Genome-wide identififi cation and characterization of a plant-specifific Dof transcription factor gene family in olive (Olea europaea) and its comparison with Arabidopsis

Mariyam,Muhammad Shafiq,Muhammad Haseeb,Rana Muhammad Atif,Syed Agha Armaghan Asad Abbas Naqvi,Numan Ali,Muhammad Arshad Javed,Fizza Gillani,Muhammad Saleem Haider 한국원예학회 2021 Horticulture, Environment, and Biotechnology Vol.62 No.6

DNA binding with one fi nger (Dof) proteins are encoded by a ubiquitous plant-specifi c transcription factor gene family thatplays a critical role in various biological processes including fruit ripening and organogenesis. The wild olive ( Olea europaeavar. sylvestris v1.0 ) genome was used to identify Dof gene family members using a set of bioinformatics tools. Gene structure,chromosome locations, phylogeny, protein motifs, miRNA targets and tissue-specifi c expression patterns were analyzed. Here, we identifi ed 51 potential Dof genes unevenly distributed on all chromosomes and a few scaff olds. Dof proteins in oliveclustered into eight subgroups (D1, B2, C3, C2.2, C1, C2.1, B1, and A) based on the established Arabidopsis classifi cation. The prevalence of segmental duplication was observed as compared to tandem duplication, and this was the main factorunderlying the expansion of the Dof gene family in olive. Tissue-specifi c expression profi ling of Oeu Dof genes revealed thatthe majority of Oeu Dof genes were highly expressed in fl owers, stem and meristem tissues. In seed and meristem tissues,cis-regulatory element (CRE) analysis revealed the presence of elements that are specifi cally responsive to light, circadian,auxin, and ABA. In addition, a comparative analysis between Dof genes in olive and Arabidopsis revealed eight groups orsub-families, although the C3 group of Arabidopsis was not represented in olive. This extensive genome evaluation of theDof gene family in olive presents a reference for cloning and functional analysis of the members of this gene family.

A Novel Compressed Sensing Technique for Traffic Matrix Estimation of Software Defined Cloud Networks

( Sameer Qazi ),( Syed Muhammad Atif ),( Muhammad Bilal Kadri ) 한국인터넷정보학회 2018 KSII Transactions on Internet and Information Syst Vol.12 No.10

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements . Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

Data Mining Techniques : More Accurate Classified Algorithm For Cardiopulmonary Diseases Prediction

Taher M. Ghazal,Syed Hakim Masood,Atif Ali,Muhammad Usama Nazir 한국차세대컴퓨팅학회 2022 한국차세대컴퓨팅학회 학술대회 Vol.2022 No.10

Data mining techniques develop a more accurate classification algorithm for patients classified as either normotensive, prehypertensive, or hypertensive. Logistic Model Tree, NBTree, and Bagging were chosen as the three classification models with tenfold cross-validation (LMT). Over 24 hours, we collected ABP readings from 1161 patients. To analyze the data, data mining techniques were used and a tool called WEKA. The data was analyzed based on age, gender, wake-up blood pressure, medication, sleep-up blood pressure, and overall blood pressure. According to bagging results, 886 cases (76.3 percent) are correctly classified, with 270 cases classified as pre-hypertensive, 436 cases as Normotensive, and 180 cases as hypertensive. NBTree's results show that 882 (75.9%) of the 1161 instances are correctly classified. Pre-hypertensive patients make up 256, normotensive patients 442, and hypertensive patients 184. Of the 1161 instances, the LMT algorithm correctly classified 878 (75.6 percent). According to the results, 275 people are pre-hypertensive, 431 are normotensive, and 172 are hypertensive. According to our findings, bagging is the most accurate classifier for the 24 hour ABP Monitoring dataset we used. Bagging achieves less overfitting because it focuses on global accuracy. It stabilizes and improves the accuracy of unstable methods compared to single classifiers.

Characterization and Comparative Evaluation of Milk Protein Variants from Pakistani Dairy Breeds

Iqra Yasmin,Rabia Iqbal,Atif Liaqat,Wahab Ali Khan,Muhamad Nadeem,Aamir Iqbal,Muhammad Farhan Jahangir Chughtai,Syed Junaid Ur Rehman,Saima Tehseen,Tariq Mehmood,Samreen Ahsan,Saira Tanweer,Saima Naz 한국축산식품학회 2020 한국축산식품학회지 Vol.40 No.5

The aim of study was to scrutinize the physicochemical and protein profile of milk obtained from local Pakistani breeds of milch animals such as Nilli-Ravi buffalo, Sahiwal cow, Kajli sheep, Beetal goat and Brela camel. Physicochemical analysis unveiled maximum number of total solids and protein found in sheep and minimum in camel. Buffalo milk contains the highest level of fat (7.45%) while camel milk contains minimum (1.94%). Ash was found maximum in buffalo (0.81%) and sheep (0.80%) while minimum in cow’s milk (0.71%). Casein and whey proteins were separated by subjecting milk to isoelectric pH and then analyzed through sodium dodecyl sulphate-polyacrylamide gel electrophoresis (SDS-PAGE). The results showed heterogeneity among these species. Different fractions including αS1, αS2, κ-casein, β-casein and β-lactoglobulen (β-Lg) were identified and quantitatively compared in all milk samples. Additionally, this electrophoretic method after examining the number and strength of different protein bands (αS1, αS2, β- CN, α-LAC, BSA, and β-Lg, etc.), was helpful to understand the properties of milk for different processing purposes and could be successfully applied in dairy industry. Results revealed that camel milk was best suitable for producing allergen free milk protein products. Furthermore, based on the variability of milk proteins, it is suggested to clarify the phylogenetic relationships between different cattle breeds and to gather the necessary data to preserve the genetic fund and biodiversity of the local breeds. Thus, the study of milk protein from different breed and species has a wide range of scope in producing diverse protein based dairy products like cheese.

Ethernet‑Based Fault Diagnosis and Control in Smart Grid: A Stochastic Analysis via Markovian Model Checking

Riaz Uddin,Ali S. Alghamdi,Muhammad Hammad Uddin,Ahmed Bilal Awan,Syed Atif Naseem 대한전기학회 2019 Journal of Electrical Engineering & Technology Vol.14 No.6

The fault diagnosis and control through fault detection, isolation and supply restoration (FDIR) technique is the part of a commonly used distribution management system application in smart grid. When the fault occurs, it becomes essential to detect and isolate the faulty section of the distribution network at once and then restore back to its running condition through tie switches. The communication between IEDs is done through diferent communication mediums such as Ethernet, wireless, power line communication etc. Therefore, formal analysis of the FDIR mechanism is required with communication network (ideally Ethernet), which helps us to predict the behavior of FDIR response upon the occurrence of fault in terms of various important probabilities, reliability study and efciency (showing the system will work properly). In this regard, for the above said analyses, this article discusses (a) the development of the Markovian model of FDIR for distribution network of smart grid considering Tianjin Electric Power Network as case study with intelligent electronic devices (IEDs) using ideal communication medium (Ethernet); (b) utilized probabilistic model checker (PRISM tool) to predict the probabilities; (c) perform the reliability analyses and (d) study the efciency of FDIR behavior for future grid using logical properties. The detailed analysis and prediction (done for the fault occurrence scenario) mainly focus in determining the (1) the probability of switching failures of FDIR in smart grid; (2) the probability of isolating the defective switch from the system within limited time and (3) the probability of restoring the system automatically within the minimum possible interval.