dataesr/scientific-paragraphs-categorization huggingface dataset:
All samples suggestive of multiple infections (more than one contig representing the same genomic region) were re-aligned using their respective IVA-assembled contigs as references in the Geneious program. More restrictive assembly parameters were used, such as allowing mapping of paired reads only when both mapped to the same contig and discarding reads that mapped simultaneously to more than one contig. To investigate if these contigs were derived from the same virus, sequences were submitted to hypermutation analysis using the Hypermut2. 0 tool available at Los Alamos HIV Database. The contigs were considered hypermutated if the p-value was ≤0. 05 when comparing the number of APOBEC G-to-A signature mutations with the control context. The overlapped region between the contigs obtained per sample was compared to all sequences publically available at the BLASTn Database 3.
Phylogenetic Analysis The consensus sequence for each sample was extracted from the reference-guided assembly described above using the 50% stringency setting and classified using maximum likelihood phylogenetic analysis performed with PhyML v. 3. 0 and the best model of nucleotide substitution defined with Model Generator  (Keane et al. , 2006; Guindon et al. , 2010). To investigate HIV-1 recombination the bootscaning tool of Simplot v. 3. 5. 1 was used with the following parameters: window = 400 pb; steps = 40 pb; T/t = 2. 0; gapstrip = on; replicas = 100; nucleotide substitution model = F84; method = Maximum Likelihood  (Lole et al. , 1999). Phylogenetic analyses were repeated for recombinant sequences considering the bootscanning breakpoint analysis (data not shown). The sequences obtained in this study were submitted to the GenBank under the accession numbers MK041550-MK041589.
RESULTS In this study, we included data previously published by our group from patients followed-up at HFI and all HIV-1 sequences obtained by NGS publicly available. A detailed description of the studied populations can be found in their respective articles  (Pessoa et al. , 2015 (Pessoa et al. , , 2016;; Alves et al. , 2017). We focused on the HIV-1 subtype classification, identification of HIV recombinants and multiple infection investigation for our novel patients. As previous published, the patients from HFI were mostly males (75%) with a median age of 38 years at the time of sample collection. Regarding our new cohort, we also found a prevalence of males (67%) among the patients from HU-UFRJ with a median age of 43. 5 years. Unlike the other centers, a greater number of female patients (62%) were observed among patients from HU-FURG, with a median age of 43 years.
ACKNOWLEDGMENTS We would like to thank the medical staff personnel of  Hospital Federal de Ipanema  ( Marianne M. Garrido  and  Sayonara R. Ribeiro ),  Hospital Universitário Dr. Miguel Riet Corrêa Jr. ( Jussara Silveira  and  Rossana Basso ), and  Hospital Universitário Clementino Fraga Filho  ( Elizabeth S. Machado ) for providing clinical follow-up and information on the studied patients at their respective clinical sites.
Despite long fermentation of ideas and some notable progress in data sharing, existing practices still place restrictions on the open and unconditional use of various genomic data after their official approval for release to the public domain or to public databases. These restrictions often come into conflict with the terms and conditions of the relevant funding bodies who supported the release of those data for the benefit of the scientific community and society. We argue that the publicly available data should be treated as open data, i. e. , a shared resource with unrestricted usage for analysis, interpretation, and publication.
These include massive increases in both the production and scale of genomic and metagenomic datasets, as well as the development of open data initiatives 1 according to which "some data should be freely available to everyone to use and re-publish as they wish, without restrictions from copyright, patents or other mechanisms of control". The sequencing revolution has resulted in the generation of myriad datasets, many of which are publicly released without an accompanying publication. These datasets are often processed and integrated into public databases and public repositories , or under scholarly commons such as the Open Science Framework 2. These resources provide users the opportunity to mine and analyze data that have been made public but are often still unpublished in the peer-review literature.
The large number of datasets integrated into these and other similar resources (totaling hundreds of thousands of datasets) and the lack of comprehensive automated mechanisms to track the publication status of each dataset make it virtually impossible for these resources to provide this information to their users. Moreover, with the advent of large global data analysis studies, which include the mining of thousands of publicly available datasets and reaching a "genomical" scale of yottabytes that even surpasses measurements in astronomical data generation  (5) , it has become challenging to appropriately acknowledge or cite every dataset that has been included in such an analysis. While in the past immediate release of data may not have enabled an 1 https://en. wikipedia. org/wiki/Open_data 2 https://osf.
io/ "outsider" to publish before the scientists producing the original data, analytical capability advances and the availability of so many datasets currently may place teams of scientists other than the originator of a given dataset in a better position to publish first. For example, the "outsider" team may have better analytical capabilities, and/or overarching protocols for analyzing more comprehensive sets of data, pre-or post-publication. Also, sequence datasets can be interrogated via numerous value-added platforms and tools from multiple groups. Taken together, these developments have rendered the Fort Lauderdale Agreement rather outdated and in dire need of a revision to reflect the current state of science and technology.
Controversy has arisen, as different points of view have been expressed regarding data ownership and best data utilization practices. For example, studies such as the American Gut (7) GEBA  (8) , and the Earth Microbiome Project  (9)  have been open and available for years prior to formal publication, resulting in many publications by the community prior to the central documents describing the core projects of data generation in an asynchronous, yet widely accepted manner. While publication of other researchers' data can lead to claims of 'data parasitism', many acknowledge that such use of data, adds value  (10) , for example, due to the increased knowledge obtained from meta analyses of multiple datasets with goals substantially different to, or unanticipated by, the original data generators.
And most importantly, as was recently argued, freely available public data without restrictions is considered "critical for progress in any scientific discipline and has been the cornerstone of sound and reproducible genomics research"  (11). In addition, when new sequence data are submitted to any of the International Nucleotide Sequence Database Collaboration (INDSC) sites, they have already gone through rigorous Quality Controls (QC) and are further scrutinized by additional QC steps at the INSDC repositories.
We acknowledge that for existing sensitive human data, some restrictions may be appropriate. However, moving forward, explicit informed consent can remove much of this obstacle, if participants are told in advance that their data will be maximally, openly used and what the potential (if often minimal) risks inherent in this use are. In the meanwhile, resistance to share sensitive data, such as those from clinical trials is gradually being curbed. Some major clinical journals such as PLoS Medicine and BMJ have explicit policies not to publish clinical trials unless the authors pledge to share the raw data. An empirical evaluation showed that trialists shared the raw data from 46% of these trials upon request and re-analyses of the raw data did not change any of the major conclusions  (12). Across the entire biomedical literature, in 2015-2017, about one-fifth of published articles shared raw data, a major increase over previous years  (13).
g. figshare) 4 and are now used quite broadly. As an example, in prokaryotic nomenclature and taxonomy, DOIs have been used for more than a decade and already provide persistent records. These can be readily incorporated into digital content prospectively or retrospectively as they provide a solid framework to monitor and track the use of all sequencing projects, including unpublished datasets  (15). It is also important to identify efficient ways to give credit for the generation of background protocols that describe the process of production and the physical effort and thinking that was invested towards producing specific datasets. Some data production entails automated, routine processes, while in other cases intensive sample collection, processing and innovation to design a protocol and execute a study are required.
Such logistic and protocols can also be efficiently linked to the datasets they generate. Furthermore, many datasets and projects are currently created by merging multiple smaller sets of data. The challenge is to adopt smart strategies so that these iterative agglomerations can still carry the DOIs or other reference of the smaller sets that they have incorporated. This becomes increasingly important for the numerous databases that collect, integrate and improve the quality and value of the public raw data or are linked to time series studies. To a large extent, these resources are also generating new data and metadata, thus enabling the community to advance their research and make new discoveries. Eventually, this approach should aim at providing an independent means of evaluating the impact of the research products that are created by individual scientists, much like current bibliometric methods.
While a lot of DNA sequence data are released in publicly accessible databases within twenty-four hours after generation, this principle was never extended to encompass other sequence data as, for instance, microbial genomes or metagenomes and associated metadata. Here, the US Department of Energy (DOE) has led the way with an immediate data release policy (i. e. the data become publicly available immediately upon generation), however, other funding agencies allow for the data to remain private until the time of publication. Yet, data analysis may take years before publication; this delay coupled with the increasing speed of generating new data creates a very different landscape compared to the past, when these policies were instituted. Thus, revisions to data release policies, are also necessary to ensure that public data can be used by the entire community in a timely fashion, without losing its value and therefore impact.
marcescens isolates revealed that it is located on a novel cryptic prophage genomic island, SmarGI1-1. This genomic island can be excised and circularized, which probably contributes to its dissemination amongst S. marcescens. 10 this work, we have characterized the genome of an SME-4-producing S. marcescens (SME-4-Sm) from a clinical isolate recovered from an University Hospital in Buenos Aires, Argentina, and compared it to the genomes of SME-Sm isolates either sequenced in this study or downloaded from the Genbank database.
Materials and Methods Bacterial strains. S. marcescens 163 (Sm163) was identified with MALDI-TOF (MALDI Biotyper CA system, Bruker Daltonics, Billerica, MA, USA). Escherichia coli TOP10 (Invitrogen, Saint-Aubin, France) was used for cloning experiments. Bla SME gene harbouring Serratia marcescens, S. marcescens S6 (SmS6),  3, 4  S. marcescens S8 (SmS8),  3, 4  S. marcescens CHE4 (SmCHE4),  8  S. marcescens AW (SmAW),  5  S. marcescens USA-1 (SmUSA-1) and S. marcescens Mex-1 (SmMEX-1) were also used in this study. The presence of SME was systematically assessed on all the genome data of S. marcescens deposited on the NCBI genome database (December 2017). Genomes of 323 S. marcescens isolates were downloaded from Public databases including seven SME-Sm and 24 fully-sequenced S. marcescens strains (Tables  S1  and S2 ).
The sequences of the SmarGI1-1 and flanking regions from S. marcescens N10-0408, N11-2820 and N12-0620 were from accession numbers KF445086, KF318367 and KF615855, respectively, in the GenBank database. The acquired antimicrobial resistance genes were identified by uploading assembled genomes to the Resfinder server v2. 1 (http://cge. cbs. dtu. dk/services/ResFinder-2. 1). 15 gressiveMauve and the Mauve distributed script stripSubsetLCBs  16  were used to extract all the regions with a minimun length of 500 bp shared by all the strains, generating a 2. 877. 168 bp core genome alignment. Recombinant variable positions were removed with Gubbins (https://github. com/sanger-pathogens/gubbins), and a Maximum Likelihood phylogeny was inferred for the alignment of the 631207 non-recombinant variable sites with RAxML using a General Time Reversible (GTR) model incorporating a gamma distribution rate among sites.
17. In parallel a whole genome phylogeny was estimated for all the S. marcescens retrieved from public databases with kSNP3  18  (Supplementary Material and methods). All genomes were annotated with Prokka,  19  and graphic representations were generated with the genoplotR 20 library in R. Nucleotide sequence accession number. The whole genome sequences generated in the study have been submitted to the Genbank nucleotide sequence database under the accession number detailed in Table  S1. The genomic islands constructed from whole genome sequences available at Genbank nucleotide sequence database where submitted to the Genbank Third Party Annotation database under accession numbers BK010577-BK010585 (Table  S1 ). The nucleotide sequence of bla SRT-3 gene has been submitted to the EMBL/Genbank nucleotide sequence database under the accession number MG234451.
Characteristics of WGS of S. marcescens 163, revealed that the bla SME-4 gene was inserted into a SmarGI1-1 prophage-like genomic island initially described in three SME-1 producing S. marcescens N11-02820, N10-00408 and N12-00620 isolates (accession numbers KF318367, KF445086, and KF615855, respectively). 10 This genomic island found in Sm163 is ∼28. 4 kb in size and bracketed by 25 bp imperfect direct repeats (Figure  1A ). While the upstream sequences of the SmarGI1-1 were identical between Sm163 and N11-02820 isolates, downstream of the attR site the sequences were similar except for an 8-kb deletion in S. marcescens 163 that included genes coding for intCPD, GNAT family, RES domain, Helicase, ATP-dependent OLD family protein (Figure  1A ).
Phylogeny of SME-producing S. marcescens. Phylogeny of the S. marcescens from this study along with the 323 S. marcescens assemblies retrieved from the NCBI genome database using KSNP3, revealed the two clades, with clade 1 representing 80% of the isolates (Figure  S1 ). Sixteen phylogenetic clusters were defined using ClusterPicker. 6 clusters were located in Clade 1, with cluster 1 being the main one. In Clade 2, 10 clusters were identified, with Sm163 beeing not related to any cluster. The presence of bla SME as well as other carbapenemase genes was screened in the 323 S. marcescens assemblies retrieved from the NCBI genome database (Figure  1S ). In total seven bla SME encoding isolates were found (Table  S1  and S2 ) that were added to the seven SME producers sequenced in the present study.
A core genome SNP-based Maximum Likelihood phylogenetic tree was obtained for the fourteen SME-producing strains and 24 completed S. marcescens genomes retrieved from the NCBI database (Table  S1 ), using the S. marcescens fgi94 genome as outgroup. Two main clades, with a mean genetic distance between them of 25. 302 non-recombinant SNPs, were revealed in the S. marcescens phylogeny (Figure  2 , Table  S1 ). The cluster numbers were those from Figure  S1  (Figure  S1  and 2 ). Thirteen out of the fourteen SME-producers were found on the clade 1, eleven of them formed a single sub-cluster of SME producers (hereafter named SME sub-cluster within the cluster I), and the two remaining, MGH315 is located outside the SME-sub-cluster but still in Cluster I, while BIDMC44 belongs to cluster III. On the other hand, S.
Conclusion This is the first report of SME-4-Sm in Argentina. Until now, SME-4 had only been reported in Brazil,  7  and in one single SME-4 producing S. marcescens from US, as reported in GenBank under the accession number KF481967, but not published yet. The increase in the use of carbapenems in Argentina,  23  together with the S. marcescens natural resistant to polymyxin, could be factors that helped to select SME-producing S. marcescens isolates. Finally, as increasing numbers of the SME carbapenemases are being reported from S. marcescens, especially from North and South America, rapid detection systems based on multiplex PCR analyses should also target bla SME genes in addition to the big 5 carbapenemases: KPC and OXA serine carbapenemases, and the IMP, VIM and NDM metallo-β-lactamases, as was previously suggested by Bush et al.
Funding This work was supported by the  Assistance Publique -Hôpitaux de Paris (AP-HP) , the  University Paris-Sud , the  Laboratory of Excellence in Research on Medication and Innovative Therapeutics (LERMIT)  supported by a grant from the  French National Research Agency  [ ANR-10-LABX-33 ] and by the  Joint Programming Initiative on Antimicrobial Resistance (JPIAMR) DesInMBL  [ ANR-14-JAMR-002 ], and by  DIM Malinf ,  Ile de France , for LD's PhD fellowship.
While the Hamamatsu S10362-13-050C MPPC was used in the INGRID and Proton Module, a newer type of MPPC, S13660 with higher gain, lower noise rate, crosstalk rate, and after-pulse rate, is used in the Water Module. The same Trip-t electronics  [19]  are used for all the three detectors. To record data from the neutrino beam, delivered typically in eight bunches with a cycle of 581 ns for each 2. 48 sec, a trigger from the J-PARC accelerator is provided to each detector. The integrated charge and hit timing of all channels are digitized and recorded with 2. 5 photoelectrons (p. e. ) threshold for each beam bunch.
Monte Carlo simulation A Monte Carlo (MC) simulation is used for the estimation of background contamination and signal detection efficiency. Three pieces of software are used for the chain of simulation: JNUBEAM  [20]  for the neutrino flux prediction, NEUT  [21]  for the neutrino interactions with nuclei, and a GEANT4  [22] -based detector simulation. JNUBEAM simulates the interaction of 30 GeV primary protons on a graphite target, the propagation of the secondary and tertiary produced mesons in the magnetic fields induced by the magnetic horns and their decays in the decay volume. The simulation uses the proton beam profiles measured by the J-PARC neutrino beam line and is tuned with external hadron production measurements, mainly from the NA61/SHINE experiment  [23, 24]. We can select either a muon neutrino beam or a muon anti-neutrino beam by changing the current polarity of the focusing magnetic horns.
In this analysis, data collected in the former beam configuration is used. The simulated on-axis neutrino beam has a mean energy of 1. 5 GeV and a 1σ standard deviation between -0. 75 GeV and +0. 85 GeV, as shown in Fig. For a given flux of incoming neutrinos, NEUT simulates the neutrino interactions with nuclei, including initial and final state interactions inside the nuclei, in order to provide the four-momenta of all induced particles. In this analysis, the version 5. 3. 3 of NEUT is used. CC quasi elastic (CCQE)-like, neutral-current (NC) elastic, CC and NC single pion production (1π), deep inelastic scattering (DIS), multi-pion production, and coherent interactions are simulated. The CCQE-like interactions, characterized by the inclusion of a single charged lepton and no mesons in the final state, are simulated with a relativistic Fermi Gas model (RFG)  [25] , random phase approximation (RPA)  [26] , and multi-nucleon (2p2h) interactions  [27].
The N CH BG WM is estimated as follows: N CH BG WM = i σ i CH Φ CH WM T CH WM ε CH i WM = ij U ij PM (N sel j PM -N BG j PM ) Φ CH WM T CH WM ε CH i WM Φ CH PM T CH PM ε CH i PM. ( 2 ) where σ i CH is the differential cross section on the CH target with i-th muon scattering angle bin. The other backgrounds are estimated by MC as summarized in Table  9  in detail. The integrated ν µ fluxes Φ are estimated to be Φ H2O WM = 3. 72 × 10 13 /cm 2 with 7. 25×10 21 POT, Φ CH PM = 3. 02 × 10 13 /cm 2 with 5. 89×10 21 POT, and Φ Fe INGRID = 2. 99 × 10 13 /cm 2 with 5. 89×10 21 POT by MC, as shown in Table  10. Although data samples used for the Proton Module and INGRID module are at the same delivered POT, the fact that the Proton Module is 1. 2 m closer to the production target than the INGRID module, leads to a small difference in the integrated flux between them.
4 AI technologies generally require large amounts of both personal and nonpersonal data to function. In health care specifically, AI technologies rely on personal information, including health-related data extracted from medical files or research participants' results. 5 Promoting AI and capturing its benefits for the health care system yet depend, in large part, on procuring a convenient access to this sensitive data. 6 Ensuring that privacy protections are in place appears essential, especially with individuals showing substantial concerns about sharing their data in the medical and clinical context. 7 uggestions to implement public open databases to promote medical research have created some controversy in Europe and in North America. In the UK, citizens rejected the care. data project launched in 2014 due to privacy concerns.
40 enerally, personal data are data that allows the direct or indirect (eg through triangulation) identification of a data subject. 41 Canadian federal law defines personal information as 'information about an identifiable individual' (art. 2, PIPEDA). This definition is similar to that embodied in the GDPR which covers 'any information relating to an identified or identifiable natural person (data subject)' (art. 4, GDPR). In the US, there are multiple definitions of personal data in the legislation, as applicable regulations vary according to sectors and states. 42.
46 Technology giants like Google, Amazon, which deal daily with large amounts of personal data including health-related ones, are generally not covered by the regulation. 47  (part 160 & 164). Under American law, any 'de-identified' information is considered non-personal, meaning that it is not subject to any data protection regulation. Under HIPAA, de-identification can be completed in two ways: either by removing specific identifiers enumerated in the law from the data set or by having a statistical expert confirm that the risk of re-identification linked to a specific data set is sufficiently small (45 CFR § 164. 514(b)(1), HIPAA). Both de-identification techniques have been proven insufficient to prevent all re-identification of data subjects. 48 The HIPAA deidentification standard has thus been considered particularly problematic in the case of genomic and genetic data.
This type of data is commonly treated as de-identified as soon as all personal information related to the dataset is removed (ie name, age, address, etc. ) and falls out of HIPAA's scope of application. This occurs even though the actual de-identification of genetic information is unlikely and despite the fact that this type of data can be sensitive. 49 In this case, other laws at the federal (the Genetic Information Non-discrimination Act, for one) or state level can sometime offer additional protection. Under the GDPR, de-identification is not automatically considered a sufficient way to prevent re-identification of individuals. As such, de-identified data remain in the category of protected personal data. The GDPR only excludes anonymous data from its scope of application. Strict conditions are set around anonymization under the GDPR.