Metallic nanoparticle synthesised by biological route: safer candidate for diverse applications

Biological synthesis of nanoparticles (NPs) involves greater prospect; however, a detailed review is required for ecofriendly, faster and stable NP formulation in large scale for different commercial applications. The present article highlighted recent updates on biological route of single and bimetallic NP synthesis wherein the chemical reducing agents are eliminated and biological entities are utilised to convert metal ions to NPs. Application of the biological reducing agents ranging from bacteria to fungi and even natural plant extracts have emerged as eco‐friendly and cost‐effective routes for the synthesis of metal nanomaterials. Potential applications of such NPs, a wide range of analytical techniques used for characterisation and factors influencing the synthesis of NPs are focused. Further, elucidation of the mechanisms associated with the NP formation using microorganisms, as well as plant‐based materials are analysed which would be helpful for wide range of readers in the field of NP research for future selection and commercial implementation.Inspec keywords: nanoparticles, nanobiotechnology, nanofabrication, reviews, microorganisms, botanyOther keywords: metallic nanoparticle, biological synthesis, chemical reducing agents, biological reducing agents, bacteria, fungi, natural plant extracts, eco‐friendly synthesis, cost‐effective synthesis, microorganisms, plant‐based materials, review     

Offspring of normal and diabetic rats fed saturated fat in pregnancy demonstrate vascular dysfunction

BACKGROUND: Disturbances of the in utero environment may "program" for disease in later life. In this study, we determined whether dietary fat supplementation and/or diabetes in pregnancy can adversely affect vascular function in the offspring. METHODS AND RESULTS: Female Sprague-Dawley rats were fed a breeding diet or a diet high in saturated fat (30% wt/wt) for 10 days before mating, throughout pregnancy, and postpartum. Endothelium-dependent relaxation to acetylcholine was blunted in isolated femoral arteries of 15-day-old weanling pups from dams fed the 30%-fat diet. Endothelial dysfunction and enhanced constrictor responses to norepinephrine were also observed in an additional study of 60-day-old offspring of dams fed 20% saturated fat. Rats with streptozotocin-induced diabetes were also fed saturated fat during pregnancy. Femoral arteries from their 15-day-old offspring showed impairment of endothelium-dependent dilation and enhanced constrictor responses to norepinephrine and the thromboxane mimetic U46619 compared with young offspring of high-fat-fed normal dams. The 30%-fat diet was also deleterious to vascular function in the maternal diabetic animals when assessed in mesenteric arteries 16 days postpartum. CONCLUSIONS: A high-fat diet in pregnancy led to vascular dysfunction in rat weanlings and young adult offspring. Vascular function further deteriorated in weanlings if the maternal rat was diabetic.     

Fault containment in weakly stabilizing systems

Research on fine tuning stabilization properties has received attention for more than a decade. This paper presents probabilistic algorithms for fault containment. We demonstrate two exercises in fault containment in a weakly stabilizing system, which expedite recovery from single failures, and confine the effect of any single fault to the constant-distance neighborhood of the faulty process. The most significant aspect of the algorithms is that the fault gap, defined as the smallest interval after which the system is ready to handle the next single fault with the same efficiency, is independent of the network size. We argue that a small fault gap increases the availability of the fault-free system.     

Interaction of CO with hydrous ruthenium oxide and development of a chemoresistive ambient CO sensor

Hydrated ruthenium oxide (RuO_x(OH)_y), the material of interest in this study was prepared by reaction of an aqueous solution of ruthenium chloride with base. This material was amorphous, made up of 20-50 nm particles and contains Ru(III) and Ru(IV), as determined by X-ray photoelectron spectroscopy. The conductivity of thick films of RuO_x(OH)_y decreased in the presence of CO in a background of air and this change was reversible. Infrared spectroscopy showed the formation of carbonates in the presence of CO, which disappeared upon replacement of CO with O₂. Upon heating RuO_x(OH)_y, there was a gradual conversion to crystalline RuO₂ beyond 200 °C. With these heated materials, the resistance change in the presence of CO at room temperature also gradually diminished. We propose that oxidation of CO on RuO_x(OH)_y leads to reduction of the ruthenium and a decrease in conductivity. With the conversion to crystalline RuO₂ upon heating, the material becomes metallic and conductivity changes are diminished. The change in conductivity of RuO_x(OH)_y with CO provides a convenient platform for an ambient CO sensor. Such a device also does not show interference from hydrocarbons (2000 ppm), ammonia (150 ppm), CO₂ (2000 ppm), NO (15 ppm) and NO₂ (15 ppm).     

Examination of Au/SnO2 core-shell architecture nanoparticle for low temperature gas sensing applications

Au/SnO₂ core-shell structure nanoparticles (NPs) were synthesized using two methods, microwave and conventional precipitation. In both cases, the size of the Au core was 12-18 nm and the thickness of the SnO₂ shell was 8-12 nm. The particle size of SnO₂ synthesized using the microwave and precipitation method was 3-5 nm and 1-2 nm, respectively. Upon heating to 400-600 °C, both particles maintained their core-shell morphology but the smaller SnO₂ particles prepared using the precipitation method were more sintered. The resistance changes in films of these particles were measured as a function of CO concentration. The Au/SnO₂ particles prepared using the microwave method showed higher sensor response than those prepared using the precipitation method, even providing a significant signal at testing temperatures approaching ambient conditions, thereby affording a new class of material for gas sensing. Both sets of core-shell particles showed higher sensor response than the SnO₂ nanoparticles. The role of the Au core as a catalyst in improving the adsorption and oxidation of CO gas is important for improving the low temperature response. In addition, the maintenance of the size of SnO₂ in the microwave method during sintering contributed to the higher response towards CO sensing.     

Towards nonideal iris recognition based on level set method, genetic algorithms and adaptive asymmetrical SVMs

We present algorithms for iris segmentation, feature extraction and selection, and iris pattern matching. To segment the inner boundary from a nonideal iris image, we apply a level set based curve evolution approach using the edge stopping function, and to detect the outer boundary, we employ the curve evolution approach using the regularized Mumford-Shah segmentation model with an energy minimization algorithm. Daubechies wavelet transform (DBWT) is used to extract the textural features, and genetic algorithms (GAs) are deployed to select the subset of informative features by combining the valuable outcomes from the multiple feature selection criteria without compromising the recognition accuracy. To speed up the matching process and to control the misclassification error, we apply a combined approach called the adaptive asymmetrical support vector machines (AASVMs). The parameter values of SVMs are also optimized in order to improve the overall generalization performance. The verification and identification performance of the proposed scheme is validated using the UBIRIS Version 2, the ICE 2005, and the WVU datasets.     

Classification Based on Hybridization of Parametric and Nonparametric Classifiers

Parametric methods of classification assume specific parametric models for competing population densities (e.g., Gaussian population densities can lead to linear and quadratic discriminant analysis) and they work well when these model assumptions are valid. Violation in one or more of these parametric model assumptions often leads to a poor classifier. On the other hand, nonparametric classifiers (e.g., nearest-neighbor and kernel-based classifiers) are more flexible and free from parametric model assumptions. But, the statistical instability of these classifiers may lead to poor performance when we have small numbers of training sample observations. Nonparametric methods, however, do not use any parametric structure of population densities. Therefore, even when one has some additional information about population densities, that important information is not used to modify the nonparametric classification rule. This paper makes an attempt to overcome these limitations of parametric and nonparametric approaches and combines their strengths to develop some hybrid classification methods. We use some simulated examples and benchmark data sets to examine the performance of these hybrid discriminant analysis tools. Asymptotic results on their misclassification rates have been derived under appropriate regularity conditions.     

ILP-assisted de novo drug design

The accurate and early detection of epileptic seizures in continuous electroencephalographic (EEG) data has a growing role in the management of patients with epilepsy. Early detection allows for therapy to be delivered at the start of seizures and for caregivers to be notified promptly about potentially debilitating events. The challenge to detecting epileptic seizures, however, is that seizure morphologies exhibit considerable inter-patient and intra-patient variability. While recent work has looked at addressing the issue of variations across different patients (inter-patient variability) and described patient-specific methodologies for seizure detection, there are no examples of systems that can simultaneously address the challenges of inter-patient and intra-patient variations in seizure morphology. In our study, we address this complete goal and describe a multi-task learning approach that trains a classifier to perform well across many kinds of seizures rather than potentially overfitting to the most common seizure types. Our approach increases the generalizability of seizure detection systems and improves the tradeoff between latency and sensitivity versus false positive rates. When compared against the standard approach on the CHB–MIT multi-channel scalp EEG data, our proposed method improved discrimination between seizure and non-seizure EEG for almost 83 % of the patients while reducing false positives on nearly 70 % of the patients studied.     

A revisit to non-maximally entangled mixed states: teleportation witness,noisy channel and discord

We constructed a class of non-maximally entangled mixed states (Adhikari et al. in Quantum Inf Comput 10:0398, 2010) and extensively studied their entanglement properties and also their usefulness as teleportation channels. In this article, we have revisited our constructed state and have studied it from three different perspectives. Since every entangled state is associated with a witness operator, we have found a suitable entanglement as well as teleportation witness operator for our non-maximally entangled mixed states. We considered the noisy channel’s effects on our constructed states to see how much it affects the states’ capacities as teleportation channels. For this purpose, we have mainly focussed on amplitude damping channel. A comparative study on concurrence and quantum discord of our constructed state of Adhikari et al. (2010) has also been carried out here.