dataesr/scientific-paragraphs-categorization huggingface dataset:
Results A total of 3,179 studies were identified via databases. Before screening, 357 duplicates and 159 studies were removed (e. g. , language, chapters and books, congress papers, or theses). According to the exclusion criteria, 2,349 studies were excluded based on the title or the abstract. After verifying the records left in full, according to the pre-established eligibility criteria, 93 studies from databases were included in the systematic review. Moreover, 14 out of 25 studies identified via citation searching were included. Finally, a total of 107 studies were included in the systematic review. Results from the process for selecting the included articles (following the recommendations of  Page et al. , 2021)  are described in the flowchart (Figure  1 ). In total, 13 studies provide a SMT value or a range of SMT values in healthy adults (Table  3 ).
, 2018). Building on the results of  Hoffman et al. (2018) , we aim to investigate and compare the suitability of using two types of iDNA sources based on ubiquitous insects -carrion flies and mosquitoes-for detecting wild mammal species in suburban forests in a large city. Specifically, we focused on four suburban areas in Berlin and applied metabarcoding using next-generation sequencing to identify species in iDNA samples from carrion flies, non-bloodfed mosquitoes, and bloodfed mosquitos. Moreover, a customized and incremental database of molecular markers was built to support the species identification analysis within this and future studies involving vertebrate, mosquito, and fly molecular studies from Berlin.
| Customized sequence database The database was built for all three markers (Database 1-4) with the goal of curating a local database of the 59 terrestrial mammal species occurring in Berlin  (Klawitter et al. , 2005). Briefly, an initial database was created by downloading sequences from the NCBI database using a list of known markers and species of interest. As sequences downloaded would be of varying fragment lengths, in silico PCR was conducted using ecoPCR within the OBItools package to obtain the fragment used in this study, forming the initial database. A second search was conducted via Genbank or BOLD, using ecoPCRSequences. sh script  (Heeger et al. , 2022; url:  doi. org/10. 5281/ZENODO. 7322543), to obtain a comprehensive list of sequences, as sequences in the NCBI database are limited to the date of download.
These additional sequences were aligned to TA B L E 2 Summary of the results of DNA barcode primers used for mammal identification in samples of each type of insect, primer used for insect identification (COI-insect). the initial database as a reference to retain sequences with the fragment of interest and fragment length at a 99%-100% identity match. Sequences were then dereplicated using obiuniq -m sample samplename and maximum likelihood tree was built with IQ-TREE  (Nguyen et al. , 2015)  using ModelFinder to select a model of nucleotide evolution  (Kalyaanamoorthy et al. , 2017)  and Macropus giganteus (GenBank accession number: NC027424) as an outgroup for mammals. A comparison was made between the phylogenetic trees constructed in this study with existing phylogenetic trees from published papers.
| Sequence analysis Primers were trimmed from the sequences of both 12 S-V5 and 16Smam metabarcodes using Geneious 8. 1 (Biomatters, New Zealand). The trimmed paired-end reads were merged using OBItools  (Boyer et al. , 2016) , chimeras removed using Vsearch  (Rognes et al. , 2016) , and the filtered sequences dereplicated to identical sequences using the OBItools pipeline. For COI data, following the removal of primers, low-quality sequences were removed and paired-end reads were concatenated with the gap between reads filled with 10 N bases. Concatenated sequences were dereplicated using OBItools to identical sequences. Unique sequences from samples were concatenated with the sequences from the database and pairwise distances were calculated using Vsearch. Based on these distances, clusters with an identity threshold of 99%-100% were created using clusterFromPairs.
py  (Heeger et al. , 2022 ). An analysis of the clusters, using clusterAnalysis. py  (Heeger et al. , 2022) , was carried out to ensure that clusters were formed between sequences from samples and those from the database. Clusters formed from sequences from samples only were not assigned to a species. To determine the effectiveness of the 12 S and 16 S primers, in silico PCR was run using Primersearch  (Rice et al. , 2000)  on sequences obtained from Genbank.
After grid search optimisation of the number of potential origins and the number of firing factors, we observed a high correlation (PCC = 0. 88), similar to Gindin et al (  [19] ; 0. 865), between simulated and experimental MRT profiles, December 15, 2021 5/39 and a lower correlation between simulated and experimental RFD profiles (PCC = 0. 70) (Fig. 3 ). Reasoning that addition of dispersive, random initiation to the PODLS (see Methods) might improve the results, we extended the grid search for this parameter and obtained an optimal correlation for RFD at PCC = 0. 75 for 5% of random initiation events, while maintaining the same correlation with MRT (PCC = 0. 88) (Fig. 3 ). These observations confirm that MRT and RFD data are consistent with each other and suggest that RFD data are furthermore informative about random initiation.
In that setting, the correlation between simulated and experimental profiles decreased markedly for MRT (PCC = 0. 72) but only slightly for RFD (PCC = 0. 72) (Fig. 4AC , blue curves). Therefore, MRT is constrained by the collective action of multiple origins, so that the sum of neighbouring DNase I HSS signals is critical, while their exact positions per 200 kb windows are not (Fig. 4A ). RFD is instead sensitive to the location rather than the magnitude of these signals. The influence of a single origin on RFD decreases rapidly with distance, due to the influence of intervening origins. To summarize, incorporating RFD as a target for simulations likely allows to test origin positioning at much higher resolution than was achieved with MRT  [19, 34]. Deriving RFD profile from MRT data (Eq. (  1 )) by numerical derivative would produce low resolution RFD December 15, 2021 7/39 The starting PODLS I 0 may be a crude approximation of the target PODLS such as given by the peaks of RFD derivative, or the DNAse HSS profile, but not a random profile.
We observed that the procedure does not improve the prediction quality after a small number of iterations (maximum of 4 in S. cerevisiae, Supplementary Table  S1 ). profiles with amplified noise, while determining MRT profile from the summation of RFD data (Eq. (  2 )) would produce MRT profiles with unreliable MRT changes over large distances. Experimental MRT and RFD profiles thus provide complementary information. We use both in the following analyses. Learning the PODLS to optimize the prediction of both MRT and RFD experimental data. Having shown that experimental MRT and RFD profiles are consistent over a wide range of scales in the context of the constant fork speed hypothesis, we assessed to which extent they could be jointly explained by a single PODLS in our replication model. At 5 kb resolution, the PODLS correspond to ∼ 575000 parameters which must be optimised.
For K562 SNS, Hela SNS and GM Bubbles, the correlations at 5 kb and 50 kb were 0. 06 and 0. 16, 0. 18 and 0. 31, and 0. 06 and 0. 17, respectively. For K562 ORC2, Hela MCM7 and HeLa MCM2, the correlations were 0. 29 and 0. 53, 0. 18 and 0. 31, and 0. 27 0. 41, at 5 kb and 50 kb resolution, respectively. Therefore, none of these experimental datasets were convincing predictors as RFD upshifts and MRT (Eq. (  8 )) of the PODLS I 4 coherent with experimental MRT and RFD profiles, for reasons that remain to be elucidated. The fact that ORC location seem to better predict the PODLS than MCM is somewhat unexpected, given experiments in Xenopus egg extracts that suggest that the multiple MCM DHs loaded around a single ORC binding site are equally competent to bind Cdc45 and trigger origin firing  [26, 15].
1 lower in correlation than our best simulations suggest that the amount of ORC2 could contain all the information needed to account for replication inhomogeneity. Surprisingly however MCM profiles in Hela used as such, allowed to obtain an intermediate correlation with MRT and RFD profile (0. 55 and 0. 44 respectively). Extracting peaks of the profile allowed to reach an higher correlation of 0. 77 and 0. 6. However these correlations are about 0. 2 lower than our optimum correlation and do not allow to conclude if MCM non uniform distribution alone account for replication inhomogeneity. In our perspectives we plan to extend our analysis to other cell lines when the dataset will be available. The already obtained PODLS will allow to push forward the determination of epigenetic marks and mechanism responsible for replication inhomogeneity in further analysis.
This means that if we increase the size of the system, the constant of reaction k on must be decreased, which is coherent as k on encompass the efficiency of encounter between one potential origin and one firing factor and being in a larger system their encounter rate is decreased. Missing data. For all simulations if a gap larger than 1. 5 Mb without data in either MRT or RFD experimental data was present, the region extended of 500 kb on both ends was removed. This mainly happen in telomeric region and centromeric region, and it mean that a chromosome can be segmented in two or more pieces. Then when comparing e. g. simulated MRT with experimental one, we also remove all gaps present in MRT that are smaller than 1. 5M extended of 500 kb on both end. These two steps remove less than 10 % of the genome for either MRT or RFD in all considered cell lines.
For both MRT and RFD when comparing with experimental data, we masked the region removed as specified in the missing data paragraph. Pearson correlations were computed at 5 kb resolution for RFD and 10 kb resolution for MRT. T 100 was defined as the replication time of the latest replicated window T99 (resp. T95) we defined as the time at which 99% (resp. 95%) of the genome was replicated. December 15, 2021 20/39 search optimisation (See previous paragraph) to obtain M RT 0 and RF D 0 that fitted best experimental one. Then a neural network N 1 was trained to predict the initiation profile I 0 + r (r being the amount of random activation obtained from the grid search optimisation part) from the simulated M RT 0 and RF D 0 (See next paragraph for details on the neural network parameters). N 1 was then applied on experimental M RT and RF D predicting the I 1 profile.
Then I 1 was used as input of a simulation to obtain M RT 1 and RF D 1. We then computed the correlation with experimental M RT and RF D. To summarise in this iteration we used M RT 0 and RF D 0 as input to produce I 1 , M RT 1 and RF D 1. We then reiterated the process twice using successively M RT 1 and RF D 1 as input and then the obtained M RT 2 and RF D 2 as input to produce M RT 3 and RF D 3. we reitirated once more the procedure and stopped as it did not improved the correlations. All the code to reproduce the steps here are detailed at (https://github. com/organic-chemistry/repli1D).
Moreover, this method requires costly marine surveys with large research vessels, taxonomic expertise to identify fish, and is invasive, which raises ethical concerns  (Trenkel et al. , 2019 ). In recent years, environmental DNA (eDNA) metabarcoding has emerged as a new tool applied in ecology  (Deiner et al. , 2017 ) , including in the marine realm (e. g. Gilbey et al. , 2021 ). eDNA is a genetic material obtained directly from environmental samples without isolating the individuals and is characterized by a complex mixture of intracellular and extracellular DNA  (Taberlet et al. , 2012 ). In the eDNA metabarcoding method, species presence is detected through water filtration, polymerase chain reaction (PCR) amplification with one or several universal primers, sequencing using a high-throughput sequencer, and comparison of sequences with a genetic reference database  (Fraija-Fernández et al.
, 2019 ) , deep-water monitoring  (Everett and Park, 2018 ) , and characterizing of fish diversity and habitat preference  (Stoeckle et al. , 2017 ). Moreover, eDNA metabarcoding could complement and even reduce the number of trawls performed by surveys  (Trenkel et al. , 2019 ). Despite these attractive aspects, eDNA metabarcoding requires laboratory facilities and equipments as well as expertise in both molecular ecology and bioinformatic to analyse the data effectively. The first comparative study between eDNA and trawling for fish  (Thomsen et al. , 2016 )  indicated that eDNA holds promise but detects lower richness than trawling. In contrast, as the availability and quality of databases improve and eDNA techniques become more refined, recent studies comparing scientific trawling and eDNA monitoring methods showed that eDNA detects a higher species richness than trawling, with especially good performance for both rare (low abundance) and pelagic species  (Weltz et al.
Data acquisition by eDNA and trawling To perform eDNA sampling, we collected water samples at 15 sites ( Figure  1 a ). At each site, we sampled seawater using Niskin bottles deployed with a circular rosette. There were nine bottles on the rosette, each of them able to hold ∼5 l of water. At each site, we first cleaned the circular rosette and bottles with freshwater, then lowered the rosette (with bottles open) to 5 m above the sea bottom, and finally closed the bottles remotely from the boat. The 45 l of sampled water was transferred to four disposable and sterilized plastic bags of 11. 25 l each to perform the filtration on-board in a laboratory dedicated to the processing of eDNA samples. To speed up the filtration process, we used two identical filtration devices, each composed of an Athena ® peristaltic pump (Proactive Environmental Products LLC, Bradenton, Florida, USA; nominal flow of 1.
85 ± 1. 5 km, minimum distance 0. 25 km, maximum distance 5. 8 km). Trawling was carried out during daylight for 30 min at a speed of around 4 knots. The catch of each haul thus integrated 3. 5 km of distance and around 20 m in the horizontal direction (trawl opening between wings). The trawl was a standard GOV 36/47 ("Grande Ouverture Vertical") with a 4-m vertical opening and a 20-mm mesh size in the codend. Taxonomic experts identified, counted, and weighed the sampled fish during the survey  (Laffargue et al. , 2021 ). We performed the fish identification at the species level, however, taxa that could not be unambiguously identified were grouped at the genus level. For example, Trachurus mediterraneus and T. trachurus were lumped in Trachurus sp. Additional information about sam-pling is available on the GitHub page indicated in the section "Data availability".
We amplified two negative extraction controls and one negative PCR control of ultrapure water (12 replicates) and sequenced them in parallel to the samples. We did not detect any contamination. We performed library preparation and sequencing at Fasteris (Geneva, Switzerland). Specifically, we prepared four libraries using the MetaFast protocol (a ligation-based method) and sequenced them separately. We carried out paired-end sequencing using a MiSeq sequencer (2 × 125 bp, Illumina, San Diego, C A, US A) on two MiSeq Flow Cell Kits (v3; Illumina), following the manufacturer's instructions. We analysed the sequence reads using the OBITools package ( http: // metabarcoding. org/ obitools ;  Boyer et al. , 2016 ) , following the protocol described by  Valentini et al. (2016). We assembled forward and reverse reads using the illuminapairedend program, with a minimum score of 40 and retrieving only the joined sequences.
We then assigned the reads to each sample using the ngsfilter program and created a separate dataset for each sample by splitting the original dataset into several files using obisplit. After this step, we analysed each replicate sample individually before merging the taxon list. We dereplicated strictly identical sequences using obiuniq. We removed sequences shorter than 20 bp, those with an occurrence < 10, and those labelled "internal" by the obiclean program due to PCR substitutions and indel errors. We performed taxonomic assignment of the sequences using the ecotag program with a genetic reference database formed by combining two sources: (i) the EMBL genetic reference database including 16128 sequences from 10546 species across all organisms (European Molecular Biology Laboratory , < www.
ebi. ac. uk > , v141, downloaded in January 2020;  Baker et al. , 2000 )  and (ii) a custom-built 12S reference database from sequenced samples taken from individual fish during previous EVHOE trawl surveys, currently containing 84 sequences belonging to 68 species of Atlantic fish. We confirmed taxonomic assignment at different taxonomic levels only when the following conditions were met: species (match > 98%), genus (96% < match ≤ 98%), family (90% < match ≤ 96%)  (Marques et al. , 2020 ). We discarded all sequences with a frequency of occurrence < 0. 001 per sequence and per library to account for tag jumps  (Schnell et al. , 2015 ). We further corrected for index-hopping  (MacConaill et al. , 2018 )  with a threshold empirically determined using experimental blanks between libraries. We only kept species and genera from the identified sequences for diversity analyses.
Data aggregation The lowest taxonomic level at which we assigned the sequences was the species level, but in some cases, we could only identify the sequences at the genus level. For ambiguous assignments, we aggregated all species belonging to the same genus to the genus level when one observation was restricted to a genus identification. For example, we merged the detected taxa Notoscopelus , N. elongatus , and N. kroyeri into the genus Notoscopelus. When the genus had only one known species in the region, we replaced it by the species. For example, the genus Sardina and the species S. pilchardus , and we combined them into S. pilchardus. As several taxa could not be unambiguously identified at the species level for the bottom trawl dataset (as previously indicated in the section "Data acquisition by eDNA and trawling"), we used the taxonomic level (species or genus) for taxa detected by trawling and by eDNA sampling ( Supplementary Table  S1  ).
To perform taxa aggregation and analyses based on fish clades, we retrieved the taxonomic classification from the Barcode of Life Data System (BOLD;  Ratnasingham and Herbert, 2007 )  and the World Register of Marine Species (WoRMS;  Horton et al. , 2022 ) , querying them online through the taxize package v0. 9. 99  (Chamberlain and Szöcs, 2013 )  in R v4. 1. 2 (R Core Team, 2023 ).
Fish traits and ph ylogen y We measured functional diversity using nine traits associated with several ecosystem functions (habitat, feeding, reproduction, and mobility; Villéger et al. , 2017 ): maximum length, average depth and its range, trophic level, position in the water column, body shape, reproduction mode, fertilization mode, and parental care type ( Supplementary Table  S2  ). We retrieved 89% of the traits from the online Fishbase database  (Froese and Pauly, 2022 )  and complemented missing values with information from experts and from a reference guide  (Quéro et al. , 2003 )  to fill a trait table to 95%. We centred and normalized all quantitative traits. For taxa detected or merged at the genus level, we randomly selected one species of that genus occurring in the Eastern Atlantic from the reference guide ( Supplementary Table  S2  ).
We measured phylogenetic divergence using the mean pairwise distance (MPD) index, defined as the average phylogenetic distance between all pairs of species  (Tucker et al. , 2017 ). We estimated phylogenetic regularity by computing the variance in pairwise distances (VPD), calculated using the R package PhyloMeasures v2. 1  (Tsirogiannis and Sandel, 2015 ). For functional diversity, we assessed the functional richness facet using the 0-order ( q = 0) functional Hill number (FD hereafter), denoting the number of equivalent functional entities at a site or in an assemblage  (Chao et al. , 2019 ). FD is computed directly from the Gower distance matrix and not based on the reconstructed functional space. For comparison with FD, we also computed the FRic index  (Villéger et al. , 2008 ) , denoting the volume of the convex hull formed by species in the functional space  (Mouillot et al.
, 2009 ). We tested the improvement in the model fit between LM and GLS using the Akaike information criterion (AIC) while fitting the GLS model using a maximum likelihood optimization ap-proach implemented in the R package nlme  (Pinheiro et al. , 2021 ). We assessed the explained GLS model variation using the Cox and Snell pseudo-R 2  (Cox and Snell, 1989 ). Second, we fitted the same linear regressions among taxa for each eDNA filter independently within each site because the purified PCR products of each filter were pooled in equal volume before sequencing, which prevented a robust comparison of the number of eDNA reads between filters. Finally, we tested whether accounting for allometric scaling in eDNA production improved the relationships between organism abundance and eDNA reads count, and we explored which allometric scaling coefficient maximized such relationships  (Yates et al.
01. We implemented GLMs with a negative binomial distribution error and a log link function to account for the overdispersion of the number of eDNA reads, using the R package MASS  (Venables and Ripley, 2002 ). We retained the model with the lowest AIC as the best model. We also estimated the explained model variation using a pseudo -R 2 based on deviance  (Zuur et al. , 2009 ). To assess whether accounting for allometry provided a better model fit than the linear relationships between the relative number of eDNA reads and the log number of individuals per trawl, for each filter we compared the R 2 of the LM model in step 2 with the pseudo-R 2 of the GLM fitted with the best b coefficient in step 3. We also tested the best allometric coefficient for the relationships with the relative number of eDNA reads per taxa per filter using an LM to assess if accounting for allometry provided a better model fit than the linear relationships between the relative number of eDNA reads and the log number of individuals per trawl.
γ di ver sity The taxonomic assignment of eDNA sequences resulted in the identification of 1039 unique sequences at the family or lower taxonomic level (33% species level, 34% genus level, and 33% family level), corresponding to 202 different taxa (128 genera or species). After data aggregation and harmonization between the two methods, we retained 92 taxa, among which 79 were Actinopterygii (27 orders; 74 genera; and 55 species), 12 Elasmobranchii (7 orders; 13 genera; and 9 species), and 1 Holocephali ( Chimaera monstrosa ). At each site, we detected 33 to 55 taxa (42. 2 on average) based on 31300 reads on average. Scientific trawl catches included 250000 fish individuals, with an average of 17000 individuals per sampling site. We identified 84 taxa, and after taxonomic harmonization we retained 70 taxa: 60 Actinopterygii (belonging to 22 orders; 57 genera; and 44 species), 9 Elasmobranchii (5 orders; 8 genera; and 5 species), and 1 Holocephali ( C.