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1. Position Specific Scoring Is All You Need? Revisiting Protein Sequence Classification Tasks

Author

Ali, Sarwan, Murad, Taslim, Chourasia, Prakash, Mansoor, Haris, Khan, Imdad Ullah, Chen, Pin-Yu, and Patterson, Murray

Subjects
Computer Science - Machine Learning
Abstract

Understanding the structural and functional characteristics of proteins are crucial for developing preventative and curative strategies that impact fields from drug discovery to policy development. An important and popular technique for examining how amino acids make up these characteristics of the protein sequences with position-specific scoring (PSS). While the string kernel is crucial in natural language processing (NLP), it is unclear if string kernels can extract biologically meaningful information from protein sequences, despite the fact that they have been shown to be effective in the general sequence analysis tasks. In this work, we propose a weighted PSS kernel matrix (or W-PSSKM), that combines a PSS representation of protein sequences, which encodes the frequency information of each amino acid in a sequence, with the notion of the string kernel. This results in a novel kernel function that outperforms many other approaches for protein sequence classification. We perform extensive experimentation to evaluate the proposed method. Our findings demonstrate that the W-PSSKM significantly outperforms existing baselines and state-of-the-art methods and achieves up to 45.1\% improvement in classification accuracy.

Published
2024

2. Sequence-Based Nanobody-Antigen Binding Prediction

Author

Sardar, Usama, Ali, Sarwan, Ayub, Muhammad Sohaib, Shoaib, Muhammad, Bashir, Khurram, Khan, Imdad Ullah, and Patterson, Murray

Subjects
Quantitative Biology - Biomolecules, Computer Science - Machine Learning
Abstract

Nanobodies (Nb) are monomeric heavy-chain fragments derived from heavy-chain only antibodies naturally found in Camelids and Sharks. Their considerably small size (~3-4 nm; 13 kDa) and favorable biophysical properties make them attractive targets for recombinant production. Furthermore, their unique ability to bind selectively to specific antigens, such as toxins, chemicals, bacteria, and viruses, makes them powerful tools in cell biology, structural biology, medical diagnostics, and future therapeutic agents in treating cancer and other serious illnesses. However, a critical challenge in nanobodies production is the unavailability of nanobodies for a majority of antigens. Although some computational methods have been proposed to screen potential nanobodies for given target antigens, their practical application is highly restricted due to their reliance on 3D structures. Moreover, predicting nanobodyantigen interactions (binding) is a time-consuming and labor-intensive task. This study aims to develop a machine-learning method to predict Nanobody-Antigen binding solely based on the sequence data. We curated a comprehensive dataset of Nanobody-Antigen binding and nonbinding data and devised an embedding method based on gapped k-mers to predict binding based only on sequences of nanobody and antigen. Our approach achieves up to 90% accuracy in binding prediction and is significantly more efficient compared to the widely-used computational docking technique.

Published
2023

3. CAMP: A Context-Aware Cricket Players Performance Metric

Author

Ayub, Muhammad Sohaib, Ullah, Naimat, Ali, Sarwan, Khan, Imdad Ullah, Awais, Mian Muhammad, Khan, Muhammad Asad, and Faizullah, Safiullah

Subjects
Computer Science - Artificial Intelligence, Computer Science - Computers and Society, Computer Science - Machine Learning
Abstract

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, CAMP, to quantify individual players' contributions toward a cricket match outcome. CAMP employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. CAMP incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate CAMP on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Man of the M}atch (MoM). The top two rated players by CAMP match with MoM in 83\% of the 961 games. Thus, the CAMP rating of the best player closely matches that of the domain experts. By this measure, CAMP significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (DLS) method.

Published
2023
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4. Robust Brain Age Estimation via Regression Models and MRI-derived Features

Author

Ahmed, Mansoor, Sardar, Usama, Ali, Sarwan, Alam, Shafiq, Patterson, Murray, and Khan, Imdad Ullah

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Quantitative Biology - Neurons and Cognition
Abstract

The determination of biological brain age is a crucial biomarker in the assessment of neurological disorders and understanding of the morphological changes that occur during aging. Various machine learning models have been proposed for estimating brain age through Magnetic Resonance Imaging (MRI) of healthy controls. However, developing a robust brain age estimation (BAE) framework has been challenging due to the selection of appropriate MRI-derived features and the high cost of MRI acquisition. In this study, we present a novel BAE framework using the Open Big Healthy Brain (OpenBHB) dataset, which is a new multi-site and publicly available benchmark dataset that includes region-wise feature metrics derived from T1-weighted (T1-w) brain MRI scans of 3965 healthy controls aged between 6 to 86 years. Our approach integrates three different MRI-derived region-wise features and different regression models, resulting in a highly accurate brain age estimation with a Mean Absolute Error (MAE) of 3.25 years, demonstrating the framework's robustness. We also analyze our model's regression-based performance on gender-wise (male and female) healthy test groups. The proposed BAE framework provides a new approach for estimating brain age, which has important implications for the understanding of neurological disorders and age-related brain changes., Comment: Published at the 15th International Conference on Computational Collective Intelligence

Published
2023

5. Virus2Vec: Viral Sequence Classification Using Machine Learning

Author

Ali, Sarwan, Bello, Babatunde, Chourasia, Prakash, Punathil, Ria Thazhe, Chen, Pin-Yu, Khan, Imdad Ullah, and Patterson, Murray

Subjects
Quantitative Biology - Genomics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Understanding the host-specificity of different families of viruses sheds light on the origin of, e.g., SARS-CoV-2, rabies, and other such zoonotic pathogens in humans. It enables epidemiologists, medical professionals, and policymakers to curb existing epidemics and prevent future ones promptly. In the family Coronaviridae (of which SARS-CoV-2 is a member), it is well-known that the spike protein is the point of contact between the virus and the host cell membrane. On the other hand, the two traditional mammalian orders, Carnivora (carnivores) and Chiroptera (bats) are recognized to be responsible for maintaining and spreading the Rabies Lyssavirus (RABV). We propose Virus2Vec, a feature-vector representation for viral (nucleotide or amino acid) sequences that enable vector-space-based machine learning models to identify viral hosts. Virus2Vec generates numerical feature vectors for unaligned sequences, allowing us to forego the computationally expensive sequence alignment step from the pipeline. Virus2Vec leverages the power of both the \emph{minimizer} and position weight matrix (PWM) to generate compact feature vectors. Using several classifiers, we empirically evaluate Virus2Vec on real-world spike sequences of Coronaviridae and rabies virus sequence data to predict the host (identifying the reservoirs of infection). Our results demonstrate that Virus2Vec outperforms the predictive accuracies of baseline and state-of-the-art methods., Comment: 11 Pages 6 Figures Accepted in conference Conference on Health, Inference, and Learning (CHIL) 2023

Published
2023

6. BioSequence2Vec: Efficient Embedding Generation For Biological Sequences

Author

Ali, Sarwan, Sardar, Usama, Patterson, Murray, and Khan, Imdad Ullah

Subjects
Computer Science - Machine Learning, Quantitative Biology - Genomics
Abstract

Representation learning is an important step in the machine learning pipeline. Given the current biological sequencing data volume, learning an explicit representation is prohibitive due to the dimensionality of the resulting feature vectors. Kernel-based methods, e.g., SVM, are a proven efficient and useful alternative for several machine learning (ML) tasks such as sequence classification. Three challenges with kernel methods are (i) the computation time, (ii) the memory usage (storing an $n\times n$ matrix), and (iii) the usage of kernel matrices limited to kernel-based ML methods (difficult to generalize on non-kernel classifiers). While (i) can be solved using approximate methods, challenge (ii) remains for typical kernel methods. Similarly, although non-kernel-based ML methods can be applied to kernel matrices by extracting principal components (kernel PCA), it may result in information loss, while being computationally expensive. In this paper, we propose a general-purpose representation learning approach that embodies kernel methods' qualities while avoiding computation, memory, and generalizability challenges. This involves computing a low-dimensional embedding of each sequence, using random projections of its $k$-mer frequency vectors, significantly reducing the computation needed to compute the dot product and the memory needed to store the resulting representation. Our proposed fast and alignment-free embedding method can be used as input to any distance (e.g., $k$ nearest neighbors) and non-distance (e.g., decision tree) based ML method for classification and clustering tasks. Using different forms of biological sequences as input, we perform a variety of real-world classification tasks, such as SARS-CoV-2 lineage and gene family classification, outperforming several state-of-the-art embedding and kernel methods in predictive performance., Comment: Accepted to PAKDD 2023

Published
2023

7. Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning

Author

Chourasia, Prakash, Murad, Taslim, Tayebi, Zahra, Ali, Sarwan, Khan, Imdad Ullah, and Patterson, Murray

Subjects
Computer Science - Machine Learning
Abstract

This paper presents a federated learning (FL) approach to train an AI model for SARS-Cov-2 variant classification. We analyze the SARS-CoV-2 spike sequences in a distributed way, without data sharing, to detect different variants of this rapidly mutating coronavirus. Our method maintains the confidentiality of local data (that could be stored in different locations) yet allows us to reliably detect and identify different known and unknown variants of the novel coronavirus SARS-CoV-2. Using the proposed approach, we achieve an overall accuracy of $93\%$ on the coronavirus variant identification task. We also provide details regarding how the proposed model follows the main laws of federated learning, such as Laws of data ownership, data privacy, model aggregation, and model heterogeneity. Since the proposed model is distributed, it could scale on ``Big Data'' easily. We plan to use this proof-of-concept to implement a privacy-preserving pandemic response strategy., Comment: 10 pages, 7 figures, Accepted at 10th International Conference on Information Management and Big Data (SIMBig 2023)

Published
2023

8. Efficient Classification of SARS-CoV-2 Spike Sequences Using Federated Learning

Author

Chourasia, Prakash, Murad, Taslim, Tayebi, Zahra, Ali, Sarwan, Khan, Imdad Ullah, Patterson, Murray, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Lossio-Ventura, Juan Antonio, editor, Ceh-Varela, Eduardo, editor, Vargas-Solar, Genoveva, editor, Marcacini, Ricardo, editor, Tadonki, Claude, editor, Calvo, Hiram, editor, and Alatrista-Salas, Hugo, editor

Published
2024
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9. Early Crack Fault Detection of Rotor Systems Based on an Optimized VMD

Author

Jiang, Li, Khan, Imdad Ullah, Yu, Hongzhang, Hua, Chunrong, Ouyang, Huajiang, IFToMM, Series Editor, Ceccarelli, Marco, Advisory Editor, Corves, Burkhard, Advisory Editor, Glazunov, Victor, Advisory Editor, Hernández, Alfonso, Advisory Editor, Huang, Tian, Advisory Editor, Jauregui Correa, Juan Carlos, Advisory Editor, Takeda, Yukio, Advisory Editor, Agrawal, Sunil K., Advisory Editor, Ball, Andrew D., editor, Ouyang, Huajiang, editor, Sinha, Jyoti K., editor, and Wang, Zuolu, editor

Published
2024
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10. Efficient Approximate Kernel Based Spike Sequence Classification

Author

Ali, Sarwan, Sahoo, Bikram, Khan, Muhammad Asad, Zelikovsky, Alexander, Khan, Imdad Ullah, and Patterson, Murray

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Several methods have been proposed to compute the similarity between sequences, such as the exact approach that counts the number of matches between $k$-mers (sub-sequences of length $k$) and an approximate approach that estimates pairwise similarity scores. Although exact methods yield better classification performance, they pose high computational costs, limiting their applicability to a small number of sequences. The approximate algorithms are proven to be more scalable and perform comparably to (sometimes better than) the exact methods -- they are designed in a "general" way to deal with different types of sequences (e.g., music, protein, etc.). Although general applicability is a desired property of an algorithm, it is not the case in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic, there is a need for an approach that can deal specifically with the coronavirus. To this end, we propose a series of ways to improve the performance of the approximate kernel (using minimizers and information gain) in order to enhance its predictive performance pm coronavirus sequences. More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e.g., Alpha, Beta, Gamma). We report results using different classification and clustering algorithms and evaluate their performance using multiple evaluation metrics. Using two datasets, we show that our proposed method helps improve the kernel's performance compared to the baseline and state-of-the-art approaches in the healthcare domain., Comment: Accepted for publication at "IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)"

Published
2022

14. SsAG: Summarization and sparsification of Attributed Graphs

Author

Ali, Sarwan, Ahmad, Muhammad, Beg, Maham Anwer, Khan, Imdad Ullah, Faizullah, Safiullah, and Khan, Muhammad Asad

Subjects
Computer Science - Discrete Mathematics
Abstract

We present SsAG, an efficient and scalable lossy graph summarization method that retains the essential structure of the original graph. SsAG computes a sparse representation (summary) of the input graph and also caters to graphs with node attributes. The summary of a graph $G$ is stored as a graph on supernodes (subsets of vertices of $G$), and a weighted superedge connects two supernodes. The proposed method constructs a summary graph on $k$ supernodes that minimize the reconstruction error (difference between the original graph and the graph reconstructed from the summary) and maximum homogeneity with respect to attributes. We construct the summary by iteratively merging a pair of nodes. We derive a closed-form expression to efficiently compute the reconstruction error after merging a pair and approximate this score in constant time. To reduce the search space for selecting the best pair for merging, we assign a weight to each supernode that closely quantifies the contribution of the node in the score of the pairs containing it. We choose the best pair for merging from a random sample of supernodes selected with probability proportional to their weights. A logarithmic-sized sample yields a comparable summary based on various quality measures with weighted sampling. We propose a sparsification step for the constructed summary to reduce the storage cost to a given target size with a marginal increase in reconstruction error. Empirical evaluation on several real-world graphs and comparison with state-of-the-art methods shows that SsAG is up to $5\times$ faster and generates summaries of comparable quality.

Published
2021

15. Efficient Sequence Embedding for SARS-CoV-2 Variants Classification

Author

Ali, Sarwan, Sardar, Usama, Khan, Imdad Ullah, Patterson, Murray, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Guo, Xuan, editor, Mangul, Serghei, editor, Patterson, Murray, editor, and Zelikovsky, Alexander, editor

Published
2023
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18. Efficient Approximation Algorithms for String Kernel Based Sequence Classification

Author

Farhan, Muhammad, Tariq, Juvaria, Zaman, Arif, Shabbir, Mudassir, and Khan, Imdad Ullah

Subjects
Computer Science - Data Structures and Algorithms
Abstract

Sequence classification algorithms, such as SVM, require a definition of distance (similarity) measure between two sequences. A commonly used notion of similarity is the number of matches between $k$-mers ($k$-length subsequences) in the two sequences. Extending this definition, by considering two $k$-mers to match if their distance is at most $m$, yields better classification performance. This, however, makes the problem computationally much more complex. Known algorithms to compute this similarity have computational complexity that render them applicable only for small values of $k$ and $m$. In this work, we develop novel techniques to efficiently and accurately estimate the pairwise similarity score, which enables us to use much larger values of $k$ and $m$, and get higher predictive accuracy. This opens up a broad avenue of applying this classification approach to audio, images, and text sequences. Our algorithm achieves excellent approximation performance with theoretical guarantees. In the process we solve an open combinatorial problem, which was posed as a major hindrance to the scalability of existing solutions. We give analytical bounds on quality and runtime of our algorithm and report its empirical performance on real world biological and music sequences datasets.

Published
2017

21. Anti-Oxidative and Anti-Apoptotic Oligosaccharides from Pichia pastoris -Fermented Cress Polysaccharides Ameliorate Chromium-Induced Liver Toxicity.

Author

Khan, Imdad Ullah, Aqsa, Aqsa, Jamil, Yusra, Khan, Naveed, Iqbal, Amjad, Ali, Sajid, Hamayun, Muhammad, Alrefaei, Abdulwahed Fahad, Faraj, Turki Kh., Lee, Bokyung, and Ahmad, Ayaz

Subjects
*GEL permeation chromatography, *REACTIVE oxygen species, *PICHIA pastoris, *OLIGOSACCHARIDES, *FREE radicals
Abstract

Oxidative stress impairs the structure and function of the cell, leading to serious chronic diseases. Antioxidant-based therapeutic and nutritional interventions are usually employed for combating oxidative stress-related disorders, including apoptosis. Here, we investigated the hepatoprotective effect of oligosaccharides, produced through Pichia pastoris-mediated fermentation of water-soluble polysaccharides isolated from Lepidium sativum (cress) seed mucilage, on chromium(VI)-induced oxidative stress and apoptosis in mice. Gel permeation chromatography (GPC), using Bio-Gel P-10 column, of the oligosaccharides product of fermentation revealed that P. pastoris effectively fermented polysaccharides as no long chain polysaccharides were observed. At 200 µg/mL, fractions DF73, DF53, DF72, and DF62 exhibited DPPH radical scavenging activity of 92.22 ± 2.69%, 90.35 ± 0.43%, 88.83 ± 3.36%, and 88.83 ± 3.36%, respectively. The antioxidant potential of the fermentation product was further confirmed through in vitro H2O2 radical scavenging assay. Among the screened samples, the highest H2O2 radical scavenging activity was displayed by DF73, which stabilized the free radicals by 88.83 ± 0.38%, followed by DF53 (86.48 ± 0.83%), DF62 (85.21 ± 6.66%), DF72 (79.9 4± 1.21%), and EPP (77.76 ± 0.53%). The oligosaccharide treatment significantly alleviated chromium-induced liver damage, as evident from the increase in weight gain, improved liver functions, and reduced histopathological alterations in the albino mice. A distinctly increased level of lipid peroxide (LPO) free radicals along with the endogenous hepatic enzymes were evident in chromium induced hepatotoxicity in mice. However, oligosaccharides treatment mitigated these effects by reducing the LPO production and increasing ALT, ALP, and AST levels, probably due to relieving the oxidative stress. DNA fragmentation assays illustrated that Cr(VI) exposure induced massive apoptosis in liver by damaging the DNA which was then remediated by oligosaccharides supplementation. Histopathological observations confirmed that the oligosaccharide treatment reverses the architectural changes in liver induced by chromium. These results suggest that oligosaccharides obtained from cress seed mucilage polysaccharides through P. pastoris fermentation ameliorate the oxidative stress and apoptosis and act as hepatoprotective agent against chromium-induced liver injury. [ABSTRACT FROM AUTHOR]

Published
2024
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22. Pichia pastoris Mediated Digestion of Water-Soluble Polysaccharides from Cress Seed Mucilage Produces Potent Antidiabetic Oligosaccharides.

Author

Khan, Imdad Ullah, Jamil, Yusra, Khan, Aiman, Ahmad, Jalwa, Iqbal, Amjad, Ali, Sajid, Hamayun, Muhammad, Hussain, Anwar, Alrefaei, Abdulwahed Fahad, Almutairi, Mikhlid H., and Ahmad, Ayaz

Subjects
*PICHIA pastoris, *POLYSACCHARIDES, *MUCILAGE, *MICROBIAL polysaccharides, *GEL permeation chromatography, *METFORMIN, *OLIGOSACCHARIDES
Abstract

Diabetes mellitus is a heterogeneous metabolic disorder that poses significant health and economic challenges across the globe. Polysaccharides, found abundantly in edible plants, hold promise for managing diabetes by reducing blood glucose levels (BGL) and insulin resistance. However, most of these polysaccharides cannot be digested or absorbed directly by the human body. Here we report the production of antidiabetic oligosaccharides from cress seed mucilage polysaccharides using yeast fermentation. The water-soluble polysaccharides extracted from cress seed mucilage were precipitated using 75% ethanol and fermented with Pichia pastoris for different time intervals. The digested saccharides were fractionated through gel permeation chromatography using a Bio Gel P-10 column. Structural analysis of the oligosaccharide fractions revealed the presence of galacturonic acid, rhamnose, glucuronic acid, glucose and arabinose. Oligosaccharide fractions exhibited the potential to inhibit α-amylase and α-glucosidase enzymes in a dose-dependent manner in vitro. The fraction DF73 exhibited strong inhibitory activity against α-amylase with IC50 values of 38.2 ± 1.12 µg/mL, compared to the positive control, acarbose, having an IC50 value of 29.18 ± 1.76 µg/mL. Similarly, DF72 and DF73 showed the highest inhibition of α-glucosidase, with IC50 values of 9.26 ± 2.68 and 50.47 ± 5.18 µg/mL, respectively. In in vivo assays in streptozotocin (STZ)-induced diabetic mice, these oligosaccharides significantly reduced BGL and improved lipid profiles compared to the reference drug metformin. Histopathological observations of mouse livers indicated the cytoprotective effects of these sugars. Taken together, our results suggest that oligosaccharides produced through microbial digestion of polysaccharides extracted from cress seed mucilage have the potential to reduce blood glucose levels, possibly through inhibition of carbohydrate-digesting enzymes and regulation of the various signaling pathways. [ABSTRACT FROM AUTHOR]

Published
2024
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23. CAMP: A context-aware cricket players performance metric.

Author

Ayub, Muhammad Sohaib, Ullah, Naimat, Ali, Sarwan, Khan, Imdad Ullah, Awais, Mian Muhammad, Khan, Muhammad Asad, and Faizullah, Safiullah

Subjects
CRICKET players, CRICKET competitions, TEAM sports, DATA mining, CRICKET (Sport)
Abstract

Cricket is the second most popular sport after soccer in terms of viewership. However, the assessment of individual player performance, a fundamental task in team sports, is currently primarily based on aggregate performance statistics, including average runs and wickets taken. We propose Context-Aware Metric of player Performance, camp, to quantify individual players' contributions toward a cricket match outcome. camp employs data mining methods and enables effective data-driven decision-making for selection and drafting, coaching and training, team line-ups, and strategy development. camp incorporates the exact context of performance, such as opponents' strengths and specific circumstances of games, such as pressure situations. We empirically evaluate camp on data of limited-over cricket matches between 2001 and 2019. In every match, a committee of experts declares one player as the best player, called Manof theMatch (MoM). The top two rated players by camp match with MoM in 83% of the 961 games. Thus, the camp rating of the best player closely matches that of the domain experts. By this measure, camp significantly outperforms the current best-known players' contribution measure based on the Duckworth-Lewis-Stern (dls) method. [ABSTRACT FROM AUTHOR]

Published
2024
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27. Reimagining Educational Leadership: Preparing Leaders for Transformative Change towards Equity, Diversity, and Social Justice.

Author

Khan, Imdad Ullah, Ahmad, Aftab, and Naz, Raheela

Subjects
EDUCATIONAL leadership, TRANSFORMATIVE learning, SOCIAL justice, LITERATURE reviews, SOCIALIZATION, CLASSROOM environment, SELF-efficacy
Abstract

The current state of educational management practices is insufficient in addressing issues of equity, diversity, and social justice in educational systems. This literature review explores the potential of transformative educational leadership for social justice, which emphasizes the importance of creating equitable and just learning environments that empower all students to succeed. It discusses social justice in educational leadership, examining its conceptualization in the literature and exploring key concepts, approaches, and empirical analyses of transformative educational leadership, equity, and inclusivity. It also highlights the challenges and opportunities for implementing transformative educational leadership in the policy and practiceof educational management. The literature selection criteria were based on choosing relevant literature published during the last 20 years that had the most citations. The study findings suggest that transformative educational leadership is an important framework for educational leadership that prioritizes equity and inclusivity. Collaborative, participatory, and culturally responsive approaches are necessary to address issues of social justice in education. However, challenges exist in implementing it in practice, including resistance to change, a lack of resources and support, and inadequate training for educational leaders. Implications for policy, research, and practice include the need for educational leadership programs to prioritize approaches that focus on social justice issues and pedagogies that promote inclusivity, dialogue, and community building. To practice transformative educational leadership, leaders must have a deep understanding of data analysis and prioritize the collection of high-quality data while ensuring that data is used in ethical and responsible ways. [ABSTRACT FROM AUTHOR]

Published
2023

29. Vibration control of an irregular structure using single and multiple tuned mass dampers.

Author

Khan, Imdad Ullah, Usman, Muhammad, and Tanveer, Muhammad

Subjects
*TUNED mass dampers, *SKYSCRAPERS, *WIND pressure
Abstract

The tuned mass damper is a passive control technique, which is used in high-rise building structures for mitigation of the vibration produced as a result of wind and earthquake loadings. Irregularity in structure increases the chances of vibrations produced from these loadings. In this paper, detailed experimental work was carried out for vibration mitigation of an irregular four-storey structure having mass irregularity at its fourth floor using a single tuned mass damper (STMD) and multiple tuned mass dampers (MTMDs), against different harmonic and seismic loadings; such work has never been carried out before. The positions of STMD and MTMDs were varied along each floor and all possible configurations of STMD and MTMDs were tested against these loadings. For comparison, the acceleration responses of each storey for all configurations of STMD and MTMDs were compared with that of the uncontrolled structure. It was found that under all types of loadings, the STMD reduces the acceleration response of all floors of the uncontrolled structure; similarly, all configurations of MTMDs show further reduction in acceleration response, demonstrating the efficiency of MTMDs over a STMD in irregular structures. [ABSTRACT FROM AUTHOR]

Published
2023
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33. Efficient Approximate Kernel Based Spike Sequence Classification

Author

Ali, Sarwan, Sahoo, Bikram, Khan, Muhammad Asad, Zelikovsky, Alexander, Khan, Imdad Ullah, and Patterson, Murray

Abstract

Machine learning (ML) models, such as SVM, for tasks like classification and clustering of sequences, require a definition of distance/similarity between pairs of sequences. Several methods have been proposed to compute the similarity between sequences, such as the exact approach that counts the number of matches between $k$k-mers (sub-sequences of length $k$k) and an approximate approach that estimates pairwise similarity scores. Although exact methods yield better classification performance, they pose high computational costs, limiting their applicability to a small number of sequences. The approximate algorithms are proven to be more scalable and perform comparably to (sometimes better than) the exact methods — they are designed in a “general” way to deal with different types of sequences (e.g., music, protein, etc.). Although general applicability is a desired property of an algorithm, it is not the case in all scenarios. For example, in the current COVID-19 (coronavirus) pandemic, there is a need for an approach that can deal specifically with the coronavirus. To this end, we propose a series of ways to improve the performance of the approximate kernel (using minimizers and information gain) in order to enhance its predictive performance pm coronavirus sequences. More specifically, we improve the quality of the approximate kernel using domain knowledge (computed using information gain) and efficient preprocessing (using minimizers computation) to classify coronavirus spike protein sequences corresponding to different variants (e.g., Alpha, Beta, Gamma). We report results using different classification and clustering algorithms and evaluate their performance using multiple evaluation metrics. Using two datasets, we show that our proposed method helps improve the kernel's performance compared to the baseline and state-of-the-art approaches in the healthcare domain.

Published
2023
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38. Lactobacillus acidophilus Supplementation Restores Gut Epithelial Integrities and Barrier Functions in Non-specific Diarrhea

Author

Hossain Ferdaus Mohd ALTAF, Sumaiya Islam FARZANA, Khan PARVEZ, Zahran EMAN, Atif Shaikh MUHAMMAD, Uddin Md NAZIM, Zinnah Kazi Mohammad ALİ, Khan IMDAD ULLAH, and Rahman Md MASUDUR

Subjects
Complementary and alternative medicine, Health Care Sciences and Services, mice,gut immunity,tight junctions,and adhesion molecules,Lactobacillus acidophilus supplementatio, Pharmaceutical Science, Pharmacology (medical), Sağlık Bilimleri ve Hizmetleri
Abstract

Objectives: The study was aimed to evaluate the roles of Lactobacillus acidophilus supplementation in maintaining intestinal epithelial integrities, tight junction proteins, and adhesion molecules in non-specific diarrhea. Methods: In this study, we used the pre-weaned BL/6 pups (3 weeks of age, same litters) as a model animal. We supplied the non-sterilized and poor-quality water to experimental pups (n=7) to develop non-specific diarrheic symptoms. Then diarrheic pups were supplemented with Lactobacillus acidophilus (LA) for three consecutive weeks. The control group (n=5) was supplied with sterilized water and no LA. The sampling and analysis were performed on day 0, day 7, day 14, and day 21. The expressions of pro-inflammatory cytokines (IL-6, TNF-a) and tight junction proteins (TJPs) of gut mucosa were determined using qRT-PCR. And the serum cytokines level was screened through sandwich ELISA. Results: The intestinal cytoskeletal integrity becomes disrupted and characterized by lower ZO-1, Occludin, Claudin-1, Claudin-5, and JAM mRNA expressions upon real-time qRT-PCR. However, Claudin-4 was found to be not affected and illustrated with a higher expression like control pups. Interestingly, supplementing Lactobacillus acidophilus was found to maintain gut integrity and effectively reduce diarrheic symptoms. Like the control pups, the Lactobacillus acidophilus supplemented pups exhibited a higher expression of gut epithelial TJPs and adhesion molecules. Moreover, the diseased pups produced significantly increased IL-6, and TNF-α production in blood serum, compared to control BL/6 pups. Conclusion: We concluded that L. acidophilus supplementation might orchestrate the equilibrium of gut health and immunity against non-specific diarrhea.

Published
2021

39. WEALTH AND WOMANHOOD: A FEMINIST MARXIST READING OF PROPERTY, INHERITANCE, AND CLASS MOBILITY IN PRIDE AND PREJUDICE.

Author

Khan, Imdad Ullah, Khan, Saira Asghar, and Salman, Saad

Subjects
*GENDER inequality, *FEMINISM, *INHERITANCE & succession, *PREJUDICES, *ECONOMIC status, *MODERN society, *SELF-consciousness (Awareness)
Abstract

This research reveals how the way female characters perform social roles, express their desires, and make their decisions in Jane Austen's Pride and Prejudice (1813), is influenced by their economic status, which was a common motive for marriage in the contemporary society of the nineteenth century. By using feminist Marxist theory, the analysis in this article shows how mobility in class, property, and inheritance interacted in Regency-era England and affected women in the novel. By exploring the textual nuances, dialogues, and characters' interactions, the study brings to light how ownership and inheritance influenced the level of agency and autonomy for women. Importantly, the study underscores the fact that Elizabeth Bennet's journey towards independence and self-awareness was shaped almost all the time by the struggle against the injustice of property standards and the class system. Generally, the research accentuates the significance of Austen's work in the context of the issues that address women's equality in the early 19th century, such as gender, property, inheritance, and social justice. This research adds to the research body on Pride and Prejudice, but also gives a holistic picture of the multilayered interconnection of class mobility, feminism and Marxism with its literary representation. [ABSTRACT FROM AUTHOR]

Published
2023

40. Students' Perception of Code-Switching as Pedagogical Tool in Multilingual EFL Classrooms: A Social Justice Perspective.

Author

Khan, Imdad Ullah, Ahmed, Aftab, and Gul, Sana

Subjects
PSYCHOLOGY of students, CODE switching (Linguistics), SOCIAL justice, NATIVE language, MINORITY students, COMMUNICATIVE competence, COGNITION
Abstract

The act of switching back and forth between two languages, varieties, or dialects, typically during a single conversation, is referred to as code-switching. In multilingual, bilingual, and translanguaging contexts, code-switching is a significant phenomenon that affects cognition, communication, and students' language acquisition. The benefits and drawbacks of language switching for L2 learners have been hotly debated ever since the 1980s. However, these studies are often more focused on teachers' beliefs and perceptions about code-switching paying little attention to how students perceive the use of code-switching in an L2 classroom. The current article's goal is to gain a better understanding of how students at an English-medium university in a Pashto-majority area of northern Pakistan feel about the practice of code-switching. The study's data was collected using questionnaires that included both closed- and open-ended questions. The results of the study suggest that students are supportive of their teachers' deliberate use of code-switching; however, in the university's multilingual classes, the language that the teacher switches to must be understood by all students to prevent the minority group that does not understand the teachers' native language from feeling excluded. The article concludes with the implication of this study for English as a medium of instruction in higher education in Pakistani and other analogous teaching environments. [ABSTRACT FROM AUTHOR]

Published
2023

41. Exploring geochemical assessment and spatial distribution of heavy metals in soils of Southern KP, Pakistan: employing multivariate analysis.

Author

Ali, Iftikhar, Khan, Imdad Ullah, Khan, Muhammad Jamil, Sardar, Tariq, Deeba, Farah, Hussain, Haziq, Ullah, Kalim, Khan, Qudrat Ullah, Khan, Mumtaz, and Khan, Muhammad Daud

Subjects
*MULTIVARIATE analysis, *HEAVY metals, *METAL content of soils, *ATOMIC absorption spectroscopy, *HEAVY metal toxicology, *PEARSON correlation (Statistics)
Abstract

Soil pollution with heavy metals is continuously expanding with the rapid development of industrialisation and agriculture. The current study was aimed to determine the skewed nature and spatial distribution of soil heavy metals pollution in southern areas of Khyber Pakhtunkhawa. Fifty four soil samples were collected from the selected sites in southern districts and analysed for various soil physico-chemical and elemental concentrations. Various metals content, i.e. (Pb, Cd, Ni, Cr) was determined after acid digested through atomic absorption spectrophotometry. Results of the metals content in soil samples showed maximum values Pb (46.38), Cd(6.74), Ni(45.58), Cr(58.08) mg/kg, bluk density 1.46 g/cm3 and lowest pH value 6.53 for Lakki cement factory (LCF) site. Other parameters like soil electrical conductivity, total dissolved solids, percent porosity and soil urease activity also indicated higher values, i.e. 504 µs/cm, 250 mg/lit, 66% and 440.50 respectively. Positive Pearson correlation was found between metals concentration and soil physico-chemical properties except percent porosity and bulk density exhibited negative correlation. The multivariate statistical analysis of the data formed principal component that accounted for a total variance of 71.1%, on the basis of which metal contamination is traced to both geogenic and anthropogenic sources. Spatial distribution of the metals concentration showed higher concentration of Cd, Cr, Ni in Lakki cement factory (LCF) soil except Pb content that was observed maximum in Indus highway Karak (IHK) soil samples. Geoamccumulation index (Igeo) of the samples sites showed moderate contamination for studied metals with some uncontaminated sites showing a value of Igeo> 0. The study concludes a baseline data for metals concentration in the area, which can be used as a beacon for various strategic plannings regarding soil resource management in order to address the metals related health issues and environmental pollution. [ABSTRACT FROM AUTHOR]

Published
2022
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42. A Global Nomad: Santiago's Journey in The Alchemist as a Narrative of Transnationalism and Globalization.

Author

Khan, Afaqullah, Khan, Imdad Ullah, and Shah, Syed Mujeebullah

Subjects
TRANSNATIONALISM, GLOBALIZATION, LITERARY criticism, INTERNATIONAL travel, NOMADS
Abstract

Transnational books have become more popular in today's age of easier travel thanks to globalization and increased cross-border migration. Literary authors that stand out for their diversity and have gained recognition on a worldwide scale are a result of globalization. Thus, literary studies and the study of globalization are beginning to converge, whether globalization is viewed as primarily economic and political or as more broadly cultural. The current article reads Paulo Coelho's "The Alchemist" as a symbolic narrative of nomadic identity and cross-border existence that is common in today's era of international travel. By writing a book with characters from several countries, Coelho has created a fictional universe in the book where knowledge flows across borders and overcomes national barriers. The protagonist Santiago's experiences and character growth are emphasized in this bildungsroman novel. He struggles to find and achieve his personal legend which becomes his major goal in the narrative. After a long and tiresome journey, Santiago finds that what he was looking for was right in front of him the whole time. The current article focuses on how the symbolic journey of the protagonist parallels modern-day internationalism and globalization. It focuses on individual sociocultural assimilation strategies as shown in the character of Santiago to understand the text in terms of modern increased intercultural contact and its impact on individuals. [ABSTRACT FROM AUTHOR]

Published
2022

44. Mini Review An-overview on Buffalo Condensed Milk and its Products with Emphasis of Their Biochemical Properties

Author

Hasan, Syed Mubashar, primary, Ullah, Shakeeb, primary, Umer, Muhammad, primary, Shah, Muhammad Kamal, primary, Zaman, Ali, primary, Hussain, Iqtidar, primary, Muhammad, Khalid, primary, Khan, Imdad Ullah, primary, Malik, M. Inam, primary, Rehman, Ateeq Ur, primary, Maris, Hamza, primary, Khan, Sumera Ali, primary, and Khan, Muhammad Shuaib, primary

Published
2021
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47. Antibacterial, Antihemolytic, Cytotoxic, Anticancer, and Antileishmanial Effects of Ajuga bracteosa Transgenic Plants

Author

Rubnawaz, Samina, primary, Okla, Mohammad K., additional, Akhtar, Nosheen, additional, Khan, Imdad Ullah, additional, Bhatti, Muhammad Zeeshan, additional, Duong, Hong-Quan, additional, El-Tayeb, Mohamed A., additional, Elbadawi, Yahaya B., additional, Almaary, Khalid S., additional, Moussa, Ihab M., additional, Abbas, Zahid Khurshid, additional, and Mirza, Bushra, additional

Published
2021
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48. CuO‐SiO2 based nanocomposites: Synthesis, characterization, photocatalytic, antileishmanial, and antioxidant studies.

Author

Yaseen, Muhammad, Farooq, Saima, Khan, Abbas, Shah, Nasrullah, Shah, Luqman Ali, Bibi, Shaista, Khan, Imdad Ullah, and Ahmad, Sajjad

Subjects
GENTIAN violet, COPPER oxide, NANOCOMPOSITE materials, PHOTODEGRADATION, TRANSMISSION electron microscopy, SCANNING electron microscopy, ETHANOL
Abstract

The abatement of toxic pollutants and drug‐resistant pathogens from water is one of the major environmental issues. In this work, we are reporting the preparation, detailed characterization and photocatalytic degradation, and biological activities of bimetallic oxides (CuO‐SiO2) nanocomposite. For this purpose, CuO, SiO2 nanoparticles, and CuO‐SiO2 nanocomposites were successfully synthesized through sol–gel process. The nanoparticles and nanocomposite were prepared by taking the Cu(NO3)2.3H2O as copper oxide precursor and TEOS as silica precursor using absolute ethanol and water as solvents The molar ratio of TEOS: Glycerol: H2O used was 1.5:1:6.6 respectively. The synthesized nanoparticles and nanocomposites were characterized by using Scanning electron microscopy (SEM), transmission electron microscopy (TEM), Fourier‐transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD) and Brunauer–Emmett–Teller (BET). The nanocomposites and individual nanoparticles were then applied as catalysts for their activities toward catalytic/photocatalytic degradation of crystal violet (CV) dye in an aqueous medium. The degradation progress was monitored by UV–Visible spectroscopic analysis. It was found that CuO‐SiO2 nanocomposite shows good photocatalytic activities toward the removal of crystal violet dye as compared to the catalytic activities of its individual constitutes. The antileishmanial and antioxidant activities results indicate that synthesized materials have promising biological attributes. [ABSTRACT FROM AUTHOR]

Published
2022
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50. Response of Cryopreserved Nili Ravi Buffalo Bull Semen to Gallic Acid Inclusion in Semen Extender.

Author

Tariq, Muhammad, Rabbani, Imtiaz, Yousaf, Muhammad Shahbaz, Khan, Imdad Ullah, Zaneb, Hafsa, Iqbal, Sajid, Khan, Alam Zeb, Shakirullah, Khan, Muhammad Shuaib, Ur Rehman, Atta, and Khan, Mumtaz Ali

Abstract

During cryopreservation, the spermatozoa faces osmo-chemical, mechanical and thermal stresses, which are predominant at dilution, cooling, equilibration, freezing and thawing stages. They damage functional and morphological characteristics of sperms. Beside these exogenous stresses there is an oxidative stress which damages the spermatozoa endogenously. These stresses can be controlled with the inclusion of antioxidants in semen extender at the time of cryopreservation. In the current study, semen from four (n=4) healthy Nili Ravi buffalo bulls was collected through artificial vagina and Gallic acid was added to the semen at 1 µM, 15 µM, 30 µM, 45 µM, 60 µM, and 100 µM. A total of six groups were prepared. One group was kept as control where no Gallic acid was added. After the addition of extender, semen was cooled to 4°C, filled in 0.5mL straws for 4 h and frozen in liquid nitrogen at -196 °C. The parameters evaluated were percentage motility, plasma membrane integrity (HOST assay), acrosomal integrity (NAR), viability (Live/Dead), DNA integrity (Acridine orange assay) and oxidative stress (TBARS assay). Five straws from each Gallic acid group were thawed individually in water bath at 37°C for 30 seconds. The addition of 15 µM GA to semen extender improved marginally the buffalo bull spermatozoa motility, viability and membrane integrity but still not sufficient to reach the statistical significance, while it has no protective effects on other parameters like Acrosomal integrity, DNA status and oxidative stress. However further studies are needed to assess the role of Gallic acid in different concentrations and in other animals. [ABSTRACT FROM AUTHOR]

Published
2022
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