dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2013). To achieve these ambitions, a cornerstone of the application of eDNA metabarcoding is the development of primer pairs for DNA amplification through polymerase chain reaction (PCR)  (Collins et al. , 2019; Leese et al. , 2021; Zhang et al. , 2020). Associated with the increased use of eDNA metabarcoding, a large variety of primer pairs have been developed, either universally  (Bagley et al. , 2019; Stat et al. , 2019)  or specifically to amplify target clades  (Kress et al. , 2015; Xia et al. , 2018). To test their efficiency, computational tools reproducing in silico PCR have been developed to simulate potential amplification of primers across a species sequence database. Primer pairs are most frequently evaluated in silico for their capacity to bind and amplify a broad range of species  (Cannon et al.
, 2020; Zhang et al. , 2020). An important feature of well-performing primer pairs is the ability to not only recover the species through the species-specificity of the targeted sequence  (Wilcox et al. , 2013; Zhang et al. , 2020) , but also assign recovered eDNA sequences to higher taxonomic levels if a more precise taxonomic resolution cannot be guaranteed, or in the absence of a high-quality reference database. Generally, the number of nucleotide differences between DNA sequences of different species amplified by primer pairs represent a good proxy of the species specificity of the amplified region. A species can be considered unambiguously assigned if the marker sequence differs from the marker sequences associated with all other species by at least one base pair, regardless of the marker size  (Boyer et al.
, 2016). Nevertheless, the main limitation of species assignment is the incomplete coverage of reference databases  (Marques et al. , 2020). While an international effort has led to the compilation of a large cytochrome oxidase 1 data base  (Weigand et al. , 2019) , this region of the mitochondrial genome has revealed a low performance as an eDNA target region for some organismal groups, such as vertebrates, in proximity with variable regions allowing for species-level discrimination  (Collins et al. , 2019). For taxonomic groups other than invertebrates, other regions such as the 12S and 16S ribosomal RNA (rRNA) have shown higher performance in discriminating between organisms and consequently, new primer pairs have been but that generally both teleo and MiFish-U primers pairs perform well to monitor fish species.
, 2016; Valentini et al. , 2016). The MiFish-U primer pair targets roughly 170 base pairs region at the beginning of the 12S mtDNA (Figure  1 ), which contains sufficient variability to identify fish to the species level  (Miya et al. , 2015). In this study, we had the following objectives: 1. We compared the potential amplification performance of teleo and MiFish-U primer pairs across a broad range of mitogenomes of fishes including both Actinopterygii and Chondrichthyes. We expect that MiFish-U, optimized for Actinopterygii, should F I G U R E 1 Schematic representation of (a) the mitochondrial mitogenome with the 12S mitochondrial rRNA gene and (b) the position and length of the metabarcode associated with the corresponding primer pair: MiFish-U and teleo recover more diversity within this taxon, while teleo should have a broader taxonomic range including Chondrichthyes.
| Primer sensitivity and species-specificity from in silico PCR We applied in silico PCR to compare the MiFish-U  (Miya et al. , 2015)  and the teleo  (Valentini et al. , 2016)  primer pairs using USEARCH::search_pcr (v11. 0. 667_i86linux64). We used two different curated 12S rRNA reference data bases: the MIDORI database  (Leray et al. , 2018 GenBank release 240) , which includes over 55 k metazoans and eukaryotic sequences, and the MitoFish data base  (Iwasaki et al. , 2013; Sato et al. , 2018 release 362) , which is a fishspecific database with over 4 k species. There is a partial taxonomic overlap between these two reference data bases. To ensure transparency and reproducibility, we did not alter them. In a first step, we screened the reference data bases for sequences covering both amplicons.
This was essential to perform comparable in silico PCRs for both primer pairs. For the in silico PCR, we explored a variety of mismatches and allowed a maximum of three mismatches for each of the four primers individually. We ran multiple in silico PCRs for both primer pairs with different numbers of primer mismatches (0-3). Following  Zhang et al. , (2020) , we also removed hits with mismatches at the last two positions of the 3' end. Next, we analyzed whether unique taxa detected by both MiFish-U and teleo primer pairs presented a phylogenetic signal at genus levels. Because genus detection might be specific to selected primer pairs, we explored the phylogenetic signal of taxa by using the standardized effect size (SES) of the phylogenetic diversity index (PD, Faith, 1992) calculated with the R package PhyloMeasures  (Tsirogiannis & Sandel, 2015).
A negative SES PD below a value of -1. 96 shows that the phylogenetic pattern in the variable is significantly clustered on the tree, whereas a positive value higher than 1. 96 indicates a significant overdispersed signal. A value around zero means that the variable is randomly distributed on the tree. We used two distributions of 100 super-trees, for Actinopterygii  (Rabosky et al. , 2018)  and Chondrichthyes  (Stein et al. , 2018)  pruned at the genus level and corresponding to the MIDORI database. We calculated the mean and the standard deviation of the SES PD values for each primer pair at genus levels.
| Species specificity of amplified sequences by teleo and Mifish-U We used the ensemble of amplified sequences from the MIDORI and MitoFish data bases to evaluate the species specificity of the teleo and MiFish-U primer pairs. Specifically, we computed the percentage of sequenced species per family for each primer pair as an indication of coverage for each data base, as well as two coefficients of taxonomic resolution from two different methods presented by  Marquina et al. , (2019) : exclusive taxonomic resolution (BE) and taxonomic resolution (BS) per family. These approaches are originally designed within a sequence clustering framework when the diversity unit corresponds to clusters instead of individual sequences and can hence be used to define an appropriate barcoding gap. In the present work, we focus on individual sequences and do not consider sequence clustering.
| Taxonomic predictability of amplified sequences by teleo and Mifish-U using neural networks We used neural networks to evaluate the capacity of the teleo and MiFish-U primer pairs to amplify sequences that can be assigned to genus and family levels. This was performed using the sequences from the in silico PCR based on the recognition of k-mer patterns in the sequences. For each of the MIDORI and MitoFish data bases, we generated four learning data sets: one for genus and one for species for both teleo and MiFish-U primer pairs. For the family data sets, we limited the selection to those with at least 10 genera in common between the teleo and MiFish-U assemblies. Similarly, for the genus data sets, we limited the selection of genera to those with at least 10 species in common. Considering the MIDORI data base, for the family sequences assignment, we selected 42 families containing 3033 sequences assigned to 2376 species for the MiFish-U data set, and 2820 sequences from 2373 species for the teleo data set.
Similarly, for the genus assignment, we selected 54 common genera with 1124 sequences from 873 species for the MiFish-U data set, and 1065 sequences from 871 species for the teleo data set. The reverse complement for all sequences was added to the data sets for invariance with respect to the read direction of the sequences. We set aside 10% of the genera for each family and 10% of the species for each genus in all data sets to evaluate the generalization power of this approach over unseen taxa. We used those sequence data sets to train a set of neural networks per primer recognizing sequences at the genus and family levels each. We repeated the training of the neural network ten times, each time with holding 10% of the species and genera randomly. For each training repetition, we trained 10 neural networks per data set with four or five hidden layers, all of them either 32, 64, 128, 256, or 512 neurons wide.
The purified PCR products were pooled in equal volumes to achieve a theoretical sequencing depth of 500,000 reads per sample per marker. Two libraries, one for each marker, were prepared using the MetaFast protocol by Fasteris (https://www. faste ris. com/dna/?q=conte nt/metaf ast-proto col-ampli con-metag enomi c-analysis), which significantly limits the tag-jump problem  (Taberlet et al. , 2012). A paired-end sequencing (2 × 125 bp) was carried out using a MiSeq (2 × 125 bp) with the MiSeq Flow Cell Kit v3 (Illumina), following the manufacturer's instructions at Fasteris facilities. Following sequencing, reads were processed to remove errors and analyzed using programs implemented in the OBITools package (http://metab arcoding. org/obitools, Boyer et al. , 2016)  as described in  Valentini et al.
, (2016). The forward and reverse reads were assembled with the ILLUMINAPAIREDEND program, using a minimum score of 40 and retrieving only joined sequences. Then, we assigned the reads to each sample using the NGSFILTER software and we created a separate data set for each sample by splitting the original data set into several files using OBISPLIT. After this step, we analyzed each sample individually before merging the taxon list for the final ecological analysis. Strictly identical sequences were clustered together using OBIUNIQ. Sequences shorter than 20 bp, or with fewer than 10 occurrences were excluded using the OBIGREP program. The OBICLEAN program was then run. We discarded all sequences labelled "internal," which most likely correspond to PCR substitutions and indel errors. Taxonomic assignment of the remaining sequences was performed using the ECOTAG program with both a local reference database and a curated database of the sequences extracted from the release 138 (standard sequences).
The curated database of the sequences extracted from the release 138 was built using the ECOPCR program  (Bellemain et al. , 2010; Ficetola et al. , 2010). We built one database per marker and we selected only sequences present in both databases for the final curated data set. We used local databases for the Mediterranean Sea  (Boulanger et al. , 2021)  and French Guiana  (Cilleros et al. , 2019)  in which we retrieved only sequences containing both amplicons (Mifish-U and teleo). After the taxonomic assignment, only sequences with a similarity higher than 98% with the reference database were kept. After the filtering pipeline, the PCR negative controls were completely clean, and no sequence reads remained in those samples. We finally analyzed the specificity of MiFish-U and teleo as described in  Collins et al.
, (2019). Immediately after demultiplexing and quality filtering (i. e. , sequences longer than 20 bp, MOTU represented by more than 10 reads and MOTU), remaining reads are classified successively to Metazoa, Chordata, Aves, Mammalia, Actinopteri, Chondrichthyes, and Hyperoartia using a conservative 98% identity threshold in relation to the reference bases (EMBL for nonfish, and local databases for Actinopteri, Chondrichthyes, and Hyperoartia). Analyses were run in R (R Development Core Team, 2019).
When considering two mismatches with the MIDORI database, teleo amplified 79 orders, 437 families, and 1829 genera, whereas MiFish-U amplified 79 orders, 394 families, and 1730 genera. With the MitoFish data base, teleo amplified 79 orders, 416 families, and 1900 genera, whereas MiFish-U amplified, 74 orders, 388 families, and 1792 genera. Considering three mismatches, the amplification of both primers further increased to offer a broad coverage (Table  1 ). We found a difference in taxonomic breadth and specificity of amplification between both primer pairs investigated. In particular, considering two mismatches, teleo specifically amplified 4 orders and 37 families in the MIDORI data base, and 5 orders and 37 families with the MitoFish data base. In contrast, MiFish-U specifically amplified no orders and 8 families in the MIDORI data base and no orders and 9 families with the MitoFish data base (Figure  S1 ).
Using the MiFish-U primer pair, we detected 14 orders of Actinopterygii (including Eupercaria) and 19 different families for a total of 46 taxa of which we assigned 39 to the species level (Figure  S4c ). Species detected by teleo and MiFish-U did not completely overlap and we found that 12 species detected with teleo were not detected with MiFish-U, while respectively 10 species detected with MiFish-U were not detected with teleo. Combining the two primers pairs allowed recovering 57 taxa, more than each other did alone. The reference database was identical for both primers, implying that differences do not stem from database bias. We found that teleo and MiFish-U were highly specific with 99% of cleaned reads attributed to Actinopteri for both primer pairs (Figure  S4e -g, Table  S1 ).
, 2016). In particular,  Zhang et al. , (2020)  found that the top six primer pairs that recovered the greatest numbers of fish taxa were all for the 12S region. However, in contrast with our results,  Zhang et al. , (2020)  found a lower performance of teleo compared with other primers pairs including MiFish-U, both in silico and in situ. Zhang et al. , (2020)  used an uncurated version of the NCBI database during their in silico analyses, in which the coverage could have been higher for some portions of the 12S gene. In regard to their in situ analyses, PCR temperatures did not match the recommended PCR conditions for the use of teleo  (Valentini et al. , 2016) , which could have led to suboptimal amplifications. Moreover, the resolution power of markers, where assemblages were dominated by the Cyprinidae family, poorly discriminated by teleo, could have led to a lower detection of taxa.
In contrast to  Zhang et al. , (2020) , our results show that teleo was performing slightly better in situ, but the shorter amplicon length from this primer pair was associated with a lower taxonomic discrimination ability. Contrasted with teleo, MiFish-U was significantly more resolutive at the species level than teleo and showed a greater discrimination at higher taxonomic levels, which can be a useful feature in the absence of a complete reference database. MiFish-U showed a systematic superior discrimination both in the evaluation with the neural network and with the quantification of species-level specificity from in silico PCRs (Figure  3 ). As a consequence, MiFish-U could allow a better discriminative ability in environment with many phylogenetically closely related species, such as resulting from recent radiations  (Doenz et al.
Primer pairs amplifying short fragments could show better result in tropical environments dominated with rare species and assemblage with greater uniformity. Another advantage of primer pairs amplifying short fragments is that they can be "mass-produced" on, for example, Illumina NovaSeq (PE100 or PE150), while longer amplicons are restricted to, for example, Illumina MiSeq (PE250 or PE300) with a lower output. Hence, small fragments can be particularly relevant to get an estimation of biodiversity including less abundant species  (Bylemans et al. , 2018 ), but at the cost of reduced discrimination. Despite the good performance of 12S primer pairs, a major current limitation is the low coverage of reference sequences  (Collins et al. , 2019) : efficient recovery of biodiversity and accurate taxonomic assignments rely on the completeness and sequence quality of the corresponding databases  (Bylemans et al.
I G U R E 3 Species-specificity of (a) teleo and (b) MiFish-U across the different families. (c) Boxplot showing the performance of the neural network to predict the taxonomic assignment at the genus and at the family level for both MiFish-U and teleo for the MIDORI database F I G U R E 4 Sampling sites considering three different geographic regions in different climatic zones and different ecosystems. The sampling sites are the Rhone river (France; a), in proximity of the cities of Jons and Brangues, the Mediterranean Sea around Carry-le-Rouet and the island of Porquerolles (France; a); and the Maroni River (French Guiana; b), upstream from the confluence with the Olemari river for the first site and downstream from the Yalou Patapte locality for the second site. In situ evaluation of the species richness (c) and total number of reads (d) recovered from both MiFish-U and teleo primer pairs across three different ecosystems, the Rhone river, the Mediterranean Sea in temperate environments, and the Maroni River in tropical environments.
discrimination of MiFish-U and teleo, by training a neural network analysis to evaluate whether DNA patterns found in MiFish-U and teleo sequences allow deeper taxonomic discrimination at the genus and family levels. When we examined the individual predictions of the neural network along a fixed reference sequence, most confident, correct species-level predictions were achieved for most of those two 12S hypervariable regions. While the discrimination reached 97%, we show that overall, both primers had a reasonable capacity to predict the genus and a lower capacity to predict family taxonomic classes. Our results suggest that family assignments from incomplete reference databases can be uncertain, especially for smaller sequences. As these hypervariable regions are well-known and exploited features of 12S sequences, the ability to learn the importance of these regions from training data is a demonstration that the neural network captured important biological structure of 12S present in both MiFish-U and teleo sequences.
on the manuscript. The study was co-funded by the Instituto de Investigaciones Marinas y Costeras (INVEMAR) through the project "Investigación científica hacia la generación de información y conocimiento de las zonas marinas y costeras de interés de la nación", BPIN code 2017011000113. This study is the contribution number 1310 of the Instituto de Investigaciones Marinas y Costeras -INVEMAR, Colombia. This project was supported by the ETH Global grant and the Monaco Explorations Foundation. LP received funding from the Swiss National Science Foundation for the project Reefish (grant number 310030E-164294). We thank SPYGEN staff for technical support in the laboratory.
F I G U R E 3 Species-specificity of (a) teleo and (b) MiFish-U across the different families. (c) Boxplot showing the performance of the neural network to predict the taxonomic assignment at the genus and at the family level for both MiFish-U and teleo for the MIDORI database F I G U R E 4 Sampling sites considering three different geographic regions in different climatic zones and different ecosystems. The sampling sites are the Rhone river (France; a), in proximity of the cities of Jons and Brangues, the Mediterranean Sea around Carry-le-Rouet and the island of Porquerolles (France; a); and the Maroni River (French Guiana; b), upstream from the confluence with the Olemari river for the first site and downstream from the Yalou Patapte locality for the second site. In situ evaluation of the species richness (c) and total number of reads (d) recovered from both MiFish-U and teleo primer pairs across three different ecosystems, the Rhone river, the Mediterranean Sea in temperate environments, and the Maroni River in tropical environments.
The sizing accuracy of the SMPS was frequently checked with monodisperse polystyrene latex particles. Additionally, the data quality was continuously verified by complementary measurements with a condensation particle counter (CPC, model 5412, Grimm Aerosol Technik, Ainring, Germany) measuring the total particle number concentration (N CN ) >4 nm. All particle data were visually inspected for malfunction and contamination, further corrected for standard temperature and pressure  (STP, 273. 15 K, 1013. 25 hPa)  as well as inlet transmission efficiency according to  Moran-Zuloaga et al. (2018). The PNSD data were used for this analysis if N CN from SMPS and CPC agreed within 15 %. CN <50 are particularly prone to diffusion losses at surfaces (e. g. , the tube surfaces of the inlet lines)  (von der Weiden et al.
Multi-modal log-normal fitting of PNSDs Each measured PNSD was fitted by a multi-modal lognormal distribution function, according to  Heintzenberg (1994) : f D p , D i , N i , σ i = n i=1 N i √ 2π ln(σ i ) × exp - ln(D p ) -ln(D i ) 2 2ln 2 (σ i ) , , (1) where D p is the particle diameter, and n is the number of aerosol size modes to be fitted (with n ≤ 3; see Sect. 2. 2). Each mode is characterized by three main parameters: the  Franco et al. :  Occurrence and growth of sub-50 nm aerosols in Amazonia mode number concentration N i , the mode geometric median diameter D i , and the mode geometric standard deviation σ i. M. A. A script was developed -similarly to the procedure in Hussein et al. (  2005 ) -to provide an automatic user-free decision algorithm to obtain the size modes according to the following steps: 1.
Identification of particle growth events We analyzed the occurrence and properties of particle growth events (Amazonian banana plots) in the sub-50 nm and Aitken-mode size range. Characteristic examples of such growth events are shown and discussed in Sect. 3. The growth event identification is based on the following main steps: 1. All data were smoothed to eliminate single exceptionally high or low values to avoid possible bias due to short intense particle peaks or dips. The moving window has two dimensions: one in time and the other in size. The SMPS measurements last 5 min to get a fullsize distribution. In order to reduce noise, we average the time window at 25 min. The second parameter is the particle size window, which accounts for five SMPS bins. This is also made to reduce noise in terms of particle size.
GOES-16 cloud brightness temperature This study uses infrared brightness temperature (Tir) data obtained by the Geostationary Operational Environmental Satellite (GOES), GOES-16, from November 2017 to April 2020. This data set comprises a total of 914 d with measurements every 10 min, as an indication of the troposphere's meteorological conditions. The Advance Baseline Imager (ABI) -a state-of-the-art 16-band radiometer on board GOES-16 -was employed in this study, specifically, Band 13, the infrared window at 10. 3 µm. These measurements are less sensitive than other infrared bands to gas absorption, which allows the cloud-top brightness temperature to be estimated. An area of 3 × 3 pixels centered at ATTO was selected for obtaining the time series of Tir, representing around 6. 0 × 6. 0 km 2.
Data availability. The data of the key results presented here have been deposited in associated data files for use in followup studies. The SMPS data used in this study are available under https://doi. org/10. 17617/3. 90 (Franco et al. , 2022a). Additional fog data used in this study are available under https://doi. org/10. 17617/3. 8z (Franco et al. , 2022b). Additional equivalent potential temperature data, the anomaly of the potential temperature, and the characteristics of the 254 particle growth events analyzed in this study are available under https://doi. org/10. 17617/3. 8y (Franco et al. , 2022c). Additional Tir data can be found on the portal https://edmond. mpdl. mpg. de/imeji/ (last access: 16 February 2022) collection /Q9NOaAlgWZTq7f9s (Machado et al. , 2021). Additional ATTO data can be found in the ATTO data portal under https://www.