dataesr/scientific-paragraphs-categorization huggingface dataset:
Data from the METABRIC dataset that were relevant to the model were selected. Focusing on the 110 genes overlapping with nodes of the logical model, exome sequencing resulted in 2,659 mutations, of which 1,431 mutations were inferred as loss of function and 1,228 as gain of function. Besides, 634 mutations have unknown or silent effects and therefore were not considered. These 3,293 model-related mutations represent 19% of all mutations of the METABRIC dataset. Note that these numbers show the intersection of a generic model and a breast-cancer-specific dataset, so this percentage could be further increased by using a model with breast-specific pathways. Patients' profiles were found to have up to 7 mutations with most patients having only one assigned mutation. PIK3CA and TP53 were found to be the most frequently mutated genes.
For CNA data, patients' profiles had up to 19 perturbations, with a median number of 2. MYC gene was the most frequent gene with copy number alterations. RNA expression data were processed and genes were separated in bimodal, unimodal and zero-inflated categories (Figure  3  and section 2. 3). All model-related genes in METABRIC cohort were found to be unimodal. Note that bimodal genes occur in several biologically meaningful situations like fusion genes such as ERG in prostate cancer or hormone genes such as ESR1 in breast cancer. We chose to explore the results of the METABRIC data with a model built specifically for breast cancer analysis  (Zañudo et al. , 2017)  in order to assess the importance of including cancerspecific genes. Indeed, ESR1 is present in the breast-specific logical model analyzed in Supplementary Material.
The methods of binarization and normalization are applied to each data type according to the previously presented rules (Figure  4  and Figure  S8 ). We further compared our binarization method to an existing tool, RefBool framework  (Jung et al. , 2017) , using the same METABRIC dataset. This tool uses a set of reference distributions and it results in p-values for each sample and gene, assessing the significance of its putative binarization. Using 0. 05 as a binarization threshold for RefBool p-values on the whole METABRIC RNA dataset (1904 samples and 24368 genes), around 4. 4 million values were binarized (9. 5% of the total). All of these binarizations resulted in active nodes and thus set to 1. Notably, RefBool was designed to use a reference dataset to binarize new data. Due to the lack of a reference healthy dataset in METABRIC, the whole dataset has been used as its own reference: each gene was compared to the distribution of that gene across all samples.
Comparatively, our method results in 2. 8 million of binarized values (6. 1% of the total), respectively 4. 2% of 1 s and 1. 9% of 0 s. There seems to be a trend for RefBool in METABRIC dataset to emphasize positive outliers at the expense of negative ones, even for roughly symmetric unimodal distributions (Figure  S18 ). Some examples of this dataset can be studied in Supplementary Material, together with the analysis on TCGA dataset, that bears healthy samples, and should be a better showcase to RefBool capabilities (Figure  S19 ).
Adding mutations information as Strict NV does not shift the probabilities' distributions (case 5). Lastly, when we consider mutations and CNA as nodes' activity and RNA expression levels as modified transition rates, it results in a combination of the previously observed patterns (Figure  6 , case 6). Nevertheless, the generic logical model we use here does not take into account key genes in breast cancer progression such as hormone receptors and their associated signaling networks. As previously mentioned, a breast-cancer-specific model  (Zañudo et al. , 2017)  was investigated using the same METABRIC dataset to personalize breast patient-specific models with similar trends to those of the generic model's study (Figure  S12 ). Zañudo et al. (2017)  model generates narrower distributions and therefore less discriminating probabilities from one patient to another, which is mainly due to the fact that it captures less information due to its lower number of nodes (especially with sparse data such as mutations).
For these reasons, and having in mind the methodological scope of present work, we will focus on the discussion on results of the more comprehensive generic model. In order to present the use of one model with more than one dataset, PROFILE method was also done and analyzed using TCGA molecular data on Fumiã and Martins (2013) generic model (Figure  S14 ). Figures such as Figure  6  are useful to identify the integration of which data in which part of the model has a greater impact in the change in phenotypes' distributions, but say little about the biological relevance of these distributions. To further investigate which combinations of methodology provides better biological or clinical insights, we compared these models' results to several signatures or clinical factors used in breast cancer studies.
Selecting Personalization Methods Using Correlation of Phenotypic Probabilities to Signature Scores To classify the relevance of the six personalization methods presented in the previous section, we studied the correlations of probabilities of the model phenotypes with representative signatures of the same phenotypic processes. This methodology allows to classify the different personalization methods and to study which one is better suited to describe the diversity of patients when tailoring a given model to a given dataset. The Spearman rank correlations of the Apoptosis probabilities from personalized models with the RNA-based "Apoptosis" signature defined in the Hallmarks  (Liberzon et al. , 2015)  gene set was computed (Figure  7A ). Sparse binary data (when using mutations or CNA data) appear to be a poor choice to recover a consistent Apoptosis probability with the logical models (cases 1, 2 and 3).
e. , Soft NV, intervening in initial states and transition rates) can be seen as biologically consistent. It is indeed expected that a genetic mutation can have a very strong and lasting effect that makes the gene independent of any regulation such as in the loss of function mutations. Conversely, the RNA expression level will affect the activity level of the genes but may not alter its regulation. Using our PROFILE methodology, we are able to provide guidelines regarding the patient-data personalizations of logical models. Firstly, it is important to consider the nature of the node (gene or protein) in order to match the proper data type to the node. In the generic model used in our study, most of the nodes are supposed to be proteins, therefore it would be advisable to focus on protein data, which is unfortunately unavailable in the METABRIC dataset.
In any case, the proposed framework could be easily adapted to the ideal case where each node would have a well-defined nature and a proper mapping of the corresponding data types. It is important to note that in the context of phospho-proteomic data (like RPPA's phosphosites), highly phosphorylated species can correspond to an inactive state that must be taken into consideration as mentioned in Supplementary Materials with TCGA data. Secondly, healthy samples should be used if they are available in the dataset. Using an independent healthy samples for RNA normalization in TCGA dataset not only improved the correlation performances (Figure  S15 , case 4) but also the qualitative trend of the results (Figure  S17 ). It can be seen that using healthy samples instead of cancer samples as a reference for RNA normalization results in a significant shift of the distribution toward high Proliferation model probabilities (Figure  S17 ).
Hyperspectral Imaging HSI extends classical RGB imaging by replacing the three channels corresponding to three large wavebands with a significantly higher number of narrow wavebands covering the entire visible spectrum (and possibly the UV and IR domains). Hyperspectral image data are also called "hypercubes" since they are three dimensional: two dimensions corresponding to the spatial domain, one dimension corresponding to the spectral domain. The information captured in each pixel is proportional to the spectral radiance coming from one direction in the camera's field. By dividing the spectral radiance measured from the object with the spectral radiance measured from a perfectly white diffuser illuminated identically as the object, the spectral reflectance factor of each pixel is obtained.
Acknowledgments A preliminary version of this work was presented at  IS&T Electronic Imaging Symposium, Material Appearance (Burlingame, USA ,  29  January to 2 February 2018) and published in the conference proceedings. 37This work was supported by the  Région Auvergne-Rhône-Alpes  within the program ARC 6, and the  French National Research Agency (ANR)  as part of the program " Investissements d'Avenir"  ( ANR-11-IDEX-0007 ), in the framework of the  LABEX MANUTECH-SISE  ( ANR-10-LABX-0075 ) of  Université de Lyon.
1 operated by the Joint Berlin MX-Laboratory at the BESSY II electron storage ring (Berlin-Adlershof, Germany)  [27]. All data were processed using the program XDS  [28]. An overview of data collection statistics can be found in Table  1. The structure was solved by means of single-wavelength anomalous diffraction (SAD), using Phaser  [29]  in combination with Phenix  [30]. Multiple rounds of manual model building and refinement were carried out using Coot  [31, 32] , Refmac  [33, 34]  and the CCP4 package  [35]. Structure refinement statistics have been summarised in Table  1. Figures showing molecular models were prepared using PYMOL 0. 99rc6 (DeLano Scientific LLC, Palo Alto, CA, USA). The surface charge distribution of PptA was calculated using ABPS  [36]  with an ionic strength value of 150 mM.
Scattering of the buffer was subtracted and the difference curves were scaled to unity protein concentration (1 mg/ml). Merging and further data analysis were carried out with version 2. 8. 4 of the ATSAS package  [40]. The program CORAL  [40]  was used to compute theoretical SAXS curves based on the crystallographic homodimer, as well as to model missing loops and extremities. The SAXS data and corresponding CORAL model of the PptA dimer have been deposited in the SASBDB data base with accession number SASDF76.
8 kDa, whereas a recently developed concentration-independent Bayesian method  [44]  yielded a value of 78. 5 kDa. These results are in good agreement with the expected mass of a  PptA dimer (79. 2 kDa). A more detailed analysis was carried out with the program CORAL, which was used to add strings of dummy residues to the high-resolution crystallographic structure in order to account for missing loop regions as well as the undefined N-and C-terminal ends of the protein. The optimised model generated by CORAL (Fig. 4C ) explains the experimental data to within experimental error (Fig. 4B , fitted curve in red), corroborating the dimerisation mode observed in the crystal lattice.
Discussion In the study presented here, we analysed the structural and biochemical properties of a phosin (PptA) from S. chartreusis. The highly unusual polyphosphate-and metal-binding fold that we identify has not been described in the literature, but bears similarity to a previously deposited crystal structure (PDB entry 3E0S). The latter, annotated as an uncharacterised protein from Chlorobaculum tepidum, was solved in the context of a structural genomics initiative. Superposition of the two structures via secondary structure matching in the program Coot  [31, 32]  yields a C a -RMSD of 2. 9 A for a total of 244 aligned residues (Fig. 6 ). Like PptA of S. chartreusis, 3E0S consists entirely of a-helices that together form a thin, pseudosymmetrical lozenge with a central tunnel running through its core.
Pharmacokinetic parameters estimated from the sparse afabicin desphosphono concentrations measured during bone resection are presented in Table  1. The exposure derived from the composite profile constructed from the sparse plasma samples at the time of resection (AUC , 20,100 ng•h/ml; maximum observed plasma concentration    and soft tissue (F) after the third oral dose of afabicin (240 mg, q12h) in patients undergoing hip replacement surgery. Data are from 14 patients, with the following exceptions: for 1 patient, the plasma sample was missing at the time of resection and the plasma concentration was extrapolated by linear regression between the two adjacent concentration data from the full PK profile in plasma. Synovial fluid was obtained for 8 patients only. For one patient no results except for plasma were reportable at the time of resection, and for another, only plasma, soft tissue, and bone marrow were collected at the time of resection.
Protein binding in plasma and synovial fluid. The afabicin desphosphono free fraction was 2% in blank plasma samples spiked with afabicin desphosphono as well as in plasma samples collected from the patients during the study. This confirmed the values obtained in previous in vitro and in vivo experiments (data not shown). Individual protein binding in synovial fluid collected during the study could not be determined due to a limited sample volume. However, protein binding was determined in synovial fluids obtained from different sources after spiking with afabicin desphosphono: individual free fractions ranged from 2. 4% to 30. 4%, with a mean value of 9. 4% and a median value of 5. 4%. Considering these free fractions, the mean ratio of the unbound AUC for synovial fluid tissue over the unbound AUC for plasma was 2.
13, 0. 19, and 0. 41 for cortical bone, cancellous bone, and synovial fluid, respectively  (23). Additionally, a study was performed in 16 FIG 3 Osteoarticular tissue-and fluid-to-plasma concentration ratios obtained for vancomycin, linezolid, ertapenem, daptomycin, dalbavancin, and afabicin. Mean ratios (median ratios for ertapenem) were obtained from published data from studies with designs similar to that of this study, i. e. , dosing in patients undergoing elective replacement surgery and analysis of the total concentration of the drugs in homogenized bone tissues or synovial fluid collected during surgery  (20) (21) (22) (23) (24) (25)  at steady state for linezolid and afabicin and after a single dose for the other antibiotics. Vancomycin, linezolid, and ertapenem were quantified in serum, whereas daptomycin, dalbavancin, and afabicin desphosphono were quantified in plasma.
patients undergoing knee or hip replacement and receiving a single i. v. dose of 8 mg/kg daptomycin prior to surgery. The overall mean bone-and synovial fluid-toplasma ratios at the time of surgery were 0. 14 and 0. 54, respectively  (24). Finally, bone samples were collected in 30 patients receiving a single i. v. infusion of 1,000 mg dalbavancin at 0. 5, 1, 3, 7, 10, or 14 days before undergoing elective orthopedic surgery. The mean bone-to-plasma AUC ratio was 0. 13  (25). All these data described in the literature show lower cortical bone, cancellous bone, and synovial fluid drug penetration for the drugs tested, with the exception of linezolid, compared to the data obtained for afabicin desphosphono when the distribution was evaluated in noninfected patients. There has been a general recognition that assessing the unbound concentrations of antibiotics in the bone tissues would be advantageous in the interpretation of the concentrations required in bone for efficacy, with the bioactive drug being believed to be only the unbound drug at the site of action.
MATERIALS AND METHODS Overall study design. This was a multicenter, open-label study to evaluate the penetration of afabicin into bone in patients undergoing elective hip replacement surgery (study Debio 1450-108, ClinicalTrials. gov identifier NCT02726438). Seventeen patients were enrolled and randomized to a group receiving afabicin (n ϭ 15) and a group not receiving afabicin (n ϭ 2). The study population consisted of male and female patients 18 to 75 years of age (inclusive) with a body mass index (BMI) of 18 to 35 kg/m 2 (inclusive) and a weight of at least 50 kg. Patients received 3 oral administrations of afabicin (as the bis-ethanolamine salt in 5% dextrose solution) at a dose of 240 mg approximately 12 h apart, without regard to food intake. They were randomized into 4 groups, with the last dose of afabicin being given approximately 2, 4, 6, or 12 h prior to the scheduled surgery time.
3) software (Certara L. A. ). The NCA was performed from the individual full plasma PK profile for each patient using actual sampling times. For the tissues, synovial fluid, as well as plasma collected at the time of bone resection, the NCA was performed from the composite profile constructed from the mean of the individual concentrations available at each nominal resection time point. The density of all biological matrices was considered 1 for calculation of the ratios. The mean and coefficient of variation (CV) data presented in this article for afabicin desphosphono are geometric means and CVs. The concentrations and PK parameters are based on total afabicin desphosphono unless specified otherwise (e. g. , AUC is total AUC and the free drug AUC is the AUC of unbound compound).
These data led us to address the question, what is the molecular structure and function of CTL4/CTLMA2 and to what extent is it conserved throughout the Anopheles genus? We report that CTL4/CTLMA2 intermolecular disulfide bond formation can occur via any two cysteines of the CXCXC motif, and that the two proteins can form higher-order oligomers via complementary electrostatic interactions. The solution structure of CTL4 and CTLMA2 was determined by small-angle x-ray scattering. Analysis of glycan binding of CTL4 and CTLMA2 suggest the heterodimer separately and synergistically recognize β1-3 and β1-4 glucose/galactose linkages. CTL4 knockdown results in increased PO activity following E. coli challenge, which is reversed by co-silencing of TEP1 or co-administration of recombinant CTL4/CTLMA2.
5 μM, ΔH = -23 ± 4 kcal/mol. An. albimanus CTL4/ CTLMA2 (Fig. 3d ) has a similar affinity for calcium with K D = 2. 82 μM, ΔH = -12. 1 kcal/mol. However, calcium binding to both An. gambiae and An. albimanus CTL4/CTLMA2 was sub-stoichiometric (N = 0. 36-0. 50). Glycan binding. CTL4 and CTLMA2 belong to the lineage of myeloid CTLs -including macrophage mannose receptor (MMR) and DC-SIGN -that form a conserved family of immune receptors in metazoans  15, 25. Among CTLs with known structure, CTLMA2 has 30% sequence identity to the carbohydrate recognition domain (CRD) of mouse scavenger receptor (SCRL, PDB ID 2OX9)  26  and porcine surfactant protein D (SP-D, PDB ID 4DN8)  27. CTLMA2 conserves residues associated with Ca 2+ binding in the glycan binding loop, and the canonical EPN motif associated with D-mannose selectivity (Fig.
However, the calculated Guinier plot is based on only seven data points; fitting over an extended range (Fig. 4c , inset) yields R G = 24. 5 Å (I 0 = 0. 64), while fitting the pairwise distribution function P(r) (Fig. 4d ) yields an R G = 25. 4 Å (I 0 = 0. 65). The P(r) distribution fit by DAMMIF 31 with 20 ab initio bead models with an average normalized structural discrepancy NSD = 1. 0 ± 0. 2. The main body of the ab initio models are similar in shape to the expected CTL4/CTLMA2 heterodimer. Additional density extends from the main body of the bead models that can reflect either the N-terminal sequence of both proteins including the CXCXC motif, or a minor population of CTL4/CTLMA2 with a higher radius gyration. To test this hypothesis we performed multi-state modeling of the SAXS profile with the program MULTIFOXS  32.
Aside from Zn-binding sites in DNA/RNA binding proteins, the CXCXC motif is uncommon in proteins of known structure. At time of publication the motif is present in only 74 representative protein-only structures at <4. 0 Å resolution in the Protein Databank. The sequences are generally linear with disulfide bonds (if any) to different strands. Angepoietin-1 has an internal disulfide bond between the first and second cysteines of a CXCXC motif, with the backbone carbonyls serving as ligands to a calcium binding site. This is not observed for CTL4/CTLMA2 since we only observe a single Ca 2+ binding site by ITC corresponding to the CTLMA2 Ca 2+ /glycan loop. (n = 3, two-tailed heteroscedastic student t-test). Bars represent mean ± SD with p ≤ 0. 05 considered significant. Steric hindrance tends to limit the formation of multiple disulfide between adjacent strands of closely spaced cysteine residues.
Analytical ultracentrifugation (AUC). Sedimentation velocity AUC was performed on a Beckman XL-I ultracentrifuge with absorbance optics set at 280 nm. First An. gambiae CTL4/CTLMA2 was analyzed under same conditions as for SEC (Fig. 2d ). To test effect of calcium (Fig. S1b ), An. gambiae and An. albimanus CTL4/ CTLMA2 was prepared at 0. 5 mg/ml in 0. 15 M NaCl, 20 mM Tris pH 7. 5 and either 10 mM CaCl 2 (Ca-TBS) or 1 mM EDTA (EDTA-TBS). Protein partial specific volume and buffer viscosity were calculated with SEDNTERP: υ (Ag) = 0. 723 cm 3 g -1 , υ (Aa) = 0. 718 cm 3 g -1 , ρ(Ca-TBS) = 1. 0061 g cm -3 , ρ(EDTA-TBS) = 1. 0054 g cm -3 , η(TBS) = 1. 002 cP. Samples were centrifuged at 20 °C, 40,000 rpm. Data analysis was performed with SEDFIT  43.
Small-Angle X-ray scattering. SAXS data was collected in-house on a BioSAXS-2000 (Rigaku Corp. ) with a DECTRIS PILATUS 100 K detector. Samples were prepared as a dilution series from 1-4. 5 mg/ml in 0. 5 M NaCl, 20 mM HEPES pH 9. 0, 0. 5 mM CaCl 2 , 1% glycerol. Data acquisition was 30 min collected as 5 min exposures to ensure no measurable radiation-induced changes within the aquisition period. Following conversion to I vs. q curves, primary data analysis using the ATSAS software package  20, 44. Buffer subtraction and Guinier analysis was performed with PRIMUS, P(r) calculation with GNOM. Construction of ab initio bead models were derived for each structure was performed with DAMMIF using D max = 80 Å, P1 symmetry. Mosquito rearing and maintenance. The An. gambiae G3 strain was obtained through BEI Resources, NIAID, NIH: An.
Acknowledgements This work was supported in part by the  National Institute of General Medical Sciences   R01GM114358  of the  National Institutes of Health  to RHGB,  National Institute for Allergy and Infectious Diseases   R01AI139060  of the  National Institutes of Health  to MP, and  National Health and Medical Research Council (NHMRC; Australia)   Program Grant 1071659 and Principal Research Fellowship   1138466  to MPJ.
genoscope. cns. fr/agc/microscope) which is an integrated software environment for data management, annotation, comparative analysis and visualization of prokaryotic genomes especially for D580 Nucleic Acids Research, 2020, Vol. 48, Database issue the review of the quality of functional annotation. Published for the first time in 2006 under the name MaGe (4), the platform has been under continuous development within the LABGeM team at CEA, and its capacities are now quite extensive  (5) (6) (7). In summary, MicroScope supports free-of-charge external submissions of assembled genomes and metagenomes (i. e. Metagenome-Assembled Genomes, MAGs) generated by any sequencing technology or collected from public sequence data archives. Submission of reads for RNA-seq and variant analysis is also available for genomes already integrated into MicroScope.