dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2015) was used to create the loom files which were uploaded to SCope. The embedding of the regulon and integrated gene expression based UMAP clustering, as seen in this article, were added to the loom file. In vitro glucose-stimulated insulin secretion assay. Fifty aggr egates wer e pr e-incubated with Krebs buffer (Human Cell Design, #BK-25) supplemented with 0. 1% BSA and 2. 8 mM glucose for 90 min prior the sequential exposure to glucose 2. 8 mM, 16. 7 or 16. 7 mM glucose plus 10 M forskolin. Each incubation lasted for 30 min and supernatants were collected for insulin measurement by ELISA (Mercodia, 10-1113-10). Intracellular insulin was extracted using acid ethanol (95% ethanol, 5% 12N hydrochloric acid) and quantified by human insulin ELISA. Data of insulin secretion and content were normalized to total protein content, measured by protein assay dye (Bio-Rad, #5000006).
Integrated analysis of scRNA-seq data identifies 12 human adult pancreatic cell types Integrating multiple human adult pancreas scRNA-seq datasets can improve the power of scRN A-seq anal yses to cr eate a compr ehensi v e human adult pancreas cell atlas. We set out to analyze and integrate fiv e pub licly availab le datasets covering a total of 35 non-diabetic, one type 1 diabetic and 15 type 2 diabetic individuals using Seurat v3. 0 canonical correlation analysis (CCA) integration tools (Figure  1 A , Supplementary Table  S5 ) ( 6-10 , 109 , 110 ). After filtering out low quality transcriptomes and data integration, unif orm manif old appr oximation and pr ojection for dimension reduction (UMAP) visualization re v ealed that 7393 cells localized into distinct clusters (Figure  1  B, Supplementary Figure  S1A, B ).
Cells from each original dataset localized together suggesting that there is good correspondence between identical cell types from different datasets. We next sought to identify pancreatic cell types (Figure  1 C ). Clustering analyses based on the expression of wellestablished cell type specific markers led to the identification of eight cell types in the human adult pancreas: beta, alpha, gamma, delta, acinar, ductal, stellate and endothelial cells (Figure  1  D, Supplementary Table  S6 ). UMAP visualization allowed the segregation of endocrine, exocrine and other lineages (Supplementary Figure  S1C ). Beta cells grouped together, away from other clusters and were marked by INS (Figure  1 E ). Other distinct clusters corresponded to alpha, gamma and delta cells based on global transcriptional similarity and GCG, PPY and SST, and other markers, respecti v ely (Figure  1 E , Supplementary Table  S6 ).
Se v eral regulatory modules are shared between different cell types within the endocrine and exocrine pancreas. Additionally, each cell type is defined by cell type-specific regulatory modules (Figure  1 D ). In the endocrine pancreas, alpha and beta cells shared endocrine regulons (MAFB, MEIS2), whereas we observed distinct activities for ARX and IRX2 regulons in alpha cells and RXRG and PDX1 in beta cells (Figure  2 D, H ), expanding previous findings  ( 82 ). Using iRegulon for visualization, PDX1 target genes include SLC6A17, PDIA6 and ABHD3, which have been reported to control insulin release ( 127 , 128 ) (Supplementary Figure  S2D ). Interestingly, gamma and delta cells overlapped with alpha and beta cells, respecti v ely, suggesting a shar ed r egula tory sta te (Figur e 2 C-D). This includes shar ed regulon activity for ARX in gamma and alpha and PDX1 in beta and delta cells (Figure  2 D, H ), consistent with their reported expression in published scRNA-seq studies  ( 5 , 6 , 10 ).
In addition, we identified JUND, EGR4, SREBF1 and STAT4 that have not yet been implicated in alpha cell identity or state. EGR1 (but not EGR4) has been shown to transcriptionally regulate GCG ( 134 ) as well as the PDX1 promoter in beta cells ( 135 ). STAT4 and JUND have been described respecti v ely in pancreatic tissue in general and in beta cells but not in alpha cells ( 136-138 ) (Figure  3 B, C ). Interestingly, these TFs were also highly expressed in primary alpha cells found in the PANC-DB scRNA-seq dataset ( 139 ) ( Supplementary Figure  S3A ). These TFs respond to the JNK and EGFR signaling pathways and may have important physiological functions. Both JUND and the JUND / JNK signaling pathway have been implicated in pancreatic cancer ( 140 , 141 ). Immunocytochemistry of the human adult pancreas confirmed the presence of nuclear JUND in islets (Figure  3Di ).
We also detected nuclear JUND protein in a subset of hiPSCs subjected to beta cell dif ferentia tion (Figure  3 E, F ). Surprisingly, we also detected JUND protein in ductal cells, despite lower JUND regulon activity in this cell type (Figure  3  C, Dii). Thus, protein expression does not necessarily mean that the TF is acti v e in a gi v en cell type. We also confirmed the expression of putati v e JUND target genes in primary alpha cells using the PANC-DB scRNAseq dataset ( 139 ) (Supplementary Figure  S3B ). Ne v ertheless, these results show that JUND is present and acti v e in a subset of pancreatic cell types in the human adult pancreas and hiPSC-deri v ed islet cells. Alto gether, this anal ysis predicts TFs acti v e in human alpha cells, recovering known as well as new candidate TFs.
Among the top regulons identified in beta cells, we retrie v ed well-known as well as new candidate regulators of beta and endocrine cell identity. Known TFs include RXR G, PDX1, NEUR OD1, PAX6 and RFX6 ( 42 , 51 , 52 , 115 , 142 , 143 ) (Figure  3 G, H ). In addition, we found that ZNF705D, ASCL2, BHLHE41 and HOXD13 were highly ranked regulons (Figure  3 H, I ). HOXD13 and BHLHE41 have been shown to be present in the exocrine pancreas ( 144 , 145 ). We confirmed that these TFs were also expressed in primary beta cells of the PANC-DB scRNAseq dataset ( 139 ) (Supplementary Figure  S3C ). Interestingly, ASCL2 has been reported to interact with ␤-catenin of the Wnt pa thway; the la tter has an established role in endocrine fate specification during in vitro differentiation ( 146-148 ). Many putati v e target genes of ASCL2 including PDX1, INS, ABCC8, FOXA1, KCNK16, FXYD2 are dir ectly r elated to glucose sensing and beta cell identity, in line with the beta cell-specific regulatory activity of ASCL2 (Supplementary Figure  S3D ) ( 117 , 149 , 150 ).
FXYD2 ␥ a, a regulatory subunit of the Na + -K + -ATPase, is a transcript e xclusi v ely e xpressed in human beta cells ( 151 ). Immunohistochemistry of human adult pancreas sections showed that ASCL2 is expressed in INS+ beta and islet cells (Figure  3 J ). Surprisingly, ASCL2 was mainly localized to the cytoplasm (Figure  3  J), which is unexpected for TFs which tend NAR  Genomics and Bioinformatics, 2023, Vol. 5, No. 3 11  to localize to the nucleus ( 152 ). Cytoplasmic localization of ASCL2 has been reported in the context of colon and breast cancer ( 153 , 154 ). Immunohistochemistry of hiPSC-deri v ed islet cells showed that BHLHE41 is expressed both in nuclei and cytoplasm of INS+ beta cells (Figure  3 K ). We also confirmed the expression of putati v e BHLHE41 target genes using the PANC-DB scRNA-seq dataset ( 139 ) (Supplementary Figure  S3E ).
We confirmed that both JUND and BHLHE41 transcript levels were decreased in siRNA-transfected cells (Figure  4  B, C, Supplementary Figure  S5A, B ). BHLHE41 and JUND knockdown did not result in global gene expression changes suggesting that perturbation of these TFs is compatible with the maintenance of GRNs (Figure  4 D ). We next sought to annotate cell types within our sc-RNAseq dataset using clustering analyses and the expression of well-established cell type specific markers (Figure  4 E-F ). This led to the identification of nine populations among the hiPSC islet cells: alpha, beta, delta, enter ochr omaffin, endocrine pr ogenitor, exocrine and nonpancr eatic cells (Figur e 4 E), in line with previous publications  ( 11 , 22 , 29-35 , 79 ). To cross-r efer ence our data with primary islets and published islet dif ferentia tion experiments, we integra ted the dataset with previously described data ( 35 ) (Supplementary Figure  S5C, D ).
Most of the cell types identified in our dataset grouped together with previous datasets  ( 35 , 36 )  and away from the primary human islet dataset  ( 8 )  suggesting that our cell populations are transcriptionally similar to previously published datasets. Next, we investigated whether BHLHE41 or JUND perturbation altered cell type proportions (Figure  4  G, Sup-plementary Table  S8 ). The number of sc-beta and scalpha cells remained unchanged upon either BHLHE41 and JUND perturba tion, suggesting tha t TF knockdown did not result in specific loss of these cells (Figure  4 G ). Sc-enter ochr omaffin cells are a kno wn off-tar get population generated during the differentiation of hiPSCs towards the beta cell fate  ( 34 , 35 ). Recently, sc-enter ochr omaffin cells were found to more closely resemble a pre-beta cell population in the fetal pancreas  ( 79 ).
BHLHE41 deficiency induces apoptosis in human adult pancreatic islets GRNs were computed using primary pancreas scRNA-seq data ( 6-10 ), but the validation experiments were carried out in hiPSC-deri v ed islet cells. Since differences exist between hiPSC-deri v ed islets and primary islets  ( 35 , 97 ) , we next assessed whether JUND and BHLHE41 modulate cell states in primary islets, using a knockdown approach. 72h after siRNA transfection into primary islet cells, we confirmed that JUND and BHLHE41 transcript le v els were decr eased (Figur e 5 A-C, Supplementary Figure  S6A, B ). While JUND depletion did not affect human islet cell viability (Pearson correlation, R = -0. 34, p = 0. 23), reduced BHLHE41 transcript le v els did correlate with increased apoptosis (Spearman correlation, R = -0.
Prediction of regulators of e x ocrine cell identity and cell states in the human adult pancreas The comprehensi v e networ k analysis abov e also provides an opportunity to predict and identify regulators of exocrine cell identity and cell states. We identified known and new TFs in acinar cells. Among the top acinar-specific regulons, we recovered well known regulators of acinar and exocrine cell identity such as PTF1A, RBPJL, GATA4 and NR5A2 ( 53 , 123 , 130 , 161 ) (Figure  6 A, B ). These findings are in line with a recent stud y tha t used single-nucleus RNA-seq on pancreatic acinar tissue ( 162 ) and the expression of TFs in acinar cells of the PANC-DB scRNA-seq dataset ( 139 ) (Supplementary Figure  S7A ). Furthermore, we identified MECOM, HEYL and TGIF1 as highly ranked regulons (Figure  6  B / C).
In addition, we observed CDX2 and PPARD as highly specific ductal regulons (Figure  6 F, G ). Both PPARD and CDX2 have been reported to be involved in human pancreatic ductal carcinoma, warranting further functional studies ( 168 , 169 ). We confirmed the expression of predicted TFs within ductal cells of the PANC-DB scRNA-seq dataset ( 139 ) (Supplementary Figure  S7B ). In summary, GRN analysis and regulon ranking allowed us to pinpoint both known and predicted candidate regulators of pancreatic exocrine GRNs and cell identity. Specifically, we identified HEYL as a candida te TF tha t might be implicated in regulating acinar cell identity, warranting further investigation.
An interactive online resource for visualization of the human adult pancreas cell network atlas To enable users to easily navigate the human pancreatic cell network atlas, we provide a loom file that allows for the visualiza tion and explora tion of the da ta using the w e b-based portal SCope ( 76 ) (. loom file and tutorial available at http: //scope. aertslab. org/#/P ancr easAtlas/ * / welcome and https: //github. com/pasquelab/scP ancr easAtlas ). Featur es such as cell type annotation as defined in this paper, gene expression and regulon activity, can be explored on the regulon and gene expression based UMAPs. This resource enables users to select and visualize up to three genes or regulons simultaneously and select subsets of cells for downstream analyses. Target genes of a specific regulon can be downloaded to facilitate further exploration, for example in iRegulon or gene ontolo gy anal ysis  ( 73 ).
Limitations of the study It is important to note that pySCENIC is a stochastic algorithm that does not produce precisely the same regulons for repeated runs, limiting reproducibility when comparing different datasets and when SCENIC is used multiple times on the same dataset ( 112 , 92 ). To mitigate this uncertainty, we ran the full pySCENIC pipeline fiv e times and only kept consistent regulons with the highest regulon acti vity. Alternati v ely, SCENIC could be automated to be run hundreds of times to further mitigate stochasticity ( 112 ). The performance of pySCENIC and other GRN inference methods suffers due to the large amount of dropout e v ents in scRN A-seq data warranting caution w hen interpr eting r esults ( 199 ). This could explain the absence of well-established pancreas TFs such as MAFA ( 200 ), MNX1 ( 66 ), NEUROG3 ( 201 ), FOXA2 ( 54 ) and NKX2-2 ( 202 ) in this analysis.
Ne v ertheless, in support of the validity of our findings, ATAC-seq, the literature and immunohistochemistry of human pancreas sections corroborate se v eral p ySCENIC pr edictions r eported her e such as BHLHE41, JUND and HEYL ( 137 , 138 , 167 ). Chen and colleagues underline the importance of using large sample sizes to deri v e the most accurate network inference possible ( 199 ), highlighting the importance of dataset integration to increase the number of cells analyzed. Howe v er, we cannot exclude tha t integra tion of da tasets may mask dif ferences between datasets of origins. In the future, it will be interesting to extend these analyses to include many more cells and patients, and to de v elop methods for better differential regulon analysis across datasets. Despite current caveats, GRN analysis has enabled the capture of biological relevant information ( 203 ).
ACKNOWLEDGEMENTS We thank  Stein Aerts ,  Kristofer Davie  and the  Stein Aerts  lab for discussions and creating the permanent SCope link,  Shengbao Suo  for sharing the MATLAB script for calculating the regulon specificity score, the  Flemish Supercomputer Center (VSC)  and  Leuven Stem Cell Institute. We thank  Stein Aerts  for feedback on the manuscript. Author contributions : Concei v ed project, V. P. , L. V. Performed experiments, F. F. , A. A. S. , M. V. H. , T. O. Analyses, F. F. , L. V. , A. A. S. , T. O. , M. V. H. , T. H. , A. R. , T. H. , T. S. , T. R. scRN A-seq anal yses, L. V. , X. Y. , A. J. , J. C. ATAC-seq analysis , L. V. Resources , M. C. , J. K. C. Wrote manuscript, L. V. , V. P. and M. C. with input from all authors. Supervision, V. P.
FUNDING Research in the Pasque laboratory was supported by the  Research Foundation-Flanders [FWO  ;  Odysseus Return  Grant  G0F7716N  to V. P. ;  FWO  grants  G0C9320N  and  G0B4420N  to V. P. ];  KU Leuven Research Fund  [ BOFZAP  starting grant  StG / 15 / 021BF  to V. P. and  C1  grant  C14 / 21 / 119  to V. P. ];  FWO SB Ph. D. Fellowship to L. V. [ 1S29419N ];  Pandarome  project  40007487 [G0I7822N ] (funded by the  FWO  and  F. R. S. -FNRS)  under the  Excellence of Science (EOS)   programme  (to M. C. , V. P. ); Research in the Cnop lab was also funded by the  Fonds National de la Recherche Scientifique (FNRS)  (to M. C. , F. F. ); Marie Sk łodowska-Curie Actions Fellowship from the  European Union 's  Horizon 2020 r esear ch and innovation programme  under the  Marie Sk łodowska-Curie  [ 801505  to T.
S. ];  Walloon Region SPW-EERWin2Wal project BetaSource  and the  Francophone Foundation for Diabetes Research  (sponsored by the  French Diabetes Federation ,  Abbott ,  Eli Lilly ,  Merck Sharp & Dohme  and  Novo Nordisk)  (to M. C. );  European Foundation for the Study of Diabetes / Boehringer Ingelheim  European Research Programme on ' Multi-System Challenges in Diabetes'  (to M. C. and F. F. ); Innovati v e Medicines Initiati v e 2 Joint Undertaking [ 115797 ] (INNODIA); this Joint Undertaking recei v es support from the Union's  Horizon 2020 r esear ch and innovation program  and ' EFPIA'  ( European Federation of Pharmaceutical Industries Associations) ; ' JDRF ' ( Juvenile Diabetes Research Foundation) ; 'The  Leona M. and Harry B. Helmsley Charitable Trust '); this manuscript used data acquired from the Human P ancr eas Anal ysis Pro gram (HPAP-RRID: SCR 016202 ) Database ( https://hpap.
Figur e 1. 1 Figur e 1. Integrated anal ysis of scRN A-seq identifies 12 pancrea tic cell types. ( A ) Schema tic of workflow used in this paper. Cells from fiv e pub lic datasets were processed uniformly from raw SRA files and integrated, resulting in one dataset of 7393 cells of 51 individuals. ( B ) Integrated gene expression based UMAP of 7393 single cells annotated by dataset of origin. ( C ) Schematic ov ervie w of di v erse cell types in the human adult pancr eas. Cr eated with BioRender. com ( D ) Integrated gene expression based UMAP of 7393 single cells annotated by cell type. ( E ) Integrated gene expression based UMAP of 7393 single cells colored by non-integrated INS, GCG, PPY and SST expression. ( F ) Integrated gene expression based UMAP of 7393 single cells annotated by disease status.
In this paper we address the scarcity of annotated data for NArabizi, a Romanized form of North African Arabic used mostly on social media, which poses challenges for Natural Language Processing (NLP). We introduce an enriched version of NArabizi Treebank  (Seddah et al. , 2020)  with three main contributions: the addition of two novel annotation layers (named entity recognition and offensive language detection) and a re-annotation of the tokenization, morpho-syntactic and syntactic layers that ensure annotation consistency. Our experimental results, using different tokenization schemes, showcase the value of our contributions and highlight the impact of working with non-gold tokenization for NER and dependency parsing. To facilitate future research, we make these annotations publicly available.
This observation is relevant to our study as we expect refining the treebank could enhance the parsing performance even though the inherent variability of this language, which, tied to its small size treebank, could bring a negative impact on such enhancements. On the other hand, the experiments conducted by  Schluter and van Genabith (2007)  demonstrate that using a cleaner and more coherent treebank yields superior results compared to a treebank with a training set five times larger. This observation highlights the significance of high-quality dataset annotations, particularly for smaller datasets. This understanding primarily drives the goal of improving the NArabizi treebank's annotations. In this context, we propose a heavily revised version of NArabizi treebank  (Seddah et al. , 2020)  that includes two novel annotation layers for Named Entity Recognition (NER) and offensive language detection.
One of the goals of this work is also to study the impact of non-gold tokenization on NER, a scenario almost never investigated by the community  (Bareket and Tsarfaty, 2021). Our primary contributions are as follows: • Using error mining tools, we release a new corrected version of the treebank, which leads to improved downstream task performance. • We show that corrections made to a small size treebank of a highly variable language favorably impacts the performance of NLP models trained on it. • We augment the treebank by adding NER annotations and offensive language detection, expanding its applicability in various NLP tasks. • We homogenize tokenization across the dataset, analyze the impact of proper tokenization on UD tasks and NER and conduct a realistic evaluation on predicted tokenization, including NER evaluation.
Related work NArabizi The Arabic language exhibits diglossia, where Modern Standard Arabic (MSA) is employed in formal contexts, while dialectal forms are used informally  (Habash, 2010). Dialectal forms, which display significant variability across regions and predominantly exist in spoken form, lack standardized spelling when written. Many Ara-bic speakers employ the Latin script for transcribing their dialects online, using digits and symbols for phonemes not easily mapped to Latin letters  (Seddah et al. , 2020). This written form, known as Arabizi and its North African variant, NArabizi, often showcases code-switching with French and Amazigh  (Amazouz et al. , 2017). Textual resources for Arabizi primarily consist of noisy, user-generated content  (Foster, 2010; Seddah et al. , 2012; Eisenstein, 2013) , complicating the creation of supervised models or collection of extensive pre-training datasets.
The original NArabizi treebank  (Seddah et al. , 2020) , contains about 1500 sentences. The sentences are randomly sampled from the romanized Algerian dialectal Arabic corpus of  Cotterell et al. (2014)  and from a small corpus of lyrics from Algerian dialectal Arabic songs popular among the younger generation. This treebank is manually annotated with morpho-syntactic information (parts-of-speech and morphological features), together with glosses and code-switching labels at the word level, as well as sentence-level translations to French. Moreover, this treebank also contains 36% of French tokens. Since its creation, this treebank spawned two derived versions that first added a transliteration to the Arabic script at the word level and sentiment and topic annotation at the sentence level  (Touileb and Barnes, 2021).
, 2011; Seddah et al. , 2012; Sanguinetti et al. , 2018; Rehbein et al. , 2019; Sanguinetti et al. , 2020). NER for Dialects and User-generated Content NER is an information extraction task that identifies and categorizes entities at the token level. It is an extensively investigated NLP task with numerous datasets and models for various languages. However, datasets for low-resource languages are rare, and NER datasets for social media platforms such as Twitter predominantly exist for English  (Ritter et al. , 2011; Derczynski et al. , 2016 Derczynski et al. , , 2017)). A prominent NER dataset for lower-than-English resource languages is the CoNLL 2002 Shared Task dataset  (Tjong Kim Sang, 2002) , which provides NER annotations for four languages: Dutch, Spanish, Chinese, and Czech. Additionally, the WikiAnn dataset  (Pan et al.
, 2017)  includes NER annotations for several lowresource languages. Nevertheless, it is derived from Wikipedia content which is not well-suited for NER tasks involving user-generated content. As mentioned above, Touileb (2022) added a NER annotation for the first version of the NArabizi treebank. However, they did not address the tokenization issues inherent in the dataset and used a different annotation scheme. The following sections delve deeper into the tokenization challenges and the differences between the two datasets.
Extending a Low-resource Language treebank In this section, we outline our methodology for expanding and enhancing the NArabizi treebank. We start by re-annotating tokenization, morphosyntactic, and syntactic layers to ensure consistency, followed by detailing the annotation guidelines and procedures for NER and Offensive Language detection. We refer to the initial treebank introduced by Seddah et al. (  2020 ) as NArabiziV1 and our extended version as NArabiziV2.