dataesr/scientific-paragraphs-categorization huggingface dataset:
Each alignment was used to infer a neighbor-joining tree using the Jukes-Cantor substitution model with 1000 bootstrap replicates to test branch support. Branches with <60% bootstrap branch support were collapsed using TreeGraph2  [57]. The 42 and 32 genomes sequences, respectively, of the group OpV2 and cactus-derived becurtovirus, with fragments derived through interspecies recombination removed (see below), were used to infer a maximum-likelihood (ML) phylogenetic tree using IQ-TREE  [59]. The model of nucleotide substitution used for the OpV2 tree was GTR+F+I+G4 and for the cactus-derived becurtovirus was TIM2+F+I+G4, as determined by ModelFinder  [60]  with 1000 bootstrap replicates to test for branch support. Datasets were assembled for the inferred replication-associated protein (Rep) and capsid protein (CP) amino acid sequences from representatives of the OpV2 and becurtovirus genotype groups, along with representative sequences from the geminivirus genera and unclassified geminiviruses (n = 49).
The Rep and CP amino acid sequence datasets were aligned using MAFFT v. 7  [56] , and the alignments were used to infer ML phylogenetic trees with models rtRev+G+I for the CP data and rtRev+G+I+F for the Rep data, as deter-mined to be best-fitting by ProtTest  [61]  and with the approximate maximum likelihood ratio test (aLRT) of branch support. Branches with <80% aLRT support were collapsed using TreeGraph2  [57] , and each ML tree was rooted with inferred Rep or CP amino acid sequences from two members of the viral family Genomoviridae  [62].
Capsid Protein Cluster Analysis The CP amino acid sequences of all geminiviruses available in GenBank were extracted and a representative dataset was generated through cluster analysis, using CD-HIT  [71]  with a 90% identity threshold. One representative from each cluster, together with representatives of the 11 genotypes of OpV2, 15 genotypes of OpV1 and one representative from the Opuntia becurtoviruses were used to generate a sequence similarity network using the enzyme function initiative-enzyme similarity tool (EFI-EST)  [72]  with a similarity score of 65 and an E-value threshold of 1 × 10 -5. The network was visualized in Cytoscape v3. 8. 2  [73]  with the organic layout.
It is interesting to note that in eight of the eleven OpV2 genotypes, the "iteron" adjacent to the TATA box occurs as two tandem TATA repeats. Sequences from genotypes 3 and 6 present two different "iteron" sequences. The OpV2 representative genotypes all share <74. 1% nucleotide pairwise identity with other geminiviruses (Supplementary Data S1). Their encoded Rep and CP proteins respectively share <76. 8% and <85. 4% amino acid sequence identity with other geminiviruses (Supplementary Data S2). Hence, the OpV2 sequences are diverse compared to the currently classified geminiviruses and likely represent a new genus. The Rep amino acid sequences encoded by OpV2 all have a rolling-circle replication, geminivirus Rep sequence (GRS), a helicase SF3, and Walker motifs  [81] , which are other important conserved features shared with geminiviruses and which are essential for the rolling circle replication functions of Rep (Supplementary Table  S2 ).
Phylogenetic analysis of representative OpV2 sequences, together with representatives of other geminiviruses (Figure  1 ), show that OpV2 clusters evolutionarily together with species in the genus Curtovirus. Not surprisingly, the highest genome-wide pairwise identities of the OpV2 sequences are with the members of the curtovirus species, such as that of beet curly top virus (AF379637) (Supplementary Data S2). In contrast, ML phylogenetic analysis of representative amino acid sequences of the encoded OpV2 Rep and CP proteins, together with those of other geminiviruses, demonstrate a more complex scenario. The OpV2 encoded CPs cluster with those of the curtoviruses, becurtoviruses, and two presently unclassified divergent geminiviruses, Limeum africanum-associated virus  [31]  and parsley yellow leaf curl virus  [35]  (Figure  2 ).
Infection Assays To assess the infectivity of OpV2, an infectious clone with two tandem copies of OpV2 DBG_56 (GenBank accession MT840871) was constructed. The infectivity assays in N. benthamiana plants showed that based on three independent assays performed with 10 inoculated plants in each, OpV2 had a 40-50% rate of systemic infection. OpV2 infectivity was further confirmed with Southern blot analysis of DNA from N. benthamiana-infected plants that revealed evidence of circular covalently closed DNA, super coiled dsDNA and single-strand DNA. No replicative forms of OpV2 were observed for the uninoculated control plants (Supplementary Figure  S1A ). Transmission electron microscopy analysis of OpV2 particles recovered from inoculated N. benthamiana plant material revealed the presence of twinned icosahedral particles (Supplementary Figure  S1B ).
Infectivity Assays To evaluate the infectivity of the Opuntia becurtovirus, an infectious clone was constructed and used to inoculate N. benthamiana plants. In three independent experiments with 10 plants each, only two plants were detectably infected through conventional PCR at 45 days post-inoculation. We were not able to detect the replicative forms of the Opuntia becurtovirus after Southern blot analysis of the infected plants (data not shown). However, qPCR quantification of both N. benthamiana plants inoculated with Opuntia becurtovirus showed their presence at very low levels with approximately 100 viral copies in 40 ng of plant material or 2 to 3 copies/ng of plant material (Supplementary Figure  S2 ). It is likely that Opuntia becurtovirus does not replicate well in N. benthamiana plants or a longer period of viral infection might be required to establish infection.
Inter-Genus Recombination Viruses in several geminivirus genera show clear evidence of inter-genus recombination  [39, 77, 93, 95, 96]. The geminivirus genomic organization, which involves bi-directionally transcribed genes and the geminivirus mode of rolling circle replication, are both factors that seem to influence the rates and patterns of recombination in these viruses  [43, 84, 97]. Recombination is clearly evident in the incongruence observed in the phylogenetic trees of the encoded Rep and CP amino acid sequences (Figure  2 ). Since the OpV2 encoded Rep and CP protein sequences cluster together with those of viruses in the genera Becurtovirus and Curtovirus, we undertook a recombination analysis to detect any events of recombination between members of these genera. We took representatives of each genus, including one sequence from OpV2 and the Opuntia becurtovirus to create a dataset.
The dataset sequences were aligned and this alignment was trimmed to retain only the nucleotide sequences of the Rep and CP coding regions (the only parts of the genomes that were clearly homologous). In the recombination analysis, seven well supported events were identified, all of them in the sequences of curtoviruses (Table  4 , Figure  5 ). Three events were identified in the CP sequence of curtoviruses, two of which were interspecies events. In one event (event 3 in Figure  5 ), the recombinant region appears to be derived from curtoviruses and becurtoviruses. Recombination events 1 and 3 span almost the entire CP nucleotide sequence. In the Rep coding region, four events were detected. One displays contributions from both curtoviruses and becurtoviruses and the other three are recombinants between curtoviruses and OpV2 (Figure  5 ).
On the other hand, OpV1 maintained similar viral loads over the two time points. These results suggests that OpV2 favors mixed infections with OpV1, but in this case it did not seem to affect the viral load of OpV1. In attempted co-infections with OpV1 and Opuntia becurtovirus and OpV2 and Opuntia becurtovirus, the fact that Opuntia becurtovirus was not detected was not surprising based on the initial single infection experiments. Therefore, we could not draw any conclusions on coinfections involving this species (data not shown). To further investigate the potential dynamics of those co-infections, infectivity assays were performed with OpV1 and OpV2 in N. benthamiana. Of 10 co-inoculated N. benthamiana plants, four were found to be co-infected, one plant was found to be infected with only OpV2, and five plants only with OpV1.
Cluster Analysis of Geminivirus Capsid Protein Geminivirus CP amino acid sequences are possibly co-diverging with their specific insect vectors, as highlighted in the phylogenetic analysis presented in  [48]. Hence, we undertook a sequence similarity network analysis of the CP amino acid sequences of all geminiviruses (with a >90% identity cut off) and further associated the identified clusters with the known insect vectors (Figure  8 ). Sequence similarity network analyses are useful for clustering groups of similar protein sequences in large datasets relatively quickly (see  [48, [106] [107] [108]  for examples). Unlike OpV1, which forms its own distinct cluster  [15] , indicating that it is likely transmitted by an insect vector that has not yet been associated with geminiviruses, OpV2 CPs cluster with those of curtoviruses, becurtoviruses (including Opuntia becurtovirus), and two unclassified viruses (parsley yellow leaf curl virus and Limeum africanum-associated virus).
Hence, a more extensive sampling strategy should be applied to test this assumption. If we consider the present results jointly with Macková et al. [30]  and Kši ňan et al. [40] , as a comprehensive dataset on C. integerrimus ploidy variation, it is apparent that diploids are a minority cytotype in the overall cytotype structure of the species. Diploids of C. integerrimus as a rare cytotype never form monoploid populations  [47]. The coexistence of diploid and tetraploid cytotypes is also extremely rare in C. integerrimus. Diploid cytotype at the Umoljani site contributes markedly to ploidy richness, reproductive mode variation, and genotypic diversity. Namely, diploids affect cytotype dynamics through reproductive interactions with sexual and asexual tetraploids, generating intermediate triploid cytotype (Table  3 ).
:8x end. ) (Table  3 ). Unlike Macková et al. [30] , in our dataset, we detected some cases of G2 peaks of tetraploid embryos (Figure  2 (S4)), and therefore we cannot precisely distinguish endosperm with endoreduplicated embryos. Finally, several cases of haploid parthenogenesis have been found in tetraploid seeds as a distinct rare pathway of asexual seed formation yielding unviable seeds and anormal development of the plant.
The occurrence of triploid cytotype in mixed-ploidy populations indicates the existing weak reproductive barriers between diploid and tetraploid cytotype, which nevertheless allow the formation of triploids but with a low frequency. The key reproductive isolation mechanism in the formation of triploids is considered to be the triploid block, which operates in different polyploid systems  [22, [57] [58] [59] [60]. The triploid block is caused by malfunction of the endosperm as a result of an imbalance between paternally and maternally imprinted genes during the endosperm development, and often leads to seed abortion  [61]. The observed endosperm imbalance in all triploid seeds originated from di-, tri-, and tetraploid mother plants in the present dataset (Table  3 ), suggesting that a small portion of seeds are insensitive to the triploid block.
5 (Applied Biosystems). To study genetic diversity in polyploid C. integerrimus populations, we determined the multilocus genetic genotypes (MLGs or simply genotypes) for each individual on the basis of microsatellite allele data for each of five loci in the software program GenoType v. 1. 2  [82]. Assignment of individuals into a particular clone was performed using the Meirmans and Van Tienderen  [82]  algorithm according to the calculation of a genetic distance matrix and a clonal threshold (set to two for the study, after testing different threshold values upon recommendations by the authors of the programs) under the stepwise mutation model option. In order to analyze clonal diversity of C. integerrimus, we included following measures: the total number of genotypes (Ng) and effective genotypes (Eff), the number of unique genotypes (Un), and Simpson's diversity index (D) calculated in the software GenoDive v.
Acknowledgments: We are greatly thankful to  Elma Silajdžić  for her help with molecular laboratory work. Božo Frajman  supported us with samples from  Austria, Germany , and Italy;  Nevena Kuzmanović  with samples from Serbia and Greece;  Danka Petrović  with samples from Montenegro. We thank  Dubravka Šoljan  for her help during collection of samples and  Mario Gomez Pacheco  for help with cytometry. We thank three anonymous referees for valuable comments on the manuscript.
Statistical Analyses For each study site, ANOVA was used to test the amendment effects on soil physical, chemical and biological parameters and maize yield. For maize yields, season was not included in the statistical model and separate ANOVA has been carried out for each season. Prior to running ANOVA, data were tested for homogeneity of variances and normality and log-transformed when required. LSD post hoc multiple comparison tests were used if the effects were significant. Differences among treatments were declared significant at the 0. 05 probability level. We also performed principal component analyses (PCA) to visually summarize the information of soil fertility parameters and maize yield. All statistical analyses and plots were carried out with R software using "car", "agricolae" and "ade4" packages  [18].
We demonstrate, for the first time, to the best of our knowledge the value of these RGB-Depth images to monitor the early stages of seedling growth. We investigate fusion strategies between RGB and depth with several neural networks architecture. The underlying motivation to use multimodal data is that complementary information give a richer representation that may be utilized to create better results than a single modality. The multimodal fusion research community has made significant progress in the past decade  [27]. Different fusion strategies have been reviewed  [28, 29]. Specifically for RGB and Depth with deep learning architectures, fusion has been extensively studied in the literature  [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41]. Mainly two types of fusion can be distinguished.
Imaging System and Data Set We have conducted similar experiments as the ones described in detail in  [16]  and shortly recalled here. A set of minicomputers, connected to RGB-Depth cameras  [45] , was used to image seedlings from the top view as illustrated in Figure  1. We used, instead of the RGB cameras of  [16] , Intel real sense cameras  [46]  (model D435) which natively produces registered RGB-Depth pairs of images and calibrated Depth maps. We installed 8 of these RGB-Depth cameras in a growth chamber where cameras followed the growth of seedlings from top view. During experiment, soil pots were hydrated to saturation for 24 h after which excess water was removed. After 24 h, seeds were sown at a depth of 2 cm, and trays were placed in a growth chamber at 20 • C/16 • C, with 16 h for photoperiod at 200 µMm -2 s -2.
In total, the database consists of 72 temporal sequences of RGB and depth images of size 66 × 66 pixels where each temporal sequence consists of 616 individual images. Example of images from the database is shown in Figure  1. RGB-Depth temporal sequences acquired during daylight were annotated by expert in biology while looking at RGB images. This ground-truth annotation consisted of four classes: soil, first appearance of the cotyledon (FA), opening of the cotyledon (OC), and appearance of the first leaf (FL). The algorithms presented in this paper for seedling emergence identification following these four phases of growth were trained, validated, and tested against this human-annotated ground-truth. In order to train robust models, we used the cross-validation approach by considering image sequences of bean varieties in three split of train, validation, and test dataset.
5) and a fully connected output layer for four classes corresponding to each event with a softmax activation. This proposed CNN architecture has been optimized on a hold-out set and was demonstrated in  [16]  to be optimal by comparison with other standard classical architectures (VGG16, ResNet, DenseNet). The network was trained from scratch since the size of the input tensor (4 channels and small spatial resolution) was different from existing pre-trained networks on large RGB data sets.
We, therefore, provide the transformer of  [54]  with time series of consecutive images of the same pot (we used the same time slot as in the other spatio-temporal methods). We used 32 transformer layers with batch size 64, feed forward layer as classification head layer and the size of our patch size was equal to 66 × 66 pixels for both architectures of Figure  5. For all our training, we used the NVDIA DGX station. This station is composed of 4 GPUs and each one of them have a RAM memory of 32 Gb. We used Python version 3. 7. 8, Tensor-flow version 2. 7. 0 and Keras library version 2. 3. 1.
Fusion Strategies The proposed deep learning methods CNN, TD-CNN-GRU, and Transformers with image or feature RGB-Depth fusion were applied to the produced dataset as described in the Section 2. The performances are provided in Tables 2-4 and Figure  6. show that all methods performed better when RGB and Depth data are fused by comparison with the sole use of RGB data. This improvement is obtained both with image fusion and with feature fusion. This demonstrate the value of RGB-Depth fusion with a gain of 5% (on average) compared to the use of the sole RGB images. This is obtained at a reasonable training time of around 1 to 3 h as detailed in Table  5. The best results are obtained with the CNN method, i. e. , the spatial method by comparison with the spatio-temporal method. This CNN is showing the best absolute performance, the smallest training time and also minimum decrease of performance between training, validation and test.
This is in agreement with our previous results found in  [16, 43] , where spatio-temporal methods outperformed memoryless spatial ones only when the kinetic of growth were homogeneous among the dataset. This was not the case in this study. The confusion matrix of the CNN method is displayed in Figure  6  for RGB images and RGB-Depth images. Interestingly errors with both RGB and RGB-Depth only occur on adjacent classes along the developmental order. These are situations where even the human eye can have uncertainty to decide the exact time of switching from one class to the next one. Remaining errors can thus be considered as reasonable errors. The confusion matrices also clearly demonstrate that the main gain brought by the Depth channel is on the stage of opening the cotyledons for which the error are divided by a factor two.
Detection of Event Changes at Night Using Depth Information The advantage of using the depth is not limited to enhance the performance during the day as shown in the previous subsection. Depth is also expected to be specifically useful during the night since the RGB cameras are then non operating while the Depth images can still be acquired. If the growth stage switches during the night the RGB imaging devices detect the switch only on the first frame of the next day time as illustrated in Figure  7. It is possible to screen for Depth alone during these nights and observe the start of a growth pattern actually occurring before the beginning of the day. We demonstrate in this subsection how to take benefit quantitatively of the sole Depth channel during these nights. We analyzed the number of switches from one growth stage to another happening on the first image acquired during the day in the data set of  [16]  and found out that it represented 35 percent of the events (see Figure  8 ).
This is similar to what we found with the dataset of this article where we had 100 sets of pots from different varieties. In these frames, we have 115 switches of growth stages with 43 happening during night time. While some could be triggered by the action of light others could also happen earlier during the night. To detect a possible change during the night, we quantitatively used Depth. We designed Algorithm 1 which acts as follows. We first detects nights where a switch between a growth stage to another growth stage is found in RGB images. During these nights, the algorithm then detects the depth frame on which the switch is the most likely to occur. In short, this is obtained by choosing the time where the average spatial depth is permanently (computed over a sliding window of 4 images = 1 h) closer to the average spatial depth of the next growth stage.
We wanted to provide basic results here which already happen to be of rather high quality without the use of such approach to robustify the model since the main goal was the fusion of the RGB and Depth for seedling growth monitoring. One may wonder about the robustness of the model proposed given the relatively small size of the plant population considered. First, the overfit measured with the best method was found to be limited together with the difference of performance between cultivars. It is important to recall here that the point of the work is to quantify the added value of RGB-Depth images by comparison with sole RGB. This is what we do on the same data sets. Interestingly, the performance with RGB images obtained with only 72 samples are similar to the larger data set used in  [16]  (90% against 88% here).
However, we cannot ensure a perfect robustness to large change of phenotypic shapes. If such variability in scale was expected, larger data sets would have to be constituted. The comparison between RGB and RGB-Depth would remain unchanged. In this work, we focused on early fusion and feature fusion of RGB and Depth. One may also consider decision fusion where the classification from the RGB image and the Depth image would be made. We performed this analysis and found a pure random decision when the classification was made on Depth alone. Therefore, at the decision level, no added value of Depth was to be expected on average. Fusion between RGB and Depth for such small images and low-cost sensors as the one considered in this study is found to be beneficial on average at earlier stages of processing (image or features).
An issue comes with the possible overlapping between plants. One solution would be to decrease the density of plants but this would come with a lower throughput for the experiments. Another solution would be to investigate the possibility to track leaves during their growth in order to decipher partial occlusions. Here again, RGB depth sensors coupled with advanced machine learning approaches could be tested to further extend the capability to monitor seedling growth  [56]. Last but not least, we can now directly apply the developed algorithms to analyze biologically in detail the statistical distribution of seedling growth events at night on large datasets. This may unravel new knowledge on the physiological impact of light on these growth kinetics in addition to their links with circadian rhythms  [57].
of Temporal Sequences Totale No. of Images during Days Totale No. of Images during Nights Totale No. of All Images Flavert 10 4240 1920 Red Hawk 10 4240 1920 Training dataset Linex Caprice 10 10 4240 4240 1920 1920 36,960 Deezer 10 4240 1920 Vanilla 10 4240 1920 Flavert 1 424 192 Red Hawk 1 424 192 Validation dataset Linex Caprice 1 1 424 424 192 192 3696 Deezer 1 424 192 Vanilla 1 424 192 Flavert 1 424 192 Red Hawk 1 424 192 Testing dataset Linex Caprice 1 1 424 424 192 192 3696 Deezer 1 424 192 Vanilla 1 424 192.