Key hormonal components regulate agronomically important traits in barley

Marek Marzec is supported by scholarships funded by the Ministry of Science and Higher Education (424/STYP/10/2015 and DN/MOB/245/IV/2015). Ahmad M. Alqudah supported by Leibniz Institute of Plant Genetics and Crop Plant Research (IPK).The development and growth of plant organs is regulated by phytohormones, which constitute an important area of plant science. The last decade has seen a rapid increase in the unravelling of the pathways by which phytohormones exert their influence. Phytohormones function as signalling molecules that interact through a complex network to control development traits. They integrate metabolic and developmental events and regulate plant responses to biotic and abiotic stress factors. As such, they influence the yield and quality of crops. Recent studies on barley have emphasised the importance of phytohormones in promoting agronomically important traits such as tillering, plant height, leaf blade area and spike/spikelet development. Understanding the mechanisms of how phytohormones interact may help to modify barley architecture and thereby improve its adaptation and yield. To achieve this goal, extensive functional validation analyses are necessary to better understand the complex dynamics of phytohormone interactions and phytohormone networks that underlie the biological processes. The present review summarises the current knowledge on the crosstalk between phytohormones and their roles in barley development. Furthermore, an overview of how phytohormone modulation may help to improve barley plant architecture is also provided.MNiS

Insight into the genetic contribution of maximum yield potential, spikelet development and abortion in barley

Societal Impact Statement To feed the world's ever‐increasing population, new genetic approaches are required. Increasing the number of living spikelets is one promising way to improve grain yield. This, in turn, increases the number of spikelets per plant, thereby increasing the total yield. We present the first evidence for genetic control of alive spikelets in barley. Discovering natural variation as well as genomic regions associated with these traits will serve as a benchmark in future breeding for improving grain yield. Summary The primary goal of most breeding programmes is to increase grain yield. However, one of the many methods for raising yield that is yet to be fully investigated is increasing the number of spikelets by minimising spikelet abortion. Spikelet abortion dramatically increases during the late reproductive phase, but the molecular and genetic mechanisms remain unknown. Here, we employed a phenotyping approach in which developed and undeveloped spikelets were detected and counted during spike development and their maximum yield potential (MYP) was investigated. We studied 20 agronomic and spikelet‐related traits using a set of 184 diverse spring barley accessions under field conditions. By employing a set of >125K  SNPs, GWAS was conducted. Our analysis revealed 26 genetic clusters associated with MYP and the number of developed and undeveloped spikelets. Most of the significant associated genomic regions were co‐located near the candidate genes of phytohormones such as ABA, auxin, and cytokinin suggesting the importance of phytohormones in keeping spikelets alive, their development, and MYP. Our findings point to a potential link between jasmonic acid and the MYP, development and abortion of spikelets. We further provide genetic evidence that sugar‐related genes and sucrose have the potential to regulate MYP, spikelet development and spikelet survival. Our findings can be used for marker‐assisted breeding and as a resource for future molecular and genetic validation. Collectively, we propose a new genetic network linking spikelet‐related traits to grain yield determinants

Automatic Identity Recognition Using Speech Biometric

Biometric technology refers to the automatic identification of a person using physical or behavioral traits associated with him/her. This technology can be an excellent candidate for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. Biometric technology can be used to design and develop automatic identity recognition systems, which are highly demanded and can be used in banking systems, employee identification, immigration, e-commerce…etc. The first phase of this research emphasizes on the development of automatic identity recognizer using speech biometric technology based on Artificial Intelligence (AI) techniques provided in MATLAB. For our phase one, speech data is collected from 20 (10 male and 10 female) participants in order to develop the recognizer. The speech data include utterances recorded for the English language digits (0 to 9), where each participant recorded each digit 3 times, which resulted in a total of 600 utterances for all participants. For our phase two, speech data is collected from 100 (50 male and 50 female) participants in order to develop the recognizer. The speech data is divided into text-dependent and text-independent data, whereby each participant selected his/her full name and recorded it 30 times, which makes up the text-independent data. On the other hand, the text-dependent data is represented by a short Arabic language story that contains 16 sentences, whereby every sentence was recorded by every participant 5 times. As a result, this new corpus contains 3000 (30 utterances * 100 speakers) sound files that represent the text-independent data using their full names and 8000 (16 sentences * 5 utterances * 100 speakers) sound files that represent the text-dependent data using the short story. For the purpose of our phase one of developing the automatic identity recognizer using speech, the 600 utterances have undergone the feature extraction and feature classification phases. The speech-based automatic identity recognition system is based on the most dominating feature extraction technique, which is known as the Mel-Frequency Cepstral Coefficient (MFCC). For feature classification phase, the system is based on the Vector Quantization (VQ) algorithm. Based on our experimental results, the highest accuracy achieved is 76%. The experimental results have shown acceptable performance, but can be improved further in our phase two using larger speech data size and better performance classification techniques such as the Hidden Markov Model (HMM)

GWAS: Fast-forwarding gene identification and characterization in temperate Cereals: lessons from Barley – A review

Understanding the genetic complexity of traits is an important objective of small grain temperate cereals yield and adaptation improvements. Bi-parental quantitative trait loci (QTL) linkage mapping is a pow- erful method to identify genetic regions that co-segregate in the trait of interest within the research pop- ulation. However, recently, association or linkage disequilibrium (LD) mapping using a genome-wide association study (GWAS) became an approach for unraveling the molecular genetic basis underlying the natural phenotypic variation. Many causative allele(s)/loci have been identified using the power of this approach which had not been detected in QTL mapping populations. In barley (Hordeum vulgare L.), GWAS has been successfully applied to define the causative allele(s)/loci which can be used in the breeding crop for adaptation and yield improvement. This promising approach represents a tremendous step forward in genetic analysis and undoubtedly proved it is a valuable tool in the identification of can- didate genes. In this review, we describe the recently used approach for genetic analyses (linkage map- ping or association mapping), and then provide the basic genetic and statistical concepts of GWAS, and subsequently highlight the genetic discoveries using GWAS. The review explained how the candidate gene(s) can be detected using state-of-art bioinformatic tools

Sustaining construction organisations in NZ: A linear regression model approach to analysing determinants of their performance

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Plasma Amino Acids Metabolomics' Important in Glucose Management in Type 2 Diabetes

The perturbation in plasma free amino acid metabolome has been observed previously in diabetes mellitus, and is associated with insulin resistance as well as the onset of cardiovascular disease in this population. In this study, we investigated, for the first time, changes in the amino acid profile in a group of people with and without type 2 diabetes (T2D) with normal BMI, from Jordan, who were only managed on metformin. Twenty one amino acids were evaluated in plasma samples from 124 people with T2D and 67 healthy controls, matched for age, gender and BMI, using amino acids analyser. Total amino acids, essential amino acids, non-essential amino acids and semi-essential amino acids were similar in T2D compared to healthy controls. Plasma concentrations of four essential amino acids were increased in the presence of T2D (Leucine, p < 0.01, Lysine, p < 0.001, Phenylalanine, p < 0.01, Tryptophan, p < 0.05). On the other hand, in relation to non-essential amino acids, Alanine and Serine were reduced in T2D (p < 0.01, p < 0.001, respectively), whereas Aspartate and Glutamate were increased in T2D compared to healthy controls (p < 0.001, p < 0.01, respectively). A semi-essential amino acid, Cystine, was also increased in T2D compared to healthy controls (p < 0.01). Citrulline, a metabolic indicator amino acid, demonstrated lower plasma concentration in T2D compared to healthy controls (p < 0.01). These amino acids were also correlated with fasting blood glucose and HbA1c (p < 0.05). Glutamate, glycine and arginine were correlated with the duration of metformin treatment (p < 0.05). No amino acid was correlated with lipid profiles. Disturbances in the metabolism of these amino acids are closely implicated in the pathogenesis of T2D and associated cardiovascular disease. Therefore, these perturbed amino acids could be explored as therapeutic targets to improve T2D management and prevent associated cardiovascular complications

The Relationship between the Academic Procrastination and Self-Efficacy among Sample of King Saud University Students

The purpose of this study is to explore the relationship between the academic procrastination and self-efficacy among students of King Saud University. It also aims to identify whether the level of Academic procrastination varies depending on variables such as type of the college, academic level, or the level of students’ achievement. Two instruments were developed: academic procrastination instrument, and self-efficacy instrument. The questionnaires were distributed to random sample of 195 students from Science and Arts colleges at King Saud University. The Findings indicate that the highest percentage of the distribution of the sample on of procrastination Academic scale is (83.6%), followed by the low percentage (9.7%) of procrastination while the lowest percentage of procrastination is (6.7%). The findings also showed that there were statistically significant differences at the level of academic procrastination due to level of achievement for favour of group who get (acceptable) in their achievement. It also found that there were no statistically significant differences due to the type of college and the academic achievement. In addition, the findings revealed that there were statistically significant differences between of the academic procrastination scale and the self-efficacy scale. Keywords: Academic procrastination, self-efficacy, university student

The Role of Media in Educational Social Construction of Children with Special Needs

This study tries to explore the function that the media plays in supporting the social construction process within the context of inclusive education services. The impact of the media is one of the multiple variables that contribute to the broad adoption of inclusive education. Other contributing aspects include: To be more specific, what part do different kinds of media play in the social construction that makes up inclusive education? It is vital to do qualitative research to shed light on the function that the media plays in building and inviting classrooms to create. The selection of the test population to evaluate the performance of the inclusive education program required a great deal of attention to detail and consideration. The data for this research was gathered using a variety of methods, including observation, interviews, and written records. When looking at the data that was acquired, a descriptive qualitative analysis was performed. It has been found, after considerable discussion, that the speed with which social construction may occur in the classroom is closely tied to the efficacy of the media in aiding student comprehension. This conclusion was reached after much deliberation

Vascular endothelial growth factor receptor inhibition enhances chronic obstructive pulmonary disease picture in mice exposed to waterpipe smoke

Background: Chronic obstructive pulmonary disease (COPD) is marked by destruction of alveolar architecture. Preclinical modelling for COPD is challenging. Chronic cigarette smoke exposure, the reference animal model of COPD, is time-inefficient, while exposure to waterpipe smoke (WPS), a surging smoking modality, was not fully tested for its histopathological pulmonary consequences. Since alveolar damage and pulmonary vascular endothelial dysfunction are integral to COPD pathology, lung histopathological effects of WPS were temporally evaluated, alone or in combination with vascular endothelial growth factor receptor (VEGFR) inhibition in mice.Materials and methods: Mice were exposed to WPS, 3 hours/day, 5 days/week, for 1, 2, 3, or 4 months. Another group of mice was exposed to WPS for 1 month, while being subjected to injections with the VEGFR blocker Sugen5416 (SU, 20 mg/kg) 3 times weekly. Control mice were exposed to fresh air in a matching inhalation chamber. Histopathological assessment of COPD was performed. Alveolar destructive index (DI) was counted as the percentage of abnormally enlarged alveoli with damaged septa per all alveoli counted. Mean linear intercept (MLI) was calculated as a measure of airspace enlargement.Results: Exposure to WPS resulted in significant increases in alveolar DI and MLI only after 4 months. Lung inflammatory score was minimal across all time-points. Importantly, combination of WPS and SU resulted in significantly increased DI, MLI, and inflammatory scores as early as 1 month post exposure.Conclusions: Combined exposure to WPS and SU results in COPD picture, highlighting the role of pulmonary vascular endothelial dysfunction in the disease

Investigating rainfall estimation from radar measurements using neural networks

Rainfall observed on the ground is dependent on the four dimensional structure of precipitation aloft. Scanning radars can observe the four dimensional structure of precipitation. Neural network is a nonparametric method to represent the nonlinear relationship between radar measurements and rainfall rate. The relationship is derived directly from a dataset consisting of radar measurements and rain gauge measurements. The performance of neural network based rainfall estimation is subject to many factors, such as the representativeness and sufficiency of the training dataset, the generalization capability of the network to new data, seasonal changes, and regional changes. Improving the performance of the neural network for real time applications is of great interest. The goal of this paper is to investigate the performance of rainfall estimation based on Radial Basis Function (RBF) neural networks using radar reflectivity as input and rain gauge as the target. Data from Melbourne, Florida NEXRAD (Next Generation Weather Radar) ground radar (KMLB) over different years along with rain gauge measurements are used to conduct various investigations related to this problem. A direct gauge comparison study is done to demonstrate the improvement brought in by the neural networks and to show the feasibility of this system. The principal components analysis (PCA) technique is also used to reduce the dimensionality of the training dataset. Reducing the dimensionality of the input training data will reduce the training time as well as reduce the network complexity which will also avoid over fitting