dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2018; Kutnjak et al. , 2021). For example, dsRNA-based approaches are usually poor at detecting DNA viruses, while VANA-based ones may perform poorly for viruses with labile particles. When interested in known viruses or potentially novel viruses but from a known family, bioinformatics methods that compare the sequenced reads to genomes in public databases are very efficient for virus detection and identification  (Stobbe and Roossinck, 2014; Massart et al. , 2019). Read-based analysis is thus particularly suited to study viral diversity of sequencing samples in terms of known viral species. Generalistic metagenome analysis tools such as, for example, Kaju  (Menzel et al. , 2016) , Kraken 2  (Wood et al. , 2019) , and Centrifuge  (Kim et al. , 2016)  show good performance in terms of sensitivity and precision in detection of present known viral species  (De Vries et al.
, 2021). For the discovery of novel viruses, use of de novo assembly to recover novel viral contigs from sequencing data is an essential step in order to overcome the incompleteness of virus reference databases, annotation errors and, importantly, the limited homology between novel viral sequences and reference genomes  (Sutton et al. , 2019). The assembly step is a staple of short-read sequencing studies, which are still the vast majority today  (Maree et al. , 2018; Kutnjak et al. , 2021). It represents its own challenges, in particular, for very short reads such as those of siRNAs and for viral populations with multiple and microdiverse variants  (Warwick-Dugdale et al. , 2019) , often leading to microdiversity-associated fragmentation and, sometimes, to chimeras in the resulting contigs  (Martinez-Hernandez et al.
, 1990)  and its derivatives or the amino acid alignment of protein-coding genes predicted from the assembled contigs using DIAMOND  (Buchfink et al. , 2015). Also, focusing the analysis on coding regions is particularly relevant for RNAseq data since the non-coding sequences of viruses are not highly represented in such samples, even if they can be well conserved in certain viral taxa. However, the main drawback of alignment-or mapping-based approaches lies on the fact that they are both computationally intensive and require expertise for filtering and interpreting the results. As for the generalistic k-mer signature approaches, they remain demanding in terms of memory and are best suited for diversity analysis tasks  (Kutnjak et al. , 2021). The emergence of machine learning tools for contig-based analysis of virome sequencing data holds much promise to streamline the discovery of novel viruses in sequencing datasets by both avoiding the time-consuming sequence similarity analyses and modeling even highly divergent sequences.
These three deep learning methods were developed for identification of viral contigs in metagenomic samples and evaluated on bacterial and human metagenomes. However, DeepVirFinder has been recently successfully used in plant-related virome studies  (Santos-Medellin et al. , 2021). In this work, we present VirHunter, a deep learning method that uses convolutional neural networks (CNNs), classifies previously assembled contigs to identify potential viral, host, and bacterial (contamination) sequences in RNAseq samples. The hybrid architecture of VirHunter combines a multi-network CNN-based module covering different k-mer sizes with a downstream random forest classifier module. We have trained VirHunter models for three different plant host species: peach, grapevine, and sugar beet. Importantly, we have shown that VirHunter is especially performant for the task of completely novel virus discovery by building 31 leave-out datasets, in which each viral family is excluded from the training dataset, and comparing the results with a standard BLAST-based solution on one side and a state-of-the-art deep learning method, DeepVirFinder, the other side.
VirHunter not only systematically outperformed DeepVirFinder in terms of virus detection but also has considerably reduced the False Positive rate. Cross-evaluation has shown that host detection accuracy remained high and decreased slightly when test sequences originated from the plant species were further phylogenetically removed from that used to train the model. We have further evaluated the detection capacity of VirHunter on in silico mutated contigs sampled from the NCBI virus database and have shown that it decreased only slightly with a progressively increased mutation rate (e. g. , True Positive rate of 0. 898 for 20% mutation rate). Moreover, we generated 12 RNAseq datasets for a range of host species and have shown that VirHunter was not only able to uncover the viruses that were previously identified but also to streamline the analyses by considerably reducing the need for manual curation.
Datasets We downloaded all complete and incomplete viral sequences from the NCBI virus database for which the host's taxonomic id belongs to Viridiplantae on 26/10/2021, which yielded 122,832 sequences. Plant sequences have been downloaded for Prunus persica (peach), Vitis vinifera (grapevine), Beta vulgaris (sugar beet), and Oryza sativa (rice) from the NCBI RefSeq genomes database. On one hand, they consist of the latest available assemblies, GCF_000346465. 2, GCF_000003745. 3, GCF_000511025. 2, and GCF_001433935. 1 for peach, grapevine, sugar beet, and rice, respectively, and of the coding region sequences (CDSs), on the other hand. In the absence of the plastid sequence in the reference assembly of the sugar beet, we used the separately available sugar beet plastid sequence (NC_059012.
1). All complete representative bacterial genomes have been downloaded from the NCBI RefSeq database on 28/10/ 2021 using the genome_updater. sh script. To simulate the discovery of completely unknown viruses that do not have expected similarities with the available data, we constructed virus family leave-out datasets by excluding in turns all the sequences of a given plant viral family from the downloaded virus dataset. The NCBI taxonomy contains 45 viral families. We excluded the Pospiviroidae and the Avsunviroidae families of viroids as they have very small genomes (average length < 1,000). All families with the number of available sequences < 100 were merged in one dataset called small families. Finally, all sequences without family labels constituted the unclassified dataset. This resulted in 31 leave-out datasets.
Moreover, we generated 12 novel virome-sequencing RNAseq datasets, sampled from peach, grapevine, and sugar beet (see Sample Preparation and Sequencing). Description of these datasets and presence of viruses identified by aligning assembled contigs against the NCBI GenBank database (see Assembly of RNAseq Datasets and Annotation of Viral Contigs) are listed in the Supplementary Table  S1.
Sample Preparation and Sequencing Total RNAs were extracted from three peach leaf samples, three grapevine phloem scrapping samples, and three sugar beet leaf samples using the CTAB method  (Chang et al. , 1993) , the Spectrum ™ Plant Total RNA Kit (Sigma-Aldrich, Saint Quentin-Fallavier, France), and the NucleoSpin RNA plant kit (Macherey-Nagel SAS, Hoerdt, France), respectively. RNAseq libraries were prepared either from total RNAs (peach and grapevine samples), messenger RNAs (grapevine samples), or ribodepleted RNAs (sugar beet samples). High-throughput sequencing was performed on an Illumina platform (Hiseq3000 or NovaSeq600) using a paired-end read length of 2 × 150 bp. Accession numbers for each of the three studies (peach, grapevine, and sugar beet) containing raw FASTQ sequencing files are provided in the Supplementary Table  S1.
Assembly of RNAseq Datasets and Annotation of Viral Contigs All of the 12 selected plant virome datasets (see Datasets) were processed with the QIAGEN CLC Genomics Workbench (v. 21. 0. 5). Briefly, reads were first quality-controlled and trimmed using default parameters and then assembled using de novo assembly (word size 50, bubble size 300, and minimum contig length 250). To identify viral contigs present in these datasets, we followed a standard three step BLAST-based approach, see e. g. ,  (Candresse et al. , 2018). 1) All contigs were aligned using the CLC built in tBLASTx tool against the NCBI nucleotide non-redundant database limited to taxonomic identifiers of viruses. Contigs having significant hits (e-value below the 10 -20 cut off) were selected. 2) Contigs were further filtered by aligning them using BLASTn and BLASTx with default parameters against the whole GenBank non-redundant nucleotide and protein databases, respectively, and keeping contigs for which the best hits correspond to plant viruses for both BLASTn and BLASTx.
Data Preprocessing To prepare the data for processing by the neural network module, datasets were preprocessed by creating representative one-hot encoded fragments (see Figure  1 ). Specifically, let us denote the virus dataset by V, the plant dataset by H (for "host")-composed of the full assembly G, the coding sequences C, the chloroplast sequence L, and the bacterial dataset by B. Given a fragment size n of 500 and 1,000 nucleotides, V was split into fragments of size n with a sliding window with an increment of n/2. Sequences shorter than n nucleotides and longer than 0. 95 × n were padded to n bp length with gaps (those shorter than 0. 95 × n are discarded), together yielding N viral fragments. Same number N of fragments of size n was randomly sampled from B. As for the plant, G was split into 0.
6 × N fragments using a sliding window with an increment of size n, C was split into 0. 3 × N fragments with a sliding window with increment of n/2, and finally 0. 1 × N fragments were sampled randomly from L. Including plastids in relatively high proportion into the plant dataset H was important to avoid the potential incorrect assignment of contigs originating from plastids to B, given the phylogenetic proximity of plastids and bacteria  (McFadden, 2001). Moreover, there are RNA viruses that are known to be replicated in tight association with plastids (mostly chloroplasts) - see e. g. ,  (Budziszewska and Obrepalska-Steplowska, 2018; Delgado et al. , 2019). Enriching for CDS sequences was necessary since the envisioned application of VirHunter is for RNAseq virome datasets. Five compositions of G/C/L proportions of H were tested (100/0/0, 90/0/10, 60/30/10, 50/ 40/10, and 45/45/10, data not shown) and the best was retained.
Fragments were further transformed from length n ACGTcharacter strings to n × 4 one-hot encoded arrays, in which an A is encoded by [1, 0, 0, 0], a C is encoded by [0, 1, 0, 0] etc. , while gaps are encoded by [0, 0, 0, 0]. Moreover, the encoded dataset is augmented by adding the reverse complement of each original fragment. Indeed, it has been shown by  Shrikumar et al. (2017)  that CNN models in genomics require the reverse-complement data augmentation combined with parameter sharing between the forward-and reverse-complement representations of the model. Class labels V, H, or B are assigned to each fragment according to its provenance.
Training The neural network and machine learning modules were trained separately for each of the three plant host species (peach, grapevine, and sugar beet) and for fragment sizes n of 500 and 1,000. The training dataset for the CNN module was built as presented in Data Preprocessing. Training batches with size 512 were prepared in a balanced manner across the three classes (virus, plant, and bacteria) from the training dataset and are split into training and validation with the ratio of 9:1. Each of the three individual networks was trained for 10 epochs, followed by 1 epoch of training on the validation set to take into account all the data. For training and testing the machine learning components, predictions for the three trained networks were obtained on 100, 000 randomly selected fragments of size n from each V and B.
Likewise, 100, 000 fragments of size n were randomly sampled from H, following the ratio described in Data Preprocessing. Predictions for random viral fragments were further subsetted in the following manner. We split the test dataset viral fragments into those having good quality predictions (prediction value for viral class ≥ 0. 8) and lowquality predictions (prediction value < 0. 8) and maintained 10, 000 randomly selected fragments from each category, yielding 20, 000 predictions. These 20, 000 predictions were further selected for plant host H and bacterial B fragments. The resulting dataset with three predictions for each of 60, 000 fragments was further split in train and test datasets with 2:1 ratio, and the machine learning module was trained with parameters max_depth = 5, n_estimators = 10, max_features = 1, and max_samples = 0.
Contig Classification VirHunter trained on fragments with n 500 was used to classify contigs with length 750 < l < 1500, while VirHunter trained on fragments with n 1000 was used to classify contigs with 1500 < l. Indeed, an ORF of 500 nucleotides corresponds to an 18 kDa protein, this size covering the vast majority of viral polymerases, movement proteins, and capsid proteins for plant viruses. Contigs with l < 750 were considered as very small for prediction by the smaller of the two models and were discarded. Each fragment of an input contig was preprocessed following the procedure presented in Data Preprocessing. Predictions were produced by the neural network module for each of these one-hot encoded fragments, yielding three probabilities of belonging to a specific class (V, H, B). These class probabilities were further processed by the random forest component, resulting in a unique class label for each of the fragments.
RESULTS VirHunter Outperforms State-of-the-Art Tools on Family Leave-Out Datasets VirHunter was trained on GPU (Nvidia Tesla T4) with n 1000 for 31 family leave-out datasets and three different plant datasets (peach, grapevine, and sugar beet), resulting in 63 leave-out models. The test datasets were prepared by random sampling of 30,000 fragments with n 1000 from the corresponding leftaside families of viral sequences, bacteria, and plant. Classification results for the viral fragments by VirHunter in this family leave-out experiment are shown in Figure  3  and in Supplementary Tables  S2, S3. We compared the capacity of VirHunter to detect novel viruses in the family leave-out setting with the BLAST-based approach on one hand and two state-of-the-art machine learning methods, DeepVirFinder and VirSorter2, on the other hand as also shown in Figure  3.
Briefly, each test dataset was aligned using tBLASTx (v2. 12. 0), preserving one best hit with parameters -max_target_seqs 1 -max_hsps 1, against the respective virus database with the leave-out family removed, and filtered by e-value < 10 -10 , percent identity > 0. 5, and alignment length > 50 amino acids (see results in Supplementary Table  S4 ) in order to emulate the annotation workflow without manual inspection; DeepVirFinder was trained on the same 31 leave-out datasets but excluding bacterial fragments from the training dataset since this method provides the possibility to have only two class labels and using the recommended parameters  (Ren et al. , 2020)  on 10 CPUs Intel Xeon CPU E5-2630 v4 (see results in Supplementary Table  S5 ); VirSorter2 was evaluated on each test dataset using pretrained models provided by authors (see results in Supplementary Table  S6 ).
Altogether, VirHunter has shown consistently better results than that of tBLASTx, for which the TP rate was frequently below the threshold 0. 5 (16 families out of 31). As for the capacity to correctly classify bacterial fragments, VirHunter has shown a systematically high TP rate, ranging from 0. 958 to 0. 983, across all the leave-out experiments. As for plant fragments, the TP rate was also satisfactory, sugar beet TP from 0. 950 to 0. 961, grapevine TP from 0. 983 to 0. 991, and peach TP TABLE 2 | Performance of VirHunter, DeepVirFinder, and VirSorter2 on 12 RNAseq virome datasets. For each of the 12 datasets shown are the number of contigs that were annotated as being viral by experts and the number of contigs in the initial assembly with length > 750. Columns "VirHunter," "DeepVirFinder," and "VirSorter2" show predictions run on these contigs by each method.
Columns "# detected" show the total number of contigs detected as being viral by each of the two methods, and columns "detected ∩ annotated" indicates how many of these were previously identified by the curators. Finally, the "tBLASTx e-value < 10 -10 " column indicates how many of "# detected" contigs align against viruses for VirHunter. RNA viruses are the most abundant pathogens infecting plants. However, RNA plant virus detection using HTS presents a number of challenges due to their genetic diversity, lack of conserved regions across viral species, short genome lengths, high mutation rate, and incomplete knowledge present in reference databases. To address this challenge, we developed a novel deep learning method, VirHunter, to detect novel and known plant viruses in RNAseq datasets.
Dataset VirHunter is particularly well-suited for the discovery of novel viruses as it was exemplified on 31 synthetic leave-out family datasets, where VirHunter systematically outperformed DeepVirFinder and VirSorter2, reference machine learning tools for virus detection. When compared with the standard tBLASTx approach, we have shown that for most (21 out of 31) leave-out families, VirHunter obtained a higher TP rate. In six cases, tBLASTx was slightly better (5. 8% on average). However, there remained four cases where we have seen a much worse performance in VirHunter results. For these specific families, it can be noted that they are particularly well-suited to the alignment-based virus identification, for example, Alphasatellitidae viruses carry high sequence similarity to Geminiviridae (which was confirmed by the majority of tBLASTx hits).
As expected, VirHunter performed better, when the plants it was trained and tested on were phylogenetically closer: grapevine and peach belong to the same rosids higher clade resulted in better mutual predictions, while sugar beet as an outgroup belonging to the caryophyllids higher clade has shown a relative drop in performance. All these three plants are eudicots  (Pin 2012 ). When the model was trained on an even further phylogenetically distant plant, rice that belongs to monocots and tested on fragments from peach, grapevine, and sugar beet, the classification accuracy of VirHunter was expectedly lower. Together this implies that to classify contigs from an RNAseq experiment, using a pretrained model trained on the exact same plant species as the host of the experimental dataset is not mandatory, but it is preferable to use one trained on a phylogenetically close plant, ideally from the same family and at least belonging to the same eudicots/monocots higher clade.
A possible avenue to explore in the future work is the feasibility of transfer learning  (Eraslan et al. , 2019) , to enable fast on-demand retraining for a new plant or building a generalistic plant model. Finally, we validated VirHunter's capacity to detect novel viruses on 12 newly acquired RNAseq datasets for peach, grapevine, and sugar beet. In these datasets, VirHunter detected at least 90% (73% for DeepVirFinder and 26% for VirSorter2) of all expert-annotated viral contigs, and in seven datasets it was 100%. Another contribution is the low rate of false positives generated by VirHunter, leaving from 19 to 277 contigs depending on the dataset to be inspected by an expert. These results indicate that VirHunter efficiently reduces the number of contigs requiring manual expert curation.
Frontiers in Bioinformatics | www. frontiersin. org May 2022 | Volume 2 | Article 867111 present in public RNAseq datasets by Edgar et al. (2021), where the authors have identified 10 5 novel RNA viruses. Finally, of note is the considerable gain of time left for expert curation of contigs by approaches similar to that presented in Assembly of RNAseq Datasets and Annotation of Viral Contigs, given the numbers in the "# detected" column, where VirHunter has shown improvement over DeepVirFinder in eight out of 12 datasets.
Frontiers in Bioinformatics | www. frontiersin. org May 2022 | Volume 2 | Article 867111 present in public RNAseq datasets by Edgar et al. (2021), where the authors have identified 10 5 novel RNA viruses. Finally, of note is the considerable gain of time left for expert curation of contigs by approaches similar to that presented in Assembly of RNAseq Datasets and Annotation of Viral Contigs, given the numbers in the "# detected" column, where VirHunter has shown improvement over DeepVirFinder in eight out of 12 datasets. ID and # # Contig VirHunter DeepVirFinder VirSorter2 plant origin Contig >750 annotated as viral # detected Detected ⋂ annotated tBLASTx hits # detected Detected ⋂ annotated # detected Detected ⋂ annotated (e-val < 10 -10 ) P1 Peach 1,009 2 35 2 14 45 2 10 1 P2 Peach P3 Peach DISCUSSION 415 685 2 2 19 23 2 2 10 10 32 49 2 2 8 7 1 1 G1 Grapevine G2 Grapevine High-throughput sequencing (HTS) is capable of broad virus 9,154 10 153 10 17,024 10 178 10 47 40 133 131 6 9 52 117 4 6 G3 Grapevine detection for both known and unknown viruses in a variety of 18,750 20 208 18 59 137 17 142 11 G4 Grapevine hosts and habitats.
Moreover, an interactive analysis tool has been developed within Ansys® Mechanical, in order to automate the application of the PSM to complex both aluminum and steel 3D welded structures subjected to multiaxial fatigue loads. The automated PSM tool has been developed taking advantage of the integrability between Ansys® Mechanical and modern programming languages, enforcing PSM compatibility requirements and automating the implementation tasks and phases of the method. In this perspective, the present work deals with the employment of the PSM tool for the fatigue strength assessment of some complex welded details adopted in modern roller-coaster structures. Due to the geometrical complexity of large-scale structures, private companies usually employ finite element beam models in order to evaluate nominal stresses, that must be compared with appropriate fatigue strength references (FAT classes) in design standards.
Automated implementation of the PSM for the fatigue design of welded structures In order to support the FE analyst in the fatigue design of complex welded structures, an interactive analysis tool has been developed in Ansys® Mechanical, the FE environment of Ansys® Workbench, by means of Ansys® Customization Toolkit (ACT). ACT allows indeed to take advantage of modern programming languages, i. e. IronPython, Microsoft® C#, Microsoft® Jscript, XML and HTML, in order to develop applications and integrate them with native CAD/FE environments available in Ansys®. The developed application provides the FE analyst a user-friendly set of tools designed to automate the fatigue strength assessment of welded structures subjected to both uniaxial and multiaxial constant amplitude loads according to the PSM.
Two track pipes and tie beam welded joint geometry The welded detail under exam is a rail module characterized by two track pipes and a tie beam component connecting them, having the dimensions reported in Fig. 4b. Model configurations characterized by both (i) full penetration welds and (ii) fillet welds were considered, in order to take into account the influence of the weld root on the fatigue strength of the welded joint, with respect to the weld toes. According to the fatigue strength assessment procedure described in Paragraph 4, the CAD model has been generated taking advantage of the symmetry plane YZ and simulated in Ansys® Mechanical with respect to the different load cases in Table  1 , as reported in Fig. 5b. A free FE mesh has been generated taking advantage of 10-node tetra elements (SOLID 187 of Ansys® element library).