dataesr/scientific-paragraphs-categorization huggingface dataset:
This capacity of PhylteR to identify putative paralogs is an important feature, as it was shown earlier that non-orthologous sequences in phylogenomic datasets could have drastic impact on results  (Philippe et al. 2017) , leading for instance to erroneous branching with high support in the reconstructed species tree in some cases  (Philippe et al. 2011). A final argument that we used to validate PhylteR consisted in exploring the syntenic nature (and lack thereof) of the sequences identified as outliers when comparing the species in a pairwise manner. We observed that outlier sequences were often (much more than expected by chance) syntenic-outliers, i. e. sequences associated with a loss of synteny when comparing the two genomes. This provides two kinds of information: on one side, that the "syntenic outliers" and the "phylogenetic outliers" largely overlap, which proves with an argument orthogonal to all the previous ones, that PhylteR (and TreeShrink to a lesser extent) captures an information about erroneous annotations; on the other side, it suggests that many "syntenic outliers" are due to errors and not to biological processes.
"Syntenic outliers" are often filtered out before performing rearrangement analyses, because their position is believed to be artefactual  (Lucas and Crollius 2017). However sometimes this outlier position is modelled as the result of a biological process  (Dalevi and Eriksen 2008). Our analysis supports this artifactual origin in Carnivora, though some syntenic outliers might proceed from retrotranscription or translocations. Here we focused on the importance of identifying outlier sequences in phylogenomic datasets in order to remove them prior to phylogenetic inference with supermatrix or supertree methods. But other usage of the tool we present here can be anticipated. For instance, correctly identifying and removing outlier sequences from multiple sequence alignments is crucial when using statistical methods based on the ratio of nonsynonymous over synonymous substitution rates (d N /d S ratio) to detect adaptive molecular evolution (see Yang and Bielawski 2000 for a review), or for correctly inferring ancestral sequences  (Yang et al.
Acknowledgments Work was funded by  ANR  Grant  18-CE02-0007  (Sthoriz) to DMDV,  ANR  Grant  19-CE45-0010  (Evoluthon) to ET, and  European Research Council  grant  ERC-2015-CoG-683257  ( ConvergeAnt  project) to FD. This is contribution ISEM 2023-XXX of the  Institut des Sciences de l'Evolution de Montpellier.
Graph classification Graph classification is a common task from Graph Theory as it aims to characterize entire graphs. The idea is to learn over graph data. Instead of predicting over the individual components (nodes and edges) of a unique graph, a graph classification model takes as input a dataset of multiple different graphs and perform independent predictions for each graph. Here, each graph is an i. i. d. datapoint associated with a label and the goal is to use a training set of labeled data to learn a mapping and predict labels. Examples of graph classification application include molecular property modeling. For instance, in enzyme identification, a collection of graphs, each representing a chemical compound, is used to train a model to predict whether a graph is an enzyme or not. The same holds for drug discovery, where molecular graphs are considered to identity desired pharmacological and ADME/T (absorption, distribution, metabolism, excretion, and toxicity) properties  (Basile et al.
Benchmark datasets This section presents several tables providing information on the datasets used by the studied articles. Table  1  describes the evaluation tasks used in the methods of the taxonomy. It provides information on popularity of evaluation tasks in the recent literature. Table  2  gathers high level information on the bipartite graph datasets. In detail, it gives the kind of graph type, the evaluation tasks the graphs have been used for and the methods in which the graphs have been used. Table  3  summarizes statistics as well as descriptive elements about the nature of the bipartite graph datasets. This is the more furnished table related to the dataset and should be considered as the reference point to have a global view of datasets.
Pytorch geometric Pytorch Geometric [69] consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use minibatch loader for many small and single giant graphs, multi gpu-support, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds.
Spektral Spektral (Grattarola and Alippi 2020) is a Python library for graph deep learning, based on the Keras API and Tensor-Flow 2. The main goal of this project is to provide a simple but flexible framework for creating Graph Neural Networks. Spektral may be used to classify users of a social network, predict molecular properties, generate new graphs with Generative Adversarial Networks (GANs), clustering nodes, predict links, and any other task where data are described by graphs. Page 11 of 16 54.
Conclusion and future directions In this work, we conducted a comprehensive survey of the literature on bipartite graph representation learning techniques. We analyzed recent approaches of bipartite graph embedding that transform a bipartite graph into a lowdimensional vector representation while preserving intrinsic properties of the original graph. We suggested a taxonomy of bipartite graph embedding algorithms that gathers the algorithms by common principles. We described the most common evaluation tasks to compare different bipartite graph embedding methods and to draw insights from algorithms performance. We also gathered lists of datasets and existing libraries to ease the implementation of further research on the subject. In order to inspire future research on the topic, we now briefly discuss specific directions potentially worth pursuing.
Background: Artificial intelligence (AI) has gained tremendous popularity recently, especially the use of natural language processing (NLP). ChatGPT is a state-of-the-art chatbot capable of creating natural conversations using NLP. The use of AI in medicine can have a tremendous impact on health care delivery. Although some studies have evaluated ChatGPT's accuracy in self-diagnosis, there is no research regarding its precision and the degree to which it recommends medical consultations. Objective: The aim of this study was to evaluate ChatGPT's ability to accurately and precisely self-diagnose common orthopedic diseases, as well as the degree of recommendation it provides for medical consultations. Methods: Over a 5-day course, each of the study authors submitted the same questions to ChatGPT.
Introduction Recently, the field of artificial intelligence (AI) has made remarkable progress. The applications of AI in health care have also gained attention  [1] [2] [3] [4] [5]. One of the most popular forms of AI involves using a natural language processing (NLP) system. In medicine, researchers have used NLP to extract unstructured data from medical records, followed by organization of the output  [6] [7] [8] [9]. Some have advocated for the use of an NLP as a prognostic or diagnostic tool  [10] [11] [12] ; however, further investigation is warranted. ChatGPT (OpenAI, San Francisco, CA, USA) was released in November 2022. ChatGPT is a sophisticated chatbot that uses an NLP model capable of both supervised and forced learning; it can understand the context of a sentence from only a few words.
ChatGPT is also thought to possess the ability to translate languages and analyze customer experience if implemented as a survey  [13]. Hence, its popularity has been growing rapidly  [14]. Despite not being explicitly designed for health care, ChatGPT has also been increasingly used in health care contexts  [3, 15]. ChatGPT can be helpful in aiding health care providers in formulating differential diagnoses or assisting patients in self-diagnosing conditions before seeking medical attention. Nonetheless, it is still unclear whether digital self-diagnostic tools truly provide health benefits to patients, and multiple studies have raised concerns about their accuracy in triage and diagnosis  [16] [17] [18] [19] [20]. If we leap into the realm of AI and its health care applications, we must first understand whether ChatGPT can accurately and precisely assist with self-diagnosis to reduce the risk of error, which would cause harm to the patient.
Study Design Over a 5-day period (February 20 to 24, 2023, between the hours of 12 AM and 3 PM), the study authors (TI, EY, AY, KT, and KF) submitted the same questions to ChatGPT (GPT version 3. 5) (see Multimedia Appendix 1 for an example). Each question was submitted daily to evaluate the variation in responses. At the end of the study period, all answers generated by the chatbot were recorded and sent to one study author (TK) for analysis. Additionally, each author who questioned ChatGPT provided the details of the operating system (OS) and browser software they used when conducting this experiment.
Summary of Answers to the Questions The summary of answers to the initial questions are presented in Table  1  and the full text of the answers is shown in Multimedia Appendix 2. In response to the question regarding CTS, ChatGPT diagnosed CTS in all answers. In response to the question regarding CM, ChatGPT either diagnosed peripheral neuropathy, multiple sclerosis, a neurological disorder, or presented differential diagnoses. Regarding the question about LSS, ChatGPT diagnosed either LSS or sciatica, or presented differential diagnoses. Regarding the questions about KOA and HOA, ChatGPT diagnosed KOA and HOA, respectively, or presented differential diagnoses. The OS and browser software used by each rater when using ChatGPT are presented in Table  2. e ND: neurological disorder; judged as a correct answer because, although it is not the disease that was assumed, it is not an error.
Recommendation for Medical Consultation Nearly 5. 6% of the generated answers omitted any recommendation to seek care. Since ChatGPT is not a substitute for medical advice, it would be prudent for the chatbot to counsel the patient to seek medical attention for diagnostic validation and management. Without this, the patient is left without guidance on the appropriate next steps. Some may think that this language is often written by a software or program to avoid medical liability should an error occur  [17]. Since ChatGPT has inconsistent diagnostic capability, one would consider this a necessary feature should this be applied to health care. Although 79. 6% of the answers recommended medical consultation for more than 10% of the total words, only 12. 8% of the answers included a strong recommendation as set by the study standards with phrasing including either the term "essential" or "recommended.
" The other phrases could be interpreted as rather vague since they indirectly recommend seeking care. Without direct language, it is possible that the patient is left confused after self-diagnosis, or worse, experience harm from a misdiagnosis. In fact, ChatGPT explicitly provides a disclaimer regarding these potential harms. Since it is not exclusively designed as a self-diagnostic tool for medical support, the inclusion of the disclaimer is understandably necessary. However, instead of solely focusing on limiting the use of AI chatbots for health care purposes to reduce the potential risk to users, several papers advocate that the following would be effective: (1) understanding and presenting the issues associated with the use of AI chatbots for health care purposes; (2) improving the technology and adapting it to appropriate health care applications; and (3) advocating for shared accountability and fostering collaboration among developers, subject matter experts, and human factors researchers  [3, 15, 39].
Other chatbots may present better/worse results, and the easily accessible data do not always offer better support for self-diagnoses compared to that offered by chatbots. We plan to compare the difference between different chatbots in the future. Fourth, the OS and browser software used should have been consistent to eliminate their potential impact on the results. Fifth, it is possible that ChatGPT was trained using the six websites we referenced  [24] [25] [26] [27] [28] [29]. However, the significance of our study was not compromised and this was unrelated to the problem of reproducibility. Finally, a GPT-4 version of ChatGPT was released just after we conducted our experiment, which may provide more accurate answers. We plan to use this most recent version in our next study.
Plant material and whole-genome sequencing We ordered the seeds of 66 accessions from the Versailles Arabidopsis Stock Center at Jean-Pierre Bourgin Institute (IJPB) and received the seeds of the remaining 6 accessions (Elh-2, Ice-1, Rab-R1, Tanz-1, Taz-0 and Zin9) from Angela Hancock (MPIPZ). The accession numbers of seeds from Versailles are shown in Supplementary Table  1. Plants were grown in greenhouses or growth chambers. For the 48 accessions that were sequenced with PacBio HiFi, high-molecular weight (HMW) DNA was prepared from 30-40 4-week-old plants using the NucleoBond HMW DNA kit. The HiFi libraries were prepared using the SMRTbell® prep kit 3. 0 and the BluePippin cartridge for enriching fragments greater than 9 kb up to 50 kb. Finally, HiFi sequencing was performed on the Sequel IIe platform at the Max Planck Genome-centre Cologne (MP-GC).
SMRTlink software (PacBio) was used to demultiplex and extract HiFi data sets. The same HMW DNA was used to prepare PCR-free short-read libraries according to the protocol of the NEBNext® Ultra™ II DNA PCR-free Library Prep Kit for Illumina® (New England Biolabs). Libraries were then sequenced with 2 x 150 paired-end reads on the NextSeq 2000 platform. As previously described  66  , for the 24 accessions that were sequenced with Oxford Nanopore, HMW DNA was extracted from 3-week-old plants according to the protocol described by Russo et al. 67. The subsequent library preparation and sequencing were performed at the GeT-PlaGe core facility, INRAE Toulouse. ONT libraries were prepared using the EXP-NBD103 and SQK-LSK109 kits according to the manufacturer's instructions and using 4 µg of 40 kbsheared DNA (Megaruptor, Diagenode) as input.
Then, we used a homology-based scaffolding tool, RaGOO v1. 1  74  , to order and orient contigs based on whole-genome alignments to the Col-CEN genome  75. Manual evaluation and correction were performed based on the whole-genome alignment and position of centromeric repeats. Finally, to close the gaps in scaffolds, we ran four rounds of MaSuRCA v4. 1. 0 76 by using the HiFi reads and the three assemblies individually. For the 24 accessions that were sequenced with Oxford Nanopore, the long reads were filtered for adapters, short (<1 kb) or low-quality reads (mean quality > 70) using Porechop v0. 2. 4 (https://github. com/rrwick/Porechop) and Filtlong v0. 2. 1 (https://github. com/rrwick/Filtlong). De novo assembly of each genome was initially performed using SMARTdenovo (https://github. com/ruanjue/smartdenovo)  77  and Flye.
To fix base errors in the initial assemblies, we polished the genome by running three rounds of Racon v1. 4. 10 78 with longreads, and four rounds of NextPolish v1. 3. 1  79  with short reads. Then, assemblies were purged using purge_dups. Similar as for the process described above for HiFi datasets, assemblies were further improved, corrected and scaffolded. Finally, scaffolds were polished using NextPolish.
Genome assembly evaluation To evaluate the completeness of each genome assembly, BUSCO v5. 2. 1  80  was used with the OrthoDB database embryophyta_odb10 (eukaryota, 2020-09-10). We evaluated genome assemblies (QV and completeness) based on the k-mers spectrum of Illumina whole-genome sequencing reads, using Merqury v1. 3  81  with default parameters, in order to estimate the goodness and completeness of the reference protein-coding genes (TAIR10, Araport11) in each genome assembly. The reference genes were aligned against the genome assemblies using blastn  82  , and reference genes were considered well assembled in genome assemblies which were aligned with identity >= 80 and coverage >= 0. 9. We also used Liftoff v1. 6. 3  83  to 'lift over' the reference genes to the 72 genome assemblies, with parameters "-copies -sc 0.
2%, which is comparable to the Col-0 reference and T2T genomes (Supplementary Figure  3 , Table  2 ); (3) on the basis of the k-mer-based estimation, the assembled genomes showed a mean of 52. 6 QV and 98. 5% completeness (Supplementary Figure  4 , Table  2 ); (4) the analysis of completeness of reference protein-coding genes by homology search against the assembled genomes (BLASTP and Liftoff), a mean of 97. 3% and 98. 4% were successfully assembled (Supplementary Figure  5 , Table  3  and 4 ); (5) on the basis of the LTR Assembly Index (mean of 21. 9), which reached the "gold standard" level (LAI > 20) (Supplementary Figure  6 , Table  5 ); (  6 ) on the basis of the completeness of centromere repeats (mean of 97%), estimated using the Illumina short-read dataset (Figure  2D , Table  6 ).
Gene prediction and annotation Protein-coding genes were annotated based on a strategy that integrated ab initio gene prediction, transcriptome-based de novo transcript assembly and homologous protein sequence alignment. First, four ab initio gene prediction tools were used: Augustus v3. 3. 3  90  , GeneMark v4. 62  91  , GlimmerHMM v3. 0. 4  92  and SNAP (version 2006-07-28)  93. Second, we collected a list of 308 public RNA-seq datasets for 28 accessions from the NCBI SRA database (Supplementary Table  12 ). The quality of short reads was checked with FastQC. Trimmomatic v0. 39  94  was used to remove potential adapter and low-quality sequences, with parameters "LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36" for single-end reads and "LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36" for paired-end reads.
To obtain the protein sequence of the transcript, the reads were then processed with Trinity v2. 14. 0  95  to assembly transcript sequences, and TransDecoder v5. 5. 0 (https://github. com/TransDecoder/TransDecoder) to identify candidate coding regions. The predicted longest open reading frames were searched against the UniPort database 96 using DIAMOND v2. 0. 4  97  with the parameter setting "-k 1 -f 6 -e 1e-5 --ultra-sensitive", and Pfam database  98  using hmmsearch from HMMER v3. 1b2 (http://hmmer. org). Then, we merged all the predicted protein sequences to generate the pan-pep library and selected representative sequences using CD-HIT v4. 6. 8  99, 100  (-c 0. 98). Third, protein sequences of Arabidopsis thaliana (447_Araport11), Arabidopsis lyrata (384_v2. 1), Oryza sativa (323_v7.
0) and Solanum lycopersicum (514_ITAG3. 2), which were downloaded from Phytozome v13 database 101 and the pan-pep library, were aligned to each genome assembly using Exonerate v2. 2. 0 102 (--percent 70 --minintron 10 --maxintron 60000). Finally, all different evidences of gene models were integrated using EVidenceModeler v1. 1. 1 103. Non-coding genes were annotated by integrating the predictions from Barrnap v0. 9 (https://github. com/tseemann/barrnap), Infernal v1. 1. 4 104 (Rfam database v14. 8) and tRNAscan-SE v2. 0. 9 105. Non-coding RNA and TE-related genes were identified by checking alignment/overlap between predicted gene models and TE sequences (TAIR10), representative gene models (Araport11), TE genes (Araport11), TE and non-coding RNA annotations of each assembly. Disease resistance genes were identified by using NLR-Annotator v2.
The SNP list of the 72 accessions was converted into FASTA format using vcf2phylip v2. 8 (https://github. com/edgardomortiz/vcf2phylip), and then was taken by IQ-TREE to reconstruct the evolutionary tree (parameters: -m GTR+ASC -bb 1000 -alrt 1000redo -safe). The gene presence-absence variation matrix (phylip format) was generated and used for tree building by IQ-TREE (parameters: -st MORPH -m MK+ASC -bb 1000 -alrt 1000 -redo -safe). To estimate population recombination rates (ρ = 4Ner, where Ne is the effective population size and r is the recombination rate of the window), we used FastEPRR v2. 0 122 with 100-kb non-overlapping window size. The nucleotide diversity of each site was calculated by VCFtools. To calculate the Ka, Ks and Ka/Ks of each orthologous gene pair (Arabidopsis lyrata as the reference), the amino acid sequences were aligned using MUSCLE, transformed into CDS alignments with PAL2NAL, and then fed into the KaKs_Calculator v2.
Gene expression analysis The RNA-seq dataset from a previous study 125 , including 79 organs and developmental stages of Arabidopsis thaliana, was downloaded from the NCBI SRA database. First, the potential adapter and low-quality sequences were identified and removed by Trimmomatic v0. 39  94  , with parameters "LEADING:3 TRAILING:3 SLIDINGWINDOW:4:15 MINLEN:36". Then, HISAT2 v2. 1. 0 126,127 was used to align the clean reads against the Col-PEK genome. Gene expression was normalized as transcripts per million (TPM), which was calculated by StringTie v2. 0. 6 128 with default parameters. Comparison of synteny between intra-population (D) and inter-population (E) genomes. The diversity in synteny was calculated for each of the 72 genomes by using the 10 kb nonoverlapping genomic window. For each genome, accumulated proportion of genomic windows with diversity in synteny, ranging from 0 to 1 with bin size of 0.
Boxplots showing the median and upper and lower quartiles. The expected dataset was made by 1,000 simulations with the same data size of the testing dataset. (G) The increase of the pan-genome size and the decrease of core-genome size in the whole population (grey), Europe (red), Asia (blue) and Africa (orange) sub-populations. Accessions were sampled as 2,000 random combinations of each given number of accessions ranging from 2 to 70. The mean number of gene families is shown with the standard deviation. Figure legends • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • n = 72 -> n = 47 • • • • • • • • • • • • • • • • • • • • • R 2 = 0. 91 n = 47 • Europe Madeira Asia Africa Admix • • • • • • • • • • • • • • • • • • • • • • R 2 = 0. 20 n = 47 • Europe Madeira Asia Africa Admix • • • • • • • • • • • • • • • • • • • • • • R 2 = • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • •• • •• •• • •• • •• • •• • •• •• •• •• •• •• •• •• •• •• •• •• • • • • • • • • • • • • • • • • • • •• •• •• •• •• •• •• •• • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • • Europe Asia Africa All • • • • • • • • Pan Core.
Acknowledgements We thank the  Max Planck-Genome-centre Cologne (MP-GC)  for DNA extraction, library preparation and sequencing,  Hequan Sun  for helpful discussions and  Neysan Donnelly  for proofreading the manuscript. We thank  Angela Hancock  and The  Versailles Arabidopsis Stock Center  for providing genetic material. This work was supported by core funding from the  Max Planck Society  and an  Alexander von Humboldt Fellowship  to Q. L. This work was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under  Germany 's  Excellence Strategy -EXC 2048/1-390686111  (to K. S. and R. M. ) and  TRR 341/1-456082119  to K. S.