Enhanced Neural Network-Based Univariate Time-Series Forecasting Model for Big Data.

Big data is a combination of large structured, semistructured, and unstructured data collected from various sources that must be processed before using them in many analytical applications. Anomalies or inconsistencies in big data refer to the occurrences of some data that are in some way unusual and do not fit the general patterns. It is considered one of the major problems of big data. Data trust method (DTM) is a technique used to identify and replace anomaly or untrustworthy data using the interpolation method. This article discusses the DTM used for univariate time series (UTS) forecasting algorithms for big data, which is considered the preprocessing approach by using a neural network (NN) model. In this work, DTM is the combination of statistical-based untrustworthy data detection method and statistical-based untrustworthy data replacement method, and it is used to improve the forecast quality of UTS. In this study, an enhanced NN model has been proposed for big data that incorporates DTMs with the NN-based UTS forecasting model. The coefficient variance root mean squared error is utilized as the main characteristic indicator in the proposed work to choose the best UTS data for model development. The results show the effectiveness of the proposed method as it can improve the prediction process by determining and replacing the untrustworthy big data.

An improved energy-efficient cloud-optimized load-balancing for IoT frameworks.

As wireless communication grows, so does the need for smart, simple, affordable solutions. The need prompted academics to develop appropriate network solutions ranging from wireless sensor networks (WSNs) to the Internet of Things (IoT). With the innovations of researchers, the necessity for enhancements in existing researchers has increased. Initially, network protocols were the focus of study and development. Regardless, IoT devices are already being employed in different industries and collecting massive amounts of data through complicated applications. This necessitates IoT load-balancing research. Several studies tried to address the communication overheads produced by significant IoT network traffic. These studies intended to control network loads by evenly spreading them across IoT nodes. Eventually, the practitioners decided to migrate the IoT node data and the apps processing it to the cloud. So, the difficulty is to design a cloud-based load balancer algorithm that meets the criteria of IoT network protocols. Defined as a unique method for controlling loads on cloud-integrated IoT networks. The suggested method analyses actual and virtual host machine needs in cloud computing environments. The purpose of the proposed model is to design a load balancer that improves network response time while reducing energy consumption. The proposed load balancer algorithm may be easily integrated with peer-existing IoT frameworks. Handling the load for cloud-based IoT architectures with the above-described methods. Significantly boosts response time for the IoT network by 60 %. The proposed scheme has less energy consumption (31 %), less execution time (24\%), decreased node shutdown time (45 %), and less infrastructure cost (48\%) in comparison to existing frameworks. Based on the simulation results, it is concluded that the proposed framework offers an improved solution for IoT-based cloud load-balancing issues.

Synthesis, in vitro and structural aspects of cap substituted Suberoylanilide hydroxamic acid analogs as potential inducers of apoptosis in Glioblastoma cancer cells via HDAC /microRNA regulation.

Glioblastoma multiforme (GBM) is a heterogeneous, aggressive brain cancer characterized by chemo-resistance and cancer stemness. Histone deacetylases (HDACs) are a group of enzymes that regulate chromatin epigenetics which were in turn found to be controlled by microRNAs (miRs). The drug employed in chemotherapy for the treatment of GBM is Temozolomide (TMZ). Unfortunately, many GBM patients exhibit chemo-resistance to this drug. Here we have synthesized various Suberoyl anilide hydroxamic acid (SAHA) analogs with many substitutions at the cap site majority of which not yet studied. These SAHA analogs have exhibited profound cytotoxicity at 2 µM, and 4 µM concentrations in GBM cancer cell line U87MG, and 1 µM, and 2 µM concentrations in breast cancer cell line MCF-7. Surprisingly, these analogs have exhibited cytotoxic effects in chronic lymphoid leukemia cells (Raji) at 64 µM, and 128 µM concentrations due to mutated p53. Among all the synthesized analogs 3-Chloro-SAHA, 3-Chloro-4-fluoro SAHA have exhibited effective cytotoxicity in all cancer cells. These potent analogs inhibited HDAC-8 enzyme activity by 2-folds in U87MG, and MCF-7 cell lines and 7-folds decrease in HDAC-8 activity was observed in Raji cell line. These analogs decreased the expression of HDAC-2, HDAC-3 genes and enhanced the expression of p53 tumor suppressor. Interestingly, these compounds decreased the expression of Rictor, the main component of the mTORC2 complex involved cancer cell metabolism. Furthermore, these molecules have decreased oncogenic microRNA expression such as miR-21 and enhanced the expression of tumor suppressor microRNAs such as miR-143. The HDAC binding ability of these molecules was highly significant and have exhibited the ability to cross blood-brain barrier (BBB), and followed the Lipinski rule of five. Thus, these molecules need to be taken up further to clinics for better therapy against GBM either singly or combination therapy.

N-acetyl l-aspartate and Triacetin modulate tumor suppressor MicroRNA and class I and II HDAC gene expression induce apoptosis in Glioblastoma cancer cells in vitro.

Glioblastoma multiforme (GBM), grade IV glioma and is aggressive, malignant primary brain cancer. Altered expression and activity of epigenetic proteins such as histone deacetylases (HDACs) are involved in GBM metastasis. Also, acetates are important to brain metabolites that regulate cell proliferation and apoptosis. Here, we have examined the effect of the acetates on the cell-cycle. U87MG cancer cells treated with N-acetyl l-aspartate (NAA) and sodium acetate have exhibited G1 phase cell-cycle arrest whereas U87MG cells treated with Triacetin (TA), and potassium acetate has induced G2/M cell cycle arrest. We have observed inhibition of histone deacetylase (HDAC) mRNA levels in acetate treated U87MG cells. Interestingly, acetates-treated U87MG cells have shown a significant reduction in the mRNA level of class II HDACs than class I HDACs. Acetate treated cells have exhibited an enhanced expression of various microRNAs such as miR-15b, miR-92, miR-101, miR-155, miR-199, miR-200, miR-223, miR-16, and miR-17 that are involved in the inhibition of cancer cell proliferation, invasion, migration, and angiogenesis. Further, these acetate molecules regulate genes involved in mammalian target of rapamycin complex 2 (mTORC2) such as mammalian stress-activated protein kinase-interacting protein (mSIN1), protein observed with Rictor 2 (Protor 2), and protein kinase C α (PKCα). The present study reveals the possible involvement of the mTORC2 complex during acetate-mediated HDAC inhibition, as well as microRNA modulation. Furthermore, molecular modeling studies were employed to understand the binding mode of these acetate molecules to mTOR, Rapamycin-insensitive companion of mammalian target of rapamycin (Rictor), and HDAC-8 proteins. Thus in this study, we have identified the pivotal role of acetates in the modulation of mTOR complex, epigenetic genes and provide structural as well as functional insights that will help in future drug discovery against GBM cancer therapy.