dataesr/scientific-paragraphs-categorization huggingface dataset:
: Karpathy et al. (2014)opose HMDB-51. 24Thedatasetconsists of 6766 annotated video clips gleaned from various sources such as Youtube or Movies. In terms of resolution, the videos share all the same 240 pixels height. However, the width of each video clip is re-sized so as to maintain its proper aspect ratio. The frame-rate of all the clips is of 30 FPS. The videos are categorized into 51 action classes. Meta-information within the dataset details for each video clip a range of information such as camera viewpoint, occlusion, occurrence of camera motion (that concerns 2∕3's of the database), video quality (which isKay et al. (2017)propose the Kinetics dataset, 25 a dataset that is similar to UCF-101 and HMDB-51, and that is mainly used for video classification. It consists of 400 classes of different actions performed by a wide spectrum of different persons.
Each activity is a class-name, and within the dataset, there are 487 different activities. There is, at around, 1000-3000 videos per class, and about 5% of the videos possess more than one label. • UCF-101: Soomro et al. (2012) propose UCF-101, 22 an extension of UCF-50. The dataset consists of 13320 web clips depicting 27 h of video, obtained from Youtube. 23 The resolution of each clip is of 320 × 240 at a frame rate of 25 FPS. The clips are categorized into 101 action classes. The videos are subjected to background clutter, camera motion, lighting changes, partial occlusion and low quality frames. Was assessed, on this dataset, a bag-of-words (BoW) approach used for action recognition that resulted in an overall accuracy of 43. 9%. • HMDB-51: categorized into high, medium, or low), the number of individuals appearing in the video.
Two methods were assessed on this dataset: Jhuang, Serre, Wolf, and Poggio (2007)'s and Laptev (2005), Laptev et al. (2008), Wang, Ullah, Klaser, Laptev, and Schmid (2009)'s, and achieved at around 23% accuracy. • Kinetics5. 2. 5. Anomaly detection • Aerial Violent Individual (AVI): Proposed by Singh et al. (2018) to train their ScatterNet Hybrid Deep Learning (SHDL) network for pose estimation, this dataset contains 2000 images. The recorded scenes are mildly dense and contain at around 2 to 10 individuals. The interesting part of the dataset is that each individual is annotated with 14 key-points allowing a detailed estimation of its pose. The images were recorded by a drone from four different heights. Unfortunately, this dataset is not publicly available. • UMN SocialForce and Web datasets: UMN SocialForce and Web datasets 28 are two datasets used by Mehran et al.
(2009) in their work. UMN SocialForce consists of 11 videos all illustrating a normal-starting situation and an abnormal ending. Web Dataset consists of 20 videos, 8 videos containing abnormal events such as panic, clashes, fights, and 12 video clips of a normal situation (pedestrians walking). • UCSD Anomaly Detection: UCSD Anomaly Dataset 29 is com-monly used for anomaly detection and it consists of at around 100 video clips. These videos are divided into two sub-datasets: Peds1 and Peds2.
• Blunsden and Fisher (2010) 32 can be used for pedestrian and group behavior analysis in a mildly crowded scene, but mainly for anomaly detection. All the scenes were scripted(ED, 2003). , scared, sad, and neutral. The dataset is exhaustively annotated. The scenes are recorded from a camera fixed at a height oriented to the ground to capture people walking. The video resolution is of 554 × 235 pixels and consists of 30 frames per second (FPS). The density of the scenes varies from an intermediate to a high concentration of individuals. •UCFCrime:TheUCFCrimedataset34was created by Sultani et al. (2018). The dataset consists of 1900 long and untrimmed surveillance videos, that totalized 128 h of recording, and that were acquired from Youtube and Liveleak 35 via text queries expressed in different languages.
(2016) propose the SALSA dataset 42 person re-identification. The sequences are divided into two scenarios depending on where they come from: an indoor scene from the entrance lobby of INRIA Labs at Grenoble, and a shopping center from Portugal. The first set consists of 28 clips stemming from a mono-camera. The The sequences are divided into two groups: a synthetic set, of second set consists of 26 video clips originating from two cameras 28 sequences and 200 frames each, and a real dataset. The syn- yielding two views for each scenario. thetic set includes simple and challenging events. The real dataset • Crowd-11: The Crowd-11 dataset is a totally annotated dataset concerns only outdoor scenarios. The events described within that was created by Dupont et al. (2017) for crowded scene the dataset are: groups appearing, disappearing, going through classification.
The dataset consists of 6272 video sequences cap- split/merge and queue events. tured from a mono-camera. Each sequence is made up of 100 • CUHK Crowd Dataset: Created by Shao et al. (2014, 2017), frames each. Each video sequence lasts for 5 s. The resolution the CUHK Crowd dataset 38 contains 474 video clips of different depends on each video. The dataset is mainly intended for be- lengths depicting 215 crowded scenes taken from various environ- havior classification and violence detection. The sequences can ments. The dataset is mainly used for mesoscopic group detection: be classified into 11 categories, namely: No Crowd, Laminar flow, clustering pedestrians into groups and crowd segmentation, even- Turbulent flow, Crossing flows, Merging flow, Diverging Flow, tually group behavior analysis and anomaly detection.
It can also Gas free, Gas jammed, Static agitated, Interacting crowd. This be used for crowd statistics. The annotations for group detection very large dataset originates from video-sharing websites such and state analysis, and crowd video classification are provided in as Youtube, Pond5 and Gettyimages, and from other datasets, the dataset. namely Agoraset, UMN, Violent Flows, CUHK Crowd, WWW Crowd Attributes, Shanghai WorldExpo'10 Crowd, Hockey fights • MuseumVisitors The MuseumVisitors Dataset, 39 created by Bar-toli, Lisanti et al. (2015), was recorded by a multi-camera setup and movies, PETS-2009. • Motion Emotion: The Motion Emotion Dataset 33 was created by Rabiee, Haddadnia, Mousavi, Kalantarzadeh et al. (2016) and Rabiee, Haddadnia, Mousavi, Nabi et al. (2016), and is used for anomaly detection in human motions and emotions.
The dataset is made up of approximately 44. 000 frames that are divided in of three IP cameras at a resolution of 1280 × 800 pixels at a frame rate of 5 FPS, inside the National Museum of Bargello in Florence, Italy. This challenging dataset, in terms of occlusion, lighting and scale changes, is intended for pedestrian and group detection under occlusion, gaze estimation, behavior analysis, 31 video clips. The dataset depicts 5 types of motions, which are: panic, fight, dealing with an obstacle, congestion, and neutral behaviors; and 6 types of emotions, which are: angry, happy, 30Violent Flows (ViF): https://www. openu. ac. il/home/hassner/data/ violentflows/. 31 https://talhassner. github. io/home/projects/violentflows/index. html. 32 Caviar Dataset: http://homepages. inf. ed. ac. uk/rbf/CAVIARDATA1/.
The objective of this study is to identify and analyze integrons and antibiotic resistance genes (ARGs) in samples collected from diverse sites in terrestrial Antarctica. Integrons were studied using two independent methods. One involved the construction and analysis of intI gene amplicon libraries. In addition, we sequenced 17 metagenomes of microbial mats and soil by high-throughput sequencing and analyzed these data using the IntegronFinder program. As expected, the metagenomic analysis allowed for the identification of novel predicted intI integrases and gene cassettes (GCs), which mostly encode unknown functions. However, some intI genes are similar to sequences previously identified by amplicon library analysis in soil samples collected from non-Antarctic sites. ARGs were analyzed in the metagenomes using ABRIcate with CARD database and verified if these genes could be classified as GCs by IntegronFinder.
, 2010). The variable region includes a gene array (GCs), each flanked by an attC site expressed under the control of Pc. Integron studies were classically focused on isolates from clinical environments, using culture-dependent methods. However, the incorporation of new sequencing methods allowed access to much more information. Metagenomic studies have allowed for the identification of new integrase sequences showing that there is large genetic diversity. In addition, metagenomic dataset analysis revealed that there is an extensive pool of GCs encoding functions required for environmental adaptation and others, mostly of unknown function  (Holmes et al. , 2003). The main obstacles to the identification of GCs are the limited tools available to recognize and predict the identification of attC sites  (Cury et al.
, 2016). Some strategies were designed to recognize attC sites of "classical" integrons  (Stokes et al. , 2001) , although this is difficult because they exhibit a relatively high diversity  (Cury et al. , 2016). This has even allowed for encryption of these sequences within protein-coding sequences  (Nivina et al. , 2020). Integrons are ancestral and usually stable structures that have played an essential role in bacterial genome evolution (Rowe-Magnus and  Mazel, 2001) , due to their GCs shuffling properties in times of stress  (Guerin et al. , 2009). Considering the presence of an intI gene, integrons have been found in 9-17% of genomes available in databases  (Boucher et al. , 2007; Cambray et al. , 2010; Cury et al. , 2016). They are distributed throughout Gram-negative bacteria, with the notable exception of α-proteobacteria, and are more prevalent among γ-proteobacteria  (Cury et al.
Our objective was to identify and analyze integrons and ARGs in microbial mat and soil samples collected in different sites of the Antarctic continent. For the analysis of integrons, we used two culture-independent methods. One of these strategies involved the construction and analysis of intI amplicon libraries from the soil. We also sequenced 17 metagenomes of microbial mats and soil by highthroughput next generation sequencing (NGS) and analyzed these metagenomic data using the IntegronFinder program, which allows intI and GC identification. part of a gene encoding the first 76 amino acids of aminoglycoside adenyltransferase (aadA6), and the other is a qacG2 gene.
K E Y W O R D S antibiotic resistance, bioinformatics, horizontal gene transfer, microbial genomics As expected, these analyses allowed us to identify a broad spectrum of intI sequences. The vast majority were not related to mobile integrons integrase sequences, although some sequences were similar to others previously recovered in amplicon libraries from soil samples of non-Antarctic environments. ARGs were analyzed in the 17 metagenomes using ABRicate with CARD database. We identified 53 ARGs by CARD, distributed in 15 metagenomes (except MTG3 and MTG4) and IntegronFinder identified three of these genes as GCs.
| Total DNA isolation and NGS metagenome sequencing Metagenomic libraries were prepared with the Nextera DNA Flex library prep kit (Illumina, San Diego, CA) where fragments of total DNA (1 μg) were inserted into vectors and sequenced with wholegenome sequencing technology (HiSeq2 × 150), at the Yale Center for Genomic Sciences. The data obtained for each metagenome had an average of 7. 8 ± 2 Gb, which in total represented 109 Gb of sequenced DNA. The raw data passed through a quality filter and were assembled de novo using IDBA-UD, with k-mer sequences between 120 and 150 long  (Peng et al. , 2012). Metagenomes of soil samples were sequenced in Macrogen (Seoul, South Korea), using Illumina HiSeq 2500 technology.
| Bioinformatic analysis of NGS total metagenomes IntegronFinder (https://github. com/gem-paste ur/Integ ronFi nder)  (Cury et al. , 2016)  was used for the detection of integrons and their most distinctive components. The options used were -local_max, --linear, attC_evalue 1 and -func_annot. Results obtained were classified as complete integrons, including an integron-integrase gene nearby attC site(s), In0 elements with just the integron-integrase gene, and CALIN (Cassette Lacking Integrase), as clusters of attC sites without intI genes. Genomic contexts of the results obtained by IntegronFinder were annotated by RAST  (Aziz et al. , 2008). SnapGeneViewer 4. 2. 11 program was used to visualize the integron's genomic context. Once the presence of an integrase gene was confirmed, the relative abundance of the contig bearing that gene was calculated.
The software bowtie2 was used for mapping the filtered reads against all contigs of each metagenome. The number of reads mapping against the contigs bearing an integrase was divided by the length of the contig to obtain the RPK value. That value was normalized per million reads in the metagenome and the paired-end nature of the reads was considered to express that value in fragments per kilobase of transcript per million reads mapped (FPKMs). To identify conserved motifs characteristic of integron integrases, amino acid sequences selected from this study were aligned against IntI reference sequences (classes 1, 2, 3, 4, and 5) (GenBank A. N. : IntI1, AAQ16665; IntI2, AAT72891; IntI3, AAO32355; IntI4, AAC38424; IntI5, AF180939). Tyrosine recombinases XerC from Escherichia coli (P0A8P6) and XerD from E.
CP2 February/2016 62°14´35´´ S 58°39´99´´ W Potter creek Soil denitrificans (WP018078134) were also included  (Nunes-Düby et al. , 1998). For the alignment, Mafft program version 3. 717  (Katoh et al. , 2002)  was used. Visualization and editing were done with Aliview program version 1. 20  (Larsson, 2014). Representative integrases of selected organisms carrying chromosomal and super-integrons were also included. For the detection of total ARGs in the assembled metagenomes, ABRicate software (https://github. com/tseem ann/abricate) was used along with the CARD database (https://card. mcmas ter. ca/). This software runs a blast search which was filtered with 50% of coverage for the query sequence and 70% of identity for the alignment. Then, we verified if these genes could be identified as GCs using IntegronFinder.
| Phylogenetic analysis of Antarctic integrases including Inti reference sequences Phylogenetic analysis of IntI included 46 amino acid sequences of good quality (considering complete ORFs and not located at the edges of the contigs) identified by IntegronFinder and 15 nonredundant sequences recovered from intI amplicon libraries, obtained in this and a previous study  (Antelo et al. , 2015). The analysis also included 42 sequences of integrases from known organisms and XerC and XerD sequences of E. coli and T. denitrificans, which were used as external branches for the construction of the tree. Reference sequences were obtained from INTEGRALL-the Integron Database  (Moura et al. , 2009)  (http://integ rall. bio. ua. pt) and NCBI GenBank. All sequences were clustered to 90% of sequence identity using CD-HIT  (Fu et al.
| Amplicon library analysis King George Island (KGI) (25 de Mayo Island) in Maritime Antarctica is the largest island in the South Shetland archipelago. Amplicon libraries of intI genes were obtained from samples collected on this island (Table  1 ). Drake coast (PD2) and Potter Cove creek (CP2) contain a total of 22 IntI clones (8 in PD2, 14 inCP2). Some of these sequences were included for phylogenetic analysis discussed below. Similar to previous studies  (Antelo et al. , 2015) , analysis of amplicon libraries indicated that the primers used, designed to amplify class 1 intI, are not specific for this class. This primer pair was designed using a limited database  (Mazel et al. , 2000)  since at that time only sequences of clinical origin were available. However, we could identify 8 clones from CP2 highly similar (more than 99% identity in amino acid sequences) to the reference gene IntI1_AAQ16665 of clinical origin.
We could identify "Mobile Integron-related" (MI-related) intI genes (CS1_14211, 88% identity to AAQ16665_IntI1). Using these strategies, amplicon library, and NGS analysis, we found that "MIrelated" intI genes represent about 21% of the total (13 from a total of 61). The analysis of the phylogenetic tree obtained with these recovered environmental sequences confirms that criteria for the classification of integrons should consider the high diversity of integrase sequences found in the environment. 3 ). IntegronFinder also annotates potential coding sequences (CDS) associated with these element classes. Then, a total of 3431 CDS were annotated within the region of 200 bp next to each integron element found in our dataset (intI and attC sites), including the 54 intI genes. However, the implication of this type of insertion in integrase activity is unknown (Rodríguez-Minguela et al.
| Annotation of antibiotic resistance genes Using the CARD database, ARGs were detected in most of the metagenomes analyzed (53 genes in 15 metagenomes) (Figure  3 ), which did not show a high richness of ARGs compared to other environmental samples  (Van Goethem et al. , 2018). However, in sample CS1, collected next to the septic chamber of BCAA we could identify 37 different ARGs. These ARGs were not identified as GCs by (Appendix Figure  A1a ) as part of a complete integron and a fragment of aadA6 gene associated with a complete integron in CS1_14211 (Appendix Figure  A1b ). This integron also included a qacG2 gene cassette, identified by Blastn. In the same metagenome, we could find several determinants of macrolides resistance like mpH, mef, OPM, and erm. In terms of geographic distribution, dfrE (dihydrofolate reductase) and vatF (streptogramin A acetyltransferase) genes seem to be the most prevalent in the metagenomes analyzed, since they were detected in 8 and 7 different samples, respectively (Figure  3 ).
| Phylogenetic analysis of Inti sequences and description of their genomic context This analysis included 61 IntI Antarctic sequences identified in this work and previous studies  (Antelo et al. , ,2015 (Antelo et al. , , , 2018)). We used 46 IntI sequences of good quality (considering complete ORFs and not located at the edges of the contigs), from those identified by NGS metagenomic analysis and 6 nonredundant IntIs recovered by amplicon library analysis obtained in this study. We also incorporated 9 Antarctic sequences obtained in previous studies  (Antelo et al. , 2015). In addition, 42 reference sequences and others recovered from the database were also included for analysis. IntI sequences identified in this study exhibited great diversity (less than 40% overall sequence identity at amino acid level) distributed throughout the tree in different groups.
| DISCUSS ION In this study, we used IntegronFinder to identify genetic elements of integrons by NGS data analysis of Antarctic soil and microbial mat metagenomes collected from different sites on the continent. We also used samples of soil collected from other sites of Antarctica for the construction of amplicon libraries of intI integron-integrase genes, using a pair of primers previously designed by  Mazel et al. (2000). All recovered IntI deduced amino acid sequences were filtered by domain conservation analysis. Several novel results were obtained. First, although intI gene search tools were used in samples of different origins, the sequences obtained from each approach were different, according to the phylogenetic analysis (Figure  1 ). Only two groups included sequences obtained using both strategies.
Group VII identified in the phylogenetic tree (Figure  1 ), contains two sequences, one obtained by NGS at Halfthree Point (HTP2_2775) and the other by amplicon library from a coastal area in the Drake sea (PD2_18), both from the Fildes peninsula (KGI). Besides, Group XV includes intI sequences obtained by amplicon libraries in this study (CP2) and one identified by NGS analysis of the CS1 metagenome. However, other groups identified in the phylogenetic tree contain sequences obtained from samples of diverse origins but using the same data collection strategy. For example, Group XVI contains sequences obtained from samples collected in KGI (MTG_5) and others in the Antarctic peninsula (MTG_8) and Mac Murdo Dry Valleys (MTG_11 and MTG_13), but all obtained by NGS data analysis. As mentioned previously, we could identify "MI-related" class 1 intI genes by NGS data analysis from one sample collected from one site exposed to human influence (CS1 metagenome) and by amplicon library with intI1 primers  (Mazel et al.
, 2000) , but identifying also, in this case, other diverse integrase genes. Previous studies have proposed that the relative abundance of "MI-related" intI1 genes would be used as a proxy for anthropogenic pollution  (Gillings et al. , 2015; Zheng et al. , 2020). These genetic platforms are usually linked to genes conferring resistance to antibiotics, disinfectants, and heavy metals, and they seem to be fixed by human selection. Mazel et al. (2000)  designed this primer pair aimed at amplifying intI1 genes with relative specificity, avoiding the amplification of class 2 and 3 intI genes. Databases available at that time were relatively small and reduced to clinical isolates. Then, by including environmental samples as templates for amplification, it was later established that these primers had a lower specificity, being able to amplify integrase genes with a spectrum of diversity greater than expected  (Antelo et al.
These resistance GCs, analyzed with Abricate using CARD and VFDB  (Chen et al. , 2005)  as databases, were only identified in integrons from sewage samples. In our case, using arbitrary thresholds, we could identify "MIrelated" integrase genes in 25% of samples (5 in a total of 20 samples), including soil and microbial mats. From this group, 32% of these genes were identified in the presumptive "contaminated" sample obtained from soil next to the septic chamber in BCAA (CS1). In this way, finding coherent identity thresholds for integrase sequence classification could be a good way to take into account the sequence diversity derived from metagenomic analysis. This could also be useful to compare with other works and gain insight into its distribution and occurrence in different environments  (Roy, et al.
2021 , Zhang et al. , 2018). Also, ARGs were identified with ABRIcate using the CARD database. Although we used low identity and coverage thresholds, there could be a bias in the method used as we looked for ARGs using CARD database with mainly "clinical" resistance genes, which are no expected to be widely present in pristine Antarctic environments. In addition, Antarctic environmental communities could be a reservoir of non-characterized antimicrobial resistance genes  (Azziz et al. , 2019). Only three GCs could be identified as ARGs using CS1 metagenome, sequenced from a sample of soil collected next to BCAA, showed the highest richness of ARGs (in relation to the total number of ORFs), which has also been reported in other studies  (Rabbia et al. , 2016; Jara et al. , 2020). In addition, CS1 was the only one in which we were able to detect human fecal contamination using the reference genome of Crassphage phage as a biomarker  (Dutilh et al.
this tool, one in CS1 metagenome, encoding class D β-lactamase (bla-OXA-205) in a complete integron associated with an IntI sequence similar to that of T. denitrificans. The second one was a fragment of a gene found also in the CS1 metagenome (76 amino acids), encoding an aminoglycoside adenyltransferase (aadA6). The other GC was located in a CALIN of MTG12 and encoded an aminoglycosidemodifying enzyme (AAC(6´)-acetyltransferase) (100% identity to AAC(6')-32). We also used the Resfams database with IntegronFinder to functionally characterize CDSs next to attC sites (in CALIN and complete integrons). With this strategy, IntegronFinder identified a total of 26 GCs as antibiotic resistance genes including blaOXA-205, located in the complete integron of contig CS1_503. This gene was also identified using the ABRIcate-CARD strategy.