dataesr/scientific-paragraphs-categorization huggingface dataset:
Early Kidney Proteome Remodeling in LBW Rats To unravel the early changes in LBW rats before pathological changes occurred in the kidney, we performed quantitative proteomic analyses on kidney cortices taken at 4 weeks of age, when the rats in both groups exhibited no pathological changes. A total of 1200 proteins were found in the rat kidney and 437 proteins were detected as differentially expressed (p < 0. 05) between LBW and NBW rats at this time point (Table  S1A ). Further analysis of the proteomic dataset using a multiple comparison method with Benjamini-Hochberg correction allowed us to calculate the adjusted p-value (q value; Table  S1B ). The threshold for the false discovery rate (FDR) was set at q = 0. 01. The dataset is available to the community through the public repository PRIDE using the accession number PXD018948.
Metabolic and Signaling Pathways Altered in LBW Kidney Analysis of the above-described proteomic data was performed using Ingenuity Pathway Analysis (IPA) software version July 2021. The LBW rat kidney differential proteome composed of 1183 proteins was compared to the IPA Knowledge database to detect enrichment in specific pathways using the 'core analysis' module. The results revealed that the main four pathways (ranked by log 10 p-value with Z > 1 and -logP < 3) altered in the LBW rat kidneys were EIF2 signaling, oxidative phosphorylation (OXPHOS; Z-score -1. 9), TCA cycle, and sirtuin signaling (Figure  4A ; Table  S2 ). Glycolysis and gluconeogenesis were also reduced, with 48% of the pathway components downregulateded in LBW kidney. These findings indicate that the main machinery required for energy transduction was inhibited in LBW kidneys, suggesting a reduction in energy metabolism.
Predicted Genetic Regulators Involved in LBW Kidney Remodeling Analysis of the proteins with altered content (p < 0. 05) in the LBW kidney allowed us to predict putative transcription factors, kinases, proteases, or even microRNAs involved in the observed changes based on the knowledge of the targets associated with specific regulators and the directionality of the expected changes. Such a bioinformatic predictive analysis was performed on the LBW kidney proteome. The results (Table  S2 ) identified a series of regulators potentially involved in LBW kidney proteome reprogramming, indicating that LBW kidney remodeling impacts different signaling pathways. The top two regulators (Z-score and p-value) were rapamycin-insensitive companion of mTOR (RICTOR) and NFE2L2 (NRF2), as a large number of their respective targets were found in the LBW differential proteome dataset (Table  3 ).
This protein is a protease that cleaves a large number of targets in the kidney, including extracellular matrix (ECM) proteins, modulators of inflammation, and proteins involved in the protein kinase A (PKA) and PKC signaling pathways  [40]. Meprins have been implicated in the pathophysiology of diabetic nephropathy (DN), acute kidney injury (AKI), and fibrosis-associated kidney disease. Studies in diabetic mouse models suggested that Meprin A plays a protective role in the kidney  [41] , raising the need to evaluate in more detail the implication of Meprin A in LBW nephropathy progression or protection. Computer analysis of the untargeted proteomic dataset generated in our work also predicted the alteration of mTOR signaling in LBW. Accordingly, a considerable number of studies have shown the involvement of mTOR and RICTOR in nephropathies  [42, 43].
Therefore, NRF2 activators might also be interesting candidates for the prevention of LBW-related nephropathy, as considered in other nephropathies  [52] [53] [54]. To conclude, the proteomic and histopathology study of an LBW rat model generated a unique dataset of interest for generating novel hypotheses on the pathogenesis of LBW nephropathy. We unraveled changes in proteins such as Meprin A, UQCR7/11 and Atp5I/L that occurred early in the disease process, suggesting that these elements are potential players in the molecular determinants of this disorder. However, our findings remain limited to the rat model used. Technical and methodological limitations also exist in our study, as only 1200 proteins were found in the rat kidney proteome. This number is low as compared to the reference rat proteome dataset although a recent analysis found a similar number of distinct proteins (1290) in rat kidney  [55].
Data collection Variables recorded were: age, sex, height, weight, severity at admission estimated by the Simpli ed Acute Physiology Score (SAPS3) [which ranges from 16 points (low severity) to 226 points (high severity)], comorbidities, (previous medication use), mode of respiratory support (high ow nasal oxygen cannula and/or noninvasive positive pressure ventilation  [NPPV]  as CPAP (continuous positive airway pressure) or BiPAP (Bi-Level Positive Airway Pressure)), use of compassionate medication [antivirals (lopinavirritonavir, remdesivir), hydroxychloroquine, and immunomodulatory agents (interleukin-6 receptor antagonists, Janus kinase inhibitor, and corticosteroids)] before admission to ICU, length of hospital stay prior to ICU admission, date of intubation, ventilatory settings within the rst week of mechanical ventilation, daily arterial blood gases, concentrations of plasma/serum biomarkers drawn within 7 days of ICU admission, including high-sensitivity C-reactive protein, D-dimer, ferritin, high-sensitivity troponin, procalcitonin, and IL-6, use of adjuvant therapies for acute respiratory failure (neuromuscular blocking agents, inhaled pulmonary vasodilators, ventilation in prone position, and extracorporeal membrane oxygenation), vasopressor agents, renal replacement therapy, antibacterial agents, antiviral agents, and other immunomodulatory agents, complications, and organ failure during the ICU stay.
Patients were followed-up until 180 days after hospital admission. Patients who died were censored at the date of death for the time-to-discharge analysis. Independent on-site monitoring was performed at each participating center in coordination with the main investigators who veri ed all data. All data were monitored and reviewed by four external investigators (OP, AE, CRS, and FFV) to detect erroneous and missing data. The study was approved by the institutional ethics board at each participating site. The requirement for informed consent from individual patients was waived because the study design was considered minimal-risk research using data collected for routine clinical practice during an ongoing public health emergency. The study was registered in ClinicalTrials. gov Identi er: NCT04379258.
Statistical analysis Following the approval of the ethics committee at each participating center, patients were collected retrospectively to allow rapid data collection and over 180 days of prospective follow-up to analyze clinical outcomes (data extraction date: April 16, 2020). The main outcome was mortality at 90-days and 180-days. Secondary outcomes were duration of mechanical ventilation, duration of ICU and in-hospital stay, reason for death and destiny at hospital discharge. Mortality outcomes were analyzed using mixed-effects logistic regression and the imputed data. The analysis was tted with a random effect in which the patients were nested in ICUs to characterize ICU-level variation and to estimate center-speci c rates of ICU-mortality. For the predictive model for ICU mortality and hospital mortality, we used the multivariate imputation by chained equations (MICE) procedure for all missing data  (13).
Incomplete dichotomous variables were imputed using a logistic regression model, while linear regression was used to impute incomplete continuous variables. We generated 10 imputed data sets. Variables included in the predictive model were as follows: age, sex, comorbidities (diabetes, hypertension, obesity), and the following pre-admission medications: angiotensin-converting enzyme inhibitors, angiotensin receptor blockers and chronic steroids. We also included in the model non-invasive respiratory support before orotracheal intubation, time from hospital admission until intubation, SAPS3, relevant variables determined within the rst 48 hours of admission to the ICU (neutrophil to lymphocyte ratio, PaO 2 to FiO 2 ratio, D-dimer, ventilatory ratio  (14) , tidal volume and positive end-expiratory pressure (PEEP), treatments given within in the rst 48 hours of mechanical ventilation (vasopressor support, antivirals, immunomodulatory therapy, steroids, anticoagulants, neuromuscular blocking agents, selective digestive decontamination, prone position).
The fractional polynomial method was used to explore the behavior of continuous variables. We applied stepwise selection with backward elimination of predictors from the full model with p < 0. 05. The nal model was then validated by bootstrapping. Using this procedure we sampled 100 samples for each imputed data set with replacement. Discrimination of the model was evaluated using the c-statistic: for a binary outcome, c is identical to the area under the receiver operating curve (AUROC). Calibration was evaluated studying the calibration slope and calibration-in-the-large (CITL). Calibration-in-the-large (CITL) indicates the difference between the observed prevalence of death and the predicted mean probability. In a perfect model, the CITL should be equal to zero, but our CITL value was small and the con dence interval contained 0.
Discussion This multicenter cohort study in 868 adult patients with severe COVID-19 undergoing invasive mechanical ventilation in Spain provides follow-up data concerning long-term survival after ICU admission. Independent predictors associated with ICU mortality were older age, higher SAPS3, the need for norepinephrine, an increased neutrophil-to-lymphocyte ratio, a failed attempt of noninvasive positive pressure ventilation, and the lack of use of a selective digestive decontamination strategy. The proportion of patients who died varied widely between centers. To the best of our knowledge, this is the rst report dealing with and long-term outcomes in a large cohort of mechanically ventilated patients with severe COVID-19. Overall, in-hospital mortality was 40. 5%, which is considerably lower than that in other high-income countries (18-24).
In our predictive model, an NLR higher than 15 on day 1 was signi cantly associated with increased mortality. Our data also con rm that NLR is a useful independent prognostic factor in COVID19 patients under mechanical ventilation. We did not nd that adjunctive therapies such as prone positioning or the use of neuromuscular blocking agents or glucocorticosteroids had a bene cial effect on mortality. It is important to highlight that the predictive model described here is based on the rst 48 hours of mechanical ventilation. One can therefore argue that these measures would not have been implemented for long enough to have a positive clinical impact on patients with severe COVID-19. Furthermore, this observational study collected the local clinical practice from centers during the pandemic outbreak.
Our data indicate that 60% of all deaths among ventilated patients occurred in the rst 30 days of ICU stay. These deaths were due to refractory hypoxemia and multiorgan failure, and were probably related to the development of progressive, brotic lung disease. Although the virus is eradicated in the most severe COVID-19 patients, the cause of lung damage is not. Linked to the in ammatory response, lung brosis emerges as a secondary event related to the progression of the pathology and worse outcomes  (43) (44) (45). We acknowledge a number of limitations. First, due to the fact that most participating centers rapidly reached ICU saturation at the critical moment of the COVID-19 outbreak, and as intensivists were facing di cult decisions, not all patients admitted to the participating ICUs during the study period were collected.
BRCA1 is a tumour suppressor gene coding for a large protein containing multiple functional domains, which interacts with multiple other proteins  [4, 5]. The BRCA1 gene is an essential component of DNA damage repair via the Homologous Recombination (HR) pathway, which is an error-free repair mechanism, and hence crucial for cellular survival. BRCA1 is also involved in chromosome segregation and mismatch repair, so that it also plays a critical role in the maintenance of genome integrity  [6]. While some BRCA1 variants are undoubtedly pathogenic, over 50% of BRCA1 variants (single nucleotide variants) are classified as Variants of Uncertain Significance (VUSs) on the ClinVar public database, thus complicating clinical decisions regarding the choice of therapies and counselling for reproductive decisions.
Additional variants present in heterozygous BRCA1 variant carriers Heterozygous BRCA1 variants can co-occur with other genetic mutations, for instance in TP53  [24, 25]. Our whole exome sequencing data were then inspected for the presence of additional variants. Interestingly, only one known pathogenic variant-a mutation in the ATM gene (c. C8373A:p. Y2791X)-was found in the non-pathogenic Y856H fibroblasts and iPS cells (Fig 4A  and 4B ). This variant leads to a truncation in the kinase domain of ATM, thus abrogating its autophosphorylation on Ser1981, which is a prerequisite of its function at DNA damage sites  [26]. Here, despite the low quality of our Western blot, no ATM autophosphorylation was observed in the iY856H_2 iPS cells compared to WT lines (Fig  4C ). Importantly, the Y856H BRCA1 variant is classified as benign (Fig  1B ).
Acknowledgments The authors sincerely thank all those patients, and their families, who have generously donated time, samples, and information. The authors are grateful for the collaboration of the breast and plastic surgical teams, breast tissue bank co-ordinators, led by  Cheryl Gillett , and the diagnostic DNA laboratory. They also wish to acknowledge their deep gratitude to  Dusko Ilic  and his group, for their help and support throughout the project. The authors are indebted to all members of the  Biomedical Research Centre (BRC) Flow Cytometry Core Facility , and particularly to  Pj Chana  and  Susanne Heck. The authors thank  Alka Saxena  (former Director,  BRC Genomics Core Facility)  for her support in whole-exome sequencing analysis. The authors also thank  Davide Danovi  and  Fiona Watt  for giving us access to their HipSci bank.
This work was supported by a  European Union Scholarship Programme , by a pilot grant from  Breast Cancer Now  (2015NovPR609), by a specialist program grant from  Blood Cancer UK  (13043; previously called Bloodwise) and  King's College London. All BRC  facilities were funded by the  National Institute for Health Research (NIHR) Biomedical Research Centre , based at  Guy's and St. Thomas' NHS Foundation Trust and King's College London. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Western blot analysis Total protein extraction from yeast cells was performed by the NaOH-TCA lysis method  [100]. Samples were separated on 4-12% SDS-PAGE gels and transferred to nitrocellulose membranes, or PVDF for SUMO detection. Western blot analysis was performed using the following antibodies: polyclonal anti-VLP (to detect p49/p45-Gag polypeptides  [93] ; 1:10,000), polyclonal anti-Integrase (8B11, a gift from J. Boeke; 1:100), polyclonal anti-p18/p22 ([61]; 1:5,000, a gift from D. Garfinkel), polyclonal anti-SUMO (  [36] ; 1:2,000) and monoclonal anti-Pab1 (Abcam; 1:1,000). Total proteins were detected upon coupling with stain-free fluorescent reagents in TGX precast gels (Mini-PROTEAN, Bio-Rad), using a ChemiDOC imager (Bio-Rad) according to the manufacturer's instructions. Quantification of signals was performed using the ImageJ software.
Gene Ontology analysis was performed on differently-expressed genes (FC>2) using the GO Slim Mapper tool available in Saccharomyces Genome Database (https://www. yeastgenome. org/goSlimMapper). For metagene analysis, the aligned reads files (BAM) were first converted to normalized (RPKM) stranded coverage files (bigWig) with 1 bp bins using bamCoverage (deepTools version 3. 3. 2)  [107]. The mean coverage was then obtained from the three replicates of each strain using bigwigCompare (deepTools). Metaplots were finally generated with computeMatrix and plotProfile (deepTools), representing the mean and standard error of read coverage over the 32 Ty1 genomic copies for each strain. Distance to the closest tDNA was determined for each Ty1 copy using ClosestBed from bedtools (version 2. 29. 0)  [108].
Statistical analyses The following statistical tests were used: a two-sided Wilcoxon rank-sum test was used to compare the median log 2 (FC) of a dataset with a hypothetical median of 0 (Figs 1C and 3G) and a Mann-Whitney test was used in S1F Fig. Alternatively , the two-sided Welch's t test was used allowing unequal variance (other figures). � p <0. 05; �� p <0. 01; ��� p <0. 001; ���� p <0. 001; ns, not significant. Statistical tests were performed using GraphPad Prism version 9. 0. 0. nup84Δ cells. Values (mean±SD, n�3) are expressed as a percentage of IP and normalized to a distinct intergenic region from Fig 2A. (B-C ) RNAP II occupancy on the intergenic region #1  [111]  (B) and intergenic region #2  [112]  (C) as determined by ChIP in WT, nup133Δ and nup84Δ cells. Values (mean±SD, n�3) are expressed as a percentage of IP.
Acknowledgments We are very grateful to  A. Asif-Laidin ,  J. Boeke ,  V. Doye ,  E. Fabre ,  D. Garfinkel  and  A. Nicolas  for reagents; to  V. Doye  in whose lab this work was initiated; to  A. Babour  and  E. Fabre  for fruitful discussions; and to  V. Doye  and  E. Fabre  for their critical reading of the manuscript.
This study has been funded by: the  Agence Nationale pour la Recherche , https://anr. fr/( ANR-18-CE12-0003 , to B. P. ,  ANR-17-CE11-0025  le Cancer, https://www. ligue-cancer. net/(comitede Paris, to B. P. ),  Centre National de la Recherche Scientifique (CNRS) , the  Medicale (INSERM)  (Apprenticeship contract to C. C. ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Here, the source was uniformly distributed in a cylinder with size corresponding to the maximum range of 177 Lu-beta particles (diameter, height = 1. 76 mm). The "Livermore" low energy physics models were adopted in Geant4 to track electrons down to an energy of 100 eV while the default production threshold of secondary electrons was set to 0. 2 μm (adapted to cell nuclear volumes) which corresponds to 1. 75 keV in liquid water. Atomic deexcitations processes, such as Auger cascades and fluorescence, were included in the simulations. The chemical composition of CM, cytoplasm, Golgi and nucleus was the same as water ( = 1 g/cm 3 ) (NIST database). The position, direction, energy, compartment of emission and event identifier (i. e. "event ID"), which identifies particles derived from the same primary, were recorded for each particle entering the nucleus of the central cell, assumed as representative for the cell population.
DSBs were scored from the simulated strand breaks as defined in  (13) , i. e. , at least two strand breaks located in opposite strands and separated by less than 10 base pairs. Genomic content of cell nuclei composed of chromatin fibers in the G0/G1 phase of the cell cycle was generated with the DNAFabric software  (28, 29). The simulation chain coupled to these geometries allowed the calculation of DSBs per source particle (SP) reaching the nucleus, as recorded in the PHSP file. Source particles characterized by the same event ID, whose tracks are related to the same primary, were simulated together until a relative standard deviation on the average DSBs per source particle of 5% was reached. The DSB yields are reported in terms of DSBs per source particle and per Gbp ( /( ) ) and, for the calculations of total number of DSBs, all nuclei are assumed to have 6 Gbp ( / ).
15 per decay, respectively). The simulated results are compared to the experimental number of DSBs/cell measured by 53BP1 foci formation as previously reported  (18). Briefly, Z-stack imaging was performed using a TCS SP5 confocal microscope (Leica) and foci were counted from at least 50 cells of 2 independent experiments using Image J software (30) (settings: median blur 1. 0, maximum projection and find maxima, noise tolerance 75 for cells and 100 for slices). The not treated average DSBs level was subtracted from the measured data.
Cells were washed with PBS and incubated with 1 μg/ml propidium iodide (Sigma Aldrich) and 10 μg/ml RNase in PBS for 30 min at RT. Cell were washed with PBS and mounted in 87% glycerol pH8. 6 (Sigma Aldrich). Zstack imaging was performed using a 4Pi confocal microscope (Leica, Mannheim, Germany) and images were analyzed using Image J software (2).
After incubation, cells were washed 3 times for 5 min at RT with PBS and 0. 1% Triton X-100 and incubated with the secondary antibody (goat antirabbit Alexa Fluor 594 [Life Technologies]; 1/1,000) in blocking buffer for 60 min at RT. Cells were mounted with Vectashield (Vector Laboratories) containing DAPI (49,6diamidino-2-phenylindole). Z-stack imaging was performed using a TCS SP5 confocal microscope (Leica), and foci were counted from 30 to 40 cells per condition using Image J software (National Institutes of Health). Foci were considered legitimate when their size was between 20 and 100 squared pixels; foci smaller or bigger were considered background staining.
This cleaning procedure was completed by STM measurements of the singlecrystal samples. The HHTP molecules were evaporated on the samples kept at different temperatures from molybdenum crucibles heated to 500 K for Ag(111) and from homemade Ta pockets and quartz crucibles heated to 550 K for Cu(111). On Ag(111), large domains of the P2* structure (see definition below) were only obtained through deposition using a low deposition rate (0. 01 ML/min) on a hot substrate (473 K). On Cu(111), another preparation procedure (temperature treatment consisting in annealing molecules deposited at room temperature beforehand) was also tested and is not further discussed below since it produces no significant difference in our STM observations. STM data were recorded in the constant current mode. Images were processed with the WSxM  33  and Gwyddion softwares.
■ ACKNOWLEDGMENTS L. Giovanelli  is gratefully acknowledged for fruitful discussions. This work was supported by the  Natural Sciences and Engineering Research Council of Canada (NSERC)  and would not have been possible without the computational resources provided by  Calcul Québec and Compute Canada. J. C. and J. A. M. -G. acknowledge financial support from  CNRS  and  CSIC  via the  IEA program  ( PIC2017-FR4/07981 ) under the ORGAN'X project. J. C. , A. C. G. -H. , and F. C. acknowledge financial support from the  French National Research Agency  through contract ORGANI' SO  ( ANR-15-CE09-0017 ). C. S. -S. , J. A. M. -G. , and J. I. M. acknowledge funding from  Spanish MINECO  (grant  MAT2017-85089-C2-1-R ),  Comunidad de Madrid  via " Programa de Investigacíon Tecnologías 2018 " ( FOTOART-CM S2018/NMT-4367 ), and the  EU Innovation Program  under grant agreement  881603  (GrapheneCore3-Graphene-based disruptive technologies).
C. S. S. acknowledges  MICINN for "Ramon y Cajal " contract ( RYC-2018-024364-I ). L. V. aknowledges support from the  French Government Program "Investissements d'Avenir" (LABEX INTER-AC-TIFS , reference  ANR-11-LABX-0017-01 ) and financial support from the  "Région Nouvelle Aquitaine ".