dataesr/scientific-paragraphs-categorization huggingface dataset:
It also enables us to pull valuable details about users (e. g. , their IDs) and their social activity (e. g. , the number of retweets/followers/friends) and media content (e. g. , images, places, and poll metadata) using the new expansion parameters. We also consider that all the tweets that discuss a particular news article will bear the truth value (i. e. , the label of the article) because it contributes to the diffusion of a rumor (true or fake), even if the tweet denies or remains skeptical regarding the veracity of the rumor. The statistical details of DAT@Z21 are shown in Table  2. Unlike most existing datasets that are focused on only text content, we aim to build a multimedia dataset that includes images. We collect the original tweet texts and attached images. Thus, from the 2,864,530 collected tweets, we remove text-only tweets, duplicated tweets, and images to obtain 395,625 tweets with attached images.
This represents a significant part ( ≃ 14%). In addition, among these tweets, almost 10% share more than one image, which shows that visual information is widely used on social media. Figure  6  shows the distribution of true and fake tweets with the number of included images. For the textual content of our dataset, we analyze the topic distribution of fake and real tweets. From Figure  7 , we can observe that the topic distribution of fake tweets is slightly different from that of the real tweets. Indeed, both distributions share most of the frequent words related to political events, such as "Donald", "trump", "Hillary", "clinton", and "election" or to the Covid-19 health crisis, such as "health", "care", "Coronavirus", and "death". The accordance of real and fake news topic distributions ensures that the fake news detection models trained on our dataset are not topic classifiers.
For users involved in spreading tweets, we find that the number of users spreading fake and true news is greater than the total number of distinct users in the dataset. This indicates that a user can engage in spreading both fake and real news. As for the users social interaction, the distribution of their followers and followees count are presented in Figure  8. We can observe that the followees and  To understand how users interact with each other, we have created an interaction graph using the Python package Networkx  [6]  that represent three types of interactions between two users: retweets, replies, and quoted tweets. Figure  9  shows the largest connected component for our original graph for users spreading real and fake tweets. We note that both users display echo-chamberness. In addition, users posting fake tweets tend to create tightly knit groups characterized by relatively highly-dense ties.
Our dataset includes spatiotemporal information that depicts the temporal user engagement for news articles, which provides the necessary information to further investigate the utility of using spatiotemporal information to detect fake news. Figure  10  depicts the geolocation distribution of users engaging in posting tweets. Given that it is often rare for the location to be explicitly provided by the users in their profiles, we consider the locations information attached with tweets. Note that to comply with Twitter's Terms of Service 12  , the full contents of user social engagements and network are not able to be directly published. Instead, we only publicly release the collected tweet IDs, so that the tweets can be rehydrated for non-commercial research use, but we also provide the instructions for obtaining the tweets using the released IDs for user convenience.
Rumor Classification Using DAT@Z21 In this section, we conduct comparative experiments on the rumor classification task using the DAT@Z21 dataset. These experiments aim to show the usefulness of our dataset with different kinds of methods. We include both monomodal methods and multimodal state-of-the-art methods as baselines to predict the credibility of tweets. We specify these methods in Section 4. 1. We then provide the experiments settings in Section 4. 2. Finally, we present the performance analysis for these methods in Section 4. 3.
Experiment Settings We randomly divided the dataset into training and testing subsets, with a ratio of 0. 7:0. 3. We use the scikit-learn library from Python to implement the traditional algorithms with default settings and we do not tune parameters. We use the Keras library from Python to implement multimodal methods. We use TF-IDF with 1000 features, word2vec and doc2vec embeddings with 200 vector size to represent texts. Because the dataset has a slightly unbalanced distribution between false and true classes (≃ 1. 17 : 1), we use the following metrics to evaluate the performance of rumor classification algorithms: PR AUC (Area Under Precision-Recall Curve), ROC AUC(Area Under Receiver Operating Characteristic Curve), precision, recall, and F 1 -score. Finally, we run each method three times and report the average score in Table  3.
Because the dataset is slightly unbalanced in favor of fake labels, the models tend to generate slightly more false positive cases. However, the PR AUC (which is an appropriate metric for unbalanced datasets), recall and F 1 -score values are satisfactory. Figure  11  illustrates the PR curve for the monomodal and multimodal methods. Given that the proportion of true and fake information is likely to be even more unbalanced in real world datasets, practical detection solutions need to handle this type of imbalance more effectively. With these experiments, we show that the DAT@Z21 dataset allows to explore various methods dealing with different types of features, by comparing their efficiency. According to the obtained results, we see that the visual content is very important since these methods obtain better results.
Conclusion and Perspectives In this work, we have constructed a comprehensive multimodal dataset DAT@Z21 for rumor classification, which contains textual, visual, spatiotemporal, user engagement, and social platform posts. We have described how we collected the dataset. To show the distinctive features between rumors and facts, we have provided a brief exploratory data analysis of news articles and the corresponding messages from the Twitter social platform. In addition , we have demonstrated the usefulness and the relevance of our dataset through a rumor classification task over several monomodal and multimodal state-of-the-art methods using the data of DAT@Z21. The DAT@Z21 dataset can be extended by including (1) news articles and tweets from other languages than English, and (2) other reliable news sources(e.
Article https://doi. org/10. 1038/s41559-023-02170-1 scRNA-seq read mapping, we substantially refined and extended previous annotations of the lamprey germline genome  10  (Extended Data Fig. 1  and Supplementary Data 1) on the basis of 63 deeply sequenced RNA-seq libraries covering six major organs, including different brain regions (Supplementary Tables  1  and 2  and Methods). After quality control and data filtering (Methods), we obtained transcriptomes for a total of 159,381 high-quality cells (72,810 for adults and 86,571 for ammocoetes). Using a detailed clustering approach and an iterative marker-gene-based annotation procedure (Methods), we identified 151 (95 neuronal) distinct cell types in the adult dataset and 120 (92 neuronal) in the larval dataset (Supplementary Table  3 ; see the online atlas).
To spatially localize cell types across the brain, we generated in situ sequencing (ISS) 11 data for 93 selected marker genes in both lamprey life stages and single-molecule RNA fluorescence in situ hybridization (smRNA-FISH) images for four genes in the larval stage (Supplementary Table  4  and Supplementary Data 2). Overall, neural cell type compositions are similar between the two stages (Extended Data Figs. 2-4a ). However, we noted a generally higher cell type specificity of gene expression patterns in adults than in ammocoetes (Extended Data Fig. 4c )-a result that is robust to controls for technical differences between datasets (Extended Data Fig. 4b-d  and Methods ). A cell type tree derived from the datasets for the adult lamprey, which is thought to be better suited for the inference of ancestral vertebrate traits than ammocoetes  12  , reflects cell type relationships based on gene expression distances (Fig.
9a, b, g, i ). Two additional types of specialized ependymal cells are the pigmented pineal epithelial cells, defined by markers that are common to the retina pigment epithelium (for example, RPE65 and RRH; Extended Data Fig. 9a ), and the KERA-expressing ependymal cells of the hindbrain and spinal cord (types: ReEpen1 and ReEpen3; Extended Data Fig. 9a, c, d ). The large number of detected ependymal cells and cell types in the adult dataset (Extended Data Fig. 3b ) probably reflects the large relative sizes of the ventricles and choroid plexuses of the lamprey brain (Extended Data Fig. 9i )  32. Notably, lamprey astrocytes are highly comparable to those from mouse in terms of their overall transcriptome signature (Fig. 2c, e ). They share key marker genes that are fundamental for the development and function of astrocytes, such as SOXE3 (Sox9), HES5 and SLC1A2 (Fig.
Sea lamprey genome annotation Bulk tissue RNA-seq reads were mapped to the sea lamprey germline genome 10 using GSNAP  79  (v. 2018-03-01) with the option to find known and new splice junctions in individual reads activated (novelsplicing, 1). The resulting BAM files for each stage and tissue were merged before being used for transcriptome assembly with StringTie 80 (v. 1. 3. 4d). Each resulting GTF file was filtered for putative assembly artefacts using GffRead 81 (v. 0. 9. 9) by discarding single-exon transcripts and multi-exon mRNAs that have any intron with a non-canonical splice site consensus (that is, not GT-AG, GC-AG or AT-AC). Individual annotated transcriptomes were then merged together with the already available set of annotated protein-coding genes from the germline genome study  10  to obtain a non-redundant set of transcripts.
Genome annotation was further refined using TransDecoder (v. 5. 3. 0; https://github. com/ TransDecoder/TransDecoder) to identify candidate coding regions within the transcript sequences; this process involves identifying the longest putative open reading frame within each transcript and then searching the corresponding peptides against SwissProt (https:// uniprot. org) using BlastP 82 (v. 2. 5. 0+) and Pfam (https://pfam. xfam. org) using HMMER  83  (v. 3. 2). Annotation quality was assessed by comparing the number of reads mapping to exonic, intronic and intergenic regions of the genome (Extended Data Fig. 1 ). Annotation completeness was also estimated using BUSCO  84  (v. 3) by comparing the set of translated longest coding sequences from each transcript against a set of metazoan-conserved single-copy orthologues from OrthoDB  85  (Supplementary Table  6 ).
Orthology assignment and gene nomenclature Homology information for the set of annotated genes was retrieved by applying the OrthoFinder 86 (v. 2. 3. 11) pipeline against a group of selected chordates: vase tunicate (Ciona intestinalis)  87  , inshore hagfish (Eptatretus burgeri; permission to use unpublished genome data was given exclusively for the purposes of the present study; personal communication), Australian ghostshark (Callorhinchus milii)  88  , spotted gar (Lepisosteus oculatus)  89  , zebrafish (Danio rerio)  90  , West Indian Ocean coelacanth (Latimeria chalumnae)  91  , western clawed frog (Xenopus tropicalis)  92  , red junglefowl (Gallus gallus)  93  , house mouse (Mus musculus) and human (Homo sapiens). By reconstructing a complete set of rooted gene trees among the analysed species, this tool allows us to establish all orthology relationships among all genes and to infer duplication events and cross-reference them to the corresponding nodes on the gene and species trees.
Proteomes were downloaded from Ensembl 94 (remaining species; v. 97) databases and used for a BlastP Best Reciprocal Hit analysis; to avoid redundancies in the blast results, only the peptides coming from the longest isoform within each gene were used. Rooted gene trees from the inferred orthogroups-that is, groups of genes descended from a single gene in the last common ancestor-were obtained using multiple sequence alignments (MAFFT 95 v. 7. 455) with IQ-TREE  96  (v. 1. 6. 12; 1,000 bootstrap replicates) and STRIDE  97. Orthology relationships can be explored in our online atlas. Throughout this work, we use mouse orthologue names to indicate lamprey gene names. This choice is justified by the fact that most lamprey genes lack a clear and consistent nomenclature and the fact that mouse is used as the main reference in our study.
Complementary DNA amplification and scRNA-seq libraries were constructed using Single-Cell 3′ Gel Bead and Library v. 2 (for larvae) and v. 3 kits (for adults and larvae), following the instructions of the manufacturer. For three larval whole brains, we additionally produced libraries using the v. 3 kit (SN580, SN582 and SN588) for adequate technical comparisons between the larval and adult datasets (main text and Extended Data Fig. 4b-d ). Complementary DNA libraries were amplified using 12 or 13 PCR cycles and quantified on a Qubit Fluorometer (Thermo Fisher Scientific). Average fragment size was determined on a Fragment Analyzer (Agilent). The libraries were sequenced using the NextSeq 500/550 High Output Kit v. 2. 5 on the Illumina NextSeq 550 system (28 cycles for Read 1, 56 cycles for Read 2, 8 cycles for i7 index and 0 cycles for i5 index).
Additional functional information was added by comparing the annotated clusters to published vertebrate neural single-cell datasets  18, 104. Datasets coming from different samples were integrated using integrative non-negative matrix factorization as implemented in LIGER v. 0. 5. 0 (ref. 105 ). The datasets were integrated at two levels: (1) integration of replicates coming from the same brain region (that is, telencephalon, diencephalon, mesencephalon, rhombencephalon and whole brain) and stage (that is, ammocoete and adult), and (2) integration, within each stage, of all replicates together in the same dataset encompassing all sampled regions. Each integrated dataset was then imported to Seurat to perform shared nearest-neighbour graph construction, clustering, differential expression analysis, 2D embedding and cluster annotation as described above.
We noticed that the number of UMIs and expressed genes per cell was consistently lower for the larval dataset (produced using Chromium kit v. 2) than for the adult one (produced using Chromium kit v. 3) (Supplementary Tables  1  and 2 ). To establish whether this difference reflected an actual biological property of the two stages, we produced three larval datasets using the v. 3 kit and compared their number of expressed genes per cell to the larval v. 2 and adult v. 3 datasets (Extended Data Fig. 4b ) (see also 'Cell dissociation and scRNA-seq data generation'). We also compared the distributions of cell-type-specific gene expression signals across datasets based on gene specificity indices calculated using the method developed by Tosches and colleagues  64. Briefly, to obtain the specificity index with this method, the mean of normalized scRNA-seq read counts of each gene (g c ) is calculated for each cell type (C) and then divided by its mean across all cells: s g,c = g c 1 N ∑ i∈c g i.
Lamprey-mouse comparisons To find cross-vertebrate similarities and differences in neural cell types, the adult integrated brain atlas was compared against a published juvenile mouse nervous system atlas  18. The two datasets were first compared via a correlation-based approach. That is, the raw UMI count matrices were extracted from both species datasets, and orthology information for the corresponding gene IDs was added; orthology relationships between mouse and lamprey were obtained from the OrthoFinder analysis (see above; Supplementary Table  7 ). The UMI counts coming from paralogues in the respective species were summed ('meta-gene' method  20  ), and the species-specific gene IDs were replaced by numeric indices (1. n, where n is the number of orthology groups between the mouse and lamprey) shared by the two species.
The new meta-gene count matrices were then normalized using SCTransform, filtered for HVGs and averaged across all annotated clusters. The expression levels were finally transformed to specificity indices (see above), which were then used for Pearson correlation analyses. Dendrograms relating cell-type families between lamprey and mouse were constructed using the pvclust 106 R package with complete hierarchical clustering and 1,000 replicates. In addition, the two datasets were compared using the SAMap (v. 0. 2. 3) algorithm  19  , a method that enables mapping single-cell transcriptomic atlases between phylogenetically distant species. A gene-gene bipartite graph with cross-species edges connecting homologous gene pairs was constructed by performing reciprocal BlastP searches between the two proteomes of the two species.
The graph was used in a second step to project the two datasets into a joint, lower-dimensional manifold representation, where the expression correlation between homologous genes was iteratively used to update the homology graph connecting the two atlases. After the analysis was run, a mapping score (ranging from 0 to 1) was computed among all possible cross-species cluster pairs. The full list of all lamprey gene names used in this study with their respective gene IDs is reported in Supplementary Table  8.
Further processing (only when required) involved contrast enhancing (saturated pixels between 0. 1% and 0. 3%) and background subtraction for noise reduction (rolling ball with a radius of 50 pixels). Extended Data Fig. 2  | Larval (ammocoete) brain dataset. a, Schematic of the sea lamprey larval brain showing the different regions dissected for this study and UMAP projections for each brain region. Each UMAP projection represents an integration of the biological replicates (as indicated in panel b) for each brain region, respectively. Additional information is available in the interactive atlas (https://lampreybrain. kaessmannlab. org/ammocoete. html). b, Barplots showing the proportions of each cell type group (as reported in a and Fig. 1b, c ) for each sample. For whole brain samples, proportions are showed separately for datasets obtained using the v2 and v3 kits.
c, Heatmap showing clustered pseudobulk brain regions based on Spearman's ρ. Median ρ = 0. 90. Extended Data Fig. 3  | Adult brain dataset. a, Schematic of the sea lamprey adult brain showing the different regions dissected in this study and UMAP projections for each brain region. Each UMAP projection represents an integration of the biological replicates (as indicated in panel b) for each brain region, respectively. Additional information available in the interactive atlas (https://lampreybrain. kaessmannlab. org/adult. html). b, Barplots showing the proportions of each cell type group (as reported in a and Fig. 1b, c ) for each sample. For whole brain samples, the proportions of neural cell types only are additionally showed. c, Heatmap showing clustered pseudobulk brain regions based on Spearman's ρ.
Acknowledgements We thank all members of the  Kaessmann group  for fruitful discussions;  Robert Frömel  for his help with library sequencing;  E. Panzariello ,  M. Sanchez-Delgado  and  N. Trost  for the brain and animal illustrations;  M. Mall  for providing temporary lab space; and  M. Cardoso-Moreira  for discussions and comments on the manuscript. The computations were performed on the Kaessmann lab server (managed by  N. Trost ) and the bwForCluster from the  Heidelberg University Computational Center  (supported by the state of  Baden-Württemberg  through bwHPC and the  German Research Foundation ,  INST 35/1134-1 FUGG ). We thank  W. Wang  of the  Northwestern Polytechnical University of Xi'an, China ;  Y. Zhang  of the  Institute of Zoology, Chinese Academy of Sciences ; and  J. Pascual-Anaya  from the  University of Málaga, Spain , for granting access to the early draft of the inshore hagfish genome (https://doi.
Data availability The raw and processed bulk and scRNA-seq data have been deposited to ArrayExpress with the accession numbers E-MTAB-11085 (bulk) and E-MTAB-11087 (single cell) (https://www. ebi. ac. uk/arrayexpress/). The genome annotation files and in situ images have been deposited to Zenodo 110 (https://doi. org/10. 5281/zenodo. 5903844). Information about gene expression, cell type annotation and gene orthology relationships across species can be visualized using the online atlas (https://lampreybrain. kaessmannlab. org/).
Code availability All code underlying the published atlas is available on GitHub (https:// github. com/f-lamanna/LampreyBrainAtlas/) and Zenodo 111 (https:// doi. org/10. 5281/zenodo. 8113793) together with detailed instructions about its usage. Article https://doi. org/10. 1038/s41559-023-02170-1 110. Lamanna, F. et al. Genome annotation files and in situ images associated with analyses of the lamprey neural cell type atlas. Zenodo https://doi. org/10. 5281/zenodo. 5903844 (2023). 111. Lamanna, F. et al. All code underlying the analyses of the lamprey neural cell type atlas. Zenodo https://doi. org/10. 5281/ zenodo. 8113793 (2023).
Data Fig. 5 | 5 Posterior forebrain, midbrain, and hindbrain neurons. a, Subtree from the dendrogram of Fig. 1cdisplaying the expression of selected marker genes for each cell type (gene names mentioned in main text are highlighted in bold). SC, spinal cord. b, Sagittal section (anterior end to the left) of a larval head (white dashed line outlines the brain) showing the expression of SLC6A5, HOXA/B5, and PKD2L1. Dashed lines separate the main brain regions. c, UMAP projection of a larval hindbrain dataset showing the expression of CHAT and TBX6/20. d-m Sagittal sections (anterior end to the left) of the adult brain showing the expression of selected marker genes. Arrows in d and e indicate the putative location of cranial nerve nuclei. See Supplementary Fig. 2 for the ISS section schemes; scale bars, 500 μm.
Among the 57 identified putative proteins, three lipases were identified as possibly involved in lipophagy due to a potential vacuolar localization. The expression of the mRNA corresponding to the 57 proteins was then searched in 3 transcriptomic datasets obtained under nitrogen starvation. Nine genes were highly regulated and were considered as encoding enzymes with a probable important function in lipid catabolism. A tertiary structure prediction of these nine candidates yielded eight functional 3D models. Among those, two downregulated enzymes, Phatr3_J54974 and Phatr3_EG00720, were highlighted as good targets for future functional genomics and purification studies to investigate their role in lipid degradation.
tricornutum genome  [48]  yielded 162 putative protein sequences, of which 90 have a significant annotation (E-value < 10 -5 ) in the pfam database. Additional screenings through sequence investigation and ESTHER database homology search narrowed the total to 74 putative protein sequences (Table  S1 ). Among them, 58 proteins with a potential TAG lipase function grouped in four families annotated as α/β hydrolase (24 proteins), class 3 lipases (18 proteins), GDSL-like lipases (8 proteins) and patatin-like phospholipases (8 proteins). Sequence alignment was performed to retrieve conserved motifs.