dataesr/scientific-paragraphs-categorization huggingface dataset:
To train ab initio predictors, a user-defined number of models are randomly chosen in a user-defined ratio between (10%) mono-exonic and (90%) multi-exonic. These models were chosen from best classified models (gold and silver). For Augustus, we performed meta parameter optimization and train a model with kfold=8. Beside ab initio predictions, we used Augustus to predict gene models with three different weights for each evidence type as suggested by REAT authors (cf. code on GitHub). At last, we used Minos ("Minos -a Gene Model Consolidation Pipeline for Genome Annotation Projects" [2019] 2022) which is gene model consolidation pipeline and produces external metrics based on DIAMOND "BLASTp/BLASTx"  (Buchfink, Xie, and Huson 2015) , Kallisto  (Bray et al. 2016) (v0. 46. 2) expression quantification, coding potential calculator (CPC2 v0.
1)  (Kang et al. 2017, 2)  and BUSCO assessments. These metrices pass through Mikado in combination with various gene models produced with different methods (as mentioned above), Minos determines the best gene model for each region based on user defined criteria (for details, see GitHub) and external metrics. Minos also put a tag on each gene model to categorize them based on a user defined threshold (we used default values) for sequence similarity coverage of homologs, BUSCO score, CPC score, TPM expression, and transcript score into "high confidence", "low confidence", and "predicted genes".
Genome annotation assessment We used two methods to compare the quality of the new gene model with the published one. We compared the BUSCO scores of the annotated protein sequences as well as genome sequence using the reference "viridiplantae. odb. 10-2020-09-10" dataset. We also used maker  (Campbell et al. 2014) (v3. 01. 04)  to calculate the AED  (Eilbeck et al. 2009 ) to evaluate the agreement of the gene models with external evidences. Maker-P was used to build the M. endlicherianum gene model V1. Further, we used the maker package to perform functional annotation via InterProScan and BLAST using agat  (Dainat 2020 ) package (v0. 9. 2). Additionally, we performed a BLAST search against A. thaliana protein sequences (Araport11) and reported the best hit for each sequence in (Suppl. Table  11 ) and used eggNOGmapper  (Huerta-Cepas et al.
RNA-Seq analysis: Weighted gene co-expression network analysis We used WGCNA  (Langfelder and Horvath 2008; 2012)  package (v1. 71) with the expression table produced by limma. We checked for and filtered out outliers as suggested by WGCNA authors (Suppl. Fig. 10 ). Then, we visualized the scale free topology model fit (R 2 ) against the soft thresholds (ßs) to pick a ß for our network construction (Suppl. Fig. 11 ). We used signed network type and "bicor" as our correlation function for WGCNA. Based on these results, we picked 16 as our soft threshold 'ß'. We experimentally chose a merging threshold if 0. 25 after exploring different values from 0. 2 to 0. 4 and investigating the relationship between eigengenes and temperature, light intensity, F v F m , and absorption (Suppl. Fig. 12 ). We built the gene co-expression network using a merging threshold of 0.
25 for modules, maximum portion of outliers as 0. 05 and minimum module size of 30. Then, we visualized the correlation between each module eigengene and temperature, light intensity, F v F m , and absorption to identify which modules are more related to each treatment (Figure  4c ). We provided a table for all genes, their module assignment, inter-and intramodular connectivity, gene significance for temperature and light intensity, correlation with temperature and light intensity, and their module membership (aka. Signed eigengene-based connectivity) in (Suppl. Table  5 ). We also visualized the graphical representation of the topological overlap matrix of our samples (Suppl. Fig. 13 ). In order to have a visual representation of gene expression in each module, we drew heatmaps for each module via pheatmap (using Euclidean method for calculating the distance and complete method clustering) (Suppl.
Fig. 14 ). GO enrichment analysis was performed via clusterProfiler package  (Yu et al. 2012; Wu et al. 2021) (v4. 4. 4) using the output of eggNOGmapper and adjusted p-value cut-off 0. 05 and q-value cut-off of 0. 05, considering only genes that are present in our GO term-to-gene table which was expressed and passed filtering as our background gene universe (Suppl. Table  6 ). Determining the proper background gene list has significant importance in enrichment analysis  (Wijesooriya et al. 2022). To see how A. thaliana's well-known genes in stress-response mechanisms (downloaded from TAIR database via keyword search) were distributed across different modules we performed BLASTp searches against the new M. endlicherianum annotated proteins. We visualized the distribution of these IDs for different stress-related keywords in (Suppl.
Phylogenetic analyses We assembled a protein database based on the protein releases from the genomes of: Anthoceros agrestis BONN  (Li et al. , 2020) , Anthoceros punctatus  (Li et al. , 2020) , Amborella trichopoda (Amborella Genome Project, 2013), Arabidopsis thaliana  (Lamesch et al. , 2012) , Azolla filiculoides  (Li et al. , 2018) , Bathycoccus prasinos  (Moreau et al. , 2012) , Brassica oleracea  (Liu et al. , 2014) , Brassica rapa  (Wang et al. , 2010) , Brachypodium distachyon (The International Brachypodium Initiative, 2010), Capsella grandiflora  (Slotte et al. , 2013) , Chara braunii  (Nishiyama et al. , 2018) , Chlorokybus atmophyticus  (Wang et al. , 2020) , Chlamydomonas reinhardtii  (Merchant et al. , 2007) , Coccomyxa subellipsoidea  (Blanc et al. , 2012) , Gnetum montanum  (Wan et al.
Homologs for proteins were detected using BLASTp with Arabidopsis and Mesotaenium proteins as query against the aforementioned proteins as database. Alignments were computed using MAFFT v7. 490  (Katoh and Standley, 2013). All phylogenies were computed with IQ-TREE multicore version 1. 5. 5  (Nguyen et al. , 2015) ; their respective best model for protein evolution was determined using ModelFinder  (Kalyaanamoorthy et al. , 2017)  according to Bayesian Information Criterion and 1000 ultrafast bootstrap replicates; 1000 ultrafast bootstrap replicates  (Hoang et al. , 2018)  were carried out and 100 Felsenstein bootstraps  (Felsenstein, 1985)  for the LDAP phylogeny.
Differential interference contrast and confocal laser scanning microscopy Differential interference contrast (DIC) imaging was done for all replicates from the table with a Olympus BX-60 microscope (Olympus, Japan) with a ProgRes C14plus camera and the ProgRes® CapturePro Software (version 2. 9. 01) (JENOPTIK AG, Jena, Germany). The morphology of chosen conditions (see Supplemental Figure  1 ) of Mesotaenium cells that were 89 h on the table was analyzed. For algae that were used for quantifying the abundance of lipid droplet per cell, a ZEISS Axioscope 7 microscope (Carl Zeiss, Germany) was used including the ZEN software (Carl Zeiss, Germany). Lipid droplet count was carried out in FIJI  (Schindelin et al. , 2012). For statistical analysis of the lipid droplet count data, we first used a Shapiro-Wilk test  (Shapiro and Wilk, 1965)  to assess normality and used Mann-Whitney U tests  (Mann and Whitney, 1947)  with R (version 3.
1 % formic acid) and 4 % solvent B (80 % acetonitrile, 0. 1 % formic acid) to 10 % solvent B within 2 minutes, to 30 % solvent B within the next 58 min, to 45% solvent B within the following 22 min, and to 90 % solvent B within the last 12 min of the gradient. All solvents and acids had Optima grade for LC-MS (Thermo Fisher Scientific). Eluting peptides were on-line ionized by nano-electrospray (nESI) using the Nanospray Flex Ion Source (Thermo Fisher Scientific) at 1. 5 kV (liquid junction) and transferred into a Q Exactive HF mass spectrometer (Thermo Fisher Scientific). Full scans in a mass range of 300 to 1650 m/z were recorded at a resolution of 30,000 followed by data-dependent top 10 HCD fragmentation at a resolution of 15,000 (dynamic exclusion enabled). LC-MS method programming and data acquisition was performed with the XCalibur 4.
0 software (Thermo Fisher Scientific). Afterwards the raw proteome data were analyzed using Max Quant software version 1. 6. 2. 10 (Cox and Mann, 2008). The database for this analysis was our new V2 gene model data. The data were then further processed by the Perseus (1. 6. 2. 2) software  (Cox et al. , 2008; Tyanova et al. , 2016). Data availability All RNAseq reads have been uploaded to NCBI SRA and can be accessed under Bioproject PRJNA832564 and SRA accessions SRR18936040 to SRR18936170. Codes and Data used for genome re-annotaiton, WGCNA and differential gene expression analysis are available on our GitHub page https://github. com/deVrieslab/Response_to_a_gradient_of_environmental_cues_in_mesotaenium_endlicherianum. Proteomic data have been uploaded to PRIDE. Furthermore, data can be interactively explored at https://mesotaenium.
control). (c) Biological theme comparison summarizing all GO-term enrichment analysis with adjusted p-value ≤ 0. 01 of DEGs against all genes that were expressed and passed the filtering in our analyses as background. The size of each circle is proportional to the count of each GO-term. Only the top 30 enriched terms are shown. (d) Wordle of the 124 genes that showed significant regulation across multiple comparisons shown in Figure  3a ; word size correspond to the number of comparisons (based on (a) ) in which a gene appeared. Figure  4 : Unsupervised gene expression clusters recover genetic programs separated by environmental cues. Gene expression clustering into 26 colored modules was performed using WGCNA; grey is the module of unclustered genes. (a) Hierarchical cluster tree of 17,095 genes.
thaliana representative protein sequences. Bar charts show the percentage of detected Mesotaenium homologs across the modules relative to the number of all Arabidopsis IDs assigned to the terms. No blast hit was not depicted. Abbreviations: proc. = process; reg. = regulation; biogen. = biogenesis; develop. = development; pos. = positive; neg. = negative; init. = initiation; GEP = Gene expression profile; med. = mediated; dep. = dependent; modif. = modification; conjug. = conjugation; anneal. = annealing; compl. = complex; synth. = synthesis; resp. = response; transf. = transferring. Figure  5 : Molecular programs for environmental responses around recurrent plant hubs. Visualization of the co-expression networks clustered by WGCNA into the modules blue (3,101 genes), yellow (1,427 genes), green (1,220 genes), purple (506 genes), and pink (718 genes).
Data and methods The LoTSS, conducted by the LOFAR High-Band Antennae (HBA), is a 120-168 MHz interferometric survey  (Shimwell et al. 2017 (Shimwell et al. , 2019 (Shimwell et al. , 2022) )  with the ultimate aim to image the full northern sky at resolutions of 6 , 20 , 60 , and 90. Its central frequency ν c = 144 MHz. The latest data release -the LoTSS DR2  (Shimwell et al. 2022 ) -covers 27% of the northern sky, split over two regions of 4178 deg 2 and 1457 deg 2 ; the largest of these contains the Sloan Digital Sky Survey (SDSS) DR7  (Abazajian et al. 2009 ) area. By default, the LoTSS DR2 provides imagery at the 6 and 20 resolutions. We show these standard products in Fig. 2  for the same sky region as in Fig. 1. In terms of total source counts, the LoTSS DR2 is the largest radio survey carried out thus far: its catalogue contains 4.
) 11 The pysynch code is publicly available online: https://github. com/mhardcastle/pysynch. and B min,2 = 46 ± 3 pT. Assuming the equipartition condition  (Pacholczyk 1970) , we find equipartition pressures P eq,1 = 4. 9 ± 0. 3 × 10 -16 Pa and P eq,2 = 4. 9 ± 0. 6 × 10 -16 Pa for the northern and southern lobes, respectively. The corresponding equipartition magnetic field strengths are B eq,1 = 43 ± 2 pT and B eq,2 = 43±2 pT. The minimum-energy and equipartition results do not differ significantly. From pressures and volumes, we estimated the internal energy of the lobes E = 3PV. We find E min,1 = 6. 2 ± 0. 5 × 10 52 J, E min,2 = 4. 3 ± 0. 6 × 10 52 J, E eq,1 = 6. 3 ± 0. 5 × 10 52 J and E eq,2 = 4. 4 ± 0. 6 × 10 52 J. Next, we could bound the ages of the lobes from below by neglecting synchrotron losses and assuming that the jets have been injecting energy in the lobes continuously at the currently observed kinetic jet powers.
Conclusion In this work, we have presented the discovery of a radio galaxy of at least 5 Mpc. We have measured its key physical characteristics, and explored possible reasons for its exceptional growth. 1. We reprocessed the LoTSS DR2, the latest version of the LOFAR's northern sky survey at 144 MHz, by subtracting angularly compact sources and imaging at 60 and 90 resolution. The resulting images  Oei et al. (in prep. ) allow us to explore a new sensitivity regime for RG lobes and thus represent promising data to search for unknown GRGs of large angular length. We will present a sample in forthcoming work. 2. We have discovered the first 5 Mpc GRG, which we dub 'Alcyoneus'. The projected proper length is l p = 4. 99 ± 0. 04 Mpc, while the true proper length is at least l min = 5. 04 ± 0. 05 Mpc.
Acknowledgements. M. S. S. L. Oei  warmly thanks  Frits Sweijen  for coding the very useful https://github. com/tikk3r/legacystamps. M. S. S. L. Oei ,  R. J. van Weeren  and  A. Botteon  acknowledge support from the  VIDI research programme  with project number  639. 042. 729 , which is financed by The  Netherlands Organisation for Scientific Research (NWO). M. Brüggen  acknowledges support from the  Deutsche Forschungsgemeinschaft  under  Germany's Excellence Strategy -EXC 2121 'Quantum Universe ' -390833306. W. L. Williams acknowledges support from the  CAS-NWO  programme for radio astronomy with project number  629. 001. 024 , which is financed by The  Netherlands Organisation for Scientific Research (NWO). The LOFAR is the Low-Frequency Array designed and constructed by  ASTRON. It has observing, data processing, and data storage facilities in several countries, which are owned by various parties (each with their own funding sources), and which are collectively operated by the  ILT Foundation  under a joint scientific policy.
The Pan-STARRS1 Surveys ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No. NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  Grant No.
BASS is a key project of the  Telescope Access Program (TAP) , which has been funded by the  National Astronomical Observatories of China , the  Chinese Academy of Sciences  (the  Strategic Priority Research Program  " The Emergence of Cosmological Structures " Grant #  XDB09000000 ), and the  Special Fund for Astronomy  from the  Ministry of Finance. The BASS is also supported by the  External Cooperation Program of Chinese Academy of Sciences  (Grant #  114A11KYSB20160057 ), and  Chinese National Natural Science Foundation  (Grant #  11433005 ). The Legacy Survey team makes use of data products from the  Near-Earth Object Wide-Field Infrared Survey Explorer (NEOWISE ), which is a project of the  Jet Propulsion Laboratory/California Institute of Technology. NEOWISE is funded by the  National Aeronautics and Space Administration.
In the present study, we capitalized on a large and longitudinal neuropsychological dataset gained from patients with a slow-growing tumor to test the hypothesis whereby structures shaping the right medial network may play an important role in the voluntary deployment of visuospatial attention. Overall, our results substantiated this contention, as surgical removal of structures in a location compatible with the SEF and CEF was specifically associated with difficulties in purposely exploring the contralesional space in a visual search task. As already suggested by Mesulam thirty years ago 1 , spatial neglect is better conceptualized as a multicomponent syndrome whose anatomical correlates may differ as a function of the used tasks that generally differ in terms of cognitive requirement  55.
To minimize the potential bias caused by abnormal lesion-related radiological signals, MRI datasets were registered to the MNI space using enantiomorphic normalization  71. This procedure was performed with the SPM12 (https:// www. fil. ion. ucl. ac. uk/spm/software/spm12/) Clinical Toolbox (https://github. com/ neurolabusc/Clinical)  72. The output resolution was 1*1*4 mm for FLAIR images and 1-mm isometric for 3DT1 images. As a first step, the tumors/resection cavities were semi-automatically drawn using MRIcron package (https://github. com/ neurolabusc/MRIcron) and further inflated by means of a three-dimensional smoothing procedure (3-mm full-width-at half-maximum [FWHM] Gaussian kernel with a threshold of 0. 3). The obtained masks were then binarized and inserted during the registration process (during enantiomorphic normalization, the area covered by a particular mask is replaced by the undamaged homologous area within the contralesional hemisphere).
In this study, we used the Matlab script originally coded by Zhang et al. 47  to perform all SVR-LSM analyses (https://github. com/yongsheng-zhang/SVR-LSM). Epsilon-SVR models with a radial basis kernel function (RBF) were used to estimate hyperplane. Hyper-parameters of SVR-LSM models were optimized using a grid searching procedure, in particular the cost (C) which corresponds to the penalty/ regularization parameter and gamma (γ) which represents the kernel coefficient. This optimization was performed by means of a 5-fold cross-validation procedure allowing to determine the combination of parameters that maximized prediction accuracy while maintaining a high level of reproducibility (see Zhang et al. 47  for a complete description of how model fit and reproducibility are estimated). In total, 66 couples of parameters were assessed for each behavioral measure of interest and for each time point i.
e. , A1, Δ1 and Δ2 (9 optimization procedures, sum-total), with C = [1 10 20 30 45 50] and γ = [0. 1 1 2 3 4 5 6 7 8 9 10]. Parameter assessment was done with a publicity available Matlab script ('svr_lsm_BasicScript_opt. m', https:// data. mendeley. com/datasets/2hyhk44zrj/2)  74 after it was checked for quality and modified for local use. Datasets associated with a poor prediction accuracy (r max < 0. 20) and/or an insufficient index of reproducibility (r max < 0. 90) were not eligible to subsequent SVR-LSM analyses. Prior to running SVR-LSM analyses, the DTLVC (direct total volume control) option was chosen to control for lesion volume directly to the lesion data (and not to the behavior performances). Note that this parameter was also taken into consideration during the optimization procedure of hyper-parameters.
A bootstrap permutation procedure (5000 permutations) was first used to estimate the significance of the voxels' feature weight. Then, a FDR correction  75  (q = 0. 05) (directly implemented in the script by Zhang et al. 47  ) was applied to threshold the resulting SVR-LSM p-maps. An extent threshold of 50 voxels was employed. Note that voxels were analyzed if they were lesioned in at least three patients. This rather low cut-off was originally selected to include structures relevant to spatial cognition, but typically less affected in the context of lower-grade glioma (i. e. , some regions of the parietal cortex  40  ). Results are reported within the AAL atlas  76. Tract-level disconnection analyses. To estimate the severity with which the main white-matter tracts were damaged by the surgical resection, we used the lesion quantification toolbox (LQT)  77  -a new Matlab toolbox that employs different population-based approaches to provide multiple measures of white-matter disconnection severity, including tract-level disconnection measures (Supplementary Note 3).
info, version 4. 6). In addition, the cerebral disconnections caused by the surgical procedure were estimated with LQT (see above). The patient benefited from an assessment of visuo-spatial attention in the context of both surgeries. Statistics for behavioral analyses. In this study, we used parametric statistics to assess between-group and within-subject differences (i. e. , t-tests, repeated-measure ANOVAs, and mixed ANOVAs); all these tests were two-tailed and a p-value of less than 0. 05 was considered as statistically significant. Post-hoc, pairwise multiple comparisons analyses were conducted with the Scheffé test which controls for the familywise error rate. Parametric statistics were preferred despite the typical nonnormality of neuropsychological data because of the large sample size and to allow the assessment of interaction effects.
Data availability The unthresholded SVR-LSM p-maps, the raw beta maps, the raw lesion maps, and the overlap maps generated in this study as well as the raw behavioral dataset are available without restriction at https://doi. org/10. 6084/m9. figshare. 16822306. v3  79. All raw behavior data are also provided in the Source data files. The 1065-HCP tractography atlas is available at https://brain. labsolver. org/. Source data are provided with this paper.
Code availability SVR-LSM analyses were performed with the Matlab code by Zhang et al. (https:// github. com/yongsheng-zhang/SVR-LSM). Optimization of SVR-LSM hyper-parameters was performed with the Matlab code by Wiesen et al. (https://data. mendeley. com/ datasets/2hyhk44zrj/2). Measures of disconnection severity were performed with Lesion Quantification Toolkit 77 (https://wustl. box. com/v/LesionQuantificationToolkit). Source data are provided with this paper.
Reconstructed forcing potential: stratospheric aerosol optical depth and radiative forcing We develop a primary forcing reconstruction for the 852/3 CE Churchill eruption using the EVA(eVolv2k) 550 nm stratospheric aerosol optical depth (SAOD) reconstruction  (Toohey and Sigl, 2017). Detailed explanations of the model selection and set-up are provided in Appendix A. We also generate a second SAOD reconstruction using the EVA_H model, which is an extension of the Easy Volcanic Aerosol Model (EVA;  Toohey et al. , 2016) , which accounts for the SO 2 injection latitude and altitude and is calibrated using a more extensive observational dataset than EVA  (Aubry et al. , 2020). The EVA_H reconstruction uses the same SO 2 mass as EVA, the latitude of Churchill (61. 38 • N) and an injection altitude of 31.
Climate model simulation Climate conditions were simulated using the Community Earth System Model version 1. 2. 2 (CESM;  Hurrell et al. , 2013). The ensemble simulation consists of 20 ensemble members performed to study the impacts of the 852/853 CE Churchill eruption on climate. To generate the ensemble members, initially a seamless transient simulation is run from 1501 BCE  (Kim et al. , 2021a)  with time-varying orbital parameters  (Berger, 1978) , total solar irradiance  (Vieira et al. , 2011; Usoskin et al. , 2014 Usoskin et al. , , 2016)) , greenhouse gases  (Joos and Spahni, 2008; Bereiter et al. , 2015)  and volcanic forcing from the HolVol v. 1. 0 (Sigl et al. , 2021)  and eVolv2k  (Toohey and Sigl, 2017)  databases. The necessary prescribed spatio-temporal distribution of volcanic sulfate aerosol for the simulations is generated using the EVA model  (Toohey et al.