dataesr/scientific-paragraphs-categorization huggingface dataset:
Virus inoculum Reads obtained from strain EBOV Gabon 2001 were mapped onto the Genbank sequence KC242800 (Zaire ebolavirus isolate EBOV/H. sapiens-tc/GAB/2002/Ilembe). Eight fixed mutations differentiating the two strains were identified (Table  S11 ). The distribution of minor variants (frequency >1%) is presented in Fig. 3. Five mutations (all with frequencies <2%) were detected in non-coding regions (NCR): 3 in the 3 0 NCR, 1 in the VP30-VP24 intergenic NCR and 1 in the 5 0 NCR. In the coding sequence, 8 mutations were identified along the GP gene and 3 in the L gene (all with frequencies <3. 5%). All details regarding the list of variants and their respective frequencies are presented in Table  S11. 3. 4. 3. Monkey sera 3. 4. 3. 1. Major variants sites. Viral reads from monkey sera were mapped using the inoculum sequence as a reference.
In the complete dataset comprising all sequences produced from monkeys, only four major variants could be detected throughout the viral genome (Table  2A ). No insertions or deletions were detected. Three minor variants induced ORF modifications, introducing premature stop codons or modifying a start codon (Table  2B ). 3. 4. 3. 2. Minor variant sites. Deep sequencing coverage enabled the identification, in the complete dataset of 35 EBOV complete genomes from monkey sera, of a total of 822 variant sites with a frequency >1%. (The nature and type of minor variant by monkey is presented in Table  S20 ). The mean number of minor variant sites (frequency >1%) per monkey was calculated as the total number of distinct variant sites in the group considered (e. g. , for samples collected at day 5 pi) divided by the number of monkeys studied.
The same variant site appearing in different samples was counted once. It was found to be similar at days 5 and 7 pi (34. 1 and 31. 9 variants/monkey, respectively), and higher at day 9 pi (40. 1 variants/monkey) (Fig. S3 ). No clear difference according to the inoculum group (10-100-1000ffu) was observed. The variant site distribution is shown for each individual monkey in Fig. 4. In the complete dataset, variant sites in NCRs were significantly over-represented (5. 6/100 nt, vs 4. 0/100 nt in coding regions, p ¼ 3 10 À6 , chi-2 test) (Fig. S4 ). Genomic regions with the highest numbers of variant residues were: 5 0 NCR (7. 7% of positions found to be variant at least once) > GP/VP30 (6. 4%) > VP40/GP & VP30/VP24 (5. 9%) > NP/VP35 (5. 5%) > 3 0 NCR (5. 2%) > VP30 (5. 0%) > GP (4. 8%) > VP24-L & NP (4.
2%) > VP40 (3. 8%) > L (3. 7%) > VP35 (3. 2%) > VP24 (2. 9%) > VP35/VP40 (2. 1%). Details are presented in supplemental data, Table  S13. The proportion of transitional mutations at variant sites was 82%. It was quite constant over time during infection (80e83%), and similar in the different inoculum groups (80e86%, Table  S14 ). In coding regions, 41% of all mutations were synonymous. This proportion was clearly higher than that (22%) obtained in a random distribution of 568 variants (the number actually observed in the complete dataset) in coding regions of the genome. It was quite stable over time (41%e47%) and in the different inoculum groups (39e45%). Ten variant sites identified in the inoculum strain were found amongst the variant sites reported from monkey sera (Table  S12 ). Seventy-two variant sites (9%) were identified in at least two different monkeys (distribution detailed in Table  S19 ).
The distribution of mutations per monkey and per sampling day is shown in Fig. S5  and reveals that 133 variant sites were repeatedly identified at days 5, 7 and 9* pi (*day 8 pi for monkey CB821); 265 were observed at least on two different days in the same animal (Fig. S6 ). Again, these repeatedly variant sites were statistically over-represented in non-coding regions (p ¼ 6 10 À4 , chi-2 test). They represent 31% of all variant sites in coding regions, and 36% of all variant sites in non-coding regions. All details regarding repeatedly variant sites can be found in supplemental data Table  S15. We investigated the distribution of minor variants in the genome with reference to variant sites (fixed mutations) observed in complete genomic sequences of Zaire Ebola virus deposited in Genbank.
Amongst the 18,871 positions of the genome analyzed, 1627 were variant in at least one Genbank sequence. Amongst these 18,871 genomic positions, 226, 308 and 822 were mutated with frequency >5%, 3% and 1%, respectively. When we identified variant sites shared by GenBank sequences and by our minor variants, we observed that the ratio (observed number of common sites/expected number of common sites in case of random distribution) was 1. 55, 1. 51 and 1. 50 for variants with frequency >5%, 3% and 1%, respectively. The excess of common variant sites with reference to a random distribution was significant upon binomial analysis for variants with frequency >5%, 3% and 1% (p ¼ 0. 02, 0. 01 and 8 10 À5 , respectively) (Table  S16 ). For variants with frequency >10%, the ratio (observed number of common sites/expected number of common sites) was 1.
Some variants detected in the inoculum strain were identified in the blood of infected macaques at days 5, 7 and 9 pi. When we compared the distribution in the viral genome of minor variants identified in infected macaques with the spectrum of variant sites (fixed mutations) observed in complete genomic sequences of Zaire Ebola virus deposited in Genbank, we observed a significant excess of common variant sites with reference to a random distribution. This suggests that similar evolutionary constraints operate for the selection of some minor variants and fixed mutations. In other words, a proportion of the fixed mutations observed in nature in Zaire Ebola virus strains exists in the form of minor variants in the virus population of infected macaques. Similarly, the excess of minor variant sites detected in non-coding regions of the genome had previously been observed amongst fixed mutations identified in Ebola virus genomes  (Jiang et al.
Acknowledgements This study was supported by  European Union Research Programmes  (REACTION, H2020 #66609 for NHP experiments; EVAg, H2020 # 653316  for reference diagnostic and sequencing material; EbolaMoDRAD, IMI # 115843  for molecular biology tests); by the  St Luke's International University, Tokyo, Japan ; by the  Inserm  and the  I3M "Reacting" Committee of the "Alliance pour les sciences de la vie et de la  sant e". The authors are deeply indebted to  Karine Barth  el emy and  Magali Gilles  for excellent technical assistance and to  Ernest Gould  for advice and review of the manuscript.
Data collection Nurses and practice characteristics Biographic and demographic data were collected during the interviews age, gender, clinical setting, number of years in practice and number of years in this department, and if they were the pain resource nurse of their unit. The pain resource nurse is in charge of applying institutional protocols regarding pain issues, organizing training sessions and all quality improvement initiatives. We also asked them to provide the number of venipuncture and needle change for ICVAP per week in their department, and how many were performed with a prophylactic approach to pain management (use of a lidocaine-prilocaine patch or equivalent).
Analysis Following an introductory meeting, an individual faceto-face semi-structured interview was conducted with each nurse by a researcher skilled in qualitative research methods (WR), in a quiet room. Interview questions were geared towards eliciting open-ended responses to acquire specific information about the nurses' thoughts associated with procedural pain, especially regarding venipuncture and needle change for ICVAP. In our study, we conducted two preliminary interviews to test the quality of the questions (ie understanding and accuracy) we had planned to use (Table  1 ). All interviews were digitally audio-recorded and then fully transcribed. The name and personal information of participants were removed from transcripts, and code numbers were assigned. These interviews were subjected to a thematic analysis informed by Grounded Theory applied to the data to extrapolate results.
The discrepancies in analysis were discussed during a meeting with the scientific committee. The first author (MF) performed the translations of the reported quotations. In qualitative research, data collection ends when none of the analysts recognize new or unique themes. This approach is known as data saturation, particularly reflective of Grounded Theory. In our study, data saturation was reached when transcripts from 17 semiqualitative interviews had been coded. It took 4 months to recruit the 17 consecutive study participants. This study follows the COREQ guidelines.
However, the computation for choosing this parameter should be done for each situation and different dataset, which is very costly in computation. Bayesian methods  [11] ,  [12] , therefore, are often used to estimate the parameters and variables simultaneously  [13]. The choice of prior distributions is crucial in Bayesian methods. In a previous work  [14] , a hierarchical structured prior distribution is defined via the Haar transformation, with the direct model f = Dz +ξ, with coefficients presented by z and transform operator by D, and ξ defines the uncertainties which relax the transformation relationship in this hierarchical model. ξ is then modelled through a heavy tailed distribution to enforce its sparsity. In particular a generalized form of Student-t distribution (details presented in Section II) has been used and can be presented with a hierarchical model by defining p(ξ|v ξ ) as a normal distribution and p(v ξ ) as an Inverse Gamma distribution, where v ξ is the variance of noise ξ.
E. Bayesian Point Estimation The JMAP computation aims at iteratively and alternately estimate the variables and parameters by maximizing the posterior distribution: ( f , z, v , v ξ , v z ) = arg max f ,z,v ,v ξ ,vz {p(f , z, v , v ξ , v z |g)} (6) Because of the huge size of dataset, the descend gradient algorithm is used since the computation of big size matrix inversion is too expensive. The iterative updating rule is: iter : f (n+1) = f (n) -γ (n) f ∇J ( f (n) ); iter : z (n+1) = z (n) -γ (n) z ∇J ( z (n) ); vz k = βz 0 + 1 2 z 2 k / (αz 0 + 3/2) , ∀k ∈ [1, N ]; v i = β 0 + 1 2 gi -H f i 2 / (α 0 + 3/2) , ∀i ∈ [1, M ]; v ξ j = β ξ 0 + 1 2 fj -[D z] j 2 / (α ξ 0 + 3/2) , ∀j ∈ [1, N ], (7) where J (f ) = 1 2 V -1 2 (g -Hf ) 2 2 + 1 2 V -1 2 ξ (f -Dz) 2 2 ; J (z) = 1 2 V -1 2 ξ (f -Dz) 2 2 + 1 2 V -1 2 z z 2 2 ; γ (n) f = ∇J ( f (n) ) 2 2 / Y H∇J ( f (n) ) 2 2 + Y ξ ∇J ( f (n) ) 2 2 ; γ (n) z = ∇J ( z (n) ) 2 2 / Y ξ D∇J ( z (n) ) 2 2 + Y z ∇J ( z (n) ) 2 2 , (8) where ∇J (•) is the gradient of J (•).
γ f and γ z are obtained by using optimized step length strategy, see  [22]. The initialization of the hyper parameters αs and βs are: α z0 = α 0 = α ξ0 = 2 + ε 1 , β z0 = β 0 = β ξ0 = ε 2 SNR = g 0 2 2 / 2 2 = g - 2 2 / 2 2 , (9) where g 0 is the unbiased sinogram and g is the contaminated sinogram with the additive noise represented by in Eq. (  1 ). A lower level of SNR refers to the dataset with more critical additive noise. The proposed BH-DTCWT method is used for the reconstructed and is compared with the conventional TV method and the HHBM method in our previous work  [14]. In the NMSE = f -f 2 2 / f 2 2. ( 10 ) The performance of TV is very good when there are sufficient number of projections, but when the number of projections become insufficient, the HHBM and BH-DTCWT methods are more robust than the TV method.
IV. CONCLUSION We proposed a Bayesian method based on a hierarchical prior model in which the Dual-Tree Complex Wavelet Transformation is used. Generalized Student-t distribution is used to enforce the sparsity of the transformation coefficient. Comparing with the state-of-the-art methods, the proposed method remain robust when dealing with reconstruction problems with less number of projections, namely insufficient dataset, in the CT reconstruction of the Shepp Logan image. Another advantage is that by using the Bayesian method we have no need to choose a value for the parameters, such as the regularization parameter in TV, and they are automatically estimated in the simulations. In the future work we will complete the comparisons by using different types of phantoms, for example phantoms with periodical textures, in order to analyse the influence of the transformation type.
are small values close to zero. III. SIMULATIONS RESULTS The simulated Shepp-Logan image of size 256 2 , shown in Fig. (3)(a), is used as the original image, and the value of the pixels are normalized to [0, 1]. Parallel projections are used as the dataset. For each projection, 256 2 detectors are used, and an image of size 256 × 256 is obtained. The red block is the zone that we choose to show the results in Fig. (4). The uncontaminated sinogram by applying 128 projections is shown in Fig. (3)(b). The sinogram with 128 projections and SNR=20dB is shown in Fig. (3)(c). The projections are distributed uniformly from 0 to π. The signal-to-noise ratio of the dataset is defined as:.
where ε 1 and ε 2 2 are small values close to zero. III. SIMULATIONS RESULTS The simulated Shepp-Logan image of size 256 2 , shown in Fig. (3)(a), is used as the original image, and the value of the pixels are normalized to [0, 1]. Parallel projections are used as the dataset. For each projection, 256 2 detectors are used, and an image of size 256 × 256 is obtained. The red block is the zone that we choose to show the results in Fig. (4). The uncontaminated sinogram by applying 128 projections is shown in Fig. (3)(b). The sinogram with 128 projections and SNR=20dB is shown in Fig. (3)(c). The projections are distributed uniformly from 0 to π. The signal-to-noise ratio of the dataset is defined as:.
0-60. 0% seroconversion of all three antibody types. However for the tOPV arm the seroconversion rate ranged from 30. 0-35. 0% for all the three serotypes (Fig. 4 ). Seroconversion rates in IPV compared to tOPV arm were significantly higher for polio type 2 (p = 0. 03) and polio type 3 (p < 0. 01). For polio type 1 type the seroconversion rates were not significantly different for IPV and tOPV arm (p = 0. 72). High serum neutralizing antibody titers were observed over period from week 18 to week 53-54 (Fig. 5 ). The analysis of week 40 SNAB titers, as predictor of week 52 shedding, showed that infants with seroconversion at week 40 from week 18 were similarly shed at week 52 than those who were not seroconverted at was estimated using all available data points. The total prevalence of shedding by serotype was estimated using only infants with complete five time data.
The study was approved by the Indian Council of Medical Research (ICMR), Health Ministry's Screening Committee (HMSC), Government of India; the Institutional Ethical Committee at National Institute of Cholera and Enteric Disease (NICED) and the Institutional Review Boards of International Vaccine Institute (IVI), South Korea; University of Virginia and University of Vermont USA. The study protocol was registered at clinical trial registry of India (CTRI/ 2012/03/002504) and at clinicaltrials. gov (ClinicalTrials. gov Identifier: NCT01571505). All participants received free primary care during the study. All mothers signed an informed consent form on behalf of their infants before enrollment into the study and were free to withdraw at any time. Article No~e00223 Three-hundred seventy-two eligible neonatal infant-mother pairs were enrolled into the Performance of Rotavirus and Oral Poliovirus Vaccines in Developing Countries (PROVIDE) study in Kolkata, India.
Acknowledgements We thank all volunteers who participated in this study and the entire team from the  National Institute of Cholera and Enteric Diseases (NICED) ,  International Vaccine Institute (IVI) ,  India , for setting up the laboratory and providing field support in Kolkata, India. We also thanks team from  Enterovirus Research Centre (EVRC)  to perform all Polio testing. We also like convey our sincere gratitude to  Dr. Jerome Kim ,  Dr. Jean-Louis Excler ,  Dr. Yun Chon  for reviewing and giving valuable suggestions in improving the manuscript. http://dx. doi. org/10. 1016/j. heliyon. 2016. e002232405-8440/© 2017 The Authors. Published by  Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4. 0/).
Data from "during" WSP implementation (periods of 10-24 months from the initiation of team meetings to certification) was excluded. Some datasets (online sensors datasets for turbidity, total organic carbon, and free chlorine; and critical control parameters used for compliance analysis including trichloroethylene/tetrachloroethylene, nickel, chromium VI, and iodine absorption) begin 1 Jan 2006 for a total of two years in the before period.
Water Quality Analysis Water quality data were provided by employees of the Suez-affiliated drinking water supplier at each study location, including the parameter, unit, date, time (if applicable), and monitoring station. Data sets were produced via either routine internal water quality monitoring or external quality control involving independent sampling and analysis by health authorities. Data from manual sampling records were pooled while data sets from online sensors were considered separately where available (locations 3 and 5). We selected twelve water quality parameters to evaluate water quality, treatment process effectiveness, and possible human health risk, including E. coli, fecal streptococci, total coliform, heterotrophic plate count, trihalomethanes (THMs), bromate, free residual chlorine, total residual chlorine, aluminum, total organic carbon, turbidity, and pH.
In some cases, water quality data represented water that did not reach the consumer, (e. g. , an alarm or scheduled maintenance event might trigger containment and disposal of a water batch and/or emergency cross-connection with alternate water supplies), but it was nevertheless included as an event relevant to the WSP and performance history. Precise historical records of maintenance, flushing, spiking, equipment failure and other events/activities that potentially affected individual samples were not available; therefore, suspected outliers remained in the dataset. All statistical analyses were performed using SAS 9. 4 software. For chemical parameters, multiple regression was applied to detect significant differences between the pre-intervention ("before") and post-intervention ("after") time periods.
Population data at the level of municipality was provided by the National Institute of Statistics and Economic Studies (INSEE) in France and the Statistical Institute of Catalonia (IDESCAT) in Spain. Because the French population data is released after a two-and-a-half-year time lag, population estimates needed to be extrapolated for the years 2014-2015 to enhance or enable comparison with cases reported at locations 1 and 3, respectively. Extrapolations were based on linear estimates of growth for each municipality using 2010-2013 data. Because the number of years in each time period varied, acute gastroenteritis was characterized as a rate: the incidence (number of new cases) per 1,000 person-years. Population data could further be matched to existing age divisions in the health data to stratify the analysis by ages under 5, 5-14, 15-64, and over 65 years at locations 1 and 3, and by ages 0-14 and over 15 at location 5.
To statistically compare acute gastroenteritis incidence before and after WSP implementation, a Poisson regression model was applied with population as the offset (person-years) and controlling for the comparison area conditions (the base level of the site variable; equation 4). Comparison area values were included to isolate the effect of the drinking water intervention, as opposed to data reporting, overall health, or other changes that may have affected the whole region. The location 5 model separated the full WSP (production and distribution) and partial WSP (distribution only) intervention areas (equation 5). Cases reported by municipality were pooled within each area and clustering was not considered in the model.
Informal Audits Lastly, qualitative questionnaires were developed to better understand the nature of the data and the WSP intervention at each site, as well as to gather information about perceptions and expectations of undertaking the WSP. Informal WSP audits were carried out in June/July 2016 at locations 1, 3, and 5, incorporating a semi-structured group discussion and a guided tour of a drinking water treatment plant. Questions were developed using the World Health Organization and International Water Association's Practical Guide to Auditing Water Safety Plans  (WHO and IWA, 2015)  as well as an interview guide used in Iceland by Gunnarsdóttir (2012; Appendix 1). Because the questionnaires were intended as a starting point for discussion, sessions were structured loosely and answers were not forced on all questions; however, information was specifically requested on significant events that took place during the study period, and expectations of change in water quality or health data as a result of the WSP (reported in tables B.
4 through B. 8). Significant changes were more easily detected in online sensor datasets with sample numbers up to 531,603. Some undesirable changes were observed at some locations (tables 3 and 4), but a widespread harmful effect was not indicated over the period of the intervention. In particular, microbial indicator levels at location 3 appeared to worsen following the WSP intervention. This might be explained by the utility managers' desire to keep chlorine levels as low as possible in response to concerns about disinfection by-products and their potential adverse health effects. Due to local socio-political pressure to eliminate chlorine usage, this location is looking to pilot an unchlorinated water supply in the near future. The WSP at this location did not emphasize control of microbial contamination due to the perceived high quality of the groundwater source.
Performing pooled analysis across locations (e. g. , via a multi-level model) would be useful, but is precluded by the small number of case studies and differences in data collection methods. A prospective, randomized controlled study design might be recommended as the gold standard for overcoming confounding and data consistency constraints. One of the three case studies did demonstrate a reduction in acute gastroenteritis incidence following WSP implementation, corresponding to about a 4% decrease in acute gastroenteritis incidence in the overall population, or 6% in the 15-64 age group (tables  5  and 6 ). This occurred at location 1, which had the largest health dataset. Location 5 full (production and distribution) and partial (distribution only) WSPs showed the opposite of the expected effect, with significantly higher post-implementation rates of acute gastroenteritis among adults in both intervention areas, relative to the comparison area.
Acknowledgements The authors greatly appreciate the assistance of all Suez staff members who facilitated site visits and data collection for this study. Special thanks extend to  Catherine Galey  and  Pascal Beaudeau  from  Santé Publique France  for providing access to public health surveillance data in France and advice regarding its interpretation. Suez, the  Seine River Basin Agency  (AESN; specifically  Véronique Lahoussine ), and the  Royster Society of Fellows at UNC Chapel Hill generously  provided financial support for this study. We also are grateful to  Maria Gunnarsdóttir  and  Olivier Schlosser  for reviewing and commenting on the draft manuscript. Statistical analysis support was provided by the  Odum Institute at the University of North Carolina at Chapel Hill.