dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analyses Determinants associated with a high level of knowledge were identified in bivariate and multivariate analyses. Variables significantly associated with a high level of knowledge in the bivariate analysis (p<0. 20) were included in the multivariate model. Percentages were compared in a chi-squared test or (depending on the number of individuals) Fisher's exact test. We calculated adjusted odds ratios (aORs) with their 95% confidence interval (CI). The threshold for statistical significance was set to p<0. 05. All statistical analyses were performed with STATA software (version 13. 0. 0, StataCorp LP, College Station, TX, USA).
Results Of the 2862 women having giving birth during the study period, 370 (12. 9%) did not receive the study questionnaire because the investigator did not attend the maternity unit during the women's stay, and 138 (5. 5%) were excluded (Fig  1 ). Next, 69 of the 2358 eligible women (2. 9%) refused to participate, and 216 (9. 2%) did not return the study questionnaire. Hence, 2069 women of the 2354 eligible women were included in the study (87. 9%). Our data on the women's knowledge about influenza disease and the influenza vaccine are summarized in Table  1. Firstly, 827 of the women (40. 1%) did not know that influenza can lead to serious complications for the mother, and 960 (46. 6%) did not know that influenza can lead to be serious complications for the baby. Secondly, 201 women (9. 8%) considered the vaccine to be "contraindicated" or "unnecessary" during pregnancy.
Confidence in information and communication technology services and systems is crucial for the digital society which we live in, but this confidence is not possible without privacy-enhancing tools and technologies, nor without risks management frameworks that guarantee privacy, data protection, and secure digital identities. This paper provides information on ongoing and recent developments in this area in the European Union (EU) space. We start by providing an overview of EU's General Data Protection Regulation (GDPR) and proceed by identifying challenges concerning GDPR implementation, either technical or organizational. For this, we consider the work currently being done by a set of EU projects on the H2020 DS-08-2017 topic, namely BPR4GDPR, DEFeND, SMOOTH, PDP4E, PAPAYA and PoSeID-on, which address and aim at providing specific, operational solutions for the identified challenges.
In essence, GDPR presents risks and opportunities, given that best practices and technology-enhanced information workflows can enable organizations to be more responsive, agile and efficient. In order to adapt to the GDPR, standards SN Computer Science for personal information management have to be created that can serve as a basis for organizations to be more efficient and GDPR compliant. In consequence, GDPR exponentially increases data security responsibilities and risks for organizations, and a strategy is required to cope with GDPR and other regulations. Information technology plays a key role in data governance, systems strategies and management, to accomplish personal data requirements, enhancing information security and developing breach-awareness capabilities aligned with those of the organization.
SN Computer Science While public bodies and large corporations have the resources and/or expertize required for efficiently adopting the GDPR, MEnts may struggle to do so due to their lack of resources and/or expertize. Hence, MEnts represent the vast majority of European enterprises with a huge impact on the European economy and employment. At the same time, MEnts are the most vulnerable entities in the adoption of the GDPR due to their lack of awareness, expertize and economic resources. The adoption of the GDPR by MEnts in a smooth manner, without incurring unnecessary or unaffordable costs, is of vital importance to the EU global economy and constitutes the high level objective of the SMOOTH project. 3 Some elements that need to be addressed in order to achieve that objective are: -Lack of data protection regulation awareness among MEnts, -Clear explanation of the GDPR mandates affecting MEnts, -Lack of affordable and simple solutions for MEnts addressing the GDPR mandates.
SMOOTH Front-End MEnts access the SMOOTH platform through a registration/subscription process where they have to fill an entry questionnaire. The questionnaire captures contextual information about the MEnt business, such as its data protection background, the personal information that is collecting from its customers/providers (if any) and the data protection mechanisms currently in place (if any). This information is highly valuable to the algorithms performing the compliance analysis in the back-end. Once the registration phase is completed, the platform front-end will offer MEnts an intuitive and assisted process to upload the resources required to carry out the automatic compliance assessment process in the back-end. An initial list of such resources includes: (1) currently used informative documents (e.
g. informed consent, terms of use, cookie policy for MEnts with online presence); (2) a sample of the customers'/providers information repository (e. g. files, database) where (personal) data is stored; (3) URL of the website(s); and (4) link to the mobile app. It is important to note that depending on the MEnt under analysis some of these resources may not be available. It should also be stressed that SMOOTH will use the customers' data repository only for the purpose of generating the compliance report. The data analysis will happen in real time and once the compliance report is delivered, all data sample from the MEnt will be removed from the platform. In any case, due to the analysis process, the SMOOTH platform becomes a data processor; therefore, MEnts, being the data controllers, will have to sign an online contract for letting the SMOOTH platform process their data.
Back-End The goal of the SMOOTH back-end is to automatically produce a compliance report against the main basic elements of the GDPR affecting MEnts. The back-end uses as input the information provided in the registration process and the resources uploaded by the MEnts to the platform. The back-end is formed by three modules each of them analysing specific resources type. The output generated by these modules will be consolidated to produce the GDPR compliance report. Following, we describe the modules comprising the SMOOTH platform back-end. 1. SMOOTEXT This module analyses the content of the (offline or online) documents used by MEnts to inform customers of the personal data collected and the purpose for which they are used as well as the procedures that customers should follow to execute their rights (e.
g. access or erasure) as data subjects according to the GDPR. We refer to those documents such as informed consent, terms of use, privacy policies, cookie policies, etc. This module validates if the conditions and elements required by the GDPR are included in the documents. In addition, it extracts from the analysed documents the list of personal data items that the MEnt declares to be storing. As a result, it reports whether any of the GDPR elements analysed is not properly addressed or not adequately described in the documents (e. g. the document does not include text to inform the user how it can execute its right to data erasure, language used is very complex syntax, etc). 2. SMOODATA This module analyses the personal information that MEnts are storing using as input the sample of customers' and providers' information repository uploaded by the MEnts.
The module assesses if the MEnt has sufficient permission to store the information that it is actually storing. That is, whether the MEnts are only storing the personal data items declared in the consent documents (analysed by the previous module) or is storing personal data (by mistake) non-agreed by users in the consent documents with the customer personal data. The module also identifies the presence of" Sensitive Personal Data" in the data repository. This type of data requires a special treatment (e. g. sensitive personal data must be encrypted). 3. SMONLINE The informative documents and data collected by MEnts through their websites and mobile apps are mostly covered in SMOOTEXT, which analyses documents such as websites/mobile apps terms of use, consent requests, cookies policies, or privacy policies and SMOODATA, which analyses the data repositories used to store the information collected by the MEnts's websites or mobile apps.
This module analyses the data flows from websites (SMONLINE WEB) and mobile apps (SMONLINE APP) to find potential privacy leaks. In the case of websites and mobile apps, the management of personal information may also involve third party companies different than the MEnts (e. g. advertising, tracking and social network integration). Hence, this module emphasizes the analysis of personal information leaks to third party companies from MEnts' websites and/or mobile apps. This information will be used together with the output of SMOOTEXT for validating if: (1) the website visitors are being informed of the collection of their (personal) data (e. g. website activity) by third party companies, (2) the terms of use informs the visitors how to execute their rights as data subjects, and (3) the visitors are asked their consent for sharing information with the identified third parties and for what purposes.
In addition, this module analyses the potential use of personal data under monetization strategies (i. e. online advertising). This module is also reporting the list of third party organizations retrieving user information from the MEnts websites/mobile apps (which may constitute privacy leakage events compromising users' privacy). Each of the above modules has an added value in its own right. Focusing on specific aspects of the GDPR, each module could well apply to larger organizations beyond the context of MEnts. In SMOOTH, the three modules will be integrated together to create the core of the SMOOTH platform. The output generated by each module will be processed and combined together to create the final GDPR compliance report to be delivered to the MEnts.
PDP4E As previously explained, privacy and data protection aspects are usually dealt with from perspectives which, despite SN Computer Science providing valuable contributions, are not aligned with systematic engineering practice. This makes engineers consider privacy as an unfamiliar aspect they often ignore  [19]. Nonetheless, in order to ensure that privacy and data protection features are effectively embedded in the products, systems and services, it seems reasonable to directly involve those who are responsible for creating and developing them-that is, put the engineers in the loop. Any legal innovation (e. g. data minimization principle, right to be forgotten, data protection impact assessment, or accountability, to name just a few) needs to go along with systematic guidance to engineers, so as to ensure that it is effectively implemented  [20].
This idea follows the "code is law" aphorism, in that features implemented by software products has practical implications of what is allowed to do as much as the legal regulation. Hence, PDP4E claims that engineers must be endowed with methodological and technological tools to systematically apply privacy of data protection principles so as to comply with the regulatory framework. These methods and tools should allow for other, competing requirements and system constraints, and they must especially bear in mind that the savvy and effort to apply them by non-privacyexperts should be taken to a minimum, by being aligned with the engineers' expertize. In order to pay effective consideration to privacy and data protection, engineers must be endowed with tools that map data protection principles, data subject rights, and controller obligations, onto engineering terms such as backlog items, database structures, business process models or deployment architectures.
Then, during the development of a project, generated artefacts are captured that provide evidences, which can be traced to specific requirements posed by the regulatory framework, so that, all in all, and through a logical argumentation process, compliance with the regulation can be claimed. In order to support that assurance process, PDP4E also provides reusable argumentation patterns that act as templates of typical techniques to achieve and justify compliance. This approach is realized into a set of interrelated but loosely coupled Privacy and Data Protection Engineering tools that PDP4E is producing (Fig. 4 , and which leverage and extend general purpose software and system engineering tools already in the context of each of the said disciplines. Thus, the privacy and data protection risk management tool is a new version of a previous security-management tool called MUSA, the requirements engineering and the model-driven design tools are extensions of Papyrus (a modular modeldriven engineering framework), together with some ancillary tools implemented on the source code analysis tool Frama-C, and the assurance tool draws from the OpenCert assurance framework.
Through the blockchain technology, references to PII shall be managed and exchanged securely. The blockchain technology was selected due to two main reasons. First and foremost, there was the need to maintain an irrevocable record of PII transactions, including permissions handling and all kinds of operations involving PII processing, for providing full control to PII owners, for accountability, and for legal assurances. On the other hand, there was need to allow multiple entities to share data and to contribute to data processing, without relinquishing control over their own databases, or without relying on a central datastore. By agreeing to participate in the PoSeID-on system, users benefit from full control over their PII, and third parties can provide an auditable ledger of all their PII-related operations to users and regulators.
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However, in remission, IR primarily affects genes involved in splicing processes (Figure  S12 ), suggesting that aberrant splicing persists in remission due to auto-regulatory processes. Overall, IR profiles are highly dynamic in CML, which becomes apparent when considering the large overlap of differentially retained introns (n = 419) in all three comparisons (Figure  3B ). We confirmed these results with an independent dataset of 59 CML patients from Branford et al. [11] , which includes samples from various stages of disease progression, and from 4 healthy controls (Figure  3C ). The number of IR events in this cohort was highly variable. While there are on average more IR events in chronic phase compared to healthy controls, the number drops significantly at blast crisis (Figure  3C , right).
Examining differential exon usage in our samples, we observed a similar trend, although not as marked as observed in differential IR, i. e. , both diagnosis and remission samples exhibit a larger number of differentially used exons (DUEs) compared to normal controls. Most of these DUEs are upregulated (diagnosis, 78%; remission, 81%; Figure  3D ). A large fraction of the DUEs at diagnosis (72%) remain differentially expressed at remission as well. Some genes of these mutual DUEs are associated with DNA repair mechanisms (top-enriched GO term; Figure  3E ). Only 26 exons in total are differentially used in diagnosis vs. remission samples (Figure  3D ). becomes apparent when considering the large overlap of differentially retained introns (n = 419) in all three comparisons (Figure  3B ). We confirmed these results with an independent dataset of 59 CML patients from Branford et al.
We have previously shown that DNA methylation regulates IR in myeloid cells  [15]. To confirm this mode of regulation in our CML cohort, we compared CpG methylation around the splice sites (±200 bp) and in the centre (200 bp) of retained and non-retained introns. We confirmed decreased DNA methylation levels near the 3 splice junction and within the body of retained introns (Figure  5B ). Similar patterns were observed in remission and control samples, suggesting that this mode of IR regulation is also present in lymphoid cells (Figure  S16 ). To further investigate the role of epigenetics in the regulation of IR in CML, we retrieved RNA-seq and chromatin immunoprecipitation sequencing (ChIP)-seq data of the K562 CML cell line from ENCODE (encodeproject. org). We analysed transcriptomics data of RNA binding protein knockdown experiments and observed significantly increased IR occurrences with the most drastic IR increase (~5 fold) following Poly(RC) Binding Protein 2 (PCBP2) knockdown (Figure  5C ).
Patient Samples We retrieved nine diagnostic specimens (total leukocytes from peripheral blood collected in ethylenediaminetetraacetic acid (EDTA) from patients enrolled in the RESIST (1 imatinib; ACTRN12610000055000), ENESTxtnd (4 nilotinib, NCT01254188) and PINNACLE (4 nilotinib + pegylated interferon, ACTRN12612000851864) trials, with matched remission specimens (major or complete molecular response at 12 or 24 months). In addition, nine CML patient samples (7 diagnostic and 2 matched remission) from the French Persistem study were obtained. All patients were diagnostically tested for the presence of the BCR/ABL1 gene fusion. Most patients also had a bone marrow cytogenetic analysis at diagnosis, confirming only the reciprocal chromosomal translocation t(9;22) (q34;q11) characteristic for CML.
mRNA Sequencing Data Analysis Paired-end RNA-sequencing reads (125 nt) were trimmed and mapped to the human reference genome hg38 using STAR v2. 7  [48]. Quality control of raw and mapped sequencing reads was performed using FASTQC v0. 11. 5 (github. com/s-andrews/FastQC), RSeQC v2. 6. 4  [49]  and multiqc v1. 2 [50]. Putative confounders and batch effects were excluded using principal components analyses (Figure  S1 ). STAR-FUSION v1. 4. 0  [51]  was used for the identification of fusion genes and Fusion Inspector (FusionInspector. github. io) for in silico validation of the predicted gene fusions. Gene expression levels specified as transcripts per million (TPM) were determined using Salmon 0. 14. 1  [52]. General statistics on alternative splicing events were determined using rMATS v3. 2.
4  [53]. We used our IRFinder algorithm (v1. 2. 0) for the detection of IR events in introns extracted from an Ensembl gtf file (regions between two adjacent exons)  [27]. IRFinder estimates the abundance of IR by computing the ratio between gene transcripts retaining an intron and the sum of all transcripts of the respective gene (more information is provided in File S1). Differentially used exons were determined using the R Bioconductor package DEXSeq  [54]. Differential exon usage (DEU) = changes in the relative usage of exons: transcripts with_exon /(transcripts with_exon + transcripts without_exon ) (1).
WGBS Data Analysis Reads were processed and aligned to the human (hg38) reference genome using Meth10X (github. com/luuloi/Meth10X) [56]. In short, the Meth10X pipeline takes raw reads in fastq format and trims the adaptors, which are then aligned to the human reference genome using bwa-meth (github. com/brentp/bwa-meth). The generated bam files were marked with duplication and merged if necessary. Estimation of the duplication rate, coverage bias (genomic features) and methylation bias in reads was carried out to provide quality control. All the metrics, such as percentage of unmapped/mapped read metrics, mapping quality distribution, GC content distribution, insert size distribution and coverage distribution, were generated by Qualimap 2  [57]  for further evaluation of the alignment. Finally, a count table and bigwig files of methylated and coverage at each CpG site in the genome was constructed for all samples.
Statistical Analyses All statistical analyses were performed in R v. 3. 6. 2 (www. r-project. org). The Wald test implemented in the R package DESeq2  [59]  was used for the identification of statistically significant differential alternative splicing events and differential gene expression analysis. p-values were adjusted for the false discovery rate using the Benjamini-Hochberg procedure. Functional enrichment analysis of differentially expressed genes and genes with differentially retained introns was performed using DAVID 6. 7  [60]  with the human genome and the set of expressed genes respectively, used as a background.
Funding: This work was supported by the  National Health and Medical Research Council  (Investigator Grant # 1177305  to J. E. J. R. , Project # 1128175  and # 1129901  to J. E. J. R. and J. J. -L. W. , # 1126306  to J. J. -L. W. , APP1027531 and  APP1104425  to S. B. , and  APP1135949  to T. P. H. ; the  NSW Genomics Collaborative  Grant (J. E. J. R. and J. J. -L. W. ); Cure the Future (J. E. J. R. ), and an anonymous foundation (J. E. J. R. ). U. S. and J. J. -L. W. hold Fellowships from the  Cancer Institute of New South Wales. U. S. also received support from the  Australian Academy of Science in form of an Australia-India Early and Mid-Career Fellowship. Financial support was provided by  Tour de Cure  ( Scott Canner Research Fellowship ) to C. G. B. and for research grants to C. G. B.
Microarray analysis Mouse primary hepatocytes transcriptomics-Analysis was performed using Mouse Gene 2. 0ST arrays (Affymetrix). Array data processing was performed using Bioconductor in the Renvironment (r-project. org). Gene expression changes were calculated after signal normalization using robust multichip averaging in the oligo package  (39). Differential gene expression was assessed using the limma package (40) with a threshold of 5% false discovery rate. Gene Ontology (GO) terms enrichment of selected clusters was performed using the clusterProfiler package  (41, 42). Human liver transcriptomics-Liver biopsies were obtained from morbidly obese patients during abdominal surgery. Patients underwent a full clinical workup to assess metabolic characteristics and surgical risks. All experimental procedures abide by the Declaration of Helsinki Principles and written informed consent was obtained from each participant as part of the ABOS protocol (ID: NCT01129297).
Cell cycle analysis Cells were washed with PBS, harvested, and fixed with cold 100% ethanol for 10 min at 4 °C. Cells were again washed with PBS, centrifuged, and incubated with a solution of 50 mg/ml of propidium iodide (PI) (Sigma-Aldrich) with 200 mg/ml of RNase A (Qiagen) and NucBlue ® (Invitrogen) for 30 min at 37 °C. Cells were then washed, resuspended in PBS, and analyzed by flow cytometry with BD LSR Fortessa X-20 cell analyzer (BD Biosciences). PI fluorescence was analyzed with blue laser 488-and 575-nm emission filters. NucBlue ® fluorescence was analyzed with UV laser 355-and 496-nm emission filters. Acquisition was performed with FACS DIVA software (BD Biosciences). PI fluorescence was analyzed on live singlecell population. The percentage of cells in each cell cycle phase was analyzed by using the FlowJo TM software algorithm (BD Biosciences).