dataesr/scientific-paragraphs-categorization huggingface dataset:
It also highlights the limitations and successes of the current system of surveillance. Moreover, it will help to identify critical time periods in the year when mosquito control strategies should be implemented to limit the spread of this virus. In future, this could help shape new local strategies to control Zika virus, dengue and other diseases carried by mosquitoes. emergence, transmission and endemic potential of the Zika virus, especially in the context of a well described outbreak, have not yet been addressed in detail. In this study, focusing on an urban centre of Brazil (Feira de Santana), we explicitly model the mosquito-vector lifecycle under seasonal, weather-driven variations. Using notified case data of both the number of suspected Zika infections and confirmed microcephaly cases, we demonstrate how the combination of high suitability for viral transmission and a low detection rate resulted in an extremely high attack rate during the first epidemic wave in 2015.
We found a time lag of 5-6 months (20-24 weeks) between the first reported Zika epidemic wave and the MC peak in case counts. This coincides with previous observations suggesting a link between the development of neurological complications in newborns and ZIKV infection during the second trimester  (Faria et al. , 2016a). We note that our lag may be offset by around 1-4 weeks, however, since the date of MC cases in our dataset represents the date of diagnostic confirmation, which is usually done postpartum. Overall, the epidemic behaviour in FSA was in sharp contrast with trends observed in notified cases across Brazil (BR) as a whole, for which the second epidemic in 2016 was approximately 6 times larger than the one in 2015 (Figure  1A ), suggesting the Bahia state as a focus point in the emergence and initial spread of ZIKV in Brazil  (Faria et al.
Discussion Using an ento-epidemiological transmission model, driven by temporal climate data and fitted to notified case data, we analysed the 2015-2017 Zika outbreak in the city of Feira de Santana (FSA), in the Bahia state of Brazil and determined the conditions that led to the rapid spread of the virus as well as its future endemic and epidemic potential in this region. Given FSA's high suitability for ZIKV mosquito-vectors and its particular geographical setting as a state commerce and transport hub, our results should have major implications for other urban centres in Brazil and elsewhere. The pattern of reported ZIKV infections in FSA was characterized by a large epidemic in 2015, in clear contrast to total reports at the country-level, peaking during 2016. Most notably for FSA was the epidemic decay in 2016 and fadeout in 2017.
In order to resolve whether this was due to a lower transmission potential of ZIKV in 2016/2017 in FSA, we calculated the daily reproductive number (R 0 ) between 2013 and 2017 but found no notable decrease in 2016. Interestingly, the maximum R 0 in that period was observed in the season 2015/2016, coinciding with El Nin ˜o (Golden Gate Weather Services, 2017) and thus in line with the hypothesis that this phenomenon may temporary boost arboviral potential  (Caminade et al. , 2017; van Panhuis et al. , 2015). By fitting our model to weekly case data we also estimated the observation rate, i. e. the fraction of cases that were notified as Zika out of the estimated total number of infections. It has previously been reported that the vast majority of Zika infections go unnoticed (Table  3 ), which is in agreement with our estimates of an observation rate below 1%.
Climate data Local climatic data (rainfall, humidity, temperature) for the period between January 2013 and May 2017 was collected from the Brazilian open repository for education and research (BDMEP, Banco de Dados Meteorologicos para Ensino e Pesquisa)  (Brazil BDMEP, 1961). The climate in FSA is defined as semi-arid (warm but dry), with sporadic periods of rain concetrated within the months of April and July. Between 2013 and 2015, mean yearly temperature was 24. 6 celsius (range 22. 5-26. 6), total precipitation was 856 mm (range 571-1141), and mean humidity levels 79. 5% (range 70. 1-88. 9%). Temperature, humidity and precipitation per day is available as Dataset 1.
Microcephaly and severe neurological complications case data A total of 53 suspected cases with microcephaly (MC) or other neurological complications were reported in FSA between January 2015 and February 2017. Using guidelines for microcephaly diagnosis provided in March 2016 by the WHO (as in  (Faria et al. , 2016c) ), a total of 21 cases were confirmed after birth and follow-up. A total of 3 fetal deaths were reported for mothers with confirmed ZIKV infection during gestation but for which no microcephaly assessment was available. The first confirmed microcephaly case was reported on the 24 th of November 2015 and virtually all subsequent cases were notified before August 2016 (with the exception of 2). The microcephaly case series can be found in Dataset 4.
Acknowledgements We are most grateful to  Wanderson Klebeler de Oliveira ,  Livia Carla Vinhal ,  Mariana Pastorello Verotti ,  Giovanini Evelim Coelho  and  Claudio Maierovitch Pessanha Henrique  from the  Brazilian Ministry of Health  for providing epidemiological data regarding Zika virus notified cases in Brazil. MML and EMC curated the Zika virus notified cases in Feira de Santana. JL and ASW received funding from the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/ 2007-2013) / ERC  grant agreement no. 268904 -DIVERSITY. MR was supported by a  Royal Society University Research Fellowship. The  European Research Council  under the European Union's  Seventh Framework Program  ( FP7/2007-2013) / ERC  grant agreement no. 614725-PATHPHYLODYN funded OP.
MUGK's contribution was made possible by the generous support of the American people through the  United States Agency for International Development Emerging Pandemic Threats Program -2 PREDICT-2  (Cooperative Agreement No. AID-OAA-A-14-00102 ). CJVA was supported by a fellowship from the  Labex EpiGenMed , via the  National Research Agency, Program for Future Investment and University of Montpellier  [ ANR-10-LA-12-01 ]. BL received funding from the  Engineering and Physical Sciences Research Council (EPSRC)  in the UK. NRF was supported by a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust  and the  Royal Society  (grant number  204311/Z/16/Z ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
truncatula genome and 39 403 Medicago truncatula coding regions with an Mt4. 0 identifier. Each long primer is triplicated for robust analysis. For Q-PCR experiments, RNA samples were treated using Perfecta DNaseI (QuantaBioSciences), and 1 µg of total RNA was used for reverse transcription using the qScript cDNA SuperMix (QuantaBioSciences), following the supplier's instructions. Nanoliter high-throughput quantitative PCR  (Morrison et al. , 2006)  was used for validation of 60 genes (using six reference genes) as described in  Camps et al. (2015). Primers are listed in Table  S1  available at the Dryad Digital Repository, http://dx. doi. org/10. 5061/dryad. s43c7. All the original microarray data are deposited in the Gene Expression Omnibus (http://www. ncbi. nlm. nih. gov/geo/) datasets (GSE74099) and at CATdb  (Gagnot et al.
Nimblegen arrays and statistical analysis Experiments were designed with the statistics group of the Institute of Plant Sciences Paris Saclay (IPS2). The following competitive hybridizations were performed: NAA treated versus control, NF treated versus control, NF+NAA treated versus control, and NF+NAA treated versus NAA treated. For each comparison, one technical replicate with fluorochrome reversal including three biological replicates was performed (i. e. two dye-switch hybridizations per comparison). Two-micron scanning was performed with an InnoScan900 scanner (InnopsysR, Carbonne, France) and raw data were extracted using MapixR software (InnopsysR, Carbonne, France). For each array, the raw data comprised the logarithm of median feature pixel intensity at wavelengths 635 nm (red) and 532 nm (green).
MapMan and Medicago Classification SuperViewer analysis The MapMan software (http://mapman. gabipd. org/web/guest/mapman version 3. 5. 1) (Thimm et al. , 2004; Usadel et al. , 2005)  was used for functional classification of genes with an affymetrix probeset. Functional GO enrichment was performed using the Classification SuperViewer tool from the University of Toronto adapted for Medicago truncatula Mt4. 0 (http://bar. utoronto. ca/ntools/cgi-bin/ ntools_classification_superviewer_medicago. cgi) to classify sets of Medicago genes according to their functions. The new tool is based on the BAR Arabidopsis Classification SuperViewer framework  (Provart and Zhu, 2003)  and is called the Medicago Classification SuperViewer (http://bar. utoronto. ca/ntools/cgi-bin/ntools_clas-sification_superviewer_medicago.
cgi). To develop this tool, we obtained GO classifications for Medicago truncatula genes from two different sources: UniProt, ftp://ftp. ebi. ac. uk/pub/databases/GO/ goa/UNIPROT/goa_uniprot_all. gaf. gz, and agriGo, http://bioinfo. cau. edu. cn/agriGO/download/item2term_61 (Du et al. , 2010). GO terms and their relationships with one another were parsed from Plant GO (http://www. geneontology. org/ontology/go. obo), and GO Slim term categories based on the most recent GO Slim mappings provided by TAIR (http://www. arabidopsis. org) were used to create the appropriate GO Slim mappings for Medicago GO terms. Go Slim mappings are based on  Berardini et al. (2004) , where approximately 15 most-relevant GO Slim categories per GO aspect (molecular function, biological process, cellular component) were defined.
Similar to TAIR GO Slim, we allowed more than one GO Slim term for a given Medicago gene in cases where a GO term has multiple parents. The data for Medicago Classification SuperViewer were added to a MySQL database tables on the BAR, and the existing Classification SuperViewer framework was used to build the Medicago Classification SuperViewer using the new database tables. Both UniProt and agriGO data are available in Medicago Classification SuperViewer for Gene Ontology enrichment tests of a user-specified gene set. The UniProt GO dataset is the default option. Histochemical and microscopic analysis GUS staining and root sections were performed as described in  Herrbach et al. (2014).
NF and auxin signaling overlap As expected, NAA had a strong effect, with 9223 genes responding to the auxin treatment and 9931 DEGs identified for the NAA+NF treatment compared with control conditions (with 8339 DEGs overlapping, Fig. 7A ). Among the 9223 DEGs, 4521 were up-regulated and 4702 were down-regulated (see Table  S2  at Dryad). Well-known auxinresponsive genes, such as the transcription factors FEZ (Medtr5g040420), SOMBRERO (Medtr2g062730), LBD16 (Medtr7g096530), and PUCHI (Medtr4g119270), the efflux carrier transporter PIN3 (Medtr4g084870), and the GH3. 1 auxin-responsive gene (Medtr0035s0150) were up-regulated in both NAA and NAA+NF conditions. Several IAA/ AUX, SAUR, and cyclin genes also responded to the auxin   S2  at Dryad). Altogether, this suggests that our system was highly responsive to auxin.
Despite the NPA pre-treatment, the roots retained sensitivity to NFs, since 304 genes responded to the NF treatment (204 up-regulated and 100 down-regulated, Table  S3  at Dryad). Among these genes were the well-known symbiotic marker genes MtENOD11  (Journet et al. , 2001) , MtN1  (Gamas et al. , 1998) , the U-Box protein MtPUB1  (Mbengue et al. , 2010) , and the transcription factor NSP1  (Smit et al. , 2005). Interestingly, approximately half (156/304) of the NF-responsive DEGs were also responsive to NAA. This included the symbiotic genes MtNSP1, MtVAPYRIN  (Murray et al. , 2011) , and MtLYK10. These common target genes of NF and auxin signaling were regulated in both a similar and an opposite manner. For example, MtLBD16 was up-regulated both by auxin and by NFs, but MtNSP1 and MtVAPYRIN were down-regulated by NAA and up-regulated by NFs.
treatment (Table In many cases, the action of NFs and NAA was additive, with a fold-change in the NF+NAA treatment that was approximately the sum of the action of NAA and NFs individually (see Tables  S2, S3  at Dryad). In some cases, one treatment effect was dominant over the other. For example, the MAPKKK14-like gene (Medtr5g071560) was predominantly regulated by NFs and the MtLBD16 transcription factor by NAA (Table  S3  at Dryad). Altogether, these data suggest a strong convergence and interaction between the NF and auxin signaling pathways, acting via common and/ or parallel pathways. Of the 9931 genes that responded differentially to the NF+NAA treatment compared with control conditions (Fig. 7 , Table  S4  at Dryad), 1526 responded specifically to the combinatorial effect of NF+NAA (616 were up-regulated and 910 down-regulated; Fig.
7 , Table  S5  at Dryad). This first 'molecular synergistic' group encompassed symbiotic genes such as MtNFP (Medtr5g019040), MtLYK3 (Medtr5g086130), and MtNSP2 (Medtr3g072710), as well as hormone-related genes such as the potential ortholog of the auxin biosynthesis gene AtTAA1 (Medtr5g033520), the auxin influx carrier MtLAX1 (Medtr5g082220), and a cytokinin oxidase gene (Medtr4g126150, MtCKX2) (Table  S5  at Dryad). Heatmap clustering of these 1526 genes clearly showed a different response to the combined NF+NAA treatment compared with NF or NAA treatments alone (Fig. 7B ), although several genes had a slight (not statistically significant) tendency to respond to NAA treatment alone, suggesting that NFs can potentiate auxin action. Among the NAA-regulated genes, we wanted to investigate genes for which NF+NAA treatment had a greater effect than NAA only.
To identify such genes, we directly compared NF+NAA with NAA-treated roots and found 1038 DEGs (see Table  S6  at Dryad). Among these DEGs, 266 were responsive to NAA and NF+NAA but not to NFs compared with control conditions. We called these genes 'synergistic group 2' (Table  S7  at Dryad). By plotting the expression levels of these genes in the NF+NAA condition compared to NAA, we found 105 genes where NFs antagonized NAA action and 161 genes for which NFs enhanced the NAA effect (Fig. 7C ). Among these 266 genes, we found no obvious symbiosisrelated genes but many hormone-related genes. For instance, the ACC oxidase MtACS3 (Medtr5g015020) and the (putative) ABA biosynthetic gene MtABA4 (Medtr6g025680) were more significantly up-regulated by the NAA+NF treatment compared to the NAA treatment alone and did not respond to the NF treatment alone (Tables S7, S8 at Dryad).
Hormone-signaling genes, such as MtIAA7 (Medtr3g106850) and one potential negative regulator of LRF, the ortholog of AtPRR7 (Medtr1g067110)  (Ruts et al. , 2012)  were also represented (Tables  S7, S8  at Dryad). Expression levels of other hormone-related genes such as the auxin biosynthesis gene MtTAR2 (Medtr3g077250) and the CK signaling gene MtRR9 (Medtr4g051330) were also different in the NAA and NF+NAA treatments compared with control conditions, but no differential expression was observed with the direct NAA comparison (see Tables S8, S2 at Dryad). The same was found for the negative regulator of LRF MtCRA2 (Medtr3g110840)  (Huault et al. , 2014). Finally, we looked at the 66 genes responding to both NFs and NF+NAA but not to NAA for genes showing a difference between NF and NF+NAA responsiveness.
To do this, we chose an expression level different by at least 30% between the NF and the NF+NAA treatments and found 51 such genes (see Table  S8  at Dryad). Most of these genes were upregulated by NFs and showed a less pronounced induction in the NF+NAA condition. Only two of them (ENOD11 and a Kunitz protease inhibitor, Medtr6g045097) were more highly induced by the NF+NAA combination compared with NFs alone. This suggests that auxin can also negatively interfere with NF signaling.
8A, B ). 'DNA or RNA binding', containing many transcription factors (TFs) and genes related to the transcriptional/translational machinery, was enriched in up-regulated genes and under-represented in the down-regulated genes. A more visual representation was obtained using the Mapman software. A total of 1305 genes could be tested, and among them 252 genes could be assigned to the global 'regulation' (Supplementary Fig. S10A ) and 218 to the 'biotic stress' categories (Fig. S10B ). A large representation in protein kinases, TFs (71 from different types), and protein degradation pathway components was observed, as well as the presence of several hormone-related genes, more specifically related to auxin and ethylene pathways (Fig. S10 ). 'Biotic stress' genes encompassed a variety of biological functions, including cell wall modification, proteolysis (E3 ligases) as well as secondary metabolism (phenylpropanoids and flavonoids), and signaling components (hormone signaling, TFs, and receptor kinases) (Fig.
Graphical summary of the gene ontology (GO) classification ranking of the 616 up-regulated (A) and 910 down-regulated (B) genes of the first synergistic group obtained using the Classification SuperViewer tool from http://bar. utoronto. ca adapted to Medicago truncatula. The data are the normed frequency of each GO category for the given sets of genes compared to the overall frequency calculated for the Mt4. 0 Medicago truncatula genome as: (Number_in_Class input_set /Number_Classified input_set )/ (Number_in_Class reference_set /Number_Classified reference_set ). Hence, a frequency above 1 means enrichment and below 1 means under-representation. Errors bars are the standard deviation of the normed frequency calculated by creating 100 gene sets from the input set by random sampling (with repeats allowed, i.
Both ACC and ABA inhibit nodulation in M. truncatula and interfere with NF signaling, leading to calcium spiking Fig. 9. Functional category classification of the positive interaction and negative interaction genes from the second synergistic group. Graphical summary of the gene ontology (GO) classification ranking of the 161 differentially expressed genes (DEGs) from the second synergistic category for which NFs enhanced the NAA effect (positive interaction, A) and the 105 DEGs for which NFs antagonized the NAA effect (negative interaction, B), using the Classification SuperViewer tool from http://bar. utoronto. ca adapted to Medicago truncatula. The data are the normed frequency of each GO category for the given sets of genes compared to the overall frequency calculated for the Mt4. 0 Medicago truncatula genome as: (Number_in_Class input_set / Number_Classified input_set )/(Number_in_Class reference_set /Number_Classified reference_set ).
, 2013)  (data not shown). Nodulation, and especially nodule organogenesis, is known to involve auxin and LR-related genes  (Breakspear et al. , 2014; Larrainzar et al. , 2015). We found LBD16 and PLT3 genes to be regulated both by NFs and NAA in our dataset, with a dominant auxin effect. Recently, MtPLT3 was shown to be involved in nodule meristem maintenance  (Franssen et al. , 2015). Interestingly, the ortholog of one of our group 1 genes, MtCKX2, was recently shown to be involved in nodulation in lotus, but it has no impact on LR density  (Reid et al. , 2016). Finally, among the 48 NF-responsive genes whose expression was attenuated in the NF+NAA condition (group 3), eight displayed strong expression in mycorrhizal transcriptomic data from the MtGeneAtlas  (Benedito et al. , 2008). There is already increasing evidence of auxin being implicated during symbiotic interactions  (Breakspear et al.
LRF candidate genes from our dataset Our transcriptomic approach revealed a large interaction between NAA-and NF-regulated genes. The main issue that now remains is to pinpoint the most interesting genes that could be responsible for NF effects on LRF. Given the primordial role of auxin in controlling LRF, genes from group 2 may display the most interesting candidates. Interestingly, group 2 genes with a positive interaction between NFs and auxin were enriched in signaling functions. One way to find such genes is to look for possible orthologs of well-known LRF regulators identified in Arabidopsis. Among such candidates, ACR4, SLR, or ARF7-like genes were only regulated by NAA and no interaction with NFs was visible in our data. Comparison between our DEGs and Arabidopsis root transcriptomic data  (Vanneste et al.
Acknowledgments We thank  T. Timmers  for help with confocal imaging and  F. Maillet  for providing purified NFs. We thank  P. Gamas  ( LIPM ) for sharing the M. truncatula genome data annotation obtained in the ' Symbimics' program  ( ANR-08-GENO-106 ) that was used for the design of the probes. This work was supported by the  French LabEx project  ' TULIP'  ( ANR-10-LABX-41 ;  ANR-11-IDEX-0002-02 ) and the POPS transcriptomic platform by the  LabEx Saclay Plant Sciences-SPS  ( ANR-10-LABX-0040-SPS ). Violaine Herrbach  was supported by the  CNRS (INEE)  and the  Novozymes company.
A good summary of these common notions may be found in the paper of Bayer  [2]  in the context of the random dot model. Much of the subsequent analog literature is concerned with proposing mathematical models  [3, 10, 19]  which imitate the perceived effect of grain "clumping". This clumping, or agglomeration, of grains is an essential visual feature of graininess. In more modern work, the main goal is grain synthesis. The most popular method seems to be to use real scanned examples of film grain. Film grain synthesis products such as DxO's "FilmPack" 2  and Grubba Software's "TrueGrain" 3  tools take this approach. More precisely, a single grain image is saved for each film type. Similarly, Schallauer and Mörzinger  [16]  copy grain from scanned analog images and synthesize a new grain image from these examples, which they then apply to the image in an additive fashion.
A similar approach is used in the Film Emulation feature of the G'MIC free software 4  using the random phase texture algorithm  [5]  to synthesize large grain textures from small stored samples. The Heeger-Bergen texture synthesis  [8]  algorithm is used by Bae et al. [1]  on a constant gray-level area in an example image to produce film grain. Stephenson and Saunders  [17]  filter white noise in the Fourier domain. Yan et al. [22]  propose an additive film grain model with signal-dependent noise. A drawback is that their approach supposes that film grain noise is spatially uncorrelated, which is clearly unrealistic. Oh et al. [15]  propose an auto-regressive model for film grain removal and synthesis. They point out that spatial correlation is crucial for producing realistic film grain. However, they consider that an input grainy image is available, and that the characteristics of the grain may be extracted.
Conclusion We have presented a film grain rendering algorithm based on the discrete evaluation of a filtered Boolean model using a Monte Carlo simulation. We have shown the film grain rendering results of the algorithm with black and white or color film grain for any digital image at any desired resolution. We have presented and analyzed two different implementations of this algorithm, which present different strengths depending upon the situation. We have provided a method to choose between the two, based on an analysis of the execution times when varying the grain parameters. We also demonstrate the visual variability of the grain when the parameters are tuned. The code of this implementation is publicly available at the IPOL website. "FilmPack" tool of DxO Result of the Boolean model Figure  10 : Limitation of the film grain model when imitating certain film emulsion types.