Current Signal Transduction Therapy - Volume 15, Issue 1, 2020

Background: Existing cancer chemotherapeutics can kill normal as well as malignant cells. To solve these issues, scientists are now more concerned about the design and discovery of potential anticancer, least toxic leads, which can promote apoptosis process and inhibition of abnormal signal transduction via hyperactivation of protein kinases such as Pim-1 due to overexpression or mutation of proto-oncogenes and tumor suppressor genes related to molecular mechanisms of senescence, cell cycle, apoptosis and metastatic invasion, thus leading to anticancer activities. Natural scaffolds play a great role in this aspect. Objective: Sea is full of biodiverse natural resources of medicinal compounds derived from marine plants, sponges, actinomycetes, cynobacteria, fungi, corals and animals. Many anticancer compounds were successfully discovered. But there are few potent compounds developed against abnormal signal transduction mechanism. Materials and Methods: Therefore, an attempt has been made in the present review to focus on molecular mechanisms of various targets in connection with the over-expression of Pim-1 mediated senescence, cell cycle, apoptosis and metastatic invasion and their potent inhibitors. Results: Biochemical mechanisms of the potent marine sourced inhibitors keeping activities against abnormal signal transduction were discussed in this study. It gives great attention to expand the capabilities in these upcoming areas to remain globally relevant. Conclusion: Existed marine sourced anticancer compounds tabulated in this study could be used as a template for further design and synthesis of promising congeneric synthetic compounds against another disease by the application of in silico high throughput screening through drug repositioning.

Background: Poly-(ADP-Ribose) Polymerase (PARP) plays a central role in recovery from single-strand DNA (ssDNA) damage via base excision repair. When PARP activity is inhibited by a NAD+ mimetic analog, ssDNA is converted into a Double-Strand Break (DSB) during the S-phase in a cell cycle. However, the DSB site is repaired in a process of Homologous Recombination (HR) that is derived by genes such as BRCA1/2, PALB2, and RAD51. Under conditions of HR dysfunction, including mutations of BRCA1/2 (called BRCAness), PARP inhibitor (PARPi) induces “synthetic lethality” in BRCAness-specific cancer cells. Indeed, clinical trials using forms of PARPi that include olaparib, veliparib and rucaparib, have revealed that PARP inhibition produces a dramatic effect that actually arrests cancer progression. Its clinical efficiency is limited, however, due to the acquisition of PARPi resistance during long-term use of this inhibitor. Thus, it is important to elucidate the mechanisms of PARPi resistance. Methods: We searched the scientific literature published in PubMed, with a special focus on kinase phosphorylation that is involved in acquiring PARPi resistance. We also summarized the possible molecular events for recovering HR system, a key event for acquiring PARPi resistance. Results: CDK1 is a critical kinase for 5’-3’ DNA end resection, which is important for generating ssDNA for recruiting HR-priming factors. CDK12 is necessary for the transcription of HR-driver genes, such as BRCA1, BRCA2, RAD51 and ATR via the phosphorylation of RNA Pol-II. PLK-1 participates in driving HR via the phosphorylation of RAD51. The PI3K-AKT-mTOR signaling cascade is involved in BRCA1 induction via an ETS1 transcriptional pathway. Even under ATMdeficient conditions, the ATR-CHK1 axis compensates for loss in the DNA damage response, which results in HR recovery. The HGF receptor Met tyrosine kinase is responsible for promoting DNA repair by activating the PARP catalytic domain. Conclusion: These kinase-based signaling pathways are biologically important for understanding the compensatory system of HR, whereas inactivation of these kinases has shown promise for the release of PARPi resistance. Several lines of preclinical studies have demonstrated the potential use of kinase inhibitors to enhance PARPi sensitivity. We emphasize the clinical importance of chemical inhibitors as adjuvant drugs to block critical kinase activities and prevent the possible PARPi resistance.

Background: In this paper, we present an approach to evaluate the information sharing in the supply chain. Materials and Methods: We propose a study of four scenarios of sharing upstream and downstream information simultaneously. Replenishment lead time is the upstream information studied in this work and demand information is the downstream one. We treat in this context the case of two-echelon (a warehouse and several retailers) and multi-products supply chain. Results: We focus our approach on the centralised decision, in which, the warehouse is the decision maker and his goal is to minimise the system cost independently. In our formulation, we consider a system cost composed of holding, ordering, penalty and transportation costs. Then, we use a Genetic Algorithm in order to approximate the optimal echelon inventory position at the warehouse and optimal allocation quantity of each item from the warehouse to the respective retailer, which minimises the system cost. Conclusion: Our approach is illustrated by some numerical experiments.

Background: Dedicated wavelength utilization and the isolation of control plane from the data plane are the important features in the design of Optical Burst Switching (OBS). The contention in bursts, link congestion and the reservation cause the burst dropping in optical networks. The slotted time and the burst assembly models incorporate the wavelength assignment and the channel reservation schemes to reduce the dropping probability. The reservation of resources prior to burst arrival and the additional delay due to the burst assembly and the offset time are the major issues in the reduction of probability. Besides, the traditional one-to-one packet transmission consumes more time due to a large number of packets handling. Materials and Methods: This paper proposes the novel OBS model that incorporates the three processes such as Open-Flow (OF)-based Orchestrator Live Node (OLN) modeling, fuzzy logic based ranking and the offset time-based reservation (without/with void filling) to overcome the issues in the traditional methods. Initially, the OLN modeling based on OF analysis includes the Flow Information Base (FIB) table for the periodical update of the link information. The fuzzy logic- based ranking of channels followed by OF-OLN predicts the status of the wavelength such as free, used and conversion. Based on the status, the channels are reserved without and with void filling to schedule the bursts effectively. The reservation scheme employs the Offset-Time Burst Assembly algorithm to allow the resource reservation prior to burst arrival. Through these processes, the reuse of wavelength and the reallocation of resources are possible in OBS. Results & Conclusion: The controlling of maximum burst transfer delay by the OTBA efficiently reduces the end-to-end delay for data traffic. The comparative analysis between the proposed OLN-WR with the existing Hybrid Burst Assembly (HBA), Fuzzy-based Adaptive Threshold (FAT) and Fuzzy-based Adaptive Hybrid Burst Assembly (FAHBA) in terms of end-to-end delay and transmitted amount of bursts assures the applicability of OLN-WR in scheduling and communication activities in OBS networks.

Background: Due to the huge development of wireless devices and mobile data traffic had gained attention towards identifying accurate solutions for more proficient utilization of the wireless spectrum. An essential issue confronting the future in wireless systems is to identify the appropriate spectrum bands to satisfy the request of future administrations. While the greater part of the radio spectrum is allocated to various services, applications and users show that spectrum usage is quite low. Materials and Methods: The spectrum sensing is performed at the start of each time slot before the data transmission. As a promising framework to improve the spectrum utilization, Cognitive Radio (CR) technique has the immense potential to meet such a necessity by permitting unlicensed users to exist together in licensed bands. In this paper Cognitive radio and Full-Duplex (FD) based two-way relay communications are developed to enhance spectrum utilization for multichannel and to decrease the false alarm rate. Results: To solve the optimization problems in spectral efficiency, soft computing techniques is proposed to minimize the self-interference and delay to the licensed users. In this proposed work the kurtosis parameter is used for channel detection to determine whether the signal is present or not. Conclusion: The performance results of the proposed method are evaluated in terms of spectral allocation and outage probability which achieves better performance than the existing Multi- Objective Genetic Algorithm (MOGA) optimization.

Background: Wireless Sensor Networks (WSNs) are self-configured infrastructure-less networks are comprising of a number of sensing devices used to monitor physical or environmental quantities such as temperature, sound, vibration, pressure, motion etc. They collectively transmit data through the network to a sink where it is observed and analyzed. Materials and Methods: The major issues in WSN are interference, delay and attacks that degrade their performance due to their distributed nature and operation. Timely detection of attacks is imperative for various real time applications like healthcare, military etc. To improve the Black hole attack detection in WSN, Projected Independent Component Analysis (PICA) technique is proposed herewith, which detects black hole attack by analyzing collected physiological data from biomedical sensors. Results: The PICA technique performs attack detection through Mutual information to measure the dependence in the joint distribution. The dependence among the nodes is identified based on the independent probability distribution functions and mutual probability function. Conclusion: The black hole attack isolation is then performed through the distribution of the attack separation message. This supports to improve Packet Delivery Ratio (PDR) with minimum delay. The simulation is carried out based on parameters such as black hole attack detection rate (BHADR), Black Hole Attack Detection Time (BHADT), False Positive Rate (FPR), PDR and delay.

Background: Position sensorless control technique for Permanent Magnets-Brush Less Direct Current (PM-BLDC) motor drive is considered in this paper. Materials and Methods: A new estimation based on sensorless technique is proposed for PMBLDC motor. Artificial Neural Network (ANN) is aided for the purpose. Results: The inputs to the ANN are the voltages of PM-BLDC motor and it estimates the sample signals to feed Zero Crossing Point (ZCP) detection circuit. The ZCP detection circuit provides ZCP signals for commutation logic which gives the commutation sequence to power switches. In order to provide the correct sample signal to ZCP detection circuit, the ANN is well trained by Genetic Algorithm (GA). The proposed sensor less control model is implemented in MATLAB/SIMULINK working platform. Conclusion: Field Programmable Gate Array (FPGA) is used to implement the proposed method. Experimental results verify the analysis and demonstrate the advantages of the proposed method.

Objective: The EEG signal extraction offers an opportunity to improve the quality of life in patients, which has lost to control the ability of their body, with impairment of locomotion. Electroencephalogram (EEG) signal is an important information source for underlying brain processes. Materials and Methods: The signal extraction and denoising technique obtained through timedomain was then processed by Adaptive Line Enhancer (ALE) to extract the signal coefficient and classify the EEG signals based on FF network. The adaptive line enhancer is used to update the coefficient during the runtime with the help of adaptive algorithms (LMS, RLS, Kalman Filter). Results: In this work, the least mean square algorithm was employed to obtain the coefficient update with respect to the corresponding input signal. Finally, Mat lab and verilog HDL language are used to simulate the signals and got the classification accuracy rate of 80%. Conclusion: Experiments show that this method can get high and accurate rate of classification. In this paper, it is proposed that a low-cost use of Field Programmable Gate Arrays (FPGAs) can be used to process EEG signals for extracting and denoising. As a preliminary study, this work shows the implementation of a Neural Network, integrated with ALE for EEG signal processing. The preliminary tests through the proposed architecture for the activation function shows to be reasonable both in terms of precision and in processing speed.