dataesr/scientific-paragraphs-categorization huggingface dataset:
Data-driven virus discovery through scanning of large public sequencing datasets is a major recent development. Re-examining existing datasets for the presence of known and novel viruses has become accessible for virologists, through new web-based platforms like Serratus 1   (Edgar et al. , 2022) , RVMT 2   (Neri et al. , 2022)  and ViroidDB 3   (Lee et al. , 2022a,b). Nevertheless, virologists and plant health stakeholders should consider the consequences, not only benefits, of these data-driven virus discovery approaches  (Lauber and Seitz, 2022). In a short timeframe, these revolutionary high-throughput sequencing and data-driven approaches have extended the need to reconsider and adapt the current framework through a multistakeholder consultation. We thus propose an improved and adapted.
Usually, RT-PCR detection tests are developed using primers designed based on assembled sequences or a reference-based assembly to account for variability. If the genetic diversity of closely related viral species is well characterized, it is worth checking the scientific literature and aligning the existing sequences to evaluate the less variable ORF/regions within the taxonomic group as a way to design primers targeting a conserved genomic region to allow for detection of the most virus variants from the species. The ability of the designed primers to detect only the targeted new viral species and not related species should be checked by comparing their sequences to databases using primer BLAST, for example, or the genome alignments already developed. It is worth nothing that the specific test will be used as a diagnostic test in the following steps of the framework to complete the biological characterization of the new or poorly characterized virus (i.
Documentation of sample context At this stage and to assist the risk assessment process, as much information as possible, whenever possible, should be collected regarding the original sample (or pool of samples) where the novel virus was detected. This includes the plant species and cultivar, sample accession number, description of the symptoms observed at the time of sampling, plant tissue collected, the viral status of the neighboring plants (if known either by onsite testing or from previous records), the incidence in the affected crop, other crops affected, recent meteorological conditions, sample collection date, geographical origin of the sample with specific map coordinates, and growth conditions of the plants  (Massart et al. , 2017). Collecting this information at the time of sampling can facilitate and minimize efforts later on.
Additionally, the documentation could include the economic importance and geographical distribution of the crop species affected, globally or domestically  (Kwibuka et al. , 2021). Optionally, plants that are taxonomically related to the infected hosts, including other crops and wild plants that could potentially be threatened by the virus or be a reservoir or alternate host, could also be documented. This set of information could help make better preliminary assessment of the potential threat (García-Arenal and  Zerbini, 2019; Hasiów-Jaroszewska et al. , 2021; Rivarez et al. , 2023). The information-gathering step, if done well, is critical and can decrease the burden during the submission of the dataset to public repositories such as the European nucleotide archive (ENA) or the sequence read archive (SRA) of GenBank.
An unexplored path: exploitation of structural features from genomic sequence toward predictive sequence-to-function viral proteomics In animal virology, many publications used machine learning approaches on databases of genomic features and biological properties from known viruses to predict the taxonomy or key biological properties of new viruses, such as host range and vector. Most of these approaches focus on nucleotide features like CG bias, CpG bias, di-codon, or dinucleotide bias  (Young et al. , 2020; Giovani et al. , 2022). For example, dinucleotide bias was used to identify host reservoirs and vector candidates for mammalian RNA viruses  (Babayan et al. , 2018) , to predict hosts of coronaviruses  (Tang et al. , 2015) , or to identify the human or avian origin of influenza A viruses (IAV) using random forest analysis  (Eng et al.
, 2016 (Eng et al. , , 2017;; Li et al. , 2019). Recent research used an original approach to identify new proteomic features potentially involved in plant virus-vector transmission, i. e. , intrinsically disordered proteins/regions, and to understand how their biophysical properties and regulation might arise from these interactions  (Tahzima et al. , 2021). As a result, it was shown that most encoded plant virus proteins contain multiple disordered features that are phylogenomically preserved and can be associated with structural, bio-physical, and evolutionary strategies. This opens a new focus for predicting the biological properties of the new plant virus from in-depth structural and functional analyses of protein sequences. Nevertheless, interpreting all these features and results should still currently be done with much caution, given the uncertainty attached to such predictions and the sometimes limited accuracy of these databases.
Public database screening and consideration on careful use of related metadata Valuable information can be gained by screening public databases of HTS data, such as the SRA of GenBank 4  , for the presence of newly identified or poorly characterized viruses  (Hily et al. , 2020). SRA is predicted to surpass 50 petabytes of data by 2023 [Sequence Read Archive (SRA) Data Working Group | DPCPSI, 2021;  Katz et al. , 2022]  and mining such an enormous amount of information for virus presence previously required heavy computational power unaffordable for most virology laboratories as well as expertise in data science. The recent development of a practical and user-friendly web-based interface called Serratus  (Edgar et al. , 2022)  represents a major advancement toward a more generalized public database screening (see text footnote 1 for more details).
Serratus uses a pre-screening strategy to look for viral RdRp motifs in SRA data (deposited until  January 2020). It has the potential to provide hints about the host range or geographical distribution of specific RNA viruses present in the sequencing datasets. Following the pre-screening of SRA, Serratus provides a database of potential viral RdRp sequences (known and unknown), which is publicly available and can be used for exploratory and further diversity or phylogenetic analyses. This database also contains the link between each RdRp (SRA origin) and their associated "palmprint, " 5  which is an RdRp "barcode" classified by taxonomy and clustered in operational taxonomic units (OTUs) with 90% identity threshold (Babaian and  Edgar, 2021). Through Serratus, the SRA datasets deposited until January 2020 can be mined by looking for an RNA virus (via a family/Genbank/SRA_id search or a taxonomic tree exploration) or by searching for the sequence (protein or nucleic) of an RdRp using the palmID search tool.
6  The palmID search tool allows finding the palmprint sequence within the provided RdRp and match it, with a minimal threshold of pairwise identity, to palmprint sequences in the palmprint database. The palmprint OTU is necessary to avoid heavy computational requirements, although it lowers the confidence in the results, thus only giving hints to the presence of the target virus, which needs further validation. Thus, the sequencing reads of the identified SRA dataset should be reanalyzed using existing bioinformatic approaches to confirm the presence of the virus of interest. Supplementary material 2 presents a practical example of the additional information that can be gained by using palmID for an emerging virus (physostegia chlorotic mottle alphanucleorhabdovirus). Nevertheless, Serratus has some limitations, mainly when the virus of interest is not detected from public SRA datasets.
Even in case of detection, the verification step (assembly and mapping) is timeconsuming and a computational burden, and sometimes inefficient, depending for example on virus representation and on sample identity (i. e. , fragmented genome or pooled sample). Another limitation is the potential misassignment of the host, in particular when the new finding involves a metagenomic dataset with unexpected virome content (i. e. , a plant virus found in a human clinical dataset or animal viruses found in plant datasets). In addition, metadata information such as the host or the country of origin of the sequenced material should also be investigated, keeping in mind that the metadata may not be accurate. If biological material is still available, contacting the authors may allow the confirmation of the detection.
It should be stressed that there could also be some implications for trade and related policies when relying on SRA mining for reporting the detection of pathogens in a country where it is not currently known to be present. In such a case, the conduct of confirmatory tests in the 10. 3389/fmicb. 2023. 1181562 Frontiers in Microbiology 08 frontiersin. org wet lab should be encouraged and given a very high priority. The ethics of reporting the presence of a pathogen in a territory without prior notification to its NPPO should also be considered. Whenever possible, it is advised to contact the dataset's original authors and notify the country's NPPO before the publication of a new country record.
Notification and exchanges with other stakeholders The additional biological information obtained will progressively feed the risk evaluation. It should be shared with stakeholders via, for example, a reporting system or ad hoc meetings with relevant plant health authorities. A meeting between the involved parties can be organized to analyze the new information obtained since the last notification (step 2. 3), to assess the status of the novel virus as a pest, and if it needs to be regulated  (EPPO, 2012; IPPC ISPM 11, 2019; IPPC ISPM 2, 2019; IPPC ISPM 21, 2021). This discussion will help identify further data gaps and to prioritize the research focus as the assessment moves forward. At this point, exchanges with the authorities, risk managers, and stakeholders will allow re-evaluation of risks posed by the virus and reach a provisional decision on its phytosanitary status.
Conclusion The recent reviews of  Hou et al. (2020)  and  Rivarez et al. (2021) , as well as the similar analysis done here for Poaceae, highlighted the need for a revision of the previous characterization framework. Conducting similar systematic reviews in other crops might give additional insights on what is the current landscape on plant virus characterization after a first identification in an HTS dataset. Essentially, this revision emphasizes adapting a progressive feedback approach during the risk evaluation process, highlights the growing importance of database mining, proposes as a keystone the disease causal association, and underlines the importance and benefits of data and effort sharing, as well as the advantages to collaborate with other researchers. It is worth noting that this is not a substitution for any decision-support scheme for a pest risk analysis or pest categorization but a complementary document, especially useful for cases where the virus is considered as non-priority or where communication with plant health authorities may be more limited.
Researchers may be overwhelmed with the number of findings in HTS datasets, or have a lack of resources and time, so this revision serves as an outline of the prioritization steps to go further than the genomic and molecular characterization of a novel virus, which will produce more useful and practical information for plant health authorities and producers/grower associations. In the long term, advances in artificial intelligence, machine learning technologies and bioinformatic tools will facilitate the process of characterization of a novel virus and reduce the resources and time needed. For example, SRA mining tools have the potential to complement conventional global surveys although a discussion around the technical and ethical considerations of using such methods should be held between the scientific community and stakeholders.
Nevertheless, the difficulty of identifying PPs and the lack of systematic methods to automatically detect them has limited their use. However, based on a recent characterization of PPs in the superior temporal sulcus, we present here a method to automatically detect PPs in the superior temporal sulcus. Local morphology within the sulcus is characterized using cortical surface profiling, and the three-dimensional PP recognition problem is performed as a two-dimensional image classification problem with class-imbalance. This is solved by using an ensemble support vector machine model (EnsSVM) with a rebalancing strategy. Cross validation and quantitative experimental results on an external dataset show the effectiveness and robustness of our approach.
This is why we want to implement this definition in order to detect PPs in an automatic and more systematic manner. Starting from the work in  Bodin et al. (2021) , we propose to automate the detection of pli-de-passages in the superior temporal sulcus. In order to do so using the presence of wall pinches (WPs) on the sulcal banks, we have to keep in mind that on one hand there is no strict quantifiable definition of WPs, and on the other hand we have a dataset on which all WPs and PPs have been delineated by hand (see next section and  (Bodin et al. , 2021) ). Therefore we decided to use supervised learning, using this dataset as a training set. In the next section we present the definition of features that locally characterise the presence of wall pinches, followed by a supervised machine learning-based method that can detect PPs using these features, hence automating the definition provided in  (Bodin et al.
Human Connectome Project (HCP) database The first dataset is composed of structural T1 MR images of 100 subjects from the Human Connectome Project (HCP) database, for which detailed information is available: https://www. humanconnectome. org/study/ hcp-young-adult/document/900-subjects-data-release. Subjects were randomly having completed the full diffusion and structural acquisitions, being non-twins, right-handed, between 22 and 40 years old. Data taken from the HCP database were acquired as follows: structural images were acquired using a modified version of Siemens Skyra 3T scanner (Siemens, Erlangen, Germany) with a maximum gradient strength of 100 ∕ , slew rate of 200 ∕ ∕ (reduced to 91 ∕ ∕ for diffusion due to peripheral nerve stimulation limits) and a 32-channel head coil. T1-weighted images were acquired using 3D MPRAGE sequence (TR/TE = 2400∕2.
Temporal Voice Areas (TVA) database The second dataset is composed of structural T1 MR images of 92 subjects, aged between 17 and 44 years old, from the Temporal Voice Areas (TVA) dataset previously used in  (Bodin et al. , 2018). The origin dataset contains onehundred and sixteen healthy subjects that were scanned (n = 116; 53 males, 63 females; mean age 23. 7 ± 5. 8) as part of published and unpublished experiments of the Voice Neurocognition Laboratory (http://vnl. psy. gla. ac. uk/) of the Institute of Neuroscience and Psychology at University of Glasgow. Participants, drawn mostly from the Glasgow student population, were of various ethnic backgrounds, education and manual lateralization and all provided written informed consent prior to participation. The experiments were approved by the local ethics committee at University of Glasgow.
Image preprocessing and identification of landmarks 2. 2. 1. Anatomical images and related maps All individual T1-images were first segmented using Freesurfer (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki), and then imported into the Morphologist pipeline of the BrainVisa (BV) software (http://brainvisa. info) (Mangin et al. , 2004)  to generate triangular meshes of the grey/white matter interface for both hemispheres of all subjects (see Figure  2-a ). These surfaces will be further referred to as ?cortical surface?. Meshes are reconstructed independently for each hemispheres and contain on average 60000 vertices. Then, we generated several morphological surface maps used for anatomical analysis including sulcal depth, depth potential function (DPF) and curvature map. The depth map for each individual surface was computed as the geodesic distance from the sulcal fundus to the outer cortex  (Rettmann et al.
STS identification and drawing The STS is a fold separating the superior temporal gyrus (STG) from the middle temporal gyrus (MTG) in the temporal lobe (see Figure  3-a ). The STS fundi were drawn semi-automatically on the cortical surface for both hemispheres and all subjects of both datasets using the SurfPaint module of the Anatomist visualization software  (Le Troter et al. , 2011). This was performed by C. B. in the context of  (Bodin et al. , 2018 (Bodin et al. , , 2021)). We manually selected the anterior and posterior extremities based on anatomical landmarks identifiable in each subject as described in  (Bodin et al. , 2018). The anterior extremity was chosen as the tip of the temporal pole, posterior to polar temporal sulcus. In most cases, this sulcus was perpendicular to the STS and a clearly visible PP separates these two folds  (Ochiai et al.
PPs identification Herein, the PPs were manually labeled on the HCP dataset by C. Bodin in the context of  (Bodin et al. , 2021)  according to the morphological criteria described in  (Bodin et al. , 2021). Wall pinches were first identified and PPs were then drawn by selecting the two intersections (two vertices) between the extremities of WPs and gyral crests (STG and MTG). A line was then automatically generated between these two vertices, as the shortest path that minimizes the DPF  (Bodin et al. , 2021). Such PP lines were drawn for the STS on both hemispheres of the 100 HCP subjects (see examples on Figure  3-c ). For all points of the STS fundus, those at the intersection with PPs lines were labelled "PP" and the rest were labelled "non-PP" (see Figure  3-d ).
Ensemble Support Vector Machines (EnsSVM) We describe here the supervised learning strategy we use to detect PPs in the STS fundus. As mentioned before, for all points of the STS fundus, after manual labelling those at the intersection with plis-depassage were labelled "PP" and the rest were labelled "non-PP". On the HCP dataset, we got a total of 15703 vertices of STS fundus, where only 865 of them were labelled as PPs, and 14208 were not, leading to an unbalanced data set with a ratio 1 ∶ 16 and PP as the minority class. This could lead to difficulties as most algorithms perform better on balanced datasets.
Augmentation of PPs data To deal with the imbalanced dataset, we proposed to augment the PPs instances by considering their nearest neighbours. If a point is a PP, then its nearest neighbours on the STS fundus also can be considered as PPs since their surrounding cortical geometry is very similar (see Figure  7-a, b ). This increases the number of PPs instances while reducing the number of Non-PPs. Here we consider the two nearest neighbors, i. e. one on each side of the vertex. Hence by doing so, not only do we rebalance the dataset to some extent, but we also define a PP on the fundus as a small area (called "PP region") around the vertex instead of a single point. Notably, by considering the 2 nearest neighbours, a PP region contains at least 3 points on the STS.
EnsSVM pipeline After the PP data augmentation, the unbalanced ratio is reduced to 1 ∶ 5. Although improved, it is still an very imbalanced dataset. In order to deal with this aspect and provide an adequate sampling of each category without bias due to the imbalance, we chose a strategy that has been succesful in many ensemble methods  (Liu, 2009; Liu et al. , 2011). Multiple subsets of the majority class (non-PPs) are randomly generated 1 , 2 , … , such that their size is the same as the minority class , i. e. | | = | |. Then, the union of each pair of and is used to train a base classifier and a decision is taken by combining all base classifier results with a majority vote. More precisely, we proposed the following ensemble SVM (EnsSVM) algorithm, illustrated in Figure  8. At first, we apply the augmentation strategy presented in section 2.
4. 1 and get a total of PPs. We then divide the data into training and test data (note that this step is only needed for the algorithm evaluation, while for a final model applied to detect PPs in new data, we learn from the full dataset). After that, the majority (non-PP) data in the training set is under-sampled times by bootstraping with replacement and the size of each subset is. We then combine each bootstraped subset with the over-sampled positive instance (PPs) to form a perfectly balanced training dataset of size 2 * that we use to train a base SVM. Finally, for a test instance, the classification decision is made through a majority voting from all base classifier results.
Post-processing Figure  9  gives an example of a result produced by our EnsSVM model. As we can see, instead of detecting single vertices, most of the results include several vertices forming a single connected region. This is expected given the fact that, as mentioned in section 2. 4. 1, after data augmentation we assume that a PP region on the STS fundus is composed of at least 3 consecutive points. Our hypothesis is that if a point is on a PP, its direct neighbors and possibly second order neighbors should as well, because their surrounding geometry is equivalent, and defining a cortical feature as a single point does not really make sense. On the other hand, a set of more than 5 consecutive points is on average more than 4mm wide and should be considered as containing more than one PP (e.
Evaluation measures on the HCP dataset In this work, our evaluation measures are based on the , a two-class problem confusion matrix with positive and negative values is illustrated in Table  1. The choice of a performance index is critical to evaluate the goodness of a classifier. Traditionally, the overall performance of machine learning algorithm is usually evaluated with the "accuracy": = + + + + (2) However, it is often meaningless to achieve high accuracy when the data is highly imbalanced. In our case, with a ratio of PPs∕Non-PPs ≈ 1∕16, a straightforward method of guessing all instances as non-PPs would achieve an accuracy 93. 75%. Although this accuracy seems high, it does not reflect the performance of the strategy used. Therefore a high overall accuracy may misrepresent the performance of classifiers in the context of an imbalanced class problem.
5 (7) where 2 is computed by setting = 2 in and 0. 5 is computed on the confusion matrix in Table  2 : Compared to the confusion matrix in Table  1 , it switches the class labels of each samples (positive samples become negative and vice versa). Using the confusion matrix in Table  2 , 0. 5 is computed as the standard 0. 5 Performances on the HCP dataset used for training and testing will be assessed using these measurements.
Statistical analysis on TVA data After assessing results on the HCP dataset, we want to validate its use on an external dataset, the TVA dataset, and quantitatively compare the distribution of PPs in the STS with the manually identified PPs of the HCP data  (Bodin et al. , 2021) , according to different depth ranges and PPs types. We also report the number of PPs per individual and compare the distribution between hemispheres per type of PPs. We also looked at the link between PPS and their position along the STS, as well as with the variations of sulcal depth along the STS fundus. A parametrisation of the STS fundus was performed as in  (Cykowski et al. , 2008; Bodin et al. , 2018) , resulting in 101 equally spaced positions along the fundus, with 0 being at the anterior extremity and 100 at the posterior extremity.