dataesr/scientific-paragraphs-categorization huggingface dataset:
Evolution and DNAm We generated a dataset consisting of DNAm profiles of 15,456 DNA samples derived from 70 tissue types from 348 mammalian species using the mammalian methylation array  (5). We evaluated whether methylation levels of cytosines (CpGs) in DNA sequences that are conserved across species would allow us to construct what could be called a phyloepigenetic tree. To avoid potential confounding by different tissue types, we generated tissuespecific phyloepigenetic trees (Fig. 1B  and figs. S2 and S3). We defined the "congruence" between traditional phylogenetic trees and phyloepigenetic trees as the Pearson correlation coefficient between distances (branch length) based on phyloepigenetic trees and evolutionary distances in traditional phylogenetic trees. We observed high congruence (0.
DNAm networks relate to individual and species traits We used signed WGCNA, an unsupervised analysis  (33) , to cluster CpGs with similar methylation dynamics across samples into comethylation modules. We then summarized their methylation profiles as "module eigengenes. " The respective eigengenes of these modules were used to identify their potential correlations with various traits within and across mammalian species. Our data analysis proceeded in two sequential phases. First, we developed several comethylation networks using data from 11,099 DNA samples from 174 species (discovery dataset finalized March 2021). A eutherian network [network 1 (Net1)] was formed from 14,705 conserved CpGs using this dataset (Fig. 2A ). Later, we generated a second dataset of 4357 samples from 30 tissues of 240 mammalian species (174 new species and 66 that are represented in the discovery set), which were not used to define modules and were used as an independent validation set.
All eutherian modules were present in the independent validation dataset according to module preservation statistics (corKME)  (34) , validating the presence of these modules (corKME > 0. 43, P < 10 -22 ; median corKME = 0. 84) (fig. S5 ). These modules were designated with colors according to WGCNA convention (Fig. 2A ). The smallest module (lavenderblush3) consisted of 33 CpGs and the largest (turquoise) had 1864 CpGs. To characterize the 55 modules with respect to species characteristics (e. g. , maximum life span and average adult weight), module eigengenes were calculated in all samples (discovery and validation set combined, 331 eutherian species). Because information on taxonomic order, tissues, maximum life span, age, sex and adult weight of each species was available, we were able to assess whether any of the module eigengenes correlated with these traits.
Using the discovery dataset, we first trained a network (Net2) in both eutherians and marsupials based on only 7956 probes that are mappable to both. The color bands under the hierarchical tree reveal that all the Net1 modules were also preserved in Net2 (Fig. 2A ). Second, we selected CpGs in Net1 modules that were also mapped to marsupials or monotremes and confirmed that their eigengene relationships to primary traits were retained in these mammalian clades (table  S3 ). For example, the magenta module, which is related to blood in eutherians, was also found to be so in monotremes (table  S3 ), which confirms that the Net1 modules can indeed be applied to other mammalian clades by selecting probes that are also mapped to those clades. A functional enrichment study, accounting for the mammalian array background, revealed that the genes neighboring to module CpGs are implicated in many biological processes including development, immune function, metabolism, reproduction, stem cell biology, stress responses, aging, and various signaling pathways (Fig.
Third, we focused on specific nonoverlapping age groups: young animals (defined as age <1. 5 times the age at sexual maturity), middle-aged, and old (defined as age >3. 5 times the age at sexual maturity; fig  S19 ). Some of these regression models were further adjusted for average species weight (denoted LifespanAdjWeight). For brevity, we will focus on linear regression models because phylogenetic regression models led to qualitatively similar conclusions (tables S13 and S14). The most significant life span-related CpGs are located in the distal intergenic region neighboring TLE4 (Pearson's r = 0. 68, P = 5. 8 × 10 -46 ; Fig. 5A  and table S11 ) and two CpGs near the promoter region of HOXA4 (r = 0. 66, P = 7. 5 × 10 -45 ; Fig. 5A , midnightblue module) and are negatively correlated with a CpG in an intron of GATA3 (r = -0.
7), with the analogous EWASs restricted to animals that are young, middle-aged, or old. EWASs of life span showed good consistency with the eigengene-based analysis in the mammalian comethylation network. As expected, the following previously discussed life spanrelated modules were enriched with CpGs implicated by our EWAS of life span: midnightblue (hypergeometric test P = 2. 2 × 10 -47 ; 67/249 overlapped CpGs), greenyellow (hypergeometric P = 2. 1 × 10 -36 ; 70/398 overlapped CpGs), tan (hypergeometric P = 6. 7 × 10 -23 ; 52/365 overlapped CpGs), and green (hypergeometric P = 5. 0 × 10 -18 ; 104/1542 overlapped CpGs). In total, 1006 genes had a differential methylation association with life span (union of cognate genes resulting from the marginal model analysis for life span and LifespanAdjWeight).
01) and contain at least five significant genes. The top three significant terms per column (EWAS) and enrichment database are shown. (E) Ingenuity potential upstream regulator analysis (40) of the differentially methylated genes related to mammalian maximum life span. Only significant (FDR < 0. 05) regulators are represented in the bar plot. (F) Venn diagram of three gene lists. Gene list 1 is the top 646 genes adjacent to 1000 life span-related CpGs (500 positive and 500 negative). Gene lists 2 and 3 are based on CpGs that are differentially methylated (nominal Wald test P < 0. 005, up to 500 positive and 500 negatively related CpGs) after OSKM overexpression in murine kidney (583 genes) and skin (686 genes)  (39). We observed significant overlap between the gene lists (nominal Fisher's exact P = 9.
9 × 10 -30 for skin and life span; P = 4. 5 × 10 -25 for kidney and life span). (G) Transcriptional factor motif enrichment analysis of life span modules and life span-related CpGs. The enrichment results for LifespanAdjWeight. negative were not significant. The overlap is assessed by a hypergeometric test for the CpGs within the motifs based on the human hg19 genome. HOXL V$HOXA4_Q2 V$ER_Q6 V$IK3_01 AAANWWTGC_UNKNOWN V$EVI1_05 V$GATA6_01 V$USF_C V$E2F_01 findings suggest that expression of life spanrelated genes might be regulated to some extent by pluripotency factors. This prompted us to investigate whether expression of any of the life span-related genes identified above are altered by transient expression of pluripotency inducing factors (Yamanaka factors OSKM) in a mouse model  (39).
Chromatin state analysis Our large-scale mammalian DNAm data confirm that located in promoter regions (-2000 to 2000 bp of TSS regions) have low methylation levels (mean = 15%; Fig. 7A ). By contrast, those in gene bodies and distal regions are highly methylated (mean = ~70%; Fig. 7A ). CpGs having a high or low mean methylation level tend to have positive or negative correlation test Z statistics for life span, respectively (Fig. 7, A  and B ). We find that CpGs with low methylation levels in long-lived species are located close to the TSS of genes and near binding sites of PRC1 (P = 6. 4 × 10 -11 ; Fig. 7C ) and PRC2 (P = 2 × 10 -6 ). To test the hypothesis that longlived species exhibit high or low methylation levels in chromosomal regions that are expected to have high or low patterns, respectively, we used chromatin states that were identified and annotated based on >1000 epigenetic datasets encompassing a diverse range of human cell and tissue types  (41).
Discussion In this study, we present an analysis of a crossspecies DNAm dataset obtained from a mammalian array platform. This platform specifically focuses on highly conserved regions of DNA, making it a valuable resource for studying methylation patterns across mammalian species  (5). The successful construction of mammalian phyloepigenetic trees suggests that the divergence of DNAm profiles is closely aligned with genetic changes throughout evolution. Sensitivity assessments reveal that the observed phyloepigenetic associations are not caused by technical issues associated with our measurement platform. Instead, the phyloepigenetic signal may stem from sources such as upstream regulators, transcription factors, or DNA sequence variations in distant regions. The conserved CpGs exhibiting the strongest phylogenetic signals are situated in intergenic regions, whereas promoter regions do not display such signals.
lifespan), and the corresponding results after adjustment for average adult weight (LifespanAdjWeight). The last column reports enrichment with respect to the RelativeAge + module (purple). We used the same significance thresholds as in Fig. but promoters demonstrate either full or partial conservation across species (2). We found that 30 of the resulting 55 modules identified from an unsupervised machine learning method were readily associated with species traits (taxonomic order, maximum life span, and average adult weight) or individual traits (chronological age, tissue, and sex). We expect that many of the remaining 25 modules will be associated with biological characteristics about which we currently have no information. As a case in point, although the yellow module was not associated with any of our primary tested traits, it did show association with response to a murine circadian rhythm disruption study (light pollution during the night; fig.
Methods summary The Mammalian Methylation Consortium generated cytosine methylation data from n = 15,456 DNA samples derived from 70 tissue types of 348 mammalian species (331 eutherians, 15 marsupials, two monotremes) using a custom-designed mammalian methylation array that targets CpGs at conserved loci in mammals  (5). DNAm data were used for phyloepigenetic tree development using 1-cor dissimilarity applied to mean methylation values per species. The choice of the correlationbased dissimilarity matrix is justified in the supplementary materials and methods. For unsupervised analysis, we formed WGCNA networks based on two sets of CpG probes in our data. The first network was generated from 14,705 conserved CpGs in 10,927 samples of 167 eutherian species. The preservation of this network was evaluated in an independent dataset comprising 3692 samples from 29 tissues of 228 mammalian species (164 new species; 64 overlapped with the training set).
ACKNOWLEDGMENTS This is Duke Lemur Center publication #1563. Funding: The majority of the funding was contributed by the  Paul G. Allen Frontiers Group  (S. H. ) and  Open Philanthropy  (S. H. ). Additional financial support for specific aspects of data generation was obtained from the following sources: National Geographic Society grant  8941-11  (B. L. ),  British Heart Foundation  ( FS/18/39/33684  to D. V. ),  Wellcome  ( WT202878/Z/16/Z  to D. T. O. ),  European Research Council  ( 788937  to D. T. O. ),  Cancer Research UK  ( 20412  to D. T. O. ),  Max Planck Research Group  Award from the  Max Planck Gesellschaft  (S. C. V. ),  UKRI Future Leaders Fellowship  ( MR/T021985/1  to S. C. V. ),  DST-NRF SARChI  chair of  Mammalian Behavioral Ecology and Physiology  ( GUN 64756  to N. C. B.
Pederson),  UCLA Jonsson Comprehensive Cancer Center  and  Eli  and  Edythe Broad Center of Regenerative Medicine and Stem Cell Research Ablon Scholars Program  (J. E. ), funds provided by the  College of Computer, Mathematical and Natural Sciences at the University of Maryland, College Park  (G. S. W. ),  Taronga Conservation Society Australia  (J. K. O. ), S. C. V. was supported by a  Max Planck Research Group Award  from the  Max Planck Gesellschaft  and a  UKRI Future Leaders Fellowship  ( MR/T021985/1 ). N. C. B. was funded by a  DST-NRF SARChI chair of Mammalian Behavioral Ecology and Physiology  ( GUN 64756 ). A. N. A was funded by  DSFP ,  King Saud University, Riyadh, Saudi Arabia. K. J. P. was supported by a postdoctoral grant from the  University of Zurich. Collection of plains zebra samples was supported by the  National Geographic Society  to B.
Data and materials availability: All data from the Mammalian Methylation Consortium are posted on the Gene Expression Omnibus website (complete dataset: GSE223748 ). Subsets of the data sets can also be downloaded from accession numbers,  GSE174758 ,  GSE184211 ,  GSE184213 ,  GSE184215 ,  GSE184216 ,  GSE184218 ,  GSE184220 ,  GSE184221 ,  GSE184224 ,  GSE190660 ,  GSE190661 ,  GSE190662 ,  GSE190663 ,  GSE190664 ,  GSE174544 ,  GSE190665 ,  GSE174767 ,  GSE184222 ,  GSE184223 ,  GSE174777 ,  GSE174778 ,  GSE173330 ,  GSE164127 ,  GSE147002 ,  GSE147003 ,  GSE147004 ). The mammalian array platform is distributed by the nonprofit  Epigenetic Clock Development Foundation  (https://clockfoundation. org/). The mammalian data can also be downloaded from the  Clock Foundation  web page: https:// clockfoundation.
org/MammalianMethylationConsortium. The manifest file of the mammalian array, genome annotations of the CpGs, and codes can be found on Zenodo (46). License information: Copyright © 2023 the authors, some rights reserved; exclusive licensee American Association for the Advancement  of Science. No claim to original US government works. https://www. science. org/about/ science-licenses-journal-article-reuse.
5. 5 Cell shading corresponds to fold enrichment between comethylation modules and each chromatin state. Numeric values correspond to the P value of such enrichments based on the hypergeometric test, and only cell values with significant P < 0. 001 (equivalent to FDR < 0. 02) are shown. The chromatin states are learned based on epigenetic datasets profiling chromatin mark signals in different human cell and tissue types, resulting in a genome annotation shared across cell types (41). The common partially methylated domains (commonPMD), solo CpGs (WCGW), and highly methylated domain (HMD) annotations are from (42). PRC1 and PRC2 binding sites are obtained from the ChIP-seq datasets of PCR1 and PCR2 from ENCODE (45).
The hardware trigger selects muon candidates with large transverse momentum or dimuon candidates with a large value of the product of the p T of the muons. In the software trigger, two oppositely charged muons are required to form a good-quality vertex that is significantly displaced from every PV, with a dimuon mass exceeding 2. 7 GeV/c 2. Simulated events are used to describe signal shapes and to compute the efficiencies needed to determine the branching fraction ratio. In the simulation, pp collisions are generated using Pythia  [40]  with a specific LHCb configuration  [41]. Decays of unstable particles are described by the EvtGen package  [42] , in which final-state radiation is generated using Photos  [43]. The interaction of the generated particles with the detector, and its response, are implemented using the Geant4 toolkit  [44, 45]  as described in ref.
Acknowledgments We express our gratitude to our colleagues in the CERN accelerator departments for the excellent performance of the LHC. We thank the technical and administrative staff at the LHCb institutes. We acknowledge support from  CERN  and from the national agencies:  CAPES ,  CNPq ,  FAPERJ  and  FINEP (Brazil) ;  MOST  and  NSFC (China) ;  CNRS/IN2P3  (France);  BMBF ,  DFG  and  MPG (Germany) ;  INFN (Italy) ;  NWO (Netherlands) ;  MNiSW  and  NCN (Poland) ;  MEN/IFA (Romania) ;  MICINN (Spain) ;  SNSF  and  SER (Switzerland) ;  NASU (Ukraine) ;  STFC  ( United Kingdom );  DOE NP  and  NSF  (U. S. A. ). We acknowledge the computing resources that are provided by  CERN ,  IN2P3 (France) ,  KIT  and  DESY (Germany) ,  INFN (Italy) ,  SURF (Netherlands) ,  PIC (Spain) ,  GridPP  ( United Kingdom ),  CSCS (Switzerland) ,  IFIN-HH (Romania), CBPF (Brazil) ,  Polish WLCG (Poland)  and  NERSC (U.
IV. CONCLUDING REMARKS In this work, we presented a solution for contactless material classification that exploits the physical properties of scattered electromagnetic signals. We did this by incorporating model-based analysis into data-based learning techniques. By exploiting the physical model of scattered signals, we extracted the complex natural frequencies and used them as descriptors for each class. We then transformed the natural frequencies into characteristics suitable for the classification problem using statistical data analysis. This signal-processing step resulted in a reduction in the dimensionality of the problem, as an added benefit. Finally, machine learning techniques performed the classification. Although this work does not include advanced clutter reduction techniques, it is something that could be added in the future to improve the performance of the proposed method.
7 and 531. 2 eV and carboxyl/ester groups of C 1s link to O=C, OH in the O 1s spectrum usually located between 531. 8 and 532. 7 eV. Analyzing those data further provides evidence about the fact that the content of graphitic carbon increases as a function of the temperature, while the amount of oxygenated groups decreases as observed by FT-IR spectroscopy. On closer inspection, sulfur was also detected in samples CS700, CS800 and CS900, while silicon was measured for CS700 and CS900, which is consistent with the EDX analysis. Two types of sulfur species on the surface of the CS700, CS800 and CS900 could be determined, thiophenic and oxidized sulfur species (sulfoxide and sulfone), with corresponding peaks at 163. 8-164. 9 eV and 168. 03-199. 19 eV, respectively (Figure  7 ). Silicon was detected in samples prepared at the lowest pyrolysis temperature, CS600 and CS700, and chlorine was detected in CS900 alone.
Note that the percentage of sulfur decreases as the pyrolysis temperature increases. Analyzing those data further provides evidence about the fact that the content of graphitic carbon increases as a function of the temperature, while the amount of oxygenated groups decreases as observed by FT-IR spectroscopy. On closer inspection, sulfur was also detected in samples CS700, CS800 and CS900, while silicon was measured for CS700 and CS900, which is consistent with the EDX analysis. Two types of sulfur species on the surface of the CS700, CS800 and CS900 could be determined, thiophenic and oxidized sulfur species (sulfoxide and sulfone), with corresponding peaks at 163. 8-164. 9 eV and 168. 03-199. 19 eV, respectively (Figure  7 ). Silicon was detected in samples prepared at the lowest pyrolysis temperature, CS600 and CS700, and chlorine was detected in CS900 alone.
32 × 1. 45 mm 2. A light source (Constellation 120E, Imaging Solutions) illuminates the substrate and the particles from above. Each particle in the image is discretized into about 55 pixels along the diameter. The initial number of particles 𝑁 0 in the field of view is determined using semi-automatic procedure. At this stage, the gas in the test section is at rest, that means 𝑅𝑒 = 0. With ImageJ as image processing software  (Schneider, Rasband, & Eliceiri, 2012) , a Gaussian blur is first employed. An attempt at tagging each individual particle is done by finding the image maxima. The particle tags are then corrected manually. In the presented experiments, the initial particle number 𝑁 0 typically varies between about 40 and 200, which corresponds to an initial coverage of the field of view ranging from 9 to 47 % and to surface number concentration ranging from 11 to 60 mm -2 , that is the particle number per square millimeters.
Acknowledgements This work was funded by the  Helmholtz Associations Initiative and Networking Fund  within the frames of the  Helmholtz Climate Initiative (HI-CAM) , the  Helmholtz  project  CORAERO  and the  Helmholtz Innovation Lab, Clean Water Technology (CLEWATEC). Funding by the  European Social Fund  and the  Free State of Saxony  (Grant Number  100284305 ) is also acknowledged.
Collected Data Our dataset consists of 8 patients aged between 8 and 16 years with AIS (Cobb angle range: 14-68 • ) and without any treatment history. They were included in our study following the IRB CPP Ile de France 2 on 20 July 2020 : n • ID RCB: 2020-A01071-38. All parents and patients received an information letter. Two data modalities were collected in our institution for each subject:.
Data Processing Some manual steps are needed to annotate the images. The first step is to make the semi-automated reconstruction of the 3D geometries of the spine from the biplanar radiographs with the SterEOS software 1. 8. 99. 21R (copyright 2015 EOS Imaging)  [12]  (EOS Imaging, Paris, France). Then, the radio-opaque markers, visible in the images, are located in 2D and their 3D position is computed using the calibration information available in the DICOMs metadata  [13]. From the surface skin mesh, the marker locations are identified. As the surface scan and the EOS reconstruction are defined in a different global frame, we use a rigid registration to bring them in the same frame. Namely, we compute the translation and rotation that minimizes the distance between the 3D markers identified in both modalities.
Accuracy of the Anatomical Model To evaluate the quality of our model, we compute several metrics related to the accuracy in shape, positions and orientations of the vertebrae. The ground-truth measurements of the vertebrae are based on the 3D annotations provided by the SterEOS software. We compare our model meshes with the reconstructed ones. The vertebrae location is defined with the center of mass of the vertebra mesh. The euclidean distance between the corresponding ground truth and model meshes is then computed with their mean absolute difference on each anatomical axis. The differences in orientations are given by the 2D projection of a vertebra orientation vector, computed according to the recommendations of the International Society of Biomechanics (ISB)  [19]  on a given anatomical plane.
Results We evaluated the created digital twin in two settings. We first quantified the capability of the model to capture the data in the standing position, and then we evaluated the precision of the model in the bending position. For each position, we assess the external accuracy, i. e. , how well does the model fit the 3D markers, as well as the internal accuracy, i. e. , how well does the model capture the shape and position of the 3D spine inside the body?.
Study Cohort Prospective cross-sectional and longitudinal data from all 948 consecutive patients enrolled between 2013 and August 4, 2021 were retrieved from the global multicenter ARCA Registry. 20 Datasets included (1) genotypic and demographic data, (2) assessments of ataxia severity (SARA  1  ) and nonataxia features (including the Inventory of Non-Ataxia Signs [INAS]  21  ), and (3) the functional staging (FARS-FS) and activities of daily living (ADL) scales of the Friedreich Ataxia Rating Scale (FARS)  22  as patient-focused outcomes. Patients had been eligible for inclusion into the ARCA Registry if they had (1) a genetically confirmed autosomal recessive cerebellar ataxia (ARCA); and/or (2) an early onset ataxia (EOA) with onset before age 40 years without evidence of an autosomal dominant family history, repeatexpansion SCA, or acquired cause (eg, subacute onset, rapid progression, alcohol intake, abnormal B12 levels, cerebrospinal fluid pleocytosis, or structural lesions on imaging), thus representing a stratum of ataxia patients known to be enriched for ARCAs.