dataesr/scientific-paragraphs-categorization huggingface dataset:
Validation of Var|Decrypt using an independent dataset of 90 multiple myeloma samples We sought to validate Var|Decrypt using an independent dataset. To this aim, we analyzed published WES data from 30 human multiple myeloma cell lines (HMCLs) and primary multiple myeloma (MM) from 59 patients  [29]. Previous analysis of these data revealed a prevalent TP53 mutational landscape and altered MAPK pathways. Reanalysing this dataset with Var|Decrypt after filtering out the putative false-positive hits (i. e. , highly mutated gene families such as MUC genes, see above) using the filtering options (frequency less than 4 mutations by gene within 20% of the cohort), and after crossing the mutated gene list with cancer gene databases (COSMIC, as in  [29] ) led to very similar identification of MM mutated hits, with frequent TP53 (47%), KRAS (40%), NRAS (30%), ATM (33%) alterations, and many epigenetic modifiers (BRD3, BRD4, SETD1B) and DNA repair proteins (FANCD2, RECQL4) (Additional file 4: Table  S3 ).
The time of the processing and the memory resource usage were evaluated during: (1) the processing of new data;  (2)  the reload of already processed data (Additional file 6: Table  S5 , Additional file 11: Fig. S5 ). These measurements indicate that Var|Decrypt is a fast-operating tool, with loading and processing times ranging from seconds to minutes (< 3 min 5 s for the larger datasets on a regular laptop with 8 GB of RAM, < 1 min and 30 s with 16 GB of RAM).
Discussion Although WES is widely used to diagnose human diseases or to discover pathological mutations in Mendelian disorders and cancers, there is a surprising paucity of accessible and easy to use tools for WES analysis. A major hurdle is the presence of hundreds to thousands of variants routinely detected in WES datasets, complicating the identification of causative mutations and prioritization of variants in complex samples such as tumor biopsies. We present here Var|Decrypt, a fully automated dynamic interface for Exome-Seq data analysis. From a  The The primary goal of our tool is to provide non-specialists with a ready-to-use solution for easy browsing and exploration of Exome-Seq datasets. The rational is that driver mutations or gene mutations that are important for the development or maintenance of a disease state should be overrepresented or enriched in a patient cohort.
This underscores the utility of dedicated analysis tools such as Var|Decrypt to highlight oncogenic signaling pathways and mutated genes that have been overlooked in other studies. Whether NOTCH pathway alteration represents a common feature of AEL and can be exploited as therapeutic vulnerability remains to be tested and is beyond the scope of this article. However, this represents an example of how biological information can be extracted from complex Exome-seq datasets without knowledge in bioinformatics. We further showed that Var|Decrypt could detect known recurrently mutated genes in human multiple myeloma and could highlight signaling pathways known to be important for this disease.
Availability and requirements Project name: Var|Decrypt. Project home page: https:// gitlab. com/ moham madsa lma/ varde crypt. Operating system(s): Linux, MacOS and Windows. Programming language: R and Python3. Other requirements: R 4. 1 or higher, Snakemake 6 or higher and pandas 1. 3. 5 or higher. License: CeCill-C (http:// www. cecill. info/ licen ces/ Licen ce_ CeCILL-C_ V1-en. html) and GPLv3. Any restrictions to use by non-academics: license needed.
Funding This work was supported by the  Laboratory of excellence Labex EpiGenMed  (program " Investissements d'avenir "  ANR-10-LABX-12-01 ); the  Laboratory of Excellence GR-Ex  (program " Investissements d'avenir "  ANR-11-LABX-0051 ,  ANR-18-IDEX-0001 );  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20180339221 );  Ligue Nationale Contre le Cancer ,  ANR-AAPG 2022-PRC-ZEBERY ;  ANR-18-CE15-0010-01 PLASMADIFF-3D ,  FFRMG  ( AAP-FFRMG-2021 ), INSERM PSCI 2020 Smooth-MM,  LabUM Metabohox , and  Institut Universitaire de France. The funding sources had no involvement in study design, data collection, analysis, interpretation or manuscript writing.
Named Entity Recognition (NER) is a key step in the creation of structured data from digitised historical documents. Traditional NER approaches deal with flat named entities, whereas entities often are nested. For example, a postal address might contain a street name and a number. This work compares three nested NER approaches, including two state-of-the-art approaches using Transformer-based architectures. We introduce a new Transformer-based approach based on joint labelling and semantic weighting of errors, evaluated on a collection of 19 th -century Paris trade directories. We evaluate approaches regarding the impact of supervised fine-tuning, unsupervised pre-training with noisy texts, and variation of IOB tagging formats. Our results show that while nested NER approaches enable extracting structured data directly, they do not benefit from the extra knowledge provided during training and reach a performance similar to the base approach on flat entities.
Similar work aiming to extract information from historical directories exists in the literature. They use flat NER approaches complete by a post-processing stage to create hierarchical entities. [6]  used directories published between 1936 and 1990 to build a database of gas stations in the city of Providence, Rhode characteristics of the books. First, entries contain two levels of entities. Top-level entities are built using bottom-level entities. For example, an address consists of two or three other types of entities: a street name, a number and sometimes a geographical feature type (e. g. depôt -warehouse). All of these spans are not formally linked as an address with flat-NER approaches, while they are linked with nested NER approaches. These approaches provide a more complex semantic view of directories, which improves searchability.
Second, directories' entries do not have the same structure throughout the corpus: the entities' enumeration pattern varies. Finally, directory entries are extracted from digitised documents using OCR models. This requires dealing with noisy texts at the NER stage. The amount of noise changes depending on the quality of the original print, the conservation status of historical sources, and the resolution of the digitised images. OCR errors are erroneous, missing, or extra characters. These multiple patterns of entities and the noisy entries encountered in the collection of directories make the use of rule-based methods inappropriate. In this article, we focus on the evaluation of several nested named entity recognition approaches. The main contributions are new nested named entity datasets using Paris trade directories built on an existing flat NER dataset, and a benchmark of three nested named entity recognition approaches using transfer-learning models fine-tuned on a ground-truth dataset and on a noisy dataset.
We use two pre-trained BERT-based models: a state-of-the-art French model and this same model pre-trained on domain-specific noisy examples to assess the impact of unsupervised training on the performances. We also compare two tagging formats to evaluate their influence on NER performance in highly structured texts. This paper is organised as follows: (i) a state-of-the-art of nested NER approaches, OCR noise, and tagging strategies on NER task; (ii) a presentation of the evaluated approaches ; (iii) an introduction of our datasets, experiments and metrics ; (iv) an evaluation of our approaches from both quantitative and qualitative point of view regarding the effects of the tagging strategy and unsupervised pre-training on transfer-learning-based models.
Each layer is trained to recognise entities of a given group according to their depth level in the dataset. [12]  suggests stacking predictions made by independent layers for each entity level. Models often use n-level outputs to improve the recognition of n+1 level entities such as  [13]  with a Layered BiL-STM+CRF model or  [24]  with a BERT+CRF model. Region-based approaches use two-step models to detect candidates and classify them. There are multiple entity listing strategies: enumeration strategies (such as n-gram) or boundariesbased strategies that aim to identify the first and last tokens of potential entities  [26]. In addition to these approaches,  [25]  introduce various methods which have in common the use of a unique tag created by concatenating multiple labels from the nested entities.
Named Entity Recognition on noisy inputs With the rise of the digitisation of historical documents, the need to extract and structure the information they contain has increased dramatically. Named Entity Recognition is a useful way to produce a high-level semantic view of these documents. Performing this task on historical documents has its own peculiarities that need to be taken into account. [10]  lists specific challenges associated with the NER task in historical documents. Most of them are processed with automatic tools such as OCR or Handwritten Text Recognition (HTR), which are likely to misread some characters. Thus, it involves dealing with noisy inputs in the NER stage. [9]  suggests pre-processing texts before a NER stage. They process a dataset of French newspapers of the 19 th century and underline the impact of OCR errors on the recognition of tree-structured named entities.
Conclusion The following benchmark concentrates on the three main properties of our dataset: nested entities, noisy inputs, and highly structured texts. We compare three nested named entity recognition approaches based on deep learning models to recognise complex entities in directories entries. We measure the impact of two tagging formats on entity recognition in short texts with repetitive patterns of entities. We evaluate the effects of domain adaptation of BERT-based models on noisy inputs. Finally, we aim to evaluate the contribution of training with nested entities on the flat named entity recognition.
State-of-the-art nested NER approaches Independent NER Layers (abbreviated as [M1]). [12] 's approach proposes to fine-tune a pre-trained BERT-based model for each level of entities in the dataset. Tags examples are given in table  2. Each model is called a layer and is completely independent of the others. The predictions made by each layer are merged to obtain nested entities. This approach reduces the risk of error propagation: a level 2 entity can be detected even if level 1 is wrong. However, the independence of the NER layers does not allow us to control the combination of the layers' predictions. The assignment of a class to a span at level N does not favour or disfavour the assignment of another class at level N+1. Transfer learning approach using joint labelling [M2]. [3]  propose a BERT-based transfer learning approach using joint labelling.
Datasets We have produced two annotated datasets using Parisian trade directories: the ground-truth dataset with non-noisy entries and a real-world dataset with the corresponding noisy OCR outputs. The use of these two datasets allows us to compare the performance of the approaches on both clean and noisy texts. Source documents: Paris trade directories from 1798 to 1854. Our aim is to detect nested entities in entries published during the 19 th century. They were printed over a long period of time by several editors using different printing techniques. The layout, content, length, and typography (font, case, abbreviations, etc. ) of the entries vary greatly, as illustrated in figure  3. Some directories organise the information into several lists, sorted by name, activity, or street name.
Directories datasets We produced two new NER datasets from the public dataset of  [1]. Both datasets are created from the same 8765 entries from 78 pages chosen in 18 different directories published from 1798 to 1861. The text is extracted using Pero-OCR. The first dataset is created by first correcting the text predicted by Pero-OCR and then annotating the ground-truth named entities. To create the second dataset, also known as the real world dataset, we first align the text produced by Pero-OCR with the ground-truth text by means of tools implemented as part of Stephen V. Rice's thesis  [19, 21]. Lastly, the named entities spans and their associated labels are projected from the first dataset text to the Pero-OCR text. Entries for which no named entities could be projected are also removed from the real world dataset, which in the end has 8445 entries.
For our experiments, we reduce the ground-truth dataset to the 8445 entries that also are in the noisy dataset. The ground-truth dataset is used to perform the evaluation of the approaches without the impact of the OCR and compare its result with one of the models trained with noisy data. Entries have been randomly selected in several directories to represent the wide variety of typographic and pattern types to be learned. There are 10 types of entities described in table  3. The maximum level of entities is 2. The entity hierarchy is described in figure 4. It's a Part-Of hierarchy: combined level-2 entities form a level-1 entity.
Experiments summary Nested NER on noisy texts The first axis of our work focusses on the comparison of nested NER approaches to directory entries. We evaluate the  ability of these approaches to detect all entities regardless of hierarchy and, in contrast, to associate the correct type of level 1 entity with any level 2 entity. We include in these experiments the use of two pre-trained transformer models and two tagging formats. The robustness of the approaches to OCR noise is given particular attention. Flat NER vs. Nested NER The second axis of this paper is to assess the impact, positive or negative, of the use of complex labels on the recognition of flat named entities. We created flat NER datasets (clean and noisy) using our tree-structured annotations. We map the joint labels to the flat labels described by  [2]  as described in figure  5  and reimplement their flat NER experiment.
Metrics We use the seqeval  [18]  library to evaluate the performance of each approach. It is well suited to the evaluation of natural language processing tasks, including sequence labelling. The tool supports the IO and IOB2 tag formats. The metrics used for the evaluation are precision, recall, and the F1-score. To compute these values, the tool first gathers tokens of the same class that follow each other. These groups of tokens constitute the predicted entities that are aligned with the ground-truth. Any difference in the boundary of the entity or its class, at any level, will be considered as an error. We look at the performance of approaches in nested NER. We calculate the F1-score for the spans illustrated in Figure  6. We can split them in two groups. First, we evaluate the ability of approaches to recognise entities independently of their structure.
Nested-NER experiments results Table  5  presents the F1-score measured for real-world (noisy OCR) and ground-truth datasets. Most of the time, the IO tagging format gives the best results on both datasets. Unsupervised pretraining of the BERT-based models with domain examples increases the results in most cases and benefits the noisy dataset. The three approaches produce close results. On the All entities F1-score value, metrics are included between 95. 6% and 96. 6% in the ground-truth dataset and between 93. 7% and 94. 3% in the noisy dataset. The independent NER layers approaches (M1) using IO tags outperforms all tests on both datasets on All and L1 entities, while the joint labelling approach with hierarchical loss give the best scores on L1+L2 and P-L1+P-L2 spans. L2 best-score is reached with M1 on the ground-truth dataset and M3 on the noisy dataset.
Flat-NER experiments results We fine-tune CamemBERT and pre-trained CamemBERT on our flat-NER datasets (derived from our nested-NER datasets) using only IO tags. Results are given in table  8. As for nested NER approaches, we observe that performances with the ground-truth dataset are better than those obtained with the noisy dataset. This result shows the impact of noise on the named entity recognition task. Unsupervised pre-training has no significant effect on the results provided with the model fine-tuned on the ground-truth dataset, but it provides better scores on the noisy dataset. Comparison between true flat-NER results and flat-NER equivalent results provided by nested NER approaches shows that fine-tuning models with structured entities does not provide a significant increase of F1-scores.
They contain similar information. There also are confusions between first-level ACT and second-level ACT entities which give the same kind of information whatever their entity level, as seen in figure  8. This first group of confusion appears in the top-left corner of figure  7. A first element of explanation is that information carried by these classes are really close from a semantic point of view which favours such kind of mistakes. DESC entity type also is represented by a few examples in datasets. The model often failed to recognise TITREP in DESC and TITREH in PER. There is a small number of these entities in the training dataset, which could explain these errors. Note that both titles entity types always contain Unicode characters which aren't included in the CamemBERT model vocabulary.
Confusion also can affect unrelated types of entities. The most often observed errors are ACT entities that mention a warehouse of merchandise, which are confused with a FT mentioned in an address. The model often misses FT type entities in text. For both datasets, we observed that punctuation between entities at the two levels are often misclassified (see the bottom line of figure  7  and highlighted tokens in figure  8 ). They are included in their right-side or left-side entity. When using the IOB2 tagging model, we also observed tokens confusions. Both the training and running time of the first approach are multiplied by the number of levels of entities in the dataset. It's  [7]. It includes the semantic distance between expected and predicted labels. We compare the performance of each approach using a fine-tuned CamemBERT model and a pre-trained and fine-tuned CamemBERT model.
We used our new nested NER datasets with clean and noisy directories entries to fine-tune models using IO and IOB2 tagging formats. The performance of the three approaches achieves high F1 score values with both datasets. M3 outperforms results on hierarchical entities (L1+L2 ) which are the most adapted to produce structured data. Fine-tuning a pre-trained BERT-based model increases performances on noisy entries. Finally, we conclude that the IO tagging format achieves the best performance in most cases. IOB2 tagging format does not improve our results on noisy outputs.
Samples were placed on ice for 20 minutes and then centrifuged at 4°C for 20 minutes at 14,000 g to pellet cell debris. Five microliters of each sample were loaded onto PEI-Cellulose thin layer chromatography (TLC) plates (Merck-Millipore) prior to ascending development with 1. 5 M KH2PO4 solution (pH 3. 4). Plates were revealed by PhosphoImaging (GE Healthcare) and analyzed using ImageQuant software (GE Healthcare). The amount of (p)ppGpp was normalized to the total amount of G nucleotides observed in each sample, the total G being the sum of GTP, ppGpp and pppGpp detected. For double expression assay the ΔrelA ΔspoT ΔymgB strain harboring pEG25-spoT and pBbS2K-ymgB or pEG25-spoT and pBbS2K-ymgB R74G or pEG25-spoT L567P and pBbS2K-ymgB R74G were subjected to the same protocol. Overnight cultures were diluted 100 times in the same medium with 0.
To study the binding of YmgB or YmgB R74G to SpoT TGS-Helical , increasing concentrations of SpoT TGS-Helical (2 to 50 µM in interaction buffer) were used with the following steps; 30s baseline, 120s association and 120s dissociation. In all experiments, a reference subtraction of the SpoT TGS-Helical on the uncoated biosensors for each concentration tested was performed. The dissociation constant (KD) was calculated using the GraphPad Prism 5. 0 software on the basis of the steady state level responses, by plotting on x axis the different concentrations SpoT TGS-Helical and on the y axis the corresponding response measured 10 seconds before the end of the association. For KD calculation, a nonlinear regression fit for xy analysis was used and one site (specific binding) as a model [corresponding to the equation y=Bmax*x/(KD+x)].
QTL mapping A genetic map containing 18,316 SNP markers for 1,332 genetic bins covering the whole Z. tritici genome bar chromosomes 14 and 18 was built using the Multipoint ultra-dense software (MultiQTL Ltd, Haifa University, Israel) (details in Supporting information 2) and was used for QTL mapping. A linkage analysis was carried out using the R/qtl software  (Broman et al. , 2003)  version 1. 42-8. This analysis included for each trait an initial Simple Interval Mapping (SIM), followed by a Composite Interval Mapping (CIM). Analyses were performed replication by replication. For SIM, 1000 genome-wide permutations were used to calculate the significant logarithm of odds (LOD) threshold. Were considered significant only QTL that showed P-values<0. 05. The CIM was carried out with the QTL with the highest LOD used as a covariate.
Identification of a candidate pathogenicity gene 2. 7. 1 QTL gene content The gene content of the QTL identified on chromosome 6 was looked into using the annotation by  Grandaubert et al. (2015). Certain reannotations were carried out using data from  Haueisen et al. (2019)  and RepeatMasker  (Smit et al. , 2013)  for TE annotation. Functions of candidate genes were predicted using the translated protein sequences as input for the InterPro database (http://www. ebi. ac. uk/interpro/search/sequence/).
Structural differences between I05 and I07 at the detected QTL A de novo genome assembly of parental strains I05 and I07 was realized with previously available PE-100 sequences obtained on an Illumina HiSeq 2000 sequencing system with a 70x mean genome coverage (BioProject: PRJNA777581, accessions SRR16762604 and SRR16762605). Illumina paired-end reads were assembled using a combination of VELVET  (Zerbino and Birney, 2008) , SOAPDENOVO and SOAPGAPCLOSER  (Luo et al. , 2012) , as previously described for the assembly of the Botrytis cinerea genome  (Mercier et al. , 2021). The contigs from the I05 and I07 assemblies covering our regions of interest were identified by BLAST of the assembled genomes on the IPO323 reference genome  (Goodwin et al. , 2011). These enabled us to identify polymorphism between I05 and I07 for our candidate genes and TE presence/absence polymorphisms in both strains.
Phenotypic characterization of mutant strains All generated mutant strains were inoculated on 'Renan' and 'Chinese Spring' (susceptible control) according to the same protocol as the previously described assays. Only visual evaluations were performed. Three clones of each mutant strain type and I05 and I07 were tested, three times each per replication. Four replications were carried out. The results obtained were analysed with a Kruskal-Wallis test and a Wilcoxon pairwise comparison. 2. 9 Diversity and selection analysis of the candidate effector from Qzt-I05-6 In addition to the I05 and I07 de novo assemblies, the assemblies of 103 Z. tritici strains collected in France in  2009 -2010 (BioProject: PRJNA777581)  and 126 strains collected in France in 2018-2019 (BioProject: PRJNA881220) were also available to us, enabling us to look into the sequence diversity of the pathogenicity gene.