dataesr/scientific-paragraphs-categorization huggingface dataset:
Thus, energetic metabolism enzymes were also among the targets of AA, in accordance with the energetic crisis and metabolic alterations described in other studies  [33]. Interestingly, several proteins implicated in DNA and RNA metabolism as well as in protein metabolism were oxidized in treated cells, including chromosome-associated kinesin KIF4A (accession number O95239), histone-binding protein RBBP4 (Q09028), ATP-dependent RNA helicase DHX8 (Q14562), U5 small nuclear ribonucleoprotein 40-kDa protein (Q96DI7), tRNA-splicing ligase RtcB homolog (Q9Y3I0), 26S proteasome non-ATPase regulatory subunit 3 (O43242), Cullin-4A (Q13619), E3 ubiquitin-protein ligase RING2 (Q99496), E3 ubiquitin-protein ligase UBR4 (Q5T4S7), E3 ubiquitin-protein ligase XIAP (P98170), and E3 SUMO-protein ligase RanBP2 (P49792) (Supplementary Table  1 ).
PTGR1 had the highest significant correlation value (r = -0. 608, P = 0) and was already highlighted in our earlier work as a predictive biomarker for resistance of breast cancer cell lines to auranofin/VitC combinational cytotoxicity  [28]. For AA alone, expressions of some antioxidant genes such as TXNRD1 (r = -0. 401, P = 1. 624 × 10 -3 ) and PRDX1 (r = -0. 384, P = 2. 678 × 10 -3 ) also correlated negatively with AA cytotoxicity. We performed a pathway analysis using the IPA software for the 246 genes. NRF2-mediated oxidative stress response was the first significant canonical pathway enriched (Supplementary Fig. 2A ). Top statistically significant molecular and cellular pathways for these genes included cellular movement and cell death and survival, among others. Finally, the top networks of these genes included cancer, cell death and survival and carbohydrate metabolism.
Protein classification according to their GO biological processes in DAVID database highlighted a significant association with signal transduction, oxidation-reduction process, and response to oxidative stress, among others (Supplementary Fig. 2B ). All these data directed us to conclude that oxidative stress response genes play a role in the resistance of cancer cells to H 2 O 2 -mediated cytotoxicity of AA. We wanted to ascertain that, for these genes, the predicted negative correlation of their expression with AA activity according to NCI-60 database are indeed valid on breast cancer cell lines. We thus calculated IC50 of AA on 13 breast cancer cell lines (Supplementary Fig. 3 ) and correlated them with the retrieved gene expression data from the Institut Curie database. Fig. 7A  shows PRDX1 gene expression (mRNA) distribution of 29 breast cancer cell lines.
(B) Histogram shows the percentages (%) of cells in G0-G1, S, G2-M phases at 0 h post-AA treatment as means ± SD calculated from 3 independent experiments. Statistical significance is assessed by one-way ANOVA with Tukey's multiple comparisons test. *, P < 0. 05. (C) MDA-MB-231 cells were labeled with BrdU for 1 h and then subjected to three treatment conditions for 2 h: non-treatment (NT), treated with 10 mM AA, or 10 mM AA plus 2000 U/ml catalase. Cells were then analyzed by flow cytometry after PI staining at different time points during recovery in growth media (0, 6 or 24 h). Representative graphs of at least 3 experiments are shown. and GGCT, very probably affecting the thioredoxin and glutathione scavenging systems. Furthermore, AA alters the redox state of many proteins implicated in the metabolic process, providing a rational explanation for a set of previously reported data that AA induced metabolic alterations, impairing glycolysis  [10] , pentose phosphate pathway, glycolysis and tricarboxylic acid cycle  [18] , impairing the Warburg metabolism and downregulating glucose transporters and pyruvate kinase M2 in cancer cells  [33].
For 2D processing, the spectra were zero filled to a 2048 × 2048 real data matrix. Before Fourier transformation, all spectra were multiplied with a squared cosine bell function in both dimensions. When applicable, excitation sculpting or a selective 90 • water flip-back pulse was used to suppress the H 2 O signal. PFGSTE translational diffusion measurements were performed by using convection compensated sequences. Either the standard Bruker double stimulated echo with bipolar gradients  [49]  or a double stimulated echo with monopolar gradients with an extended phase cycle  [50]  was used. The diffusion encoding/decoding gradients were varied linearly between 2% and 95% of their maximum output over 32 increments. The duration of these gradients and the diffusion delay time were chosen so that at the highest gradient strength the intensity of the signals of interest was decreased to at least 10% of the intensity at the lowest gradient strength.
The obtained intensity decays were fitted to the appropriate Stejskal-Tanner equation  [51]  using an in-house Matlab script. The 3D HNHA experiments were recorded on uniformly 15 N-enriched VA samples using identical acquisition and processing parameter set. The 15 N-VA was dissolved up to 1. 5 mM in acetonitrile, or up to 0. 5 mM in 100 mM DPC/90%H 2 O + 10%D 2 O, pH = 7. 4. The amide H N and H α regions of the spectra were already sparse even if the acquisition of the 15 N dimension was omitted and the experiment was run in 2D fashion by recording the H α -H N plane within an hour. To fully resolve the limited number of peak overlaps then, the 15 N dimension could be acquired with a restricted number of time domain points. The direct H N (t 3 ), indirect H α (t 2 ) and indirect 15 N (t 1 ) dimensions were recorded by sampling 2048, 64 and 8, data points, with acquisition times of 121.
The set of actual 3 J HNHA (DPC) values (Table  1 ) could then be obtained by multiplying each 3 J HNHA (DPC) values calculated from the HNHA spectrum by a uniform factor of 1. 043. The long-range HNCO experiments were recorded on uniformly 13 C-, and 15 N-enriched VA dissolved up to 2. 81 mM in acetonitrile, or up to 5. 8 mM in 150 mM DPC/90%H 2 O + 10%D 2 O, pH = 7. 4. For both samples, the direct H N (t 3 ), indirect 15 N (t 2 ) and indirect 13 C (t 1 ) dimensions were recorded by sampling 2048, 1 and 128, data points, respectively, resulting in the acquisition of a 2D H N -C plane without the 15 N dimension. The acquisition time was uniform in t3 (104. 4 ms), and t2 (0. 2 ms) while in t1 it was slightly longer in DPC solution (42. 3 ms) than in acetonitrile (30. 3 ms) to increase the resolution.
[55]  The calculated trajectories were visualized using VMD 1. 9. 1  [56]  or Pymol v1. 3 while ptraj or cpptraj programs [57] (provided with the AMBER software) were used for in-depth analysis. A cluster analysis protocol was used to select the 3D conformation which ideally reflects the time-averaged position of the atomic coordinates using the built-in DBSCAN  [58]  algorithm of cpptraj. Thus, according to the coordinate RMSD of a selected set of atoms it was possible to distinguish a cluster of converged conformations from less similar structures (or 'noise') that appeared during the trajectory. Additionally, the algorithm determines the snapshot conformation which's atomic coordinate RMSD is minimal with respect to the centroid of the dominant cluster. This conformation is then used to represent the MD trajectory.
Design The study represents a single-centre parallel group randomised controlled trial which will compare assessment and/or interventions carried by an HSCP team, comprised of a senior physiotherapist, a senior occupational therapist and a senior medical social worker, in the ED with usual ED care. The Consolidated Standards of Reporting Trials (CONSORT) guidelines will be followed to ensure the standardised conduct and reporting of the research. This protocol has been registered on Clinical-Trials. gov (NCT03739515) and prepared in accordance with the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) guidelines (see Fig. 1 ). The checklist is presented in Additional file 1.
Power calculation We estimated our sample size based on our primary outcome (ED length of stay) using G*Power version 3. 1. Using data from the Patient Experience Time (PET) database employed in the ED at UHL, the average ED length of stay for patients aged 65 and older for the period 2016-2017 was 13. 95 h (standard deviation 12. 49 h)  [14]. Estimating a 40% decrease in ED length of stay in the intervention group (mean 8. 37 h), and with a 20% attrition rate to follow-up, a sample size of 258 patients (129 in each group) is required to achieve 90% power with two-tailed tests at an alpha level of 0. 05.
Assessment of patient-oriented outcomes include the Barthel Index for Activities of Daily Living  [15]  as a global measure of function and the EuroQoL's 5level of the EQ-5D (EQ-5D-5 L) to measure health-related quality of life  [16] , which will be conducted at baseline as well as at follow-up (30 days and 6 months, with quality of life also assessed at 4 months). In addition, patient satisfaction with their index visit will be explored using the 18-item Patient Satisfaction Questionnaire (PSQ-18)  [17]  at the time of the visit. An economic analysis will estimate the incremental cost-effectiveness of the HSCP team from the perspective of the Irish public health service, compared with usual care. We will estimate healthcare costs from reference costs from national data sources. Participants' responses to the EQ-5D-5 L questionnaire will be used to estimate health state utilities using the Irish value set  [18] , and quality-adjusted life years (QALYs) for each treatment group will be estimated across all time points.
Data analysis Each participant in the study will be assigned a numerical code in order to link data collected at baseline to the data collected at the follow-up at 30 days, 4 and 6 months. Aggregate data will be anonymised. Appropriate descriptive statistics will be used to describe the baseline characteristics of study participants. These will include proportions, percentages, ranges, means and standard deviations, and medians and interquartile ranges (where data are not normally distributed). Differences between the two groups in terms of ED length of stay and hospital length of stay will be analysed using an independent samples t test if they meet the assumptions of normality; otherwise, we will employ the nonparametric alternative Wilcoxon Mann-Whitney test with bootstrapping to calculate an effect size 95% confidence interval (CI).
CE-MS analysis was performed as previously described  29. Briefly, CE-MS analyses were performed using a Beckman Coulter Proteome Lab PA800 capillary electrophoresis system (Beckman Coulter, Fullerton, USA) on-line coupled to a micrOTOF II MS (Bruker Daltonic, Bremen, Germany). The electro-ionization sprayer (Agilent Technologies, Palo Alto, CA, USA) was grounded, and the ion spray interface potential was set to -4. 5 kV. Data acquisition and MS acquisition methods were automatically controlled by the CE via contact-close-relays. Spectra were accumulated every 3 s, over a range of m/z 350 to 3000. MosaiquesVisu software package was applied to deconvolve mass spectral ion peaks representing identical molecules at different charge states into single masses  30. Migration time and ion signal intensity (amplitude) were normalized using internal polypeptide standards  31.
Each polypeptide present in the list was defined by its normalized migration time [min], molecular mass [kDa], and signal intensity detected. Using a Microsoft SQL database, all detected polypeptides were deposited, matched, and annotated in order to allow for further comparison between the groups. The criteria applied to considered a polypeptide identical was that within different samples the mass deviation was lower than 50 ppm for masses <4 kDa, 150 ppm for masses >6 kDa, and between 50-150 ppm for masses between 4-6 kDa. Acceptable migration time deviation was between 1 and 2. 5 minutes.
These peptides were then included in a support vector machine (SVM) mathematical model using the proprietary software MosaCluster (version 1. 7. 0) that allows the classification of samples in the high dimensional data space  33, 34. MosaCluster calculates classification scores based on the amplitudes of the ageing peptides. Classification is performed by determining the Euclidian distance (defined as the SVM classification score) of the vector to a maximal margin hyperplane. The SVM model was optimized by the take-one-out procedure (systematic reduction of peptides in the classifier) with respect to an optimal result after cross-validation. This led to the inclusion of forty peptides in the SVM model with C and the gamma values in the SVM model of 3. 2 and 0. 008, respectively. This SVM model of mouse aging related urinary peptides was used to study the effect of apelin treatment.
Slides were scanned using a Nanozoomer 2. 0 RS (Hamamatsu Photonics SARL) and image analysis was realized in a blind fashion using Image J software (https://imagej. nih. gov/ij/). First medullar part of the slides and background was discarded. Glomerular volume was estimated from Masson Trichrome stained slides. All the glomeruli present on a slide (mean n = 180) were isolated and area was measured. Volume is estimated according to Weibel  35  as Volume = Area 1. 5 × 1. 38/1. 01. Blue staining was then quantified and expressed as percent of glomeruli area (% glomerular fibrosis), or cortex area without glomeruli (% tubular fibrosis). For fibrosis estimation in Sirius Red stained slides, the red color area was quantified in the renal cortex and expressed as percent of total cortex area. For F4/80 stained slides, the area of inflammatory cells was measured and expressed as percent of total cortex area.
Acknowledgements We address a special thanks to all the personal of ANEXPLO animal facility (Toulouse, France) and transcriptomic GeTQ plateform (Toulouse, France). This work has been partly supported by  INSERM  ( Institut National de la Santé et de la Recherche Médicale ), by the  Conseil Regional Occitanie  and by the  "Fondation pour la Recherche Médicale " (Grant Number  DEQ20170336759 ).
The use of MaBoSS, a tool based on Monte-Carlo kinetic algorithm to perform stochastic simulations on logical models results in model state probabilities, and allows for a semi-quantitative study of the model phenotypes and perturbations. As a proof of concept, we use a published generic model of cancer signaling pathways and molecular data from METABRIC breast cancer patients. For this example, we test several combinations of data incorporation and discuss that, with these data, the most comprehensive patient-specific cancer models are obtained by modifying the nodes' activity of the model with mutations, in combination or not with CNA data, and altering the transition rates with RNA expression. We conclude that these model simulations show good correlation with clinical data such as patients' Nottingham prognostic index (NPI) subgrouping and survival time.
The generic logical model can be any model in standard format, automatically translated into a format specific to MaBoSS (Markovian Boolean Stochastic Simulator), a tool that simulates continuous time Markov processes on Boolean networks  (Stoll et al. , 2012 (Stoll et al. , , 2017)). The biological data are extracted from existing repositories or from private sources into a data frame per data type. The merging of these two inputs provides a personalized logical model per patient. Therefore, we define a personalization of a logical model as a specification of a generic logical model using available patient data. We present here a framework to tailor a logical model to patient-specific multi-omics data, thereby personalizing these generic models to particular patients or sets of patients with the goal to treat these patients in a personalized manner.
Simulations With MaBoSS MaBoSS software is applied to obtain probabilities for each of the model states of the system using continuous time Markov chain simulations on the Boolean network  (Stoll et al. , 2012 (Stoll et al. , , 2017)). Its principles are summarized in Figure  2  and in Figure  S5  for a more comprehensive version. MaBoSS uses a specific language for associating transition rates, k 0→1 (or k up ) and k 1→0 (or k down ), to each node (Figure  2B ), enabling to account for different time scales of the processes described by the model. Given some initial conditions (i. e. , either 0 or 1 state for each node), MaBoSS applies Monte-Carlo kinetic algorithm (or Gillespie algorithm) to the network. This algorithm provides a stochastic way to choose a specific transition among several possible ones (Figure  2C ), to perform asynchronous updates and finally to infer a corresponding time for this transition (Figure  S5D ).
01 (see Figure  S9 ). For any study using MaBoSS, to insure that the state space is well explored, it is advised to start with a higher number of trajectories at first and reduce it when the median deviation is below a reasonable threshold. Examples of MaBoSS applied to biological questions can be found in  Calzone et al. (2010) ;  Cohen et al. (2015) ;  Remy et al. (2015) ; or  Montagud et al. (2017). Any logical model in SBML qual format  (Chaouiya et al. , 2013)  can be exported from GINsim  (Chaouiya et al. , 2012)  into MaBoSS format, allowing the use of any logical model from databases for the PROFILE framework.
Mutations Based on the variant classification provided by the data, inactivating mutations (nonsense, frame-shift insertions or deletions and mutation in splice or translation start sites) are assumed to correspond to loss of function mutations and therefore the corresponding nodes of the model are forced to 0. Missense mutations are matched with OncoKB database  (Chakravarty et al. , 2017). For each mutation present in the database, an effect is assessed (gain or loss of function assigned to 1 and 0, respectively) with a corresponding confidence based on expert and literature knowledge. Mutations targeting oncogenes (resp. tumor-suppressor genes), as defined in the 2020+ driver gene prediction method  (Tokheim et al. , 2016) , are assumed to be gain of function mutations (resp. loss of function) and therefore assigned to 1 (resp.
e. , the one that minimizes this maximum difference  (Hartigan and Hartigan, 1985). Values below 0. 05 indicate a significant multi-modality. In PROFILE, this dip statistic is computed using the R package diptest. The Bimodality Index (BI) evaluates the ability to fit two distinct Gaussian components with equal variance  (Wang et al. , 2009). Once the best 2-Gaussian fit is determined, along with the respective means µ 1 and µ 2 and common variance σ , the standardized distance δ between the two populations is given by δ = | µ 1 -µ 2 | σ (1) and the BI is defined by BI = [π(1 -π)] 1 2 δ (2) where π is the proportion of observations in the first component. In PROFILE, BI is computed using the R package mclust. Finally, the kurtosis method corresponds to a descriptor of the shape of the distribution, of its tailedness, or non-Gaussianity.
Bimodal genes processing: Gaussian mixture models In PROFILE, a 2-component Gaussian mixture model is fitted using mclust R package resulting in a lower mode M 0 and a upper mode M 1 (Figure  4 ). Each data point X has a probability to belong to M 0 or M 1 such as Prob(X gene i ,sample j ∈ M 0,gene i ) + Prob(X gene i ,sample j ∈ M 1,gene i ) = 1 (3) For these bimodal genes, the normalization processing is defined as: Norm(X gene i ,sample j ) = Prob(X gene i ,sample j ∈ M 1,gene i ) (4).
Reference expression dataset For the processing of expression data, two main options are available in PROFILE depending on what reference dataset is taken into account. We can either binarize/normalize genes based on distribution patterns across the whole cancer cohort or based on healthy patients. In the latter case, the type of gene distributions (bimodal, unimodal and zero-inflated) and the corresponding parameters (like inter-quartile range) are defined based on distribution patterns for healthy samples only, and the binarization/normalization is then applied on cancer patients. In the datasets under consideration in the present work, only the TCGA RNA dataset includes healthy samples. Except otherwise stated, genes are processed based on cancer cohort and not based on healthy samples.
Tuning node activity status, we define Strict Node Variants or Strict NV, i. e. , forcing the activity (either present or absent) of a node. Tuning both initial states and transition rates, we define Soft Node Variants or Soft NV, i. e, nodes whose initial activation proportion is close to that of the patient and whose transition rates promote maintenance in its state of activity. data types with the same personalization method raises some ambiguity issues. For instance, a gene can be inferred as a loss of function from the mutation data and can be found as amplified from CNA data. In this case, we consider that the information from mutations always overrides the information coming from CNA or binarized RNA/protein. Since both RNA and protein expression are available in the TCGA dataset, we explored the possibility to combine the two data types as follows: the RNA expression level is taken into account to define soft node variants only if there is no corresponding data in the protein dataset for that specific node.
Comparison With Clinical Data In order to assess the relevance of the different scenarios of model personalization, we investigate the correlation with biological and clinical factors. For METABRIC dataset, signatures from the Molecular Signature Database (MSigDB) described in  Liberzon et al. (2015)  were used to classify the relevance of Proliferation and Apoptosis probabilities obtained from different personalization methods. We selected the Hallmarks "G2M Checkpoint" (resp. "Apoptosis"), a gene set composed of 200 genes (resp. 161) to correlate with the Proliferation (resp. Apoptosis) model probabilities. Genes used to personalize the models are excluded from the gene set, which reduces it to 185 (resp. 150) genes. Signature scores are then computed with the Gene Set Variation Analysis (GSVA) method, described in  Hänzelmann et al.
(2013)  and implemented in GSVA R package. Correlations are assessed based on Spearman rank method and 95% confidence intervals are obtained by bootstrap (n = 1, 000). For the METABRIC cohort, the patient's Nottingham prognostic index (NPI) and survival data are also gathered. NPI is a prognostic score based on clinical features such as tumor size, tumor grade and node status. Regarding the survival data, there is data for all but one of the 1980 METABRIC patients. The overall survival time points are between 0 and 355 months with a median survival time of 283 months and 646 events (patients died of disease). Kaplan-Meier fits are obtained using the survival R package.
Breast Cancer Data Processing Our framework has been applied to 2,509 breast cancer patients' molecular data that were collected from METABRIC. Patients' data types include exome mutations, CNA and RNA expression as well as clinical data such as survival data. One thousand nine hundred and four patients of the 2,509 total have all these data types available  (Curtis et al. , 2012; Pereira et al. , 2016)  (Figure  S8A ). Data were processed as described in previous sections. The logical model of cancer pathways  (Fumiã and Martins, 2013)  was chosen as a working example as it is a generic model with a relatively big number of nodes that span several pathways relevant to cancer. This model was initally used to study the effects of microenvironment conditions, to simulate the response to driver mutations in colorectal cancer progression and the effect of genes' perturbations as therapeutic targets  (Fumiã and Martins, 2013).