dataesr/scientific-paragraphs-categorization huggingface dataset:
Orthology detection and database searches B. duncani genes were assigned to OrthoMCL (https://OrthoMCL. org) groups using the orthology assignment tool available through the VEuPathDB (https://VEuPathDB. org) Galaxy workspace  59, 60. Translated B. duncani proteins (4,170) in FASTA format were assigned to groups on the basis of the OG6r9 blast database using default settings. Output files generated by the OrthoMCL pipeline included a mapping file between B. duncani gene IDs and OrthoMCL v. 6 group IDs (this file was used to query the OrthoMCL database to determine degrees of evolutionary conservation) and a file of leftover proteins that did not map to any OrthoMCL groups but did cluster with at least one other B. duncani protein (these were considered species-specific gene duplications or families).
RNA-seq processing for gene-expression analysis RNA-seq FASTQ files were assessed for quality using FastQC (v0. 11. 8). Adapter sequences as well as the first 11 bp of each read were trimmed using Trimmomatic (v0. 39). Tails of reads were trimmed using Sickle with a Phred base quality threshold of 25, and reads shorter than 18 bp were removed. Reads were then aligned to the B. duncani genome assembly using HISAT2 (v2. 2. 1). Only properly paired reads with a mapping quality score of 40 or higher were retained, with filtering done using Samtools (v1. 11). StringTie (v2. 2. 1) was run with the -e parameter to estimate the abundance of each gene in TPM (transcripts per million).
ChIP-seq data analysis Quality of the reads was analysed by FastQC (https://www. bioinformatics. babraham. ac. uk/projects/fastqc/). Sequence adaptors were removed using Trimmomatic v0. 39 (http://www. usadellab. org/ cms/?page=trimmomatic). Bases with Phred quality scores below 20 were trimmed using Sickle v1. 330 (https://github. com/najoshi/ sickle). Reads were mapped against the B. duncani assembly using Bowtie2 (v2. 4. 4) using default parameters, and PCR duplicates were removed by PicardTools MarkDuplicates v2. 18. 0 (Broad Institute). Only properly paired reads were retained using Samtools v1. 11 (http:// samtools. sourceforge. net/). To obtain the read coverage per nucleotide and generate genome browser tracks, we used BedTools (v2. 27. 1) and custom scripts to normalize the counts by millions of mapped reads.
To compare histone modifications with gene expression profiles, we generated a heat map using H3K9me3 and H3K9ac read counts, as well as the RNA-seq TPM levels for each gene. Read counts for H3K9me3 and H3K9ac were normalized to millions of mapped reads and gene length, while TPM was determined by Stringtie (see RNA-seq methods). Log scaling of all counts was used for the heat map, and genes were sorted from high to low TPM. A separate Excel spreadsheet of ChIP-seq data is provided in Supplementary Information (Supplementary Data file 2).
Steady-state kinetics of DHFR-TS activity DHFR kinetic experiments were performed by incubating purified enzyme (0. 1 µM) with a saturating concentration of DHFR ligand (NADPH or dihydrofolate (DHF) at 300 µM) and measuring the reaction rate at varying concentrations of the complementary ligand (DHF or NADPH, respectively). The experiments were performed in a 96-well plate in a reaction buffer consisting of 25 mM Tris pH 8, 20 mM NaCl, 50 mM l-arginine, 0. 5% glycerol, 1 mM dithiothreitol and 1 mM EDTA. Enzyme activity was determined by measuring the decrease in absorbance at 340 nm as NADPH is converted to NADP+. The plates were incubated at 37 °C, and OD 340 measurements were taken using a BioTek SynergyMx microplate reader for 1 min. Rate constants for steady-state kinetic experiments were estimated by fitting the data to a Michaelis-Menten hyperbolic curve ((v = V max [S]/(K m + [S])), where v is the reaction rate, [S] the concentration of the substrate, and K m is the Michaelis constant) using the curve-fitting programme in GraphPad Prism.
Acknowledgements We thank  R. Gao  for her contribution to the initial efforts to sequence the B. duncani genome. C. B. M. 's research was supported by grants from the  National Institutes of Health  ( AI097218 ,  GM110506 ,  AI123321  and  R43AI136118 ), the  Steven and Alexandra Cohen Foundation  ( Lyme 62 2020 ), and the  Global Lyme Alliance. S. L. 's research was supported by grants by the  US National Science Foundation  ( IIS 1814359 ) and the  National Institutes of Health  ( 1R01AI169543-01 ). K. G. L. R. 's research was supported by the  National Institutes of Allergy and Infectious Diseases  ( R01 AI136511 ,  R01 AI142743-01  and  R21 AI142506-01 ), the  University of California, Riverside  ( NIFA-Hatch-225935 ) and the  Health Institute Carlos III  ( PI20CIII/00037 ).
Data availability All the datasets generated for the current study are available in the NCBI/SRA repository, as Bioproject PRJNA821606, as follows: PacBio HiFi reads (SRA accession number SRR18778747), B. duncani genome assembly (NCBI Genome submission: SUB11253661), RNA-seq (SRA accession number SRR18907291), RNA Isoseq (SRA accession number SRR18902718), Hi-C reads (SRA accession number SRR19325692). Source data are provided with this paper.
Advancements in genome sequencing have facilitated whole genome characterization of numerous plant species, providing an abundance of genotypic data for genomic analysis. Genomic selection and neural networks, particularly deep learning, have been developed to predict complex traits from dense genotypic data. Autoencoders, a neural network model to extract features from images in an unsupervised manner, has proven to be useful for plant phenotyping. This study introduces an autoencoder framework, GenoDrawing, for predicting and retrieving apple images from a low-depth single nucleotide polymorphism (SNP) array, potentially useful in predicting traits that are difficult to define. GenoDrawing demonstrated proficiency in its task while using a small dataset of shape-related SNPs, and multiple experiments were conducted to evaluate the impact of SNP selection and shape relation.
Results indicated that the correct relationship of SNPs with visual traits had a significant impact on the generated images, consistent with biological interpretation. While using significant SNPs is crucial, incorporating additional, unrelated SNPs results in performance degradation for simple NN architectures that cannot easily identify the most important inputs. The proposed GenoDrawing method is a practical framework for exploring genomic prediction in fruit tree phenotyping, particularly beneficial for small to medium breeding companies to predict economically significant heritable traits. Although GenoDrawing has limitations, it sets the groundwork for future research in image prediction from genomic markers. Future studies should focus on using stronger models for image reproduction, SNP information extraction, and improved dataset balance in terms of shape for more precise outcomes.
Neural networks and deep learning (DL) are subfields of artificial intelligence that use multiple interconnected nodes, called artificial neurons, to process information and make predictions. These networks can have multiple layers, allowing for complex and non-linear analysis of data. Deep learning algorithms have demonstrated improved accuracy and speed compared to other artificial intelligence-based methods  [29]. Deep neural networks (DNN) have been already applied for genomic prediction in several fields, using real  [18, 28]  and simulated  [2]  SNP marker data, stimulated by the growing availability of easy-to-use DNN frameworks such as PyTorch  [20]  and TensorFlow  [1]. Within the DL fields of study, generative models (i. e. unsupervised models that learn from patterns to finally generate new examples that could have been derived from the original dataset) encompass a vast and complex area  [9]  and are living a new golden age after the publication of the generative adversarial networks (GANs) where one neural network compete to generate new synthetic data that can fool a discriminator network  [7].
Genotypic data A matrix of 303,239 SNP data for the 356 apple genotypes was retrieved from the apple RefPop  [14]  and prepared for the machine learning process by encoding the dataset to a numerical scale (0, 0. 5, 1 representing AA, Aa, aa alleles, respectively). To train the predictor model, two SNP datasets were extracted from the matrix: one of 150 size and shape-related SNPs (tSNPs) from literature  [13]  or identified by GWAS in our laboratory (data not published), and the one of 150 randomly selected SNPs (rSNPs) using NumPy library  [8]. The random selection of SNPs was renewed every time a new model was trained.
The decoder followed the inverse structure with a final sigmoid activation in the last layer(Figure  1 , Decoder). The model was compiled using the perceptual loss of the VGG network  [33]  as the loss function, and the stochastic gradient descent (SGD  [23] ) algorithm was used as optimizer. Training was performed for 35 epochs, over apple cuts of the training dataset. The SNPs and embedding values of each genotype in the training dataset were used to train a two layers model to generate an embedding predictor model. This model consisted of i) a linear layer of 300 units with sigmoid activation, which received as input the 150 SNPs; and ii) a 64 units linear layer with sigmoid activation which connected with the 300 units layer and served later as a connection to the decoder when the model was fully trained (Figure  1 , Embedding predictor).
Embedding predictor target dataset generation. To generate a target dataset for the embedding predictor model, we utilized a trained autoencoder to reduce the dimensionality of each image to a vector of 64 values. Subsequently, each vector representing an image was grouped by genotype, and a mean vector was computed. This mean vector represented the target for the embedding predictor models and was also utilized in the decoder to produce a mean image representing the mean image for the genotype (Figure  2 ). The resulting dataset of generated images was utilized in the evaluation of the metrics by estimating their FSI and their SR. 1 ) were encoded into vectors of size 64 (2) using the encoder. These vectors could be decoded into images  (3). Also, the vectors were used to estimate a mean embedding value per genotype (4).
General approach The general approach (Figure  3 ) consisted of training two models: 1) an image compressor model (autoencoder), trained with images of apple sections, and 2) an embedding predictor, a model that predicts the mean compressed values, also known as embeddings, of a genotype based on its single nucleotide polymorphisms (SNPs). Both models were then merged into a SNPs-to-Image model which we named GenoDrawing. Images were separated into a training and a validation datasets conserving similar shape typologies distributions (Figure  3. 1). The training dataset contained the 90% of the images (10271 of 320 genotypes). The rest (1340 of 36 genotypes) were used for validation (Figure  3. 2). The autoencoder model was fitted with the training dataset to ultimately produce vectors of 64 embedding values per image (Figure  3.
3-4). These vectors were used to generate a 10271x64 matrix, which was then reduced to a 320x64 matrix using the mean embedding values per genotype. The autoencoder model was then used in the 320x64 matrix to retrieve the average image for each genotype. The same process was followed for the validation dataset without retraining the image compressor. Image and genotypic datasets (Figure  3. 5) were used for training and validating the embedding predictor model. The dataset of random SNPs was renewed in each iteration (Figure  3. 6). The decoder section of the autoencoder and the embedding predictor were assembled into the GenoDrawing to retrieve the predicted image (Figure  3. 7-8). Figure  3 : Graphical abstract of the general approach. The dataset was divided into two splits conserving each similar fruit shape distribution (1,2).
15 to a 0. 09 along the 35 epochs. The mean encoder values per genotype were used together with SNP data to train the embedding predictor and reconstruct the apple section images. The embedding predictor was trained through the number of epochs needed until an early stopper was activated. For all the SNP datasets tested, a slow decrement in the mean absolute error was observed through the various iterations of training on the validation dataset, with a stagnation that arrived earlier or later depending on the number of SNPs used. There was no evidence of overfitting. Figure  4 : On the left side, original apple section. On the right side, output given by the autoencoder for the correspondent apple.
Comparing the impact of trait-related and randomly selected SNPs on embedding prediction models for GenoDrawing. To compare the accuracy of the embededing prediction model using targeted or random SNPs, we trained 100 models for each case. Using tSNPs consistently led to lower mean absolute error (MAE) values. On average, the MAE values obtained using tSNPs was 0. 0864 with a standard deviation of 0. 0034. When rSNPs were used, the average MAE was 0. 0944 with a standard deviation of 0. 0017 (Figure  S1 ). The best two embedding predictors among the 100 previously described (MAE 0. 0857 and 0. 0889 for the targeted and random SNP datasets, respectively) were used along with the decoder model to generate the predicted images and, eventually, test the accuracy of GenoDrawing. Mean decoded and predicted images were analyzed to determine the Fruit Shape Index External (FSI  [11] ), and the Shoulder Ratio (SR, ratio between the width at the most top and bottom points of the apple)(Figure  S3 ).
Deviation of FSI and SR values with the tSNP dataset was lower, with MAE of 0. 0386 and 0. 0317 for the FSI and SR, respectively, compared to MAE of 0. 0651 and 0. 0493 for the same to metrics in the rSNPs dataset. To determine the quality threshold for the MAE values, the mean and standard deviation of the FSI and SR were used to produce 10,000 datasets of 36 samples. These datasets were generated according to a N (µ, σ 2 ) distribution based on the validation dataset, and subsequently compared to the FSI and SR of the images produced by GenoDrawing. The resulting MAE superior boundary was established at 0. 075 with a standard deviation of 0. 0084 for the FSI, and at 0. 0529 with a standard deviation of 0. 0054 for the SR. Given that these boundary values exceed those produced by both rSNPs and tSNPs derived models, it may be concluded that the predictions are not merely the result of random sampling from the original distribution.
Once the non-randomness of the models had been confirmed, the distributions corresponding to both the rSNPs and tSNPs based versions of the embedding prediction were compared to the decoded means and the original data distributions for each genotype in the validation dataset (Figure  5 ). From Wasserstein distances between the distributions we infer that tSNPs models generate distributions closer to the ones of the Decoded and Genotype Means, 0. 0209 and 0. 0259 respectively, than the rSNPs models, (0. 0469 and 0. 0331, respectively) (Table  S1 ). Additionally, it was observed that there exists a small difference between the distribution of the decoded mean image and the original images. Specifically, the Wasserstein distance values between the decoded mean distribution and the original image distributions were relatively small for both the FSI attribute, 0.
Effect of Adding Non-Shape-Related Markers on Embedding Predictor Model Accuracy. To investigate the effect of increasing the number of markers selected without knowing the biological relevance, we added to the tSNP dataset an increasing number of SNPs in nine steps up to a total of: 150, 300, 1650, 4650, 10150, 20150, 50150, 100150, and 300150. We used a similar approach with the random-selected SNPs, increasing the size of the selection at each step. For each of these steps and datasets, 15 models were trained. The embedding predictor models with only 150 tSNPs were generally more effective in achieving a low mean absolute error (MAE) compared to models that include additional SNPs. The exception was observed when using 300150 targeted version, where three outliers showed lower MAEs than their random selection counterparts.
Notably, a unique case was observed where a targeted version consisting of 300 SNPs achieved the lowest observed MAE of 0. 0835 (Figure  6 ). In this study, we employed an autoencoder network to simplify the complexity of the apple images into 64 embeddings. This number of embeddings was deemed sufficient to account for small variations in the images while circumventing the complexity associated with adjusting a large number of embeddings to a reduced dataset of SNPs. Although changes in the number of embeddings can produce different outcomes, given the scope of this study, we selected the number that aligned better with our objectives. The images produced from the embeddings successfully captured a reliable and straightforward representation of the apples, effectively reproducing the original image structure.
In our experiments the reconstructions were similar to the original images in terms of shape but were less visually alike. Furthermore, MSE tendency to generate blurry boundaries between figures  [31]  proved to be highly detrimental to the comparison of image shapes in our study, preventing a proper examination of the data. this The embedding predictor structure employed in this study was relatively simple, consisting of two single layers with 300 units each and sigmoid activation. This approach was selected following an examination of several architectures, including deeper models with skip connections, convolutions, and direct models, on the 150 SNP version predictions. The simplicity of this approach allowed for the exploration of different input sizes while minimizing training time. For larger datasets, however, more complex architectures capable of studying sequences while retaining position relevance could prove beneficial.
Relevance of SNP selection in genome prediction models Genome prediction models are typically founded on dense SNP data. Nevertheless, generating and managing large SNP datasets remains a challenge for small to medium-sized breeding companies, despite the relatively low cost of high-throughput genotyping methods. Strategies such as genotyping-by-sequencing (GBS) have been developed to reduce genotyping costs  [4] ; however, the use of smaller arrays would be more conducive to the implementation of this methodology. To this end, we reduced the genotyping array to a small array of 150 SNPs that we found to be significant for different shape traits. We tested the model with both random and selected SNPs and consistently observed an improvement in results when using the selected SNPs. This supports the notion that the selected SNPs are indeed significant and might represent a good health sign for their relationship with apple shape (Figure  S1 ).
Limitations By estimating the mean shape appearance for a genotype we aimed to cover the variability within genotype, but apples can be very influenced by environmental effect, leading to a wide range of phenotypes within the same individual  [5]. Even though most of the genotypes counted with multiple biological replicates and two year imaging, this is one of the biggest challenges faced in genomic prediction. Furthermore, the overrepresentation of flat-globose apples limits the capacity of the model trained, forcing it to be biased toward this category (Figure  S2 ). Removing the number of examples from this category has been considered, but this considerably would limit the size of the dataset in term of number of genotypes which might hurt the learning process more than helping it. For traits with such a low separation between classes a larger genotype dataset, including more extreme phenotypes may help to improve accuracy in future studies.
Conclusions Through the GenoDrawing SNP-to-Image method presented in this work, we propose a new framework to study genomic prediction for fruit trees phenotype. Our study showed that using traitassociated SNPs significantly improves the performance of GenoDrawing in predicting fruit shape compared to using random SNP selections. The consistent better optimization during training with trait-associated SNPs and the fact that the results are within the dataset distribution boundaries also provide evidence that this methodology is moving in the right direction. Our approach of using a low number of SNPs to generate predictions at a relatively low cost might be particularly relevant for small to medium breeding companies. Additionally, the same approach could be applied to predict other heritable traits with higher economic impact such as physiological disorders or genetic disease severity.
Acknowledgments FJR is recipient of grant  PRE2019-087427  funded by  MCIN/AEI/ 10. 13039/501100011033  and by " ESF Investing  in your future. This research was supported by project  PID2021-128885OB-I00  funded by  MCIN/AEI/10. 13039/501100011033  and by " ERDF A way of making Europe ". This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under.
Acknowledgments I am most grateful to the staff of the  Journal of Venomous Animals and Toxins including Tropical Diseases  for their new invitation to contribute with a review paper to the journal. My thanks go also to  Dr. Aniela Matuszewska  ( University of Silesia, Poland ) for comments on fossil resins and for the permit to use some data from her recent article. To  Adriano Kury  ( Museu Nacional, Rio de Janeiro ) and  Eric Ythier  ( Syntech Research, France ) for their very useful comments on previous versions of the ms.
Consecutive serosurveys, preferably over a period of time, can help revealing trends and identifying risk groups. In Germany, a seroprevalence of 4% was measured in children in the period 2003 to 2006  [16]  and 9% in adults in 1997 to 1999 and in 2008 to 2011  [17]. To assess the incidence of Bb infections in a population, however, repeated measurements of the same individuals are necessary to determine a seroconversion. Estimates of seroprevalence and seroincidence are useful for prioritisation of public health interventions. They help to assess changes in the risk of acquiring Bb infections. Furthermore, seroprevalence estimates serve as a basis to account for pre-test probabilities of serological tests in the context of clinical diagnoses of LB in children and adolescents. Here, we aimed at analysing and comparing the seroprevalence of Bb IgG antibodies among children and adolescents in Germany in two nationwide surveys conducted 11 years apart, identifying factors associated with seropositivity, seroconversion and seroreversion and estimating their annual rates based on longitudinal data of individuals.
Statistical analyses Seroprevalence and predictors of seropositivity We used Pearson's chi-squared test to test associations for categorical data and t-test for continuous data. We calculated two-sided p values and considered results statistically significant at a threshold of < 0. 05. For all analyses, we used the statistical software R (version 4. 2. 1)  [20]. Visualisations were realised using the R-package ggplot2. To improve representativeness, we adjusted for the clustered study design and applied study-specific weights  [18, 21] , using the R-package survey. We assessed associations with seropositivity calculating odds ratios (OR) and 95% confidence intervals (CI), using univariable logistic regression. Variables for multivariable analyses were selected from available study variables.