dataesr/scientific-paragraphs-categorization huggingface dataset:
The cross-validations were replicated ten times with different partitions of datasets at each time, to reduce the random error introduced by the random partition of small datasets. Finally, the averages of RMSE and rRMSE were calculated for each factor combination, for each view zenith angle, and for each estimation step. To simplify the ablation study, five common bands were used here as spectral reflectance input to the GPR regression model (section 2. 3). These five bands were the blue (B, 475 nm), green (G, 560 nm), red (R, 668 nm), red edge (RE, 717 nm), and near-infrared (NIR, 842 nm) bands. This set of bands is widely used in commercial multispectral cameras, such as Rededge (Micasense, Washington, US), P4-multispectral (DJI, Shenzhen, China), Airphen (Hiphen, Avignon, France), MiniMCA (Tetracam, Bolton, UK), and Sentera-6X (Sentera, Minneapolis, US).
AIC c takes into account both the error and the parsimony of the model, and a model with a low AIC c value is preferred  (Burnham and Anderson, 2004). AIC c was calculated as follows: AIC c = -2log(L( θ) ) + 2K + 2K(K + 1) n -K -1 (2) Where L( θ) is the likelihood of the Gaussian Process estimation, K is the number of parameters to be determined in the GPR model and n is the number of samples used to calibrate this model. In GPR, the loglikelihood of the model was calculated using a Python sci-kit package  (Pedregosa et al. , 2011; Williams and Rasmussen, 2006) , and the number of parameters was the number of bands plus two according to Equation  (1). In this study, when multiple sub-models were calibrated on different sub-datasets due to factor differentiation (section 2. 3. 2), the sum of AIC c values from multiple sub-models was calculated as a criterion.
When one model was calibrated for the whole dataset, the AIC c value of this model was used as a criterion. The optimal set of bands comes from the model with a minimum sum of AIC c values. No crossvalidation was applied to the models when calculating AIC c since the choice of model with AIC and cross-validation are asymptotically equivalent when maximum likelihood estimation is used  (Stone, 1977). For the best view zenith angle, plant density estimation performances obtained with direct, indirect, and baseline methods were finally evaluated, using optimal factor differentiation and optimal band set. These performances were quantified using the coefficient of determination (R 2 ), the RMSE, and the rRMSE.
For example, barley leaves were wider than wheat leaves in our experiments and that could lead to differences in GF-Density relationship. The large diversity in growth stages, sites, and species in our dataset (Table  1 ) and the above results allow us to further discuss the results obtained by  Gnädinger and Schmidhalter (2017) ,  Wilke et al. (2021)  and  Liu et al. (2017). First, the poor relationship between GF and density for maize observed by  Gnädinger and Schmidhalter (2017)  was probably due to the non-differentiation of three growth stages, four cultivars, and six cultural practices, all of which led to strongly different GF values for the same density. Second, our results confirm those of  Wilke et al. (2021) , i. e. , accurate plant density estimates can be obtained thanks to GF estimates when differentiating species and growth stages.
This fact could keep the spectra-GF relationship generally unchanged. Furthermore, differentiating the species and growth stages in addition to the sites has a negative effect on the estimation (Table  3 , Spectra-GF), probably because this led to too small training datasets and unstable GPR performance. Using the 45 • spectral observations for GF estimation generally performed better than using 0 • observations (Table  3 , Fig. 6 ). One possible reason is the larger projection area of the plants in the 45 • view zenith angle could help the small plants in the early stages to be more easily detected by spectrometer and camera  (Jin et al. , 2017; Liu et al. , 2017)  (Fig. 11 ). The forward band selection method with AIC c (Fig. 5 ) showed the importance of choosing the right number of bands as inputs to the estimation model.
Despite the already large dataset collected, more data would thus be needed to confirm these band selections. Alternatively, using a common band set with five bands: B-G-R-RE-NIR led to slightly poorer performance than using selected optimal band sets Fig. 6 ). On one hand, this result shows that forward band selection was effective in selecting an optimal band set for a specific dataset because this method yielded the optimal performance. On the other hand, it shows that the common band set was sufficient for practical use, as it performed similarly to the selected band sets. Note that, despite the small RMSE of around 0. 02 obtained for GF estimation, the rRMSE was moderate (between 26 % and 33 %, Fig. 6 ) because the overall GF values were also small for the early stages. This explains the moderate plant density performances obtained with the indirect method (Table  4 , Fig.
At an earlier stage (e. g. , 1-leaf), plants have less overlap, which could facilitate morphological analysis, while at a later stage (e. g. , 3-leaf), plants are larger so they are easier to detect. This may explain the difference in the best growth stages with different methods. Our study emphasizes an important limitation of methods that exploit the relationship between density and GF: the estimation is affected by site, stage, and possibly species. A possible solution for this problem is to calibrate one model for each site, stage, and species. To avoid laborious manual counting that is necessary for calibration, image-based counting methods, e. g. , based on deep learning detection algorithms  (Liu et al. , 2020; Shubhra et al. , 2018; Tseng et al. , 2022) , could be used because they have higher estimation accuracy, and their lower throughput would be enough to build a small dataset for calibration.
Conclusion In this study, the straw cereal plant density at early stages was estimated from spectral reflectance measured at the nadir or from 45 • view zenith angle, with (indirect method) or without (direct method) using GF as a proxy. The results were compared to those obtained with a popular image-based method, using a large and diverse dataset including different sites, species, and growth stages. According to the ablation study performed with five common spectral bands, the spectra-GF estimation (first step of indirect estimation) was site-specific and stage-specific; the GF-density estimation (second step of indirect estimation) was site-specific, stage-specific, and species-specific; the spectra-density estimation (direct estimation) was not specific at all. Using a 45 • view zenith angle showed slightly better performance on average so 45 • was chosen.
First, the commonly used band set of commercial multispectral cameras (B, G, R, RE, NIR) performed almost as well as chosen bands from the spectrometer in spectra-GF estimation, showing there is enough information in this band set for the density estimation task. Second, this reflectancebased method is robust to a degradation in spatial resolution, which means it allows higher flying altitudes and higher throughput, while keeping the same accuracy. A general model of direct density estimation may be possible, but this somewhat unexpected result did not explain its capability in handling different growth stages. This will need to be confirmed with a larger dataset with different varieties, different sites, and different growth stages as factors.
Acknowledgements This work was supported by the  Agence Nationale de la Recherche , programme  Investissements d'avenir ,  ANR-11-INBS-0012  ( Phenome ), the  Young Scientists Fund of the National Natural Science Foundation of China  (No. 42201437 ), the  Young Scientists Fund of the Natural Science Foundation of Jiangsu Province, China  (No. BK20210411 ), the  National Key R&D Program of China  (No. 2021YFD2000105 , No. 2022YFE0116200 ), and the  Project of Seed Industry Revitalization in Jiangsu Province, China  ( JBGS[2021]007 ). One author of this article,  Tiancheng YANG , was funded by the  Chinese Scholarship Council  (Number  202006850003 ). We are very thankful to  Gaetan DAUBIGE ,  Mario SEROUART ,  Benoit DE-SOLAN , and  Vincent MERCIER  from  ARVALIS  and  INRAe  for helping deploy the experiments in France.
numata, H. pardalinus, H. elevatus, H. hecale, H. ismenius, H. besckei and H. ethilla). For H. melpomene and H. cydno belonging to the so-called melpomene clade, their genomes were mapped with a substitution rate of 0. 02. Alignment file manipulations were performed using SAMtools v0. 1. 3  (Li et al. , 2009). After mapping, duplicate reads were excluded using the MarkDuplicates tool in Picard (v1. 1125; http://broadinstitute. github. io/picard) and local indel realignment using IndelRealigner was performed with  GATK (v3. 5; DePristo et al. , 2011). Invariant and polymorphic sites were called with GATK HaplotypeCaller, with options-min_base_quality_score 25 --min_mapping_quality_score 25 -stand_emit_conf 20 --heterozygosity 0. 015.
Structure, IBD and population sizes inferences FST, dXY and π were calculated in overlapping windows of 25 kb based on linkage disequilibrium (LD) decay  (Dasmahapatra et al. , 2012)  using custom scripts provided by Simon H. Martin (https://github. com/simonhmartin), and the genome-wide average was calculated using our own scripts (available from https://github. com/angelesdecara). Distance in km between sampling sites was measured along a straight line, not taking into account potential physical barriers. Following  (Rousset, 1997) , in a 2dimensional habitat, under a model of isolation by distance (IBD) differentiation, measured as FST /(1 -FST), should increase as a function of the logarithm of distance. Therefore, we tested for the existence and intensity of an IBD signal among species and between populations of H.
numata using a linear model. If IBD is stronger in species not polymorphic for the inversion we should observed significantly steeper slopes in these species. To test this, we measured IBD (i) within populations of each species separately, (ii) for all H. numata within the Amazonian region (excluding Atlantic forest populations) and (iii) for all H. numata including the Atlantic region. The slopes of FST /(1 -FST) versus log(distance) were calculated using the R package lsmeans  (Lenth, 2016) ; the slope difference among species or between populations within species was estimated with an ANOVA and its significance evaluated with function pairs of this package (Text S1 and see example script on https://github. com/angelesdecara/). Admixture  (Alexander et al. , 2009)  analyses were run on a subset of the 68 H.
numata genomes, keeping only 15 individuals from Peru to have a more balanced representation of individuals across the geographic distribution. Filters were applied to keep biallelic sites with minimum mean depth of 8, maximum mean depth of 200 and at most 50% genotypes missing. We only kept 1 SNP per kilobase to remove linked variants using the thinning function in vcftools, and we obtained the optimal number of clusters using cross-validation for values of K from 1 to 10  (Alexander & Lange, 2011). Principal component analyses (PCA) were performed with the same filters as for admixture, using the same H. numata genomes as for the admixture analyses, using plink2  (Chang et al. , 2015). In order to estimate demographic parameters independently of the effect of selection on diversity, we performed stringent filtering on the dataset.
The input dataset consisted of 4092 genomic regions, each 1kb in length and spaced at approximately 30kb intervals (above the value at which LD decays at more than half of its value) and with genotypes in at least one of the two samples of each taxon We used as priors for coalescence times (τ) and genetic diversity (θ), Gamma functions with α=1 and β=100, and for migration rates α = 0. 002 and β = 0. 00001. These priors were chosen to allow good convergence while also ensuring non informativity. In order to calculate the highest posterior density interval, we used the library HDInterval in R, and to integrate such posterior densities we used the library sfsmisc in R. We rescaled the results using a mutation rate of 1. 9e-9 mutation/bp/generation  (Martin et al. , 2016)  and 4 generations per year (i.
Population structure revealed by PCA is in line with the admixture analysis (Fig  2B ). Individuals from the Atlantic populations of H. numata clustered together to one side of the first PCA axis, whereas all other individuals from all other populations clustered to the other side. The second axis of the PCA separates individuals from French Guiana from the other samples of the upper Amazon. This separation was not found with Admixture (i. e. with K = 3) from the complete dataset. To better investigate the existence of a hierarchical population structure, we excluded individuals from the Atlantic populations and compared individuals from French Guiana to a randomly sampled set of Peruvian individuals. In this case we found a clear separation in two groups corresponding to French Guiana and Peru (Fig  S2A ).
Funding This work was funded by grants  Hybevol  ( ANR-12-JSV7-0005 ) and  Supergene  ( ANR-18-CE02-0019-01 ) from the  Agence Nationale de la Recherche  and  European Research Council Grant MimEvol  ( StG-243179 ). AVLF acknowledges support from  Fundação de Amparo à Pesquisa do Estado de São Paulo -(FAPESP)  ( Biota-Fapesp  grants  2011/50225-3 ,  2013/50297-0  and  2021/03868-8 ) and  Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)  ( 421248/2017-3  and  304291/2020-0 ). KLSB acknowledges the financial support of  FAPESP  Process #  2012/16266-7. Brazilian specimens are registered under SISGEN ( A701768 ).
To obtain a molecular dissection of the cell wall reshuffling during the germination process, we compared MAS NMR data recorded on three morphotypes of Aspergillus fumigatus: dormant (dorC), swollen (swoC), and germinating (gerC) conidia. We first focused on the mobile polysaccharide fraction since the comparison of 2D 13 C-13 C DP-INADEQUATE spectra showed strong differences (Fig. 4 ). A new spin system was observed in gerC compared to dorC, with 13 C contributions at 109, 84, and 78 ppm (Fig. 4A ). We identified it as galactosaminogalactan based on the connectivity patterns suggesting a beta-pyranose linked at the C1 position, no acetyl, or NH 2 group linked to the C2 position and by comparison to previous studies  (55). Galactosaminogalactan is a linear but heterogeneous polymer composed of galactopyranose (in contrast to galactofuranose found in galactomannan) and N-acetyl-galactosamine/galactosamine linked through α-1,4 linkages (56) (Fig.
8C ). In dorC, the molar ratio of rigid cell wall polysaccharides were in line with previous data obtained by destructive biochemical methods. On the other hand, while a previous study on mycelia  (40)  reported, respectively, 50%, 42%, and 8% for these polysaccharides, our data show substantial differences for the main three polysaccharides between the infectious (germinating conidia, i. e. , 63%, 25%, and 12%, respectively) and postinfectious (mycelia) stages. β-1,3-glucan is the major structural polysaccharide component of the cell wall conferring rigidity and turgor pression resistance and has been targeted for drug development  (5, 86). Our data indicate that the dorC to swoC transition is associated with a change of this glucan organization, with a decrease in level from 67% to 46% during transition.
MAS NMR Spectroscopy. All MAS NMR experiments were performed on a 14. 1 T NMR spectrometer (Bruker Biospin) using 3. 2-mm or 4-mm HCN MAS probes. AFM Analysis. Experiments were performed at room temperature using a Multimode 8 AFM (Bruker). For melanin ghosts imaging, the experiments were performed in liquid using contact mode and oxide-sharpened microfabricated silicon nitride (Si 3 N 4 ) AFM probes with triangular cantilevers of nominal spring constant of 0. 01 N/m (MSCT, Bruker). For recombinant RodA imaging, the experiments were done in air using the ScanAsyst mode and ScanAsyst air AFM probes with a nominal spring constant of 0. 4 N/m (Bruker). Data, Materials, and Software Availability. All study data are included in the article and/or SI Appendix.
ACKNOWLEDGMENTS. We are grateful for the support for equipment from the  French Government Programme Investissements d'Avenir France BioImaging (FBI, Agence Nationale de la Recherche ,  N° ANR-10-INSB-04-01 ) and the  French Government Investissement d'Avenir programme, Laboratoire d'Excellence "Integrative Biology of Emerging Infectious Diseases"  ( ANR-10-LABX-62-IBEID ). We thank the  ANR  grants  ANR-16-CE11-0020-02  to I. G. , A. L. , and V. A. ;  ANR-21-CE17-0032-01  to V. A. ), as well as the  Swiss National Science Foundation  (for early postdoc mobility project  P2EZP2_184258  to  A. Lends. This work has benefited from the  Biophysical and Structural Chemistry Platform at Institut Européen de Chimie et Biologie IECB, Centre National de la Recherche Scientifique CNRS Unité d'Appui et de Recherche UAR 3033 ,  INSERM US001 , and the  CNRS  (IR-RMN FR3050 and Infranalytics  FR2054 ).
For 18S rDNA, PCR program started with 10min of initial denaturation at 96°C followed by 35 cycles including 20s of denaturation at 96°C, 30s of annealing at 54°C, 30s of extension at 72°C and ended with a final extension of 10min at 72°C. A total of three negative controls corresponding to blank extractions and PCR were included for each gene. The PCR products were then sent to Biofidal sequencing company for purification and 2x300bp Miseq sequencing (Illumina). A total of 12,836,921 and 14,248,382 reads were obtained from 16S and 18S rDNA respectively and were demultiplexed using the Mothur pipeline  (Schloss, 2013). Reads were trimmed based on size (270-310bp and 400-550bp for the 16S and 18S rDNA, respectively) and sequences were conserved if they contained no ambiguous position and if both strands of the paired-end reads aligned together.
Chimeras were removed using CHIMERAVSEARCH. The alignment was performed using Silva v. 132 database and reads that did not align were filtered out. However, for sequences that matched gregarine, a supplemental local alignment (BLAST) was performed against the Genbank database due to the lack of reference sequences in Silva. Clustering was performed using Opticlust method allowing a maximum dissimilarity rate of 3%. After removing singletons and subsampling, 5,886 and 5,264 OTUs were retrieved respectively for 16S rDNA and 18S rDNA. Contaminant OTUs were removed if their proportion in samples were not at least 10-fold in comparison with controls. Finally, after quality control, data were homogenized by sampling 1,000 and 500 reads respectively for 16S rDNA and 18S rDNA. Miseq sequences have been deposited on XXX under the project number XXX (ongoing process).
, 2015). Because the activity periodization measured by the LAM system matches the flight activity recorded by video tracking realized on Ae. albopictus flight behavior  (Wilkinson et al. , 2014) , and because mosquito walk distance usually do not exceed 20% of total mosquito distance in laboratory experiments  (Ziegler et al. , 2022) , we considered that the recorded activity was mainly due to flight. Two experiments involving 16 parasitized males, 16 parasitized females, 16 unparasitized males and 16 unparasitized females were conducted for 24h in a BSL2 insectary with 18h/6h day/night and 28°C. Activities were binned into a 10min interval. Whenever individuals died before the end of the registration, they were removed from the final dataset.
Statistical and data analysis Prevalence data were analyzed with a binomial model and a probit link function (glm, R package stats). Statistical analyses of fixed effects (sex and time from introduction) were performed with a Type II Wald χ 2 test (Anova, R package car). Geographical distance between mosquito populations were calculated based on ellipsoid distance (pointDist, R package enmSdm). Microbiota diversity has been estimated using taxa number for richness (specnumber, R package Vegan), Shannon index for alpha diversity (diversity, R package Vegan) and Bray-Curtis dissimilarity index for beta diversity (vegdist, R package Vegan). Richness and alpha diversity have been analyzed using generalized linear models (glm, R package lme4) and linear models (lm, R package lme4) respectively, meanwhile beta diversity has been analyzed using permutational multivariate analysis (adonis2, R package vegan).
Microbiota For the founder effect and desiccation experiments, the densities of As. taiwanensis per mosquito were modeled with a linear mixed model and a negative binomial generalized mixed model respectively (lmer and glmer. nb, R package lme4). The experiment replicates were used as random factors. The fixed effects (sex, density or desiccation time) and their interaction were tested with type II Wald χ 2 test (Anova, R package car). For the Experiment 2, flight activities over the entire recording period were represented as a heatmap (Heatmap, R package Complexheatmap). The total activities for each mosquito were modeled with a negative binomial generalized linear mixed model since data were zero inflated (glmer. nb, R package lme4). Experiment replicates were used as a random variable.
Ascogregarina taiwanensis abundances correlate with the microbiota composition, the mosquito origin and the population age To compare dynamics of As. taiwanensis abundances within the microbiota among different mosquito populations, a metabarcoding analysis was conducted on a subset of 248 male and female mosquitoes (Table  S1 ). The eukaryotic and prokaryotic microbiota of Ae. albopictus was dominated by two OTUs classified as Wolbachia (Figure  2A ) and three OTUs classified as Ascogregarina (Figure  2B ). The three Ascogregarina OTUs could be confidently classified within the Ascogregarina taiwanensis species based on a maximum likelihood phylogeny conducted on the same 18S rDNA region with closely related taxa (Figure  3A ). Interestingly, 9 different OTUs of Ascogregarina were identified among the complete dataset meanwhile only 2 OTUs were identified for Wolbachia.
We then modelled the variations of those three OTUs with the time after mosquito introduction, sex and their interaction. Through likelihood ratio test, we showed that none of those OTUs significantly differed between sex but Otu000002 and Otu000003 showed a significant covariation with the time after mosquito introduction (see Table  S2 ). Otu000002 and Otu000003 increased in abundance in old populations while Otu000001 did not significantly vary with the age of the population (Figure  4 ). Since abundance is defined as the proportion of sequences occupied by a given OTU in its community (eukaryotes in this case), we performed qPCR to estimate the absolute density of microorganisms for each marker used in this study. DNA quantities were limited so we could not perform this test for all the 248 samples, we separated the dataset in 3 eukaryotic communities based on the metabarcoding (Figure  2B ).
Acknowledgement. Part of the mosquito collection was enabled by the international network of the ' Mosquito Microbiome Consortium ' (https://mosquito-microbiome. org/). This project was funded by the  French National Research Program for Environmental and Occupational Health of Anses , grant number  19-95. We would also like to thank the  EC2CO program  for its support to this project (project name ' INTERASCO ') as well as the  IDEX Breakthrough  (project name ' MICRO-BEHAVE '). Sampling collections were also supported by the  Thailand Program Management Unit for Human Resources &   Institutional Development, Research and Innovation (PMU-B) ,  NXPO , grant number  B17F640002. This study used resources from the iBio platform and the  FR BioEEnViS.