dataesr/scientific-paragraphs-categorization huggingface dataset:
Hindcasting paleomagnetic data We assess the success of coarse predictions by ''hindcasting", i. e. , by predicting the past. This technique is routinely used in numerical weather prediction and goes as follows. One assimilates data up to a specified time in the past and computes model states that are compatible with the data up to that time. One then evolves each state by the model, without assimilating more data. The trajectories one obtains in this way ''predict" what happened in the past. Thus, hindcasting assesses how successful a prediction strategy is for predicting the future, by testing how successful it performs for past events. For the hindcasts illustrated in Figs. 9-12, we assimilate Sint-2000 data, however similar results are obtained when PADM2M is used for assimilation. The assimilation is done by D-IMP and 200 samples, 5 data points per sweep for the G12 model, by S-EnKF with 400 samples for the G12 SDE model, by S-IMP with 400 samples for B13, and by SIR with 400 samples for P09, for the reasons outlined in Section 3.
Indeed, we can perform hindcasts every 1000 years for all four models we consider, and compute the probability of a reversal to occur within a given horizon as a function of time. The results for all four low-dimensional models for a 4 kyr horizon when assimilating Sint-2000 data are shown in Fig. 13. We note that the probability graphs of all four models ''peak" when the dipole indeed reverses. However, the B13 model assigns a low probability to the event ''a reversal occurs within 4 kyr" at all times, even when a reversal is about to happen, with the maximum probability being about 30%. The graphs of the other three models, P09, G12 and G12 based SDE, look qualitatively similar to each other, and are somewhat noisier than the graph obtained with B13. We obtain qualitatively and quantitatively similar results when PADM2M data are assimilated.
IRBS comparison of data assimilation based prediction strategies We compute IRBS for all four models, and when assimilating synthetic and paleomagnetic data. Experiments with synthetic data are essential here because these tests reveal whether or not the models are intrinsically predictable by the proposed strategy. Synthetic data are generated by the low-dimensional models using the state trajectories already shown in Fig. 2. Each data point has associated Gaussian errors whose variance is such that the mean of the relative paleointensity divided by the standard deviation of the errors is the same for Sint-2000 and for each of the four synthetic data sets. As before, we consider 4 kyr and 8 kyr horizons. Our results are summarized in Table  2. We find that all four models yield IRBS larger than one when synthetic data are used and when the horizon is 4 kyr.
In our experiments, however, we have to adjust the synthetic data to have roughly the same errors as the paleomagnetic data, and to acknowledge that data of other field or flow components are not available at this point. Thus, our synthetic data experiments suggest that, with the currently available paleomagnetic data, G12 can only predict dipole reversals within a 4 kyr horizon, and not for longer horizons. We observe a significant drop in IRBS for all models and considered horizons when hindcasting paleomagnetic data. The reason is that model error can be expected to be significant, since all models are simplified representations of Earth's dipole dynamics. However, the results we obtain with either paleomagnetic data set, Sint-2000 or PADM2M, are very similar and predictions based on any of the four models still score IRBS larger than 1 for a 4 kyr horizon.
05 Myr ago onwards, and include two reversals. We apply this strategy to Sint-2000 and PADM2M and consider a 4 kyr horizon. We show thresholds and associated IRBS values over the training data in the left panel of Fig. 14. We observe a welldefined extremum with IRBS well above one at an intensity threshold of 0. 175 for both data sets (IRBS is 2. 17 for Sint-2000 and 1. 22 for PADM2M). This graph thus suggests that relying on an intensity threshold may indeed be a meaningful way of predicting reversals within a 4 kyr time-window. However, a posteriori using this optimal intensity threshold of 0. 175 fails to predict several reversals, not only within the verification data, but also within the training data. Failure to correctly predict several reversals occurs independently of whether we use Sint-2000 or PADM2M (failures occurring when using Sint-2000 are illustrated in the bottom right panel of Fig.
Specifically, the optimal threshold based on Sint-2000 is 97. 5%, and for PADM2M threshold values between 90% and 95% are optimal, leading to IRBS values of 1. 63 for Sint-2000, and 1. 31 for PADM2M. When these optimal thresholds are used, we obtain an IRBS of 1. 13 for the verification data of Sint-2000 and between 1. 98 and 3. 97 for the verification data of PADM2M (with optimal thresholds between 90% and 95%). In addition, both reversals within the verification data sets, whether Sint-2000 or the PADM2M, are correctly predicted (see Fig. 15 ). While the G12 probability threshold-based strategy is somewhat successful, it also has weaknesses. For example, it leads to one false alert and fails to predict the reversal ending the Cobb mountain subchron (see zoom (c) in Fig. 15 ), when considering training data of Sint-2000.
However, the false alert precedes a reversal by only 13 kyr and the reversal is correctly predicted by a later alert. In view of the much longer ''typical" chron durations, such a false alert may be viewed as a ''slightly too early" warning. Note that assessing the success of predictions by just relying on IRBS ignores the fact that predicting a reversal slightly too early is an error that is less severe than not predicting it at all. Failing to predict the reversal ending the Cobb mountain subchron is of greater concern. This reversal occurred, according to the Sint-2000 data set, to within 4 kyr of the previous one. Failure to predict this reversal thus may result from inaccuracies within the Sint-2000 data. However, it may also suggest that the G12 model is incapable of producing two successive reversals within a few thousand years.
This could be due to the fact that the deterministic G12 dynamics imposes a ''minimum time" between reversals which may be significantly larger than what can be observed for this event. Similar issues arise when using the PADM2M data set. In this case, no false alert occurs before the Cobb mountain subchron. However, a false alarm does occur shortly after (1kyr after the subchron), again indicating some incompatibility of the G12 model with this quick sequence of two reversals. The G12 model in combination with PADM2M and a probability threshold-based prediction strategy further fails to predict the upper Olduvai reversal (1. 77 Myr ago) in the training data set. In this case, the alert is triggered only once the reversal actually occurred. We did not observe this behavior when using Sint-2000, which suggests that this behavior may indicate the limits of probability threshold-based strategies, especially in view of uncertainties in Sint-2000 or PADM2M.
Training data Verification data We also apply the probability threshold-based prediction strategy to synthetic data of the G12 model, which yields positive results. We find an optimal probability-threshold of 87. 5% and associated IRBS of 7. 92 for the verification data, as well as fully successful predictions of all reversals. These tests indicate that some of the above issues could be caused by intrinsic limitations of the G12 model. Finally, we also test probability threshold-based strategies for the three stochastic low-dimensional models P09, B13 and G12 based SDE. For the B13 and G12 based SDE models, no probability thresholds leading to IRBS significantly larger than one can be found, whether considering Sint-2000, PADM2M or synthetic data. This is reminiscent of the results we obtained by the intensity threshold-based strategy (see above).
In other words, neither B13 nor the G12 based SDE model seem to provide successful probability threshold-based predictions, even when considering synthetic data produced by the models. The situation is again different for the P09 model. When using Sint-2000 data, the optimal threshold is 0. 55, and the associated IRBS is 1. 4 for the training data, and 0. 99 for the verification data. Considering PADM2M data leads to a different, perhaps more encouraging result. We obtain an optimal threshold of 0. 275 yielding an IRBS of 1. 19 for the training data, and 2. 47 for the verification data. However, when we consider synthetic data, we obtain a lower optimal probability threshold of 0. 125, leading to an IRBS of 1. 96 for the training data, and 0. 5 for the verification data, failing to successfully predict reversals.
This purely data-based strategy fails to predict several reversals in both the training and verification data sets. This was found to be true for Sint-2000 and PADM2M data and suggests that, given the available data, paleomagnetic intensity can become low without necessarily being followed by a reversal within the next 4 kyr. 4. Similar intensity threshold-based prediction tests applied to synthetic data of the three stochastic models (B13, P09, G12 based SDE) suggest that the intensity of these models can be low without necessarily being followed by a reversal within the next 4 kyr. The deterministic G12 model on the other hand seems to have an intensity threshold, i. e. , a reversal of the G12 dipole will necessarily occur if its intensity drops below a threshold. 5. Probability threshold-based predictions raise an ''alert" for a reversal to occur within the next 4 kyr if the probability of a reversal inferred from low-dimensional models and data assimilation exceeds a given threshold.
This strategy yields improved coarse predictions provided the G12 model is used. In contrast, stochastic models (B13, P09 and G12 based SDE) give unsatisfactory results. However, even when using the G12 model, probability threshold-based predictions have weaknesses. These are likely due to uncertainties of the Sint-2000 and PADM2M data we have not properly accounted for, as well as an inability of G12 to produce nearby reversals. The resulting ''partial" failures, however, are not critical, and we conclude that a probability threshold-based strategy using the G12 model is more reliable than a purely data-based intensity threshold-based strategy. 6. Similar probability threshold-based prediction tests applied to synthetic data from the four low-dimensional models (B13, P09, G12 and G12 based SDE) further suggest that this strategy indeed fails for all stochastic models (B13, P09, or G12 based SDE), but not for the deterministic G12 model.
However, this limit does not preclude that general macroscopic conditions for a reversal to occur within a wider time window could be found by examining the long-term dynamic behavior of the dipole field itself, which indeed displays a rich low-frequency temporal spectrum  (Constable and Johnson, 2005). In this context, the horizon of predictability of the coarse behavior of the dipole field may be larger than that of the detailed behavior of the full field of the Earth's dynamo. This is the possibility we investigated here with the help of data of the past behavior of the dipole field, as documented by Sint-2000 and PADM2M, tentative low-dimensional models of the geodynamo, and data assimilation. Two key results of geophysical relevance were obtained. One is that the available paleointensity data (Sint-2000 or PADM2M) do not seem to display any intensity threshold below which a reversal can be guaranteed to occur within the next 4 kyr.
Another important limitation of the present study, which we already stressed, is the limited amount of reversals documented in the Sint-2000 and PADM2M data sets. This limitation, combined with the uncertainties affecting the data, may well impact IRBS, the exact values of the various thresholds we computed and, therefore, the significance of our results. However, the consistency of our findings with respect to the data, i. e. , whether we use the Sint-2000 or PADM2M data sets, is encouraging. Our study also revealed a number of interesting properties of the low-dimensional models we considered. While all four models succeed at assimilating the signed paleointensity data with comparable success (average errors after data assimilation are no larger than 8%), and appear to be intrinsically predictable in the coarse sense we defined, only predictions based on the deterministic G12 model pass the set of tests we devised.
However, even the G12 model may not be considered as ''satisfactory" for the purpose of coarse dipole predictions. For example, it fails to properly handle fast sequences of two successive reversals (such as those bounding the Cobb mountain subchron). It also produces sequences that display an intensity threshold that can be used to raise successful reversal alerts for G12, contrary to the paleointensity data as documented by Sint-2000 and PADM2M. Moreover, the G12 model is unable to properly reproduce the observed reversal frequency when scaled to the millennium time scale. Nonetheless, the successes of the G12 model in combination with the probability threshold-based prediction strategy indicates that these predictions may improve if ''better" low-dimensional models could be obtained. It is interesting in this respect to compare dipole data of the G12 model (not using any data assimilation) with the signed paleointensity data of Sint-2000 and PADM2M, and to investigate the causes of its success and failures.
Comparing Figs. 1 and 2 (see also Fig. 8 ) makes it clear that the G12 dipole data is more regular than the paleomagnetic data. The fact that an intensity threshold can be found in the case of G12, and not in the case of the paleomagnetic data, can be traced back to this regularity. Local minima that do not lead to reversals in the G12 synthetic data are all of comparable magnitude. This is not the case in the paleomagnetic data. This is also not the case in the synthetic data produced by the three stochastic models B13, P09 and G12 based SDE, which were also found to lack reliable intensity thresholds (with the only possible exception of P09, which however displays a very low and poorly defined intensity threshold, as described in Section 4. 4. 1). In this respect, the dipole variable of the G12 model may be too regular when compared to Sint-2000 or PADM2M.
g. ,  Sarson and Jones, 1999)  could also be tested. With modern computers, data assimilation for such models is feasible, even over geological time scales. Any improvements, however, will depend on the validity of our underlying assumption that general conditions for reversals to occur are dictated by the average large-scale behavior of the dipole field, and not by the detailed morphology of the field, which plays a role only once the reversal is just about to happen. Although our study suggests this could be the case, this still needs to be confirmed. For the time being, and based on what could be achieved using the G12 model and assimilating Sint-2000 and PADM2M data (up to 1 kyr ago), it is reassuring to see that no warning of any reversal is currently being raised for the next few millennia by our probability threshold-based approach.
Acknowledgements We thank  Jean-Pierre Valet  of the  Institut de Physique du Globe de Paris  and  Leah Ziegler  of  Oregon State University  for assistance in using the SINT-2000 and PADM2M datasets. We thank  Bruce Buffett ,  University of California at Berkeley ,  Francois Petrélis  and  Christophe Gissinger  of  Ecole Normale Supérieure, Paris , for interesting discussion and for providing their models for this study. We also thank  Olivier Sirol  of  IPGP  for valuable help with computing. We finally thank the reviewers for their very useful comments, which helped improve our manuscript and provided us with many suggestions for future additional work. This material is based upon work supported by the  U. S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics Program  under contract  DE-AC02005CH11231 , by the  National Science Foundation  under grants  DMS-1419044  and  DMS-1619630 , by  IPGP 's  Visiting Program, CNRS PNP program , and by the  Alfred P.
Identification of miR-30a gene targets in human epidermis To decipher the molecular networks controlled by miR-30a in epidermis, we investigated potential gene targets. Transcriptome data already deposited in the Gene Expression Omnibus (GEO) database on NCBI identified 5 transcriptome projects in which miR-30a expression was modulated (overexpression or silencing) in cultured cells. The potential targets (transcripts repressed after miR-30a overexpression or induced after AGING miR-30a silencing), identified in at least 2 of the 5 transcriptomes, were selected for further analysis. Twenty-four genes were thus identified, 14 of which were potentially expressed in human epidermis according to the Human Protein Atlas (HPA) database and 8 of which exhibited conserved miR-30a response element (MRE) in their 3'-UTR (Figure  4A ).
We therefore selected 5 genes (AVEN, FBXL20, IDH1, TDG, TBPL1) with at least 1 conserved miR-30a MRE and potentially expressed in the epidermis according to the HPA database. Each of these was then tested on RNA samples from long-term culture of skin equivalents mimicking chronological aging. There was a strong decrease in AVEN and IDH1 transcript expression in the late phase of culture, suggesting that these 2 genes might be repressed in aged epidermis (Figure  4B ). We also observed that IDH1, AVEN and LOX were repressed at the mRNA and protein level in keratinocytes from aged skin (Figure  S4 ). We then selected AVEN, encoding a caspase inhibitor, and IDH1 encoding the isocitrate dehydrogenase 1, as putative miR-30a targets potentially involved in human epidermis aging. The study also focused on LOX, encoding a lysyl oxidase known to be involved in keratinocyte differentiation  [13]  and to be directly regulated by miR-30a in cancer cells  [14].
Microarray analysis MicroRNA expression profiles keratinocytes from young (<5 years), adult (19-40 years) and aged skin (>60 years) were analyzed using a whole human genome microarray containing 30,434 probes (GeneChip TM miRNA 4. 0 Array; Affymetrix, Santa Clara, CA, USA). RNA amplification, microarray hybridization and scanning, data normalization and analysis were performed at the ProfileXpert genome facility (Lyon, France) as previously described  [40]. Significantly modulated microRNAs were selected by fold change ≥ 1. 5 and pvalue ≤0. 05 (multiple unpaired t-test. FDR = 5%). The microarray data have been deposited into the Gene Expression Omnibus database at NCBI (https://www. ncbi. nlm. nih. gov/geo/) and are available with GEO accession number GSE101493.
Protein extraction and western blotting Protein extraction and immunoblotting on nitrocellulose membrane were performed as previously described  [41]. The membrane was incubated overnight at 4°C in TBS-T with primary antibody specific to IDH1 (Cell Signaling Technology, Danvers, USA #D2H1, 1/1000), LOX (home-made antibody, 1/250), AVEN (Sigma, St-Louis, USA #HPA020863, 1/100) or ACTIN (Millipore, Billerica, USA #MAB 1501 Clone C4, 1/5000). The membrane was then washed 3 times in TBS-T and incubated with secondary antibody for 1 hour at room temperature. The peroxidase-conjugated secondary antibodies were goat anti-mouse IgG and goat anti-rabbit IgG (Thermo Fisher Scientific, Waltham, USA). Proteins were detected using an enhanced chemiluminescence system (Thermo Fisher Scientific, Waltham, MA, USA) and the signal was detected by the Fusion Fx system (Vilber Lourmat, Collégien, France).
Identification of miR-30a targets and luciferase assays The Gene Expression Omnibus (GEO) database (https://www. ncbi. nlm. nih. gov/geo/) was used to identify putative targets of miR-30a by comparison of 5 transcriptomes performed on cells after miR-30a modulation (Accession numbers: GSE12908, GSE16569, GSE29921, GSE36565, GSE41607). Genes identified by at least 2 of the 5 transcriptomes were selected for further analysis. The Human Protein Atlas database  [43]  was then used to select predicted target genes encoding proteins expressed in human epidermis. A final search for conserved miR-30a MicroRNA Recognition Elements (MRE) in the 3'UTR of the selected genes was performed using TargetScan 7. 1 (www. targetscan. org). A 3'-UTR sequence of AVEN (194 pb), IDH1 (240 pb) or LOX (306 pb) containing the candidate miR-30a AGING binding sites was amplified by PCR from human genomic DNA.
Histology and immunofluorescence microscopy RE samples were fixed in Zinc Formal-Fixx TM (Thermo Fisher Scientific, Waltham, MA, USA) for 24 hours at 4°C, embedded in paraffin and then cut into 5 μm sections. After dewaxing and rehydration, tissue sections were either stained with HES to check morphology or permeabilized and blocked by 1% bovine albumin serum (BSA) + 10% goat serum + 0. 3M glycine in PBS-T for 1 hour at room temperature. Sections were then incubated with primary antibody diluted in PBS + 0. 1% goat serum overnight at 4°C: INVOLUCRIN (Sigma, St-Louis, MO, USA #I-9018, 1/200), K10 (Dako, Agilent, Santa Clara, CA, USA #M7002, 1/50), K1 (Covance, Princeton, NJ, USA #PRB-149P, 1/1,000), LORICRIN (Covance, Princeton, NJ, USA #PRB-145P, 1/1000), K14 (Novocastra, Leica, Nanterre, France #NCL-LL002, 1/200), AVEN (Sigma, St Louis, MO, USA #HPA020863, 1/100), IDH1 (Cell Signaling Technology, Danvers, USA #D2H1, 1/1,000).
Secondary Alexa-488 and Alexa-546-conjugated antimouse or anti-rabbit (Molecular Probes, Eugene, Or, USA) was incubated 1 hour at room temperature. Nuclear counterstaining using DAPI was carried out. Sections were then mounted in Permafluor TM Aqueous Mounting Medium (LabVision, Thermo Fisher Scientific, Waltham, MA, USA). Negative controls were performed by omitting the primary antibody. To study LOX expression, tissue sections were etched and fixed in Bouin's fixative (Microm Microtech, Francheville, France) and then LOX expression was detected as previously described  [46]. Ki-67 expression was detected as previously described  [12]. Image acquisition was performed using a Nikon microscope (Nikon TE300, Champigny-sur-Marne, France) with a coolsnap fx CCD camera (Photometrics, Tucson, AZ, USA) with MetaVue software (Universal Imaging Corporation, West Chester, PA, USA).
Statistical analysis All data are represented as means for at least 3 independent experiments. Statistical significance was calculated by a two-tailed Student's t-test for unpaired samples to compare 2 groups and by one-way analysis of variance (ANOVA) to compare 3 or more groups. All statistical analyses were performed using Prism (version 6. 0, GraphPad Software Inc. , San Diego, CA, USA). All results are expressed as mean ± SD. Mean differences were considered to be significant when P<0. The expression level of miR-30a-3p, miR-30a-5p, AVEN, IDH1 and LOX transcripts were evaluated by qPCR in RNA samples from keratinocytes prepared from young skin at early passage (passage 2) and replicative senescent keratinocytes (passage 6). P16 was used as a marker of senescence. Results are mean +/-SD from three independent samples.
ACKNOWLEDGEMENTS The authors thank  Gisèle Froment ,  Didier Nègre  and  Caroline Costa  from the lentivectors production facility  SFR Biosciences Gerland Lyon Sud  ( UMS3444/US8 ). We thank  Naima El Kholti  from  LBTI-Primatiss facility (Lyon ) for her expertise in histology. We also thank  Nicolas Gadot  from  Plateforme de Recherche Anatomopathologique  ( Centre Léon Bérard, Lyon ) for his help in analyzing reconstructed epidermis. The.