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Map-based cloning of a recessive gene v_1 for virescent leaf expression in cotton(Gossypium spp.)

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Journal of Cotton Research 2018年第2期1卷 35-43页

作者： ZHANG Youping WANG Qiaolian ZUO Dongyun CHENG Hailiang LIU Ke ashraf javaria LI Simin FENG Xiaoxu YU John Z. SONG Guoli State Key Laboratory of Cotton Biology Institute of Cotton Research of Chinese Academy of Agricultural Sciences U.S. Department of Agriculture-Agricultural Research Service (USDA-ARS) Southern Plains Agricultural Research Center College Station

Background： Virescence, as a recognizable phenotype available for research on chloroplast development and in the early development stage of cotton, is not only photosynthesis but also for heterosis exploitation in cotton Methods： In current study, for fine mapping of virescent-1 （V1） in cotton, three populations with a total of 5 678 individuals were constructed using T582 which has the virescent trait. Tobacco rattle virus, TRV1 and TRV2 （pYL156）, were used as vectors for the virus-induced gene silencing （VlGS） assay. Results： The V1 gene was fine-mapped to a 20 kb interval on chromosome 20 of tetraploid cotton. We identified only one candidate gene with four single nucleotide polymorphisms between parents, among which the single nucleotide polymorphism at the position of 1 082 base pair caused the change of amino acid residue from Arg （3-79） to Lys （]-582）. The relative expression of the candidate gene in virescent plants was extensively lower than that in normal plants. Nullification of the gene by VlGS significantly turned the green leaf of normal cotton plants into yellow. We named this candidate gene as GhRVL. Conclusions： This study will facilitate the further research on virescent formation, and will be useful for breeding of hybrid cottons.

关键词： Cotton Virescence T582 VIGS

Improved Gossypium raimondii genome using a Hi-C-based proximity-guided assembly

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Journal of Cotton Research 2021年第3期4卷 261-267页

作者： YANG Qiuhong ZUO Dongyun CHENG Hailiang ZHANG Youping WANG Qiaolian javaria ashraf FENG Xiaoxu LI Simin CHEN Xiaoqin LIU Shang SONG Guoli State Key Laboratory of Cotton Biology Institute of Cotton ResearchChinese Academy of Agricultural SciencesAnyangHenanChina

Introduction:Genome sequence plays an important role in both basic and applied *** raimondii,the putative contributor of the D subgenome of upland cotton(***,highlights the need to improve the genome quality rapidly and ***:We performed Hi-C sequencing of *** and reassembled its genome based on a set of new Hi-C data and previously published *** also compared the reassembled genome sequenee with the previously published G raimondii genomes for gene and genome sequence ***:A total of 9842%of scaffold sequences were clustered successfully,among which 99.72%of the clustered sequences were ordered and 99.92%of the ordered sequences were oriented with *** evaluation of results by heat-map and collinearity analysis revealed that the current reassembled genome is significantly improved than the previous one(Nat Genet 44:98-1103,2012).Conclusion:This improvement in G raimondii genome not only provides a better reference to increase study efficiency but also offers a new way to assemble cotton ***,Hi-C data of *** may be used for 3D structure research or regulating analysis.

关键词： Gossypium raimondii Hi-C Genome assembly Heatmap and collinearity

Networks of biochemical mechanisms, transcriptional regulation, and ionic transport underpin salinity tolerance in cotton

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Networks of biochemical mechanisms, transcriptional regulati...

中国生物工程学会第十一届学术年会暨2017年全国生物技术大会

作者： Muhammad Ali Abid Chengzhen Liang Waqas Malik Zhigang Meng Zhu Tao Zhaoghong Meng javaria ashraf Sandui Guo Rui Zhang Biotechnology Research Institute Chinese Academy of Agricultural Sciences Genomics Lab Department of Plant Breeding and Genetics Faculty of Agricultural Sciences and Technology Bahauddin Zakariya University

Salt stress is a severe threat to agricultural productivity in many parts of the world. Cotton(Gossypium hirsutum) is an economically important fiber crop globally, and is regarded as a model crop for salinity tolerance research. Although cotton is a relatively salt-tolerant plant, its growth and development, as well as fiber yield and quality, are greatly reduced by severe salt conditions. Salinity tolerance studies in cotton have focused on finding the key molecular genetic processes activated in response to salt stress, for breeding salt-tolerant cotton genotypes. In response to salt stress, cotton exhibits a variety of changes at the molecular, biochemical,and physiological levels. Photosynthetic pathways and metabolism play pivotal roles in redox balance and ion homeostasis. Several stress signaling pathways e.g. abscisic acid, salt overly sensitive(SOS),mitogen-activated protein kinase(MAPK), reactive oxygen species(ROS),and membrane bound Na+/H+antiporters, all participate in salinity tolerance. The activation and regulation of specific genes drive this physiological and biochemical plasticity. In cotton, the roles of several transcription factors(e.g. WRKY, ZFP, NAC, DREB, bZIP and ERF) in salt stress tolerance have been well documented. We also highlight the potential areas for future investigation to elucidate the key pathways for effective breeding of salt-tolerant cotton genotypes.

关键词： Cotton Salt stress Antioxidants Na+/H+ antiporters Transcription factors

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球孢白僵菌对柑橘木虱内共生菌的影响

武夷科学 2021年第2期37卷 119-126页

作者：戴佳旺王会停崔施施方元徐宏涛赖文昶杨晨李小芳李新安 HAFIZA javaria ashraf 王联德福建农林大学闽台作物有害生物生态防控国家重点实验室福建福州350002 福建农林大学生物农药与化学生物学教育部重点实验室福建福州350002

为明确球孢白僵菌[Beauveria bassiana(Balsamo)]侵染柑橘木虱后其体内共生菌的变化,研究虫生真菌、柑橘木虱及内共生菌三者间的互作关系,采用荧光原位杂交技术(FISH),测定2个球孢白僵菌(FAFU-12和FAFU-16)菌株亚致死浓度(LC_(25))对柑... 详细信息

为明确球孢白僵菌[Beauveria bassiana(Balsamo)]侵染柑橘木虱后其体内共生菌的变化,研究虫生真菌、柑橘木虱及内共生菌三者间的互作关系,采用荧光原位杂交技术(FISH),测定2个球孢白僵菌(FAFU-12和FAFU-16)菌株亚致死浓度(LC_(25))对柑橘木虱F_(1)代内共生菌Wolbachia和Carsonella的影响。结果表明,与对照相比,FAFU-12和FAFU-16处理组中柑橘木虱F1代2种内共生菌红色荧光分布范围减少,荧光强度减弱。球孢白僵菌亚致死剂量处理改变了柑橘木虱F_(1)代内共生菌Wolbachia和Carsonella的含量及分布,2种内共生菌参与了虫生真菌侵染宿主致病过程中的互作。

关键词：球孢白僵菌柑橘木虱内共生菌亚致死剂量荧光原位杂交技术

A Saliency Based Image Fusion Framework for Skin Lesion Segmentation and Classification

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Computers, Materials & Continua 2022年第2期70卷 3235-3250页

作者： javaria Tahir Syed Rameez Naqvi Khursheed Aurangzeb Musaed Alhussein Department of Computer Science COMSATS University IslamabadIslamabad CampusPakistan Department of Electrical and Computer Engineering COMSATS University IslamabadWah CampusPakistan Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia

Melanoma,due to its higher mortality rate,is considered as one of the most pernicious types of skin cancers,mostly affecting the white *** has been reported a number of times and is now widely accepted,that early detection of melanoma increases the chances of the subject’s ***-aided diagnostic systems help the experts in diagnosing the skin lesion at earlier stages using machine learning *** thiswork,we propose a framework that accurately segments,and later classifies,the lesion using improved image segmentation and fusion *** proposed technique takes an image and passes it through two methods simultaneously;one is the weighted visual saliency-based method,and the second is improved HDCT based saliency *** resultant image maps are later fused using the proposed image fusion technique to generate a localized lesion *** resultant binary image is later mapped back to the RGB image and fed into the Inception-ResNet-V2 pre-trained model-trained by applying transfer *** simulation results show improved performance compared to several existing methods.

关键词： Skin lesion segmentation image fusion saliency detection skin lesion classification deep neural networks transfer learning

Fruits and Vegetable Diseases Recognition Using Convolutional Neural Networks

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Computers, Materials & Continua 2022年第1期70卷 619-635页

作者： javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam University of Wah WahCanttPakistan National University of Technology(NUTECH) IJP RoadIslamabadPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsanKorea

As they have nutritional,therapeutic,so values,plants were regarded as important and they’re the main source of humankind’s energy *** pathogens will affect its leaves at a certain time during crop cultivation,leading to substantial harm to crop productivity&economic selling *** the agriculture industry,the identification of fungal diseases plays a vital ***,it requires immense labor,greater planning time,and extensive knowledge of plant *** approaches are developed and tested by different researchers to classify plant disease identification,and that in many cases they have also had important results several ***,the proposed study presents a new framework for the recognition of fruits and vegetable *** work comprises of the two phases wherein the phase-I improved localization model is presented that comprises of the two different types of the deep learning models such asYouOnly Look Once(YOLO)v2 and Open Exchange Neural(ONNX)*** localizationmodel is constructed by the combination of the deep features that are extracted from the ONNX model and features learning has been done through the convolutional-05 layer and transferred as input to the YOLOv2 *** localized images passed as input to classify the different types of plant *** classification model is constructed by ensembling the deep features learning,where features are extracted dimension of 1×1000 from pre-trained Efficientnetb0 model and supplied to next 07 layers of the convolutional neural network such as 01 features input,01 ReLU,01 Batch-normalization,02 *** proposed model classifies the plant input images into associated labels with approximately 95%prediction scores that are far better as compared to current published work in this domain.

关键词： Efficientnetb0 open exchange neural network features learning softmax YOLOv2

Convolutional Bi-LSTM Based Human Gait Recognition Using Video Sequences

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Computers, Materials & Continua 2021年第8期68卷 2693-2709页

作者： javaria Amin Muhammad Almas Anjum Muhammad Sharif Seifedine Kadry Yunyoung Nam ShuiHua Wang University of Wah Wah Cantt47040Pakistan National University of Technology(NUTECH) Islamabad44000Pakistan COMSATS University Islamabad Wah CampusWah CanttPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics University of LeicesterLeicesterUK

Recognition of human gait is a difficult assignment,particularly for unobtrusive surveillance in a video and human identification from a large ***,a method is proposed for the classification and recognition of different types of human *** proposed approach is consisting of two *** phase I,the new model is proposed named convolutional bidirectional long short-term memory(Conv-BiLSTM)to classify the video frames of human *** this model,features are derived through convolutional neural network(CNN)named ResNet-18 and supplied as an input to the LSTM model that provided more distinguishable temporal *** phase II,the YOLOv2-squeezeNet model is designed,where deep features are extricated using the fireconcat-02 layer and fed/passed to the tinyYOLOv2 model for recognized/localized the human gaits with predicted *** proposed method achieved up to 90%correct prediction scores on CASIA-A,CASIA-B,and the CASIA-C benchmark *** proposed method achieved better/improved prediction scores as compared to the recent existing works.

关键词： Bi-LSTM YOLOv2 open neural network resNet-18 gait squeezeNet

Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

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Computers, Materials & Continua 2021年第8期68卷 2451-2467页

作者： javaria Amin Muhammad Sharif Muhammad Almas Anjum Yunyoung Nam Seifedine Kadry David Taniar Department of Computer Science University of Wah47040Pakistan Department of Computer Science Comsats University IslamabadWah Campus47040Pakistan College of Electrical and Mechanical Engineering National University of Sciences&Technology(NUST)Islamabad44000Pakistan Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea Department of Mathematics and Computer Science Faculty of ScienceBeirut Arab University115020Lebanon Faculty of Information Technology Monash UniversityClaytonVictoria3800Australia

Coronavirus 19(COVID-19)can cause severe pneumonia that may be *** diagnosis is *** tomography(CT)usefully detects symptoms of COVID-19 *** this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and *** the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung *** the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification ***3 is an effective decoder that helps to refine segmentation of lesion *** model was trained using fine-tuned hyperparameters selected after extensive ***,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented *** computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for *** performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital *** results were better than those of existing methods.

关键词： Deeplabv3 fusion genetic algorithm gabor watershed

Malaria Parasite Detection Using a Quantum-Convolutional Network

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Computers, Materials & Continua 2022年第3期70卷 6023-6039页

作者： javaria Amin Muhammad Almas Anjum Abida Sharif Mudassar Raza Seifedine Kadry Yunyoung Nam University of Wah Wah CanttPakistan National University of Technology(NUTECH) IslamabadPakistan COMSATS University Islamabad Vehari CampusVehariPakistan COMSATS University Islamabad Wah CampusPakistan Faculty of Applied Computing and Technology Noroff University CollegeKristiansandNorway Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538Korea

Malaria is a severe illness triggered by parasites that spreads via mosquito *** underdeveloped nations,malaria is one of the top causes of mortality,and it is mainly diagnosed through ***-assisted malaria diagnosis is difficult owing to the fine-grained differences throughout the presentation of some uninfected and infected ***,in this study,we present a new idea based on the ensemble quantum-classical framework for malaria *** methods comprise three core steps:localization,segmentation,and *** the first core step,an improved FRCNN model is proposed for the localization of the infected malaria ***,the RGB localized images were converted into YCbCr channels to normalize the image intensity ***,the actual lesion region was segmented using a histogram-based color thresholding *** segmented images were employed for classification in two different *** the first method,a CNN model is developed by the selection of optimum layers after extensive experimentation,and the final computed feature vector is passed to the softmax layer for classification of the infection/non-infection of themicroscopicmalaria ***,a quantum-convolutionalmodel is employed for informative feature extraction from microscopicmalaria images,and the extracted feature vectors are supplied to the softmax layer for ***,classification results were analyzed from two different models and concluded that the quantum-convolutional model achieved maximum accuracy as compared to *** proposed models attain a precision rate greater than 90%,thereby proving that these models performed better than the existing models.

关键词： Quantum convolutional RGB YCbCr histogram Malaria