dataesr/scientific-paragraphs-categorization huggingface dataset:
py script allowing mismatches, clonal reads were removed using the MarkDuplicates function within picard tools (http://broadinstitute. github. io/picard/), and methylation levels at each cytosine covered by at least 4 reads were determined using the bs_seeker2-call_methylation. py script. For visualization of methylation levels in each sequence context or in all contexts together, wig files were generated using a custom perl script (BSseeker2_2_ wiggleV2_CG_CHG_CHH_allmC. pl) based on the BS-Seeker2 CGmap output files. Information regarding the mapability, coverage, global percent CG, CHG, and CHH methylation levels, and non-conversion rates are presented in Supplementary Data 3. DMR calling. DMRs were determined as described in Zhou et al. ,  37. Briefly, DMRs in the CG, CHG or CHH contexts were identified in 100 bp non-overlapping bins based on either pair-wise comparisons between each mutant and three independent wild-type data sets, or between all three wild-type data sets from one tissue and all three wild-type datasets from another tissue.
These wild-type (WT) versus WT DMRs were subsequently combined to generate a set of 100 bp hyper and hypo DMRs amongst all pairwise tissue comparisons (e. g. "WTvsWT all tissues hypo" DMRs; Supplementary Data 7 and Supplementary Fig. 4a ). Methyl-cutting assay. Genomic DNA was isolation from either flower, ovule, leaf, or rosette tissues using the CTAB method. 0. 5 μg of DNA was digested with the DNA methylation-sensitive restriction enzyme AluI (Thermo Scientific, Cat# FD0014) following the manufacturer's instructions. PCR amplification of selected sites (site 32, site 61, site 75, and actin) was performed using either digested or undigested DNA as the templates using the primers listed in Supplementary Data 15 and resolved on an 1. 5% agarose gel. In addition to the undigested DNA control, the Actin site, which lacks DNA methylation and the AluI recognition motif, was used as an internal loading control.
24nt-siRNA and DNA methylation data analyses and visualization Chromosome scale analysis of 24nt-siRNA and DNA methylation patterns. The levels of 24nt-siRNAs or CHH methylation for the three wild-type controls and pol-iv mutants were determined for each tissue in 5 kb bins across the five Arabidopsis chromosomes with the HOMER 57 annotatePeaks. pl script using ether the "tair10 -size given -len 1 and -ratio" or the "tair10 -size given -len 1 and -fpkm" options, respectively. For 24nt-siRNAs, a pseudo count of one was added to all values prior to a log 2 transformation. After averaging the wild-type data, the 24nt-siRNA and methylation values were visualized as barplots in R using RStudio with the circularize package  67  (Fig. 2a  and Supplementary Fig. 3a ). For CHH methylation, between 1 and 3 bins, depending on the tissue, had values over 0.
3%, but were capped at this value to facilitate visualization on a chromosome-wide scale. PCA analyses. For Fig. 6a  (PCA), raw values for 24nt-siRNA levels at the master clusters in the wild-type controls and pol-iv mutants for each tissue were calculated with the HOMER  57  annotatePeaks. pl script using the "-noadj, -size given and -len 1" options and the PCA was plotted in R using RStudio with the DEseq2 59 and ggplot2 packages. For Fig. 6c , the WTvsWT all tissues hypo CHH DMRs (Supplementary Data 7) were filtered to include only DMRs overlapping with the 10 24nt-siRNA classes (Supplementary Data 6), resulting in 11,511/12,532 DMRs. For all cytosines (CG, CHG, and CHH) within these DMRs, the coverage and frequencies of C and T bases were calculated from the ATCGmap output files from BS-seeker2 for each genotype.
Pie charts. For Fig. 2e  the fraction of DMRs (Supplementary Data 12) overlapping 24nt-siRNA clusters for each of the 10 classes (Supplementary Data 6), or none, were determined with BEDOPS 62 using the "-e 1" option. For Fig. 3b , the fraction of total 24nt-siRNAs present in specific categories were determined from raw read counts of a wild-type control from each tissue with the HOMER 57 annotatePeaks. pl script using the "tair10 -size given -noadj and -len 1" options. Venn diagrams. Scaled Venn diagrams showing the overlaps between reduced 24nt-siRNA clusters (Supplementary Data 11) or hypo CHH DMRs (Supplementary Data 12) for the different sets of clsy single, double, and quadruple mutants were generated using VennMaster (version 0. 38. 2)  70  (Fig. 3c  and Supplementary Figs. 7-10). Scaled Venn diagrams showing the overlaps between siren loci (Supplementary Data 5), reduced 24nt-siRNA clusters (Supplementary Data 11), and CLSY3 chIP peaks (BEDOPS  62  "-e 1" option; Supplementary Data 14) were also generated using VennMaster  70.
Unscaled Venn diagrams for 24nt-siRNA clusters (Supplementary Fig. 6b ) were generated with the same data using venny (https://bioinfogp. cnb. csic. es/tools/venny/index. html). Coefficient of variation. To assess the degree of variation in the levels of 24nt-siRNAs and CHH methylation between clsy1,2and clsy3,4-dependent Fl,Rs,Lv,Ov 24nt-siRNA clusters across tissues, the coefficient of variation (cv = standard deviation average for each cluster across the 4 tissues) was calculated and plotted as boxplots in R using RStudio and P-values were calculated using t-tests (Fig. 5c ). Violin and boxplots. For all violin and boxplots showing the expression levels of 24nt-siRNAs (Fig. 4b , Supplementary Figs. 6a, 7-10b, 11c, d, and 15a), the data was generated with the HOMER  57  annotatePeaks. pl script with the "tair10 -size given -fpkm and -len 1" options and plotted in R using RStudio.
Acknowledgements We thank colleagues and lab members for helpful comments and discussions. This work was supported by the  NIH  ( GM112966 ) and  Hearst Foundation  to J. L. M. Z. was supported by the  111  Project (Grant  B14027 ), the  Hundred-Talent Program of Zhejiang University, a Pioneer Fund Postdoctoral Award , and a postdoctoral fellowship from the  Paul F. Glenn Center for Biology of Aging Research at the Salk Institute. This work was also supported by the  NGS Core Facility  and the  Integrative Genomics and Bioinformatics Core Facility  at the  Salk Institute  with funding from  NIH-NCI CCSG :  P30 014195 , the  Paul F. Glenn Center for Biology of Aging Research at the Salk Institute , the  Chapman Foundation , and the  Helmsley Charitable Trust.
Data availability Illumina sequencing data (smRNA-seq, MethylC-seq, mRNA-seq, and ChIP-seq) has been deposited in the NCBI Gene Expression Omnibus (GEO) and are accessible through the GEO series accession number GSE165001. The raw data for Figs. 1c, 5d , and Supplementary Fig. 11f  as well as the raw images for the gels in Supplementary Fig. 11e  are provided as a Source Data file. Source data are provided with this paper.
Monitoring the chemical and structural transformation of the lamella intermediates via insitu XAS and XRD To understand the chemical and structural changes occurring in the reaction mixture during the synthesis, we performed in-situ Cu K-edge X-ray absorption spectroscopy (XAS) and XRD measurements using synchrotron radiation. For these studies, a custom 3-neck flask was used, which quite accurately replicates the conditions of a typical synthesis (see Figure  S6 ). XAS provides information on the Cu oxidation state as well as the coordination geometry and can offer insight into how these evolve with time. For these experiments, the reaction temperature T2 was set at 270 o C, as this is the highest limit of the syntheses studied above. The reaction with TDPA will be discussed as a representative example as the data were essentially identical for all of the phosphonic acid ligands (Figures  S7-S10 ).
Acknowledgments This worked was primarily financed by the  H2020 Marie Curie Individual Fellowship grant SURFCAT  with agreement number  837378  and contribution from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (grant agreement no. 715634-HYCAT ) L. C. -A. and P. A. thank the  Swiss National Science Foundation (SNSF)  for financial support from grant number  200021L_191997/1  and from the  NCCR Catalysis  (grant number  180544 ), a  National Centre of Competence in Research  funded by the  Swiss National Science Foundation , respectively. The authors thank the  European Synchrotron Radiation Facility in Grenoble, France  for the provision of synchrotron radiation beamtime at the Swiss-Norwegian beamline  BM31  and thank  Dr Wouter van Beek  for assistance.
However, these constraints are in general much weaker than the limits from cosmology. In this paper we revisit the scenario in which neutrinos decay into dark radiation after becoming non-relativistic and obtain updated limits based on the newer data from Planck 2018. In order to take advantage of the greater precision of the new data, the analysis we perform is also more accurate. We find that, under the assumption that neutrinos decay after becoming non-relativistic, the neutrino mass bound from Planck 2018 data (in combination with BOSS baryon acoustic oscillation (BAO) data and Pantheon SN1a data) is relaxed to m ν 0. 42 eV (95% C. L. ). 1  While this represents a remarkable relaxation of the constraints as compared to the case of stable neutrinos, we note that it is much stronger than the limit derived from Planck 2015 data for the same decaying neutrino scenario, m ν 0.
While this is a simple parameterization of neutrino decays, our bounds can easily be applied to specific models, as done in great details in Ref. [36]. The CMB can be used to constrain the masses and decay widths of neutrinos that decay prior to recombination. When neutrinos decay while still relativistic, decay and inverse decay can prevent neutrinos from free-streaming. If this happens before recombination, it can alter the well-known 'neutrino drag' effect that manifests as a phase-shift at high-'s in the CMB power spectrum  [37] [38] [39] [40]. The plot shows the current constraints on decaying neutrinos in the m ν -Γ ν parameter space. The colored regions are excluded by current data while the white region is allowed. The orange dashed line represents Γ ν = H(a nr ). Our study focuses on the region below this line, meaning decay happens after neutrinos have become non-relativistic.
The light grey region shows current constraints on neutrino mass and lifetime coming from the requirement that neutrinos are free streaming close to recombination  [13]. Our analysis excludes the red (blue) region labelled "Planck 2015"("Planck 2018") based on the data (Planck+BAO+Pantheon). The vertical brown line shows the projected KATRIN sensitivity. Therefore, CMB data can place a constraint on the decay width of neutrinos. The resulting bound depends on neutrino masses, and was recently updated in Ref. [13] , τ ν ≥ 4 × 10 6 s (m ν /0. 05 eV) 5. This bound excludes the grey region at the top of Fig. 1. In addition, based on the analysis in this paper, part of the 'late-decay' parameter space can also be excluded based on the gravitational impacts of massive neutrinos on the CMB and LSS.
Details of the analysis In this section we perform a numerical scan over the parameter space to obtain updated limits on the neutrino mass and lifetime. We perform comprehensive MCMC analyses with the MontePython-v3 3   [48, 49]  code interfaced with our modified version of CLASS. We fit the decaying neutrino model to a combination of the following data-sets: • The Planck 2018 high-TT, TE, EE + low-data TT, EE + lensing data  [7]. We will also compare these results with the use of Planck 2015 data to disentangle the effects of our improved formalism and that of the new data. • The BAO measurements from 6dFGS at z = 0. 106  [50] , SDSS DR7 at z = 0. 15  [51] , BOSS DR12 at z = 0. 38, 0. 51 and 0. 61  [52] , and the joint constraints from eBOSS DR14 Ly-α autocorrelation at z = 2. 34  [53]  and cross-correlation at z = 2.
Main results: updated limit on the neutrino mass and lifetime The results of our analyses are presented in Figs. 6. For very late decays, Log 10 (Γ ν /[km/s/Mpc]) 2. 5, no relaxation of the constraints on m ν /eV is visible, in agreement with what was found in Ref. [18]. The impact of the new Planck data is visible as a significantly improved bound on the sum of neutrino mass, namely we find m ν < 0. 127 eV (95%C. L. ), an improvement of about ∼ 35% over 2015 data, in good agreement with Ref. [7]. For Log 10 (Γ ν /[km/s/Mpc]) 2. 5, one can see that the bound relaxes as expected, although not as much with Planck 2018 data as for Planck 2015 data. Taking the intersect of the non-relativistic decay line with our 2σ limit, we find that Planck 2018 excludes neutrino decaying non-relativistically with masses m ν > 0.
Due to the well-known degeneracy between A s and e -2τ reio , Planck 2015 data, which was limited in polarization, were unable to place a tight constraint on A s , and thus the constraining power on the sum of neutrino mass and lifetime was limited. The precise measurements of low-polarization from Planck 2018 leads to constraints on τ reio that are tighter by a factor of two than those from Planck 2015. As a result, parameters degenerate with τ reio such as A s are now much better constrained. Consequently, the constraints on the sum of neutrino mass and lifetime have significantly improved with Planck 2018 data. To confirm this simple argument, we perform another MCMC run with Planck 2015 data and a tight gaussian prior on τ reio = 0. 0540 ± 0. 0074, chosen to match the optical depth to reionization reconstructed from 20%.
However, we have verified that this bug had no impact in the short-lived case (Γν > 10 2. 5 km/s/Mpc). Planck 2018. Given that the constraints on m ν are independent of Γ ν below Γ ν 10 3 , and the scaling above Γ ν 10 5. 5 is monotonic, we focus on the parameter space Log 10 (Γ ν /[km/s/Mpc]) ∈ [3, 5. 5] to accelerate convergence. Our results are presented in Fig. 7 , where one can see that this simple prescription leads to constraints that are very similar to those from the full Planck 2018 data. We attribute the remaining differences to the additional constraining power of Planck 2018 data on the parameters ω cdm and ω b , which are mildly correlated with m ν (see Fig. 6 , top panel). Note that our constraints are a factor of two weaker than those advocated in Ref. [35] , which performed a 'model-independent' reconstruction of the neutrino mass as a function of redshift, but neglects the decay products.
Acknowledgements PD is supported in part by  NSF  grant  PHY-1915093. ZC is supported in part by the  National Science Foundation  under Grant Number  PHY-1914731. ZC is also supported in part by the  US-Israeli BSF  grant  2018236. YT is supported by the  NSF  grant  PHY-2014165  and  PHY-2112540. Fermilab  is operated by  Fermi Research Alliance, LLC  under contract number  DE-AC02-07CH11359  with the  United States Department of Energy. This work has been partly supported by the  CNRS-IN2P3  grant  Dark21. The authors acknowledge the use of computational resources from the  Dark Energy computing Center  funded by the  Excellence Initiative of Aix-Marseille University  -A*MIDEX, a  French "Investissements d'Avenir" programme  ( AMX-19-IET-008 -IPhU ). PD and YT thank the  Aspen Center for Physics , which is supported by  National Science Foundation  grant  PHY-1607611 , where part of this work was performed.
5. 3   km/s/Mpc. 5 Let us note that the implementation of the BAO/f σ8 DR12 likelihood used in Ref. [18]  within the MontePython code had an issue that led to constraints on mν that were somewhat milder than the true bounds. MontePython has since then been corrected, leading to an improvement on the constraints on the stable/long-lived (Γν < 10 3 km/s/Mpc) case by about.
In addition, theory needs to provide models of dense matter properties across all relevant densities. To achieve these goals, the field should take advantage of synergies with multi-institutional nuclear theory centers and coalitions such as the FRIB Theory Alliance. • Astronomy: in astronomy, increases in the sensitivity of gravitational wave and neutrino detectors, expanded samples and improved analysis of spectroscopic data on ancient stars, asteroseismology, time domain observations to identify transient phenomena, and a new nuclear γ-ray mission are ongoing developments of special importance for nuclear astrophysics. Future capabilities that would be important for the community include an advanced x-ray telescope and a capability for space-based UV spectroscopy such as JWST and LUVOIR (see ASTRO2020  [3] ).
How did we get here? More than 100 x-ray bursters are known in the Galaxy and most of them show bright bursts several times a day. Consequently, x-ray observatories such as RXTE have obtained data on thousands of bursts that are available in databases such as MinBAR  [370]. More recently, neutron-star interior composition explorer (NICER) has provided a large amount of new data with unprecedented x-ray spectral coverage  [371]. Observations of the response of long-term burst behavior to changes in accretion rate are available as well  [372]. These observations reveal a wide diversity in burst phenomenology from source to source and among bursts of the same source. These include single, double, and triple bursts; short bursts (∼10 s), long bursts (∼100 s), intermediate long bursts (hours), and superbursts (hours to days); short recurrence time bursts; radius expansion bursts; and bursts with millisecond period oscillations  [363].
As such, electron-capture rates are critical in linking observables to the nature of the progenitor system, one of the key open questions. The sensitivity of thermonuclear supernovae nucleosynthesis to electron-capture rates has been analyzed in a few works with conflicting results  [476] [477] [478]. Charge-exchange reaction measurements have been used to constrain these electron-capture reactions by benchmarking theoretical shell-model calculations and quantifying nuclear uncertainties  [479]. These data are available in a dedicated database  [480] [481] [482]. Of particular importance are new techniques that have been developed to perform these measurements far from stability, such as the use of the active target AT-TPC with the S800, and in the future the HRS, spectrometer at FRIB  [483, 484].
This will enable precision determination of ground-state properties, including nuclear masses using Penning traps, storage rings, and MR-TOF. Measurements of weak transition strengths using total absorption spectroscopy, neutron counting, and charge-exchange reaction measurements will also become possible. To reach all the relevant nuclei out to the limit of neutron stability will require the FRIB400 energy upgrade of the new FRIB facility. Fusion measurements involving neutron-rich nuclides will provide experimental grounding of nuclear theory calculations needed for pycnonuclear fusion reaction rate estimations. Meanwhile expanding the set of observed crust cooling sources will improve the fidelity of crust cooling models by providing more data with which to test theory. Early and regular coverage of the observed light curve are particularly crucial to breaking model degeneracies  [362].
A multi-messenger observation of such an event would provide transformational data for the understanding of dense matter. Recently, supernova simulations have used more modern treatments of dense matter that better match the constraints provided by astronomical observations and laboratory measurements, but the robust analysis of future observations requires three-dimensional simulations based on detailed knowledge of the properties of nuclear matter and neutrino-matter interactions. Accurate nuclear physics, for example the weak interactions of neutron rich rare isotopes that can be constrained with FRIB experiments, will also be needed to interpret such observations. Capitalizing on these opportunities for dense-matter science will require a concerted effort in theory, experiment, simulations, and modeling to keep up with detector sensitivity improvements and the expected wealth of data.
Such training should be tailored to the needs of the field. Centers should facilitate the collection and sharing of experiences in the field with such trainings. Centers and networks: centers and networks (section 8) have the opportunity to apply particularly impactful action on all the points in this section. To further broaden the impact and lower access barriers it is important to expand networks to smaller, less-connected institutions both nationally and internationally. As they are broad-reaching and multi-institutional, centers have an excellent opportunity to provide a support network for those who might not otherwise have access to interdisciplinary training, research opportunities, and resources. This could include community-wide mentorship and networking programs, a centralized location for job notices and workshop announcements, mental health and wellness resources, assistance with translation or data accessibility issues, and a diverse database of potential speakers for conference and seminar planning.
Acknowledgments We thank all participants of the 2020 JINA Horizons meeting for their active participation that formed the basis of this paper. The organization of the meeting and the writing of this paper have been supported by the  US National Science Foundation  through awards  PHY-1430152  ( Joint Institute for Nuclear Astrophysics JINA-CEE ) and OISE-1927130 (IReNA), and in part by the  ExtreMe Matter Institute EMMI  at the  GSI Helmholtzzentrum für Schwerionenforschung in Darmstadt, and the 'ChETEC' COST Action  ( CA16117 ) supported by  COST (European Cooperation in Science and Technology).
(2016)  that only those emissions that are confined close to the meridional plane can propagate down to the altitudes of DEMETER. In this article, we present statistics of low altitude EN events found in the burst mode data gathered by the DEMETER spacecraft. We focus on the wave propagation properties and latitudinal and frequency distributions of these emissions. The observational results and the methods used to obtain them are presented in Section 2. The observed properties of EN are compared to statistics obtained from ray tracing simulations in a cold plasma, which is the main topic of Section 3. We assume purely meridional propagation (Section 3. 2) and use minimization methods to estimate the distribution of wave normal angles and frequencies in the source that produces the best nonlinear least squares fit of the latitudinal distribution of incident rays to the experimental data.
Dataset and Processing Methods The data used in the wave analysis were acquired by the ICE (system of four electrical sensors) and IMSC (triaxial set of magnetic sensors) instruments onboard the DEMETER spacecraft -see  Berthelier et al. (2006)  and  Parrot et al. (2006)  for a detailed description of these instruments. The spacecraft was operational from June 2004 to December 2010. It followed a nearly sun-synchronous circular orbit, altering between approximately 10:30 MLT and 22:30 MLT, and kept an altitude of about 715 km, later (December 2005) changed to 660 km. In the survey mode, DEMETER provided continuous wave measurements with one electric and one magnetic field component in a frequency range up to 20 kHz. More detailed data were available in the burst mode, which was triggered only in preselected regions, mostly at lower geomagnetic latitudes  (Němec et al.