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Fulltext Reliable geographical forwarding in cognitive radio sensor networks using virtual clusters

Zubair S, Fisal N

Sensors (Basel), 2014;14(5):8996-9026.
PMID: 24854362 DOI: 10.3390/s140508996

The need for implementing reliable data transfer in resource-constrained cognitive radio ad hoc networks is still an open issue in the research community. Although geographical forwarding schemes are characterized by their low overhead and efficiency in reliable data transfer in traditional wireless sensor network, this potential is still yet to be utilized for viable routing options in resource-constrained cognitive radio ad hoc networks in the presence of lossy links. In this paper, a novel geographical forwarding technique that does not restrict the choice of the next hop to the nodes in the selected route is presented. This is achieved by the creation of virtual clusters based on spectrum correlation from which the next hop choice is made based on link quality. The design maximizes the use of idle listening and receiver contention prioritization for energy efficiency, the avoidance of routing hot spots and stability. The validation result, which closely follows the simulation result, shows that the developed scheme can make more advancement to the sink as against the usual decisions of relevant ad hoc on-demand distance vector route select operations, while ensuring channel quality. Further simulation results have shown the enhanced reliability, lower latency and energy efficiency of the presented scheme.
An ensemble of bioinformatics and machine learning approaches to identify shared breast cancer biomarkers among diverse populations

Sultan G, Zubair S

Comput Biol Chem, 2024 Feb;108:107999.
PMID: 38070457 DOI: 10.1016/j.compbiolchem.2023.107999

Breast cancer continues to be a prominent cause for substantial loss of life among women globally. Despite established treatment approaches, the rising prevalence of breast cancer is a concerning trend regardless of geographical location. This highlights the need to identify common key genes and explore their biological significance across diverse populations. Our research centered on establishing a correlation between common key genes identified in breast cancer patients. While previous studies have reported many of the genes independently, our study delved into the unexplored realm of their mutual interactions, that may establish a foundational network contributing to breast cancer development. Machine learning algorithms were employed for sample classification and key gene selection. The best performance model further selected the candidate genes through expression pattern recognition. Subsequently, the genes common in all the breast cancer patients from India, China, Czech Republic, Germany, Malaysia and Saudi Arabia were selected for further study. We found that among ten classifiers, Catboost exhibited superior performance with an average accuracy of 92%. Functional enrichment analysis and pathway analysis revealed that calcium signaling pathway, regulation of actin cytoskeleton pathway and other cancer-associated pathways were highly enriched with our identified genes. Notably, we observed that these genes regulate each other, forming a complex network. Additionally, we identified PALMD gene as a novel potential biomarker for breast cancer progression. Our study revealed key gene modules forming a complex network that were consistently expressed in different populations, affirming their critical role and biological significance in breast cancer. The identified genes hold promise as prospective biomarkers of breast cancer prognosis irrespective of country of origin or ethnicity. Future investigations will expand upon these genes in a larger population and validate their biological functions through in vivo analysis.
Fulltext Mobility-Enhanced Reliable Geographical Forwarding in Cognitive Radio Sensor Networks

Zubair S, Syed Yusoff SK, Fisal N

Sensors (Basel), 2016;16(2):172.
PMID: 26840312 DOI: 10.3390/s16020172

The emergence of the Internet of Things and the proliferation of mobile wireless devices has brought the area of mobile cognitive radio sensor networks (MCRSN) to the research spot light. Notwithstanding the potentials of CRSNs in terms of opportunistic channel usage for bursty traffic, the effect of the mobility of resource-constrained nodes to route stability, mobility-induced spatio-temporal spectral opportunities and primary user (PU) protection still remain open issues that need to be jointly addressed. To this effect, this paper proposes a mobile reliable geographical forwarding routing (MROR) protocol. MROR provides a robust mobile framework for geographical forwarding that is based on a mobility-induced channel availability model. It presents a comprehensive routing strategy that considers PU activity (to take care of routes that have to be built through PU coverage), PU signal protection (by the introduction of a mobility-induced guard (mguard) distance) and the random mobility-induced spatio-temporal spectrum opportunities (for enhancement of throughput). It also addresses the issue of frequent route maintenance that arises when speeds of the mobile nodes are considered as a routing metric. As a result, simulation has shown the ability of MROR to reduce the route failure rate by about 65% as against other schemes. In addition, further results show that MROR can improve both the throughput and goodput at the sink in an energy-efficient manner that is required in CRSNs as against compared works.
Fulltext Assessing routing strategies for cognitive radio sensor networks

Zubair S, Fisal N, Baguda YS, Saleem K

Sensors (Basel), 2013;13(10):13005-38.
PMID: 24077319 DOI: 10.3390/s131013005

Interest in the cognitive radio sensor network (CRSN) paradigm has gradually grown among researchers. This concept seeks to fuse the benefits of dynamic spectrum access into the sensor network, making it a potential player in the next generation (NextGen) network, which is characterized by ubiquity. Notwithstanding its massive potential, little research activity has been dedicated to the network layer. By contrast, we find recent research trends focusing on the physical layer, the link layer and the transport layers. The fact that the cross-layer approach is imperative, due to the resource-constrained nature of CRSNs, can make the design of unique solutions non-trivial in this respect. This paper seeks to explore possible design opportunities with wireless sensor networks (WSNs), cognitive radio ad-hoc networks (CRAHNs) and cross-layer considerations for implementing viable CRSN routing solutions. Additionally, a detailed performance evaluation of WSN routing strategies in a cognitive radio environment is performed to expose research gaps. With this work, we intend to lay a foundation for developing CRSN routing solutions and to establish a basis for future work in this area.
Fulltext Improved Reconstruction of MR Scanned Images by Using a Dictionary Learning Scheme

Ikram S, Shah JA, Zubair S, Qureshi IM, Bilal M

Sensors (Basel), 2019 Apr 23;19(8).
PMID: 31018597 DOI: 10.3390/s19081918

The application of compressed sensing (CS) to biomedical imaging is sensational since it permits a rationally accurate reconstruction of images by exploiting the image sparsity. The quality of CS reconstruction methods largely depends on the use of various sparsifying transforms, such as wavelets, curvelets or total variation (TV), to recover MR images. As per recently developed mathematical concepts of CS, the biomedical images with sparse representation can be recovered from randomly undersampled data, provided that an appropriate nonlinear recovery method is used. Due to high under-sampling, the reconstructed images have noise like artifacts because of aliasing. Reconstruction of images from CS involves two steps, one for dictionary learning and the other for sparse coding. In this novel framework, we choose Simultaneous code word optimization (SimCO) patch-based dictionary learning that updates the atoms simultaneously, whereas Focal underdetermined system solver (FOCUSS) is used for sparse representation because of a soft constraint on sparsity of an image. Combining SimCO and FOCUSS, we propose a new scheme called SiFo. Our proposed alternating reconstruction scheme learns the dictionary, uses it to eliminate aliasing and noise in one stage, and afterwards restores and ﬁlls in the k-space data in the second stage. Experiments were performed using different sampling schemes with noisy and noiseless cases of both phantom and real brain images. Based on various performance parameters, it has been shown that our designed technique outperforms the conventional techniques, like K-SVD with OMP, used in dictionary learning based MRI (DLMRI) reconstruction.
Dengue and zika seropositivity, burden, endemicity, and cocirculation antibodies in Nigeria

Asaga Mac P, Tadele M, Airiohuodion PE, Nisansala T, Zubair S, Aigohbahi J, et al.

Ann Med, 2023 Dec;55(1):652-662.
PMID: 37074313 DOI: 10.1080/07853890.2023.2175903

INTRODUCTION: Mosquito-borne infections are of global health concern because of their rapid spread and upsurge, which creates a risk for coinfections. DENV and ZIKV are transmitted by Aedes aegypti and A. albopictus and are prevalent in Nigeria and neighbouring countries. However, their seroprevalence, burden, hidden endemicity and possible cocirculation are poorly understood in Nigeria.
METHODS: We conducted a cross-sectional study of 871 participants from three regions of Nigeria. All serum samples were analysed using malaria RDT and the immunoblot molecular diagnostic assay recomLine Tropical Fever for the presence of arboviral antibody serological marker IgG (Mikrogen Diagnostik, Neuried, Germany) with DENV and ZIKV Nonstructural protein 1 (NS 1), DENV and ZIKV Equad (variant of the envelope protein with designated mutations to increase specificity), according to the manufacturer's instructions.
RESULTS: The overall IgG antibody seropositivity against DENV-flavivirus was 44.7% (389/871); 95% CI (41.41-47.99), while ZIKV-flavivirus was 19.2% (167/871); 95% CI (0.16-0.21), and DENV-ZIKV-flavivirus cocirculation antibody seropositivity was 6.2%5 (54/871); 95% CI (0.6-0.7) in the three study regions of Nigeria. The study cohort presented similar clinical signs and symptoms of flaviviruses (DENV and ZIKV) in all three study regions.
CONCLUSION: This study highlighted an unexpectedly high antibody seropositivity, burden, hidden endemicity, and regional spread of mono- and co-circulating flaviviruses (DENV and ZIKV) in Nigeria.Key messagesDengue flavivirus sero-cross-reactivity drives antibody-dependent enhancement of ZIKV infection.Both viruses share common hosts (humans) and vectors (primarily Aedes aegypti), and are thus influenced by similar biological, ecological, and economic factors, resulting in epidemiological synergy.Additionally, the actual burden in epidemic and interepidemic periods is grossly or chronically unknown and underreported. Despite this trend and the potential public health threat, there are no reliable data, and little is known about these arboviral co-circulation infections.