This dataset includes a compelete statistics of the EAC mapping validation, data of sensitivity experiments, and the quantitative results of EAC encroachment (area and distance) during July 2015 to September 2017.

Magnetic Resonance Imaging measurement data used in the PhD thesis "Quantitative Multi-Parameter Mapping in Magnetic Resonance Imaging". The data is provided in a file format used by the BART toolbox (DOI: 10.5281/zenodo.592960).

Further information about the individual datasets:

data_invivo_b0map Type: B0 Map Object: Single-slice of volunteers brain Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence TR|TE1|TE2 [ms]: 400|4.92|7.38 FA [deg]: 60 FOV [mm]: 240

data_invivo_b1map Type: B1 Map Object: Single-slice of volunteers brain Sequence: Preconditioned RF pulse with TurboFLASH Readout TR|TE [ms]: 6830|2.19 FA [deg]: 8 FOV [mm]: 240

data_invivo_irflash Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR FLASH TR|TE [ms]: 3.8|2.26 FA [deg]: 8 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 7

data_invivo_irbssfp Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 45 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 7

data_invivo_irbssfp_shim Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 45 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 7 missing shim break

data_vfa_b0map Type: B0 Map Object: Single-slice of volunteers brain Sequence: Two GRE Acquisitions with different TE, "gre_field_mapping" Sequence TR|TE1|TE2 [ms]: 400|4.92|7.38 FA [deg]: 60 FOV [mm]: 240

data_vfa_b1map Type: B1 Map Object: Single-slice of volunteers brain Sequence: Preconditioned RF pulse with TurboFLASH Readout TR|TE [ms]: 6830|2.19 FA [deg]: 8 FOV [mm]: 240

data_vfa_irflash Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR FLASH TR|TE [ms]: 3.8|2.26 FA [deg]: 8 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

data_vfa_irbssfp_20 Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 20 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

data_vfa_irbssfp_40 Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 40 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

data_vfa_irbssfp_45 Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 45 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

data_vfa_irbssfp_50 Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 50 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

data_vfa_irbssfp_60 Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 60 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

data_vfa_irbssfp_70 Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 70 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

data_vfa_irbssfp_77 Type: Radial Single-Shot Dataset Object: Single-slice of volunteers brain Sequence: IR bSSFP TR|TE [ms]: 4.5|2.25 FA [deg]: 77 T_RF [ms]: 1 BWTP: 4 FOV [mm]: 240 #Tiny GA: 13

Images and analyses used to support the manuscript published https://doi.org/10.1091/mbc.E22-08-0372

The knowledge of relaxation times is essential for understanding the biophysical mechanisms underlying contrast in magnetic resonance imaging. Quantitative experiments, while offering major advantages in terms of reproducibility, may benefit from simultaneous acquisitions. In this work, we demonstrate the possibility of simultaneously recording relaxation-time and susceptibility maps with a prototype Multi-Echo (ME) Magnetization-Prepared 2 RApid Gradient Echoes (MP2RAGE) sequence. T1 maps can be obtained using the MP2RAGE sequence, which is relatively insensitive to inhomogeneities of the radio-frequency transmit field, . As an extension, multiple gradient echoes can be acquired in each of the MP2RAGE readout blocks, which permits the calculation of and susceptibility maps. We used computer simulations to explore the effects of the parameters on the precision and accuracy of the mapping. In vivo parameter maps up to 0.6 mm nominal resolution were acquired at 7 T in 19 healthy volunteers. Voxel-by-voxel correlations and the test-retest reproducibility were used to assess the reliability of the results. When using optimized paramenters, T1 maps obtained with ME-MP2RAGE and standard MP2RAGE showed excellent agreement for the whole range of values found in brain tissues. Simultaneously obtained and susceptibility maps were of comparable quality as Fast Low-Angle SHot (FLASH) results. The acquisition times were more favorable for the ME-MP2RAGE (≈ 19 min) sequence as opposed to the sum of MP2RAGE (≈ 12 min) and FLASH (≈ 10 min) acquisitions. Without relevant sacrifice in accuracy, precision or flexibility, the multi-echo version may yield advantages in terms of reduced acquisition time and intrinsic co-registration, provided that an appropriate optimization of the acquisition parameters is performed.

Here we present the creation of a modular and realistic digital brain phantom to serve as a ground-truth to assess the quality of different reconstruction algorithms for Quantitative Susceptibility Mapping (QSM). The phantom is derived from high-resolution, quantitative MRI data of a healthy volunteer, features a realistic morphology including a non piece-wise constant susceptibility distribution.

epitope description:GQGQSGYYPTSPQQSGQEAT,antigen name:Other Triticum aestivum (Canadian hard winter wheat) protein,host organism:Homo sapiens

Recommended reporting template for EPMA quantitative mapping

Quantitative Mapping of Computational Boundaries

A Statistical Field Theory Approach to Phase Transitions in NP-Hard Problems Author: Zixi Li (Oz Lee) Affiliation: Noesis Lab (Independent Research Group) Contact: lizx93@mail2.sysu.edu.cn

  Overview

Classical computability theory tells us that computational boundaries exist (halting problem, P vs NP), but it doesn't answer: where exactly are these boundaries? This paper presents the first quantitative mapping of… See the full description on the dataset page: https://huggingface.co/datasets/OzTianlu/Quantitative_Mapping_of_Computational_Boundaries.

Biological Magnetic Resonance Bank Entry 26960: Quantitative mapping of MAP2c phosphorylation and 14-3-3 binding sites reveals key differences between MAP2c and Tau

Example of standardization report generated by XMapTools

OverviewProject: Optimising an acquisition protocol and reconstruction pipeline for quantitative susceptibility mapping in the head and neck.Dates: 2018 - 2024Funding: Cancer Research UK Multidisciplinary Award #24348This is a dataset of 3D multi-echo GRE data for analyzing the repeatability of QSM reconstruction algorithms in the head and neck (HN) region. It consists of data from 10 healthy subjects (detailed in subjects.tsv), each of whom was scanned a total of six times, across two sessions.The dataset also contains, for each subject and session, a brain mask (obtained using FSL BET), and a segmentation file which contains ROI segmentations for brain and HN ROIs (obtained using FSL FIRST and manual segmentation). The key for the segmentations can be found in dseg.tsvAccessThis data is covered by a CC-BY license (https://creativecommons.org/licenses/by/4.0/). You are free to share this data and adapt it for any purpose, but you must attribute this dataset.If you use this dataset in any publications, please cite:Karsa A, Punwani S, Shmueli K. An optimized and highly repeatable MRI acquisition and processing pipeline for quantitative susceptibility mapping in the head-and-neck region. Magn. Reson. Med. 2020; 84: 3206-3222. https://doi.org/10.1002/mrm.28377Cherukara MT, Shmueli K. Comparing repeatability metrics for quantitative susceptibility mapping in the head and neck. Magn. Reson. Mater. Phy. 2025. https://doi.org/10.1007/s10334-025-01229-3For queries, please contact Matthew Cherukara (m.cherukara@ucl.ac.uk) or Karin Shmueli (k.shmueli@ucl.ac.uk)

MRI Datasets from the GUFI Traveling Heads experiment at 7T.

2 Subjects 10 Sites

The same quantitative imaging protocol at all sites consisting of:

B1 and B0 mapping MP2RAGE QSM CEST Relaxometry

The sites were organized in the German Ultrahigh Field Imaging network (GUFI, www.mr-gufi.de) and discriminate by hard- and software differences of the 7T systems from different generations (same vendor): Configuration 1: Magnet: Passively shielded, Gradient Coil: 38mT/m, RFPA: 8kW, RF Coil: 24ch, Software: VB Datasets: BER_20181211, HEI_20190205 Configuration 2: Magnet: Passively shielded, Gradient Coil: 38mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: ES_20181008, ES_20190813 Configuration 3: Magnet: Passively shielded, Gradient Coil: 70mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: MAG_20190114, LEI_20190115, WIE_20190404 Configuration 4: Magnet: Actively shielded, Gradient Coil: 70mT/m, RFPA: 8kW, RF Coil: 32ch, Software: VB Datasets: BN_20181009 Configuration 5: Magnet: Actively shielded, Gradient Coil: 80mT/m, RFPA: 11kW, RF Coil: 32ch, Software: VE Datasets: ERL_20181019, ERL_20190226, ERL_20190618, JUL_20181212, JUL_20190604, WUE_20190125, WUE_20190617

One full dataset includes:

b0fieldHZ: B0 field mapped in Hz

b1map_mtflash_reg: rel. B1 map registered to the mtflash dataset for B1 correction of relaxometry data

b1rel: rel. B1 map original image space (100*measured flip/nominal flip)

brainmask_mp2rage: brain mask calculated with CBS tools, ANTS and FSL for MP2RAGE data

CEST_NOE: rNOE map derived from the CEST analysis

CEST_APT: APT map derived from the CEST analysis

CEST_MT: MT map derived from the CEST analysis

CEST3D06: CEST image data for B1=0.6uT

CEST3D06: CEST image data for B1=0.9uT

CEST3DWASABI: Correction data for the CEST calculation

gre_qsm: QSM Map calculated from the GRE data in ppB

gre_qsm_mag: Multiecho-GRE magnitude image data for QSM

gre_qsm_phs: Multiecho-GRE phase image data for QSM

mp2rage_inv1: MP2RAGE image data first inversion contrast

mp2rage_inv2: MP2RAGE image data second inversion contrast

mp2rage_T1_corr: MP2RAGE derived T1 map after additional transmit B1 correction with B1 data

mp2rage_T1_gdc_brain: MP2RAGE T1 map after brain extraction and gradient distortion correction (used for inter-site comparisons)

mp2rage_uni_corr: MP2RAGE uniform images after additional transmit B1 correction with B1 data

mp2rage_uni_gdc_brain: MP2RAGE uniform images after brain extraction and gradient distortion correction (used for inter-site comparisons)

mpm_PD: Proton Density map (in %) derived from the multiparametic analysis of the mtflash data

mpm_T1: T1 map (in s) derived from the multiparametic analysis of the mtflash data

mpm_T2s: T2* map (in ms) derived from the multiparametic analysis of the mtflash data

mtflash3dPD: Multiecho FLASH images in PD weighting for multiparametic analysis

mtflash3dT1: Multiecho FLASH images in T1 weighting for multiparametic analysis

Not all data may be available for every measurement.

For further information on the dataset and the methods used for analysis please refer to the corresponding paper: M. N. Voelker et al., “The Traveling Heads 2.0: Multicenter Reproducibility of Quantitative Imaging Methods at 7 Tesla,” Neuroimage, p. 117910, Feb. 2021. https://doi.org/10.1016/j.neuroimage.2021.117910 Please cite if you use the GUFI data!

The first upload (TH2_data_ES_s1.zip) consists of the one full dataset derived at the first measurement at configuration 2 of subject 1 and was intended for the review process (CEST results of this upload were refined during review) of the corresponding paper. The full dataset (TH2_alldata.zip) was uploaded as an update under this project number.

Data reported in research published in Crop Science, “Mapping the quantitative field resistance to stripe rust in a hard winter wheat population ‘Overley’ × ‘Overland.’” Authors are Wardah Mustahsan, Mary J. Guttieri, Robert L. Bowden, Kimberley Garland-Campbell, Katherine Jordan, Guihua Bai, Guorong Zhang from USDA Agricultural Research Service and Kansas State University. This study was conducted to identify quantitative trait loci (QTL) associated with field resistance to stripe rust, also known as yellow rust (YR), in hard winter wheat. Stripe rust infection type and severity were rated in recombinant inbred lines (RILs, n=204) derived from a cross between hard red winter wheat cultivars ‘Overley’ and ‘Overland’ in replicated field trials in the Great Plains and Pacific Northwest. RILs (n=184) were genotyped with reduced representation sequencing to produce SNP markers from alignment to the ‘Chinese Spring’ reference sequence, IWGSC v2.1, and from alignment to the reference sequence for ‘Jagger’, which is a parent of Overley. Genetic linkage maps were developed independently from each set of SNP markers. QTL analysis identified genomic regions on chromosome arms 2AS, 2BS, 2BL, and 2DL that were associated with stripe rust resistance using multi-environment best linear unbiased predictors for stripe rust infection type and severity. Results for the two linkage maps were very similar. PCR-based SNP marker assays associated with the QTL regions were developed to efficiently identify these genomic regions in breeding populations.Field response to YR was evaluated in seven trials: Rossville, KS (2018 and 2019), Hays, KS (2019), Pullman, WA (2019 and 2020) and Central Ferry, WA (2019 and 2020). An augmented experimental design was used at Rossville, KS with highly replicated checks and two full replications of RILs (n=187 in 2018; n=204 in 2019). The field experiment at Hays was arranged in a partially replicated augmented design with one or two replications of each RIL (n=194). The parental checks (Overley and Overland) were represented in three blocks for each of the two field replications at Hays, and RILs were distributed among blocks; not all RILs were present in each replication. RILs were arranged in an augmented design with two replications at Pullman (n=204 RILs) and Central Ferry (n=155 RILs in 2019; n=204 in 2020). At Pullman and Central Ferry.The trials at Rossville, KS were inoculated using an inoculum consisting of equal parts of four isolates that were all virulent to Yr9. Two isolates were collected in Kansas in 2010 and had virulence to Yr17 but not QYr.tamu-2B. The other two isolates were from Kansas in 2012 and had virulence to QYr.tamu-2B, but not Yr17. Susceptible spreader rows (KS89180B, carrying Yr9) were inoculated several times during the tillering stage in the evenings with an ultra-low volume sprayer using a suspension of 2 mL of fresh urediniospores in 1 L of Soltrol 170 isoparaffin oil. Trials at Pullman, WA and Central Ferry, WA were evaluated under natural inoculum supplemented by a mixture of isolates collected in the previous field season. The trial at Hays, KS was evaluated under natural infection.Data collection at Rossville, KS began once the susceptible check (KS89180B) had an infection severity coverage of ~10% and continued until senescence. In Rossville, disease ratings (IT and SEV) were collected on 16, 22, and 28th of May 2019. Most ratings in Rossville were taken some time after heading from Zadoks stages 55 to 70. In Pullman, disease ratings were collected on July 1 and 12. In Central Ferry, disease ratings were taken on 12th and 18th of June 2019. The second rating date was used for subsequent statistical analysis. In Hays, disease ratings were taken on June 1, 2019, when the plants were in early booting or heading stages (Zadoks 31-41). Stripe rust evaluations were measured using two disease rating scales: IT (0-9; from no infection to highly susceptible, Line and Qayoum, 1992) and SEV based on visual estimation of the percent flag leaf area affected by the pathogen including associated chlorosis and necrosis (0-100%).DNA was extracted from seedlings, and genotyping-by-sequencing was conducted as described previously (Guttieri, 2020) on a subset of 189 lines (187 RILS and 2 parents) of which 23 RILs were F6-derived and 164 RILs were F9-derived. Single nucleotide polymorphisms (SNPs) were identified in parallel using reference-based calling in the TASSEL pipeline (Bradbury et al., 2007) using both the IWGSC v2.1 reference genome (Zhu et al., 2021) and the Jagger reference sequence (Wheat Genomes Project (http://www.10wheatgenomes.com/10-wheat-genomes-project-and-the-wheat-initiative/). The TASSEL pipeline was executed with the following parameters: minimum read count = 1, minimum quality score = 0, minimum locus coverage = 0.19, and minimum minor allele frequency = 0.005, minimum heterozygous proportion = 0, and removal of minor SNP states. The resulting SNP datasets from each reference sequence were filtered in TASSEL by taxa (RILs) and sites (SNPs). The RILs were filtered to include those RILs for which at least 20% sites were present. The sites were filtered to include sites for which > 60% of RILs were called, minor allele frequency (MAF) > 0.25, maximum allele frequency < 0.75, maximum heterozygous proportion = 0.25, and removal of minor SNP states. The ABH plugin in TASSEL was applied to this reduced dataset to identify parental genotypes.Resources in this dataset:Resource Title: Multilocation Stripe Rust Data File Name: MultiLocRawData_Yr.xslxResource Title: OvOv_CS_TasselSNPCalls File Name: KSM17-OvOv-parentsmerge1.hmp.txt Resource Description: Output of TASSEL GBS SNP calling pipeline using Chinese Spring v2 refseq. Starting point for map construction pipeline.Resource Title: OvOv GBS SNP Calls Jagger RefSeq File Name: KSM17-OvOv-Jaggerpmerge1.hmp.txt Resource Description: TASSEL output from reference-based SNP calling using the Jagger reference sequenceResource Title: QTL-Associated KASP Markers with IT and SEV BLUPs File Name: KASP_Data_IT_SEV.xlsx Resource Description: Multilocation best linear unbiased predictors (BLUPs) for stripe rust infection type and severity of recombinant inbred lines. KASP assay results for QTL-associated SNPs, coded Overley = 2, Overland = 0, Het = 1, Missing = "."

Abstract Background: Mitochondrial membrane potential (ΔΨm) arises from normal function of the electron transport chain. Maintenance of ΔΨm within a narrow range is essential for mitochondrial function. Methods for in vivo measurement of ΔΨm do not exist. We use 18F-labeled tetraphenylphosphonium (18F-TPP+) to measure and map the total membrane potential, ΔΨT, as the sum of ΔΨm and cellular (ΔΨc) electrical potentials. Methods: Eight pigs, five controls and three with a scar-like injury, were studied. Pigs were studied with a dynamic PET scanning protocol to measure 18F-TPP+ volume of distribution, VT. Fractional extracellular space (f_ECS) was measured in 3 pigs. We derived equations expressing ΔΨT as a function of VT and the volume-fractions of mitochondria and f_ECS. Seventeen segment polar maps and parametric images of ΔΨT were calculated in millivolts (mV). Results: In controls, mean segmental ΔΨT = -129.4±1.4 mV (SEM). In pigs with segmental tissue injury, ΔΨT was clearly separated from control segments but variable, in the range -100 to 0 mV. The quality of ΔΨT maps was excellent, with low noise and good resolution. Measurements of ΔΨT in the left ventricle of pigs agree with previous in in-vitro measurements. Conclusions: We have analyzed the factors affecting the uptake of voltage sensing tracers and developed a minimally invasive method for mapping ΔΨT in left ventricular myocardium of pigs. ΔΨT is computed in absolute units, allowing for visual and statistical comparison of individual values with normative data. These studies demonstrate the first in vivo application of quantitative mapping of total tissue membrane potential, ΔΨT.

For each QTL, we include cofactors use to generate models as well as markers and physical positions for the peak of the QTL and the 95% confidence interval. Marker names include the physical location on the linkage group, with names referring to the contig and nucleotide position in the M. zebra UMD2a assembly. LOD values, percent phenotypic variance explained by that QTL, allelic effects, and additive, dominance, and heritability calculations are for the peak marker in the QTL. QTL listed in gray are suggestive at the 10% significance level, while those in black meet 5% genome-wide significance based on values indicated. (XLSX)

Supplementary datasets for quantitative fate mapping:

Dataset S1. All quantitative fate maps. Related to Figure 1.

Dataset S2. inDelphi predicted mutant allele probabilities for hgRNAs in MARC1 mice and iPSC line. Related to Figure 4 and Figure 7.

Dataset S3. Simulated phylogenies, single cell lineage barcodes, Phylotime reconstructed trees, fate map and set of MARC1 hgRNAs used for all experiments. Related to all Figures.

In crop genetic studies, the mapping of longitudinal data describing the spatio-temporal nature of agronomic traits can potentially elucidate the factors influencing their formation and development. Here, we combine the mapping power and precision of a MAGIC wheat population with robust computational methods to track the spatio- temporal dynamics of traits associated with wheat performance. NIAB MAGIC lines were phenotyped throughout their lifecycle under smart house conditions. Growth models were fitted to the data describing growth trajectories of plant area, height, water use and senescence and fitted parameters were mapped as quantitative traits. Single time points were also mapped to determine when and how markers became and ceased to be significant. Assessment of temporal dynamics allowed the identification of marker-trait associations and tracking of trait development against the genetic contribution of key markers. We establish a data-driven approach for understanding complex agronomic traits and accelerate research in plant breeding.

Chen and Ritland supplementary data

This research was funded in whole or in part by National Science Centre, Poland 2020/39/D/HS6/01993. For the purpose of Open Access, the author has applied a CC-BY public copyright license to any Author Accepted Manuscript (AAM) version arising from this submission.

Table of quantitative issues reported for each analytical grid (TRI, municipality, neighbourhood) and for each flood scenario. European Directive 2007/60/EC of 23 October 2007 on the assessment and management of flood risks (OJ L 288, 06-11-2007, p. 27) influences the flood prevention strategy in Europe. It requires the production of flood risk management plans to reduce the negative consequences of flooding on human health, the environment, cultural heritage and economic activity. The objectives and implementation requirements are set out in the Law of 12 July 2010 on the National Commitment for the Environment (LENE) and the Decree of 2 March 2011. In this context, the primary objective of flood and flood risk mapping for IRRs is to contribute, by homogenising and objectivating knowledge of flood exposure, to the development of flood risk management plans (WRMs). This data set is used to produce maps of issues on an appropriate scale.