which is caused by LoF variants in one of these prioritised genes, NF2. We identified a single 5'UTR variant in two unrelated probands in this cohort (ENST00000338641:-66-65insT; GRCh37: chr22:29999922A >AT) that segregates with disease in three additional affected relatives across the two families (Supplementary Fig.
In this work, we used variant frequencies in a large population dataset to study the global impact of a specific class of non-coding variants with a predicted functional effect. Previous studies using non-coding constraint have focused on entire regulatory regions  33  or concentrated exclusively on splicing  34, 35. These and other studies  36  have concluded that signals of constraint and selection are likely confined to individual bases  33  and diluted out when studying larger regions. Our results support this assertion; as the signal of negative selection associated with all UTR variants is not discernible from synonymous variants. We show the power of grouping individual non-coding bases by functional effect to identify subsets of variants with strong signals of selection.
Methods Ethics statement. We have complied with all relevant ethical regulations. This study was overseen by the Broad Institute's Office of Research Subject Protection and the Partners Human Research Committee, and was given a determination of Not Human Subjects Research. Informed consent was obtained from all participants. Study dataset. We used the 15,708 whole genome sequenced individuals from version 2. 1. 1 of the Genome Aggregation Database (gnomAD), which is fully described in our companion paper  22. These data were downloaded from https://gnomad. broadinstitute. org/downloads and queried using Hail version 0. 2 (https://hail. is). Definition of 5'UTRs. The start and end positions and sequence of the 5'UTRs of all protein-coding genes were downloaded from Ensembl biomart (Human genes GRCh37.
4 GRCh37 release files and used to annotate lists of all possible coding, UTR and uORF stop bases. To remove biases due to gene context and distance from the coding sequence, we created a set of matched UTR bases which comprised the 3 bases immediately upstream and downstream of the stop. Conserved bases were defined as those with PhyloP > = 2. We also checked for a significant difference between the entire distribution of scores using a Wilcoxon rank sum test for all stop-removing compared to matched UTR bases (P = 8. 1 × 10 -9 ). Identifying disease gene lists. Developmental disease genes were downloaded from The Developmental Disorders Genotype-Phenotype Database (DDG2P) on the 6th October 2018. We included only genes categorised as 'confirmed' or 'probable'. Genes with a known dominant LoF mechanism were identified using the 'allelic requirement' and 'mutation consequence' annotations.
Genes intolerant and tolerant to LoF variants were identified using data from Karczewski et al. 2019  22. Genes were ordered by their loss-of-function observed/ expected upper bound fraction (LOEUF) scores and the top and bottom sextiles were categorised as tolerant and intolerant, respectively. We downloaded data from The Clinical Genome Resource (ClinGen) Dosage Sensitivity Map on 21st January 2019 (https://www. ncbi. nlm. nih. gov/projects/ dbvar/clingen/). Genes manually curated as haploinsufficient were defined as those with a score of 3 (sufficient evidence). In addition, we added genes curated as severe or moderately haploinsufficient by the MacArthur lab (https://github. com/ macarthur-lab/gene_lists/tree/master/lists). Searching for uORF-perturbing variants in HGMD and ClinVar. Lists of all possible uAUG-creating and stop-removing SNVs were intersected with all DM variants from HGMD pro release 2018.
Class 7-remaining genes intolerant to LoF variants or with > = 2 highconfidence Pathogenic LoF variants in ClinVar. Class 6-all genes not classified into any other class. The nine gene classes were grouped into three categories corresponding to low (classes 2, 3, 4 and 5), moderate (class 6) and high (classes 7 and 8) likelihood that high-impact uORF-perturbing variants would have a deleterious effect. Sequencing of individuals with neurofibromatosis type 2. A cohort of 1134 unrelated individuals with neurofibromatosis type 2 were recruited to the Centre for Genomic Medicine at St Mary's Hospital, Manchester. All individuals fulfilled both Manchester and NIH criteria for diagnosis. Ethical approval for use of these samples and anonymised associated clinical data was obtained from the North West Greater Manchester Central Research Ethics Committee (reference 10/ H1008/74).
Informed consent was obtained from all participants. All patients were sequenced across the NF2 gene. Two individuals were identified to carry a single 5'UTR variant (ENST00000338641:-66-65insT; GRCh37:chr22:29999922A >AT). Both carriers were confirmed to have no variants in SMARCB1 or LZTRA1 and no coding variants in NF2. The -66-65insT variant segregated with disease in 3 affected siblings in one family and in affected parent and child in another (Supplementary Fig. 3b ). Phenotypic data for all family members can be found in Supplementary Table  3. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article. Matthew Solomonson  3, 4  , Jose Soto 13 , Grace Tiao  3, 4  , Kathleen Tibbetts 13 , Charlotte Tolonen 13 , Christopher Vittal  4, 18  , Gordon Wade 13 , Arcturus  Wang 3, 4, 18  , Qingbo Wang  3, 4, 15  , Nicholas A.
Acknowledgements N. W. is supported by a  Rosetrees  and  Stoneygate Imperial College Research Fellowship. This work was supported by the  Wellcome Trust  ( 107469/Z/15/Z ), the  Medical Research Council (UK) , the  NIHR Biomedical Research Unit in Cardiovascular Disease at Royal Brompton and Harefield NHS Foundation Trust and Imperial College London , the  NIHR Imperial College Biomedical Research Centre , the  Fondation Leducq  ( 11 CVD-01 ), a  Health Innovation Challenge Fund  award from the  Wellcome Trust and Department of Health, UK  ( HICF-R6-373 ), and by  NIDDK   U54DK105566  and  NIGMS R01GM104371. D. G. E. and M. J. S. are funded by the  NIHR Biomedical research centre Manchester  ( IS-BRC-1215-20007 ). L. C. F. is supported by the  Swiss National Science Foundation  ( Advanced Postdoc.
CUMS modi ed caecal microbiome composition. A more in-depth taxonomic analysis of bacterial types revealed several changes in the microbiome composition, and variations appeared at different phylogenetic levels. Nine divisions were identi ed by pyrosequencing. In all samples, the majority of caecal bacteria (ranging from 92 to 98% of total 16S) belonged either to the Firmicutes (ranging from 49. 3 to 94. 4%) or to the Bacteroidetes phylum (ranging from 2. 5 to 46. 8%), with a small proportion (2-8% of the identi ed sequences) of bacteria from seven others phyla: Actinobacteria, Candidatus Melainobacteria, Candidatus Saccharibacteria (TM7), Cyanobacteria, Proteobacteria, Tenericutes and Verrucomicrobia (Suppl. Database S1). Moreover, 16 classes, 26 orders, 53 families, and 123 genera were identi ed.
3C , Suppl. Database S1). We also noticed the lowering of another Bacteroidetes (Candidatus Armanti lum and Odoribacter) and of members of the genus Akkermansia. At the species level, of the 389 taxa assigned, 275 species were found in control mice and 337 species in the CUMS mice, corresponding to 223 species recovered in both groups (Fig. 3D ). Among them, only 27 were shared by all animals (core microbiome).
Amplicon sequencing data analysis. Analysis of amplicon sequencing data was carried out using the MEGAN pipeline  [49]. After demultiplexing, combined raw sequencing data plus metadata were ltered to exclude low-quality reads. Next, data were denoised and clustered using the MIRA 4 software (http://miraassembler. sourceforge. net). Sequences with ≥ 98% similarity were binned and assigned to the same OTU to approximate species-level phylotypes. Representative sequences of each OTU, derived from clusters or singletons, were assigned at different taxonomic level by using the Ribosomal Database Project II Classi er  [50]. To avoid a potential bias linked to variation of sequence coverage between samples, the data were normalized to 100000 sequences per samples. Rarefaction curves were constructed to evaluate sequencing depth.
Relative abundances of each OTU were compared according to the different experimental conditions. Bacterial richness and diversity across samples were estimated by calculating the following indexes as previously described  [3] : Shannon index, Evenness index, OTU's number, Simpson's index of diversity, and Simpson's reciprocal index. PCA was conducted to appreciate overall distance between microbial communities, using relative abundance and taxa-to-taxa distance estimates. Obtained 16S rRNA gene sequences have been deposited into NCBI's Sequence Read Archive database (https://www. ncbi. nlm. nih. gov/sra) under accession number SRP153311. Analysis of intracaecal bacterial load by qPCR. The amount of total bacteria was assessed by amplifying 0. 5 ng of DNA extracted from each fecal sample with pan-bacterial primers targeting the 16S rRNA gene as previously described  [3].
Background: The rapid and often uncontrolled rural-urban migration in Sub-Saharan Africa is transforming urban landscapes expected to provide shelter for more than 50% of Africa's population by 2030. Consequently, the burden of malaria is increasingly affecting the urban population, while socio-economic inequalities within the urban settings are intensified. Few studies, relying mostly on moderate to high resolution datasets and standard predictive variables such as building and vegetation density, have tackled the topic of modeling intra-urban malaria at the city extent. In this research, we investigate the contribution of very-high-resolution satellite-derived land-use, land-cover and population information for modeling the spatial distribution of urban malaria prevalence across large spatial extents.
As case studies, we apply our methods to two Sub-Saharan African cities, Kampala and Dar es Salaam. Methods: Openly accessible land-cover, land-use, population and OpenStreetMap data were employed to spatially model Plasmodium falciparum parasite rate standardized to the age group 2-10 years (PfPR 2-10 ) in the two cities through the use of a Random Forest (RF) regressor. The RF models integrated physical and socio-economic information to predict PfPR 2-10 across the urban landscape. Intra-urban population distribution maps were used to adjust the estimates according to the underlying population.
Land-use (LU) The LU maps utilized here were the outcome of a processing chain involving the computation of LC-based spatial metrics derived from the maps mentioned above and information derived from OpenStreetMaps (OSM)  [53]. Linear elements extracted from OSM such as the street network and various parcel types were imported into a PostGIS database and processed to create streetblock polygons. Subsequently, a machine learning classifier assigned a LU value to each street-block through supervised training. The complete processing chain was a reproduction of the work by  Grippa et al. ([54] ; Additional file 1). The LU classification allowed for the classification of the urban surface according to its different urban functions (i. e. , residential/non-residential). The residential classes were categorized into either formal or informal settlements.
Ancillary data To complement the previous datasets, we extracted the Normalized Difference Vegetation Index (NDVI) from the raw VHR satellite images, and terrain height information (30 m resolution) from the NASA/NGA Shuttle Radar Topography Mission (SRTM)  [58]. In the case of Dar es Salaam, OSM vector features such as wetlands, streams and rivers were used due to the high level of detailed information available for the city, in a large part thanks to community mapping projects. For instance, 'Ramani Huria' , one of the largest community projects in Dar es Salaam aiming to mitigate hazard and flooding risk, has mapped detailed urban infrastructure such as the drainage network and buildings for more than 4 million people in the city since 2018  [29]. Table  1  summarizes the complete set of variables examined.
Plasmodium falciparum prevalence data Data of community surveys were extracted from an open access online database  [59]  that accompanied the publication of changing malaria prevalence across The average survey sample size in Kampala and Dar es Salaam, is 82 and 175, among individuals aged 0-16 years old, respectively. All surveys were random selections of communities or schools. Further information regarding the key characteristics of the malaria dataset can be found in Additional file 2. The mean PfPR 2_10 values were 6. 76% and 7. 76% for Kampala and Dar es Salaam, respectively. 17. 6% of the data points reported zero PfPR 2_10. Finally, we used 1-kilometer buffers around each geolocated survey to extract aggregated values for each predictor mentioned previously in Table  1  (i. e. proportions for categorical features, mean values for continuous ones and the mean distance to the "Wetland", "River" and "Stream" classes), similar to research employing survey data and geographical variables  [6, 64].
Modelling and quality assessment methods To train the PfPR 2_10 models, we used a random forest (RF) regressor which has shown to be resilient to overfitting, capturing non-linear relationships and appropriate for heavily contextual models  [65]. RF is an ensemble of regression decision trees, trained on random data bootstraps (bagging). In a standard RF configuration, each computed tree is trained using a random sub-sample of about 70% of the initial data. The average prediction of all computed trees is used as the final output  [65]. The hyper parameters that require fine-tuning in RF are (i) the number of considered features for each decision split in each tree (feature bagging) and (ii) the total number of decision trees built. In this study, the former was determined through cross validation (value of 1), while the latter was set at a computationally efficient number (1000) through the R software's caret package  [66].
Relationship between satellite indicators and malaria prevalence Previous research using remotely sensed datasets was able to distinguish malaria risk across the urban fabric in SSA cities, albeit using coarser resolution information  [6, 9]. Given the nature of datasets and differences in objectives, only basic distinctions could be made (land cover classes such as building density and vegetation). Building density was in general negatively associated with malaria prevalence while vegetation exhibited positive associations. Although very informative, these models often neglect the importance of the underlying socio-economic relationship of different urban settlements with malaria risk. Here, our results support the notion that malaria prevalence is combination of physical factors such as urban land cover that favors the emergence of vector breeding sites, but also the type of surrounding communities (informal, formal), which provides an indication on their capability to cope with the burden of malaria.
Malaria data limitations and model assessment The PfPR 2_10 RF models are temporal composites of surveys ranging almost a decade, and consequently the temporal dimension was assumed stationary. We assumed that the extracted signal is mostly invariant as we focused on urban regions that have not undergone major transitional changes but might have expanded (i. e. , large informal settlements, the planned residential center). Moreover, it was a necessary sacrifice in order to assemble a dataset large enough to capture the fine-scale spatial variability. Nonetheless, improvements in the modeling process can be expected if temporal effects are to be integrated. Furthermore, significant variations exist within the malaria datasets used in this study, with respect to sample sizes, survey locations and years of survey, which are likely to bias the results.
Intra-urban human population distribution maps as an additional tool to address urban malaria To our knowledge this was the first study making use of fine-scale population data distributed through VHR information in order to adjust the PfPR 2_10 estimates across two cities. As population density varies greatly across the urban fabric, efforts should be made to not only present the malaria prevalence as an abstract variable, but according to the underlying population at risk. It should also be emphasized that the population information used in both cities comes from a census carried out in 2002 and a temporal mismatch between the imagery and population counts can exist, even though relative patterns are expected to be similar. Nonetheless, population projection techniques can be applied to simulate both population and malaria cases in future dates.
Conclusions This research provides a framework to predict intraurban malaria at a fine scale spatial resolution, coupling machine learning algorithms, very-high-resolution satellite derived indicators, and geospatial and survey data. Focusing on Dar es Salaam and Kampala as case studies, we conclude that the predictive dataset appears to be robust for modelling the intra-urban spatial distribution of malaria prevalence across large scales. Within both cities, urban malaria prevalence is not evenly distributed and varies intrinsically across the urban fabric. Informal settlements, urban agriculture and locations near wetlands and riparian vegetation are highlighted as potential hotspots. Population adjusted estimates indicate higher prevalence values in highly populated administrative units.
Availability of data and materials The datasets that support the conclusions of this manuscript are deposited in the open access Zenodo scientific repository (https ://zenod o. org/recor d/38714 97#. XtTWP PZuKD A). The raw satellite imagery is property of AIRBUS DS, France, all rights reserved. The OpenStreetMap data used in this are publicly accessible and copyrighted under the mention "©OpenStreetMap contributors, CC BY-SA". The Shuttle Radar Topography Mission information were extracted from https ://www. usgs. gov/.
1e ). To stabilize ATPase head engagement, we introduced two pairs of cysteine residues at the predicted SMC heterodimerization interface and induced disulphide bond formation by addition of copper phenanthroline, in the presence of ATP. The cross-linking procedure resulted in the essentially complete conversion of Smc1-Smc3 monomers into a single species on SDS-PAGE (Fig. 1b ; Extended Data Fig. 1b ). We collected a cryo-EM dataset from cross-linked c-link, vitrified in the presence of ATPγS-Mg 2+ , that yielded a map with an overall resolution of 3. 2Å, using the gold standard Fourier shell correlation (FSC) = 0. 143 criteria (Fig. 1c , Extended Data Fig. 2d ). Density corresponding to two ATPγS-Mg 2+ molecules was observed between the respective Walker A and Walker B motifs of each subunit, and the cognate signature motif of its partner (Fig.
Discussion Recent data show that ATP-dependent Smc2-Smc4 head dimerization induces dissociation of the N-kleisin from the Smc2 coiled coil to open the condensin ring  33. A comparison of the ATPγS-bound CtSmc1 structure to Smc2 and Smc4 as well as the nucleotide free and ATPγSbound form of bacterial SMC ATPases and Rad50 showed that the conformational changes associated with ATP-mediated head engagement are conserved between SMCs (Extended Data Fig. 4 ). SMC coiled-coil arms can adopt a conformation in which they are intimately aligned in a manner refractory to ATPase engagement  34-40,41 ,42. Thus, it is possible that controlled remodeling of the SMC arms to promote or preclude ATP-sandwich formation is a central paradigm for the regulation of SMC-kleisin function. Our results suggest that the cohesin release factors are not an absolute requirement for conformational changes within the isolated Smc1-Smc3-Scc1module per se.
EM Data Processing Micrograph movies were recorded using a Titan Krios (FEI, ThermoFisher) operating at 300keV, equipped with a K2-Summit (Gatan, ThermoFisher) direct electron detector configured in counting mode. Dose, magnification, and effective pixel size are detailed in Table  S2. Micrograph movie alignment, dose-weighting and summing was performed using MOTIONCORR2  50. Contrast-transfer function estimation was performed using GCTF  51. Initial particle sets were picked with Gautomatch (Kai Zhang, MRC-LMB), extracted, and subjected to 2D classification in Relion 2. 1 52. Resulting classes corresponding to protein complexes were subsequently employed as references for auto-picking in Relion 2. 1. Following several iterations of 2D classification, classes exhibiting clear secondary structure features were selected for 3D classification, using a simulated density map generated from a hypothetical model of the Smc3-Smc1 heterodimer low-pass filtered to a resolution of 40 Å as a reference.
Functional domains are indicated according to the conserved domain database from NCBI. DNA PolB ε: DNA polymerase type-B epsilon subfamily catalytic domain. Exo: DNA polymerase family B 3′ to 5′ exonuclease domain. DUF1714: domain of unknown function. d Transcript accumulation at four silent loci detected by RT-qPCR, normalized to the ACTIN2 gene with pol2a-8 set to 1. Asterisks mark statistically significant differences (unpaired two-sided Student's t test, P < 0. 05). Error bars represent standard error of the mean across three biological replicates. e Number of PCGs and TEs detected as differentially expressed in pol2a-12. f Proportion of TE superfamilies of all TEs in the Arabidopsis genome and of TEs upregulated in pol2a-12. g Changes in transcript accumulation in pol2a-12 relative to L5 control plants represented along chromosome 3 by log 2 ratios of average reads per kilobase per million mapped reads (RPKM) over non-overlapping 100 kb bins (black, left y-axis).