dataesr/scientific-paragraphs-categorization huggingface dataset:
Data curation and preparation for CECs screening Correctness of the SMILES was evaluated by checking whether the valency matches the structures. Subsequently, adjusted KNIME workflows of  Mansouri et al. (2016)  and  McEachran et al. (2018)  were used to generate "MS-  (Mansouri et al. , 2016)  and QSAR-ready" structures  (McEachran et al. , 2018) (Figure SM-1). The first step of the workflow was the removal of inorganic compounds, which are defined here as all substances without the element carbon. Subsequently, organic mixtures were split and each mixture constituent was entered into CECscreen as a separated entry with a link to the other constituents. In addition, any resulting inorganic components related to salts, plus water were removed. The final step to create "MS-ready" structures was chemical transformation and neutralization of the compounds to the forms that could be found in HRMS.
To create "QSAR-ready" structures, stereochemistry information was removed from the "MS-ready" structures. A more detailed description of the standardization and transformation steps in the workflows was published by  McEachran et al. (2018). The original workflows were adjusted by removing the organometallic filter and retaining the stereochemistry information for the "MS-ready" structures. Furthermore, log P values were predicted based on the "MSready" structures using the XLogP  (Wang et al. , 1997)  node from the KNIME CDK plug-in  (Beisken et al. , 2013). OpenBabel  (O'Boyle et al. , 2011)  was used to convert the "MS-and QSAR-ready" SMILES into canonical SMILES, InChIs and InChIKeys. The "doBy" package  (Højsgaard and Halekoh, 2006)  in R was used to summarize the CECs list based on unique "MS-ready" InChIKeys and duplicates were removed.
Simulation of metabolites The comprehensive and open source software tool BioTransformer was used for the prediction of metabolites  (Djoumbou-Feunang et al. , 2019). CDK objects representing structures were generated using the "QSAR-ready" canonical SMILES and saved in an SDF file that was imported in the BioTranformer software  (Djoumbou-Feunang et al. , 2019). Metabolites were predicted using the Phase I (CYP450) transformation option and the program was run in Windows via the command line. Data retrieved included metabolite ID, predicted Phase I metabolite InChI and InChIKey, biotransformation reaction, enzymes involved and precursor "QSAR-ready" InChIKeys. Further data curation was performed similar to the curation work of the aggregated CECs list. In addition, compound name and CAS numbers were retrieved based on the InChIKeys using the "webchem" package  (Szöcs et al.
CompTox Chemicals Dashboard The CompTox Chemicals Dashboard  (Williams et al. , 2017)  was used to retrieve existing and predicted data on toxicity, exposure and prevalence in the literature of the entries in the aggregated list. Batch searches based on CAS numbers were performed containing 5000 compounds per batch. Information extracted from the CompTox Chemicals Dashboard included predicted median exposure in mg/kg bodyweight /day, availability of Toxicity Value data, ToxCast data (number of assays and percentage active), number of PubMed articles, chemicals and products database count and TEST toxicity predictions including 48 h Daphnia magna exposure LC50, developmental toxicity, Ames mutagenicity, oral rat LD50, Tetrahymena pyriformis IGC50 and 96 h fathead minnow LC50.
Software and hardware All data mining, curation and modelling was performed on a HP desktop computer with an Intel® Core™ i7-4790 CPU 3. 60 GHz processor and 16. 0 GB RAM with a 64-bit Windows 10 operating system. Data handling and curation was performed using version 3. 5. 1 of the R Project for Statistical Computing software  (Team R, 2013)  with the "webchem"  (Szöcs et al. , 2020) , "doBy"  (Højsgaard and Halekoh, 2006) , "RChemMass"  (Schymanski, 2019) , enviPat  (Loos et al. , 2015)  and rcdk  (Guha, 2007)  packages installed. Rcdk was installed to access the functionalities of the JAVA Chemistry Development Kit  (Steinbeck et al. , 2003). Furthermore, the KNIME Analytics Platform version 4. 0. 0  (Berthold et al. , 2009)  was used with the Marvin Chemistry (ChemAxon. Marvin. , 2014) and CDK extensions  (Beisken et al.
Identification, aggregation and curation of CECscreen The questionnaire provided to HBM4EU members resulted in the identification of 11 databases of CECs (Table  1 ), as well as the CompTox Chemicals Dashboard, which included at that stage 40 CECs lists (Table  SM-1 ), several of which were integrated via the NORMAN Suspect List Exchange (https://www. norman-network. com/nds/SLE/). In total 145,284 entries were included in the databases, which contained 107,655 unique CAS numbers and 109,237 unique SMILES. For 30,740 entries with missing structural information in the databases, no SMILES structure could be retrieved with the "webchem" package  (Szöcs et al. , 2020)  in R and these entries were therefore excluded (as described in the methods). Furthermore, another 3227 inorganics were excluded.
Multi-component entries were separated and the organic mixture constituents were reinserted, resulting in a total number of 116,758 entries in the compiled list. Removal of duplicates and standardization of structures resulted in a total of 70,397 unique "MS-ready" structures and 66,071 unique QSAR-ready structures, which corresponds to 29,504 unique chemical formulas (DOI: https://doi. org/10. 5281/zenodo. 3956586). The monoisotopic masses range based on the "MS-ready" structures was from 16 to 6981 Da (Fig. 2 ). CECscreen and related metabolites are also directly incorporated into MetFrag  (Ruttkies et al. , 2016)  (https://msbi. ipb-halle. de/MetFrag/).
Metabolite predictions: CECscreen_Metabolite_DB BioTransformer was able to predict Phase I metabolites for 51,094 out of the 66,071 (77. 3%) QSAR-ready structures, resulting in 434,953 transformations. After duplicate removal and standardization of structures 306,279 unique metabolites remained. 205,765 products were formed by hydroxylation, 43,429 by epoxidation, 22,121 by dealkylation, 13,029 by oxidation, 12,039 by desaturation, 6878 by reduction. The remaining 3018 products were formed by other types of reaction like ring opening, deisopropylation, dearylation, desulfurization, dehydrogenation, NS-cleavage, dechloroethylation, deboronation, dealkoxylation, depropargylation and formation of iminium or pyridinium (Fig. 3 ). As many of these predicted compounds may or may not already be known in databases, the R package "webchem"  (Szöcs et al.
CompTox Chemicals Dashboard results: CECscreen_Comptox_DB CompTox Chemicals Dashboard information was retrieved for 56,445 out of the 69,526 CAS numbers reported in CECscreen (Table  2 ). 5. 895 (8. 5%) of the matches included estimations of exposure and 18,553 (26. 7%) also contained information from the chemicals and products database. For 11,266 (16. 2%) of the matches ToxVal data was available, while 6544 (9. 4%) of the matches also included ToxCast data. Source links to the ToxVal data is also included in the curated database. TEST toxicity prediction could be retrieved for between 24,639 (35. 4%) and 37,541 (54. 0%) of the entries depending on the endpoint. Furthermore, for 15,190 (21. 8%) to 39,812 (57. 3%) entries predictions related to several chemical properties could be retrieved.
Discussion The aim of the present study was to establish a database of CECs (CECscreen) for screening of these compounds in human biological samples, using non-targeted HRMS and thus to provide a resource for chemical annotation of the exposome. The database includes not only the CECs themselves, but also their corresponding Phase I metabolites that are likely to be present in human samples. The database was established by aggregating and curating existing chemicals databases containing CECs selected via a survey and by taking necessary steps to make the database "MS-ready". Furthermore Phase I metabolites for all parent compounds included in the database were simulated and information related to physicochemical properties, environmental fate, and toxicity were retrieved and predicted. Combining and aggregating the CECs lists resulted in 70,397 unique "MS-ready" (i.
For OPERA predictions a confidence index is provided which is a measure of the reliability of prediction when the query chemical falls within the AD and is calculated based on the prediction accuracy of the five most similar compounds of the query chemical. A detailed description of the implementation of such an approach is reported in  Sahigara et al. (2012). The current state of CECscreen and associated files function as a first step to facilitate screening for CECs in human populations. The overlap based on unique InChIKey between CECscreen and other exposome focused databases like the Human Blood Exposome Database, Exposome Explorer and DSSTox is illustrated in Fig. 4. The largest overlap of CECscreen is with the DSSTox database, which is also incorporated into METLIN (72. 4%). However, this is still only a minor fraction of the total DSSTox database (6.
4%) which also incorporates a large number of endogenous compounds and predicted metabolites. Furthermore, over half of the Exposome Explorer database is also included into CECscreen. The overlap of CECscreen with the Blood Exposome Database is relatively small. A total of 13,175 compounds overlap between the databases which accounts for 18. 7% and 20. 2% of CECscreen and the Blood Exposome Database, respectively. The difference between the databases is most likely the result of the large number of blood related endogenous markers that are included in the Blood Exposome Database, which are outside the scope of CECscreen and the different aggregation methods used. The limited overlap between the Blood Exposome Database and.
, 2014). Both of these techniques greatly reduce the total amount of features to be considered and therefore facilitate compound annotation. For an illustration of such an application of CECscreen in an annotation approach, see Figure SM-2. There are many other prioritization approaches available, e. g. signal intensity, characteristic isotope pattern or selection based on functional groups  (Hollender et al. , 2017). However, careful consideration should be given to the type of approach to be used to reach optimal efficiency. For instance, prioritization based on the highest peak intensities would not be optimal to screen for lower intensity CECs in a matrix with a high intensity of endogenous markers  (Andra et al. , 2017). CECscreen and CECscreen_Metabolite_DB are large: by aggregating all available information in various existing databases, the number of CECs is high and this number is further expanded due to the inclusion of simulated Phase I metabolites.
, 2020; Dührkop et al. , 2020; Dührkop et al. , 2019). The in silico fragmenter MetFrag can combine database searches and MS/MS fragmentation prediction together and in this regard increase annotation confidence  (Ruttkies et al. , 2016). To facilitate this process, CECscreen is directly incorporated into MetFrag under the names of "HBM4EU_CECscreen_MF_Jul2020" and "HBM4EU_CEC-screen_MF_Jul2020plusTPs" without and with Phase I metabolites, respectively. Several further steps can be undertaken in an attempt to reduce the percentage of false positive annotations. Based on the features generated by LC-HRMS, these include the use of information on retention time characteristics, mass defect, and isotope/adduct patterns  (Scheubert et al. , 2013)  to characterize modules of features with correlated intensity levels found by hierarchical clustering  (Uppal et al.
, 2017). If sufficient pathway level information is available, such clusters can be assessed for pathway level correlations to increase the annotations confidence for correlated features present in the same cluster. The approach has been described successfully with databases that contain pathways of endogenous metabolites such as KEGG  (Uppal et al. , 2017). Similar databases containing information on CECs metabolic pathways would need to be developed to apply this approach on a large scale. The effective application of this database in screening human biological samples for CECs is dependent on the compartment that is measured. Phase I metabolites might be detected more frequently in blood samples whereas the metabolites detected in urine are often the result of Phase II metabolism. The BioTransformer software used to simulate the Phase I metabolites also includes a module to create Phase II metabolites, which can be easily applied to the "QSAR-ready" structures included in the CECscreen  (Djoumbou-Feunang et al.
, 2019) , however, considering the possibility that multiple Phase II metabolites are simulated from the Phase I metabolites, this will enlarge the volume of the database dramatically and could slow down the annotation procedure significantly. Furthermore, there are uncertainties in the validity of the metabolites as these were the product of simulation and not measurement. For example, multiple metabolites were simulated for the same compound but no information was provided on the likeliness of occurrence. As a result, some predicted metabolites may be detected more frequently, whilst others might not occur at all. Therefore, when available, CAS numbers and compound names were retrieved for the simulated metabolites in order to provide users a means to perform literature searches to determine if an annotated match is feasible.
However, subsequent steps are still necessary to confirm the annotated metabolite (e. g. in vitro metabolism studies  (Jia and Liu, 2007) , MS/MS mass shifts  (Boix et al. , 2016) ). BioTransformer covers a wide range of chemical substrates including xenobiotics and different metabolic biotransformation reactions. Therefore, the simulated Phase I metabolites is considered to be an appropriate first step to facilitate CECs metabolite identification in exposome research  (Djoumbou-Feunang et al. , 2019). Initiatives are in progress that measure and document existing metabolites of a wide range of CECs such as the activities within the US EPA,  NORMAN-SLE and PubChem (US EPA, 2004; LCSB-ECI, 2020). These measured metabolites should in time also be included in CECscreen. The presence of predicted metabolites in annotation databases such as CECscreen will help finding new metabolites in samples, which in turn will add to the knowledge on existing metabolites.
The approach described here resulted in the first version of the CECscreen database tailored for annotation of CECs in human samples and offers users metadata to prioritize compounds based on their own research question. Most of the generated metadata was the result of prediction models and simulations. As the understanding of CECs increases over time, actual measured values should be added to the databases. The addition of information on compound class, usage and production volumes would also be a valuable contribution to the database for prioritization and application  (Fischer, 2017). Furthermore, including adduct species information that are specific for certain LC-HRMS methods is valuable as well, although can cause file size inflation. All information required to produce these species are included in the database.
Hosting CECscreen, CECscreen_Metabolite_DB, CECscreen_CompTox_DB and CECscreen_OPERA_Predictions are hosted by the NORMAN Suspect List Exchange  (NORMAN, 2015)  and also available on Zenodo  (Meijer et al. , 2020)  via DOI: https://doi. org/10. 5281/zenodo. 3956586. The database is also included in the CompTox Chemicals Dashboard  (Williams et al. , 2017) (https://comptox. epa. gov/dashboard/chemical_lists/ CECSCREEN), PubChem via the NORMAN-SLE Classification Browser (https://pubchem. ncbi. nlm. nih. gov/classification/#hid=101) and is incorporated in MetFrag (https://msbi. ipb-halle. de/MetFrag/) (Ruttkies et al. , 2016).
Acknowledgements JM, ML, TH, JA, SH, LD, AC, CH, MK, RV and JV acknowledge financial support by the  HBM4EU  project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  733032. In addition, JV and RV acknowledge funding by  EXPANSE  ( EC H2020 , grant agreement No  874627 ) and EXPOSOME-NL. EXPOSOME-NL is funded through the  Gravitation program  of the  Dutch Ministry of Education, Culture , and Science and the  Netherlands Organization for Scientific Research (NWO  grant number  024. 004. 017 ). ELS acknowledges funding support from the  Luxembourg National Research Fund (FNR)  for project  A18/BM/12341006. Support was also provided to DIW by the  US National Institute of Health , award number  U2C ES030859. ELS thanks  Evan Bolton ,  Jeff Zhang ,  Paul Thiessen  ( Pub-Chem ),  Antony Williams  ( US EPA ),  Steffen Neumann  ( IPB Halle ) and  Natalia Glowacka  for their assistance in the PubChem, CompTox,  Met-Frag  and  NORMAN-SLE website integration of CECscreen , respectively.
, 2015) US EPA CPCAT database 43,600 Database containing chemical (includes SPIN 2000) information and usage in consumer products (Epa, 2017) OECD HPV 2007 4645 OECD list of high production volume chemicals (IOMC, 2009) EC EDS list 433 European Commission List of possible endocrine disrupting chemicals (European Commission, 2017) TEDX list 1409 List of potential endocrine disrupting compounds (TEDX, 2017) US EPA Chemicals 74,705 Collection of CECs lists retrieved Dashboard lists from the US EPA Chemistry Dashboard (Epa, 2017).
These algorithms are part of crowd analysis. Within the field of computer vision, crowd analysis is gaining more and more interest. Understanding the crowd mechanisms, that explain what could endanger massive gatherings is of utmost concern for security forces. Many studies have been conducted to understand human and crowd behavior within a crowded scene  (Walia & Kapoor, 2016). According to many surveys  (Grant & Flynn, 2017; Lamba & Nain, 2017; Zhan et al. , 2008) , crowd analysis is subdivided into    Grant and Flynn (2017). • The use of deep neural networks in crowd analysis, without ignoring some very recent works that still rely on the extraction of hand-crafted features and do not use deep learning methods; • As Deep Learning is ubiquitous in computer vision, we do not dedicate a section to feature engineering, but we talk about the detection of frequent important scene elements like pedestrians and groups; • We point out the subtopics of crowd analysis that lack attention in the literature; • Exploration of multiple sources of data: may they be from live video-surveillance or from private/public datasets; • And finally, the use of annotators.
Their contribution is required to enrich existing datasets and to create new ones. Many subareas of crowd analysis are still at their genesis due to data scarcity. The remainder of this paper is organized as follows: Section 2 gathers previous reviews on crowd analysis. Many of them adopt a particular taxonomy to describe the research axis. Some reviews do not present crowd analysis as a whole but analyze one of its subtopics. Hence, we divided the section into subsections according to the topic/subtopic that is presented. Section 3 is dedicated to pedestrian and group detection. We did not include these studies within crowd analysis (Section 4), because we consider pedestrian and group detection as essential tools for crowd analysis but not a subtopic itself. Section 4 presents the part of the recent literature of crowd analysis.
A visualization of these taxonomies is proposed in Fig. 1. Zhan et al. (2008) 's survey does not mention the datasets that were used for crowd analysis. Grant and Flynn (2017)  study crowd analysis and divide it into two broad categories: crowd counting and crowd behavior analysis. A visualization of the taxonomy, that they propose, can be observed in Fig. 2. The authors show that former studies on human activity recognition focused on individual scenes. The interest for group actions or actions within a crowd came later. For crowd behavior analysis, the review mentions works on group analysis, the detection of abnormal events, and crowd motion. For crowd statistics, the authors evoke the use of a measure used in traffic flow  (TRB, 2000) , to estimate crowd's density, the Level of Service (LoS).
As highlighted by the authors, the reviewed works do not tackle many challenging problems, for example: • they focus mainly on small crowds; • the images on which they work are easy to process; • the studies do not tackle demographics-related issues; • and, these studies do not intend to tackle activity or behaviorrelated questions. One finding of this review is that few research is held on identifying dangerous crowd environments because of data scarcity. A mention is given to some popular datasets used for crowd analysis. However, as stressed by  Tripathi, Singh, and Vishwakarma (2018) , despite the ubiquity of Deep Learning, few of the mentioned studies are Deep Learning-based. Lamba and Nain (2017)  start by highlighting the inability of handcrafted methods to model crowd dynamics because of occlusion and cluttered scenes.
Contrary to  Zhan et al. (2008) , the other reviews were written very recently and after that Deep Learning had been used in many fields of computer vision, including crowd analysis. However, only  Tripathi et al. (2018)  focused its review on the application of Deep Learning to crowd analysis. Contrary to others,  Tripathi et al. do not propose many taxonomies. For instance,  Zhan et al. propose three taxonomies to categorize each work connected with crowd analysis which allow us to observe the field from several points of view. Moreover, while  Grant and Flynn (2017)  propose a taxonomy for crowd analysis and for its datasets,  Lamba et al. propose a taxonomy for crowd analysis and feature extraction.
When they compare the methods on KTH and Hollywood2 datasets, the authors used as baseline handcrafted methods: HOG3D (Histogram of Oriented Gradients 3D) and HOG/HOF (Histogram of Oriented Gradients/Histogram of Optical Flow). It is worth mentioning that HOG/HOF+Mining and dense trajectories are state-of-the-art feature extractors for both datasets. We can visualize the categories of feature engineering and feature extraction explored in this review in Fig. 7. Apart from KTH and Hollywood2, the review does not propose a set of datasets that can be used for anomaly detection in videos of crowded scenes. By putting forth the fact that anomaly detection is a challenging domain within the unsupervised learning research area,  Kiran, Thomas, and Parakkal (2018)  talk mostly about anomaly detection and evoke Fig.
7. Taxonomies proposed by  Chong and Tay (2015). anomaly prediction. They highlight the issue arising from the paucity of annotated data despite the availability of raw video data. They define anomaly as the detection of unseen objects and infrequent events. The survey mentions six datasets among the frequently uses ones: UCSD Anomaly detection, CUHK Crowd, and UMN Social Force that we describe later in the datasets Section 5. 2. It introduces less used datasets, that we do not speak about later, like the Subway entry and exit datasets, The Train dataset, The Queen Mary University of London U-turn, and a newly introduced dataset, the lv dataset, by  Leyva, Sanchez, and Li (2017). Starting from the premise that normality is defined by a static background, normal crowd appearance, no change in trajectory, etc.
, the mentioned datasets comprise videos where the background is still. The survey reviews deep learning methods used for unsupervised and semi-supervised anomaly detection in videos. It classifies them in terms of the model's type and detection criteria. More precisely, it explores three types of statistical models: reconstruction models, predictive models, and deep generative models. We depict the categories of the statistical models explored by this review in Fig. 8. The authors discuss the experiments they undertook on CUHK Crowd and UCSD datasets to compare the models. The performance metrics used are Precision-Recall (PR) and Receiver-Operator-Characteristics (ROC) curves. We regret that the chosen datasets are not intended for anomaly detection in massively crowded scenes.