Photoprintable nanowire–polymer blends synthesized by dynamic emulsion polycondensation

In this article, we report a photoprintable nanocomposite synthesized at room temperature in less than 10 s by aggressive mixing of two monomer solutions. Polycondensation occurs in the dispersed phase of the dynamic emulsion, microdroplets, in which the nanofillers are suspended. Hence, the synthesized composite microparticles have a high dispersion and high loading of nanofillers. Specifically, silver nanowire (AgNW)–nylon 66 blends with various Ag weight fractions are synthesized and studied. The poly(vinyl pyrrolidone) (PVP)-functionalized AgNWs (by polyol synthesis) are singly dispersed and tethered to the nylon matrix by PVP with a slight depression of the glass-transition temperature. The Ag–nylon powder melts under low-intensity visible continuous wave laser radiation; this allows convenient photoprinting of the nanocomposite structures. Photoprinting is demonstrated at a radiation intensity of 0.3 kW/cm² (405 nm laser) and a scan rate of 5 mm/s for a 1.49 wt % Ag content. This enhanced photothermal characteristic of the nanocomposite, which allows printing at low radiation intensities, is attributed to the efficient coupling of light to the AgNW plasmon modes and the high dispersion of nanowires in the polymer. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47670.     

An Update on Glioblastoma Biology,Genetics, and Current Therapies: Novel Inhibitors of the G Protein-Coupled Receptor CCR5

The mechanisms governing therapeutic resistance of the most aggressive and lethal primary brain tumor in adults, glioblastoma, have increasingly focused on tumor stem cells. These cells, protected by the periarteriolar hypoxic GSC niche, contribute to the poor efficacy of standard of care treatment of glioblastoma. Integrated proteogenomic and metabolomic analyses of glioblastoma tissues and single cells have revealed insights into the complex heterogeneity of glioblastoma and stromal cells, comprising its tumor microenvironment (TME). An additional factor, which isdriving poor therapy response is the distinct genetic drivers in each patient’s tumor, providing the rationale for a more individualized or personalized approach to treatment. We recently reported that the G protein-coupled receptor CCR5, which contributes to stem cell expansion in other cancers, is overexpressed in glioblastoma cells. Overexpression of the CCR5 ligand CCL5 (RANTES) in glioblastoma completes a potential autocrine activation loop to promote tumor proliferation and invasion. CCL5 was not expressed in glioblastoma stem cells, suggesting a need for paracrine activation of CCR5 signaling by the stromal cells. TME-associated immune cells, such as resident microglia, infiltrating macrophages, T cells, and mesenchymal stem cells, possibly release CCR5 ligands, providing heterologous signaling between stromal and glioblastoma stem cells. Herein, we review current therapies for glioblastoma, the role of CCR5 in other cancers, and the potential role for CCR5 inhibitors in the treatment of glioblastoma.     

Factors affecting the antilisterial effects of nisin in milk

The ability of Listeria monocytogenes to proliferate in milk and the antilisterial activities of nisin are well documented. Although milk fat was reported to reduce the antimicrobial activities of nisin, there is little information on the influence of milk fat on the antilisterial activities of nisin in refrigerated milk, and whether pasteurization and homogenization influence these activities. Fresh, pasteurized, and homogenized milk samples (0.1%, 2.0%, and 3.5% fat) were treated with nisin (0-500 IU/ml) and challenged with 10(4) CFU/ml L. monocytogenes strain Scott A. The organism was most sensitive to nisin in skim milk, showing rapid decline in cell numbers to <10 CFU/ml after 12 days at 5 degrees C following treatment with 250 IU/ml. An initial decline in cell numbers in 2% and whole milk was followed by regrowth of the organism. Loss of the antilisterial effects of nisin was confirmed in homogenized whole milk, whether raw or pasteurized, but not in raw or pasteurized whole milk that was not homogenized. Tween 80, a nonionic emulsifier, partially counteracted the loss of the antilisterial activity of nisin, whereas lecithin, an anionic emulsifier, had no effect. These results demonstrate that the chemical composition and treatment of foods may play an important role in the antilisterial effects of nisin.     

On thermal conductivity of micro‐ and nanocellular polymer foams

Many theoretical and empirical models exist to predict the effective thermal conductivity of polymer foams. However, most of the models only consider the effect of porosity, while the pore size effect is ignored. The objective of this study is to understand the effect of pore size on the thermal conductivity of polymer foams, especially when it reduces to the micro and nanometer scales. A wide range of pore sizes from 1 nm to 1 mm were studied in conjunction with the porosity effect using finite element analysis and molecular dynamics simulation methods. Experimental data was used to validate the modeling result. It is shown that the pore size has significant effect on thermal conductivity, even for microcellular and conventional foams. The contribution of heat conduction through air is negligible when pore size is reduced to the micrometer scale. The extremely low thermal conductivity of nanofoams is attributed to extensive diffusive scattering of heat carriers in the solid phase of polymer matrix, instead of air. This study provides quantitative understanding of the pore size effect on thermal conductivity of polymer foams. It is also shown that polyetherimide (PEI) nanofoams could have a thermal conductivity as low as 0.015 W/m‐K. POLYM. ENG. SCI., 2013. © 2013 Society of Plastics Engineers     

Nonlinear model predictive control for hydrobatics: Experiments with an underactuated AUV

Hydrobatic autonomous underwater vehicles (AUVs) can be efficient in range and speed, as well as agile in maneuvering. They can be beneficial in scenarios such as obstacle avoidance, inspections, docking, and under-ice operations. However, such AUVs are underactuated systems—this means exploiting the system dynamics is key to achieving elegant hydrobatic maneuvers with minimum controls. This paper explores the use of model predictive control (MPC) techniques to control underactuated AUVs in hydrobatic maneuvers and presents new simulation and experimental results with the small and hydrobatic SAM AUV. Simulations are performed using nonlinear model predictive control (NMPC) on the full AUV system to provide optimal control policies for several hydrobatic maneuvers in Matlab/Simulink. For implementation on AUV hardware in robot operating system, a linear time varying MPC (LTV-MPC) is derived from the nonlinear model to enable real-time control. In simulations, NMPC and LTV-MPC shows promising results to offer much more efficient control strategies than what can be obtained with PID and linear quadratic regulator based controllers in terms of rise-time, overshoot, steady-state error, and robustness. The LTV-MPC shows satisfactory real-time performance in experimental validation. The paper further also demonstrates experimentally that LTV-MPC can be run real-time on the AUV in performing hydrobatic maneouvers.     

Bayesian hypothesis testing for pattern discrimination in brain decoding

Research in cognitive neuroscience and in brain–computer interfaces (BCI) is frequently concerned with finding evidence that a given brain area processes, or encodes, given stimuli. Experiments based on neuroimaging techniques consist of a stimulation protocol presented to a subject while his or her brain activity is being recorded. The question is then whether there is enough evidence of brain activity related to the stimuli within the recorded data. Finding a link between brain activity and stimuli has recently been proposed as a classification task, called brain decoding. A classifier that can accurately predict which stimuli were presented to the subject provides support for a positive answer to the question. However, it is only the answer for a given data set and the question still remains whether it is a general rule that will apply also to new data. In this paper we try to reliably answer the neuroscientific question about the presence of a significant link between brain activity and stimuli once we have the classification results. The proposed method is based on a Beta-Binomial model for the population of generalization errors of classifiers from multi-subject studies within the Bayesian hypothesis testing framework. We present an application on nine brain decoding investigations from a real functional magnetic resonance imaging (fMRI) experiment about the relation between mental calculation and eye movements.     

Knowledge mining sensory evaluation data: genetic programming, statistical techniques, and swarm optimization

Knowledge mining sensory evaluation data is a challenging process due to extreme sparsity of the data, and a large variation in responses from different members (called assessors) of the panel. The main goals of knowledge mining in sensory sciences are understanding the dependency of the perceived liking score on the concentration levels of flavors’ ingredients, identifying ingredients that drive liking, segmenting the panel into groups with similar liking preferences and optimizing flavors to maximize liking per group. Our approach employs (1) Genetic programming (symbolic regression) and ensemble methods to generate multiple diverse explanations of assessor liking preferences with confidence information; (2) statistical techniques to extrapolate using the produced ensembles to unobserved regions of the flavor space, and segment the assessors into groups which either have the same propensity to like flavors, or are driven by the same ingredients; and (3) two-objective swarm optimization to identify flavors which are well and consistently liked by a selected segment of assessors.     

A TCP offload accelerator for 10 Gb/s Ethernet in 90-nm CMOS

This programmable engine is designed to offload TCP inbound processing at wire speed for 10-Gb/s Ethernet, supporting 64-byte minimum packet size. This prototype chip employs a high-speed core and a specialized instruction set. It includes hardware support for dynamically reordering out-of-order packets. In a 90-nm CMOS process, the 8-mm/sup 2/ experimental chip has 460 K transistors. First silicon has been validated to be fully functional and achieves 9.64-Gb/s packet processing performance at 1.72 V and consumes 6.39 W.