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The p53 protein is a key tumor suppressor and the most commonly mutated and down-regulated protein in human tumors. It functions mainly through interaction with DNA, and p53 acts as a transcription factor that recognizes the so-called p53 target sites on the promoters of various genes. P53 has been shown to exist as many isoforms, including three C-terminal isoforms that are produced by alternative splicing. Because the C-terminal domain is responsible for sequence-nonspecific binding and regulation of p53 binding, we have analyzed DNA recognition by these C-terminal isoforms. Using atomic force microscopy, we show for the first time that all C-terminal isoforms recognize superhelical DNA. It is particularly noteworthy that a sequence-specific p53 consensus binding site is bound by p53α and β isoforms with similar affinities, whilst p53α shows higher binding to a quadruplex sequence than both p53β and p53γ, and p53γ loses preferential binding to both the consensus binding sequence and the quadruplex-forming sequence. These results show the important role of the variable p53 C-terminal amino acid sequences for DNA recognition.

The Device-to-Device (D2D) networks bear a close resemblance to future Internet of Things (IoT) networks. IoT plays a significant role in the FinTech industry, especially in data security and context-aware applications. D2D communication in cellular networks has emerged as a competent technology for the upcoming future cellular networks. To improve spectral efficiency and data secrecy, we propose a two-phase network coding based two-way Decode-and-Forward (DF) relaying policy for D2D communication. Firstly, a suitable relay node is selected considering the end-to-end signal-to-interference plus noise ratio (SINR). Then, a XOR-based DF operation is performed when the signal transmitted by both the D2D users (DUEs) is successfully decoded at the relay node. In order to analyze the performance of the proposed two-way communication system, we derive outage probability expression as a function of various system parameters like SINR, position of both the relays and DUEs, power splitting factor, number of relays, and data rate. Further, we also derive an expression for error probability and average throughput of D2D link to evaluate a fair comparison of the proposed approach with the traditional methods. The analytical results are also validated with the simulation results to justify the efficacy of the proposed method.

Data security and resource allocation are two important terms associated with Internet of Things (IoT). This recent technical evolution has made its mark in industrial applications making the network more flexible and computation friendly through connecting all the devices. As a subset of IoT, the framework of Industrial IoT (IIoT) is based on huge number of nodes with continuous process of multiple works at a time. Due to this, multi-objective network, interference in the path always becomes reason for the loss of network resources as well as the security of data becomes vulnerable. In most of the previous works, dedicated channel states are considered for fixed resources which remains a major issue of IIoT network flexibility along with security. In this paper, both the problems are incorporated by calculating the channel security and using convolutional neural network (CNN) optimal channel state extracted for different applications. This results as a fast system with proper utilization of resources and validated with mathematical analysis and simulations.

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification.

An essential factor of the DNA damage response is 53BP1, a multimeric protein that inhibits the resection-dependent double-strand break (DBS) repair. The p53 protein is a tumor suppressor known as a guardian of the genome. Although the interaction between 53BP1 and its p53 partner is well-known in regulating gene expression, a question remains whether genome injury can affect the interaction between 53BP1 and p53 proteins or p53 binding to DNA. Here, using mass spectrometry, we determine post-translational modifications and interaction properties of 53BP1 and p53 proteins in non-irradiated and γ-irradiated cells. In addition, we used Atomic Force Microscopy (AFM) and Fluorescent Lifetime Imaging Microscopy combined with Fluorescence Resonance Energy Transfer (FLIM-FRET) for studies of p53 binding to DNA. Also, we used local laser microirradiation as a tool of advanced confocal microscopy, showing selected protein accumulation at locally induced DNA lesions. We observed that 53BP1 and p53 proteins accumulate at microirradiated chromatin but with distinct kinetics. The density of 53BP1 (53BP1pS1778) phosphorylated form was lower in DNA lesions than in the non-specified form. By mass spectrometry, we found 22 phosphorylations, 4 acetylation sites, and methylation of arginine 1355 within the DNA-binding domain of the 53BP1 protein (aa1219-1711). The p53 protein was phosphorylated on 8 amino acids and acetylated on the N-terminal domain. Post-translational modifications (PTMs) of 53BP1 were not changed in cells exposed to γ-radiation, while γ-rays increased the level of S6ph and S15ph in p53. Interaction analysis showed that 53BP1 and p53 proteins have 54 identical interaction protein partners, and AFM revealed that p53 binds to both non-specific and TP53-specific sequences (AGACATGCCTA GGCATGTCT). Irradiation by γ-rays enhanced the density of the p53 protein at the AGACATGCCTAGGCATGTCT region, and the binding of p53 S15ph to the TP53 promoter was potentiated in irradiated cells. These findings show that γ-irradiation, in general, strengthens the binding of phosphorylated p53 protein to the encoding gene.

The birth of beyond 5G (B5G) and emerge of 6G have made personal and industrial operations more reliable, efficient, and profitable, accelerating the development of the next-generation Internet of Things (IoT). We know, one of the most important key performance indicators in 6G is smart network architecture, and in massive IoT applications, energy-efficient ubiquity networks rely mainly on the intelligence and automation for industrial applications. This article addresses the energy consumption problem with a massive IoT system model with dynamic network architecture or clustering using a multiagent system (MAS) for industrial 6G applications. The work uses distributed artificial intelligence (DAI) to cluster the sensor nodes in the system to find the main node and predict its location. The work initially uses the backpropagation neural network (BPNN) and convolutional neural network (CNN), which are, respectively, introduced for optimization. Furthermore, the work analyzes the correlation of mutual clusters to allocate resources to individual nodes in each cluster efficiently. The simulation results show that the proposed method reduces the waste of resources caused by redundant data, improves the energy efficiency of the whole network, along with information preservation.

Wireless sensor network enhances the classic features of wireless communication with cognitive capabilities for efficient spectrum usage. This work focuses on the dynamic correlation between the secondary users (SUs) based on their statistical behaviour while performing the cooperative communication in cognitive radio sensor network. The proposed approach addresses the problem of uneven and repetitive communication between the SUs in a cooperative communication scenario. The authors’ objective is to use a novel approach based on the Gaussian copula theory and advanced particle swarm optimisation algorithm to analyse the dependencies of time-varying spectrum sensing behaviour of multiple SUs. Here, time delay in prediction reduces due to the analysis of the dynamic correlation between the time delay in spectrum sensing results for the same set of channels. The simulation results show the performance of the proposed approach outperforming the other well-known techniques.

The importance of DNA structure in the regulation of basic cellular processes is an emerging field of research. Among local non-B DNA structures, inverted repeat (IR) sequences that form cruciforms and G-rich sequences that form G-quadruplexes (G4) are found in all prokaryotic and eukaryotic organisms and are targets for regulatory proteins. We analyzed IRs and G4 sequences in the genome of the most important biotechnology microorganism, S. cerevisiae. IR and G4-prone sequences are enriched in specific genomic locations and differ markedly between mitochondrial and nuclear DNA. While G4s are overrepresented in telomeres and regions surrounding tRNAs, IRs are most enriched in centromeres, rDNA, replication origins and surrounding tRNAs. Mitochondrial DNA is enriched in both IR and G4-prone sequences relative to the nuclear genome. This extensive analysis of local DNA structures adds to the emerging picture of their importance in genome maintenance, DNA replication and transcription of subsets of genes.

The advent of Industrial Internet of Things (IIoT) has determined the proliferation of smart devices connected to the Internet and injected a vast amount of data into it, which may undergo many computational stages at several clusters. On the one hand, the benefits brought by these technologies are well known; however, in the envisaged scenario, the exposure of data, services and infrastructures to malicious attacks has definitely grown. Even a single breach on any of the links of the data–service–infrastructure chain may seriously compromise the security of the end-user application. Therefore, the logical and smart clustering while satisfying security and reliability is a key issue for IIoT networks. A novel clustering method proposed based on power demand assures security of data information in IIoT-based applications. First, security capacity of the system is calculated from mutual information of primary channel and eavesdropping channel. Then, under the maximum transmit power constraint, an optimal transmit power is found based on deep learning technique, which maximizes security capacity of the system. Finally, the network is clustered according to the calculated power demand. Experimental results accredit the proposed method has higher security and reliability, as well as lower network time overhead and power consumption.

The Internet of Things (IoT) is an integrated part of the future network which requires comprehensive awareness, reliable delivery and intelligent processing. Therefore, it faces the challenge of energy efficiency during real-time implementations based on respective applications. The work proposes an efficient and green dynamic clustering mechanism based on power demand and information volume. The machine learning approach is based on dynamic cluster formation by avoiding any information loss. Initially, among all the available Poisson distributed nodes, the master node divides them into two temporary clusters based on their power requirements. Then, the power requirements within these two clusters are equalized by a set threshold and based on application requirements, the dynamic clusters are formed by measuring the amount of information in each cluster. The simulation results presents that the proposed method equalizes the power demand of the network and maximizes the information in the cluster, which further improves the energy efficiency of the whole network. The results are also compared with traditional methods to justify the proposed work.

This proposed work addresses the problem of dynamic and real-time power allocation to the cluster head (CH) in a cognitive radio sensor network (CRSN). The work is based on spectrum sensing outputs by these secondary users (SUs) for a nonergodic system. In general, a cooperative sensing network consists of multiple nodes communicating with each other about their respective spectrum sensing output, to perform the desired work. Therefore, the SU nodes of each cluster sense the spectrum all the time, which results in a continuous power consumption in CRSN. Now, the total amount of energy, which is used to allocate all the CHs at a particular place for spectrum sensing is being avoided and saved. This is performed, by the use of autocorrelation error for predicting the real-time behavior of primary users extended by the implementation of vector quantization to identify nearby active SU nodes and based on distributed artificial intelligence for power allocation. The simulation results and the mathematical derivation validate the proposed method.

This paper considers the power allocation for cooperative sensing when the system's statistical properties cannot be deduced for any given real-time scenario. For such type of systems, the power allocation varies and the standard optimization techniques fail. Therefore, a simple and robust method based on Euclidean Distance (ED)is implemented which measures the Autocorrelation Error (AE) of the Power spectra of the channel at different instants. Here, ED refers to the shortest path between the two active nodes or users. The proposed method is analyzed and simulated for 10 channels keeping ED constant. Blind Source Separation (BSS) is primarily used to identify dominating frequency components in the corresponding channels followed by the evaluation of AE. The results illustrate the active nodes along with the importance of identifying these nodes for future prediction which will help in 5G Communications using Cognitive Radio (CR) network. Further, it can be inferred that during spectrum sensing there is a need of allocating power to the specific nodes only, rather than all the nodes, which will use the power consumption by the secondary users efficiently. The probability of detection and probability of false alarm is set to QoS degradation by the Primary User (PU).

In this paper an energy efficient and dynamic cluster formation technique is proposed. Cluster formation is the first and the main priority for any Wireless Sensor Network (WSN). In our work, the proposed technique is combination of Firefly algorithm and Hierarchical Maximum Likelihood (HML) for an Optical Wireless Sensor Network (OWSN). It utilizes the unique property of Firefly algorithm and overcomes the problem of it by introducing HML in it. As a result the parameters of the network changes with respect to the requirement of suitable position of nodes. The power distribution in the nodes is also accurately done as HML works on the property of maximum likelihood property. It means the nodes become active when they are selected based on the closest value of source node to form a cluster. The cost function also minimized.

In the Internet of things (IoT) paradigm, wireless sensor networks (WSNs) contribute hugely by connecting all the devices for smart application purposes. The smart applications include smart healthcare system, as a key phenomenon of new era of medical service. Smart healthcare system consists of different techniques and approaches, and remote health monitoring is one of those important techniques of healthcare system, which helps doctors to monitor health of remotely located patients with different health statistics provided by sensors. This whole process should be fast, prompt in response and energy efficient which is tough with limited battery power of sensors. The article proposes a novel method for efficient utilization of power in WSNs, using artificial neural network-based technique self-organizing map (SOM) for clustering and distributed artificial intelligence (DAI) for power distribution in the nodes. The hybrid approach of SOM and multi-agent-based DAI results in better performance compared to other existing methods. The performance of the proposed method is validated with mathematical analysis and simulation results, which justifies the significance of the work for IoT environment.

In a recently published paper, we have found that SARS-CoV-2 hot-spot mutations are significantly associated with inverted repeat loci and CG dinucleotides. However, fast-spreading strains with new mutations (so-called mink farm mutations, England mutations and Japan mutations) have been recently described. We used the new datasets to check the positioning of mutation sites in genomes of the new SARS-CoV-2 strains. Using an open-access Palindrome analyzer tool, we found mutations in these new strains to be significantly enriched in inverted repeat loci.

In any cognitive radio sensor networks (CRSNs), the secondary users (SUs) and primary users (PUs) share the opportunity to use the authorized frequency band. Here, the SU nodes can only transmit in the temporarily idle spectrum when it is not in use by any PU nodes. The proper estimation and detection of primary nodes are important for the energy-efficient spectrum access. The work basically presents a state of the art in implementing Bayesian-based convolutional neural network (CNN) for addressing the issue of energy constraints in next-generation Internet of things (Nx-IoT) using CRSN. Initially, we use blind source separation to extract the energy features and cyclic spectrum features of the signal and carry on the asymptotic autocorrelation calculation to the extracted signal. Finally, we construct the corresponding training set for CNN training and establish a suitable spectrum sensing model for Nx-IoT. Theoretical analysis and simulation results validate a suitable B-CNN spectrum sensing model along with energy-efficient cooperative communication between the SU nodes.

The recent technical evolution is revolving around Internet of Things (IoT). The Internet of Softwarized Things (IoST) as a subset of IoT, is making its mark mostly towards industrial applications to connect all the devices and improve the computation capability and networking flexibility. The Industrial IoT (IIoT) consists of a large network, where the multiple works are processed continuously at a time. Therefore, multi-objective interference issue in the path remains as obstacle, for which the networking resources are lost. The existing works were performed with fixed resources and dedicated channel states which make the network less flexible with more time response. In this paper, the problem is addressed with optimal resource allocation using convolutional neural network (CNN) to extract the optimal channel state for different applications, which ease the computations along with efficiency. Furthermore, the proposed method is validated with the mathematical analysis and simulation.

SARS-CoV-2 is an intensively investigated virus from the order Nidovirales (Coronaviridae family) that causes COVID-19 disease in humans. Through enormous scientific effort, thousands of viral strains have been sequenced to date, thereby creating a strong background for deep bioinformatics studies of the SARS-CoV-2 genome. In this study, we inspected high-frequency mutations of SARS-CoV-2 and carried out systematic analyses of their overlay with inverted repeat (IR) loci and CpG islands. The main conclusion of our study is that SARS-CoV-2 hot-spot mutations are significantly enriched within both IRs and CpG island loci. This points to their role in genomic instability and may predict further mutational drive of the SARS-CoV-2 genome. Moreover, CpG islands are strongly enriched upstream from viral ORFs and thus could play important roles in transcription and the viral life cycle. We hypothesize that hypermethylation of these loci will decrease the transcription of viral ORFs and could therefore limit the progression of the disease.

The Internet of Things (IoT) realizes the interconnection of different paradigms in information technologies along with their connectivity. With its evolution, the cost and energy efficiency along with ease of life stands as a challenge towards its deployments. Advances in Artificial Intelligence coupled with IoT connectivity provide lots of potentials for real-time communication applications. To address the issue of energy-efficient computing for these applications, we propose an improved dynamic clustering algorithm which can be implemented in IoT applications which comprises of heterogeneous Wireless Sensor Networks (WSNs). Initially, we use neural network and Copula theory to process the information quantity based on power demand by individual clusters. This avoids the information redundancy and the waste of resources caused by repeated construction of similar type of clusters (as in conventional methods). According to the power requirement, the nodes based on the applications are divided into two initial clusters, and compared with the set thresholds. These are then used to assign logical values to the nodes in the cluster. Since, the amounts of observation information of the nodes are not always useful due to random energy consumption; the Back Propagation Neural Network (BPNN) is used to optimize the amount of information to form the final dynamic cluster efficiently. The simulation results showed that the proposed method can effectively utilize the information in the cluster and balance the inter-cluster cooperative communication energy efficiently for IoT applications. The effectiveness of the proposed approach and the superiority compared with the traditional methods had also been demonstrated through the simulation results. We hope our work can further stimulate the investigations on energy and cost efficient methodologies for smart IoT applications.

Smart cities are claimed to be smart if the new technologies are capable of providing desired sustainable outcome. The sustainable properties of smart city applications require less energy consumption and efficient resource allocation. The Internet-of-Things (IoT), 5G, and fog networks have emerged as the most crucial researched areas due to their numerous applications for smart cities to provide the desired sustainable outcome. The sustainable properties of Wireless Sensor Networks (WSNs) play a vital role in the deployment of these technologies into the physical world and efficient utilization of the available spectrum is a major problem faced here. As a potential solution of this, Cognitive Radio (CR) merged with WSN as Cognitive Radio Sensor Networks (CRSNs) make the smart perspective with high resource management through cooperative communication. The proposed work establishes a dynamic correlation between Secondary Users/nodes (SUs) in a single cluster according to their statistical behavior at the time of performing smart cooperative communication in CRSNs to improve sustainability of smart world IoT applications. Distributed Artificial Intelligence (DAI) is used to calculate the real-time resource allocation to these clusters using their respective Coordinator Agent (CoA) based on the dynamic behaviors. To improve sustainability in the smart city applications, the time delay in the prediction of vacant channels is reduced which results in making these applications become more energy efficient. The effectiveness of the proposed work is illustrated with mathematical analysis and simulation results confirm its better sustainable performance compared to the existing techniques.

The article addresses the Cluster Head (CH) identification, based on the theory of linear-regression for Multi-input Multi-output (MIMO) technique in wireless sensor networks. The CH location is initially unknown while assuming a prior knowledge of absolute location of any three or four sensors. Thus, we will calculate the positions of the unknown sensors using distributed gradient descent due to its excellent localization accuracy. The network is assumed to have a cooperative communication among the clusters as well as multiple CHs. Mathematical analysis and simulations justifies our proposed approach along with comparison of previous literatures.

This letter addresses the problem of the fusion center (FC) positioning based on the novel approach of hierarchical maximum likelihood clustering for cooperative communication. In a cooperative communication system, the power allocation for all the nodes, including FC, should not be the same all the time, as decision of all the nodes is not required. Using the proposed scheme, the location of the FC based on the cluster formation in hierarchical approach can be made closer to the dense clusters.

The role of local DNA structures in the regulation of basic cellular processes is an emerging field of research. Amongst local non-B DNA structures, the significance of G-quadruplexes was demonstrated in the last decade, and their presence and functional relevance has been demonstrated in many genomes, including humans. In this study, we analyzed the presence and locations of G-quadruplex-forming sequences by G4Hunter in all complete bacterial genomes available in the NCBI database. G-quadruplex-forming sequences were identified in all species, however the frequency differed significantly across evolutionary groups. The highest frequency of G-quadruplex forming sequences was detected in the subgroup Deinococcus-Thermus, and the lowest frequency in Thermotogae. G-quadruplex forming sequences are non-randomly distributed and are favored in various evolutionary groups. G-quadruplex-forming sequences are enriched in ncRNA segments followed by mRNAs. Analyses of surrounding sequences showed G-quadruplex-forming sequences around tRNA and regulatory sequences. These data point to the unique and non-random localization of G-quadruplex-forming sequences in bacterial genomes.

In this paper the Bit Error Rate (BER) is considered for Cognitive radio users using spectrum sensing algorithms. The proposed modified algorithm shows a substantial improvement in BER for high data rate applications of CR. The algorithms are exclusively made for Cognitive transmitters. All the simulations are been performes using real time signal using NI-USRP 2920. The energy is beinf found using Welche's power spectral density and Probability of detection and probability of false alarm are being compared using FFT/NFFT conventional Periodogram and then the BER is compared with the Welch's Periodogram which is modified. The BER performance analysis has been evaluated and compared with standard error function for binary FSK. Simulation results show that the substantial improve in bit error rate for high data rate cognitive radio users.