dataesr/scientific-paragraphs-categorization huggingface dataset:
Writing the screenplay of the workshop. First thing to do when writing the screenplay of the workshop is to begin by the end, that is here, playing Spot It! with the Raspberry, i. e. "Matching symbols between two cards". Students have to resolve the problem, compare their solutions and draw up a list of the different instructions used. This is the" Resolve" part. Those instructions correspond to the different steps of the resolution of the problem: these steps will give the subjects of the different exercises of the "Apply" part. Once again students have to propose a list of exercises for each subject, solve them and then write scripts corresponding to solutions of similar exercises. Those solutions give the "Discover" part. Three main steps can be established in the resolution of the problem.
The taxonomic classification of viral sequences is frequently used for the rapid identification of pathogens, which is a key point for when a viral outbreak occurs. Both Oxford Nanopore Technologies (ONT) MinION and the Illumina (NGS) technology provide efficient methods to detect viral pathogens. Despite the availability of many strategies and software, matching them can be a very tedious and time-consuming task. As a result, we developed PIMGAVir and Vir-MinION, two metagenomics pipelines that automatically provide the user with a complete baseline analysis. The PIMGAVir and Vir-MinION pipelines work on 2nd and 3rd generation data, respectively, and provide the user with a taxonomic classification of the reads through three strategies: assembly-based, read-based, and clustering-based. The pipelines supply the scientist with comprehensive results in graphical and textual format for future analyses.
Regardless of the chosen analytical approach, the preprocessing steps always start with the steps of base calling  [35] , demultiplexing  [35, 36] , filtering and quality control  [37]. The read-only strategy relies on the identification of reads by taxa using an algorithm with Centrifuge software  [38]  and the NCBI RefSeq sequence database, as is the case with the Metrichor/EPI2ME cloud platform (Metrichor Ltd. , Oxford, UK). Strategies based on assembly and clustering share, with the previous strategy, the pre-processing steps to which the assembly  [39, 40] , polishing  [41] [42] [43] , or clustering  [32, 44]  steps must be added as described in the MicroPIPE  [45] , NanoCLUST  [46] , and mothur  [47]  workflows. Nowadays, it is very common in a research laboratory to combine the different sequencing technologies available to perform metagenomic studies.
Description of the Two Pipelines Given that the objectives of these two pipelines are similar, they share the use of identical computer packages as for example with the use of megahit for the assembly step (Table  1 ). However, they also differ in the use of dedicated packages (Table  1 ), such as the use of filtering or demultiplexing tools that are specific to 3G data processing. On the other hand, both pipelines use the same database that will be used for the filtering steps of the contaminants, e. g. , Silva to remove bacterial sequences or NR refseq for the identification of viral sequences (Table  2 ). [51]  0. 10. 1 PIMGAVir filtering seqtk  [52]  1. 3 PIMGAVir filtering megahit  [24]  v1. 2. 9 PIMGAVir/Vir-MinION assembly flye  [40]  v2. 9 Vir-MinION assembly quast  [53]  v5. 0. 2 PIMGAVir assembly spades  [25]  3 As shown in Figure  1A , the pipeline executes the pre-processing task to trim the raw data and remove contaminants.
Then, according to the user option, the reads_filtering script will filter out the reads not belonging to desired taxa. At this point, the pipeline will execute one or more strategies (namely, read_based, ass_based, and clust_based) in parallel to proceed with the taxonomic classification. Double applications perform both the clustering and assembly methods to present the user with a pool of comparable results. The pipeline builds a specific data structure following the logical schema "strategy-application" to be easily surfable. For example, Figure  1B  depicts the data structure created during the analysis step. The PIMGAVir pipeline uses a set of local-viral databases to perform both the filtering and taxonomic tasks (Figure  1C ). The pipeline runs under the Ubuntu 20. 04 operative system, and a set of bash scripts performs the workflow.
Each strategy, once called, executes a few scripts and produces a collection of results (text, HTML, and pdf) and log files (Figure  2A ). Most of the scripts lean on a group of applications and databases to accomplish their task. Figure  2B  shows the databases and applications used by every script. Finally, the user has the freedom to run every one of the mentioned scripts as an autonomous process as long as the input format is respected. The three strategies have been designed to run independently, allowing the user to run on parallel computing systems. The PIMGAVir pipeline has been tested on a cluster configured on the SLURM workload manager, running on multiple samples at once. The following is an example of the SLURM script to run the PIMGAVir pipeline (Figure  2C ). Regarding the Vir-MinION pipeline, after the pre-processing step, which is executed as the default step, the pipeline runs one or more methods in parallel, according to the user choice.
The Vir-MinION pipeline runs under Ubuntu 20. 04 and uses NVIDIA technology to make its processing. The Vir-MinION utilizes a collection of bash scripts to perform the workflow. For its part, the bash scripts invoke a group of applications and databases to accomplish their task. Figure  3B  shows the databases and applications used by every script. As shown in Figure  3C , the pipeline builds a specific data structure following the logical schema "strategy-application" to be easily surfable.
Double applications perform both the clustering and assembly methods to present the user with a pool of comparable results. The pipeline builds a specific data structure following the logical schema "strategy-application" to be easily surfable. For example, Figure  1B  depicts the data structure created during the analysis step. The PIMGAVir pipeline uses a set of local-viral databases to perform both the filtering and taxonomic tasks (Figure  1C ). The pipeline runs under the Ubuntu 20. 04 operative system, and a set of bash scripts performs the workflow. Each strategy, once called, executes a few scripts and produces a collection of results (text, HTML, and pdf) and log files (Figure  2A ). Most of the scripts lean on a group of applications and databases to accomplish their task. Figure  2B  shows the databases and applications used by every script.
Finally, the user has the freedom to run every one of the mentioned scripts as an autonomous process as long as the input format is respected. The three strategies have been designed to run independently, allowing the user to run on parallel computing systems. The PIMGAVir pipeline has been tested on a cluster configured on the SLURM workload manager, running on multiple samples at once. The following is an example of the SLURM script to run the PIMGAVir pipeline (Figure  2C ). Regarding the Vir-MinION pipeline, after the pre-processing step, which is executed as the default step, the pipeline runs one or more methods in parallel, according to the user choice. The read_based strategy carries out the taxonomic classification using the demultiplexing results as input to generate an overall view of what the sample contains.
The clust_based approach, as the name suggests, identifies the clusters obtained from the meta-barcoding step and executes the taxonomic classification on them. In the ass_based mode, the pipeline performs the assembly step from the shotgun, producing their taxonomic classification. As in the same case of PIMGAVir, the Vir-MinION pipeline relies on local-viral DBs to guarantee its capability in connection-less conditions and save runtime. The use of both taxonomic classifiers (Kraken2 and Kaiju) gives the user the possibility to compare the results. The outcomes are presented in graphical and text tabular layouts for further analysis. The Vir-MinION pipeline runs under Ubuntu 20. 04 and uses NVIDIA technology to make its processing. The Vir-MinION utilizes a collection of bash scripts to perform the workflow.
The assembly-based strategy groups metagenomic contigs into potential genomes to study the functional roles of microbial populations. Thus, the combined analysis of these results can help to better define the most plausible viral metagenomic composition of samples. In addition, the adoption of multi-solution software specific to viral genome analysis has increased the reliability and computational efficiency of these pipelines where possible. For example, the choice of the assembler is fundamental before executing the taxonomic classification, and many software progams/algorithms exist to perform this task. Moreover, when working with a new dataset, it is common to generate a few assemblies testing different programs with different parameters, to compare the results and thus be more confident we are doing the best with the data.
From this perspective, SPAdes and MEGAHIT are the two most commonly used assemblers today. SPAdes uses much more memory than MEGAHIT, so it is often more suitable for working with one or a few genomes (such as from an isolate or enrichment culture). However, if working with high-diversity metagenomic samples, sometimes, the memory requirements for SPAdes are too high, and MEGAHIT (which uses much less memory) can handle the task instead. Since the PIMGAVir pipeline uses both assemblers to produce the assembled genomes, the Vir-MinION pipeline (upon the same philosophy) accomplishes the assembly steps using either MEGAHIT and Flye assemblers. Of course, the consistency of the databases is also a crucial point during the taxonomic classification, and we have chosen to classify every "object" (whether reads, clusters, or assembled genome) with different software (kraken2, kaiju, or blastn) querying to several viral databases.
Background and study aims Artificial intelligence (AI) is set to impact several fields within gastroenterology. In gastrointestinal endoscopy, AI-based tools have translated into clinical practice faster than expected. We aimed to evaluate the status of research for AI in gastroenterology while predicting its future applications. Methods All studies registered on Clinicaltrials. gov up to November 2021 were analyzed. The studies included used AI in gastrointestinal endoscopy, inflammatory bowel disease (IBD), hepatology, and pancreatobiliary diseases. Data regarding the study field, methodology, endpoints, and publication status were retrieved, pooled, and analyzed to observe underlying temporal and geographical trends.
However, the development of AI algorithms still requires the collection of cases and supervised annotation, which is a time-consuming process that may require several years to be finalized. For this reason, it is relevant to understand how this translational process works. For instance, observational studies are needed to collect large and robust databases to train AI algorithms. On the other hand, prospective interventional trials are requested before such algorithms can be clinically implemented. Clusterization of studies on one specific topic may be expected to lead to rapid implementation in clinical practice, while some other fields may be marginalized due to paucity of data or challenges in algorithm generation. To predict the future of AI in gastroenterology, we systematically reviewed all the studies registered on the clinicaltrials.
Methods This systematic review was based on data retrieved from Clinicaltrials. gov (https://clinicaltrials. gov), a free database listing 390,330 studies (November 2021). This database has been widely used in the past when conducting systematic reviews to analyze research trends in gastroenterology (such as in IBD)  [9, 10]  and daily endoscopic practice  [11] [12] [13] , forecasting future research efforts and drawing patient-centered management plans or guideline proposals. Search-filter strategy, data extraction sub-classifications, and study nomination process are reported in the Appendix.
Data extraction Each study entry was searched for the following information: 1) geographical setting; 2) funding (academic/hospital); 3) year of registration; 4) field of application (gastrointestinal endoscopy/ IBD/liver disease/other); 5) type and design of study (interventional/observational, single-center/multicenter); 6) patient tion and characterization of colorectal neoplasia (52/103, 50 %). Image analysis was also more frequently reported than data analysis for pancreaticobiliary (six of 10 [60 %]), liver diseases (eight of nine [89 %]), and IBD (six of eight [75 %]). Overall, 48 of 103 study entries (47 %) were interventional and 55 (53 %) observational. In 2018, one of eight studies (12. 5 %) were interventional, while in 2021, 21 of 34 (61. 8 %) were interventional, with an inverse ratio between observational and interventional studies during the study period.
The majority of the studies were planned as singlecenter (74 of 103 [72 %]) and more were in Asia (45 of 103 [44 %]) and Europe (44 of 103 [43 %]). Conclusions AI implementation in gastroenterology is dominated by computer-aided detection and characterization of colorectal neoplasia. The timeframe for translational research is characterized by a swift conversion of observational into interventional studies. population (number, age); 7) state of recruitment (completed/ ongoing/not started); 8) study-endpoints; and 9) study status (published/unpublished). One-dimensional frequency distributions (absolute, relative) were determined for the analyzed study characteristics. Dataset quantitative acquisition, processing, and statistical evaluation were carried out using Microsoft Excel software for Microsoft Windows.
Study characteristics An initial literature search using the Clinicaltrials. gov results database identified 115 related study records. Eight study entries were excluded as unrelated to gastroenterology, two due to lack of AI technology, while one study was excluded as a duplicate. Thus, 103 study entries were included in our analysis. ▶ Fig. 1  shows the methodological process used by the authors to select study entries. Twenty-one of 103 (20 %) and 27 of 103 (26 %) had already closed or completed enrollment at the time of the search, while 49 of 103 (48 %) were open (status not reported for six study entries). The study end points and area of focus of the individual AI studies are summarized in ▶ Table  1  and Supplementary Table  2  respectively. Supplementary Table 1 shows the publication status of completed studies.
Discussion According to our study, research efforts surrounding AI in gastroenterology were primarily focused on detecting and characterizing colorectal neoplasia. At the same time, the use of machine learning for personalized medicine received far less attention and was found to be applied primarily in hepatology. In addition, the time of conversion between observational and interventional study entries was approximately 2 years, suggesting a fast translation of AI research in the clinical setting. There may be three main reasons for the dominance of colorectal neoplasia as the primary driver of AI-based research. First, deep learning developed for non-medical imaging, i. e. , face/object-recognition software, coupled with high-performing graphical interfaces, was technologically optimized for real-time endoscopy.
However, it could be argued that this also applies to upper gastrointestinal neoplasia. Thus, the sec-ond reason may be the high prevalence of colorectal polyps with a polyp-to-patient ratio up to 3:1, facilitating the collection of a critical volume of cases needed for training an adequate deep learning system. We can estimate that thousands of frames must be annotated to train and thoroughly test a reliable deep learning model for polyp detection (i. e. , computeraided diagnosis for detection  [CADe] ) and characterization (computer-aided diagnosis for characterization  [CADx] ). In this regard, upper gastrointestinal neoplasia, such as Barrett's esophagus or early gastric cancer, may be penalized by its very low prevalence outside a tertiary center. Of note, there was a clear tendency for a single-center research setting, preventing the coalescence of multicenter/national databases that may be required for less prevalent disorders.
Third and finally, CADe implementation exploits the clinical relevance of the conversion of any increase in adenoma detection rate in additional colorectal cancer prevention, as well as the progressive expansion of population-based colorectal cancer screening programs in several Western and Eastern countries. The progressive inversion of the gradient between observational and interventional studies was expected. In the observational phase, databases are prospectively collected to train and test AI algorithms in an artificial setting, i. e. , standalone performance. In the interventional phase, such algorithms are validated against the reference standard in a clinical setting through randomized or sequential trials. On the other hand, what was somewhat unexpected was the very short time of conversion between the two phases of AI translation in clinical practice, which is well in line with the subitaneous appearance in the gastrointestinal endoscopy market of several devices approved by regulatory agencies for colorectal polyp detection/ characterization.
Thus, AI research appears as one of the fastest channels to shift innovation from bench to bedside. Disappointingly, only a limited number of studies on personalized medicine based on the use of machine learning were documented during the timeframe of our analysis. This represents a substantial difference between oncology and genetics, where most AI models are aimed at patient prognosis or re- ▶ Fig. 3  Main field of proposed application for AI in gastroenterol- ogy. sponse prediction. A possible reason for the small number is the difficulties faced in collecting large enough databases for rare and infrequently encountered diseases, such as liver or pancreatic cancer or IBD  [14]. In fact, most of the study entries in these fields were related to image analysis, irrespective of whether that was endoscopy-based or using ultrasound or cross-sectional imaging.
Artificial intelligence" filter applied Entries not related to clinical gastroenterology & hepatology excluded (n = 8) database (September 2021) 115 retrieved study entries screened 107 study entries reviewed Entries related to clinical gastroenterology & hepatology but not AI-focused excluded (n = 2) 105 study entries reviewed Multiple versions of the same study entry excluded & duplicates (n = 2) Aimed at creating new machine learning algorithms (n = 6) Aimed at validating existing deep learning platforms (n = 75) 103 study entries reviewed Sub-categorization into digestive & hepatobiliary systemStudies inactive for more than 2 years (n = 0) Flowchart of the systematic review.
PREFACE A NOTE FOR READERS this booK teLLs stories about onLine Liberties shapeD by technicaL architectures and infrastructures. It originates from a three-year research project called NEXTLEAP (nextleap. eu, NEXT-generation Techno-Social and Legal Encryption, Access and Privacy, funded by the European Commission in the frame of the H2020 Collective Awareness Platforms (CAPS) programme). The purpose of NEXTLEAP, which ran from 2016 to 2018, was to 'create, validate, and deploy communication and computation protocols that can serve as pillars for a secure, trust-worthy, annotable and privacy-respecting Internet 1 that ensures citizens' fundamental rights' 2 : as such, it was an interdisciplinary project at its core. Its consortium included computer scientists and social scientists working in close dialogue with one another in an attempt to build a protocol that 'actually works'.
Network-level anonymity End-to-end encryption does not usually allow a user to be anonymous to the server or a third party without additional network-level encryption. Thus, network-level initiatives, such as P2P routing services or anonymous remailers, which can add supplementary privacy properties to end-to-end encrypted messaging, are worth mentioning here. Metadata protection and traffic obfuscation is still an area of active research, stimulating experiments with standards and architectures (e. g. Vuvuzela's usage of 'noise' to obfuscate metadata, discussed in  Van den Hooff et al. 2015). There seem to be no functional standards on this level yet; however, some solutions, such as Tor or I2P, tend to serve as references or de facto standards for different projects. The Tor hidden service protocol offers a platform to develop decentralised and encrypted instant messenger servers.
Tor only provides anonymity for network addresses, but not metadata such as the sender, recipient and time of message, which are kept in the email header at the time of email or can be deduced by the server. Historically, work has been carried out on anonymous high-latency remailers to fix these transport metadata leaks in federated messaging, these falling under three types: Cypherpunk Anonymous Remailer, Mixmaster and Mixminion. The last of these is not currently active, according to the project's website. 23  A number of experimental network-level tools, while not guaranteeing anonymity, provide some level of encryption. Zero Tier One is an end-to-end encrypted, peer-to-peer virtual network that provides static network addresses which remain stable even if the user changes physical WiFi/networks.
Several secure messaging projects propose solutions to the problem of data storage. Indeed, despite the guarantees of 'no personal data collection', some projects still store key data on the servers (such as usage statistics, device information, keys, usernames or friend relations). Developers tend to explain such practices by reference to technical requirements (e. g. proposing a better user experience based on the usage statistics collected) but many developers remain keen to seek alternatives that involve minimal data storage and use stronger forms of decentralisation. A related issue is a powerful 'double' narrative about end-to-end encryption. If on the one hand, it is associated with a very strong discourse on empowerment and the better protection of fundamental civil liberties, several projects show in parallel a desire/need to defend themselves from associations with criminal activity and allegations such as 'encryption is used by jihadists'  (Sanger and Perlroth 2015).
Creating and concealing an online presence In response to the recent increasing variety of use-cases for encrypted secure messaging, a number of privacy-enhancing tools offer several different solutions to conceal private communications or other online exchanges. From the more popular centralised solutions such as Wire, Telegram, Signal and WhatsApp to less widely used decentralised platforms such as Ricochet or Briar -some of which will be the subject of the following chapters -and email clients supporting PGP encryption, these solutions are tailored to protect against specific 'adversaries'. Security and privacy features worked into different protocols offer various degrees of protection and let users 'hide' different parts of their online identities. The great variety of concealing devices that are being developed for our communicative tools is a response to the increasingly complex relationship between Internet users and the circulation of their personal data online.