dataesr/scientific-paragraphs-categorization huggingface dataset:
Besides these, 50 compounds (3% of all reported) were generated using NTS workflows that included MS/MS confirmation. Consequently, the majority of the confirmed and tentatively identified compounds stem from suspect screening using candidate lists or searches of ESI mass spectral libraries. Different groups have access to different suspect lists and different mass spectral libraries, which often are vendor specific. Obviously, this influenced the compounds identified by the various participants. This may also be one of the major reasons for the poor overlap between the LC-MS and GC-MS datasets. The LC-ESI-MS libraries are usually relatively small and rich in compounds relevant for the life sciences, pharmaceutical research, and environmental and forensic toxicology, while the GC-EI-MS libraries are large, more diverse, and also include a large range of industrial chemicals.
Using feasible suspect lists One of the main outcomes of the collaborative effort is the generation of an extensive database with house dust contaminants. More than 2300 compounds were identified or tentatively identified of which close to 1700 could be assigned exact chemical structures. The database greatly expands our knowledge base of contaminants in house dust. As a comparison, a recent compilation of house dust contaminants by Zhang et al. [49]  included a total of 485 compounds, including ca 250 compounds from an earlier compilation by Mercier et al. [50]. In addition, a GC×GC-MS screening of contaminants in house dust revealed 10,000 peaks of which 370 could be characterized (145 PAHs, 52 phthalates, 8 nitro compounds, and chlorine/brominecontaining compounds)  [20]. A more recent study using both LC-QTOF-MS and GC-QTOF-MS reported 271 house dust contaminants of which 163 could be unambiguously confirmed by reference standards  [21].
The list of dust contaminants generated in the collaborative trial will be amended with additional compounds from the above cited studies and with indoor air contaminants reported by members of the NORMAN Association. This will result in a NORMAN indoor environment contaminants suspect list, which will be shared through the NORMAN suspect list exchange program. NORMAN partners are also working on compiling and curating a master list termed BSusDat^containing information needed in NTS workflows for screening of known environmentally relevant compounds (both lists can be found at www. norman-network. net, Bdatabases^tab). Next to unique identifiers such as StdInChIKey, MS Ready InChIKey, SMILE, and QSAR model-predicted ecotoxicological limit values, exact masses of expected adduct ions in both positive and negative ionization modes and related RTIs are now available for more than 40,000 substances (as of October 2018).
Work is currently in progress on obtaining experimental and predicted mass fragments for all compounds to support identifications of these suspects in the NORMAN Digital Sample Freezing Platform (DSFP; www. norman-data. eu). The BCompTox Chemistry Dashboard^at the U. S. Environmental Protection Agency (U. S. EPA) is an even larger well-curated repository of compound information. It contains many potential candidate lists, including the NORMAN lists, but also contains useful additional data to support identification, such as physicochemical properties, literature references, patent data, functional uses, and (eco)toxicological data. NORMAN and U. S. EPA closely cooperate on further development of SusDat and the two databases are interlinked. The above and other similar initiatives are in progress to expand the support of suspect screening and NTS workflows, including MS spectra predictions, and prioritization of compounds found in such studies.
Work is currently in progress to generate RTIs for all compounds on the SusDat list which, once fully implemented, greatly facilitate the suspect qualification process. Although the NIST library of EI-MS spectra includes GC RI information, it is not always easy to use. Some participants in the collaborative trial did not appear to use this information, which resulted in misassignments. Some MS software allows the use of RI information for ranking of the NIST spectral search hit list, which reduces the probability of false positives. However, even if the NIST database contains more than 72,000 compounds, RIs are still lacking for many compounds encountered in a complex matrix, such as house dust. A simple QSRR, such as the Abraham general solubility model developed for curation of the collaborative trial, may then be used for prediction of LRIs.
In this context, the development of automatic routines for suspect list and database curation and generation of BMS-ready^structures is of importance  [55]. These tools have been adopted in the CompTox database and the substances have been desalted, desolvated, and had stereochemistry removed to represent the forms of chemicals observed via HRMS. Easy access to curated MS-ready structures will greatly facilitate suspect screening of known unknowns. In the future, the NORMAN SusDat and the Dashboard records may be linked to those from open spectral libraries (e. g. , MassBank and MoNa) and fragmentation prediction resources (e. g. , MetFrag, CFM-ID, and Mass Frontier) to further streamline the process to raise the identification confidence  [56]. In addition, further ranking of candidate chemicals using data source ranking or functional use filtering has proven effective.
Using open-source data processing platforms The poor overlap between the GC-MS and LC-MSderived datasets from individual participants is hypothesized to be mainly influenced by the varying experiences of the laboratories, the time spent for analyzing the data, and varying access to tools such as mass spectral libraries, dust-relevant suspect lists, and data-processing tools, rather than instrumental limitations. This hypothesis was confirmed after uploading selected LC-MS data to the recently developed Norman DSFP and perform suspect screening using the complete list of compounds identified in the study. Briefly, the dust LC-MS raw data files (from different vendors) were converted to mzML format, imported to DSFP together with instrumental metadata, contributor details, and retention times of the retention index calibrants.
An internally standardized procedure of peak-picking and componentization (using previously optimized parameters) is utilized to create a component list, which then can be matched against any list of suspected substances considering their exact mass, fragmentation, and retention time plausibility through QSRR RTI models. The tentative results were obtained in a short time and the number of identified compounds exceeded on average 500. Additional compound lists can be found at the NORMAN Suspect List Exchange (https://www. normannetwork. com/?q=node/236). Suspect screening against all the compounds in the NORMAN SusDat database allowed for provisional identification of additional 476 compounds not reported by the participants. The FOR-IDENT platform offers another open access tool (https://water.
for-ident. org), as previously discussed. It uses retention time/RTI, accurate mass/empirical formula, and mass spectra information of uploaded (suspects or nontarget screening) mzML files or raw data files from LC-MS vendors, which are compared with the information of compounds in the STOFF-IDENT database (formula, logD, and in silico fragmentation spectra from the MetFrag tool and MassBank entries). The FOR-IDENT platform gives on this basis molecular identification suggestions with prioritization levels.
Using harmonized terms and identification levels when untargeted analysis data From the results of the collaborative trial, it is clear that not even established users of untargeted analytical techniques a common use of the terms Btarget,^Bsuspect,^and Bnontarget^analysis and the identification confidence levels 2-5  [16, 26]. It seemed to be a more coherent view on the terms and levels among the LC-HRMS community, which is logical. That community was instrumental in the development of a common nomenclature and identification confidence system. In future reporting of untargeted analysis data, LC-HRMS users are encouraged to strictly follow the guidelines laid out by Schymanski et al. [16]. The identification confidence scheme  [16, 26]  appeared less easily applicable for the GC-MS participants, most likely due to differences in preferred workflows.
Reza Aalizadeh is a Ph. D. student in analytical chemistry at the National and Kapodistrian University of Athens, Greece. He is working on the development and application of chemometric techniques and workflows to facilitate the identification of unknowns by nontarget screening in samples analyzed by highresolution mass spectrometry. Nikiforos Alygizakis is a Marie Skłodowska-Curie fellow, working at the Environmental Institute in Kos, Slovakia. His main research interests are digital archiving of liquid and gas chromatography high-resolution mass spectrometry data, wide-scope retrospective screening, and prioritization of substances in nontarget screening datasets. Christoph Moschet holds a Ph. D. degree in environmental chemi stry from the Swiss Federal Institute of Technology in Zürich, Switzerland.
Methodology This section provides details for the procedure involved in the selection, inclusion and exclusion of research articles. The review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines  [15]. Overall, three databases were selected to conduct this review, namely, Scopus, IEEE Xplore and Science Direct. The keywords utilised to select relevant articles from the databases are listed in Table  1  along with the number of retrieved research papers. The research articles were manually screened by reading the title and abstract. The database search returned (n = 13,875) records from three online databases. After removal of duplicate articles, only 2823 articles were left for review. The titles and abstracts of these 2823 articles were manually screened for relevance, resulting in the exclusion of 2415 records.
The remaining 408 articles were selected for full-text review and content analysis. For inclusion in the final list, articles were required to be published between 2009 and 2019 and to be related to flood monitoring, forecasting or mapping. These inclusion criteria resulted in 91 relevant articles. In regard to exclusion criteria, the articles about IoT protocols in flood monitoring were not included in this review, as this is not the core focus of this study. Furthermore, duplicated articles in different databases were also discarded, and only the articles written in English were considered in this review. The PRISMA flow diagram for the systematic literature review can be seen in Figure  2.
The novelty in the study was the introduction of measuring river slope through the rangefinder sensor. The same authors in  [59] , Mousa et al. progressed their work further in  [60]  by introducing L1 regularization for fault detection and missing data points in real-time sensor applications. The proposed study also utilised ANN to compensate for the change in the environmental condition, accounting for how such change affects the readings obtained from sensors. In this ANN approach, the readings from multiple temperature sensors was obtained and the temperature variation between the ultrasonic sensor and ground determined in order to compensate for the error. The study highlights the fact that acquired data from sensors may not always be reliable as sensors may be damaged or covered with dirt; thus, early warning monitoring systems can issue false alarms.
Moreover, Mostafa et al. [68]  proposed a WSN along with a multi-agent system to classify whether the data coming from the sensors are valid or invalid. The study suggested an optimal model for aggregation and classification and is divided into three steps, namely, sensors verification phase, data aggregation and classification, and the database interaction step. Review of the IoT-related literature presented above has revealed the potential of IoT-based sensors in early warning system. The most obvious findings to emerge from this subsection is that sensor-based approaches are more accurate in terms of calculating water level. However, the limitation of such approaches is that they only offer a reading at a single point and the only way to validate the reading is to visit the site due to the unavailability of visual data.
Data Collection and Early Warning System The accuracy of any deep learning computer vision application is dependent on the quality and quantity of the input dataset serving the neural network architecture for learning purposes  [69]. Fuentes et al. [70]  published the first image segmentation dataset of water body along with test results for three different CNNs. The deep learning approach on the image segmentation dataset proved to be very reliable, as the algorithm was able to learn on different images which reduced the need for manual filtering. The study concluded that Tiramisu image segmentation performed best on the water segmentation dataset. However, the dataset that the study utilised to train the model consisted of only 300 images with no benchmark available to compare. Understandably, data scarcity is a major limitation constraining AI initiative.
For this reason, several studies have utilised crowdsourced social media images and textual data about flooding as a means of training and validating machine learning outputs in computer vision  [71, 72]  In addition, Helber et al. [73]  have proposed a dataset consisting of 27,000 geo-referenced labelled images which are divided into ten different classes. The collected images were from the Sentinel-2 satellite which opens up opportunities for a wide range of applications. The benchmark was created by using pre-existing CNN ResNet-50 architecture for the evaluation of the proposed dataset. The dataset and ground truths were collected manually to compare the accuracy of different algorithms. The ground truths were compared with predicted labels to evaluate the accuracy. A confusion matrix  [73]  was utilised to evaluate the performance of the proposed algorithm.
[83]  developed software to automatically create near real-time flood mapping for the images retrieved from satellites. The proposed software can segment out the water body, cloud shadows, and terrain shadow from an image. However, the software is only limited to the USA and can cover any land region between 80 degrees south and 80 degrees north. Moreover, Martinis et al. [75]  presented a two-phase flood monitoring system. First, the flood data is collected through moderate resolution imaging spectroradiometer (MODIS) and then it activates the second phase of the crisis management component which includes acquiring a large amount of spatial data from the satellite utilizing synthetic aperture radar (SAR). The study mentioned that TerraSAR-X-based flood mapping service could be triggered to derive high-resolution information for inundation mapping.
57%. Even though the evaluation of this method could have benefitted from the use of a larger number of images, this level of performance is considered outstanding. Similarly, Sumalan et al. [102]  developed a classification algorithm to classify images taken from UAVs into three different classes, namely, grass, buildings and flooded area. The proposed study developed an algorithm which is based on a local binary pattern so that it can extend to red and green channels in the RGB domain and to the h channel of HSV. The UAV was utilized to collect images, and the dataset was grouped into three categories. The histograms of the different classes were grouped together so that the histogram of the new image was compared against one of the three groups to predict the class of the input image. Instead of using the histogram approach, deep learning can be utilized to classify the images and the videos collected from UAVs autonomously into disaster and non-disaster categories  [103].
Kamilaris et al. [104]  utilized a deep learning model based on visual geometry group (VGG) to establish if an image is to be categorized into a disaster or not. The training used 544 images containing different images, some of which are non-disaster type and the others relate to disasters such as fires, earthquakes, collapsed buildings, tsunamis and flooding. By employing data augmentation techniques on the small dataset, an accuracy of 91% was achieved, with a suggestion that this accuracy can reach 95% with a larger dataset. However, little is known about how the accuracy of VGG architecture compares with other existing state-of-the-art CNN architectures. This subsection presented the critical analysis of the cited literature in terms of proposed technology and the type of experimental setup for mapping of the flood events.
Computer Vision and Data Fusion for Flood Mapping The accuracy of predictive models can be increased by fusing the data from different sources  [105]. Zoka et al. [106]  focused on combining data coming from radar SAR and optical data to monitor the water stretch in a wetland area after a flood. The study used a combination of categorical and spectral approaches, where radiometric changes were observed from optical sensing imagery, and thematic changes were observed from categorical processing. Moreover, the study suggested that the proposed methodology can be used to manage water storage capacity, and the flood extent mapping accuracy can be increased by merging categorical and spectral processing. In a similar approach, Chaouch et al. [107]  utilized satellite images from radar SAR and Landsat to improve coastal flood inundation mapping.
Different images from the satellites and aerial view were fused along with a digital elevation model (DEM) to make the proposed method of mapping more accurate. The proposed algorithm achieved an accuracy of 83% with the authors emphasizing that the accuracy can be further improved by pre-processing the data (removing inherent speckle noise from images) and increasing the quantity of the dataset. In addition, Senthilnath et al. [108]  implemented a computer simulation of hierarchical clustering approach along with multi-purpose sensors including SAR for analysing data during the flood, and Linear Imaging Self-Scanning III (LISS III) for analysing the area before the flood. The data from both the sensors were mapped together to obtain the flooded and non-flooded areas. There are also studies fusing sensor and satellite image data.
These techniques are utilized to segment the water surface, whereas for segmenting out the lagoon area, the CNN can be retrained with the addition of one more class, i. e. , lagoon entrance. For adding one more class, additional data needs to be collected which will require an understanding of a new feature map of an image. Hence, further research should be undertaken to investigate semantic segmentation. Furthermore, future research should be undertaken to automate the control process of a lagoon entrance by incorporating remote sensing and computer vision techniques. This will allow relevant data to be collected and visualized to understand the impacts of a change in weather conditions on berm height. At present, berm height is understood to be the product of wave run-up and the height of a berm which continues to increase until reaching the maximum height of a wave run-up  [134].
Data Acquisition Following cryoprotection the brains and spinal cords of the fetuses and the brain of the adult control were immediately removed and blocked. Forty micrometers thick sections were cut on a freezing microtome and mounted in saline onto gelatinized slides. The brains were cut in the parasagittal plane and the spinal cords horizontally. One in 3 sections of the adult brain and 1 in 4 sections in the fetuses were retained to explore CS neuron distribution. Sections were examined in UV light with oil-immersion objectives using a Leitz fluorescence microscope equipped with a D-filter set (355-425 nm). High-precision maps were made using Mercator software running on Exploranova technology, coupled to the microscope stage. At least 1 in every 10 sections (1. 6 mm apart in the fetuses and 1.
2014a). Because the present study uses a parasagittal plane of section, we converted the atlas from the coronal plane into the parasagittal plane using a 7 T MRI scan of the contralateral hemisphere used to create the  Markov et al. (2014a)  atlas. ITK-SNAP software (http://www. itksnap. org) made it possible to create the virtual parasagittal plane displaying the appropriate area locations. Histological boundaries were confirmed using Nissl-, CO-, and AChE-stained sections. Boundary locations in the developing brains were checked using Nissl-(not shown) and AChE-stained sections (Fig. 3 ). In the youngest fetus the primary visual cortex is readily apparent as a region of very low levels of AChE activity. At more anterior levels presumptive auditory cortex is relatively strongly labeled by AChE.
Funding  LABEX CORTEX  ( ANR-11-LABX-0042 ) of  Université de Lyon  ( ANR-11-IDEX-0007 ) operated by the  French National Research Agency (ANR)  (H. K. , C. D. );  ANR-15-CE32-0016 ,  CORNET (H. K. ) ;  ANR-17-NEUC-0004 ,  A2P2MC (H. K. ) ;  ANR-17-HBPR-0003 ,  CORTICITY (H. K. ) ;  ANR-14-CE13-0036 , Primacor (C. D. );  Chinese Academy of Sciences President's International Fellowship Initiative. Grant No. 2018VBA0011  (H. K. );  FRC "Rotary-Espoir en Tête " 2018 (H. K. and C. D. );  Fondation pour la Recherche Médicale , Equipe FRM-DEQ20160334943 (C. D. ).
The association between high initial DNA methylation level at transcription start site (TSS) regions and the genes responding to cytokine treatments was determined from Illumina-Methylation-450-Bead-Chip and whole-genome bisulphite sequencing data of untreated MCF7 cells. Since these data may be of interest for the understanding of the pathophysiology of the human breast cancers, we extended this study to a human breast cancer cohort (TCGA databank). The diversity of DNA methylation patterns in human cancer cells offered the opportunity to investigate the crosstalk between cytokine-activated genes, their methylation levels and their in vivo response to their microenvironment.
Results Identification of genes upregulated in response to the pro-inflammatory cytokines, Il6, Il1b, and TNFa, in the human breast cancer cell line MCF7 Previous analyses of genes upregulated by CAF conditioned media suggested that cytokines secreted by CAFs preferentially impact genes exhibiting a high methylation level at their 5ʹ end  [9]. To gain further insight into this process, we first identified genes upregulated by specific cytokines in an independent model. MCF7 cells were chosen since this breast cancer cell line has been widely studied for expression, DNA methylation and response to many factors, and therefore extensive public datasets are available and can be compared with the large number of breast tumour samples from TCGA databank. Among the molecules secreted by the tumoural microenvironment, pro-inflammatory cytokines play a major role in tumour progression and drug resistance  [20] [21] [22] [23]  thus orienting our selection of cytokines to be analysed towards the interleukins IL6, IL1b, and TNFa.