Effect of Biopolymer Blend Matrix on Structural,Optical and Biological Properties of Chitosan–Agar Blend ZnO Nanocomposites

The present research is focused on the development of ecofriendly biopolymer blend based nanocomposites to enhance the effect of cytotoxic activity. Novel eco-friendly synthesis of pure Chitosan–Agar blend and Chitosan–Agar/ZnO nanocomposites was successfully synthesized by in-situ chemical synthesis method. The influence of Chitosan–Agar (1:1 wt/wt%) concentrations (0.1, 0.5, 1 and 3 g) was studied. The presence of ZnO nanoparticles in Chitosan–Agar polymer matrix was confirmed by UV, FTIR, XRD, FESEM, EDAX and TEM. The crystallite size of the nanocomposites in the range of 12–17 nm is observed from XRD analysis. PL and UV reveal that Nanocomposites shows an blue shift by increase in the blend concentrations. TEM analysis shows that 0.1 and 3 g of Chitosan–Agar/ZnO Nanocomposites are in spindle and spherical shape with polycrystalline nature. The prepared Nanocomposites shows the respectable Antibacterial activity against Gram-positive (Staphylococcus aureus and Bacillus subtilis) and Gram-negative (Pseudomonas aureginosa and Klebsilla pneumonia) bacteria. The potential toxicity of Chitosan–Agar/ZnO nanocomposites was studied for normal (L929) and breast cancer cell line (MB231). The result of this investigation shows that the Chitosan–Agar/ZnO nanocomposites deliver a dose dependent toxicity in normal and cancer cell line.     

Analysis of healthy and tumour DNA methylation distributions in kidney‐renal‐clear‐cell‐carcinoma using Kullback–Leibler and Jensen–Shannon distance measures

DNA methylation is an epigenetic phenomenon in which methyl groups get bonded to the cytosines of the DNA molecule altering the expression of the associated genes. Cancer is linked with hypo or hyper‐methylation of specific genes as well as global changes in DNA methylation. In this study, the authors study the probability density function distribution of DNA methylation in various significant genes and across the genome in healthy and tumour samples. They propose a unique ‘average healthy methylation distribution’ based on the methylation values of several healthy samples. They then obtain the Kullback–Leibler and Jensen–Shannon distances between methylation distributions of the healthy and tumour samples and the average healthy methylation distribution. The distance measures of the healthy and tumour samples from the average healthy methylation distribution are compared and the differences in the distances are analysed as possible parameters for cancer. A classifier trained on these values was found to provide high values of sensitivity and specificity. They consider this to be a computationally efficient approach to predict tumour samples based on DNA methylation data. This technique can also be improvised to consider other differentially methylated genes significant in cancer or other epigenetic diseases.Inspec keywords: cancer, tumours, DNA, genetics, molecular biophysicsOther keywords: tumour DNA methylation distributions, kidney‐renal‐clear‐cell‐carcinoma, Kullback–Leibler distance measure, Jensen–Shannon distance measure, epigenetic phenomenon, methyl groups, cytosines, hyper‐methylation, probability density function distribution, average healthy methylation distribution     

Performance of relay-aided downlink DS-CDMA system using transmitter preprocessing based on feedback information

In this contribution, we investigate the performance of relay-aided downlink (DL) direct sequence code division multiple access (DS-CDMA) system with multi-user transmitter preprocessing (MUTP) based on vector quantized channel impulse responses (VQ-CIRs). Specifically, the CIRs are estimated with the aid of training-sequence based estimation technique at each of the relays. These estimated CIRs are then VQ and the magnitudes and phases are fed back to the base station (BS) through feedback channels, that conflict noise and fading. At the BS, the CIRs recovered using a linear detector are then exploited to formulate the preprocessing matrix to mitigate the DL multi-user interference at the relays. Our study shows that the attainable bit-error-rate (BER) degrades when noise and fading contaminated VQ-CIRs are invoked to realize the preprocessing matrix. Nevertheless, the resultant BER performance of the MUTP based on VQ-CIRs acquired via ideal feedback remains close to that achieved with perfect CIRs assumption.     

Cutting Chai, Jugaad, and Here Pheri: towards UbiComp for a global community

This paper attempts to re-imagine ubiquitous computing and technologies for populations in resource-poor, digitally unstable, and diversely literate environments. Extending UbiComp’s frame of reference to include any ICT with a ubiquitous presence, we articulate how technologies are adopted, accessed, used, and diffused in three urban slums of India. We showcase important local practices surrounding technology diffusion and their widespread implications for entrenching ICT use through sharing, learning, training, renewing, and extending use and access. We do this by discussing three main processes at the intersection of technology consumption, resource constraints, and cultural production specific to low-income communities in India: Cutting Chai or sharing technology ownership and maintenance to cut costs, Jugaad or workarounds in the face of resource constraints, and Here Pheri or gray market activity that subvert legal business processes. We also suggest a few design principles to provoke new kinds of inquiry and practice in the design and implementation of UbiComp for a global community.     

Influence of substrate temperature on CeF3 thin films prepared by thermal evaporation

We have investigated the effect of substrate temperature on the structural, compositional and electrical properties of cerium fluoride thin films prepared by thermal evaporation method. The structure of cerium fluoride is hexagonal and the growth orientation changes with increase in substrate temperature. The substrate temperature favors the growth of vertical nanorods on the surface of the thin films. The compositional analysis confirms the formation of cerium oxyfluoride, leading to free fluoride ions. Electrical conductivity increases with increasing substrate temperature.     

Deep Neural Artificial Intelligence for IoT Based Tele Health Data Analytics

Tele health utilizes information and communication mechanisms to convey medical information for providing clinical and educational assistances. It makes an effort to get the better of issues of health service delivery involving time factor, space and laborious terrains, validating cost-efficiency and finer ingress in both developed and developing countries. Tele health has been categorized into either real-time electronic communication, or store-and-forward communication. In recent years, a third-class has been perceived as remote healthcare monitoring or tele health, presuming data obtained via Internet of Things (IOT). Although, tele health data analytics and machine learning have been researched in great depth, there is a dearth of studies that entirely concentrate on the progress of ML-based techniques for tele health data analytics in the IoT healthcare sector. Motivated by this fact, in this work a method called, Weighted Bayesian and Polynomial Taylor Deep Network (WB-PTDN) is proposed to improve health prediction in a computationally efficient and accurate manner. First, the Independent Component Data Arrangement model is designed with the objective of normalizing the data obtained from the Physionet dataset. Next, with the normalized data as input, Weighted Bayesian Feature Extraction is applied to minimize the dimensionality involved and therefore extracting the relevant features for further health risk analysis. Finally, to obtain reliable predictions concerning tele health data analytics, First Order Polynomial Taylor DNN-based Feature Homogenization is proposed that with the aid of First Order Polynomial Taylor function updates the new results based on the result analysis of old values and therefore provides increased transparency in decision making. The comparison of proposed and existing methods indicates that the WB-PTDN method achieves higher accuracy, true positive rate and lesser response time for IoT based tele health data analytics than the traditional methods.

     

Desorption Kinetics of Polyether Lubricants from Surfaces

Desorption or evaporation is one of the mechanisms for loss of perfluoropolyalkylether (PFPE) lubricants from the surfaces of data storage media. One approach to minimizing PFPE loss to desorption is the use of lubricants with increasing molecular weight or increasing average chain length. In order to understand the effects of chain length on the lubricant evaporation kinetics we have studied the desorption kinetics of monolayer films of oligomeric ethers with varying chain length adsorbed on the surface of graphite. The desorption pre-exponents, v, and desorption barriers, E _des , have been measured for poly(ethylene glycol) dimethyl ethers, CH₃O(CH₂CH₂O) _m CH₃, with m=1,2,3,4,8 and 10. These are models for the PFPE known as Fomblin Z, which has a structure CF₃O(CF₂CF₂O) _x (CF₂O) _y CF₃. The results show that the desorption pre-exponents are independent of chain length and have an average value of v=10^18.7±0.3 s^–1. The E _des for the poly(ethylene glycol) dimethyl ethers vary non-linearly with chain length and can be fit with a power law expression of the form E _des =a+bN , where N is the total number of atoms in the oligomer backbone (N=3m+3) and the scaling exponent has a value of 1/2. This non-linear dependence of E _des on chain length has also been observed in recent studies of the desorption kinetics of straight chain alkanes from graphite. A desorption mechanism is described that explains the non-linearity of E _des for the poly(ethylene glycol) dimethyl ethers. The implication for the lifetime of lubricants on data storage media is that the long chain PFPE lubricants desorb more rapidly than one might expect based on simple linear scaling of the E _des of lower molecular weight PFPEs.     

Novel Li4Ti5O12/Sn nano-composites as anode material for lithium ion batteries

Li₄Ti₅O₁₂/Sn nano-composites have been prepared as anode material for lithium ion batteries by high-energy mechanical milling method. Structure of the samples has been characterized by X-ray diffraction (XRD), which reveals the formation of phase-pure materials. Scanning electron microscope (SEM) and transmission electron microscope (TEM) suggests that the primary particles are around 100 nm size. The local environment of the metal cations is confirmed by Fourier transform infrared (FT-IR) and the X-ray photoelectron spectroscopy (XPS) confirms that titanium is present in Ti⁴⁺ state. The electrochemical properties have been evaluated by galvanostatic charge/discharge studies. Li₄Ti₅O₁₂/Sn-10% composite delivers stable and enhanced discharge capacity of 200 mAh g⁻¹ indicates that the electrochemical performance of Li₄Ti₅O₁₂/Sn nano-composites is associated with the size and distribution of the Sn particles in the Li₄Ti₅O₁₂ matrix. The smaller the size and more homogeneous dispersion of Sn particles in the Li₄Ti₅O₁₂ matrix exhibits better cycling performance of Li₄Ti₅O₁₂/Sn composites as compared to bare Li₄Ti₅O₁₂ and Sn particles. Further, Li₄Ti₅O₁₂ provides a facile microstructure to fairly accommodate the volume expansion during the alloying and dealloying of Sn with lithium.