dataesr/scientific-paragraphs-categorization huggingface dataset:
We employed BRANE Cut  [40] , a Biologically-Related Apriori Network Enhancement method based on graph cuts, previously developed by our team. It has been proven to provide robust meaningful inference on real and synthetic datasets from  [41, 42]. In complement to classical analysis, such as differential expression or gene clustering, the graph optimization of BRANE Cut on T. reesei RNA-seq is likely to cast a different light on relationships between transcription factors and targets. While cellulose is the natural inducer of cellulase production, authors in  [43]  showed that, in Trichoderma reesei , the lactose is capable to play the role of cellulase inducer. For this reason, this carbon source is generally used in the industry to induce the cellulase production in T. reesei. Efficient enzymatic hydrolysis of cellulose requires the synergy of three main catalytic activities: cellobiohydrolase, endoglucanase and β-glucosidase.
Before mapping, poly N read tails were trimmed, reads with less than 40 bases were removed, and reads with quality mean lower than 30 were discarded. Reads were then aligned against the Trichoderma reesei genome version 2 (from the Joint Genome Institute database) using Bowtie (version 0. 12. 9) [80]. Alignments from reads matching more than once on the reference genome were removed using samtools (Java version)  [81]. To compute gene expression, Trichoderma reesei genome annotation version 2 from Joint Genome Institute database was used. All overlapping regions between alignments and referenced exons (or genes) were counted using HTSeq-count 0. 5. 3  [82]. The RNA-seq gene expression data and raw fastq files are available on the GEO repository (www. ncbi. nlm. nih. gov/geo/) under accession number: GSE82287.
Semantic annotation is the process in which semantic concepts are linked to natural language. It helps in boosting the search and access of resources and can be used in information retrieval systems to increase the queries from the user. In this paper, we are interested in identifying ontological concepts in scientific text contained in spreadsheet. We developed a tool which is able to handle various types of spreadsheet. Furthermore, we used the benefits of NCBO Annotator API provided by BioPortal to enhance the semantic annotation functionalities covering spreadsheet data. Table2Annotation developed strengths in certain criteria like speed, error handling and complex concept matching.
Semantic Annotation Challenges There are benefits of semantic annotation but there are also some challenges faced during annotation of biological texts or other resources. Some of these challenges are: • Text ambiguity in bio terms. Terms usually has more than one meaning causing ambiguity making it unclear and difficult to annotate. • Given texts having incorrect spellings or grammar. • Positions of words in a sentence for example "drought and salinity tolerance" means "drought tolerance and salinity tolerance" but in this case we might have matching on only "salinity tolerance". • In biomedical context, all protein has associated genes with the same name making it difficult for annotation on gene and protein texts. • Abbreviations and short hand texts can also complicate annotations. The challenges faced in annotation can be tackled by two methods which are term-to-concept matching method which has to do with matching some parts of provided texts to structured knowledge databases, dictionaries or vocabularies and machine learning (ML) method which has to do with creating annotators for specific purposes and usage instead of general usage  [4].
Challenges of Semantic Annotation Tools There are diverse tools used in semantic annotation  [1]. These tools also have some challenges faced when using them and to list a few as follows: • Speed. This is one of the most common challenge. Annotations performed on huge datasets can take a lot of time to finish. • Language specific. Most of the annotators are in English which makes applying semantic annotation in other languages a challenge. • Document format. Annotators that support input of documents can face the problem of having to annotate different document formats and not supporting a particular format could be a challenge. • Text Variation. According to  [4] , challenges are faced also due to the fact that there are different kinds of biomedical texts and variations among variations of text for example in biomedical and clinical text.
Overview of Table2Annotation tool In this section, we describe the solution proposed to build the ontology matching system. Our solution is using NCBO annotator Web Service API as a primary information retrieval. NCBO annotator annotates data with MGrep term-to-concept matching tool and retrieves sets of annotations which are later expanded using various methods of semantic matching making this annotator to go through 2 stages. This is a unique annotator because of the method used to associate concepts instead of looking for a concept which will best match the provided context. This annotator uses BioPortal  [6]  and UMLS Metathesaurus ontologies vocabularies and although it doesn't support disambiguation of terms, it is suitable for real time processing. This annotator is available for free and is implemented through web services.
This annotator can be used on AgroPortal  [7]  and BioPortal. The overview diagram of the Table2Annotation tool is shown on supplementary file S1. The flow of the system is quite simple and understandable. The system starts by taking an input dataset which is a file in CSV, Excel, etc. and then processes this file by reading the data and fetching the necessary data to be annotated. It takes the necessary data and calls an external API provided by AgroPortal to annotate this data. The results returned from this process is processed by taking the URI, concept-id and the matched words. Finally the annotated terms are being saved and written to an output file for the user to access. The operation of the matching system is described diagrammatically on supplementary file S2. In building this Table2Annotation tool we decided to use the NCBO annotator (AgroPortal API) to support annotation of terms.
Running Table2Annotation Table2Annotation is a Java based program which is currently executed through command line interface. The user needs to have a dataset which he/she wants to annotate first before anything. Table2Annotation is compiled after the code and all the functions explained in previous chapter have been fully implemented. The compilation of Table2Annotation is done with all the necessary libraries included in the java project. To run the system the user needs to input as parameters the following; Input file (mandatory), Column (mandatory) , Suggestions (optional) , Slice (optional), Separator (optional), Sheet (optional). Firstly the user provides the path to the input file (dataset) and then secondly provides the name of the column to be annotated. These two parameters are mandatory and the rest are optional.
Results In this section we describe the results gotten from our Table2Annotation tool. It also describes the context to obtaining the results and an evaluation of the system. First of all to begin test running the system, we need to have a dataset to test with and this is shown in figure  1. The data is quite small as we want to use a small data to better show clarity on the results gotten but its still the same using a large dataset anyways. The dataset contains a "PROPERTY" column which is the column with the terms that will be annotated.
The filters (slice and suggestions) also help to filter the results according to users expectations. The systems has strengths but also has some weaknesses like connection and dependency. The system uses an external API and this can cause problems. Firstly the system cannot work offline as it needs internet to call the external API and this can be seen as a weakness. Secondly what happens if the external API is down for some reasons? this means that we cannot use the current system too. These weaknesses can be solved by building a full annotation system which isn't dependent of any available external annotation API. In the future, we think we can improve algorithms to handle grammar problems and disambiguation. This algorithms should use language dictionaries to be able to transform terms without meaning (short forms) to something understandable for better matching to concepts.
This paper presents a 3D brain tumor segmentation network from multi-sequence MRI datasets based on deep learning. We propose a three-stage network: generating constraints, fusion under constraints and final segmentation. In the first stage, an initial 3D U-Net segmentation network is introduced to produce an additional context constraint for each tumor region. Under the obtained constraint, multi-sequence MRI are then fused using an attention mechanism to achieve three single tumor region segmentations. Considering the location relationship of the tumor regions, a new loss function is introduced to deal with the multiple class segmentation problem. Finally, a second 3D U-Net network is applied to combine and refine the three single prediction results. In each stage, only 8 initial filters are used, allowing to decrease significantly the number of parameters to be estimated.
Wang et al. (2019)  proposed a novel 2D fully convolution segmentation network WRN-PPNet based on the pyramid pooling module. Zhao et al. (2018)  integrated fully convolutional neural networks (FCNNs)  (Long et al. , 2015)  and conditional random fields to segment brain tumor. Havaei et al. (2017)  implemented a two-pathway architecture that learns about the local details of the brain tumor as well as the larger context feature. Wang et al. (2017)  proposed to decompose the multi-class segmentation problem into a sequence of three binary segmentation problems according to the sub-region hierarchy. Kamnitsas et al. (2017a)  proposed an efficient fully connected multi-scale CNN architecture named deepmedic, which reassembles a high resolution and a low resolution pathway to obtain the segmentation results.
Furthermore, they used a 3D fully connected conditional random field to effectively remove false positives. Isensee et al. (2017)  modified the U-Net to brain tumor segmentation and use data augmentation to prevent the over-fitting. Kamnitsas et al. (2017b)  introduced EMMA, an ensemble of multiple models and architectures including DeepMedic, FCNs and U-Net, and won the first position in BraTS 2017 competition. However, the previous methods only directly apply the four MRI modalities to achieve segmentation. For multi-modal medical image segmentation task, the fusion strategy takes an important role to achieve an accurate segmentation result. In general, we can category the network architectures into single-encoder-based method and multiencoder-based method, as presented in  Zhou et al. (2019b).
However, the methods mentioned above evaluated the attention mechanism only on the single-modal image dataset and did not consider the fusion issue on the multi-modal medical images. Inspired by the attention mechanism, in this paper, we propose a three-stage multimodality fusion network based on attention mechanism and additional constraint information for brain tumor segmentation. The preliminary results of this work have already presented at  Zhou et al. (2020)  and  Zhou et al. (2019a). This journal version is a substantial extension. The main contributions of our method are four fold: (1) A novel three-stage-network is presented for multi-modal brain tumor segmentation to reduce the memory requirement and accelerate the training process. (2) An additional constraint information is introduced and integrated to the multi-encoder based network architecture to allow the network to use fewer parameters and to enhance the segmentation accuracy.
Data and preprocessing The datasets used in the experiments come from BraTS 2017 dataset  (Bakas et al. , 2017). The training set includes 210 HGG patients and 75 LGG patients. Each patient has four image modalities including T1-weighted (T1), contrast enhanced T1-weighted (T1c), T2-weighted (T2) and Fluid Attenuation Inversion Recovery (FLAIR) images. All data used in the experiments have been pre-processed with a standard procedure. The N4ITK  (Avants et al. , 2009)  method is first used to correct the distortion of MRI data, and intensity normalization is applied to normalize each modality of each patient. To exploit the spatial contextual information of the image, we used the 3D image, we cliped and resized the image from 155.
Qualitative results In order to evaluate the effectiveness of our model, we randomly selected several examples on BraTS 2017 dataset and visualized the results in Figs. 6 7 8. Since the quantitative results of U-Net is not good, so we will not compare our results with it. Except the best method in single model  (Isensee et al. , 2017) , the other related works did not provide the available code, so we cannot do the related comparison. 3. 4. 2. 1. Evaluation of our method. Fig. 6  shows the comparison results between different methods in stage 2: backbone, backbone with new loss, backbone with new loss and context constraint. We can observe the backbone network generates many false predictions, for example, it detects some false pixels in the three tumor sub-regions. However, the false predictions are corrected when the new loss is applied, but there are still some false pixels, like in the enhancing tumor and edema regions, some isolated pixels are failed to be detected.
Sequence Read Mapping, Assignment and Count Nucleotide sequence reads generated from each RNA-seq dataset were uploaded to the Galaxy web platform, and the public server at usegalaxy. org was used for part of the data analysis  (Afgan et al. , 2018). Reads were mapped on the Marseillevirus T19 genome (GenBank accession number NC_013756. 1) using HISAT2 software  (Kim et al. , 2015)  with default parameters (see entire protocol in Supplementary Figure  S3 ), and considering a maximum intron length of 5000 bp as previously described  (Cherif Louazani et al. , 2018). Reads that did not align to the viral genome were mapped against Acanthamoeba castellanii Neff nuclear genome (GenBank accession number AHJI00000000. 1) using HISAT2 software default parameters adapted to eukaryotic genomes with maximum intron length set to 500 000 bp and excluding all discordant alignment  (Kim et al.
, 2015). Unaligned reads at this step were then mapped against the amoeba mitochondrial genome (GenBank accession number NC_001637. 1) using similar parameters only differing in maximum intron length, this time set to 1000 bp to correspond to the mitochondrial genome organization  (Burger et al. , 1995). Mapping results were analyzed using HTseq-count software, with the union mode  (Anders et al. , 2015). Only uniquely mapped reads were used in further analyses.
Marseillevirus Reads Assembly To validate gene structures of Marseillevirus, viral reads of all infection time points were pooled then assembled into contigs with Trinity genome-guided approach using jaccard-clip and trimmomatic options  (Grabherr et al. , 2011). Contigs were then mapped against the viral genome using PASA pipeline with stringent overlap parameters  (Haas et al. , 2003). Contigs were then inspected for ORFs using Transdecoder  (Haas et al. , 2013). Predicted ORFs of over 100 aa were aligned against the annotated MRSV proteins using protein BLAST  (Altschul et al. , 1997). Predicted ORF that did not align to known MRSV proteins were searched for homologs against the NCBI non-redundant protein sequences database and inspected for functional domains using the NCBI Conserved Domain Database search tool.
Marseillevirus Gene Expression Count Normalization and Cluster Analysis For Marseillevirus expression analysis, the raw counts were normalized, considering gene length and sequencing coverage, by means of Transcripts Per Million reads (TPM)  (Conesa et al. , 2016). Normalized read counts ranged from 32 to 318,169. To reveal transcriptional patterns during Marseillevirus infection, we clustered gene transcription profiles using hierarchical and k-means clustering methods. We first log-transformed the normalized read count profiles and centered this data by the mean. Both cluster analyses were performed with the Cluster 3. 0 program  (de Hoon et al. , 2004) , using Euclidean distance as the similarity metric, and setting the number of clusters to 3. Data visualization was done using the Java TreeView program  (Saldanha, 2004).
Expression Profile Validation by Real-Time Quantitative PCR To validate the expression profiles observed with the RNAseq analysis, we performed reverse transcription real-time polymerase chain reactions (RT-qPCR) for six different genes (Supplementary Table  S1 ). We included two genes with high level of expression from each temporal category defined in the RNA-seq data for this assay. Specific primers for each gene were designed using the primer-blast tool on the National Center for Biotechnology Information platform  1. Virus infection and RNA extraction was performed as previously described. RT-qPCR was performed in a one-step reaction using the QuantiTect SYBR Green RT-PCR Kit (QIAgen, France), following the manufacturer's recommendations. Assays were performed in a Bio-Rad Real-Time PCR Detection System (Bio-Rad) using the following thermal conditions for all genes, 30 min at 50 • C for the reverse transcription step, followed by 15 min at 95 • C and 40 amplification cycles of 15 s at 94 • C, 30 s at 60 • C, and 30 s at 72 • C.
De novo Motif Search and Statistical Analysis We used MEME (Motif Discovery Tool, version 5. 0. 4  (Bailey et al. , 2009)  for de novo motif prediction in the intergenic sequences of MRSV. For that purpose, we used a fasta formatted file containing all the 426 intergenic regions sequences (size cutoff = 8 nucleotides), where each sequence corresponds to the upstream sequence from the strand where the gene was observed. This fasta file was used as input to a stand-alone version of MEME running on a local Dell Server (2 Intel Xeon E5-4610 v2 2. 3GHz processors, 64 threads, 128GB RAM, CentOS Linux release 7. 5. 1804). We executed MEME in parallel (60 threads) with the following configuration:  (1)  motif length varying between 8 and 10 nucleotides (an additional search was performed considering motif length varying between 5 and 15 nucleotides); (2) search for zero or more motifs in each intergenic region; (3) no upper limit for the number of sequences per intergenic region.
The command line used to generate the results is reported below: command: meme in_file. fasta -dna -o meme_output_file. txt -csites 5000 -mod anr -nmotifs 10 -minw 8 -maxw 10 -p 60 All graphs and statistical analyses reported here were done using R and Python  (McKinney, 2010; Wickham, 2016). We developed Python scripts to parse the MEME output file filtering each motif occurrence reported based on its, respectively e-value into a dataframe using the Pandas and Numpy packages  (Van Der Walt et al. , 2011). We have selected the best four ranked motifs for forward analyses in R environment. We used additional software for de novo motif prediction, the BaMMmotif server  (Kiesel et al. , 2018) , in order to validate the results found in MEME search.
The functional annotation of differentially expressed genes was conducted using BlastKOALA  (Kanehisa et al. , 2016)  and Amoebadb  (Aurrecoechea et al. , 2011). Parallelly, the expression of the amoeba mitochondrial genes was inspected for a potential temporal transcriptional pattern. After the normalization of raw read counts for gene length and library size with the Transcripts Per Million reads method (TPM)  (Conesa et al. , 2016) , a principal component analysis was performed on the nine samples. In absence of clustering of the samples by time of infection, normalized read counts were log-transformed and genes were clustered using Euclidean distance and average linkage. Data clustering and visualization were performed using ClustVis webtool  (Metsalu and Vilo, 2015). The functional classification of mitochondrial genes followed the results of  Gawryluk et al.
RESULTS Predicted Genes of the Marseillevirus Were Validated by RNA-Seq Data A ribosomal depletion approach was used for preparation of the RNA to be sequenced. Acanthamoeba castellanii strain Neff was infected with MRSV, and cells were collected at different times. Nucleotide sequencing datasets obtained from these separate cell collections for times p. i. of 0, 1, 2, 4, 5, 6, 8, 10, and 12 h, were mapped to the virus genome. All 457 initially predicted genes of MRSV  (Boyer et al. , 2009)  were validated by at least 10 reads superimposed on the corresponding ORF (considering raw data from all time-point datasets). The total number of reads normalized by means of transcripts per million reads (TPM) exhibited a highly differential distribution among the genes (min = 32; max = 318,169; median = 7,036), indicating a large difference in the expression level of the respective genes (Supplementary Figure  S4  and Supplementary Table  S2 ).
Predicted genes of MRSV lacking valid start codon were considered in the second validation step. An alignment of these genes with the assembled transcripts database helped validate seven additional contigs. These contigs confirmed the genome sequence of the predicted genes lacking start codon as they presented 100% identity with the genomic sequence. The last validation step inspected the presence of potential novel protein coding genes and non-coding transcripts. For this, assembled contigs that did not match known Marseillevirus genes were searched for homology at the nucleotide and protein level. In total, the read assembly allowed the definition of the 3prime UTRs of 75 genes, the 5-prime UTRs of 27 genes and internal transcripts corresponding to 44 predicted MRSV genes (Supplementary Table  S5 ).
MEME detected a total of four motifs with e-value smaller than cutoff (1e-5). The most significant motif MEME detected was the previously predicted promoter AAAATATTTT (1224 motif occurrences, e-value = 2. 0e-170), followed by the motifs SAAARRRAAR (1484 motif occurrences, e-value = 6. 3e-130), TYTYTCTTTB (1506 motif occurrences, e-value 5. 7e-76) and CSAVARAV (1306 motif occurrences, e-value 1. 3e-48) (Supplementary Figure  S7 ). The promoter motifs were found in multiple copies occurring throughout the entire intergenic region, although most of them occur up to 500 nt distant from the start codon (Supplementary Figure  S8 ). Motifs search using BaMMmotif server resulted in very similar promoter motifs as obtained using MEME software, thus corroborating the initial results (Supplementary Figure  S9 ).
Structural Genes of the Amoebal Translation Apparatus Are Downregulated To further investigate the amoebal response to infection, we analyzed the evolution of the transcriptional activity at the gene level using he reference annotation of Acanthamoeba castellanii Neff genome that includes 14973 genes. Only 3036 genes could be assigned at least one read in at least one dataset. The expression data for amoebal genes are depicted in Supplementary Table  S9. Three groups of samples could be defined regarding the amoebal gene expression using an unsupervised clustering of samples: early group (0, 1, and 2 h p. i. ), intermediate group (4, 5, 6, and 8 h p. i. ) and late group (10 and 12 h p. i. ). In absence of biological replicates and considering the intragroup similarities observed (average biological coefficient of variation of 0.