dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusions We describe a procedure for students to become acquainted with the Gene Ontology classification system which is widely used in genomics and systems biology research to characterize gene function. Grouping genes with GO Terms and the DAVID database is based on a protocol that we use with our summer interns to profile gene expression data related to behavioral neuroscience studies  (Crusio et al. , 2017). Grouping genes in this way identifies genes that function in like processes and also provides information about the overall distribution of a set of genes associated with a particular tissue or process. This tutorial will familiarize early stage students with a biological database and teach them how to mine it and extract useful information from (for?) a sample list of genes. Survey response data from twelve students indicate that they believe they have benefited intellectually from this work and that they enjoy this type of learning experience.
○ The first sentence of that section is "DAVID is primarily a clustering program that groups genes based on different criteria related to GO terms. " However, the protocol, demonstrated in Video 1, only shows how to obtain all the functional annotations of all genes from the list inputted, there is no clustering analysis here. The graph that provides clustering is the "Functional Annotation Clustering", but I couldn't get it to work; I get an error message stating that there are too many genes on my list (3000), which by the way was incorrect.
○ The authors state that "DAVID links to other databases that contain complementary information like The Gene Ontology". This is incorrect: the Gene Ontology is the source of the data (annotations) presented in DAVID. The Gene Ontology therefore does not contain complementary information; it contains the source data. (Note also that DAVID is much out of date with respect to the GO data; the last update was in 2018, and the previous one in 2009. This should not impact the ability to use this tool for demonstration purposes, but for research project, as those mentioned by the students in their comments, the gene enrichment analysis tool from the GO website should be favored, as its data is updated monthly).
We apologize for the confusion. We have changed the word "profiling" to "analyses" for clarification. The first sentence of that section is "DAVID is primarily a clustering program that groups genes based on different criteria related to GO terms. " However, the protocol, demonstrated in Video 1, only shows how to obtain all the functional annotations of all genes from the list inputted, there is no clustering analysis here. The graph that provides clustering is the "Functional Annotation Clustering", but I couldn't get it to work; I get an error message stating that there are too many genes on my list (3000), which by the way was incorrect. We apologize for the confusion. We have omitted the word "clustering" from the sentence. The authors state that "DAVID links to other databases that contain complementary information like The Gene Ontology".
This is incorrect: the Gene Ontology is the source of the data (annotations) presented in DAVID. The Gene Ontology therefore does not contain complementary information; it contains the source data. We have removed the word "complimentary" from the sentence. (Note also that DAVID is much out of date with with respect to the GO data; the last update was in 2018, and the previous one in 2009. This should not impact the ability to use this tool for demonstration purposes, but for research project, as those mentioned by the students in their comments, the gene enrichment analysis tool from the GO website should be favored, as its data is updated monthly). Students do in fact use the Gene Ontology site in addition to DAVID for their research projects. Based on a suggestion made by Reviewer 3, we have now added an optional gene enrichment exercise using the Gene Ontology tools.
William Grisham University of California, Los Angeles (UCLA), Department of Psychology, Los Angeles, CA, USA This article is aimed at describing an easily searchable database that can be used by students to search by gene ontologies. The point of the article is to provide a guide to use this tool in education at the high school level, but the article could provide a good starting point for college level instructors and even industry trainers. The article describes the underlying question well and chooses a gene, RAB5A, whose function should be readily understood by most biologists. The submitted article is timely because the DAVID tool described is not very intuitive. Thus, an easy to follow guide such as the article provides is most welcome. Notably, database tools are becoming more elaborate, and their complexity has created a barrier for effectively use in teaching, particularly at lower levels.
This addition would make this article of much more value to educators who could then see if incorporating this tool into their pedagogy is worthwhile. Response: To address this point, we have conducted and provided the results of a student survey designed to determine the viability of the method. The survey questions and responses are provided in the newly added Tables, 1, 2, and 3. To summarize the results, students report that they benefited The benefits of publishing with F1000Research: Your article is published within days, with no editorial bias • You can publish traditional articles, null/negative results, case reports, data notes and more • The peer review process is transparent and collaborative • Your article is indexed in PubMed after passing peer review • Dedicated customer support at every stage • For pre-submission enquiries, contact research@f1000.
Are sufficient details provided to allow replication of the method development and its use by others? YesIfany results are presented, are all the source data underlying the results available to ensure full reproducibility? No source data required Are the conclusions about the method and its performance adequately supported by the findings presented in the article? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Molecular microbiology; STEM outreach I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Reviewer Report 21 March 2019 https://doi. org/10. 5256/f1000research. 19752. r45177 © 2019 Grisham W.
This is an open access peer review report distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Typos and errors: Figure 2 'David' should be DAVID. Corrected Question 4 Table 1 should be 92% (11/12) and not 96%. Corrected Lists are inconsistent: sometimes lines end with a period, sometimes not. Corrected Competing Interests: No competing interests were disclosed. Reviewer Report 09 July 2019 https://doi. org/10. 5256/f1000research. 21308. r50598 © 2019 Lovering R.
the rationale for developing the new method (or application) clearly explained? Yes Is the description of the method technically sound? Yes Are sufficient details provided to allow replication of the method development and its use by others? Yes If any results are presented, are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions about the method and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Is Reviewer Expertise: Molecular microbiology; STEM outreach. Version 2 I confirm that I Reviewer Report 02 May 2019 https://doi. org/10. 5256/f1000research. 20696. r47231.
Fixed wing aircraft have been controlled using different techniques like backstepping  [15] , sliding mode  [15] , robust nonlinear control  [16] , neuronal network  [17]  and even super-twisting controls  [18]. A comparison between non-linear controllers based on backstepping, sliding mode and nested saturations, were done in Reference  [19]  showing that sliding mode control has good performance in yaw and roll movements. The sliding mode control strategy uses less energy consumption than the others but suffers from high frequency chattering. In order to reduce such phenomenon and retain the benefits of a control based on sliding modes, a Super-Twisting control technique was proposed. (3) The present paper also demonstrates the correct operation of the Super-Twisting control technique when applied to fixed wing vehicles using the aerodynamic parameters obtained by the XFLR5 software in the numerical simulation of the complete lateral dynamic and in real-time experiments.
SAR Data Since rice cultivation in Camargue takes place between May and October of each year, we focus our analysis of SAR data only on this period. For this reason, thirty-one S1 images obtained from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation operating at the C-band (frequency = 5. 406 GHz, wavelength ~ 6 cm) were downloaded for the period between 08 April 2017 and 29 October 2017. The 31 S1 images used were acquired in the Interferometric Wide Swath (IW) imaging mode with the VV and VH polarizations. In addition, the images were generated from the high-resolution Level-1 Ground Range Detected (GRD) product with 10 m × 10 m pixel spacing. These Sentinel-1 images are available via the Copernicus website (https://scihub. copernicus. eu/dhus/#/home). To calibrate the S1 images (radiometric and geometric correction); the Sentinel-1 Toolbox (S1TBX) developed by the ESA (European Spatial Agency) was used.
SAR Data Since rice cultivation in Camargue takes place between May and October of each year, we focus our analysis of SAR data only on this period. For this reason, thirty-one S1 images obtained from the Sentinel-1A (S1A) and Sentinel-1B (S1B) satellite constellation operating at the C-band (frequency = 5. 406 GHz, wavelength ~6 cm) were downloaded for the period between 08 April 2017 and 29 October 2017. The 31 S1 images used were acquired in the Interferometric Wide Swath (IW) imaging mode with the VV and VH polarizations. In addition, the images were generated from the high-resolution Level-1 Ground Range Detected (GRD) product with 10 m × 10 m pixel spacing. These Sentinel-1 images are available via the Copernicus website (https://scihub. copernicus. eu/dhus/#/home). To calibrate the S1 images (radiometric and geometric correction); the Sentinel-1 Toolbox (S1TBX) developed by the ESA (European Spatial Agency) was used.
Random Forest Classification Random Forests (RF) is a learning method for classification based on generating a large number of decision trees, where each is constructed using a different subset of the training set. These subsets are usually selected by sampling at random and with replacement from the original data set. The decision trees are then used to identify a classification consensus by selecting the most common output. It has demonstrated its ability to yield high quality classification with a high computational time. In our study, we classified rice and non-rice plots by introducing the extracted metrics into the random forest classifier available via the "R" open statistical computing software (Package "randomForest"). The random forest is trained using the values of the position of the maximum (b), the standard deviation (c), and the correlation coefficient (R 2 ) of the VV/VH Gaussian fitting, the variance of VV/VH, and the slope S of the VH linear fitting for the 832 reference plots for the two classes (Rice and other crops).
In fact, 5 folds cross-validation is first performed using the 832 reference plot. To do so, the dataset is randomly divided into 5 folds of equal size. Four folds are used to train the model, and the fifth fold is used for the validation phase. This operation is repeated five times, so that each fold will be a possible validation set. The number of trees chosen is 300 and the number of features used in the construction of each tree is 2. Finally, the map obtained using the trained RF classifier was further compared to the available national data. The overall accuracy, Kappa, and F 1 score were calculated for the obtained confusion matrix  [28, 29] F 1 score = 2TP 2TP + FP + FN ( (8) ) 9 where TP is the number of the rice plots truly classified as rice plots, TN is the number non-rice plots truly classified as non-rice plots, FP is the number of non-rice plots falsely classified as rice, FN is the number of rice plots falsely classified as non-rice plots, N is the total number of plots, P 0 is the observed accuracy and P e is the expected accuracy.
The results show that the high significant metrics are the variance of the VV/VH and the slope of the linear fitting of the VH, whereas the less significant metrics are the maximum position and standard deviation of the Gaussian fitting of VV/VH. The rice area map of the Camargue region was finally established by applying the generated random forest classifier (Figure  9a ). The estimated rice area in 2017 obtained using the proposed classification is 9740 ha. Comparing this area to the rice area from the IGN data of 2011, we observe that the rice cultivation area has significantly decreased in the region from 16,000 ha in 2011 to 9740 ha in 2017 (our estimation). The obtained rice area was then compared to the rice area declared in the RPG data (Figure  9b ). Using the proposed classification the rice area was estimated as 9740 ha slightly lower than the declared area (10,015 ha).
Discussion In this study we mapped rice areas in the Camargue region using Sentinel-1 SAR backscattering time series and two classification approaches: random forest and decision tree. We first analyzed the SAR backscattering coefficient over the entire agricultural crop existing in the area and showed that the rice plots have a specific temporal profile in VH polarization and in the VV/VH ratio. Then, we mapped then rice areas based on metrics derived from the VH and VV/VH time series. The obtained results were finally compared to existing national data. This validation indicated that the proposed approach has good performance where the overall accuracy was 96. 6% and Kappa coefficient was better than 92. 5% (using RF classifier). To properly assess the quality of the obtained results, we compared the classification results with results obtained by other classification methods using the same dataset.
On the other hand, the 5 folds cross-validation of the RF classifier in our approach shows that the producer accuracy reaches 99. 0% while the user accuracy reaches 98. 4%. Figure  10  presents the classified rice areas produced using their proposed approach (using the DNN). Since their approach uses the same dataset as our study (Sentinel-1 time series and ground data) and was accomplished for the same year (2017), a comparison was also performed between the obtained rice class of Ndikumana et al. [25]  and the RPG-2017. The 9976 plots classified in our proposed approach were checked for being rice or other crop in the agricultural land cover map proposed by Ndikumana et al. [25]  and then compared to the previous results obtained for RPG data. Table  4  presents the confusion matrix produced by comparing Ndikumana's rice areas and RPG rice areas based on the classified plots in our approach.
Lasko et al. [24]  have mapped rice areas using Sentinel-1A time series in Hanoi, Vietnam by applying the random forest classifier. The highest overall accuracy (93. 3%) was obtained when using the VV/VH ratio. Clauss et al. [34]  also mapped rice areas over six different study sites using S1 temporal series by applying the decision tree. They chose the VH polarization due to the high dynamic range of backscatter over rice areas. They achieved an overall accuracy of 83%. In their study they recommend that increasing the temporal density of the used Sentinel-1 images would help better increase the accuracy of the classification. Most studies tend to use classical machine learning methods applied on optical or/and SAR data in order to map rice areas. Among the several discussed studies, our proposed method remains powerful since it combines the high accuracy, the simplicity of the applied method and the availability of the used dataset.
The obtained accuracy is equivalent and even better than accuracies obtained by complex methods that may require large input datasets. Although the supervised classification highly depends on the study site (mainly on existing crop types) and ground data, the proposed approach could be used in any agricultural area with similar rice cropping practices and land cover. However, the metrics and the threshold used in this study should be adapted for a good application of this method in other geographical contexts. The method uses the SAR temporal signature of rice plots during its growing season to classify the rice areas. The temporal behavior of the SAR backscattering for rice plots allows us to extract metrics that best describe the rice cultivation, regardless of the surrounding agricultural land cover.
Conclusions In this paper, a simple yet powerful tool for mapping the rice area over the Camargue region of France using a Sentinel-1 SAR data time series was introduced. The SAR backscattering time series of 11 agricultural crop types in the area were first analyzed during the period between May 2017 and September 2017. The analysis of the SAR temporal behavior revealed that rice crops are clearly described by Gaussian profile of the "VV/VH" time series, an increasing linear profile of the "VH" time series, and a high fluctuation in the VV/VH signal when compared to other crop types. To classify rice areas using this description, the position of the maximum, standard deviation, and correlation coefficient of the Gaussian fitting curve of VV/VH, the variance of the VV/VH signal, and the slope of the linear fitting of the VH signal were extracted for each plot during the period between May 2017 and September 2017.
Acknowledgments: The authors wish to thank the  French Space Study Center  (2018  TOSCA ) and the  National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA)  for supporting this work. The authors also wish to thank  Laure Hossard  of the  National Institute of Agricultural Research (INRA)  for providing the terrain reference data, providing vector map on Camargue area, land use ground data, and contacts to farmers.
Raw data analyses were performed using the software MassARRAY TYPER 4. 0. We filtered out monomorphic SNPs (i. e. , declared as polymorphic SNPs from ddRADseq but proved to be monomorphic after validation by MassArray), loci with weak or ambiguous signal (loci displaying more than three clusters or unclear cluster delineation), and those with Mendelian discordance (checked in 11 captive-born families).
| Individual identification and parentage assignment We then tested our SNP panel for individual identification and parentage assignment and compared its efficiency to STRs. For individual identification, the probability of identity (PID, the probability that two individuals hold the same genotype) was assessed and compared to that of STRs, based on the same N = 37 breeders. To test for the resolution of the panel of SNPs in our context, calculations were also done based on the SNP allelic frequencies of the future breeders (PID Juv; N = 148 related and nonrelated individuals, captive-born in 2007 and 2008 that will be mature in the next years). The combined PID over loci was calculated using the analysis module in GenAlEx 6. 5 for unrelated individuals (PID-unrel) or relatives (i. e. , siblings and PID-sibs)  (Peakall & Smouse, 2012).
| Assessment of reliability to estimate relatedness and inbreeding The selection of the most reliable estimate of relatedness is essential for the genetic monitoring of the breeding program. Relatedness indices were calculated using seven different estimators: five moment-based estimators  (Li, Weeks, & Chakravarti, 1993; Lynch & Ritland, 1999; Queller & Goodnight, 1989; Ritland, 1996)  and two likelihood-based estimators  (Anderson & Weir, 2007; Wang, 2007). All above estimators were implemented in the COANCESTRY version 1. 0. 1. 5 software  (Wang, 2011). To determine bias and precision of the different estimators, we used COANCESTRY to generate 100 pairs of genotypes for different relationship categories, that is, unrelated (UR), first cousins (FC), half-siblings (HS), full siblings (FS), and parent-offspring (PO), based on observed allele frequencies at each locus estimated in the broodstock population, which is representative of the genetic diversity of the species  (Roques et al.
, 2018). We choose that option in COANCESTRY that takes into account some level of inbreeding. Relatedness coefficients were estimated for these groups of simulated individuals (PO SIM, FS SIM, HS SIM, and FC SIM) as well for two groups of individuals of known kinship (i. e. , empirical and EMP; 36 individuals with full siblings, FS EMP and 16 individuals with parent-offspring, PO EMP). Based on the simulated dataset, COANCESTRY also calculates a matrix of correlation coefficients among the seven different relatedness estimators and the true simulated values. The best estimator is the one that has the highest correlation with expected values, that is, 0. 5 for PO and FS, 0. 25 for HS, 0. 125 for FC, and 0 for UNR  (Wang, 2011). Comparisons of relatedness between STRs and SNPs and among relatedness categories were carried out using nonparametric tests (Mann-Whitney or Wilcoxon tests).
We also considered the most accurate estimator, the one with smallest interquartile ranges for each category of kinship and with the weakest overlay between relationship categories. Because inbreeding will be considered for the breeder's selection in captivity, we also evaluated reliability and precision of SNPs and STRs in estimating inbreeding. First, we calculated F values in wild-born breeders (BREEDERS, N = 36) and F1 juveniles (F1-JUV; N = 33 juveniles; software KINGROUP; F TrioML and F Dyad ML, for SNPs and STR, respectively). Because the F1 generations produced in captivity are issued from only a subset of breeders, some of them being related, we expect inbreeding to be higher in F1 than in wild breeders because of selection and genetic drift. We used Wilcoxon rank sum tests to test for significance.
Recent works have also proposed identity disequilibrium (ID) as a measure that may capture variance in the level of inbreeding within a population  (Stoffel et al. , 2016). The INBREEDR package  (Stoffel et al. , 2016)  allows the exploration of several parameters to quantify ID (as measured by the g2 statistic;  David, Pujol, Viard, Castella, & Goudet, 2007 ). An especially interesting purpose of this package is to test the effects of the number of loci on the precision and magnitude of inbreeding, g2, by simulations. We specified the number of simulated individuals to n_ind = 50, the subsets of loci to be drawn (i. e. , 1-17 and 1-79 for STRs and SNPs, respectively), the heterozygosity of noninbred individuals (i. e. , the expected heterozygosity He in the base population, BREEDERS; of 0.
Results of parentage assignment in the captive stock of F1 ju- veniles indicate a high level of assignment for individuals of known parent pairs. All but four (i. e. , 97%, 4/128) have been assigned to the parent pairs recorded in the captive stock database of origin with 95% confidence. For three (ID#149, 150, and 209) of these four individuals, the identification has been likely mistaken during sampling and handling in captivity: Individual 149 was genetically assigned to the parents' pairs of individual 150, and individual 209 was assigned to a parent pairs that was also crossed the same year (both assignations with 95% confidence). This may lead us think to family mixing during handling in the tanks at young stages or during tagging procedures. If we accept this "misidentification" hypothesis, genetic assignment then reached 99% success.