dataesr/scientific-paragraphs-categorization huggingface dataset:
Genome association analysis Unique markers (genes and variants) and combinations were both investigated to find a genomic pattern of host adaptation (bovine and poultry). For unique genes, GFF files of all genomes produced by Prokka 7  were computed by Panaroo  (Tonkin-Hill et al. , 2020) , a graph-based pangenome clustering tool that is able to account for errors introduced during annotation. The clustering identity threshold was increased to 90%. For unique variants, VCF files from iVARCall2 were merged to identify the presence and absence of variants in each genome. The position of variants was identified in S. Mbandaka SA20026234. Genes identified by Panaroo or genes containing discriminating variants were annotated using BLAST and UniProt (UniProt, 2021). To explore possible combinations of markers for host adaptation, we developed a python 3 script called MarkerFinder.
" Sensitivity was calculated as TP/(TP + FN) and specificity as TN/(FP + TN). A false-positive result is defined as a positive signal for an identification assay for a gene or variant belonging to a non-targeted phenotype. A falsenegative result is defined as a negative result for a gene or variant belonging to the targeted phenotype. The MarkerFinder tool developed is available at https://github. com/madeleinevlt/MarkerFindr.
Worldwide wild animal genome dataset and phylogenetic analysis To explore the hypothesis of possible cross-contamination between wild and farm animals, we extracted, from the open access Salmonella EnteroBase database, 2,465 genomes in row reads (on October 2021) whose serovar was confirmed as S. Mbandaka, the MLST profile as ST413, and epidemiologic information-host or country of isolation-available. Among these genomes, 42 were retained because they were isolated from wild animals, with 10 genomes from "Avian, " 2 from "Canine, " 2 from "Marsupial, " 4 from "Reptile, " and 23 "No-determined. " Among these 42 genomes, none were isolated from France or Europe. Finally, considering that only Aves could have been physically responsible for contamination between different continents, we chose nine of the ten genomes from strains isolated from wild birds ("Avian").
One genome was retried due to read errors. Among the final nine genomes selected, six were from coastal regions with five genomes coming from strains isolated along the east coast of North America and one from the west coast. Six genomes were from the United States, two from Canada, and one from Mexico. The accession numbers and epidemiologic information for these genomes are reported in Supplementary Table  1  ("Wild animal DATA" tab). The variant calling (SNPs) phylogenetic analysis was carried out as described above with the nine genomes from North America and a subset of 214 genomes from France. These 214 genomes (n = 100 bovine, n = 114 poultry) were selected from the total panel of 304 genomes so that they were representative of the different groups identified by the phylogenetic analysis of bovine and avian strains from France.
Sequence type genetic diversity and SNP core genome phylogenetic analysis All 304 S. Mbandaka genomes belong to the MLST profile ST413. To investigate the genome diversity of the dataset and identify clustering revealing possible host adaptation, a maximum likelihood phylogenetic tree was built taking into account core genome SNP distances. The tree was rooted using the strain S. Mbandaka 100,727 (NCBI ID SRR6860551) belonging to ST506. This ST was identified as genetically closest to ST413 according to hierarchical clustering (HC) cgMLST EnteroBase analysis (Zhou et al. , 2020) (data not shown). The phylogenetic analysis revealed 12 major groups supported by 100% bootstrap values and each containing more than five strains. Among these 12 groups, seven groups (A, B, C, D, F, H, and K) contained 118 out of a total of 164 (72%) analyzed genomes sampled from poultry.
Accessory genome analyses The virulome and accessory genome were analyzed to better characterize this serovar and to identify possible genomic differences between strains isolated from the bovine and poultry sectors. Two approaches were followed to identify conserved genes and mutations related to possible bovine and poultry host associations within S. Mbandaka strains. The first approach, described in this paragraph, explores the presence/absence of virulence and resistance genes compiled in open access databases targeting known virulence factors, SPI genes, multi-drug resistance genes, and genes conferring heavy metal or biocide resistance. Plasmid and phage sequences were also queried. The second approach, described below, refers to a genomic pattern analysis that computes statistical sensitivity, specificity, and accuracy of accessory genes and core variants according to host associations and specific phylogenetic groups identified.
Virulome analysis Infection of cells by Salmonella requires different steps. After moving to reach epithelial intestine target cells with its flagella, adhesion factors, such as fimbriae and adhesins present on the S. Mbandaka surface, enabling strong attachment to the surface of host cells. Gene clusters csg, fim, bcf, and lpf, coding for both curli and chaperonne/placier type fimbriae were found in all analyzed strains from France and, although genes coding for SPI-3 were not observed as a whole, misL and mgtCB genes coding for adhesins were also observed in the dataset (Supplementary Table 2, "SPI" and "vfdb" tabs). The fimH gene was identified in all genomes, and comparison of translated sequences revealed no mutations that could be associated with any bovine or avian host. Only one sequence revealed 1 SNP mutation (i.
e. , poultry ACT20SMb17 fimH nucleotide sequence), but this mutation is synonymous. Combined analysis of the BLAST results obtained by the SPIFinder and VFDB databases with both thresholds set at 90% identity and 80% identity (data not shown) revealed that SPI-1, SPI-2, SPI-4, and SPI-9, coding for type III (T3SS-1 and T3SS-2) and type I secretory apparatus (T1SS) responsible for survival and proliferation in various intracellular environments, were identified in all the S. Mbandaka genomes (Supplementary Table 2, "SPI" and "VFDB" tabs). SPI-1 genes, encoding T3SS-1 apparatus that is required for invasion of intestinal epithelium cells, such as inv, prg, and org gene clusters and sicA, sipABCD, sspABC, sopB, sopE, and sopE2 regulator/effector genes were identified in all genomes. In the same way, SPI-2 genes encoding components of the T3SS-2 apparatus required for bacterial virulence and proliferation in macrophages, such as ssa, ssc, and sse gene clusters and pipB, pipB2, and soxS regulator genes were also identified within all genomes from France (Supplementary Table 2, "SPI" and "VFDB" tabs).
Interestingly, the siiABCDEF SPI-4 gene cluster, encoding a type I secretion apparatus that contributes to the colonization of bovine intestines, was also observed in the dataset, with the exception of five out of the 164 poultry genomes. The few inconsistencies between BLAST results of SPIFinder and VFDB could be explained by the use of different alleles for the same gene in the two databases. It is also important to take into account the identity percentage applied to the analyses. Likewise, only an in-depth analysis of the sequences of these gene clusters within each genome could highlight either SNP or indell type differences between the strains or possible recombination events. Additionally, even though SPI-9 was identified by the SPIFinder database BLAST analysis, operon STY2876, STY2877, STY2878, and STY2875 were not displayed by VFDB BLAST analysis.
Investigation of wild animal contamination in French herds The nine North American genomes from wild birds selected from the Salmonella EnteroBase database clustered together with six genomes from France (five poultry samples and one bovine) (Figure  3 ). Interestingly, the genomes from France were from strains isolated on farms close to the west coast or along a river that flows to this coast. Among the five poultry samples, strains S18LNR1211 and S16LNR3059 were isolated from farms located close to the Atlantic coast in Bretagne. Strain 2019LSAL01500 was sampled in the same region and the last two strains S18LNR0214 and S18LNR1879 were isolated further inland, but along a river that flows to the Atlantic coast. Bovine sample ACT1919846 was isolated from manure close to the north-western coast in the Normandie region.
Funding This study was supported by funding from the  French Ministry of Agriculture, Food and Forestry , through the grant  CASDAR-RT EMISSAGE  (No. 1710 ). MDSV was the recipient of a doctoral fellowship co-funded by the grant  CASDAR-RT EMISSAGE  (No. 1710 ) and two  French Agrifood Institutes  (ACTALIA and  IFIP-Institut du Porc).
We show the accuracy and speed of PyBDEI on simulated data and compare it to the gold standard Bayesian tool BEAST2  (Bouckaert et al. , 2019)  and the deep-learning-based tool PhyloDeep  (Voznica et al. , 2022). We find that our approach outperforms the competitors and makes the BDEI model applicable to very large data sets. Lastly, we apply PyBDEI to infer the epidemiological parameters that shaped the Ebola epidemic in Sierra-Leone in 2014. Our estimator is freely available at github. com/evolbioinfo/bdei.
PhyloDeep took 0. 2 CPU seconds per tree, which is faster than our method's time but does not include the training time of deep learning predictors (hundreds of hours). To our knowledge, the only other available maximum-likelihood estimator for BDEI is implemented in the TreePar package  (Stadler and Bonhoeffer, 2013). However, as it suffers from underflow issues for BDEI already on trees of small size, its developers suggest using BEAST2 instead (private communication). The average time of PyBDEI convergence on the large data set was 3 minutes and 53 seconds on 1 CPU, and required 990 iterations. Parallelization on 2 CPUs reduced it to 2 minutes and 2 seconds (1. 9 times faster). The speed up is close to the number of cores, which shows the efficiency of parallelization of master equation resolution despite the pre-and post-processing steps (tree reading, distribution of jobs between the threads, combining their results), which are always performed on one CPU.
The accuracy of estimations improves with the data set size (as expected, see our simulations). In the world of rapidly growing sequencing data sets  (Hodcroft et al. , 2021) , we can gain important insights on epidemic spreads by harvesting all available information. PyBDEI is applicable to very large data sets (4 minutes on a 10 000-tip tree), making parameter and CI estimation instantaneous with respect to phylogenetic tree reconstruction times (hours). Our approach could be easily used in a Bayesian setting as well, and could potentially be implemented in BEAST2.
This permits us to avoid possible stability time restrictions and only choose the time steps for precision. The core of our estimator is implemented in C++ and uses the NLopt library ( Johnson ) for non-linear optimization. The parallelization is achieved with the C++ thread pool tools  (Williams, 2012). To facilitate the use of our estimator in Python and perform additional validation of input trees, we wrapped the core estimator into a Python 3 library PyBDEI. PyBDEI uses ETE 3 framework for tree manipulation  (Huerta-Cepas et al. , 2016)  and NumPy package for array operations  (Harris et al. , 2020). Our estimator is available as a command-line program and a Python 3 library via PyPi (pypi. org/project/ pybdei), and via Docker/Singularity (hub. docker. com/r/evolbioinfo/bdei). Its source code, the simulated and real data used for its assessment, as well as the Snakemake  (Köster and Rahmann, 2012)  data analysis pipelines, and the installation and usage documentation are available on GitHub at github.
, 2016) (v1. 10: github. com /tothuhien/lsd2/ tree/v1. 10, under strict molecular clock with outlier removal) using tip sampling dates, and reconstructed the ancestral characters for country with PastML  (Ishikawa et al. , 2019) (v1. 9. 34, MPPA+F81). Lastly, we extracted 10 SLE forests from these trees to represent the Ebola epidemic in SLE between July 30 2014 (when the SLE government began to deploy troops to enforce quarantines (News24, 2014)) and September 12 2015 (the last SLE sample in our dataset) by (  1 ) cutting each tree on July 30 2014 to remove the more ancient part (with a different health policy); (2) among the July-31-on trees, picking those whose root's predicted character state for country was SLE (light-green branches at the level of July 31 2014 in Figure  S2 ); (3) removing the non-SLE subtrees (indicated with other colours in Figure  S2 ) from the selected July-31-on SLE trees to focus on the epidemic within the country, without further reintroductions.
More precisely, while an instance p represents the entity itself, it is attached to several time slices which represents its state at various points in time. Some recent work go further by generalizing this approach to describe any kind of statement context  [9]. However, as pointed out in  [1] , the main drawback is the proliferation of entities represented due to the multiple time-slices, which both increases the size of the dataset and makes reasoning more complex. In TSN, as in other approaches  [12] , time-slicing is handled by creating a new version of the whole hierarchy for every change, regardless of whether every territory actually evolves or not. Regarding TSN, this approach is legitimate as they represent territory nomenclatures defined by a central organism which seldom issues a new version.
Evaluation Our algorithm was evaluated on several datasets which all had particularities. Tests were carried out using the TopBraid API, which allows to evaluate SHA-CLRules. This section will tackle these evaluations, and their results. All datasets complemented with data description as well as the algorithm results are available online in the git resource. A script is also provided to convert CSV tables to HHT knowledge graphs. Finally, the git resource provides several SPARQL queries, and a query comparison with TSN.
French Third Republic This dataset is the simplest the algorithm was tested on. It focuses on the French territory from 1870 to 1939. Only one hierarchy is represented, which has three levels (Commune, Arrondissement and Département). Data was represented using HHT, after being converted from CSV tables. Finally, all the building blocks (Communes, here) are described across the whole timespan of the knowledge graph, making this dataset fully-compliant with our approach. It is interesting to note that out of 211 hht:CompositeChange, only 181 are geometrically qualified. This difference is due to cases where a territory disappears to be replaced by another one occupying the exact same geometry, which were not considered in our approach. Such cases occur in this particular dataset mostly due to the reintegration of some part of the French territory following the first World War.
The analysed changes were compared with results from an economists' study regarding territory evolution on this same period. The feature changes detected matched those found manually. Interestingly enough, it was shown that, by listing changes by year, it was possible to identify easily identify periods of great changes. In this dataset, for example, 241 feature changes occur in 1926, which coincides with the greatest territorial reform of the Third Republic. Humanities researches are also provided with a deeper analysis due to the aggregation of feature changes in composite changes. This dataset was also used to evaluate HHT-SHACL FDD. In order to have accurate results, it was mandatory to check that original data had been properly converted. The HHT-SHACL FDD approach was instrumental in that regard.
NUTS To evaluate our algorithm on a larger knowledge graph, it was used to detect changes in the NUTS (Nomenclature of territorial units for statistics) 2  nomenclature from 2010 to 2016. More precisely, this dataset describes the NUTS and LAU (Local Administrative Units) 3  nomenclatures for 14 of the 28 countries of the European Union, and was generated from an existing RDF dataset describing the NUTS hierarchy, and tables published on Eurostat describing the LAU levels. Some countries were removed due typically to arbitrary and non documented changes in territory IDs in the found data. The countries still described vary from the original dataset due to the removal of some LAU2 units which did not respect the hypothesis of description across the whole timespan. A high amount of name changes are found, mostly due to spelling variants.
France: Region reform The goal of this evaluation was to test the behaviour of the algorithm when confronted to unproperly formalized data. This dataset was created by combining datasets provided by INSEE. Timeslicing was handled by creating one timeslice for each territory described in the original datasets, meaning that most territories are described as having two versions regardless of any difference between those two versions. As a consequence, in this dataset, Fragmentation is carried out poorly, with some hht:Unit having two temporally-consecutive hht:UnitVersion which describe the same properties. In addition, some building blocks aren't described across the whole timespan. Those inconsistencies with the algorithm's hypothesis lead to flaws in the results. Changes are detected between two versions that are describing the exact same properties, as they should be described as the same version.
Noise is added to the mock observations according to the depth in each filter (to the greatest depth as quoted in Tables  1  and 2 ). Similarly, known noise characteristics of existing ground-based data have been added to the galaxies' mock Figure  3. An illustration of the deepest filters available in COSMOS-Web and their depths across the spectrum. At the top are the filter transmission profiles for existing COSMOS data sets that are ground-based (light gray), space-based (dark gray), and new additions from JWST for COSMOS-Web (blue). These filters are separated between those that have 5σ point-source depths between 24 and 26. 5 mag (top subpanel), and those that reach depths beyond 26. 5 mag (bottom subpanel; see  Weaver et al. 2022b , for more details). We also include recent coverage from COSMOS-DASH at 1.
At z > 6 we fit all galaxies so that we adequately sample the full range of true EoR source properties. Thus, in Figure  5 , there appears to be a dearth of sources at 4  z < 6 due to this differential sampling of parameter space, but the apparent differential is simply visual (e. g. , there are 1 K sources modeled in the 4 < z < 6 bin). To understand the improvement in the photometric redshifts provided by COSMOS-Web data, we compare our inferred mock photometric redshift quality to those from the COSMOS2020 catalog. Specifically,  Weaver et al. (2022b)  found that sources with i-band magnitude between 25 < i < 27 have 5% photometric redshift precision. Over the same i-band magnitude range, we infer that these JWST data will improve that statistic to 2. 5%. Both precisions are measured using the normalized median absolute deviation (σ NMAD ) of Δz/(1 + z), a quantity analogous to the standard deviation of a Gaussian but less sensitive to outliers.
Scheduling of the Observations and First Epoch of Data COSMOS-Web was awarded a total of 208 hr, but due to changes in overhead and the dithering pattern described above, COSMOS-Web will take a total of 255 hr to execute. We requested relatively low zodiacal background observations (<10th-20th percentile) and to tile the mosaic at a nearly uniform position angle on the sky to avoid gaps within the mosaic. COSMOS-Web is observable in windows in April (PA ≈ 105) and December/January (PA ≈ 290) of each year. In order to maximize the amount of overlap between the prime (NIRCam) and parallel (MIRI) observations, we will observe roughly half of the mosaic in each window. The first epoch of observations consists of six visits covering ∼77 arcmin 2 with NIRCam and was observed on 2023 January 5-6.
When multiple mass cuts are quoted by a given study, we show number densities above this mass limit for consistency. The quiescent galaxy sample from IllustrisTNG100 was selected using the publicly available 60  SFR  (Pillepich et al. 2018; Donnari et al. 2019 ) and stellar mass value that corresponds to the mass within twice the half-mass-radius of each object (Rodriguez-Gomez et al. 2016). Note that there are no quiescent galaxies in the IllustrisTNG100 volume beyond z > 4 using this definition. We similarly selected the quiescent galaxy sample from the public EAGLE galaxy database 61  (McAlpine et al. 2016) using the recommended aperture size of 30 physical kpc. These hydrodynamical simulations are calibrated to reproduce physical properties in the local universe and predict the SFR and M å values at high redshift.
Acknowledgments We thank the anonymous reviewer for helpful suggestions, which greatly improved the manuscript. We thank the entire  JWST team , including scientists, engineers, software developers, and the instrument and commissioning teams for making this amazing telescope a reality. We thank our program coordinator  Christian Soto , our  NIRCam  reviewer  Dan Coe , and our  MIRI  reviewer  Stacey Bright  for helping us to optimize our program and ensuring that the entire program is schedulable. We also thank the  CEERS team  for their quick release of simulations and observed data products, early testing and modification of the data reduction pipeline, and assisting with preparation for COSMOS-Web data reduction. Support for this work was provided by  NASA  through grant  JWST-GO-01727  and  HST-AR-15802  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc.
The Cosmic Dawn Center (DAWN) is funded by the  Danish National Research Foundation  under grant No. 140. J. D. R. was supported by  JPL , which is under a contract for  NASA  by  Caltech. This research is also partly supported by the  Centre National d'Etudes Spatiales (CNES). O. I. , C. L. , and H. H. M. C. C. acknowledge the funding of the  French Agence Nationale de la Recherche  for the project  iMAGE  (grant  ANR-22-CE31-0007 ). B. E. R. was supported in part by  NASA  grant  80NSSC22K0814. M. T. and F. G. acknowledge the support from grant  PRIN MIUR 2017 20173ML3WW_001. B. T. acknowledges support from the  European Research Council (ERC)  under the  European Unionʼs Horizon 2020 research and innovation program  (grant agreement  950533 ) and from the  Israel Science Foundation  (grant  1849/19 ).
The authors acknowledge Research Computing at the  Rochester Institute of Technology  (2019) for providing computational resources and support for the work reported in this publication. This work used the CANDIDE computer system at the IAP supported by grants from the  PNCG ,  CNES , and the  DIM-ACAV  and maintained by  S. Rouberol. We acknowledge use of the lux supercomputer at  UC Santa Cruz , funded by  NSF MRI  grant  AST1828315.
Despite the construction of continuous online learning, the risk for science prevails in that the web content may spread without timely updates and without relevant concepts, in a "Tower of Babel" effect, driven by growing interactions of plain scientific categorizations that are barely updated. However, even though web mining technology is extensively practiced in scientific applications, there is still hardly any Scientific Knowledge Graph providing users with comprehensive dynamic modeling of the "commonwealth" of web usage and proposing a language for its interpretation; GRAPHYP 2 is intended to be a contribution in this direction.