dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We want to thank  Silas Kiragu , who is responsible for maintaining the trials in Embu and Machanga site, and  John Mukalama , who implemented and maintained the trials in Aludeka and Sidada. Also, we want to thank  John Waruingi  for helping with sample processing over the years, and  Dr. Moses Thuita  for coordinating the trials for many years, and  Britta Jahn-Humphrey  for organizing and overseeing the measurement of most SOC and TN in recent years. Additional a thanks to  Moritz Bach  and  Julian Koller  for their contribution in the measurement of SOC and TN from 2021 and to  Lukas Burgdorfer  and  Franziska Büeler  for their contribution to the data from 2018. This study was funded by the  Swiss National Science Foundation (SNSF ; grant number  172940 ) and by the  DSCATT project  " Agricultural Intensification and Dynamics of Soil Carbon Sequestration in Tropical and Temperate Farming Systems"  ( N°AF 1802-001 ,  N°FT C002181 ), supported by the  Agropolis Foundation  (" Programme d'Investissement d'Avenir"   Labex Agro ,  ANR-10-LABX-0001-01 ) and by the  TOTAL Foundation  within a patronage agreement.
We used shifted Beta distributions as prior distributions for these parameters. Given that the null expectation was that the Bayesian score (BS) would be 0, we used a Beta (2,2) distribution (i. e. , with a slightly enhanced probability for 0. 5) and shifted them by -0. 5 to center on 0. After a burn-in phase of 10,000 steps, we sampled the posterior distribution every 10 of 100,000 steps of MCMC chains using open-Bugs  (Lunn et al. , 2009)  linked to R with the BRugs package  (Thomas et al. , 2006). We visually checked for convergence of these chains.
| Measurement of leaf nutritional quality traits Leaf nitrogen content (LNC, % of dry mass) was determined nondestructively on 15 May, 2018, on 4-6 leaf replicates from the control cages of the field experiment, and using near-infrared spectroscopy (NIRS) with a portable near-infrared spectrometer (FieldSpec 2500; Analytical Spectral Devices Inc). We used a Leaf Clip and a white background panel for the measurement of reflectance, with calibration previously performed on a large range of tetraploid wheat accessions from the domestication continuum  (Ecarnot et al. , 2013). For each leaf, we recorded two spectra on the adaxial leaf surface one-third and two-thirds down the length of the leaf, and the mean of these two spectra was used to estimate nitrogen content. We complemented the nutrient data with destructive enzymatic measurements of the soluble protein content (LPrC; % of dry mass) and digestible carbohydrate content (LChC; % of dry mass) of wheat accession leaves.
For this, we first extracted the median values of the posterior distributions for the Bayesian scores (BS, see above). We finally estimated the effect of the discriminant function (DF), that is, the linear combination of leaf traits that separates the modern from the ancestral subspecies obtained from the LDA, on the two feeding responses of the herbivore, using linear models. All statistical analyses were performed with the R software (R Development Core Team, 2005), using the appropriate packages and scripts publicly available (see "Data Availability" section). We reported observed p-values and their Shannon information transforms S-value = -log 2 (p-value), which measure the degree of incompatibility between the data and the null hypothesis of the model  (Greenland, 2019; Wasserstein et al.
| Feeding responses of the locusts Both the field experiment and the lab paired-choice trials suggested that locusts selected wild ancestors over domesticated modern accessions (Figure  3 ). The ordinal mixed model including a subspecies effect was reasonably supported regarding the feeding damage index (FD: p-value =. 004 and Shannon information transform S-value = 8; see Materials & Methods above for further details). We did not detect an effect of accession identity on the plant-scale feeding damage index. Paired-choice trials under laboratory-controlled conditions confirmed that wild emmer accessions were more palatable than that of wheat durum accessions, with a mean difference of 14% in the feeding selectivity proportions (FS; paired t-test; pvalue <. 001; S-value > 10). Furthermore, the Bayesian posterior distributions supported an accession effect on herbivore selectivity; in particular, the two ancestral accessions A5 and A9 as well as the modern accession M1 were preferred by the herbivores, while the modern accession M3 was rejected (Figure  S1 ).
In addition, we performed paired food selection trials directly comparing modern wheat accessions and their wild ancestors under laboratory-controlled conditions. One hundred food-choice trials were conducted between April 17 and 27, 2018, in order to test all pairs of the 10 wild emmer × 10 durum wheat accessions. We usedF I G U R E 2Designs of the field experiment (a, b) and the laboratory paired-choice experiment (c). (a) The field experiment consisted in 4 treatment conditions, with 6 field cages each: Control with ancestral wheat (CA), control with modern wheat (CM), herbivory with ancestral wheat (HA), and herbivory with modern wheat (HM). (b) Each field cage included two replicates per genotype, for a total of 20 pots. Each circle represents a pot; the values within a circle are the accession numbers (from 1 to 10) and the values positioned in index are the repetition numbers (1 or 2).
François Vasseur: Methodology (supporting). Cyrille Violle: Methodology (supporting). Elena Kazakou: Conceptualization (equal); data curation (equal); funding acquisition (equal); methodology (equal); project administration (equal); supervision (equal); validation (equal); writing -review and editing (lead). ACK N OWLED G M ENTS This work was supported by the DINER project, publicly funded by the Labex CEMEB and funds from the French Agricultural Research Centre for International Development (CIRAD). CV was supported by the European Research Council (ERC) Starting Grant Project StG-2014-639706-CONSTRAINTS. We thank the CEFE (Centre d'Ecologie Fonctionnelle et Evolutive) and the CBGP (Centre de Biologie pour la Gestion des Populations) for providing us access to the LabEx CeMEB platform "terrain d'expérience CEFE," 43°38′20.
Introduction Rice (Oryza sativa L. ) is the most important staple food crop worldwide, with an average production of around 700 million tons per year (FAOSTAT database: http:// www. fao. org/ faost at/ en/# home). Rice is a high yield crop when grown in paddy fields under waterlogged conditions. Waterlogged conditions guarantee high rice productivity while preventing the growth of weeds, but are also responsible for the low water use efficiency (0. 60 kg yield/m 3 water) of rice compared with other major cereals, such as maize (2. 19 kg yield/m 3 water) and wheat (2. 46 kg yield/m 3 water)  (Liu et al. 2009; Kuschel-Otárola et al. 2020). Global climate change has increased both the intensity and duration of drought periods  (Trenberth et al. 2014 ) thereby reducing the availability of water for irrigation and threatening the production of high waterdemanding crops including rice grown in paddy fields.
The PCR products were purified with AMPure XP beads (Beckman Coulter) using a genomic sample preparation platform (BRAVO, Agilent technologies). The concentration of amplicons was measured with an ultrasensitive fluorescent stain (Quant-ITTMPicoGreen ® dsDNA Assay kit, Invitrogen), and the size was determined using the Agilent High Sensitivity DNA kit (Agilent Technologies). Equimolar amplicon pools were paired-end sequenced (Illumina Miseq), following the manufacturer's guidelines. The raw data were uploaded in the NCBI database under project number PRJNA810017.
Trimming and Identification of Sequences The 5' and 3' primers (on R1 and R2 reads) and unknown bases (n) were removed from the sequences using the Phyton package 'Cutadapt'  (Martin 2011 ). The reads were analyzed according to FROGS pipeline guidelines  (Kozich et al. 2013; Escudié et al. 2018 ) using the galaxy workbench at Genotoul (http:// bioin fo. genot oul. fr). The clustering step was performed with the 'swarm' method thus avoiding the need for an identity threshold. The sequence-clusters (Operational Taxonomic Units, OTUs) produced by FROGS use a similar view as the amplicon sequence variant (ASV) delineation  (Callahan et al. 2017)  but with the advantage of limiting the risk of overestimating diversity if copies of the SSU rRNA gene differ within a given individual. Following the pipeline designer's advice, a de-noising step was performed with a maximum distance of aggregation of 1 followed by a second step with a maximum distance of aggregation of 3.
Chimera products were filtered out with the FROGS 'remove chimera' tool. A filter was then applied to remove sequenceclusters with sequences in less than five samples to avoid artificial sequence clusters. Sequence clusters were identified with 'Blast + '  (Camacho et al. 2009 ) using the Silva 128 18S database. Sequence cluster assignments were filtered based on the quality of the match with a Blast threshold of 95% and 95% Blast identity. Subsequently, all sequence clusters were checked at the phylum level and those belonging to a non-fungal taxon were removed.
Microbial Ecology Analyses All statistical analyses were performed in R (R core team 2021). The contingency matrix of the sequence cluster count was filtered: sequence-clusters that occurred at least 25 times with a total of at least 100 reads were retained. Drought and control samples were filtered separately. For each sample, the counts assigned to each sequence cluster were log-transformed, i. e. divided by its total, summed with a pseudo-fraction (0. 001), and then log transformed. The thus log-transformed data were adjusted for the plate effect (using the control and drought samples together), using a linear model, remove-BatchEffect, in the R package LIMMA  (Ritchie et al. 2015). This normalized contingency matrix was used for the follow up analyses. Data on rice grain yield were averaged across replicates.
From the normalized contingency matrix, sequence cluster richness, evenness and diversity index estimators were calculated using the VEGAN R package  (Oksanen et al. 2015)  and BIODIVERSITY R  (Kindt & Coe 2005). Statistical differences in these parameters were analyzed using two-way ANOVA with the R package CAR  (Fox & Weisberg 2011). In order to reduce the size of the sequence-cluster dataset and to extract the most relevant information concerning the sequence-cluster effect on yield, the differences within the fungal community under the different treatments were analyzed using redundancy analysis (RDA) with grain yield as constraining covariate, using the function rda in the VEGAN R package. Redundancy analysis summarizes the variation in the microbial community that is explained by yield and drought.
Genome-Wide Association Analysis Marker-based estimates of narrow-sense heritability were computed based on all the cultivars used in the present study  (Kruijer et al. 2015). GWA analyses were performed of all sequence clusters with a frequency higher than 5%, using the 82,858 SNPs with a minor allele frequency (MAF) > 0. 05. Univariate GWAS was performed of each fungal sequence cluster under drought and control conditions separately, as well as for the RDA axes described above. We used the approach of Kang and colleagues  (Kang et al. , 2010)  implemented in the STAT-GENGWAS package (https:// cran. r-proje ct. org/ web/ packa ges/ statg enGWAS/ index. html) using the function runSingleTraitGwas. For the sequence-cluster univariate GWAS, we only used the most abundant fungal taxa (17 sequence clusters under control and 15 under drought conditions).
Linkage Disequilibrium Analysis and Identification of Candidate Genes The linkage disequilibrium (LD) decay rate in indica rice varieties is about 75 Kb  (Mather et al. 2007 ). We consequently defined windows of 75 Kb upstream and 75 Kb downstream of the SNP of interest to search for candidate genes using the TRIO R package  (Schwender et al. 2014). Genes located in the selected windows were obtained from the MSU Rice Genome Annotation Project database (http:// rice. plant biolo gy. msu. edu), version 7. 0. For 24 loci, in silico gene expression analyses were performed with RiceXPro (http:// ricex pro. dna. affrc. go. jp/) and IC4R Rice Expression Databases (http:// expre ssion. ic4r. org/).
Rice Genetic markers Associated with Root Mycobiota To study the role of the host in the recruitment of root mycobiota and how the recruitment changes under drought, a root fungal community was analyzed to identify which sequence clusters differed the most between well-watered and drought conditions. Univariate GWA analysis was used to search for genomic associations with the most abundant sequence clusters. The root fungal community sequencing data resulted in a total of 15,271,794 sequences belonging to 2,687 different sequence clusters. A total of 2,347 sequence clusters were found in both treatments, while 61 were specific to the drought treatment and 278 to the control treatment (Additional file 1: Fig. S1a ). The total number of sequence reads (abundance) and the total number of sequence clusters (richness) were lower under drought conditions, while the diversity (Shannon) and species balance (J-evenness) indexes were higher under the drought treatment (Additional file 1: Fig.
Gene Candidates LD analysis with a window of 150 kb around each SNP was used to identify candidate genes (within chromosomal intervals) underlying the significant SNPs identified by GWA analysis, providing a list of a priori candidate genes that possibly control the association between root associated fungi and/or grain yield. The list of candidate genes and their functional annotation was retrieved from the MSU Rice database (Table  2 , Additional file 1: Table  S1 ). When looking at the SNPs with the highest LOD score from the GWAS sequencecluster (Additional file 1: Table  S1 ), we identified candidate genes related to protease activity (serine protease SUBTILISIN, LOC_Os04g35140), cellulose synthesis protein (e. g. CELULOSE SYNTHASE-LIKE PROTEIN H2 (CSLH2) LOC_Os04g35020), anti-microbial defense (DEFENSIN-LIKE, DEFL, LOC_Os04g31250), Bric-àbrac/Tramtrack (BTB/POZ, LOC_Os10g28970) and EXO70 exocyst complex (LOC_Os04g31330), among other kinases and phosphatases (Table  1 ).
We found several candidate genes, putatively involved in plant-microbe interactions, which may also be involved in sequence cluster abundance in rice roots, and possibly in rice yield. When zooming in on individual candidate genes, we found a number of candidate genes for disease resistance and plant development, including two that encode RALF-LIKE (RALFL) peptides, for instance. The RALF protein family is conserved across species and has a role in root development and as signaling molecules for stress adaptation responses (e. g. drought)  (Sharma et al. 2016). Both RALFL candidate loci are downregulated in rice after root treatment with jasmonic acid (-4 Log 2 Fold) and abscisic acid (-4 Log 2 Fold), and Magnaporthe oryzae infection (-3 Log 2 Fold) (data in the RiceXPro database: https:// ricex pro.
Acknowledgements We would like to thank the staff of the  International Rice Research Institute (IRRI)  for their immense help running the field experiment and collecting and processing the root samples. We thank  N. de Brouwer  for his help grinding roots. At the EcogenO platform (within  ANAEE research infrastructure and Biogenouest ), we thank  S. Michon-Coudouel  and  M. Biget  for their technical support in library preparation and sequencing. We thank  J. G. Maciá-Vicente  for advice and help with some of the R scripts.
Proliferation test Cell proliferation was investigated using the highly advanced IncuCyte® Cell Count proliferation assay (Sartorius) in this study. Untransfected cells were seeded at a density of 1. 10 3 cells per well in a 96-well plate, while for transfection, cells were seeded at a higher density of 5. 10 3 cells per well. The IncuCyte® system allowed for time-lapse imaging at regular 2-hour intervals over a 72-hour period, employing a 10X magni cation to capture detailed cellular dynamics. The acquired images were then subjected to meticulous analysis using the sophisticated IncuCyte® software, facilitating a comprehensive assessment of cell proliferation dynamics. The analysis parameters included both con uence analysis and cell-by-cell analysis, ensuring precise quanti cation of cell growth over time.
Cytotoxic assay We used the IncuCyte cytotoxicity assay (Sartorius) designed for adherent cell lines. Untransfected cells were seeded at a density of 5. 10 3 cells per well, followed by a 24-hour incubation period to allow cell adhesion and growth before transfection. At 24 hours post-transfection, the culture medium was replaced with 100 µl of fresh medium containing IncuCyte Cytotox Green reagent (#4633, Sartorius) at a nal concentration of 250 nM. Using the real-time imaging capability of the IncuCyte system, cell images were captured at regular 2-hour intervals over 72 hours, at 10X magni cation. Acquired images were subjected to the analysis using IncuCyte software, where uorescence quanti cation was used as an indicator to estimate cell death rate per mm 2. Then, cell toxicity was assessed after normalization with control cells.
Statistical analysis Data were analyzed using GraphPad Prism software (v. 9) (Dotmatics, US) or R-Studio (R version 4. 3. 1 (2023-06-16). For comparisons of two groups, Student's t-test was performed, while one-way or two-way ANOVA for comparisons of at least two groups. Data are expressed as means ± standard deviation (SD) or standard error of mean (SEM) as stated, and differences were considered statistically signi cant at p < 0. 05 (* p ≤ 0. 05, ** p ≤ 0. 01; *** p ≤ 0. 001). Each experiment was performed at least three times independently to ensure the reproducibility of the results.
(e. g. Grötsch et al. , 2003; Cantrell, 2004; Swart et al. , 2005; Al Suwaidi et al. , 2005; Morad et al. , 2012; Nader et al. , 2013; Lawrence et al. , 2015; Marchionda et al. 2018). No later dolomite cements were recorded in the samples. Locally, other mineral phases (anhydrite and calcite) possibly postdating the dolomites were observed; they fill fractures and intercrystalline pores in the dolomites and may locally replace some dolomite crystals (Fig. S1C ). No clear relationship was observed between dolomite petrography and Δ47/U-Pb thermochronometry data (Fig. S1B ). Indeed, when comparing samples with low temperatures (and old ages) and samples with high temperatures (and young ages) a clear increase in crystal size and/or in non-planar textures was not recorded. Furthermore, dolomite crystals display a uniform dull red CL and lack zonation/overgrowths and reaction borders, (Fig.
S2. CLUMPED ISOTOPE (Δ47) ANALYSES AND DATA PROCESSING Simultaneous measurements of Δ47, δ 18 O and δ 13 C compositions of carbonate samples were performed at Institut de Physique du Globe de Paris (IPGP) with a Thermo Scientific MAT253 gas-source mass spectrometer after digestion of carbonate powder in 104% phosphoric acid. About 5 mg of carbonate (dolomites) was reacted at 90°C in a common acid bath for 1 h. The methods used for carbonate digestion, CO2 purification and isotope measurements follow the procedure detailed in  Bonifacie et al. (2017). Each measurement consisted of 70 cycles of comparison between the CO2 extracted from the sample against a working internal reference CO2 gas [Oztech; with δ 13 C = -3. 72‰ and δ 18 O = -6. 06‰, versus VPDB (Vienna Pee Dee Belemnite), verified with the international carbonate reference material NBS19].
For constructing correction frames for processing raw ∆47 values, CO2 gases driven to isotopologue equilibrium at both 1000°C and 25°C were used. The equilibrated CO2 gas standards in this study have bulk isotopic compositions spanning the entire range of measured samples, were purified and analyzed in the same way as carbonate samples or carbonate standards and were typically run every four to five analyses. The raw ∆47 data were first corrected for linearity effects using a fixed common equilibrated gas line slope fitted to the equilibrated CO2 gases at both 1000°C and 25°C. Subsequently, the raw ∆47 values (expressed relative to the working gas) were transferred into the Carbon Dioxide Equilibrated Scale (CDES) using the CO2 gases driven to isotopologue equilibrium at both 1000°C and 25°C with theoretically predicted Δ47 values of 0.
S3. U-PB GEOCHRONOLOGY BY LA-ICPMS: DATA ACQUISITION AND PROCESSING Uranium and Pb isotopic ratios were measured in situ on thin sections (30-60μm thick) by LA-ICP-MS at Frankfurt Isotope and Element Research Center (FIERCE) of the Goethe University of Frankfurt using a method similar to that described in  Gerdes and Zeh (2006). The measurements were acquired using a Thermo-Scientific Element XR ICP-MS, coupled to a RESOlution (Resonetics) 193 nm ArF Excimer laser (CompexPro 102, Coherent) equipped with a S-155 two-volume ablation cell (Laurin Technic, Australia). Signal strength at the ICP-MS was tuned for maximum sensitivity while keeping oxide formation below 0. 3% (UO/U) and element fraction low (e. g. , Th/U = 1). The data were acquired in fullyautomated mode overnight in three different analytical sessions during which a spot size of 213µm was used.
This is also supported by results of  Godeau et al. (2018)  where samples analyzed with the same device and by using the same method as described here agreed within uncertainy (+/-1%) with ID solution mode analysis. The analytical data are summarized in Table  S2  and presented in detail in Table  S3. This latter contains the complete analytical data set of the 3 analytical sessions including that of the reference material and the Metadata table. Data were plotted in the Tera-Wasserburg diagram (Extended Data) and ages calculated as lower intercepts using Isoplot 3. 71 (www. Isoplot. com). All uncertainties are reported at the 2σ level. Additional data on the 15 dolomite samples investigated with Δ47/U-Pb thermo-chronometry are reported in Figure  S2  and Table  S2. Constraints on the thermal history of the studied samples (presently at 2833-3366 m depth) are needed to interpret the Δ47 dataset here presented.
This improves the model's ability to understand context and capture long-range spatial relationships in an image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative feature maps from transformer encoder blocks. We established the potential of the single-stage detector for the fine-grained image recognition task, achieving state-of-the-art 93. 4% accuracy, as well as outperforming existing onestage recognition models. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.
• We replaced the Yolov5 detection heads with three transformer heads at the output to detect discriminative fused features extracted using the transformer encoder blocks. To validate the algorithm's performance, comparison studies were conducted, and the empirical results indicate that the model is capable of recognizing sub-classes with high precision and accuracy. The rest of the article is structured as follows: Section 2 discusses related work in detail, providing an overview of the existing two-stage and one-stage methods to overcome the fine-grained recognition problem. Section 3 first introduces the existing Yolov5 backbone structure, subsequently modifies the backbone using transformer encoder blocks, and finally compares the proposed model to the existing model. Section 4 summarizes the experimental setup and presents the training and validation results from a fine-grained dataset using evaluation metrics, demonstrates the proposed model's results on the test set, and finally compare the results with the state-of-the-art fine-grained recognition methods.