Context aware Routing to Assist Routing Decisions for Quality Improvement in Multi Hop Ad hoc Networks

The context information is an intriguing aspect of decision making. The context-awareness can be useful in the ad hoc networks in which nodes are mobile, and the conditions are dynamic. In ad hoc networks, routing protocols are intended to discover the route over multi-hop wireless links under varying conditions. The context-awareness can assist the routing protocols in determining the appropriate path. This paper investigates into choosing the appropriate route by applying the context information and presents the approach to improve the decision making and the quality of the route. We consider nodes, connecting links, and different layers as the context. The paper introduces the scalability and flexibility in the set of parameters that govern the eminence of the node inter-connection that, in turn, influences the overall quality of the route. We propose the context-aware dynamic routing protocol (CADR) and present the approach, algorithm, and analysis. We simulate the protocol by taking the flexible combination of the context attributes and the values, also compares the performance with AODV. The simulation results show that the protocol chooses the appropriate route as per the considered attributes and weight, and provide the enhanced performanc

Design Analysis and Implementation of Stock Market Forecasting System using Improved Soft Computing Technique

In this paper, a stock market prediction model was created utilizing artificial neural networks. Many people nowadays are attempting to predict future trends in bonds, currencies, equities, and stock markets. It is quite challenging for a capitalist and an industry to forecast changes in stock market prices. Due to the numerous economic, political, and psychological aspects at play, forecasting future value changes on the stock markets is quite challenging. In addition, stock market forecasting is a difficult endeavor because it relies on a wide range of known and unknown variables. Many approaches, including technical analysis, fundamental analysis, time series analysis, and statistical analysis are used to attempt to predict the share price; however, none of these methods has been demonstrated to be a consistently effective prediction tool. Artificial neural networks (ANNs), a subfield of artificial intelligence, are one of the most modern and promising methods for resolving financial issues, such as categorizing corporate bonds and anticipating stock market indexes and bankruptcy (AI). Artificial neural networks (ANN) are a prominent technology used to forecast the future of the stock market. In order to understand financial time series, it is often essential to extract relevant information from enormous data sets using artificial neural networks. An outcome prediction neural network with three layers is trained using the back propagation method. Analysis shows that ANN outperforms every other prediction technique now available to academics in terms of stock market price predictions. It is concluded that ANN is a useful technique for predicting stock market movements globally

I/O Performance Characterization of Lustre and NASA Applications on Pleiades

In this paper we study the performance of the Lustre file system using five scientific and engineering applications representative of NASA workload on large-scale supercomputing systems such as NASA s Pleiades. In order to facilitate the collection of Lustre performance metrics, we have developed a software tool that exports a wide variety of client and server-side metrics using SGI's Performance Co-Pilot (PCP), and generates a human readable report on key metrics at the end of a batch job. These performance metrics are (a) amount of data read and written, (b) number of files opened and closed, and (c) remote procedure call (RPC) size distribution (4 KB to 1024 KB, in powers of 2) for I/O operations. RPC size distribution measures the efficiency of the Lustre client and can pinpoint problems such as small write sizes, disk fragmentation, etc. These extracted statistics are useful in determining the I/O pattern of the application and can assist in identifying possible improvements for users applications. Information on the number of file operations enables a scientist to optimize the I/O performance of their applications. Amount of I/O data helps users choose the optimal stripe size and stripe count to enhance I/O performance. In this paper, we demonstrate the usefulness of this tool on Pleiades for five production quality NASA scientific and engineering applications. We compare the latency of read and write operations under Lustre to that with NFS by tracing system calls and signals. We also investigate the read and write policies and study the effect of page cache size on I/O operations. We examine the performance impact of Lustre stripe size and stripe count along with performance evaluation of file per process and single shared file accessed by all the processes for NASA workload using parameterized IOR benchmark

An Application-Based Performance Evaluation of NASAs Nebula Cloud Computing Platform

The high performance computing (HPC) community has shown tremendous interest in exploring cloud computing as it promises high potential. In this paper, we examine the feasibility, performance, and scalability of production quality scientific and engineering applications of interest to NASA on NASA's cloud computing platform, called Nebula, hosted at Ames Research Center. This work represents the comprehensive evaluation of Nebula using NUTTCP, HPCC, NPB, I/O, and MPI function benchmarks as well as four applications representative of the NASA HPC workload. Specifically, we compare Nebula performance on some of these benchmarks and applications to that of NASA s Pleiades supercomputer, a traditional HPC system. We also investigate the impact of virtIO and jumbo frames on interconnect performance. Overall results indicate that on Nebula (i) virtIO and jumbo frames improve network bandwidth by a factor of 5x, (ii) there is a significant virtualization layer overhead of about 10% to 25%, (iii) write performance is lower by a factor of 25x, (iv) latency for short MPI messages is very high, and (v) overall performance is 15% to 48% lower than that on Pleiades for NASA HPC applications. We also comment on the usability of the cloud platform

Performance Analysis of Scientific and Engineering Applications Using MPInside and TAU

In this paper, we present performance analysis of two NASA applications using performance tools like Tuning and Analysis Utilities (TAU) and SGI MPInside. MITgcmUV and OVERFLOW are two production-quality applications used extensively by scientists and engineers at NASA. MITgcmUV is a global ocean simulation model, developed by the Estimating the Circulation and Climate of the Ocean (ECCO) Consortium, for solving the fluid equations of motion using the hydrostatic approximation. OVERFLOW is a general-purpose Navier-Stokes solver for computational fluid dynamics (CFD) problems. Using these tools, we analyze the MPI functions (MPI_Sendrecv, MPI_Bcast, MPI_Reduce, MPI_Allreduce, MPI_Barrier, etc.) with respect to message size of each rank, time consumed by each function, and how ranks communicate. MPI communication is further analyzed by studying the performance of MPI functions used in these two applications as a function of message size and number of cores. Finally, we present the compute time, communication time, and I/O time as a function of the number of cores

Gemcitabine Combination Nano Therapies for Pancreatic Cancer

Pancreatic cancer is one of the deadliest causes of cancer-related death in the United States, with a 5-year overall survival rate of 6 to 8%. These statistics suggest that immediate medical attention is needed. Gemcitabine (GEM) is the gold standard first-line single chemotherapy agent for pancreatic cancer but, after a few months, cells develop chemoresistance. Multiple clinical and experimental investigations have demonstrated that a combination or co-administration of other drugs as chemotherapies with GEM lead to superior therapeutic benefits. However, such combination therapies often induce severe systemic toxicities. Thus, developing strategies to deliver a combination of chemotherapeutic agents more securely to patients is needed. Nanoparticle-mediated delivery can offer to load a cocktail of drugs, increase stability and availability, on-demand and tumor-specific delivery while minimizing chemotherapy-associated adverse effects. This review discusses the available drugs being co-administered with GEM and the limitations associated during the process of co-administration. This review also helps in providing knowledge of the significant number of delivery platforms being used to overcome problems related to gemcitabine-based co-delivery of other chemotherapeutic drugs, thereby focusing on how nanocarriers have been fabricated, considering the modes of action, targeting receptors, pharmacology of chemo drugs incorporated with GEM, and the differences in the physiological environment where the targeting is to be done. This review also documents the focus on novel mucin-targeted nanotechnology which is under development for pancreatic cancer therapy

An Application-Based Performance Characterization of the Columbia Supercluster

Columbia is a 10,240-processor supercluster consisting of 20 Altix nodes with 512 processors each, and currently ranked as the second-fastest computer in the world. In this paper, we present the performance characteristics of Columbia obtained on up to four computing nodes interconnected via the InfiniBand and/or NUMAlink4 communication fabrics. We evaluate floating-point performance, memory bandwidth, message passing communication speeds, and compilers using a subset of the HPC Challenge benchmarks, and some of the NAS Parallel Benchmarks including the multi-zone versions. We present detailed performance results for three scientific applications of interest to NASA, one from molecular dynamics, and two from computational fluid dynamics. Our results show that both the NUMAlink4 and the InfiniBand hold promise for application scaling to a large number of processors

Antibody-Drug Conjugates for Cancer Therapy: Chemistry to Clinical Implications

Chemotherapy is one of themajor therapeutic options for cancer treatment. Chemotherapy is often associated with a low therapeutic window due to its poor specificity towards tumor cells/tissues. Antibody-drug conjugate (ADC) technology may provide a potentially new therapeutic solution for cancer treatment. ADC technology uses an antibody-mediated delivery of cytotoxic drugs to the tumors in a targeted manner, while sparing normal cells. Such a targeted approach can improve the tumor-to-normal tissue selectivity and specificity in chemotherapy. Considering its importance in cancer treatment, we aim to review recent efforts for the design and development of ADCs. ADCs are mainly composed of an antibody, a cytotoxic payload, and a linker, which can offer selectivity against tumors, anti-cancer activity, and stability in systemic circulation. Therefore, we have reviewed recent updates and principal considerations behind ADC designs, which are not only based on the identification of target antigen, cytotoxic drug, and linker, but also on the drug-linker chemistry and conjugation site at the antibody. Our review focuses on site-specific conjugation methods for producing homogenous ADCs with constant drug-antibody ratio (DAR) in order to tackle several drawbacks that exists in conventional conjugation methods

Biodegradable PEG-PCL Nanoparticles for Co-delivery of MUC1 Inhibitor and Doxorubicin for the Confinement of Triple-Negative Breast Cancer

Combating triple-negative breast cancer (TNBC) is still a problem, despite the development of numerous drug delivery approaches. Mucin1 (MUC1), a glycoprotein linked to chemo-resistance and progressive malignancy, is unregulated in TNBC. GO-201, a MUC1 peptide inhibitor that impairs MUC1 activity, promotes necrotic cell death by binding to the MUC1-C unit. The current study deals with the synthesis and development of a novel nano-formulation (DM-PEG-PCL NPs) comprising of polyethylene glycol-polycaprolactone (PEG-PCL) polymer loaded with MUC1 inhibitor and an effective anticancer drug, doxorubicin (DOX). The DOX and MUC1 loaded nanoparticles were fully characterized, and their different physicochemical properties, viz. size, shape, surface charge, entrapment efficiencies, release behavior, etc., were determined. With IC(50) values of 5.8 and 2.4 nm on breast cancer cell lines, accordingly, and a combination index (CI) of < 1.0, DM-PEG-PCL NPs displayed enhanced toxicity towards breast cancer cells (MCF-7 and MDA-MB-231) than DOX-PEG-PCL and MUC1i-PEG-PCL nanoparticles. Fluorescence microscopy analysis revealed DOX localization in the nucleus and MUC1 inhibitor in the mitochondria. Further, DM-PEG-PCL NPs treated breast cancer cells showed increased mitochondrial damage with enhancement in caspase-3 expression and reduction in Bcl-2 expression.In vivo evaluation using Ehrlich Ascites Carcinoma bearing mice explicitly stated that DM-PEG-PCL NPs therapy minimized tumor growth relative to control treatment. Further, acute toxicity studies did not reveal any adverse effects on organs and their functions, as no mortalities were observed. The current research reports for the first time the synergistic approach of combination entrapment of a clinical chemotherapeutic (DOX) and an anticancer peptide (MUC1 inhibitor) encased in a diblock PEG-PCL copolymer. Incorporating both DOX and MUC1 inhibitors in PEG-PCL NPs in the designed nanoformulation has provided chances and insights for treating triple-negative breast tumors. Our controlled delivery technology is biodegradable, non-toxic, and anti-multidrug-resistant. In addition, this tailored smart nanoformulation has been particularly effective in the therapy of triple-negative breast cancer. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s10924-022-02654-4

Novel Paclitaxel Nanoformulation Impairs De Novo Lipid Synthesis in Pancreatic Cancer Cells and Enhances Gemcitabine Efficacy

Pancreatic cancer (PanCa) is a highly lethal disease with a poor 5 year survival rate, less than 7%. It has a dismal prognosis, and more than 50% of cases are detected at an advanced and metastatic stage. Gemcitabine (GEM) is a gold standard chemotherapy used for PanCa treatment. However, GEM-acquired resistance in cancer cells is considered as a major setback for its continued clinical implementation. This phenomenon is evidently linked to de novo lipid synthesis. PanCa cells rely on de novo lipid synthesis, which is a prime event in survival and one of the key drivers for tumorigenesis, cancer progression, and drug resistance. Thus, the depletion of lipogenesis or lipid metabolism can not only improve treatment outcomes but also overcome chemoresistance, which is an unmet clinical need. Toward this effort, our study reports a unique paclitaxel−poly(lactic-co-glycolic acid) (PLGA) nanoparticles (PPNPs) formulation which can target lipid metabolism and improve anticancer efficacy of GEM in PanCa cells. PPNPs inhibit excessive lipid formation and alter membrane stability with compromised membrane integrity, which was confirmed by Fourier transform infrared and zeta potential measurements. The effective interference of PPNPs in lipid metabolic signaling was determined by reduction in the expression of FASN, ACC, lipin, and Cox-2 proteins. This molecular action profoundly enhances efficacy of GEM as evident through enhanced inhibitory effects on the tumorigenic and metastasis assays in PanCa cells. These data clearly suggest that the ablation of lipid metabolism might offer an innovative approach for the improved therapeutic outcome in PanCa patients