dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sk lodowska-Curie  grant agreement No  860370. This work was also funded by  Spanish Ministry of Science, Innovation and Universities  under grant  RTI2018-097088-B-C32 , from  Xunta de Galicia-Consellería de Cultura, Educación e Ordenación Universitaria Accreditation   2019-2022 ED431G-2019/04  and Reference Competitive Group Accreditation  2021-2024 ED431C2021/048 , co-funded by ( ERDF/FEDER   programme ).
Conclusions In this review, we discussed the use of ANN to generate, augment and evaluate RIRs. From the reviewed studies, we see that GAN, CNN, and MLP networks are used to generate RIRs as well as to estimate room geometry and absorption coefficients. In most studies, the authors have shown that better results can be achieved by applying additional augmentation to the generated RIRs. We can also conclude that the choice of datasets for training, as well as the choice of an acoustic model consisting of certain neural networks, strongly determines the results obtained when performing evaluation tasks of the generated RIRs, such as ASR. The authors of the IR-GAN algorithm, whose acoustic model was based on the TDNN, achieved the best results. They were able to obtain the best WER value when the AIR dataset was selected as the training data.
The training dataset consisted of a total of 200 high-quality images divided into n = 40 images of tissue-mimicking silicone phantoms with different optical properties ranging from μ a = 0. 005 to 0. 05 mm -1 for absorption and from μ s ' = 0. 5 to 3 mm -1 for reduced scattering; n = 52 images of hands from different Caucasian men and women in various configurations; n = 108 images from ex vivo and in vivo swine organs in several orientations (stomach, small bowel, colon, kidney, pancreas, liver, and spleen). Additional information regarding SSOP and its deep learning implementation (code and sample data) are available as follows: https:// healt hphot onics. org/ resso urces/ sfdi-resou rces/.
Statistical analysis All statistical analyses were performed using the GraphPad Prism software for macOS (GraphPad Software, Inc. , San Diego, CA, United States), version 9. 1. 1. Data are shown as mean and standard deviations (SD) unless otherwise indicated. Capillary lactates were normalised according to systemic lactates to reduce variability. In order to assess a possible correlation between normalised lactates and SSOP-StO2, a Pearson's rank correlation coefficient was calculated. A Student's t test was used to compare continuous variables after confirmation of a parametric distribution using the Kolmogorov-Smirnov normality test. A p value < 0. 05 was considered statistically significant.
Discussion In the present experimental pure bowel ischaemia model, the Trident imaging system could provide accurate quantification of tissue StO2, which is a useful biomarker of tissue viability and is also correlated with a validated marker of tissue perfusion  [24]. Capillary lactates have been substantially studied by our research group as a ground truth assessing NIR optical imaging technologies  [9, 27]. Local bowel capillary lactates were normalised via systemic lactates to reduce the variability between animals. The high number of paired normalised capillary lactates and StO2 datasets (175) at different time points allowed to compute a negative correlation between those two parameters. Previously, our group reported a correlation between StO2 and lactates using HSI within similar ranges (r = -0.
As compared to FA, endogenous imaging methods including SSOP can provide a greater amount of quantitatively significant data by characterising several tissue components including StO2, which reflects intracellular metabolic changes induced by ischaemia. HSI is an eminent well-known example of endogenous imaging. Its accuracy in assessing perfusion during oesophagectomy has been studied in 22 patients by discriminating between gastric conduits with and without laparoscopic ischaemic preconditioning (StO2 66 vs. 78%, p = 0. 03)  [37]. In colorectal surgery, HSI has been compared between to FA, showing comparable results in detecting the optimal demarcation line  [38]. Although HSI is an efficient technology, it does not allow for a video-rate acquisition and the large datasets require postprocessing algorithms to discriminate features according to spectral curves.
Funding This research was funded by the  European Research Council (ERC)  under the European Union's  Horizon 2020 Research and Innovation Programme  under Grant Agreement No. 715737  (QuantSURG; S. G. ),  France Life Imaging WP3  (S. G. ), the  French programme "Investissement d'Avenir " run by the  "Agence Nationale de la Recherche"  under Grant Agreement No. ANR-11-INBS-006  (S. G. ), the  University of Strasbourg IdEx (S. G. )  and the  European Union 's  Horizon 2020 Research and Innovation Programme  under the  Marie Skłodowska-Curie Grant  Agreement No. 857894-CAST  (S. G. , M. D. ).
The study focused on the more prevalent, multisystemic rare conditions that require multidisciplinary care, services, supports and therapeutic interventions. Results: 29 rare conditions were selected across 18 ERNs, for care pathway development. Multidisciplinary input from multiple specialisms was relevant for all pathways. A high level of engagement was experienced from clinical leads and patient organisations. CPGs were identi ed for 26 of the conditions. Nurse specialist, Psychology, Medical Social Work and Database Manager roles were deemed essential for all care pathways. Access to the therapeutic Health Service Professionals: Physiotherapy, Occupational Therapy, and Speech and Language Therapy were seen as key requirements for holistic care. Genetic counselling was highlighted as a core discipline in 27 pathways demonstrating the importance of access to Clinical Genetics services for many people with RDs.
Condition selection We initially selected 30 rare conditions, across 18 ERNs, due to their estimated higher prevalence in the Irish population, based on preliminary data derived from the Irish ERN applications and Orphanet data  (32). Conditions were considered only if they were multisystemic and chronic in nature requiring multidisciplinary care and services. Rare cancers and rare infectious diseases were not included, as these do not fall under the remit of the Irish National Rare Diseases O ce (NRDO) but are covered, in Ireland, by separate national health service policy, commissioning and clinical governance structures.
Disease name Nurse Specialist Physio. Occup. Therapy. Speech and Language Therapy Psychol. Medical Social Worker Gen. Counselling Database Manager Dietetics Hereditary Haemorrhagic Telangiectasia ✓ ✓ ✓ ✓ ✓ Marfan syndrome ✓ ✓ ✓ ✓ ✓ ✓ ✓.
Registries Inclusion of database managers for registry development and curation was deemed a core requirement in line with ERN priorities. Registries are recognised as an invaluable resource for capturing epidemiological disease information and natural history, identifying patient cohorts available for clinical research, assessing therapeutic outcomes, generating evidence and monitoring CoEs for diseasespeci c key performance indicators that can be used to gauge ERN activity and impact. Centralised registry development to ensure data interoperability and uniform database structure is essential for integration into the ERN IT system (46). The use of Orphacodes within our care pathways promotes aligned codi cation with ERN RD registries, which are committed to Orphacode designation as a key parameter, and implementation of the EC eHealth Network Guideline on the electronic exchange of health data under the Cross Border Directive 2011/24/EU  (47).
Conclusion This pilot study explored the development of an optimal process for national RD care pathway design in the absence of agreed, common, best practice methodology. Partnership with RD patient representatives and service users was central to the delivery of this study. The high level of engagement experienced from clinical leads and patient organisations highlights the strong endorsement of this care pathway initiative by key RD stakeholders. RD patient needs and health care professional interventions common across all pathways were identi ed, highlighting the core roles of therapeutic HSCPs, Nurse Specialist, Psychology, Medical Social Work, Database Manager and Genetic Counselling for delivering optimum rare disease care. Effective care pathways offer the best opportunity to translate best practice, evidence-based guidelines into local healthcare services leading to service redesign and more targeted resources to optimise patient health outcomes.
Comparison of in silico typing to available phenotypes showed 84-96% concordance between phenotypic and genotypic predictions (supplementary dataset 1). A random subset of the SA1 dataset (n=371) was available at the time for phenotyping, which identified 32 isolates resistant to bedaquiline (BDQ-R). When combined with the in silico AMR predictions, 13 (10%) pre-XDR isolates could be reclassified as extensively-drug resistant (XDR), while 52 remained pre-XDR (41%), and 61 were not tested (49%) (Figure  1 ). Additionally, 17 MDR-TB, one RR-TB and one sensitive TB also had BDQ-R phenotypes. To predict how BDQ resistance emerged (clonally from a single event, or multiple times independently), we first created a maximum-likelihood phylogeny using core single nucleotide polymorphisms (SNPs) from the SA1 dataset.
Truncation of mmpR5 was observed in all lineages across a large South African dataset To contextualise our SA1 dataset into a broader South African cohort with accompanying MICs, we downloaded all publicly available South African M. tuberculosis isolates from the ENA and CRyPTIC consortium 13 datasets (n=5,253; hereafter referred to as "SA2"). Overall, we found that our SA1 dataset was wellsampled across the known diversity of M. tuberculosis in South Africa, with the exception of a large lineage 2. 2 clade primarily isolated in Cape Town (as noted previously; Figure  4 ). tuberculosis isolates (SA2) plus the SA1 dataset, built using core SNPs with IQTree v2. 2. 0. We used the antimicrobial resistance prediction tool ARIBA  15  with a custom reference gene set to rapidly identify truncated genes, including mmpR5.
ARIBA was able to identify 42/47 gene truncations (89%) from our exhaustively manually curated SA1 dataset. Three of the five missing calls were caused by IS insertions, and two had ambiguous heterogeneous calls that ARIBA did not report. Nevertheless, as ARIBA captured the majority of truncations, we used it to identify non-synonymous mutations and truncations in mmpR5, mmpL5 and mmpS5 in the much larger SA2 dataset. We identified 76 isolates in the SA2 dataset with predicted truncations resulting from 18 mutations, 11 of which were unique to the SA2 dataset (including 7 frameshifts, 1 IS6110 insertion, and 3 nonsense mutations) (supplementary dataset 1). Three additional isolates had one of two different frameshift mutations that resulted in altered proteins but not truncation. Plotting truncations onto the large SA1+SA2 phylogeny produced the same scattered prevalence as with the smaller SA1 dataset, confirming that truncations of MmpR5 occurred independently (Figure  4 ).
We also identified 50 isolates with one of 30 additional non-synonymous variant combinations in MmpR5. When assessing all variants in mmpR5 across both datasets, 43 mutations were unique to the SA2 dataset, eight mutations were found in both the SA1 and SA2 datasets, while 20 were unique to the SA1 dataset (not including the IS promoter mutations, which were not identified in the SA2 dataset). Across the SA1 and SA2 datasets we identified 73 separate variants in mmpR5  In addition to mmpR5 truncations, we also looked for evidence of truncation in mmpL5 and mmpS5 in the SA2 dataset. Overall we found 6 isolates with MmpL5 truncations, caused by 5 different mutations (Q333fs, Q702nonsense, R734fs, R115fs, and G138fs). There was no evidence of IS6110 interruption within or upstream of mmpL5. ( We only identified a single isolate with a mutation leading to a truncation in mmpS5 (P101fs).
The BDQ minimum inhibitory concentration (MIC) is increased in isolates with mmpR5 truncation Of the 79 MmpR5-truncated (or otherwise frameshifted) isolates found in the SA2 dataset, 75 were from the CRyPTIC dataset and had phenotypic data. 51 (68%) were BDQ-R (based on an ECOFF threshold of 0. 25 mg/L for resistance), while the remainder were BDQ-S. Four of the six isolates with MmpL5 truncations had phenotypic data and were all sensitive. The single isolate with MmpS5 truncated had no phenotypic data. As before, we found a number of BDQ-S isolates with predicted MmpR5 truncations. To gain better insight into the effect of MmpR5 truncation on resistance beyond the binary phenotypes, we plotted the BDQ minimum inhibitory concentrations (MIC) for 1,961 South African CRyPTIC isolates and separated them based on predicted truncation of MmpR5 and MmpL5 (no MIC was available for the MmpS5 truncated isolate).
mmpR5 has been the focal point of multiple BDQ-R-association studies 4,10 , but few have looked at truncation as an overall effect relating to BDQ-R. Our study also highlights the effect of mmpR5 truncation in a sizable cohort of M. tuberculosis samples, including not only our primary SA1 dataset, but also a large number of publicly available M. tuberculosis samples with paired MIC data (SA2)  13. We assessed disruption of mmpR5 using local assembly to simultaneously identify multiple means of truncation, including frameshifts, nonsense mutations, and also IS elements, which have been reported previously in separate studies  10, 19. We show that the most common mutations occur prior to the predicted DNA binding region of mmpR5, suggesting inactivation of the protein, although this remains to be confirmed in vitro.
25 mg/L). This explains why binary phenotyping alone reports a mix of both sensitivity and resistance  21. While we found that truncations in mmpR5 were important, we were limited in our ability to determine the specific contribution of certain mutations to BDQ susceptibility. Like other studies, we found a number of mutations, many of which were only represented by a single isolate  4, 10. For each isolate with a truncation of mmpR5 we used a phylogenetically closely related control without the truncation, to estimate the effect of the truncation on MIC. Most such pairs differed also by other SNPs than just those in mmpR5, making it difficult to assess the contribution of the mmpR5 mutation alone. In practical terms, we found that ARIBA is a robust tool for screening large datasets and identifying gene truncations, which was only slightly less sensitive than exhaustive manual curation.
However, we found it highly sensitive to data quality (including at regions of high GC content 21 especially in mmpL5), so recommend that predictions be separately and rigorously assessed. We acknowledge some other limitations of this study. Firstly, we focused mainly on truncations and disruptions of the predicted protein sequences, and so did not rigorously assess synonymous mutations. While we looked for promoter disruptions in the SA1 dataset, we did not look for promoter disruptions in the SA2 dataset as we used ARIBA to screen genes only. Finally, we focused our IS analysis on IS6110 only, and did not explore other IS movement. Overall, we recommend the continued use of MICs to identify modest shifts in BDQ resistance and local assembly tools, like ARIBA, to detect gene inactivations caused by truncations or disruptions.
Quality control: All raw reads were run through the quality control and decontamination pipelines in Clockwork v0. 10. 0 (https://github. com/iqbal-lab-org/clockwork) using the reference genome H37Rv (accession: NC_000962. 3). Samples that had <25x average coverage and/or >5% contamination were removed, leaving 505 M. tuberculosis samples (referred to as SA1).
Variant calling and regenotyping: Per-sample variant calling was completed using Clockwork v0. 10. 0 (https://github. com/iqbal-lab-org/clockwork) against the reference genome H37Rv (accession: NC_000962. 3). Clockwork is a variant caller which runs two other independent callers (SAMtools 22 , Cortex 23 ) on each isolate, and then builds a graph genome of the (reference genome and) two sets of variant calls, and remaps the reads to this graph, in order to adjudicate between the two callers where they disagree  24. Having done this, Minos 24 v0. 12. 0 was used to collect a non-redundant list of all segregating variants in the cohort, harmonise variant representation in positions where indels overlap SNPs (or other indels), and then remap the decontaminated reads one more time, to genotype all samples at all variants, providing consistent VCFs for all samples.
Publicly available South African TB: Raw sequencing reads for all publicly available South African M. tuberculosis samples from the European Nucleotide Archive (ENA) until October 2020 and the CRyPTIC dataset  13  were downloaded (n=5,802). All samples underwent quality control (described above), leaving 5,253 M. tuberculosis samples (referred to as SA2). Specific isolate sets used for each analysis is given in Supplementary Dataset 1. In silico lineage assignment and resistance profiling: Mykrobe 25 v0. 9. 0 was run on default settings using the raw reads against the 2020-01-14 TB panel. The output consisted of json files containing resistance predictions, mutation evidence, and lineage predictions.
Phylogenetic reconstruction: Single nucleotide polymorphisms (SNPs) were substituted into the H37Rv genome using BCFtools 26 consensus (v1. 10. 2) to create a whole-genome alignment of all samples (https://github. com/LeahRoberts/Mtb_South_Africa/tree/main/scripts). Repeat regions (e. g. PE/PPE genes) and genes known to be involved in antimicrobial resistance (including 100 base pairs upstream and downstream) were masked (https://github. com/LeahRoberts/Mtb_South_Africa/tree/main/mask_files) in the alignment. SNP-sites 27 (v2. 5. 1) using the '-c' flag was used to obtain all variant positions, as well as all constant sites using the '-C' flag. The final alignment of variant position and constant sites were then used to generate a phylogeny with IQTree.
Investigation of bedaquiline-associated mutations: Several genes were investigated for their role in bedaquiline resistance based on previous literature, which we refer to as "bedaquiline-R-associated" genes (mmpL5, mmpL3, mmpS5, atpE, amiA2, pepQ, era, rv1816, rv3249c, rv0678/mmpR5)  2, 13, 31, 32. For the SA1 dataset, we performed de novo assembly of all isolates using Shovill v1. 1. 0 (https://github. com/tseemann/shovill) (which uses SPAdes 33 ) with '--gsize 4. 4M' to investigate "bedaquiline-R-associated" genes variants and their effect on the predicted proteins. We then used blastn 34 v2. 9. 0 and a custom script (https://github. com/LeahRoberts/Mtb_South_Africa/tree/main/scripts) to determine nucleotide similarity and the predicted amino acid translation of the "bedaquiline-Rassociated" genes from the de novo assemblies.
The predicted protein sequences were then aligned with MAFFT 35 v7 at default settings and visualised using a neighbour joining tree created through Jalview 36 v2. 11. 2. 5. We used ISmapper 14 v2. 0 to characterise IS6110 insertion sites in the isolate genomes using the decontaminated reads and the reference genome H37Rv (accession: NC_000962. 3). To check for duplicated genes in our SA1 dataset, we mapped the decontaminated isolate reads to the H37Rv genome using BWA-MEM 37 v0. 7. 17-r1188 and checked coverage across the entire genome and at specific gene sites (mmpR5, mmpL5, mmpS5) using pysamstats v1. 1. 2 (https://github. com/alimanfoo/pysamstats) and a custom script (https://github. com/LeahRoberts/Mtb_South_Africa/tree/main/scripts) to revise our conclusions to the appropriate rationale.
For the SA2 dataset, we used ARIBA 15 v2. 14. 6 to broadly identify truncated or interrupted genes by creating a database consisting of a multi-fasta of the "bedaquiline-R-associated" genes, which were then used as the query database when running ARIBA. We then used ARIBA summary to concatenate all results and identify isolates where the gene prediction from ARIBA was "no". Isolates with predicted truncations were analysed with Shovill, blastn, MAFFT, Jalview and ISmapper (as above). To control for poor quality data that could affect ARIBA's local assembly, we did not consider predicted truncations where the sample's Shovill assemblies generated >500 contigs. We also checked sample read quality using fastp 38 v0. 22. 0 with the following parameters: "--detect_adapter_for_pe --dedup --length_required 50".
a. 80 10 S094 ERS14298650 frameshift P48fs:RAAVL n. a. 80 10 S125 ERS14298681 frameshift P48fs:RAAVL R 80 10 S134 ERS14298690 frameshift P48fs:RAAVL R 80 10 S172 ERS14298728 frameshift P48fs:RAAVL n. a. 80 10 S194 ERS14298750 frameshift P48fs:RAAVL n. a. 80 10 S257 ERS14298813 frameshift P48fs:RAAVL S 80 10 S279 ERS14298835 frameshift P48fs:RAAVL R 80 10 S393 ERS14298949 frameshift P48fs:RAAVL R 80 '* This isolate was not included in results relating to MmpR5 truncation 204 205 Figure 3: protein alignment and neighbour joining tree of MmpR5 predicted protein sequences from the SA1 dataset.
The simulation programs most commonly used for interactive digital twin experiments are an elastic network model, BioSpring  [31]  , and molecular dynamics simulations using the Gromacs package  [32]. BioSpring was designed for interactivity and we will describe it first. The BioSpring software package represents each molecular component through a variable network of harmonic springs, enabling interactive dynamic testing. The extended elastic network model (aENM) combines the usual harmonic spring network functions with unbound force field terms between atoms or pseudoatoms, as described in [33]. aENM supports the use of multiple cutoff distances to determine the final spring network. Thus, more or less weight can be assigned to secondary or ternary structures as needed. In a typical study, a cutoff distance of 9Å was used for scaffold particles involved in secondary structure elements, whereas it was reduced to 5Å for other particles belonging to loops or side chains.
The two resulting spring networks were merged into a final aENM model after the particles of the two previous aENM models were connected by a spring when they were closer than the cutoff distance of 5Å. This approach significantly simplified the creation of multiscale scenarios, providing the ability to combine atomistic and coarse-grained models. Owing to code optimizations that include parallel computing and GPU programming, this approach can be used on desktops or laptops to create molecular assemblies. As part of a digital twin, BioSpring is remarkably efficient in preparing numerical simulations or developing biomolecular models that incorporate qualitative experimental data. For the example concerning the MutS mismatch repair protein, we used a reduced representation of the protein derived from the 1W7A crystal structure.
The augmented elastic network representation combines a specific coarse-grained elastic network  [34]  , an optimized repulsive steric Lennard-Jones term, and a coulombic potential. Springs were generated automatically within a 9Å cutoff, except for connections between the MBD domains and the rest of the complex that were generated with a 4. 4Å cutoff to provide sufficient flexibility for the deformations. When a more detailed representation was required, for instance, when an explicit membrane or solvent representation was involved in the molecular system, molecular dynamics simulations were the method of choice. We performed many digital twin experiments using the Gromacs molecular dynamics software suite. Here, we describe a typical setup, such as that used for the water channel example discussed in Section 3.