Identification of Candidate mRNA Isoforms for Prostate Cancer-Risk SNPs Utilizing Iso-eQTL and sQTL Methods

Single nucleotide polymorphisms (SNPs) impacting the alternative splicing (AS) process (sQTLs) or isoform expression (iso-eQTL) are implicated as important cancer regulatory elements. To find the sQTL and iso-eQTL, we retrieved prostate cancer (PrCa) tissue RNA-seq and genotype data originating from 385 PrCa European patients from The Cancer Genome Atlas. We conducted RNA-seq analysis with isoform-based and splice event-based approaches. The MatrixEQTL was used to identify PrCa-associated sQTLs and iso-eQTLs. The overlap between sQTL and iso-eQTL with GWAS loci and those that are differentially expressed between cancer and normal tissue were identified. The cis-acting associations (FDR < 0.05) for PrCa-risk SNPs identified 42, 123, and 90 PrCa-associated cassette exons, intron retention, and mRNA isoforms belonging to 25, 95, and 83 genes, respectively; while assessment of trans-acting association (FDR < 0.05) yielded 59, 65, and 196 PrCa-associated cassette exons, intron retention and mRNA isoforms belonging to 35, 55, and 181 genes, respectively. The results suggest that functional PrCa-associated SNPs can play a role in PrCa genesis by making an important contribution to the dysregulation of AS and, consequently, impacting the expression of the mRNA isoforms.     

Artificial neural network-driven federated learning for heart stroke prediction in healthcare 4.0 underlying 5G

In recent years, smart healthcare, artificial intelligence (AI)-aided diagnostics, and automated surgical robots are just a few of the innovations that have emerged and gained popularity with the advent of Healthcare 4.0. Such technologies are powered by machine learning (ML) and deep learning (DL), which are preferable for disease diagnosis, identifying patterns, prescribing treatments, and forecasting diseases like stroke prediction, cancer prediction and so forth. Nevertheless, much data is needed for AI, ML, and DL-based systems to train effectively and provide the desired outcomes. Further, it raises concerns about data privacy, security, communication overhead, regulatory compliance and so forth. Federated learning (FL) is a technology that protects data security and privacy by limiting data sharing and utilizing model information of distributed systems to enhance performance. However, existing approaches are traditionally verified on pre-established datasets that fail to capture real-life applicability. Therefore, this study proposes an AI-enabled stroke prediction architecture consisting of FL based on the artificial neural network (ANN) model using data from actual stroke cases. This architecture can be implemented on healthcare-based wearable devices (WD) for real-time use as it is effective, precise, and computationally affordable. In order to continuously enhance the performance of the global model, the proposed FL-based architecture aggregates the optimizer weights of many clients using a fifth-generation (5G) communication channel. Then, the performance of the proposed FL-based architecture is studied based on multiple parameters such as accuracy, precision, recall, bit error rate, and spectral noise. It outperforms the traditional approaches regarding accuracy, which is 5% to 10% higher.     

Electrochemical bath deposition technique: Deposition of CdS thin films

Cadmium sulphide thin films have been deposited onto chromium plated stainless steel substrates under the influence of electric field. The various deposition parameters such as speed of rotation of the substrates, temperature of the chemical bath, molar concentrations of solution and the strength of the electric field were kept at optimized conditions. The electrochemical photovoltaic (ecpv) cells are formed with CdS film electrodes. The properties of CdS films andecpv cells are monitored with selective values of the electric field employed in the controlled precipitation technique. This relatively new technique is described and the possible film formation mechanism suggested.     

Production of volatile compounds by hairy root cultures of Cichorium intybus L under the influence of fungal elicitors and their analysis using solid‐phase micro extraction gas chromatography–mass spectrometry

Hairy root cultures of Cichorium intybus L produced volatile aromatic compounds under the influence of fungal elicitors. It was observed that the intensity of the production of volatile aromatic compounds in the hairy root cultures of C intybus with 10 ml l^?1 media filtrate (MF) of Phytopthora parasitica var nicotiana reached a maximum on the 21st day, as seen by a quantitative flavour profiling method. It was noted that, during the time course, treatment with 10 ml l^?1 MF of P parasitica to the MS basal liquid medium containing hairy root cultures of chicory resulted in changed root morphology with cell wall thickening and shear. The components of the volatile aromatic compounds were identified as a propyl isovalerate, undecanal, nonanol, isoamyl nonanoate and 2‐decene‐1‐ol. The mass spectra of all these compounds matched well with the NIST/EPA/NIH library and by comparing with the Kovats index of volatile compounds. The major fruity note was due to the presence of two of the major volatile components, namely propyl isovalerate and isoamyl nonanoate, which were also produced at higher concentration on day 21. This response of chicory hairy root cultures to the fungal elicitor producing volatile aromatic compounds would imply eco‐physiological functions, possibly for plant defence system. Copyright © 2003 Society of Chemical Industry     

Novel Method for DNA-Based Elliptic Curve Cryptography for IoT Devices

Elliptic curve cryptography (ECC) can achieve relatively good security with a smaller key length, making it suitable for Internet of Things (IoT) devices. DNA-based encryption has also been proven to have good security. To develop a more secure and stable cryptography technique, we propose a new hybrid DNA-encoded ECC scheme that provides multilevel security. The DNA sequence is selected, and using a sorting algorithm, a unique set of nucleotide groups is assigned. These are directly converted to binary sequence and then encrypted using the ECC; thus giving double-fold security. Using several examples, this paper shows how this complete method can be realized on IoT devices. To verify the performance, we implement the complete system on the embedded platform of a Raspberry Pi 3 board, and utilize an active sensor data input to calculate the time and energy required for different data vector sizes. Connectivity and resilience analysis prove that DNA-mapped ECC can provide better security compared to ECC alone. The proposed method shows good potential for upcoming IoT technologies that require a smaller but effective security system.     

Regression of experimental brain tumors with 6-thioxanthine and Escherichia coli gpt gene therapy

The identification of transgenes with antitumor activity is critical to the development of gene therapy of cancer. Retrovirus-mediated transfer of the Escherichia coli gpt gene into rat C6 glioma cells without subsequent selection still inhibited the proliferation of this mixed polyclonal population upon addition of the prodrug, 6-thioxanthine, with an ID50 of 4.1 microM, whereas parental C6 cells were not affected at a concentration of 500 microM. In a time-course assay, effects of the prodrug on the mixed polyclonal cell proliferation required at least 10 days of exposure. In mixed co-cultures, a bystander effect was not present over the first 4 days of prodrug exposure, but required trypsinization of the co-cultures and replating at lower densities. This "modified" bystander assay thus revealed a 50% decrease in C6 cell proliferation, even when the initial ratio of gpt-expressing to parental C6 cells was as low as 1:19. In a nude mouse model of subcutaneous tumors, co-grafts of C6 glioma and gpt-retrovirus producer cells displayed retarded growth upon exposure to 6-thioxanthine (6-TX). In a nude mouse model of intracerebral tumors, grafting of the gpt-retrovirus producer cells leads to an 80% reduction in intracerebral tumor volumes after 6-TX treatment. This reduction results in a 28% increase in the mean time of survival of animals that harbor intracerebral tumors (p < 0.0005). These antitumor effects indicate that the gpt/6-TX enzyme/prodrug pair is a promising alternative to the thymidine kinase gene and ganciclovir combination in the gene therapy of cancer.     

EMM: Extended matching market based scheduling for big data platform hadoop

Hadoop has emerged as a popular choice for processing Big data. Its cluster is used to process large scale jobs. The performance of a cluster is largely dependent upon the different kind of scheduling policies employed for job processing. However, a single type of scheduling policy may not be suitable for different kind of jobs. Inefficient performance of a cluster is an apparent outcome of inappropriate scheduling policies. These policies are either too complex or they are too elementary to understand the diverse jobs and their needs. Most of them follow a fixed pattern, which cannot be considered as a common solution for different jobs. The effect of such a non-fitting mechanism is lower resource utilization and poor cluster performance. In this paper, a pluggable scheduling mechanism is proposed for efficient and adaptive processing of the jobs. It utilizes the Matching Market concept for the allocation and further adaptively accommodates the diverse needs of the multiple jobs by understanding the varying requirements of the tasks. The experimental results reveal an enhanced resource utilization and improved cluster performance with an overall reduction in makespan. In certain instances, we have seen resource utilization improved up to 80% and performance improvement up to 60% with the proposed technique. Cluster efficiency is increased up of 31%. The evaluation and comparisons were conducted on various scheduling policies using different benchmarks of Hadoop with the same data and identical configurations. The proposed system has shown significant improvement in cluster efficiency.

     

A novel parallel prefix adder for optimized Radix-2 FFT processor

Multidimensional Systems and Signal Processing - The Fast Fourier Transform (FFT) is the basic building block for DSP applications where high processing speed is the critical requirement. Resource...     

Fully coupled modeling and design of a piezoelectric actuation based valveless micropump for drug delivery application

The precise control over the drug delivery involved in several vital applications including healthcare is required for achieving a therapeutic effect. For such precise control/manipulation of the drugs, micropumps are used. These micropumps are basically of two types viz. check valve-based and valveless micropumps. The valveless micropumps are preferable due to the congestion-free operation of diffuser/nozzle valves. In this paper, design optimization of a valveless piezo-electric actuation based micropump is carried out using COMSOL Multiphysics 5.0 by coupling two Multiphysics interface modules namely fluid–structure interaction and piezoelectric physics modules. Using simulation studies, the influence of pump design parameters including diffuser angle, diffuser length, neck width, chamber depth, chamber diameter and diaphragm thickness on net flow rate is studied. An optimal set of design parameters for the proposed micropump is identified. Further, the influence of actuation frequency on the flow rate is analysed. It is found that the proposed micropump is capable to deliver a net flow rate of 20 µl/min and a maximum back pressure attainable is 200 Pa.

     

Design optimization of an electromagnetic actuation based valveless micropump for drug delivery application

Microsystem Technologies - A valveless micropump based on an electromagnetic actuation for drug delivery application has been designed. The parametric studies are performed to examine the effects...