2-(Phenylazo)pyridineplatinum(II) Catecholates Showing Photocytotoxicity, Nuclear Uptake, and Glutathione-Triggered Ligand Release

Platinum(II) complexes Pt(pap)(an-cat)] (1) and Pt(pap)(py-cat)] (2) with 2-(phenylazo)pyridine (pap), 4-2-(anthracen-9-ylmethylene)amino]ethyl]benzene-1,2-diol (H(2)an-cat), and 4-2-(pyren-1-ylmethylene)amino]ethyl]benzene-1,2-diol (H2py-cat) were prepared, and their photoinduced cytotoxicity was studied. The complexes were found to release catecholate ligand in the presence of excess glutathione (GSH), resulting in cellular toxicity in the cancer cells. The catecholate complex Pt(pap)(cat)] (3) was prepared and used as a control. Complex 3, which is structurally characterized by X-ray crystallography, has platinum(II) in a distorted square-planar geometry. The complexes are redox-active, showing responses near 0.6 and 1.0 V versus SCE in N,N-dimethylformamide/0.1 M tetrabutylammonium perchlorate corresponding to a two-step catechol oxidation process and at -0.3 and -1.3 V for reduction of the pap ligand. Complex 1 showed remarkable light-induced cytotoxicity in HaCaT (human skin keratinocytes) and MCF-7 (human breast cancer) cells, giving IC50 value of similar to 5 mu M in visible light of 400-700 nm and >40 mu M in the dark. The 2',7'-dichlorofluorescein diacetate (DCFDA) assay showed the generation of reactive oxygen species (ROS), which seems to trigger apoptosis, as is evident from the annexin V-fluorescein isothiocyanate (FITC)/propidium iodide (PI) assay. The fluorescence microscopic images showed significant nuclear localization of the complexes and free ligands. A mechanistic study revealed possible reduction of the coordinated azo bond of pap by cellular GSH, releasing the catecholate ligand and resulting in remarkable photochemotherapeutic action of the complexes.

On the Two-Step Mechanism for Synthesis of Transition-Metal Nanoparticles

The two-step particle synthesis mechanism, also known as the Finke-Watzky (1997) mechanism, has emerged as a significant development in the field of nanoparticle synthesis. It explains a characteristic feature of the synthesis of transition metal nanoparticles, an induction period in precursor concentration followed by its rapid sigmoidal decrease. The classical LaMer theory (1950) of particle formation fails to capture this behavior. The two-step mechanism considers slow continuous nucleation and autocatalytic growth of particles directly from precursor as its two kinetic steps. In the present work, we test the two-step mechanism rigorously using population balance models. We find that it explains precursor consumption very well, but fails to explain particle synthesis. The effect of continued nucleation on particle synthesis is not suppressed sufficiently by the rapid autocatalytic growth of particles. The nucleation continues to increase breadth of size distributions to unexpectedly large values as compared to those observed experimentally. A number of variations of the original mechanism with additional reaction steps are investigated next. The simulations show that continued nucleation from the beginning of the synthesis leads to formation of highly polydisperse particles in all of the tested cases. A short nucleation window, realized with delayed onset of nucleation and its suppression soon after in one of the variations, appears as one way to explain all of the known experimental observations. The present investigations clearly establish the need to revisit the two-step particle synthesis mechanism.

Absolute/Convective Instability Transition in a Backward Facing Step Combustor: Fundamental Mechanism and Influence of Density Gradient

Hydrodynamic instabilities of the flow field in lean premixed gas turbine combustors can generate velocity perturbations that wrinkle and distort the flame sheet over length scales that are smaller than the flame length. The resultant heat release oscillations can then potentially result in combustion instability. Thus, it is essential to understand the hydrodynamic instability characteristics of the combustor flow field in order to understand its overall influence on combustion instability characteristics. To this end, this paper elucidates the role of fluctuating vorticity production from a linear hydrodynamic stability analysis as the key mechanism promoting absolute/convective instability transitions in shear layers occurring in the flow behind a backward facing step. These results are obtained within the framework of an inviscid, incompressible, local temporal and spatio-temporal stability analysis. Vorticity fluctuations in this limit result from interaction between two competing mechanisms-(1) production from interaction between velocity perturbations and the base flow vorticity gradient and (2) baroclinic torque in the presence of base flow density gradients. This interaction has a significant effect on hydrodynamic instability characteristics when the base flow density and velocity gradients are colocated. Regions in the space of parameters characterizing the base flow velocity profile, i.e., shear layer thickness and ratio of forward to reverse flow velocity, corresponding to convective and absolute instability are identified. The implications of the present results on understanding prior experimental studies of combustion instability in backward facing step combustors and hydrodynamic instability in other flows such as heated jets and bluff body stabilized flames is discussed.

REDUCED ORDER MODELLING OF COMBUSTION INSTABILITY IN A BACKWARD FACING STEP COMBUSTOR

This paper develops a fully coupled time domain Reduced Order Modelling (ROM) approach to model unsteady combustion dynamics in a backward facing step combustor The acoustic field equations are projected onto the canonical acoustic eigenmodes of the systems to obtain a coupled system of modal evolution equations. The heat release response of the flame is modelled using the G-equation approach. Vortical velocity fluctuations that arise due to shear layer rollup downstream of the step are modelled using a simplified 1D-advection equation whose phase speed is determined from a linear, local, temporal stability analysis of the shear layer just downstream of the step. The hydrodynamic stability analysis reveals a abrupt change in the value of disturbance phase speed from unity for Re < Re-crit to 0.5 for Re > Re-crit, where Remit for the present geometry was found to be approximate to 10425. The results for self-excited flame response show highly wrinkled flame shapes that are qualitatively similar to those seen in prior experiments of acoustically forced flames. The effect of constructive and destructive interference between the two contributions to flame surface wrinkling results in high amplitude wrinkles for the case when K-c -> 1.

One-step synthesis of a cyclic 2,17-dioxo3,3](4,4 `)biphenylophane and first preparation of a microporous polymer network from a macrocyclic precursor by cyclotrimerization

One-step synthesis of a cyclic 2,17-dioxo3,3](4,4') biphenylophane (MC) was achieved in high yield; its structure was verified by single crystal X-ray analysis. As a first example, a microporous polymer network was formed from macrocycle MC via acid-catalysed cyclotrimerization yielding a BET surface area of ca. 570 m(2) g(-1).

Oriented nanometric aggregates of partially inverted zinc ferrite: One-step processing and tunable high-frequency magnetic properties

In this work, it is demonstrated that the in situ growth of oriented nanometric aggregates of partially inverted zinc ferrite can potentially pave a way to alter and tune magnetocrystalline anisotropy that, in turn, dictates ferromagnetic resonance frequency (f(FMR)) by inducing strain due to aggregation. Furthermore, the influence of interparticle interaction on magnetic properties of the aggregates is investigated. Mono-dispersed zinc ferrite nanoparticles (<5 nm) with various degrees of aggregation were prepared through decomposition of metal-organic compounds of zinc (II) and iron (III) in an alcoholic solution under controlled microwave irradiation, below 200 degrees C. The nanocrystallites were found to possess high degree of inversion (>0.5). With increasing order of aggregation in the samples, saturation magnetization (at 5 K) is found to decrease from 38 emu/g to 24 emu/g, while coercivity is found to increase gradually by up to 100% (525 Oe to 1040 Oe). Anisotropy-mediated shift of f(FMR) has also been measured and discussed. In essence, the result exhibits an easy way to control the magnetic characteristics of nanocrystalline zinc ferrite, boosted with significant degree of inversion, at GHz frequencies. (C) 2015 AIP Publishing LLC.

Rapid and facile one-step synthesis of LiTaO3 nanorods

Single crystalline LiTaO3 nanorods (length of 2.5-6 mu m and diameter of 200-500 nm) were synthesized via a facile molten-salt technique. An individual single crystalline nanorod exhibited a piezoelectric coefficient of 8 pm V-1. An improved optical frequency-doubling efficiency was observed in the case of LiTaO3 nanorods as compared to that of cubic crystallites of similar size.

NUMERICAL STUDIES OF DYNAMO ACTION IN A TURBULENT SHEAR FLOW. I.

We perform numerical experiments to study the shear dynamo problem where we look for the growth of a large-scale magnetic field due to non-helical stirring at small scales in a background linear shear flow in previously unexplored parameter regimes. We demonstrate the large-scale dynamo action in the limit where the fluid Reynolds number (Re) is below unity while the magnetic Reynolds number (Rm) is above unity; the exponential growth rate scales linearly with shear, which is consistent with earlier numerical works. The limit of low Re is particularly interesting, as seeing the dynamo action in this limit would provide enough motivation for further theoretical investigations, which may focus attention on this analytically more tractable limit of Re < 1 compared to the more formidable limit of Re > 1. We also perform simulations in the regimes where (i) both (Re, Rm) < 1, and (ii) Re > 1 and Rm < 1, and compute all of the components of the turbulent transport coefficients (alpha(ij) and alpha(ij)) using the test-field method. A reasonably good agreement is observed between our results and the results of earlier analytical works in similar parameter regimes.

Different Modes of Transactivation of Bacteriophage Mu Late Promoters by Transcription Factor C

Transactivator protein C is required for the expression of bacteriophage Mu late genes from lys, I, P and mom promoters during lytic life cycle of the phage. The mechanism of transcription activation of mom gene by C protein is well understood. C activates transcription at Pmom by initial unwinding of the promoter DNA, thereby facilitating RNA polymerase (RNAP) recruitment. Subsequently, C interacts with the (sic) subunit of RNAP to enhance promoter clearance. The mechanism by which C activates other late genes of the phage is not known. We carried out promoter-polymerase interaction studies with all the late gene promoters to determine the individual step of C mediated activation. Unlike at P-mom, at the other three promoters, RNAP recruitment and closed complex formation are not C dependent. Instead, the action of C at P-lys, P-I, and P-P is during the isomerization from closed complex to open complex with no apparent effect at other steps of initiation pathway. The mechanism of transcription activation of mom and other late promoters by their common activator is different. This distinction in the mode of activation (promoter recruitment and escape versus isomerization) by the same activator at different promoters appears to be important for optimized expression of each of the late genes.

Human Action Recognition in Compressed Domain using PBL-McRBFN Approach

Large variations in human actions lead to major challenges in computer vision research. Several algorithms are designed to solve the challenges. Algorithms that stand apart, help in solving the challenge in addition to performing faster and efficient manner. In this paper, we propose a human cognition inspired projection based learning for person-independent human action recognition in the H.264/AVC compressed domain and demonstrate a PBL-McRBEN based approach to help take the machine learning algorithms to the next level. Here, we use gradient image based feature extraction process where the motion vectors and quantization parameters are extracted and these are studied temporally to form several Group of Pictures (GoP). The GoP is then considered individually for two different bench mark data sets and the results are classified using person independent human action recognition. The functional relationship is studied using Projection Based Learning algorithm of the Meta-cognitive Radial Basis Function Network (PBL-McRBFN) which has a cognitive and meta-cognitive component. The cognitive component is a radial basis function network while the Meta-Cognitive Component(MCC) employs self regulation. The McC emulates human cognition like learning to achieve better performance. Performance of the proposed approach can handle sparse information in compressed video domain and provides more accuracy than other pixel domain counterparts. Performance of the feature extraction process achieved more than 90% accuracy using the PTIL-McRBFN which catalyzes the speed of the proposed high speed action recognition algorithm. We have conducted twenty random trials to find the performance in GoP. The results are also compared with other well known classifiers in machine learning literature.

An Optical How Feature and McFIS Based Approach for 3-Dimensional Human Action Recognition

We propose to develop a 3-D optical flow features based human action recognition system. Optical flow based features are employed here since they can capture the apparent movement in object, by design. Moreover, they can represent information hierarchically from local pixel level to global object level. In this work, 3-D optical flow based features a re extracted by combining the 2-1) optical flow based features with the depth flow features obtained from depth camera. In order to develop an action recognition system, we employ a Meta-Cognitive Neuro-Fuzzy Inference System (McFIS). The m of McFIS is to find the decision boundary separating different classes based on their respective optical flow based features. McFIS consists of a neuro-fuzzy inference system (cognitive component) and a self-regulatory learning mechanism (meta-cognitive component). During the supervised learning, self-regulatory learning mechanism monitors the knowledge of the current sample with respect to the existing knowledge in the network and controls the learning by deciding on sample deletion, sample learning or sample reserve strategies. The performance of the proposed action recognition system was evaluated on a proprietary data set consisting of eight subjects. The performance evaluation with standard support vector machine classifier and extreme learning machine indicates improved performance of McFIS is recognizing actions based of 3-D optical flow based features.

Action Recognition from Motion Capture Data using Meta-cognitive RBF Network Classifier

Action recognition plays an important role in various applications, including smart homes and personal assistive robotics. In this paper, we propose an algorithm for recognizing human actions using motion capture action data. Motion capture data provides accurate three dimensional positions of joints which constitute the human skeleton. We model the movement of the skeletal joints temporally in order to classify the action. The skeleton in each frame of an action sequence is represented as a 129 dimensional vector, of which each component is a 31) angle made by each joint with a fixed point on the skeleton. Finally, the video is represented as a histogram over a codebook obtained from all action sequences. Along with this, the temporal variance of the skeletal joints is used as additional feature. The actions are classified using Meta-Cognitive Radial Basis Function Network (McRBFN) and its Projection Based Learning (PBL) algorithm. We achieve over 97% recognition accuracy on the widely used Berkeley Multimodal Human Action Database (MHAD).

Single-step synthesis of carbon nanotubes/iron/iron oxide composite films through inert-ambient CVD using ferric acetylacetonate as a precursor

We have developed a unique single-step chemical vapor deposition (CVD) route for the synthesis of composite thin films containing carbon nanotubes (CNTs). CVD was carried out in an inert ambient using only iron(III) acetylacetonate as the precursor. Depositions were conducted at 700 degrees C on stainless steel substrates in argon ambient in the absence of any reactive gases (such as oxygen, hydrogen). By changing the deposition parameters, especially the pressure in the CVD reactor, the form of carbon deposited could be changed from amorphous to carbon nanotubes, the latter resulting in Fe-Fe3O4-CNT films. X-ray diffraction, Raman spectroscopy, X-ray photoelectron spectroscopy, and electron microscopy together confirm the formation of the three-component composite and illustrate the nanoscale mixing of the components. Elemental iron formed in this process was protected from oxidation by the co-deposited carbon surrounding it. Irrespective of the substrate used, a composite coating with CNTs was formed under optimum conditions, as verified by analyses of the film formed on polycrystalline alumina and silicon substrates.

Human action recognition in H.264/AVC compressed domain using meta-cognitive radial basis function network

In this paper, we propose a H.264/AVC compressed domain human action recognition system with projection based metacognitive learning classifier (PBL-McRBFN). The features are extracted from the quantization parameters and the motion vectors of the compressed video stream for a time window and used as input to the classifier. Since compressed domain analysis is done with noisy, sparse compression parameters, it is a huge challenge to achieve performance comparable to pixel domain analysis. On the positive side, compressed domain allows rapid analysis of videos compared to pixel level analysis. The classification results are analyzed for different values of Group of Pictures (GOP) parameter, time window including full videos. The functional relationship between the features and action labels are established using PBL-McRBFN with a cognitive and meta-cognitive component. The cognitive component is a radial basis function, while the meta-cognitive component employs self-regulation to achieve better performance in subject independent action recognition task. The proposed approach is faster and shows comparable performance with respect to the state-of-the-art pixel domain counterparts. It employs partial decoding, which rules out the complexity of full decoding, and minimizes computational load and memory usage. This results in reduced hardware utilization and increased speed of classification. The results are compared with two benchmark datasets and show more than 90% accuracy using the PBL-McRBFN. The performance for various GOP parameters and group of frames are obtained with twenty random trials and compared with other well-known classifiers in machine learning literature. (C) 2015 Elsevier B.V. All rights reserved.

Compressed domain human action recognition in H.264/AVC video streams

This paper discusses a novel high-speed approach for human action recognition in H.264/AVC compressed domain. The proposed algorithm utilizes cues from quantization parameters and motion vectors extracted from the compressed video sequence for feature extraction and further classification using Support Vector Machines (SVM). The ultimate goal of the proposed work is to portray a much faster algorithm than pixel domain counterparts, with comparable accuracy, utilizing only the sparse information from compressed video. Partial decoding rules out the complexity of full decoding, and minimizes computational load and memory usage, which can result in reduced hardware utilization and faster recognition results. The proposed approach can handle illumination changes, scale, and appearance variations, and is robust to outdoor as well as indoor testing scenarios. We have evaluated the performance of the proposed method on two benchmark action datasets and achieved more than 85 % accuracy. The proposed algorithm classifies actions with speed (> 2,000 fps) approximately 100 times faster than existing state-of-the-art pixel-domain algorithms.