dataesr/scientific-paragraphs-categorization huggingface dataset:
However, only two genes (Kmt5b and Pisd-ps1) had a significant dose-dependent relationship, further supporting a haploinsufficient disease model. Functional enrichment analyses by IPA of the WT versus HET and WT versus KO mDEG (q < 0. 05) datasets separately showed foremost that the KO state predicts significantly increased organismal death (P = 2. 01 × 10 -25 ; z = 3. 229) that was not present in the HET state. Both the HET and the KO conditions predict significantly decreased morphogenesis of neurons (P = 7. 52 × 10 -8 to 5. 58 × 10 -28 ; KO > HET) and decreased neuritogenesis (P = 3. 01 × 10 -7 to 9. 61 × 10 -27 ; KO > HET; table  S9 ). Furthermore, microtubule dynamics and organization of the cytoskeleton were both also predicted to be significantly decreased in the HET and KO conditions (table  S9 ).
6F  and table  S9 ). Shared upstream regulators of the HET and KO conditions included Harvey rat sarcoma viral oncogene homolog (HRAS; 1. 4 × 10 -13 to 3. 69 × 10 -13 ), FGF2 (1. 5 × 10 -9 to 1. 06 × 10 -11 ), estrogen (1. 25 × 10 -8 to 4. 4 × 10 -11 ), and TGFB1 (3. 2 × 10 -6 to 8. 4 × 10 -8 ). Among mDEGs (HET or KO) with an annotated human ortholog (Ensembl release 107), six genes-COL1A2, FSTL1, TGFB1, FOXC1, SLC6A13, and F13A1-were up-regulated in both the mouse (q < 0. 05) and the hDEG (P < 0. 05) datasets (Fig. 6G ). Similarly, the down-regulation of WDFY3 and PLCB1 were shared between the mDEG and the hDEG datasets (Fig. 6G ). Axonal guidance signaling and the Glycoprotein VI Platelet (GP6) signaling pathway were both predicted in the top 20 significant canonical pathways for the hDEG (P < 0.
05) and mDEG (q < 0. 05; HET and KO) datasets (Table  1 ). Furthermore, FGF2 and CCR2 were predicted as significant upstream regulators in all three datasets (hDEG and both mDEG sets; tables S7 and S10), highlighting shared brain and ECM effects. Last, down-regulated, but not up-regulated mDEGs (q < 0. 05), were enriched for known autism-associated genes by simulation testing (P = 9. 99 × 10 -7 ; Fig. 6H ), suggesting that down-regulated mDEGs (specifically, Reln, Plxna4, Grik3, Hivep2, Sox5, Plcb1, Wdfy3, Dst, Kcnq3, Grin2b, and Nfix) may contribute to the phenotypes observed. Four of these genes-Grin2b, Plcb1, Reln, and Wdfy3-were validated by quantitative reverse transcription polymerase chain reaction (PCR) testing, confirming down-regulation in the HET versus WT mouse brain (fig. S7 ).
While PLCB1 down-regulation survived FDR correction in the hDEG dataset, this gene alone was not a strong enough signal for significance in this simulation test. Both up-regulated and downregulated hDEGs (P < 0. 05) were significant for autism-associated genes by simulation testing (P = 3. 9 × 10 -5 to 9. 99 × 10 -7 ); however, these datasets are lower confidence and likely contained some falsepositive signal.
Growth changes in primary patient cells and mouse models To test the RNA-seq predicted effects of KMT5B haploinsufficiency, we evaluated fibroblasts from two patients in the study with different variants [p. (H273R) and p. (F154SfsX4); patients 2 and 10, respectively]. KMT5B mutant primary cell lines grew   (A, C,  and D ) Volcano plots illustrate the genes that are significantly up-(red) or down-regulated (blue) in the (A) human and (C and D) mouse datasets. HET, heterozygous; KO, homozygous knockout; black dots, nonsignificant genes. A functional enrichment summary is shown for the human dataset (hDEGs, P < 0. 05) in (B) and for the KO mouse (mDEGs, q < 0. 05) in (F). Orange, activated; blue, inhibited. Venn diagrams show (E) the number of mDEGs that overlap between the genotypes tested and (G) genes that overlapped between the hDEG and mDEG datasets.
(F) Simulation testing for gene overlaps between the (H) mDEG and (I) hDEG down-regulated genes and high-risk autism genes from SFARI Gene. Dashed line, observed overlap. Gene in bold was found in both the human and mouse datasets (q < 0. 05). significantly slower than two control lines (P < 0. 05 at 48 hours and P < 0. 005 at 72 hours; Student's t test; Fig. 7A ); this was not due to increased cell death in the mutants (Fig. 7B ), supporting the hypothesis that KMT5B expression contributes to regulation of cell division  (36, 37)  and predicted as cancer enrichments in the hDEG analyses. We have reported persistent lower body weight and shorter body length in the Kmt5b gene trap haploinsufficient mouse model on a C57BL/6N strain background  (33). We independently confirmed this growth defect using the gene trap allele on a C57BL/6JInv background (fig.
S10 ), suggesting that increased expression of Ttr is not due to hyperthyroidism. TTR is often used as a diagnostic marker for choroid plexus tissue. In mice, the choroid plexus is established between E12. 5 and E13, reaching full size by E16. 5  (45). Ttr expression remains high until birth and decreases postnatally  (46). Overabundance of choroid plexus tissue has been linked to increased CSF production and hydrocephalus  (47). Kmt5b mRNA is highly expressed in the mouse brain at E14. 5, the time point at which we collected additional transcriptomic data. Our mDEG dataset highlights a net effect of gene up-regulation with Kmt5b loss. This is consistent with other published studies  (21)  and with the increased H4K20me1 (permissive chromatin Fig. 7. Cell growth and death in human fibroblasts and mouse brains.
Our data did not show an up-regulation of Kmt5c or Nsd2/Mmset/Whsc1 transcripts (table  S6 ), two genes that have been proposed as an alternative H4K20 dimethylating enzymes  (48) , as a compensatory reaction to Kmt5b loss. We did not identify any overlaps between our mDEG dataset and that of Wang et al. (21)  who used adeno-associated virus-short hairpin RNA to knock down Kmt5b expression specifically in the prefrontal cortex of young adult mice. These results suggest that where and when KMT5B is present in the developing brain may matter. A recent study identified a difference in the fraction of migrating cells during peak cortical neurogenesis when ventricular zone Kmt5b was knocked down at E13. 5 via in utero electroporation  (15) , in agreement with our transcriptomic predicted changes in cell migration over this time period.
Collectively, the data described in this study provide many exciting avenues for future research connecting KMT5B to the developing brain. This study is the first to document the in situ expression of Kmt5b transcripts in the mouse brain over developmental time (embryonic stages through adulthood). It is also the first report of transcriptomic data from the embryonic brain in a model of germline Kmt5b loss, a period that is likely critical to the development of humans carrying disruptive KMT5B variants  (18). Both our hDEG and mDEG datasets predict increases in TGF-β signaling concomitant with ECM remodeling in the mutant conditions. TGF-β is thought to be neuroprotective and is up-regulated following many types of brain damage  (49). Whether increased cell stress due to loss of KMT5B/H4K20me2-mediated DNA repair contributes to the phenotypes observed in this patient population should be further investigated.
TGF-β is also known to be a potent stimulator of ECM synthesis, including collagens, which were strongly up-regulated in the mDEG and hDEG datasets. Collagen I (Col1a1/Col1a2), proteoglycan link protein 1 (Hapln1), and lumican (Lum) have been shown to contribute to folding of the neocortex  (50). All three of these genes were significantly up-regulated in the mDEG mutant dataset. Given the report of polymicrogyria in patient 34, changes in cortical folding may be an interesting avenue for future research. Last, TGF-β has been shown to regulate axon growth in neurons  (51). Axon guidance signaling was significantly enriched in our hDEG and mDEG datasets. Given the additional roles for the ECM in axon growth and guidance  (52) , human cell and mouse resources should be used to further explore this process in the context of KMT5B function.
In silico analyses For multiple sequence alignment, orthologs were extracted from the UniProt database using Blast  (56)  with the human KMT5B (NP_060105. 3) as the reference amino acid sequence. The homologous sequences retrieved were aligned using COBALT (57) with default parameters. For each missense variant, the protein stability changes upon mutation (ΔΔG; negative values indicate destabilization) using mCSM  (30) , and the number of interatomic contacts of the native residue using RING (31) was calculated. For both computations, the 3D structure of the SET domain of KMT5B in complex with S-adenosyl-L-methionine [Protein Data Bank code: 3S8P (  58 )] was used. The structure representations were created with PyMOL (59). Structural models for the variants were obtained using MODELLER  (60).
html; downloaded 7 September 2018) using TopHat v2. 1. 0 (Bowtie2 v2. 2. 6), followed by transcript assembly using cufflinks v2. 2. 1. Transcript differences were assessed using cuffdiff on merged transcriptome replicates by genotype. Data visualization and mining were performed using cummeRbund for R (v3. 5). Gene orthologs (human and mouse) were identified using the Ensembl release 106 Biomart tool. Gene set enrichment analyses were performed using QIAGEN Ingenuity Pathway Analysis (IPA; v01-20-04) where noted. We also compared our up-and down-regulated mDEG and hDEG gene lists to annotated autism risk genes from the SFARI Gene Database (downloaded 31 March 2021; n = 1003) that were performed using R v 4. 1. 1. For comparison, 10 6 random sets of genes of the same relative sizes of the test and SFARI dataset were sampled.
Statistical analysis Statistical analyses have been described in the above Materials and Methods sections. To compare the clinical characteristics in males and females, we performed Fisher's exact tests. For the RNA-seq results, we used Bioconductor DESeq2 to compare expression levels in the human samples and cuffdiff to compare transcript levels in the mouse samples. Raw P values were FDR-corrected for both datasets. When comparing the growth of the cell lines, we used Student's t tests. The size of the mouse brains were compared using a one-way ANOVA with Tukey's correction.
After counting the colonies, the plating efficiency (PE) and the survival fraction (SF) were calculated according to the following formulas: The survival fraction of cells has been reported to control cells (non-irradiated). The surviving fraction (SF) was fitted with the linear-quadratic model (ln(SF) = -(αD + βD 2 )) for X-ray and the linear model (ln(SF) = (a*D)) for carbon ions using the non-linear regression tool of SigmaPlot 15 (Systat Software GmbH, Erkrath, Germany)  43. Micronucleus assay. After irradiation, for each experimental condition, the cells were detached, counted and seeded at 10 4 density on 10 mm coverslips. Cytochalasin B (Sigma-Aldrich Chemie GmbH, Burlington, MA, United States), at a concentration of 3 μg/mL in culture medium, was added 20 h prior to fixing the cells.
Acknowledgements The in vitro experiments were done at  IFIN-HH  and  CEA  for which we acknowledge the  Romanian Ministry of Research, Innovation , and Digitalization through:  543PED/2019  and  PN 23210202 F1P1. Irradiations with carbon ions were performed at  Ganil France  using beam time obtained under the experiments nr P1304-H of the iPAC 2021 call. Hyperspectral and 3D imaging on CytoViva equipment was possible to be done in UPB due to European Regional Development Fund through  Competitiveness Operational Program 2014-2020, Priority axis 1 , Project No. P_36_611 ,  MySMIS  code  107066 ,  Innovative Technologies for Materials Quality Assurance in Health, Energy and Environmental-Center for Innovative Manufacturing Solutions of Smart Biomaterials and Biomedical Surfaces-INOVABIOMED.
10. 1029/2023GL104631 2 of 12  Pollack & Smerdon, 2004). In contrast, proxy-based methods mostly indicate a long-term cooling from CE 1000 to around CE 1700-1800  (Klippel et al. , 2020; Mann, 2021; PAGES 2k Consortium, 2013 , 2019). The global geothermal climate database was originally compiled with the International Heatflow Commission (IHFC)  (Huang & Pollack, 1998; Huang et al. , 2000). It is a database of opportunity that has been compiled from measurements taken over several decades with different instrumentation, in varied settings and with no commonly adopted protocol or quality control. It is also relatively clustered spatially. Around half of sites (471 out of 1012) were logged before CE 1985, with profile tops mostly 20 m or deeper (see Figure  1 ), meaning the observed post CE 1970 surface air temperature rise may not be recorded.
There are also potentially non-climatic influences which have been investigated with modeling, direct observations and comparisons with instrumental data  (Bartlett et al. , 2006; Beltrami et al. , 2005; Cermak et al. , 2017; Gonzàlez-Rouco et al. , 2006 , 2009; Majorowicz & Skinner, 1997; Melo-Aguilar et al. , 2018 , 2020; Skinner & Majorowicz, 1999). This means that the global database is potentially subject to variable and unquantified levels of measurement error and non-climatic influence, termed as noise hereafter (see also Text S1 in Supporting Information S1). Partly for this reason but also because the ill-posed nature of the inverse problem  (Menke, 1989) , a smoothing operator is required to prevent over-fitting. Without this, a reconstruction with unrealistically large swings in temperature could result.
Although there are empirical approaches to choose this smoothing, there is no single value that can apply equally well in the range of situations encountered. We identify four main reasons for studying this data set here: (a) spatial variability in the geothermal reconstruction has not been evaluated or attributed in detail (e. g. ,  Beltrami & Bourlon, 2004; Huang et al. , 2000) ; (b) it remains unclear why proxy-based reconstruction show cooling until Industrialization (e. g. ,  Esper et al. , 2012; Klippel et al. , 2020; Mann, 2021; Neukom et al. , 2019)  whereas geothermal reconstruction show the opposite (e. g. ,  Cuesta-Valero et al. , 2021; Masson-Delmotte et al. , 2013; Pollack & Smerdon, 2004) ; (c) it is unclear whether the heterogeneity in the geothermal database has been fully accounted for and for example, whether noisier data have caused reconstructions to be smoother than necessary  (Clow, 1992; Hopcroft et al.
, 2007) ; and (d) 91 profiles have been added to the IHFC database since CE 2000, with recent additions in Australia which was otherwise dominated by much older measurements (Figure  1 ). For these reasons we have developed a Bayesian hierarchical model to infer past surface temperature histories from the global geothermal database  (Huang & Pollack, 1998). This approach allows uncertainties to be robustly treated in a probabilistic framework  (Denison et al. , 2002). Both the observational noise level at each site and the required smoothing of the climatic history are inferred directly from the data. A key advantage of this is that noisier data are given less weight in the final result and data which can support more detailed climate histories are not subject to excessive smoothing. We applied this method to the available global IHFC database and evaluate spatial variability and uncertainty in the resultant reconstructions.
Model Setup We use 1012 profiles in the University of Michigan/International Heatflow commission database  (Huang & Pollack, 1998) , see http://heatflow. world/project (IHFC henceforth). Our global analysis is performed on a 5° × 5° latitude/longitude grid shown in Figure  1. Given the heterogeneous distribution of sites, this results in 172 occupied grid cells. A one-dimensional finite element heat conduction model  (Lewis et al. , 1996)  is used to calculate the temperature-depth profile at all 1012 sites over time  (Hopcroft et al. , 2007). The model starts at 1400 CE and runs forward in time with a 5-year timestep up to the timestep that is closest to the latest date of logging among the profiles that are contained within each grid cell. The spatial discretization uses the depths of the site measurements, with five padding layers above the shallowest measurement and a total of 480 elements vertically.
, 2000). This contrasts with the results for the Northern Hemisphere and is most likely because of the inclusion of a large number of new profiles in the IHFC database in recent years as shown in Figure  1. The last 35 years of the SH reconstruction shows a trend that does not agree with the preceding decades or observations. This is because the number of sites included in the average is very low and is therefore biased toward the results in few gridcells. The alternative prior makes very little difference to the results for the Southern Hemisphere. In contrast with the North, over the Southern Hemisphere there is no two-phase warming over the 20th Century, in agreement with the observed temperature record. We performed three additional tests to isolate the impact of the developments made in this study.
Data Availability Statement Code developed for this study is available from https://dx. doi. org/10. 5281/zenodo. 7108225. This comprises shell scripts for downloading the IHFC database and processing the files and the Bayesian hierarchical model written in C++. NetCDF files containing the posterior mean and credible intervals of the reconstructed ground surface temperatures on the 5° × 5° grid are available from https://doi. org/10. 5281/zenodo. 7761459 for all cases discussed in this work. The IHFC borehole database is available from https://geothermal. earth. lsa. umich. edu or from the US National Oceanographic and Atmospheric Administration https://www. ncei. noaa. gov/products/ paleoclimatology/borehole. The PAGES2k v2. 0. 0 global temperature field reconstructions are available from https://doi.
One of the major challenges for large-scale radio surface arrays, such as the Giant Radio Array for Neutrino Detection (GRAND), is the requirement of an autonomous online trigger for radio signals induced by extensive air showers. The NUTRIG project lays the foundations for the development of a pure, efficient, and scalable trigger in the context of GRAND. For this purpose, a GRAND prototype setup of four detection units has been deployed at Nançay, France, which currently serves as the main testing facility for the deployment of this autonomous trigger. This work provides a detailed description of the GRAND@Nançay setup, and a first analysis of background data gathered on site. Initial tests of signal recovery in laboratory conditions are also presented. Finally, near-future plans are outlined to scale NUTRIG to larger pathfinder arrays such as GRANDProto300.
Acknowledgments This work is part of the  NUTRIG  project, supported by the  Agence Nationale de la Recherche  ( ANR-21-CE31-0025 ;  France ) and the  Deutsche Forschungsgemeinschaft (DFG ;  Projektnummer 490843803 ; Germany). In addition, this work is supported by the  CNRS Programme Blanc MITI  ( 2023. 1 268448 GRAND ;  France ) and the  Programme National des Hautes Energies (PNHE; France)  of  CNRS/INSU  with  INP  and  IN2P3 , co-funded by  CEA  and  CNES. Computations were performed using the resources of the  CCIN2P3 Computing Center (Lyon/Villeurbanne, France) , a partnership between  CNRS/IN2P3  and  CEA/DSM/Irfu.
Data Selection As a first step, observations of PSP between 2018 January 1 and 2022 October 1 were collected, encompassing the first 13 perihelia (E1 -E13) of the PSP mission. Level 2 magnetic field data from the fluxgate magnetometer (FGM;  Bale et al. 2016) , as well as Level 3 plasma moment data from the Solar Probe Cup (SPC) for E1-E8 and the Solar Probe Analyzer (SPAN) from the Solar Wind Electron, Alpha and Proton (SWEAP) suite for E9-E13  (Kasper et al. 2016) , were analyzed. Data from the SCaM product  (Bowen et al. 2020 ) obtained during E1 have also been analyzed and will be presented as a high-quality case study. The plasma data consist of moments of the distribution function computed on board the spacecraft, including the proton velocity vector V p , number density n p , and temperature T p.
When available, electron number density data derived from the quasi-thermal noise from the FIELDS instrument  (Moncuquet et al. 2020)  were preferred over SPAN or SPC data for estimating proton number density. In order to calculate the proton density from the electron density, charge neutrality must be considered, leading to a ≈4% abundance of alpha particles. Therefore, electron density from quasi-thermal noise was divided by 1. 08. The second step involved obtaining magnetic field and particle measurements from the SO mission between 2018 June 1 and 2022 March 1. Magnetic field measurements from the Magnetometer (MAG) instrument  (Horbury et al. 2020 ) have been analyzed using burst magnetic field data when available. Particle moment measurements for our study are provided by the Proton and Alpha Particle Sensor (SWA-PAS) on board the Solar Wind Analyser (SWA) suite of instruments (Owen et al.
Data Processing Quality flags for the magnetic field and particle time series have been taken into account, and time intervals missing 1% and/or 10% in the magnetic field and particle time series have been omitted from further analysis. Additionally, the mean value of the cadence between successive measurements δτ in the magnetic field time series has been estimated for each of the selected intervals, and time intervals that were found to have a mean cadence of δτ 250 ms were discarded. Due to poor data quality, all PSP intervals exceeding R ; 0. 5 au have also been discarded. Spurious spikes in the plasma time series were eliminated by replacing outliers exceeding three standard deviations within a moving average window covering 200 points with their median values  (Davies & Gather 1993).
Case Study: SCaM Data Set, E1 The high-resolution data from the first perihelion of the PSP (R ≈ 0. 17 au) from 2018 November 1 to 11 were analyzed. A total of 33 intervals, each with a duration of 12 hr, were obtained, and the PSD was estimated, with subsequent intervals overlapping by 50%. The analysis covered 18 bins of the θ BV angle, but in the following we will only focus on those bins closer to the parallel and perpendicular directions, with θ BV 5°a nd θ BV 85°, respectively. It is worth noting that the second half of E1 displayed significantly different characteristics compared to the first half, with the solar wind exhibiting higher speeds and a greater number of magnetic switchbacks  (Bale et al. 2019). It is well established that power spectra exhibit different characteristics when different solar wind speeds are considered owing to the different types of fluctuations they transport  (Borovsky et al.
49 and -1. 55, is observed. This suggests that the MHD range spectral index of the perpendicular spectrum for fast streams may not evolve in a consistent manner with increasing distance in the inner heliosphere. It is worth noting, however, that within 0. 1 au only four intervals with V sw 400 km s -1 were sampled by PSP. More data from fast streams near the Sun are needed to statistically confirm these findings. For parallel fluctuations, the inertial range scaling remains remarkably similar across all heliographic bins, with the spectral index progressively steepening toward smaller scales from -5/3 toward -2, where a narrow range of scales over which the local spectral index obtains a constant value appears. In contrast to slow wind streams, the high-frequency point in fast wind streams does not remain anchored in a normalized wavenumber but gradually drifts toward larger scales with distance.