dataesr/scientific-paragraphs-categorization huggingface dataset:
gov NCT01988506). The advantageous function of low-dose rIL-2 in this context has been linked to the expansion of Tregs that play a major role in controlling immune responses and establishing tolerance  [22]. In cancer immunotherapy, high-dose intermittent rIL-2 therapy has increased long-term survival for some patients with metastatic renal cell carcinoma  [23]  and rIL-2 therapy alone or in combination with a peptide vaccine has resulted in clinical improvement for patients with metastatic melanoma  [24, 25]. However, the use of rIL-2 to enhance immune restoration in infectious diseases such as in human immunodeficiency virus (HIV-1) infection did not have similar success. Indeed, two major clinical trial studies, SILCAAT and ESPRIT, have been conducted to expand the CD4 + T-cell pool in HIV-1-infected patients and despite a substantial and sustained increase in the CD4 count as compared with antiretroviral therapy (HAART) alone, rIL-2 plus HAART yielded no clinical benefit  [26].
Our results show that these markers were more abundantly expressed on CD39 + FoxP3 + CD25 + CD134 + in comparison to both CD39 -FoxP3 + and CD39 -FoxP3 -CD25 + CD134+ cells as represented by the mean fluorescence intensity (MFI) for each molecule (Fig  8B ). ICOS expression was similar in all subsets but Tbet and PD-1 expressions were higher in CD39 -FoxP3 -as compared to the other subsets. Similar data were obtained with HIV-specific CD39 + FoxP3 + cells from patients, with CTLA-4 being significantly higher on these cells (S2  CMV-specific cells decreased from 3. 62% to 1. 44% (before and after depletion, respectively) and within this subset, there was a significant decrease in CD39 + Foxp3 + cells (30. 3% vs 3. 66% before and after depletion respectively), which demonstrates depletion efficacy (S3 Fig).
Importantly, we observed an increase of about 25-30% in TNF-α production (p<0. 05) after CD39 + CD4 + depletion in CD134 + CD25 + CD4 + CMV-(mean± SEM, 6. 55± 0. 44% vs 9. 92± 2. 78%) and HIV-(mean± SEM, 6. 2± 3% vs 8. 1± 3. 7%) specific cells, demonstrating that CD39 + Tregs have a suppressive function and are able to inhibit cytokine production. Similar Altogether, these data indicate that antigen-specific CD39 + FoxP3 + CD25 + CD134 + cells display bona fide Tregs characteristics. Globally, these results underline the negative impact of HIV-specific Tregs on HIV-specific effector responses and on the control of HIV replication and reveal the effect of IL-2 decreasing a population of HIV-specific Tregs expressing CD39. At the same time, we observed after IL-2 therapy a trend for an increase in CD39 -FoxP3 -CD25 + CD134 + effector cell frequency at wk36 compared to wk0 (Fig  6F ).
As such, therapeutic interventions aiming at Tregs depletion may directly contribute to the reduction of the size of virus reservoir  [42]. In addition, IL-2 signaling through CD25 facilitates HIV replication in vitro and facilitates homeostatic proliferation of CD25 + FoxP3 + CD4 + Tregs  [39]. Thus the implication of HIV-specific CD39 + FoxP3 + to the establishment of HIV reservoir needs to be clarified and is currently under investigation. In contrast to HIV-specific CD39 + FoxP3 + , both HIV-specific CD39 -FoxP3 + and CD39 -FoxP3 -non-Tregs, which are enriched in IFN-γ producing effectors, inversely correlated with viral load after treatment interruption. These data are in line with previous LCMV mouse studies, where daily therapeutic low-dose IL-2 increased virus-specific T-cell responses, thus resulting in decreased viral burden  [43].
This study was supported by the  Investissement d'Avenir program  managed by the  ANR  under reference  ANR-10-LABX-77  and by the  Agence Nationale pour la Recherche sur le SIDA et les hepatites virales (ANRS) , the  Vaccine Research Institute (VRI). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Standardised protocols, validation studies, quality control programmes, database and nomenclature development, and training should support the implementation and decentralisation of WGS. Ideally, WGS data collected for surveillance purposes should be publicly available, in real time where possible, respecting data protection policies. WGS data are suitable for surveillance and outbreak purposes and for answering scientific questions pertaining to source attribution, antimicrobial resistance, transmission patterns, and virulence, which will further enable the protection and improvement of public health with respect to food-borne disease. Table 1 Key features of k-mer, single nucleotide polymorphism (SNP) and multilocus sequence typing (MLST) approaches relevant to laboratories providing routine public health functions Features K-mer SNP MLST Epidemiological concordance Intermediate High High Discrimination Intermediate High High Stable strain nomenclature No No Yes International standardisation No No Yes Scalability No No Yes Speed Intermediate Slow SNP calling, slow comparisons Slow allele calling, fast comparisons.
The mission of PulseNet International is to implement standardised genotyping methods and share information in real-time within regional and national laboratory networks to support surveillance and outbreak response enabling the direct comparison of inter-laboratory data irrespective of geography. The primary method of molecular subtyping used by PulseNet International for the identification and investigation of outbreaks has been pulsed-field gel electrophoresis (PFGE), with multilocus variable-number tandem repeat analysis (MLVA) applied to selected organisms  [3]. The use of standardised, validated protocols and analysis procedures by all participants coupled with consistent interpretive criteria has enabled countless successful outbreak investigations, both within single countries and also spanning across borders  [4].
Moreover, genome sequencing elucidates the actual phylogenetic relationship among isolates. This renders the data useful for answering broader questions outside the relatively narrow scope of outbreak detection and response. For example, the same data used for routine surveillance could also be used for precise microbiological attribution studies, to elucidate transmission pathways and common properties of persistent strains, and to identify potential intervention points along the food safety continuum. The vision of PulseNet International is for WGS to be used in all public health laboratories to identify, characterise and subtype food-borne pathogens, largely replacing existing phenotypic and molecular methods in support of preparedness and response to foodborne illness at the local, national, regional and global levels.
Preparation and sequencing There are several technologies available for genome sequencing. Collectively known as massively parallel sequencing, they produce billions of nt sequences during each run, where each genome is sequenced multiple times in small random pieces (reads and contigs) to generate very large datasets  [16]. Even though sequencing platforms (e. g. Illumina, etc. ) have different biochemistry and arrays, the workflow is similar: (i) DNA extraction; (ii) library preparation, which usually includes shearing the DNA either mechanically or enzymatically, adding adaptors and barcodes/indexes, and amplification; (iii) template preparation, either by bridge amplification or emulsion PCR; and (iv) sequencing (the sequencing run itself is highly automated).
Whole genome MLST, the method chosen by PulseNet International For integration into routine public health surveillance and for maintaining inter-laboratory comparability, the gene-by-gene, i. e. extended MLST, approach offers a number of compelling features. The extended MLST schemes assess information from coding regions only, and collapse different types of mutations into a single allelic change. Allele information is assessed by comparing new sequences with an allele database that contains all genes ('loci') present in the typical several hundred strains used to create the scheme. The number of genes assessed may range from typically seven housekeeping genes to a several thousand  [19]. The biggest schemes contain the genes in the core genome (genes present in nearly all strains of the same species, core genome (cg)MLST), or the whole or pan genome (all core genes plus accessory genes present in any strain used to create the allele database, whole genome (wg)MLST).
Storage space Each sequencing run generates gigabytes (GB) of data, with sequence read sets (SRS) of 100-500 megabyte (MB) in size for each isolate. Many laboratories anticipate analysing thousands of isolates each year, and storage on existing systems will likely be prohibitively expensive. Generally speaking, it is desirable to retain the SRSs (and not just the final processed data assembled) so that they may be used for future analyses by using alternative methods such as SNPs or assessment of mobile genetic elements e. g. from phages. Some PulseNet International members currently store the SRSs within their own reference laboratory's data storage. Some also submit them in close to real-time to one of the databases of the International Nt Sequence Database Collaboration (INSDC, http://www.
insdc. org), such as the Sequence Read Archive (SRA) at the National Center for Biotechnology Information and the European Nt Archive at the European Molecular Biology Laboratory, as well as the DNA Databank of Japan Sequence Read Archive (DRA). Using a public domain archive removes the storage cost from individual laboratories and makes the data available to the wider scientific community in perpetuity, but also necessitates public data sharing. This is problematic for some countries with respect to data protection (see section on data sharing below). Additionally, it is expected that very few countries in the future will have the capacity to store the ever growing amount of raw sequence data in-house. Strategies to manage the information associated with each isolate (i. e. the descriptive data, sometimes referred to as meta-data) are being developed and implemented by some PulseNet International network laboratories (see data sharing section below).
Another potential storage solution consists of commercial clouds (e. g. BaseSpace, Amazon S3), with users paying per GB of storage; storage costs using these options are typically lower than in-house solutions; however, many organisations prohibit the use of commercial clouds due to institutional data security policies. Additionally, if an outside-house storage solution is pursued, Internet connections with adequate bandwidth must be available. Given the great diversity in resources across PulseNet International laboratories, a one-size-fits-all solution might not be possible but renders publicly available storage options compelling.
Within a single country or region, cloud-based bioinformatics tools can be maintained by local specialists, be partitioned from secure corporate or in-house computing networks as needed, and in many cases be scaled to meet demand (Figure  2 ). For PulseNet International laboratories with modest numbers of isolates, making use of cloud-based or other web-based public analysis tools may facilitate entry into WGS (as could outsourcing the actual sequencing instead of purchasing the equipment). The actual analysis tools can be used from open sources or commercial software. Additionally, outsourcing analyses to a third party is an alternative, e. g. a PulseNet regional coordinator may serve as a third party analyst as it was done in PulseNet Latin America and the Caribbean for the analysis of PFGE data while waiting for individual participants to acquire the analytical software  [2, 20].
No confidential information is handled on this computing cluster. Within the PulseNet laboratory, microorganism experts will perform quality controls, cluster analysis on MLST data, as well as evaluating virulence and other factors. Following data interpretation, laboratory experts will use this information for outbreak investigations, reporting and sharing with other agencies. When required, SNP analysis will be performed on subsets of closely related strains. the past and arrive at the same results is a necessity. Presently, many PulseNet International laboratories do not have ready access to bioinformatics expertise. In deciding to use open source or commercial software, issues to consider include the need and ability to modify and tailor the source code, the ability of the nonbioinformatician PulseNet participants to learn and use the tools, the process for and cost of maintenance and updates, version control and other quality measures, and the cost of licenses and bioinformatics personnel.
Nomenclature Despite achieving success in standardising molecular subtyping and inter-laboratory comparability across 86 countries, PulseNet International has not been able to implement global PFGE nomenclature for foodborne pathogens, largely due to the complexities (and costs) of implementing a central, global PFGE database requiring frequent manual curation. To date, each country or region maintains their own PFGE databases and nomenclature, with the exchange of data files to compare results across disparate nomenclatures as needed. In addition to its superior performance, WGS provides at long last the opportunity for a truly global nomenclature, facilitating laboratory-based surveillance at the international level and opening the door to future reductions in the burden of food-borne disease.
Allele nomenclature Allele nomenclature provides the names and definitions of all loci included in the wg/cgMLST scheme, as well as correspondence between allele sequences and allele identifiers (Figure  3 ). The latter consists of unique identifiers, integers, that each correspond to a particular allele sequence for a pre-defined locus on the genome, and which do not change over time. Assignment of allele identifiers converts the WGS data for an isolate into a series of identifiers, one per locus. The resulting allelic profiles are used to determine genetic similarity between isolates. For PulseNet International laboratories to maintain real-time outbreak detection and inter-laboratory comparability, it is crucial that allele calling can be done reliably in real-time; i. e. automated, and that the nomenclature scheme used is the same in all laboratories.
This will ultimately require a global allele-calling algorithm, which is still in the early stages. Issues to be addressed are the use of sequence reads vs assemblies, assembly method, locus start and end definitions, parameters for locus identification and allele sequence extraction, algorithm accuracy per locus, etc. ; the amount of manual curation may initially be high but ultimately the actual allele calling will be fully automatic. The wg/cgMLST approach will also necessitate an international database of alleles; there are several features of such a database that would be highly beneficial to international public health users (Table  2 ). Organisations producing WGS data have differing policies on data sharing, so a global allele nomenclature database that strives to meet the needs of most is optimal.
For example, it must be possible to submit not only whole genomes as raw reads or assembly, but also individual new alleles detected through the use of a local cached allele nomenclature database. In case of full genomes, it must be possible to have them automatically deleted once alleles have been called unless the submitter wants the full sequence to become available to the general public and/or the allele database curators. Because of the substantial size of raw read data, an allele calling platform would also need sufficient bandwidth to support such large amounts of data being continuously uploaded by organisations worldwide, along with computational resources available to process alleles and return results in real-time. A flexible system that has an open interface would facilitate integration with existing organisations' or other third party systems.
STRAIN NOMENCLATURE submissions, the provision to the user of an automated quality assessment is essential. The selection of additional reference alleles will require manual intervention by curators, but this is expected to be infrequent. New loci should not routinely be added to an existing wgMLST scheme since this invariably will also require revision of the strain nomenclature database (see next section). Security features would mitigate inadvertent or malicious use (e. g. submissions of large amounts of artificial sequences) while ensuring that individual allele sequences and their identifiers remain publicly accessible -perhaps by requiring user registration for submissions. Finally, a process to solicit input from stakeholders and the development of a collaboration agreement or terms of service would facilitate use by many public health organisations.
6 It must be possible for authorised curator users to annotate individual allele sequences with information, e. g. to include them as a reference allele, and to add additional loci to derive allele nomenclature for, as the known pan genome grows. after a potential outbreak has already been flagged by one country/region, with the exception of the US and Canada who permit direct access to each other's national databases. National or regional databases are accessed directly by internal network members. With the limited utility of PFGE data outside of outbreak detection and response, there has not been cause to make these data more widely available on a routine basis. Also, sharing of real-time PFGE results outside the network has historically been strictly controlled, executed according to agreements negotiated by coordinating country/regional laboratories and their submitters (typically state, provincial, or local laboratories) whose individual privacy legislations stipulate conditions for sharing.
However, genome sequences can be used for many more purposes than routine public health surveillance and outbreak response, in particular they are highly suitable for basic research and activities aligned with the One Health approach [25]. Ideally, the genome sequences collected for surveillance purposes should be publicly available, if not in real time then within a reasonable time frame, e. g. within 12 months of sequence generation. These data should comprise both the sequences and some descriptive data about the isolate, as the sequences themselves alone have extremely limited utility. Publicly available repositories within the INSDC accessible via the Internet would be the ideal storage location, i. e. the SRA, the European Nt Archive and the DNA Data Bank of Japan as these are synchronised with each other on a regular basis.
Each organisation or country/region determines itself which sequence data to upload, along with what descriptive data (e. g. time, place and type of isolate) would be uploaded to these repositories. Current data sharing procedures in some PulseNet International participating laboratories show promise in providing a suitable balance between protecting the privacy of patients and the utility of data generated and shared in real-time for global surveillance; these procedures include providing very little descriptive data for each isolate initially followed by the addition of information about the isolate after a 6-12 month delay. At present, not all public sequence repositories allow for user-defined controls (e. g. sharing WGS data with a specific user subset immediately after upload and during a period of embargo before public release); however, such functionality as well as an improved interface may accelerate implementation of WGS and data sharing by more organisations.
Given the expertise, infrastructure, mission and stability of the public repositories, integrating the global allele nomenclature databases, the allele-calling algorithms and possibly the strain nomenclature databases into these public spheres with an appropriate curator interface for subject matter experts would be desirable at the global level. Storing the allele identifiers along with the WGS data in the sequence repository would effectively make the latter searchable for the presence of loci, specific alleles and specific mutations. Since the principle of allele nomenclature is species independent, it could even be applied to all species rather than only those relevant for food-and waterborne diseases, to the benefit of all users of these databases.
Routine quality parameters Quality of the raw reads must be assessed; if the minimum quality metrics are not met, the reads must be discarded and sequencing repeated. All platforms produce raw reads of both sequences and the individual quality scores. A primary quality metric is coverage, defined as the number of raw reads for the average read length over the genome (i. e. the number of times each base in the genome is contained in individual raw reads). A minimum coverage of 30x is typically enough for routine surveillance, but this is platform-dependent and also may vary by organism  [21, 26]. Other quality metrics to be checked routinely include the average Phred or Q scores as a measure of base-calling accuracy. Low quality reads should not be uploaded to a repository or PulseNet database, open or closed to the public.
Ensuring standardisation and competence As basic tenets of quality control, written standard operating procedures must be in place for both sequencing and analysis and the procedures should only be performed by trained personnel. Participation in an external quality assessment (EQA) programme is recommended. While each country/region sets its own quality control requirements, many PulseNet International participating countries submit and pass an organism-specific certification test before they are permitted to submit data to a repository or to most PulseNet country/regional databases (although this is not the case in Europe or PulseNet Asia-Pacific). The certification process documents each participant's highest level of competence in producing raw sequence data, performing analysis using bioinformatics tools, and checking data quality (participants may be certified in producing raw sequence data, analysing the results, or both).
Beyond the laboratory For the integration of WGS for routine surveillance, all public health-related professionals must be prepared for the use of genomic data to support public health decision making. WGS should not be portrayed as a panacea; it does not replace the need for high quality epidemiological data to be interpreted in context with laboratory results  [30]. Knowledge transfer must occur not only for the participating public health laboratories, but also for the national and regional epidemiologists and other immediate stakeholders. PulseNet member laboratories often serve as the interface between food-borne disease laboratory and epidemiology, and between public health and food monitoring. Understanding basic genomics and bioinformatics concepts by all members of the food safety continuum is needed.
Conclusion To meet the needs of real-time surveillance, PulseNet International will standardise WGS-based subtyping using extended MLST-based approaches; specifically, wgMLST. This delivers optimal resolution and epidemiological concordance while providing unambiguous nomenclature. In addition, it is computationally efficient and realistic for most public health laboratories to use on a daily basis. Standardised protocols, validation studies, quality control programmes, database and scheme development, and training materials all must support the implementation and decentralisation of any new technique. As was done previously with PFGE, PulseNet International is presently disseminating these important elements among network members. Training, in particular, is a keystone for the success of standardised PulseNet activities; this will require dedicated and sustainable resources.