dataesr/scientific-paragraphs-categorization huggingface dataset:
Cell growth inhibition was determined by an MTS assay according to the manufacturer's instructions (Promega, Madison, WI, USA). Briefly, the cells were seeded in 96-well plates (2. 5 × 10 3 cells/well) containing 100 µL of growth medium. After 24 h of culture, the cells were treated with the tested compounds at 10 different final concentrations. After incubation for 72 h, 20 µL of Cell Titer 96 ® AQ ueous. One Solution Reagent was added for 2 h before recording absorbance at 490nm with a spectrophotometric plate reader. The concentration-response curves were generated with Graph Prism software and the 50% inhibition concentration values (IC 50 ) were calculated from polynomial curves (four or five-parameter logistic equations).
Acknowledgements This work is partly supported by the  National Natural Science Foundation of China  under Grants No. 11735003 , No. 11835015 , No. 11975041 , No. 12047503  and No. 12125507 , the  Chinese Academy of Sciences  under Grant No. XDB34030000 , the  China Postdoctoral Science Foundation  under Grant No. 2022M713229 , the  Fundamental Research Funds for the Central Universities  and the  Thousand Talents Plan for Young Professionals. M. P. V. would also like to thank the  IJCLab of Orsay , where part of this work has been done, for its long-term hospitality.
Single human-Candida cell interactions were measured using the AFM CellHesion ® module under human cell growth conditions in a heated (Bruker, JPK BIOAFM) petri dish connected to a CO 2 tube and grown overnight (6  10 4 cells). The spring constant of the cantilevers was calibrated using the thermal noise method, having a sensitivity of approximately 18 to 35 nm/V and a spring constant (0. 017 to 0. 044 N/m), consistent with the nominal spring constant (0. 06 N/m; Bruker, USA). The probes were then used to measure interaction forces within a 3 μm  3 μm area at the center of the cell (Figure  S1a, b ) using an applied force of 0. 5 nN with 20 μm/s approach and retract speeds and contact times of 0 (immediate retraction), 5, 10 and 20 s. Each force curve was analyzed using the JPK Data Processing software (JPK, Berlin, Germany), for which adhesion force, work of adhesion and rupture distance histograms were calculated.
ACKNOWLEDGMENTS The work was supported by a  Mitacs Globalink travel grant  (Ref. IT12948 ) to EN, a  Natural Science and Engineering Research Council   Discovery Grant  ( NSERC  DG;  06649-2018 ) to  TESD , and  Agence Nationale de la Recherche  grant ( Project-ANR-17-CE11-0023 ) to ED. EN was partially supported by the  Faculty of Graduate Studies  and Research at the  University of Regina , which is situated on the traditional territories of the nêhiyawak, Anihsinapek, Nakoda, Dakota, and Lakota peoples, and the homeland of the  Métis/Michif Nation. We would like to thank the technical team at "caractérisation plateforme" at  LAAS-CNRS  especially,  Charline Blatché ,  Sandrine Souleille  and  Camille Gironde  for their training and guidance,  Emmanuelle Trévisiol  ( Biosoft joint lab, LAAS-CNRS ) for assistance generating the micropatterned substrates and  Miguel Cacho Teixeira  who provided the C.
Study Design and Participants The CKD-REIN study is a prospective cohort carried out in 40 nephrology clinics in France, nationally representative with respect to geography and facility legal status, public or private. From 2013 through 2016, we included 3033 adult patients with CKD stage G3 to G5 (eGFR <60 ml/min per 1. 73 m 2 ) and any type of primary kidney disease, not on dialysis or transplanted. At baseline, fasting blood and second morning urine samples were collected together with extensive information about patient and disease characteristics. Patients were then followed-up annually over 5 years. Study protocol and cohort profile have been published in detail elsewhere. 15, 17 The study was approved by the institutional review board of the French National Institute of Health and Medical Research (IRB00003888) and is registered at ClinicalTrials.
gov (NCT03381950). For the peptidome analysis, a casecohort sample of 1054 participants was selected, including all incident cases of KF and cardiovascular events over the first 3-year follow-up, and a random sample of enrolled patients, all of them with urinary sample stored at baseline or within the next 3 months (Figure  1 ). The validation cohort included 326 patients with CKD stage G3 to G5 and UP data from the Ghent cohort study, a single-center study that included 526 patients with CKD Stages G1 to G5 not on dialysis, from Ghent University Hospital outpatient nephrology clinic (Belgium), between 2011 and 2014. 18, 19 This study was approved by the local ethical committee at the Ghent University Hospital (2010/033; B67020107926). Written informed consent was obtained from all participants.
Demographic, Clinical and Laboratory Data In the CKD-REIN study, patients' characteristics (age, sex, body mass index, blood pressure, and primary kidney disease), and comorbidities (cardiovascular disease, hypertension, and diabetes) were collected at baseline from medical records. GFR was estimated using the CKD-epidemiology collaboration equation  20  on the basis of cystatin C and serum creatinine (isotope dilution mass spectrometry traceable enzymatic method) measured in a single laboratory from stored blood samples. UACR was also measured centrally based on stored urine samples. For missing data (8 for eGFR, 13 for UACR), we used the closest routine laboratory value to the inclusion date. In the Ghent cohort study, GFR was estimated using the CKD-epidemiology collaboration equation with serum creatinine.
24 Of the 1054 patients selected in the case-cohort study, 1000 had urinary pepidome data available for this analysis (Figure  1 ). Urine samples from Ghent, Belgium, were analyzed in Hannover, Germany on a P/ACE MDQ capillary electrophoresis system (Beckman Coulter) coupled on-line coupled to a micrOTOF II mass spectrometer (Bruker Daltonic).
Data Processing Mass spectral ion peaks representing identical molecules at different charge states were deconvoluted into single masses with MosaiquesVisu software. 25 The software automatically examined all mass spectra from a capillary electrophoresis-mass spectrometry analysis for signals with a signal-to-noise ratio of at least 4 present in 3 consecutive spectra. Capillary electrophoresis migration time was calibrated by local regression with more than 1700 reference signals. Normalization of urine peptidome data require correction for both analytical variances during profiling (e. g. , signal suppression in the mass spectrometer) and for variability caused by biological issues (e. g. , dilution of urine by different hydration states of the urine donors). With that aim, mass spectrometry signal intensities were normalized relative to 29 "housekeeping" peptides.
Peptide Sequencing For sequencing of peptides, the urine samples were analyzed on a Dionex Ultimate 3000 RSLC nano flow system (Dionex, Camberly, UK) coupled to an Orbitrap Velos MS instrument (Thermo Fisher Scientific), using data-dependent higher-energy collision dissociation MS/MS sequencing of a maximum of the top 20 ions, as described in Klein et al. 27  Data files were analyzed using Proteome Discoverer 1. 2 (Thermo Fisher Scientific) and were searched against the Uniprot database. No fixed modifications were selected; oxidation of methionine, lysine and proline were selected as variable modifications. The peptide data were extracted using high confidence peptides, which are best ranking peptides with an Xcorr $1. 9, a delta mass between experimental and theoretical mass AE 5 ppm, absence of cysteines in the sequence (because cysteines without reduction and alkylation form disulphide bonds), absence of oxidized proline in protein precursors other than collagens or elastin.
Statistical Analysis Patients' baseline characteristics were reported as percentage for qualitative variables and as mean AE SD or median (lower quartile-upper quartile) for quantitative variables. In all analyses, peptides abundances and continous variables were used as linear predictors. In the CKD-REIN case-cohort, a clustering algorithm with selection of relevant variables and number of groups was first carried out with the sequenced urinary peptides to classify patients at baseline. Peptides present in less than 20 patients or with a near zero variance were excluded. The algorithm used the integrated completedata likelihood (VarSelLCM package in R Statistical software, R Core Team). 28 Patients' baseline characteristics were described according to identified clusters. Several 3-year KF risk prediction models were then developed in the CKD-REIN case-cohort with different sets of predictors as follows: (i) sequenced peptides; and (ii) sequenced peptides and risk factors including age, sex, eGFR, and UACR (log transformation); two supplementary models were created to explore the prediction potential of nonsequenced peptides as follows: (iii) all peptides, (iv) all peptides and risk factors.
Only peptides that had more than 20 patients with a nonzero value were predictor candidates. We used a Cox's proportional hazards model regularized by elastic net penalty to develop each model (glmnet package in R Statistical software, R Core Team). 29 All predictors were standardized before training. Two-fold stratified crossvalidation repeated 50 times was performed to choose the optimal hyperparameters with the "one standard error" rule  30  and the Harrell c-index. Mean c-index was estimated and its 95% CI obtained with the percentile method through resampling. Finally, each optimal model was fitted in the entire data set. In accordance with clinical proteomics guidelines,  31, 32  we then assessed the performance of the identified peptide KF signature in Ghent, an external validation cohort, using the c-index and its 95% bootstrap CI (percentile method).
This study has major strengths with respect to standards requested for proteomic analyses. 31, 43, 44 It is based on a large cohort of well-phenotyped CKD patients with adequate demographic and clinical data (type of nephropathy and comorbidities), clearly defined clinical questions, patient selection, and outcomes, and detailed information about the sampling methodology because urinary samples of the CKD-REIN study have been collected and stored following strict standard operating procedures. The case-cohort design of the study was cost-effective and allowed us to use Cox proportional hazards models and Harell c-index to evaluate the 90-peptide signature performance as for the entire cohort. Moreover, we validated our findings in an external independent cohort with less severe CKD. Interestingly, the samples were collected from another site (Ghent University Hospital) than the CKD-REIN cohort, and peptidome analysis was also performed in another laboratory confirming the high interlaboratory precision, stability, and reproducibility of our biomarkers regardless of the analytical platform.
DISCLOSURE ZAM reports having received grants for CKD-REIN and other research projects from Amgen, Baxter, Fresenius Medical Care, GlaxoSmithKline, Merck Sharp and Dohme-Chibret, Sanofi-Genzyme, Lilly, Otsuka, Astra Zeneca, Vifor, and the French government, and fees and grants to charities from Astra Zeneca, Boehringer Ingelheim, and Glax-oSmithKline. HM is the cofounder and co-owner of Mosaiques Diagnostics. JS is employed by Mosaiques Diagnostics. BS coordinates the CKD-REIN cohort study, funded by the Agence Nationale de la Recherche through the 2010 Cohortes-Investissements d'Avenir program and by the 2010 national Program Hospitalier de Recherche Clinique, and supported through a public-private partnership currently involving Fresenius Medical Care, Glax-oSmithKline, Vifor Fresenius and Boehringer Ingelheim France.
ACKNOWLEDGMENTS We acknowledge the  CKD-REIN study coordination  staff for their efforts in setting up the CKD-REIN cohort ( Marie Metzger ,  Elodie Speyer ,  Céline Lange , and all the clinical research associates) as well as the CKD-REIN clinical sites and investigators: Alsace:  Prs. T. Hannedouche et B. Moulin  ( CHU, Strasbourg ),  Dr. A. Klein  ( CH Colmar );  Aquitaine: Pr. C. Combe  ( CHU, Bordeaux ),  Dr. J. P. Bourdenx  ( Clinique St Augustin, Bordeaux ),  Dr. A. Keller ,  Dr. C. Delclaux  ( CH, Libourne ),  Dr. B. Vendrely  ( Clinique St Martin, Pessac ),  Dr. B. Deroure  ( Clinique Delay, Bayonne ),  Dr. A. Lacraz  ( CH, Bayonne ); Basse Normandie:  Dr. T. Lobbedez  ( CHU, Caen ),  Dr. I. Landru  ( CH, Lisieux );  Ile de France: Pr Z. Massy  ( CHU, Boulogne -Billancourt ),  Pr.
Funding CKD-REIN  is funded by the  Agence Nationale de la Recherche  through the  2010 «Cohortes-Investissements d'Avenir » program  ( ANR-IA-COH-2012/3731 ) and by the 2010 national  Program Hospitalier de Recherche Clinique. CKD-REIN  is also supported through a public-private partnership with Fresenius Medical Care and Glax-oSmithKline (GSK) since 2012 and Vifor France since 2018,  Sanofi Genzyme  (2012 to 2015),  Baxter  and  Merck Sharp & Dohme-Chibret  ( MSD France ) from 2012 to 2017, Amgen (2012 to 2020), Lilly France (2013 to 2018),  Otsuka Pharmaceutical  (2015 to 2020),  AstraZeneca  (2018 to 2021) and  Boehringer Ingelheim France since 2022. Inserm Transfert  set up and has managed this partnership since 2011. The work was also supported by a grant from the  Agence Nationale de la Recherche  (Proteomark_CKD,.
Acknowledgements The authors acknowledge funding from the  Agence Nationale de la Recherche , through the grants no. ANR-18-CE07-0025  ( Pomac  project) and no. ANR-21-CE07-0028  ( Flips  project), and from the  European Union 's  Horizon 2020 research and innovation program  under grant agreement no. 814557  (C123 project). R. R. , E. A. Q. and J. C. are thankful to  Universite Ćlaude Bernard Lyon  1 for R. R. doctoral fellowship.
ACKNOWLEDGMENT C. M. , G. G. and K. C. acknowledge the support of the  CNRS  and  Chimie ParisTech ,  PSL University. C. M. thanks  MESRI (Paris-Saclay University ) for a Ph. D. fellowship. This work was financially supported by an  ERC   Consolidator Grant PhotoMedMet  to  G. G. ( GA 681679 ) and has received support under the program " Investissements d' Avenir " launched by the  French Government  and implemented by the  ANR  with the reference  ANR-10-IDEX-0001-02 PSL  (G. G. ). V. G. thanks  CNRS ,  UPS , and  Ecole Polytechnique  for financial support. This work was granted access to the HPC resources of CINES under the allocation 2020-A0070810977 made by  GENCI.
Results and Discussion A first difficulty with 1 is that it was sparingly soluble in most common NMR solvents. This was rather unexpected, as the numerous polyketide pigments extracted from Cortinarius spp. had their NMR spectra recorded in usual solvents. Among the different investigated solvents, TFA-d represented the best option to facilitate the dissolution and acquisition of NMR data. Alternatively, DMSO-d 6 also afforded 1 H and 13 C NMR data of convenient quality, but the 1 H NMR signals were too broad to disclose a satisfying HMBC spectrum. The 1 H NMR spectrum of 1 revealed three aromatic protons ((δ H 7. 47 (1H, s), 7. 53, and 8. 00)) with the two latter being meta-arranged (1H both, d, J = 2. 3 Hz), an oxygenated methylene group (δ H 5. 82 (2H, s)), and a methoxy group (δ H 4. 34 (3H, s)).
Results and Discussion A first difficulty with 1 is that it was sparingly soluble in most common NMR solvents. This was rather unexpected, as the numerous polyketide pigments extracted from Cortinarius spp. had their NMR spectra recorded in usual solvents. Among the different investigated solvents, TFA-d represented the best option to facilitate the dissolution and acquisition of NMR data. Alternatively, DMSO-d6 also afforded 1 H and 13 C NMR data of convenient quality, but the 1 H NMR signals were too broad to disclose a satisfying HMBC spectrum. The 1 H NMR spectrum of 1 revealed three aromatic protons ((δH 7. 47 (1H, s), 7. 53, and 8. 00)) with the two latter being meta-arranged (1H both, d, J = 2. 3 Hz), an oxygenated methylene group (δH 5. 82 (2H, s)), and a methoxy group (δH 4. 34 (3H, s)).
that NMR-based structure elucidation is at high risk of being inaccurate when the ratio of H/C atoms is less than one, as it is the case here  [15]. The scarce information available from the NMR data of such structures is likely to result in several possible candidates, and it is therefore important to ensure that all possibilities have been duly considered  [16]. To verify our structural hypothesis, we elected to submit the NMR spectroscopic data of 1 to a Computer-Assisted Structure Elucidation (CASE) workflow  [17] , through the ACD/Structure Elucidator (ACD/SE) expert system  [18]. Briefly, this program takes the benefit of the HRMS-deduced molecular formula and of the 1D ( 1 H and 13 C) and 2D (COSY, HSQC, and HMBC) NMR spectra to create Molecular Connectivity Diagrams (MCD). This graphically depicts all atoms and XHn moieties, along with their chemical shifts and properties (valence, hybridization, and admissibility of a neighbor heteroatom).
and 3 ) listed in order of increasing spectroscopic discrepancies. Average deviations are reported according to the prediction method-dA, HOSE-code; dN, neural network; and dI, incremental approach-along with the maximal 13 C NMR chemical shift deviation for each prediction method. Unfortunately, the average mean deviations obtained between experimental and predicted 13 C NMR chemical shifts proved to be of the same order and were higher than typically observed differences in such analyses (viz. dn < 3 ppm, with n = A, I and N). As ACD/Predictors  [18]  were trained on databases containing NMR spectra recorded in conventional solvents, it is rather probable that the discrepancies could be a consequence of the NMR data being acquired in TFA-d. That is a very rarely chosen NMR solvent, but known to exert a substantial influence on 13 C NMR chemical shifts  [23].
The MCD created from NMR data which were acquired in DMSO-d6 is shown in Figure  S15. As a result of structure generation, 20 structures were obtained in 34 min. The three top-ranked structures are presented in Figure  6. Unfortunately, the average mean deviations obtained between experimental and predicted 13 C NMR chemical shifts proved to be of the same order and were higher than typically observed differences in such analyses (viz. d n < 3 ppm, with n = A, I and N). As ACD/Predictors  [18]  were trained on databases containing NMR spectra recorded in conventional solvents, it is rather probable that the discrepancies could be a consequence of the NMR data being acquired in TFA-d. That is a very rarely chosen NMR solvent, but known to exert a substantial influence on 13 C NMR chemical shifts  [23].
General Experimental Procedures Ultraviolet (UV) spectra were measured on a Lightwave II+ WPA 7126 V. 1. 6. 1 spectrophotometer. Infrared (IR) spectra were recorded with a PerkinElmer type 257 spectrometer. 1 H and 13 C NMR data were recorded on a Bruker 700 MHz NMR spectrometer equipped with a 5 mm TXO cryoprobe using TFA-d or DMSO-d 6 as solvent. The solvent signals were used as references. HRESIMS measurements used an Agilent 6546 Accurate-Mass Q-TOF hyphenated with a 1290 Agilent Infnity II LC system. The chromatographic system was fitted with a Zorbax RRHD Eclipse Plus C 18 column (2. 1 × 50 mm, 1. 8 µm). Sunfire preparative C 18 column (150 × 19 mm, i. d. 5 µm; Waters) was used for preparative HPLC separations using a Waters Delta Prep equipped with a binary pump (Waters 2525) and a UV-visible diode array detector (190-600 nm, Waters 2996).
Electronic spectral studies The absorption spectra of 1 and 2 were recorded in conc. range 200-700 nm in methanol. The UV-vis spectrum showed bands at and 266 nm in the UV region that were attributed to electronic transitions (π-π*) of the aromatic chromophore while LMCT bands (ligand-tometal charge transfer) appeared at 347 and 354 nm. These complexes and 2 also displayed lower energy dd bands due to the 2 Eg → 2 T 2g transition centered at 678 and 680 nm in the visible region. The electronic spectral data indicated that the complexes formed distorted square pyramidal geometry around Cu(II) well-corroborated with single X-ray crystal diffraction studies  [41].
Lipophilicity Lipophilicity is used to determine various biological parameters of the complexes, such as cellular uptake, metabolism, bioavailability, and lipid solubility. The study has gained importance as it is ideal for predetermining the ADMET properties of drug candidate correlate structure and lipophilicity profiles, thus, rendering them a safe throughput in  clinical trials. The lipophilicity can be calculated in terms of Log P, demonstrating the membrane permeability of the complexes. The SwissADME software calculated Log P values of complexes 1 and 2 as 1. 28 and 2. 82, respectively. The greater lipophilic nature of complex 2 than complex 1 was reflected by their Log P value, followed by their enhanced biological activity  [58].
Molecular docking studies Molecular docking studies with DNA and RNA biomolecule were conducted to further confirm the binding mode as well as selective recognition sites for 1 and 2, were carried out  [59]. The docked pose model of 1 and 2 with DNA (PDB ID: 1BNA) delineated that the complexes preferentially target the minor groove GC region in proximity to G-24, C-23, G-22, G-4, G-4, A-5 and A-6 nucleobase pairs (Fig. S7  & S8a). The Schiff base ligand in the complexes was found to intercalate within the DNA nucleobases, while the imidazole moiety interacted through hydrogen bonding interactions stabilizing the overall molecular interactions. The binding energies were found to be -248 KJmol -1 and -270 KJ mol -1 for complexes 1 and 2, respectively, suggesting the higher binding propensity of complex [C 16 H 15 CuN 3 O 4 S] with DNA duplex.
The Docking profile of the complexes 1 and 2 with tRNA (PDB ID: 6tRNA) demonstrated that these complexes fit snugly into the RNA intercalation site i. e. , inserted into the active pocket of the anticodon arm in proximity to C-28, C-27, A-29, G-26, C-25, G-30, C-40, G-42, G-43 and U-41 (Fig. S7  & S8b). In addition, van der Waals and hydrophobic interactions also play a major role in the stronger binding of the complexes with tRNA. The binding energies for complexes 1 and 2 were found to be -265 KJmol -1 and -299 KJmol -1 , respectively, which validates the higher binding propensity of complex 2. Furthermore, the higher negative binding energy values of the complexes with tRNA as compared to ct-DNA demonstrated that they were better  targeted towards tRNA therapeutic biomolecule.
Acknowledgements The  SAIF Panjab University, Chandigarh  is gratefully acknowledged for providing elemental analysis and  ESI-MS Facilities. We are also thankful to the  Department of Chemistry, Aligarh Muslim University , for providing the spectroscopic facilities. The authors (S. Z. ) sincerely acknowledge the  University Grant Commission (UGC) ,  CSIR-HRDG  (SRF Scheme-2020: 09/112(0660 ) 2020-EMR-I ),  New Delhi , and  Department of Chemistry, AMU  through  UGC  assisted  DRS-SAP, DST-FIST , and  DST PURSE Programme  for providing financial assistance during the research work.
This pipeline provides a basis from data collection and model training, to model validation and model deployment in commercial fields. Accurate model training is the foundation of the pipeline. However, in natural environments, the deployment dataset is frequently different from the training data (domain shift) causing the model to fail, so a robust model is essential to build a reliable solution. Although we demonstrate our pipeline in a sorghum field, the pipeline can be generalized to other grain species. Our pipeline provides a highresolution head density map that can be utilized for diagnosis of agronomic variability within a field, in a pipeline built without commercial software.