dataesr/scientific-paragraphs-categorization huggingface dataset:
Applying the method to GB1 protein. Having established the method applicability with DENV C, we proceeded to test it with the B1 immunoglobulin binding domain of streptococcal protein G (GB1), which contains 56 amino acid residues and a structure of four stranded β-sheets with one long α-helix on top (Fig. 6a ), as shown by X-Ray diffraction crystallography as well as by NMR (PDB ID: 2GB1 and 5JXV)  35, 36, 43. GB1 has been extensively studied by different biophysical methods and is one of the smallest stable folded globular domains known. A pH interval of 1. 5 was assayed as well, but now changing the pH from 6. 5 to 8. 0. No major conformational changes were seen (Fig. 6b ), only minor local switches (Fig. 6c, d ). As the overall protein structure remains highly stable within that pH range  37  , we went further and tested if the intensities of the 1 H-  15  N HSQC peaks revealed any changes (Fig.
GB1 structure and 1 H-15 N HSQC spectra from pH 6. 5 to 8. 0. (a) GB1 experimental structure. GB1 is a domain of immunoglobulin G binding protein that is negatively charged, with 6 cationic and 10 anionic residues out of 56. It consists of four β-sheets, named β1 to β4 (colored in orange), plus one α-helix, named α1 (colored in pink), connected by short loops (PDB ID 5JXV  43  ). (b) Superimposed GB1 1 H-  15  N HSQC spectra at pH 6. 5 (blue), 7. 3 (red), 7. 6 (green) and 8. 0 (black). Major chemical shifts corresponding to large conformational rearrangements are not observed. (c and d) Zoom on spectral regions where all types of responses to the pH variation are observed: peaks that vary in intensity and/or display a small chemical shift perturbation. Most peaks neither vary in intensity nor in chemical shift.
UCSF Chimera v1. 9 software 54 was used for protein structure visualization  43. G. We employed another physiological interval of 1. 5 pH units, from 6. 5 to 8. 0 (Fig. 6 ). The data shows that also with this protein there are no major conformational changes. Moreover, the key regions of the protein are also readily identified (Fig. 7 ), namely at the single amino acid level (Fig. 8 ). Therefore, the pH range tested (6. 0-8. 0) is particularly suitable since most proteins isoelectric point is not close to the physiological pH (since they would not be functional as they could precipitate) and, apart from histidines, titratable amino acids are usually not affected at this pH range, as inferred from Fig. S1. Even if minor conformational changes occur, these would be readily visible in the NMR spectra through chemical shift perturbation (CSP) studies, allowing it to be taken into account in the analysis.
Briefly, α0 region is more malleable than the other secondary structure domains and α1 is partially flexible when compared to α2, α3 and α4. The N-terminal IDP region as well as the first amino acids of the α-helices and the loop domains are accessible to the solvent, in agreement with the available DENV C structure (PDB ID 1R6R  32  ). Overall, the results are consistent with the current understanding of DENV C structure and function  25, 26, [28] [29] [30] [31] [32]  , supporting the use of the proposed methodology to investigate protein architecture. Importantly, the findings concerning the flexible fold region are particularly new and shed a new light on the protein biological activity. The term "flexible fold", to refer to that section of the protein, was first used by us upon comparing DENV C NMR structure with the capsid protein X-Ray crystallographic structure of the closely related West Nile virus  26, 32, 33.
The pH was measured using a Docu-pH meter (Sartorius) calibrated with standard solutions. The initial pH was 6. 50. For each pH step, the pH was adjusted with microliter additions of 0. 15 M NaOH solution. The concentration of added salt amounted to less than 1 mM. Spect a were collected in steps of ~0. 4 pH units from pH 6. 5 to 8. 0. Data were processed using Bruker TopSpin TM 4. 0 and analyzed with CCPNMR  53  for.
Acknowledgements This work was supported by  Fundação para a Ciência e a Tecnologia -Ministério da Ciência, Tecnologia e Ensino Superior (FCT-MCTES, Portugal)  projects  PTDC/QUI-BIQ/112929/2009, PTDC/SAU-ENB/117013/2010  and  PTDC/BBB-BQB/3494/2014 ,  Calouste Gulbenkian Foundation  ( FCG, Portugal ) project  Science Frontiers Research Prize 2010 ,  European Union Marie Skłodowska-Curie Research and Innovation Staff Exchange   H2020-MSCA-RISE-2014  project  INPACT  (Grant  644167 ),  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brazil , grant numbers  471239/2012-7  and  306669/2013-7 ) and  Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ, Brazil , grant numbers  E-26/110. 636/2012 , E-26/110. 092/2013and E-26/201. 167/2014 ). AFF acknowledges FCT- MCTES  fellowship  SFRH/BD/77609/2011.
MS acknowledges  FCT-MCTES fellowship   PD/BD/128202/2016. ICM acknowledges consecutive funding from the  FCT-MCTES  fellowship  SFRH/BPD/74287/2010  and the  Program "Investigador FCT " ( IF/00772/2013 Research Contract ). This work was also supported by  LISBOA01-0145-FEDER-007391  project, cofunded by  FEDER , through  POR Lisboa 2020 -Programa Operacional Regional de Lisboa, PORTUGAL 2020 , and  Fundação para a Ciência e a Tecnologia. The NMR spectrometers at FCT NOVA are part of  Rede Nacional de RMN (PTNMR) , supported by  FCT-MCTES  ( ROTEIRO/0031/2013 -PINFRA/22161/2016 ) co-funded by  FEDER  through  COMPETE 2020, POCI , and PORL and FCT through PIDDAC.
Motivation: Low-coverage next-generation sequencing (LC-NGS) methods can be used to genotype bi-parental populations. This approach allows the creation of highly saturated genetic maps at reasonable cost, precisely localized recombination breakpoints, and minimize mapping intervals for quantitative-trait locus analysis. The main issues with these genotyping methods are (1) poor performance at heterozygous loci, (2) a high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. Recent methods like Tassel-FSFHap or LB-Impute are excellent at addressing issues 1 and 2, but nonetheless perform poorly when issues 3 and 4 are persistent in a dataset (i. e. "noisy" data). Here, we present an algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately types heterozygous chromosomic regions, corrects erroneous data, and imputes missing data.
We compare its performance with Tassel-FSFHap, LB-Impute, and Genotype-Corrector using simulated data and three real datasets: a rice single seed descent (SSD) population genotyped by genotyping by sequencing (GBS) by whole genome sequencing (WGS), and a sorghum SSD population genotyped by GBS. Availability: NOISYmputer, a Microsoft Excel-Visual Basic for Applications program that implements the algorithm, is available at mapdisto. free. fr. It runs in Apple macOS and Microsoft Windows operating systems.
Even then, low-quality markers might not be detected easily and could alter dramatically the quality of the imputation and the final genetic map. Here, we present an algorithm for imputation of LC-NGS data that eliminates the need of complex pre-filtering of noisy data, accurately finds heterozygous chromosomic regions, corrects erroneous data, and imputes missing data. We test its accuracy using simulated data, and we compare its performance with Tassel-FSFHap, LB-Impute and Genotype-Corrector using three real datasets: (1) a rice SSD population genotyped by GBS and (2) by WGS and (3) a sorghum SSD population genotyped by GBS. The algorithm is implemented in NOISYmputer, a user-friendly computer program (see "Availability and system requirements" section).
Data preparation -Convert the VCF to an 'ABH-' matrix of genotypes (plain text file) encoded as "A" (homozygous parent 1), "B" (homozygous parent 2, "H" (heterozygous) and "-" (missing data), using parental information. If parental genotypes contain many missing data, it is desirable to pre-impute them from the population. LB-Impute -either stand-alone or from within MapDisto  (Heffelfinger, Fragoso, & Lorieux, 2017). -and Tassel-FSFHap can both perform this task. -Split the ABH-matrix into individual chromosomes. NOISYmputer has commands to execute these tasks automatically. The VCF conversion to ABH-format is executed by the Java MapDisto Addons. The matrix-splitting command has an option to compress individual chromosomes files. It uses the 7-zip utility, available for free at 7-zip. org.
Application note Overall, accuracy was very high in all situations and comparable to values obtained by authors of Tassel-FSFHap, LB-Impute, and higher than Genotype-Corrector accuracy when MDR exceeded 0. 4. To test the efficiency of NOISYmputer with real data, we compared it with FSFHap, LB-Impute and Genotype-Corrector using and three datasets: one high-quality dataset, and two noisy datasets. We also considered ABHgenotypeR  (Furuta, Ashikari, Jena, Doi, & Reuscher, 2017)  in early tests but did not go on with full testing as the algorithm showed very poor performance with noisy data. PlantImpute  (Hickey, Gorjanc, Varshney, & Nettelblad, 2015)  and AlphaPlantImpute  (Gonen et al. , 2018)  were also designed for imputation of bi-parental genetic data, but they only handle DNA chipbased SNP data in their present form.
Data description The high-quality real "Rice_GBS" dataset was composed of GBS data (~0. 67 X coverage, "Yale protocol"  (Heffelfinger et al. , 2014) ) for a population of 187 rice SSD lines from the cross [IR64 x Azucena], obtained at the IRD, Montpellier, France  (Fragoso et al. , 2017). The two noisy real datasets were (1) "Rice_WGS", composed of WGS data (~1. 55 X coverage, "Genoscope protocol" (Karine Labadie, pers. comm)) for 212 SSD lines from the same [IR64 x Azucena] cross, and (2) "Sorghum_GBS", composed of GBS data (~0. 77 X coverage, "Cornell protocol"  (Elshire et al. , 2011) ) for a population of 150 sorghum SSD lines from the cross [SSM1611 x SSM249] kindly provided to us by Dr. Mbaye Ndoye Sall (ISRA/CERAAS, Thies, Senegal). Note that a 1. 55X WGS represents lower coverage than a 0.
Methods for algorithm comparison In real data, we do not know the true state at each locus, so we cannot estimate imputation accuracy directly. One alternative way is to calculate the final genetic map and to compare it with existing, high-quality maps. A correctly imputed dataset should generate a map size coherent with maps calculated from high-quality marker data. An imputed dataset that contains substantial rate of genotyping errors will generate map expansion, that is, a genetic map longer -in centimorgans (cM) -than its expected size, due to falsely imputed recombination breakpoints. Map expansion was calculated using the smallest map as a baseline. Maps were calculated for chromosome 1 of each dataset, after full imputation of the few remaining missing data using the NOISYmputer "Full imputation" command.
Marker orders were left as determined by the RefSeq. Recombination fractions were obtained with the "Calculate final map" command in NOISYmputer, which is equivalent to the MapDisto 2 two-point method that can use heterozygotes to calculate recombination frequencies in SSD populations  (Heffelfinger et al. , 2017). We could have also used multipoint mapping as implemented in Mapmaker/EXP  (Lander et al. , 1987)  or R/qtl  (Broman, Wu, Sen, & Churchill, 2003) , but these algorithms cannot use residual heterozygosity to calculate recombination frequencies in RIL-SSD populations. Note that two-point estimation of recombination fractions on fully imputed data is equivalent to multipoint estimation as implemented in Mapmaker/EXP or R/qtl. To test NOISYmputer and other methods' accuracy on real data, we used the high-quality Rice_GBS dataset.
We compared map sizes to the map obtained with LB-Impute combined with BP-Impute and a custom R script that selects the most probable genotype, as extensive testing on this dataset showed that this method provided accurate imputation  (Fragoso et al. , 2016 (Fragoso et al. , , 2017)). To test performance of the four algorithms with noisy data, we (1) compared the maps they generated for the Rice_WGS dataset to the map obtained for the Rice_GBS dataset and (2) compared map sizes for Sorghum_GBS datasets to the consensus sorghum map  (Mace et al. , 2009). Additionally, in order to avoid any bias in map expansion estimation of chromosome 1 only, we calculated the total map size for all chromosomes with NOISYmputer. Finally, we also tested NOISYmputer's ability to estimate residual heterozygosity in RILs-SSD, which was simply calculated as the percentage of heterozygous calls over all chromosomes, and compared with the expected residual heterozygosity,   = 0.
5 (-1) , where g is the number of selfing generations. For Genotype-Corrector tests, we observed that its 'vcf2map' command requires a VCF with phased data (A=ref, B= alt) to generate proper ABH-matrices. That is, 'vcf2map' doesn't seem to have an option to use parental information for phasing. Thus, we used the MapDisto Addons VCF converter instead, available in NOISYmputer. NOISYmputer was run in 64-bit Windows 10 (Apple Bootcamp), and Tassel-FSFHap and LB-Impute were run in macOS with Java 1. 8 JRE, on a portable computer equipped with a "Kaby Lake" Intel Core i5 CPU 7267U@3. 1 GHz and 16 GB of RAM. Genotype-Corrector was run in Red Hat Enterprise Linux Server release 5. 8 (Tikanga), on a Dell PowerEdge M610 computer (server) equipped with an Intel Xeon CPU E5620@2. 40GHz and 198 GB of RAM.
Results High-quality real data (Rice_GBS dataset) All four algorithms performed well when applied to the Rice_GBS high-quality data (Table  1  and Table  S3 , Rice_GBS entry). NOISYmputer produced the shortest map (183. 4 cM), with no apparent imputation incoherence (Figure  S1 ). LB-Impute introduced incorrect heterozygote calls at recombination breakpoints (Figure  S2 ), leading to a map size of 192. 1 cM and map expansion of 4. 7 % but this can be corrected with BP-Impute  (Fragoso et al. , 2017). FSFHap produced very similar results compared with NOISYmputer, with a map size of 184. 3 cM a map expansion of 0. 5% (Figure  S3 ). Genotype-Corrector introduced incorrectly heterozygote calls at random sites, leading to a 7% map increase (Figure  S4 ), but this could be easily corrected with further and simple chunk correction.
Noisy real data (Sorghum_GBS dataset) With noisy data, we observed important differences in map sizes between the different methods (Table  1  and Table  S3 , Sorghum_GBS entry). NOISYmputer generated the shortest map, at 181. 3 cM, coherent with the consensus sorghum map size of 191. 8 cM  (Mace et al. , 2009)  (Figure  S5 ). LB-Impute inaccurately imputed many singletons, resulting in very severe map expansion (53. 4X) (Figure  S6 ). FSFHap also inaccurately imputed many singletons, although in lesser extent than LB-Impute, resulting in severe map expansion (5. 8X) (Figure  S7 ). Genotype-Corrector was unable to eliminate some noisy data and converted them to heterozygous calls, resulting in severe map expansion (3. 7X) (Figure  S8 ). The total map size calculated with NOISYmputer, at 1,330.
4 cM, matched well with the framework sorghum map size (1,355. 4 cM). The residual heterozygosity calculated with NOISYmputer, at 1. 43%, matched very well to the expected heterozygosity (1. 56 %). Very noisy real data (Rice_WGS dataset) The noisiest dataset, Rice_WGS, also revealed the greatest differences in final map sizes between the different algorithm methods (Table  1  and Table  S3 , Rice_WGS entry). NOISYmputer generated the shortest map, at 181. 3 cM, coherent with the Rice_GBS map size of 183. 4 cM (Figure  S9 ). The inability of FSFHap to eliminate noisy data, especially clustered distorted SNPs, and the inaccurate imputation of many small heterozygous chromosome chunks, conducted to severe map expansion (78. 5 X) (Figure  S10 ). Genotype-Corrector was also unable to eliminate some noisy data and converted them to heterozygous calls, resulting in severe map expansion (6.
Speed The four tested algorithms used different programming languages, so comparing them for speed is somewhat unfair. Also, LB-Impute imputes VCF files, while NOISYmputer, FSFHap and Genotype-Corrector impute Hapmap-like files, which induces another unfairness towards LB-Impute. For GBS data, we found that NOISYmputer was greatly faster than LB-Impute (~96 X) and Genotype-Corrector (~28X), and approximately half as fast as FSFHap. For WGS data, NOISYmputer was slightly faster than FSFHap, and ~90X faster than Genotype-Corrector (Table  1 ).
Furthermore, its authors do not seem to have tested it against FSFHap or LB-Impute, which are specifically designed for bi-parental population imputation  (Miao et al. , 2018). Instead, they compared it with Beagle, designed for diversity panels and LinkImpute, designed for non-model organisms with no RefSeq available. This might explain why they found Genotype-Corrector to perform better than alternative methods. Finally, we estimated its total run time at 4. 35 days for the complete Rice_WGS dataset, which would make it impractical for very large datasets. One way to even increase imputation accuracy could be to use combinations of NOISYmputer and LB-Impute or FSFHap, which leverage very efficient hidden Markov chain-based methods. For instance, one could use an intermediate output (e. g.
, after the incoherence filter) of NOISYmputer as input to LB-Impute or FSFHap. We are currently in the process of converting NOISYmputer's code to Java, in order to further improve its speed. This will allow us to explore wider parameter spaces and, for instance, find sets of parameter values that lead to convergence of an objective function, e. g. expected map size or overall heterozygosity. Although previous methods have made significant advances in addressing the challenges listed above, the noisiness of imputed datasets are still evident by expanded genetic maps, excess heterozygosity, and probabilistically unlikely recombination events contained within a short physical interval. Here, we introduce an algorithm that, in a series of steps, addresses each source of error to create higher-quality datasets for improved trait mapping and genomicsassisted breeding.
Summary of imputation results for chromosome 1 of one high-quality and two noisy datasets, using NOISYmputer, LB-Impute, Tassel-FSFHap and Genotype-Corrector (G-Corrector). ( * ): SNP number after pre-filtering; ( ** ): SNP number after ABH conversion. ( *** ): LB-Impute could not handle the Rice_WGS dataset. See TableS3 more details.
Acknowledgements We thank  Dr. Mbaye Ndoye Sall  ( ISRA/CERAAS, Thies, Senegal ) for kindly sharing the GBS data for the sorghum SSD population,  Karine Labadie  ( CEA, Institut de Génomique, Genoscope, Evry, France ) for sharing the WGS data for the rice RIL population,  Christine Tranchant  ( IRD, Montpellier, France ) for her help with retrieving the Rice_WGS data and  François Sabot  ( IRD, Montpellier, France ) for coordinating the  IRIGIN  project.
Availability and system requirements The algorithm is implemented in Visual Basic for Applications (VBA). NOISYmputer, a Microsoft Excel application that implements the algorithm, is available at no charge for academic use (see information at mapdisto. free. fr/NOISYmputer). Researchers who intend to use NOISYmputer for commercial purpose should contact us. NOISYmputer runs in Apple OS X or macOS and Microsoft Windows operating systems. The VCF converter needs Java 1. 8. In Windows, 64-bit versions of both the OS and Excel should be preferred for very large datasets. The version for Apple macOS runs better in the 32-bit Excel 2011, as the 2016 and later versions of Excel (2019 or 365) implement a slow, sometimes buggy VBA interpreter.
Retain the SNPs that mark recombination breakpoints (at one or both sides of the breakpoint). Accuracy tests on simulated data Method for accuracy estimation 9. Impute data-step 2. Now that we have identified heterozygous regions, we can use frequencies to refine the allele calling. Define a smaller window of m SNPs (m < n) around each tested locus. In each individual or line, calculate To test NOISYmputer's accuracy, we used simulated datasets generated as follows: a chromosome of 180 cM containing 10001 SNPs was simulated for 200 SSD lines in MapDisto 2. The calculated map in MapDisto was 172. 7 cM. The resulting matrix of genotypes was then imported into NOISYmputer and different types of noise were added to the data using NOISYmputer simulation code: (1) variable missing data rate (MDR), (2) variable MDR and segregation distortion on clustered SNPs, and (3) combinations of missing data rate, A → B or B → A calling error rate, and A or B → H calling error rate.
All the instruments are driven by a data logger (Campbell ® CR6), which stores locally the data and then upload them to a remote server every hour during the day using a 3G modem. SMS (Short Message Service) alerts can be sent depending on scheduled conditions (e. g. low battery voltage, water level threshold, all samples of the automatic sampler collected). The platform has been designed to be as autonomous as possible: it is powered by a battery supplied by a solar panel. Limiting the power consumption of the platform was one of the main technical challenges, because of the quantity of instruments integrated. A simple 100 W solar panel is sufficient to power up the entire platform, even during winter or low insulation conditions. The RIPLE platform has been designed to facilitate its use and maintenance.
There have been studies that have developed autonomous monitoring platforms in hostile environments to remain unattended for several months or years  (Musko et al. , 2009; Bhatti and Ridley, 2014; Clauer et al. , 2014; Peters et al. , 2014; Morschhauser et al. , 2017)  but little or no for monitoring of water and solid (both fine and coarse particles) discharges in rivers  (Mueller et al. , 2013; Navratil et al. , 2011; Comiti et al. , 2014; Griffiths et al. , 2014). This study presents the development of a multi-instrumental platform called RIPLE (River Platform for monitoring Erosion). The different variables measured relate to the flow discharge, water quality, fluxes of fine and coarse sediment and optionally the properties of suspended fine sediments (settling velocity). A user interface is also presented that enables data visualization and remote configuration of the platform.
Two instruments, i. e. PASS (automatic water sampling) and SCAF, are now integrated in RIPLE but were not included in the case study presented in Section 6. They therefore appear as optional in Table  1. The devices integrated in RIPLE are all controlled by the Campbell CR6 data logger (Figure  2 ). This data logger was chosen because of its robustness in isolated sites and under difficult environmental conditions (humidity, temperature), its flexibility in terms of ports (universal ports), Ethernet port, Secure Digital (SD) memory card slot, large number of possible expansion modules (CDM-A108: analog expansion module, SDM-SIO1A: RS-232 RS-485 RS-422 expansion modules) enabling to control the large number of instruments integrated. This control system has a wireless connection option, allowing to remote control the system from the car or from a shelter during bad weather.
It means that the entire section of the river on which the platform is located and the adjacent river reach (about ten meters upstream and downstream) should be visible in the image. This makes it possible to understand the hydraulic behaviour of the river reach under all water level conditions. The control camera is usually installed at the opposite river bank to the staff gauge in order to see both the staff gauge and most of the sensors. The consumption of the camera is 5 W and it can be turned off while it is not taking pictures. The camera supports for HTTP, UDP and FTP protocols. The camera is connected to a Power over Ethernet (PoE) injector, which in turn is connected to the data logger via an Ethernet cable (see Appendix A). The PoE injector is connected to the power supply (12 V).