dataesr/scientific-paragraphs-categorization huggingface dataset:
However, PI/LPI methods assume exponential growth, equal growth rates between organs, and constant plastochron (delay separating the apparition of two successive organs). These requirements must be a priori tested, and are not systematically fulfilled. As an alternative, organ size may be used as a convenient proxy for developmental time. This approximation was successfully employed to analyse leaf development  (Gonçalves et al. , 2017; Maugarny-Calès et al. , 2019)  using MorphoLeaf software developed in our lab  (Biot et al. , 2016). However, this approach is limited because growth is not always linearly related to time and because it does not allow comparing growth dynamics of organs developing at distinct growth rates. In addition, it is preferable to measure development as a function of chronological time instead of morphological one.
Neither PI/LPI nor size as a temporal proxy provide explicit temporal information in standard time units. Because the analysis of complete organ morphogenesis over a long period is difficult to achieve with live imaging, we propose a new approach to reconstruct and quantify morphogenesis as a function of time from static data collected on different individuals (see Figure  1  and Supplementary Figure  S1 ). As previously mentioned, this implies being able to assign an age to any sampled organ. For this, we first introduce a mathematical model of the sequential apparition of organs that we apply to estimate initiation times, thus allowing the temporal registration of growth dynamics between organs. Second, we use calibration curves relating organ age and size to estimate the ages of samples collected at arbitrary timepoints.
MorphoLeaf software was modified to integrate this temporal calibration in the reconstruction of developmental trajectories, thus providing shape analyses as continuous functions of chronological time. We used this methodology to analyse the morphogenesis of a series of rosette leaves in Arabidopsis thaliana, whose size and shape significantly evolve according to apparition rank  (Hunter et al. , 2006; Tsukaya et al. , 2000). Our results reveal invariant developmental patterns at both global (blade) and local (tooth) scales, that were hidden by contrasted sizes and shapes. Our study also shows uncoupling between tissue growth at global and local scales. Finally, we illustrate how our method allows dissecting Quantitative Plant Biology 3 the spatiotemporal effects of mutations on morphogenesis, thus contributing to deciphering the molecular basis of morphogenetic processes.
MorphoLeaf new release The new version of MorphoLeaf software offers the possibility to enter estimated growth kinetics parameters [L ∞ , t 50 and n in equation (2)]. An age is then automatically attributed to all individual organs, computed from blade length measures. This information is then stored in the measurement files generated by the application, thus allowing shape analyses as functions of time. Using these kinetics parameters, MorphoLeaf now also generates leaf growth trajectories based on standard chronological time instead of blade length.
We first quantified relative telomere length by qPCR across the lifespan for each donor and computed the rate of telomere shortening per cell division. Using a mixed effects linear model to compare rates of change over time, Dex showed an 8. 7% non-significant acceleration in the telomere shortening rate (Fig. 8D ); directly comparing the linear slopes of each Dextreated cell line to its corresponding control showed an average acceleration of 27. 6% across the three donors (p<0. 05, paired t-test). Telomere length was also estimated through a DNA methylation-based algorithm  (66). This measure documented an effect of similar magnitude towards accelerated shortening rate per population doubling (+17. 0%, p<0. 05, Supplementary Material Fig. S10A-B ). These data thus provided converging evidence that chronically stressed cells experience more rapid loss of telomeric repeats during each event of genome replication, consistent with allostatic overload.
Chronological age can also be predicted with high accuracy through DNA methylation-based algorithms, known as epigenetic clocks  (70) (71) (72) (73) (74). Here, we generated a longitudinal DNA methylation dataset using the EPIC array  (26) , and deployed an approach that increases DNAmAge accuracy using a principal component-adjustment of the classic epigenetic clocks  (75). Applied across the cellular lifespan, we can longitudinally quantify the rate of epigenetic aging relative to population doublings. The PC-PhenoAge clock  (74)  showed a significant 36. 4% increase in the epigenetic aging rate (p<0. 01, Fig. 8E ). A similar increase was found with the PC-Hannum clock (+33. 1%, p<0. 05), whereas non-significant effects were obtained using other clocks (Horvath Skin and Blood, Horvath Pan Tissue, and GrimAge, Supplementary Material Fig.
Age-related hypermetabolism and cell death occur near-synchronously Given the premature age-related rise in cell death (see Fig. 1 ) and the profound mitochondrial and bioenergetic recalibrations associated with increased energy consumption in Dex-treated cells (see Fig. 2  and Fig. 3 ), we leveraged the longitudinal nature of our dataset to examine whether cell death was temporally related to hypermetabolism. Plotting both mortality (% cell death) and total energy expenditure (JATPtotal/cell volume) relative to population doublings demonstrated that chronic Dex caused a left shift in the mortality curves, shifting the trajectory of each donor towards an earlier onset and progression along their lifespan (Fig. 8G , upper panels). Strikingly, Dex caused a similar and near synchronous left shift in energy expenditure trajectories, reflecting an early onset, hypermetabolic state that develops over weeks (Fig.
This variability likely reflects true inter-individual differences. Thus, although this multi-donor design increases experimental variability, it importantly guards against the overfitting of results to a single donor/cell line, and therefore likely yields more robust and generalizable findings. In relation to the stressor, Dex is admittedly a simplistic model of stress, whereas chronic psychosocial stressors in humans involves the action of multiple hormones and metabolic factors. Therefore, although this targeted glucocorticoid stressor provides a strong proofof-concept of the cell-autonomous energetic and molecular consequences of allostatic load, additional studies with other (combinations of) stress mediators are warranted. Finally, our analyses of the RNA sequencing and DNA methylation omics datasets are only partial, and more complete analyses of these data beyond the scope of the present manuscript could yield new insights into the global, longitudinal recalibrations and mechanisms underlying glucocorticoid-driven cellular allostatic load and the resulting allostatic overload.
To examine these and related questions, we have made available as a community resource the multi-omics dataset of chronic Dex (as well as other exposures, see Data Availability Statement)  (26). In summary, we have defined multiple cell-autonomous features of GC-induced allostatic load, and mapped the long-term consequences associated with cellular allostatic overload in primary human fibroblasts. In particular, our work quantifies the added energetic costs of chronic anticipatory responses at the cellular level, thereby defining hypermetabolism as a feature of allostatic load. We also document mitochondrial allostatic load including elevated mtDNA copy number and mtDNA density per cell, in parallel with co-occurring mtDNA instability and cytokine hypersecretion (both peaking around 20-50 days).
mtDNA copy number Cellular DNA was extracted using DNeasy Blood and Tissue Kit (Qiagen #69504). Duplex qPCR reactions with Taqman chemistry were used to quantify mitochondrial (mtDNA, ND1) and nuclear (nDNA, B2M) amplicons, as described previously  (103). Primers and probes utilized were the following: and NGS Libraries were generated using an enzymatic DNA fragmentation approach with Ion Xpress Plus Fragment Library Kit (Thermo Fisher Scientific). Sequencing was performed using an Ion Torrent S5XL platform with an Ion 540 chip, and signal processing and base calling were performed through the pre-processing embedded in house pipeline as described elsewhere  (104). Demultiplexed reads were mapped according to the mtDNA reference sequence (NC_012920. 1), and further analysis was performed through the eKLIPse pipeline(58) (https://github.
DNA methylation and DNAmAge Global DNA methylation was evaluated on the same genomic material used for other DNAbased measurements. DNA samples were submitted to the UCLA Neuroscience Genomic Core (UNGC) for bisulfite conversion and hybridization using the Infinium Methylation EPIC BeadChip kit (Illumina). DNA methylation data was processed in R (v4. 0. 2), using the 'minfi' package(112) (v1. 36. 0). Data was normalized using functional normalization (Fun Norm), and RCP and ComBat adjustments were applied using the 'sva' package(113) (v3. 12. 0) to correct for probe-type and plate bias, respectively.
Statistical analyses All statistical analyses were performed using GraphPad Prism (v9. 0) and R (v4. 0. 2) using RStudio (v1. 3. 1056). All analyses were restricted to matching timepoints between control and the Dex or Dex+mitoNUITs treatments from the CellularLifespan dataset  (26). In graphs of lifespan trajectories, datapoints are connected using the Akima spline curve. Lifespan average graphs show each passage as a single datapoint, and bars represents mean ± standard error of the mean (SEM). The percent differences between groups (i. e. , Control vs Dex; or Dex vs mitoNUITs) was computed separately for each cell line (i. e. , Donors 1, 2, 3), and groups were compared by two-way ANOVA treating each timepoint as a unique observation. Analyses of gene expression (i. e. , HK2, PFK, UQCRC1, COX7A1, POLQ, PPARGC1A, and NRIP1, TPP1, TERC) were similarly performed by two-way ANOVA across all donors, without adjustment for multiple comparisons.
Data availability statement All data presented in this manuscript are available for download at our Cellular Lifespan Study shiny app: https://columbia-picard. shinyapps. io/shinyapp-Lifespan_Study, with additional data described in  (26). The raw RNA sequencing and DNA methylation are available at GEO superseries #GSE179849. Source data and fibroblast images are available at https://doi. org/10. 6084/m9. figshare. 18441998. v2 and https://doi. org/10. 6084/m9. figshare. 18444731. v1, respectively. All code r script for data preprocessing is available at https://github. com/gav-sturm/Cellular_Lifespan_Study. Other information is available upon request from the corresponding author.
Model Variables directly available in the ICG-EMO riverine database Institute (country) Q TN NO3 NO2 NH4 TP PO4 Si DIC DOC POC TOC Fe TALK SPM MIRO&CO RBINS (BE) x x x x x x x x MARS3D- MANGA4 Ifremer (FR) x x x x x x ECOHAM Uni-Hamburg /HZG (DE) x x x x x x x x x GPM Uni-Oldenburg (DE) x x x x x x x x x x DFLOW-FW Deltares (NL) x x x x x x x GETM-JRC- ERSEM JRC (EU) x x x x x GETM-ERSEM- BFM Cefas (UK) x x x x x NEMO-SCOBI SMHI (SE) x x x x x Here "Uni" stands for "University of". Q stands for fresh water discharge. van  Leeuwen et al. 10. 3389/fmars. 2023. 1129951  Frontiers in Marine Science frontiersin. org.
Available observational data Observations were obtained from the OSPAR Common Procedure Eutrophication Assessment Tool (COMPEAT, see GitHub -ices-tools-prod/COMPEAT, ICES, 2022) for validation and weighting purposes. This tool is applied in the OSPAR Comprehensive Procedure (COMP) for the determination of the eutrophication status of marine areas based on observational data submitted by each member country. The observations, which are the same as those used in the official eutrophication assessments, have a higher spatial and temporal coverage in coastal areas than in offshore waters (see Appendix B; Supplementary Materials). Thus, spatial averages of the observations over assessment areas that comprise coastal and more open waters may not be fully representative of these assessment areas.
Simulated area-averaged results will always be representative of the full area, complicating a direct comparison with the observational averages (see, e. g. ,  Garcia-Garcia et al. , 2019). Observations were available for DIN, DIP and Chl. Due to the low confidence in the in-situ Chl observations (Appendix B; Supplementary Figure  3 ) related to data scarcity (both temporally and spatially), satellite data (a reanalysis product,  Van der Zande et al. , 2019; Lavigne et al. , 2021)  were added to the Chl observational database in COMPEAT. To calculate a combined seasonal mean concentration per assessment area from in-situ and Earth Observation (EO) data a weighting factor was applied, based on the OSPAR confidence rating of in-situ data availability which is implemented in the COMPEAT tool.
Where the combined temporal and spatial confidence of in-situ Chl was high, 50:50 (in-situ/EO) data was used to derive the assessment area mean. If in-situ confidence was moderate, 30:70 (in-situ/EO) was used, while where in-situ confidence was low, 10:90 (in-situ/EO) was applied  (OSPAR, 2022a). Coastal waters are optically challenging environments for satellite wavelength observations, due to shallow depths and high levels of suspended matter. Retrieving accurate Chl estimates from satellite data is therefore more challenging in coastal waters than in offshore waters  (Lavigne et al. , 2021). Observational time series were extracted for the stations with the most complete temporal coverage over the simulated period (available in the ICES COMPEAT tool), for validation purposes. Unfortunately, using these data results in a strong spatial bias, as most observations were obtained in near-shore waters of the southern North Sea.
Current state scenario The current state scenario (hereafter, CS) applied the ICG-EMO database of European rivers for the riverine nutrient inputs of the selected period. This database contains daily values for flow and nutrients for 368 rivers discharging onto the European Shelf, following optimization to daily values from originally sourced observational data  (Lenhart et al. , 2010; ICG-EMO, 2021). For an overview of the rivers included see Appendix A (Supplementary Materials). The average atmospheric deposition rates for NO x , NH 3 and Total Nitrogen over the North Western Continental Shelf as estimated by EMEP (EMEP, 2020) were used for atmospheric input of nutrients, with values provided in Table  2. For the open sea boundaries, information from CMEMS (Copernicus Marine Service, EU, https://marine.
smhi. se/for the HYPE model suite, with E-HYPE the European application), representing conditions around the year 1900  (Enserink et al. , 2019). These loads, which did not include hydrological or morphological changes in river basins (e. g. reservoir construction, dams and barriers, etc. ), are simulated per coastal area and are not necessarily associated with actual rivers. Nevertheless, this dataset provides a consistent set of pre-eutrophic nutrient loads going into the marine environment on the European Shelf. Local, more detailed studies offer additional information. Kerimoglu et al. (2018)  describe historic riverine loads for the German Bight based on simulations of the detailed catchment model MONERIS  (Venohr et al. , 2011; Gadegast & Venohr, 2015). They found significantly lower historical DIP levels for major German rivers compared to the E-HYPE historical scenario.
Note that E-HYPE historical nutrient levels were not used, only the E-HYPE estimate of the percentage change in riverine nutrients compared to current day loads. E-HYPE coastal areas were then linked to actual rivers, and the CS and HS riverine loads were derived from the observation-based ICG-EMO riverine database for 2006-2014, using 100% and the reduction percentage estimates, respectively. The reduction percentages are shown in Figure  4 , while Appendix A provides the same information as a table. No change was applied to rivers with pre-eutrophic loads higher than current loads (mainly Scottish rivers north of Inverness where populations have declined), in order to preserve reduction effects from other rivers. River freshwater discharges were kept at current day levels and therefore are equal to those of the simulated period: this choice was made to allow for easier definition of (achievable) nutrient reductions in the current situation.
Importance of observational data Observational data are of prime importance for the applied approach: as input data, as validation data and as data used for weighting different model results in the ensemble. This immediately highlights issues with both availability of observations and their temporal and spatial resolution. Chlorophyll a levels encompass diatom contributions, but these phytoplankton can only grow when silicate is available for them to build their cell walls. In the applied riverine inputs database information on silicate is lacking for Belgian, Danish, Irish and Spanish rivers. Total nitrogen data is lacking for British and Spanish rivers. Without realistic values for these nutrient inputs the models will invariably struggle to reproduce observed concentrations in the adjacent coastal zones.
More coordinated riverine monitoring within Europe and subsequent central storage of sample data for easy access could address these issues. The observations used in the weighting method were obtained from the COMPEAT tool built by OSPAR, that draws on the ICES marine database (Supplementary Materials Figure  S1 ). Even though N and P are the main driving nutrients of primary production in shelf seas and therefore constitute the basis of our understanding of eutrophication processes, measurements of N and P concentrations in many areas (for example the Channel Well Mixed Tidally Influenced; CWMTI, Figure  5 ) are severely limited in number. This complicates comparison with model results offering more temporal and spatial resolution, and increases uncertainty. Figure  S1  in the supplementary materials highlights the spatial bias of the observations (available mainly in near-coastal zones), while the annual results (Figures S9-S15, Appendix F) highlight issues with temporal observational coverage.
An objective way to further reduce uncertainties is to resort to weight-averaged values, estimated from the comparison between model outputs and observations, and apply these weights to the individual model results before taking the ensemble average  (Almroth and Skogen, 2010). The present exercise used this weighted-ensemble-mean method to provide pre-eutrophic values, or reference values, for the indicators of eutrophication in coastal and shelf areas. For this the availability of observational data in the COMPEAT tool was essential. More observational evidence would therefore also increase confidence in the weighted ensemble result.
The ensemble approach presented here has demonstrated its use for policy purposes by defining a baseline for nutrient reduction measures; it may be useful for other environmental questions as well. For eutrophication modelling the next step should be to consider climate change impacts on the marine environment, and how these changes impact on derived thresholds for eutrophication indicators, both in the immediate and intermediate (policy) future. Maritime and Fisheries Fund. Deltares and IFREMER were partly supported by the Jerico-S3 project, funded by the European Union's Horizon 2020 research and innovation programme under grant agreement No 871153. RBINS received financial support from BELSPO through the project ReCAP, which is part of the Belgian research program FED-tWIN.
A very special thanks to Hjalte Parmer for providing the ICES data that are used within the COMPEAT tool. We thank the members of ICG-Eut and TG-COMP for the fruitful discussions, as well as OSPAR for commissioning this work. Part of the maps in this manuscript were made using the free package M_Map: Pawlowicz, R. , 2020. "M_Map: A mapping package for MATLAB", version 1. 4m, [Computer software], available online at www. eoas. ubc. ca/~rich/ map. html. Computing facilities for Deltares were provided by the DECI resource Cartesius based in The Netherlands at SURFsara with support from PRACE. The support of Maxime Mogéfrom SURFsara, The Netherlands is gratefully acknowledged. UK riverine data was processed from raw data provided by the Environment Agency, the Scottish Environment Protection Agency, the Rivers Agency (Northern Ireland) and the National River Flow Archive.
French water quality data was provided by Agence de l'eau Loire-Bretagne, Agence de l'eau Seine-Normandie and IFREMER, while flow data was provided by Banque Hydro. German and Dutch riverine data was provided by the University of Hamburg (Johannes Paetsch, Hermann Lenhart), with some additional German river data supplied by IOW (Ulf Graewe). Irish flow data was provided by Hydrodata and the Environment Protection Agency (Hydronet), while water quality data was obtained from OSPAR RID reports. Norwegian flow data was supplied by NVE's Anne Fleig (afl@ nve. no), water quality data was obtained from NIVA (www. niva. no) and Tore Høgåsen (tore. hogaasen@niva. no). Danish water quality data was provided by the National Environmental Research Institute (NERI). Water quality data for Baltic rivers was provided by the University of Stockholm and the Baltic Nest (www.
The success of games has the power to raise technologies from a niche into the mainstream (mass-acceptance) and on the other hand, a technology can get completely rejected by the users, if there are not enough successful applications as it was the case for the Windows Phone  15. In research, new knowledge is continuously generated for the development of new hardware, software and interaction approaches. The broad upcoming of immersive technologies opens opportunities for new tasks to be performed remotely, which were yet not possible with traditional tools. Immersive technologies are very interesting for tasks involving spatial information, for instance collaborative engineering. However, they also involve many issues to be solved for a broader acceptance, like the effect of cybersickness or usability.
This is the case for all conditions from all datasets in this paper (Supplementary Table  1 ). In other words, differences and similarities between cell phenotypes not discernible to the human eye can still, in many cases, be assessed by deep learning (DL), demonstrating that microscopy images contain much more information than what can be seen  6, 7. It is a great opportunity for biology and medicine to discover and investigate sounder, more subtle, and broader classes of phenotypes and biomarkers. However, as phenotype difference cannot be seen, DL approaches offer few options and no guarantee that discrimination is not based on a bias, an experimental artifact or a biological phenotype irrelevant to the considered assay. Conclusively, phenotype differences may be too subtle to be visually accessible.
5 ). Results demonstrated the capability of conditional image generation to reproduce cell phenotypes induced by compound treatment (Fig. 1a ). Furthermore, synthesis could be performed for all treatments from the same cell image, and latent traversals enabled us to see gradual changes that need to be performed in order to transform an untreated (DMSO) cell to any compound treated cell (Fig. 1b ). We engineered a web interface to ease visualization and manipulation of such data on many examples for all datasets considered in this work (data can be manipulated here https://www. phenexplain. bio. ens. psl. eu/hd. html and here https://www. phenexplain. bio. ens. psl. eu/lda_ translation. html). Interestingly, beyond the phenotypes themselves, the synthetically treated cell images displayed a lower cell count, which is consistent with the fact that these treatments are toxic at high concentration (Fig.