dataesr/scientific-paragraphs-categorization huggingface dataset:
Multivariate analysis and selection of promising diploid and tetraploid genotypes using phenotypic and genomic information A PCA analysis that included the molecular and phenotypic information was implemented; in this case, the molecular traits correspond to the two first components of the PCA implemented with only molecular information and phenotypic information corresponding to the means values generated for each genotype in each feature (TPC, AA_DPPH, AA_FRAP and, AAC). A new cluster analysis was developed, and the genetic groups were described at the phenotypic level. Promising genotypes to TPC, AAC, and AA of the AGROSAVIA collection were identified and selected using an Index Selections (IS) calculated through the selection index Rpackage  (Goyani, 2021) , adjusting the weights of each character according to their heritability (h 2 ) that was computed from phenotypic and genotypic data using the heritability R-package  (Kruijer, 2019).
Genome-wide association studies The GWAS analyses were conducted using the mean values of TPC, AAC, and AA (AA_DPPH and AA_FRAP) obtained for each genotype. These analyses were implemented in the mixed population and independently in diploid and tetraploid populations because the CCC has a clear population genetic structure in function to the ploidy level  (Berdugo-Cely et al. , 2017). The complete dataset was analyzed under a tetraploid approach, while the diploid and tetraploid datasets were analyzed under their respective ploidy model approach. This study implemented the GWAS analyses through single-locus and multi-locus methods in the GWASpoly  (Rosyara et al. , 2016)  and mrMLM  (Zhang et al. , 2020 ) R-packages, respectively. Kinship (K) matrices and population structure were included as random and fixed effects to remove associations related to the genetic structure.
A mixed linear model (MLM) implemented in GWASpoly was carried out for the single-locus method using the eight available models in this R-package (general, additive, diplogeneral, diplo-additive, 1-dom-alt, 1-dom-ref, 2-dom-alt, and 2-dom-ref)  (Rosyara et al. , 2016). The p-values were adjusted with the False Discovery Rate (FDR) method  (Benjamini and Hochberg, 1995)  at p<0. 001 to reduce the false-positive rate for the marker-trait association. On the other hand, in the multilocus GWAS implemented in mrMLM, six models (mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were evaluated  (Zhang et al. , 2020)  using the default parameters. The threshold values to determine associated markers are usually established by corrections methods (e. g. , Bonferroni), but those are sometimes too strict, missing some essential associations  (Yousaf et al.
, 2021). Some studies  (Phan et al. , 2018; Riaz et al. , 2018; Yousaf et al. , 2021)  have instead used a threshold of 3. 0 or higher to determine the significant SNP-trait associations (STAs) or Quantitative Trait Nucleotide (QTNs). For this study, we established that STAs or QTNs with values of LOD (logarithm of the odds) scores higher than 3. 5 were significantly associated. We reported LOD scores identified after evaluating all models for each STA (detected in a single-locus model) or QTN (detected in multi-locus models). The associated SNPs reported in this study were detected for a minimum of two models in each GWAS model. We constructed the Quantile-Quantile (q-q) and Manhattans plots for each evaluated model in R software. We graphed the plots using the model that detected the highest number of STAs in GWASpoly, while the graphs reported to mrMLM included the QTNs detected in all multi-locus models.
Candidate genes The STAs and QTNs detected in common by single and multi-locus models (LOD score > 3. 5) were searched against the potato genome of Solanum tuberosum group Phureja DM1-3 PGSC v4. 03 in the Spud database -Potato Genomic Resource (PGSC Data Download;  Xu et al. , 2011; Sharma et al. , 2013). Those genes were proposed as candidate genes associated with TPC, AA (AA_DPPH and AA_FRAP), and ACC.
Diploid accessions Cha_2, Cha_16, Phu_7, Phu_75, and Phu_48 were selected (SI>7) based on their ascorbic acid contents above 5. 24 mg AA. The tetraploid accessions And_186, And_209, and And_210 and the diploids Cha_49 and Cha_16 genotypes are particularly interesting for Genome-wide LD decay calculated to mixed, tetraploid, and diploid populations. The x-axis represents the physical distance in base pairs (pb) and the y-axis represents the average pairwise correlation coefficient (r 2 ) of SNPs. presenting high values for more than two traits (Figure  3A  and Supplementary Table  S1 ). This strategy allowed us to select 11 genotypes as promising. Additionally, a selection index calculated using the joint data of the four traits allowed the addition of nine genotypes to the list of promising materials (SI>11431).
, 2017; Byrne et al. , 2020; Naeem et al. , 2021). Then, until this moment, this could be the first GWAS that identified STAs and QTNs that flanked genes with a possible genetic association with TPC, AA, and AAC in potatoes using diploid and tetraploid genotypes. In other cultivated plant species, GWAS revealed genes associated with TPC in rice  (Xu et al. , 2016) , apple  (McClure et al. , 2019) , sorghum  (Habyarimana et al. , 2019; Kimani et al. , 2020) , barley  (Han et al. , 2018) , and tomatoes  (Ruggieri et al. , 2014)  and associated with AAC in tomatoes  (Ruggieri et al. , 2014; Sauvage et al. , 2014; Ye et al. , 2019). Here, we implemented single and multi-locus GWAS analyses of the complete dataset that included diploid and tetraploid potatoes and also ran the analyses separating the potatoes by their ploidy level.
, 2004; Simko et al. , 2006). In this study, we confirmed that the number of SNPs was enough to find STAs and QTNs for the analyzed traits. However, the number of STAs and QTNs in the diploid population was minimal. In the single and multi-locus GWAS analyses, the number of significant associations found for TPC, AAC, and AA (AA_DPPH and AA_FRAP) traits depended on the dataset/populations (mixed, diploid, and tetraploid) and the GWAS models (single and multi-locus) used. At the level of the dataset, the number of detected STAs and QTNs was higher in the mixed (in single locus GWAS) and tetraploid (in multi-locus GWAS) datasets than in the diploid dataset (in both GWAS models: single and multilocus), probably due to the higher sample number in the mixed and tetraploid populations. The size population affects the obtention of reproducible and robust results in GWAS analyses  (Uffelmann et al.
, 2021) ; for this, it would be necessary to increase the number of individuals in the diploid population to ensure that the number of genetic associations detected was not affected by this difference in population size. In sugarcane, a species with a complex population structure based on ploidy levels, GWAS identified the highest number of genetic associations in the complete dataset and the largest subpopulations. Associations detected in the subpopulations were not detected in the complete dataset, probably because of the population size and structure population  (Yang et al. , 2020). Similar results were found in our study; the highest number of the STAs and QTNs detected in the diploid and tetraploid populations were not detected in the complete dataset. This result suggests that GWAS cannot detect the overall genetic associations even with a correction by population structure in a mixed population.
Then, GWAS in potatoes should be conducted using complete datasets and data separated by ploidy level to identify the highest number of genetic associations. The single-locus GWAS detected more genetic associations than the multi-locus model. In contrast, the LOD scores and the phenotype variation estimated (PVE-r 2 ) were higher in multilocus than in the single-locus GWAS. The conventional single-locus GWAS, such as the general linear model (GLM) and mixed linear model (MLM), is considered less robust because it neglects the overall effects of multiple loci and needs corrections for critical values. QTLs with minor effects are sometimes not detected, and the effect estimations (r 2 ) are less accurate with single-locus models. GWAS multi-locus methods would have lower false-positive error and higher statistical power in detecting a higher number of genetic associations  (Wang et al.
Acknowledgments We want to thank  Rauĺ Ivań Valbuena , curator of the potato germplasm bank of AGROSAVIA until 2021, for providing qualitative phenotype information about the potato color traits of the genotypes used in this study. We want to thank  Benjamıń Rojano  from the  National University  for facilitating the biochemical analyses. We would also like to thank  Andreś Corteś ,  Jaime Andreś Osorio , and  Gina Garzoń  for kindly reviewing the manuscript. Finally, we want to thank the reviewers and the editor of this document, since all their comments allowed us to generate a more robust document.
Pulsed field gel electrophoresis Isolates were typed by PFGE according to the standardized protocol for Molecular subtyping from Pulsenet  (19)  and previously described (20) using a CHEF DR II electrophoresis unit (Bio-Rad Laboratories, Mississauga, ON). Restriction digestions were completed using SalI for M. haemolytica, ApaI for P. multocida, and SacII for H. somni. Electrophoresis gels were run at 6 V/cm and electrophoresis conditions were a 4. 0 s initial switch time and 40 s final switch time for 20 h at 12 • C. Resulting patterns were analyzed using Bionumerics Version 7. 1 software (Applied Maths Inc. , Austin, TX). Dendrogram and minimum spanning tree (MST) analysis were based on similarity matrices generated from UPGMA clustering of Dice coefficient values with 1. 0% tolerance and 0.
Cut test and Fermentation Degree The dried beans were cut lengthwise into halves for maximum surface exposed. Both halves of each surface were inspected under artificial light and divided into five groups (fully brown, purplebrown, fully purple, slaty and mouldy) as showed in Fig. 4. The total of dried beans in each group were counted and expressed in percentage and replicated for three times. Table  1  shows the results of the cut test for the fermented and dried cocoa beans and the mean data after fermentation and dried periods. According to the data, The rate of brown beans varied from 80. 33 to 88. 67%, the number of purple-brown beans was 3. 33 to 5% and purple beans reaching 8 to 14. 67% for A and B batches. Similar data were found by Efraim et al  [27]  and Afoakwa et al  [28]  by investigating the effect of fermentation time, the type of drying and the method of storage before fermentation.
This leads to a total complexity of O(N(hs + h 2 k 2 + s 2 g )) where k is a bound on the number of symbionts per host in the sampled reconciliations (s in the worst case), and N is the number of samples in the Monte Carlo approach. The datasets presented give a good idea of the size of the data we can consider with this new method. We here give the computation time for the Sequential heuristic. Computation on the Cinara aphid dataset, with a size of 25 leaves for the symbiont tree, 9 leaves for the host, and 13 gene families takes about 3 minutes on a single laptop core, including the rate estimation steps. This is a dataset on which it would be possible to use the Monte Carlo approach. The pylori dataset is larger, the symbiont has 119 leaves, the host 7 leaves, and there are 1034 gene families, of which 322 have 119 leaves.
Simulated datasets 4. 1. 1. Description of the simulation process. Our probabilistic model can be used for simulation, however in order to test our method, we chose to use an exterior simulation framework. We Hugo  Menet et al. used the available software Sagephy developed by  Kundu and Bansal (2019). Sagephy generates three embedded trees and allows replacing transfers on top of additive ones. We used the parameters proposed by the same team in another article  (Kordi et al. , 2019) , as representative of small (D 0. 133, T 0. 266, L 0. 266), medium (D 0. 3, T 0. 6, L 0. 6) and high (D 0. 6, T 1. 2, L 1. 2) transfer rates, without replacing transfers. The software enables to specify an inter transfer rate, corresponding to the probability for a gene transfer to be between symbionts hosted by different hosts ("inter" transfer).
When a horizontal transfer is chosen during generation of the gene tree (inside a symbiont tree and knowing a host/symbiont reconciliation), the transfer is chosen to be an inter host one with the inter transfer rate. So an inter transfer rate of 0 corresponds to our inference model of only intra transfer, and of 1 corresponds to a case where transfers are only between symbionts in separate hosts. We constructed two simulated datasets, one with a combination of the different rates for the DTL parameters, and one with only medium rates but with different inter transfer rates. For the first dataset, we used all 9 combinations of small, medium and high rates for the symbiont generation and the gene generation, with only intra host gene transfer (i. e. an inter transfer rate of zero). For the second dataset, we used only medium rates for both symbiont and genes generation, but we used 6 inter transfer rates going from 0 to 1.
For both datasets, and for each set of rates, we generated 50 instances consisting of 1 host tree with 100 leaves, 1 symbiont tree and 5 gene trees, each generated in the pruned version of the other trees (branches that do not reach present are pruned before the generation of the next tree). We then selected host leaves with a probability of 0. 08 to simulate unexhaustive sampling, resulting in host trees with an average size of 8 leaves. We thus simulate extinct lineages, and even with a simulation inter transfer rate of 0, some gene transfers will be inter. This ended up to 399 instances for the first dataset and 226 instances for the second one, and at least 29 instances of 5 genes for each set of parameters. We compared the results from three approaches. (1) The "2-level" heuristic which is a 2-level reconciliation between the gene and symbiont trees, ignorant of the host tree.
Denoting by L t,sim the list of simulated transfers and L t,obs the list of observed transfers, and P obs (T ) the estimation of our approach for the probability of transfer T. (7) Precision = T ∈L t,sim P obs (T ) T ∈L t,obs P obs (T ) and Recall = T ∈L t,sim P obs (T ) T ∈L t,sim 1 4. 1. 2. The 3-level method infers more true transfers than the 2-level method. Overall the Monte Carlo and sequential approaches give similar results on these simulated datasets, and better results (in particular for recall and to a lesser extent for precision) than the 2-level approach 12 Hugo Menet et al. Peer Community Journal, Vol. 3 article e47 https://doi. org/10. 24072/pcjournal. 273 (Figure  4 ). In most cases, the faster Sequential heuristic can advantageously replace the Monte Carlo one because they have the same recall and precision.
A host-symbiont co-evolution test. The reconciliation likelihood difference between 3-level inference and 2-level inference is a marker of host-symbiont co-evolution. Indeed, Figure  5 (A)  shows that when the simulation model is less dependent from the host phylogeny (inter transfer rates of 0. 6, 0. 8 and 1. 0), the likelihood difference between the 2-level and 3-level inference methods are mostly in favor of the 2-level. It happens for almost all instances in the simulation dataset with with no intra transfers (inter transfer rates of 1. 0), the farthest one from the model behind our heuristic that privileges intra transfers. For all these instances a preference for 2-level reconciliation (according to the likelihood) is more likely when few transfers are inferred (we sum over 1 to 5 gene families generated for each host and symbiont instance).
This is a sign of the precision of the method to not classify 2-level instances as 3-level ones. In a model with only intra transfers (inter transfer rate of 0), we have a very good recall for the detection of the 3-level model, almost all only intra transfer instances are classified as 3-level as they should be. A more detailed exam of this recall is presented in Figure  5  (B) with the first simulated dataset, with only intra transfer and varying DTL parameters. Figure  5  (B) shows the likelihood difference when only intra transfers occur in the simulations. We see that when the number of transfers is higher, the likelihood difference better reflects the Hugo  Menet et al. 13  Peer Community Journal, Vol. 3 (2023), article e47 https://doi. org/10. 24072/pcjournal. 273 mode of simulation.
For instance in the Cinara aphids dataset, the genes considered are mostly similar, and do not really make the number of independent transfers increase, and with only one intra transfer, that necessitates an additional loss to occur, the 2-level model displays a better likelihood than the 3-level. If more independent transfers were present, we can suppose that some of them might not necessitate such a loss and the test would favor a host symbiont co-evolution. All these features deserve further tests to know their domain of validity and to draw biological conclusions. In particular, the inference of the symbiont tree, with the use of amalgamation, Hugo  Menet et al. 17  Peer Community Journal, Vol. 3 (2023), article e47 https://doi. org/10. 24072/pcjournal. 273 from an input distribution of universal unicopy gene tree would deserve to be tested against other standard methods as concatenate or species tree reconstruction with 2-level reconciliation model as it is implemented in SpeciesRax  (Morel et al.
Estimating straw cereal plant density at early stages is important for field crop management and phenotyping. Usual plant density estimation methods include manual counting and image-based counting, both of which have limited throughput, due to the need for high spatial resolution images. In this study, we explored the potential of high-throughput estimations with spectral information. A large and diverse dataset was collected on micro plot field experiments, encompassing six sites, three leaf stages, and four species of straw cereals. Canopy spectral reflectance was acquired with a spectrometer, both in 0 • or 45 • view zenith angle, perpendicularly to the row direction. Two reflectance-based approaches were then tested. In the direct approach, density was directly estimated from reflectance using Gaussian process regression (GPR) and spectral bands selected based on Akaike's information criterion.
Reyniers et al. (2004)  showed that the crop coverage of wheat obtained from spectral data is more related to sowing density in the early season. This result shows the possibility to estimate wheat seedling density from crop coverage. Habibi et al. (2021)  combined the accurate deep learning method and the high-throughput reflectance-based method in a two-step soybean plant density estimation. In the first step, deep-learning method was used to get the plant density with high accuracy, and the density value was used as input for the next step. In the second step, reflectance information and climate information were used to estimate plant density in high-throughput, with a moderate accuracy. Zhang et al. (2022)  estimated the stand density of evergreen trees based on the linear relationship between fractional vegetation cover (FVC) and stand density.
With 45 • view zenith angle, using an azimuth direction perpendicular to the crop row further reduces the overlap between plants inside one row  (Baret et al. , 2010). This observation set was also used in the work of  Liu et al. (2017)  and  Jin et al. (2017)  for wheat seedling density. Besides changing the acquisition geometry, another solution to further limit the soil influence is to constrain the reflectance-based density estimation by first estimating GF, and second relating estimated GF to density. Indeed, canopy reflectance is strongly related to GF  (Baret et al. , 2007; Gitelson et al. , 2002) , which is itself proportional to plant density when the plants are of similar size with little overlap such as in the case of early-stage plants  (Wilke et al. , 2021). The previous literature review on plant density methods shows that there is currently no comparison of the performances between imagebased and reflectance-based methods achieved over the same dataset.
Furthermore, the possible degradation of performances as a function of sensor spatial resolution for both types of methods is still lacking. Therefore, in this work, we developed two reflectance-based approaches to estimate plant density from nadir or 45 • observations. In the first approach, plant density was estimated directly using a machine learning regression algorithm. In the second approach, GF was first estimated from spectral data, and then related to density. By introducing GF as a proxy, we wanted to make the estimation more interpretable. These two approaches were compared to a popular image-based method, trained on a large and diverse dataset comprising six sites, three leaf stages, and four straw cereal species. In summary, this research has the following objectives: (a) Evaluate the performance of two reflectance-based methods and one image-based method for estimating cereal straw plant density in terms of accuracy and robustness to changes in sensor spatial resolution.
Spectrometer measurements In France, canopy reflectance data were collected with an SM-3500 spectrometer (Spectral Evolution, Massachusetts, US), with 737 bands ranging from 343 to 2517 nm, and with full-width at half maximum (FWHM) between 1. 5 nm and 3. 8 nm. In China, canopy reflectance data were collected with an ASD FieldSpec 4 spectrometer (Analytical Spectral Devices, Colorado, US), with bands ranging from 350 to 2500 nm, and with FWHM between 1. 1 and 1. 4 nm. Spectral data were measured at 0 • and 45 • view zenith angles. In the 45 • measurements, the azimuth was perpendicular to the direction of the rows. The acquisition geometry was designed such that (1) the area covered by the 25 • field of view (FOV) of the spectrometer was similar in 0 • and 45 • situations, and (2) the area covered by the FOV was large enough to represent the plot while not exceeding the plot boundaries (Fig.
Forty-nine canopy reflectance spectra were removed from the dataset, either because of inaccurate reflectance calibration, or because the shadows of nearby buildings and trees accidentally covered the plots. Among these 49 removed samples, 9 samples were acquired from 0 • view zenith angle, and 40 samples were acquired from 45 • view zenith angle. In total, there were 262 samples in the nadir view and 231 samples in the 45 • view zenith angle for assessing the reflectance-based methods presented in section 2. 3. Some preprocessing operations were applied to the spectral data. First, the bands of the ASD spectrometer were interpolated into the bands of the SM-3500 spectrometer. Then, a Savitzky-Golay filter with a window of 5 and a polynomial order of 2 was applied to reduce the influence of noise  (Savitzky and Golay, 1964; Virtanen et al.
We tested if the calibration of separate sub-models for one or several factor(s) would significantly improve the estimation performance obtained when not differentiating the above three factors. We tested all the possible factor combinations for each of the three estimation steps presented in section 2. 3, i. e. , spectra-density for direct estimation and spectra-GF and GF_rgb-density (baseline method) for indirect estimation. The estimation performance was quantified with the root mean squared error (RMSE), and the relative root-mean-square error (rRMSE). The rRMSE was obtained by dividing the RMSE by the mean value of the target variable (GF or density) of the full dataset. Due to the potentially low number of samples attained when differentiating several factors, the RMSE and rRMSE were computed using five-fold cross-validation when the dataset had more than five samples, and leave-one-out cross-validation otherwise.