dataesr/scientific-paragraphs-categorization huggingface dataset:
Computational analysis For RNA-seq, data for amiR-Lsd1/Prmt6-treated AR100Q mice, AR100Q mice, and WT mice comparisons were analyzed by Rosalind (https:// rosalind. onramp. bio/), with a HyperScale architecture developed by Rosalind Inc. (San Diego, CA), while data for WT mice and amiR-Lsd1/Prmt6-treated WT mice were analyzed on local computational resources. In both cases, reads were trimmed using cutadapt  102  , quality scores were assessed using FastQC and reads were aligned to the Mus musculus genome (mm10) using STAR  103. Individual sample reads were quantified using HTseq and normalized via relative log expression using DEseq2 104 , which also was used for differential expression analyses. P-values were adjusted for multiple hypothesis testing using the Benjamini and Hochberg method.
Antigen-antibody complexes were washed four times with lysis buffer. Bound protein was eluted with 30 µL of NuPAGE LDS Sample Buffer (4X) and 0. 1 M DTT, heated at 70 °C for 5 min, and loaded onto 10% SDS-PAGE gels. The following antibodies were used for analysis: anti-LSD1 (Abcam, #ab17721, 1:1000), anti-PRMT6 (Proteintech, #15395-1-AP, 1:1000; Bethyl, #A300-929A, 1:2000), anti-AR (441, Santa Cruz, #sc-7305; H-280, Santa Cruz, #sc-13062, 1:1000), anti-FLAG (Sigma-Aldrich, #7425, 1:1000), anti-GFP (Roche, #11814460001, 1:1000), anti-calnexin (Enzo, #ADI-SPA-860, 1:2500), and anti-β-tubulin (Sigma-Aldrich, #T7816, 1:10,000). Quantifications were performed using ImageJ 1. 51 software. Nicotinamide adenine dinucleotide (NADH) staining  16  was performed on slides with flash-frozen in liquid nitrogen skeletal muscles previously embedded in optimal cutting temperature (OCT) compound (Qpath, #411243), and cut in 10 µm thick-cross sections with a cryostat (Leica, #CM1850UV).
Acknowledgements We thank  Dr. Kenneth Fischbeck  ( NINDS, NIH ) for insightful comments and suggestions. We also thank  Morena Simonato  ( CNR, Italy ) for technical support,  Dr. Sergio Robbiati  at the  Model Organism Facility , and  Dr. Giorgina Scarduelli  at the  Advanced Imaging Facility at Dept. CIBIO, University of Trento  for technical help. We would like to thank  Veronica De Sanctis  and  Roberto Bertorelli  at the  NGS Facility at Dept. CIBIO, University of Trento  for the RNA sequencing experiments and the  CRIBI, University of Padova , for the cDNA library preparation. We thank  Dr. Lyle Ostrow , and the  ALS Postmortem Tissue Core  for providing the postmortem tissue. This work was supported by  Dulbecco-Telethon Institute/Fondazione Telethon-Italy  ( TCP12013  and  GGP19128  to M.
Data availability All data generated or analyzed during this study are included in this article and its Supplementary Information files. All requests for raw data and materials should be addressed to the corresponding author. Any data and materials that can be shared will be released via a material transfer agreement. Raw RNAseq FASTQ files and counts data for nontreated (AR100Q), treated (amiR-Lsd1/Prmt6) SBMA mouse models and WT controls have been deposited in the Gene Expression Omnibus (GEO) database under accession code GSE193539. Source data are provided with this paper.
Seventeen animals (twelve males and five females) remained in our final dataset. Two males and two females had been using extensively the military zone. No pair of collared animals moved together as part of the same sounder. Due to the very limited female data, we excluded them from the analyses and only present results from the analysis of male data. Note however that exploration of the female data does not suggest that their behavior differ largely from that of males, with regard to aspects investigated here (not shown). Also, for brevity, we hereafter refer to the animals studied in the analyses as 'wild boars', without specifying their sex each time.
Acknowledgments This work was supported by the  REPOS  project through the  I-site Montpellier Université d'Excellence  (grant number  ANR-16-IDEX-0006 ) and by the  François Sommer foundation. We are indebted to the various landowners who permitted to work on their land, to  S. Perret ,  D. Degueldre ,  G. Fréchet ,  J. Nicot  for assistance with wild boar captures, and to  R. Mathevet  and  J. -M. Chanabe  for some aspects of project management. D. Cornelis  kindly offered the first collars used in the study. L. Latorre  and  J. Miquel  developed the audiologgers. M. Valeix  kindly reviewed a draft of the manuscript. We also thank the map data copyrighted OpenStreetMap contributors for using the map data available from https://www. openstreetmap. org.
Using three different datasets, we show that our methods accurately represent several biodiversity indicators in a twodimensional latent space: MOTU richness per sample, sequence α-diversity per sample, Jaccard's and sequence β-diversity between samples. We show that our nonlinear methods are better at extracting features from eDNA datasets while avoiding the major biases associated with eDNA. Our methods outperform traditional dimension reduction methods such as Principal Component Analysis, t-distributed Stochastic Neighbour Embedding, Nonmetric Multidimensional Scaling and Uniform Manifold Approximation and Projection for dimension reduction. Our results suggest that NNs provide a more efficient way of extracting structure from eDNA metabarcoding data, thereby improving their ecological interpretation and thus biodiversity monitoring.
Typically, these sequences are assigned to known taxonomic units stored in a genetic reference database. The incompleteness of genetic reference databases precludes the identification of many species  (Marques et al. , 2020) , thus working with Molecular Operational Taxonomic Units (MOTUs), representing a cluster of similar sequences, may be required  (Deiner et al. , 2017; Floyd et al. , 2002; Mathon et al. , 2022). MOTUs are then defined by a consensus sequence. The attribute attached to an eDNA MOTU is the relative frequency of the sequences in each MOTU and the nucleotide sequence itself. Both attributes can be directly related to ecosystem states and properties  (Bakker et al. , 2017). Therefore, eDNA data has the potential to reveal ecological patterns that distinguish sampled sites along environmental or human pressure gradients  (Marques et al.
In the last decade, many nonlinear techniques have been proposed for dimensionality reduction  (Facco et al. , 2017; Nguyen & Holmes, 2019)   (Diaz-Papkovich et al. , 2021). However, the interpretation of t-SNE and UMAP plots remains challenging due to the lack of global structure in the reduced space representation  (Battey et al. , 2021). Although these methods perform well in clustering similar samples, distances between clusters are not always meaningful  (Becht et al. , 2019). On the other hand, neural networks (NNs) have been shown to provide a good representation of learning capacity for various datasets  (Sze et al. , 2017). NNs are complex mathematical models consisting of many operators called neurons connected as a network. Within the framework of ordination and dimensionality reduction, there are contrastive methods, such as UMAP and t-SNE, that work on learning features by satisfying distances between observations.
The potential of VAE and DML to ordinate eDNA samples in a low-dimensional space has not yet been demonstrated. In this study, we present two new methods, one based on VAE and the other on DML, to visualize eDNA biodiversity indicators. We tested our methods on three different published eDNA datasets: a fish eDNA dataset collected in the Mediterranean Sea  (Boulanger et al. , 2021) , and two eukaryotic plankton eDNA datasets from the Tara Ocean expedition  (de Vargas et al. , 2015). We used both the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequences. To validate these two new methods, we compare them with other classical methods: PCA, t-SNE and UMAP for the VAE-based method, and Nonmetric Multidimensional Scaling (NMDS) for the DML-based method.
| Sensitivity To perform a cross-validation of our two new methods, we set a global random seed to split 80% of the original dataset in the training set and 20% in the validation set. We repeated the tests until the results were stable, ensuring that we did not overfit by monitoring the loss on both the training and validation set (Figure  S1 ). We implemented the models in R (version 4. 1. 3, R Core Team, 2022) using TensorFlow  (Abadi et al. , 2016)  and Keras  (Chollet & Others, 2015)  libraries.
| Datasets We tested our methods on three different published eDNA datasets: a fish eDNA dataset collected in the Western Mediterranean Sea  (Boulanger et al. , 2021)  and two eukaryotic plankton eDNA datasets from the Tara Ocean Campaign  (de Vargas et al. , 2015). Details are given in Table  1. eDNA samples from the Western Mediterranean Sea were collected at 77 stations in six marine regions covering the Western Mediterranean, including fished and no-take protected areas  (Boulanger et al. , 2021). eDNA extraction and amplification were performed at the SPYGEN facility. PCR amplification was performed using the teleo primer pair, targeting a 64 bp fragment of the mitochondrial DNA 12S rRNA gene specific for teleost fishes and elasmobranches  (Valentini et al. , 2016). Data collection and sample processing are described in detail in Appendix S1.
The two encoders process the two samples, combining the number of sequences found for each MOTU detected and the latent encoding z AE from the AE of the nucleotide sequences detected in each sample, and project them into two points in z 1 and z 2. To optimize the model, we calculate the Euclidean distance between the two points in z 1 and z 2 and compare it with Jaccard's β-diversity via a loss function (the mean square error (MSE)). In z 1 and z 2 , we find the 2D representation of all the data points (Figure  S3c, d ). TA B L E 1 Descriptive table of the three eDNA datasets: Western Mediterranean fish dataset  (Boulanger et al. , 2021) , Tara Ocean Dictyochophyceae dataset  (de Vargas et al. , 2015) , and Tara Ocean Telonemia dataset  (de Vargas et al. , 2015). Details on the calculation of sequence α-diversity can be found in the Appendix S1.
β-diversity matrix, uses the distance matrix calculated on the βdiversity between sequences within the Hill number framework. In addition, we examined the ability of our methods to represent the MOTU richness and sequence α-diversity indicators through linear correlations with the two dimensional space. For simplicity, we only reported the correlations for the axis with the highest correlation denoted as V. The baseline methods were performed using R version 4. 1. 3 with the packages 'stats' for PCA, 'umap'  (Konopka, 2019)  for UMAP, 'Rtsne'  (Kjaer et al. , 2022)  for t-SNE, 'TensorFlow' with 'Keras' for VAE. The NMDS method was performed using the function metaMDS from the package 'vegan' specifying 'Jaccard's' as distance. We used the MRM function of the 'ecodist' package to perform the tests.
We computed the Euclidean distance matrix between each pair of samples in the 2D latent spaces using the function dist from the package 'stats' and the Jaccard's β-diversity using the library 'betapart'. We calculated the distance matrix of sequence β-diversity between each pair of samples using the Hill number framework  (Abadi et al. , 2016). The genetic distance between each pair of sequences was computed with the function dist. gene from the package 'ape'. Sequence β-diversity was calculated with the function beta. fd. hill from package 'mFD', with parameters q = 1 and τ = 'mean'  (Chao et al. , 2020; Magneville et al. , 2022) , using Sørensen's β-diversity. We have visualized the 2D space results of ENNBetaDist in the Western Mediterranean fish dataset on a geographic map (Figure  4 ).
| VAESeq Out of the three datasets considered, the highest R 2 values were achieved by VAESeq and AEgen + PCA (Table  2 , Table  S2 ). Indeed, when we tested the correlation between the distance matrix of samples in 2D latent spaces and the Jaccard's distance matrix (TEST 1), the R 2 values of AEgen + PCA and VAESeq (VAESeq: R 2 =. 34; AEgen + PCA: R 2 =. 31) are almost 10 times higher than those of PCA (R 2 =. 04), and 3 times higher than those of t-SNE (R 2 =. 11), and UMAP (R 2 =. 11). The worst-performing method was PCA, which is the only linear method used. For example, in the Western Mediterranean fish dataset, the first principal component of PCA explained only 2. 9% of the variance in the data, and the correlation was not significant in any test (Figure  S2a , Table  2 ). VAE failed to extract information for the three datasets when the nucleotide sequence was not used (R 2 =.
In TEST 2, instead of using the distance matrix based on Jaccard's β-diversity, we use the distance matrix calculated on the β-diversity between sequences within the Hill number framework. The best results for each test are shown in bold. based on genetic β-diversity (for VAESeq: R 2 =. 48; for AEgen + PCA R 2 =. 43). VAESeq and AEgen+PCA were able to accurately represent the gradient of MOTU richness and sequence α-diversity for all three datasets, where the other classical methods failed (Table  S2 ).
| ENNBetaDist For Tara Ocean datasets, both models succeeded in representing the Jaccard's β-diversity (for ENNBetaDist: R 2 =. 44; For NMDS: R 2 =. 51), and the sequence β-diversity (for ENNBetaDist: R 2 =. 61; For NMDS: R 2 =. 55) (Table  3 ). For the Western Mediterranean fish dataset ENN-BetaDist succeeded in representing the Jaccard's β-diversity (R 2 =. 41) and the sequence β-diversity (R 2 =. 74), while NMDS failed due to the presence of one outlier (Jaccard's β-diversity R 2 =. 01; sequence β-diversity R 2 =. 04; Figure  S3 ). By removing the outlier, NMDS and ENNBetaDist achieved similar β-diversity estimates. Moreover, for the representation of MOTU richness and sequence α-diversity, ENN-BetaDist proved to be better than NMDS in representing both indicators, achieving values of R 2 >.
| Latent spaces representations and ecological interpretation on Western Mediterranean eDNA dataset The 2D latent space representations of the eDNA fish samples using two new methods (Figure  S5 ) revealed marked gradients both F I G U R E 4 Geographical map of the Western Mediterranean fish dataset (a), illustrating the resulting 2D latent space of ENNBetaDist. To achieve this visualization, we employed the HSV (hue-saturation-value) approach, which allowed us to transform the coordinates of eDNA samples into colors (b). Specifically, the HSV hue component of each sample was determined based on its distance from zero in the 2D latent space, resulting in a colour gradient. The HSV value component was derived from the slope of the vector defined by the sample and the origin, introducing further colour variations.
Furthermore, we reported the resulting 2D latent space of ENN-BetaDist in the geographical map of the Western Mediterranean Sea using an HSV approach (Figures  4a, b ). The geographical map of the dataset showed a clustering of protection levels (Figure  4a ). This was supported by a significant association tested by a generalized linear model (GLM) between the level of protection and the distance of the samples from zero in the 2D latent space (HSV hue value) (likelihood ratio test on the GLM, p-value =. 001). Remarkably pink clusters on the map corresponded to the protected areas located in the Mediterranean Sea and its immediate surroundings. Moving from west to east, these areas were identified as Cerbere-Banyuls, Carry-le-Rouet and Riou, Porquerolles, Cap Roux and Calvi (Figure  4c ).
| DISCUSS ION The arrival of big data in ecology, facilitated by new technologies  (Besson et al. , 2022; Farley et al. , 2018)   2  and 3 ). Thus, the 2D representation obtained reveals the ecological information underlying the structure of fish communities. In addition, we highlight how VAESeq outperforms other dimensionality reduction techniques, such as PCA, t-SNE, UMAP and even a simple VAE without sequence information added, in the visualization of ecosystem properties (Table  2 ). Likewise, ENNBetaDist outperforms NMDS in 11 of the 12 tests performed and manages to cope with the presence of an outlier in the Western Mediterranean fish dataset (Table  3 ). In contrast, linear methods such as PCA result in poor dimensionality reduction to ordinate eDNA samples (Table  2 ; Figure  S2a ).
This is due to the complexity of eDNA data  (Miya, 2022; Xiong et al. , 2022). Indeed, despite its potential in biodiversity monitoring  (Mathon et al. , 2022; Pawlowski et al. , 2022; van der Heyde et al. , 2022) , eDNA metabarcoding can be limited by false reads due to contamination, errors that can occur during the extraction, PCR or sequencing process  (Bohmann et al. , 2014; Calderón-Sanou et al. , 2020; Creer et al. , 2016; Ficetola et al. , 2016; Hering et al. , 2018). Although field and laboratory practices can mitigate some of this, the risk of error cannot be eliminated and must be considered  (Burian et al. , 2021). Furthermore, eDNA metabarcoding sampling produces large, high-resolution datasets that are complex and highly dimensional, with a single observation from the experimental system containing measurements describing multiple traits  (Hallam et al.
The sequences turn out to be crucial for good information extraction. Instead of relying solely on the number of sequences identified per MOTU or the nucleotide sequences as in  Cordier et al. (2021)  and  Flück et al. (2022) , our methods combine both information (Table  S1 ). VAESeq is based on VAE that is optimized to reconstruct the input data. ENNBetaDist is a DML method that also uses the diversity information (here the β-diversity) as a distance metric between samples. Using VAESeq for data extraction has the advantage of treating each data independently because it does not rely on any pairwise distance between samples. In this case, the model is free to discover connections and highlights possible new ones in a fully unsupervised learning process. Alternatively, ENNBetaDist helps to represent samples in a 2D latent space according to an input metric.
ENNBetaDist is also able to represent the data and the three biodiversity indicators in the 2D space despite the strong perturbation due to an outlier, in contrast to the NMDS method (Table  3 , Figures S3-S5). In addition, two new methods allow users to define the output dimensionality while preserving the global geometry (i. e. relative positions in the 2D latent space) better than classical methods. Furthermore, in the case of the Western Mediterranean eDNA dataset, ENNBetaDist revealed a clustering of protected sites (pink cluster on the map, Figure  4a, c ). This result of an effect of protection on β-diversity supports previous eDNA studies analyzing the same dataset  (Boulanger et al. , 2021; Dalongeville, Boulanger, et al. , 2022; Dalongeville, Nielsen, et al. , 2022)  and non-eDNA studies in the Mediterranean  (Giakoumi et al.
V9 rDNA metabarcoding dataset (De Vargas et al. , 2015) collected across tropical and temperate oceans during the circumglobal Tara Oceans expedition. The analysis was based on metabarcoding data from 129 stations in various oceanic provinces worldwide, using 18S ribosomal DNA sequences across the intermediate plankton-size spectrum. All details on data collection, extraction, and sequencing can be found in the article by de Vargas et al. (2015). We selected the Dictyochophyceae and Telonemia subsets by taxonomic identification, resulting in two smaller datasets of similar sizes to the Western Mediterranean one whose specifications are shown in Table.
Correlation plots between the ordination of data samples in the 2D spaces of the VAESeq and ENNBetaDist with MOTU richness (a, d), sequence α-diversity (b, e) with one axe of the 2D latent spaces (V), and between the Euclidean distance between the samples in the 2D latent spaces of the two methods and the sequence β-diversity matrix (c, f) on the Western Mediterranean fish dataset. The R 2 values for each correlation test performed are shown in each figure (p <. 001).
, makes dimensionality reduction, as well as data visualization, important analytical tools for ecological interpretations. In this study, we introduce two new deep learning-based methods that combine different types of NNs to ordinate eDNA samples and visualize ecosystem properties in a two-dimensional space: the first is based on variational autoencoder (VAE), and the second on deep metric learning (DML). The strength of our new methods lies in their ability to combine multiple inputs simultaneously, namely the number of sequences found for each molecular operational taxonomic unit (MOTU) detected and their corresponding nucleotide sequence. Using three different datasets -a fish eDNA dataset collected in the Mediterranean Sea (Boulanger et al. , 2021), and two eukaryotic plankton eDNA datasets from the Tara Ocean expedition (de Vargas et al.
Western Mediterranean Fish Dataset Tara Ocean Dictyochophyceae Dataset Tara Ocean Telonemia Dataset Tot number of samples 394 319 321 Tot number of MOTUs 290 223 237 Min MOTU richness 1 1 1 Max MOTU richness 92 57 49 Min sequence length 53 76 103 Max sequence length 76 151 151 Min sequence α-diversity 1 1 1 Max sequence α-diversity 16. 74 10. 54 10. 41.
R 2 p-value R 2 p-value R 2 p-value R 2 p-value Western Mediterranean Fish Dataset NMDS 0. 0821. 001 0. 0124. 001 0. 0117. 001 0. 0369. 006 ENNBetaDist 0. 9018. 001 0. 7116. 001 0. 4106. 001 0. 7415. 001 Tara Ocean Dictyochophyceae Dataset NMDS 0. 7527. 001 0. 6907. 001 0. 3802. 001 0. 5337. 001 ENNBetaDist 0. 9612. 001 0. 8639. 001 0. 4440. 001 0. 6288. 001 Tara Ocean Telonemia Dataset NMDS 0. 5238. 001 0. 4918. 001 0. 6428. 001 0. 5640. 001 ENNBetaDist 0. 9245. 001 0. 8034. 001 0. 4411. 001 0. 5802. 001.
In fungi, the most abundant transcription factor (TF) class contains a fungal-specific 'GAL4like' Zn2C6 DNA binding domain (DBD), while the second class contains another fungal-specific domain, known as 'fungal_trans' or middle homology domain (MHD), whose function remains largely uncharacterized. Remarkably, almost a third of MHD-containing TFs in public sequence databases apparently lack DNA binding activity, since they are not predicted to contain a DBD. Here, we reassess the domain organization of these 'MHD-only' proteins using an in silico error-tracking approach. In a large-scale analysis of ~17,000 MHD-only TF sequences present in all fungal phyla except Microsporidia and Cryptomycota, we show that the vast majority (>90%) result from genome annotation errors and we are able to predict a new DBD sequence for 14,261 of them.
Furthermore, it has been postulated that the MHD might also participate in DNA target discrimination  [16, 17]. In terms of protein domain organization, the domain pair or bigram  [18]  composed of the Zn2C6 DBD combined with the MHD is the most frequent in the Zn2C6-TF family. For example, of the 54 Zn2C6-TF from the Saccharomyces cerevisiae S288C strain, 44 (81. 5%) contain a Zn2C6-MHD domain pair  [12]. Strikingly, in the InterPro protein family database  [19] , approximately one-third of the proteins exhibiting an MHD (InterPro ID: IPR007219) are not predicted to contain a zinc finger motif of the Zn2C6 or C2H2 types (InterPro ID: IPR001138 or IPR013087). These TFs, which apparently lack a DNA binding activity, represent the second largest fungal TF class after the Zn2C6 TF and will be called 'MHD-only' hereafter.