Theranostic Interpolation of Genomic Instability in Breast Cancer

Breast cancer is a diverse disease caused by mutations in multiple genes accompanying epigenetic aberrations of hazardous genes and protein pathways, which distress tumor-suppressor genes and the expression of oncogenes. Alteration in any of the several physiological mechanisms such as cell cycle checkpoints, DNA repair machinery, mitotic checkpoints, and telomere maintenance results in genomic instability. Theranostic has the potential to foretell and estimate therapy response, contributing a valuable opportunity to modify the ongoing treatments and has developed new treatment strategies in a personalized manner. “Omics” technologies play a key role while studying genomic instability in breast cancer, and broadly include various aspects of proteomics, genomics, metabolomics, and tumor grading. Certain computational techniques have been designed to facilitate the early diagnosis of cancer and predict disease-specific therapies, which can produce many effective results. Several diverse tools are used to investigate genomic instability and underlying mechanisms. The current review aimed to explore the genomic landscape, tumor heterogeneity, and possible mechanisms of genomic instability involved in initiating breast cancer. We also discuss the implications of computational biology regarding mutational and pathway analyses, identification of prognostic markers, and the development of strategies for precision medicine. We also review different technologies required for the investigation of genomic instability in breast cancer cells, including recent therapeutic and preventive advances in breast cancer.     

Liaison of phenolic acids and biological activity of escalating cultivars of Daucus carota

The objective of the present study was to analyze and compare the phenolic compounds and their antioxidant capacities of new lines of Dacus carota. The selected cultivars showed high variation in the contents of total phenolics (30.26–65.39 mg/100 g FW) and total ascorbic acid (41.12–58.36 mg/100 g FW). Analysis on RP-HPLC revealed that hydroxycinnamic acids and its derivatives were major phenolic compounds present in D. carota extracts, whereas 5-caffeolquinic acid was a major hydroxycinnamic acid (ranged from 30.26 to 65.39 mg/100 g FW). DCP cultivar showed high total antioxidant capacity (77.69 mg/100 g), 2,2-diphenyl-1-picrylhydrazyl (DPPH) scavenging capacity (52.36 mg/100 g), superoxide radical scavenging capacity (53.69 mg/100 g), and hydroxyl radical scavenging capacity (51.91 mg/100 g). A linear relationship was found between total phenolic acid contents and antioxidant capacity. Both phenolic compounds and antioxidant capacities varied significantly (ρ < 0.05) among cultivars. DCP cultivar was found to be a rich source of phenolics and ascorbic acid with high antioxidant activity.     

Structural Characterization of Oat Bran (1→3), (1→4)-β-D-Glucans by Lichenase Hydrolysis Through High-Performance Anion Exchange Chromatography with Pulsed Amperometric Detection

Cereal mixed linkages (1 → 3) (1 → 4)-β-D-glucan is a linear polysaccharide composed of glucose units. Oat β-glucan is a natural polymer. The main products of β-glucanase are oligosaccharides with DP3 and DP4, i.e., 3-Ob-cellobiosyl-D-glucose and 3-Ob-cellotriosyl-D-glucose, which represent over 90% of the molecule. Keeping in mind all the benefits of oat bran, the present study was planned to investigate the structural properties of oat bran, high-performance anion exchange chromatography with pulsed amperometric detection was used to examine these oligosaccharides. The structural analysis of oat bran of two oat varieties revealed that the ratio of soluble and insoluble triose to tetraose in β-glucan fraction was 1.44 and 1.78, respectively, for Avon variety; while the ratio of soluble and insoluble triose to tetraose in β-glucan fraction for Sargodha-81 was 1.49 and 1.77. The major units determined were cellotriose and cellotetraose. Other units cellopentaose and hexaoses were also existed but in minor fractions. Lichenase hydrolysis high-performance anion exchange chromatography with pulsed amperometric detection appeared to be the best choice for structural analysis of purified samples of mixed-linkage β-glucan.     

Plasmodium species aware based quantification of malaria parasitemia in light microscopy thin blood smear

Malaria is a serious worldwide disease, caused by a bite of a female Anopheles mosquito. The parasite transferred into complex life round in which it is grown and reproduces into the human body. The detection and recognition of Plasmodium species are possible and efficient through a process called staining (Giemsa). The staining process slightly colorizes the red blood cells (RBCs) but highlights Plasmodium parasites, white blood cells and artifacts. Giemsa stains nuclei, chromatin in blue tone and RBCs in pink color. It has been reported in numerous studies that manual microscopy is not a trustworthy screening technique when performed by nonexperts. Malaria parasites host in RBCs when it enters the bloodstream. This paper presents segmentation of Plasmodium parasite from the thin blood smear points on region growing and dynamic convolution based filtering algorithm. After segmentation, malaria parasite classified into four Plasmodium species: Plasmodium falciparum, Plasmodium ovale, Plasmodium vivax, and Plasmodium malaria. The random forest and K‐nearest neighbor are used for classification base on local binary pattern and hue saturation value features. The sensitivity for malaria parasitemia (MP) is 96.75% on training and testing of the proposed approach while specificity is 94.59%. Beside these, the comparisons of the two features are added to the proposed work for classification having sensitivity is 83.60% while having specificity is 94.90% through random forest classifier based on local binary pattern feature.     

Runoff Analysis for a Small Watershed of Tono Area Japan by Back Propagation Artificial Neural Network with Seasonal Data

The rainfall runoff (R-R) process was studied for two small sub-basins having different sizes in a mountainous catchment of Tono area Japan. The runoff and other meterological data have been collected in this catchment for the last 14 years. The major objective of this study was to construct numerical models for these sub-basins to predict runoff after 1/2 and 1 h. The effects of season and the size of the catchment on R-R process were also investigated. The hydrogeological conditions of the catchment were studied prior to the analyses. The data obtained for summer (rainy) and winter (dry) seasons were treated separately in order to study the seasonal effects on the model development. The back propagation artificial neural network technique (BPANN) and the multivariate autoregressive and moving average models (ARMA) were adopted for the analysis. It was found that for very small catchments the seasonal effects are dominant and therefore separate models should be developed for each season to obtain better forecasting estimates. It was also found that the predictions by BPANN models were better than multivariate ARMA models for intense rains having complex R-R relationships in summer. On the other hand, both the modelling techniques yielded almost similar results for smaller rains in winter. It was also found clearly that the accuracy of prediction decreased with the increase of the time period for prediction.     

Multi‐agent‐based sharing power economy for a smart community

In this paper, a multi‐agent‐based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small‐scale battery ESSs (each owned by community users), which are connected together to form a unified‐ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified‐ESS. This decision is based on the price of electricity at ‘time of use’ and ‘state of charge’ of unified‐ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of ‘no profit, no loss’. The objective of the proposed PDH is to limit the purchase of electricity on ‘high priced’ hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi‐agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on‐peak, off‐peak, and mid‐level (mid‐peak) prices of a real‐time price signal during 24 h of a day. Results clearly show vital financial benefits of ‘sharing power economy’ for end users and efficient use of power within the smart community. Copyright © 2017 John Wiley & Sons, Ltd.     

Exchange Bias, Memory and Freezing Effects in NiFe2O4 Nanoparticles

Single-phase NiFe₂O₄ nanoparticles embedded in SiO₂ matrix have been synthesized by sol-gel method. Average particle size lies in the range 8?C12 nm. Magnetic measurements are taken by SQUID-magnetometer with a maximum applied field of ??7 T and temperature down to 4.2 K. An exchange bias effect in nanoparticles is due to the existence of strong core-shell interactions and it vanishes as the particle size decreases (<4?nm). Spin disorder and frustration appear at the core-shell interface due to broken bonds on the surface. We have observed the exchange bias effect via hysteresis loop shift, when the sample is cooled in an applied field of 5?T. In both AC and DC fields, our system exhibit memory effects at the halted temperatures. Furthermore, a sharp increase of coercivity at low temperatures (<50 K) is observed, which is attributed to increased surface anisotropy at low temperatures. For saturation magnetization vs. temperature data, Bloch??s T ^3/2 law (M(T)=M(0)?(1?BT ^b )) is fitted well and yields: B=4??10^?05 K^?3/2 and b=1.53. All these measurements prove the presence of exchange bias (core-shell interactions), memory effects, validation of Bloch??s T ^3/2 law and freezing effects in nickel ferrite nanoparticles dispersed in SiO₂ matrix.