dataesr/scientific-paragraphs-categorization huggingface dataset:
Training DL method (StarDist) The deep neural network must be trained and the hyperparameters tuned. We therefore split the training images into training and validation as most standard deep learning approaches do. 20% of the training images were used as validation images. With less than 5 training images, one image was kept for validation. Therefore, the procedure that we show here requires at least 2 training images, one to train the weights of the network, the other to validate the hyper-parameters. The official implementation of StarDist (https://github. com/stardist/stardist) was used to train, validate, and evaluate the performances with our dataset. For tracking, we provide a wrapper to perform the detection process using a trained StarDist model (https://partage. imt. fr/index. php/s/npwHJHZebxqGMPi).
Generating tracklets with probabilistic eMHT The eMHT algorithm is implemented on the open-source imaging platform Icy (plugin Spot Tracking), which is coded in Java language. Instead of translating all the code into Python, we decided to directly call the Spot Tracking plugin in Icy in a headless mode from the TraSE-IN platform. To ensure compatibility with Icy, we implemented different functions for inputs (detections) and outputs (tracks) wrapping. We believe that calling implemented tracking solutions in major imaging platforms such as Icy and ImageJ is an efficient and sustainable solution because the main imaging platforms are open-source, the tracking plugins (e. g. , Spot tracking in Icy, Trackmate in ImageJ) are regularly updated by developers, wrapping input and output variables in TraSE-IN is much less tedious and prone to implementation errors than translating all the Java code of tracking methods in Python, and TraSE-IN will easily integrate new tracking solutions developed on other platforms, if needed.
Calcium Signal and Spike-Time Extraction We used the constrained FOOPSI implementation in the CaImAn Python library (OASIS method of order 2)  30. In Hydra, calcium spikes correspond to single action potentials  16. Therefore, we added a clustering step to aggregate the action potentials (electrical spikes) predicted with the previous deconvolution FOOPSI algorithm (less than 5 frames away). The clustering consists of first temporal Gaussian blurring (𝜎 = 5 frames) of the estimated electrical spikes, followed by local maxima extraction. Finally, we filtered out electrical spikes with low probability/intensity using a user-defined threshold to reduce the number of erroneous predictions. Since these computed probabilities/intensities vary wildly between different signals, using a fixed threshold would not work across different signals.
The presence of the calcium-insensitive fluorophore in the nucleus of each neuron allows visualization of each neuron even while it is inactive and significantly increases accuracy of tracking individual neurons over time. The high-resolution spinning disc confocal imaging also increases the accuracy of tracking individual neurons longitudinally because they are easier to detect in each frame due to the high spatial resolution, and easier to track frame-to-frame due to the high temporal resolution. Software improvements include more accurate segmentation of neurons in each frame using a machine learning algorithm (StarDist  40  ) specifically trained to detect Hydra nuclei, an accurate single neuron tracking protocol using a state-ofthe-art probabilistic algorithm (eMHT  44  ) that is directly executed on the native open-source platform Icy  45  , tracklet stitching  25  , and, finally, a user-friendly Python pipeline for signal extraction, signal processing, and spike prediction for all neurons in an entire behaving animal.
However, as fluorescent labels and optics improve, shorter exposure times and higher frame rates may be possible to allow whole animal volumetric imaging while Hydra is behaving. In addition to hardware improvements, multiple improvements can also be implemented on the software side. In terms of segmentation, even if Stardist provides accurate detection with relatively few manual annotations (∼ 2 image patches for a total of ~ 1,000 segmented nuclei), transfer learning from preannotated public datasets would be a very valuable resource for the segmentation of cells in various imaging set-ups. In terms of tracking, we designed TraSE-IN so it does not code for a specific method, but rather executes state-of-the-art tracking algorithms directly from their native platform. In the present version, TraSE-IN is executing the eMHT probabilistic algorithm in Icy (https://icy.
Funding Statement This research was supported by the  Leon Levy Fellowship in Neuroscience  and  K99 NS127851  (A. H. ),  French ANR-PRCI  " Rebirth " (T. L. and R. R. ) and the  NSF  ( 2203119 ;WY, NT, RY)  Vannevar Bush Faculty Award  ( ONR N000142012828 ; (WY, NT, RY).
The appearance of genetic variants impacts vaccination efficiency and therapeutic options, generating a need to map and relate mutations observed in the proteome and the genome. We develop an user-friendly web-service software (Viral Instant Mutation Viewer or VIMVer) which allows a direct identification of mutations in the genome and its counterpart in the viral proteome. Since its emergence in 2019, the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), responsible for the COVID-19 pandemic, has generated an overwhelming amount of data while becoming one of the most studied viruses of the Nidovirales family. We originally develop this tool during the COVID pandemic, thus for any SARS-CoV-2 nucleotide sequence the web-service gives a fast identification, mapping, and display of new mutations simultaneously at the nucleotide and amino acid level in comparison to a reference sequence (Wuhan-1).
MATERIAL AND METHODS Our goal is to propose a tool to quickly analyze viral genomes VIMVer rapidly detects mutations and their consequences on the protein structure relative to a reference strain present in the database. VIMVer workflow is developed under Ubuntu 22. 04 LTS and is an early stage web-service developed in python 3. 8+ using the Django framework, and meant to be friendly to the virology community. Django has a heavy and rigorous but adaptable architecture which simplifies any integration of new functions during the development. The workflow is presented in Figure  1  and can be summarized as follow : a nucleic sequence is submitted as an input to a search engine currently a blastn (3) to query the VIMVer reference database. The multiple sub-alignments generated by blastn are optimized through a realign procedure using Muscle version (4) to proof the alignment.
Database Any viral reference sequence could be implemented in the database and constituted of the retro-translated genomic sequence(s) from the corresponding viral proteins sequences. In this context, viral genome sequences are treated as modular sequences representing their translated states. Therefore, genomes coding for polyproteins, which are subsequently processed into multiple proteins, are preannotated at the genomic level. Currently, the development focus of VIMVer lies on the SARS-CoV-2 genome. Consequently, the database currently consists of the complete SARS-CoV-2 genome (Wuhan-1: NC_045512. 2 / YP_009725297. 1), encompassing 26 proteins in both nucleotide and amino acid sequences. Each sequence type is organized into separate files in FASTA format. To accomplish this, the entire genome was divided into sub-sequences to match each functional protein.
These nucleotide sub-sequences were meticulously retro-translated to ensure the preservation of codon-wise reading frames and to address ribosomal frameshifts, a characteristic feature of CoV genomes. Notably, the first open reading frame (ORF) of CoV involves a ribosomal frameshift, leading to the synthesis of two polyproteins, pp1a and pp1ab  (8). This frameshift has implications for the translation of the landmark protein nsp12, as its N-terminal is encoded at the end of ORF1a, overlapping nsp11, while the remaining coding sequence is shifted by +1 frameshift. In our local database, we have merged nsp11 at the beginning of nsp12 to align with the coding reading frame, resulting in the duplication of the corresponding retrotranslated sequence's n th nucleotide. Therefore, both genomic and protein nsp12 sequences are accurately assigned and numbered in our database.
Search engine and Alignments As shown in Figure  1 , we are using Blastn (3) mainly to confront the query to our database. Blastn parameters are left by default. In case that the inquiry is too distant from the reference or that the inquiry's sequence has too many 'N's (unspecified nucleotide(s)), blastn produces multiple sub-alignments for a hit. Muscle (v3. 8) is used (4) to correct the alignment  (5). All alignments are done in respect to the reference nucleotide sequences to serve as anchors for the following codon-wise co-translation. Here Blastn was selected for it ease to deploy and parametrization considering the size of the database. The output is a list of as many hits blastn found for a maximum sequences of the prepared dataset. Each, hit opens a two frames window that shows on the left panel the proteins alignments and on the right panel the nucleotides alignments.
Deployment Viral Instant Mutation Viewer is deployed on a apache2 server hosted from our laboratory server. The ssl protocol was obtained with Encrypt's services. For users or developers who want to install VIMVer locally the source code is freely available, however it will not be functional straight away after download. Indeed the settings and configurations file has to be protected. A setting file (setting. py) including an encryption key used to run Django functionality needs to be present in the path This setting file is generated once by the administrator when creating the VIMVer project within django. Moreover, it is also within the setting file that the administrator will define its server setting prior deployment Details for installation are provided in the GitHub repository.
In this version queries have to be SARS-CoV or SARS-CoV2 related to retrieve hits from the blastn. When the query is submitted : VIMVer generates a job-key, which is only shown once. On the same page, there is a link to the PANGOLIN web-service (6) (link to:pangolin), to retrieve information to determine the lineage of the query sequence. The result, screened in Figure  3  is returned as a table for which each entry corresponds to a "blastn" hit, described by the score, length and identity percentage. Considering that our database is small, the E-value is not relevant and therefore not shown, albeit calculated, instead the score is shown as it represents the number of aligned residues which allows to infer the identity percentage. If the query nucleotide sequence is not a silent mutation, then the corresponding hit is marked as "Mutant" in red.
At the end of the page, the user can download the blastn results in fasta format. To access the resulting alignment sampled in Figure  4 , click on the 'pick' button on the same line. As proof of concept, we tested our project in a real-life scenario. To do so, we are looking for two well-known proteins: the domain RdRp and the spike protein S Indeed, the S protein is the third ORF of the SARS-CoV2 genome, and the gene most prone to accumulate mutations that influence the infectivity of emerging variant  (7). If one wants to observe and analyze the spike nucleotide and protein sequences evolution, straight out of sequencing campaign or from database analysis VIMVer makes the task easy and quick. The nsp12 gene (the main replicative RdRp core) is also a point of interest for our project since it is often reported with incorrect numbering due to the nidovirales frameshift  (8, 9).
As an example, a random entry in GISAID (EPI_ISL ID : 14769247) is selected and examined for mutations. We selectively focus on the RdRp domain for the frameshift management and the spike protein for its high mutation susceptibility. The goal is to process a whole genome, that is why the test sequence has to be a complete genome with a high coverage rate. On the query page of VIMVer, a single fasta sequence was pasted in the input text box. In the alignments of the RdRp domain and the spike protein respectively in Figure  4  & supplementary Figure1, the numbering is respected and each mutation is easily identified as silent or otherwise. In the RdRp example the frameshift is discretely managed and one non-synonymous mutation (P323L) is found in the RdRp domain. In the spike example several non-synonymous mutations were found : T19I, L24-, P25-, P26-, A27S, H69-, V70-, G142D, V213G, G339D, S371F, S373P, S375F, T376A, D405N, R408S, K417N, N440K, L452R, S477N, T478K, E484A, F486V, Q498R, N501Y, Y505H, D614G, H655Y, N679K, P681H, N764K, D796Y, Q954H, N969K.
DISCUSSION In its final stage, VIMVer, designed as a transversal tool, should target audience from research studies to clinical applications. It could be implemented as an automatized survey workflow synergized with wet-lab applications and resources, such as the database on RNA viruses described in the VAZyMolO DB (9) used in structural and functional studies  (11, 12, (12) (13) (14) (15) (16) (17) (18). Further developments concerning the online VIMVER is to extend the reference database, to the annotated sequences of VAZyMolO. The latter is a highly curated database of soluble functional or structural protein domains. The combination of both would then allow not only to spot an emerging mutation but to correlated it with structural information and compared it within a defined domain family.
The connection of VIMVer to protein structure analysis software, such as AlphaFold  (19)  or other structural prediction and analysis tools, should facilitate the connection of genotype to phenotype for emerging virus gene products. The main spirit of VIMVer is to propose a deep analysis of each mutation found in the user's genomic sequence of interest against the corresponding completely annotated reference. Considering its specific conception, VIMVer diverges from existing web-services like the remotely close web-service like NextClade (2) that does not provide specific annotated protein related dataset.
The first, based on lexical substitution, uses the predictions of a BERT masked language model 2. The second captures lexical-semantic relations between biterms by means of analogy between term representations provided by a FastText static model. Our experiments were carried out with two main datasets. The first is a dataset composed of synonymous 3  biterms extracted from IATE 4  , a terminology resource for translation. The second is a dataset of semantically related biterms created by semantic projection using various resources of the environment domain in French. Semantic projection is a method that extends relations between STs to the MWTs (e. g. , the relation between dry and wet is extended to the MWTs dry air and wet air). It is often used to identify semantic relations between MWTs  (Morin and Jacquemin 1999; Daille and Hazem 2014; Hazem and Daille 2018).
Section 2 presents a state of the art on the acquisition of semantic relations from DSMs and in particular by means of analogy and lexical substitution. Section 3 presents the experimental framework: the general resources selected to build our test data and carry out our experiments, the adaptation of analogy and lexical substitution methods to our data, the trained language models and the evaluation metrics. Section 4 describes the extraction of synonymous biterms from IATE and the experiments carried out on them. Section 5 presents the creation by semantic projection of a resource containing opposition and hierarchical relations and experiments aimed to compare our methods on the acquisition of these relations. Section 6 discusses and compares the results of the different methods. The paper concludes with a summary and a presentation of future avenues of research.
The existence of analogies in static embeddings has been theoretically justified by  Allen and Hospedales (2019). While most studies focus on analogy between words or between simple terms  (Chen et al. 2018; Gladkova, Drozd, and Matsuoka 2016; Köper, Scheible, and im Walde 2015) ,  Chaudhri et al. (2022)  attempt to solve analogy equations between simple and multi-word terms in the biological domain in English. Their dataset is composed of quadruplets of terms such as Carbon Phospholipid : fatty acid tail :: polar amino acid : polar side chain. They are interested in relations that are specific to the biological field such as a type of. They solve analogy equations like a : b :: c :? with a seq2seq 6  model  (Sutskever, Vinyals, and Le 2014)  with three encoders that model the input of a, b and c respectively.
Corpus We created our test data and carried out our experiments using the French monolingual corpus PANACEA Environment (ELRA-W0065) 9  built in the framework of the PANACEA project 10. The corpus is made up of 35,453 documents (50 million words) related to the environment domain. These documents have different levels of specialization and belong to various genres; they were crawled on the web from encyclopedias, blogs, government and non-governmental organization websites. Due to the heterogeneous nature of the environment domain, this corpus is more heterogeneous than a typical specialized corpus which normally only contains specialized texts (Bernier-Colborne 2017). The corpus has been pre-processed: we extracted the text from the original files and normalized the characters.
Lexical relation databases Three test datasets were built from three French databases which provide lexical relations between simple terms and between simple words. These test datasets are: RefDicoenviro, RefIATE et RefDicosyn. RefDicoenviro is made up of 830 pairs of simple terms (nouns or adjectives) linked by lexical relations and extracted from DiCoEnviro 11  , a specialized dictionary of the environment domain. Relations between these term pairs fall into three categories (Bernier-Colborne and Drouin 2016): • ANTI (116 pairs): opposite and contrastive relations (froid:chaud 'cold:hot'; flore:faune 'flora:fauna'); • HYP (191 pairs): hyponymy and hypernymy (biomasse:combustible 'biomass:fuel'; consommation:surconsommation 'consumption:over-consumption'); RefDicosyn is made up of 833,891 pairs of synonymous nouns and adjectives extracted from Dicosyn 12  , such as empreinte:impact 'footprint:impact'.
Distributional semantic models We used two types of distributional semantic models: a contextual one, CamemBERT  (Martin et al. 2020) , and an static one, FastText  (Bojanowski et al. 2017). The CamemBERT-large model, pre-trained for the masked token prediction task is a language model for French. It has been pretrained on the French subcorpus of the OSCAR multilingual corpus. Its tokenizer is SentencePiece  (Kudo and Richardson 2018). Its vocabulary is a mix of whole words and wordpieces. Words absent from the vocabulary are tokenized into subwords. For example, piscicole 'fish farming' is split into the wordpieces pis, s, and cicole. FastText is a library for learning static models, but unlike Word2Vec and Glove, it computes embeddings for both the words and their n-grams of characters. This specificity can be exploited to build models that contain representations of the MWTs and of their parts and in which the representations of MWTs are independent of those of their components.
Acquisition of synonymy between biterms Our first experiment concerns the acquisition of synonymy with lexical substitution and analogy. We start with synonymy because of the availability of resources. In order to test the methods, we need data composed of pairs of semantically related biterms from the environment domain. However, few terminology resources provide relations between terms, and especially between MWTs. Among the online resources dealing with the environment, IATE is the only one that provides a significant number of synonymous biterms that can be used as a dataset for our experiments. We will first explain how we extracted the data from IATE. Then, we present the test data, the experiment and the results for the two methods, lexical substitution and analogy.
The remaining set, DataIATE, contains 928 biterm pairs. In order to further select the data for the experiments, we performed an additional analysis upon DataIATE. The results are summarized in Table  1. DataIATE includes 563 biterm pairs that share one lexical word and have identical syntactic structure, such as environnement de travail 'work environment' and milieu de travail 'workplace' (they share the noun travail 'work' and have the same Noun Preposition Noun structure). These biterm pairs are referred to as DataIATE_MLM in the following.
Acquisition of synonymy between biterms using a masked language model Test dataset for the MLM experiments. The MLM method presented in Section 3. 2 requires pairs of biterms with the same syntactic structure and one common lexical element. The dataset we used is DataIATE_MLM from which we removed the pairs which contain lexical elements not included in the model's vocabulary. As explained in Section 3. 3, out-of-vocabulary words are split in wordpieces and cannot be retreived. 396 pairs remain in the dataset once these pairs are removed. Besides, we now need contexts to feed the MLM model. For each biterm that occurs in the 396 pairs of DataIATE_MLM, we extracted 100 contexts or less from PANACEA taking into account some quality criteria of good contexts as defined by Kilgarriff et al. 13 (2008) (some biterms occur less than 100 times in PANACEA).
The quality criteria that we applied are the following: (i) the context length has 10 to 100 words; (ii) the contexts are sentences; (iii) the contexts contain the biterm under consideration and at least one other term of the environment domain. We used the set of the French terms of the environment domain in IATE for the verification of the third condition. We removed from the dataset the biterms without context that meets the three context quality criteria, such as taxe d'émission 'emission tax'. We end up with a final test set made up of 317 biterm pairs and 24,265 contexts. Experiment. The objective of the experiment being the acquisition of lexical semantic relations between biterms of the environment domain, the rank of the semantically related terms should not be considered relative to all the tokens of the model, but only to the simple terms of the domain in IATE.
Identification of synonymy between biterms by means of analogy Test dataset for analogy. The test dataset for the analogy experiments is DataIATE_FastText (see Section 4. 1). We built W 1 : W 2 :: M W T 1 : M W T 2 quadruplets where (i) M W T 1 and M W T 2 share a lexical element W 3 ; (ii) W 1 is the other lexical element in M W T 1 and W 2 is the other lexical element in M W T 2 ; (iii) W 1 and W 2 are synonymous. We built three sets of quadruplets from three French relation databases presented in Section 3. 1: RefIATE, RefDicosyn and RefDicoenviro_qsyn (simple term pairs classified as QSYN in RefDicoenviro). Quadruplet_RefIATE contains 20 quadruplets, Quadruplet_RefDicoenviro_qsyn 63 quadruplets and Quadruplet_RefDicosyn 156 quadruplets. Experiment. Because we look for relations between biterms, the rank is calculated with respect to the 5,002 nominal biterms extracted from IATE that occur at least 5 times in PANACEA (DataIATE_FastText).