Mohammad Ali Jinnah University
  • Islamabad, Sindh, Pakistan
Recent publications
For several strategies formulated to prevent atherosclerosis, Apolipoprotein A1 Milano (ApoA1M) remains a prime target. ApoA1M has been reported to have greater efficiency in reducing the incidence of coronary artery diseases. Furthermore, recombinant ApoA1M based mimetic peptide exhibits comparatively greater atheroprotective potential, offers a hope in reducing the burden of atherosclerosis in in vivo model system. The aim of this review is to emphasize on some of the observed ApoA1M structural and functional effects that are clinically and therapeutically meaningful that might converge on the basic role of ApoA1M in reducing the chances of glycation assisted ailments in diabetes. We also hypothesize that the nonenzymatic glycation prone arginine amino acid of ApoA1 gets replaced with cysteine residue and the rate of ApoA1 glycation may decrease due to change substitution of amino acid. Therefore, to circumvent the effect of ApoA1M glycation, the related mechanism should be explored at the cellular and functional levels, especially in respective experimental disease model in vivo.
Mycoplasma pneumoniae causes respiratory infections and other mucous membrane related inflammations. To explore the intra-specific variations and evolution of M. pneumoniae , a pan-genomic analysis was performed on 150 strains. In genome size evaluation of M. pneumoniae , on average 687 genes with low S.D (8.43) showed overall consistency in the gene count of 150 strains. This species is potentially pathogenic and highly evolving as 134 out of 150 showed almost all pathogenic factors with above 90% homology except 16 strains which belong to Spain, USA, China, and Japan. M. pneumoniae pan genome is an open pan genome showed total genes of 103174 in which 70359 (68.19%) core genes, 32241(31.24%) shared genes and 574 (0.55%) unique genes. Adhesin P30, Protein TopJ, ADP-ribosylating toxin CARDS toxin, GLPF, GLPK, GLPO, GLT and IgG-blocking protein M are the part of core genome. Adhesin P1, HMW1 and HMW2 genes are the part of shared genome. HMW3 and Cytadherence-associated protein P65 are the part of the unique genome in some strains. Therefore, comparative neighborhood gene analysis revealed many different neighbor genes pattern for HMW3 and P65. Phylogenetic analysis was exhibited in two main groups/clades of strains and identified major ancestral lineage within M. pneumoniae species.
Code comments are considered an efficient way to document the functionality of a particular block of code. Code commenting is a common practice among developers to explain the purpose of the code in order to improve code comprehension and readability. Researchers investigated the effect of code comments on software development tasks and demonstrated the use of comments in several ways, including maintenance, reusability, bug detection, etc. Given the importance of code comments, it becomes vital for novice developers to brush up on their code commenting skills. In this study, we initially investigated what types of comments novice students document in their source code and further categorized those comments using a machine learning approach. The work involves the initial manual classification of code comments and then building a machine learning model to classify student code comments automatically. The findings of our study revealed that novice developers/students’ comments are mainly related to Literal (26.66%) and Insufficient (26.66%). Further, we proposed and extended the taxonomy of such source code comments by adding a few more categories, i.e., License (5.18%), Profile (4.80%), Irrelevant (4.80%), Commented Code (4.44%), Autogenerated (1.48%), and Improper (1.10%). Moreover, we assessed our approach with three different machine-learning classifiers. Our implementation of machine learning models found that Decision Tree resulted in the overall highest accuracy, i.e., 85%. This study helps in predicting the type of code comments for a novice developer using a machine learning approach that can be implemented to generate automated feedback for students, thus saving teachers time for manual one-on-one feedback, which is a time-consuming activity.
Under salt and toxic metal stress condition, genetic make-up and proteins including some enzymes undergo specific changes to combat the posed harsh environment. Carbonic anhydrase is ubiquitous enzyme associated with respiratory disorder and cancerous condition. Strategies investigated and adopted by microorganisms to cope with hypersaline by bioinformatics analysis including homology modeling, Zinc metal interaction prediction, sequence analysis for Carbonic Anhydrase. This study shows that carbonic anhydrase acquired more acidic residues on its surface as countermeasure and may form salt bridges with positive ions in response to hypersaline environment. It also reduced its surface area and had more polar residues. Zinc metal interaction with Nitrogen of Histidine residues were almost conserved in the studied and modelled structure with fewer changes in catalytic region of sequence pattern. Salt tolerance achieved by foster promising approaches at the genetic and proteomic level by modifying protein sequence. Surface residues are exposed to a hypersaline medium and are mainly composed of polar and acidic residues. The enzyme exists in its compact form and reduces its surface footprint exposed to outside environment. Beta pleated secondary structure configuration increases flexibility to counter, ions disruptive effect.
Klebsiella pneumoniae is the causative agent of numerous types of infections in humans, includes urinary tract infections (UTIs), respiratory tract infections, and bloodstream infections. The hypervirulent Klebsiella pneumoniae associated with severe community-acquired infections. Hypervirulent Klebsiella pneumoniae(hvKP) is more virulent than classical Klebsiella pneumoniae (cKP) and it is an evolving pathotype due to the acquisition of genetic determinants which is responsible for its open genome. In the present study, we have performed the pan-genome analysis of Klebsiella pneumoniae for the determination of virulence proteins and genomic variation among its strains using a tool pangenome analysis pipeline (PGAP-1.2.1). The whole genome sequences of fifty-one Klebsiella pneumoniae strains was downloaded from NCBI using Refseq database. These sequences were converted into PGAP required extension files. Thirty-nine proteins and their corresponding protein sequences involved in the pathogenesis of the organism were downloaded from UniprotKB for BLAST analysis. After blast analysis, twenty-five strains were selected out of fifty-one strains based on 90-100% homology. A wide variety of selected pathogenic proteins were present in core and shared genome which includes siderophores specifically (salmochelin), adhesion proteins, extracellular toxins, and complement resistant proteins. The presence of carbapenem resistance and hypermucoviscosity proteins in unique genome indicated that Klebsiella pneumoniaeis continuously evolving through horizontal gene transfer (HGT). Additionally, the presence of siderophores like salmochelin and aerobactin indicates the presence of hypervirulent Klebsiella pneumoniae. Phylogenetic tree shows that strain GCF_000009885 (NTUH-K2044) was distant from all the other strains.
Carrot, Daucas carota is another important crop that is most cultivated throughout India and consumed by human beings and animals. The root knot nematode (RKN) Meloidogyne incognita infestation significantly reduces the yield of carrot at initial inoculums of 230–2300 J2/g soil. One strategy to address these concerns is to develop an effective agro-phyto-remediator to these tiny enemies that have zero toxicity to non-target organisms and can be applied at very low cost. Biochemical studies reveals that in certain cruciferous plants like Brassica rapa, Brassica botrytis and Raphanus sativous having nematicidal principle as α tetraethylene and 5-1-3-butenyl 2,2 bithienyl, polyacetylene compounds like trans 3,11-trideca-1-3,11-triene 5,7,9 trizene etc. targeted the percent mortality of Meloidogyne incognita juveniles increased almost equally from higher 100% upto 6.25% dilution after 24, 48, and 72 h exposure period of Raphanus sativus leaf extract, while Brassica botrytis caused significant percent mortality of Meloidogyne juveniles i.e. 100% was observed within 24 h exposure with leaf extract in its 100 and 50% concentrations whereas leaves extract of turnip was most effective and showed 100% J2 killed followed by 85.67–96.75% mortality with 50–6.25% dilation after 72 h exposure. Histo-pathological and molecular studies show infection of Meloidogyne incognita increased transpiration, photosynthesis or water content and decreased the level of sugars, ascorbic acid and fruit quality. In present study, observed high metabolic activities with intense cytoplasm and nuclei in giant cells produced by nematodes in the carrot.Keywords Meloidogyne Meloidogyne juveniles Daucas carota Agro-phytoremediatorProtein amino lipid
With the evolving technological era, the optical character recognition systems have substantial execution, considering the widespread use of daily hand-written human transaction. Optical Character Recognition (OCR) is an implementation of Computer Vision that digitizes numerous hand dealt documents for further analysis and formatting. OCR is achieved by various ways such as discriminative analysis and deep learning. This paper focuses on evaluating deep learning models on a hand-written compiled dataset of Urdu digits. The evaluation is performed for deep convolutional learning algorithms; VGGNet16, InceptionV3, ResNet50, and DenseNet121. The convolutional models are pre-trained on the ImageNet. The model weights of fully connected layers have been evaluated, reducing the training time of the convolutional layers. The testing accuracy for the compiled dataset is observed as, ResNet50 with 96%, InceptionV3 with 95%, VGGNet16 with 95% and DenseNet121 with 94%.
The root-knot nematode Meloidogyne incognita is one of the most damaging plant-parasitic nematodes and is responsible for significant crop losses worldwide. Rising human health and environmental concerns have led to the withdrawal of commonly used chemical nematicides. There has been a tremendous demand for eco-friendly bio-nematicides with beneficial properties to the nematode hosting plants, which encourages the need for alternative nematode management practices. The current study was undertaken to determine the nematicidal potential of cotton seed cake (CSC) against second-stage juvenile (J2) hatching, J2 mortality, and J2 penetration of M. incognita in tomato plants in vitro. J2s and egg masses of M. incognita were exposed to four concentrations (250, 500, 750, and 1000 mg/L) of CSC extracts. The higher J2 mortality and inhibition of J2 hatching were found at 1000 mg/L, while the least effective result was observed at 250 mg/L of the CSC extract. The CSC extract applied with the concentrations mentioned above also showed inhibition of J2 penetration in tomato roots; 1000 mg/L showed the highest inhibition of penetration, while 250 mg/L displayed the least inhibition. Using gas chromatography-mass spectroscopy, we identified 11 compounds, out of which 9,12-Octadecadienoic acid, Hexadecanoic acid, and Tetradecanoic acid were found as major compounds. Subsequently, in silico molecular docking was conducted to confirm the nematicidal behavior of CSC based on binding interactions of the above three major compounds with the targeted protein acetylcholine esterase (AChE) of M. incognita. The values of binding free energy are −5.3, −4.5, and −4.9 kcal/mol, observed for 9,12-Octadecadienoic acid, n-Hexadecanoic acid, and Tetradecanoic acid, respectively, suggesting that 9,12-Octadecadienoic acid binds with the receptor AChE more efficiently than the other two ligands. This study indicates that CSC has nematicidal potential that can be used to control M. incognita for sustainable agriculture.
The ever-increasing market turbulence has turned today’s corporate landscape more competitive and complex. Particularly during the last two decades, the increased utilization of Information & Communication Technologies (ICTs) globally transformed the services sector in terms of ease of business processes and improved client service delivery. However, in the current knowledge-based era, these tools & technologies would only be meaningful if these are appropriately utilized by a knowledgeable workforce. In other words, this knowledge age has changed the success mantra of business competitiveness by re-shifting the focus from ICT-based transformations to knowledge-based transformations, though the availability of ICT systems has further augmented the organizational capabilities. Moreover, truly capitalizing on these warrants a knowledge-enabled work culture and recognizing as such the strategic significance of in-house Intellectual Capital (IC) that serves as a prime mover of achieving a sustainable competitive advantage. However, the maximum potential of IC for deriving multi-stakeholder value has not been fully achieved. Therefore, by administering 12 face-to-face semi-structured interviews at Australian Professional Service Firms (PSFs), this research offers a novel perspective on IC valuation by presenting the concept of ‘Triple Value Bottomline’ coupled with ‘IC Best Practices for PSFs’. These collectively offer IC evaluation, measurement and management mechanisms. Overall, the findings reveal immense potential of IC for achieving diverse value outcomes for multi-stakeholders in PSFs.
In this paper, a fast reaching law based integral terminal sliding mode controller has been designed for photovoltaic based DC microgrid system. The proposed microgrid system comprised of photovoltaic system as main energy source and fuel cell, battery and supercapacitor as auxiliary energy sources. To avoid the stress on individual energy sources, an energy management system has been devised to allocate the load among these energy sources. Using the Lyapunov stability criteria, the stability of proposed framework has been validated. The performance of the proposed controller has been verified by simulating the dc microgrid under varying environmental conditions and load demands in MATLAB/Simulink platform. The comparison of the proposed control laws against PID and lyapunov controllers has been provided in order to assess its accuracy and robustness. The hardware-in-loop experiments have been performed to verify the real-time efficacy of the proposed microgrid system.
The purpose of this study is to analyze the influence of GDP, urbanization, trade openness, financial development, and renewable energy consumption on CO2 emissions in Pakistan using yearly time series data from 1985 to 2018. The study utilized the cointegration technique and Granger causality for empirical estimation. The results of the study indicated that urbanization, financial development, and trade openness upsurge CO2 emission. Whereas using renewable energy resources is favorable for the environment and possesses negative relation with CO2 emission. All variables possess long-run relation with Co2 emission. Granger causality shows unidirectional causality from GDP and renewable energy to CO2 emission. The study contains insight for policymakers in Pakistan with beneficial policy recommendations to work toward a sustainable green environment.
Gait analysis is typically associated with the pattern of the human walk. Determining it with computational means can be helpful in many ways—from identifying individual humans to detecting gait-related diseases. In comparison to the expensive approaches and devices, which are limited to laboratories, smartphones with motion sensors are low-cost solutions through which we can analyze mobility and gait patterns. Thus, in this work, we present the usage of smartphone sensors for data acquisition followed by machine learning-based gender classification, which is a baseline for different gait-related tasks. In this regard, we collected data from 14 persons in different tracks, paces, and movement styles; after adequate normalization, iterative feature elimination, and Monte-Carlo experiment-based ML training, we found the Decision Tree is the most optimal algorithm with attaining 90.6% balanced-accuracy.
Traditional methods for recruiting have few restrictions, which leads the HR and Candidate both away from the expectations. Scanning through hundreds of resumes to find the best candidates for a job is a time-consuming process and it may lead to biasness. Nowadays, many companies have come on the ground for talent acquisition using internet-based platforms. These platforms, also known as “job portals”, facilitate the candidate as well as the recruiter in the hiring process. HR can set the criteria based on skills, years of experience, education, etc. to filter out eligible candidates. Apparently, this may have a major impact on reducing the tasks. However, there are still an ample amount of resumes satisfying the criteria that need to be analyzed manually. In this paper, we are proposing a tool to automatically shortlist and rank the candidates based on their job profiles. It simplifies this process by calculating a score for a resume against the job description, by using cosine similarity, which makes it easy for the HR department to get to the eligible candidate.
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985 members
Sohail Iqbal
  • Department of Electronic Engineering
Kamran Azim
  • Biosciences
Murtaza Khan
  • Faculty of Computing
Samreen Javed
  • Department of Computer Science
Prem Singh
  • Department of Computer Science
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Mohammad Ali Jinnah University. 22-E, Block-6, P.E.C.H.S.. Karachi., 75400, Islamabad, Sindh, Pakistan
Website
https://www.jinnah.edu
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021-111-87-87-87, 34311325-6, 34543321-25