dataesr/scientific-paragraphs-categorization huggingface dataset:
A completeness analysis of the A. castellanii reference transcriptome available (GCA_000313135. 1, BioProject PRJNA66753)  5  against the BUSCO Eukaryota_odb10 database (version 3. 0. 2)  61  was performed to define our assembly strategy. The obtained metrics were not satisfactory, thus we decided to perform a de novo assembly of our data to obtain a more recent and exhaustive transcriptome. This de novo assembly was carried out with Trinity (version 2. 6. 6)  62. The presence of possible contamination in our assembly was further investigated with taxon-annotated GC content-coverage plot using Blobtools  63. Taxonomic assignment was based on the taxonomic classification of their BLASTn and BLASTx results ensuring the most accurate results as recommended in the tool documentation. Finally, the transcriptome was validated by mapping back the preprocessed reads to the assembly using Bowtie2, when nearly 100% mapped properly.
Differential expression analysis. Transcript quantifications were estimated by mapping all pre-processed RNA-Seq paired reads for each sample to the final assembled transcriptome using Bowtie2. The expression levels of mapped paired reads were normalized using the TMM normalization method with RSEM  64. No filtering was done based on the expression values. Quality check of replicates was performed using the post-transcriptome assembly downstream analyses tools of Trinity. The normalized reads were then used for the differential expression analyses using the DESeq2 26 at the transcript level using the Trinity 'run_DE_analysis. pl' script. Differentially expressed transcripts were identified using the Trinity 'analyze_diff_expr. pl' script with default parameters: an absolute log2 fold change ≥ 4 and a false discovery rate (FDR) p-value ≤ 0.
Dynamic exclusion was set to 35 s (Input sample) or 40 s (Phosphopeptides) to avoid repeated sequencing of identical peptides. Data Processing for protein identification and quantification. All data were searched using Andromeda  66  with MaxQuant software (version 1. 5. 3. 8)  67, 68 against a custom-generated database constituted of the non-redundant protein sequences from the Uniprot A. castellanii strain Neff proteome and the Trinity database (129,654 entries after curation), known mass spectrometry contaminants (Max-Quant contaminant database with the proteome of Parachlamydia acanthamoebae strain UV7 to control for contamination because this strain was used in the laboratory) and reversed sequences of all entries. The custom FASTA can be downloaded from zenodo. org (DOI 10. 5281/zenodo.
Acknowledgements This work was supported by the  Agence Nationale de la Recherche  (grant  ANR-17-CE13-00001-01  "Amocyst") (A. S-L. )and by the  Novo Nordisk Foundation  (grant  NNF14CC0001 ) (L. J. J. and M. L-. P). A. S-. L. received a JEDI (justice, equity, diversity, and inclusion) award from  Life Science Editors  and we would like to thank  Marie Bao  for editorial assistance. We acknowledge the  Ebioinf bioinformatics core facility of EBI  for providing the computing infrastructure and resources. Finally, we thank  Gwenaelle Quere  and  Amélie Jessu  for technical support,  Vincent Delafont  for technical assistance and for the critical reading, and  Lesley-Ann Giddings  for proofreading the article.
Data availability All transcriptomic raw data generated in this study have been deposited in the NCBI SRA database, under the BioProject database identifier PRJNA794325. The MS proteomics data generated in this study have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository, under the project accession number PXD031788. FASTA used for the computational analysis and the protein identification and quantification used in this study are available on Zenodo at https://doi. org/10. 5281/ zenodo. 5869928. The raw data for all figures generated in this study are included in the supplementary data files or in the source data file. The following genomes were used in this study: Acanthamoeba castellanii strain Neff [GCA_000313135. 1, BioProject PRJNA66753], Acanthamoeba castellanii strain Namur [GCA_903821525.
4% (1) 5. 5% (14) 14. 9% (38) Our transcriptome vs BUSCO eukaryota_odb10 Complete Complete and Single- Complete and Fragmented Missing database (255 Single-copy genes) copy duplicated Acanthamoeba castellanii strain Neff [This study] 94. 2% (240) 42. 0% (107) 52. 2% (133) 1. 6% (4) 4. 2% (11).
Long-term data collection To describe the long-term evolution of the lagoon, historical data on different environmental and biological parameters were collected from multiple sources. For meteorological data, daily rainfall and mean air temperature data were collected from Meteó-France at the Bastia-Poretta Airport meteorological station (42°32′26″ N; 9°29′07″E; Figure  1 ). In order to characterize climatic long-term trends, an annual dataset of cumulative rainfall and mean air temperature from 1950 to 2021 was constructed. Potential multiannual monotonic trends were investigated through the Mann-Kendall (MK) trend test (modifiedmk package in R,  (Hamed and Rao, 1998; Patakamuri and O'Brien, 2020) , applied to annual cumulative rainfall and mean air temperature means. The Theil-Sen slope estimator was provided in the case of significant MK (i.
e. , p-value<0. 05). Then, an analysis that focused on the 2000-2021 period was performed on cumulative annual rainfall and mean annual air temperature to characterize meteorological conditions over the study period. This was compared to the standard reference value over the reference period established for France  according to the World Meteorological Organization guidelines (WMO-No. 1203 © World Meteorological Organization, 2017). Over the same period, a monthly database was also constructed to discriminate and compare the extension of dry periods between different years. In order to describe the global context of the study site, monthly cumulative water discharge and nutrient (nitrate, NO - 3 ) contents of the two main rivers of the watershed (Bevinco and Golo rivers) were obtained from publicly accessible data websites (http://www.
naiades. eaufrance. fr/, https://www. hydro. eaufrance. fr/) at several available sampling stations from 2000 to 2021, located at 9 km (Bevinco) and 13 km (Golo) upstream the lagoon. Annual mean values of NO - 3 discharge were then obtained based on monthly available data, by multiplying the nutrient concentration by water discharge. Values obtained were then expressed as tonnes of NO - 3 per year. A monthly database for salinity (PSU), water temperature (°C), ammonium (NH + 4 , µM), nitrate (NO - 3 , µM), nitrite (NO - 2 , µM), phosphates (DIP, µM), silicates (SiOH, µM), and chlorophyll a (µg L -1 ) data for the lagoon was constructed based on different sources  (Garrido et al. , 2013; Garrido et al. , 2016; REPHY, 2019  (SEANOE https://doi. org/10. 17882/47248); RNEB data; this work). This covers the 2000-2020 period, at two main stations, representing distinct basins inside the lagoon: North Basin (NB) and South Basin (SB) stations (Figure  1 ).
When possible, mean values for the entire basin were calculated. Potential relationships between nutrient concentrations in the lagoon, salinity of the lagoon, NO - 3 inputs and flow from main rivers (Bevinco for the northern and Golo for the southern part), and monthly cumulative rainfall were assessed through the non-parametric Spearman's rank-order correlation tests applied on available monthly data. Qualitative information on the opening of the grau and the outlet of the Golo river were obtained by visual analysis of available satellite pictures from Landsat 4/5 (30-m resolution), Landsat 7 (15-m resolution), and Sentinel2 (10-m resolution) systems (https://glovis. usgs. gov/). Sixteen-day cycle data were available from January 2000 to October 2021. For each image, the outlets were assigned a status of "opened" (1), "closed" (0), or "partially opened" (0.
5). To represent dominant trends over the study period, a mean value was then calculated per year. All the years considered within our study presented at least 10 observations seasonally distributed along the year. Furthermore, information on human-mediated openings of the grau was collected from the Natural Reserve of the Biguglia Lagoon (RNEB data) and expressed as the declared number of days of artificial openings per year. In order to analyze biological compartments and evolution over time of different trophic levels, multiple sources were exploited. Data on fish captures (t), fish species evolution, and major invasive species were obtained from professional fishermen, managers of the RNEB, and literature  (Bouchoucha, 2010; Etourneau, 2012). Macrophytes (angiosperms and macroalgae) mean percentage coverage over the basin and dominant species information were obtained from the Observatoire des Lagunes (OBSLAG) network data (monthly samples during the summer period from 2000 to 2021 at 15 stations in the Biguglia lagoon) and following the method described in  Pasqualini et al.
Some missing months are due to the impossibility of sample analysis performance or technical problems impeding field sampling  (2020). Data were treated for the two main sampling stations separately, NB and SB. For class-level analysis, original data were grouped into five main classes: "Bacillariophyceae" (or diatoms), "Dinophyceae" (or dinoflagellates), "Cyanophyceae", "Cryptophyceae", and "Chlorophyta". All individuals that could not be assigned to one of these five classes were grouped in the class "Others". To explore the evolution of the two dominant classes, diatoms and dinoflagellates, we calculated their ratio as the abundance of dinoflagellates over the sum of diatoms and dinoflagellates. A finer qualitative analysis of dominant and blooming (cellular density >100,000 cell L -1 ) taxonomic units was also performed.
| Genotyping errors and microsatellites characteristics Genotyping errors (e. g. , presence of null alleles, allelic loss) were tracked using the program MICROCHECKER v. 2. 2. 3  (van Oosterhout et al. , 2004). FSTAT v. 2. 9. 3. 2  (Goudet, 1995 (Goudet, , 2001) )  was used to check for deviation from Hardy-Weinberg equilibrium for each locus (10,000 randomizations) and linkage disequilibrium for all pairs of loci (exact G-tests). When necessary, p-values were adjusted for multiple comparisons using the Bonferroni procedure  (Bonferroni, 1936).
| Delimitation of species To genetically verify the species identification, we first built a phylogenetic tree using the Cyt-b sequences generated in this study and additional sequences retrieved from public databases (6 sequences of C. pusillus, 12 of C. leucogaster, and two sequences of C. atsinanana as an outgroup, Genbank database, NCBI, see Figure  2 ). The Cyt-b sequences were aligned using the webPRANK server (http://www. ebi. ac. uk/goldm an-srv/webpr ank/) with default parameters  (Löytynoja & Goldman, 2010). The phylogenetic tree was constructed using the Bayesian method implemented in MrBayes 3. 2. 7 software  (Ronquist et al. , 2012) , with 10,000,000 generations. The automatic Smart Model Selection (SMS) in PhyML  (Lefort et al. , 2017)  was employed to choose the adequate substitution model (HKY85 + G + I), based on Akaike Information Criterion (AIC,  Burnham & Anderson, 2002).
, 2005)  as implemented in the online software STRUCTURE HARVESTER v. 0. 6. 94  (Earl & vonHoldt, 2012). Independent runs for the different K values were averaged using CLUMPP v. 1. 1. 2 (Jakobsson & Rosenberg, 2007)  as implemented in CLUMPAK  (Kopelman et al. , 2015) , also implementing the DISTRUCT  (Rosenberg, 2004)  procedure to display graphical results. In CLUMPAK, the LargeKGreedy algorithm was used, with a random input order and 2000 repeats. Genetic differentiation between the two species was measured using F ST (theta estimator,  Weir & Cockerham, 1984 ) calculated with FSTAT v. 2. 9. 3. 2  (Goudet, 1995 (Goudet, , 2001)). The significance level was assessed by exact G-tests assuming random mating within samples with 10,000 permutations.
| Gene flow between species and historical demography As potential incongruent patterns between mitonuclear markers could be the result of incomplete lineage sorting or hybridization, we tested whether gene flow between C. pusillus and C. leucogaster may explain the observed patterns (see Section 3) using an Approximate Bayesian Computation (ABC) framework  (Beaumont et al. , 2002). We ran the ABC analyses, with the ABCtoolbox package v. 2. 0  (Wegmann et al. , 2010) , and the programs SIMCOAL 2. 0 and ARLSUMSTAT  (Laval & Excoffier, 2004) , on two different datasets. In the first dataset, the incongruent individuals were grouped   S2. A rejection sampling approach was used to simulate the data under each scenario. A set of summary statistics (for microsatellites: the mean number of alleles, mean total number of alleles, mean heterozygosity, mean total allelic range, pairwise F ST and genetic distance (δμ)2; for Cyt-b: standard deviation over loci of the heterozygosity for each population, mean heterozygosity, number of private polymorphic sites and pairwise F ST ) were used to compare the simulated and observed data.
To discriminate between the three models, we used a rejection method implemented in the R "abc" package  (Csilléry et al. , 2012). We then evaluated the ABC performances in model choice and parameter estimates using the two following methods. First, we randomly picked 1000 pseudo-observed datasets from all simulations generated for each scenario and determined how often the ABC procedure correctly predicted the correct scenario. Each pseudoobserved dataset was used as the observed data to calculate the marginal densities of the different scenarios. Second, to evaluate how well the selected scenario fits with the observed data, we carried out a principal component analysis on the summary statistics computed from each model and plotted the observed data. Since the ring hypothesis postulates a population expansion of C.
| Within-species genetic diversity Microsatellite loci genetic diversity indices (A: the number of alleles per locus; AR: allelic richness, calculated using the rarefaction method; El  Mousadik & Petit, 1996) , observed (H o ) and expected (H e ) heterozygosities were estimated using the "hierfstat" package  (Goudet & Jombart, 2001)  in R v. 4. 0. 1 (R Core Team, 2020). The within-species population genetic structure was investigated using STRUCTURE with the same parameterization presented above. Cockerham, 1984)  between ex-provinces (Madagascar) or Comoro archipelago using FSTAT v. 2. 9. 3. 2. (Goudet, 1995). Significance levels of global F ST values were assessed by exact G-tests assuming random mating within samples with 10,000 permutations. Pairwise F ST values significance levels were evaluated using permutations, and p-values were adjusted for multiple comparisons using the Bonferroni procedure  (Bonferroni, 1936).
Finally, isolation by distance inter-specific patterns was assessed between geographic locations within species by regressing pairwise linearized F ST (F ST /(1-F ST )) and log-transformed geographic distances, using Mantel tests (9999 permutations, "ecodist" R package, Goslee & Urban, 2007), following  Rousset (1997). Genetic variability and population structuration were also determined from Cyt-b sequences. First, intra-specific diversity was evaluated by calculating nucleotide diversity (i. e. , a set of genetic alleles usually transmitted together) per species with ARLEQUIN software. Subsequently, the haplotype network, allowing the reconstruction of genealogic links between haplotypes of each species, was built using the NETWORK software v. 5. 0. 1. 1, with the Median Joining method (fluxu s-engin eering.
| Gene flow between species and historical demography Regardless of the dataset used for the ABC analysis, the posterior probabilities significantly selected model 3, which assumes recent gene flow between the species since their divergence (pp = 0. 51-0. 59; Figure  4 ). The two other models (Figure  4 , models 1 and 2) had low posterior probabilities (pp = 0. 14-0. 15 and pp = 0. 26-0. 34, respectively) and were rejected. The goodness of fit between the selected model and the observed data was validated by the PCA analysis in which the data fit well with the observed data (Figure  S4 ). The ABC analyses showed a good power of discrimination between the three models (Figure  S5 ), with a misclassification proportion of 0. 35, 0. 31, and 0. 07 for models 1, 2, and 3, respectively. Most of the model confusions were found between models 1 and 3, which involved no gene flow and ancient migration.
tors that gave rise to C. pusillus and C. leucogaster species (Naidoo et al. , 2016), we further constructed a phylogenetic tree including Cyt-b sequences from C. pumilus to have an overview of the relationship between these regional species of Chaerephon. To decipher the F I G U R E 2 Bayesian phylogenetic tree of Chaerephon species based on cytochrome b sequences from 161 individuals sequenced herein and 75 sequences retrieved from Genbank. Node values correspond to posterior probabilities. Nodes supported by more than 0. 9 are indicated. Chaerephon atsinanana is used as an outgroup. Individuals sequenced in this study are highlighted in blue and red for C. pusillus and C. leucogaster, respectively. The black-colored sequences are publicly available sequences from both species. In purple, 53 individuals morphologically identified as C.
No allelic dropout was detected, but MICROCHECKER indicated that stuttering might have resulted in scoring errors in locus TabrA10 since very few heterozygotes were present (only two alleles were detected, separated by one pair) in the C. pusillus. However, since the rarest allele was also found in C. leucogaster, leading to 26 heterozygous genotypes, we were confident that this allele was a real allele. No linkage disequilibrium between loci nor departures from HWE were detected in C. pusillus and C. leucogaster datasets. This was also indicated by low and nonsignificantly different from zero F IS values (F IS = 0. 053, p =. 04 and F IS = 0. 039, p =. 17, respectively, adjusted nominal level = 0. 01).
e. , a minimum of 18 individuals per island); therefore, increasing the number of samples and polymorphic markers is of utmost importance to test these hypotheses, and thoroughly examine the breadth of genetic diversity. 4. 2 | Mitonuclear discordance Our present dataset suggests that 53 individuals out of 161 (33%) carried the Cyt-b haplotype of C. leucogaster while bearing the Nuc DNA from C. pusillus. Interestingly, nearly half of the C. pusillus individuals sequenced for mt DNA bear the Cyt-b haplotype of.
Average or Bulk velocity The average velocity can be obtained by the following equations: With: u + =u/uτ, y + =(y uτ)/ν, for these equations we need to know the velocity profile which is given for the inner region by the law of the wall  [17] : • The linear profile in the viscous sublayer where ν≫νt u + = y + (7) • The logarithmic profile in the overlap or log-law layer where νt≫ν Figure  (1)  presents linear and logarithmic profiles (equations 7 and 8) with DNS data for a friction Reynolds number Reτ=642  [20]. For the logarithmic profile, we use κ=0. 4  and B=5 which seem to be suitable. These approximations (curves of equations 7 and 8) are very close to DNS data. However, the velocities are not accurate near the intersection between the two velocity curves and also in the outer region for high y +.
Determination of friction factor from DNS data We obtain values of f from DNS by both Reynolds numbers (equation 4) or from the average velocity (equation 2). f values obtained from Reynolds numbers: In table  (1) , friction factor f is obtained from Re and Reτ of DNS data  (Iwamoto, 2002). f values obtained from the average velocities: The trapezoidal rule solves the integral (equation 6) by approximating the region under the data and provides the area from elementary area for each Δy + , as: For each Reynolds number we obtained the area which provides the average velocities 𝑢𝑢 𝑚𝑚𝑚𝑚𝑚𝑚 + and therefore the friction factor (table  2 ). This method is particularly interesting for Reτ=2003  [19]  where the Reynolds number is not provided and therefore we are unable to use the first method. However, for Iwamoto's data this second method allows a second estimation of f.
Comparison and proposition of a new calibration for Dean's correlation Figure  (2)  shows that Dean's correlation (equation 5) does not provide accurate predictions of friction factor particularly for low Reynolds numbers. We will consider the DNS values in order to recalibrate this correlation which we write as: f = a Re -b Fig. 2. Friction factor from Dean's correlation and DNS data. Different calibrations are proposed: In the first, we use the values of f obtained from Re of Iwamoto until 642 (Table  1 ), the coefficients are a=0. 0702 and b=0. 25. In the second, the calibration is based on f obtained from the average velocity (Table  2 ), we obtain a=0. 0652 and b=0. 241. Finally, the third calibration which seems the most adequate uses both the first for Iwamoto's data and the second for Re=2300, we obtain a=0.
Law-of-the-wall method This method is based on linear and logarithmic velocity laws. The friction factor f is obtained by the average velocity (equation 2). The average velocity is obtained by integrating the linear profile until y + =11. 5 and the logarithmic profile for y + >11. 5. MATLAB provides the result directly with the "quad" function which uses recursive adaptive Simpson quadrature. Table (3) presents the results: q1 is the result of the first integral related to the linear profile and q2 the second one for the logarithmic velocity profile. Table  3. Friction factor obtained by the law-of-the-wall method. Figure  (6)  presents the friction factor as a function of the Reynolds number obtained by the law of-the-wall method. This result is compared to DNS data and the recalibrated Dean's correlation.
ODE-Log method In this method the friction factor f is obtained by the average velocity (equation 2) through two velocity profiles. The first until y + =20 is obtained by a method based on the momentum equation (  14 ) and a suitable eddy viscosity profile  [3]  and the second by the log-law. In this equation, νt + represents the dimensionless eddy viscosity given by an analytical equation  [5]. We can solve this equation by ODE45 function of MATLAB. Figure  (7)  presents the velocity profile (red solid line) obtained by the ordinary differential equation ODE (  14 ) for y + <20 which is in very good agreement with DNS data. For the average velocity (equation 6), we use the trapezoidal rule for y + <20 and the "quad" function for the log-law. Table (  4 ) presents the results of f obtained by equation (  2 ).
Reynolds numbers are obtained by equation (  3 ). In order to evaluate the friction factor results obtained by the ODE-log method, we plot f = f(Re) with DNS data and the results from the law-of-the-wall method (figure  8 ). For Re>395, ODE-log and law-of-the-wall (linear-log) methods provide similar results. They are in good agreement with DNS data for intermediate Re numbers. For low-Re the result from ODE-log method seems somewhat less accurate. This result is unexpected because the velocity profile obtained by the momentum equation (equation  14 ) is more accurate for 0>y + >20 and therefore the average velocity and the friction factor f should be improved. 9 ) provides an explanation of this unexpected result, where for low Re, ODE-log method is somewhat less accurate than the law-of-the-wall method, through an analysis of the DNS data of mean velocities for 𝑅𝑅𝑅𝑅 𝜏𝜏 = 109.