dataesr/scientific-paragraphs-categorization huggingface dataset:
For litter size variables, the number of piglets at the beginning of the analyzed period was used as a covariate. For litter performance variables, the body weight of the litter at the beginning of the analyzed period was used as a covariate. The post-weaning piglets' performances and IgG concentration were submitted to an analysis of variance with treatment as the main effect. Alternatively, if the variables were not normally distributed, data were processed using Kruskal-Wallis non-parametric test, with treatment as the main effect. The experimental unit was the sow for lactation variables, the pen for post-weaning variables, and the piglet for IgG concentration in the blood. The.
B. vulpis is distinct from the core clade and is closely associated to the heterogeneous species and strains that are introduced next (Figures  1  and 2 ). Circular tree illustrating the phylogenetic relationship of novel Brucella isolates. The phylogenetic tree was visualized with iTOL (v6) and is based on pairwise analysis of average nucleotide identities (fastANI v1. 32)  [5]  and hierarchical clustering (UPGMA method). Publicly available genome sequences retrieved from the NCBI database (see Data Availability Statement) were complemented with own unpublished genome sequences. Colored rings indicate the host/environment where the strain was originally isolated from. From inside to outside: 'host' and 'group', presenting the exact host/environment and a rough grouping, respectively. The group 'Vertebrate' excludes mammals, and the group 'Mammal' excludes humans.
inopinata-like 3 (BO3), since it clusters together with the distinct clade of novel Brucella consisting of B. inopinata strains BO1 and BO2 as well as frog isolates; however, BO3 was most similar to the Pac-Man frog strain B13-0095 (Figure  2 )  [44]. Furthermore, from our NCBI database query, we retrieved three sequences of recent clinical isolates-B. sp. 2280 (BioSample: SAMN12091575), B. sp. 6810 (BioSample: SAMN15962648), and B. sp. 458 (BioSample: SAMN18395631)-that all cluster close to or within the B. inopinata clade that also contains some of the African bullfrog isolates (Figure  2 ). These clinical isolates originated from human lymph nodes, blood, and lung tissue, and were associated with brucellosis and granuloma formation, respectively. In conclusion, the majority of novel Brucella that have been isolated over the past 15 years comprise genetically heterogeneous strains that form the relatively diverse noncore clade, basal to the genetically highly homogeneous core clade including only two novel species.
[5]e illustrating the phylogenetic relationship of novel Brucella isolates. The phylogenetic tree was visualized with iTOL (v6) and is based on pairwise analysis of average nucleotide identities (fastANI v1. 32)[5]and hierarchical clustering (UPGMA method). Publicly available genome sequences retrieved from the NCBI database (see Data Availability Statement) were complemented with own unpublished genome sequences. Colored rings indicate the host/environment where the strain was originally isolated from. From inside to outside: 'host' and 'group', presenting the exact host/environment and a rough grouping, respectively. Papio sp. )[17]. The strains have identical 16S rRNA gene sequences with two nucleotide mutations when compared to the classical Brucella species. In addition, these isolates share an identical high-copy-number IS711 profile with several baboon isolate-specific IS711 copies[17].
s 2022, 10, x FOR PEER REVIEW 6 of 32 Figure 2. Circular [5]e illustrating the phylogenetic relationship of novel Brucella isolates. The phylogenetic tree was visualized with iTOL (v6) and is based on pairwise analysis of average nucleotide identities (fastANI v1. 32)[5]and hierarchical clustering (UPGMA method). Publicly available genome sequences retrieved from the NCBI database (see Data Availability Statement) were complemented with own unpublished genome sequences. Colored rings indicate the host/environment where the strain was originally isolated from. From inside to outside: 'host' and 'group', presenting the exact host/environment and a rough grouping, respectively. Papio sp. )[17]. The strains have identical 16S rRNA gene sequences with two nucleotide mutations when compared to the classical Brucella species.
5 mL of loading dye (5% Serva Blue G-250, 750 mM 6-aminocaproic acid), and run in NativePAGE TM 3-12% Bis-Tris Gel (Invitrogen). The proteins were transferred onto a polyvinylidene difluoride membrane and probed with 1:5,000 diluted polyclonal antibodies against Atp6/subunit a, Atp1/subunit a, and Atp9/subunit c (a gift from J. Velours). The tested proteins were revealed using peroxidase-labeled antibodies (Promega) at a 1:5,000 dilution and the ECL reagent of Pierce ECL Western Blotting Substrate (Thermo Scientific). SDS-PAGE analysis and immunological detection of proteins from isolated mitochondria or whole cells was performed as previously described  (Schagger and von Jagow 1991). The open source program Image J (http://rsbweb. nih. gov/ij/) was used to quantify the immunological signals and statistical significance between samples was evaluated using Student's t-test.
Funding This work was supported by the  National Institute of Health  ( 5R01GM111873-02 ) to LMS and  J-PdR , the  Agence Nationale de la Recherche  ( ANR-09-BLAN-00500 ) to  J-PdR , and ( ANR-10-IDEX-0002 ) to HB and the  National Science Centre of Poland  ( UMO-2011/01/B/NZ1/03492 ) to RK.
We also build a generalized score, by combining the PASTA properties with PScore, possibly the best performing first-generation predictor, based on pi-pi interactions  [23]. Significant improvement over PScore in the performance of the generalized score would signal that β-pairing information may be crucial to better characterize LLPS behavior. The development of effective LLPS predictors is very much depending on the availability of reliable LLPS datasets, for both positive  [31] [32] [33]  and negative  [26]  sets. The latter may be built based on proteomes from different organisms. Interestingly, our results depend on the choice of the positive set and, to a lesser extent on the choice of the negative set.
Data Sets We considered two different positive sets of phase-separating proteins: the PP dataset, including 121 proteins, from the PhaSePro database (https://phasepro. elte. hu, accessed on 30 June 2020)  [33] , and the LLPS dataset, including 453 proteins, as compiled in  [26]  by merging different datasets. The LLPS set contains the PP sequences as a subset. We as well considered two different negative sets, both compiled in  [26] : the hsnLLPS set, including 18108 sequences from the Swiss-Prot human proteome after removal of human proteins included in phase separation datasets (such as LLPS), and the nsLLPS dataset, including 3911 proteins from different organisms (C. elegans, C. reinhardtii, D. melanogaster, H. sapiens, M. musculus, R. norvegicus, S. cerevisiae, S. pombe, X. laevis), reflecting the organism composition of the LLPS dataset.
The script provided in  [23]  does not output any PScore for several sequences from the LLPS, hsnLLPS, nsLLPS datasets compiled in  [26]. This occurs when the sequence length is smaller than 140 residues (the length of the shortest sequence in the PP dataset used in  [23]  as a positive set) or when letters different from the ones coding for the 20 natural amino acids are present in the protein FASTA sequence. Since our analysis is based on the use of PScore, we removed those sequences from the LLPS, hsnLLPS, nsLLPS datasets used in this work, which then contain 442, 16,360, 3503, sequences, respectively.
Training Procedure by k-Fold Cross Validation Cross validation is a very useful technique to assess the performance of any predictive machine learning algorithm. It is basically a resampling method that takes into account different parts of the full data as the test set and then the remaining part as the training set. For a k-fold cross validation algorithm, the entire dataset, that is the union of the positive and negative set, is randomly partitioned into k subsets of equal size. In our work, we choose one among the k subsets as the test set and the union of the remaining k -1 ones as the training set. During the training procedure for each of our defined scoring functions we optimised the weights (α, β or γ) by maximising AUC on the training set. In all the cases, the simplex algorithm was used to maximise the AUC on the training set  [34].
Positive Sets As a first possibility for the positive set we use PP (https://phasepro. elte. hu, accessed on 30 June 2020)  [33] , which provides manually curated protein regions from a variety of organisms, whose association with liquid-liquid phase was experimentally validated in the literature, either "in vitro" or "in vivo". PP was used as a positive set for the development and training of PScore  [23]. As a second possibility for the positive set we use LLPS  [26] , obtained by merging PP with two other data sets for liquid-liquid phase separation, REV and LPS-D. The REV data set is a subset of PhaSepDB (http://db. phasep. pro/, accessed on 30 December 2020); it includes proteins whose involvement in either "in vivo" or "in vitro" liquid-liquid phase separation can be found in the literature  [32].
Negative Sets Both choices for the negative set were assembled in  [26]. As a first possibility we use hsnLLPS, the Swiss-Prot human proteome from which all proteins that appear in any of the liquid-liquid phase separation data sets were removed. As a second possibility we use nsLLPS, a collection of proteins sampled from the proteomes of 9 different organisms in order to reproduce the frequencies with which sequences from different organisms appear in the LLPS dataset, after removal of all proteins that appear in any of the liquid-liquid phase separation data sets. For LLPS, hsnLLPS, and nsLLPS we had to consider only the entry sequences yielding a PScore, resulting in 442, 16,360, 3503, entries, respectively (see Section 2. 1).
The LLPS Positive Set We optimised the parameters α, β and γ in the different scoring functions by maximising the AUC on the training set and then used the optimised parameters to find AUC and MCC on the test sets (see Section 2. 4 for details on AUC and MCC). Training was performed using a k-fold cross validation with k = 4 such that the full dataset (the union of the positive and the negative set) is split randomly in k equally sized subsets with one of them used in turn as the test set and the union of the other k -1 ones as the training set (see Section 2. 5 for details on the cross validation training procedure). The PScore function was already trained previously  [23] , so we evaluated its performance on the test set by computing the corresponding AUC and MCC for each realisation of k-fold cross validation, whereas we did not evaluate its performance on the training set.
Further comparison of the vacant C. albicans ribosome with the ribosomes from S. cerevisiae and from H. sapiens revealed that in the E-site of C. albicans ribosome, there is a P56Q alteration in ribosomal protein eL42 (fig. S1A ). This is the only difference found in the organization of the functional sites between C. albicans, S. cerevisiae, and H. sapiens (table  S1 ). The eL42 P56Q substitution decreases the volume of the binding pocket. To establish whether the reduction of the pocket volume affects the binding of inhibitors, we compared the S. cerevisiae ribosome [Protein Data Bank (PDB) 4U3U] and H. sapiens (PDB 5LKS) ribosome in complex with CHX (fig. S3A ) to the vacant C. albicans ribosome (at 2. 3 Å) with a superimposed CHX molecule. CHX binds to the 60S subunit in the E-site, preventing translocation of tRNA from P-to E-site (Fig.
S1, C to E ). While eL41 is not currently annotated in Candida species, eL41 protein was evident in the experimental density. A conserved DNA sequence in the genome of C. albicans SC5314 between orf19. 1676 and orf19. 1677 was identified with the help of curators at the Candida Genome Database (13) that specifies a 25-residue polypeptide similar to S. cerevisiae eL41 that fits into the density and therefore appears to be C. albicans eL41 (fig. S7 ). A 2. 32-Å [at FSC (Fourier shell correlation) = 0. 143] highresolution ribosome structure of one of the most pathogenic fungi, C. albicans, is reported. Our results explain the resistance of C. albicans to CHX and demonstrate specific features of the C. albicans ribosome. On the basis of structural and cell-free translation data, we propose that CHX cannot bind to the C.
Zero-loss images were recorded semiautomatically, using the UCSF Image script  (14). The GIF Quantum energy filter was adjusted to a slit width of 20 eV. Images were collected at a various nominal magnification, yielding pixel sizes from 0. 413 to 1. 012 Å, and with a defocus range of -0. 5 to -3. 0 m. Movie images were collected with 24 frames dose-fractionated over 18 s. In total, we collected 9807, 2040, 2152, 1310, and 3199 micrographs for the vacant C. albicans ribosome or in complex with ANM, BLS, PHY, and CHX, respectively. Motion correction, CTF (contrast transfer function) estimation, manual-and template-based particles picking, two-dimensional (2D) classification, ab initio volume generation, CTF global and local refinements, and nonuniform 3D refinement were performed using cryoSPARC (v 3.
Inhibition of cell-free translation by CHX, PHY, ANM, and BLS We prepared CFTSs from the C. albicans and S. cerevisiae strains used for structural analyses. These CFTSs were programmed with LUC mRNA, and then their capacity to produce the enzyme in the presence of increasing concentrations of CHX was examined. C. albicans belongs to a subclade in which the "universal" genetic code is altered; in this "CTG-clade," the CUG codon is translated as Ser, not Leu  (20, 21). Therefore, spLUC was used as the translational reporter because the coding region for this enzyme lacks CUG codons and functions as a reporter in C. albicans  (22). Translation of spLUC RNA using CFTSs from these yeasts can thus be directly compared without concern for their use of different genetic codes. C. albicans and S. cerevisiae CFTSs were prepared using the method 1 protocol for S.
Data deposition The final models and associated maps are deposited with the PDB and Electron Microscopy Data Bank (EMDB) with the following accession codes: 7PZY and EMD-13737 for the vacant C. albicans ribosome, 7Q0P and EMD-13749 for complex with ANM, 7Q0R and EMD-13750 for complex with BLS, 7Q0F and EMD-13744 for complex with PHY, and 7Q08 and EMD-13741 for complex with CHX, respectively.
Materials and methods For the morphological identification of the material collected, we used the identification keys provided in  Faccoli (2008) ,  Gomez et al. (2018)  and  Smith et al. (2020). Molecular identification was based on DNA barcodes. DNA extraction, purification and amplification followed the methodology described in  Ruzzier et al. (2020). PCR products were purified using Exonuclease and Antarctic Phosphatase (GE Healthcare) and sequenced at the BMR Genomics Service (Padova, Italy). The sequences were edited using MEGA 11  (Tamura et al. 2021 ) and subsequently, translated with Transeq (EMBOSS) to exclude the presence of stop codons in the coding region. An analysis of the sequences obtained was run through the integrated bioinformatics platform Barcode of Life Data (BOLD) System database to assess the identity of the species.
In order to investigate the origin of the Italian Cnestus mutilatus population, the single barcode produced by  Colombari et al. (2022)  was used in a haplotype network analysis in  POPART (Leigh and Bryant 2015) ; as input, we used all the Cnestus mutilatus public barcodes available on the BOLD System. Maps were produced using QGIS 3. 16. The basemap originates from the CartoDB Positron, combined with © MapTiler topo and OpenStreetMap data. The reference system of the data is WGA84 -EPSG:4326. We provide identification remarks only for those species that represent extremely relevant or new European records.
Acknowledgements The author thanks  Cesare Bellò ,  Margherita Attorino  and  Pietro Berton  ( World Biodiversity Association ) for sharing collecting data and photographs of C. mutilatus and  Giacomo Cavaletto ,  Isabel Martinez-Sañudo  and  Davide Rassati  ( DAFNAE, Università degli Studi di Padova ) for data sharing, support and constructive comments provided during the manuscript realisation. In addition, the authors thanks  Rachele Amerini  ( World Biodiversity Association ) for maps realisation. The research has been funded by  DOR  ( DAFNAE University of Padova ),  HOMED  project (funded from the  European Union 's  Horizon 2020 Research and Innovation Programme  under grant agreement no. 771271 ) and the "Entrusting support tasks in the area of Plant health -Commodity risk assessment for  High Risk Plants" EFSA-DAFNAE University of Padova  research agreement.
e. physical activity should be started close to hyperglycaemia (approximately 1. 8 g/l) and the basal insulin flow must be reduced before and during activity. Our findings aim to provide solutions to the above open problems. A metamodel for glucose-insulin dynamics that is, subject to carbohydrate ingestion and aerobic physical activity is derived; it incorporates major existing knowledge-based models. The common core, Bergman's minimal model, is highlighted. Our own new model is challenged with clinical data and in silico testing. We derive a comprehensive and universal definition of the underlying FIT parameters as the insulin sensitivity factor (ISF). Our approach allows us to extend and specialize these notions to any model regardless of its complexity. In addition, the relevance of physical activity modelling is assessed on clinical data.
Model parameter identification from clinical data. We perform an identification procedure on clinical data to demonstrate the fitting performance of our model (Fig. 3 ). After defining a new mathematical model, we verify that accounting for physical activity is essential for capturing the insulin-glucose dynamics of an individual. An identification procedure is implemented on clinical data obtained under free-living conditions to answer this first question. The patient of interest is a 50 year-old male practising for a triathlon. Physical activity is detected when the aerobic threshold (approximately 50% of VO 2max ) is reached. Identification is used to validate modelling assumptions and assess the different models that are available in the current literature. We test multiple configurations with either nonlinear terms or parameters designed to represent the long-term effect of physical activity on the ISF.
Methods Mathematical modelling. A mathematical model was developed in this work as an extension of the socalled flexible insulin therapy model  18  , also named the 'Minimal Model Control-oriented'  11. The latter has demonstrated its long-term ability to reproduce the insulin-glucose dynamics of type 1 diabetes while remaining quite elementary, comprehensive to practitioners, and mathematically consistent in terms of its equilibrium points as well as its a priori identifiability 18 from glycaemia measurements. The series generation approach of the GenSSI MATLAB © toolbox was used to show that the parameters were structurally and globally identifiable. A structural and practical identifiability analysis was performed on the flexible insulin therapy model  18  by Garcia-Tirado  11  , who discussed arguing potential identification issues due to the low sensitivity of the output to certain model parameters.
By using the MATLAB GenSSi toolbox, it was shown that (4) fulfilled the multi-experiment identifiability conditions so that the seven parameters could be derived from the measured output, under well chosen scenarios (Fig. 1  in the Supplementary information). When possible, we found that running the optimization algorithm on a two day-long data-set provided that were consistent with the parameters observed in clinical practice. The clinical data used in this study were provided by Nantes University Hospital. They were obtained from free-living conditions; the patients had to declare their meals and wear smartwatches that were able to acquire heart rate and actimetry data. The maximum heart rate was determined via an estimate given by Gellish et al. , which is a curvilinear definition linking maximum heart rate with age 20.
Unfortunately, this simulator does not include physical activity data and does not model the effects of physical activity on glycaemia. In 2019, Resalat et al. proposed a statistical virtual patient population for the glucoregulatory system in type 1 diabetes patients, including physical activity  15. Insulin-glucose dynamics were modelled by using the results of Hovorka  22  , and physical activity was accounted for with Hernandez equations  12. The data of 20 patients with type 1 diabetes obtained under different meals and aerobic activity scenarios were used to generate a virtual patients population. The cohort was then validated by creating virtual twins of the 20 real patients and assessing their relative times spent in the glycaemic target. This simulator was used to assess the performance of our control law (8) with a two-day scenario including multiple meals and one physical activity period.
Acknowledgements The work by J. G. acknowledges the financial support by  FAME , the  NexT research cluster 'Human Factors for Medical Technologies '. The clinical data came from the NCT04572009 study and we acknowledge the financial support provided by  Société Française de Diabétologie  which support the clinical trial. E. S. acknowledges the financial support provided by " Fondation pour la Recherche Médicale " under grant  FRM FDM201806006247.
Data availability The main data supporting the results in this study are available within the paper and its Supplementary Information. The clinical data came from the NCT04572009 study (https:// clini caltr ials. gov/ ct2/ show/ NCT04 572009) | (2022) 12:8017 | https://doi. org/10. 1038/s41598-022-11772-x www. nature. com/scientificreports/ which is still in progress. Here we share the raw sample data of one patient and we provide the flexible insulin therapy parameters for all patients in the Supplementary Information.
SNP genotyping and determining RhD status All participants were genotyped for the rs590787 SNP, through a genomewide SNP array, using HumanOmniExpress and HumanExomeBeadChips  [11]. Rhesus status (RhD-positive or -negative) was determined based upon the presence or absence of the rs590787 polymorphism in the gene sequence of each individual  [14]. In total, 921 donors were retained for downstream analysis based on completeness of datasets available. For males, n = 384 RhD-positive and n = 75 RhD-negative. For females, n = 385 RhD-positive and n = 77 RhD-negative.
NanoString transcriptomics Total RNA was extracted from the TruCulture cell pellets for the 1000 MI donors using NucleoSpin 96 miRNA kit (Macherey-Nagel). RNA concentrations were measured using Quantifluor RNA system kit (Promega) and RNA integrity numbers were determined using the Agilent RNA 6000 Nano kit (Agilent Technologies). The expression levels of 560 immune-related genes were quantified before and after stimulation using NanoString hybridisation arrays, producing highly reproducible transcriptomic data. Gene expression data were normalised as previously described  [13].