dataesr/scientific-paragraphs-categorization huggingface dataset:
Dimension reduction was performed using PCA and then UMAP on the top 10 PCs and 15 nearest neighbors for visualization. Clusters of cells within the data were calculated using the Louvain algorithm within Scanpy with a resolution of 0. 4. For Figure  1 , 10 ng/ml EREG single nuclei dataset from in vitro grown HIOs was first processed through the standard CellBender 38 workflow to remove ambient RNA introduced in the nuclei isolation preparation. Then the same dataset was filtered to remove cells with less than 1200 or greater than 6,000 genes, or greater than 17,500 UMI counts per cell, and 0. 1 mitochondrial cell counts. Data matrix read counts per gene were log normalized prior to analysis. After log normalization, highly variable genes were identified and extracted. Data was then scaled by ztransformation.
Dimension reduction was performed using PCA and then UMAP on the top 18 PCs and 15 nearest neighbors for visualization. Clusters of cells within the data were calculated using the Louvain algorithm within Scanpy with a resolution of 0. 5. For Figure  2 , the 10 ng/mL EREG single nuclei dataset from transplanted HIOs and was first processed through the standard CellBender 38 workflow to remove ambient RNA introduced in the nuclei isolation preparation. Then the same dataset was filtered to remove cells with less than 400 or greater than 8,000 genes, or greater than 30,000 UMI counts per cell, and 0. 2 mitochondrial cell counts. Data matrix read counts per gene were log normalized prior to analysis. After log normalization, highly variable genes were identified and extracted. Data was then scaled by z-transformation.
Label transfer of tHIO dataset onto human fetal dataset We utilized Seurat's recommended pipeline to perform single-cell reference mapping using the same cells as the reference data (human fetal intestine) and query data (tHIOs). PCAs are first performed on reference and query data. Then a set of anchors are identified and filtered based on the default setting of the function FindTransferAnchors. With the computed anchors, reference. reduction parameter set to PCA, and reduction. model set to UMAP, the function MapQuery returns the projected UMAP coordinates of the query cells mapped onto the reference UMAP. We then integrated the projected UMAP (colored in red) and the reference UMAP (colored in light gray) to visualize the result of our reference-based mapping in Fig. 3.
tHIO muscle contractions and ENS function Muscle contraction and ENS function was assayed as previously described  12, 39. Following HIO transplantation as outlined in the previous section, tHIOs were matured for 10-12 weeks before harvest. tHIOs were cut into strips ~2 × 6 mm in size and the epithelium mechanically removed as previously described  12. No chelation buffer was used, and all manipulations occurred in oxygenated Kreb's buffer while on ice ((NaCl, 117 mM; KCl, 4. 7 mM; MgCl2, 1. 2mM; NaH2PO4, 1. 2 mM; NaHCO3, 25 mM; CaCl2, 2. 5 mM and glucose, 11 mM), warmed at 37 °C and gassed with 95% O2 + 5% CO2). These strips were mounted in an organ bath chamber system (Radnoti) to isometric force transducers (ADInstruments) and contractile activity was continuously monitored and recorded using LabChart software (ADInstruments).
Data and code availability statement Sequencing data generated and used by this study are deposited at EMBL-EBI ArrayExpress. Data sets for human fetal intestine (ArrayExpress: E-MTAB-9489, https://www. ebi. ac. uk/arrayexpress/experiments/E-MTAB-9489/, and previously published work  26  ); Data sets for whole cell single cell sequencing of 1 ng/mL, 10 ng/mL, 100 ng/mL EREG HIOs and 100 ng/mL EGF HIO (ArrayExpress: E-MTAB-13463, https://www. ebi. ac. uk/arrayexpress/experiments/ E-MTAB-13463/,); Data sets for single nuclear RNA sequencing of 10 ng/mL HIOs and 10 ng/mL EREG tHIOs (ArrayExpress: E-MTAB-13469, https://www. ebi. ac. uk/arrayexpress/experiments/ E-MTAB-13469/). Code used to process raw data can be found at https://github. com/jason-spence-lab/Childs_2023. git.
1:500 (Abcam Cat#ab14106), goat anti-E-Cadherin 1:500 (R&D Systems Cat# AF748), mouse anti-E-Cadherin 1:500 (BD Transduction Laboratories Cat# 610181), rabbit anti-PECAM 1:200 (Sigma Cat# HPA004690), mouse anti-TUBB3 1:200 (BioLegend Cat# 801201), sheep anti-SM22 1:500 (Novus Cat# AF7886), chicken anti-MAP2 1:2000 (Abcam Cat#ab92434), goat anti-VIM 1:200 (R&D Systems Cat#AF2105), rabbit anti-c-KIT 1:500 (Abcam Cat#ab32363) and mouse anti-VE-CAD 1:1000 (R&D Systems Cat#MAB9381). FISH probes were acquired from ACDbio and stained using the RNAscope multiplex fluorescent manual protocol and kit.
See methods section for further explanation on calculations. Statistical significance was determined using an unpaired Welch's t test (ns -P = >0. 05). Extended Data Figure 3: Gene expression analysis of EREG-grown HIOs show presence of smooth muscle, neural cells, and endothelial cells not seen in control EGF-grown HIOs. a) UMAP visualization of Louvain clustering by cell type of all HIO samples sequenced (100 ng/mL EREG, 10 ng/mL EREG, 1 ng/mL EREG, and 100 ng/mL EGF). b) UMAP visualization of clustering by sample type (100 ng/mL EREG blue, 10 ng/mL EREG purple, 1 ng/mL EREG green, and 100 ng/mL EGF grey). Inlays break out each sample (red) individually against rest of dataset (grey). c) Dot plot of combined HIO dataset highlighting expression of canonical lineage genes used for cluster annotation.
d) Bar charts showing the cell type abundance (% of total cells) within each cluster for each sample sequenced. Colors are consistent with the cell type annotation in panel A. e) UMAP visualization of Louvain clustering by cell type of 1 ng/mL EREG and accompanying dot plot of expression of canonical lineage genes used for cluster annotation. f) UMAP visualization of Louvain clustering by cell type of 10 ng/mL EREG and accompanying dot plot of expression of canonical lineage genes used for cluster annotation. g) Dot plot of individual HIO dataset highlighting expression of major ENS neuronal cell types seen in the developing human intestine. Enteric ganglion cells (TUBB3, SYN1), submucosal secretomotor (VIP), enteric glial cells (S100b -glial network; SOX10 -EGC nuclei), ICC's (ANO1), cholinergic neurons (CHAT) and Schwann cells (MPZ, PLP1).
CC-BY-ND 4. 0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made The copyright holder for this preprint this version posted November 6, 2023. ; https://doi. org/10. 1101/2023. 11. 06. 565830 doi: bioRxiv preprint Extended DataFigure 4: EREG-grown tHIO pattern two off target epithelial clusters. a) Bar plot of clusters 5 and 7 showing predicted organ identity using the scoreHIO R package. Clustered mapped somewhere between gastric epithelium and intestinal epithelium. b) Dot plot of human fetal dataset highlighting expression of canonical lineage genes used for cluster annotation in label transfer and SingleR analysis.
On average, two recombination events were detected per RIL per chromosome. Altogether, the population size and recombination events provided 4,515 unique genetic markers and 4,568 distinguishable genomic loci/bins suitable for mapping, improving the previously available map  [71]  (Supplementary table  1 ). The detected loci had a size-range from 60 Mb to 1. 7 Kb, with an average locus size of 180 Kb and a median of 11 Kb (Supplementary table  2 ). Given the high local recombination frequency, relatively small loci were overrepresented towards the chromosome tips (Figure  1C ). Together, our dataset enables precise mapping of QTLs and eQTLs, especially towards the tips of the chromosomes.
Discussion Our RNA-sequencing data obtained from a Tomato RIL population (S. lycopersicum (cv. Moneymaker; MM) x S. pimpinellifolium (G1. 1554; PI))  [17, 49] , allowed for the construction of a detailed and high resolution genetic map, describing the genotypes using 4,515 SNP markers. This is over five times more than previously reported in  Kazmi et al. , 2012 [71] , which used 865 markers. However, intrinsic to RNA-seq data, only SNPs present in the coding parts of the genes (mRNA's) could be used. Therefore, determining the exact locus where recombination took place would need additional genome sequencing as described in  [70]. By measuring transcript levels (i. e. mRNA abundances) in the seeds of a tomato RIL population that had matured in different maternal nutrient environments, we show that the maternal environment affects both regulation and the genetic architecture of gene expression in progeny seeds.
Methodology/Principal findings The study is a time trend analysis of the data aggregated from the database of dengue surveillance system. The database consisted of clinically diagnosed and laboratory-confirmed cases of dengue in southern Vietnam from 2000 to 2015. In the study period, the mean age of dengue cases increased from 12. 2 ± 8. 8 years old (y/o) to 16. 8 ± 13. 3 y/o between 2000 and 2015. Majority of severe cases were observed in the age group of 5-9 y/o and 10-14 y/ o. Overall, the mortality and case fatality rates (CFR) were lowest during 2010 to 2015, and all four serotypes of dengue were observed.
Study design The study is a time trend analysis of the data aggregated from the dengue surveillance system database. The database comprised of clinically diagnosed and laboratory-confirmed cases of dengue from the 19 provinces of southern Vietnam between the study period of 2000 and 2015. The data was collected by Pasteur Institute of Ho Chi Minh City (PIHCMC) as a part of National Program for Dengue Control (NPDC) (S1 Text) (Fig  1 )  [14] [15] [16] [17]. In 2005, to consolidate and standardize the data reporting and generating statistical reports of dengue cases, a software program named 'CTSXH' built on Microsoft Access was deployed in all 19 provinces of southern Vietnam by PIHCMC. All information on dengue cases was collected and entered in this program by provincial medical centers for storing and generating statistical reports.
Study population/data collection All cases with a discharge diagnosis of dengue were reported in the surveillance system database. The discharge diagnoses information included patient demographics (subject's identification, age, date of birth, sex, and province), clinical data (subject's identification number, date of admission, date of discharge, and severity of the case), and diagnostic laboratory data (cumulative number of samples sent or tested with serology and virus isolation, polymerase chain reaction (RT-PCR), number of positive samples (serology, virus isolation, and RT-PCR), number of samples serotyped, and number of positive samples per serotype). After removal of cases with incomplete information from data collected, monthly reports were developed. All the cases in monthly reports were categorized into 'clinical dengue' or 'laboratory-confirmed dengue'.
If this ratio was found to be other than 1, the line listings were checked for ensuring the precision. In case of an identified reporting bias, the data from the particular year and province were deleted. The serology data was collected from PIHCMC using MAC-ELISA technique. The data was then verified by comparing the data from collection lists with periodic monthly data reports and then discrepancies in the data counts between these two sources were investigated. Age-specific annual census data were collected from the General Statistics Office of Vietnam (GSO) survey in 1999, 2009, and 2014. For the years without age-stratified census data, the average population growth rate was calculated and applied between the surveys. E. g. , for the 2015 census data, the annual population growth rate of 2009 to 2014 was used.
Statistical analysis The data was managed with 'CTSXH' program and analyzed on Microsoft Excel. The provincial mean age and standard deviation of dengue cases were calculated for each year, and Mann-Kendall test was performed for all dengue cases to detect the trend of the mean age and severity over time. Age-adjusted provincial case morbidity, mortality, and fatality rates were calculated using weighted average, and their temporal trends were described using annual percentage change (APC) for year 'i'. The average annual percentage change (AAPC) estimates, which were the year-to-year changes in percentage and the average of year-to-year changes in percentages over the studied period 'i' to 'n' respectively: APCðiÞ Incidence ¼ I i I iÀ 1 � 100 AAPCði; nÞ Incidence ¼ P n i¼2 ððI i =I iÀ 1 Þ À 1Þ n À 1 � 100 where n is the total number of years, I i is the 3-year moving average incidence rate for year i and I i-1 is the 3-year moving average incidence rate centered at the year of interest i-1.
4 provinces in the analysis set of 2000, 6 provinces in 2001, 8 provinces in 2002, 12 provinces in 2003, 13 provinces in 2004, 16 provinces in 2005, and 19 provinces between 2006 and 2015 in the analysis (S1 Table). The final adjusted dataset included 686,868 dengue cases from 2000 and 2015 with an overall incidence rate of 145 cases per 100,000 (13. 7% reduction). A total of 92,669 laboratory-confirmed cases were present from 2000 and 2015. From these laboratory-confirmed cases, 32,572 were tested for the identification of serotypes. The incidence rate of dengue cases increased from 41 cases per 100,000 population in 2000 to 250 cases per 100,000 in 2007. From 2007 to 2015, the reported incidence rate decreased with the exception of two peaks in 2012 and 2015 (Fig2). Between 2000 and 2015, the mean age of clinical dengue cases was significantly increased from 12.
Acknowledgments We thank  Vu Thien Thu Ngu  for data acquisition and data analysis/interpretation. We would also like to thank current and ex-employees of Sanofi who have supported in the development of the project and establishing contact between  Sanofi  and the  Pasteur Institute of Ho Chi Minh City :  Nguyen Thuy An ,  Truong Tho Loc ,  Nicolas Baurin , and  Marine Bauduin. Finally , we want to thank  Saikrishna Kandalam, PhD ;  Atharva Moghe ,  MS (Sanofi)  for providing medical writing and editorial support for this manuscript.
, 2019). Yankee whaling records have previously been incorporated in sperm whale habitat suitability models  (Johnson et al. , 2016)  and have been used to provide information on seasonal changes in historical sperm whale habitat use  (Sahri et al. , 2020). However,  Sahri et al. (2020)  specifically questioned whether Yankee records might provide different conservationrelevant information on sperm whales in comparison with modern baselines because the modern distribution may not be effectively protected by an MPA placement prioritized on the basis of historical distribution. We generated habitat suitability models for sperm whales in the WIO based on historical and modern records to compare ecological information content between these different data types. We sought to identify patterns and environmental determinants of habitat suitability, congruence, and differences in model performances and predictions and potential explanations for observed differences.
Sperm whale occurrence data We collated historical and modern sperm whale occurrence data for the WIO and identified environmental correlates of sperm whale habitat by building 2 habitat suitability models based on historical-only and modern-only occurrence data. Historical distribution records for sperm whales dating from 1792 to 1912 in the WIO were obtained from the Census of Marine Life collection of American offshore whaling logbooks, which includes records of 36,909 sperm whales seen by American whalers  (Smith et al. , 2012). This archive has been used previously to investigate sperm whale distribution in the Eastern Indian Ocean  (Johnson et al. , 2016)  and Indonesia  (Sahri et al. , 2020). In the absence of modern standardized cetacean survey effort covering the entire WIO, we conducted a review of sperm whale occurrence records from recent (1995-2020) scientific surveys.
We searched online species occurrence databases (www. obis. org, www. marinespecies. org) and conducted internet and Clarivate searches (www. webofknowledge. com) for this 25-year period with a Boolean combination of terms (sperm whale*, Physeter*, Physeter macrocephalus*). We reviewed and retained search results if they included map locations or precise coordinates of sperm whale occurrences or if the author could be contacted for data access and if reports were available in English and published in the primary or gray literature. The latter selection criteria likely introduced a bias because records published in other languages would have been missed. We populated our database by extracting coordinates or digitizing maps of occurrence and survey effort. We plotted occurrences with QGIS 3.
Spatial protection assessment and differences in relative habitat suitability To assess how different data types can influence spatial management decision-making, we used the habitat predictions from our different models to investigate 2 related mechanisms that have been proposed to limit the recovery potential of threatened species and the conservation impact of MPAs. First, we assessed whether spatial protection MPA placement in the WIO is residual with respect to sperm whales (i. e. , provides ineffective protection because it contains low habitat suitability for target species  (Devillers et al. , 2014). For each model, we compared predicted habitat suitability values for each 1/6 • × 1/6 • raster cell inside MPAs versus outside with Kruskal-Wallis tests and regional MPA coverage obtained from the World Database of Protected Areas (IUCN-UNEP, 2021).
Historical and modern sperm whale records We compiled 543 sperm whale records within the WIO from the Yankee whaling era and 219 records from the modern era, following rarefaction, originating from a variety of survey platforms (oceanographic vessels, patrol vessels, aircrafts, small boats) and methods (visual transects, passive acoustics) (Appendix S3). The whaling and modern survey data sets provided broad geographical coverage of the WIO (Figure  1c, d ) and associated environmental conditions (Figure  3 ). Modern sperm whale records that did not meet our selection criteria (e. g. , because of the resolution at which the records had been gridded  [Escalle et al. , 2015] ) were limited to a few records from a relatively small geographical range (n = 40) (Appendix S4). Value ranges of environmental variables associated with historical records were greater than that in the modern data set, with the exception of seabed slope (Figure  3b ).
DISCUSSION We used sperm whale occurrence records from across 4 centuries to compare habitat predictions between historical and  modern data types. By accounting for how different data sets were collected in selection of model background points, we interpreted our model predictions in terms of ecological information content and historical population change. Our findings will help inform future management and planning decisions for conservation and industry impact assessment. Overall, predicted habitat was modestly consistent between the historical and modern models; the latter had a more geographically homogenous distribution. Our comparisons revealed a noteworthy inconsistency in the relationship between sperm whale occurrence and environmental variables between eras concerning the influence of seabed slope.
ACKNOWLEDGMENTS We thank  V. Ridoux  and the  PELAGIS Observatory-UMS 3462  ( La Rochelle University-CNRS ) and  Office Français de la Biodiversité  for the access to aerial observations of the REM-MOA South West Indian Ocean survey. We thank  P. Sabarros  and  P. Bach  ( IRD ) for facilitating access to fishery data. We are grateful to  MRAG Ltd  for their data on daily observations of cetaceans in the  Chagos Archipelago (BIOT) , particularly  J. Clark  for data curation and  T. Franklin ,  S. Browning ,  D. Hughes ,  R. Hartnell ,  J. White , and  Y. Barnes. We thank  M. Procknik  from the  New Bedford Whaling Museum  for help with accessing whaling logs and  The Science Museum, London , for use of their microfilm reader. We thank  N. Casajus  for his assistance with coding of the R compendium,  R.
Chang  at the  Royal Veterinary College  for advice on statistics and methodology, and  P. Boersch-Supan  and  C. Collins  for assisting with GIS and extraction of geomorphic features. T. B. L. was funded by the synthesis center CESAB of the  French Foundation for Research on Biodiversity  (FRB; www. fondationbiodiversite. fr), the  Mediterranean Centre for Environment and Biodiversity Laboratory of Excellence (CeMEB LabEx)  (https://www. labex-cemeb. org),and the  Bertarelli Program in Marine Science. Open access publishing facilitated by The  University of Western Australia , as part of the  Wiley -The University of Western Australia agreement via the Council of Australian University Librarians.
Experimental animals Twenty-one male Wistar rats three months old and weighing 390-440 g (Department of Pharmacology, University of Zagreb School of Medicine, Zagreb) were used in all experiments. Animals were housed in plastic cages with a maximum of 3 per cage, in a temperaturecontrolled environment under a 12 h/12 h light and dark cycle with free access to food and water. Animal procedures were approved by the Ethics Committee of the University of Zagreb School of Medicine (permit No EP 108/2017) and conducted according to the European Union Directive for the protection of laboratory animals (Directive 2010/63/EU, 2010). All data were presented in accordance with the ARRIVE guidelines (Percie du Sert et al. , 2020).
Acknowledgement This study was supported by  Ipsen, France ;  Croatian Science Foundation , Croatia (Project  ID :  IP-2014-09-4503 ) and  University of Zagreb Support project ,  Croatia. V. M. R. Muñoz-Lora  was a guest Ph. D. student at the  University of Zagreb School of Medicine  and received a scholarship from the  São Paulo Research Foundation  (FAPESP;  BEPE scholarship   2018/13575-5 , regular scholarship  2017/07741-7 ).
The subsampled genome datasets comprise 1,093,860 sequences from 63 countries (SI Appendix, Fig. S1 ), and each sequence was labeled according to the corresponding Phylogenetic Assignment of Named Global Outbreak (PANGO) lineage definition  (22). Continually emerging VOCs have led to repeated COVID-19 waves globally, reflected by the GR of cases (Fig. 1 A  and B ). The GR, with its peaks and troughs, mirrors the upsurge and decline of waves of monthly new confirmed infections (SI Appendix, Fig. S2 ). To investigate the lineage diversity during the COVID-19 pandemic, we used three diversity metrics: richness (the number of lineages), evenness (the relative abundance of lineages), and Shannon's index [a larger value of the diversity index indicates a larger number and more evenly distributed frequencies of lineages  (24) ].
These metrics were calculated based on the subsampled dataset generated using three subsampling strategies (Box 1 and SI Appendix, Fig. S3 ). When considering the average situation across 63 countries, the richness of lineage diversity has exhibited a boom-and-bust pattern driven by the transmission of SARS-CoV-2 variants, i. e. , the transition from Alpha to Delta, and to Omicron variants (Fig. 1C ). To mitigate SARS-CoV-2 spread caused by VOCs, most countries implemented IT restrictions and PHSM. Subsequently, vaccine rollout was launched in some countries starting in early 2021. However, there were no consistent relationships between COVID-19 pandemic dynamics and control measures at the national scale (Fig. 1 D  and E ). The spatiotemporal distributions of COVID-19 vaccines exhibit a high degree of heterogeneity and inequality (SI Appendix, Fig.