dataesr/scientific-paragraphs-categorization huggingface dataset:
Community building A final general high-level objective of ChEESE-1P was that of community building by integrating around HPC research institutions, academia, industry, and public governance bodies concerned with geohazards. To this end and, in addition to the aforementioned engagement with the IUB members, the consortium pursued various actions and activities that included: 1. Training activities. ChEESE-1P organised 15 training courses including 5 PRACE Advanced Training Courses (PATC) on the use of flagship codes and 2 EU-ASEAN schools. In total, training activities involved over 400 attendees from 45 different countries in Europe, North and Latin-America, and ASEAN Member States. 2. A Zenodo collection of ChEESE-1P codes and tools (see Table  3  for references) for code distribution and data sharing with efforts already in place to integrate the ChEESE catalog as a future European Plate Observing System (EPOS-ERIC) service including definition of a metadata scheme for software and ensure interoperability with the EPOS catalog (starting from the EPOS Volcano Thematic Core Service software metadata model).
ChEESE-2P: towards the next project phase ChEESE-2P (2023-2026) is continuing with the preparation and optimisation of flagship codes on computational seismology, tsunami science, volcanology, and magnetohydrodynamics in order to bring them to the next level and consolidate the role of the CoE in harnessing the scientific community, the EuroHPC infrastructures, the National Competence Centers (NCCs), and public and private industry parties. However, ChEESE-2P includes also community codes from geodynamics and glacier hazards, two disciplines that were untapped during the first project phase. Enlarging the application domains pursues the engagement of other geophysics communities and the attraction of potential end-users from other countries (e. g. eastern and northern Europe). Similarly to the first project phase, the flagship code preparation roadmap articulates around the adequacy of applications to the new EuroHPC heterogeneous hardware systems (including porting, optimisation, audits, and performance monitoring) and on co-design with major EU initiatives on OpenSequana (EuPEX) and RISC-V (EuPilot) exascale hardware prototypes, ensuring an effective integration of co-design hints back to the main applications.
The second phase of the project also considers critical exascale technical challenges not fully addressed yet, including code resilience, fault-tolerance, (run-time) load balancing, energy efficiency, code performance portability and containerisation for deployment on pre-exascale tier-0 (LUMI, Leonardo, MareNostrum-5) and petascale tier-1 EuroHPC infrastructures. This includes the preparation of services and policy recommendations for an eventual EuroHPC emergency (urgent computing) access mode in case of geohazards, for which containerisation of components or whole workflows allowing fast and reliable deployment and setup of simulations is particularly relevant. ChEESE-2P farms a new generation of 9 Pilot Demonstrators ( Table  9 ) to replace those predecessors that reached a higher TRL and that, to some extent, were transferred elsewhere in the ecosystem for further development or downstream service implementation.
The new PDs are underpinned by concepts like multi-physics, multi-scale, multi-source, multi-hazard, and integration of phenomena that will require developments in flagship codes in order to incorporate new physics (functionalities), couplings, and forcing terms. During the project, the PDs will materialise in various capability and capacity Simulation Cases (SCs) defined on the basis of scientific interest, social relevance, window of opportunity (e. g. hazardous events that have occurred recently or are likely to occur in the near future) and homogeneous geographic coverage across Europe and beyond. The resulting datasets (e. g. hazard maps) will be EPOS-compliant and follow an EOSC-enabled quality approach, including automatic FAIR validation. In addition, and in order to foster the urgent computing access mode in EuroHPC tier-0/tier-1 systems for emergency management during high-impact geohazardous events (earthquakes, tsunamis, and volcanoes), 4 PDs will be exploited for urgent computing service enabling, including related technical challenges and access policy recommendations.
Acknowledgements This work has been funded by the  European Union   Horizon 2020 research and innovation program  under the  ChEESE  project, Grant Agreement No. 823844 , by the  European High Performance Computing Joint Undertaking (JU) , Grant Agreement No  101093038 , and by 4 different  Partnership for Advanced Computing in Europe (PRACE)  projects from calls  20  and 21 for granting  ChEESE activities  with a total of 170M core hours on different machines: VOHA ( ID 2019215114 ),  TSUCAST  ( ID 2019215169 ),  SEISVIEW  ( ID 2019215212 ) and  TsuHazAP  ( ID 2020225386 ). We thank two anonymous referees for their constructive comments. This article is in memoriam of our colleague and friend  Dimitri Komatitsch  (1970-2019) [81] and to his invaluable legacy in computational seismology.
Designed for large-scale, time-domain, full-waveform modelling and inversion ranging from laboratory ultrasound studies to planetary-scale seismology. SPECFEM3D [2] CS 1 1 Solves acoustic (fluid), elastic (solid), coupled acoustic/elastic-poroelastic or seismic wave propagation in any type of conforming mesh. It can also be used for ocean acoustics (doi:10. 5281/zenodo. 6394675). SeisSol [3] CS 1 1 Solves seismic wave propagation (elastic, viscoelastic) and dynamic rupture problems on heterogeneous 3D models using high-order Discontinuous Galerkin (DG) discretisation and local time-stepping on unstructured adaptive tetrahedral meshes (doi:10. 5281/zenodo. 7598601). xSHELLS [4] MHD 1 1 Solves rotating incompressible flows and magnetic fields in spherical shells including the coupled induction equation for MHD and temperature.
Based on a semi-spectral approach combining finite differences in radius and spherical harmonics, semi-implicit second-order time scheme. PARODY_PDAF [5] MHD 2 1 Solves the Navier-Stokes and Maxwell equations in the MHD approximation in spherical shell geometry, offering the possibility of ensemble simulations using the Parallel Data Assimilation Framework (PDAF) library (doi:10. 5281/zenodo. 6415313). ExaHyPE [6] CS 2 2 Solves systems of hyperbolic PDEs including seismic wave propagation problems using high-order DG discretisation with local time-stepping on tree-structured adaptive Cartesian meshes (doi:10. 5281/zenodo. 6387677). ASHEE [7] V 3 2 Solves multiphase fluid dynamic model for compressible volcanic plume mixtures composed of gaseous components and solid particle phases (doi:10.
5281/zenodo. 6560777). T-HySEA [8] L-HySEA [9] T 3 2 T-HySEA solves propagation of tsunamis generated by earthquakes including seafloor deformation, propagation and inundation of coastal areas under the shallow water approach in spherical coordinates and using high-order Finite Volume discretisation. Similarly, L-HySEA solves the Savage-Hutter equations for landslide triggered tsunamis in a two layer shallow water system where the landslide represents the bottom layer and the tsunami the top layer (doi:10. 5281/zenodo. 6400825). FALL3D [10] V 3 2 Solves set of advection-diffusion-sedimentation (ADS) equations for the atmospheric transport and ground deposition of particles and aerosols using a Finite Volume explicit scheme (doi:10. 5281/zenodo. 7267808).
Numerical simulations of CLR dynamics Herein, we present the results of numerical simulations of system (1) using ode45 in Matlab to integrate the differential equations during the rainy seasons, with the impulses giving each year's initial conditions. We take T = 250 days, which is the length of rainy season in Cameroon  [37]  and also corresponds to the length of coffee production period. For the simulations, we suppose that all leaves are initially healthy, which means that I(0) = 0 leaf, with S(0) = 500 leaves. Infection is initiated by uredospores, with U (0) = 3000 spores. There are no flowers and berries initially, F (0) = 0 flower and B(0) = 0 berry, because simulations start at the beginning of the production period. The initial conditions are then (S(0), I(0), U (0), F (0), B(0)) = (500, 0, 3000, 0, 0).
The pipeline uses third-party software to infer homologous genes in various species and highlight differences in the expression of the core-genes. To illustrate the methodology and demonstrate its usefulness, we focus on the emergence of the highly virulent Leptospira subclade known as P1+, which includes the causative agents of leptospirosis. Here, we expand on the genomic study through the comparison of transcriptomes between species from P1+ and their related P1-counterparts (low-virulent pathogens). In doing so, we shed light on differentially expressed pathways and focused on describing a specific example of adaptation based on a differential expression of PerRA-controlled genes. We showed that P1+ species exhibit higher expression of the katE gene, a well-known virulence determinant in pathogenic Leptospira species correlated with greater tolerance to peroxide.
Software Annotator-RNAtor for RNA comparisons -Overall analyses (Fig 2 ): Further comparative expression analyses were conducted as follows. The genome sequences of the nine evaluated Leptospira species (Table  1 ), were annotated using Prokka v1. 14. 5. For each dyad possibility, a standalone BLASTP search was performed using the respective protein sequences (. faa) files. Network connections were established using a python programming package NetworkX version 2. 6. 2  [26] , with a similarity threshold set at 60%. Essentially, all proteins showing more than 60% similarity with one of the members (putative homologues) were clustered together. Each cluster of proteins was assigned a name (e. g. , Lsp_1), which was then used to replace the original locus tags in the. gff file generated by Prokka.
Protein files were also screened for putative homologues using GET_HOMOLOGUES  [27]  through OrthoMCL method. The reannotated. gtf files were utilized to map the reads to their corresponding genomes using BWA. -Input Data and formats using the GUI: The pipeline accepts whole-genome sequence FASTA files. If the genomes are already annotated, it requires genome FASTA, protein FASTA, genome feature files and coding sequences for Annotator. The RNAtor module requires related FASTQ files as input. Currently, the pipeline supports analysis of both paired-end short-read as well as long-read nanopore data. -Third party software and dependencies, data processing and clustering: The pipeline utilizes Prokka for annotating the genomes, followed by BLAST+ programs  [28, 29]. NetworkX  [26]  and GET_HOMOLOGUES v16052022  [27]  are used for screening homologues in subsequent steps  [27].
Homologues can be screened based on the percent similarity/identity as parameters. Additionally, GET_HOMOLOGUES provides options to cluster homologues using Bidirectional best-hit , COGtriangles v2. 1  [30] , or OrthoMCL v1. 4  [31]. At the end of the process, a tab-separated file is generated, in which Annotator-RNAtor creates a presence (locus) or absence (-) map, facilitating the finalization of the analyses. The RNAtor menu in Annotator-RNAtor implements the bwa  [32]  and bwamem commands. Annotator-RNAtor is designed to utilize the annotation files from NCBI. RNAtor invokes Pandas and FeatureCounts of Subread  [33]. Supported platforms and availability: Annotator-RNAtor is built using Python3 with PyQt bindings for a user-friendly GUI interface. The efficiency of Annotator-RNAtor relies on running tasks in parallel, utilizing multiple processors and GNU Parallel.
The pipeline has been tested on Linux/Ubuntu with 8 GB RAM and 64-bit processors. It includes a user manual with a detailed hands-on tutorial and an installation script that verifies dependencies. Alternatively, due to constant version upgrades in software, the package also includes a conda recipe file to install required dependencies in conda environment. The stand-alone pipeline can be downloaded from GITHUB via https://github. com/BactSymEvol/Annotator. -Statistical analyses: In the RNAtor module, the. gtf and. sam files were utilized to perform read counts using featureCounts v2. 0. 1 from the Subread package. The count files for each sample were combined into a table using a custom script, and these results were subsequently analyzed using DESeq2 version 3. 14  [34]. To retain only genes from the soft-core genome and exclude genes from shell and cloud genomes, genes without any counts in less than 8 species were not considered.
The genomes were grouped into P1+ and P1-categories. The data were normalized and transformed using the VarianceStabilizingTransformation (VST) function to yield a homoscedastic matrix of values. Further, Log 2 fold change values were extracted with an FDR-adjusted p values (padj) cut-off of 0. 05. To facilitate the interpretation of results, the differentially regulated genes are presented using the locus tag of Leptospira interrogans serovar Manilae str UP-MMC-NIID-LP. We investigated the biological functions of the gene differentially expressed (Fold change>2 and padj <0. 05) and the putative pathways that could link them through a STRING  [35]  association analysis and COG  [36]. The STRING analysis enabled the identification of protein interactions and functional enrichment in the two datasets, which consisted of up-and down-regulated genes in P1 + species.
To accomplish this, the protein-coding sequences from both datasets were concatenated and analyzed collectively using STRING v11. 5  [35] , with L. interrogans serovar Lai str 56601 serving as the reference. Of note, L. interrogans serovar Manilae, which had been the reference genome throughout the study, is not yet included in the STRING database. For the analysis, the highest confidence interaction score of 0. 900 was employed to focus solely on strong interactions, and any disconnected nodes (protein) in the network were hid. On the other hand, the protein-coding sequences from both datasets were classified based on Clusters of Orthologous Genes (COG) using eggNOG mapper (options --evalue 0. 001 --score 60 --pident 40 --query_cover 20 --subject_cover 20 --target_orthologs all --pfam_realign denovo)  [37, 38].
PerRA alignment analysis Ortholog sequences of the PerRA protein in Leptospira species were searched with BLASTP version with Jalview software (using Clustal for sequence coloring  [40] ). Multiple sequence alignments of all P1+ and P1-species were visualized using Alvis software version 0. 1. The residues of PerRA involved in the coordination of the regulatory metal were represented using Mol* 3D Viewer of PDB website.
General description of Annotator and RNAtor workflow There are two primary modules to analyse data in this pipeline (Fig  2 ). In the initial steps, Annotator module assigns homologous sequences in the different species studied and reannotates the genomes. Annotator uses two different methods namely NetworkX  [26]  and GET_HOMOLOGUES  [27]  to assign homology. The user can define similarity cut-off for NetworkX and identity cut-off for GET_HOMOLOGUES. This will depend on the phylogenetic proximity of the strains studied (herein we use 60% and 45% respectively). In the end, the pipeline reannotates the homologous genes with common user-defined unique tags that are found in the different species (convert GFF to GTF). A gene association table is generated at the end of this module which shows presence-absence of genes in the genomes (S4 Table ).
Singletons are not reannotated using the pipeline and can be seen as locus tags in the table. Once the gene association table is generated, the RNAtor module is used to map transcriptome data onto the genomes to generate a counts matrix file. This matrix is generated using the. GTF files (containing reannotated common unique names), BWA for mapping and featureCounts scripts to count the number of reads by genes. The final counts comparison file (S4 Table ) shows the expression of the homologous genes in all the species studied. Species-specific gene counts can also be seen.
Apart from these, Hidden Markov approach also exist which can be used to detect homologies but may require higher computational power and well-curated datasets. In the current pipeline, homology analysis is performed using the Annotator module which considers protein similarity searches instead of DNA, making it more sensitive. DNA:DNA alignments cannot detect homology post divergence of 200-400 million years. Compared to this, protein:protein or protein:translated alignment are more accurate. Protein:protein alignments with expectation values < 0. 001 can reliably be used to infer homology, whereas DNA:DNA expectation values < 10 -6 often occur by chance, and 10 -10 is a more widely accepted threshold for homology based on DNA:DNA searches  [51]. We used two methods relying on BLAST to search for homologues.
NetworkX  [26]  uses a graph method to screen out homologues from the BLAST results (based on the user-provided threshold) whereas GET_HOMOLOGUES  [27]  uses identity to screen them. These approaches both have their benefits depending on the type of data being analysed. For example, if the purpose of the analysis is to find gene presence/absence in unrelated species or genera the NetworkX method provides more flexibility when searching for similar sequences. It also takes into account fragmented proteins that are not considered as homologues by other tools. This may provide an advantage while working with fragmented assemblies as well. In this study, we analyzed 9 Leptospira genomes (Fig  1  and Table  1 ) along with their transcriptome data. Annotator module was used to extract homologous genes and generate a gene presence/absence table using both NetworkX and OrthoMCL in GET_HOMOLOGUES method.
Acknowledgements We thank  Farah Martin  for help with plasmid constructs,  Robert Gaultney  for RNA extraction. This research was supported by the  Canadian Institutes of Health Research (CIHR)  (# 450862 ) (FJV); by the  Institut Pasteur  through grant  PTR 30-2017  (MP and FJV) and by  Institut Pasteur & INRS-Centre Armand-Frappier  through their  Pasteur International   Joint Research Units program  " LEptospirosis Pasteur NETwork (LePNet) " (MP and FJV). CN received a Ph. D. studentship Calmette & Yersin from the  Institut Pasteur International Network. AGG was funded by a  PTR2019-310  grant (NB). FJV received a  Junior 1 and Junior 2 research scholar  salary award from the  Fonds de Recherche du Québec-Santé. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the article.
2. 13. 13 0 and HMMer version 3. 3. 1 against the reference proteomes database of the 68 Leptospira spp. Only the sequences with an e-value  0. 01 were retained. The results were subsequently aligned by MAFFT version 7. 467 using the L-INS-I algorithm, and tree inference was computed with IQ-TREE version 2. 0. 6 with the best-fit model JTTDCMut + F + R5. Direct homologues of PerRA were extracted from this phylogeny, and the final alignment was refined using MAFFT (version 7. 467 using the L-INS-i algorithm) with only these sequences. The alignment of PerRA among Leptospira spp. was visualized.
d ir = d tr SI NR th (8) As the interfering nodes and also the intended transmitter must be located within the receiver FOV, the MUI zone for a certain pair of intended T x and R x can be defined as the circular segment with a radius d ir and center angle 2ψ, as illustrated in Figure  3. Therefore, this study will provide an estimation of the MUI zone using MATLAB simulations that consider the parameters summarized in Table  1. fering nodes. The simulations are made assuming a seven-lane straight road, with the RX located in the middle lane (lane 4), whereas the intended TX is placed along all the scenarios lane by lane. The acquired findings are shown in Figure  4 , where the horizontal and vertical axes denote the longitudinal (i. e. , distance from the receiver) and lateral (i. e. , lane number) locations of TX, respectively.
The software solution is developed as a Linux driver that directly communicates with the cape and the Linux networking stack. The VLC interface is configured in OpenVLC as a new communication interface that can benefit from a wide range of Linux services. The driver makes use of the BBB platform's Programmable Real-time Units (PRU)  [42]. Next, the output signal is processed by a driver which converts the digital signals generated by the BBB platform into beams of the modulated LED light. For simplicity and time-efficiency reasons, the VLC prototype has been developed on a Beagle Bone Black (BBB) platform. The BBB is a low-cost, community-supported development platform for developers  [41]. This board is able to manage all digital tasks such as networking, coding, and decoding the information, benefiting from the Linux operating system facilities.
The BBB platform is interconnected with a computer, and together they construct the digital information to be sent. Thus, the BBB platform transforms the data to send into a binary string and performs the coding and the modulation of the data, in accordance with the specified requirements. The software solution is developed as a Linux driver that directly communicates with the cape and the Linux networking stack. The VLC interface is configured in OpenVLC as a new communication interface that can benefit from a wide range of Linux services. The driver makes use of the BBB platform's Programmable Real-time Units (PRU)  [42]. Next, the output signal is processed by a driver which converts the digital signals generated by the BBB platform into beams of the modulated LED light. At the receiver side, the light beam reaches the photosensitive element-a reversed bias PIN photodiode connected to a transimpedance circuit which transforms the incident light into an electrical current, and then into a signal that will be further processed in order to extract the data.
In a simple form, signal processing refers to signal amplification from mV levels to a few volt levels and signal band-pass filtering that removes the DC component, the low-frequency noise components introduced by incandescent and fluorescent light sources, and also high-frequency noise components such as light-generated shot noise and electronic component-generated thermal noise. For the best results, the band-pass region is established in accordance with the selected code's power spectral distribution for the given data rate. Next, the signal is digitized and fed to the BBB platform which extracts and processes the data. It should be emphasized here that the challenges associated with the development of the analog front end for vehicular VLC applications are related to the correct tuning of parameters of the transimpedance amplifier gain with respect to the parasitic capacitance of the circuit, as the properties of the selected electronic components (photodiode, operational amplifier, etc.
69 cases of flexible pes planus aged between 18-25, who applied to the orthopedics and traumatology clinic and met the inclusion criteria, will be included in the study. Participants will be randomly assigned to one of 3 groups according to the order of arrival. The primary outcomes are measures navicular drop and pedobarographic analysis before and after intervention, and the secondary outcomes include balance, femoral anteversion and lower extremity muscle strength. cases will be randomized to three different exercise groups: structured pes planus exercise group, exergame, and serious game group, and will be subjected to 18 sessions. ''After the treatment, the initial measurements will be repeated. essential 1b-iv) RESULTS section in abstract must contain use data Report number of participants enrolled/assessed in each group, the use/uptake of the intervention (e.