dataesr/scientific-paragraphs-categorization huggingface dataset:
Influenzanet Versus Traditional ILI Surveillance Influenza surveillance in Europe is traditionally pooled by the European Influenza Surveillance Network (EISN), which combines epidemiological and virological surveillance of influenza. The EISN network includes a set of sentinel GPs in each country who collect information from patients reporting symptoms of ILI. The sentinel GPs report the aggregated number of ILI consultations, by age group, to the EISN via The European Surveillance System (TESSy) database, based on which the EISN calculates the ILI rates. A sample of these patients is also tested for virological confirmation of influenza. The sentinel GPs usually represent 1% to 5% of the GPs working in the country or region  [27]. The EISN is coordinated by the European Centre for Disease Control and Prevention (ECDC) since 2008 and participates in the wider World Health Organization (WHO) Regional Office for European Region influenza network and in the WHO Global Influenza Surveillance and Response System.
Since 2014, the ECDC and WHO/Europe have a single joint Web-based bulletin called "Flu News Europe"  [28]. The incidence of ILI among Influenzanet participants is determined in near real time using a syndromic case definition. From season 2011-12 onwards, all Influenzanet countries apply the case definition for ILI recommended by the ECDC when reporting feedback to participants on whether their reported symptoms might be due to ILI. Additionally, as the participants' individual symptoms data are available, this enables exploring different case definitions when analyzing the data (see Textbox 1). Graphic representation of the results is dynamically updated on the Influenzanet website. Previous studies have established a positive correlation between the incidence of ILI determined by Influenzanet and that estimated through the clinical surveillance by sentinel GPs  [3, 22, 23, [29] [30] [31] [32] [33].
The data have shown that this fraction varies greatly by country  [22] , being also dependent on the severity of symptoms (and therefore, on the ILI case definition used  [33, 34] ) and the season  [22, 34]. Textbox 1. Influenza-like illness case definitions. The following case definition for influenza-like illness (ILI) is recommended by the European Centre for Disease Control and Prevention (ILI ecdc ). From season 2011-12 onwards, all countries participating in Influenzanet use the same questionnaires and apply this case definition when reporting feedback to participants on whether their reported symptoms might be due to ILI: • Sudden onset of symptoms; • AND at least 1 of the following systemic symptoms: Fever or feverishness (chills), Malaise, Headache, Muscle pain; • AND at least 1 of these respiratory symptoms: Cough, Sore throat, Shortness of breath.
During the first seasons of Influenzanet, the questionnaire did not include some of the symptoms above; additionally, participants could only report fever if they measured their temperature. Therefore, the ILI ecdc definition could not be applied. To overcome this and to allow comparing data across seasons, the following case definition was developed (ILI hist , for historic reasons): • Sudden onset of symptoms; • AND Fever (≥38°C temperature); • AND at least 1 of these systemic symptoms: Headache, Muscle pain; • AND at least 1 of these respiratory symptoms: Cough, Sore throat ILI ecdc has a higher sensitivity, because more participants with influenza will fit the definition. Conversely, ILI hist has a higher specificity, since fewer participants who do not have influenza will fit the definition.
The ILI hist case definition was first used in the Netherlands and Belgium in 2003 to closely match the Dutch general practitioners' ILI case definition (sudden onset of symptoms with a prodromal phase of an already existing nonsickening respiratory infection of at the most 3 to 4 days; and fever (≥38°C temperature); and at least one of the following symptoms: cough, runny nose, sore throat, frontal headache, retrosternal pain, or muscle pain). In addition to these, multiple case definitions can be used within the Influenzanet system to analyze the symptoms data provided by the participants. Among the Influenzanet volunteers who did seek medical care, in southern Europe (France, Italy, Portugal, and Spain) and Belgium, the participants reported to generally visit a GP within 1 to 2 days after the onset of ILI symptoms, whereas in northern Europe (Sweden, the United Kingdom, the Netherlands, Denmark) with the exception of Belgium, participants generally sought medical care only 5 to 7 days after the onset of symptoms  [22].
Alternatively, or additionally, the reduced risk among seniors may possibly also be because of a smaller contact rate with infectious individuals. A small risk reduction was also observed in Influenzanet participants who practiced more than 1 hour of sports per week. Finally, public transportation did not appear to increase the risk of developing ILI relative to driving a car, riding a bicycle, or walking as a primary mode of transportation  [22]. The results of these risk factor analyses have been shown to be consistent across all Influenzanet countries  [39]. The identification of ILI risk factors is one of two main ways that ILI surveillance data have been used to gain a better understanding of ILI control and prevention; the evaluation of intervention effectiveness is another, as discussed in the next section.
According to a large meta-analysis of 90 reports containing 116 datasets of randomized or quasi-randomized studies of VE in healthy adults, the overall effectiveness of inactivated parenteral influenza vaccines was estimated to be 16% (95% CI 5-25) against ILI and 60% (95% CI 53-66) against confirmed influenza  [40]. Ideally, large-scale randomized controlled trials should be undertaken to assess vaccine efficiency, but it is impractical to conduct them every year. Also, because of global recommendations for influenza vaccination, placebo-controlled trials that could clarify the effects of influenza vaccines in individuals are no longer considered possible on ethical grounds  [41]. For these reasons, most data on influenza VE come from observational studies. The I-MOVE (Influenza-Monitoring Vaccine Effectiveness) network  [42]  aims at measuring influenza VE in Europe and has been operating since 2007, coordinated by ECDC.
For the 2014-15 season, the overall VE against influenza A(H3N2) was 14. 4% (95% CI -6. 3 to 31. 0), against A(H1N1)pdm09 was 54. 2% (95% CI 31. 2-69. 6), and against B was 48. 0% (95% CI 28. 9-61. 9). Because Influenzanet also collects data on whether participants have been vaccinated for influenza, it allows measurement of ILI incidence in the self-reporting cohorts of vaccinated and unvaccinated participants, and therefore, the system could potentially be used as a complementary tool to measure the effectiveness of the influenza vaccine against ILI close to real time  [45]. Several of the Influenzanet national project teams have carried out studies to assess VE among participants during specific years. For example, UK Influenzanet data were used to estimate the effectiveness of the influenza vaccine in the postpandemic influenza season of 2010-11  [46].
Additional VE Analyses There are a few important considerations when using Influenzanet data as a complementary tool to estimate the effectiveness of the influenza vaccine. The influenza vaccine only protects against the influenza virus, but ILI may be caused by other infections. Additionally, vaccinated and unvaccinated participants cannot be compared directly, as participants who decide to take the vaccination may do so because they belong to a risk group, for instance, those with a chronic disease or those of older age; thus, differences in ILI rate between vaccinated and unvaccinated participants can be because of either the vaccine or an a priori difference between both groups  [32]. Finally, influenza infections may develop asymptomatically. At the request of the National Institute for Health and the Environment in the Netherlands, we estimated VE in the Dutch Influenzanet participants for the 2014-15  [49]  and 2015-16  [50]  seasons, stratified by age and underlying chronic disease, as the number of samples collected by the GPs sentinel networks was too low to allow for these stratified analyses.
With 14,000 participants overall yearly, the Dutch Influenzanet database covers 0. 08% of the 17 million population in the Netherlands. This large size warrants estimates of VE stratified for particular risk groups, namely, the presence of chronic conditions and older age. Here, we present these estimates, addressing the top two abovementioned considerations. Notably, we considered only the ILI cases in the weeks when there was virological confirmation of influenza by the Dutch Sentinel Practice Network, and of these cases, we used only the number of ILI cases above a seasonal baseline incidence for nonepidemic ILI. By excluding the weeks when there was no virological confirmation of influenza, we excluded the period when most ILI cases were likely because of noninfluenza infections; and by considering only the number of ILI cases above the typical number of ILI cases measured in the absence of circulating viruses, one can therefore obtain a more accurate proxy of VE against influenza.
The VE calculated in this way is here designated as VE(influenza), also shown in Figure  3  (see Multimedia Appendix 2 for further details). If considering the full period of 25 weeks during which Influenzanet collected data (mid-November to the end of April), then the estimated VE against ILI (ie, VE(ILI) in Multimedia Appendix 2) is considerably low. However, VE increases substantially when considering only ILI onsets during the weeks of virological confirmed influenza (17 weeks in 2014-15, 11 weeks in 2015-16) and subtracting the seasonal baseline (ie, VE(influenza) in Table  A1  and Figure  3 ). Namely, the estimated VE in chronic patients almost doubled when considering only the influenza epidemic period with the baseline subtracted, that is, VE(Influenza)~41% in both seasons, compared with when considering the whole data collection period, that is, VE(ILI)~25% in both seasons; even greater VE increases were found for the participants with a chronic condition over 60 years of age (VE=62% [95% CI    The 2014-15 season had a circulating influenza A(H3N2) virus that mismatched with the strain in the vaccine  [49].
Further Studies The Influenzanet system gathers a variety of valuable data on ILI activity. The analyses of Influenzanet data summarized in this paper reflect only a portion of what is possible. Influenzanet has the potential to monitor the geographical spread of ILI using the postal codes of the participants. Additionally, demographic data could be used to monitor ILI activity in different more or less vulnerable subgroups of the population. Moreover, extra questions can be included in the Web-based intake questionnaire at any time and entire new questionnaires added in any particular season. For instance, a stress-related questionnaire was added in the Netherlands in the 2004-05 season, revealing significant trends between stress/personality and self-reported ILI  [52]. Multivariable logistic regression analysis on ILI was performed to test the predictive power of stress and personality.
One would, however, also need to take into consideration that the proportion of ILI in the study sample could increase because of the participation of a select group of participatory surveillance volunteers with ILI symptoms, as has been observed in the study that tested this approach  [56]. Finally, data validation is key to greater acceptance and credibility in the field of public health. In winter, the sentinel GPs that integrate the EISN are asked to take nose and/or throat swabs from a subset of patients with ILI for virological determination. These data inform the national and international decisions by health policy makers. The GP samples, however, do not cover the large part of the population that does not seek medical health care for ILI. A system of self-sampling (ie, swabbing nostrils and/or throat and then sending the swabs for virological testing) among Influenzanet participants could help to overcome this limitation.
This study showed that people can self-sample in a reasonable time frame and that these samples were viable for antigenic characterization and molecular detection, which decreases the need for medical personnel to obtain samples. Self-sampling by the callers provided among the earliest reports of influenza circulating in the community and led to the detection of several strains of the virus  [57]. These encouraging results have led to a self-sampling study with Influenzanet participants being planned in the United Kingdom to strengthen the validation of the participatory surveillance data. We also plan to integrate the Influenzanet data with social networks, news streams, health forums, clinical records, and routine data to have a better understanding of the socioeconomic aspects of ILI epidemics in Europe and some behavioral insights on, for example, attitude toward influenza vaccination.
Early Warning Detecting an earlier rise of ILI activity in certain subgroups could make Influenzanet a fast early-warning system. Indeed, it is often suggested that self-reporting surveillance systems might be able to detect changes in disease activity earlier than the traditional surveillance systems  [32, 33, 59, 60]. This is because of the self-reported data becoming instantaneously available for automated analyses, whereas in the traditional systems there is a delay from when data are collected in the medical facilities until they are available for centralized analyses. However, run-time detection of disease activity above baseline should not be confused with detection of a newly emerging disease. For a participatory surveillance system such as Influenzanet to become a viable system for early warning of the first cases of a new disease, a greater proportion of the population needs to be engaged  [32] ; so far the platform has been able to recruit at the most 0.
An important cornerstone for enhancing active participation is the information feedback offered to participants. The participants receive weekly emails containing a newsletter with country-specific data and influenza-related news articles written by professional science journalists, which helps keeping them involved and motivated. From a science communication point of view, an interesting finding from the Dutch project is that Influenzanet has been able to attract and keep engaged many people without previous experience in scientific research, citizen science projects, or science activities in their daily lives. This contradicts previous findings that participants may be restricted to a self-selected group with previous experience and interest in science  [63]. One way the Dutch Influenzanet reaches out to both current and potential participants is by having a well-known science communicator serve as the ambassador of the project.
Conclusions Influenzanet can complement traditional health care-based systems by providing data that are not otherwise available. Influezanet is able to achieve this because it allows collecting data also from people who do not seek health care (and are therefore not accounted in traditional ILI surveillance), and it additionally gathers detailed information about the participants that is not routinely collected elsewhere. Due to its uniform nature across countries, it both allows for direct comparisons of ILI activity between countries and provides a platform to monitor the geographical spread of ILI throughout Europe. Moreover, Influenzanet provides an important channel for influenza awareness and health literacy in Europe. With its speed and flexibility, the system could be extended to detect diseases other than influenza, including those that emerge in low-income settings such as dengue, leptospirosis, severe acute respiratory syndrome, Ebola, Middle East respiratory syndrome, and Zika virus, and where community engagement is vital  [35, [64] [65] [66].
If so, this novel Internet monitoring system based on voluntary participants could develop into an important weapon to fight influenza as well as other contagious diseases globally. Influenzanet in Europe is an example of best practice here, not only by engaging citizens to report information that enables to complement the data obtained by traditional disease surveillance systems but also by providing a flexible and wide reach health literacy channel, delivering back reliable and updated information to the population about disease activity, transmission, and prevention strategies. During the second International Workshop on Participatory Surveillance (Amsterdam, the Netherlands, April 15-17, 2013) a letter of intent on cooperation and data exchange has been agreed between Influenzanet, Flu Tracking, and Flu Near You.
Acknowledgments We gratefully acknowledge the expertise and advice that several contributors have given to Influenzanet at different stages. In particular, we thank  Sander P van Noort  and  M Gabriela M Gomes  for their crucial contributions since the early start of the Influenzanet system: Sander excelled in his data analyses and in producing an interactive graphical interface displaying results in real time, within and across countries and seasons; Gabriela recognized the importance of the participatory surveillance system, at a time when it was only present in the Netherlands and  Belgium , and facilitated Sander's work, promoted and enabled the adaptation of the system to Portugal, and its subsequent expansion to other European countries, as well as its adaptation to  Mexico and Brazil.
Material and Methods Bacterial strains and plant inoculation. The X. translucens type strain CFBP 2054 (syn. ATCC 19319, DSM 18974, ICMP 5752, LMG 876, NCPPB 973; GenBank acc. no. LT604072. 1) was used in this study. Bacteria were cultivated at 28 °C in Peptone sucrose agar (PSA) medium. Triticum aestivum cv. Chinese Spring plants were maintained in a growth chamber with cycles of 12 h of light at 21 °C and 50% relative humidity (RH) and 12 h of dark at 21 °C and 50% RH. Leaves of 49 days-old plants were infiltrated with a bacterial suspension in water with an optical density at 600 nm (OD 600 ) of 0. 5 using a needleless syringe. Plants inoculated with water were used as controls. For transcriptomic and proteomic analyses, leaves and root tissues were harvested 1 day post-inoculation (dpi), when symptoms were not visible yet.
Bioinformatic analysis of transcriptome data. Mapping and alignment of sequence reads was done with Burrows-Wheeler transform using the BWA-MEM algorithm, which automatically chooses between local and end-to-end alignments, supports single reads and performs chimeric alignment  26. The BWA-MEM algorithm is robust to sequencing errors and applicable to a wide range of sequence lengths from 70 bp to a few megabases. Specifically, reads were aligned to Triticum aestivum coding sequences (CDS; IWGSC1. 0 + popseq. 29) and non-coding RNA (ncRNA). Subsequently, sequence counts were collected by Samtools idxstats filtering of bam alignment files for reads with MAPQ ≥ 3. Differentially expressed genes (DEGs) were called via DESeq2 1. 10. 0 Bioconductor package using local fit and betaPrior parameter set to False.
DESeq2 implements differential expression analysis based on the Negative Binomial distribution. Independent filtering was enabled  27. A false discovery rate (FDR) threshold of 0. 05 was set for DEG calling  28. Sample clustering and principal component analyses were performed upon variance stabilizing transformation of expression data (DESeq2 package). Transcripts were called as differentially expressed when the FDR-BH adjusted p values were below 0. 05 and fold-changes over 2. The transcriptomics data have been deposited in the European Bioinformatics Institute (EMBL-EBI) with the dataset identifier E-MTAB-5891. Protein extraction and digestion. The used protocol for total protein extraction was based on a method combining trichloroacetic acid (TCA)/acetone precipitation with SDS and phenol extraction, which works well with both root and leaf samples (Wu et al.
Precursor masses selected from previous measurement were excluded for 30 s, and 10 ppm was used as exclusion window. The samples were acquired using internal lock mass calibration on m/z 371. 1010 and 445. 1200. LC-MS/MS data analysis. Mass spectrometer raw files were analyzed using MaxQuant (version 1. 5. 3. 28)   with the match between runs, matching time window of 2 min and label free quantification (LFQ) options selected. LFQ intensities are the output of the MaxLFQ algorithm (Cox et al. 2014). They are based on the (raw) intensities and normalized on multiple levels to ensure that profiles of LFQ intensities across samples accurately reflect the relative amounts of the proteins. Tandem MS spectra were searched against UniProt T. aestivum (Version 2015-10, 100,800 entries) and Uniprot X.
Proteomics data processing. Once identified, all protein groups without contaminants and decoy entries as well as groups without "only identified by site" were chosen. Resulting protein groups were considered as identified in the following cases: (i) All replicates in each condition contain a valid quantitative value; (ii) control condition contains 2 or 3 valid values and treated condition can contain any value, and (iii) treated condition contains 2 or 3 valid values and control condition contain any value. This process led to many cases of missing values for one or the other condition. Indeed, proteomics data analysis is often hampered by missing values. There are two types of missing values: the ones resulting from absence of detection -missing at random (MAR) -and the ones resulting from their absence -missing not at random (MNAR).
The latter are biologically relevant. Missing data imputation has been conducted using the R package 'imputeLCMD' (https://cran. r-project. org/web/packages/imputeLCMD/). For the MAR values, we used the KNN imputation (Troyanskaya et al. 2001), while for the MNAR values, we used the stochastic minimal value approach. Proteins were considered differentially expressed when the adjusted p values were below 0. 05 and the logarithmic fold change was over 0. 5 or when the logarithmic fold change was over 2. For p value correction we used the Bioconductor package called qvalue  30. To obtain functional annotation, sequences of identified proteins were mapped using The Arabidopsis Information Resource, version 10 (http://www. arabidopsis. org), by BLASTX with E value cut-off ≤e  -10  ). The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD006870.
MapMan analyses. To display the large datasets onto diagrams of metabolic pathway, we used the MapMan tool  31. Figures were generated by exporting DESq2-normalized expression data to the MapMan application (http://mapman. gabipd. org). CDS were assigned to MapMan bins via IWGSP_MIPSv2. 2. txt mapping file, as available in the MapMan repository (http://mapman. gabipd. org/web/guest/mapmanstore). For the proteomic analysis, we compared sequences of the all differentially expressed proteins against the TAGI database (DFCI TGI -http://occams. dfci. harvard. edu/pub/bio/tgi/data/) using the NCBI Blast+application suite (http://ftp. ncbi. nlm. nih. gov/blast/executables/blast+/LATEST/), to obtain the best matching T. aestivum gene identifier for each protein in our lists. All aligned genes had an identity score >80%.