dataesr/scientific-paragraphs-categorization huggingface dataset:
Ancestry inferences To determine which groups of avocados are spanned by the CGB and SR genotypes, we ran two independent ancestry analyses using datasets 1 and 3, because each matrix presented a different number of SNPs, 4931 and 227, respectively (Table  1 ). These datasets included genotypes with high genetic ancestry (> 80%) to one of the genetic groups (ME, GU, WI, and CO, the Colombian group) determined in the unsupervised non-parametric genetic cluster analysis. In the case of the CO group, we selected samples representing the two possible genetic sub-populations detected in the Colombian collection (Andean and Caribbean) through phylogenetic analysis. We used samples with a clear genetic ancestry for these five genetic clusters as reference genotypes to implement a supervised analysis in ADMIXTURE software  (Alexander and Lange 2011).
Reconstruction of evolutionary demographic scenarios We implemented Approximate Bayesian Computation (ABC) using the DIYABC Random Forest v. 1. 0 software  (Collin et al. 2021) , an extended version of DIYABC v. 2. 1. 0. This tree-based classification method uses supervised machine learning to classify evolutionary relationship scenarios and estimate their parameter robustness based on backward simulations. With that in mind, we simulated 120,000 sets for the 4931 selected loci of dataset 1 (Table  1 ), 10,000 trees per scenario, and 1000 as the number of out-of-band testing samples to estimate the historical parameters given the scenario with the highest posterior probability. Because there is no experimental estimate of the SNP mutation rate available for avocados, the mean mutation rate followed an a priori uniform distribution.
SNP discovery in avocado panels The sequencing of 384 avocado genotypes from CGB and SR generated over 3,132,393,704 sequence reads (Table  S1 ), which were combined with available sequences from 71 reference genotypes  (Talavera et al. 2019) , and the outgroup, P. schiedeana  (Rendón-Anaya et al. 2019). In CBG and SR genotypes, the number of DNA sequences obtained was similar from leaf (CGB-3 M to 52 M) and root (SR-1. 3 M to 56 M) tissues (Table  S1 ). However, regarding the SNP calling, the matrices obtained from leaf DNA sequences had the highest number of SNPs. In contrast, matrices including sequences from root tissues had many missing data and a low number of polymorphisms. Therefore, creating a single dataset was inviable due to significant differences in SNP numbers between leaf and root tissue sequences.
We then generated three different SNP datasets depending on the goal of each analysis, each including different samples (CGB, SR, RM, and O). The SNPs numbers ranged from 227 in dataset 3, which included 289 genotypes from SR and CGB, as well as RM materials from ME, WI, GU, Colombian Andean, and Colombian Caribbean groups, with high ancestry (> 80%) for each genetic cluster, to 4931 SNPs in dataset 1, which consisted of 199 genotypes from CGB and RM genotypes (Table  1 ). The global SNP summary statistics were calculated using dataset 1, which had the highest SNP number (4931) mapped in 8082 contigs of the Hass genome reference assembly. This dataset had a Ts/Tv proportion of 1. 54, with 2988 Ts and 1943 Tv substitutions. These SNPs presented an MDS mean of 55, ranging from 15 (Ctg0042_632283 SNP) to 245 (Ctg1142_29090); while the nucleotide diversity (Pi) presented a mean of 0.
1 ). The clade that first diverged corresponded to samples belonging to the ME group. The accessions from the GU group formed the next clade in position, followed by the clade with the WI materials. The CO and WI groups were related in two supported clades in the most derived phylogeny positions. The CO clade was divided into two subgroups. Based on passport data, the avocado genotypes mainly from the Andean region (Colombian Andean: Cauca, Huila, Quindio, Risaralda, Santander, Meta, and Tolima provinces) were regrouped in a well-supported clade (BS = 100%), while a second weaker-supported clade (BS = 43%) regrouped samples from the Caribbean region (Colombian Caribbean: Bolivar, Cesar, and Magdalena provinces). From this filtered dataset (without hybrids), seven accessions from the Colombian germplasm fell in the Mesoamerican clades, four genotypes ("Waldin", QUIBU_11, VADU_10, and VADU_14) in the WI clade, one ("Hass") in the GU clade, and two ("Topa Topa", and "Duke_7") in the ME clade (Fig.
14), but lower compared to the differences between the Colombian groups against ME (pairwise F ST of 0. 48 and 0. 50 for Caribbean and Andean), GU (pairwise F ST of 0. 46 and 0. 48 for Caribbean and Andean), and WI (pairwise F ST of 0. 27 and 0. 31 for Caribbean and Andean) groups in the K = 5 analysis (Fig. 4c ). In both analyses using K = 4 and K = 5, the genetic differences among the Mesoamerican groups were intermediate and presented values between 0. 21 (ME vs. GU) and 0. 30 (WI vs. ME) (Fig. 4a, c ). These results suggest a strong genetic structure and differentiation between the avocado genetic groups, particularly the Colombian and Mesoamerican groups. The gene flow according to directional migration rates (m) calculated for K = 4 (Fig. 4b ) and K = 5 (Fig. 4d ) in BayesAss converged and did not show differences between the several simulations implemented for each dataset.
Enabling genomic resources for avocado rootstocks This study does not report a unique marker dataset due to significant differences in SNP coverage for sequences gathered from leaf and root tissues. Consequently, we had to split the reference mapping and SNP calling steps by tissue type among sequences to generate three datasets for each research question. Regardless of the final number of SNPs in each dataset, these variants enable us to approach the research questions at different levels of resolution. The efficiency of nucleic acid extraction processes varies depending on the tissue's properties, such as physical characteristics (e. g. , lignification level) and chemical composition (e. g. , presence of secondary metabolites). Therefore, when utilizing root tissue for genomic approaches, it is recommended to sample tissues with consistent and optimized conditions (e.
Our current data may offer some tendencies in this regard. Reports by  Rendón-Anaya et al. (2019)  and  Solares et al. (2023)  utilized whole-genome resequencing (WGR), which confers more coverage of highly recombined regions experiencing insufficient linkage disequilibrium, a common trend in long-lived allogamous tree species  (Kelleher et al. 2012). Unfortunately, so far, we have been unable to carry out those detailed genome-wide reconstructions of the inter-racial divergence and selection profiles against the new Colombian subgroups because the NCBI-uploaded assembly of Rendón-Anaya et al. (  2019 ) lacked the pseudomolecule information. A new reanalysis of our dataset using chromosome-level information of the recent Gwen's reference genome by  Solares et al. (2023)  could provide more resolution for divergence and selective sweep mapping, which may, in turn, sustain the distinctiveness of the Colombian clade.
B-CJA and D-DP performed DNA extraction and genomic library preparation. B-CJA carried out bioinformatic analyses to discover SNPs and filtered and prepared input datasets. B-CJA, CAJ, L-HF, C-SI, R-HP, and YR made data analyses and interpreted results. All authors wrote and approved the submitted version. Funding Open Access funding provided by Colombia Consortium. This research was supported by the Ministerio de Agricultura y Desarrollo Rural (MADR) of Colombia under the funds TV17 and TV21-22 in the project "Diseño e implementación de un Plataforma de genotípificación para el Banco de Germoplasma Vegetal de Colombia conservado por AGROSAVIA" with 1001386 code. The sampling of rootstocks was possible thanks to a grant from Sistema General de Regalías (SGR-Antioquia) awarded to N-AAA, contract number 1833.
Acknowledgements We thank the owners and technicians of the eight avocados "Hass" orchards for donating rootstocks tissue, and the field assistants  H. M Arias ,  J. M. Bedoya ,  K. Y. Calle ,  L. E. Cano ,  E. Carranza-Hernández ,  S. A. Guzmán ,  J. A. Henao ,  L. M. Mejía ,  A. M. Otálvaro ,.
Data availability We made available this study in Spanish to promote further discussion among researchers and producers within Colombia and the region (Supplementary File S1). This published article and supplementary material include all data generated or analyzed during this study. The data for this study was submitted to NCBI under BioProject: PRJNA878519, which contains 384 raw Illumina sequencing data. Published sequencing data used in this study from  Talavera et al. (2019)  were from NCBI PRJNA564105 number. The sequences of P. schiedeana used as an outgroup were downloaded from NCBI with SRR8295605 accession number published by  Rendón-Anaya et al. (2019).
In this work, we propose to study the performance of a model trained with a sentence embedding regression loss component for the Automated Audio Captioning task. This task aims to build systems that can describe audio content with a single sentence written in natural language. Most systems are trained with the standard Cross-Entropy loss, which does not take into account the semantic closeness of the sentence. We found that adding a sentence embedding loss term reduces overfitting, but also increased SPIDEr from 0. 397 to 0. 418 in our first setting on the AudioCaps corpus. When we increased the weight decay value, we found our model to be much closer to the current stateof-the-art scores, with a SPIDEr score up to 0. 444 compared to a 0. 475 score. Moreover, this model uses eight times less trainable parameters than the current state-of-the-art method Multi-TTA.
II. BASELINE SYSTEM DESCRIPTION We use an encoder-decoder architecture widely used in AAC systems, with an encoder pre-trained on AudioSet  [7]  to extract a strong representation of sounds events. More specifically, we used the CNN14_DecisionLevel_Att audio encoder from the Pre-trained Audio Neural Networks study (PANN)  [8]. This architecture gives the best results on the classification of sound events in the audio captioning dataset part when compared to the other PANN architectures available. We have found that freezing weights does not decrease performance while significantly speeding up the training process. This encoder provides sequences of embeddings of dimension 31×2048 for ten-second long audio recordings. On top of that, we add a projection layer to get 256-dimensional embeddings to match the input dimension of the decoder d model.
The decoder is a standard transformer decoder  [9]  with 6 layers, 4 attention heads per layer, a global embedding size d model set to 256 and a global dropout probability of 0. 2. We also used the GELU  [10]  activation layer in the decoder. The decoder is trained using teacher forcing, i. e. gives the ground truth previous reference tokens to the model to predict the next one. The baseline criterion is the standard CE loss over the whole sequence between the output probabilities and the reference token classes. During inference, we used the beam search algorithm with a beam size set to 2 since higher value does not bring improvements. We conditioned the sentence generation to improve performance and overall caption quality: by limiting the prediction length to a minimum of 3 tokens and a maximum of 30 tokens and by forbidding the model to generate the same token twice, except for stop-word tokens predefined in the Natural Language ToolKit (NLTK)  [11]  package.
A. Sentence-BERT model The Sentence-BERT  [12]  (SBERT) model is a transformerbased model which combines a BERT  [13]  model with a pooling and a projection layer to produce a single embedding of a fixed size of 768 values for a given sentence. Available SBERT models have been trained on two text databases: the Stanford Natural Language Inference (SNLI)  [14]  the Multi-Genre Natural Language Inference (MultiNLI)  [15]. These datasets contain pairs of two sentences annotated with contradiction, entailment or neutral label. To learn sentence semantic, two SBERT embedding are fed to a classification layer, which must predict the label of the pair.
A. Dataset We train and evaluate our models on the AudioCaps  [24]  dataset, which is the largest known audio-language dataset with human generated captions. The audio files are 10-second clips from AudioSet  [7]  and are extracted from YouTube videos. Since some of the original videos are removed or unavailable, our version of the dataset contains 46230 over 49838 files in training subset, 464 over 495 in validation subset and 912 over 975 files in testing subset. Each audio is described by one caption in the training subset and five captions in the validation and testing subsets.
The diversity of words used could be taken into account in future captioning metrics, or directly by the model during learning like in  [31]. ACKNOWLEDGMENT This work was partially supported by the Agence Nationale de la Recherche LUDAU (Lightly-supervised and Unsupervised Discovery of Audio Units using Deep Learning) project (ANR-18-CE23-0005-01), and was granted access to the HPC resources of IDRIS under the allocation 2022-AD011013739 made by GENCI.
Texts conception The texts were created using the database Manulex, a word frequency list based on a corpus of readers used in French primary schools, from 1st to 5th grades. This database allows words to be selected according to their standard frequency index (SFI) by grade level, as well as the number of letters in a word  (Lété et al. , 2004). The texts were  Free license developed from the 1500 most frequent words for each school level. The texts consisted of 6 sentences, each sentence corresponding to a font. The texts differed in content and length according to the reading expertise group (number of words per text: 4, 5, 11, 65, 75 depending on the expertise group). The texts contain couples of complex letters in French: "cl", "il", "rm", "ff" ("ff" except for Beginner Group) and numbers.
Procedure The methodology used is inspired by  Rello and Baeza-Yates (2016)  and  Sparrow et al. (2016). In experiment 1, six texts were presented on paper books in six fonts (Fig. 1 ). In experiment 2, false-words were displayed on a screen of 24 ′′ with a resolution of 1024-768 pixels, and eye movement characteristics during reading were recorded by Tobii pro-spectrum (driven with software Tobii pro Lab). False-words are used to control the visual decryption of each letter, the reader being unable to refer to their lexical field for help or correction. For each experiment, participants chose their comfortable reading size:  12, 14, 16, 20, 24, 28, 34, 40. Afterwards, all texts and false-words were presented with the same size for the tests on the 6 fonts. A withinsubject repeated-measures design was used to evaluate the font effect on the reading for each group (LV and NV group).
The procedure (reading and comprehension questions) was repeated for the other five texts (in the same font size, each text being printed in a different font). A short pause was taken after the third text. Finally, the experimenter gave all six texts back to the subject, in the same fonts and size in which they had read them, and asked them to rank the fonts in order of subjective preference. The reading of each text was recorded to facilitate data processing. Reading time was measured when the results were tallied. Reading duration was timed using a stopwatch. The number of reading errors was recorded.
Acknowledgments We would like to thank the participants who took part in the study and our associative and socio-educational partners who helped us in the constitution of the groups. In particular,  Centre Technique Régional pour la Déficience Visuelle CTRDV ; also IFMKDV and  S3AS  (Association  PEP69 ), the  Cité Scolaire René Pellet  (EREA-DV), and  CPHV (Fondation Asile des aveugles). We would also like to thank  Erell Collet , Master's student, for her participation in the collection of data during her research internship in the  DIPHE laboratory (University of Lumière Lyon 2).
, 2018) ). Also, none of the above studies consider directly the cellular mechanism that generates stochastic gene transcriptional bursts. In the present work, we aimed to shed light on the gene transcriptional mechanism behind the rise-then-fall trajectory of cell-to-cell variability in gene expression observed during the cellular differentiation process  (Richard et al. , 2016). To this end, we analyzed a collection of published single-cell transcriptomic datasets from various cell differentiation systems, comprising both single-cell RT-qPCR (scRT-qPCR)  (Bargaje et al. , 2017; Guo et al. , 2010; Moignard et al. , 2013; Moussy et al. , 2017; Richard et al. , 2016; Stumpf et al. , 2017)  and single-cell RNA-sequencing (scRNA-seq).
Further, by leveraging the stochastic gene transcriptional model behind CALISTA, we were able identify possible mechanisms behind the overt trajectories of cell differentiation on the transcriptional uncertainty landscapes  (Coulon et al. , 2010). For two additional single-cell datasets, we also evaluated the single-cell RNA-velocity using the recently published Velocyto method  (La Manno et al. , 2018). The two-state model parameter analysis, combined with RNA-velocities, provided insights into the mechanism regulating cell fate decisions, specifically on the role of stochastic gene transcriptions in the differentiation processes and on the possible mechanism generating this stochasticity.
Main steps of CALISTA workflow Herein, we briefly describe the main steps involved in the calculation of single-cell transcriptional uncertainty using CALISTA  (Papili Gao et al. , 2020). Pre-processing. Given an N Â G single-cell expression matrix M, where N denotes the number of cells and G the number of genes, the pre-processing in CALISTA involves two steps: a normalization of the expression data m n,gi. e. the number of transcripts of gene g in the n-th cell, and a selection of the most variable genes  (Papili Gao et al. , 2020). Cell clustering. CALISTA clustering follows a two-step procedure. The first step involves a greedy optimization strategy to find cell clustering that maximizes the total cell likelihood, i. e. the sum of the likelihood value for all cells. The singlecell likelihood value is computed as the joint probability of the cell's gene expression data, which is set equal to the product of the probabilities of the mRNA counts for the selected genes based on the mRNA distribution from the two-state stochastic gene transcription model.
To avoid issues with numerical overflow, we use the logarithm of the cell likelihood. By performing the greedy optimization multiple times, a consensus matrix containing the number of times two cells in the dataset are put in the same cluster, is generated. In the second and final step, CALISTA generates the cell cluster assignments by using k-medoids clustering based on the consensus matrix. The final outcome of CALISTA's clustering is the assignment of cells into K clusters and the optimal model parameters for the two-state gene transcription model: θ g, k ð Þ¼ θ on ,θ off , θ t È É k g : for each gene g in cluster k  (Papili Gao et al. , 2020). In this case, θ on is the normalized rate of the promoter activation, θ off is the normalized rate of the promoter inactivation, and θ t is the normalized rate of mRNA production when the promoter is active.
For the z axis, we plot the NLL values. The transcriptional uncertainty landscape surface is reconstructed by estimating local approximation of individual cell 3D coordinates on a regular 30Â30 grid by using a publicly available Matlab (R2020a) surface fitting package called gridfit. Pre-processing and analysis of single-cell expression datasets Bargaje et al. scRT-qPCR dataset. The dataset includes the expression profiles of 96 genes from 1896 single cells at eight different time points (day 0,  1, 1. 5, 2, 2. 5, 3, 4, 5)  during the differentiation of human pluripotent stem cells (iPSCs) into either mesodermal (M) or endodermal (En) fate  (Bargaje et al. , 2017). By employing CALISTA, we obtained five cell clusters and detected a bifurcation event, which gives rise to the two final cell fates.
After lineage inference, we pseudotemporally ordered cells along the inferred differentiation paths (for more details, see  (Papili Gao et al. , 2020) ). Treutlein et al. scRNA-sequencing dataset. The dataset includes the gene expression profiles of 405 cells during reprogramming of mouse embryonic fibroblast (MEF) into a desired induced neural (iN) and an alternative myogenic (M) cell fate  (Treutlein et al. , 2016). We pre-processed the data using CALISTA to select the 40 most variable genes (10% of the number of cells) for the transcriptional uncertainty analysis. CALISTA identified four different subpopulations and successfully recovered the bifurcation event (for more details, see  (Papili Gao et al. , 2020) ). Richard et al. scRT-qPCR dataset. The dataset contains the expression profile of 91 genes measured from 389 cells at six distinct time points  (0, 8, 24, 33, 48, 72 h)  during the differentiation of primary chicken erythrocytic progenitor cells (T2EC)  (Richard et al.
, 2016). Following the CALISTA pre-processing step, we removed cells in which less than 75% of the genes are expressed. Then, we selected the subset of genes with at least one non-zero expression values. A total of 354 cells and 88 genes were considered in the transcriptional uncertainty analysis. Based on eigengap heuristics  (Papili Gao et al. , 2020; von Luxburg, 2007) , we grouped cells into six optimal clusters and ordered cells along the inferred linear trajectory (see the Extended data S8 Figure  (Gao et al. , 2023) ). Stumpf et al. scRT-qPCR dataset. The dataset comprises the single-cell expression of 97 genes at seven time points  (0, 24, 48, 72, 96, 120, 168 h)  during neural differentiation of mouse embryonic stem cells (E14 cell line)  (Stumpf et al. , 2017). In the data pre-processing, we excluded cells in which less than 70% of genes are expressed.
, 2023) ). Moignard et al. scRT-qPCR dataset. The dataset contains the single-cell expression level of 18 transcription factors measured in a total of 597 mouse bone marrow cells during hematopoietic differentiation. By applying CALISTA, we successfully identified the five subpopulations and the two branching points detected in the original study  (Moignard et al. , 2013) : long-term hematopoietic stem cells (HSC) differentiating into megakaryocyte-erythroid progenitors (PreM) or lymphoid-primed multipotent progenitors (LMPP); LMPP cells differentiating into granulocyte-monocyte progenitors (GMP) and common lymphoid progenitors (CLP) (for details see  (Papili Gao et al. , 2020) ).
Finally, the Pearson correlation coefficients between the burst size S g,k and NLL g,k and between the burst frequency F g,k and NLL g,k are computed for each dataset to quantify the pairwise associations between these variables. The statistical significance of the correlation coefficients is determined using the t-test-specifically, by evaluating the score t ¼ r ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðn À 2Þ=ð1 À r 2 Þ p where r is the sample correlation coefficient.
RNA velocity analysis Cells and genes were first filtered based on the pre-processing strategy in the original publication by La Manno and colleagues  (La Manno et al. , 2018) , which resulted in a total of 1720 cells and 1448 genes from human glutamatergic neurogenesis, and a total of 18140 cells and 2141 genes from the mouse hippocampus dataset. We further reduced the number of genes to only the top 500 highly variable genes for the transcriptional uncertainty analysis. CALISTA employs a maximum likelihood approach and assigns a likelihood value to each cell based on its gene expression based on the mRNA distribution governed by the two-state model of stochastic gene transcription. The singlecell transcriptional uncertainty is evaluated as the negative logarithm of the likelihood (NLL) value for a cell.