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农业科学学报:英文版 2023年第9期22卷 2673-2686页

作者： YUE Kai LI Ling-ling XIE Jun-hong Zechariah EFFAH sumera ANWAR WANG Lin-lin MENG Hao-feng LI Lin-zhi College of Agronomy Gansu Agricultural UniversityLanzhou 730070P.R.China State Key Laboratory of Aridland Crop Science Gansu Agricultural UniversityLanzhou 730070P.R.China Council for Scientific and Industrial Research Plant Genetic Resources Research InstituteBunso 00233Ghana Department of Biosciences Durham UniversityDurham DH13LEUK Department of Botany Government CollegeWomen UniversityFaisalabad 38000Pakistan College of Resources and Environment Science Gansu Agricultural UniversityLanzhou 730070P.R.China

The effect of nitrogen(N)fertilizer on the development of maize kernels has yet to be fully ***-mRNA analyses could help advance our understanding of how kernels respond to *** study analyzed the morphological,physiological,and transcriptomic changes in maize kernels under different N rates(0,100,200,and 300 kg ha–1).The result showed that increasing N application significantly increased maize grains’fresh and dry weight until N reached 200 kg ha–*** levels of indole-3-acetic acid,cytokinin,gibberellin,and a lower level of ethylene were associated with increased N *** obtained 31 differentially expressed genes(DEGs)in hormone synthesis and transduction,and 9 DEGs were regulated by 14 differentially expressed microRNAs(DEMIs)in 26 *** candidate DEGs and DEMIs provide valuable insight for manipulating grain filling under different N rates.

关键词： maize kernels phytohormones high-throughput sequencing microRNA

Drivers and breaks in the cholangiocarcinoma immune microenvironment

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Hepatobiliary Surgery and Nutrition 2022年第2期11卷 320-323页

作者： Caitlin B.Conboy sumera I.Ilyas Gregory J.Gores Division of Medical Oncology Mayo ClinicRochesterMinnesotaUSA Division of Gastroenterology and Hepatology Mayo ClinicRochesterMNUSA

Clinically effective immune checkpoint inhibitors(ICI)have transformed cancer treatment over the last *** progress was built on a foundation of understanding tumor immunology,immune checkpoint biology,and biomarkers for sensitivity to ICI(1).Monoclonal antibodies disrupting the PD-L1/PD-1 axis or CTLA-4 are highly effective in tumors that are primed for an anti-tumor immune response but suppressed by immune checkpoints(1).This is the case for cancer types with abundant neoantigens,such as melanoma and lung cancer,and in tumors regardless of histology that have elevated tumor mutation burden(TMB)or microsatellite instability(MSI)due to mismatch repair deficiency(dMMR).Elevated expression of PD-L1 is a biomarker of immunosuppression and predicts sensitivity to PD-(L)1 blockade in several tumor types,including lung and gastroesophageal *** cholangiocarcinoma(CCA),the immune landscape and factors predicting response to immunotherapy are less well understood(2).

关键词： lung elevated landscape

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

Multiple solutions of Cu-C6H9NaO7 and Ag-C6H9NaO7 nanofluids flow over nonlinear shrinking surface

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Journal of Central South University 2019年第5期26卷 1283-1293页

作者： Liaquat Ali LUND Zurni OMAR Ilyas KHAN sumera DERO School of Quantitative Sciences Universiti Utara Malaysia06010 SintokKedahMalaysia Faculty of Mathematics and Statistics Ton Duc Thang UniversityHo Chi Minh CityVietnam

Model of Casson nanofluid flow over a nonlinear shrinking surface is *** of Tiwari and Das is applied to nanofluid comprising of sodium alginate with copper and *** governing nonlinear equations incorporating the effects of the viscous dissipation are transformed into boundary value problems (BVPs) of ordinary differential equations (ODEs) by using appropriate similarity *** resulting equations are converted into initial value problems (IVPs) using the shooting method which are then solved by Runge-Kutta method of fourth *** order to determine the stability of the dual solutions obtained,stability analysis is performed and discovered that the first (second) solution is stable (unstable) and physically realizable (unrealizable).Both the thickness of the thermal boundary layer as well as temperature increase when the Casson parameter (β) is increased in the second solution.

关键词： Cu-C6H9NaO7 Ag-C6H9NaO7 shrinking surface dual solution stability analysis nanofluid

Mobile Devices Interface Adaptivity Using Ontologies

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Computers, Materials & Continua 2022年第6期71卷 4767-4784页

作者： Muhammad Waseem Iqbal Muhammad Raza naqvi Muhammad Adnan Khan Faheem Khan T.Whangbo Department of Software Engineering The Superior UniversityLahore53700Pakistan INP-ENIT University of ToulouseTarbes65000France Riphah School of Computing&Innovation Faculty of ComputingRiphah International University Lahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea Department of Computer Engineering Gachon UniversitySeongnam13557Korea

Currently,many mobile devices provide various interaction styles and modes which create complexity in the usage of *** context offers the information base for the development of Adaptive user interface(AUI)frameworks to overcome the *** this purpose,the ontological modeling has been made for specific context and *** type of philosophy states to the relationship among elements(e.g.,classes,relations,or capacities etc.)with understandable satisfied *** contextmechanisms can be examined and understood by anymachine or computational framework with these formal definitions expressed in Web ontology language(WOL)/Resource description frame work(RDF).The Protégéis used to create taxonomy in which system is framed based on four contexts such as user,device,task and *** competency questions and use-cases are utilized for knowledge obtaining while the information is refined through the instances of concerned parts of context *** consistency of the model has been verified through the reasoning software while SPARQL querying ensured the data availability in the models for defined *** semantic context model is focused to bring in the usage of adaptive *** exploration has finished up with a versatile,scalable and semantically verified context learning *** model can be mapped to individual User interface(UI)display through smart calculations for versatile UIs.

关键词： User context adaptive interfaces human computer interaction

Roman Urdu News Headline Classification Empowered with Machine Learning

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Computers, Materials & Continua 2020年第11期65卷 1221-1236页

作者： Rizwan Ali naqvi Muhammad Adnan Khan Nauman Malik Shazia Saqib Tahir Alyas Dildar Hussain Department of Unmanned Vehicle Engineering Sejong UniversitySeoul05006Korea Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan School of Computational Sciences Korea Institute for Advanced StudySeoul02455Korea

Roman Urdu has been used for text messaging over the Internet for years especially in Indo-Pak *** from the subcontinent may speak the same Urdu language but they might be using different scripts for *** communication using the Roman characters,which are used in the script of Urdu language on social media,is now considered the most typical standard of communication in an Indian landmass that makes it an expensive information *** Text classification is a solved problem but there have been only a few efforts to examine the rich information supply of Roman Urdu in the *** is due to the numerous complexities involved in the processing of Roman Urdu *** complexities associated with Roman Urdu include the non-availability of the tagged corpus,lack of a set of rules,and lack of standardized spellings.A large amount of Roman Urdu news data is available on mainstream news websites and social media websites like Facebook,Twitter but meaningful information can only be extracted if data is in a structured *** have developed a Roman Urdu news headline classifier,which will help to classify news into relevant categories on which further analysis and modeling can be *** author of this research aims to develop the Roman Urdu news classifier,which will classify the news into five categories(health,business,technology,sports,international).First,we will develop the news dataset using scraping tools and then after preprocessing,we will compare the results of different machine learning algorithms like Logistic Regression(LR),Multinomial Naïve Bayes(MNB),Long short term memory(LSTM),and Convolutional Neural Network(CNN).After this,we will use a phonetic algorithm to control lexical variation and test news from different *** preliminary results suggest that a more accurate classification can be accomplished by monitoring noise inside data and by classifying the *** applying above mentioned different machine learnin

关键词： Roman urdu news headline classification long short term memory recurrent neural network logistic regression multinomial naïve Bayes random forest k neighbor gradient boosting classifier

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

Overexpression of Slug is associated with malignant progression of esophageal adenocarcinoma

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World Journal of Gastroenterology 2008年第7期14卷 1044-1052页

作者： Paras Jethwa Mushal naqvi Robert G Hardy Neil A Hotchin Sally Roberts Robert Spychal Chris Tselepis CRUK Institute for Cancer Studies University of BirminghamUnited Kingdom Department of Clinical and Surgical Sciences Royal Infirmary of EdinburghUnited Kingdom School of Biosciences University of BirminghamUnited Kingdom Sandwell and West Birmingham Hopsitals NHS Trust BirminghamUnited Kingdom

AIM: To characterise expression of known E-cadherin repressors; Snail, Slug and Twist in the development of esophageal adenocarcinoma. METHODS: E-cadherin, Slug, Snail and Twist mRNA expression in Barrett's metaplasia and esophageal adenocarcinoma specimens was examined by real-time reverse transcription-polymerase chain reaction (RT-PCR). Semi-quantitative immunohistochemistry was used to examine cellular localisation and protein levels. The effect of Slug on epithelial mesenchymal transition (EMT) markers was examined by transfection of Slug into an adenocarcinoma line ***: Cellular localisation of Slug in Barrett's metaplasia was largely cytoplasmic whilst in adenocarcinoma it was nuclear. Semi-quantitative analysis indicated that Slug was more abundant in adenocarcinoma compared to matched Barrett's metaplastic specimens. Snail and Twist were expressed in adenocarcinoma but were cytoplasmic in location and not induced compared to Barrett's mucosa. These observations were supported by mRNA studies where only Slug mRNA was shown to be over-expressed in adenocarcinoma and inversely correlated to E-cadherin expression. Overexpression of Slug in OE33 mediated E-cadherin repression and induced the mesenchymal markers vimentin and ***: Progression to adenocarcinoma is associated with increased Slug expression and this may represent a mechanism of E-cadherin silencing.

关键词： Slug Oesophagus Cancer Barrett's metaplasia Epithelial-mesenchymal transition

Hybrid GrabCut Hidden Markov Model for Segmentation

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Computers, Materials & Continua 2022年第7期72卷 851-869页

作者： Soobia Saeed Afnizanfaizal Abdullah N.Z.Jhanjhi Mehmood naqvi Mehedi Masud Mohammed A.AlZain Department of Software Engineering UniversitiTeknologi Malaysia-UTM81310Malaysia School of Computer Science and Engineering SCE Taylor’s UniversitySubang Jaya47500Malaysia Department of Engineering and Technology Mohak CollegeAlbertaCanada Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia

Diagnosing data or object detection in medical images is one of the important parts of image segmentation especially those data which is less effective to identify inMRI such as low-grade tumors or cerebral spinal fluid(CSF)leaks in the *** aim of the study is to address the problems associated with detecting the low-grade tumor and CSF in brain is difficult in magnetic resonance imaging(MRI)images and another problem also relates to efficiency and less execution time for segmentation of medical *** tumor and CSF segmentation using trained light field database(LFD)datasets of MRI *** research proposed the new framework of the hybrid k-Nearest Neighbors(k-NN)model that is a combination of hybridization of Graph Cut and Support Vector Machine(GCSVM)and Hidden Markov Model of k-Mean Clustering Algorithm(HMMkC).There are four different methods are used in this research namely(1)SVM,(2)GrabCut segmentation,(3)HMM,and(4)k-mean clustering *** this framework,on the one hand,phase one is to perform the classification of SVM and Graph Cut algorithm to create the maximum margin *** research use GrabCut segmentation method which is the application of the graph cut algorithm and extract the data with the help of scaleinvariant features *** the other hand,in phase two,segment the low-grade tumors and CSF using a method adapted for HMkC and extract the information of tumor or CSF fluid by GCHMkC including iterative conditional maximizing mode(ICMM)with identifying the range of *** evaluation is also performing by the comparison of existing techniques in this *** conclusion,our proposed model gives better results than *** proposed model helps to common man and doctor that can identify their condition of brain *** future,this will model will use for other brain related diseases.

关键词： SVM tumor CSF k-NN MRI grabcut segmentation HMM

Automated White Blood Cell Disease Recognition Using Lightweight Deep Learning

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Computer Systems Science & Engineering 2023年第7期46卷 107-123页

作者： Abdullah Alqahtani Shtwai Alsubai Mohemmed Sha Muhammad Attique Khan Majed Alhaisoni Syed Rameez naqvi College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Computer Science HITEC University TaxilaTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia

White blood cells(WBC)are immune system cells,which is why they are also known as immune *** protect the human body from a variety of dangerous diseases and outside *** majority of WBCs come from red bone marrow,although some come from other important organs in the *** manual diagnosis of blood disorders is difficult,it is necessary to design a computerized *** have introduced various automated strategies in recent years,but they still face several obstacles,such as imbalanced datasets,incorrect feature selection,and incorrect deep model *** proposed an automated deep learning approach for classifying white blood disorders in this *** data augmentation approach is initially used to increase the size of a ***,a Darknet-53 pre-trained deep learning model is used and finetuned according to the nature of the chosen *** the fine-tuned model,transfer learning is used,and features engineering is done on the global average pooling *** retrieved characteristics are subsequently improved with a specified number of iterations using a hybrid reformed binary grey wolf optimization *** that,machine learning classifiers are used to classify the selected best features for final *** experiment was carried out using a dataset of increased blood diseases imaging and resulted in an improved accuracy of over 99%.

关键词： White blood cells augmentation deep features feature selection classification