24 papers · ranked by Valyu relevance
Min Tu, Yin Li
Kernel hardness is one of the most important single traits of wheat seed. It classifies wheat cultivars, determines milling quality and affects many end-use qualities. Starch granule surfaces, polar lipids, storage protein matrices and Puroindolines potentially form a four-way interaction that controls wheat kernel…
Xiaoling Ma, Muhammad Sajjad, Jing Wang, Wenlong Yang + 4 more
Background Kernel hardness, which has great influence on the end-use properties of common wheat, is mainly controlled by Puroindoline genes, Pina and Pinb. Using EcoTILLING platform, we herein investigated the allelic variations of Pina and Pinb genes and their association with the Single Kernel Characterization System…
Xiaoyan Li, Yin Li, Xiaofen Yu, Fusheng Sun + 2 more
Kernel hardness is a key trait of wheat seeds, largely controlled by two tightly linked genes Puroindoline a and b (Pina and Pinb). Genes homologous to Pinb, namely Pinb2, have been studied. Whether these genes contribute to kernel hardness and other important seed traits remains inconclusive. Using the high-quality…
Josh Alman, Yunfeng Guan
In batch Kernel Density Estimation (KDE) for a kernel function f : R m × R m → R, we are given as input 2n points x (1), . . . , x(n) , y(1), . . . , y(n) ∈ R m in dimension m, as well as a vector v ∈ R n . These inputs implicitly define the n×n kernel matrix K given by K[i, j] = f(x (i) , y(j) ). The goal is to…
Meriem Aoun, Jose M. Orenday-Ortiz, Kitty Brown, Corey Broeckling + 3 more
'Craig F. Morris' 'Alecia M. Kiszonas' 'Shailender Kumar Verma'] Super soft kernel texture is associated with superior milling and baking performance in soft wheat. To understand the mechanism underlying super soft kernel texture, we studied proteomic changes between a normal soft and a super soft during kernel…
Sonia Goel, Kalpana Singh, Balwant Singh, Sapna Grewal + 5 more
Wheat cultivars are genetically crossed for improving end use quality for apt traits as per need of baking industry and broad consumer’s preferences. The processing and baking qualities of bread wheat underlie into genetic make-up of a variety and influence by environmental factors and their interactions. WL711 and…
Xianfang He, Maoang Lu, Jiajia Cao, Xu Pan + 7 more
'Haiping Zhang' 'Cheng Chang' 'Jianlai Wang' 'Chuanxi Ma' 'Hongxiang Ma'] The grain hardness index (HI) is one of the important reference bases for wheat quality and commodity properties; therefore, it is essential and useful to identify loci associated with the HI in wheat breeding. The grain hardness index of the…
Nathalie Geneix, Michèle Dalgalarrondo, Caroline Tassy, Isabelle Nadaud + 4 more
Grain hardness is an important quality trait of cereal crops. In wheat, it is mainly determined by the Hardness locus that harbors genes encoding puroindoline A (PINA) and puroindoline B (PINB). Any deletion or mutation of these genes leading to the absence of PINA or to single amino acid changes in PINB leads to hard…
Weidong Luo
Kernelization is a significant topic in parameterized complexity. Turing kernelization is a general form of kernelization. In the aspect of kernelization, an impressive hardness theory has been established [Bodlaender etc. (ICALP 2008, JCSS2009), Fortnow and Santhanam (STOC 2008, JCSS 2011), Dell and van Melkebeek…
Joanna K. Banach, Katarzyna Majewska, Krystyna Żuk-Gołaszewska
Grain of the highest hardness was produced from durum wheat grown without the growth regulator, at the lowest sowing density (350 seeds m^-2^) and nitrogen fertilization dose of 80 kg ha^-1^. The highest values L and b were determined in the grain of wheat cultivated without additional agrotechnical measures (growth…
Michael J. Burns, Jonathan S. Renk, David P. Eickholt, Amanda M. Gilbert + 9 more
Lack of high throughput phenotyping systems for determining moisture content during the maize nixtamalization cooking process has led to difficulty in breeding for this trait. This study provides a high throughput, quantitative measure of kernel moisture content during nixtamalization based on NIR scanning of uncooked…
Authors not listed
Chemical hardness is one of the fundamental concepts in chemical reactivity theory, rigorously defined within the framework of Conceptual Density Functional Theory (CDFT). The associated maximum hardness principle (MHP), which postulates that, a favorable direction of reaction is towards the state of maximum hardness…
Anant Agnihotri, Michael Krebsbach, Florentin Reiter, Thomas Wellens
Quantum support vector machines are classification algorithms that rely on quantum-generated kernels. The fidelity quantum kernel commonly used in quantum support vector machines suffers from exponential concentration as system size increases, preventing an efficient scaling beyond fewqubit systems. We introduce the…
Feng Xing, Jianwei Xiao, Bin Wen, Jijun Zhao + 3 more
'Yongjun Tian'] Abstract Understanding temperature-dependent hardness of covalent materials is not only of fundamental scientific interest, but also of crucial technical importance. Here we proposed a temperature-dependent hardness formula for diamond-structured covalent materials on the basis of the dislocation…
Ping Li
The1 term "CoRE kernel" stands for correlation-resemblance kernel. In many applications (e.g., vision), the data are often high-dimensional, sparse, and nonbinary. We propose two types of (nonlinear) CoRE kernels for non-binary sparse data and demonstrate the effectiveness of the new kernels through a classification…
Mirko Polato, Fabio Aiolli
In many personalized recommendation problems available data consists only of positive interactions (implicit feedback) between users and items. This problem is also known as One-Class Collaborative Filtering (OC-CF). Linear models usually achieve state-of-the-art performances on OC-CF problems and many efforts have…
Samir F. Matar, Vladimir L. Solozhenko
Novel ultra-hard hexacarbon C6 allotropes are proposed based on crystal chemistry rationale and geometry optimization onto ground state structures. Similar to diamond, the orthorhombic, tetragonal and trigonal C6 are cohesive networks of C4 tetrahedra illustrated by charge density projections exhibiting sp3-like carbon…
Samir F. Matar, Vladimir L. Solozhenko
Oppositely to diamond which is built of corner sharing C4 tetrahedra with C(sp3), the presence of mixed carbon hybridization (sp2 and sp3) in three carbon allotropes derived from so-called 'glitter' has been shown through DFT-based quantum calculations to produce stable systems sharing some properties of diamond with…
Authors not listed
Metastable states and the conformational transitions in between them are key to understanding dynamical behaviour and function of large-scale molecular systems. By combining basic dimensionality reduction techniques with a state-of-the art approximation of the Koopman operator associated to molecular dynamics…
Sriram K Vidyarthi, Rakhee Tiwari, Samrendra K Singh
After harvesting almond crop, accurate measurement of almond kernel sizes is a significant specification to plan, develop and enhance almond processing operations. The size and mass of the individual almond kernels are vital parameters usually associated with almond quality, particularly head almond yield. In this…
Jarosław Zaklika, Piotr Ordon, Ludwik Komorowski
The theory of reactivity based on cDFT has been supplemented with the new method of calculation of the atomic and local indices. With the use of previously derived relationship of the electron density gradient to the softness kernel and to the linear response function we deliver analytical scheme to obtain significant…
Samir F. Matar, Vladimir L. Solozhenko
Stable tetragonal C9 and C12 with original topologies have been devised based on crystal chemistry rationale and unconstrained geometry optimization calculations within the density functional theory (DFT). The two new carbon allotropes are characterized by corner- and edge-sharing tetrahedra, they are mechanically…
Yang Hu, Zhiwu Zhang
Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting the genetic architecture of these traits toward ultimate yield improvement. High-throughput…
Suvo Banik, Karthik Balasubramanian, Sukriti Manna, Sybil Derrible + 1 more
Identifying key descriptors and understanding important features across different classes of materials are crucial for machine learning (ML) tools to both predict material properties and reveal the physics underlying any process of interest. Traditionally, the predictive modeling of elastic properties of materials is…