Size-dependent deposition of inhaled nanoparticles in the rat respiratory tract using a new nose-only exposure system

Concerns about the potential health effects of exposure to nanomaterials have led to a growing number of in vivo inhalation toxicity studies using nanoparticle aerosols. Estimates of aerosol deposition within the respiratory tract are important for these studies to enable: (a) the interpretation of the results, in particular, the evaluation of dose–response relationships; (b) comparison with the results of other related studies; and (c) the extrapolation of results from animal models to human. Unfortunately, only a limited number of studies have been undertaken to investigate respiratory tract deposition efficiencies for nano-sized aerosol particles. This is of particular importance as deposition efficiencies are predicted to vary significantly over the nano-size range for some elements of the respiratory tract. In this study, female Wistar-Kyoto rats were exposed in a new design nose-only inhalation exposure system to spark generated radioactive iridium-192 nanoparticle aerosols of four particle sizes chosen to cover the majority of the nano-size range (nominal sizes: 10, 15, 35, and 75 nm). The content of iridium-192 in the lung, head, gastrointestinal tract, and various other organs and tissues was measured. Aerosol deposition efficiencies in the whole respiratory tract and components (head airways, lung, alveolar region, and tracheobronchial region) were estimated and compared with the predictions of the Multiple Path Particle Dosimetry (MPPD) model (v2.11). The experimentally derived deposition efficiencies were broadly consistent with, but typically higher than, model predictions and the results of comparable studies in the literature.

Copyright © 2016 American Association for Aerosol Research     

Space charge behavior in low density polyethylene at pre-breakdown

It has been known that the electrical breakdown of insulating materials is strongly affected by the formation of space charge in the bulk of the materials. Many researchers have attempted to study the relationship between the space charge and the breakdown; however, it has not been clarified yet. Although the pulsed electroacoustic (PEA) method has been widely used to observe space charge profiles, previous works have not shown clear evidence of the influence of the space charge on the breakdown. Therefore, we have developed a new PEA system with an interval of 0.5 ms to observe the space charge distribution continuously under the ultra-high electric field. Using this system, we observed the space charge dynamics in the low-density polyethylene (LDPE) at and around the breakdown. We also investigated the dependence of the applied electric field on space charge behavior. From the results, it is found that the injected charge packet moved faster and deeper under a relatively lower electric field rather than that under a higher electric field. Furthermore, we found that the maximum electric field in each specimen was almost the same when the breakdown happened in a specimen.     

Circulating Tumor Cells (CTC) and Cell-Free DNA (cfDNA) Workshop 2016: Scientific Opportunities and Logistics for Cancer Clinical Trial Incorporation

Despite the identification of circulating tumor cells (CTCs) and cell-free DNA (cfDNA) as potential blood-based biomarkers capable of providing prognostic and predictive information in cancer, they have not been incorporated into routine clinical practice. This resistance is due in part to technological limitations hampering CTC and cfDNA analysis, as well as a limited understanding of precisely how to interpret emergent biomarkers across various disease stages and tumor types. In recognition of these challenges, a group of researchers and clinicians focused on blood-based biomarker development met at the Canadian Cancer Trials Group (CCTG) Spring Meeting in Toronto, Canada on 29 April 2016 for a workshop discussing novel CTC/cfDNA technologies, interpretation of data obtained from CTCs versus cfDNA, challenges regarding disease evolution and heterogeneity, and logistical considerations for incorporation of CTCs/cfDNA into clinical trials, and ultimately into routine clinical use. The objectives of this workshop included discussion of the current barriers to clinical implementation and recent progress made in the field, as well as fueling meaningful collaborations and partnerships between researchers and clinicians. We anticipate that the considerations highlighted at this workshop will lead to advances in both basic and translational research and will ultimately impact patient management strategies and patient outcomes.     

Interactive topic modeling

Topic models are a useful and ubiquitous tool for understanding large corpora. However, topic models are not perfect, and for many users in computational social science, digital humanities, and information studies—who are not machine learning experts—existing models and frameworks are often a “take it or leave it” proposition. This paper presents a mechanism for giving users a voice by encoding users’ feedback to topic models as correlations between words into a topic model. This framework, interactive topic modeling (itm), allows untrained users to encode their feedback easily and iteratively into the topic models. Because latency in interactive systems is crucial, we develop more efficient inference algorithms for tree-based topic models. We validate the framework both with simulated and real users.     

Generation of optimal artificial neural networks using a pattern search algorithm: application to approximation of chemical systems

A pattern search optimization method is applied to the generation of optimal artificial neural networks (ANNs). Optimization is performed using a mixed variable extension to the generalized pattern search method. This method offers the advantage that categorical variables, such as neural transfer functions and nodal connectivities, can be used as parameters in optimization. When used together with a surrogate, the resulting algorithm is highly efficient for expensive objective functions. Results demonstrate the effectiveness of this method in optimizing an ANN for the number of neurons, the type of transfer function, and the connectivity among neurons. The optimization method is applied to a chemistry approximation of practical relevance. In this application, temperature and a chemical source term are approximated as functions of two independent parameters using optimal ANNs. Comparison of the performance of optimal ANNs with conventional tabulation methods demonstrates equivalent accuracy by considerable savings in memory storage. The architecture of the optimal ANN for the approximation of the chemical source term consists of a fully connected feedforward network having four nonlinear hidden layers and 117 synaptic weights. An equivalent representation of the chemical source term using tabulation techniques would require a 500 x 500 grid point discretization of the parameter space.     

Physics-guided machine learning from simulated data with different physical parameters

Knowledge and Information Systems - Physics-based models are widely used to study dynamical systems in a variety of scientific and engineering problems. However, these models are necessarily...     

Characterization of protective coatings for planar automotive gas sensors

Alumina support material suitable for use as a planar automotive gas sensor support was coated in thin films of yttria-stabilised zirconia (YSZ) and titania. The morphology, composition, thickness and homogeneity of the coating was measured. The coating was applied to the ‘green’ form of a tape cast alumina substrate which was subsequently fired at 1500 °C to produce the final form of the coated alumina. The YSZ coating gave a continuous 5 μm thick coating with no evidence of mixed oxide formation between the YSZ and the alumina substrate. XRD indicated a face centred cubic Y doped ZrO₂ or primitive tetragonal Zr_0.9Y_0.1O_1.95 phase. The titania coatings were much thinner (<1 μm) with signs of trace amounts of aluminium titanium oxide (Al₂TiO₅) as well as rutile titania in XRD. Spot analysis using X-ray photoelectron spectroscopy showed a fairly regular titania coverage. Atomic force microscopy analysis showed a particle size of 1–3 μm for the YSZ coating and 0.5 μm for titania.     

AGATHA: Using heuristic search to automate the construction of case law theories

In this paper we describe AGATHA, a program designed to automate the process of theory construction in case based domains. Given a seed case and a number of precedent cases, the program uses a set of argument moves to generate a search space for a dialogue between the parties to the dispute. Each move is associated with a set of theory constructors, and thus each point in the space can be associated with a theory intended to explain the seed case and the other cases in the domain. The space is large and so an heuristic search method is needed. This paper describes two methods based on A* and alpha/beta pruning and also a series of experiments designed to explore the appropriateness of different evaluation functions, the most useful precedents to use as seed cases and the quality of the resulting theories.     

Divided attention during adaptation to visual-motor rotation in an endoscopic surgery simulator

We investigated how cognitive demand affects the acquisition of adaptive hand–eye coordination in a low-fidelity endoscopic simulator. Participants performed a peg transfer task. In the pre-exposure and post-exposure phases participants moved objects between pegs with forceps while directly viewing the pegboard. In the exposure phase participants completed that task while indirectly viewing the pegboard through a camera and monitor with a 90° clockwise visual rotation. A control group completed the experiment as described whereas an experimental group concurrently performed a short-term memory task (digit rehearsal) during the exposure phase. Performance was significantly disrupted during early exposure-phase performance for both groups. The cognitive task caused an additional initial performance decrement for the experimental group, but their performance quickly converged on control-group performance. Both groups performance improved over trials. Competition for attentional resources imposed by memory demands can impair simulator performance, but performers can quickly learn to compensate for the increased attentional demand.