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Mechanisms and molecular approaches for heat tolerance in rice (Oryza sativa L.) under climate change scenario

Journal of Integrative Agriculture 2018年第4期17卷 726-738页

作者： syed adeel zafar Amjad Hameed Muhammad Amjad Nawaz MA Wei Mehmood All Noor Muzammil Hussain Mehboob-ur-Rahman National Key Facility for Crop Gene Resources and Genetic Improvement/Institute of Crop Sciences Chinese Academy of Agricultural Sciences Beijing 100081 P.R.China Nuclear Institute for Agriculture and Biology (NIAB) Faisalabad 38000 Pakistan Department of Biotechnology College of Engineering Science Chonnam National University Chonnam 59626 Republic of Korea Institute of Crop Sciences Chinese Academy of Agricultural Sciences~Key Laboratory of Crop Physiology and Ecology Ministryof Agriculture Beijing 100081 P.R.China State Key Laboratory of Mycology/Institute of Microbiology Chinese Academy of Sciences Beijing 100101 P.R. China Plant Genomics & Molecular Breeding Laboratories National Institute for Biotechnology and Genetic Engineering (NIBGE)Faisalabad 38000 Pakistan

Rice, a staple cereal crop in many parts of the world, has been confronted with multiple environmental stresses including high temperature, negatively impacts the booting as well as anthesis growth stages. The situation is further complicated by the changing climatic conditions, resulting in gradual escalation of temperature as well as changing the rainfall pattern and frequency, thus raising a concern of food security worldwide. The situation can be combat by developing rice varieties with excellent genetics with improved morpho-physiological, biochemical, and molecular mechanisms, together can minimize the adverse effects of heat stress. Here, several strategies(encompassing genetic and genomic, and mechanisms involved) for mitigating the impact of high temperature on rice have been discussed. Finally, the utilization of genomic knowledge in augmenting the conventional breeding approaches have been comprehensively elaborated to develop heat tolerant germplasm.

关键词： heat stress rice climate change molecular markers heat stress responses

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DEGENERATED PANICLE AND PARTIAL STERILITY 1(DPS1) encodes a CBS domain containing protein required for anther cuticle and panicle development in rice

DEGENERATED PANICLE AND PARTIAL STERILITY 1(DPS1) encodes a ...

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中国生物工程学会第十三届学术年会暨2019年全国生物技术大会

作者： syed adeel zafar Suyash B.Patil Muhammad Uzair Jingjing Fang Jinfeng Zhao Tingting Guo Shoujiang Yuan Muhammad Uzair Qian Luo Jianxin Shi Lukas Schreiber Xueyong Li National Key Facility for Crop Gene Resources and Genetic Improvement Institute of Crop Science Chinese Academy of Agricultural Sciences Shandong RiceResearch Institute Joint International Research Laboratory of Metabolic and Developmental Sciences School of Life Sciences and Biotechnology Shanghai Jiao TongUniversity Institute of Cellular and Molecular Botany University of Bonn

Degeneration of apical spikelets and reduced panicle fertility are common reasons for low seed-setting rate in rice. However, little is known about the underlying molecular *** we report a novel degenerated panicle and partial sterility 1(dps1) mutant that showed panicle apical degeneration and reduced fertility in middle spikelets. dps1 plants were characterized by small whitish anthers with altered cuticle morphology and absence of pollen grains. Amounts of cuticular wax and cutin were significantly reduced in dps1 anthers. Panicles of dps1 plants showed an accumulation of reactive oxygen species(ROS), lower antioxidant activity and increased programmed cell death. Map-based cloning revealed that DPS1 encodes a mitochondrial-localized protein containing a cystathionine β-synthase(CBS) domain which showed the highest expression in panicles and anthers. DPS1 physically interacted with mitochondrial thioredoxin proteins Trx1 and Trx20 and it participated in ROS scavenging. Global gene expression analysis in dps1 revealed that biological processes related to fatty acid metabolism and ROS homeostasis were significantly affected and the expression of key genes involved in wax and cutin biosynthesis were *** results suggest that DPS1 plays a vital role in regulating ROS homeostasis, anther cuticle formation and panicle development in rice.

关键词： anther cuticle CBS domain Oryza sativa panicle apical degeneration reactive oxygen species seed setting rate

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Ab-initio calculations of structural,electronic,and optical properties of Zn_3(VO_4)_2

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Chinese Physics B 2018年第3期27卷 220-224页

作者： Nisar Ahmed S Mukhtar Wei Gao syed zafar Ilyas Department of Physics and Applied Mathematics Pakistan Institute of Engineering and Applied Sciences Department of Physics Allama Iqbal Open University Department of Chemical and Materials Engineering the University of Auckland

The structural, electronic, and optical properties of Zn3（VO4）2 are investigated using full potential linearized aug- mented plane wave （FP-LAPW） method within the framework of density functional theory （DFT）. Various approaches are adopted to treat the exchange and correlation potential energy such as generalized gradient approximation （GGA）, GGA＋U, and the Tran-Blaha modified Becke-Johnson （TB-mBJ） potential. The calculated band gap of 3.424 eV by TB- mBJ is found to be close to the experimental result （3.3 eV）. The optical anisotropy is analyzed through optical constants, such as dielectric function and absorption coefficient along parallel and perpendicular crystal orientations. The absorption coefficient reveals high absorption （1.5 ×10^6cm^-1） of photons in the ultraviolet region.

关键词： density functional theory Zn3(V04)2 electronic structure optical properties

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Hybrid Global Optimization Algorithm for Feature Selection

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Computers, Materials & Continua 2023年第1期74卷 2021-2037页

作者： Ahmad Taher Azar zafar Iqbal Khan syed Umar Amin Khaled M.Fouad Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Faculty of Information Technology and Computer Science Nile UniversitySheikh ZaidEgypt

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization *** experimental study is performed in two stages to assess the proposed *** I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO *** based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)*** phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT *** phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical *** experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable *** from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

关键词： Particle swarm optimization(PSO) time-variant acceleration coefficients(TVAC) genetic algorithms differential evolution feature selection medical data

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Studying the patterns of alien and native floras of some habitats in Srinagar city, Kashmir, India

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Ecological Processes 2015年第1期4卷 17-28页

作者： syed Mubashir Andrabi zafar Ahmad Reshi Manzoor Ahmad Shah Salman Qureshi Department of Botany University of KashmirSrinagar 190006Jammu and KashmirIndia Department of Geography Humboldt University of BerlinRudower Chaussee 1612489 BerlinGermany School of Architecture Birmingham City UniversityThe Parkside Building5 Cardigan StreetBirmingham B47BDUK

Introduction:Urban flora is an important component of human-dominated ecosystems that links urban dwellers with *** study aims at compiling and analysing the alien and native vascular plant species of some selected habitats in the Srinagar city,Kashmir,***:Bi-seasonal sampling was employed and each habitat type(viz.,graveyards,grasslands,orchards and wastelands)was sampled at seven different ***:The number of species recorded from graveyards was 96;from grasslands 112;141 from orchards and 110 from *** level of invasion among habitats varied from 67.27%to 68.75%,with a mean value of 67.86%;out of clubbed species list,64.84%were ***,Poaceae,Brassicaceae and Fabaceae were the biggest families in all four habitat ***:Our research shows that across habitats,alien plant species are the predominant part of their floras.

关键词： Alien Flora Herbs Srinagar city Level of invasion

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Privacy Preservation in IoT Devices by Detecting Obfuscated Malware Using Wide Residual Network

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Computers, Materials & Continua 2024年第11期81卷 2395-2436页

作者： Deema Alsekait Mohammed Zakariah syed Umar Amin zafar Iqbal Khan Jehad Saad Alqurni Applied College Department of Computer Science and Information TechnologyPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The widespread adoption of Internet of Things(IoT)devices has resulted in notable progress in different fields,improving operational effectiveness while also raising concerns about privacy due to their vulnerability to virus ***,the study suggests using an advanced approach that utilizes machine learning,specifically the Wide Residual Network(WRN),to identify hidden malware in IoT *** research intends to improve privacy protection by accurately identifying malicious software that undermines the security of IoT devices,using the MalMemAnalysis ***,thorough experimentation provides evidence for the effectiveness of the WRN-based strategy,resulting in exceptional performance measures such as accuracy,precision,F1-score,and *** study of the test data demonstrates highly impressive results,with a multiclass accuracy surpassing 99.97%and a binary class accuracy beyond 99.98%.The results emphasize the strength and dependability of using advanced deep learning methods such as WRN for identifying hidden malware risks in IoT ***,a comparison examination with the current body of literature emphasizes the originality and efficacy of the suggested *** research builds upon previous studies that have investigated several machine learning methods for detecting malware on IoT ***,it distinguishes itself by showcasing exceptional performance metrics and validating its findings through thorough experimentation with real-world *** WRN offers benefits in managing the intricacies of malware detection,emphasizing its capacity to enhance the security of IoT *** summarize,this work proposes an effective way to address privacy concerns on IoT devices by utilizing advanced machine learning *** research provides useful insights into the changing landscape of IoT cybersecurity by emphasizing methodological rigor and conducting comparative performance *** research co

关键词： Obfuscated malware detection IoT devices Wide Residual Network(WRN) malware detection machine learning

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Hard exudates referral system in eye fundus utilizing speeded up robust features

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International Journal of Ophthalmology(English edition) 2017年第7期10卷 1171-1174页

作者： syed Ali Gohar Naqvi Hafiz Muhammad Faisal zafar Ihsanul Haq International Islamic University (IIUI)

In the paper a referral system to assist the medical experts in the screening/referral of diabetic retinopathy is suggested. The system has been developed by a sequential use of different existing mathematical techniques. These techniques involve speeded up robust features（SURF）, K-means clustering and visual dictionaries（VD）. Three databases are mixed to test the working of the system when the sources are dissimilar. When experiments were performed an area under the curve（AUC） of 0.9343 was attained. The results acquired from the system are promising.

关键词： referral system speeded up robust features eye fundus visual dictionaries

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Enhancing Collaborative and Geometric Multi-Kernel Learning Using Deep Neural Network

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Computers, Materials & Continua 2022年第9期72卷 5099-5116页

作者： Bareera zafar syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Ummul Baneen Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54890Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Department Software Gachon UniversitySeongnam13120Korea

This research proposes a method called enhanced collaborative andgeometric multi-kernel learning (E-CGMKL) that can enhance the CGMKLalgorithm which deals with multi-class classification problems with non-lineardata distributions. CGMKL combines multiple kernel learning with softmaxfunction using the framework of multi empirical kernel learning (MEKL) inwhich empirical kernel mapping (EKM) provides explicit feature constructionin the high dimensional kernel space. CGMKL ensures the consistent outputof samples across kernel spaces and minimizes the within-class distance tohighlight geometric features of multiple classes. However, the kernels constructed by CGMKL do not have any explicit relationship among them andtry to construct high dimensional feature representations independently fromeach other. This could be disadvantageous for learning on datasets with complex hidden structures. To overcome this limitation, E-CGMKL constructskernel spaces from hidden layers of trained deep neural networks (DNN).Due to the nature of the DNN architecture, these kernel spaces not onlyprovide multiple feature representations but also inherit the compositionalhierarchy of the hidden layers, which might be beneficial for enhancing thepredictive performance of the CGMKL algorithm on complex data withnatural hierarchical structures, for example, image data. Furthermore, ourproposed scheme handles image data by constructing kernel spaces from aconvolutional neural network (CNN). Considering the effectiveness of CNNarchitecture on image data, these kernel spaces provide a major advantageover the CGMKL algorithm which does not exploit the CNN architecture forconstructing kernel spaces from image data. Additionally, outputs of hiddenlayers directly provide features for kernel spaces and unlike CGMKL, do notrequire an approximate MEKL framework. E-CGMKL combines the consistency and geometry preserving aspects of CGMKL with the compositionalhierarchy of kernel spaces extracted from DNN hidde

关键词： CGMKL multi-class classification deep neural network multiplekernel learning hierarchical kernel spaces

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CNN Channel Attention Intrusion Detection SystemUsing NSL-KDD Dataset

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Computers, Materials & Continua 2024年第6期79卷 4319-4347页

作者： Fatma S.Alrayes Mohammed Zakariah syed Umar Amin zafar Iqbal Khan Jehad Saad Alqurni Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer and Information Sciences King Saud UniversityRiyadh11362Saudi Arabia College of Computer and Information Sciences Prince Sultan UniversityRiyadh11586Saudi Arabia Department of Educational Technologies College of EducationImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia

Intrusion detection systems(IDS)are essential in the field of cybersecurity because they protect networks from a wide range of online *** goal of this research is to meet the urgent need for small-footprint,highly-adaptable Network Intrusion Detection Systems(NIDS)that can identify *** NSL-KDD dataset is used in the study;it is a sizable collection comprising 43 variables with the label’s“attack”and“level.”It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks(CNN).Furthermore,this dataset makes it easier to conduct a thorough assessment of the suggested intrusion detection ***,maintaining operating efficiency while improving detection accuracy is the primary goal of this ***,typical NIDS examines both risky and typical behavior using a variety of *** the NSL-KDD dataset,our CNN-based approach achieves an astounding 99.728%accuracy rate when paired with channel *** to previous approaches such as ensemble learning,CNN,RBM(Boltzmann machine),ANN,hybrid auto-encoders with CNN,MCNN,and ANN,and adaptive algorithms,our solution significantly improves intrusion detection ***,the results highlight the effectiveness of our suggested method in improving intrusion detection precision,signifying a noteworthy advancement in this *** efforts will focus on strengthening and expanding our approach in order to counteract growing cyberthreats and adjust to changing network circumstances.

关键词： Intrusion detection system(IDS) NSL-KDD dataset deep-learning machine-learning CNN channel Attention network security

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Network Security Enhanced with Deep Neural Network-Based Intrusion Detection System

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Computers, Materials & Continua 2024年第7期80卷 1457-1490页

This study describes improving network security by implementing and assessing an intrusion detection system(IDS)based on deep neural networks(DNNs).The paper investigates contemporary technical ways for enhancing intrusion detection performance,given the vital relevance of safeguarding computer networks against harmful *** DNN-based IDS is trained and validated by the model using the NSL-KDD dataset,a popular benchmark for IDS *** model performs well in both the training and validation stages,with 91.30%training accuracy and 94.38%validation ***,the model shows good learning and generalization capabilities with minor losses of 0.22 in training and 0.1553 in ***,for both macro and micro averages across class 0(normal)and class 1(anomalous)data,the study evaluates the model using a variety of assessment measures,such as accuracy scores,precision,recall,and F1 *** macro-average recall is 0.9422,the macro-average precision is 0.9482,and the accuracy scores are ***,macro-averaged F1 scores of 0.9245 for class 1 and 0.9434 for class 0 demonstrate the model’s ability to precisely identify anomalies *** research also highlights how real-time threat monitoring and enhanced resistance against new online attacks may be achieved byDNN-based intrusion detection systems,which can significantly improve network *** study underscores the critical function ofDNN-based IDS in contemporary cybersecurity procedures by setting the foundation for further developments in this *** research aims to enhance intrusion detection systems by examining cooperative learning techniques and integrating up-to-date threat knowledge.

关键词： Machine-Learning Deep-Learning intrusion detection system security privacy deep neural network NSL-KDD Dataset

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