dataesr/scientific-paragraphs-categorization huggingface dataset:
Data protection procedures Digitised resources of the Faculty of Biology of the AMU are very valuable assets resulting from the work of many people for several months of the project lifetime. In the event of their loss as an outcome of a failure of the storage system where the data are intended to be stored, their restoration would be extremely difficult, if not impossible. Therefore, the key issue is to ensure adequate protection measures by implementing procedures that ensure the performance of backups. This allows us to reinstate resources and restore the full functionality of the system. As already mentioned in the AMUNATCOLL system, we deal with two types of data: metadata and graphic data in the form of scans and photos. Therefore, two different approaches were used, which considered the nature of working with the system and the resources available.
Metadata describing the specific features of the collected specimens were stored in a database and were especially prepared for this purpose. Two-level data protection is used here. The first level is the registration of changes to individual records. In the case of a record change, its previous version is saved in the database with the annotation describing who and when the change was made. This allows us to restore the contents of the record, e. g. , in case of a mistake. Another type of database content protection is to perform regular snapshots of the database. This is done automatically at least once a day at night when modifications have been detected. This way, the different versions of the database are preserved from many months back. A slightly different approach has been designed and implemented for iconographic data.
Due to the considerably greater data capacity compared to metadata, the number of copies has been limited to three. The first copy is the data directly used by the AMUNATCOLL system for daily operational work. The second and third are backups run only in case of an emergency. The difference between them is that the second copy is stored on the disk system in the same location as the main copy, while the third is a geographic copy. In the case of the second copy, its physical proximity facilitates the process of possible data recovery after the failure. Locating the third copy on another system makes it resistant to much more serious threats, such as fire or flooding.
Conclusions The creation of a uniform IT system in the AMU-NATCOLL project ensures interoperability, communication with other databases, and the possibility of creating applications based on it that allow for multidimensional data analysis, including geoinformatics. Moreover, it allowed for equipping the research team of the project with tools enabling effective work on digitised biological resources and opening up cooperation with other entities implementing projects in the field of biodiversity. It should be noted that the guarantee of the durability of the operation and further development of the system is provisioned by hardware infrastructure and organisational facilities brought by the project partner Poznan Supercomputing and Networking Center (PSNC 2021). It worth emphasising that the challenges defined in the AMUNATCOLL project, especially in the field of data processing, are among the most demanding.
This is due both to a specific quantitative indicator (over two million records), as well as a capacity indicator (hundreds of terabytes of multimedia). This work focuses on the presentation of the essential elements of the AMUNATCOLL system related to the description of digitised objects, their storage both during development and for later sharing for operational purposes, and the preparation and use of dedicated tools for the automation of work supporting effective processing. The metadata structure needed to store taxonomic data was based on the ABCD standard and extended with fields specific to the project requirements. The design and structure of the database as also individual, addressing the needs related to the storage of descriptive data, as well as information on iconography and supporting the functionality of the portal.
Carrying out the digitisation process on such a large scale with the participation of a large team would not have been possible without technological support. For this purpose, a set of tools and infrastructural, application and procedural solutions supporting these activities were developed. Not only logistical issues but also the different nature of the data being processed (concerning specimens or iconography) were taken into account. The need to properly secure the results of the work carried out was done in developing appropriate backup procedures that would allow the system to be properly restored in the event of technical problems. Acknowledgements. This work has been supported by the AMUNATCOLL project and has been partly funded by the European Union and Ministry of Digital Affairs from the European Regional Development Fund as part of the Digital Poland Operational Program under grant agreement number: POPC.
Since the first classification and annotation of miRNAs  (7, 8) , accurately identifying them as well as the regulatory networks in which they are involved has proven difficult  (9, 10). Accurate prediction of known and novel miRNAs along with their targets is however essential for increasing our understanding of the miRNA biology  (3). Nowadays, a common experimental practice is to identify miRNAs and their expression patterns using next generation sequencing technologies (NGS)  (11). Commonly, NGS experiments are able to generate more than 20 million sRNA-seq reads, thus promoting the development of algorithms to transform and process such data into biological information  (12). Currently, there are two computational strategies for the discovery of miRNAs: 1) genome-based approaches that rely on the mapping of the sRNA-seq reads to a reference genome and subsequent evaluation of the sequences generating the characteristic hairpin structure of miRNA precursors  (9) ; 2) machine-learning approaches which rely on the biogenesis features extracted from the knowledge on miRNA sequences available in databases such as miRBase  (13)  and on the analysis of the duplex structure of miRNAs  (14).
We extensively benchmarked BrumiR on animal and plant species using simulated and real datasets. The benchmark results demonstrate that BrumiR is very sensitive, besides being the fastest tool, and its predictions were supported by the characteristic hairpin structure of miRNAs. Finally, we also applied BrumiR to the discovery of miRNAs of Arabidopsis thaliana and identified three novel high-confidence miRNAs involved in root development. These putative miRNAs were not discovered before by any other software, thereby showing the potential of using different approaches even in the case where high quality genomes are available. The code of BrumiR is freely available at https://github. com/camoragaq/BrumiR.
To evaluate the performance of BrumiR, we applied it to discover mature miRNAs on simulated sRNA-seq reads from 10 animal and 10 plant species (Figure  2A ). We compared BrumiR to the state-of-the-art genome-based miRNA discovery tools miRDeep2 (  21 ) and miR-PREFeR  (22) , which were developed specifically for animal (miRDeep2) and plant (miR-PREFeR) species. For each tested species, we generated two synthetic datasets with different error-rates (0. 01 and 0. 02) using the miRsim tool implemented and provided by the BrumiR toolkit (https://github. com/camoragaq/miRsim), and the high-confidence miRNAs annotated in the miRBase database (see Methods section). A total of 20 datasets with an average of 11. 5 million reads were simulated. The list of simulated miRNAs was considered as the ground truth, and benchmark metrics (Figure  2C ) were computed to assess the performance of BrumiR and of the other software (See Methods section) (Supplementary Table  S2 ).
BrumiR recovered more mature miRNAs than the others, on average 92% (opposed to 41% and 64% for miRDeep2 and miR-PREFeR, respectively), and presented the highest average recall across all the simulated datasets (Figure  2B ). BrumiR recovered more than 90% of the simulated mature miRNAs in 17 of the 20 simulated datasets (Figure  2B ). In particular in the H. sapiens, M. musculus and D. melanogaster datasets, BrumiR recovered three times more candidates than MiRDeep2 (Figure  2B ). As concerns precision, BrumiR tended to generate more putative candidates than MiR-PREFeR (median 316 vs 264) and miRDeep2 (median 445 vs 308). The slightly higher number of BrumiR candidates resulted in lower average precision than miRDeep2 (0. 59 vs 0. 69) and MiR-PREFeR (0. 63 vs 0. 87). This is due to the fact that BrumiR does not use the hairpin structure filter employed by the other software.
If we consider both precision and recall (F-Score), BrumiR was the top performer in 16 of the 20 datasets evaluated (Figure  2C ). With animal species, BrumiR always reached a higher F-score than miRDeep2. With plant species, BrumiR was better or comparable to miR-PREFeR on most datasets, except for Z. mays and P. pattens where miR-PREFeR reached a higher F-Score (Figure  2C ). In terms of computational time, BrumiR was the fastest method. In particular, BrumiR core was on average 30X faster than miRDeep2 and 10X times faster than MiR-PREFeR (see Table  S3 ). The speed of BrumiR relies on efficient alignment-free and graph-based approaches. Overall, we demonstrated with simulated data that BrumiR discovers putative mature miRNAs without a reference genome across different eukaryotic species achieving the highest accuracy and computational efficiency.
The hairpin structure of mature miRNAs is found in most of the BrumiR candidates. In order to assess the performance of BrumiR on real data, we collected public datasets for the same plant and animal species evaluated in the synthetic benchmark (Figure  2A ). On average, 15. 4 and 18. 2 raw million reads were used for the animal and plant datasets (Supplementary Table  S2 ), respectively. The predictions of BrumiR were compared against those of the state-of-the-art tools encompassing reference and de novo based methods   (14, 21, 22). In particular, we included mirnovo that similarly to BrumiR can discover mature miRNAs directly from the reads. Before running the tools, low-quality reads were removed using fastp (26) (~10%, see Methods section). All the predicted miRNAs for each tool were annotated using the miRBase database to identify known and novel predictions.
BrumiR achieved an accuracy (F-Score) better (animals) or comparable (plants) to the one obtained by the other software (Figure  3B3 ). Moreover, BrumiR consistently reached the highest recall for most of the datasets evaluated (Figure  3B2 ). The precision values of BrumiR were slightly lower for some datasets (Figure  3B1 ), however, none of the methods performed well on this metric (Figure  3B3 ). In particular, miRdeep2 reached the highest precision (~0. 7) on animal species and all methods performed poorly on the evaluated plant species (average precision <0. 3). The low precision with plant species may be the product of a low number of entries annotated in miRBase for plants (10. 414 vs 38. 471 animals) as well as of a higher complexity of plant miRNAs  (38). The BrumiR toolkit also provides a tool to determine the hairpin loop of miRNA precursor sequences, which is the main structural feature of miRNAs  (40).
BrumiR2reference maps the BrumiR predicted mature miRNA to the reference genome using an exhaustive alignment (See Methods section), generates precursor sequences, computes its secondary structure, and checks the hairpin structure using a variety of criteria inferred from analyzing more than 30,000 miRBase precursor sequences from animal and plant species (see Methods section). We used BrumiR2reference as a double validation for all the predicted mature miRNAs generated by BrumiR for the animal and plant datasets (Figure  3C ). On average, BrumiR2reference identified a valid precursor sequence having the characteristic hairpin structure for over 70% of the BrumiR candidates (Figure  3C ). In terms of speed, BrumiR core was the fastest tool. BrumiR was on average 120X and 220X times faster than miRDeep2 and miR-PREFeR, respectively (See Table  S4 ).
Overall, we demonstrated that BrumiR is a competitive tool for discovering mature miRNAs without a reference genome. We showed that it was the most sensitive on most of the datasets tested. The performance of our method was not only faster, but also better or comparable to the state-of-the-art tools. Moreover, we also provide a new mapper approach to be used when a reference genome is available, to further verify if a precursor sequence of the predicted mature miRNA is present in the genome. BrumiR therefore represents a reliable alternative for the discovery of mature miRNAs in model and non-model species with or without a reference genome. Three curated novel miRNAs fulfilling all the recommended criteria to annotate miRNAs in plants  (49)  were discovered by BrumiR (Figure  S9 ) (Table  S6 ).
However, the higher number of predictions of BrumiR results in a lower accuracy in some of the datasets evaluated, representing a potential weakness of our method. To address this, we developed the BrumiR2ref tool to refine the BrumiR predictions, thus reducing the number of false-positive putative miRNAs. We plan to further reduce the false-positive rate of BrumiR by using a random-forest classifier trained on the high confidence mature sequences available in miRBase. The latter will improve the accuracy of BrumiR even in the case when a reference genome is not available. It is important to observe that the miRNA annotations remain incomplete and although miRBase is the main repository for miRNAs, it cannot be considered as the gold-standard for most species (many of the entries have not been correctly validated, for example)  (9).
For this reason, the predictions of BrumiR are not based on miRBase in any step of the algorithm. However, miRBase can be used with all due precaution in a posterior analysis to verify the miRNAs inferred in case of not having any reference genome. In terms of computational resources and usability, BrumiR is the fastest method and provides a stand-alone package for running locally all the analyses. It further generates an output that is compatible with the bandage software  (43) , which can be employed to visualize and explore the results of BrumiR in a user-friendly way. Moreover, BrumiR reports other sequences expressed in the sRNA-seq data among which are putative isomiRs and longer non-coding RNAs, thereby providing additional biological insight. Finally, we tested the effectiveness of BrumiR on sequenced sRNA-seq libraries from the roots of A.
A critical parameter of any de Bruijn graph approach is the k-mer size  (28). We observed that the length of all mature miRNA sequences stored in the miRBase database (v21) (13) have a minimum value of 18 nt (Supplementary Figure  S1 ). We thus empirically set the k-mer size equal to 18. BCALM compacts the nodes of the de Bruijn graph into maximal unipaths by gluing all the nodes of the graph with an in-degree and an out-degree equal to one, thus generating the so-called unipath graph  (27). The unipath graph is the starting point of BrumiR (Figure1A). Notice that the unipath graph generated by BCALM does not represent what is expected for a set of mature miRNAs (one connected component for each miRNA) and therefore further graph operations are needed. BrumiR uses a minimum k-mer frequency (KM value) of 5 and all k-mers with lower frequency are ignored, without losing most of the information contained in the sequencing reads (Supplementary Figure  S2 ).
BrumiR then builds a second overlap graph, computes again the connected components, and selects the most expressed candidate as the representative of each cluster. The other members of each connected component are classified as putative isomiRs and saved in a file for later analysis. Identifying other expressed RNA sequences. In sRNA-seq experiments, different types of RNAs are expressed, some of which, such as small non-coding RNA elements, may have similar length with miRNAs  (33). The RFAM database (  34 ) is a collection of curated RNA families including three functional classes of RNAs (non-coding, cis-regulatory elements, and self-splicing RNAs), which are classified into families according to their secondary structure and sequence information (Covariance Models)  (34). We downloaded 3,017 RNA families present in RFAM (v14.
1) and excluded 529 miRNA families  (35). The sequences of 2,488 RFAM families were concatenated (a total of 2,736,549 sequences) and used to build a 16mer database with the KMC3 k-mer counter tool (36) ("-fm -n100 -k16 -ci5"). All distinct 16-mers with a frequency lower than 5 were excluded, leading to a total of 6,204,556 distinct 16-mers related to RNA elements. Additionally, we downloaded all the mature miRNA sequences from miRBase (v22. 1) (13) and built a 16-mer database with KMC3 ("-fm -n100 -k16 -ci1 mature. fa. gz"). RFAM 16-mers matching 16-mers from the 16-mer mature miRBase database were excluded from RFAM, leading to a 16-mer RFAM database with a total of 6,204,487 distinct 16-mers. Finally, the BrumiR candidates  (18-24 length)  were matched to the 16-mer RFAM database, and matching candidates were excluded and reported as sequences potentially associated to other RNA elements.
1) (13) using a modified version of the bpRNA program  (41)  (Figure  S6 ). Benchmarking BrumiR using simulated sRNA-seq reads. We simulated synthetic reads from animal and plant species, and compared the results of BrumiR to those obtained with the miRDeep2 (  21 ) and miR-PREFeR (  22 ) tools. The sRNA-seq reads were simulated using miRsim (https://github. com/camoragaq/miRsim), a tool that we developed specifically for simulating sRNA-seq reads from a list of known miRNA mature sequences. miRsim is based on wgsim (https://github. com/lh3/wgsim), which is a widely used tool for simulating short Illumina genomic reads. miRsim includes functionalities specific of sRNA-seq reads such as variable depth/coverage and shorter read lengths. miRNA mature sequences were obtained from miRBase  (13)  for animal (High Confidence) and plant species.
The animal species that we considered were: Homo sapiens, Mus musculus, Drosophila melanogaster, Danio rerio, and Caenorhabditis elegans, while the following plant species were included: A. thaliana, Oryza sativa, Physcomitrella patens, Zea mays, and Solanum lycopersicum. Supplementary Table  S1  provides further details (i. e. number of reads, number of mature miRNAs etc. ) for each simulated dataset. MiRDeep2 was run on the animal datasets with the default parameters providing the respective reference genome. Similarly, miR-PREFeR was run with the default parameters on the plant datasets. BrumiR was run with the default parameters on both the animal and plant datasets. The list of simulated miRNAs was considered as the ground truth, and precision, recall and F-Score quality metrics were computed to assess the performance of each discovery tool.
The benchmark metrics were defined as follows: where: TP= true positive elements predicted as miRNAs present in the miRBase input list. FP= false positive elements predicted as miRNAs but not present in the miRBase input list. FN= false negative elements not predicted as miRNAs, but that were present in the miRBase input list. Benchmarking BrumiR using real sRNA-seq reads. We downloaded publicly available sRNA-seq data for the plant and animal species listed in the synthetic benchmark, and two datasets for each species were included (Supplementary Table  S2 ). Additionally, we included mirnovo  (14) , a tool that can discover miRNAs without a reference genome. The predictions of BrumiR were benchmarked along with MiRDeep2 (v2. 0. 1. 2)  (21)  region, and exact overlap in the antisense miRNA sequence  (9).
Methods section), and the new graph is cleaned again by removing tips (Figure 1. 4). Clusters of unipaths (connected components) with topologies related to sequencing errors are also removed (Figure 1. 5, Methods section). BrumiR attempts to re-assemble all unipaths within a connected component (CC) of the graph, and those with between 18 and 24 nt are classified as putative miRNAs, while longer re-assembled unipaths (>24 nt) are classified as other longer sequences (Figure 1. 6). BrumiR then restores missing connections by re-clustering the putative miRNAs performing an all-vs-all comparison. The most expressed miRNA is selected as the representative of the cluster (Figure 1. 7) and the remaining members are classified as potential isomiRs (Figure 1. 7). The final BrumiR step uses the RFAM database to discard predicted miRNAs matching to other classes of RNA (e.
ACKNOWLEDGEMENTS This work was supported by  CONICYT   BECAS CHILE DOCTORADO 2016/FOLIO 72170320  granted to CM, by a post-doctorate fellowship from the  Agence National de Recherche  ( ANR-GREEN 17_CE20_0031_01 ) granted to MGF, as well as by  Fondo Nacional de Desarrollo Científico y Tecnológico (FONDECYT)-ANID  grant  1170926 ,  ANID PCI-Redes Internacionales entre Centros de Investigación  grant  REDES180097  and  Instituto Milenio iBio -Iniciativa Científica Milenio MINECON  granted to E. A. V. This research was performed using the computing facilities of the  LBBE/PRABI  and the  France Génomique e-infrastructure  ( ANR-10-INBS-09-08 ).
3 , 3 Methods section), and the new graph is cleaned again by removing tips (Figure 1. 4). Clusters of unipaths (connected components) with topologies related to sequencing errors are also removed (Figure 1. 5, Methods section). BrumiR attempts to re-assemble all unipaths within a connected component (CC) of the graph, and those with between 18 and 24 nt are classified as putative miRNAs, while longer re-assembled unipaths (>24 nt) are classified as other longer sequences (Figure 1. 6). BrumiR then restores missing connections by re-clustering the putative miRNAs performing an all-vs-all comparison. The most expressed miRNA is selected as the representative of the cluster (Figure 1. 7) and the remaining members are classified as potential isomiRs (Figure 1. 7). The final BrumiR step uses the RFAM database to discard predicted miRNAs matching to other classes of RNA (e.
Cardiac disease diagnosis and identification is problematic mostly by inaccurate segmentation of the cardiac left ventricle (LV). Besides, LV segmentation is challenging since it involves complex and variable cardiac structures in terms of components and the intricacy of time-based crescendos. In addition, full segmentation and quantification of the LV myocardium border is even more challenging because of different shapes and sizes of the myocardium border zone. The foremost purpose of this research is to design a precise automatic segmentation technique employing deep learning models for the myocardium border using cardiac magnetic resonance imaging (MRI). The ASPP module (Atrous Spatial Pyramid Pooling) was integrated with a proposed 2D-residual neural network for segmentation of the myocardium border using a cardiac MRI dataset.
• A contrast enhancement method is proposed for preprocessing the input cardiac MRI dataset. Various performance metrics are used to relate the performance of recent models and the proposed model in deep learning contexts. The rest of the paper is organized as follows. Section 2 describes related work. Section 3 describes materials and methods for the dataset, preprocessing, proposed model, and evaluation criteria. The experimental results are discussed in Section 4. Section 5 concludes the work and provides direction for future work.