dataesr/scientific-paragraphs-categorization huggingface dataset:
Crosses are expected RI positions from  Alexandersen et al. (2020) , and bars are colored in orange if the deletion is observed in that position in our data. (Right panels) Number of counts and ORF name for the 13 deletions with most counts observed in each replicate. Orange bars correspond to orange crosses in the left panel and represent canonical TRS. Blue bars correspond to putative noncanonical ORFs detected in our data. Names for noncanonical ORFs are hexadecimal numbers representing the position of the corresponding start codon (AUG) in the standard 5 ′ -to-3 ′ sense in the reference sequence (GISAID ID: EPI_ISL_414631), see also Supplemental Table  2. validated by a conventional approach. We performed RT-qPCR analysis on total RNA extracted from HEK293 (transduced with ACE) infected with SARS-CoV-2 or mock-infected (negative control) cells.
Comparison of sgDI-tector results on our data with existing bioinformatic tools Several techniques have been used so far to detect sgRNA expression levels in coronaviruses. Firstly, the reads per kilobase of transcript per million mapped reads (RPKM) have been directly used for several coronaviruses, once the RPKM of each sgRNA is properly "decumulated" from downstream ORFs, which are present in each ORF transcript  (Irigoyen et al. 2016). Despite its simplicity, we show in Figure  5A  that, in our case, the decumulation approach gave results which failed to correlate with those obtained with the junction analysis performed by sgDI-tector. Moreover, the decumulation resulted in several ORFs having a negative number of counts, and this can happen for two reasons: the (unavoidable) errors in the estimation of transcription levels from the NGS data (especially for short ORFs) and the presence of other DVGs which are not considered in the decumulation procedure.
In addition to these two technical advantages, sgDI-tector is user-friendly. We consider sgDI-tector to be the most user-friendly bioinformatic tool to estimate SARS-CoV-2 sgRNA from NGS data. Indeed, although STAR is a well-known mapping tool widely used in transcriptomics, it requires a large number of nontrivial options and parameters, which must be tuned accurately to detect sgRNAs. Periscope is a pipeline based on a workflow management system (snakemake) that is easy to install but, similarly to STAR, it has several parameters that the user must know and the full list of mandatory options is not provided. sgDI-tector is a Python script that is easy to run. It only needs the DI-tector script in the working directory (together with the tools required to run DI-tector, that is BWA and samtools), and all efforts have been made so that sgDI-tector operates with the lowest possible number of settings that can be easily selected based on virology knowledge.
Another possibility is that for some noncanonical ORFs the initiation of translation could be driven by non-AUG start codons  (Kearse and Wilusz 2017)  and thus escape the sgDI-tector algorithm. Moreover, from our analysis we had access to the full set of DVG produced by SARS-CoV-2 during its life cycle (see Table  1 ). We used RT-qPCR to confirm the presence of noncanonical sgRNAs detected by sgDI-tector in FIGURE 7. Logo of the RI positions around the TRS-putative sequences obtained from DI-tector. The conservation plotted as total height of the letters representing nucleotides is obtained as log 2 (4)-Σ n f i (n) log 2 (f i (n)), where f i (n) is the frequency of nucleotide n in position i. Therefore a height equal to 2 corresponds to perfect conservation. The horizontal axis is the position with respect to the reference sequence (GISAID ID: EPI_ISL_414631).
Viral titers, infection with SARS-CoV-2 and total RNA extraction The SARS-CoV-2 hCoV-19/France/GES-1973/2020 GISAID ID: EPI_ISL_414631 strain was supplied by the National Reference Centre for Respiratory Viruses hosted by Institut Pasteur (Paris, France). Vero-E6 cells were used for the amplification and titration of viral stocks. Vero-E6 monolayers were infected with SARS-CoV-2 in the presence of 0. 1% TPCK trypsin (Sigma) at a multiplicity of infection (MOI) of 0. 0001 plaque-forming units (pfu) per cell. When the cytopathogenic effect was apparent, the culture supernatant was collected and centrifuged for 5 min at 850g. The efficiency of virus amplification was evaluated by titrating the supernatant on Vero-E6 cells, in a standard plaque assay adapted from  Matrosovich et al. (2006). For SARS-CoV-2 infection ST-CH ACE-2 , cells were seeded into polylysine-coated (SIGMA) T150 flasks 1 d before infection (20 × 10 6 cells/flask).
Virus infections were carried out at an MOI of 1. Viruses were diluted with DMEM 0%FCS to obtain a final inoculum volume of 5 mL. Cells were incubated with virus for 1 h at 37°C with gentle shaking. Twenty-five milliliters of DMEM containing 0% FCS was added to each T150 flask, and cells were incubated at 37°C until infections were stopped by cell lysis 24 h later. Total RNA was extracted from either SARS-CoV-2-or mock-infected ST-CH ACE-2 using TriLS (TriLS, Sigma) reagent protocol previously described in detail in Sanchez  David et al. (2016). All experiments with SARS-CoV-2 were conducted under strict BSL3 conditions. Raw data collection, preprocessing and normalization scheme NGS libraries were built using a TruSeq mRNA-Seq library preparation kit  (Cat#20020594 Illumina) , according to the manufacturer's recommendations.
Quality control was performed on an Agilent Bioanalyzer. Sequencing was performed on the Illumina NextSeq500 platform to generate single-end 75 bp reads bearing strand specificity. Reads were cleaned of adapter sequences and low-quality sequences using cutadapt version 2. 9. Only sequences at least 25 nt in length were considered for further analysis. Bowtie version 2. 1. 0, with default parameters, was used for alignment on the reference genome (hCoV-19/France/GES-1973/ 2020, GISAID accession ID: EPI_ISL_414631). SARS-CoV-2 genome coverages were computed with bedtools genomecov for each strand. For several analyses performed in this work, we needed to use data from the three biological replicates of our experimental setup. To compare in a more robust way the data from independent experiments, we normalized all the counts as follows: For each biological replicate, we took the number of reads mapped to the SARS-CoV-2 genome and divided this by the value obtained for Replicate 1 (the raw number of reads are reported in Supplemental Table  1 ).
The three values that we obtained in this way (1 for Replicate 1, and other values for other replicates) are used to rescale all the number of reads before taking any sgDI-tector www. rnajournal. org 285 Cold Spring Harbor Laboratory Press on February 28, 2022 -Published by rnajournal. cshlp. org Downloaded from average over biological replicates. In particular, averages of normalized data have been used for Tables  3  and 1 , and for Figures  2  and 4.
What are the major results described in your paper and how do they impact this branch of the field? In our paper we introduce a new algorithm to quantify and analyze subgenomic RNA produced during coronavirus infections, starting from next-generation sequencing (NGS) data. Our tool, which we named sgDI-tector, is the first specifically designed for this aim. In our paper, we collected NGS data from cells infected with SARS-CoV-2 and compared the results obtained through sgDI-tector with other approaches, showing that sgDI-tector is extremely effective. Finally, our software has been designed to be as userfriendly as possible, which I think is very important! What led you to study RNA or this aspect of RNA science? I have always been fascinated by the ability of viruses to compress the information in their genomes, while dealing at the same time with the immune pressure induced by the host: It is amazing (and also a bit scary!) how viruses evolved to exploit very complex mechanisms to code for multiple proteins in their genetic code, for instance through inducing ribosomal frameshifting or through subgenomic RNA production.
ACKNOWLEDGMENTS We would like to thank  J. Pipoli da Fonseca ,  L. Lemée  from  Biomics Platform ,  C2RT ,  Institut Pasteur, Paris, France for RNA NGS, IBISA  and the  Illumina   COVID-19  Projects' offer. The authors would like to thank members of the Tangy, van der Werf laboratories and the National Reference Center ( CNR ) for Respiratory Viruses at the  Institut Pasteur  for support and valuable discussions. We acknowledge the  ANR (Agence Nationale de la Recherche)  and  FRM (Fondation de la Recherche Médicale)  for funding this work through the  AAP Flash-Covid 19  project  SARS-Cov-2immunRNAs. A. V. K. received support from  ANR  through the grant  ANR-LBX-62 IBEID CoV-2SENSING/COVID 19. RNA NGS has been supported by France Génomique  ( ANR-10-INBS-09-09 ). Received August 30, 2021; accepted December 7, 2021.
DATA DEPOSITION The data collected and used for this work have been deposited in NCBI's Gene Expression Omnibus  (Edgar et al. 2002)  and are accessible through GEO Series accession number GSE180632, at https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE180632. sgDI-tector code is publicly available on Github, at https://github. com/adigioacchino/sgDI-tector.
Understanding team collaboration processes is key for the development of new technologies towards increasing groups effectiveness. In particular, the need and challenges of coordinating potentially large-scale, self-organized, collaborative initiatives have been made even more salient by the COVID-19 pandemic. Key to this challenge is the difficulty for participants to situate themselves within the larger social context. At the individual level, the Quantified-Self movement has showcased how insights from one's own data can trigger changes in behavior, sometimes leading to fundamental insights through self-research. Building on these premises, here we present CoSo (Collaborative Sonar), a digital platform for participatory collective sensing and social research. CoSo is a web and mobile data collection platform for team network reconstruction and visualization.
It leverages a "group-in-the-loop" intrinsic motivator to collect data on collaborative activities performed by a team through a mobile app, with summary statistics and visualizations of the collected data made available on a web dashboard. We showcase its use and discuss the perspectives offered by promoting grouplevel metacognition and collective introspection. By highlighting the invisible relationships in a group, CoSo facilitates in-the-wild studies, remote/hybrid data collection, and multi-modal sensing approaches of group interactions within professional and other social contexts.
Smartphone apps on iOS and Android  [5]  have also been used to derive interaction networks using Bluetooth scanning. Yet, recent legislation on the use of Bluetooth for contact-tracing studies in iOS has limited such attempts to authorized governmental applications. Moreover, such studies focus on face-to-face interactions, limiting the full understanding of social interactions at play. Beyond physical interactions, passively obtained personal data from social platforms and mobile phone data allows for the collection of large interaction datasets  [10] , generating insights on friendship networks with a statistical power previously unattainable. However, such data is obtained in a non-controlled manner, and the resulting interactions remain hard to qualitatively assess. To enhance such quantitative insights, recent studies in health and psychiatry  [14, 24]  have leveraged digital mobile applications that use active methods of experience sampling, including explicit self-reports that may range from occasional and detailed survey instruments to more frequent, brief and in-the-moment questionnaires that are referred to as "ecological momentary assessment" (EMA).
Such an approach has proved to promote regular, long-term engagement in symptom self-report studies during the COVID19 pandemic ("Quantified Flu" project  [26] ), demonstrating the benefits of including participants in the loop of the experimental research study. Building on these insights, we present a new web and mobile data collection platform for team network reconstruction called CoSo (Collaborative Sonar). CoSo focuses on a group in the loop intrinsic motivator (Fig. 1 ) to collect data on activities performed by a team through a mobile app, and present summary statistics and visualizations of the collected data on a companion web dashboard. In the following, we describe the features of the platform, showcase its use on a case study, and discuss the perspectives offered by promoting group-level metacognition and collective introspection.
DESIGN CoSo is composed of three main applications. First, a backend gathers all user data and information, and provides an API. Second, a frontend web application allows for data visualization, team management, filling surveys and receiving notifications. Finally, a mobile application for Android and iOS allows users to journal tasks, receive push notifications, fill surveys and gather immediate insights about their logged data. The researchers running the study have a special administrative access on the front end to create surveys and communications to be sent to all or some participating teams. The open-source code is made available here: https://gitlab. com/interactiondatalab/coso.
External tools. In addition to the self-reported data collected from the app, we implemented in the web interface the ability to connect to OAuth based external services. This allows for the passive gathering of data from other communication channels the team is using. For example, in the context of iGEM we collect user edits on the team digital lab notebook on the iGEM MediaWiki instance. In addition, we implemented a connector to Slack, such that metrics about team communications can be extracted. 3. 1. 4 Notification system. In order to increase user engagement with the app, we implemented a notification system that regularly reminds the users to record their activity. We chose to send a reminder every week on Sundays if the user did not record any activity during the past week. Finally, social studies often require direct communication between the research team and the participants.
Visualization of individual-level and team-level data. To incentivize the data collection process, CoSo provides participants with a visualization dashboard of the collected data. The web interface contains interactive data visualization cards with an export function (Fig. 3 ). These data visualizations represent the records of team activity on the app (activity journaling) and on the external services. In particular, the ability to obtain data visualizations by plugging external services containing pre-existing data lowers the cost of entry to the CoSo app, highlighting the benefits of further self-report through the app to refine the insights on team dynamics. In addition, we implemented gamification features within the mobile application. The Insight tab within the app shows the user their current and longest streak (number of consecutive days with an activity recorded), along with various user-level and team-level metrics: the total number of activities recorded by the user, the five most frequent activity categories recorded by the user, their top five collaborators, the number of teams in the study, the total number of tasks and collaborations recorded, and finally the five users with the longest streak.
CASE STUDY Here, we present the results of a pilot study involving one team participating in the 2020 iGEM competition, using an early version of the app over 50 days (Fig. 4a ). In this version, users could only report ego-centered activities (i. e only activities in which they participated), and the web dashboard was not implemented. Out of the 20 student members actively participating in the iGEM competition, 19 used the app. Activity reports were collected using the CoSo app. The dataset obtained consisted of the activity category, the user ID who created the activity report, the timestamp and the list of teammates reported as collaborators in the activity. We find that the most reported activities involve a large number of team members: Team meetings, Meetups, Education / Outreach event, Planning tasks and Brainstorming.
DISCUSSION In this study we introduce the CoSo platform for team interaction studies. Compared to other app-based survey collection softwares  [13, 14] , CoSo is designed to be deployed across a large number of teams with a focus on gathering intra-and inter-team interactions through activity reports and surveys on team interactions. Moreover, CoSo uses gamification strategies within the app and data visualizations to promote engagement. These could be extended with visualizations and leaderboards providing social comparison to further incentivize active usage. Beyond the particular setting of team studies, this technology can be used to record general interactions between individuals identifying as a group, and allow them to gather insights on the group overall structure and dynamics that might be invisible to the individuals involved.
Figure4: Example data generated with the CoSo app. a) User engagement over time. Lines correspond to users and are ordered chronologically by the date of first report. Squares indicate activities, colored by the number of reports in that period. While 19 users have declared an activity, some users display a stronger and more regular engagement, with ∼30% of users contributing 80% of the reports. b) Summary statistics of the activity reports: number of times an activity category was reported, proportion of team members who reported a category, and proportion of members who were mentioned as collaborators in a category. c) Collaboration network between users. Links depict collaborations reported at least 5 times. d) Evolution of the team network across 10 consecutive time windows in the study.
ACKNOWLEDGMENTS We thank  Lionel Deveaux ,  Romain Delory ,  Elmar Tavares ,  Radhika Beaumé  and  Hugo Laplace  for their involvement in the technological design and implementation,  Savandara Besse  for her help with setting the case study, and  Rathin Jeyaram  and  Robert Ward  for their insights on the general design of the app and visualizations. Thanks to the Bettencourt Schueller Foundation long term partnership, this work was partly supported by the  CRI Research Fellowship  to  Marc Santolini. This work was further supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  872944. Ethical approval for the case study was granted by the  IRB00003888.
Big data analysis assumes a significant role in Earth observation using remote sensing images, since the explosion of data images from multiple sensors is used in several fields. The traditional data analysis techniques have different limitations on storing and processing massive volumes of data. Besides, big remote sensing data analytics demand sophisticated algorithms based on specific techniques to store to process the data in real-time or in near real-time with high accuracy, efficiency, and high speed. In this paper, we present a method for storing a huge number of heterogeneous satellite images based on Hadoop distributed file system (HDFS) and Apache Spark. We also present how deep learning algorithms such as VGGNet and UNet can be beneficial to big remote sensing data processing for feature extraction and classification.
Storage refers to systems where data is stored and preserved in completely different steps because it moves through the pipeline. Data storage decisions rely on numerous factors, for instance, the quantity of information, frequency, the number of queries to a storage system, and uses of data for the ensuing step. Data processing depends on the storing model of the satellite image and refers to image processing. The Hadoop framework is introduced that depends on the MapReduce programming model and the Hadoop distributed file system  [8] , which can prepare, store, and analyze a lot of circulated unstructured information. Apache Spark works on top of the current HDFS system to provide improved and additional services. It brings MapReduce to the next stage with less costly data shuffles with capabilities such as in-memory data storage and real-time processing.
It provides the flexibility, scalability, and speed required to meet the challenges of big data. Spark incorporates two major libraries: SQL (Structured Query Language) for querying large amounts of structured data, as well as MLlib  [15] , which contains the main learning algorithms and statistical methods. The remainder of this paper is structured as follows. The related work is presented in Section 2. The proposed approach is described in Section 3. Section 4 describes our experimentation and results. Section 5 contains a brief conclusion with recommendations for future research.
Related Works We have explored what big remote sensing data are, thus their importance and complex properties. The demand for storing and processing big datasets has encouraged the development of data storage and database systems in recent years as well as various platforms for processing and analyzing these data. With this advancement, the analysis systems and data models for big data have been improved over time. Storage methods and techniques have undergone an unprecedented transformation that favors the distribution of data through the use of clusters and clouds. We will present various applications of CNN architecture that are among the most recent for the treatment of satellite images; specifically, those that are related to satellite image classification and segmentation. We will also discuss the various implementation variations of this type of architecture.
Specifically, the distribution of data with Hadoop technology is already in place or becomes unavoidable. Spark technology integrates the data parallelism mode through its RDD (Resilient Distributed Dataset) model  [23]. RDD ensures pannability by storing each image satellite data partition in memory on its server between two iterations. Furthermore, each RDD is divided into several partitions that can be calculated on the various nodes of the cluster, allowing for data distribution and parallelization. In Spark, all functions are carried out on RDDs  [24]. In fact, the big satellite image data is incorporated in strips into the RDDs  [24]. Additionally, using HDFS the couple key/value can be efficiently generated to the image partitions in order to conceal the remote sensing data heterogeneity.
Hadoop's open-source distributed processing software is used in these clusters. One machine in the cluster is usually referred as NameNode, another as JobTracker; these are the masters. The remaining machines in the cluster serve as both DataNode and TaskTracker which are the slaves  [29]. Hadoop and Spark clusters are well known for increasing the speed of data analysis applications. They are also highly scalable: if increasing data volumes exceed a cluster's processing power, additional cluster nodes can be added to increase throughput. These clusters are terribly proof against failure, as every bit of data is derived to alternative cluster nodes, guaranteeing that data are not lost. Figure  3  shows the two main categories of machine roles in a Hadoop deployment: master nodes and slave nodes.