dataesr/scientific-paragraphs-categorization huggingface dataset:
Characterization of CpEO by GC/MS Analysis Identification of the major chemical compounds of CpEO was performed by gas chromatography coupled to a mass spectrometer (GC/MS) analysis (Shimadzu GCMS-QP2010 SE, Kyoto, Japan), equipped with an Rtx®-5MS capillary column (30 m × 0. 25 mm × 0. 25 μm. The CpEO (55 μL) was placed in a vial (2 mL vial with 250 μL inserts) and injected for data acquisition. The operating conditions of the GC-MS/MS were optimized as follows: 70 eV, carrier gas (He), a flow rate of 1. 7 mL. min -1 and a pressure of 53. 5 KPa. Injector and detector interface temperatures were 25 °C and 230 °C, respectively. The oven temperature program was 100 °C for 3 min -1 and then 310 °C at a heating rate of 3. 5 °C min -1 and maintained at 310 °C for 5 min -1. The identification of the constituents of the essential oils was investigated by comparing the mass spectra and Kovats index (IK) values with those of the research reference library.
AGC Target was set at 5 × 10 5 ions with automatic filling limit and maximum IT at 500 ms. The instrument was controlled by Xcalibur™ software version 4. 1. Equipment maintenance, including mass calibration, was performed between the analysis of the different batches. Each sequence began with the injection of a mixture of derivatized (deuterated) internal standards, a solvent blank (pyridine), an extraction blank, and a derivatization reagent blank, followed by the injection of seven QC samples to ensure system equilibration. The analytical information obtained from the last injected QC samples was retained as part of our QA protocol. Subsequently, serum samples were injected randomly, and QC samples were injected every five samples. Two more QC samples were also injected at the end of the sequence, followed by the injection of the mixture of internal standards to assess any shift in retention time and mass accuracy during the acquisition sequence.
Quality control of data acquisition The mixture of isotope-labeled internal standards was analyzed at both the beginning and end of each sample sequence to assess any shift in retention time and mass accuracy during data acquisition. Therefore, isotope-labeled internal standards were used for quality assurance of data acquisition, establishing an acceptance threshold of ±0. 1 min and 5 ppm for retention time and mass accuracy. The stability of instrument performance was ensured through a rigorous quality control plan. At the beginning of each sequence, a solvent blank (pyridine), an extraction blank, a derivatization reagent blank, and seven quality control (QC) samples were injected. Subsequently, serum samples were injected randomly, and QC samples were injected every five samples. QC samples were analyzed throughout the two analysis sequences to correct for any analytical variability in terms of signal intensity within and between batches of samples.
Data pre-processing GC-EI-HRMS raw data files (*. raw) were initially converted to *mzML format using MSCovert  (Chambers et al. , 2012). The *mzML files were subsequently loaded into MS-DIAL environment  (Lai et al. , 2018)  for data pre-processing using the default MS-DIAL parameters slightly modified as follows: mass range, 50-650 Da; retention time range, 5-20 min; "mass slice width", 0. 1 Da; "mass accuracy for centroiding", 0. 025 Da. A preliminary annotation was achieved using all records with Kovats RI downloaded from the MS-DIAL webpage. The RI obtained from the alkanes mixture injected under the same analytical conditions was used to score the candidate annotations. Samples were then aligned using a 0. 1 retention time tolerance and EI similarity of 70%. Peaks were filtered using the ratio sample max/blank average of 5.
Statistical analysis The statistical analysis of the data was performed in R environment (version 4. 0. 1). The mixOmics package  (Rohart et al. , 2017)  was used to perform Principal Component Analysis (PCA) and sparse Partial Least Squares-Discriminant Analysis (sPLS-DA). The robustness of each model was evaluated by R2X, R2Y and the cross-validated R2adj parameters. The models were validated calculating the classification accuracy of the model (AUC) after Monte Carlo Cross Validation  (Xu and Liang, 2001 ). In addition, a time 0 centering as proposed by Narduzzi et al. (Narduzzi et al. , 2020a (Narduzzi et al. , , 2021) )  was applied to perform geometric trajectory analysis. Variable Importance in Projection (VIP) score greater than 1. 2 was established as threshold to select the features to be annotated.
GC-HRMS data deconvolution and pre-processing Careful tuning of the various parameters in MS-DIAL was necessary to improve the deconvolution process and obtain consolidated data. After the deconvolution process, a total of 874 peaks were obtained and aligned in all chromatograms (blanks included) resulting from the whole sample set analysis. The peak count filter (5%) and the blank filtering (5fold change) excluded another 550 peaks. The remaining 324 peaks were normalized through the LOWESS algorithm using the QC samples, which were injected under the same analytical conditions, to correct the batch effect in the data.
Statistical analysis and biomarkers selection The data table with 324 peaks underwent statistical analysis in R environment. Blank samples and conditioning QCs were excluded from this analysis, therefore only samples and punctuating QCs were considered. First, a PCA was performed to evaluate the quality of the data. As shown in supplementary material (Fig. 1S ), QC samples plotted in a central position and a reduced distribution across the PCA plot indicating a good reliability of the data. Thus, QC samples could be excluded from further analysis. The data underwent sPLS-DA to study whether there was a clear effect of the treatment on the pigs' metabolism. As shown in Fig. 1A , the sPLS-DA did not show any clear separation between the three groups, with a major overlap of their confidence area (the colored circles in the plots).
Biomarker identification The automatic annotation of the 86 compounds carried out by MS-DIAL were manually curated. Also, not all the 86 compounds were annotated by the automatic analysis; therefore, the unknowns were excluded from further analysis. Thus, after manual curation, further peaks were excluded from the final list because of their uncertain identification or the evident misidentification. In total, only 40 compounds out of the initial 86 were retained and considered hereafter. The dataset with the 40 identified biomarkers underwent a sPLS-DA analysis to confirm that they retained the discriminant power (Fig. 3 ). As shown, the separation between the two groups remained straightforward with an AUC >0. 99. Subsequently, the samples collected before Aroclor 1260 exposure were reintegrated into the statistical analysis, considering only the 40 variables of the identified biomarkers to allow trend analysis.
)  or with different exposure doses, in order to understand how far our results mirrored the other studies. A preliminary literature search was conducted using two commercially available databases: Reaxys® and Embio® (Elsevier). In both cases, two major effects could be reported for NDL-PCBs regardless of species and doses: hepatotoxicity and neurotoxicity. Although the serum analyses performed in the present study did not allow to determine the origin of the metabolic interference observed, the wide evidence reported in literature allowed to speculate it in our data. Perturbation in lipids metabolism has already been reported in literature in several experiments involving NDL-PCB exposure. It is wellknown that NDL-PCBs perturb lipids absorption in liver  (Shi et al. , 2019)  and they are associated to non-alcoholic fatty liver disease (NAFLD) and non-alcoholic steatohepatitis (NASH).
Overall, the 18S based, rather than the 16S based, community structure had a greater similarity to pigment-based estimations. Relative to the pigment data, the proportion of haptophytes in the 18S dataset, and diatoms and cryptophytes in the 16S dataset, were underestimated. This study highlights that 18S metabarcoding tends to reflect biomass-based community organization of eukaryotic phytoplankton. Because there were lower copy numbers of plastid 16S than 18S per genome, metabarcoding of 16S probably approximates cell abundance-based community organization. Changes in biomass organization of the pigment-based community were sensitive to environmental changes. Taken together, multiple methodologies are recommended to be applied to more accurately profile the diversity and community composition of phytoplankton in natural ecosystems.
2009). Furthermore, the taxonomic resolution of phytoplankton by chemotaxonomy is relatively low (class level, at best)  (Eker-Develi et al. 2012). Molecular approaches targeting nuclear 18S ribosomal RNA genes have been applied to characterize the diversity and community structure of unicellular eukaryotes at lower taxonomic ranks (e. g. , family, genus, and even species levels)  (Belevich and Milyutina 2022; Chen et al. 2019; Guo et al. 2016; Liu et al. 2021; Rii et al. 2018). With increasing sequencing depth and improvements in publicly available reference databases, rare eukaryotic taxa can be routinely sampled and classified  (Tragin et al. 2018; Wang et al. 2022; Ye et al. 2022). Although metabarcoding of 18S rRNA genes covers both pigmented (phytoplankton) and non-pigmented eukaryotes (protozoa and fungi), the operational taxonomic units (OTUs) of eukaryotic phytoplankton taxa can be retrieved after classification to reassemble the eukaryotic phytoplankton communities  (Kirkham et al.
Diversity and taxonomic composition based on 18S, 16S, and pigment analysis The raw sequencing data (4,635,358 reads of 18S rRNA genes and 4,755,849 reads of 16S rRNA genes) were processed and analyzed using QIIME2. A total of 356,968 and 550,538 reads were retained for nuclear 18S and plastid 16S rRNA genes after quality control, respectively. A total of 712 OTUs and 5531 amplicon sequence variants (ASVs) were detected for 18S and 16S rRNA genes, respectively. By rarefying at 3789 reads per sample for 18S and 3000 reads per sample for 16S, the richness of both molecular markers in surface waters was mapped, showing a decrease towards the sea in July (Fig. 5A-C, E-G ), but the opposite trend in November (Fig. 5D, H ). Regression analyses showed that the importance of environmental factors in driving the alpha diversity of eukaryotic phytoplankton was different when different molecular markers were considered (Fig.
2%), which had much lower proportions in 16S-based communities, however, appeared to be much more abundant in both 18S (average 36% and 3%) and pigment (average 43% and 18%) datasets. The haptophyte pigment was frequently abundant (0 -34%) in the HPLC samples, but this group was rarely identified in the plastid 16S (< 1. 3%), and hardly detected (< 0. 7%) in the 18S dataset. Other Ochrophyta (i. e. , the ochrophytes not including diatoms) frequently occurred with low to moderate proportions of reads (0. 2% -21%) in the 18S dataset, however, was detected in only a few samples by 16S sequencing, and was never identified by pigment analysis (Fig. 7 ). Blooms of dinophytes in seven samples (Chl-a percentages > 50%, and total Chl-a > 1 µg/L) were well reflected by their high sequence proportions in the 18S   7 ).
However, these signals of dinophyte blooms were not distinguishable in the 16S dataset. In hypoxic waters, pigment-based biomass of chlorophytes appeared to be lower than those in normoxic samples, which, however, was not pronounced in the 18S and 16S datasets (arrows in Fig. 7 ). The community composition of eukaryotic phytoplankton resolved at the order level showed that some orders were identified only in one of the 18S and 16S datasets (Supplementary Fig. S7 ). For example, orders Gymnodiniales and Suessiale, Prorocentrales, and Gonyaulacales were not resolved in the 16S (Supplementary Fig. S7A-C ). Two diatom species Cyclotella sp. (Order Thalassiosirales) and Guinardia delicatula (Order Rhizosoleniales) appeared to be dominant in the 18S dataset but were not identified from the 16S (Supplementary Fig.
S7D-F ). The most dominant taxa within cryptophytes were assigned to two different orders in 18S (Order Cryptomonoadeles) and 16S datasets (Order Pyrenomonadales) (Supplementary Fig. S7J-L ). Regression analysis showed that the ratio of 18S rRNA gene abundances between two of the four microalgal groups generally related well to their ratio of Chl-a contents (0. 3 < R 2 < 0. 66, P < 0. 05; Fig. 8 ). However, the correlations between chlorophytes and cryptophytes (R 2 = 0. 12, P < 0. 01), and between haptophytes versus all other groups were weak (0. 05 < R 2 < 0. 12; Fig. 8 ). There were no significant relationships between the ratio of 18S and plastid 16S rRNA gene abundance of any two of the five major microalgal groups (Supplementary Fig. S8A ). The relations between the ratio of plastid 16S and the ratio of pigment content were weak or not significant (Supplementary Fig.
7 ). It was found that the 18S-based community structure of eukaryotic phytoplankton was highly concordant with that based on pigment content. This judgment is based on at least the following three facts: (1) the rRNA gene abundance ratio between any two groups of dinophytes, chlorophytes, diatoms, and cryptophytes generally well reflected the ratio of their pigment contents (Fig. 8);  (2) the dinophyte blooms identified in the pigment analysis were also well captured in the 18S dataset (Fig. 7 ); and (3) 18S-and pigment-based community distances were well correlated with each other (Fig. 9C ). This result is consistent with  Gong et al. (2020b) , who demonstrated similar relationships between 18S rRNA gene abundance and pigment content in a study of the community structure of eukaryotic phytoplankton in a temperate estuary of USA.
The water depths (surface, middle, and bottom layers) and months (July and November) of the collected samples are annotated in (A-C). Chl-a, chlorophyll a; DIN, dissolved inorganic nitrogen; DSi, dissolved silicate; SRP, soluble reactive phosphorus diversity may be compensatory to the approach of nuclear 18S rRNA genes of eukaryotic phytoplankton (particularly for the lineages which were commonly detected from both 16S and 18S datasets), which frequently leads to an overestimation of species diversity due to intragenomic polymorphisms  (Zou et al. 2021). It has long been known that the ratio cellular C: Chl-a is a sensitive indicator of the physiological state of phytoplankton, usually ranging from 10 to 130, and tending to be higher in larger cell size, at higher levels of irradiance, and nitrate availability and growth rate, and at lower temperatures  (Geider et al.
The proportion of haptophytes was less than 0. 7% in the 18S dataset, although this could be underestimated, considering the pigment-based biomass of haptophytes in PRE was up to 0. 2 μg Chl-a/L, and 34% of the eukaryotic phytoplankton community in the present study and in a previous investigation  (Chai et al. 2016). This underestimation of haptophytes in the 18S dataset could be due to the eukaryote-universal 18S rRNA gene primers being strongly biased against Prymnesiales (Haptophyta)  (Liu et al. 2009; Yeh et al. 2021) , and their higher GC content (57%) in 18S than many other groups, which undermines the efficiency of PCR amplification  (Liu et al. 2009). This bias could also explain the observations that the 18S ratio of haptophytes and other microalgal groups poorly reflects their pigment-based biomass proportions (Fig.
8 ). Similarly, the cryptophyte biomass (average 18%) might also be underestimated in the 18S dataset (average 3%). Since the 18S ratio of cryptophytes to other groups was well correlated with their pigment ratio (Fig. 8 ), indicating other cellular characteristics (e. g. , having fewer rRNA gene copies per cell than other microalgae) may underlie the disproportional rRNA gene of this group. Given the importance of haptophyte and cryptophyte biomass in the phytoplankton of many PRE samples, these two groups could be investigated using group-specific primers targeting 18S rRNA genes; cellular rRNA gene copies have yet to be quantified for a better understanding of their diversity and quantity in coastal systems. The plastid 16S-based community structure of eukaryotic phytoplankton was much different from those of both the pigment-and 18S-based structures.
It was also demonstrated that 515F and 926R have good coverage for the plastid 16S  (Mcnichol et al. 2021). Similar to a previous study that found contrasting relative abundances of diatoms and cryptophytes in 18S and 16S datasets across some Antarctic samples  (Hamilton et al. 2021) , the proportions of diatoms, cryptophytes, haptophytes in the 16S databases were also found to be much lower than those in the 18S-based datasets. There are many 18S rRNA gene copies (thousands to hundreds of thousands) in a microalgal cell, whereas the plastid 16S rRNA gene copy numbers have been reported to be much lower and less variable, often one to few dozen copies per genome in some phytoplankton species  (Bennke et al. 2018; Needham and Fuhrman 2016). This suggests that 16S-datasets likely approximate cell abundance-based structures rather than a biomass-oriented organization of eukaryotic communities, to which pigment and 18S datasets are more relevant.
Nevertheless, the copy number of plastid 16S of more phytoplankton species, and the relationship between cell abundance and total plastid 16S copy number has yet to be further experimentally explored. Furthermore, there were discrepancies in identifying lower-ranked taxa using these two markers in this study. Only a few phytoplankton genera were commonly detected  (Trefault et al. 2021 ). The differences in recovering lower-ranked taxa could contribute to not only their proportional differences in the communities but also the diversity estimators using 18S and 16S rRNA gene sequencing. In addition, it should be noted that, although the pigmented dinophyte 16S appeared to be abundant, underestimation of their abundance is still possible, since the plastid rRNA genes of many dinoflagellates are difficult to amplify and there are not many sequences curated in the PhytoREF database  (Decelle et al.
Concluding remarks The molecular and pigment data collected from a tropical estuary were analyzed to explore whether similar variational patterns in diversity, quantity, and community structure of eukaryotic phytoplankton could be obtained using different methodologies. In general, it was found that there were insignificant or poor correlations among 18S rRNA, plastid 23S rRNA gene abundance, and Chl-a content, and between richness of 18S OTUs and plastid 16S ASVs of eukaryotic phytoplankton. The 18S-and the pigment-based community structure were more similar to each other than to the 16S-based structure. Not surprisingly, these inconsistencies resulted in different sets of major environmental drivers being identified for the datasets obtained using 18S, 16S, and pigment approaches. The discrepancies between the two molecular approaches might be caused by primer bias, different genome sizes and gene copy numbers among phytoplankton groups, and insufficient reference sequences with high taxon coverage in the database.
Pigment analysis To measure the Chl-a content and phytoplankton structure, 0. 5-1 L of subsamples were prefiltered on board using a 200-µm mesh, then filtered onto 0. 7-μm GF/F filters (47 mm in diameter; Whatman, Little Chalfont, Buckinghamshire, UK) under low light and vacuum pressure (< 0. 03 MPa) and immediately frozen in liquid nitrogen. Pigment analysis was performed using HPLC (Dionex UltiMate 3000 LC system, Thermo Scientific)  (Zhong et al. 2020b). Thirteen diagnostic pigments were used to estimate the relative contributions of five eukaryotic phytoplankton groups [dinoflagellates, diatoms, chlorophytes (including prasinophytes), cryptophytes, haptophytes (a combination of haptophytes_8 and hapto-phytes_6)] and two prokaryotic groups (Prochlorococcus and Synechococcus) to the total Chl-a using the CHEMTAX program (ver.
Processing of sequencing data The sequence data were processed using QIIME2  (Bolyen et al. 2019). ASVs were generated by trimming the raw amplicon sequences of primers using cutadapt  (Martin 2011)  and inputting them into the DADA2 pipeline (ver. 1. 8)  (Callahan et al. 2016). Reads were filtered with the following parameters: truncLen and trimLen = c(0, 0), truncQ = 2, maxEE = 2, and then the forward and reverse reads were merged using the default parameters (minOverlap = 12, maxMismatch = 0). Chimeras were removed using the removeBimeraDenovo command. For plastid 16S rRNA genes, taxonomy was assigned using the classifier tool implemented in QIIME2 against the PhytoREF database  (Decelle et al. 2015). Considering that evolutionary rates of plastid genes were significantly slower than nuclear ones  (Wolfe et al.
1987) , and the number of plastid 16S reads (on average 10,587 reads per sample) obtained was much lower than that of nuclear 18S (on average 30,365 reads per sample), it was decided to define 16S sequence diversity at a finer resolution by clustering the reads into ASVs. The taxonomic identity of these ASVs was also examined using BLASTn searches against the plastid database in NCBI (https:// ftp. ncbi. nlm. nih. gov/ refseq/ relea se/ plast id). For 18S rRNA gene sequences, the qualified reads were clustered into OTUs at 97% identity to minimize inflation of OTU richness due to intragenomic polymorphisms, as suggested by  Zou et al. (2021). The obtained OTUs were classified using the Protist Ribosomal Reference database (PR2, ver. 4. 14)  (Guillou et al. 2012)  and SILVA (ver. 138)  (Quast et al.
2012). For diversity and community structure of eukaryotic phytoplankton communities, the macroalgae (Rhodophyta, Streptophyta and Ulvophyceae) and non-pigmented dinoflagellates, Cercozoa, Ciliophora, Mesomycetozoa, Radiolaria and Fungi were discarded, and only the OTUs of the photosynthetic groups, i. e. , pigmented dinophytes (including Gymnodiniales, Peridiniales, Gonyaulacales, Suessiales, Prorocentrale and Dinophysiales), Bacillariophyta, Chlorophyta, Cryptophyta, Haptophyta, and Ochrophyta were retained for subsequent analyses. The reads generated from MiSeq sequencing of 18S and plastid 16S rRNA genes have been deposited in the NCBI database (accession numbers: PRJNA904229 and PRJNA904929).
Non-metric multidimensional scaling (NMDS) was based on Bray-Curtis dissimilarities of the phytoplankton community structure and performed using the package vegan in R. Analysis of similarity (ANOSIM) was conducted to statistically test the hypothesis that the community structure of phytoplankton based on variations in the five major eukaryotic groups (diatoms, dinoflagellates, chlorophytes, haptophytes and cryptophytes) was independent of the three methodologies. Principal components analysis (PCA) was performed to visualize the differences in phytoplankton community structure generated using the three methods. The relationships between community structure and environmental factors were explored using canonical correspondence analysis (CCA). All statistical analyses were carried out using the packages in R (ver.
RESULTS We investigate genetic and linguistic variation in 261 Kriolu speakers from the nine Cabo Verdean islands (Brava, Fogo, Santiago, Maio, Sal, Boa Vista, São Nicolau, São Vicente, Santo Antão, Figure  F1 ). With novel genome-wide genotyping autosomal data (Figure  SF1 ), we first describe genetic differentiation patterns in Cabo Verde and other enslaved-African descendants in the Americas from previous datasets, in particular with respect to continental Africa and Europe. Second, we deploy local-ancestry inferences and determine the best proxy source-populations for admixture patterns in each Cabo Verde island. Then, we describe Runs of Homozygosity and genetic isolation-by-distance patterns at reduced geographical scale within Cabo Verde. We also investigate Kriolu utterance diversity with respect to geography and socio-cultural co-variables and, then, investigate jointly genetic and linguistic admixture patterns throughout the archipelago.