dataesr/scientific-paragraphs-categorization huggingface dataset:
Implementation orsum is a Python-based enrichment results filtering tool. The filtering is based on a simple principle: a term is discarded if there is a more significant term that annotates at least the same genes. In this case, the remaining more significant term becomes the representative term for the discarded term. The inputs for orsum are enrichment analysis results containing term IDs ordered by statistical significance (from the most to the least significant; significance values are not given) and Gene Matrix Transposed (GMT) file. GMT is a common format to store annotation data, where each row stores an annotation term and the annotated genes. orsum has two parameters related to the sizes of the considered terms. The first parameter, "minimum term size", is used to discard terms annotating only a small number of genes.
tsv" additionally contains the terms represented by them. • A heatmap presenting the top representative terms, colored according to the quartile of their ranks in each input enrichment result. • A barplot presenting the top representative terms and how many terms they represent. In the output figures, the number of terms to be presented is adjustable (default and maximum 50). orsum is implemented in Python 3. The source code is available on https:// github. com/ ozano zisik/ orsum and the package can be installed via bioconda (https:// anaco nda. org/ bioco nda/ orsum).
This simple filtering principle has three consequences: (i) There is a moderate correlation between the number of genes a term annotates and the number of terms it represents. As expected, larger terms represent more terms. (ii) When a term is more significant than its ancestor, both terms are kept as representative terms. Although the redundancy is retained in such a case, this ensures that more significant and specific biological information is presented to the user. (iii) orsum works with only the annotation databases that are hierarchically organized such that terms are in a parent-child relationship, e. g. GO and Reactome. For the summarization of enrichment analysis results obtained from databases that are not hierarchically structured, we recommend the clustering approaches. orsum can process multiple lists of enriched terms resulting from the analysis of different conditions or from the analysis of the same condition with different tools.
Conclusions In this study, we developed orsum, a Python-based tool for the filtering of enrichment analysis results. orsum is easy to use, the applied principle for filtering is easy to understand, the sizes of the resulting representative terms can be adjusted, and the output files are informative even at a quick glance. orsum's ability to work with multiple enrichment results will allow it to be used in comparative or integrative studies, for instance, in the investigation of multiple diseases or multiple -omics datasets.
Acknowledgements We thank the members of European Joint Programme on Rare Diseases, in particular  Annika Jacobsen ,  Eleni Mina ,  Friederike Ehrhart ,  Nazli Sila Kara ,  Nuria Queralt Rosinach  and  Tooba Abbassi-Daloii  for their support and interesting discussions. We thank  Alberto Valdeolivas ,  David P. Hirst ,  Lionel Spinelli  and  Thien-Phong Vu Manh  for their valuable comments on the manuscript.
Material and methods A retrospective review of all endovascular interventions for HAS after liver transplantation that occurred between June 2013 and November 2020 was performed at a single institution that performs 70-80 liver transplants per year. The patients were selected utilizing a procedural database (X-plore) using the different keywords (stenting, hepatic artery, transplant, liver, PBA). Approval for this retrospective review study was obtained from the institutional review board. To provide the optimal treatment for the patients, we used a score that combines the following criteria  (Kodama et al. , 2006; Hamby et al. , 2013) : -Us: resistive index < 0. 5, systolic accelerating time > 10 ms, peak systolic velocity > 200 cm/s -CT, MR or angiography A significant artery stenosis was suspected when typical tardus parvus pattern, defined by a RI < 0.
Statistical analysis Chart review was used to determine graft survival and identify additional interventions or complications during the follow up period. Primary, primary-assisted patency, and graft survival rates were recorded and analyzed using survival analysis using the Kaplan-Meier method. The Chi 2 test was used for comparisons among the three groups. A p value < 0. 05 was considered statistically significant. The statistical analysis was performed using SPSS software (version 24; SPSS, Chicago, IL).
Monocyte Isolation Monocytes, isolated from whole blood, were also used in adhesion assays, to assess whether data obtained from U937 cells aligned with that of human blood monocytes. Citrated whole blood, from the London Transfusion Service, was used to obtain monocytes, allowing extended time between blood withdrawal and experiments. One millilitre of 6% w/v Dextran 500 (Merck: D-1037) was added per 10 mL of blood, using 60 mL in total. The tubes were left for one hour at a 45 • angle, allowing for red blood cell sedimentation. The leukocyte-rich plasma was prudently removed and placed over a NycoPrep 1. 068 (Accurate Chemical, NY, USA) gradient: 3 mL of NycoPrep was used for every 10 mL of plasma. The plasma was then centrifuged at 2000 rpm for 15 min. The supernatant plasma layer was removed, and the monocytes isolated at the interface  [42].
PBMC were fed into the chambers at 10 6 cells/mL at a shear rate of 1 dyne/cm 2. For adhesion under flow, video sequences were taken after 7 min of flow: for static adhesion the flow rate was stopped for 15 min prior to recording fields for analysis. Images were then quantitated off-line using Image-Pro (Media Cybernetics, Rockville, MD, USA) software.
Data Analysis and Statistical Analysis In the plate adhesion assay, Fluoroskan results were used to calculate percentage adhesion to the wells. These were averaged across the repeats and plotted as bar charts. All assays were performed with varied numbers of repeats, as indicated in figures. In some, mean values of all experiments are shown, in others typical results, as stated. Statistical analysis, by one-way analysis of variance (ANOVA), was performed using GraphPad Prism (Version 8), or by paired t tests. A p-value of <0. 05 was deemed statistically significant. The standard error of repeats is shown on the graphs.
Acknowledgments: We thank  W Palinski  for the gifts of antibodies. The earlier part of the work was done when R. N. P was previously at the  Centre of Cardiovascular Biology and Medicine, King's College London. We thank  Jaymi Patel ,  Melanie Marshall ,  Mojtaba Posh-Mashad ,  Abagail Aitkin ,  Andrew Furness ,  Sam Chabuk ,  Lily Lamperti  and  Nafisa Bari  for assistance with experiments.
To discriminate between these possibilities, we analyzed the phylogenetic relationships of 20 great ape Hepatocystis sequences (i. e. , three Kanyawara-derived sequences and 17 unpublished sequences amplified fortuitously during previous molecular epidemiological studies of ape malaria parasites  1, 2, 7  ) relative to 56 previously published Hepatocystis cytB sequences (Supplementary Data 2) isolated from a wide range of hosts, including African and Asian monkeys, bats, and other mammals (Fig. 3 ). To determine the species origin of the ape fecal samples, we sequenced host mitochondrial D loop fragments and discarded sequences from samples from which this gene did not amplify. Of the 17 unpublished sequences, 15 were derived from chimpanzee fecal samples, while one was amplified from a dried blood spot (LIpts50094), collected from a captive chimpanzee living in the Limbe Wildlife Centre near Douala, Cameroon (Supplementary Data 3).
Between November and May, the proportion of positive samples was greater than the dataset mean (31. 1%) in 9 out of 11 sampling months, and that samples were more likely to test positive for malaria parasites during this part of the year (N = 404 samples; positive samples expected = 125. 6 ± 9. 3; positive samples observed = 163; binomial test, p < 0. 0001; Fig. 4a ). By contrast, the proportion of samples that tested positive was greater than the dataset mean in only 3 out of 13 sampling months between June and October, and samples were generally less likely to test positive for malaria parasites during these months (N = 474 samples; positive samples expected = 147. 4 ± 10. 1; positive samples observed = 110; binomial test, p < 0. 0001; Fig. 4a ). Taken together, these results highlight a seasonal pattern of infection.
These results highlight ambient temperature as an important climatic predictor of seasonal variation in chimpanzee Plasmodium infection probability for this community. Demographic variables also influenced infection probability. As reported previously  13  , fecal samples of younger chimpanzees (including the youngest chimpanzee in the dataset, 3. 7 months old at time of sampling) were more likely to test positive for malaria parasites than were older individuals (Fig. 4b, d ; Table  2 ). This finding is consistent with the epidemiology of P. falciparum in humans, in which children under five years of age experience the bulk of morbidity and mortality before gradually developing immunity to the parasite upon repeated exposure  [56] [57] [58]. Interestingly, however, while most chimpanzees adhered to this pattern, some notable outliers were evident.
For example, the fourth youngest chimpanzee in the dataset (NT; mean age: 1. 28 years; Fig. 4b ) tested positive for Plasmodium infection in 0/12 samples (model prediction: 7. 6 ± 2. 8 positive samples; p < 0. 0001). Although the father of this chimpanzee (OG) exhibited an unremarkable pattern of infection (positive in 6/15 samples), her mother (NP) tested positive for Plasmodium in 1/29 samples, and her maternal grandfather (LK) was positive in 0/25 samples (Fig. 4b ). It remains unclear whether such striking outliers are completely refractory to infection or are capable of controlling parasitemia such that it is below the limit of detection in feces. Future studies will be necessary to evaluate whether this pattern is attributable to genetic (e. g. , erythrocyte receptor polymorphism) or other factors.
Sequences were derived from N = 878 fecal samples, collected from the Kanyawara chimpanzee community in Kibale National Park, western Uganda, between 2013 and 2016. Color corresponds to malaria parasite species, inferred from the phylogenetic relationships of sequences to previously published reference sequences (green: P. gaboni, red: P. reichenowi, blue: P. billcollinsi, violet: P. vivaxlike, orange: Hepatocystis spp. , magenta: P. malariae). Newly generated sequences are listed in Supplementary Data 3, and previously published reference sequences used in this figure are listed in Supplementary Data 1. b Distribution of parasite species isolated from the Kanyawara chimpanzees. Malaria parasites were amplified in 31. 1% of samples (dotted line). A majority of parasites amplified were members of the sub-genus Laverania (i.
e. , relatives of P. falciparum): P. gaboni (23. 6%), P. reichenowi (8. 0%), and P. billcollinsi (4. 9%). Members of the primate malaria clade were amplified in a minority of samples: P. vivax-like (1. 2%) and P. malariae-like (0. 1%). In addition, Hepatocystis was amplified from 0. 4% of samples. 1-degree spatial resolution. Forest cover estimates were derived from high-resolution global maps of tree canopy cover (30 m, downsampled to 10 km), published by Hansen et al. 62. The resulting dataset of 2436 samples included only those data points for which all predictor variables were available. Mean ambient temperature during the 30 days prior to sample collection ranged from 9. 8 to 27. 3 °C (median: 23. 8 °C). Mean daily rainfall during the 30 days prior to sample collection ranged from 0 to 14.
e. , by screening samples in 8-10 replicates versus one replicate; Table  3 ). Finally, we extrapolated these relationships between ecological variables and the probability of chimpanzee Laverania infection across equatorial Africa to identify hypothetical exposure hotspots. To characterize ecological variation across equatorial Africa, we derived composite rasters from mean ambient temperature and intra-day temperature variation measurements Fig. 4  Longitudinal analysis of malaria parasitism in the Kanyawara cohort of wild chimpanzees. a The proportion of chimpanzee fecal samples (N = 878) that tested positive for malaria parasites varied by month of sampling (dashed line corresponds to dataset mean). Bar length corresponds to proportion of samples that tested positive during a given month of sampling (monthly mean is listed in parentheses).
Samples collected between November and May tended to be more likely to test positive for malaria parasites, while samples collected between June and October tended to be less likely. b Demographic variables also influenced infection probability. Samples collected from younger chimpanzees were generally more likely to test positive for malaria parasites than were samples collected from older chimpanzees. This result highlights the early age of infection onset (including the youngest sample in the dataset, collected from a 3-month-old female), indicative of a high magnitude of ongoing transmission. Despite this observation, a subset of chimpanzees (e. g. , NT) deviated from this trend, potentially indicative of resistance to infection. c Predicted infection probabilities, derived from the Kanyawara GLMM (Table  2 ), demonstrate that seasonality of infection is partially driven by variation in mean ambient temperature.
Mean ambient temperature (measured directly via weather monitoring stations) was positively correlated with infection probability across the range of temperature values observed at this sampling site (20. 0-23. 0 °C; p < 0. 0001). Raw data (binary) are plotted as dots and stratified vertically for visualization. d Infection probabilities predicted by the Kanyawara GLMM also demonstrated that the youngest study subjects tended to be the most likely to test positive for malaria parasites (p < 0. 0001). Raw data (binary) are plotted as dots and stratified vertically for visualization. recorded between March 2000 and February 2017 the African continent at 1-degree spatial and 1-month temporal resolution. Using these composite temperature rasters and the Hansen et al. 62  forest cover dataset, we projected the predicted probabilities of chimpanzee Laverania infection (derived from the pan-African model) across the spatial extent of chimpanzee habitat (as defined by the International Union for the Conservation of Nature, IUCN; Fig.
4c and 5a, Table  2 ), which peaks at ~24. 5 °C in wild chimpanzee populations, close to the ~25. 0 °C empirical transmission optimum of P. falciparum in humans  27, 33. The consistency of this result is underscored by the fact that these two approaches use non-overlapping datasets and alternative methods of temperature measurement (Kanyawara: direct measurement; pan-African: remote sensing). In addition to mean ambient temperature, intra-day temperature variationwhich has also been shown to influence the epidemiological processes at the parasite-mosquito interface  28, 37  -was correlated with infection probability in both datasets (Tables  2  and 3 ). However, the direction of this correlation differed in these analyses, which suggests a more complicated relationship with malaria infection (e.
While Hepatocystis parasites are known to cross species barriers rather promiscuously  54  , our study suggests that chimpanzees are susceptible to monkey Hepatocystis infection (Fig. 3 ). Although some, or all, Hepatocystis sequences derived from chimpanzee fecal samples may have resulted from the consumption of Hepatocystis-infected monkeys, we also amplified one Hepatocystis sequence from the blood of a captive chimpanzee, providing evidence for a productive infection. This is the first description of a chimpanzee Hepatocystis infection and suggests that crossspecies transmission of Hepatocystis between monkeys and chimpanzees (and potentially humans) may have been Fig. 5  Ecological niche modeling of malaria parasitism in wild chimpanzee reservoirs across equatorial Africa. a Analysis of N = 2436 wild chimpanzee fecal samples collected from 55 sampling sites across equatorial Africa demonstrate that mean ambient temperature (inferred from MODIS remote sensing datasets; see Methods) and forest cover  62  are critical determinants of chimpanzee Laverania epidemiology.
The pan-African GLMM (Table  3 ) demonstrates that (1) infection probability peaks ~24. 5 °C (dotted vertical line), consistent with the empirical transmission optimum of P. falciparum  27  , and (2) forest cover is positively correlated with infection probability (p = 0. 001), consistent with the observation that forest-dwelling Anophelines constitute the primary vectors of these parasites  16, 17. Color corresponds to three categories of forest cover: 54% (25th percentile), 90% (50th percentile), and 99% (75th percentile). Raw data (binary) are plotted as dots and stratified vertically for visualization. b, c Given the ecological relationships identified in this study, we extrapolated infection probabilities across chimpanzee habitat in equatorial Africa. We derived composite rasters from mean ambient temperature and intra-day temperature variation measurements recorded between 2000 and 2017 across the African continent.
Using these composite temperature rasters and the Hansen et al. 62  forest cover dataset, we projected the predicted probabilities of chimpanzee Laverania infection (derived from the pan-African model) across the spatial extent of chimpanzee habitat. The monthly mean pixel value of each raster is plotted, and error bars correspond to standard deviation of pixel values on each raster. d Stratification by month highlights the seasonality of these infections. Across chimpanzee habitat, infection probability peaks between the months of January and May, and infection probability declines between June and September. Because mosquito vectors of ape malaria parasites readily bite humans  17  , these maps can serve as a baseline proxy for spatiotemporal variation in the risk of human exposure in areas where humans and apes overlap.
Fecal samples-collected only upon direct observation of defecation and only when a positive identification was certain -were preserved in RNAlater (1:1 vol/vol) and stored at -20 °C until exportation and DNA extraction. For cross-sectional analyses, 2436 chimpanzee fecal samples were collected from 55 chimpanzee field sites, distributed across equatorial Africa (Fig. 1 ; Supplementary Table  2 ). This dataset included 1936 wild chimpanzee samples, previously collected for molecular studies of simian retroviruses  [73] [74] [75] [76] [77] [78]  or malaria parasites  1, 7  , and 500 samples that were newly collected for these analyses (Table  1 ). Samples were only included in this dataset if collection dates were recorded and corresponding ecological variables (see below) were available. This cross-sectional dataset comprised a combination of samples that were either collected from habituated chimpanzees occupying long-term research sites or opportunistically from non-habituated chimpanzees during ape and biodiversity surveys.
For each sample, we defined mean ambient temperature, intra-day temperature variation, and rainfall as the average of measurements of the corresponding variable during the 30 days prior to sample collection. For cross-sectional analyses, ecological variables were estimated using remote sensing datasets. For each sample, we downloaded daytime and nighttime Land Surface Temperature (LST) measurements from the MODerate Resolution Imaging Spectroradiometer (MODIS) thermal sensor on board the NASA-Terra satellite at 1 km 2 spatial resolution and 1-day temporal resolution (MOD11A1v005; http://modis. gsfc. nasa. gov/)  60 at the geospatial point of sample collection. We subsequently performed quality control to remove low quality or missing data (e. g. , resulting from cloud cover) and downsampled spatial resolution to 10 km 2 to increase applicability of variables across the sampling site.
Rainfall estimates were derived from the Global Precipitation Climatology Project (GPCP V2. 3) 61 at 1-day temporal resolution and 1-degree spatial resolution (https://www. esrl. noaa. gov/psd/). Again, for each sample, we defined mean ambient temperature, intra-day temperature variation, and rainfall as the average of measurements of the corresponding variable during the 30 days prior to sample collection. Forest cover estimates were derived from high-resolution global maps of tree canopy cover (30 m, downsampled to 10 km), published by Hansen et al. 62. The resulting dataset included only those data points for which all predictor variables were available (N = 2436 samples). Statistics and reproducibility. We developed two separate generalized linear mixed models to identify the ecological and demographic determinants of Laverania prevalence in wild chimpanzee hosts.
These statistical analyses were performed in R (version 3. 4. 3) using the lme4 package  80. Geospatial mapping. Given the ecological relationships identified in the Pan-African GLMM, outlined above, we extrapolated infection probabilities across wild chimpanzee habitat in equatorial Africa. To characterize ecological variation across equatorial Africa, we derived composite rasters from mean ambient temperature and intra-day temperature variation measurements recorded between March 2000 and February 2017 across the African continent at 1-degree spatial and 1-month temporal resolution. Using these composite temperature rasters and the Hansen et al. 62  forest cover dataset, we projected the predicted probabilities of chimpanzee Laverania infection (derived from the pan-African model) across the IUCN-designated spatial extent of chimpanzee habitat, according to the following equation: Predicted probability ¼ 1 1 þ e À ðintercept þ 0:355 ATÀ0:164 AT 2 þ 0:032 FC þ 0:208 TVÞ ð3Þ where intercept is -2.
538 for samples screened using conventional PCR (i. e. , one replicate) and -1. 374 for those screened using intensive PCR (i. e. , 8-10 replicates), and AT, TV, and FC are each corrected by subtracting the means of the chimpanzee dataset (23. 4 for AT, 9. 1 for TV, and 77. 2 for FC). All geospatial analyses were performed in ArcMap (v10. 6 Environmental Systems Research Institute Inc. ).
Acknowledgements This work was supported by grants from the  National Institutes of Health   R01AI091595 ,  R01AI120810 ,  R01AI050529 , and  P30AI045008  (B. H. H. ); R01HL139337 (M. T. D. ), the  National Geographic Society  (E. J. S. ), the  International Primatological Society (E. J. S. ) , and the  American Society of Primatologists  (E. J. S. ), as well as fellowships from  Harvard University  (E. J. S. ) and the  National Science Foundation  (E. J. S. ). We thank  Dr. Kristen Skillman  for help in putting the manuscript together, as well as two referees for their review and thoughtful comments on this manuscript.
Data Description Here we report the whole genome sequencing data of Lactiplantibacillus plantarum IMI 507027, together with its safety-related features such as antimicrobial resistance and presence of virulence factors. The whole genome sequence consisted of 37 contigs with a total size of 3235,614 bp, a GC% of 44. 51, and an N50 contig length of 366,540 bp. The average sequencing coverage was 518x. During the annotation step, contigs below 200 bp in length were removed, obtaining an annotated assembly of 31 contigs (3,234,779 bp). The annotation produced 3126 genes, of which 3030 were CDS (coding sequences), 38 miscellaneous RNAs (non-categorised non-coding RNA), 2 rRNAs (ribosomal RNAs), 1 tmRNA (transfer-messenger RNA) and 55 tRNAs (transfer RNA). The NCBI Genome database contains 613 L.