dataesr/scientific-paragraphs-categorization huggingface dataset:
The sfermion masses and X t are interpreted as DR-renormalized parameters at the scale Q = M S. The left plot in figure  3  is obtained with FeynHiggs, while the right plot is obtained with different modules of the FlexibleSUSY package -namely, FlexibleSUSY proper, HSSUSY and FlexibleEFTHiggs. In each plot the blue dotted line is the result of the pure FO calculation, the black dashed line is the result of the pure EFT calculation, and the red solid line is the result of the hybrid calculation. The yellow band corresponds to the value of the Higgs mass, as measured by ATLAS and CMS  [3]  within one standard deviation of the experimental accuracy. The comparison between the different curves in figure  3  shows that, both in FeynHiggs and in FlexibleSUSY, the hybrid calculation does indeed agree with the FO calculation at small M S and with the EFT calculation at large M S.
[305] [306] [307]  in the determination of the strong gauge coupling. The procedure proposed in ref. [296]  leads to a larger estimate for the theory uncertainty of the FO calculation compared to the one proposed in ref. [300] , and the value of M S for which the FO and EFT calculations have a similar estimated uncertainty is lowered below 1 TeV. FeynHiggs: In 2019, ref. [290]  presented a new estimate for the theory uncertainty of the FO (namely, full one-loop and gaugeless two-loop) calculation of the Higgs masses implemented in FeynHiggs, updating the estimate based on ref. [64]  that had been available in the code since 2004. Even in this case, the main purpose was the comparison with the uncertainties of the EFT and hybrid calculations of the Higgs masses implemented in the same code.
The shape of the grey-shaded bands in figure  5  shows that the EFT calculation is expected to be less accurate at low M S , where the missing O(v 2 /M 2 S ) effects are most relevant. However, for values of M S large enough that the EFT prediction for the SM-like Higgs mass is compatible with the LHC measurement, the grey-shaded bands have already shrunk to an almost-constant width, meaning that the O(v 2 /M 2 S ) effects are essentially negligible. Finally, the comparison between the three shaded bands in each plot of figure  5  shows that, for both codes, the uncertainty estimate of the hybrid calculation in this scenario essentially coincides with the one of the EFT calculation as soon as M S 2 TeV. For low values of M S , the uncertainty estimate of the hybrid calculation of FeynHiggs essentially coincides with the one of the FO calculation in the same code as soon as M S 500 GeV, whereas the uncertainty estimate of the hybrid calculation of FlexibleEFTHiggs remains smaller than the one of the FO calculation of FlexibleSUSY (the latter is obtained following ref.
A. 1. 4. Himalaya Himalaya is a library of functions allowing for the inclusion of the three-loop corrections of refs. [67, 68]  in existing FO or EFT calculations of the lighter CP-even Higgs mass that adopt the DR renormalization scheme for the SUSY parameters. URL: https://github. com/Himalaya-Library/Himalaya Model(s): MSSM. Inputs: Soft SUSY-breaking parameters, µ, tan β and M A. The SUSY-breaking parameters are defined in the DR scheme, at a scale M S of the order of the SUSY masses. Highscale boundary conditions are not supported. The code also requires the gauge and Yukawa couplings of the MSSM, as well as the parameter v, all defined in the DR scheme at the scale M S. Outputs: One-, two-and three-loop corrections to M h for the FO calculation in the DR scheme. One-, two-and three-loop contributions to ∆λ for the EFT calculation.
URL: https://github. com/debtosh/SuSeFLAV Model(s): The MSSM and its seesaw extension with three heavy right-handed neutrino superfields. The Higgs-mass calculation in the latter is the same as in the MSSM. Inputs: The soft SUSY-breaking parameters are given as input in the DR scheme at a high scale, namely the GUT scale for SUGRA boundary conditions and the messenger scale for GMSB boundary conditions. The parameter tan β is given at Q = M Z. The parameters µ 2 and B µ are fixed by the minimum conditions of the Higgs potential. SLHA format: SLHA1 conventions for input and output. Strategy: FO calculation of the Higgs masses. The gauge and Yukawa couplings of the MSSM are extracted from the SM input parameters at the scale Q = M Z , while the soft SUSY-breaking parameters are given as input at Q in.
At the EW scale, the running gauge and Yukawa couplings of the SM and the relation between λ SM and M h are determined at two loops from the interpolation formulas of ref. [191]. Language: SusyHD is a Mathematica program. What to cite: Ref. [234]  A. 2. Beyond-MSSM codes In this section we collect codes for the computation of the Higgs-mass spectrum in SUSY models that feature an extended particle spectrum with respect to the MSSM. In particular, three of the listed codes are devoted to the NMSSM, and one to the µνSSM. We remark that both of the "spectrumgenerator generators" listed in section A. 3 can be used to produce codes that compute the Higgs mass spectrum of the NMSSM, and that SARAH can also produce a code for the µνSSM. A. 2. 1. munuSSM munuSSM computes the mass spectrum of the neutral scalars of the µνSSM (namely, Higgs bosons and sneutrinos) with full one-loop corrections.
It relies on FeynHiggs to include also corrections beyond one loop in the MSSM limit. The code also computes the decays of all non-colored (neutral and charged) scalars. URL: https://gitlab. com/thomas. biekoetter/munussm Model(s): The µνSSM. Inputs: The superpotential and soft-SUSY-breaking parameters, the sneutrino vevs and tan β. The non-SM input parameters that determine the tree-level masses of the neutral scalars are defined in the DR scheme at a scale of the order of the SUSY masses. The soft SUSY-breaking parameters in the stop and sbottom sectors can optionally be defined in the OS scheme. High-scale boundary conditions are not supported. Outputs: Pole masses of the eight CP-even and seven CP-odd neutral scalars of the model. Effective couplings of all neutral scalars to SM particles.
A. 3. 1. FlexibleSUSY FlexibleSUSY is a package that uses the analytic results for RGEs, tadpoles and self-energies provided by SARAH, as well as numerical routines from SOFTSUSY, to generate stand-alone spectrum generators for any SUSY (or non-SUSY) model defined by the user. When available, higher-order corrections beyond those provided by SARAH are included for specific models. In heavy-SUSY scenarios where the EFT valid below the SUSY scale is the SM, the package also provides a hybrid calculation of the SM-like Higgs mass following the FlexibleEFTHiggs approach (see section 5. 3). The module FlexibleBSM generates stand-alone codes for the pure EFT calculation of the Higgs masses, with user-supplied boundary conditions. Finally, the package contains pre-generated spectrum generators for several SUSY models, namely MSSM, NMSSM, E 6 SSM and MRSSM.
Acknowledgements The authors of this report wish to thank all of the colleagues who, directly or indirectly, contributed over the past seven years to the progress of the Higgs-mass calculations in SUSY models and to the success of the KUTS initiative. The work of P. S. and  M. Go. has been supported in part by the  French "Agence Nationale de la Recherche" (ANR) , in the context of the  LABEX ILP  ( ANR-11-IDEX-0004-02 ,  ANR-10-LABX-63 ) and of the grant " HiggsAutomator " ( ANR-15-CE31-0002 ). The work of S. H. has been supported in part by the  MEINCOP (Spain)  under contract  FPA2016-78022-P  and under contract  PID2019-110058GB-C21 , in part by the  Spanish Agencia Estatal de Investigación (AEI) , in part by the  EU Fondo Europeo de Desarrollo Regional (FEDER)  through the project  FPA2016-78645-P , in part by the " Spanish Red Consolider MultiDark "  FPA2017-90566-REDC , and in part by the  AEI  through the grant  IFT Centro de Excelencia Severo Ochoa   SEV-2016-0597.
G. L. is grateful to the  Technion-Israel Institute of Technology  and the  University of Toronto  for partial support during completion of this work. The work of H. R. and N. M. was partially funded by the  Danish National Research Foundation , grant number  DNRF90. H. R. is also supported by the  German Federal Ministry for Education and Research (BMBF)  under contract number  05H18VFCA1. Part of this work was supported by a STSM Grant from  COST Action   CA16201  PARTICLEFACE. D. S. and T. K. acknowledge support by the  DFG  grant  STO 876/2-2. C. E. M. W. is supported in part by the  U. S. Department of Energy  under contracts  DEAC02-06CH11357  at  Argonne  and by the  DOE  grant  DE-SC0013642  at the  University of Chicago. The work of B. C. A has been partially supported by  STFC  Consolidated HEP theory grants  ST/P000681/1  and  ST/T000694/1.
The work of S. B. has been supported in part by the  Research Executive Agency (REA)  of the  European Union  through the  ERC Advanced Grants MC@NNLO  ( 340983 ) and MathAm (395568). The work of T. N. D. is funded by the  Vietnam National Foundation for Science and Technology Development (NAFOSTED)  under grant number  103. 01-2020. 17. F. D. acknowledges support of the  DFG  grant  SFB CRC-110  " Symmetries and the Emergence of Structure in QCD ".
To do so, we used our ATAC-seq datasets to identify any changes in chromatin accessibility in proximal and distal limbs of both genotypes and a wild type forebrain as control  (Fernandez-Guerrero et al. , 2020). First, we selected the ATACseq peaks corresponding to non-genic elements located throughout the locus outside of the gene cluster. We then evaluated them visually using heatmaps and found a high correspondence between the accessibility status and the tissue of origin (Figure  2c left ). This was confirmed by hierarchical clustering analysis of the non-genic elements (Supplementary Figure  2d  left panel). However, when we evaluated the accessible gene promoters in the HoxD cluster, there were only minor differences between the PFL samples, but noticeable differences between the DFL samples, especially on Hoxd13 and Hoxd11 (Figure  2c  right).
, 2018). The posterior-proximal forelimb (P-PFL) region containing the Hoxd13 ectopic domain was micro-dissected in duplicate (Figure  4a , dashed triangle). The remaining portion of the limbs were processed for Hoxd13 in situ hybridization as controls for the dissection (Supplementary Figure  4a ). To determine if HOXD13 binding is altered in the P-PFL, we also generated HOXD13 CUT&RUN from wild type distal forelimb cells and compared these with a previously reported HOXA11 whole forelimb dataset  (Desanlis et al. , 2020). We first determined whether the HOXD13 consensus binding motif was the same between the control DFL and the inv2 P-PFL samples. De novo motif finding identified the previously reported motif  (Heinz et al. , 2010; Jerković et al. , 2017; Sheth et al. , 2016)  in both control and inv2 samples.
, 1997). While our datasets produced from both single-cell RNA sequencing and DNA binding analyses of various HOX proteins do not allow us to completely discriminate between these two possibilities, it is clear that the former explanation alone cannot account for the observed MD phenotypes. Indeed, studies involving combined Hoxa11/Hoxd11 mutations in mice observed mesomelic dysplasia phenotypes when at least half the total dose was removed  (Davis et al. , 1995). In our single cell RNA-seq experiment we observe in our inv2 mice a 40 to 55 % reduction of Hoxa11 and a modest 15 to 30% reduction of Hoxd11, which together are not sufficient to elicit the described phenotype. In addition, the weak reduction in Hoxd11 transcript was not scored in WISH where, if anything, a slight gain was observed.
Sequenced DNA fragments were processed as previously reported  (Amândio et al. , 2020)  with a minor modification: the BAM file was converted to BED prior to peak calling with bedtools version 2. 27 (Quinlan 2010). Peak calling was done using MACS2 (v2. 1. 1. 20160309) callpeak (--no-model --shift -100 -extsize 200 --call-summits --keep-dup all). For hierarchical clustering and heatmap analysis, two sets of bed files were generated. First, the peak regions were collected from the three wildtype ATAC datasets, then merged with bedtools (version 2. 27. 1, citation) to remove redundant elements. The merged peaks in the region chr2:73950000-75655000 excluding the Lnpk and Mtx2 gene bodies as well as the region from Evx2 to Hoxd1 constitute the first set composed of non-genic elements. The second one contains the promoters (-1kb, +100bp from TSS) that overlapped with at least one peak in a wild-type ATAC dataset in the region from Evx2 to Hoxd1.
Post-amplified DNA was cleaned and size selected using 1:1 ratio of DNA:Ampure SPRI beads (A63881) followed by an additional 1:1 wash and size selection with HXB. HXB is equal parts 40% PEG8000 (Fisher FIBBP233) and 5M NaCl. Sequenced DNA fragments were processed as previously reported  (Amândio et al. , 2020)  with slight modifications: PCR duplicates were removed with picard before the BAM to BED conversion and in MACS2 using the option --keep-dup all instead of --keep-dup 1. Motif enrichment was performed on individual samples with HOMER version 4. 10  (Heinz et al. , 2010)  using default conditions on peaks identified as significant from MACS2 in the second replicate. All samples were mapped to wild type mm10. The E11. 5 whole forelimb HOXA11 ChIP-Seq datasets  (Desanlis et al. , 2020 )(SRR8290670 and SRR8290672) were down-sampled to 25mio reads with seqtk version 1.
3 (https://github. com/lh3/seqtk/) using a RNG seed of 4 and then processed following a previously reported workflow  (Beccari et al. , 2020). Differential binding analysis was performed with DiffBind 2. 14. 0  (Ross-Innes et al. , 2012)  on replicate sample peak sets identified by MACS2 for HOXD13 in wild type distal forelimb and HOXA11 in wild type forelimb, with default conditions using DESeq2 1. 24. 0. Hierarchical clustering analysis was performed with deepTools plotCorrelation, and the heatmap was generated with deepTools plotHeatmap.
Probes cover the region chr2:72240000-76840000 (mm9) producing 2x coverage, with moderately stringent masking and balanced boosting. Sequenced DNA fragments were processed as previously reported  (Yakushiji-Kaminatsui et al. 2018)  but the mapping was performed on mm10 and the reads in chr2:72402000-7700000 were selected. The mutant inv2 genome was characterized from Sanger sequencing data around the inversion breakpoints. A custom R (www. r-project. org) script based on the SeqinR package  (Charif and Lobry, 2007)  allowed the construction of a FASTA file for the inverted chromosome 2 from the wild-type sequence and the exact position and sequence of breakpoints. The sequence of the mutant chromosome 2 (available at 10. 5281/zenodo. 4456654) was then compiled with other wild-type chromosomes to form the mutant inv2 genome.
For samples that were mapped to the inv2 mutant genome, the same workflow as described above was used. Heatmaps in Figure  2  were plotted with pyGenomeTracks 3. 3  (Lopez-Delisle et al. , 2020; Ramírez et al. , 2018)  and subtraction heatmaps in Figure  2  and 2S were plotted with a custom tool available at https://github. com/lldelisle/scriptsForBoltEtAl2021. The TAD separation scores in Figure  2S  were computed with HiCExplorer hicFindTADs version 3. 5. 1.
Single cell RNA-seq Embryos were collected and stored in 1X PBS treated with DEPC and held on ice while genotyping was performed. Embryos with the desired genotype were selected and the posterior portion of each of the forelimbs was isolated for each replicate. The cells were digested in collagenase and stored in 1X PBS containing 10% FCS and 0. 2mM EDTA to prevent cellular aggregation. The cells samples were transferred to the EPFL Gene Expression Core Facility for preparation into 10X GEMs and reverse transcription according to the 10X Chromium 3. 1 protocol. Sequencing reads were processed with Cell Ranger 3. 1. 0 for demultiplexing, barcode processing, and 3' gene counting using a modified gene annotation file (10. 5281/zenodo. 4456702). Clustering analysis was performed with Seurat 3.
2. 3 with R 3. 6. 3. All commands used are available (https://github. com/lldelisle/scriptsForBoltEtAl2021). In order to evaluate the inferred true distribution of expression, not the distribution of detected expression, we used a new method based on Monte-Carlo Markov Chain (Lopez-Delisle and Delisle manuscript in preparation). Briefly, this method assumes that the original distribution of expression (probability density function: pdf) can be approximated by a number of Gaussians provided by the user. We evaluate the likelihood of the Gaussian parameters by using the raw expression and the total number of UMI observed for each cell assuming they follow a Poisson distribution. Then, we do a Markov chain Monte Carlo (MCMC) to obtain an interval confidence for the pdf. In order to compare models obtained with different number of Gaussians, we use the model with the highest value of evidence.
slightly reduced in the central region. On the posterior region of the proximal limb Hoxd11 and Hoxd12 show a very slight increase. (d) Pearson correlation hierarchical clustering on the ATAC datasets in Figure 2. The color of each bin represents the average read density in that region. In the left panel, this analysis evaluates the ATAC profiles on the non-genic portions of the HoxD locus. Each sample clustered most closely based on tissue of origin. The right panel uses the same clustering analysis but only evaluating the ATAC profiles on accessible Hoxd gene promoters. The promoters of Hoxd genes in the two inv2 samples cluster closely with the wild type PFL sample indicating changes in the accessibility of their promoters in the inversion configuration. (e) All embryos collected from the PLE:lacZ transgenic experiment (Figure 2b).
ACKNOWLEDGEMENTS We thank  Dr. Leonardo Beccari  for early insights on this project,  Dr. Josef Zakany  for assistance with skeletal preparations and analysis,  Dr Alexandre Mayran  for advice on single cell RNA-seq as well as members of the  Duboule laboratories  for comments and discussion. We are grateful to  Sandra Gitto  and  Thi Hanh Nguyen Huynh  for their help with mice breeding, as well as to  Bastien Mangeat  and the  EPFL Gene Expression Facility  staff.
Data Acquisition Thirty-five healthy volunteers (HC) (12 female/23 male, mean age ± SD: 61. 2 ± 9. 16 years) participated in the present study. Subjects were scanned at the Paris Brain Institute (ICM -Institut du Cerveau), Paris, France. All subjects signed informed consent forms. The study was approved by the local ethics committee (Ethics Committee: Comité de Protection des Personnes [CPP] Ile de France VI -RCB: 2014-A00725-42). Scans were performed on a 3T SIEMENS Prisma Scanner. The protocol included (i) 3-shell DWI sequence (TR = 10,400 ms, TE = 59 ms, voxel size = 1. 7 × 1. 7 × 1. 7 mm 3 , number of gradient directions per shell = 64, 32, and 8 at, respectively, b = 2,500, 700, and 300 s/mm 2 ) and (ii) magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence for R1 mapping (TR = 5,000 ms, TE = 2.
The modularity maximization also depends on a resolution parameter (γ), which makes it sensitive to different scales. If γ < 1, then the network is partitioned into larger modules, while for γ > 1 the method tends to find smaller modules. To determine at which resolution the modular structure is best described, that is, when it maximizes the quality functions, for each connectome we iterated over γ values ranging from 0. 5 to 3 with steps of 0. 1. At each step, we ran the Louvain algorithm 1,000 times  (Blondel et al. , 2008). Then, the resolution parameter (γ) with highest Q was selected on the basis of the highest Rand index  (Traud et al. , 2011)  similarity and created a consensus modularity using the netneurotools package  (Netneurolab, 2020).
Network Neuroscience The R1-weighted connectome deeply affects the R1 weight distribution and therefore the estimated modular structure. To tackle these thresholding issues, new algorithms have recently been proposed  (Schiavi et al. , 2020; Smith et al. , 2015)  that aim to reduce the number of false positive streamlines by using microstructural and anatomical priors. Future studies need to clarify how such methods could be applied to combine tractography with complementary measures. Finally, one limitation of this study is the relatively small sample size. Unfortunately, we are not aware of any publicly available dataset that includes quantitative MRI metrics (besides diffusion derived ones) that are sensitive to myelin. In conclusion, the R1-weighted connectome complements the structural connectome derived from dMRI and could provide new biomarkers for many pathologies that affect the brain.
ACKNOWLEDGMENTS We thank the  ICEBERG study group  and particularly  Marie Vidailhet ,  MD (Pitié-Salpêtrière Hospital, Paris , Principal investigator),  Jean-Christophe Corvol, MD, PhD  ( Paris Brain Institute, Paris , clinical and genetic data),  Isabelle Arnulf, MD, PhD  ( Pitié-Salpêtrière Hospital, Paris , clinical and sleep data),  Rahul Gaurav, MS, (Pitié-Salpêtrière Hospital, Paris , data analysis),  Nadya Pyatigorskaya, MD, PhD , ( Pitié-Salpêtrière Hospital, Paris , data analysis); for their help in collecting data.
Background: Life scientists routinely face massive and heterogeneous data analysis tasks and must find and access the most suitable databases or software in a jungle of web-accessible resources. The diversity of information used to describe life-scientific digital resources presents an obstacle to their utilization. Although several standardization efforts are emerging, no information schema has been sufficiently detailed to enable uniform semantic and syntactic description-and cataloguing-of bioinformatics resources. Findings: Here we describe biotoolsSchema, a formalized information model that balances the needs of conciseness for rapid adoption against the provision of rich technical information and scientific context. biotoolsSchema results from a series of community-driven workshops and is deployed in the bio.
tools registry, providing the scientific community with >17,000 machine-readable and human-understandable descriptions of software and other digital life-science resources. We compare our approach to related initiatives and provide alignments to foster interoperability and reusability. Conclusions: biotoolsSchema supports the formalized, rigorous, and consistent specification of the syntax and semantics of bioinformatics resources, and enables cataloguing efforts such as bio. tools that help scientists to find, comprehend, and compare resources. The use of biotoolsSchema in bio. tools promotes the FAIRness of research software, a key element of open and reproducible developments for data-intensive sciences.
Automated retrieval of such citation recommendations would be particularly useful in the context of virtual research environments where life scientists combine bioinformatics tools into data-intensive workflows. All of these tasks depend on the availability of a shared human-understandable and machine-processable controlled vocabulary and syntax to precisely describe bioinformatics software and data resources. We thus propose biotoolsSchema. Our objective is 2-fold: (i) to provide a technical means to formalize and express rich bioinformatics resource metadata required to achieve at least tasks T1-5 and (ii) to provide incentives for bioin-formatics resource providers to enrich their tool metadata for better human/machine accessibility, readability, and reusability. biotoolsSchema is a formalized information model that puts the description of a broad range of bioinformatics resources on a rigorous and consistent syntactic and semantic basis.