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| 1 | Reverse vaccinology assisted designing of multiepitope-based subunit vaccine against SARS-CoV-2显示文摘Background:Coronavirus disease 2019(COVID-19)linked with severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)cause severe illness and life-threatening pneumonia in humans.The current COVID-19 pandemic demands an effective vaccine to acquire protection against the infection.Therefore,the present study was aimed to design a multiepitope-based subunit vaccine(MESV)against COVID-19.Methods:Structural proteins(Surface glycoprotein,Envelope protein,and Membrane glycoprotein)of SARS-CoV-2 are responsible for its prime functions.Sequences of proteins were downloaded from GenBank and several immunoinformatics coupled with computational approaches were employed to forecast B-and T-cell epitopes from the SARS-CoV-2 highly antigenic structural proteins to design an effective MESV.Results:Predicted epitopes suggested high antigenicity,conserveness,substantial interactions with the human leukocyte antigen(HLA)binding alleles,and collective global population coverage of 88.40%.Taken together,276 amino acids long MESV was designed by connecting 3 cytotoxic T lymphocytes(CTL),6 helper T lymphocyte(HTL)and 4 B-cell epitopes with suitable adjuvant and linkers.The MESV construct was non-allergenic,stable,and highly antigenic.Molecular docking showed a stable and high binding affinity of MESV with human pathogenic toll-like receptors-3(TLR3).Furthermore,in silico immune simulation revealed significant immunogenic response of MESV.Finally,MEV codons were optimized for its in silico cloning into the Escherichia coli K-12 system,to ensure its increased expression.Conclusion:The MESV developed in this study is capable of generating immune response against COVID-19.Therefore,if designed MESV further investigated experimentally,it would be an effective vaccine candidate against SARS-CoV-2 to control and prevent COVID-19. | Muhammad Tahir ul Qamar Farah Shahid Sadia Aslam Usman Ali Ashfaq Sidra Aslam Israr Fatima Muhammad Mazhar Fareed Ali Zohaib Ling-Ling Chen | 2020 | Infectious Diseases of Poverty2020，9，5： | 2 |
| 2 | Kinematic Analysis and Synthesis of an Adjustable Six-bar Linkage显示文摘 | Gordon R Pennock Ali Israr | 2009 | Mechanism and Machine Theory2009，44，： | 1 |
| 3 | Key Factors for Determining Students' Satisfaction in Distance Learning Courses: A Study of Allama Iqbal Open University显示文摘 | Afzaal Ali Israr Ahmad | 2011 | Contemporary Educational Technology2011，2，2： | 1 |
| 4 | Key Factors for Deter- mining Students' Satisfaction in Distance Learn- ing Courses: A Study of Allama Iqbal Open Uni- versity 显示文摘 | Afzaal Ali Israr Ahmad | 2011 | Contemporary Educational Technolo- gy2011，2，2： | 1 |
| 5 | Deep Learning Method to Detect the Road Cracks and Potholes for Smart Cities显示文摘The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance. | Hong-Hu Chu Muhammad Rizwan Saeed Javed Rashid Muhammad Tahir Mehmood Israr Ahmad Rao Sohail Iqbal Ghulam Ali | 2023 | Computers, Materials & Continua2023，，4： | 1 |
| 6 | On the monetary measures of global liquidity显示文摘This study constructs and examines the dynamics of theoretical and atheoretical measures of global liquidity,using monthly data on the components of broad money over the period 2001 M12-2017 M12 for 39 high income countries.We group the countries into five regional blocks as categorized by the World Bank:East Asia and the Pacific,Europe and Central Asia,Latin America and the Caribbean,Middle East and North Africa,and North America.The atheoretical measures exploited by this study comprise of the simple-sum,GDP-weighted growth rates and PCA based aggregation methods;whereas theoretical measures include the currency equivalent and Divisia index techniques of monetary aggregation.We employ a graphical approach to investigate the trends and dynamics of the aggregates overtime,and a cross-correlation between cyclical components of global real economic activity and the lag of cyclical components of the measures of global liquidity to gauge the strength of their associations.The findings of this study reveal that theoretical measures outperform atheoretical ones in effective delineation of financial and liquidity conditions,and policy stance.Their cyclical components are also strongly associated with those of global real business activity.The currency equivalent measure,besides being a leading indicator of the shift in policy stance,has a sturdy association with global real business activity.Moreover,the theoretical measures,as noted by some empirical studies,contain some information content that the atheoretical lack. | Israr Ahmad Shah Hashmi Arshad Ali Bhatti | 2019 | Financial Innovation2019，5，1： | 0 |
| 7 | Applications of beneficial plant growth promoting rhizobacteria and mycorrhizae in rhizosphere and plant growth:A review显示文摘Because of climate change and the highly growing world population,it becomes a huge challenge to feed the whole population.To overcome this challenge and increase the crop yield,a large number of fertilizers are applied but these have many side effects.Instead of these,scientists have discovered beneficial rhizobacteria,which are environmentally friendly and may increase crop yield and plant growth.The microbial population of the rhizosphere shows a pivotal role in plant development by inducing its physiology.Plant depends upon the valuable interactions among the roots and microbes for the growth,nutrients availability,growth promotion,disease suppression and other important roles for plants.Recently numerous secrets of microbes in the rhizosphere have been revealed due to huge development in molecular and microscopic technologies.This review illustrated and discussed the current knowledge on the development,maintenance,interactions of rhizobacterial populations and various proposed mechanisms normally used by PGPR in the rhizosphere that encouraging the plant growth and alleviating the stress conditions.In addition,this research reviewed the role of single and combination of PGPR,mycorrhizal fungi in plant development and modulation of the stress as well as factors affecting the microbiome in the rhizosphere. | Ashiq Khan Zitong Ding Muhammad Ishaq Israr Khan Anum Ali Ahmed Abdul Qadir Khan Xusheng Guo | 2020 | International Journal of Agricultural and Biological Engineering2020，13，5： | 0 |
| 8 | Neural Machine Translation Models with Attention-Based Dropout Layer显示文摘In bilingual translation,attention-based Neural Machine Translation(NMT)models are used to achieve synchrony between input and output sequences and the notion of alignment.NMT model has obtained state-of-the-art performance for several language pairs.However,there has been little work exploring useful architectures for Urdu-to-English machine translation.We conducted extensive Urdu-to-English translation experiments using Long short-term memory(LSTM)/Bidirectional recurrent neural networks(Bi-RNN)/Statistical recurrent unit(SRU)/Gated recurrent unit(GRU)/Convolutional neural network(CNN)and Transformer.Experimental results show that Bi-RNN and LSTM with attention mechanism trained iteratively,with a scalable data set,make precise predictions on unseen data.The trained models yielded competitive results by achieving 62.6%and 61%accuracy and 49.67 and 47.14 BLEU scores,respectively.From a qualitative perspective,the translation of the test sets was examined manually,and it was observed that trained models tend to produce repetitive output more frequently.The attention score produced by Bi-RNN and LSTM produced clear alignment,while GRU showed incorrect translation for words,poor alignment and lack of a clear structure.Therefore,we considered refining the attention-based models by defining an additional attention-based dropout layer.Attention dropout fixes alignment errors and minimizes translation errors at the word level.After empirical demonstration and comparison with their counterparts,we found improvement in the quality of the resulting translation system and a decrease in the perplexity and over-translation score.The ability of the proposed model was evaluated using Arabic-English and Persian-English datasets as well.We empirically concluded that adding an attention-based dropout layer helps improve GRU,SRU,and Transformer translation and is considerably more efficient in translation quality and speed. | Huma Israr Safdar Abbas Khan Muhammad Ali Tahir Muhammad Khuram Shahzad Muneer Ahmad Jasni Mohamad Zain | 2023 | Computers, Materials & Continua2023，，5： | 0 |
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