Combinatory Effects of Cerium Dioxide Nanoparticles and Acetaminophen on the Liver—A Case Study of Low-Dose Interactions in Human HuH-7 Cells

The interactions between pharmaceuticals and nanomaterials and its potentially resulting toxicological effects in living systems are only insufficiently investigated. In this study, two model compounds, acetaminophen, a pharmaceutical, and cerium dioxide, a manufactured nanomaterial, were investigated in combination and individually. Upon inhalation, cerium dioxide nanomaterials were shown to systemically translocate into other organs, such as the liver. Therefore we picked the human liver cell line HuH-7 cells as an in vitro system to investigate liver toxicity. Possible synergistic or antagonistic metabolic changes after co-exposure scenarios were investigated. Toxicological data of the water soluble tetrazolium (WST-1) assay for cell proliferation and genotoxicity assessment using the Comet assay were combined with an untargeted as well as a targeted lipidomics approach. We found an attenuated cytotoxicity and an altered metabolic profile in co-exposure experiments with cerium dioxide, indicating an interaction of both compounds at these endpoints. Single exposure against cerium dioxide showed a genotoxic effect in the Comet assay. Conversely, acetaminophen exhibited no genotoxic effect. Comet assay data do not indicate an enhancement of genotoxicity after co-exposure. The results obtained in this study highlight the advantage of investigating co-exposure scenarios, especially for bioactive substances.     

Guava (Psidium guajava) leaf powder: novel adsorbent for removal of methylene blue from aqueous solutions

Batch sorption experiments were carried out using a novel adsorbent, guava leaf powder (GLP), for the removal of methylene blue (MB) from aqueous solutions. Potential of GLP for adsorption of MB from aqueous solution was found to be excellent. Effects of process parameters pH, adsorbent dosage, concentration, particle size and temperature were studied. Temperature-concentration interaction effect on dye uptake was studied and a quadratic model was proposed to predict dye uptake in terms of concentration, time and temperature. The model conforms closely to the experimental data. The model was used to find optimum temperature and concentration that result in maximum dye uptake. Langmuir model represent the experimental data well. Maximum dye uptake was found to be 295mg/g, indicating that GLP can be used as an excellent low-cost adsorbent. Pseudo-first-order, pseudo-second order and intraparticle diffusion models were tested. From experimental data it was found that adsorption of MB onto GLP follow pseudo second order kinetics. External diffusion and intraparticle diffusion play roles in adsorption process. Free energy of adsorption (DeltaG degrees ), enthalpy change (DeltaH degrees ) and entropy change (DeltaS degrees ) were calculated to predict the nature of adsorption. Adsorption in packed bed was also evaluated.     

Efficient light trapping in silicon solar cells by ultrafast‐laser‐induced self‐assembled micro/nano structures

A novel ultrafast laser processing technique is used to create self‐assembled micro/nano structures on a silicon surface for efficient light trapping. Under appropriate experimental conditions, light reflection (including scattering) of the Si surface has been reduced to less than 3% for the entire solar spectrum and the material appears completely black to the naked eye. A post‐chemical cleaning is applied to remove laser‐redeposited material and induced defects. Optical, morphological, and structural characterizations have been carried out on as‐laser‐treated and post‐chemically cleaned surfaces. Finally, we report for the first time the total efficiency of over 14% and high external quantum efficiency (EQE) results on photovoltaic devices fabricated on the ultrafast‐laser‐induced micro/nano structured silicon wafer, which can be further improved upon process optimization. Copyright © 2011 John Wiley & Sons, Ltd.     

Controlled hidden markov models for dynamically adapting patch clamp experiment to estimate Nernst potential of single-ion channels

This paper presents novel kernel-based stochastic learning algorithms for controlling the kinetics of single-ion channels in a patch clamp experiment. The algorithms yield efficient estimates of the equilibrium (Nernst) potential of an ion channel. The equilibrium potential of an ion channel is the applied external potential difference required to maintain electrochemical equilibrium across the ion channel. The algorithm adaptively controls the exploration of the learning algorithm to achieve an optimal balance between exploration and exploitation. An important feature of the resulting algorithm is that it is guaranteed to minimize the experimental effort. We illustrate the efficiency of the algorithms for the experimentally determined current voltage curve of a bi-ionic single potassium ion channel.     

Achieving stabilization in interferometric logic operations

Interferometric systems with amplitude beam splitters can implement reversible operations that, on detection, become Boolean operators. Being passive, they consume no energy, do not limit the operating bandwidth, and have negligible latency. Unfortunately, conventional interferometric systems are notoriously sensitive to uncontrolled disturbances. Here the use of polarization in a common-path interferometric logic gate with and without polarization beam splitters is explored as an attractive alternative to overcome those difficulties. Two of three device configurations considered offer significant stability and lower drive modulator voltage as advantages over the previous systems. The first experimental tests of such a system are reported. Common-path interferometry lends itself to even more stability and robustness by compatibility with no-air-gap, solid optics.     

Intramuscular EMG classifier for detecting myopathy and neuropathy

This article presents an automatic diagnostic system to classify intramuscular electromyography (iEMG) signals, thereby detecting neuromuscular disorders. To this end, we tailored the center symmetric local binary pattern (CSLBP) to analyze one-dimensional ($1$-D) signals. In this approach, the $1$-D CSLBP feature extracted from a decimated iEMG signal is fed to a combination of classifiers, which in turn assigns a set of labels to the signal, and ultimately the signal category is determined by the Boyer-Moore majority voting (BMMV) algorithm. The proposed framework was investigated with a benchmark iEMG dataset that contains signals recorded from three different muscles: biceps brachii (BB), deltoideus (DE), and vastus medialis (VM). In a repeated $10$-fold cross-validation, CSLBP-Combined-Classifiers-BMMV (CSLBP-CC-BMMV) achieved an average classification accuracy of $92.80$%, $94.25$%, and $93.71$% for the iEMG signals recorded from BB, DE, and VM muscle, respectively. Interestingly, the performance of CSLBP-CC-BMMV surpassed the other published approaches and ensemble learning methods that are akin to our scheme in terms of classification accuracy and computational time.