Genome‑wide association analyses of leaf rust resistance in cultivated emmer wheat

Leaf rust, caused by Puccinia triticina (Pt), constantly threatens durum (Triticum turgidum ssp. durum) and bread wheat (Triticum aestivum) production worldwide. A Pt race BBBQD detected in California in 2009 poses a potential threat to durum production in North America because resistance source to this race is rare in durum germplasm. To find new resistance sources, we assessed a panel of 180 cultivated emmer wheat (Triticum turgidum ssp. dicoccum) accessions for seedling resistance to BBBQD and for adult resistance to a mixture of durum-specific races BBBQJ, CCMSS, and MCDSS in the field, and genotyped the panel using genotype-by-sequencing (GBS) and the 9 K SNP (Single Nucleotide Polymorphism) Infinium array. The results showed 24 and nine accessions consistently exhibited seedling and adult resistance, respectively, with two accessions providing resistance at both stages. We performed genome-wide association studies using 46,383 GBS and 4,331 9 K SNP markers and identified 15 quantitative trait loci (QTL) for seedling resistance located mostly on chromosomes 2B and 6B, and 11 QTL for adult resistance on 2B, 3B and 6A. Of these QTL, one might be associated with leaf rust resistance (Lr) gene Lr53, and two with the QTL previously reported in durum or hexaploid wheat. The remaining QTL are potentially associated with new Lr genes. Further linkage analysis and gene cloning are necessary to identify the causal genes underlying these QTL. The emmer accessions with high levels of resistance will be useful for developing mapping populations and adapted durum germplasm and varieties with resistance to the durum-specific races

Characterizing HMW-GS alleles of decaploid Agropyron elongatum in relation to evolution and wheat breeding

Bread wheat quality is mainly correlated with high molecular weight glutenin subunits (HMW-GS) of endosperm. The number of HMW-GS alleles with good processing quality is limited in bread wheat cultivars, while there are plenty of HMW-GS alleles in wheat-related grasses to exploit. We report here on the cloning and characterization of HMW-GS alleles from the decaploid Agropyron elongatum. Eleven novel HMW-GS alleles were cloned from the grass. Of them, five are x-type and six y-type glutenin subunit genes. Three alleles Aex4, Aey7, and Aey9 showed high similarity with another three alleles from the diploid Lophopyrum elongatum, which provided direct evidence for the Ee genome origination of A. elongatum. It was noted that C-terminal regions of three alleles of the y-type genes Aey8, Aey9, and Aey10 showed more similarity with x-type genes than with other y-type genes. This demonstrates that there is a kind of intermediate state that appeared in the divergence between x- and y-type genes in the HMW-GS evolution. One x-type subunit, Aex4, with an additional cysteine residue, was speculated to be correlated with the good processing quality of wheat introgression lines. Aey4 was deduced to be a chimeric gene from the recombination between another two genes. How the HMW-GS genes of A. elongatum may contribute to the improvement of wheat processing quality are discussed

Elastic Properties of Boron-Nitride Nanotubes through an Atomic Simulation Method

The elastic properties of the boron-nitride nanotubes are studied based on an atomic simulation method that is called atomic-scale finite element method. The Tersoff-Brenner potential is used to describe the interaction between boron and nitrogen atoms, and the computational method is established in an atomic-scale scheme similar to the classical finite element method. Young’s modulus is evaluated for the boron-nitride nanotubes, and their buckling behavior is analyzed. It is shown that the diameter has an obvious influence on Young’s modulus of BNNTs, and the buckling is little related to the length of the nanotubes

The Design of Fall Detection and Positioning Alarm System Base on WCDMA

Abstract- In this paper, a novel fall detection and automatic alarm system has been designed and presented, which consists of sensor module, control module and communication module. The sensor modules composed of 3-axis accelerometers are placed on object’s pocket to monitor the object’s acceleration. System is driven by an inexpensive and low consumption microcontroller. When object is fall, the system will detect it by the abnormal acceleration and make accurate positioning through GPS immediately. The alarm as well as location information is sent via 3G network to the mobile phone number that has been set. The system has the advantages of portable, low-power and accuracy

An Effective Recognition Method for Road Information Based on Mobile Terminal

This paper describes a design of fast recognition of road information based on mobile terminal. Firstly, based on the HOG algorithm, we study and verify the effects of different parameters on the performance of the algorithm. Secondly, we test 800 images randomly selected from the INRIA pedestrian dataset to obtain the optimal parameters for the mobile terminal and the proportion of video resolution and detection window. Then, under the same test conditions, the time overheads of the SVMLight and the LibSVM are recorded and SVMLight training time is significantly less than LibSVM. Thirdly, we design and implement a real-time road information recognition and warning system on the Windows platform and Android platform. Features include real-time pedestrians detection, voice warning, and road signs recognition. When the vehicle speed is less than 30 km/h, the video resolution is less than 720 × 576 and the detection window/image ratio is less than 1 : 50; the system can guarantee low delay and high recognition rate (97.2%)

2D Sketch based recognition of 3D freeform shapes by using the RBF Neural Network

This paper presents a novel free-form surface recognition method from 2D freehand sketching. The approach is based on the Radial basis function (RBF), an artificial intelligence technique. A simple three-layered network has been designed and constructed. After training and testing with two types of surfaces (four sided boundary surfaces and four close section surfaces), it has been shown that the method is useful in freeform surface recognition. The testing results are very satisfactory

Underwater Sonar Signals Recognition by Incremental Data Stream Mining with Conflict Analysis

Sonar signals recognition is an important task in detecting the presence of some significant objects under the sea. In military, sonar signals are used in lieu of visuals to navigate underwater and/or locate enemy submarines in proximity. In particular, classification algorithm in data mining has been applied in sonar signal recognition for recognizing the type of surfaces from which the sonar waves are bounced. Classification algorithms in traditional data mining approach offer fair accuracy by training a classification model with the full dataset, in batches. It is well known that sonar signals are continuous and they are collected as data streams. Although the earlier classification algorithms are effective in traditional batch training, it may not be practical for incremental classifier learning. Since sonar signal data streams can amount to infinity, the data preprocessing time must be kept to a minimum to fulfill the need for high speed. This paper presents an alternative data mining strategy suitable for the progressive purging of noisy data via fast conflict analysis from the data stream without the need to learn from the whole dataset at one time. Simulation experiments are conducted and superior results are observed in supporting the efficacy of the methodology