dataesr/scientific-paragraphs-categorization huggingface dataset:
Overview of ex vivo diffusion MRI diffusion data processing for tractography We processed the ex vivo diffusion datasets for deterministic and probabilistic tractography analyses to generate insights into both qualitative anatomic relationships of dAAN tracts and quantitative dAAN connectivity properties. Deterministic tractography yields more visually informative qualitative data about the neuroanatomic relationships between tracts (e. g. , sites of tract branching and crossing)  (54, 151) , whereas probabilistic tractography provides a more reliable quantitative measure of connectivity between nodes because it accounts for uncertainty in tract trajectories and can detect tracts on and past grey matter boundaries (177). Diffusion data from all three specimens were processed for deterministic fiber tracking, and diffusion data from specimens 2 and 3 were processed for probabilistic fiber tracking.
At the completion of this process, we updated the Harvard Ascending Arousal Network Atlas and now release version 2. 0 of the atlas (doi:10. 5061/dryad. zw3r228d2; download link: https://datadryad. org/stash/share/aJ713eXY12ND56bzOBejVG2jmOFCD2CKxdSJsYWEHkw).
Acknowledgements: We dedicate this work in memoriam to  Marty Samuels, MD , the  Miriam Sydney Joseph  Distinguished Professor of Neurology at  Harvard Medical School  and Founding Chair of the  Department of Neurology at Brigham and Women's Hospital. Dr. Samuels  mentored generations of neurologists, neuroimagers, and neuropathologists, of which we were fortunate to be a part. Dr. Samuels  was an avid and insightful student and teacher of neuroanatomy and neuropathology of human consciousness, from whom we learned much. He was an early supporter of the histologic-radiology correlation studies performed here, for which we are grateful. We are grateful to the families for the brain donation of their loved ones, and to the individuals who donated their own brains for this study to advance medical science.
Data and materials availability: All ex vivo diffusion MRI data, along with the node regions of interest used for the connectivity analyses, are available at (https://openneuro. org/datasets/ds004640/versions/1. 0. 1). All histology and immunostain data are available at (https://histopath. nmr. mgh. harvard. edu). Version 2. 0 of the Harvard Ascending Arousal Network Atlas is available at Dryad Data Repository (doi:10. 5061/dryad. zw3r228d2; download link: https://datadryad. org/stash/share/aJ713eXY12ND56bzOBejVG2jmOFCD2CKxdSJsYWEHkw). The minimally preprocessed in vivo fMRI data are available at https://db. humanconnectome. org, and our processed fMRI data are available at https://openneuro. org/datasets/ds003716/versions/1. 0. 0. All code used to process the ex vivo MRI data are available at https://github.
imaging (DW-SE-EPI) sequence, with 60 different diffusion-weighted measurements at b = 4057 s/mm 2. One dataset of 3D DW-SE-EPI data with b = 0 s/mm 2 was also acquired to calculate quantitative diffusion properties in each voxel. Additional sequence parameters were: gradient strength = 12. 4 G/cm, duration ∂ = 13. 4 msec, intertemporal pulse offset ∆ = 25 msec, repetition time (TR) = 1000 msec, echo time (TE) = 72. 5 msec, and spatial resolution = 562 x 609 x 641 µm. Total diffusion scan time was 2 hours and 10 minutes.
A burst in transposon activity preceded adaptive radiation We manually characterised transposable elements (TEs) in the genome following a protocol 18 for TE classi cation that characterises TEs through a combination of homology-based prediction and manual inspection to nd structural motifs and de ne sequence boundaries. To this end, we created the rst species-speci c database of TEs of an orchid genome, containing a total of 436 novel sequences speci c to O. sphegodes. Using this database, we identi ed a total of 4. 05 Gb repetitive elements, occupying 78. 06% of the O. sphegodes genome (Fig. 1c , Supplementary Table  11 ). Overall, O. sphegodes represents the largest orchid genome assembled to date and exhibits the highest abundance of long terminal repeat (LTR) elements (74. 57% of the genome), more than Vanilla planifolia (10.
We used Genotype-By-Sequencing (GBS) data to investigate the genetic differentiation between species and scan the genome for signatures of selection. Chord between-population genetic distance 66 was calculated per 1 Mb window to identify the most similar/dissimilar species at a given chromosomal region in the O. sphegodes genome. This analysis revealed that segregating polymorphisms between species are distributed across the genome, and that overall genetic (dis)similarity between O. sphegodes and the three other species is roughly equal (Fig. 4a , Supplementary Fig. 12 ), as may be expected in a species radiation. Yet cumulatively, the four species were clearly separable in a principal coordinate analysis (PCoA; Fig. 4b ). These ndings are in line with previous population analyses suggesting that many polymorphisms in the genome are shared among all species 11 , whilst few barrier loci may separate them.
Furthermore, global F ST outlier analysis (FDR < 0. 01) of a GBS dataset from 126 individuals  11  revealed highly differentiated regions in chr 2 (327-346 Mb; Fig. 1c  and 4c ). Interestingly, in contrast to the genome-wide pattern, especially O. sphegodes and O. exaltata are clearly separated in the highly differentiated c. 20 Mb region on chr 2, as seen by PCoA of this region (Fig. 4d ). Whether this pattern is due to divergent selection and suppression of effective recombination via e. g. , divergence hitchhiking or an inversion, is currently unknown. Overall, using the metaphor of genomic islands of speciation 67 , these results suggest that adaptation involving individual loci (or small genomic segments) rather than entire genomes, characterises these reproductively isolated sympatric species.
Hi-C library preparation and assembly scaffolding A total of 0. 5 g leaf tissue were xed in nuclei isolation buffer and 36% formaldehyde, followed by cell lysis, chromatin digestion with the enzyme HindIII-HF (New England Biolabs, Ipswich, MA, USA, R3104L), re-ligation, DNA re-extraction and library preparation as described  78, 79. Four Hi-C libraries were sequenced on an Illumina NovaSeq 6000 SP FlowCell. To assemble the chromosomes, raw Hi-C data was mapped against the genome assembly using the ArimaGenomics mapping script (https://github. com/ArimaGenomics/mapping_pipeline/blob/master/01_mapping_arima. sh). Subsequently, Salsa v2. 3  80, 81  was used to build scaffolds (total scaffolds 2 511, N50 = 4. 8 Mb); Juicebox Assembly Tools (JBAT)  82  and the 3D-DNA pipeline v-180114 83 were used to order scaffolds to chromosomes.
Gene and non-coding RNA annotation Gene annotation was carried out via an integrative approach that included de novo gene prediction, homology-based prediction, and transcriptome-based prediction. AUGUSTUS was used as part of the BRAKER2 pipeline  84  and trained with RNA-seq data  13  from oral tissues to predict coding regions in the repeat-masked genome. ProtHint (https://github. com/gatech-genemark/ProtHint) was used to generate protein hints using the protein database liliopsida_odb10-v. 2020-09-10 from OrthoDB10, to score intron intervals, start and stop codons from ultra-conserved proteins of the monocot lineage. This extrinsic evidence was given to GeneMark 85 to self-train, and improve prediction accuracy of AUGUSTUS. Proteomes from P. equestris  20  , C. goeringii 86 , O. sativa ssp.
japonica cultivar Nipponbare IRGSP-1. 0 87 , and A. o cinalis Aspof. V1 88 were downloaded and used as input for GeMoMa 89 for homology-based prediction. The transcriptome of O. sphegodes  13  was mapped against the genome using PASA (Program to Assemble Spliced Alignment) https://github. com/PASApipeline/PASApipeline/blob/master/docs/index. asciidoc). Valid transcript alignments were clustered based on genome mapping location and assembled into gene structures. PASA assemblies were incorporated into gene predictions to correct exon boundaries, add UTRs, and update gene structures. AHRD (https://github. com/groupschoof/AHRD) was used to lter genes based on SwissProt, trEMBL, and TAIR10 databases, and candidate genes for pollinator attraction from previous studies were manually included. Functional annotation was carried out using TRAPID  90  for Gene Onthology (GO) terms, InterPro-Scan, KEGG, and iTAK Classi er  91  for transcription factors and protein kinases.
Repetitive element annotation We used a published approach 18 to classify and manually annotate repetitive elements in the genome. Putative TEs identi ed with RepeatModeler v2. 0. 1 were blasted against the TRansposable Elements Platform (TREP) database (v. 2019, https://trep-db. uzh. ch/). Sequences with strong hits to a known family were selected (plus their anking regions) and blasted against the O. sphegodes genome to identify other copies belonging to the same TE family. Multiple sequence alignments between those clusters of sequences were performed to create consensus TE sequences, and to search for structural motifs that could help classify them. Sequences with no strong hits to a known family were blasted against PTREP (Protein TRansposable Elements Platform) or Dfam (https://dfam.
Analysis of LTR divergence and insertion time LTR_harvest  93  was used to identify LTRs with complete structure (-xdrop 37 -motif tgca -motifmis 1minlenltr 100 -maxlenltr 3000 -mintsd 2), expected to be detectable for young LTR copies. The insertion time was estimated based on the sequence divergence between two LTRs of the same type. 5' and 3' LTR of each full-length paralogue were aligned using Mafft  94  and divergence between them was estimated using the Kimura two-parameter model using the distmat program implemented in the EMBOSS package (https://emboss. sourceforge. net/). LTRs insertion age was estimated using a substitution rate of 1. 3 × 10 - 8 substitutions per site per year  95.
We considered 83. 78 Ma and 114. 92 Ma as the minimum-and maximum-age calibrations of the stem age of orchids  99. We used an optimal smoothing parameter determined by the "random subsample and replicate" cross-validation method to accommodate rate heterogeneity. A total of 1000 bootstrap replicates with the topology xed to the concatenated ML tree and branch lengths allowed to vary were also generated using RAxML for calculating the con dence intervals of age estimates. Results from the dating of the bootstrapped trees were then summarised and visualised on the concatenated ML tree using TreeAnnotator (part of the BEAST2 package 100 ). We used CAFE v5. 0. 0  44  to identify gene family contractions and expansions, using the reconstructed species tree. Only gene families were kept in which among-species difference in size was less than 100.
First, nucleotide sequences of the previously identi ed single-copy orthologues were translated into amino acids with TranslatorX v1. 1 within the MitoPhAST-master v3. 0 package 101 , then given as input for MUSCLE v5. 102 for multiple sequence alignment (MSA). The resulting MSAs were trimmed with trimAL v1. 2 103 (parameter -automated1 to select optimal cut-off based on alignment similarity) to remove poorly aligned regions or spurious sequences, and the best-t model for each tree was computed with ModelTest-NG v0. 1. 7 104 (parameters -f ef -h f -s 3 -tr phyml). The nal trees were built with phyml v3. 3. 20220408 (parameters -d nt -c 4 -m AIC -f modelfreq -o modelparam) (https://github. com/stephaneguindon/phyml) using the parameters from ModelTest-NG and 1001 bootstrap replicates.
Funding This was supported by the  University of Zurich , a Ph. D. project funded by the  University Research Priority Program "Evolution in Action: from Genomes to Ecosystems"  (to UG and PMS), the  Swiss National Science Foundation (SNSF ; grant  31003A_155943  to PMS and grant  IZLRZ3_163885  to UG), the  German Research Foundation (DFG ; project  446145319  to PMS) and the  2022 PRIN Program  (to SC). This work made use of infrastructure services provided by the  Science IT team of the University of Zurich  (www. s3it. uzh. ch) and was supported via bwHPC by the  High Performance and Cloud Computing Group at the Zentrum für Datenverarbeitung of the University of Tübingen , state of  Baden-Württemberg , and the  DFG  (grant  INST 37/935-1 FUGG ).
Data availability The genome assembly and raw sequencing data generated for this study, including ONT data, Illumina data and HiC data, were submitted to NCBI under BioProject number PRJNA994461 [@Reviewers, we are in the process of completing the NCBI submission under this accession number]. The RNA-seq data used for genome annotation can be found on NCBI under accession PRJNA574279  13. The GBS data used for population genetic analysis can be found in the NCBI accession PRJNA257331  11. Chromosome comparison shows a high degree of collinearity and some chromosome rearrangements (see also Supplementary Fig. 9 ).
The analytical models used to model the cumulative infiltrations are described in Sect. 3. The BC, vGM, and KG models were fitted to the hydraulic functions of the loamy soil, as defined by  Carsel and Parrish (1988) , before computation of sorptivity to investigate the impact of the choice of the hydraulic models on sorptivity estimation and related impact on cumulative infiltrations. The different synthetic cumulative infiltrations were discussed with regard to the sorptivity estimation method, the choice of hydraulic models for the WR and HC functions, and the choice of the analytical model. All the computations were performed using Scilab software  (Campbell et al. , 2010)  and are available on Zenodo  (Lassabatere, 2022). https://doi. org/10. 5194/hess-27-895-2023 Hydrol. Earth Syst. Sci.
Illustrative example of computation of sorptivity and dependency upon initial and final conditions The studied synthetic loamy soil was defined by  Carsel and Parrish (1988)  considering the vGM hydraulic model (Eq. 28) with the hydraulic parameters tabulated in Table  2  (column "vGM"). In addition, we used the BC and KG models (Eqs. 26 and 29), with the parameters tabulated in Table 2 (column "BC" and "KG"). Related hydraulic parameters were optimized by fitting BC and KG models to the vGM model and are available in the HYDRUS software suite (Radcliffe and Simunek, 2010). Figure  7  depicts the water retention curve (Fig. 7a ) and unsaturated hydraulic conductivity (Fig. 7b ) and demonstrates the proper alignment of BC and KG models on the vGM model, despite the slightly greater discrepancy for the BC model close to saturation for the hydraulic conductivity (Fig.
7b ). At first, we illustrate the use of the mixed formulation, S M , for the computation of sorptivity for a final water pressure head at the surface of h f = -150 mm and an initial water pressure head of h i = -10 4  mm = -10 m. This initial condition corresponds to the isostatic water pressure head in equilibrium with a water table positioned at 10 m below the ground. The value of -150 mm, considered at the surface, is often used for water infiltration with tension disk experiments to deactivate the macropores and infiltrate only into the matrix (e. g. ,  Timlin et al. , 1994; Malone et al. , 2004; Lassabatere et al. , 2014). Practically, the computation of di-mensional sorptivity with the proposed procedure S M can be performed using the codes developed for this paper and downloadable from Zenodo  (Lassabatere, 2022).
7d , plateaus with dashed lines). Note for vGM and KG models, the airentry water pressure head is zero, then the plateaus begin at h f = 0, whereas the air-entry water pressure head is lower than zero for the BC model, h a = -111. 5 mm, inducing an earlier beginning of the plateau (Fig. 7d , BC versus vGM and KG). Another interesting point is the difference between the three models. The BC model exhibits much larger sorptivity values, followed by the vGM model and then the KG model. However, the three models are meant to represent the same water retention and hydraulic conductivity functions, i. e. , the same soil. That result shows that the selection of the hydraulic model for the water retention and hydraulic conductivity functions substantially impacts the value of sorptivity, even when the models are fitted to the same data and represent the same soil.
High -resolution climate projections are mandatory for many applications and impact assessments in environmental and management studies. In response to the needs in Vietnam, this study constructs a new precipitation and temperature daily dataset for Vietnam, at a high spatial resolution of 0. 1° × 0. 1°, based on the outputs of 35 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The Bias Correction and Spatial Disaggregation (BCSD) method is adopted to bias-correct monthly GCM simulations using observation data, then subsequently temporally disaggregate them into daily data. The new dataset is called CMIP6-VN, covering the present-time period 1980-2014 and future projections for 2015-2099 from both CMIP6 tier-1 (Shared Socioeconomic Pathways (SSPs) 1-1.
In the present study, we produce a new high-resolution (10-km) climate dataset for Vietnam by statistically downscaling 35 CMIP6 GCMs and eight SSPs scenarios. The Biases Corrected Spatial Disaggregation (BCSD) method  21, 22  was applied. It is noteworthy that we were able to collect daily temperature and rainfall observed station data in order to generate a gridded-based observation dataset over Vietnam, which served as important and mandatory inputs for the BCSD scheme. The new dataset, called CMIP6-VN, contains downscaled values of 4 variables, i. e. daily rainfall (pr), daily mean 2m-temperature (tas), daily maximum 2m-temperature (tasmax), and daily minimum 2m-temperature (tasmin) for the historical period 1980-2014 and the future period 2015-2099. CMIP6-VN is expected to be a valuable input with the most updated information for studies on climate change assessment and impacts in Vietnam.
Methods Study area. We focus on Vietnam in this study. Vietnam has a diverse topography spanning over a long range of latitudes, a long coastal line, and a monsoon-influenced climate. Based on specific criteria for radiation, temperature, and rainfall  23, 24  , Vietnam is divided into seven climatic regions, including (1) the Northwest (denoted as R1), (2) the Northeast (denoted as R2), (3) the Red River Delta (denoted as R3), (4) the North Central (denoted as R4), (5) the South Central (denoted as R5), (6) the Central Highland (denoted as R6), and (7) the South region (denoted as R7) (Fig. 1 ). In this study, the CMIP6-VN dataset is developed for the inland territory of Vietnam.
Data acquisition. To guide the statistical downscaling process, we collected daily-observed precipitation (pr) and near-surface temperatures (daily average tas, daily maximum tasmax, and daily minimum tasmin) from 481 and 147 stations, respectively, of the Vietnam Meteorological and Hydrological Administration (VMHA) for the period 1980-2014 (Fig. 1 ). The data underwent prior verification by VMHA's Documentation Center, following Fig. 1  The study domain Vietnam and its 7 climate regions. Green and blue dots show the locations of 157 temperature and 481 precipitation stations, of which the data are used in this study. The red circles indicate 12 random stations selected for the validation of the OBS gridded dataset. The topography over Vietnam (shaded, in m) is obtained from the 5-minute Gridded Global Relief Data (ETOPO5)  46.
established operational processes. We subsequently applied the three-sigma (five-sigma) rule to identify any suspect values in the temperature (rainfall) data and re-examined each identified case. Monthly rainfall and near-surface temperatures from 35 CMIP6 GCMs (Table  1 ) are downscaled for the historical period 1980-2014 and future period 2015-2099 under the eight SSPs 1-1. 9, 1-2. 6, 2-2. 5, 3-4. 0, 3-7. 0, 4-3. 4, 4-6. 0, 5-3. 4, and 5-8. 5. The CMIP6 GCMs data are acquired via the Earth System Grid Federation website (ESGF, https://esgf-node. llnl. gov/projects/cmip6/). Building the gridded observation dataset. We interpolated the observed station data of rainfall and temperature into a 0. 1° × 0. 1° gridded dataset (hereafter called OBS) using the Spheremap  25  and Kriging 26 interpolation techniques, respectively.
94° × 0. 94°r1i1p1f1 x - x x - - - x MRI-ESM2-0 1. 13° × 1. 13°r1i1p1f1 x x x x x x x x NESM3 1. 88° × 1. 88°r1i1p1f1 x - x x - - - x NorESM2-MM 1. 25° × 0. 94°r1i1p1f1 o - o o o - - o TaiESM1 1. 25° × 0. 94°r1i1p1f1 o - - - o - - o UKESM1-0-LL 1. 88° × 1. 25°r1i1p1f2 x x x x x - - x Table  1. List of 35 CMIP6 GCMs and associated scenarios considered in this study. Note: x: available for both precipitation and temperature o: only precipitation -: not available The BC firstly applies the quantile mapping (QM) method  32  , which corrects the biases in the GCM data when compared to the OBS at the resolution of 1° × 1°. For each variable on each grid cell in each month, cumulative distribution functions (CDFs) for both the OBS and historical GCM data are separately generated. Transfer functions (TFs) that map the model CDFs onto the OBS CDFs are subsequently developed.
Then, the biases in the GCM monthly outputs are corrected by applying the TFs to transform the GCM data to the corresponding OBS data of the same CDF quantile. Those QM TFs are assumed to be stable through the historical and future periods and thus are applied to correct the future projected variables. For temperatures, after the QM step, the previously saved climatic trends are added back to the QM model data. Then, the climatological mean bias adjustment between the GCM and the OBS temperature at each grid point is subsequently applied, producing the BC temperature data at the intermediate resolution of 1° × 1°. In the SD step, the BC data are interpolated to the resolution of 0. 1° × 0. 1° following a three-step procedure: (1) bilinearly interpolating the additive (for temperatures) and multiplicative (for precipitation) change factors estimated between the BC GCM fields and the OBS climatology to the targeted high-resolution of 0.
The implementation of the BCSD approach in this study contains two main phases: -Phase 1 -Testing: the climatological fields of the training period 1980-2004 from the OBS and GCMs are used to develop the TFs between the simulations and observations. Then, the BCSD is applied to the independent (testing) period 2005-2014, and the results (hereafter called BCSD-CMIP6) are compared with OBS to examine the performance of the BCSD in reproducing past climate conditions. -Phase 2 -Future downscaling: To maximize the construction period of the BCSD approach  33, 34  , the total 35 years from 1980 to 2014 are used to generate the TFs and to guide spatial disaggregation for the future period 2015-2099. The BCSD is applied to all GCM models and SSPs scenarios listed in Table  1  to generate the targeted CMIP6-VN dataset.
Data Records The generated OBS and CMIP6-VN datasets are stored in the figshare repository  35. Both datasets include daily values of rainfall (pr) and temperatures (tas, tasmax, and tasmin) and cover the historical period of 1980-2014. In addition, CMIP6-VN provides future projections for 2015-2099 with the 7 SSPs. The datasets are in the Network Common Data Form (netCDF) classic format. For CMIP6-VN, one compressed tar file is created for each model, in which each variable for each historical/scenario period is saved in one netCDF file. The total data size for OBS and CMIP6-VN is 480 Mb and 76. 31 Gb, respectively.