dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability All data needed to reproduce analyses including metadata, site characteristics and values of each metric (for example, species richness, functional richness) for each site and year are available at Figshare (https://doi. org/10. 6084/m9. figshare. 22227841). Biodiversity composition data are available at GitHub (https://github. com/Ewelti/EuroA-quaticMacroInverts/raw-data).
in the 'Online Figures. docx' file in the 'plots' folder at https://github. com/Ewelti/EuroAquat-icMacroInverts). Extended Data Fig. 1| Trend estimates for community subsets. Overall estimates and distributions of trends in a, non-native species richness, b, non-native abundance, c, native taxon richness, d, native abundance, e, Ephemeroptera, Plecoptera, and Trichoptera (EPT) taxon richness, f, EPT abundance, g, insect taxon richness, and h, insect abundance. Bars around estimates indicate 80%, 90%, and 95% credible intervals. Trend estimates for native taxa (c, d) are restricted to the 1,299 sites at which taxa were identified to species or a mixed taxonomic resolution. Trend estimates for non-native species (a, b) are restricted to the 898 (of 1,299) sites at which non-native species were detected.
, 2021b). However, NH 3 emissions from passenger cars are usually not regulated  (Suarez-Bertoa and Astorga, 2018). Therefore, to provide evidence in making policies for NH 3 emission regulation, it is important to investigate the local influence of NH 3 emissions on PM concentrations. Therefore, to achieve a more comprehensive simulation of PM and related precursor gas, this study couples software of two open-source CFD: OpenFOAM (OpenFOAM user guide) and Code_Saturne  (Archambeau et al. , 2004) , with gas-phase chemistry and aerosol module SSH-aerosol  (Sartelet et al. , 2020). Both OpenFOAM and Code_Saturne own wide users. Therefore, coupling SSH-aerosol with software of both CFD may satisfy more needs. Simulations of the PM concentrations in a 2-D street canyon are conducted. The coupled model is validated by comparison to field measurements.
Model description The coupling method between CFD and chemistry modules is similar to the literature  (Gao et al. , 2022; Kurppa et al. , 2019). OpenFOAM v2012 and Code_Saturne 6. 2 are used to solve the governing equations of the flow field and transport equations of gas and particle mass fractions. The inflow conditions, pollutants' background concentrations, and emission rates are obtained from regional models and are linearly interpolated into each time step; this will be introduced in Sect. 3. This simulation method is called the transientcondition method (TCM) in this study. However, because time-varying flow fields and concentration fields are expensive to compute in terms of computational time, conducting CFD simulations with fixed boundary conditions and emission rates at specific time points is considered a practical method for evaluating street-level pollutant concentrations  (Wu et al.
Validation with field measurements and comparison of simulated concentrations with the two CFD software Reproducing the flow field is important in this study. Meanwhile, the observation data on wind velocity are not available. Therefore, we conducted a velocity validation for OpenFOAM v2012 using data from a wind tunnel experiment  (Blackman et al. , 2015). The predicted mean velocity agreed well with the experimental values. The details can be found in Appendix B. Figure  4  compares the simulated concentrations with those obtained from the field measurements. In the field measurements, the measured concentration was obtained from averaging over two measurement points near the leeward and windward walls in the street canyon. In this section, the simulated results and discussion are based on the spatially averaged values in the street canyon (27.
5 Obs + CFD , ( 4 ) MG = exp ln Obs -ln CFD , (5) NMSE = (Obs i -CFD i ) 2 Obs × CFD , (6) where Obs i and CFD i are the measured and CFD-simulated concentrations for the compound/species i, respectively. The overbar represents the mean value of the entire dataset. The ideal values are 1 for MG and 0 for FB and NMSE. Previous research has suggested that |FB| < 0. 3, 0. 7 < MG < 1. 3 and NMSE < 4 are acceptable for simulated concentrations  (Hanna et al. , 2004). Table  1  shows the statistical indicators for spatially averaged concentrations of NO 2 and PM 10 in the street canyon from 05:00 LT to 17:00 LT. For NO 2 and PM 10 , the mean and 90 % percentile concentrations simulated with CFDchemistry are closer to the measurements than those simulated with CFD-passive. In addition, the FB, MG, and NMSE values of CFD-chemistry are closer to the ideal values than those of CFD-passive.
Conclusions Particles in urban environment impose adverse impacts on pedestrians' health. Conventional CFD methods regarding particles as passive scalars cannot reproduce the formation of secondary aerosols and may lead to uncertain simulations. Therefore, to increase the simulation accuracy of particle dispersion, we coupled the CFD software OpenFOAM (OF) and Code_Saturne (CS) with SSH-aerosol, a modular box model to simulate the evolution of primary and secondary aerosols. The main processes involved in the aerosol dynamics (coagulation, condensation/evaporation, and dry deposition) were considered. We simulated a 12 h transient dispersion of pollutants from traffic emissions in a street canyon using the unsteady RANS model. The simulation domain was generated to model a street canyon where field measurements are available.
The flow field was based on the WRF model. The background concentrations of gas and particles were obtained from regional-scale simulations with a chemistry transport model. The particle diameter range (0. 01 to 10 µm) was divided into six size sections. The following conclusions were drawn from the results of this study. 1. The simulated spatially averaged values in the street canyon were validated from field measurement using validation metrics. For both OF and CS, the simulated NO 2 and PM 10 concentrations based on the coupling model (CFD-chemistry) achieved better agreement with the measurement data than the conventional CFD simulation which considered pollutants as passive scalars (CFD-passive). The differences between the OF and CS results were not obvious and were mainly due to the differences in the turbulence scheme.
In parallel, OxPhos defects accelerate telomere erosion and epigenetic aging per cell division, consistent with evidence that excess energy expenditure accelerates biological aging. To explore potential mechanisms for these effects, we generate a longitudinal RNASeq and DNA methylation resource dataset, which reveals conserved, energetically demanding, genome-wide recalibrations. Taken together, these findings highlight the need to understand how OxPhos defects influence the energetic cost of living, and the link between hypermetabolism and aging in cells and patients with mitochondrial diseases.
Using this model, we show that both genetic and pharmacological mitochondrial OxPhos defects trigger marked hypermetabolism in a cell-autonomous manner. We identify mtDNA instability, activation of the ISR, increased secretory activity, and transcriptional upregulation of transcriptional/translational stress pathways as potential contributors to hypermetabolism. Finally, we report that OxPhos defects and hypermetabolism are linked to accelerated telomere shortening and epigenetic aging in fibroblasts, and provide a publicly available longitudinal dataset to query epigenetic and transcriptional signatures conserved across both cellular models. Our analyses highlight how the associated resource dataset can serve as a discovery platform to identify potentially targetable pathways linking OxPhos defects to hypermetabolism, as well as downstream mechanisms linking hypermetabolism to cellular and clinical phenotypes.
Finally, oxygen tension can have a marked effect on the metabolism and replicative lifespan of cultured fibroblasts 61 , and chronic hypoxia improves survival in fibroblasts with complex I defect and the Ndufs4 mouse model of Leigh syndrome  62. We, therefore, repeated longitudinal experiments in SURF1 cells at low (3%) O 2 in parallel with atmospheric (∼21%) O 2 (Supplementary Fig. 5a ). Compared to 21% O 2 , the low oxygen condition did not improve population doubling rates (Supplementary Figure  5b, c ), nor did it correct or alter hypermetabolism (Supplementary Fig. 5d, e ). Results of the low O 2 "hypoxia" experiments, as well as the full lifespan aging trajectory of control cells beyond 150 days, are available in the resource dataset (see Data availability statement). Inhibition of the mitochondrial F o F 1 ATP synthase triggers hypermetabolism.
OxPhos defects upregulate energy-demanding cellular programs. From our longitudinal RNAseq dataset, we noted changes in the totality of genes related to the ribosomal machinery, which is produced in proportion with cellular biosynthetic demands (Supplementary Figure  10a ). Despite their significantly reduced growth rate, both SURF1 (+19%, p = 2. 4e -10, g = 2. 59) and Oligo-treated cells (+50%, p < 0. 01, g = 0. 86) showed a marked time-dependent upregulation in the ribosomal machinery, consistent with the hypersecretory phenotype (secreted proteins must be transcribed and translated) as well as the elevated metabolic demands of translation that compete with cell growth  44, 76  (Supplementary Fig. 10b ). To characterize the genome-wide gene regulatory changes associated with these cellular phenotypes and to gain insights into the potential cause(s) of hypermetabolism in response to OxPhos defects, we next deployed time-sensitive models of gene regulation and DNA methylation.
Both SURF1 and Oligo-treated cells displayed a significant perturbation of transcription and translation processes (Fig. 6d ). Upregulated genes were enriched for pathways related to Golgi vesicle transport, fibroblast growth factor (FGF) binding, VEGF receptor signaling pathway, and the unfolded protein response, a signature consistent with increased secretion and inter-cellular signaling activity. Downregulated genes were overrepresented for processes relating to cell division, consistent with the slower division rates (i. e. , quiescence or senescence) of SURF1 and Oligo-treated cells. Lifespan gene expression trajectories in this dataset showed some noteworthy features of OxPhos defects and hypermetabolism at the single-gene level: (i) ISR-related genes are robustly upregulated in a time-dependent manner by up to ~16-fold for the transcription factor CHOP (DDIT3), and ~60-fold for its downstream target GDF15; (ii) the age-related upregulation of senescence-related genes (e.
g. , p21/CDKN1A) occurs prematurely in hypermetabolic SURF1 and Oligo cells; (iii) key nucleotide metabolism enzymes such as thymidine kinase 1 (TK1) are robustly downregulated in SURF1 cells, possibly contributing to mtDNA instability  78  ; and (iv) telomere and longevity-related genes such as the metabolic sensor SIRT1 and the telomere protection complex component CTC1 are upregulated two to fourfold (Fig. 6e ). These broad changes in gene expression, largely consistent with previous in vitro work  3, 79  , prompted us to examine another major layer of gene regulation, DNA methylation. DNA methylation recalibrations in OxPhos-induced hypermetabolism. To examine nuclear DNA methylation (DNAm) and create a resource dataset with broad utility for pathway discovery, we measured DNA methylation levels at 865,817 CpG sites (Illumina EPIC array) in Control, SURF1, and Oligo-treated cells at multiple time points across their cellular lifespan (n = 66).
Next, we leveraged our DNAm dataset to quantify biological age using validated multivariate algorithms or "clocks" (DNA-mAge, or epigenetic clocks) trained, in human tissues, to predict chronological age and mortality  57, 85. Five different DNA-mAge clocks that rely on different CpG sets and include a modification that improves their accuracy  86  were applied directly to our fibroblast time series DNAm data. These results showed that relative to the rate of epigenetic aging in control cells with normal OxPhos function, the rates of biological aging per population doubling were accelerated by an average of 131% in SURF1 cells (p < 0. 05, g = 1. 5), and to a lesser extent in Oligotreated cells (+54%, p < 0. 05, g = 0. 6, Fig. 8f, g ), thus independently supporting the findings of accelerated telomere shortening.
For this reason, to our knowledge, it is currently not possible to mechanistically isolate the influence of hypermetabolism on DNA instability and accelerated telomere shortening. We speculate that the diversion of energetic resources  43  , as well as substrates including nucleotides  78  , may contribute to reduced DNA replication fidelity, which in turn could contribute to both mtDNA instability and telomere attrition, independent of cell division. Fourth, our longitudinal RNASeq and DNAm datasets reveal conserved recalibrations implicating developmental and translation-related pathways, as well as cell-cell communication, with OxPhos defects and hypermetabolism. These identified pathways overlap with previously identified multi-omic overrepresentation analysis performed on iPSC-derived neurons from SURF1 patients  92.
Rather than pursuing a single potential explanation, here we attempted to deeply phenotype both cellular models of hypermetabolism and to produce a foundational dataset covering several key processes and pathways previously implicated in the pathogenesis of OxPhos defects in humans and animal models. Our dataset, therefore, provides a foundation that can be used as a resource to develop targeted, mechanistic experiments to (i) determine the origin and modifiability of hypermetabolism in the context of OxPhos defects in vitro and in vivo, and (ii) resolve the mechanism(s) linking hypermetabolism to human aging biology. A usual suspect to explain decreased cellular and physiological bioenergetic efficiency is the uncoupling of the OxPhos machinery. While this article was under revision, a case of clinically suspected hypermetabolism in twin boys harboring a complex V (F o F 1 ATP synthase) defect was reported  104.
9 ). A resource webtool with all data from this study, including the longitudinal RNAseq and DNAm data, is available and can be explored for genes or processes of interest (see Data Availability Statement). Altogether, these translational data, therefore, provide a basis to rationalize some unexplained clinical features of mitochondrial diseases and suggest that intracellular and systemic energy tradeoffs (rather than ATP deficiency) may contribute to the pathogenesis of mitochondrial diseases. The proposed explanatory framework of cellular and physiological hypermetabolism calls for well-controlled studies to further understand the extent to which hypermetabolism is a bystander or a harbinger of morbidity and early mortality in patients with mitochondrial diseases. Our translational findings highlight the need for collaborative partnerships that bridge the cellular, clinical, and patient-reported aspects of mitochondrial diseases and aging.
Methods Human cohorts. Data were meta-analyzed from 17 mitochondrial disease cohorts listed in Table  1. Inclusion criteria included (1) cohorts with a genetic diagnosis for all participants and (2) including measures for at least one of the primary outcomes (resting heart rate, catecholamine levels, resting VO 2 or VO 2 relative to work rate, BMI, mortality). Eligible cohorts included participants from five countries, including China, Denmark, England, Italy, and the USA. Studies were published between 2003 and 2019, covering a 16-year period. Each cohort with its sample size, female/male distribution, genetic diagnoses (nDNA vs mtDNA), and symptomatology are listed in Table  1 , with additional information about data extraction provided here. Each cohort included its own control group, so group-level averages (not patient-level data) were used to compute effect sizes as % difference between mitochondrial diseases and control, and standardized Hedges g for each outcome measure (e.
The final Seahorse injection included Hoechst nuclear fluorescent stain (ThermoFisher Scientific #62249) to allow for automatic cell counting. After each run, cell nuclei were counted automatically using the Cytation1 Cell Imager (BioTek), and raw bioenergetic measurements were normalized to relative cell counts on a per-well basis. ATP metrics were determined using the P/O ratios of OxPhos and glycolysis as previously described by Mookerjee et al. 59. These conversions assumed energy sourced was derived entirely from glucose. All J ATP measurements take into account non-mitochondrial, and proton leak-derived oxygen consumption, thereby reflecting the mitochondrial ATP-synthesis-related flux (Supplementary Fig. 2a ). The code and raw data are available as detailed in the 10 samples/lane), and underwent RiboZero Gold purification.
DNA was extracted using the DNeasy kit (Qiagen cat#69506) according to the manufacturer's protocol and quantified using the QUBIT broad-range kit (Thermo Fisher Scientific cat#Q32852). At least 375 ng of DNA was submitted in 30 µl of ddH 2 O to UNGC for bisulfite conversion and hybridization using the Infinium Methylation EPIC BeadChip kit. Sample positions across plates were randomized to avoid batch variation effects on group or timebased comparisons. All DNA methylation data were processed in R (v4. 0. 2), using the 'minfi' package (v1. 36. 0). Quality control preprocessing was applied by checking for correct sex prediction, probe quality, and sample intensities, and excluding SNPs and non-CpG probes. Data were then normalized using Functional Normalization. Using the R package 'sva' (v3.
Lab personnel lab who performed the assays were provided with de-identified samples and were blind to other data. DNAmAge. DNAmAge was calculated using the online calculator (https:// dnamage. genetics. ucla. edu/new) with normalization using the age of the cell line donor as the input age. This outputted the Horvath1 (i. e. , PanTissue clock), Horvath2 (Skin&Blood clock), PhenoAge, Hannum, and GrimAge estimated DNAmAges. PC-based DNAmAges were then obtained using the principal component method (https://github. com/MorganLevineLab/PC-Clocks)  86. The rates of epigenetic aging for each cell line were determined from the linear slope of timepoints between 25 and 75 days. This period ensures that Oligo treatment has taken effect, and avoids late-life changes in the behavior of DNAm clocks, providing the time window where the signal is most stable.
Data availability The RNAseq and DNA methylation datasets for this project are available under the GEO SuperSeries GSE179849. All data preprocessing and analysis code is available on GitHub (https://github. com/gav-sturm/Cellular_Lifespan_Study). Data presented in this manuscript was generated as part of the Cellular Lifespan Study, which includes metabolic and endocrine experimental treatments across multiple donors described in detail in  119. The complete multi-omic fibroblast dataset for the present study is available without restriction and can be accessed, visualized, and downloaded using our web tool: https://columbia-picard. shinyapps. io/shinyapp-Lifespan_Study/. This fibroblast dataset, along with brightfield images and seahorse assay files, are additionally available at FigShare.
Acknowledgements We are grateful to  Jane Newman ,  Renae Stefanetti ,  Robert W Taylor , and  Gráinne S Gorman  ( Wellcome Center for Mitochondrial Research ) for contributing data for Cohort 2,  Rohit Sharma  and  Vamsi Mootha  ( Massachusetts General Hospital ) for contributing data for Cohort 4,  Robert McFarland  ( Wellcome Center for Mitochondrial Research ) for contributing data for Cohort 17, and other investigators whose work contributed to the meta-analysis in Fig. 1. We thank  Marlon McGill  for technical assistance with parts of this project and  Herman Pontzer  for analytical advice. The cellular studies and analyses were supported by  NIH  grant  R01AG066828  and the  Baszucki Brain Research Fund  to M. P. , the  J. Willard and Alice S. Marriott Foundation ,  Muscular Dystrophy Association, Nicholas Nunno Foundation, JDF Fund for Mitochondrial Research, and Shuman Mitochondrial Disease Fund  to M.
Dataset 1 The first dataset contains 30 head injured patients admitted at the Universitary Hospital of Grenoble (CHUGA) between January 2020 and April 2021 (Radiomic-TBI cohort; NCT04058379). Inclusion was prospective, conditioned to patient agreement and an Abbreviated Injury Score (AIS) ≥ 3  16  , corresponding to the presence of an injury visible in the CT-scan acquired the day of admission. The following clinical data were retrieved at the admission in the Intensive Care unit (ICU): Age, Glasgow Coma Scale (GCS), Mean Arterial Pressure (MAP), presence/absence of antiaggregants and Hemoglobin (Hb) rate. The data needed to compute the TILsum was retrieved daily during the 8 first days in the ICU. Finally, Marshall  17  and Rotterdam 18 scores were computed from the CT-scans. Among the 30 patients of the cohort, one was excluded because of a primary admission outside of the CHUGA, as described on the flowchart on Fig.
Dataset 2 The second dataset contains 12 patients suffering a severe non penetrating TBI (GCS ≤ 8) admitted at the Universitary Hospital of Grenoble between August 2018 and April 2021 (RadioxyTC cohort; Health Data Hub index F20220207212747). TILsum score during the 5 first days in the ICU was retrieved and TILsum until the 8th day was estimated from clinical reports, in order to estimate the overall maximum TILsum score. This dataset was used to perform an external validation. Characterization of this dataset can be found in the Supplementary Table  S4.
Preprocessing All 84 CT-scans were extracted from the Hospital Storage System in DICOM and then converted to NIfTI format thanks to the MP3 software  21. The brain was extracted with a MATLAB-based Skull removal algorithm  22. Then, all images from a patient were rigidly co registered to his first CT-scan obtained at admission using the FLIRT algorithm from the FSL toolbox  23  and finally resampled at 1mm 3.
Segmentations On Dataset 1, we segmented TBI lesions on the CT-scans in 6 different ways. First, we applied the DeepMedicbased  24  Convolutional Neural Network (CNN) called BLAST-CT  14  , which aims to automatically segment 4 lesions typical of TBI : IPH, EAH, Od and IVH leading to the BLAST-CT segmentation. Then, this segmentation was manually corrected by JAdB and AA, respectively anesthesiologist and neuroradiologist with 2 and 10 years of experience, using ITK-SNAP software  25  , to obtain the 4-class manual segmentation. This manual segmentation was then refined by splitting EAH between subdural hemorrhage (SDH), epidural hemorrhage (EDH) and subarachnoïd hemorrhage (SAH), and by distinguishing petechiae (Pe) from IPH, leading to the 7-class manual segmentation. Finally, we used two transfer learning techniques to refine the automatic segmentations of BLAST-CT: a Fine-Tuning approach  26  in order to obtain an automatic 4-class segmentation named CNN2 and a Transfer Learning approach  27  which lead to two 7-class automatic segmentations named CNN3 and CNN4, depending on.
6 ± 1. 3 [2-6] 4. 9 ± 1. 1 [3-6] Rotterdam score 3. 0 ± 0. 8 [2-5] 4. 4 ± 1. 2 [2-6] TILsum maximum during the 8 first days in ICU 6. 3 ± 2. 6 [1-10] 18. 9 ± 4. 5 [11-28]  their initialization. The principal characteristics of the 4 different automatic segmentations evaluated are summarized on Table  2. On Dataset 2, manual segmentations at 4 and 7 classes were drawn by FDH (anesthesiologist, 1 year of experience), JAdB, and AA.