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Abstract Influenza is a highly contagious acute viral illness that affects the respiratory system, posing a significant global public health concern. Influenza B virus (IBV) causes annual seasonal epidemics. The exploration of molecular biology and reverse genetics of IBV is pivotal for understanding its replication, pathogenesis, and evolution. Reverse genetics empowers us to purposefully alter the viral genome, engineer precise genetic modifications, and unveil the secrets of virulence and resistance mechanisms. It helps us in quickly analyzing new virus strains by viral genome manipulation and the development of innovative influenza vaccines. Reverse genetics has been employed to create mutant or reassortant influenza viruses for evaluating their virulence, pathogenicity, host range, and transmissibility. Without this technique, these tasks would be difficult or impossible, making it crucial for preparing for epidemics and protecting public health. Here, we bring together the latest information on how we can manipulate the genes of the influenza B virus using reverse genetics methods, most importantly helper virus-independent techniques.

Abstract Long extrachromosomal circular DNA (leccDNA) regulates several biological processes such as genomic instability, gene amplification, and oncogenesis. The identification of leccDNA holds significant importance to investigate its potential associations with cancer, autoimmune, cardiovascular, and neurological diseases. In addition, understanding these associations can provide valuable insights about disease mechanisms and potential therapeutic approaches. Conventionally, wet lab-based methods are utilized to identify leccDNA, which are hindered by the need for prior knowledge, and resource-intensive processes, potentially limiting their broader applicability. To empower the process of leccDNA identification across multiple species, the paper in hand presents the very first computational predictor. The proposed iLEC-DNA predictor makes use of SVM classifier along with sequence-derived nucleotide distribution patterns and physicochemical properties-based features. In addition, the study introduces a set of 12 benchmark leccDNA datasets related to three species, namely Homo sapiens (HM), Arabidopsis Thaliana (AT), and Saccharomyces cerevisiae (SC/YS). It performs large-scale experimentation across 12 benchmark datasets under different experimental settings using the proposed predictor, more than 140 baseline predictors, and 858 encoder ensembles. The proposed predictor outperforms baseline predictors and encoder ensembles across diverse leccDNA datasets by producing average performance values of 81.09%, 62.2% and 81.08% in terms of ACC, MCC and AUC-ROC across all the datasets. The source code of the proposed and baseline predictors is available at https://github.com/FAhtisham/Extrachrosmosomal-DNA-Prediction . To facilitate the scientific community, a web application for leccDNA identification is available at https://sds_genetic_analysis.opendfki.de/iLEC_DNA/.

Influenza B virus (IBV) stands as a paradox, often overshadowed by its more notorious counterpart, influenza A virus (IAV). Yet, it remains a captivating and elusive subject of scientific inquiry. Influenza B is important because it causes seasonal flu outbreaks that can lead to severe respiratory illnesses, including bronchitis, pneumonia, and exacerbations of chronic conditions like asthma. Limitations in the influenza B virus’s epidemiological, immunological, and etiological evolution must be addressed promptly. This comprehensive review covers evolutionary epidemiology and pathogenesis, host-virus interactions, viral isolation and propagation, advanced molecular detection assays, vaccine composition and no animal reservoir for influenza B virus. Complex viral etiology begins with intranasal transmission of influenza B virus with the release of a segmented RNA genome that attacks host cell machinery for transcription and translation within the nucleus and the release of viral progeny. Influenza B virus prevalence in domesticated and wild canines, sea mammals, and birds is frequent, yet there is no zoonosis. The periodic circulation of influenza B virus indicates a 1–3-year cycle for monophyletic strain replacement within the Victoria strain due to frequent antigenic drift in the HA near the receptor-binding site (RBS), while the antigenic stability of Yamagata viruses portrays a more conservative evolutionary pattern. Additionally, this article outlines contemporary antiviral strategies, including pharmacological interventions and vaccination efforts. This article serves as a resource for researchers, healthcare professionals, and anyone interested in the mysterious nature of the influenza B virus. It provides valuable insights and knowledge essential for comprehending and effectively countering this viral foe, which continues to pose a significant public health threat.

Survival prediction integrates patient-specific molecular information and clinical signatures to forecast the anticipated time of an event, such as recurrence, death, or disease progression. Survival prediction proves valuable in guiding treatment decisions, optimizing resource allocation, and interventions of precision medicine. The wide range of diseases, the existence of various variants within the same disease, and the reliance on available data necessitate disease-specific computational survival predictors. The widespread adoption of artificial intelligence (AI) methods in crafting survival predictors has undoubtedly revolutionized this field. However, the ever-increasing demand for more sophisticated and effective prediction models necessitates the continued creation of innovative advancements. To catalyze these advancements, it is crucial to bring existing survival predictors knowledge and insights into a centralized platform. The paper in hand thoroughly examines 23 existing review studies and provides a concise overview of their scope and limitations. Focusing on a comprehensive set of 90 most recent survival predictors across 44 diverse diseases, it delves into insights of diverse types of methods that are used in the development of disease-specific predictors. This exhaustive analysis encompasses the utilized data modalities along with a detailed analysis of subsets of clinical features, feature engineering methods, and the specific statistical, machine or deep learning approaches that have been employed. It also provides insights about survival prediction data sources, open-source predictors, and survival prediction frameworks.

Stuttering is a speech disorder characterised by the repetition, prolongation, or blocking of sounds, syllables, or words, which can cause significant social and emotional difficulties for those who experience it. To help find and diagnose stuttering early, it is important for clinicians and researchers to accurately separate stuttering from normal speech. This helps them understand the disorder, find possible causes, and come up with effective interventions and treatments. The UCLASS dataset was used in this study, and 40 MFCC features were taken out to see how well different machine learning classifiers could indicate the difference between normal and stuttering speech. The major problem is the UCLASS imbalanced dataset. The authors address it with the synthetic minority oversampling technique. After machine learning experimentation’s, we propose a novel hybrid model that performs better than individual machine learning. In hybrid, the lightweight SutterNet model takes the best features from the data and then make prediction. The results indicate that the evaluated classifiers showed varying levels of performance. Overall, the results suggest that hybrid classifiers have the potential to accurately classify normal and stuttering speech, which could have important implications for the early identification and diagnosis of stuttering, as well as the development of assistive technologies and effective interventions and treatments.

Steering angle controller is a core module of autonomous vehicles, where a slight miscalculation can cause severe accidents. Following safety precautions, development of robust and precise steering angle predictor is an active area of research. However, existing simulator based steering angle predictors lack in predictive performance and have not been evaluated on same benchmark datasets. Furthermore, most of them are evaluated on simulated datasets and their potential on real-world data as well as in cross-domain evaluation of both types data (real-world, simulated) remain unexplored. To accelerate and expedite research related to steering angle prediction contributions of this paper are manifold: 1) It presents two benchmark datasets that are developed using Udacity and CARLA simulators. 2) Following the need for comparative study, over both simulated datasets, it benchmarks the performance of existing predictors under 2 different evaluation settings namely: same-track and cross-track evaluation. 3) In cross-domain evaluation, it explores generalization potential of predictors by training predictors on simulated data and evaluating them on 2 real-world datasets and vice versa. 4) It presents a robust and precise steering angle predictor that utilizes skip connections for proper gradient flow among different convolutional layers. In same-track evaluation where predictors are trained and evaluated on same-track data, proposed predictor outperforms existing predictors by achieving least Mean Absolute Error (MAE) of 0.13, 0.19 and 0.065 over lake track, jungle track and CARLA based datasets, respectively. Similarly, in cross-track evaluation where predictors are trained on one track and are evaluated on other track data, once again proposed predictor outperforms existing predictors by producing average least MAE errors of 0.33 and 0.06 over Udacity and CARLA datasets, respectively. Over two real-world datasets, Sully Chen and Comma.ai, the proposed predictor demonstrates superior performance compared to existing simulator-based predictors, achieving the lowest MAE of 2.41 and 0.50, respectively.

Human respiratory syncytial virus (RSV) is an underlying cause of lower respiratory illnesses in children, elderly and immunocompromised adults. RSV contains multiple structural and non-structural proteins with two major glycoproteins that control the initial phase of infection, fusion glycoprotein and the attachment (G) glycoprotein. G protein attaches to the ciliated cells of airways initiating the infection. The hypervariable G protein plays a vital role in evolution of RSV strains. We employed multiple bioinformatics tools on systematically accessed large-scale data to evaluate mutations, evolutionary history, and phylodynamics of RSV. Mutational analysis of central conserved region (CCR) on G protein-coding sequences between 163 and 189 positions revealed frequent mutations at site 178 in human RSV (hRSV) A while arginine to glutamine substitutions at site 180 positions in hRSV B, remained prevalent from 2009 to 2014. Phylogenetic analysis indicates multiple signature mutations within G protein responsible for diversification of clades. The USA and China have highest number of surveillance records, followed by Kenya. Markov Chain Monte Carlo Bayesian skyline plot revealed that RSV A evolved steadily from 1990 to 2000, and rapidly between 2003 and 2005. Evolution of RSV B continued from 2003 to 2022, with a high evolution stage from 2016 to 2020. Throughout evolution, cysteine residues maintained their strict conserved states while CCR has an entropy value of 0.0039(±0.0005). This study concludes the notion that RSV G glycoprotein is continuously evolving while the CCR region of G protein maintains its conserved state providing an opportunity for CCR-specific monoclonal antibodys (mAbs) and inhibitors as potential candidates for immunoprophylaxis.

Abstract Background Pakistan has one of the highest burdens of Hepatitis C virus (HCV) infection globally. To achieve the World Health Organization’s goals for HCV elimination, there is a need for substantial scale-up in testing, treatment, and a reduction in new infections. Data on the population impact of scaling up treatment is not available in Pakistan, nor is there reliable data on the incidence of infection/reinfection. This project will fill this gap by providing important empirical data on the incidence of infection (primary and reinfection) in Pakistan. Then, by using this data in epidemic models, the study will determine whether response rates achieved with affordable therapies (sofosbuvir plus daclatasvir) will be sufficient to eliminate HCV in Pakistan. Methods This prospective multi-centre cohort study will screen 25,000 individuals for HCV antibody (Ab) and RNA (if Ab-positive) at various centers in Pakistan- Karachi (Sindh) and Punjab, providing estimates of the disease prevalence. HCV positive patients will be treated with sofosbuvir and daclatasvir for 12-weeks, (extended to 24-weeks in those with cirrhosis) and the proportion responding to this first-line treatment estimated. Patients who test HCV Ab negative will be recalled 12 months later to test for new HCV infections, providing estimates of the incidence rate. Patients diagnosed with HCV (~ 4,000) will be treated and tested for Sustained Virological Response (SVR). Questionnaires to assess risk factors, productivity, health care usage and quality of life will be completed at both the initial screening and at 12-month follow-up, allowing mathematical modelling and economic analysis to assess the current treatment strategies. Viral resistance will be analysed and patients who have successfully completed treatment will be retested 12 months later to estimate the rate of re-infection. Conclusion The HepFREEPak study will provide evidence on the efficacy of available and widely used treatment options in Pakistan. It will also provide data on the incidence rate of primary infections and re-infections. Data on incidence risk factors will allow us to model and incorporate heterogeneity of risk and how that affects screening and treatment strategies. These data will identify any gaps in current test-and-treat programs to achieve HCV elimination in Pakistan. Study registration This study was registered on clinicaltrials.gov (NCT04943588) on June 29, 2021.

Influenza B virus (IBV) significantly impacts the health and the economy of the global population. WHO global health estimates project 1 billion flu cases annually, with 3 to 5 million resulting in severe disease and 0.3 to 0.5 million influenza-related deaths worldwide. Influenza B virus epidemics result in significant economic losses due to healthcare expenses, reduced workforce productivity, and strain on healthcare systems. Influenza B virus epidemics, such as the 1987–1988 Yamagata lineage outbreak and the 2001–2002 Victoria lineage outbreak, had a significant global impact. IBV’s fast mutation and replication rates facilitate rapid adaptation to the environment, enabling the evasion of existing immunity and the development of resistance to virus-targeting treatments. This leads to annual outbreaks and necessitates the development of new vaccination formulations. This review aims to elucidate IBV’s evolutionary genomic organization and life cycle and provide an overview of anti-IBV drugs, resistance, treatment options, and prospects for IBV biology, emphasizing challenges in preventing and treating IBV infection.

Current medical treatments for diastasis recti often involve exercises to strengthen the core muscles, along with abdominal binders or supports. However, there is limited evidence comparing the effectiveness of surface electromyography (SEMG) biofeedback-assisted core strengthening exercises combined with kinesiotaping to other approaches. This study aimed to assess the impact of three interventions on core strength, inter-rectus distance, and quality of life in postpartum women with diastasis recti. The interventions included core strengthening exercises with kinesiotaping and SEMG biofeedback-assisted core strengthening with kinesiotaping. This randomized controlled trial (NCT05897255) included 24 postpartum women divided into three groups. We measured inter-rectus distance, quality of life using the Short Form Health Survey 36, and core strength using the McGill torso battery test. The SEMG biofeedback provided auditory and visual cues. We used one-way analysis of variance to compare outcomes between groups, while a t-test for within-group analysis. Both the SEMG biofeedback-assisted and non-assisted core strengthening exercises with kinesiotaping groups showed significantly greater improvements in energy, bodily pain, general health, physical functioning, and limitations due to physical problems than the core strengthening group. Additionally, the SEMG biofeedback-assisted group demonstrated a greater reduction in inter-rectus distance. There were no statistically significant differences in core strength improvement among the three groups. Core strengthening exercises with SEMG-assisted kinesiotaping were superior to core strengthening alone in reducing inter-rectus distance, enhancing physical function, energy levels, and general health, and decreasing bodily pain and limitations due to physical problems. Core strength improvements were similar across all groups.

Key Clinical Message Thyroid storm represents a critical and life‐threatening complication from hyperthyroidism, with a notable mortality risk. Limited literature reports have explored the correlation between thyroid storm and peri‐myocarditis, although the precise pathophysiological underpinnings remain unclear. The pathophysiology of how thyroid storm and peri‐myocarditis are associated is not clearly understood; however, unfavorable prognostic factors include atrial fibrillation and recurrent thyrotoxicosis. Here, we present a case concerning recurrent peri‐myocarditis concomitant with a thyroid storm.

Hormone peptides are small signaling molecules that regulate key cellular processes such as cell growth, and differentiation. Hormone peptide identification is important for understanding their potential associations with certain diseases such as attention deficit hyperactivity disorder, diabetes, and psychiatric disorders. A comprehensive understanding of hormone peptides’ roles in cellular signaling, and immune regulation can provide insights into their therapeutic potential. Hormone peptides are identified through wet-lab approaches which are restricted by resource-intensive processes, limited scalability, and cost ineffectiveness. In an effort to substitute experimental approaches with computational predictors, researchers leveraged the capabilities of machine learning (ML) classifiers. These classifiers have inherent dependency over statistical vectors that are generated by extracting amino acids’ distinctive patterns from peptide sequences. Classifiers utilize these vectors for discriminating peptides into hormone and non-hormone classes. However, the performance of current predictors is constrained due to their inability to effectively extract discriminative amino acids patterns from peptide sequences. Following the need for a powerful predictor, the paper in hand presents a novel sequence encoder namely, CTD-G that transforms peptide sequences into statistical vectors by extracting 3 different types of amino acids patterns namely composition, transition, and distribution. Across public benchmark dataset, the proposed CTD-G encoder potential is compared with 56 existing encoders under two different evaluation strategies namely intrinsic and extrinsic. In Intrinsic evaluation, TSNE-based visualization demonstrates reduced overlap between clusters of hormone and non-hormone peptides with the proposed encoder’s statistical vectors compared to existing encoders. Extrinsic evaluation demonstrates the superiority of the proposed encoder, as 7 out of 11 ML classifiers achieve better performance with its statistical vectors compared to those from existing encoders. Furthermore, the proposed predictor outperforms existing hormone peptide classification predictors by 1.5% in accuracy, 5.36% in sensitivity, 1.80% in specificity, and 2.62% in MCC. To facilitate the scientific community, a web application is available at https://sds_genetic_analysis.opendfki.de/.

In the modern era of the fourth industrial revolution, where collaborative systems are the real innovation, people are still not prepared for the results of the third revolution, namely, artificial intelligence readiness (AIR). Hence, people didn’t have enough time to adapt to the change. This paper aimed to explore the impact of resistance to change on artificial intelligence readiness and also explores the mediating role of task-oriented leadership, moderating role of high-performance work system. The study employs a quantitative research design and collects data of 450 working employees from hospitality sector of Pakistan. SmartPLS is used to analyze structural equation modeling (SEM) to through mixed-methods approach. The study findings are consistent with the proposed study hypotheses that resistance to change has positively impacts RI readiness and task-oriented leadership. Furthermore, the findings also elaborate the mediation of task-oriented leadership between latent constructs and moderating role of high-performance work system between resistance to change and task-oriented leadership. The study concluded that the hospitality sector should focus on developing task-oriented leadership and high-performance work systems to overcome resistance to change and increase its AI readiness.

Many real-world decision-making issues frequently involve competing sets of criteria, uncertainty, and inaccurate information. Some of these require the involvement of a group of decision-makers, where it is necessary to reduce the various available individual preferences to a single collective preference. To enhance the effectiveness of multi-criteria decisions, multi-criteria decision-making is a popular decision-making technique that makes the procedure more precise, reasonable, and efficient. The "Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)" and "Elimination and Choice Transforming Reality (ELECTRE)" are prominent ranking methods and widely used in the multi-criteria decision-making to solve complicated decision-making problems. In this study, two $ m $-polar fuzzy set-based ranking methods are proposed by extending the ELECTRE-Ⅰ and TOPSIS approaches equipped with cubic $ m $-polar fuzzy (C$ m $PF) sets, where the experts provide assessment results on feasible alternatives through a C$ m $PF decision matrix. The first proposed method, C$ m $PF-TOPSIS, focuses on the alternative that is closest to a C$ m $PF positive ideal solution and farthest away from the C$ m $PF negative ideal solution. The Euclidean and normalized Euclidean distances are used to determine the proximity of an alternative to ideal solutions. In contrast, the second developed method is C$ m $PF-ELECTRE-Ⅰ which uses an outranking directed decision graph to determine the optimal alternative, which entirely depends on the C$ m $PF concordance and discordance sets. Furthermore, a practical case study is carried out in the diagnosis of impulse control disorders to illustrate the feasibility and applicability of the proposed methods. Finally, a comparative analysis is performed to demonstrate the veracity, superiority, and effectiveness of the proposed methods.

Requirements classification is a key component of software development life cycle. It enhances our understanding about project requirements, which in turn enables us to effectively identify and mitigate risks that could lead to project failure. Existing requirements classification predictors do not utilize feature selection methods competence in their predictive pipelines and lack in performance. To empower the process of automatic requirements classification, contributions of this paper are manifold. Firstly, it explores the potential of 7 filter-based feature selection techniques and 11 traditional machine learning classifiers. Secondly, for the first time it investigates combined potential of traditional feature selection and 9 diverse types of deep learning predictors. Thirdly, it presents a hybrid computational predictor namely FNReq-Net that reaps combine benefits of traditional feature selection and a novel deep learning predictor based on attention mechanism. Over two public benchmark datasets, large-scale experimental results reveal feature selection not only improves predictive performance of traditional machine learning predictors, but it also improves performance of deep learning predictors. The proposed FNReq-Net predictor outperforms state-of-the-art functional and non-functional requirements classification predictors by 4% and 1% in terms of F1-score over Promise and Promise-exp datasets, respectively.

Abstract Deep exploration of histone occupancy and covalent post-translational modifications (e.g., acetylation, methylation) is essential to decode gene expression regulation, chromosome packaging, DNA damage, and transcriptional activation. Existing computational approaches are unable to precisely predict histone occupancy and modifications mainly due to the use of sub-optimal statistical representation of histone sequences. For the establishment of an improved histone occupancy and modification landscape for multiple histone markers, the paper in hand presents an end-to-end computational multi-paradigm framework “Histone-Net”. To learn local and global residue context aware sequence representation, Histone-Net generates unsupervised higher order residue embeddings (DNA2Vec) and presents a different application of language modelling, where it encapsulates histone occupancy and modification information while generating higher order residue embeddings (SuperDNA2Vec) in a supervised manner. We perform an intrinsic and extrinsic evaluation of both presented distributed representation learning schemes. A comprehensive empirical evaluation of Histone-Net over ten benchmark histone markers data sets for three different histone sequence analysis tasks indicates that SuperDNA2Vec sequence representation and softmax classifier-based approach outperforms state-of-the-art approach by an average accuracy of 7%. To eliminate the overhead of training separate binary classifiers for all ten histone markers, Histone-Net is evaluated in multi-label classification paradigm, where it produces decent performance for simultaneous prediction of histone occupancy, acetylation, and methylation.

Background: The plant pathogens significantly affect quality and marketability of several cereal crops. Of these, seed-borne phytopathogens are responsible for quality and quantity losses in crops on commercial scale. The aim of this study was to isolate and detect the seed-borne mycoflora from seed lots of various bread wheat (Triticum aestivum L.) genotypes widely grown in Bahawalpur division of south Punjab, Pakistan. Methods: Eleven commercial wheat cultivars/genotypes were investigated through standard blotter paper method. The samples were randomly collected from farmer fields and grain markets. The collected samples were brought to the laboratory and analyzed with different methods to detect the mycoflora. Results: Twenty-one fungal species were recovered from seeds lots of diversified gene pool. The most frequently isolated fungi were Alternaria alternata, Fusarium monoliformae, Aspergillus flavus, Helminthosporium spp., Curvularia spp., Bipolaris sorokiniana, Phoma spp. and Penicillium spp. The one devasting fungal specie Alternaria alternata (88.8%) was the dominating species in all wheat genotypes included in the study. Conclusion: These results divulge the presence of fungal pathogens in all wheat cultivars and could exert adverse effect on their allometric traits. So, there is a potential need to control these seed borne pathogens to minimize the crop yield losses.

Background: We aimed to test the hypothesis that there is an association between hypertrophic cardiomyopathy and dilated aorta in a case-control, matched-design fashion. Methods: Of 65,843 studies done from November 2011 to December 2015, we found, after detailed evaluation by a single author, 153 cases of hypertrophic cardiomyopathy and 3,213 controls who were classified as normal clinically and echocardiographically. Controls were defined as normal patients referred to the echocardiography laboratory with no diagnoses and no known risk factors for dilated aorta (e.g., aortic stenosis, hypertension, aortic regurgitation). Clinical chart review showed none of the risk factors for dilated aorta, and echocardiography did not reveal any abnormalities. Of these 3,213 patients, 153 controls were matched to cases by age and sex by propensity score. Dilated aorta was defined according to clinical, Goldstein, and Lang's criteria. Results: The prevalence of a dilated sinus of Valsalva was 9 times higher in hypertrophic cardiomyopathy patients than controls (OR = 9.4, P = 0.003). The 9-fold higher prevalence in hypertrophic cardiomyopathy patients persisted after adjusting for height, weight, and aortic pathology. Association of dilated mid-ascending aorta with hypertrophic cardiomyopathy was significant after adjustment for height and body surface area but became borderline insignificant after adjusting for weight and aortic valve pathology. Conclusion: Hypertrophic cardiomyopathy appears to be associated with a dilated sinus of Valsalva, even after adjusting for height, weight, and aortic valve pathology.

Pakistan is a lower middle-income country in South Asia with a population of 225 million. No estimate for surgical care access exists for the country. We postulate the estimated access to surgical care is less than the minimum 80% to be achieved by 2030. We conducted a randomized, stratified two-stage cluster household survey. A sample of 770 households was selected using 2017 census frames from the Pakistan Bureau of Statistics. Data was collected on choice of hospital and travel time to the chosen hospital for C-section, laparotomy, open fracture repair (OFR), and specialized surgery. Analysis was conducted using Stata 14. Access to all Bellwether surgeries (C-section, laparotomy, and open fracture repair) in Pakistan is estimated to be 74.8%. However, estimated access in rural areas and the provinces of Balochistan, Khyber Pakhtunkhwa (KP) and Sindh is far less than in urban areas and in Punjab and Islamabad. Estimated access to C-sections is more compared to OFR, laparotomy, and specialized surgery. Health system strengthening efforts should focus on improving surgical care access in rural areas and in Balochistan, KP, and Sindh. More focus is required on standardizing the availability and quality of surgical services in secondary-level hospitals.

Climatic variables are important conditions for plant growth, development and reproduction. Citrus medica L. var. sarcodactylis Swingle (Rutaceae: Citrus) is one of the traditional bulk Chinese medicinal materials in China with the effects of bacteriostasis, anti-inflammatory, anti-oxidation, anti-cancer cells, regulating the immun. Analyzing the impact of climate change on geographical distribution of C. medica L. var. sarcodactylis can provide strong support for its production layout and agricultural zoning. In our paper, MaxEnt and ArcGIS were applied to simulate the suitable areas of C. medica L. var. sarcodactylis in China from the perspectives of bioclimate, soil, topographic factors and human activities, and the future climate scenarios generated by global climate models (GCMs) were selected to predict its suitable areas in 2050s and 2090s. Results showed that, 1) Under current climate condition, areas of the total, most, moderately and poorly suitable habitats of C. medica L. var. sarcodactylis in China were 177.36×104 km2, 22.27×104 km2, 51.96×104 km2 and 103.13×104 km2 respectively. The range of the most suitable habitat was the narrowest, which was located in the middle east of Sichuan, western Chongqing in the upstream of the Yangtze River Basin, southern Guizhou and western Guangxi in the upstream of the Pearl River Basin, central and southern Yunnan and Southeast Tibet in the Middle-Lower reaches of the Southwest River Basin and western Taiwan. 2) Under the future climate change scenarios, the total suitable area showed a significant increase trend in 2090s, and the change of most, moderately and poorly suitable habitats showed no obvious law. 3) Under SSP1-2.6, SSP2-4.5 and SSP5-8.5 scenarios, the centroid of the most suitable habitat of C. medica L. var. sarcodactylis would move to the northwest, southeast and southwest respectively.

Streamflow prediction is vital to control the effects of floods and mitigation. Physical prediction model often provides satisfactory results, but these models require massive computational work and hydrogeomorphological variables to develop a prediction system. At the same time, data-driven prediction models are quick to apply, easy to handle, and reliable. This study investigates a new hybrid model, the wavelet bootstrap quadratic response surface, for accurate streamflow prediction. Wavelet analysis is a well-known time-frequency joint analysis technique applied in various fields like biological signals, vibration signals, and hydrological signals. The wavelet analysis is used to denoise the time series data. Bootstrap is a nonparametric method for removing uncertainty that uses an intensive resampling methodology with replacement. The authors analyzed the results of the studied models with different statistical metrics, and it has been observed that the wavelet bootstrap quadratic response surface model provides the most efficient results.

Background: Quercetin, a natural polyphenol with demonstrated broad-spectrum antiviral, anti-inflammatory, and antioxidant properties, has been proposed as an adjuvant for early-stage coronavirus disease 2019 (COVID-19) infection.Objective: To explore the possible therapeutic effect of quercetin in outpatients with early-stage mild to moderate symptoms of COVID-19.Methods: This was an open-label randomized controlled clinical trial conducted at the department of medicine, King Edward Medical University, Lahore, PK. Patients were randomized to receive either standard of care (SC) plus an oral quercetin supplement (500 mg Quercetin Phytosome®, 1st week, TDS: 2nd week, BDS) (n = 50, quercetin group) or SC alone (n = 50, control group).Results: After one week of treatment, patients in the quercetin group showed a speedy recovery from COVID-19 as compared to the control group, i.e., 34 patients (vs. 12 in the control group) tested negative for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (p = 0.0004), and 26 patients (vs. 12 in the control group) had their COVID-19-associated acute symptoms resolved (p = 0.0051). Patients in the quercetin group also showed a significant fall in the serum lactate dehydrogenase (LDH) mean values i.e., from 406.56 ± 183.92 to 257.74 ± 110.73 U/L, p = 0.0001. Quercetin was well-tolerated by all the 50 patients, and no side effects were reported.Conclusion: Our results, suggest the possible therapeutic role of quercetin in early-stage COVID-19, including speedy clearance of SARS-CoV-2, early resolution of the acute symptoms and modulation of the host’s hyperinflammatory response.Clinical Trial Registration:clinicaltrials.gov, identifier NCT04861298

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network “EL-RMLocNet”, predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).

Prior to standardization, new features, algorithms, and solutions have to be rigorously evaluated and verified using different methods. In this regard, testbeds are considered as one of the most important and effective experimental platforms for performing tests, thus paving the way towards the real-world implementation of many solutions, from the most basic to the most disruptive and innovative ones. Compared to computer simulators, testbeds provide near-realistic implementation and can obtain practical results while posing less risk to live networks. They are also cheaper compared to field trials. Given the crucial role of testbeds in the cellular network ecosystem, it is imperative to create awareness regarding the design challenges associated with constructing an effective and efficient testbed deployment. In this tutorial, we discuss the design considerations and challenges we experienced while deploying an outdoor testbed that we call TurboRAN—a 5G and beyond network testbed facility. It is designed to facilitate the evaluation of early real-world use case algorithm implementations and deployments. We present the process and methodology used to select components, including antennas, amplifiers, base stations, and cabinets, to name a few, and describe the integration of the components to construct a fully operational testbed. Finally, we discuss a use case that demonstrates the TurboRAN’s ability to conduct real-world experiments. The use case demonstrates the detrimental impact of sub-optimal handover parameter configuration on network performance—a big challenge in modern cellular communication. This work intends to inspire and guide the development of a cellular testbed by providing a generalized framework that describes step-by-step the efforts made for its construction.

Cancer is a fatal disease and a significant cause of millions of deaths. Traditional methods for cancer detection often have limitations in identifying the disease in its early stages, and they can be expensive and time-consuming. Since cancer typically lacks symptoms and is often only detected at advanced stages, it is crucial to use affordable technologies that can provide quick results at the point of care for early diagnosis. Biosensors that target specific biomarkers associated with different types of cancer offer an alternative diagnostic approach at the point of care. Recent advancements in manufacturing and design technologies have enabled the miniaturization and cost reduction of point-of-care devices, making them practical for diagnosing various cancer diseases. Furthermore, machine learning (ML) algorithms have been employed to analyze sensor data and extract valuable information through the use of statistical techniques. In this review paper, we provide details on how various machine learning algorithms contribute to the ongoing development of advanced data processing techniques for biosensors, which are continually emerging. We also provide information on the various technologies used in point-of-care cancer diagnostic biosensors, along with a comparison of the performance of different ML algorithms and sensing modalities in terms of classification accuracy.

Background: Pakistan is a lower middle-income country in South Asia with a population of over 220 million. With the recent development of national health programs focusing on surgical care, two areas of high priority for research and policy are access and financial risk protection related to surgery. This is the first study in Pakistan to nationally assess geographic access and expenditures for patients undergoing surgery. Methods: This is a cross-sectional study of patients undergoing laparotomy, cesarean section, and surgical management of a fracture at public tertiary care hospitals across the country. A validated financial risk protection tool was adapted for our study to collect data on the socio-economic characteristics of patients, geographic access, and out-of-pocket expenditure. Results: A total of 526 patients were surveyed at 13 public hospitals. 73.8% of patients had 2-hour access to the facility where they underwent their respective surgical procedures. A majority (53%) of patients were poor at baseline, and 79.5% and 70.3% of patients experienced catastrophic health expenditure and impoverishing health expenditure, respectively. Discussion: A substantial number of patients face long travel times to access essential surgical care and face a high percentage of impoverishing health expenditure and catastrophic health expenditure during this process. This study provides valuable baseline data to health policymakers for reform efforts that are underway. Conclusions: Strengthening surgical infrastructure and services in the existing network of public sector first-level facilities has the potential to dramatically improve emergency and essential surgical care across the country.

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

Background: Male infertility is an emerging health issue in the world today. Surgical interventions for the treatment of male infertility are available but are quite expensive. Herbal interventions pose a popular alternative for the treatment of infertility. However, much has to be learned regarding their safety and efficacy.Objective: The aim of the study was to investigate the efficacy of herbal interventions in male infertility and also assess the possibility of these interventions as complementary and alternative medicine (CAM) in the future.Method: From inception until 16 December 2021, all articles emphasizing the efficacy of herbal interventions in the treatment of male infertility are included in this review. Seven databases are searched. The literature obtained is screened and extracted. Semen parameters, hormonal concentration, and conception are the outcomes of interest.Results: A total of 19 articles were included in this review. Herbal interventions might improve semen parameters in males with infertility. Among all the interventions, Hochu-ekki-to and W. somnifera have shown the most promising results and should be studied further in a larger sample size.Conclusion: This systematic review has demonstrated the efficacy of herbal interventions, especially Hochu-ekki-to and W. somnifera, in treating male infertility.

Summary: Protein-protein interaction (PPI) prediction is essential to understand the functions of proteins in various biological processes and their roles in the development, progression, and treatment of different diseases. To perform economical large-scale PPI analysis, several artificial intelligence-based approaches have been proposed. However, these approaches have limited predictive performance due to the use of in-effective statistical representation learning methods and predictors that lack the ability to extract comprehensive discriminative features. The paper in hand generates statistical representation of protein sequences by applying transfer learning in an unsupervised manner using FastText embedding generation approach. Furthermore, it presents “ADH-PPI” classifier which reaps the benefits of three different neural layers, long short-term memory, convolutional, and self-attention layers. Over two different species benchmark datasets, proposed ADH-PPI predictor outperforms existing approaches by an overall accuracy of 4%, and matthews correlation coefficient of 6%. In addition, it achieves an overall accuracy increment of 7% on four independent test sets. Availability: ADH-PPI web server is publicly available at https://sds_genetic_analysis.opendfki.de/PPI/

Comparative religion is a field of study through which views of various religions about a particular topic or sets of topics can be collated, interpreted, and systematically compared for attaining useful insights and broadening the understanding of religious beliefs, behaviors, and actions [i]. The current research furthers the study of comparative religion by elaborating the conceptions or myths related to eclipses as found in various religions and cultures in the world. Written as a narrative literature review, it aims to collate these conceptions and opinions for comparative analysis. In this paper, we have studied the ‘myths’ and ‘mythology’ of the religions and cultures spread across Australia, Asia, the Americas, Europe, Africa, and the Middle East. It is found that some common themes exist in the beliefs held by various people and religions. However, if we compare these themes with each other, they are found to be considerably different indicating that they have not come from a common source; people have been creating them at various points in history. However, very different to them is the Islamic perspective on the topic. It profoundly differs from the conceptions held in other religions. It does not say that the eclipses are caused by some giant creature eating up the sun, or because of the sun being imprisoned, or because of a fight between some ‘gods’, and so on. It rather explains them as a phenomenon of nature that invites reflection and pondering. This raises a very important question to ponder upon: Why did Islam not adopt any myths to explain the concept of eclipse unlike any other religion even though it could use them for its benefit? This paper answers it by explaining the nature and objective of the Prophet of Islam and the source of his knowledge which was that very Creator who created the sun and the moon and everything else. Another important point that the current research highlights is that there is an inseparable connection between the fields of history, science, religion, politics, culture, and psychology; none of them can be separated from each other if one wishes to obtain a holistic understanding of this topic as well as many other matters of the past, present, and future.

Physician communication is integral for improving patients’ experience and clinical outcomes. Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS) scores are used by the Centers for Medicare & Medicaid Services to assess the quality of care provided to the patients. We conducted a prospective pre–post-intervention study from March 2018 to March 2020 and included patients and residents of the community-based university-affiliated Internal Medicine Residency Program. Residents’ communication was evaluated through resident surveys and results were shared with them and attending physicians within 48 h. To correlate with the effect over a 4-year period, 839 HCAHPS surveys were obtained for attending physicians as an outcome measure. The mean overall HCAHPS score and doctor's communication scores increased by 8.52 and 6.06 points post-intervention. The doctor's performance score for respect increased by 6.18 points, listening increased by 3.12 points, and explanation increased by 8.23 points. This study demonstrates that timely, structured, and individualized resident feedback in an academic medicine service can result in a sustainable increase in the attendings’ overall HCAHPS and doctor's communication scores.

Exponential growth in the industrial sector causes the accumulation of toxic wastewater in the ecosystem. The unsafe disposal of such wastewater not only adversely affects the freshwater resources but also pose negative impact on the environment and human health due to the presence of heavy metals, organic/inorganic pollutants, and high amount of nitrogen, phosphorus, and sulfur in it. It demands for the effective wastewater treatment that depends on the effluent quality, industrial process, chemical/energy requirement, economic viability, operational flexibitily, and residual utilization. However, conventional treatment methods are unable to meet all the requriements which demands for the eco-friendly and economical alternatives. Currently, paradigm shift from wastewater treatment and dispoal of, to its utilization in circular biorefinery context is underway. In this context, microalgae-based industrial wastewater is vital option due its pollutant accumulation ability, environmental sustainablity, biorefinery potential, and zero-waste process chain development. This review highlights the characteristics of different industrial effluents and overviews the axenic and binary algal-treatment systems. Economical and technological barriers are the major hindrance in the implementation of algal-based treatment methods; for which biomass valorization has been briefly discussed. This review highlights the need of developing closed-loop integrated processes for wastewater treatment and reuse in sustainable manner. Further research in the development of such schemes specially at large-scale level must be the focus of future studies.

Rhino-orbital-cerebral mucormycosis (ROCM), a rare but fatal fungal infection, has recently emerged as a serious complication after corticosteroids therapy in COVID-19 patients, predominantly in diabetic and immunocompromised patients. The World Health Organization (WHO) COVID-19 current guidelines recommend corticosteroids administration in hospitalized COVID-19 patients requiring supplementary oxygen or mechanical ventilation. Herein, we report a case series of seven patients with COVID-19; three mild, three moderate, and one severe, from Lahore, Pakistan; all were using corticosteroids for managing their early mild symptoms of COVID-19 at home for around 2–3 weeks without a physician’s advise, presented, and admitted with ROCM to Mayo hospital, Lahore, from March to June 2021. Out of the seven patients, five patients had uncontrolled diabetes mellitus (DM) as comorbidity. Eye pain, facial swelling and pain, nasal blockage, and black coloration around eyes, on palate, and oral mucosa were the presenting complaints at the time of admission. All the patients had radiographic imaging, including computed tomography (CT), paranasal sinuses (PNS), or brain magnetic resonance imaging (MRI) carried out at the hospital, which confirmed mucosal thickening and adjacent sinus bony erosions with intracranial extension. All the patients were treated with local debridement of the infected necrotic tissue along with intravenous liposomal Amphotericin B and Posaconazole or Amphotericin B depending on the case. Due to timely management, in six out of seven patients, prognosis was good due to early diagnosis and treatment, while one patient with severe COVID-19 illness deteriorated and died. The misuse of corticosteroids for managing early mild symptoms of COVID-19 in diabetic and other immunocompromised patients can lead to fatal ROCM, which can further increase their risk of developing severe COVID-19 and mortality. It is stressed that only physician’s recommended therapeutic advice should be followed for managing early mild symptoms of COVID-19 in self-isolation and avoid the unnecessary use of corticosteroids. This case series also emphasizes that COVID-19 diabetic patients treated with corticosteroids need more vigilant monitoring and high suspicion of early diagnosis and treatment of invasive fungal infection. Early diagnosis and management can reduce morbidity and mortality.

Small non-coding RNAs (ncRNAs) are attracting increasing attention as they are now considered potentially valuable resources in the development of new drugs intended to cure several human diseases. A prerequisite for the development of drugs targeting ncRNAs or the related pathways is the identification and correct classification of such ncRNAs. State-of-the-art small ncRNA classification methodologies use secondary structural features as input. However, such feature extraction approaches only take global characteristics into account and completely ignore co-relative effects of local structures. Furthermore, secondary structure based approaches incorporate high dimensional feature space which is computationally expensive. The present paper proposes a novel Robust and Precise ConvNet (RPC-snRC) methodology which classifies small ncRNAs into relevant families by utilizing their primary sequence. RPC-snRC methodology learns hierarchical representation of features by utilizing positioning and information on the occurrence of nucleotides. To avoid exploding and vanishing gradient problems, we use an approach similar to DenseNet in which gradient can flow straight from subsequent layers to previous layers. In order to assess the effectiveness of deeper architectures for small ncRNA classification, we also adapted two ResNet architectures having a different number of layers. Experimental results on a benchmark small ncRNA dataset show that the proposed methodology does not only outperform existing small ncRNA classification approaches with a significant performance margin of 10% but it also gives better results than adapted ResNet architectures. To reproduce the results Source code and data set is available at https://github.com/muas16/small-non-coding-RNA-classification.

Attackers increasingly seek to compromise organizations and their critical data with advanced stealthy methods, often utilising legitimate tools. In the main, organisations employ reactive approaches for cyber security, focused on rectifying immediate incidents and preventing repeat attacks, through protections such as vulnerability assessment and penetration testing (VAPT) security information and event management (SIEM), firewalls, anti-spam/anti-malware solutions and system patches. Such system have weaknesses in addressing modern modern stealthy attacks. Proactive approaches, have been seen as part of the solution to this problem. However, approaches such as VAPT have limited scope and only works with threats that have already been discovered. Promising methods such as threat hunting are gaining momentum, enabling organisations to identify and rapidly respond to any potential attacks, though they have been criticised for their significant cost. In this paper, we present a novel hybrid model for uncovering tactics, techniques, and procedures (TTPs) through offensive security, specifically threat hunting via adversary emulation. The proposed technique is based on a novel approach of inducing adversary emulation (mapping each respective phase) model inside the threat hunting approach. The experimental results show that the proposed approach uses threat hunting via adversary emulation and has countervailing effects on hunting advance level threats. Moreover, the threat detection ability of the proposed approach utilizes minimum resources. The proposed approach can be used to develop the offensive security-aware environment for organizations to uncover advanced attack mechanisms and test their ability for attack detection.

Seawater desalination is an affordable and viable solution to the growing freshwater scarcity problem in water scarce regions. The current study focuses on cost analysis of Vacuum Membrane Distillation (VMD) setup for removing salts from water. The membrane used in the flat sheet VMD module was Polytetrafluoroethylene (PTFE) with 250 mm × 200 mm dimensions and 165 µm thickness. The experiments were carried out with variations in parameters such as velocity, pressure, concentration, and temperature. For the cost analysis, the operational, maintenance, instrumentation, and capital cost of the lab model was considered and then upscaled. A range of experiments was performed for NaCl and KCl under variations of operating parameters. It was noted that, for the NaCl solution, the increase in temperature from 50 °C to 70 °C doubled the permeate flux. However, for the conditions tested, the concentration shift from 0.25 M to 0.75 M decreased the permeate flux by 1.4% because the increase in ion concentrations along the membrane lowers the vapor pressure, restricting the permeate flux. The results trend for the KCl solution was similar to the NaCl; at temperature T1, it was noted that increased concentration from 0.25 M to 0.75 M significantly reduces the permeate flow. The reduction in permeate flow was nonlinear for a given pressure 30 kPa and velocity 5.22 m/s, but linear for all other variables. It was also observed that with an increase in temperature from 60 °C to 70 °C, the permeate flux for concentration 0.25 M was 49% for all the combinations of pressure and velocity. In addition, permeate flow increased 53% from temperature 50 °C to 60 °C and 49% from temperature 60 °C to 70 °C for both the solutions at a concentration of 0.25 M. This shows that the temperature also had a profound impact on the permeate flux. The economic analysis and market survey shows that the cost of clean water at the lab level was high which can be significantly reduced using a large-scale setup providing 1,000,000 L/H of distilled water.

Dengue is a mosquito-borne flaviviral serious febrile illness, most common in the tropical and subtropical regions including Pakistan. Vitamin D is a strong immunomodulator affecting both the innate and adaptive immune responses and plays a pivotal role in pathogen-defense mechanisms. There has been considerable interest in the possible role of vitamin D in dengue viral (DENV) infection. In the present prospective cross-sectional study, we assessed a possible association between serum vitamin D deficiency (VDD) and susceptibility towards severe dengue fever (DF) illness. Serum vitamin D levels were measured at the time of hospitalization in 97 patients diagnosed with dengue fever (DF), dengue hemorrhagic fever (DHF) or dengue shock syndrome (DSS) at Mayo Hospital, King Edward Medical University, Lahore, PK, from 16 November 2021 to 15 January 2022. In terms of disease severity, 37 (38.1%) patients were DF, 52 (53.6%) were DHF grade 1 and 2, and 8 (8.2%) were DSS. The results revealed that most patients (75 (77.3%)) were vitamin-D-deficient (i.e., serum level < 20 ng/mL), including 27 (73.0%) in DF, 41 (78.8%) in DHF grade 1 and 2, and 7 (87.5%) in DSS. The degree of VDD was somewhat higher in DSS patients as compared to DF and DHF grade 1 and 2 patients. Overall, serum vitamin D levels ranged from 4.2 to 109.7 ng/mL, and the median (IQR) was in the VDD range, i.e., 12.2 (9.1, 17.8) ng/mL. Our results suggest that there may be a possible association between VDD and susceptibility towards severe dengue illness. Hence, maintaining sufficient vitamin D levels in the body either through diet or supplementation may help provide adequate immune protection against severe dengue fever illness. Further research is warranted.

University students are a sub-group of the population at high risk of COVID-19 infection, and their judgments on vaccination affect the public attitudes towards vaccination. Thus, the present study aimed to assess the knowledge, attitudes, perceptions, and acceptance of the COVID-19 vaccine among pharmacy and non-pharmacy students. A cross-sectional study was conducted by enrolling pharmacy (375) and non-pharmacy (225) students from the universities in Lahore. Chi-square analysis was used for significant frequency distributions and a 5-point Likert scale was used to score attitude, perception, and acceptance. The majority of the students were aged between 19–24 years, hailing from urban and middle-class families with good self-reported health. The preferred vaccine was Pfizer, followed by Sinopharm and Sinovac. The major source of information was social media, followed by government campaigns and family members. The pharmacy students demonstrated better knowledge and positive attitudes toward COVID-19 vaccination. The non-pharmacy students scored higher for the questions based on scientific leads, myths, and baffling conspiracies. The non-pharmacy students showed higher hesitancy/barrier total scores related to their trust in the health system, COVID-19 vaccine storage, and efficacy. Data suggested that pharmacy students exhibited better knowledge, positive attitudes, and perceptions about COVID-19 vaccination. Overall, vaccine efficacy and safety were mutual concerns. Nonetheless, non-pharmacy students were hesitant due to mistrust in the health system of Pakistan.

A qualitative exploration of pharmacists’ perceptions regarding COVID-19 conspiracies and their willingness to get vaccinated. A semi-structured questionnaire guide was developed using ground theory to conduct in-depth interviews. A total of 36 participants gave consent for an audio-recorded interview. Results have shown that most of the respondents believed that SARS-CoV-2 is a natural virus, not man-made, that causes a disease just like other viruses and it is absurd to believe that the vaccine is being used by foreign powers for the implantation of microchips just to control humans. A general opinion thatwhich reflected from the in-depth interview is that the pharmaceutical companies may be hiding some important information on COVID-19 to promote the sale of their product. Some doubts on the reliability and trustworthiness on the COVID-19 vaccine safety and efficacy data were noticed among the respondents. Factors leading to COVID-19 vaccine hesitancy were adverse reaction, cost of COVID-19 vaccine, and limited data on safety and efficacy profile of COVID-19 vaccine. COVID-19 vaccine hesitancy among health professionals is a major hindrance to our current fight against COVID-19 pandemic. Findings of this study are alarming, and the stakeholders must consider this ongoing vaccination campaign as an opportunity to formulate a mechanism to ensure high vaccination rate among general public and healthcare providers in Pakistan. KEY POINTS What was already known? According to World Health Organization (WHO), vaccine hesitancy is one of the ten major threats to global healthcare system and it is a major barrier to achieve herd immunity around the globe. Pakistan has begun vaccinating its people in a systematic phase-wise manner under which the healthcare workers and elderly people are prioritized for vaccination. Previous experience tells us that vaccine hesitancy is a major problem in Pakistan and it is better to understand perceptions of pharmacists about COVID-19 vaccine who are the primary source of information for most of general population. What this study adds: This study is first of its kind to explore vaccine hesitancy among Pakistani pharmacists and the results of this study show that majority of the participants were willing to get COVID-19 vaccine and few of them have even got themselves vaccinated at the start of vaccination campaign. Many among the willing participants considered cost of vaccine, adverse reactions, limited data, safety, and efficacy as major hindrance to their decision to get vaccine. Few participants were found highly vaccine-hesitant because of their staunch belief in the prevalent myths and rumors about COVID-19 vaccine.

Viral-host protein protein interaction (PPI) analysis is essential to decode the molecular mechanism of viral pathogen and host immunity processes which eventually help to control viral diseases and optimize therapeutics. The state-of-the-art viral-host PPI predictor leverages unsupervised embedding learning technique (doc2vec) to generate statistical representations of viral-host protein sequences and a Random Forest classifier for interaction prediction. However, doc2vec approach generates the statistical representations of viral-host protein sequences by merely modelling the local context of residues which only partially captures residue semantics. The paper in hand proposes a novel technique for generating better statistical representations of viral and host protein sequences based on the infusion of comprehensive local and global contextual information of the residues. While local residue context aware encoding captures semantic relatedness and short range dependencies of residues. Global residue context aware encoding captures comprehensive long-range residues dependencies, positional invariance of residues, and unique residue combination distribution important for interaction prediction. Using concatenated rich statistical representations of viral and host protein sequences, a robust machine learning framework "LGCA-VHPPI" is developed which makes use of a deep forest model to effectively model complex non-linearity of viral-host PPI sequences. An in-depth performance comparison of the proposed LGCA-VHPPI framework with existing diverse sequence encoding schemes based viral-host PPI predictors reveals that LGCA-VHPPI outperforms state-of-the-art predictor by 6%, 2%, and 2% in terms of matthews correlation coefficient over 3 different benchmark viral-host PPI prediction datasets.

Background: Patients with coronavirus disease 2019 (COVID-19) could experience multiple coinfections, and judicial antimicrobials, including antibiotics, is paramount to treat these coinfections. This study evaluated physicians’ perception, attitude, and confidence about antimicrobial resistance (AMR) and antimicrobial prescribing in patients with COVID-19.Methods: A self-administered and validated online questionnaire comprised of six sections was disseminated among physicians working in public sector hospitals in Punjab, Pakistan, using the convenience sampling method from April to May 2021. The study also assessed the validity and reliability of the study questionnaire using exploratory factor analysis and Cronbach’s alpha. In addition, the descriptive and inferential statistics present survey results.Results: A total of 387 physicians participated in this study. The study showed that the questionnaire demonstrated good internal consistency (Cronbach’s alpha = 0.77). Most physicians (n = 221, 57.1%) believed that AMR is a considerable problem in Pakistan. Less than a quarter of respondents (n = 91, 23.5%) consulted with local antibiotic resistance data to prescribe antibiotics in COVID-19 patients. However, the respondents were confident to select a suitable antibiotic (n = 229, 59.2%). More than three-quarters of the respondents believed that advice from a senior colleague (n = 336, 86.8%), infectious disease (ID) physician (n = 315, 81.4%), and implementing antimicrobial stewardship programs (ASPs) could facilitate appropriate prescribing of antibiotics in COVID-19 patients. Multivariate logistic regression revealed that physicians with more than 10 years of experience had higher odds of consulting local guidelines for antibiotic therapy (OR, 4.71 95% CI: 1.62–13.73, p = 0.004) than physicians with less than 5 years of experience. Similar trends were found for consulting national guidelines and local resistance data to select an empiric antibiotic therapy.Conclusion: AMR-related awareness was optimal among physicians. Only a few physicians looked up local antibiotic resistance data before prescribing antibiotics to COVID-19 patients empirically. The significant approaches advised by physicians to reduce AMR risk among COVID-19 patients were the implementation of ASPs combined with advice from ID physicians.

Cough acoustics contain multitudes of vital information about pathomorphological alterations in the respiratory system. Reliable and accurate detection of cough events by investigating the underlying cough latent features and disease diagnosis can play an indispensable role in revitalizing the healthcare practices. The recent application of Artificial Intelligence (AI) and advances of ubiquitous computing for respiratory disease prediction has created an auspicious trend and myriad of future possibilities in the medical domain. In particular, there is an expeditiously emerging trend of Machine learning (ML) and Deep Learning (DL)-based diagnostic algorithms exploiting cough signatures. The enormous body of literature on cough-based AI algorithms demonstrate that these models can play a significant role for detecting the onset of a specific respiratory disease. However, it is pertinent to collect the information from all relevant studies in an exhaustive manner for the medical experts and AI scientists to analyze the decisive role of AI/ML. This survey offers a comprehensive overview of the cough data-driven ML/DL detection and preliminary diagnosis frameworks, along with a detailed list of significant features. We investigate the mechanism that causes cough and the latent cough features of the respiratory modalities. We also analyze the customized cough monitoring application, and their AI- powered recognition algorithms. Challenges and prospective future research directions to develop practical, robust, and ubiquitous solutions are also discussed in detail.

Channa are the freshwater and important food fish species in Pakistan belonging to family Channidae. However, identification and phylogenetic analysis based on molecular tools of these species in Pakistan was not well known. Herein, the current investigation was conceptualized, which dealt with mitochondrial DNA sequences from three geographically distinct populations of this species from Pakistan’s water system. DNA from fin tissues was extracted. COI region of mtDNA was amplified using universal primers for fish. PCR products were sequenced. Phylogenetic analysis conducted in the present study, i.e. neighbor-joining (NJ) cladogram, maximum likelihood, K2P genetic divergence and histogram suggests that the studied species of family Channidae are genetically different. The K2P intraspecific divergences were lower than interspecific divergences. The clades in the evolutionary tree for three species were clearly separated.

Wildlife populations are at a risk of extinction mainly because of human-wildlife conflict (HWC). The present study was designed to evaluate the ongoing HWC with special reference to Common Leopard (Panthera pardus) in Ayubia National park through field study as well as a literature-based approach. Questionnaire interview surveys were designed for wildlife officials working in the park and the locals who bear the cost for leopard conflict through livestock depredation and crop damage. The study showed that human-leopard conflict in the study area has been increasing. More than 60% of people considered livestock depredation as the major reason for their negative perception towards the common leopard. Among livestock, goats were more vulnerable which showed that leopards mostly preferred smaller prey. A number of reported human injuries and deaths on account of Human-Leopard conflict in the study area helped conclude that human-wildlife conflict is a significant issue. Mitigation measures may hence be recommended, such as livestock compensation schemes and community-based conservation approaches, etc. It is critical to avoid human-Leopard conflict not only to keep the public and their property safe but also to help conserve this important species of common leopard (Panthera pardus).

Electric power grids are lagging in flexibility and time-response. A smart grid is an improved version of electrical grids that leverages Internet of Things (IoT) based devices to improve the overall infrastructure from the grid stations to intelligent appliances. It provides better understanding of supply and demand and overall flow of data depending based upon the requirements. Modern approach towards Smart grid envisions to provide electricity consumers with the opportunity to manage their respective power usage. Population increase has played a major role in the adoption of smart grid as a lot of electrical energy is consumed in the residential sector and a lot of architectures have been proposed for better flow of information from the smart meter to connectors and devices for improved customer participation. Customer needs have been very important in the smart grid. However, the customers have never been provided with the ease of choosing their own kind of benefits from the smart grid. In this work, we propose an enhanced architecture working effectively for multiple users based on their requirements. The users would be able to choose their type of scheduling techniques based on their requirements. These requirements may include cost reduction and increasing user comfort for better consumption of electricity and reliable systems. These requirements can be achieved using different Bio inspired computing based scheduling algorithms. Furthermore, in this work, we provide a comparison of these bio inspired scheduling techniques, i.e., Enhanced Differential Evolution, Bacterial Foraging Algorithm and Grey Wolf Optimization integrated in smart grid architecture for providing better consumption of electricity and achieving reliable systems. These algorithms mainly aim to schedule load, minimize electricity bills and maximize the user comfort depending on user demand.

In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this article for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove. The integrity of generated neural word embeddings is evaluated using intrinsic and extrinsic evaluation approaches. Considering the lack of publicly available benchmark datasets, it provides a first-ever Roman Urdu public dataset which consists of 3241 sentiments annotated against positive, negative, and neutral classes. To provide benchmark baseline performance over the presented dataset for Roman Urdu sentiment analysis, we adapt diverse machine learning (Support Vector Machine, Logistic Regression, Naive Bayes), deep learning (convolutional neural network, recurrent neural network), and hybrid deep learning approaches. Performance impact of generated neural word embeddings based representation is compared with other most widely used bag of words based feature representation approaches using diverse machine and deep learning classifiers. In order to improve the performance of Roman Urdu sentiment analysis, it proposes a novel precisely extreme multi-channel hybrid methodology which makes use of convolutional and recurrent neural networks along with pre-trained neural word embeddings. The proposed hybrid approach outperforms adapted machine learning approaches by the significant figure of 9% and deep learning approaches by the figure of 4% in terms of F1-score.

The transport sector has proven to be the largest contributor to global CO2 emissions. To reduce CO2 emissions and improve mileage, the existing research has proposed different fuel models for vehicles such as Plug-in Hybrid Electric Vehicles (PHEVs), Electric Vehicles (EVs), solar and hydrogen Vehicles. However, these vehicles suffer from a range of issues and solutions are required to increase range, and improve charging. In this context, we propose A Novel Hybrid Fuel Framework for Modern Vehicles, to reduce CO2 emissions and increase vehicle mileage, by managing energy resources efficiently through the application of Fuzzy Logic. It considers three different energy sources i.e., gasoline, solar and electric power, to charge a vehicle, and suggest a modification in the architecture of EVs is made for the availability of all these energy resources. We use Visual Studio to implement fuzzy logic based algorithm designed to simulate the proposed system and added a small gasoline engine to the existing architecture of EVs to provide energy resources that overcome charging issues during long-range travel. We use the Statistical Package for Social Sciences (SPSS) tool to evaluate the performance of the proposed framework for CO2 emissions and fuel efficiency. The proposed framework achieves the best mileage of 57.6 Kilometers per liter (Km/l) with a 660 Cubic Centimeter (CC) gasoline engine which is 111.11% more efficient than existing frameworks. Moreover, CO2 emissions through our proposed framework are 41.52 Grams per Kilometer (G/Km) which are 53% lower than current frameworks. The proposed framework also improves the charging duration of batteries i.e., a 10 Kilowatt-Hour (KwH) battery can be charged in 1 hour and 15 minutes.

Being the true and final word of Allah swt, the Quran provides us guidance about all spheres of our life – individual as well as communal. The principles of reformation of society that it outlines begin with the individuals and then they expand to include families, communities, nations, and ultimately the whole of mankind. If the first building block - the individual - is not solid and strong, it will result in the weakness of all the subsequent levels. Islam, therefore, puts a strong emphasis on the holistic personality development of individuals. It has provided detailed guidance about all the essential aspects and ingredients of a confident Muslim personality. The details of these ingredients are abundantly elaborated through the blessed words and noble actions of the best of the creations, Muhammad ﷺ which have been recorded in the books of ahadith. We need to make ourselves aware of these ingredients and utilize them to shape our beings. We are more in need to remain in the pursuit of self-improvement in today’s times than ever before. This article is an attempt to present an overview of these essential ingredients and explain their basis, challenges in their implementation, and the factors that help us employ them. May Allah swt make us realize our true potential and enable us to perform our responsibilities as Muslims and as the messengers of the Messenger of Allah ﷺ in the most befitting manner.