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Abstract Background The genetic diversity of malaria parasites contributes to their ability to adapt to environmental changes, develop drug resistance and circumvent the host immune system. This study aimed to analyse the genetic diversity of the Pfmsp1 and Pfmsp2 genes in Plasmodium falciparum and the Pvmsp-3α gene in Plasmodium vivax isolates from District Nowshera in Pakistan. Methods Blood samples from 124 consenting patients with uncomplicated malaria presenting to different hospitals from the Nowshera district were collected between March and August 2019, representing 28 P. falciparum and 96 P. vivax isolates. The genomic DNA extracted from the isolates were subjected to nested PCR and allele-specific analysis. Pvmsp-3α amplified fragments were further treated with restriction fragment length polymorphism (RFLP)-based Hha1 restriction enzyme. Results Of the analyzed P. falciparum, 21 distinct alleles were detected, including 14 alleles for Pfmsp-1 and 7 alleles for Pfmsp-2. The sub-allelic families MAD20 (50%) of Pfmsp-1and FC27 (75%) of Pfmsp-2 were predominant. The multiplicity of infection (MOI) was calculated as 1.4 and 1.2 for Pfmsp-1 and Pfmsp-2, respectively, with an overall mean MOI of 1.34. In P. vivax, 4 allelic variants, Pvmsp-3α types A, B, C and D, were detected, while RFLP digestion of amplicons, detected 9 sub-allelic variants (A1-A4, B1, B2, C1, C2 and D1) at the Pvmsp-3α locus. Conclusion This first ever report of molecular characterization of P. falciparum and P. vivax genotypes from District Nowshera, Pakistan reveals moderate to high allelic diversity in parasite population from District Nowshera, Pakistan.

Effective malware detection is critical to safeguarding digital ecosystems from evolving cyber threats. However, the scarcity of labeled training data, particularly for cross-family malware detection, poses a significant challenge. This research proposes a novel architecture ConvNet-6 to be used in Siamese Neural Networks for applying Zero-shot learning to address the issue of data scarcity. The proposed model for malware detection uses the ConvNet-6 architecture even with limited training samples. The proposed model is trained with just one labeled sample per sub-family. We conduct extensive experiments on a diverse dataset featuring Android and Portable Executables’ malware families. The model achieves high performance in terms of 82% accuracy on the test dataset, demonstrating its ability to generalize and effectively detect previously unseen malware variants. Furthermore, we examine the model’s transferability by testing it on a portable executable malware dataset, despite being trained solely on the Android dataset. Encouragingly, the performance remains consistent. The results of our research showcase the potential of deep convolutional neural network (CNN) in Siamese neural networks for the application of zero-shot learning to detect cross-family malware, even when dealing with minimal labeled training data.

Astrovirus MLB1 (HAstV-MLB1) is non-enveloped RNA virus that cause acute gastroenteritis infection. Despite research progress about infection and pathogenesis of HAstV-MLB1, Currently, no vaccine has been developed to effectively combat this pathogen. The current study is based on immunoinformatics and reverse vaccinology approaches to design next-generation, multi-epitope-based vaccine models against HAstV-MLB1. Genome-wide whole proteome data of HAstV-MLB1 strain was retrieved, and a series of analyses were conducted to explore effective B and T-cell epitopes that hold significant antigenic nature with no toxicity and allergenicity. A set of vaccine constructs were designed by different combination of lead B and T-cell epitopes with diverse linkers and adjuvants sequences. The model vaccine structures were analyzed via rigorous criteria of physiochemical properties, antigenicity, and molecular docking with HLA and TLR4 immune receptors to ensure their efficacy and safety. Based on the lowest binding energy of −82.48 kcal/mol against the HLA receptor, the MLB1-C2 vaccine model with β-definsin adjuvant was prioritized for molecular dynamic and immune simulations analyses to assess its stability and immunogenic potential. These analyses revealed that the MLB1-C2 construct has feasible molecular stability and potential to boost strong immune responses in the host cell. Besides, the model was predicted to be non-toxic, non-allergenic, and antigenic, ensuring broad population coverage and capable to elicit a robust immune response. The in-silico cloning analysis highlighted a possible gene expression potential of the MLB1-C2 construct in E.coli commercial recombinant vector molecule. The findings of the current study provide an essential template for the development of a advanced next-generation effective vaccine against HAstV-MLB1.

Background: Typhoid fever, caused by Salmonella enterica serovar Typhi, is a significant public health concern due to the escalating of antimicrobial resistance (AMR), with limited treatment options for extensively drug-resistant (XDR) S. Typhi strains pose a serious threat to disease management and control. This study aimed to investigate the genomic characteristics, epidemiology and AMR genes of XDR S. Typhi strains from typhoid fever patients in Pakistan. Methodology: We assessed 200 patients with enteric fever symptoms, confirming 65 S. Typhi cases through culturing and biochemical tests. Subsequent antimicrobial susceptibility testing revealed 40 cases of extensively drug-resistant (XDR) and 25 cases of multi-drug resistance (MDR). Thirteen XDR strains were selected for whole-genome sequencing, to analyze their sequence type, phylogenetics, resistance genes, pathogenicity islands, and plasmid sequences using variety of data analysis resources. Pangenome analysis was conducted for 140 XDR strains, including thirteen in-house and 127 strains reported from other regions of Pakistan, to assess their genetic diversity and functional annotation. Results: MLST analysis classified all isolates as sequence type 1 (ST-1) with 4.3.1.1. P1 genotype characterization. Prophage and Salmonella Pathogenicity Island (SPI) analysis identified intact prophages and eight SPIs involved in Salmonella's invasion and replication within host cells. Genome data analysis revealed numerous AMR genes including dfrA7, sul1, qnrS1, TEM-1, Cat1, and CTX-M-15, and SNPs associated with antibiotics resistance. IncY, IncQ1, pMAC, and pAbTS2 plasmids, conferring antimicrobial resistance, were detected in a few XDR S. Typhi strains. Phylogenetic analysis inferred a close epidemiological linkage among XDR strains from different regions of Pakistan. Pangenome was noted closed among these strains and functional annotation highlighted genes related to metabolism and pathogenesis. Conclusion: This study revealed a uniform genotypic background among XDR S. Typhi strains in Pakistan, signifying a persistence transmission of a single, highly antibiotic-resistant clone. The closed pan-genome observed underscores limited genetic diversity and highlights the importance of genomic surveillance for combating drug-resistant typhoid infections.

The cyber realm is overwhelmed with dynamic malware that promptly penetrates all defense mechanisms, operates unapprehended to the user, and covertly causes damage to sensitive data. The current generation of cyber users is being victimized by the interpolation of malware each day due to the pervasive progression of Internet connectivity. Malware is dispersed to infiltrate the security, privacy, and integrity of the system. Conventional malware detection systems do not have the potential to detect novel malware without the accessibility of their signatures, which gives rise to a high False Negative Rate (FNR). Previously, there were numerous attempts to address the issue of malware detection, but none of them effectively combined the capabilities of signature-based and machine learning-based detection engines. To address this issue, we have developed an integrated Anti-Malware System (AMS) architecture that incorporates both conventional signature-based detection and AI-based detection modules. Our approach employs a Generative Adversarial Network (GAN) based Malware Classifier Optimizer (MCOGAN) framework, which can optimize a malware classifier. This framework utilizes GANs to generate fabricated benign files that can be used to train external discriminators for optimization purposes. We describe our proposed framework and anti-malware system in detail to provide a better understanding of how a malware detection system works. We evaluate our approach using the Figshare dataset and state-of-the-art models as discriminators. Our results showcase enhanced malware detection performance, yielding a 10% performance boost, thus affirming the efficacy of our approach compared to existing models.

Abstract Tumor-infiltrating lymphocytes, specialized immune cells, are considered an important biomarker in cancer analysis. Automated lymphocyte detection is challenging due to its heterogeneous morphology, variable distribution, and presence of artifacts. In this work, we propose a novel Boosted Channels Fusion-based CNN “BCF-Lym-Detector” for lymphocyte detection in multiple cancer histology images. The proposed network initially selects candidate lymphocytic regions at the tissue level and then detects lymphocytes at the cellular level. The proposed “BCF-Lym-Detector” generates diverse boosted channels by utilizing the feature learning capability of different CNN architectures. In this connection, a new adaptive fusion block is developed to combine and select the most relevant lymphocyte-specific features from the generated enriched feature space. Multi-level feature learning is used to retain lymphocytic spatial information and detect lymphocytes with variable appearances. The assessment of the proposed “BCF-Lym-Detector” show substantial improvement in terms of F-score (0.93 and 0.84 on LYSTO and NuClick, respectively), which suggests that the diverse feature extraction and dynamic feature selection enhanced the feature learning capacity of the proposed network. Moreover, the proposed technique’s generalization on unseen test sets with a good recall (0.75) and F-score (0.73) shows its potential use for pathologists’ assistance.

Abstract Diabetic retinopathy (DR) is a diabetes complication that can cause vision loss among patients due to damage to blood vessels in the retina. Early retinal screening can avoid the severe consequences of DR and enable timely treatment. Nowadays, researchers are trying to develop automated deep learning-based DR segmentation tools using retinal fundus images to help Ophthalmologists with DR screening and early diagnosis. However, recent studies are unable to design accurate models due to the unavailability of larger training data with consistent and fine-grained annotations. To address this problem, we propose a semi-supervised multitask learning approach that exploits widely available unlabelled data (i.e., Kaggle-EyePACS) to improve DR segmentation performance. The proposed model consists of novel multi-decoder architecture and involves both unsupervised and supervised learning phases. The model is trained for the unsupervised auxiliary task to effectively learn from additional unlabelled data and improve the performance of the primary task of DR segmentation. The proposed technique is rigorously evaluated on two publicly available datasets (i.e., FGADR and IDRiD) and results show that the proposed technique not only outperforms existing state-of-the-art techniques but also exhibits improved generalisation and robustness for cross-data evaluation.

Prevention of Clostridium difficile infection is challenging worldwide owing to its high morbidity and mortality rates. C. difficile is currently being classified as an urgent threat by the CDC. Devising a new therapeutic strategy become indispensable against C. difficile infection due to its high rates of reinfection and increasing antimicrobial resistance. The current study is based on core proteome data of C. difficile to identify promising vaccine and drug candidates. Immunoinformatics and vaccinomics approaches were employed to construct multi-epitope-based chimeric vaccine constructs from top-ranked T- and B-cell epitopes. The efficacy of the designed vaccine was assessed by immunological analysis, immune receptor binding potential and immune simulation analyses. Additionally, subtractive proteomics and druggability analyses prioritized several promising and alternative drug targets against C. difficile. These include FMN-dependent nitroreductase which was prioritized for pharmacophore-based virtual screening of druggable molecule databases to predict potent inhibitors. A MolPort-001-785-965 druggable molecule was found to exhibit significant binding affinity with the conserved residues of FMN-dependent nitroreductase. The experimental validation of the therapeutic targets prioritized in the current study may worthy to identify new strategies to combat the drug-resistant C. difficile infection.

Malware has evolved to pose a major threat to information security. Efficient anti-malware software is essential in safeguarding confidential information from these threats. However, identifying malware continues to be a challenging task. Signature-based detection methods are quick but fail to detect unknown malware. Additionally, the traditional machine learning archetype requires a large amount of data to be effective, which hinders the ability of an anti-malware system to quickly learn about new threats with limited training samples. In a real-world setting, the majority of malware is found in the form of Portable Executable (PE) files. While there are various formats of PE files, samples of all formats such as ocx, acm, com, scr, etc., are not readily available in large numbers. Therefore, building a conventional Machine Learning (ML) model with greater generalization for data-scarce PE formats becomes a hefty task. Consequently, in such a scenario, Few-Shot learning (FSL) is helpful in detecting the presence of malware, even with a very small number of training samples. FSL techniques help to make predictions based on an insufficient number of samples. In this paper, we propose a novel architecture based on the Relation Network for FSL implementation. We propose a Discriminative Feature Embedder for feature extraction. These extracted features are passed to our proposed Relation Module (RM) for similarity measure. RM produces the relation scores that lead to improved classification. We use PE file formats, i.e., ocx, acm, com, and scr, after transforming them into images. We employ five-shot learning and then one-shot learning, which produces 94% accuracy with only one training instance. We observe that the proposed architecture outpaces the baseline method and provides enhanced accuracy by up to 94% with only one sample.

Detection of Tumor-Infiltrating Lymphocytes (TILs) has a high prognostic value in cancer diagnosis due to their ability to identify and kill cancer cells. However, this task is non-trivial due to their diverse morphology, overlapping boundaries, and presence of artifacts. Vision Transformers (ViTs) have the ability to capture long-range relationships, but they lack local correlation in the images and require large training datasets. In this work, we propose a Channel Boosted Hybrid Vision Transformer (CB-HVT) to detect lymphocytes in histopathological images. The proposed network constitutes: 1) channel generation module; 2) channel exploitation module; 3) channel merging module; 4) region-aware module; and 5) detection and segmentation head. The proposed CB-HVT exploits the learning capacity of both CNN and ViT-based architectures to capture lymphocytic diverse morphology. In addition, we developed a feature fusion block to systematically and gradually merge the diverse feature maps to improve the learning capability of the network. The attention mechanism in the fusion block retains the most contributing features. We evaluated the effectiveness of the proposed CB-HVT on two publicly available datasets for lymphocyte detection in histopathological images. The proposed network showed good results as compared to the existing architectures in terms of F-Score (LYSTO: 0.88 and NuClick: 0.82). In addition, the performance of the proposed CB-HVT on an unseen test set reveals its significance as a valuable tool for pathologists for real-time lymphocyte detection.

Leishmania tropica is a vector-borne parasitic protozoa that is the leading cause of leishmaniasis throughout the global tropics and subtropics. L. tropica is a multidrug-resistant parasite with a diverse set of serological, biochemical, and genomic features. There are currently no particular vaccines available to combat leishmaniasis. The present study prioritized potential vaccine candidate proteins of L. tropica using subtractive proteomics and vaccinomics approaches. These vaccine candidate proteins were downstream analyzed to predict B- and T-cell epitopes based on high antigenicity, non-allergenic, and non-toxic characteristics. The top-ranked overlapping MHC-I, MHC-II, and linear B-cell epitopes were prioritized for model vaccine designing. The lead epitopes were linked together by suitable linker sequences to design multi-epitope constructs. Immunogenic adjuvant sequences were incorporated at the N-terminus of the model vaccine constructs to enhance their immunological potential. Among different combinations of constructs, four vaccine designs were selected based on their physicochemical and immunological features. The tertiary structure models of the designed vaccine constructs were predicted and verified. The molecular docking and molecular dynamic (MD) simulation analyses indicated that the vaccine design V1 demonstrated robust and stable molecular interactions with toll-like receptor 4 (TLR4). The top-ranked vaccine construct model-IV demonstrated significant expressive capability in the E. coli expression system during in-silico restriction cloning analysis. The results of the present study are intriguing; nevertheless, experimental bioassays are required to validate the efficacy of the predicted model chimeric vaccine.

Abstract Background Plasmodium vivax apical membrane antigen-1 (pvama-1) is an important vaccine candidate against Malaria. The genetic composition assessment of pvama-1 from wide-range geography is vital to plan the antigen based vaccine designing against Malaria. Methods The blood samples were collected from 84 P. vivax positive malaria patients from different districts of Khyber Pakhtunkhwa (KP) province of Pakistan. The highly polymorphic and immunogenic domain-I (DI) region of pvama-1 was PCR amplified and DNA sequenced. The QC based sequences raw data filtration was done using DNASTAR package. The downstream population genetic analyses were performed using MEGA4, DnaSP, Arlequin v3.5 and Network.5 resources. Results The analyses unveiled total 57 haplotypes of pvama-1 (DI) in KP samples with majorly prevalent H-14 and H-5 haplotypes. Pairwise comparative population genetics analyses identified limited to moderate genetic distinctions among the samples collected from different districts of KP, Pakistan. In context of worldwide available data, the KP samples depicted major genetic differentiation against the Korean samples with Fst = 0.40915 (P-value = 0.0001), while least distinction was observed against Indian and Iranian samples. The statistically significant negative values of Fu and Li’s D* and F* tests indicate the evidence of population expansion and directional positive selection signature. The slow LD decay across the nucleotide distance in KP isolates indicates low nucleotide diversity. In context of reference pvama-1 sequence, the KP samples were identified to have 09 novel non-synonymous single nucleotide polymorphisms (nsSNPs), including several trimorphic and tetramorphic substitutions. Few of these nsSNPs are mapped within the B-cell predicted epitopic motifs of the pvama-1, and possibly modulate the immune response mechanism. Conclusion Low genetic differentiation was observed across the pvama-1 DI among the P. vivax isolates acquired from widespread regions of KP province of Pakistan. The information may implicate in future vaccine designing strategies based on antigenic features of pvama-1.

Abstract Interaction between devices, people, and the Internet has given birth to a new digital communication model, the internet of things (IoT). The integration of smart devices to constitute a network introduces many security challenges. These connected devices have created a security blind spot, where cybercriminals can easily launch attacks to compromise the devices using malware proliferation techniques. Therefore, malware detection is a lifeline for securing IoT devices against cyberattacks. This study addresses the challenge of malware detection in IoT devices by proposing a new CNN-based IoT malware detection architecture (iMDA). The proposed iMDA is modular in design that incorporates multiple feature learning schemes in blocks including (1) edge exploration and smoothing, (2) multi-path dilated convolutional operations, and (3) channel squeezing and boosting in CNN to learn a diverse set of features. The local structural variations within malware classes are learned by Edge and smoothing operations implemented in the split-transform-merge (STM) block. The multi-path dilated convolutional operation is used to recognize the global structure of malware patterns. At the same time, channel squeezing and merging helped to regulate complexity and get diverse feature maps. The performance of the proposed iMDA is evaluated on a benchmark IoT dataset and compared with several state-of-the CNN architectures. The proposed iMDA shows promising malware detection capacity by achieving accuracy: 97.93%, F1-Score: 0.9394, precision: 0.9864, MCC: 0. 8796, recall: 0.8873, AUC-PR: 0.9689 and AUC-ROC: 0.9938. The strong discrimination capacity suggests that iMDA may be extended for the android-based malware detection and IoT Elf files compositely in the future.

Abstract Ransomware attacks pose a serious threat to Internet resources due to their far-reaching effects. It’s Zero-day variants are even more hazardous, as less is known about them. In this regard, when used for ransomware attack detection, conventional machine learning approaches may become data-dependent, insensitive to error cost, and thus may not tackle zero-day ransomware attacks. Zero-day ransomware have normally unseen underlying data distribution. This paper presents a Cost-Sensitive Pareto Ensemble strategy, CSPE-R to detect novel Ransomware attacks. Initially, the proposed framework exploits the unsupervised deep Contractive Auto Encoder (CAE) to transform the underlying varying feature space to a more uniform and core semantic feature space. To learn the robust features, the proposed CSPE-R ensemble technique explores different semantic spaces at various levels of detail. Heterogeneous base estimators are then trained over these extracted subspaces to find the core relevance between the various families of the ransomware attacks. Then, a novel Pareto Ensemble-based estimator selection strategy is implemented to achieve a cost-sensitive compromise between false positives and false negatives. Finally, the decision of selected estimators are aggregated to improve the detection against unknown ransomware attacks. The experimental results show that the proposed CSPE-R framework performs well against zero-day ransomware attacks.

Monitoring Indicators of Compromise (IOC) leads to malware detection for identifying malicious activity. Malicious activities potentially lead to a system breach or data compromise. Various tools and anti-malware products exist for the detection of malware and cyberattacks utilizing IOCs, but all have several shortcomings. For instance, anti-malware systems make use of malware signatures, requiring a database containing such signatures to be constantly updated. Additionally, this technique does not work for zero-day attacks or variants of existing malware. In the quest to fight zero-day attacks, the research paradigm shifted from primitive methods to classical machine learning-based methods. Primitive methods are limited in catering to anti-analysis techniques against zero-day attacks. Hence, the direction of research moved towards methods utilizing classic machine learning, however, machine learning methods also come with certain limitations. They may include but not limited to the latency/lag introduced by feature-engineering phase on the entire training dataset as opposed to the real-time analysis requirement. Likewise, additional layers of data engineering to cater to the increasing volume of data introduces further delays. It led to the use of deep learning-based methods for malware detection. With the speedy occurrence of zero-day malware, researchers chose to experiment with few shot learning so that reliable solutions can be produced for malware detection with even a small amount of data at hand for training. In this paper, we surveyed several possible strategies to support the real-time detection of malware and propose a hierarchical model to discover security events or threats in real-time. A key focus in this survey is on the use of Deep Learning-based methods. Deep Learning based methods dominate this research area by providing automatic feature engineering, the capability of dealing with large datasets, enabling the mining of features from limited data samples, and supporting one-shot learning. We compare Deep Learning-based approaches with conventional machine learning based approaches and primitive (statistical analysis based) methods commonly reported in the literature.

Swarm intelligence has been applied to replicate numerous natural processes and relatively simple species to achieve excellent performance in a variety of disciplines. An autonomous approach employing deep reinforcement learning is presented in this study for swarm navigation. In this approach, complex 3D environments with static and dynamic obstacles and resistive forces such as linear drag, angular drag, and gravity are modeled to track multiple dynamic targets. In this regard, a novel island policy optimization model is introduced to tackle multiple dynamic targets simultaneously and thus make the swarm more dynamic. Moreover, new reward functions for robust swarm formation and target tracking are devised to learn complex swarm behaviors. Since the number of agents is not fixed and has only the partial observance of the environment, swarm formation and navigation become challenging. In this regard, the proposed strategy consists of four main components to tackle the aforementioned challenges: 1) Island policy-based optimization framework with multiple targets tracking 2) Novel reward functions for multiple dynamic target tracking 3) Improved policy and critic-based framework for the dynamic swarm management 4) Memory. The dynamic swarm management phase translates basic sensory input to high-level commands and thus enhances swarm navigation and decentralized setup while maintaining the swarm’s size fluctuations. While in the island model, the swarm can split into individual sub-swarms according to the number of targets, thus allowing it to track multiple targets that are far apart. Also, when multiple targets come close to each other, these sub-swarms have the ability to rejoin and thus form a single swarm surrounding all the targets. Customized state-of-the-art policy-based deep reinforcement learning neuro-architectures are employed to achieve policy optimization. The results show that the proposed strategy enhances swarm navigation by achieving a high cumulative reward and a low policy loss. The simulations show that the proposed framework can efficiently track multiple static and dynamic targets in complex environments.

Drones are unmanned aerial vehicles (UAV) utilized for a broad range of functions, including delivery, aerial surveillance, traffic monitoring, architecture monitoring, and even War-field. Drones confront significant obstacles while navigating independently in complex and highly dynamic environments. Moreover, the targeted objects within a dynamic environment have irregular morphology, occlusion, and minor contrast variation with the background. In this regard, a novel deep Convolutional Neural Network(CNN) based data-driven strategy is proposed for drone navigation in the complex and dynamic environment. The proposed Drone Split-Transform-and-Merge Region-and-Edge (Drone-STM-RENet) CNN is comprised of convolutional blocks where each block methodically implements region and edge operations to preserve a diverse set of targeted properties at multi-levels, especially in the congested environment. In each block, the systematic implementation of the average and max-pooling operations can deal with the region homogeneity and edge properties. Additionally, these convolutional blocks are merged at a multi-level to learn texture variation that efficiently discriminates the target from the background and helps obstacle avoidance. Finally, the Drone-STM-RENet generates steering angle and collision probability for each input image to control the drone moving while avoiding hindrances and allowing the UAV to spot risky situations and respond quickly, respectively. The proposed Drone-STM-RENet has been validated on two urban cars and bicycles datasets: udacity and collision-sequence, and achieved considerable performance in terms of explained variance (0.99), recall (95.47%), accuracy (96.26%), and F-score (91.95%). The promising performance of Drone-STM-RENet on urban road datasets suggests that the proposed model is generalizable and can be deployed for real-time autonomous drones navigation and real-world flights.

Introduction: Viral hemorrhagic septicemia virus (VHSV) is the most lethal pathogen in aquaculture, infecting more than 140 fish species in marine, estuarine, and freshwater environments. Viral hemorrhagic septicemia virus is an enveloped RNA virus that belongs to the family Rhabdoviridae and the genus Novirhabdovirus. The current study is designed to infer the worldwide Viral hemorrhagic septicemia virus isolates’ genetic diversity and evolutionary dynamics based on G-gene sequences.Methods: The complete G-gene sequences of viral hemorrhagic septicemia virus were retrieved from the public repositories with known timing and geography details. Pairwise statistical analysis was performed using Arlequin. The Bayesian model-based approach implemented in STRUCTURE software was used to investigate the population genetic structure, and the phylogenetic tree was constructed using MEGA X and IQ-TREE. The natural selection analysis was assessed using different statistical approaches, including IFEL, MEME, and SLAC.Results and Discussion: The global Viral hemorrhagic septicemia virus samples are stratified into five genetically distinct subpopulations. The STRUCTURE analysis unveiled spatial clustering of genotype Ia into two distinct clusters at K = 3. However, at K = 5, the genotype Ia samples, deposited from Denmark, showed temporal distribution into two groups. The analyses unveiled that the genotype Ia samples stratified into subpopulations possibly based on spatiotemporal distribution. Several viral hemorrhagic septicemia virus samples are characterized as genetically admixed or recombinant. In addition, differential or subpopulation cluster-specific natural selection signatures were identified across the G-gene codon sites among the viral hemorrhagic septicemia virus isolates. Evidence of low recombination events elucidates that genetic mutations and positive selection events have possibly driven the observed genetic stratification of viral hemorrhagic septicemia virus samples.

Objective: To determine role of endobronchial washings/biopsy in diagnosis of smear negative pulmonary tuberculosis and evaluate their association with computed tomography findings. Study Design: Cross-sectional - analytical study. Place and Duration of Study: Department of Pulmonology, Pak Emirates Military Hospital, Rawalpindi Pakistan, from Jan to Jun 2020. Methodology: Patients having symptoms of pulmonary tuberculosis and abnormal findings on chest imaging with negative sputum smears for acid fast bacilli were selected for endobronchial washings. Bronchoscopy for washings was carried out in these patients and sample was sent for Acid fast bacilli stain, Mycobacterium tuberculosis gene Xpert and acid fast bacilli culture and sensitivity bacterial culture and fungal hyphae. In case of mass lung endobronchial and/or transbronchial lung biopsy was done and sample was sent for histopathology Active pulmonary tuberculosis was labelled as per definition. Results: Out of 105, 75 (71.4%) were male and 30 (28.6%) were female. Mean age was 55.69 ± 17.33 years. Total 40 (38.09%) were diagnosed with pulmonary tuberculosis (based on smear, genexpert, culture and histopathology). In these 40 patients, computed tomographic findings showed consolidation in 10 (25%) cases, cavity in 7 (17.5%), bronchiectasis in 3 (7.5%), mass in 1 (2.5%), pleural effusion in 2 (5%), lymphadenopathy in 1 (2.5%) and tree-in-budappearance in 12 (30%) cases. Conclusion: High resolution computed tomography and endobronchial washing both combined together increase the diagnostic yield in smear negative cases.

Abstract Background Plasmodium falciparum is an obligate intracellular parasite of humans that causes malaria. Falciparum malaria is a major public health threat to human life responsible for high mortality. Currently, the risk of multi-drug resistance of P. falciparum is rapidly increasing. There is a need to address new anti-malarial therapeutics strategies to combat the drug-resistance threat. Methods The P. falciparum essential proteins were retrieved from the recently published studies. These proteins were initially scanned against human host and its gut microbiome proteome sets by comparative proteomics analyses. The human host non-homologs essential proteins of P. falciparum were additionally analysed for druggability potential via in silico methods to possibly identify novel therapeutic targets. Finally, the PfAp4AH target was prioritized for pharmacophore modelling based virtual screening and molecular docking analyses to identify potent inhibitors from drug-like compounds databases. Results The analyses identified six P. falciparum essential and human host non-homolog proteins that follow the key druggability features. These druggable targets have not been catalogued so far in the Drugbank repository. These prioritized proteins seem novel and promising drug targets against P. falciparum due to their key protein–protein interactions features in pathogen-specific biological pathways and to hold appropriate drug-like molecule binding pockets. The pharmacophore features based virtual screening of Pharmit resource predicted a lead compound i.e. MolPort-045–917-542 as a promising inhibitor of PfAp4AH among prioritized targets. Conclusion The prioritized protein targets may worthy to test in malarial drug discovery programme to overcome the anti-malarial resistance issues. The in-vitro and in-vivo studies might be promising for additional validation of these prioritized lists of drug targets against malaria.

The emerging monkeypox virus (MPXV) is a zoonotic orthopoxvirus that causes infections in humans similar to smallpox. Since May 2022, cases of monkeypox (MPX) have been increasingly reported by the World Health Organization (WHO) worldwide. Currently, there are no clinically validated treatments for MPX infections. In this study, an immunoinformatics approach was used to identify potential vaccine targets against MPXV. A total of 190 MPXV-2022 proteins were retrieved from the ViPR database and subjected to various analyses including antigenicity, allergenicity, toxicity, solubility, IFN-γ, and virulence. Three outer membrane and extracellular proteins were selected based on their respective parameters to predict B-cell and T-cell epitopes. The epitopes are conserved among different strains of MPXV and the population coverage is 100% worldwide, which will provide broader protection against various strains of the virus globally. Nine overlapping MHC-I, MHC-II, and B-cell epitopes were selected to design multi-epitope vaccine constructs linked with suitable linkers in combination with different adjuvants to enhance the immune responses of the vaccine constructs. Molecular modeling and structural validation ensured high-quality 3D structures of vaccine constructs. Based on various immunological and physiochemical properties and docking scores, MPXV-V2 was selected for further investigation. In silico cloning revealed a high level of gene expression for the MPXV-V2 vaccine within the bacterial expression system. Immune and MD simulations confirmed the molecular stability of the MPXV-V2 construct, with high immune responses within the host cell. These results may aid in the development of experimental vaccines against MPXV with increased potency and improved safety.

Abstract The mitotic activity index is a key prognostic measure in tumour grading. Microscopy based detection of mitotic nuclei is a significant overhead and necessitates automation. This work proposes deep CNN based multi-phase mitosis detection framework “MP-MitDet” for mitotic nuclei identification in breast cancer histopathological images. The workflow constitutes: (1) label-refiner, (2) tissue-level mitotic region selection, (3) blob analysis, and (4) cell-level refinement. We developed an automatic label-refiner to represent weak labels with semi-sematic information for training of deep CNNs. A deep instance-based detection and segmentation model is used to explore probable mitotic regions on tissue patches. More probable regions are screened based on blob area and then analysed at cell-level by developing a custom CNN classifier “MitosRes-CNN” to filter false mitoses. The performance of the proposed “MitosRes-CNN” is compared with the state-of-the-art CNNs that are adapted to cell-level discrimination through cross-domain transfer learning and by adding task-specific layers. The performance of the proposed framework shows good discrimination ability in terms of F-score (0.75), recall (0.76), precision (0.71) and area under the precision-recall curve (0.78) on challenging TUPAC16 dataset. Promising results suggest good generalization of the proposed framework that can learn characteristic features from heterogenous mitotic nuclei.

Autonomous Vehicles (AVs) promise to disrupt the traditional systems of transportation. An autonomous driving environment requires an uninterrupted, continuous stream of data and information based on complex traffic data sets and predictive measurements to make critical and real-time decisions in uncertain situations. Such an environment fosters a self-organizing system where vehicles must be seamlessly connected and various other services and intelligent decisions to manage traffic flow must be executed in an emergent manner. To proceed towards this vision, in this paper, we develop a traffic flow management model which is based on a novel two-phase approach for AVs to optimize traffic during congestion periods. In the first phase of our approach, we build an adaptive traffic signal control, using Deep Reinforcement Learning (DRL) to optimize traffic flow on road intersections during the periods when traffic is congested. In the second phase, we implement a Smart Re-routing (SR) technique for the traffic approaching intersections. Re-routing is used to carry out load-balancing of traffic to alternate paths to avoid congested road intersections. The experimental evaluation of the proposed approach is validated using simulations that demonstrate up to 31% improved performance efficiency compared to the traditional settings using pre-timed signals and without re-routing. The two-phase approach improves the overall traffic flow while reducing delays and minimizing long traffic queues’ lengths. This approach is useful for making infrastructure intelligent enough to handle traffic congestion and balance traffic flow efficiently.

The emergence of multi-drug resistance in clostridium difficile is an urgent bio-threat associated with high morbidity and mortality. The availability of core genome sequences for pathogen strains provides the opportunity to rationally approach novel therapeutic targets. No preventive vaccine against C. difficile infections is available commercially to date. The core genome data of 63 complete genome sequences of C. difficile was utilized in the current study. A precise immune informatics layout has been followed for the identification of potential vaccine target proteins that are essential for the pathogen and show no homology to human as well as human gut flora proteome. Two antigenic outer membrane proteins were prioritized for the lead epitopes prediction based on the reverse vaccinology approach. The multi-epitope chimeric vaccines were engineered using lead T cell and B cell epitopes in combination with the adjuvant peptides and linker sequences involved in enhancing the immunogenic responses. Four vaccine constructs were designed from overlapping epitopes. Based on multiple physiochemical properties, including the antigenicity, allergenicity, solubility and, molecular docking scores, the vaccine construct V2 was finalized for further analysis. The in silico restriction and cloning analysis inferred the V2 vaccine construct cloning capability within the bacterial expression system. The molecular modeling and molecular dynamic simulation showed favorable molecular interaction of V2 in the host cell and ensured the molecular stability of the V2 prioritized construct. Multi-epitope-based vaccine designing is an effective approach to develop potential therapeutics with better safety, increased potency, and logistically viable. The chimeric vaccine construct prioritized in the current study speculates to effectively bind with human immune receptor proteins and possibly triggers a potent immune response against C. difficile invasion.

Breast cancer (BC) is known to be the most common malignancy among women throughout the world. Plant-derived natural products have been recognized as a great source of anticancer drugs. In this study, the efficacy and anticancer potential of the methanolic extract of Monotheca buxifolia leaves using human breast cancer cells targeting WNT/β-catenin signaling was evaluated. We used methanolic and other (chloroform, ethyl acetate, butanol, and aqueous) extracts to discover their potential cytotoxicity on breast cancer cells (MCF-7). Among these, the methanol showed significant activity in the inhibition of the proliferation of cancer cells because of the presence of bioactive compounds, including phenols and flavonoids, detected by a Fourier transform infrared spectrophotometer and by gas chromatography mass spectrometry. The cytotoxic effect of the plant extract on the MCF-7 cells was examined by MTT and acid phosphatase assays. Real-time PCR analysis was performed to measure the mRNA expression of WNT-3a and β-catenin, along with Caspase-1,-3,-7, and -9 in MCF-7 cells. The IC50 value of the extract was found to be 232 μg/mL and 173 μg/mL in the MTT and acid phosphatase assays, respectively. Dose selection (100 and 300 μg/mL) was performed for real-time PCR, Annexin V/PI analysis, and Western blotting using Doxorubicin as a positive control. The extract at 100 μg/mL significantly upregulated caspases and downregulated the WNT-3a and β-catenin gene in MCF-7 cells. Western blot analysis further confirmed the dysregulations of the WNT signaling component (*** p< 0.0001). The results showed an increase in the number of dead cells in methanolic extract-treated cells in the Annexin V/PI analysis. Our study concludes that M. buxifolia may serve as an effective anticancer mediator through gene modulation that targets WNT/β-catenin signaling, and it can be further characterized using more powerful experimental and computational tools.

Critical heat flux (CHF) is an essential parameter that plays a significant role in ensuring the safety and economic efficiency of nuclear power facilities. It imposes design and operational restrictions on nuclear power plants due to safety concerns. Therefore, accurate prediction of CHF using a hybrid framework can assist researchers in optimizing system performance, mitigating risk of equipment failure, and enhancing safety measures. Despite the existence of numerous prediction methods, there remains a lack of agreement regarding the underlying mechanism that gives rise to CHF. Hence, developing a precise and reliable CHF model is a crucial and challenging task. In this study, we proposed a hybrid model based on an artificial neural network (ANN) to improve the prediction accuracy of CHF. Our model leverages the available knowledge from a lookup table (LUT) and then employs ANN to further reduce the gap between actual and predicted outcomes. To develop and assess the accuracy of our model, we compiled a dataset of around 5877 data points from various sources in the literature. This dataset encompasses a diverse range of operating parameters for two-phase flow in vertical tubes. The results of this study demonstrate that the proposed hybrid model performs better than standalone machine learning models such as ANN, random forest, support vector machine, and data-driven lookup tables, with a relative root-mean-square error (rRMSE) of only 9.3%. We also evaluated the performance of the proposed hybrid model using holdout and cross-validation techniques, which demonstrated its robustness. Moreover, the proposed approach offers valuable insights into the significance of various input parameters in predicting CHF. Our proposed system can be utilized as a real-time monitoring tool for predicting extreme conditions in nuclear reactors, ensuring their safe and efficient operation.

Lumpy skin disease is a fatal emerging disease of cattle, which has started to gain extensive attention due to its rapid incursions across the globe. The disease epidemic causes economic loss and cattle morbidity. Currently, there are no specific treatments and safe vaccines against the lumpy skin disease virus (LSDV) to halt the spread of the disease. The current study uses genome-scan vaccinomics analyses to prioritize promiscuous vaccine candidate proteins of the LSDV. These proteins were subjected to top-ranked B- and T-cell epitope prediction based on their antigenicity, allergenicity, and toxicity values. The shortlisted epitopes were connected using appropriate linkers and adjuvant sequences to design multi-epitope vaccine constructs. Three vaccine constructs were prioritized based on their immunological and physicochemical properties. The model constructs were back-translated to nucleotide sequences and codons were optimized. The Kozak sequence with a start codon along with MITD, tPA, Goblin 5′, 3′ UTRs, and a poly(A) tail sequences were added to design a stable and highly immunogenic mRNA vaccine. Molecular docking followed by MD simulation analysis predicted significant binding affinity and stability of LSDV-V2 construct within bovine immune receptors and predicted it to be the top-ranked candidate to stimulate the humeral and cellular immunogenic responses. Furthermore, in silico restriction cloning predicted feasible gene expression of the LSDV-V2 construct in a bacterial expression vector. It could prove worthwhile to validate the predicted vaccine models experimentally and clinically against LSDV.

Intelligent traffic management systems have become one of the main applications of Intelligent Transportation Systems (ITS). There is a growing interest in Reinforcement Learning (RL) based control methods in ITS applications such as autonomous driving and traffic management solutions. Deep learning helps in approximating substantially complex nonlinear functions from complicated data sets and tackling complex control issues. In this paper, we propose an approach based on Multi-Agent Reinforcement Learning (MARL) and smart routing to improve the flow of autonomous vehicles on road networks. We evaluate Multi-Agent Advantage Actor-Critic (MA2C) and Independent Advantage Actor-Critical (IA2C), recently suggested Multi-Agent Reinforcement Learning techniques with smart routing for traffic signal optimization to determine its potential. We investigate the framework offered by non-Markov decision processes, enabling a more in-depth understanding of the algorithms. We conduct a critical analysis to observe the robustness and effectiveness of the method. The method’s efficacy and reliability are demonstrated by simulations using SUMO, a software modeling tool for traffic simulations. We used a road network that contains seven intersections. Our findings show that MA2C, when trained on pseudo-random vehicle flows, is a viable methodology that outperforms competing techniques.

Introduction The genomic miscellany of malaria parasites can help inform the intensity of malaria transmission and identify potential deficiencies in malaria control programs. This study was aimed at investigating the genomic miscellany, allele frequencies, and MOI of P. falciparum infection. Methods A total of 85 P. falciparum confirmed isolates out of 100 were included in this study that were collected from P. falciparum patients aged 4 months to 60 years in nine districts of Khyber Pakhtunkhwa Province. Parasite DNA was extracted from 200µL whole blood samples using the Qiagen DNA extraction kit following the manufacturer’s instructions. The polymorphic regions of msp-1, msp-2 and glurp loci were genotyped using nested PCR followed by gel electrophoresis for amplified fragments identification and subsequent data analysis. Results Out of 85 P. falciparum infections detected, 30 were msp-1 and 32 were msp-2 alleles specific. Successful amplification occurred in 88.23% (75/85) isolates for msp-1, 78.9% (67/85) for msp-2 and 70% (60/85) for glurp gene. In msp-1, the K1 allelic family was predominantly prevalent as 66.66% (50/75), followed by RO33 and MAD20. The frequency of samples with single infection having only K1, MAD20 and RO33 were 21.34% (16/75), 8% (6/75), and 10.67% (8/75), respectively. In msp-2, both the FC27 and 3D7 allelic families revealed almost the same frequencies as 70.14% (47/67) and 67.16% (45/67), respectively. Nine glurp RII region alleles were identified in 60 isolates. The overall mean multiplicity of infection for msp genes was 1.6 with 1.8 for msp-1 and 1.4 for msp-2, while for glurp the MOI was 1.03. There was no significant association between multiplicity of infection and age groups (Spearman’s rank coefficient = 0.050; P = 0.6) while MOI and parasite density correlated for only msp-2 allelic marker. Conclusions The study showed high genetic diversity and allelic frequency with multiple clones of msp-1, msp-2 and glurp in P. falciparum isolates in Khyber Pakhtunkhwa, Pakistan. In the present study the genotype data may provide valuable information essential for monitoring the impact of malaria eradication efforts in this region.

Leishmania tropica is a tropical parasite causing cutaneous leishmaniasis (CL) in humans. Leishmaniasis is a serious public health threat, affecting an estimated 350 million people in 98 countries. The global rise in antileishmanial drug resistance has triggered the need to explore novel therapeutic strategies against this parasite. In the present study, we utilized the recently available multidrug resistant L. tropica strain proteome data repository to identify alternative therapeutic drug targets based on comparative subtractive proteomic and druggability analyses. Additionally, small drug-like compounds were scanned against novel targets based on virtual screening and ADME profiling. The analysis unveiled 496 essential cellular proteins of L. tropica that were nonhomologous to the human proteome set. The druggability analyses prioritized nine parasite-specific druggable proteins essential for the parasite’s basic cellular survival, growth, and virulence. These prioritized proteins were identified to have appropriate binding pockets to anchor small drug-like compounds. Among these, UDPase and PCNA were prioritized as the top-ranked druggable proteins. The pharmacophore-based virtual screening and ADME profiling predicted MolPort-000-730-162 and MolPort-020-232-354 as the top hit drug-like compounds from the Pharmit resource to inhibit L. tropica UDPase and PCNA, respectively. The alternative drug targets and drug-like molecules predicted in the current study lay the groundwork for developing novel antileishmanial therapies.

Clinical epidemiological studies have reported that viral infections cause autoimmune pathology in humans. Host-pathogen protein sequences and structure-based molecular mimicry cause autoreactive T cells to cross-activate. The aim of the current study was to implement immunoinformatics approaches to infer sequence- and structure-based molecular mimicry between viral and human proteomic datasets. The protein sequences of all the so far known human-infecting viruses were obtained from the VIPR database, and complete human proteome data were retrieved from the NCBI repository. Based on a predefined, stringent threshold of comparative sequence analyses, 24 viral proteins were identified with significant sequence similarity to human proteins. PathDIP identified the enrichment of these homologous proteins in nine metabolic pathways with a p-value < 0.0001. Several viral and human mimic epitopes from these homologous proteins were predicted as strong binders of human HLA alleles, with IC50 < 50 nM. Downstream molecular docking analyses identified that lead virus-human homologous epitopes feasibly interact with HLA and TLR4 types of immune receptors. The vast majority of these top-hit homolog epitopic peptides belong to the herpes simplex and poxvirus families. These lead epitope biological sequences and 3D structural-based molecular mimicry may be promising for interpreting herpes simplex virus and poxvirus infection-mediated autoimmune disorders in humans.

The present study aimed to gain insight into the genetic origin of the Shina population from Gilgit-Baltistan, Pakistan. We partially performed the mitochondrial DNA (mtDNA) control region of healthy unrelated individuals of Shina tribe residing in the remote area of Gilgit-Baltistan to investigate their maternal lineages. The present study is the first report about Shina’s genetic structure, origin, and relationship with the surrounding north-western Pakistani population. The mtDNA sequences of the Shina samples were compared with the revised Cambridge Reference Sequence (rCRS) and the HVR-1 D-loop region was covered. The comparison with rCRS identified overall 38 haplotypes and 08 haplogroups for Shina samples. Among these haplotypes, 18 were shared by more than one individual of the Shina tribe. The obtained mtDNA sequences of Shina were compared with surrounding north-western Pakistani population groups, i.e. Kho, Kashmiri, and Pathan. The genetic diversity (i.e. 1.0424) and power of discrimination (i.e. 0.9266) of the Shina was found equivalent to surrounding north-western groups. The haplogroups frequencies, phylogenetic tree and network analysis identified the west Eurasian ancestral origin of Shina group with nearby maternal ancestral relationships with the Kashmiri population. However, no close genetic relationship of Shina was depicted with nearby residing Kho population group.

Background Some single nucleotide polymorphisms (SNPs) in the cytochrome P450 (CYP)2B6 gene lead to decreased enzyme activity and have an impact on drug metabolism. The present study was designed to investigate the patterns of genetic distinction across a hypervariable region of the CYP2B6 gene, known to contain important SNPs, i.e. rs4803419 and rs3745274, among five major ethnic groups of the Pakistani population. Methods Arlequin v3.5.DnaSPv6.12. and network 5 resources were used to analyze population genetic variance in the partial CYP2B6 gene sequences obtained from 104 human samples belonging to Punjabi, Pathan, Sindhi, Seraiki and Baloch ethnicities of Pakistan. The partial CYP2B6 gene region analyzed in the current study is previously known to possess important SNPs. Results The data analyses revealed that genetic variance among samples mainly came from differentiation within the ethnic groups. However, significant genetic variation was also found among the various ethnic groups. The high pairwise Fst genetic distinction was observed between Seraiki and Sindhi ethnic groups (Fst = 0.13392, P-value = 0.026) as well as between Seraiki and Balochi groups (Fst = 0.04303, P-value = −0.0030). However, the degree of genetic distinction was low between Pathan and Punjabi ethnic groups. Some SNPs, including rs3745274 and rs4803419, which are previously shown in strong association with increased plasma Efavirenz level, were found in high frequency. Besides, a novel SNP, which was not found in dbSNP and Ensemble databases, was identified in the Balochi ethnicity. This novel SNP is predicted to affect the CYP2B6 splicing pattern. Conclusion These results may have significant implications in Pakistani ethnicities in the context of drugs metabolized by CYP2B6, especially in Seraiki and Balochi ethnicity. The novel heterogeneous SNP, found in the present study, might lead to altered drug-metabolizing potential of CYP2B6 and, therefore, may be implicated in non-responder phenomenon.

This study reports the isolation of three new C20 diterpenoid alkaloids, Chitralinine A–C (1–3) from the aerial parts of Delphinium chitralense. Their structures were established on the basis of latest spectral techniques and single crystal X-rays crystallographic studies of chitralinine A described basic skeleton of these compounds. All the isolated Compounds (1–3) showed strong, competitive type inhibition against acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) in comparison to standard allanzanthane and galanthamine however, chitralinine-C remained the most potent with IC50 value of 11.64 ± 0.08 μM against AChE, and 24.31 ± 0.33 μM against BChE, respectively. The molecular docking reflected a binding free energy of −16.400 K Cal-mol−1 for chitralinine-C, having strong interactions with active site residues, TYR334, ASP72, SER122, and SER200. The overall findings suggest that these new diterpenoid alkaloids could serve as lead drugs against dementia-related diseases including Alzheimer’s disease.

Reverse image search has been a vital and emerging research area of information retrieval. One of the primary research foci of information retrieval is to increase the space and computational efficiency by converting a large image database into an efficiently computed feature database. This paper proposes a novel deep learning-based methodology, which captures channel-wise, low-level details of each image. In the first phase, sparse auto-encoder (SAE), a deep generative model, is applied to RGB channels of each image for unsupervised representational learning. In the second phase, transfer learning is utilized by using VGG-16, a variant of deep convolutional neural network (CNN). The output of SAE combined with the original RGB channel is forwarded to VGG-16, thereby producing a more effective feature database by the ensemble/collaboration of two effective models. The proposed method provides an information rich feature space that is a reduced dimensionality representation of the image database. Experiments are performed on a hybrid dataset that is developed by combining three standard publicly available datasets. The proposed approach has a retrieval accuracy (precision) of 98.46%, without using the metadata of images, by using a cosine similarity measure between the query image and the image database. Additionally, to further validate the proposed methodology’s effectiveness, image quality has been degraded by adding 5% noise (Speckle, Gaussian, and Salt pepper noise types) in the hybrid dataset. Retrieval accuracy has generally been found to be 97% for different variants of noise

Generative adversarial network (GANs) is one of the most important research avenues in the field of artificial intelligence, and its outstanding data generation capacity has received wide attention. In this paper, we present the recent progress on GANs. First, the basic theory of GANs and the differences among different generative models in recent years were analyzed and summarized. Then, the derived models of GANs are classified and introduced one by one. Third, the training tricks and evaluation metrics were given. Fourth, the applications of GANs were introduced. Finally, the problem, we need to address, and future directions were discussed.

Brain tumor analysis is essential to the timely diagnosis and effective treatment of patients. Tumor analysis is challenging because of tumor morphology factors like size, location, texture, and heteromorphic appearance in medical images. In this regard, a novel two-phase deep learning-based framework is proposed to detect and categorize brain tumors in magnetic resonance images (MRIs). In the first phase, a novel deep-boosted features space and ensemble classifiers (DBFS-EC) scheme is proposed to effectively detect tumor MRI images from healthy individuals. The deep-boosted feature space is achieved through customized and well-performing deep convolutional neural networks (CNNs), and consequently, fed into the ensemble of machine learning (ML) classifiers. While in the second phase, a new hybrid features fusion-based brain-tumor classification approach is proposed, comprised of both static and dynamic features with an ML classifier to categorize different tumor types. The dynamic features are extracted from the proposed brain region-edge net (BRAIN-RENet) CNN, which is able to learn the heteromorphic and inconsistent behavior of various tumors. In contrast, the static features are extracted by using a histogram of gradients (HOG) feature descriptor. The effectiveness of the proposed two-phase brain tumor analysis framework is validated on two standard benchmark datasets, which were collected from Kaggle and Figshare and contain different types of tumors, including glioma, meningioma, pituitary, and normal images. Experimental results suggest that the proposed DBFS-EC detection scheme outperforms the standard and achieved accuracy (99.56%), precision (0.9991), recall (0.9899), F1-Score (0.9945), MCC (0.9892), and AUC-PR (0.9990). The classification scheme, based on the fusion of feature spaces of proposed BRAIN-RENet and HOG, outperform state-of-the-art methods significantly in terms of recall (0.9913), precision (0.9906), accuracy (99.20%), and F1-Score (0.9909) in the CE-MRI dataset.

Abstract Background The Plasmodium falciparum apical membrane antigen-1 (PfAMA1) is considered as an ideal vaccine candidate for malaria control due to its high level of immunogenicity and essential role in parasite survival. Among the three domains of PfAMA1 protein, hyper-variable region (HVR) of domain I is the most immunogenic. The present study was conducted to evaluate the extent of genetic diversity across HVR domain I of the pfama1 gene in P. falciparum isolates from Hazara division of Pakistan. Methods The HVR domain I of the pfama1 was amplified and sequenced from 20 P. falciparum positive cases from Hazara division of Pakistan. The sequences were analysed in context of global population data of P. falciparum from nine malaria endemic countries. The DNA sequence reads quality assessment, reads assembling, sequences alignment/phylogenetic and population genetic analyses were performed using Staden, Lasergene v. 7.1, MEGA7 and DnaSP v.5 software packages respectively. Results Total 14 mutations were found in Pakistani isolates with 12 parsimony informative sites. During comparison with global isolates, a novel non-synonymous mutation (Y240F) was found specifically in a single Pakistani sample with 5% frequency. The less number of mutations, haplotypes, recombination and low pairwise nucleotide differences revealed tightly linked uniform genetic structure with low genetic diversity at HVR domain I of pfama1 among P. falciparum isolates from Hazara region of Pakistan. This uniform genetic structure may be shaped across Pakistani P. falciparum isolates by bottleneck or natural selection events. Conclusion The Pakistani P. falciparum isolates were found to maintain a distinct genetic pattern at HVR pfama1 with some extent of genetic relationship with geographically close Myanmar and Indian samples. However, the exact pattern of gene flow and demographic events may infer from whole genome sequence data with large sample size of P. falciparum collected from broad area of Pakistan.

Tuberculosis (TB), caused by the Mycobacterium tuberculosis infection, continues to be a leading cause of morbidity and mortality in developing countries. Resistance to the first-line anti-TB drugs, isoniazid (INH) and rifampicin (RIF), is a major drawback to effective TB treatment. Genetic mutations in the β-subunit of the DNA-directed RNA polymerase (rpoB) are reported to be a major reason of RIF resistance. However, the structural basis and mechanisms of these resistant mutations are insufficiently understood. In the present study, thirty drug-resistant mutants of rpoB were initially modeled and screened against RIF via a comparative molecular docking analysis with the wild-type (WT) model. These analyses prioritized six mutants (Asp441Val, Ser456Trp, Ser456Gln, Arg454Gln, His451Gly, and His451Pro) that showed adverse binding affinities, molecular interactions, and RIF binding hinderance properties, with respect to the WT. These mutant models were subsequently analyzed by molecular dynamics (MD) simulations. One-hundred nanosecond all-atom MD simulations, binding free energy calculations, and a dynamic residue network analysis (DRN) were employed to exhaustively assess the impact of mutations on RIF binding dynamics. Considering the global structural motions and protein–ligand binding affinities, the Asp441Val, Ser456Gln, and His454Pro mutations generally yielded detrimental effects on RIF binding. Locally, we found that the electrostatic contributions to binding, particularly by Arg454 and Glu487, might be adjusted to counteract resistance. The DRN analysis revealed that all mutations mostly distorted the communication values of the critical hubs and may, therefore, confer conformational changes in rpoB to perturb RIF binding. In principle, the approach combined fundamental molecular modeling tools for robust “global” and “local” level analyses of structural dynamics, making it well suited for investigating other similar drug resistance cases.

COVID-19 is a respiratory illness that has affected a large population worldwide and continues to have devastating consequences. It is imperative to detect COVID-19 at the earliest opportunity to limit the span of infection. In this work, we developed a new CNN architecture STM-RENet to interpret the radiographic patterns from X-ray images. The proposed STM-RENet is a block-based CNN that employs the idea of split–transform–merge in a new way. In this regard, we have proposed a new convolutional block STM that implements the region and edge-based operations separately, as well as jointly. The systematic use of region and edge implementations in combination with convolutional operations helps in exploring region homogeneity, intensity inhomogeneity, and boundary-defining features. The learning capacity of STM-RENet is further enhanced by developing a new CB-STM-RENet that exploits channel boosting and learns textural variations to effectively screen the X-ray images of COVID-19 infection. The idea of channel boosting is exploited by generating auxiliary channels from the two additional CNNs using Transfer Learning, which are then concatenated to the original channels of the proposed STM-RENet. A significant performance improvement is shown by the proposed CB-STM-RENet in comparison to the standard CNNs on three datasets, especially on the stringent CoV-NonCoV-15k dataset. The good detection rate (97%), accuracy (96.53%), and reasonable F-score (95%) of the proposed technique suggest that it can be adapted to detect COVID-19 infected patients.

Malware is a key component of cyber-crime, and its analysis is the first line of defence against cyber-attack. This study proposes two new malware classification frameworks: Deep Feature Space-based Malware classification (DFS-MC) and Deep Boosted Feature Space-based Malware classification (DBFS-MC). In the proposed DFS-MC framework, deep features are generated from the customized CNN architectures and are fed to a support vector machine (SVM) algorithm for malware classification, while, in the DBFS-MC framework, the discrimination power is enhanced by first combining deep feature spaces of two customized CNN architectures to achieve boosted feature spaces. Further, the detection of exceptional malware is performed by providing the deep boosted feature space to SVM. The performance of the proposed malware classification frameworks is evaluated on the MalImg malware dataset using the hold-out cross-validation technique. Malware variants like Autorun.K, Swizzor.gen!I, Wintrim.BX and Yuner.A is hard to be correctly classified due to their minor inter-class differences in their features. The proposed DBFS-MC improved performance for these difficult to discriminate malware classes using the idea of feature boosting generated through customized CNNs. The proposed classification framework DBFS-MC showed good results in term of accuracy: 98.61%, F-score: 0.96, precision: 0.96, and recall: 0.96 on stringent test data, using 40% unseen data.

Honeybee (Apis mellifera) population is going down across the globe due to honeybee pathogens. This greatly influences the bee-associated commercial food products production. Likewise, mosquitoes are prominent vector responsible for spreading life-threatening human diseases, including malaria and dengue. The plant-based insecticides are a better substitute to the recent control practices of honeybee pathogenic bacteria and mosquito. Here, we performed in vitro screening of Nepeta clarkei Hook. f. (Labiatae) aqueous extracts against three honey bee gut bacterial isolates including Paenibacillus larvae an infamous honeybee bacterial pathogen. The inhibitory zone was produced in the range of 6–14 mm diameters against three honey bee bacterial isolates. Likewise, fourth instars larvae of Culex (Diptera/Culicidae) were also subjected to check the possible larvicidal efficacy of N. clarkei. A normal media supplemented with N. clarkei in different concentrations (0.025% 0.05%, 0.1%, 0.15%, and 0.2%) affected the growth of larvae significantly. The lethal concentration at which 50% of larvae failed to become pupate was found to be 0.1% after 24 h of exposure. Considerable reductions in larval growth and pupal development of mosquito suggested that this plant should be utilized in mosquito control programmes.