dataesr/scientific-paragraphs-categorization huggingface dataset:
1B. The two most prominent features revealed by the DREM analysis are a subset of paths (W1-W4) showing broad peaks of induction around 3 h and another subset (W9-W11) showing broad peaks of repression between 3 and 6 h. The remaining paths correspond to early up-regulated genes (W5 at 1 h 30 min and W6 at 20 min), early down-regulated genes (W8 at 1 h 30 min), and late mildly up-regulated genes (W7 at 3-12 h). Notably, ∼half of the genes in the early and late responsive paths (W5-W8), as well as many of the genes in the other paths, were uniquely identified in our γ-IR time course (SI Appendix, Fig. S2B ). As expected, the 218 DE genes included based solely on the sog1 γ-IR time course displayed no significant changes in expression in the wild-type DREM model (SI Appendix, Fig. S2C ). Thus, while many genes show peaks of induction or repression at commonly assessed time points (1 h 30 min to 3 h), this DREM model reveals additional expression modules that peak earlier or later, providing insights into the DNA damage response.
To shed light on the biological functions of these gene sets, GO analyses were performed, revealing largely distinct enrichment terms for the DREM paths that capture the major processes previously connected with the DNA damage response [Fig. 1C , SI Appendix, Fig. S3 , and Source Data 2  (44) ]. These include DNA repair and DNA metabolism terms for genes upregulated in paths W1, W2, and-to a lesser extent-W3, cell cycle and associated terms for genes down-regulated in paths W9-W11, cell death terms for genes in paths W4 and W6, and respiratory burst along with other reactive oxygen speciesassociated terms for paths W6 and W7 (Fig. 1  C and D and SI   44 ) for all enriched GO terms remaining after the REVIGO similarity filter  (100). (D) Table of select genes associated with the processes indicated above each column based on GO category enrichments and/or manual curation.
The gene names and/or the number of genes per category (in parentheses) are colored based on the path in which they reside. (E) Select motifs enriched in the indicated DREM paths. The full set of enriched motifs identified are presented in SI Appendix, Fig. S5  [Source Data 3  (44) ]. Below each motif, E-values calculated against all Arabidopsis promoter sequences, and similar TF families, identified via comparisons with the motifs in the DAP-seq database (48) using Tomtom  (98) , are indicated. Three similar, but independently identified motifs that correspond to the previously described mitosis-specific activator (MSA) element (AACGG) (55) are marked with an asterisk. Appendix, Fig. S3 ). In addition, several paths are enriched for immunity and defense terms (Fig. 1  C and D and SI Appendix, Fig.
S3 ), which is consistent with previous work showing both transcriptional and genetic connections between the plant immunity and DNA damage responses  (49, 50). Thus, although many of the genes in the DREM model remain poorly characterized (Dataset S3A), grouping the various DNA damage-responsive genes into paths enabled the identification of pertinent GO terms. Furthermore, these analyses revealed that, broadly speaking, the transcriptional response to DNA damage starts with a transient induction of general stress genes, which is coincident with the sustained induction of DNA repair genes, and is followed, after a short delay, by the repression of cell cycle genes. Together, these findings add an important layer of functional and temporal information to the DNA-damage gene-regulatory network.
For example, the three most strongly up-regulated paths (W1-W3) were assigned to numerous NAC TFs (Fig. 1A  and SI Appendix, Fig. S4 ), and known NAC motifs (CTT[N 7 ]AAG)  (48, 51, 52)  were identified in the promoters of a high percentage of the genes in these paths [Fig. 1E , SI Appendix, Fig. S5 , and Source Data 3  (44) ]. Thus, although SOG1 (also known as NAC008) was not assigned to these paths because there was no SOG1-gene interaction data available in the AGRIS  (45) (46) (47)  and DAP-seq  (48)  databases, the NAC family was well represented. Similarly, all members of the MYB3R TF family present in the DAP-seq database (MYB3R1, -4, and -5) were assigned to the two most strongly down-regulated paths in the DREM model (W10 and W11) (Fig. 1A  and SI Appendix, Fig. S4 ), but only the repressor MYB3Rs (Rep-MYB3Rs), MYB3R3 and MYB3R5, have known roles in the DNA damage response  (53).
2A , SI Appendix, Fig. S6A , and Dataset S3B). Consistent with the iden-tification of fewer DE genes in the sog1 mutant (771; FC ≥ 2 and FDR ≤ 0. 01), most of the genes included in the sog1 DREM model based solely on their regulation in the wild-type γ-IR time course (1,451 of 1,624) reside in paths S3 and S4 and showed little to no concerted changes in expression across the entire sog1 γ-IR time course (SI Appendix, Fig. S6 B  and C ). The remaining 173 genes, which did not pass the thresholds for differential expression in the sog1 γ-IR time course, displayed subtle, but more concerted, expression changes and were assigned to paths S1, S2, or S5 in the sog1 DREM model (SI Appendix, Fig. S6C ). Thus, nearly two-thirds (1,451 of 2,395) of the transcriptional DNA damage response is completely dependent on SOG1.
As detailed below, assessment of the features remaining in the sog1 DREM network (Fig. 2  A and B and SI Appendix, Fig. S6D ) and visualization of the sog1 expression patterns within the context of the wild-type DREM model (Fig. 2C ) reveal both SOG1-dependent and SOG1-independent aspects of the DNA damage response. Analysis of the path S1 genes from the sog1 DREM model revealed substantial overlaps with the genes present in paths W5 and W6 of the wild-type DREM model (Fig. 2B ). Furthermore, the expression levels of many of the W5 and W6 genes were comparable between the wild-type and sog1 datasets at the 20-min time point (Fig. 2C ), demonstrating that a large portion of the initial burst of gene expression observed in the wild-type DNA damage response occurs in a SOG1-independent manner.
2  A and C and SI Appendix, Fig. S6A ). Interestingly, two-thirds of these genes correspond to those found in paths W1-W4 of the wild-type DREM model (Fig. 2B ), which normally peak between 1 h 30 min and 6 h (Figs. 1A  and 2C ). However, essentially none of these genes are induced at the proper time (between 1 h 30 min and 6 h) in the sog1 mutant (Fig. 2C ). As with the other features revealed by the sog1 DREM model, these expression profiles were independently verified (SI Appendix, Fig. S7 ). While many of the late-induced genes in the sog1 mutant correspond to general stress-response genes, several well-known DNA repair genes, including GMI1, BRCA1, PARP2, and PARG2 are also present [Dataset S3B and Source Data 2  (44) ]. Thus, we posit that this latent response may be triggered by defects in DNA repair and the persistence of unrepaired DNA lesions that may be accentuated by the loss of SOG1 function.
SOG1 Is a Transcriptional Activator that Directly Targets Nearly Half of the Genes Strongly Induced by DNA Damage. As described above, SOG1 is required for the induction or repression of thousands of genes in response to γ-IR. Until recently  (27) , only a few SOG1 target genes had been identified, namely SIAMESE-RELATED 5 (SMR5) and SIAMESE-RELATED 7 (SMR7)  (26) , FLAVIN-DEPENDENT MONOOXYGENASE 1 (FMO1)  (22) , CYCLIN B1;1 (CYCB1;1) (28), and BREAST CANCER SUSCEPTIBILITY 1 (BRCA1)  (25). To determine which of the other genes regulated by SOG1 are direct targets, chromatin immunoprecipitation and sequencing (ChIP-seq) experiments were conducted (Dataset S4A) using transgenic lines in which the sog1 mutation was complemented by a SOG1-3xFLAG construct driven by the endogenous SOG1 promoter (SI Appendix, Fig.
S10 A  and B ). As SOG1 binding is expected to precede the transcriptional regulation of its targets, the ChIP-seq experiments were performed at two early time points after irradiation, 20 min and 1 h. Analysis of the resulting ChIPseq profiles identified 307 SOG1 peaks (Dataset S4B) that are mainly located in promoters and transcribed regions (SI Appendix, Fig. S10C ) and were assigned to 310 immediately adjacent gene targets (Dataset S4 C and D). These peaks were enriched relative to both input and wild-type ChIP samples (SI Appendix, Fig. S10D ) and, attesting the reproducibility and quality of the ChIP-seq experiments, similar enrichment patterns were observed for both the 20-min and 1-h datasets (Fig. 3A ). Furthermore, four of the five initially identified SOG1 targets (SI Appendix, Fig.
S10E ), and 104 of the 146 recently identified SOG1 ChIP-seq targets  (27) , were among the 310 SOG1 targets identified after γ-IR treatment (SI Appendix, Fig. S10F ). Thus, the SOG1 ChIP-seq results presented here are consistent with, but greatly expand upon, the previously identified SOG1 targets. To determine whether genes targeted by SOG1 are induced, repressed, or both in response to DNA damage, their expression patterns after γ-IR treatment were determined from the wild-type and sog1 γ-IR time-course datasets (Fig. 3B ). Over 80% of these genes were induced by γ-IR in a SOG1-dependent manner, while none were significantly repressed (Fig. 3B ), demonstrating that SOG1 acts, either by itself or in association with other factors, as a transcriptional activator. In agreement with this finding, 206 of the 310 target genes were identified as part of the wild-type DREM model.
Not only do all these genes map to up-regulated gene paths (Fig. 3C  and SI Appendix, Fig. S10G ), they correspond to 43% of all of the strongly up-regulated genes (e. g. , W1-W3) and 85% of the most highly induced genes (e. g. , W1) (Fig. 3C ). Strikingly, the same motif identified from the path W1-W3 genes of the DREM network (CTT[N 7 ]AAG) (Fig. 1E ) was found directly under nearly all (289 of 307) of the SOG1 peaks (Fig. 3D  and Dataset S4E). This motif is similar to motifs observed for other NAC TFs  (48, 51, 52)  and is also consistent with the findings of Ogita et al. (27) , as this motif was identified in the promoter regions of approximately half of their SOG1 target genes and was shown to be necessary and sufficient for SOG1 binding based on in vitro and in vivo assays. Together, these analyses reveal several hundred direct targets of SOG1 and demonstrate that although SOG1 is required for both the induction and repression of DNA damage-responsive genes, it acts strictly as a transcriptional activator, directly controlling ∼8% of the DNA damage transcriptional response.
4 ). Together, these analyses demonstrate that SOG1 rapidly (within 20 min) and directly targets key HR and DNA metabolism factors to facilitate DNA repair in a coordinated fashion. Consistent with SOG1 only directly targeting ∼8% of the DREM network, there are 33 TF genes, from 14 different TF families, that are directly regulated by SOG1, revealing the first tier of a complex cascade of transcriptional responses initiated by SOG1 (Fig. 4  and SI Appendix, Fig. S11 ). Among these SOG1 targets are six other NAC TFs that are highly induced by γ-IR, highlighting the prominent role of this TF family in the DNA damage response. SOG1 also directly regulates TCP20, which encodes a TF that binds the CYCB1;1 promoter (79) and may help reinforce its up-regulation. Further extrapolation of the SOG1 TF network based on previously annotated gene-TF interactions from the AGRIS (45-47) and DAP-seq databases  (48)  allows the inference of a putative second tier within the network (SI Appendix, Fig.
S13B  and Dataset S1), a time when hundreds of genes are strongly down-regulated in the wild-type DREM model (Fig. 1A ). In agreement with previous studies showing minimal expression changes between wild-type and myb3r1,3,5 triple mutants in early seedling stages  (90) , comparison of the 6-d-old mock-treated seedlings (wildtype vs. myb3r1,3,5) revealed only 24 up-regulated genes, including just two G2/M phase genes and a single down-regulated gene (Dataset S5A). However, after γ-IR treatment, the DNA damage response was clearly altered in the myb3r1,3,5 triple mutant compared with the wild-type control. On a global level, the γ-IR response observed in the wild-type dataset was similar to the 3-h time point from the wild-type DREM model, but in the myb3r1,3,5 triple dataset, the genes in two of the three cell cycle-enriched paths (W10 and W11) were less repressed overall (Fig.
5A ). At the level of individual genes, 80 loci significantly less repressed in the myb3r1,3,5 mutants after DNA damage (Dataset S5 B and C) (FC ≥ 2 and FDR ≤ 0. 05) were determined by considering both the experimental conditions (γ-IR vs. mock treatments) and the genotypes (wild-type vs. myb3r1,3,5). Nearly all of these genes (78 of 80) are present in the wild-type DREM model, constituting 72. 3% of the path W11 genes (47 of 65), 24. 8% of the path W10 genes (28 of 113), and 0. 5% of the path W9 genes (3 of 571) (Fig. 5B  and SI Appendix, Fig. S13C ). Functionally, 71 of these 80 genes are associated with the G2/M phase of the cell cycle (54, 57) (Fig. 5C  and Dataset S5B). Approximately one-third of these genes (28 of 70) were previously shown to be repressed in a Rep-MYB3R-dependent manner either under normal growth conditions  (90)  or after exposure to DNA damage (53) (Dataset S5B).
However, the remaining two-thirds (42 of 70) represent newly identified Rep-MYB3R-regulated genes (Dataset S5B). Finally, these 80 genes are likely direct targets of the Rep-MYB family, as they almost all (72 of 80) possess MSA motifs in their promoters and/or are associated with previously defined MYB3R3 peaks by ChIP-seq (q-value > 25 under nondamaged conditions)  (90)  or by ChIP-qPCR after DNA damage (53) (Fig. 5D  and SI Appendix, Fig. S13D ). Furthermore, the association of MYB3R3 with these genes is quite specific, as the same DREM paths affected in the myb3r1,3,5 triple mutants displayed the highest levels of promoter-proximal MYB3R3 enrichment (SI Appendix, Fig. S13E ). In contrast to the genes less repressed in the myb3r1,3,5 mutants after DNA damage, the genes significantly less induced showed poor overlap with genes in the wild-type DREM model (SI Appendix, Fig.
S13C ) and none overlapped with G2/M-expressed genes or MYB3R3 peaks (Dataset S5B), suggesting that these genes may not be directly regulated by the Rep-MYB3R family. Taken together, these findings more than double the number of cell cycleregulated genes known to be regulated by the Rep-MYB3R family and demonstrate that these TFs control a large portion of the most strongly repressed genes in the wild-type DREM network in response to DNA damage.
Materials and Methods Plant Materials. Arabidopsis lines include the Columbia (Col) wild-type strain, the sog1-1 EMS mutant provided by A. Britt, Department of Plant Biology, University of California, Davis, CA, which is in a mixed Landsberg erecta (Ler)/ Col background containing a CYCB1;1 promoter::GUS fusion (13), and the myb3r1,3,5 triple mutant provided by M. Ito, Graduate School of Bioagricultural Sciences, Nagoya University, Chikusa, Nagoya, Japan  (90). mRNA-seq Experiments. Growth conditions and library preparation. For all mRNA-seq experiments, seeds were sterilized, sown on half MS media with 0. 6% plant agar, stratified at 4 °C for 3 d, and transferred into a growth chamber [constant light (100 μmol•m -2 •s -1 ) and temperature  (23 °C) ]. For the wild-type, sog1, and myb3r1,3,5 datasets, 6-d-old seedlings were γ-IR at a dose of 100 Gy using a Co60 radioactive source.
See SI Appendix, Materials and Methods for details regarding dose rates and experimental designs. Total RNA was extracted from pooled seedlings using the Quick-RNA MiniPrep kit (Zymo Research R1055) and mRNAs were purified from 1 to 2 μg of total RNA using the NEBNext Poly(A) mRNA Magnetic Isolation Module (New England Biolabs E7490) before library preparation with the NEBNext Ultra or UltraII RNA Library Prep Kit (New England Biolabs E7530 or E7770). All sequencing was performed on an Illumina HiSEq 2500 in 50-bp single-end mode. Data processing. For all mRNA-seq experiments, Illumina reads were mapped to the TAIR10 genome using STAR (93) (Dataset S1). Expression values for all genes and samples were retrieved using the analyzeRepeat. pl script from HOMER  (94)  with the -noadj option. These values were used to generate the principle component analysis plots shown in SI Appendix, Fig.
S1A  with ggplot in R studio and to identify DE genes (FC ≥ 2 with an FDR ≤ 0. 01) based on the experimental conditions (i. e. , γ-IR vs. mock) (Dataset S2) or genotype (i. e. , wild-type vs. mutant) (Dataset S5A) using the getDiffExpression. pl script from HOMER  (94)  with the -DESeq2 and -repeats options. To identify DE genes based on both the experimental conditions and genotypes for the "wt vs myb3r1,3,5" experiment (Dataset S5 B and C), the analysis was performed in R studio using DESeq2 with design = batch + Condition + Group + Condition:Group. To visualize the gene-expression data, University of California, Santa Cruz (UCSC) browser tracks were generated using the makeUCSCfile script from HOMER  (94) , normalizing to 10 million reads. Tracks showing the difference between the average mock-and γ-IR-treated samples (Fig.
1B ) were generated using deepTools  (95)  as detailed in SI Appendix, Materials and Methods. Heatmaps showing the expression of individual genes during the γ-IR time course were generated using the normalized log 2 FC values generated by DESeq2 and plotted in R studio using pheatmap. Venn diagrams comparing DE gene sets were generated using VennMaster  (96). DREM Analysis. For data inputs and models, see Source Data 1  (44). Three types of files were generated as inputs for the DREM models. The first is a list of the 33,323 TAIR10 gene IDs, the second is a set of gene-TF interactions, and the third contains the expression values from the RNA-seq experiments. See SI Appendix, Materials and Methods for details regarding the content and generation of these files. As detailed in SI Appendix, Materials and Methods, the input files described above were used to generate the wildtype and sog1 DREM models by a two-step process using the DREM v2.
0. 3  (42, 43). Motif Analysis. As detailed in SI Appendix, Materials and Methods, the MEME suite (97) was used to identify motifs enriched in the promoters of genes present in the individual DREM paths. All found motifs were then compared with the DAP-seq database of motifs (48) using Tomtom  (98)  to search for motif matches overlapping by at least 5 bp with a distance coefficient calculated by the Pearson method showing an E-value < 10. Only the matches with an E-value < 0. 01 were kept. GO Analysis. As detailed in SI Appendix, Materials and Methods, the GO enrichments for each DREM path were retrieved using GO-TermFinder (99) via the Princeton GO-TermFinder interface (https://go. princeton. edu/cgi-bin/ GOTermFinder), simplified using the REVIGO (100) (revigo. irb. hr/), and visualized as unclustered heatmaps in Treeview.
Digestion with Proteinase K was performed at 45 °C for 1 h. The DNA was purified by phenol-chloroform-IAA extraction and precipitated with ethanol. Pellets of both inputs and IPs were resuspended in 30 μL of TE, pH 8. 0. All buffers are described in Dataset S6. Library preparation, sequencing, and analysis. Libraries were prepared from 25 μL of the IP samples or 1 μg of the input samples using the NEBNext Ultra II DNA Library Prep Kit (New Englabnd Biolabs E7645). Sequencing was performed on an Illumina HiSEq 2500 in 50-bp single-end mode. Reads were mapped to the TAIR10 genome using bowtie2 (101) (Dataset S4A), UCSC browser tracks normalized to 10 million reads were generated using HOMER  (94) , and, as detailed in SI Appendix, Materials and Methods, heatmaps and metaplots showing SOG1 ChIP enrichments were generated using the deepTools suite  (95).
Motifs under the SOG1 peaks were determined using the MEME tool (102) from the MEME suite  (97)  with the following options (-nostatus -maxsize 7500000 -nmotifs 10 -minw 6 -maxw 18 -revcomp -psp -bfile). MYB3R3 ChIP-Seq Analysis. Using data from GSE60554  (90) , the closest TAIR10 gene to each peak was determined using annotatepeaks. pl from HOMER  (94). Mapping of the ChIP-seq reads and analysis of ChIP enrichment profiles using deepTools were as described for the SOG1 ChIP-seq. Venn diagrams were generated using VennMaster  (96)  based on known G2/M-expressed genes  (54, 57)  or previously defined MYB3R3 ChIP peaks  (90). Cytoscape. Using Cytoscape 3. 4. 0, networks for either the 141 SOG1 target genes that were assigned to functional categories based on the GO analysis and their TAIR10 annotations [Source Data 4  (44) ] or the DREM network targets of the 33 TFs downstream of SOG1 identified from the AGRIS  (45) (46) (47)  and DAP-seq.
(48) , databases [SI Appendix, Fig. S11  and Source Data 4  (44) ] were constructed and colored based on the log 2 FC in expression 3 h after γ-IR. For Fig. 4 , genes underlined in blue represent those that have a human or mouse ortholog, identified using the PANTHER  (103)  and Thalemine tools  (104) , that were shown to be targeted and up-regulated by p53 based on 13 genome-wide studies in humans (  105 ) or a single study in primary mouse embryo fibroblasts study  (106).
ACKNOWLEDGMENTS. We thank the J. A. L. laboratory and colleagues at the  Salk Institute  for helpful discussions; and  Dr. C. Huang ,  Dr. L. Song , and  Dr. Y. He  for bioinformatics support. Work in the J. A. L. laboratory was supported by the  Rita Allen and Hearst Foundations  (to J. A. L. ). C. B. was supported by the  Catharina Foundation Fellowship. N. V. was supported by the  Jesse and Caryl Philips Foundation. This work was also supported by the  Next-Generation Sequencing Core Facility  and the  Integrative Genomics and Bioinformatics Core Facility  at the  Salk Institute , with funding from  NIH-National Cancer Institute Cancer Center Support  Grant  P30 014195 , the  Glenn Foundation for Medical Research , the  Chapman Foundation , and the  Helmsley Charitable Trust.
We present an implementation of the Hall term in the non-ideal magnetohydrodynamics equations into the adaptive-mesh-refinement code RAMSES to study its impact on star formation. Recent works show that the Hall effect heavily influences the regulation of the angular momentum in collapsing dense cores, strengthening or weakening the magnetic braking. Our method consists of a modification of the two-dimensional constrained transport scheme. Our scheme shows convergence of second-order in space and the frequency of the propagation of whistler waves is accurate. We confirm previous results, namely that during the collapse, the Hall effect generates a rotation of the fluid with a direction in the mid-plane that depends on the sign of the Hall resistivity, while counter-rotating envelopes develop on each side of the mid-plane.
(2014)  uses the grid-based code PLUTO  (Mignone et al. 2007 ). The Hall effect is also implemented in the AMR codes BAS-TRUS  (Tóth et al. 2008 ) and ZEUS  (Stone & Norman 1992; Krasnopolsky et al. 2010) , as well as in the multimethods code GIZMO  (Hopkins 2015 (Hopkins , 2017)). The aim of this paper is first to present an original implementation of the Hall effect in the adaptive-mesh-refinement (AMR) code RAMSES and to apply it in protostellar collapse studies. The paper is organized as follow. First, we briefly recall the basics of the theory about the Hall effect in Section 2. Second, we detail our numerical implementation in Section 3 and present validation tests of our scheme in Section 4. Section 5 is devoted to the protostellar collapse models in which we incorporate the Hall effect.