dataesr/scientific-paragraphs-categorization huggingface dataset:
A review of the manufacturer package leaflets, an online Multi-Drug Symptom/Interaction Checker (DoubleCheckMD) and further by therapeutic area experts reduced the number of signals to five. The ranking of some drug-adverse reaction pairs on the basis of their signal index differed between the two methods. Conclusions: Our two data mining methods were equally able to generate suspected signals using the pooled safety data from a phase IIIb/IV clinical trial. This analysis demonstrated the possibility of utilising clinical studies safety data for key pharmacovigilance activities like signal detection and evaluation. This approach can be applied to complement the spontaneous reporting systems which are limited by under reporting.
Clinical trials and other controlled studies usually provide more complete and homogeneously compiled data which are submitted to the regulatory authorities and presented in standard formats. Indeed, all recruited patients comply with specific inclusion and exclusion, are well described, and are regularly monitored by the research team to uncover any ADRs. Efficacy and safety analysis are usually well documented and carefully controlled. The analytical methods used in post-marketing pharmacovigilance to analyse ADR data and generate suspected ADR signals include biostatistics and data mining algorithms (DMA)  [8]. Data mining is the technique of extracting hidden associations or patterns of association in large datasets when manual inspection is not feasible. The most commonly used data mining methods in pharmacovigilance include proportional reporting ratio (PRR)  [9] , a Bayesian confidence propagation neural network (BCPNN)  [10] , and the multi-item gamma Poisson shrinker (MGPS)  [11, 12].
All of these are based on a quantitative measure of disproportionality between observed and expected reports of a certain drug-event combination when comparing with all other reports of adverse events and drugs in the dataset. Each of these data mining methods derives a different statistical measure to qualify the ratio of observed-to-expected reports, consequently generating a signal indicative of a safety problem. The PRR has been used on the European Medicines Agency EudraVigilance database, the BCPNN by the Uppsala Monitoring Centre on the WHO safety database, and the MGPS on the database of the Food and Drug Administration (FDA)  [13]. The aim of this study was to examine the feasibility and usefulness of two DMAs to generate and identify signals exploiting the safety database of a large multi-country phase IIIb/IV clinical study.
Data Source This analysis utilised data from a multi-centric phase IIIb/IV clinical study that was conducted between July 2007 and July 2009, in twelve sites located in seven African countries (Burkina Faso, Gabon, Nigeria, Rwanda, Uganda, Zambia, Mozambique)  [14]. Each site compared three of the four artemisinin-based combination therapies (ACTs) for treatment of uncomplicated malaria i. e. amodiaquine-artesunate (ASAQ), dihydroartemisininpiperaquine (DHAPQ), artemether-lumefantrine (AL) or chlorproguanil/dapsone and artesunate (CD+A). In each site, one of the three ACTs was already approved as first line treatment for uncomplicated malaria and was already being utilised in the respective populations. Throughout the study period, safety was assessed through both direct observation using physical exam, vital signs and laboratory tests by the clinician and the caretaker reporting an event to the clinic after the patient has been discharged.
Signal Detection The two methods applied were the proportional reporting ratio (PRR)  [9] , and the Bayesian confidence propagation neural network (BCPNN)  [10]  -a frequentist and a Bayesian based framework, respectively. Classically, pharmacovigilance data mining methods are formulated with contingency table structured databases  [16]. Each spontaneous report may involve several suspected drugs and several observed events, leading to J (total number of) drugs and I (total number of) events mentioned at least once in a report (Table  1 ). The suspected AE signals were generated using the R software  [17]  by applying PhViD package  [18]  which is a data mining package containing several pharmacovigilance signal detection methods extended to the multiple comparison setting.
Signals Evaluation We adopted a common pharmacovigilance practice  [19, 13]  where the clinical validity of the drug-event associations are identified by the proposed method and reviewed by an expert in the therapeutic area. The signal detection and evaluation was done following procedures described elsewhere  [20]. Briefly, signals were evaluated in two stages, first by comparing the generated signals with the already known drug-events relationship using the manufacturer package leaflets; secondly, they were submitted to a clinician with experience on malaria treatments. The drug manufacturer package leaflets for DHAPQ, AL and ASAQ were used to eliminate ADRs already mentioned onto the leaflet. The Multi-Drug Symptom/Interaction Checker software DoubleCheckMD (http://doublecheckmd. com) -was also used for AL since it is the only ACT that was found in that database.
Ethical Considerations and Patients Safety The clinical trial  [14]  was registered prior to the enrollment the first patient in the ClinicalTrials. gov registry (NCT00393679, http://clinicaltrial. gov/ct2/show/NCT00393679) and in the Pan African Clinical Trials Registry. (/PACTR2009010000911750, http://www. pactr. org/). Permission to use the related safety dataset were obtained from all site investigators of the study team.
Results A total of 6,238 adverse events were reported within the 28 days of follow up after taking any of the four ACTs resulting into a total of 346 drug-AE combinations. The severity or any perceived relationship with the drug was not taken into account in this analysis, and multiple events were often reported after treatment with a drug. From the 346 drug-AE combinations, the two DMAs generated nine suspected signals which were subjected to review by the experts (Table  2 ). Drug-AE combinations involving ASAQ had more signals (n = 5) than the other ACTs while DHAPQ had only one. A third of the generated signals (three out of nine) were already listed on the manufacturer package leaflets and in the Double-CheckMD. Comparing the frequency of such reports in our database with the information in the leaflets, the signal ASAQ-Anemia was reported for about one in five patients in our database making it more frequent compared to the one in 100 patients indicated onto the leaflet.
Similarly, the signal AL-Pyrexia was reported by one third of the patients which is slightly more frequent than in the leaflet (greater than 1 in every 10 patients treated). After review, two signals, i. e. AL-Malaria and AL-Pyrexia, were eliminated as they were due to the underlying disease. The remaining five signals were as follows: i) 'AL-Eye discharge'; ii)'ASAQ-increased Alanine amino Transferase (ALT)'; iii) 'ASAQ-Bronchopneumonia'; iv) 'ASAQ-Neutrophil count increased'; and v) DHAPQ-''haemorrhagic diarrhoea'', and resulted in the new hypotheses that may need further evaluation. Both DMAs used in this analysis produced similar signals with similar frequency suggesting they were of comparable performance for this dataset. There was a slight difference in the ranking done on the lower bound of the confidence interval that takes into account the variance of the disproportionality measure of association.
Suspected signals ASAQ-Anaemia, ASAQ-increased Alanine amino Transferase, AL-Malaria and AL-Eye discharge had the same ranking by both methods (Figure  1 ) according to the respective method's signal index. A drug-event combination like 'AL-Pyrexia' had many reports in the database though it did not translate into higher signal index and was ranked lowest by PRR though other combinations like 'AL-eye discharge' had fewer reports in the database.
Discussion Each of the two DMAs used a different method of calculating the signal index and the ranking done on the lower bound of confidence interval of drug-AE signal indices differed. Both algorithms produced equal number of signals, suggesting that for this dataset their performance was comparable. A drug-event combination like 'AL-Pyrexia' had many reports in the database but did not translate into higher signal index and hence ranked lower than other combinations with fewer reports like 'AL-eye discharge'. This could be due to the fact that similar AEs occurred among the other drugs of interest. Additionally, some conditions in the signalled drug-event combinations were related to each other and therefore removing one combination affected the appearance of the other as a signal, e. g.
Overall, there is limited data available on safety in children during the drug development process as children are excluded from most randomized clinical trials. Moreover, in clinical practice some significant AEs may not be recognised and documented because paediatric patients cannot communicate discomfort associated with the drug. Neonates and infants cannot communicate meaning that the detection of ADRs requires a kin mother/care taker, paediatrician or a laboratory test. The signal detection process using phase IIIb/IV clinical studies data illustrates the potential utility of this approach for future drug monitoring, especially in Africa where many clinical studies are conducted and safety data are usually archived after producing the trial report. Clinical trials databases are usually well designed, of high quality and high level of completeness for clinical information and hence are more effective for signal detection.
Indeed the use of spontaneously reported AE data have been associated with a number of limitations when analysing with the inter-product quantitative DMA such as underreporting, lack of precision in the AE definition, uncertainty in estimating the extent of the drug-  exposed population, unreliability due to the highly variable quality of the reports  [22, 23]. Even with such limitations, DMA continue to be a useful pharmacovigilance tools, utilised by international organisations and regulatory authorities that possess large spontaneously reported AE databases which routinely generate and monitor signals. The use of clinical studies databases would offer an ideal complement to spontaneous reporting systems (SRS), since such databases do not suffer the caveats of the SRS and could facilitate a rapid identification of new signals.
Data mining has been used to explore cardiovascular clinical trials databases  [24] , and other specialist databases like the US Vaccine Adverse Event Reporting System  [25]. Clinical trial data have typically been amenable to safety data mining techniques because of the small sample sizes and narrow variations in database designs. However, their high intrinsic quality and completeness in clinical information make them invaluable sources of information. On the other hand the small sample size setback can be averted by pooling of safety databases from different multi-country, multi-site studies as was the case for the present study. Data mining algorithms (DMAs) for quantitative signal detection are practical on large, late stage trials or pooled studies, though screening for signals ought to begin early in the process.
After identifying safety signals or areas of interest, they can be tracked over time and lead to further actions. Standardised documentation provides evidence of continuous proactive vigilance and data can be compared to spontaneous reports and observational data. One limitation of DMAs is the potential variation in statistical properties of the standard signalling threshold when applied to different datasets. Different thresholds may be needed for more common versus rare outcomes/events which is not yet currently done in practice. Another limitation is that a strong association between an adverse reaction and a drug that is not of specific interest (e. g. for large values of 'b' in Table  1 ) may reduce the likelihood of detecting a true signal between that adverse event and the drug of interest.
This situation could occur when a drug that is an indication for the adverse event of interest is included in the dataset (and thus included in the 'all other drugs' comparison group). A specific limitation to the PRR method is the impossibility of calculating the PRR estimate in the absence of adverse events of interest reported for the comparison drug (s) (i. e. , if b = 0 in Table  1 ). Further, since the PRR is a ratio of two proportions, its value is unstable for small sample sizes as it is the case for most clinical trials. The methods used in this analysis worked well in the identification of single drug-event signals but are not suitable for the identification of drug interactions since they consider only one drug at a time. Other methods such as association rules (a priori algorithms) and clustering algorithms are more appropriate for the identification of drug interactions.
Association rules approach was used by Harpaz et al  [26]  using data from FDA spontaneous adverse events reporting system which demonstrated the opportunity to develop and use novel algorithms to detect drug interactions. The signals evaluation method in this analysis was adopted because there is no structured database of possible ADRs in Africa other than the VigiBase which is hosted at the WHO safety monitoring centre in Uppsala  [27]. Such a database would considerably improve the efficiency of signal detection process by allowing for filtering or flagging reaction reports of previously unknown reactions. Only five signals remained after the evaluation process that involved use of manufacturers' package leaflets, a Multi-Drug Symptom/Interaction Checker (DoubleCheckMD) and experts' opinions.
These are regarded as new hypotheses that may need further studying in pharmacoepidemiological studies. In conclusion, this paper has demonstrated the possibility of utilising data mining algorithms with clinical studies safety data for key pharmacovigilance activities like signal generation and evaluation especially when datasets from multi-centre clinical studies are pooled. These methods can be used to facilitate the initial signal monitoring steps of ''signal strengthening'', ''signal follow-up'' and also for hypothesis generation before the implementation of epidemiological and experimental studies. This study also contributes to the limited but important literature on paediatric pharmacovigilance, especially in Sub-Saharan Africa.
A two by two table for the adverse event-drug pair. Adverse Event; a = the number of reports involving the drug of interest j and adverse event of interest i combination; b = reports of adverse event of interest i observed with other drugs; c = reports of all other AEs with drug j; d = reports of all other AEs with the other drugs; and a+b+c+d = the total number of reports in the dataset. doi:10. 1371/journal. pone. 0096388. t001.
Acknowledgments We would like to thank the research teams at the participating sites and members of the trial management group at the  Institute of Tropical Medicine, Antwerp, Belgium , who contributed significantly to this study. We would like to thank the leadership of 4ABC study team who kindly provided the safety database for this analysis. In addition we would like to thank the editor and the anonymous reviewers for their guidance and comments. The '4ABC study' was funded by the  EDCTP.
Sections were examined by two pathologists using a Leica DMRB microscope that was equipped with a Leica digital camera connected to an Imaging Research MCID analysis system (MCID software, Cambridge, UK). Parasite load in digestive sections was scored on five selected fields at a magnification of ×400 as described previously  (Certad et al. , 2010a; Certad et al. , 2010b). Histopathological lesions at different sites were scored as described previously  (Certad et al. , 2010a; Certad et al. , 2010b)  with slight modifications. Briefly, 0 -no lesion, 1 -inflammation and/or regenerative changes, 2 -low-grade intraepithelial neoplasia, 3 -high-grade intraepithelial neoplasia (in this category, carcinomas in situ and intramucosal adenocarcinomas were also included), 4 -adenocarcinoma (invading the submucosa), 5 -adenocarcinoma with invasion into the muscularis and deeper.
PCR reactions were performed in a 96-Well MasterCycler EP, Eppendorf PCR System. Cycle conditions included initial denaturation at 95°C for 5 minutes followed by 30 cycles of denaturation at 94°C for 20 seconds, annealing at 58°C for 15 seconds, extension at 72°C for 45 seconds and a final extension step lasting 4 minutes at 72°C. After purification and quantification, the resulting amplicon products were pooled in an equimolar manner and barcode adaptor sequences were incorporated. The monitoring of the barcoded library was performed using a BioAnalyzer instrument (Agilent Technologies). Then, all the samples were pooled in an equimolar manner and, finally, the library was amplified by emPCR to obtain a sequencing template. The sequencing was performed using the Ion PGM 200 Sequencing Kit according to the manufacturer's instructions (Life Technologies), and the data analysis was performed using CLC Genomics Workbench 5 modules.
Funding This work was supported by the  Ministry of Research, France  ( EA3609/4547 Université de Lille 2 ), the  Federative Institute of Research 142  ( Institut Pasteur de Lille, France ) and the  French National Research Agency  (grant numbers  ANR-09-ALIA-009  and  ANR-10-ALIA-004 ). S. B. was supported by a scholarship from the  Catholic Institute of Lille.
Genetic Characterization of Ostreopsis Strains Ostreopsis strains cultivated in the laboratory were identified by genetic molecular analysis of the ITS1-5. 8S-ITS2 region and partial LSU (D1-D3), which had already been used for genetic identification of Ostreopsis species  [50]. Sequences obtained for the strains from Monaco IFR-OST01MO (Genbank #KJ239219) and from Villefranche-sur-Mer IFR-OST03V (Genbank #KJ239220) were 1272 bp long and 100% identical. In the phylogenetic tree inferred from ITS1-5. 8S-ITS2 (Figure  5 ), the sequences clustered with the sequences of other Ostreopsis cf. ovata from France and from various areas of the Mediterranean Sea, especially Italy and Spain, and the Atlantic Ocean. It is genetically different from the Asian clade and since no sequence from the type locality is available, it is not possible to ascertain which genotype actually corresponds to Ostreopsis cf.
Sequences Alignment and Phylogenetic Analysis Molecular phylogeny was inferred from both a matrix of ITS1-5. 8S-ITS2 and a concatenated ITS1-5. 8S-ITS2-partial LSU matrix (data not shown). To prepare the ITS-5. 8S dataset, the sequences of the two studied strains were aligned with 73 sequences of Ostreopsis and 7 sequences of Coolia retrieved in Genbank, using MUSCLE software  [65]. The alignment was refined by eye using the BioEdit version 7. 0. 0. [66, 67]. Evolutionary models were examined with jModeltest version 0. 1. 1  [68]  Bayesian Inference (BI) analysis was run using Mr Bayes version 3. 1. 2 [69]. Initial Bayesian analyses were run with a GTR model (nst = 6) with rates set to invgamma and nucleotide frequencies set to equal. Each analysis was performed using four Markov chains (MCMC), with one million cycles for each chain.
Trees were saved to a file every 100 cycles and the first 2000 trees were discarded. Therefore, a majority-rule consensus tree was created from the remaining 8000 trees in order to examine the posterior probabilities (pp) of each clade. Neighbor-joining (NJ) analysis was performed using MEGA software version 5. 05  [70] , with Maximum Composite Likelihood method. Bootstrap analysis (1000 pseudoreplicates) was used to assess the relative robustness of branches  [71].
The MS analysis worked in positive mode, with mass range set at m/z 300-1450. The conditions of API-ES source were as follows; drying gas (N 2 ); flow rate, 12 mL• min -1 ; drying gas temperature, 325 °C; nebulizer, 50 psig; capillary voltage, 4800 V; fragmentor 165 V. All the acquisition and analysis data were controlled by an Agilent LC/MSD ChemStation (Agilent, Les Ulis, France). Turning mix (G 2421-60001) was used for lock mass calibration in our assay. Separation was carried out on a Kinetex 2. 6 μm (100 × 4. 6 mm) column (Phenomenex, Le Pecq, France). Linear gradient elution was accomplished in 20 min with water (eluent A) and 95% acetonitrile/water (eluent B) both containing 0. 2% of acetic acid, at 0. 7 mL• min -1 flow rate. The gradient was as follow: 0-20 min from 20% to 40% B, 20-21 min from 40% to 100% B, 21-28 min 100% B, 28-29 min from 100% to 20% B, and re-equilibration with 20% B.
Cyanobacteria-Climate Relations Contribution to biomass for each generic group was examined in relation to meteorological parameters in order to determine dependencies toward regional climate gradients. All relations are summarized in Figure  6  as second order polynomial curves obtained with SPSS SigmaPlot 12. Three dominant areas could be distinguished: a colder, wetter, less sunny area dominated by Aphanothece/Aphanocapsa and Woronichinia, a hotter, drier, sunny area dominated by Microcystis and Anabaena, and an intermediate, transitional area associated with Aphanizomenon and Planktothrix. This distribution is in accordance with cyanobacterial growth rate and thermal optima as exposed in  [35, 36]. Climatic optima, summarized in Table  2 , were extrapolated for each genus and defined as the parametric value leading to the highest % biovolume.
From 2004 to 2011, no nutrient monitoring was conducted in these lakes. As a consequence, no relevant trophic status data could be integrated to this work, and our conclusions are only based on climatic data. Other parameters can influence cyanobacteria distribution, such as lake morphology and/or nutrient form availability to phytoplankton  [37, 38]. Nutrient data should be included as a priority in any cyanobacteria monitoring program.
Experimental Section This study is based on public health weekly survey data from recreational lakes monitored every year from 2004 to 2011 in Brittany, north-western France (Figure  8 ). These sites were selected according to cyanobacteria survey continuity criteria: 26 sites out of a total of 40 lakes monitored for cyanobacteria from 2004 to 2011 could be considered as continuously monitored from June to September, i. e. , with at least seven years of monitoring out of eight, with a sampling frequency of at least every two weeks. The lakes characteristics range from 0. 2 to 51 × 10 6 m 3 , with depths from 2 to 45 m and watershed areas from 0. 2 to 676 km². Monthly meteorological data (precipitation rates, temperature, global radiation intensity, etc. ) were collected from Météo France database (Climathèque) from 1982 to 2011.
Meteorological data were gridded and mapped with Golden Software Surfer 8 and ESRI ArcGis Desktop 9, whereas regional climate evolution rates were estimated with SPSS Sigma Plot 12. Only four out of 26 lakes were subject to nutrient monitoring prior to this study. As a consequence, no relevant trophic status data could be integrated to this work. Cyanobacteria data, i. e. , cell densities, species composition and microcystin concentrations, were collected from the regional public health authorities (French Agence Régionale de Santé (ARS) of Brittany). All samples were analyzed by four different local laboratories for cyanobacteria composition and microcystin concentration. Cyanobacteria were identified according to  [39] [40] [41]  and counted with light microscope according to  [42]. All cell densities data were first converted to biovolume with the use of mean cell dimensions and relevant geometric formulas  [43] , to account for differences in contribution from larger species associated with lower cell densities (e.