dataesr/scientific-paragraphs-categorization huggingface dataset:
Jørgensen, Jensen & Midtiby, 2017) IP41(K. Wang et al. , 2022) IP41, for crop pest 46,567 original images of crop The image data consist of pictures Dataset has a low imbalance rate recognition pests from 41 classes from search engines (Google, Baidu, 360, Yahoo, and Bing), and pictures were taken by farmers in natural settings. Sugarbeets2016(Chebrolu Agricultural dataset for the > 10,000 images include RGB Robot of four-channel multi-spectral Collected data on a sugar beet field during et al. , 2017) plant classification, and NIR images for sugar beet a crop season, covering the various growth localization, and mapping plants and more than nine stages of the plants on sugar beet fields different types of weed species Insect pest Xie 2018 Insect pest classification 4500 insect images with 40 Images were captured by the use of Most of these pest images were captured in types collected from a crop digital cameras real-life conditions field.
Another molecular mechanism of metalinduced neurotoxicity is the modification of the synaptic structure targeting important scaffolding proteins such as SHANK3 or cytoskeleton proteins (e. g. , F-actin and tubulin). The molecular mechanisms involved may be oxidative stress or competition with zinc-or copper-binding sites in synaptic scaffold and/or cytoskeleton proteins. Two research articles reported in this Special Issue used the 2011-2014 National Health and Nutrition Examination Survey (NHANES) data. Barahona et al. determined  the relationship between blood and urinary manganese levels and cognitive function using the NHANES data  [6]. This is the first study to examine associations between blood and urinary manganese levels and cognitive function in an elderly population. Blood and urinary manganese levels were inversely associated with the CERAD (Consortium to Establish a Registry for Alzheimer's Disease) after adjusting for all covariates; these associations were no longer significant after adjusting for the medical history.
The findings suggest that increased blood and urinary manganese levels are associated with poorer cognitive function in an elderly population. Metal neurotoxins not only affect cognition and memory but also neuromotor functions. This is well illustrated in the article from Gbemavo and Bouchard  [7] , who examined the relationship between blood concentrations of various metals and hand grip strength using the NHANES 2011-2014 dataset. They show that blood concentrations of mercury and manganese are not associated with grip strength, whereas lead is associated with a weaker grip strength in women, but not in men, even at low levels of exposure. In addition, low blood selenium levels were associated with weaker grip strength. Methylmercury is an environmental toxicant of high concern, and chronic methylmercury exposure is associated with diabetes mellitus and metabolic syndrome.
Analysis of metabarcoding data DNA sequences were analysed using a combination of USEARCH v7. 0. 1090 (32Bit)  (Edgar, 2010)  and QIIME v 1. 8. 0  (Caporaso et al. , 2010). Forward and reverse reads were merged using USEARCH. Each primer set was separated using Cutadapt  (Martin, 2011) , removing primers and adapters. For each primer set, fastq files were quality filtered, removing reads with expected error > 0. 5 and short sequences <200 bp. Reads were then truncated to 250 bp, 370 bp and 150 bp for 16S, 18S V4 and 18S V9, respectively, and converted to FASTA files. The FASTA files were dereplicated, abundance sorted and had their singleton sequences removed. OTUs (Operational Taxonomic Units) were clustered using the UPARSE clustering algorithm at 97%  (Edgar, 2013). Chimeras were filtered using UCHIME  (Edgar et al.
, 2011) , OTUs were mapped back to the original reads, and an OTU table was produced. Both 16S and 18S sequences were classified against the SILVA 132 database  (Quast et al. , 2013)  using UCLUST  (Edgar, 2010). OTUs with less than four sequence reads per cluster were excluded from the downstream analyses. In addition, prokaryotic OTUs present in less than three samples were also excluded from the 16S dataset. Within the 16S dataset, contaminants such as Eukarya, mitochondria, and unknown domain were removed; within both 18S datasets some Eukarya sequences (Chloroplastida, some fungi species and large metazoans such as Salpida, Chelicerata, Chilopoda, and Eutelostomi) were also manually removed during analysis, since they likely represented contamination. Abundant and frequent OTUs, as well as those identified as being potentially pathogenic, hydrocarbon or plastic degraders were further classified against the full NCBI database.
Plastic and paint concentration and types We found a total of 371 likely plastic particles of different sizes, shapes, colours, malleability, and polymer compositions. The mean concentration of plastics at the sea surface along South Brazil was 4461 items. km -  , varying from 2989 items. km -  at the oceanic station 5 to 19,267 items. km -  at the coastal station 1. The two highest plastic concentrations were at stations located in Rio Grande do Sul waters (stations 1 and 9, Fig. 2 -I ). Microplastics (< 5 mm) represented 68% of the plastic items and were dominant in all the sampling stations, except at station 9; mesoplastics (5 -200 mm) represented 32% of the dataset. There was significant difference (ANOVA; F = 11. 924, p = 0. 014) between the concentration of micro and mesoplastics at the Santa Catarina region (Fig.
Prokaryotic and eukaryotic diversity on plastics After sequence processing and quality filtering, the 16S dataset contained 21 samples with 408,805 reads comprised into 444 prokaryotic OTUs. The 21 plastic samples had Bacteria associated with biofilms and 17 had Archaea OTUs. The Archaea group was composed by phyla Euryarchaeota and Thaumarchaeota, but none of the Archaea OTUs had four or more reads per sample, and thus were not included in further analyses. The number of prokaryotic OTUs per sample varied from 30 to 288 OTUs (mean 109 ± 12. 8 SD). Representatives from 20 phyla of Bacteria were found associated with plastics (Fig. 3 ), with dominance of Proteobacteria over the entire dataset (abundance of 64%, composed by Alpha, Delta and Gammaproteobacteria), followed by Bacteroidetes (14%), Cyanobacteria and Firmicutes (6% each).
Six bacterial phyla presented abundances of between 1 and 3% (Verrucomicrobia, Planctomycetes, Epsilonbacteraeota, Actinobacteria, Thermotogae and Chloroflexi), and the remaining phyla together represented less than 2% of the total 16S dataset (Fig. 3 -I ). The prokaryotic community of sampled plastics was highly similar in terms of OTU richness over the latitudinal gradient (26°S -34°S). The four most abundant bacterial groups also showed a high frequency of occurrence (Proteobacteria, Bacteroidetes, and Firmicutes FO 100%; Cyanobacteria FO 95%) (Fig. 3 -II ). Within the Proteobacteria phylum, Gammaproteobacteria was the most abundant group (47% abundance, FO 100%), followed by Alphaproteobacteria (16% abundance, FO 100%) and Deltaproteobacteria (less than 1% abundance, FO 71%). Within Gammaproteobacteria, we found six Oceanospirillales OTUs, which had an abundance of less than 1% in the 16S dataset.
The most abundant prokaryotic OTU, representing 23% of the dataset (OTU_1), was classified as uncultured bacteria (uncultured Ralstonia) by SILVA, and it also matched many uncultured bacteria from environmental samples (mostly sediments) on NCBI (Genbank MN723154. 1) (Table  1 ). Some bacteria OTUs in our samples closely matched potential pathogens such as Vibrio parahaemolyticus from shrimp (100%, Genbank reference MT549171. 1), and Escherichia coli from wastewater (100%, Genbank reference CP055438. 1), as well as many Pseudomonas species. We also found bacterial groups previously described as plastics and/or hydrocarbon degraders, such as polycyclic aromatic hydrocarbons (PAH), a common plastic-associated contaminant  (Adams et al. , 2007; Diepens and Koelmans, 2018) : Ralstonia sp. (Biki et al.
, 2021; Ryan et al. , 2007) , Erythrobacter species  (Gao et al. , 2015) , Bacillus species  (Wright et al. , 2020b) , Alcanivorax and Arenibacter species  (Delacuvellerie et al. , 2019; Sekiguchi et al. , 2011) , Pseudomonas species  (Balasubramanian et al. , 2010) , and Colwellia sp. (Urbanek et al. , 2018). The most abundant OTUs had closest matches to sequences from environmental sequence surveys in the full NCBI database, and some showed low matches to any cultured representative (e. g. OTU_12, Table  1 ). Most OTUs had matches to taxa observed in untreated water, as well as marine sediments and coastal waters, including species associated with marine organisms such as coral, fish, sponge, ascidian and shrimp (Table  1 ). Two 18S markers were used to analyse the diversity of eukaryotic life on the sampled plastics.
After sequence processing and quality filtering, the 18S V4 dataset contained 22 samples with 189,466 reads, whereas the 18S V9 dataset contained 28 samples with 876,534 reads, clustered into 337 and 655 OTUs, respectively. The number of OTUs per sample within the 18S V4 dataset ranged from 5 to 65 (average 31 ± 3. 2 SD), and within the 18S V9 from 18 to 231 (average 68 ± 8. 1 SD). The 18S V4 and V9 markers detected different taxonomic groups, with some exclusive groups in each dataset: Telonema and Ichthyosporea were shown only with the 18S V4 marker, whereas Bryozoa, Haptophyta, Excavata, Centrohelida, Picozoa, and Hemichordata (among others) were present only within the 18S V9 dataset (Fig. 4 ). No eukaryotic group had FO 100% within the 18S V4 dataset: Chlorophyta, Charophyta and Cnidaria had FO 95% each, followed by Fungi with FO 91%, and Diatom, Dinoflagellata, and 'Other Stramenopiles' with FO 77% each.
In addition, the groups Chrysophyta, Cercozoa, Ciliophora, Nematoda, Chaetognatha, Syndiniales, and 'Other Alveolata' displayed FO > 50% each. The remaining groups identified by this marker had FOs lower than 50% each (Fig. 4 ). The 18S V9 marker showed a different community composition in terms of taxa diversity. Some groups presented higher frequencies of occurrence: Fungi had FO 100%, followed by Chlorophyta with FO 96%, and Diatom, 'Other Stramenopiles' and Cnidaria with FO 93% each, while Cercozoa, Crustacea and Ciliophora had FO 86% each. Invertebrates such as Tunicata, Nemotoda, Chaetognatha, and Mollusca occurred in over 50% of samples within the 18S V9 dataset (Fig. 4 ). The most frequent eukaryotic OTUs detected by both molecular markers also matched many uncultured eukaryotic OTUs from the marine environment (seawater and sediments), as well as with cultured species from coastal zones (e.
g. coastal water, intertidal pools), and symbionts with marine sponge and radiolarian (Table  2 ). Some harmful eukaryotes were found in the plastisphere from the Western South Atlantic, such as dinoflagellates of genus Alexandrium, whose species are known for being toxic to marine life and humans  (Donald et al. , 2012). Fungi groups found in our samples were composed of some parasites/pathogens, as well as hydrocarbon degradersthese groups are detailed in  Lacerda et al. (2020). Richness of OTUs per plastic fragment was variable between individual plastic items, and some samples had low richness. There was no significant difference in the OTU richness of plastics of distinct categories (Fig. 5 ), as well as according to location (Kruskal-Wallis, p > 0. 05), except within the 16S dataset where we found significant difference in bacterial OTUs richness (Kruskal-Wallis, p = 0.
006) between RS and SC. Likewise, the community composition was highly variable between plastic items within each DNA marker dataset (Fig. 6 ). There was significant difference in community composition according to size class within the 18S V9 dataset (PERMANOVA: F = 1. 276, p = 0. 04), and according to location in both the 18S V4 (PERMANOVA: F = 1. 988, p < 0. 001) and V9 (PERMANOVA: F = 1. 495, p = 0. 009) datasets.
, 2013; Kirstein et al. , 2019; Hansen et al. , 2021) , we did not observe this in samples from the natural environment. As previously described for epiplastic fungi in the Western South Atlantic  (Lacerda et al. , 2020) , there was no difference between OTU richness or community composition among plastic polymers within prokaryotic and general eukaryotic datasets at the region. This finding supports the suggestions of  Oberbeckmann and Labrenz (2020)  and  Oberbeckmann et al. (2021)  that microorganisms opportunistically colonize different plastic substrates, and are not specific to plastic polymers. However, significant differences in community composition according to size class was observed within the 18S V9 dataset, which we believe could be due to the higher availability of physical space on mesoplastics for the settlement of larger-sized taxa.
Acknowledgments This study was conducted within the activities of project  TALUDE , funded by  Chevron Brasil Upstrem Frade Ltda  (Grant number  CW702315 ), PETROBRAS, through the  Brazilian Inter-Ministerial Commission  for the Resources of the Sea (CIRM), that supplied diesel for the ship for all surveys. We thank  Dr. Juliana Di Tullio  for scientific cruise organization, and the crew of FURG's  R/V Atlântico Sul. The authors thank the  Coordination for the Improvement of Higher Education Personnel (CAPES)  that provided a scholarship to ALdFL and LdSR, and also provided access to the  Portal de Periódicos  and financial support through  Programa de Excelênca Acadêmica -PROEX. The  National Council for Research and Development (CNPq)  provided a scholarship to ALdFL ( SWE 206250/2017-7 ), and research fellowships to ERS ( PQ 310597/2018-8 ),  MCP  ( PQ 312470/2018-5 ), and  FK  ( PQ 435612/2018-2 ).
JDT was funded by a  University of Salford Career Development Fellowship. This article was produced in international cooperation and is within the scope of the  Capes PrInt Program  (public notice  041/2017 ), and is a contribution to the  Brazilian Long-Term Ecological Research Program (PELD, CNPq Proc. 441492/2016-9 , and  FAPERGS -Proc. 16/2551-0000102-2 ), the  Brazilian National Institute of Science and Technology -INCT-Mar  funded by  CNPq  Grant Number  610012/2011-8 , and  TRIATLAS  Project ( H2020 ). We thank  Mikaele, Lijainah  and  Nathalia  for the assistance with the FTIR analysis. We thank  Dr. Thorunn Helgason ,  Dr. Sally James  and  Dr. Peter Aston , of the  University of York Genomics & Bioinformatics Laboratory , for assistance, support and technical expertise with sequencing.
1 Shrimp and marine fish OTU_9 2% 43 Floodplain lake water 100 MF439455. 1 Alkalibacterium sp. 100 MH044645. 1 B. schlosseri (ascidian) OTU_22 2% 62 Marine water 99 KX935277. 1 Salinimonas sp. 100 CP064795. 1 Marine sediment OTU_34 2% 48 Surface sewater (China) 100 KC002482. 1 Afipia sp. 100 EF371496. 1 Yellow Sea OTU_21 2% 52 Coastal sewater 100 LC496437. 1 Loktanella sp. 100 LR722710. 1 Coastal water Notes: OTU number; Relative Abundance (R. A. ); Frequency of occurrence (FO%); Environmental Samples (Source of uncultured bacterium) and Cultured samples (Species); Similarity (ID %); Genbank accession number; and Source/Highlights.
82 JX178854. 1 Coast (China) OTU_24 Cnidaria 61 Seawater 100 HM799922. 1 Liriope tetraphylla 100 KT722405. 1 Coast (Brazil) OTU_97 Fungi 61 Savanna soil 99 EU490070. 1 Cladosporium halotolerans 100 MN859971. 1 Marine sponge OTU_18 Chaetognatha 57 - - - Sagitta enflata 99 LC581989. 1 Seawater (Japan) OTU_489 Fungi 57 Antarctica snow 99 KR131435. 1 Aspergillus penicillioides 99 AF548066 Air OTU_44 Fungi 57 Seawater 100 JF826393. 1 Wallemia mellicola 100 AY741380. 1 Hypersaline water OTU_33 Dinoflagellata 57 Radiolarian (symbiont) 100 U52353. 1 Brandtodinium nutricula 99 U52357. 1 Sargasso Sea Notes: OTU number; Taxonomy identified by SILVA database (Group); Environmental Samples (Source) and Cultured samples (Species); Frequency of occurrence (FO%); Similarity (ID%), Genbank accession number; and Source/Highlights.
Wind, temperature and radiative flux measurements Wind and temperature measurements are performed at six levels on a 45-m mast located 1-km upwind of Concordia station. Temperature is measured using mechanically-ventilated Vaisala HMP-155 thermo-hygrometers and wind speed and direction are obtained with R. M. Young 05103 aerovanes. 30-min average data have been used in this paper. Details on data acquisition and processing are given in  (Genthon et al. , 2021). The downward long-wave and short-wave radiative fluxes are measured at the Dome C Baseline Surface Radiation Network (BSRN) station using two Kipp and Zonen CM22 secondary standard pyranometers and two Kipp and Zonen CG4 pyrgeometers  (Lanconelli et al. , 2011; Driemel et al. , 2018).
Humidity measurements The near-surface atmosphere over the Antarctic Plateau is frequently supersaturated with respect to ice, which prevents from correctly measuring humidity with conventional capacitive hygrometers since the excess of moisture with respect to saturation condenses once the air gets in contact with the sampling device or the sensor  (Genthon et al. , 2017). The humidity data used in this study were obtained with advanced HMP-155 thermohygrometers deployed at three levels on the 45-m mast  (Genthon et al. , 2022). In a nutshell, the innovative technology behind these adapted HMP-155 consists of heating the air aspirated in the intake such that the sensor always samples an under-saturated air. A separate thermometer measures the ambient air temperature in order to retrieve the relative humidity with respect to liquid phase (RHl) of the ambient air.
Acknowledgements. We gratefully thank  Roxanne Jacob  for examining additional photographs during fog events at Dome C. This research was conducted in the framework of the CALVA 1013 and PRE-REC observation programs with the support of the french polar institute (IPEV) and the  Programma Nazionale di Ricerche in Antartide (PNRA). Radiosounding data have been acquired from the database of the IPEV/ PNRA  project ' Routine Meteorological Observation at Station Concordia ' www. climantartide. itwith the help of  Paolo Grigioni. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  951596 ).
Data availability. The Dome C temperature, wind and humidity datasets used in this study are thoroughly described in Genthon et al. (2021Genthon et al. ( , 2022) )  and distributed on PANGAEA (Genthon et al. , 2021a(Genthon et al. ,  , b, 2022)). Radiosonde data are freely distributed at www. climantartide. it. The tropospheric depolarization lidar data can be obtained at http://lidarmax. altervista. org/englidar/_AntarcticLIDAR. php. BSRN data are available on PANGAEA  (Driemel et al. , 2018)  Author contributions. EV, LR and CG designed and conducted the study. LR and EV analysed the data and LR made the figures. MDG collected and processed the lidar data. CG collected and maintained the meteorological measurements on the mast at Dome C. AH, JBM and AB provided scientific expertise on cold microphysics and contributed to the results' interpretation.
Meteorological measurement Both sites are equipped with meteorological and flux stations. Matter (CO 2 , H 2 O) and energy fluxes, meteorological data (temperature, rain, pressure, etc. ) radiation and soil variables (temperature, water content and heat fluxes) were measured at a half-hourly time step. The water filled pore space (WFPS) was calculated by multiplying the measured soil water content at 30 cm soil depth by the minimum bulk density measured at 30-60 cm soil depth (1. 52 and 1. 6 at FR-Lam and FR-Aur, respectively). The measurement methodology for each variable is described in details in  Béziat et al. (2009)  and  Tallec et al. (2013). These variables were used to set up the data set gap-filling procedure as described in  Bigaignon et al. (2020).
N 2 O flux calculation N 2 O fluxes (F N2O ) were calculated for each cycle and each chamber from the slope of concentration vs. time. Data were recorded after a delay of 50 s to account for the travel distance between the chamber and the analyzer (20 m). The set-up design necessarily induced a disturbance of the gas diffusion from the soil after chamber closure  (Mello and Hines 1994; Kroon et al. 2008). Given these experimental conditions, no linear fitting could be applied and an exponential regression model (derived from the Fick's law of diffusion) was preferred (Eq. (  1 )). C(t) = C max + (C 0 -C max ) ( e -kt ) (1) with C(t) the N 2 O molar fraction at time t, C 0 the initial molar fraction of N 2 O and t 0 the first time point when the exponential regression is fitted to the measurement, C max the asymptotic molar fraction of N 2 O at infinite time and k the rate constant.
Gap-filling of N 2 O emissions datasets Some daily data were missing because of hardware dysfunction, field operations implying the removal of chambers or data removal after quality check (20% and 25% of gap in the time series of FR-Lam and FR-Aur site, respectively). In the aim of estimating consistent N 2 O emissions, the dataset used in this study was gap-filled using the methodology described in  Bigaignon et al. (2020)  which combines linear interpolation and artificial neuronal networks (ANN). A specific ANN was created for each operating period and for each site to maximize the accuracy of the gap-filling procedure. Bare soil and growing season periods of each rotation crop were specifically addressed by the methodology (for details see  Bigaignon et al. , 2020). From 2013-08-21 to 2014-10-14, N 2 O emissions measurements were stopped at the FR-Aur site, resulting in a huge gap in the data, including two bare soil periods and a winter wheat growing season.
We then applied the same equation to estimate uncertainties due to spatial variation U FN2O-day-spatial by calculating SD of daily F N2O-chamber average. Total daily uncertainty (U FN2O-day-meas ) was then integrated by considering independently U FN2O-day-temporal and U FN2O-day-spatial according to Eq. (  6 ): U FN2O-day-meas = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ ̅ ∑ U 2 FN2O-day-temporal + ∑ U 2 FN2O-day-spatial √ (6) For multiple chamber measurements over a large field, spatial variation is expected to be the main source of uncertainty. To assess spatial variability, we selected days when all 6 chambers were well operational and calculated the average and the uncertainties due to spatial variation of F N2O-day. For both sites, the absolute daily standard uncertainty related to spatial variation increases positively with daily flux intensity (data not shown).
Data analysis We used an empirical multifactor index ( WFPS × N supply GLAI ), combining the cumulative effect of WFPS and of soil mineral N availability weighed by the crop development index, GLAI, to assess the correlation with N 2 O emission intensity. N supply is the total amount of mineral and/or organic nitrogen applied (in kgN ha -1 ), WFPS and GLAI are the mean WFPS (in %) and the mean GLAI (in m 2 m -2 ) over the whole three months of the season, respectively. N supply includes N input from fertilizers over the whole three months of the season and also N input from the incorporated crop and/or cover crop organic residues. As organic matter mineralization highly depends on temperature and degree days accumulation  (Van Schöll et al. , 1997; Delin and Engström, 2010) , an experiment (data not shown) was carried out to estimate the best time to study the residual N effect.