Molecular dynamics simulations of PPI dendrimer-drug complexes

Dendrimeric nanoparticles are potential drug delivery devices which can enhance the solubility of hydrophobic drugs, thus increasing their bioavailability and sustained release action. A quantitative understanding of the dendrimer-drug interactions can give valuable insight into the solubility and release profile of hydrophobic drug molecules in various solvent conditions. Fully atomistic molecular dynamics (MD) simulations have been performed to study the interactions of G5 PPIEDA (G5 ethylenediamine cored poly(propylene imine)) dendrimer and two well known drugs (Famotidine and Indomethacin) at different pH conditions. The study suggested that at low pH the dendrimer-drug complexes are thermodynamically unstable as compared to neutral and high pH conditions. Calculated Potential of Mean Force (PMF) by umbrella sampling showed that the release of drugs from the dendrimer at low pH is spontaneous, median release at neutral pH and slow release at high pH. In addition, Molecular Mechanics Poisson-Boltzmann Surface Area (MM-PBSA) binding free energy calculations were also performed at each umbrella sampling window to identify the various energy contributions. To understand the effect of dendrimer chemistry and topology on the solubility and release profile of drugs, this study is extended to explore the solubility and release profile of phenylbutazone drug complexed with G3 poly(amidoamine) and G4 diaminobutane cored PPI dendrimers. The results indicate that the pH-induced conformational changes in dendrimer, ionization states, dendrimer type and pK(a) of the guest molecules influence the free energy barrier and stability of complexation, and thus regulate drug loading, solubility and release.

Dielectric and electrical conductivity properties of multi-stage spark plasma sintered HA-CaTiO3 composites and comparison with conventionally sintered materials

One of the different issues limiting the wider application of monolithic hydroxyapatite (HA) as an ideal bone replacement material is the lack of reasonably good electrical transport properties. The comprehensive electrical property characterization to evaluate the efficacy of processing parameters in achieving the desired combination of electroactive properties is considered as an important aspect in the development of HA-based bioactive material. In this perspective, the present work reports the temperature (RT-200 degrees C) and frequency (100 Hz-1 MHz) dependent dielectric properties and AC conductivity for a range of HA-CaTiO3 (HA-CT) composites, densified using both conventional pressureless sintering in air as well as spark plasma sintering in vacuum. Importantly, the AC conductivity of spark plasma sintered ceramics similar to upto 10(-5) (Omega cm)(-1)] are found to be considerably higher than the corresponding pressureless sintered ceramics similar to upto 10(-8) (Omega cm)(-1)]. Overall, the results indicate the processing route dependent functional properties of HA-CaTiO3 composites as well as related advantages of spark plasma sintering route. (C) 2013 Elsevier Ltd. All rights reserved.

Probabilistic least squares approach to ordinal regression

This paper proposes a novel approach to solve the ordinal regression problem using Gaussian processes. The proposed approach, probabilistic least squares ordinal regression (PLSOR), obtains the probability distribution over ordinal labels using a particular likelihood function. It performs model selection (hyperparameter optimization) using the leave-one-out cross-validation (LOO-CV) technique. PLSOR has conceptual simplicity and ease of implementation of least squares approach. Unlike the existing Gaussian process ordinal regression (GPOR) approaches, PLSOR does not use any approximation techniques for inference. We compare the proposed approach with the state-of-the-art GPOR approaches on some synthetic and benchmark data sets. Experimental results show the competitiveness of the proposed approach.

Multi-task learning using shared and task specific information

Multi-task learning solves multiple related learning problems simultaneously by sharing some common structure for improved generalization performance of each task. We propose a novel approach to multi-task learning which captures task similarity through a shared basis vector set. The variability across tasks is captured through task specific basis vector set. We use sparse support vector machine (SVM) algorithm to select the basis vector sets for the tasks. The approach results in a sparse model where the prediction is done using very few examples. The effectiveness of our approach is demonstrated through experiments on synthetic and real multi-task datasets.

Validation based sparse Gaussian processes for ordinal regression

This paper proposes a sparse modeling approach to solve ordinal regression problems using Gaussian processes (GP). Designing a sparse GP model is important from training time and inference time viewpoints. We first propose a variant of the Gaussian process ordinal regression (GPOR) approach, leave-one-out GPOR (LOO-GPOR). It performs model selection using the leave-one-out cross-validation (LOO-CV) technique. We then provide an approach to design a sparse model for GPOR. The sparse GPOR model reduces computational time and storage requirements. Further, it provides faster inference. We compare the proposed approaches with the state-of-the-art GPOR approach on some benchmark data sets. Experimental results show that the proposed approaches are competitive.

Modeling and analysis of MOS capacitor controlled by independent double gates

This paper, for the first time, explores the charcatersictics of MOS capacitor controlled by independent double gates by numerical simulation and analytical modeling for its possible use in RF circuit design as a varactor. By numerical simulation it is shown how the quasi-static and non-quasi-static characteristics of the first gate capacitance could be tuned by the second gate biases. Effect of body doping and energy quantization are also discussed in this regard. A semi-empirical quasi-static model is also developed by using the existing incomplete Poisson solution of independent double gate transistors. Proposed model, which is valid from accumulation to inversion, is shown to have excellent agreement with numerical simulation for practical bias conditions.

Experimental Study on Substrate Noise Effects of a Pulsed Clocking Scheme on PLL Performance

In this brief, the substrate noise effects of a pulsed clocking scheme on the output spur level, the phase noise, and the peak-to-peak (Pk-Pk) deterministic period jitter of an integer-N charge-pump phase-locked loop (PLL) are demonstrated experimentally. The phenomenon of noise coupling to the PLL is also explained through experiments. The PLL output frequency is 500 MHz and it is implemented in the 0.13-mu m CMOS technology. Measurements show a reduction of 12.53 dB in the PLL output spur level at an offset of 5 MHz and a reduction of 107 ps in the Pk-Pk deterministic period jitter upon reducing the duty cycle of the signal injected into the substrate from 50% to 20%. The results of the analyses suggest that using a pulsed clocking scheme for digital systems in mixed-signal integration along with other isolation techniques helps reduce the substrate noise effects on sensitive analog/radio-frequency circuits.

Capacity of a Gaussian MAC with energy harvesting transmit nodes

We consider a Gaussian multiple access channel (GMAC) where the users are sensor nodes powered by energy harvesters. The energy harvesters may have finite or infinite buffer to store the harvested energy. First, we find the capacity region of a GMAC powered by transmit nodes with an infinite energy buffer. Next, we consider a GMAC with the transmitting nodes equipped with a finite energy buffer. Initially we assume perfect knowledge of the buffer state information at both the encoders and the decoder. We provide an achievable region for this case. We also generalize the achievable region when only partial information about buffer state is available at both the encoders and the decoder.

Selective and Efficient Detection of Nitro-Aromatic Explosives in Multiple Media including Water, Micelles, Organogel, and Solid Support

Selective detection of nitro-aromatic compounds (NACs) at nanomolar concentration is achieved for the first time in multiple media including water, micelles or in organogels as well as using test strips. Mechanism of interaction of NACs with highly fluorescent p-phenylenevinylene-based molecules has been described as the electron transfer phenomenon from the electron-rich chromophoric probe to the electron deficient NACs. The selectivity in sensing is guided by the pK(a) of the probes as well as the NACs under consideration. TNP-induced selective gel-to-sol transition in THF medium is also observed through the reorganization of molecular self-assembly and the portable test trips are made successfully for rapid on-site detection purpose.

Nonlinear and linear autopilot performance comparison of tactical flight vehicle

n this paper, three-axis autopilot of a tactical flight vehicle has been designed for surface to air application. Both nonlinear and linear design synthesis and analysis have been carried out pertaining to present flight vehicle. Lateral autopilot performance has been compared by tracking lateral acceleration components along yaw and pitch plane at higher angles of attack in presence of side force and aerodynamic nonlinearity. The nonlinear lateral autopilot design is based on dynamic inversion and time scale separation principle. The linear lateral autopilot design is based on three-loop topology. Roll autopilot robustness performance has been enhanced against unmodeled roll disturbances by backstepping technique. Complete performance comparison results of both nonlinear and linear controller based on six degrees of freedom simulation along with stability and robustness studies with respect to plant parameter variation have been discussed in the paper.

Trends in aerosol optical depth over Indian region: Potential causes and impact indicators

The first regional synthesis of long-term (back to similar to 25 years at some stations) primary data (from direct measurement) on aerosol optical depth from the ARFINET (network of aerosol observatories established under the Aerosol Radiative Forcing over India (ARFI) project of Indian Space Research Organization over Indian subcontinent) have revealed a statistically significant increasing trend with a significant seasonal variability. Examining the current values of turbidity coefficients with those reported similar to 50 years ago reveals the phenomenal nature of the increase in aerosol loading. Seasonally, the rate of increase is consistently high during the dry months (December to March) over the entire region whereas the trends are rather inconsistent and weak during the premonsoon (April to May) and summer monsoon period (June to September). The trends in the spectral variation of aerosol optical depth (AOD) reveal the significance of anthropogenic activities on the increasing trend in AOD. Examining these with climate variables such as seasonal and regional rainfall, it is seen that the dry season depicts a decreasing trend in the total number of rainy days over the Indian region. The insignificant trend in AOD observed over the Indo-Gangetic Plain, a regional hot spot of aerosols, during the premonsoon and summer monsoon season is mainly attributed to the competing effects of dust transport and wet removal of aerosols by the monsoon rain. Contributions of different aerosol chemical species to the total dust, simulated using Goddard Chemistry Aerosol Radiation and Transport model over the ARFINET stations, showed an increasing trend for all the anthropogenic components and a decreasing trend for dust, consistent with the inference deduced from trend in Angstrom exponent.

Tailor-Made Hollow Silver Nanoparticle Cages Assembled with Silver Nanoparticles: An Efficient Catalyst for Epoxidation

A novel approach toward the synthesis of hollow silver nanoparticle (NP) cages built with building blocks of silver NPs by layer-by-layer (LbL) assembly is demonstrated. The size of the NP cage depends on the size of template used for the LbL assembly. The microcages showed a uniform distribution of spherical silver nanoparticles with an average diameter of 20 +/- 5 nm, which increased to 40 +/- S nm when the AgNO3 concentration was increased from 25 to 50 mM. Heat treatment of the polyelectrolyte capsules at 80 degrees C near their pK(a) values yielded intact nano/micro cages. These cages produced a higher conversion for the epoxidation of olefins and maintained their catalytic activity even after four successive uses. The nanocages exhibited unique and attractive characteristics for metal catalytic systems, thus offering the scope for further development as heterogeneous catalysts.

Combinatorial Exploration of the Structural Landscape of Acid-Pyridine Cocrystals

The structural landscape of acid-pyridine cocrystals is explored by adopting a combinatorial matrix method with 4-substituted benzoic acids and 4-substituted pyridines. The choice of the system restricts the primary synthon to the robust acid-pyridine entity. This methodology accordingly provides hints toward the formation of secondary synthons. The pK(a) rule is validated in the landscape by taking all components of the matrix together and exploring it as a whole. Along with the global features, the exploration of landscapes reveals some local features. Apart from the identification of secondary synthons, it also sheds light on the propensity of hydration in cocrystals, synthon competition, and certain topological similarities. The method described here combines two approaches, namely, database analysis and high throughput crystallography, to extract more information with minimal extra experimental effort.

Influence of exhaust gas heating and L/D ratios on the discharge efficiencies for an activated carbon natural gas storage system

A transient 2D axi-symmetric and lumped parameter (LP) model with constant outflow conditions have been developed to study the discharge capacity of an activated carbon bed. The predicted discharge times and variations in bed pressure and temperature are in good agreement with experimental results obtained from a 1.82 l adsorbed natural gas (ANG) storage system. Under ambient air conditions, a maximum temperature drop of 29.5 K and 45.5 K are predicted at the bed center for discharge rates of 1.0 l min(-1) and 5.0 l min(-1) respectively. The corresponding discharge efficiencies are 77% and 71.5% respectively with discharge efficiencies improving with decreasing outflow rates. Increasing the LID ratio from 1.9 to 7.8 had only a marginal increase in the discharge efficiency. Forced convection (exhaust gas) heating had a significant effect on the discharge efficiency, leading to efficiencies as high as 92.8% at a discharge of 1.0 l min(-1) and 88.7% at 5 l min(-1). Our study shows that the LP model can be reliably used to obtain discharge times due to the uniform pressure distributions in the bed. Temperature predictions with the LP model were more accurate at ambient conditions and higher discharge rates, due to greater uniformity in bed temperatures. For the low thermal conductivity carbon porous beds, our study shows that exhaust gas heating can be used as an effective and convenient strategy to improve the discharge characteristics and performance of an ANG system. (C) 2013 Elsevier Ltd. All rights reserved.