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10 results for “kernel weight”
Data for Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets
<p>Data used in MRI experiments in paper 'Weighted Manifold Alignment using Wave Kernel Signatures for Aligning Medical image Datasets'. For each volunteer, breath-hold data (folder bhs) and dynamic free-breathing (folder dyn) data is provided in NIFTI format.</p>
Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
<p>The mapping population consists of 166 recombinant inbred lines (RILs) derived from a cross between HD3086 and HI1500.</p> <p><strong>Phenotypic data</strong><br>The RILs population along with parents were evaluated under four conditions namely timely sown irrigation (TSIR) taken as control, timely sown restricted irrigation (TSRI), late sown irrigation (LSIR), and late sown restricted irrigation (LSRI) conditions at Delhi, and under restricted irrigation condition at Indore. From each plot, 20 random spikes were harvested and spikes from each plot were threshed separately. While cleaning, care was taken to prevent metal and dust contamination. The grain iron concentration (GFeC) and grain zinc concentration (GZnC) were measured using Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000 M/s Oxford Inc, USA). The thousand kernel weight (TKW) was recorded by counting 1000 grains manually and weighted with an electronic balance.</p> <p><strong>Genotypic data</strong><br>DNA was extracted from 21 days old seedlings using CTAB method (Murray and Thompson, 1980). Genomic DNA quality was determined using 0.8% agarose gel electrophoresis with λ DNA as the standard and quantified using nanodrop. The 35K SNP Axiom breeders' array was used for genotyping of parents and the RILs population.</p>
Kernel weight contribution to yield genetic gain of maize: A global dataset of maize yield, kernel number, and kernel weight over the last century
<p>Studies characterizing the effect of a century of plant breeding on physiological traits are highly needed to identify candidate traits for future improvement in maize (<em>Zea mays</em> L.). A global evaluation of kernel weight progress over time requires the assembly of large and reliable data documenting genetic improvements in this trait across commercial breeding programs in different regions. We compiled a global dataset of yield and yield components from 34 published and unpublished studies comparing two or more maize cultivars from different decades of commercial release under field conditions. The dataset includes 750 entries of kernel weight data (requirement to be included in the systematic review), of which 642 and 666 include data entries of grain yield and kernel number, respectively. We also extracted the metadata describing experimental site information, agronomic management practices, and genotypic information. This dataset can be useful to identify trends of yield improvement across management conditions, with proper consideration of the trade-off between kernel number and kernel weight in maize.</p>
Kernel weight contribution to yield genetic gain of maize: A global dataset of maize yield, kernel number, and kernel weight over the last century
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Mapping of the QTLs governing grain micronutrients and thousand kernel weight in wheat (Triticum aestivum L.) using high density SNP markers
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Data from: Modelling mobile agent-based ecosystem services using kernel weighted predictors
1. Agriculture benefits from ecosystem services provided by mobile agents, such as biological pest control by natural enemies and pollination by bees. However, methods that can generate spatially explicit predictions and maps of these ecosystem services based on empirical data are still scarce. 2. Here we propose a generic statistical model to derive kernel functions to characterize the spatial distribution of ecosystem services provided by mobile agents. The model is similar in spirit to a generalized linear model, and uses data of landscape composition and ecosystem services assessed at target sites to estimate parameters of the kernel. The approach is tested in a simulation study and illustrated by an empirical case study on parasitism rates of the diamondback moth Plutella xylostella. 3. The simulation study shows that the scale parameter of the exponential power kernel can be estimated with limited bias, whereas estimation of the shape parameter is difficult. For the case study the model provides biologically relevant estimates for the kernel associated with parasitism of Plutella xylostella. These estimates can be used to generate ecosystem service maps for existing or planned landscapes. The case study reveals that predictions can be sensitive to the parameter values for the width and shape of the kernel, and to the link function used in the statistical model. 4. In the last two decades numerous empirical studies assessed ecosystem services at target sites and related these to the surrounding landscape. Our method can take advantage of these data by estimating underlying kernels that can be used to map the spatial distribution of ecosystem services. However, empirical data that can discriminate between alternative kernel shapes remain critical.
Data from: Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (Triticum aestivum L.) using genome-wide association study
<p>The study material in GWAS panel with 280 common bread wheat genotypes was selected from All India Coordinated Research Project on Wheat and Barley to map the genomic regions responsible for enhanced Grain Zinc Content (GZnC), Grain Iron Content (GZnC) and Thousand Kernel weight (TKW).</p> <p><strong>Phenotypic data:</strong></p> <p>The GWAS panel was evaluated at five different environments: E1-University of Agricultural Sciences, research farm, Dharwad (15°29'20.71"N, 74°59'3.35"E, 750m AMSL), E2-ICAR- Indian Agricultural Research Institute, New Delhi (28°38′30.5″N, 77°09′58.2″E, 228 m AMSL), E3-Indian Agricultural Research Institute, Jharkhand (24°16'58.4"N, 85°21'16.1"E, 651m AMSL), E4-ICAR-Indian Institute of Wheat and Barley, Karnal (29°41'8.2644''N, 76°59'25.9692''E, 250m AMSL), and E5-Punjab Agricultural University, Ludhiana (30o54' N, 75o48'E, 247m AMSL). Around 20 g of grain sample from each genotype were used for phenotyping GFeC and GZnC through high-throughput Energy Dispersive X-ray Fluorescence (ED-XRF) machine (model X-Supreme 8000; Oxford Instruments plc, Abingdon, United Kingdom) calibrated with glass beads-based values. To record TKW, the Numigral grain counter was used to count the grain number, the reading was set at 1000 grains and the weight of the grains was recorded in grams with an electronic balance. The GFeC, GZnC were expressed as milligram per kilogram (mg/kg), GPC in percentage (%), TKW in grams (gms).</p> <p><strong>Genotypic data:</strong></p> <p>Genomic DNA of the GWAS panel was extracted from the leaves of 21 days-old seedlings by Cetyl Trimethyl Ammonium Bromide (CTAB) method. The panel was genotyped using Axiom Wheat Breeder's Genotyping Array (Affymetrix, Santa Clara, CA, United States) having 35,143 genome-wide SNPs. The monomorphic, markers with minor allele frequency (MAF) of <5%, missing data of >20%, and heterozygote frequency >25% were removed from the analysis. The remaining set of 14,790 high-quality SNPs was used in GWAS analysis. The detailed information of the methods and software used, data analysis and GWAS is available at DOI: 10.1038/s41598-022-15992-z.</p>
Data from: Modelling mobile agent-based ecosystem services using kernel weighted predictors
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Data from: Genetic dissection of grain iron and zinc, and thousand kernel weight in wheat (Triticum aestivum L.) using genome-wide association study
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Data for publication: Weighted manifold alignment using wave kernel signatures for aligning medical image datasets
<p>2D dynamic sagittal MR images, corresponding to volunteers E-H in the publication "Weighted manifold alignment using wave kernel signatures for aligning medical image datasets".</p>
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