Size‐Optimized Ultrasmall Porous Silica Nanoparticles Depict Vasculature‐Based Differential Targeting in Triple Negative Breast Cancer

Rapid sequestration and prolonged retention of intravenously injected nanoparticles by the liver and spleen (reticuloendothelial system (RES)) presents a major barrier to effective delivery to the target site and hampers clinical translation of nanomedicine. Inspired by biological macromolecular drugs, synthesis of ultrasmall (diameter ≈12–15 nm) porous silica nanoparticles (UPSNs), capable of prolonged plasma half‐life, attenuated RES sequestration, and accelerated hepatobiliary clearance, is reported. The study further investigates the effect of tumor vascularization on uptake and retention of UPSNs in two mouse models of triple negative breast cancer with distinctly different microenvironments. A semimechanistic mathematical model is developed to gain mechanistic insights into the interactions between the UPSNs and the biological entities of interest, specifically the RES. Despite similar systemic pharmacokinetic profiles, UPSNs demonstrate strikingly different tumor responses in the two models. Histopathology confirms the differences in vasculature and stromal status of the two models, and corresponding differences in the microscopic distribution of UPSNs within the tumors. The studies demonstrate the successful application of multidisciplinary and complementary approaches, based on laboratory experimentation and mathematical modeling, to concurrently design optimized nanomaterials, and investigate their complex biological interactions, in order to drive innovation and translation.     

Electrodeposition of palladium-dotted nickel nanowire networks as a robust self-supported methanol electrooxidation catalyst

Journal of Materials Science - Mass activity and long-term stability are two major issues in current fuel cell catalyst designs. While supported catalysts normally suffer from poor long-term...     

ICT for socio-economic development: A citizens’ perspective

There is a widespread belief that information and communication technologies (ICTs) can play a significant role in the socio-economic development of a developing country. ICT has the potential to affect many aspects of economic and societal activities such as GDP growth, employment, productivity, poverty alleviation, quality of life, education, and healthcare. While the literature provides a myriad of definitions and elements of socio-economic development, the focus tends to be on theoretical conceptualizations from various disciplines and impacts from isolated individual projects. In particular, the impact of ICT on socio-economic development has not been carefully examined from the viewpoint of the ultimate stakeholder, the citizens of a country, who are the final consumers of the technology. This study fills this gap by focusing on the citizens’ view in describing ICT-driven socio-economic development in a developing country. A theoretical framework influenced by the “capabilities approach” was developed to guide this research, and the interpretive stance was used to conduct the study. More specifically, the narrative research method, which is seldom used in IS research but is appropriate for this study, was used. Narratives allow deeper and profound insights into social representations and participants' beliefs about the role of ICT in socio-economic development. Using this methodology, a model of the impact dimensions of socio-economic development is presented.     

Importance of satellite-retrieved rain/no-rain information on short-range weather predictions

The impact of assimilating rain (satellite-retrieved rainfall is greater than zero) and no-rain (satellite-retrieved rainfall is equal to zero) information retrieved from the Tropical Rainfall Measuring Mission (TRMM) 3B42 precipitation is assessed during Indian summer monsoon 2013 using the weather research and forecasting (WRF) model. Daily three parallel experiments are performed with and without satellite rainfall assimilation for short-range weather forecasts. Additional two experiments are performed daily to evaluate the sensitivity of cumulus parameterization on the WRF model predictions when precipitations are used for assimilation. Precipitation assimilation improves the 48 h low-level temperature, moisture, and winds predictions. Rainfall prediction is also improved over central India when satellite-retrieved rainfall information are assimilated compared to without rainfall assimilation (CNT) experiments. More improvements are seen in moisture forecasts when the Kain–Fritsch (KF) cumulus convection parameterization scheme is used against the Grell–Devenyi ensemble (GD) scheme, whereas for temperature and wind speed forecasts the Grell convection parameterization scheme performed better over the Indian region. Overall, precipitation assimilation improved the WRF model analysis and subsequent model forecasts compared with without precipitation assimilation experiments. Results show that no-rain observations also have a significant positive impact on short-range weather forecasts.     

A universal two-way approach for estimating unknown frequencies for unknown number of sinusoids in a signal based on eigenspace analysis of Hankel matrix

We develop a novel approach to estimate the \(n\) unknown constituent frequencies of a noiseless signal that comprises of unknown number, \(n\), of sinusoids of unknown phases and unknown amplitudes. The new two-way approach uses two constraints to accurately estimate the unknown frequencies of the sinusoidal components in a signal. The new approach serves as a verification test for the estimated unknown frequencies through the estimated count of the unknown number of frequencies. The Hankel matrix, of the time domain samples of the signal, is used as a basis for further analysis in the Pisarenko harmonic decomposition. The new constraints, the existence factor and the component factor, have been introduced in the methodology based on the relationships between the components of the sinusoidal signal and the eigenspace of the Hankel matrix. The performance of the developed approach has been tested to correctly estimate any number of frequencies within a signal with or without a fixed unknown bias. The method has also been tested to accurately estimate the very closely spaced low frequencies.     

The effect of plasmon field on the coherent lattice phonon oscillation in electron-beam fabricated gold nanoparticle pairs

By using electron beam lithography, we fabricated pairs of gold nanoparticles with varying interparticle separation. Double-beam femtosecond transient absorption spectroscopy was used to determine the coherent lattice oscillation frequency as a function of the interparticle separation in the presence of the plasmon field excited by the monitoring probe light. We found that the fractional shift in the coherent lattice phonon oscillation frequency follows an exponential decay with respect to the interparticle gap scaled by the disc diameter with the same decay constant as that previously observed for the fractional shift in the surface plasmon electronic oscillation resonance frequency. This strongly suggests that it is the near-field coupling between the particles that shifts both the coherent electronic oscillation (plasmon) frequency and the coherent lattice oscillation (phonon) frequency. The similar trend in the effect of interparticle coupling on the plasmon frequency and the phonon frequency is essentially a reflection of the universal scaling behavior of the distance decay of the interparticle plasmonic near-field. It is shown that the observed decrease in the lattice oscillation frequency with decrease in the interparticle distance is the result of a reduction in the effective free electron density within each nanoparticle pair partner as a result of the polarizing perturbation of the plasmonic field of the other nanoparticle in the pair.     

Normally Oriented Adhesion versus Friction Forces in Bacterial Adhesion to Polymer‐Brush Functionalized Surfaces Under Fluid Flow

Bacterial adhesion is problematic in many diverse applications. Coatings of hydrophilic polymer chains in a brush configuration reduce bacterial adhesion by orders of magnitude, but not to zero. Here, the mechanism by which polymer‐brush functionalized surfaces reduce bacterial adhesion from a flowing carrier fluid by relating bacterial adhesion with normally oriented adhesion and friction forces on polymer (PEG)‐brush coatings of different softness is studied. Softer brush coatings deform more than rigid ones, which yields extensive bond‐maturation and strong, normally oriented adhesion forces, accompanied by irreversible adhesion of bacteria. On rigid brushes, normally oriented adhesion forces remain small, allowing desorption and accordingly lower numbers of adhering bacteria result. Friction forces, generated by fluid flow and normally oriented adhesion forces, are required to oppose fluid shear forces and cause immobile adhesion. Summarizing, inclusion of friction forces and substratum softness provides a more complete mechanism of bacterial adhesion from flowing carrier fluids than available hitherto.     

Firework inspired load balancing approach for wireless sensor networks

Wireless Networks - In Wireless Sensor Networks (WSNs), where power consumption is a huge concern, the improvement of the network’s lifetime is an area of constant study and innovation. The...     

Graphical models for interactive POMDPs: representations and solutions

We develop new graphical representations for the problem of sequential decision making in partially observable multiagent environments, as formalized by interactive partially observable Markov decision processes (I-POMDPs). The graphical models called interactive influence diagrams (I-IDs) and their dynamic counterparts, interactive dynamic influence diagrams (I-DIDs), seek to explicitly model the structure that is often present in real-world problems by decomposing the situation into chance and decision variables, and the dependencies between the variables. I-DIDs generalize DIDs, which may be viewed as graphical representations of POMDPs, to multiagent settings in the same way that I-POMDPs generalize POMDPs. I-DIDs may be used to compute the policy of an agent given its belief as the agent acts and observes in a setting that is populated by other interacting agents. Using several examples, we show how I-IDs and I-DIDs may be applied and demonstrate their usefulness. We also show how the models may be solved using the standard algorithms that are applicable to DIDs. Solving I-DIDs exactly involves knowing the solutions of possible models of the other agents. The space of models grows exponentially with the number of time steps. We present a method of solving I-DIDs approximately by limiting the number of other agents’ candidate models at each time step to a constant. We do this by clustering models that are likely to be behaviorally equivalent and selecting a representative set from the clusters. We discuss the error bound of the approximation technique and demonstrate its empirical performance.