dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgments This study was supported by  Institut Europlace de Finance, Kayrros , the  DFF Sapere Aude  grant (no. 9064-00049B ), the  Academy of Finland  grant (no. 330422 ), and the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 Research and Innovation Programme  (grant agreement no. 947757 TOFDRY ). We also acknowledge support by the  Villum Foundation  through the project " Deep Learning and Remote Sensing for Unlocking Global Ecosystem Resource Dynamics" (DeReEco) , the  Pioneer Centre  for AI, DNRF grant number P1, and the  Independent Research Fund Denmark  through the grant " Monitoring Changes in Big Satellite Data via Massively-Parallel Artificial Intelligence " ( 9131-00110B ).
Hereafter we refer to this process as DIRAC  1. The spin configuration of the lowest energy encountered during this process is returned as the predicted ground state. Note that starting from the same configuration forces the agent to learn a strategy with the same starting point, which drastically reduces the potential trajectory space and thus requires less data for training. Ending at the same configuration makes the agent always finish the MDP within finite steps. This finite-horizon MDP forces the agent to pick the right move without allowing too many regrets. We emphasize that the vanilla strategy DIRAC 1 has several limitations. First, it can only handle one single uniform initialization {↑, ⋯ , ↑}, rather than multiple random initializations. This drastically hinders DIRAC's performance as significant performance improvements would be achieved by simply taking the best solution found across multiple initializations.
Those instances were chosen to be small so that their exact ground states can be calculated by the branch-and-bound based solver Gurobi within tolerable computing time. For each given n initial , we empirically computed P 0 as the fraction of 1000 random instances for which the ground state is found by DIRAC (and confirmed by Gurobi  48  ). We found that DIRAC enables a much faster finding of ground states than the Greedy, SA, and PT algorithm. In fact, all DIRAC variants (DIRAC 1 , DIRAC m , DIRAC-SA and DIRAC-PT) facilitate the finding of ground states. For example, in the case of D = 2 and L = 10 (Fig. 4a ), n * initial (the minimum value of n initial where P 0 reaches 1. 0) of PT is 322,800, while n * initial of DIRAC m is only 600. In fact, for DIRAC m the ground states can be found with only one gauge randomization for some instances.
Application on general NP-hard problems It has been shown that many NP-hard problems, including all of Karp's 21 NP-complete problems (such as the max-cut problem, 3-SAT problem and the graph coloring problem), have Ising formulations  11. Hence, we anticipate that DIRAC (with some modifications) could help us solve a wide range of NP-hard Fig. 4  | Performance of different methods in finding the ground state. To compare the ability of finding the exact ground states, we evaluated different methods on small spin glass systems for which the true ground state can be computed by the branch-and-bound-based solver Gurobi  49. In (a-c), the quantity we chose to compare is the probability of finding the ground state, denoted as P 0 , which is empirically calculated as the fraction of 1000 random instances for which the ground state is found (and confirmed by Gurobi).
Radiosonde Data Daily radiosonde measurements are collected at 1200 UTC by Meteo-France at Reunion Island since June 2011. Meteo-Modem M10 radiosondes were manually launched at Roland Garros airport (20. 9 • S, 55. 5 • E) until April 2018 when a Meteomodem robotsonde was located in the Regional Specialized Meteorological Center (RSMC). Profiles of temperature and horizontal wind have a vertical resolution <100 m at heights up to 32 km. Absolute accuracies are 0. 3 • C, 0. 15 m/s and 1 • for temperature, horizontal wind speed and wind direction, respectively. Quality of temperature and wind measurements allow us to capture GW perturbations with vertical wavelengths of 1-15 km at heights <32 km above La Réunion  [36]. Figure  1  visualizes 100 m resampled vertical profiles of temperature and horizontal wind on 8-11 October 2017 during the period of the study.
Radiosonde Data Daily radiosonde measurements are collected at 1200 UTC by Meteo-France at Reunion Island since June 2011. Meteo-Modem M10 radiosondes were manually launched at Roland Garros airport (20. 9°S, 55. 5°E) until April 2018 when a Meteomodem robotsonde was located in the Regional Specialized Meteorological Center (RSMC). Profiles of temperature and horizontal wind have a vertical resolution <100 m at heights up to 32 km. Absolute accuracies are 0. 3 °C, 0. 15 m/s and 1° for temperature, horizontal wind speed and wind direction, respectively. Quality of temperature and wind measurements allow us to capture GW perturbations with vertical wavelengths of 1-15 km at heights <32 km above La Réunion  [36]. Figure  1  visualizes 100 m resampled vertical profiles of temperature and horizontal wind on 8-11 October 2017 during the period of the study.
Lidar Data The Rayleigh-Mie-Raman (RMR) lidar, initially installed at the university of La Réunion, was upgraded when it was moved to the Maïdo Observatory  [37]  with a new telescope of 1. 2 m diameter and a powerful laser. It provides vertical profiles of temperatures from 30 to 90 km and water vapor from the boundary layer to the lower stratosphere  [38]. It belongs to the Network for Detection of Atmospheric Climate Change (NDACC) international network and has routinely operated twice a week during night-time since 2013. The Rayleigh lidar principle is based on the measurement of the atmospheric density, pro-portional to the molecular Rayleigh scattering, and the determination of the temperature by downward integration of the hydrostatic law  [39]. The profile is initialized at the top assuming a seed temperature from the NRLMSISE-00 empirical atmospheric model  [40].
The raw RMR temperature profiles are originally obtained with 1 min integration time and 150 m vertical resolution. Vertical smoothing and time binning are applied to improve the signal-to-noise ratio of the measurements depending on the scientific objectives. For nightly mean profiles available on the NDACC database a 2 km vertical smoothing is applied using a Hanning filter. The present study processes individual temperature profiles of 15 min integration time and 150 m vertical resolution to characterize GWs. Because of the time and vertical resolution, only GWs with vertical wavelengths of >1 km and observed periods of >1 h can be analyzed. A 5 min lag is used between two successive profiles of 9 and 10 October. The statistical uncertainty is computed from the photon counting noise that follows a Poisson statistical law  [41].
However, only GWs with horizontal wavelengths larger than 180 km (6 ∆x) may be well resolved  [59]. The Climate Data Store cloud server ensures fast access to post-processed ERA5 data on 37 pressure levels from 1000 to 1 hPa with high resolution in the lower stratosphere. Figure  2  compares the dynamic fields derived from WACCM and ERA5 datasets at ≈10. 5 km height on 9 October 2017 at 1200 UTC above the South-West Indian Ocean (SWIO). Temperature and winds are consistent but better resolved for ERA5. The synoptic fields are characterized by a local heating in the temperature (Figure  2a, b ), a split of the subtropical jet in the zonal wind (Figure  2c, d ) and planetary waves in the meridional wind (Figure  2e ,f) at the height level of the jet above La Réunion. The Weather Research and Forecasting model (WRF) has become an international community mesoscale model for both atmospheric research and operational forecasting since its initial public release in 2000  [60, 61].
The meridional and zonal wavenumbers, the normalized frequency (the ratio between the frequency and the inertial frequency ω/f) and the amplitude of GWs are used as inputs to initialize rays in the GROGRAT model. The inertial period is about 33 h at the latitude of La Réunion. To identify GW tropospheric sources, the model is firstly initialized with background fields of the temperature, horizontal wind and geopotential altitude every 6 h from ERA5 model data with 37 pressure levels from 1000 to 1 hPa and horizontal resolutions of 0. 25 • × 0. 25 • for the region (43 • E-67 • E, 6 • S-30 • S). Secondly, ERA5 data with 137 levels are used to trace the vertical propagation of GWs from the stratosphere up to the top of the middle atmosphere. The image processing software ImageJ (https://imagej.
MIL and GWs in the Middle Atmosphere on 9-10 October 2017 Lidar, nightglow and radiosounding datasets from June 2016 to April 2017 were scanned to select simultaneous high-quality and long duration observations in the presence and in the absence of a MIL above La Réunion. The night of 9 October 2017 reveals the presence of a MIL with an amplitude of about 30 K which disappears the next night. It was accompanied with a strong activity of GWs. Temperature lidar operated for nearly 5 and 4 h on 9 and 10 October, respectively. Figure  8a ,b visualize the difference between the two consecutives nightly mean profiles. The MIL is characterized by a temperature minimum around 78 km and a warm layer above at heights of 80-87 km in comparison with the nightly mean profile of 10 October 2017. Figure  8c  shows the five consecutive hourly profiles on 9 October.
Mesoscale Modeling in the Troposphere and the Stratosphere Simulated atmospheric background horizontal wind is shown to be in agreement with hourly ERA5 data during the simulation period (Figure  15 ). Comparison between WRF and ERA5 outputs indicates that the weakening of the zonal wind and the strengthening of the meridional wind at 10 km height are well reproduced in time and intensity from 9 October at 1200 UTC to 11 October at 1200 UTC above La Réunion. The mesospheric zonal wind also weakens while the stratospheric zonal wind strengthens from 9 October in the ERA5 dataset. The evolution of the stratospheric zonal wind is consistent with observations of radiosonde (Figure  12b ) and WRF (Figure  15a ) at the top level. In addition, the comparison between vertical profiles of WRF and observed horizontal winds on 9 and 10 October at the troposphere and the lower stratosphere at heights between 3 km and 30 km above La Réunion.
Mesoscale Modeling in the Troposphere and the Stratosphere Simulated atmospheric background horizontal wind is shown to be in agreem with hourly ERA5 data during the simulation period (Figure  15 ). Comparison betwe WRF and ERA5 outputs indicates that the weakening of the zonal wind and strengthening of the meridional wind at 10 km height are well reproduced in time a intensity from 9 October at 1200 UTC to 11 October at 1200 UTC above La Réunion. T mesospheric zonal wind also weakens while the stratospheric zonal wind strengthe from 9 October in the ERA5 dataset. The evolution of the stratospheric zonal wind consistent with observations of radiosonde (Figure  12b ) and WRF (Figure  15a ) at the t level. In addition, the comparison between vertical profiles of WRF and observed ho zontal winds on 9 and 10 October at 1130 UTC indicates that mean profiles of zonal a meridional wind are consistent in the troposphere and the lower stratosphere at heig between 3 km and 30 km above La Réunion.
This behavior is directly observable on the keogram of Figure  7  close to 2200 UTC where structures are clearly visible. This implies a high variation of the relative intensity during this period and consequently a strong activity of GWs. In fact, large amplitudes in the details d 1 suggest possible GW breaking in the upper mesosphere and the mesopause at heights of 62-68 km below the MIL and 84-90 km, respectively. Table  2  gathered the observed and intrinsic GW parameters deduced from a series of nightglow images using ImageJ software. Intrinsic parameters are obtained after correction with the following Doppler relation between intrinsic frequency ω and observed frequency ω obs : ω = ω obs -ku -lv (1) where k and l are, respectively, the zonal and meridional wavenumbers, and u and v are, respectively, the mean zonal and meridional local wind speeds at the OH layer altitude.
3 km x 5. 3 km at the OH layer altitude. Small variations of the radiance are induced by GWs. For the 10 October (in red), the increase in the radiance can be attributed to an increase in the GW activity, as already observed during  In fact, large amplitudes in the details d1 suggest possible GW breaking in the upper mesosphere and the mesopause at heights of 62-68 km below the MIL and 84-90 km, respectively. Table  2  gathered the observed and intrinsic GW parameters deduced from a series of nightglow images using ImageJ software. Intrinsic parameters are obtained after correction with the following Doppler relation between intrinsic frequency ω and observed frequency ωobs:  The wind speed is estimated with the empirical model HWM14. Table  2  provides typical values of horizontal wavelengths and observed phase speed ranging from 8 to 16 km and 27 to 47 m/s, respectively.
Thus, stronger GW activity occurs in the first period of the night with smaller periods. Results obtained using ImageJ software are gathered in Table  3  for the night of 10 October. At the beginning of the night, a quasi stationary GW moving toward the northwest is observed but after wind correction, the wave moves westward with an intrinsic phase speed of about 14 m/s. Nevertheless, the calculated vertical wavelength is very small (0. 3 km), which forbid the evaluation of the potential energy Ep (Equation (2)) using the cancelation factor CF: E p = 1 2CF 2 ( g N ) 2 ( dI I ) 2 (2) where CF is the cancelation factor  [86]  depending on the vertical wavelength λv. The spectral analysis of the N/S and E/W keograms was performed using two different methods. In the first method, the power spectral density (PSD) for each line is calculated and then summed and averaged by the number of lines (Figure  23a )  [84].
Results obtained using ImageJ software are gathered in Table  3  for the night of 10 October. At the beginning of the night, a quasi stationary GW moving toward the northwest is observed but after wind correction, the wave moves westward with an intrinsic phase speed of about 14 m/s. Nevertheless, the calculated vertical wavelength is very small (0. 3 km), which forbid the evaluation of the potential energy Ep (Equation (2)) using the cancelation factor CF:  The validity of Equation (  2 ) is for λv > 10 km, so along the night, the potential energy could be calculated for serial 5 only with λv = 19 km (CF = 0. 49), leading to a value of Ep = 2200 J/kg. Extrapolation of the GW-EP without dissipation plotted in Figure  11  gives a value of 1600 J/kg for the altitude of 87 km, which is quite consistent with that calculated from the nightglow measurements.
Indeed, MILs are frequently observed on Lidar observations over La Réunion  [24]. Moreover, planetary wave breaking is also known as another important cause for the formation of lower MILs, which was not the case in the present study. Regarding the simulation of MILs with WACCM, France et al. [29]  pointed at either a warm bias in the mesosphere or a too-large planetary wave activity to explain an overestimation of MIL occurrences within WACCM. Discrepancies between observations and modeling in the upper atmosphere are also a motivation to investigate the physical reasons and to improve climate models  [1]. on 11 April 2023) and stored at LATMOS (UMR 8190). The WACCM and nightglow data are produced and stored by the LPC2E (UMR 7328) and ONERA, respectively.
Acknowledgments: The authors thank scientific and technical teams who contributed to the data acquisition at the  Maïdo Observatory  (https://osur. univ-reunion. fr/losu-reunion,accessed on 7 April 2023),  Météo-France  for the radiosounding and Meteosat data, the  ECMWF  for providing reanalysis ( ERA5 ),  NCAR  for the WRF source code (https://www. mmm. ucar. edu/models/wrf,accessed on 7 April 2023) and  S. D. Eckermann  who provided the GROGRAT model.
Data Availability Statement: The authors thank the TIMED/SABER science team for providing the data used in this study. The SABER data were downloaded from the SABER website: (http://saber. gats-Inc. com/data. php, accessed on 11 April 2023). Nightly Lidar temperature data are available on the NDACC Web site: (http://www. ndaccdemo. org/stations/reunion-island-maido-france, accessed.
Acknowledgments The authors thank  Hanna Kokko  for helpful discussions,  Philip Mostert  for mathematical advice, and an anonymous reviewer for comments that greatly improved the manuscript. H. t. B. and T. R. H. were supported by the  Swiss National Science Foundation , on grant no. 310030B_182836  awarded to  Hanna Kokko. T. R. H. was additionally supported by the  Forschungskredit of the University of Zurich , grant no. FK-21-122  awarded to T. R. H. , and the  Research Council of Norway , grant no. 313570  awarded to Jane Reid. F. M. thanks the  CNRS  for general support.
Multimediator model. In presence of correlated mediators, traditional mediation analysis methods might lead to individual relative mediated effects that add up to more than 100%, which suggests that some pathways are overlapping and the joint and individual effects remain unidentifiable. To address this limitation, we extended the multimediate algorithm  (Jerolon et al. , 2020) , which uses the counterfactual multiple mediation framework, to the survival data setting based on the Aalen additive hazards model. The R code is available in the Github repository (https://github. com/AllanJe/multimediate). Our novel multimediate algorithm  (Jerolon et al. , 2020)  is able to identify individual mediated effects of several mediators simultaneously while taking into account correlated mediators. In this setting, relative mediated effects could never add up to more than 100%.
The Framingham Heart Study (FHS) is funded by National Institutes of Health contract N01-HC-25195. The laboratory work for this investigation was funded by the Division of Intramural Research, National Heart, Lung, and Blood Institutes, National Institutes of Health and an NIH Director's Challenge Award (D. Levy, Principal Investigator). ADR was supported by a fellowship from "la Caixa" Foundation (ID 100010434) (fellowship code "LCF/BQ/DR19/11740016"). MTP was supported by the Strategic Action for Research in Health sciences (CP12/03080 and PI15/00071) , which are initiatives from Instituto de Salud Carlos III and the Spanish Ministry of Science and Innovation and co-funded with European Funds for Regional Development (FEDER), by the Third AstraZeneca Award for Spanish Young Researchers (AstraZeneca, Spain), and by the State Agency for Research (PID2019-108973RB-C21).
Acknowledgements The  Strong Heart Study  is funded by grants from the  National Heart, Lung, and Blood Institute (NHLBI)  (contract numbers  75N92019D00027 ,  75N92019D00028 ,  75N92019D00029  and  75N92019D00030 ) and previous grants ( R01HL090863 ,  R01HL109315 ,  R01HL109301 ,  R01HL109284 ,  R01HL109282 , and  R01HL109319  and cooperative agreements:  U01HL41642 ,  U01HL41652 ,  U01HL41654 ,  U01HL65520  and  U01HL65521 ) and by the  National Institute of.
To date, studies on apoptosis in echinoderms mainly focus on the caspase-dependent extrinsic apoptotic pathway, while knowledges regarding the intrinsic apoptotic pathway is still limited. In this study, 55 genes belong to 7 families of the intrinsic apoptotic pathway, namely, Bcl-2, caspase, AIF, Cyt c, Apaf-1, Smac, HTRA2 and Endo G, were identified by screening of the genomic database of the tropical sea cucumber Holothuria leucospilota. The Bcl-2 family showed significant gene expansion in H. leucospilota but not in other echinoderms, while the pro-apoptotic factor Endo G is generally lost in echinoderms. Transcriptomic data showed that the genes of the intrinsic apoptotic pathway were ubiquitously expressed in all tissues, and all embryonic and larval developmental stages of H. leucospilota.
Cross-genomic analysis of genes in the intrinsic apoptotic pathway For cross-genomic analysis to identify genes in the intrinsic apoptotic pathway, eleven representative species of deuterostomes were selected from the GenBank database, including H. sapiens (GCA_000001405. 29), G. gallus (GCA_016699485. 2), X. tropicalis (GCA_000004195. 4), D. rerio (GCA_000002035. 6), C. intestinalis (GCA_000224145. 3), B. lanceolatum (GCA_900088365. 1), P. flava (GCA_001465055. 1), S. kowalevskii (GCF_000003605. 2), A. planci (GCF_001949145. 1), S. purpuratus (GCF_000002235. 5), and A. japonicas (GCA_002754855. 1) were selected for comparison with the genome of H. leucospilota (GenBank: PRJNA747844). Referring to the functional annotations in the SwissProt database, genes from 8 gene families (Bcl-2, caspase, AIF, Cyt c, Apaf-1, Smac, HTRA2 and Endo G) of the intrinsic apoptotic pathway were identified, and the numbers of genes were counted.
Phylogenetic tree and structural domain analysis The Lifemap program (http://lifemap-ncbi. univ-lyon1. fr/) was used to construct species evolutionary trees, and iTOL (https://itol. embl. de/) and Evolview software (http://www. evolgenius. info/evolview) were used for visualization. The phylogenetic trees of the caspase and AIF families were constructed based on amino acid (p-distance) by the neighbor-joining method (pairwise deletion) with 1000 bootstrap replicates using MEGA X. The functional domains of the caspase and AIF family were analyzed using the InterPro database (https://www. ebi. ac. uk/interpro/), Pfam database (https://pfam. xfam. org/), UniProt database (https://www. uniprot. org/) and SMART program (http://smart. emblheidelberg. de/) from the EMBL-EBI database.
Transcriptomic analysis for gene expression Transcript levels of intrinsic apoptotic pathway-involved genes, including the caspase family, AIF family, Apaf-1, Smac and HTRA2, in various tissues and developmental stages of H. leucospilota were analyzed with transcriptome data  (Li et al. , 2022a (Li et al. , , 2022b)). TBtools (https://github. com/CJ-Chen/TBtools) was used to create a heatmap. The tissues investigated including the oral tentacles, body wall, muscle, intestine, rete mirabile, transverse vessel, respiratory tree, Polian vesicles, coelomocytes, Cuvierian tubules, ovaries and testes. The developmental stages investigated including the fertilized eggs, 2-cells, 4-cells, 8-cells, 16-cells, morula, blastula, rotated-blastula, early-gastrula, late-gastrula, early-auricularia, mid-auricularia, auricularia, doliolaria, pentactula and juveniles as described before  (Huo et al.
Assessment of improved cassava genotypes using these multidisciplinary methodologies  4, 15, 16  has demonstrated the relevance of different varietal preferences and varying trait prioritizations because of variation in the agro-ecological and social dimensions. Likewise, the participatory evaluation of new cassava genotypes and their derived food products  4, 17  elicited the varietal effects on the quality of different types of gari-eba and fufu, as well as the effects of the cultural diversity and practices of the producing and processing populations. 6  Combining informed demand-led breeding with socially inclusive approaches, methodological innovation and triangulated transdisciplinary evidence (datasets) offer great potential for improving and redefining genetic gains, enhancing adoption and increasing social impact, which are key objectives of public breeding.