Effects of parylene-C photooxidation on serum-assisted glial and neuronal patterning

The increasing use of patterned neural networks in multielectrode arrays and similar devices drives the constant development and evaluation of new biomaterials. Recently, we presented a promising technique to guide neurons and glia reliably and effectively. Parylene-C, a common hydrophobic polymer, was photolithographically patterned on silicon oxide (SiO2) and subsequently activated via immersion in serum. In this article, we explore the effects of ultraviolet (UV)-induced oxidation on parylene's ability to pattern neurons and glia. We exposed parylene-C stripe patterns to increasing levels of UV radiation and found a dose-dependent reduction in the total mass of patterned cells, as well as a gradual loss of glial and neuronal conformity to the patterns. In contrast, nonirradiated patterns had superior patterning results and increased presence of cells. The reduced cell adhesion and patterning after the formation of aldehyde and carboxyl groups on UV-radiated parylene-C supports our hypothesis that cell adhesion and growth on parylene is facilitated by hydrophobic adsorption of serum proteins. We conclude that unlike other cell patterning schemes, our technique does not rely on photooxidation of the polymer. Nonetheless, the precise control of oxygenated groups on parylene could pave the way for the differential binding of proteins and other molecules on the surface, aiding in the adhesion of alternative cell types. © 2010 Wiley Periodicals, Inc. J Biomed Mater Res, 2010

Particle swarm optimisation assisted classification using elastic net prefiltering

A novel two-stage construction algorithm for linear-in-the-parameters classifier is proposed, aiming at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage to construct a sparse linear-in-the-parameters classifier. For the first stage learning of generating the prefiltered signal, a two-level algorithm is introduced to maximise the model's generalisation capability, in which an elastic net model identification algorithm using singular value decomposition is employed at the lower level while the two regularisation parameters are selected by maximising the Bayesian evidence using a particle swarm optimization algorithm. Analysis is provided to demonstrate how “Occam's razor” is embodied in this approach. The second stage of sparse classifier construction is based on an orthogonal forward regression with the D-optimality algorithm. Extensive experimental results demonstrate that the proposed approach is effective and yields competitive results for noisy data sets.

On the evolution of monopoly pricing in Internet-assisted search markets

This study examines the evolution of prices in markets with Internet price-comparison search engines. The empirical study analyzes laboratory data of prices available to informed consumers, for two industry sizes and two conditions on the sample (complete and incomplete). Distributions are typically bimodal. One of the two modes of distribution, corresponding to monopoly pricing, tends to attract such pricing strategies increasingly over time. The second one, corresponding to interior pricing, follows a decreasing trend. Monopoly pricing can serve as a means of insurance against more competitive (but riskier) behavior. In fact, experimental subjects who initially earn low profits due to interior pricing are more likely to switch to monopoly pricing than subjects who experience good returns from the start.

Evaluation of reach and grasp robot-assisted therapy suggests similar functional recovery patterns on proximal and distal arm segments in sub-acute hemiplegia

This paper provides some additional evidence in support of the hypothesis that robot therapies are clinically beneficial in neurorehabilitation. Although only 4 subjects were included in the study, the design of the intervention and the measures were done so as to minimise bias. The results are presented as single case studies, and can only be interpreted as such due to the study size. The intensity of intervention was 16 hours and the therapy philosophy (based on Carr and Shepherd) was that coordinated movements are preferable to joint based therapies, and that coordinating distal movements (in this case grasps) helps not only to recover function in these areas, but has greater value since the results are immediately transferable to daily skills such as reach and grasp movements.

Global snow mass measurements and the effect of stratigraphic detail on inversion of microwave brightness temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as Snow Water Equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment (NASA CLPX) and the Helsinki University of Technology (HUT) microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 GHz and 37 GHz vertically polarised microwaves are consistent with Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and Special Sensor Microwave Imager (SSM/I) retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10 cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method then it is equivalent to ±13 mm SWE (7% of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

Dimensionality reduction assisted tensor clustering

This paper is concerned with tensor clustering with the assistance of dimensionality reduction approaches. A class of formulation for tensor clustering is introduced based on tensor Tucker decomposition models. In this formulation, an extra tensor mode is formed by a collection of tensors of the same dimensions and then used to assist a Tucker decomposition in order to achieve data dimensionality reduction. We design two types of clustering models for the tensors: PCA Tensor Clustering model and Non-negative Tensor Clustering model, by utilizing different regularizations. The tensor clustering can thus be solved by the optimization method based on the alternative coordinate scheme. Interestingly, our experiments show that the proposed models yield comparable or even better performance compared to most recent clustering algorithms based on matrix factorization.

A variational approach to retrieve rain rate by combining information from rain gauges, radars, and microwave links

Accurate and reliable rain rate estimates are important for various hydrometeorological applications. Consequently, rain sensors of different types have been deployed in many regions. In this work, measurements from different instruments, namely, rain gauge, weather radar, and microwave link, are combined for the first time to estimate with greater accuracy the spatial distribution and intensity of rainfall. The objective is to retrieve the rain rate that is consistent with all these measurements while incorporating the uncertainty associated with the different sources of information. Assuming the problem is not strongly nonlinear, a variational approach is implemented and the Gauss–Newton method is used to minimize the cost function containing proper error estimates from all sensors. Furthermore, the method can be flexibly adapted to additional data sources. The proposed approach is tested using data from 14 rain gauges and 14 operational microwave links located in the Zürich area (Switzerland) to correct the prior rain rate provided by the operational radar rain product from the Swiss meteorological service (MeteoSwiss). A cross-validation approach demonstrates the improvement of rain rate estimates when assimilating rain gauge and microwave link information.

Solvothermal synthesis of one-dimensional chalcogenides containing group 13 elements

The solvothermal synthesis and characterisation of [C6H16N2][GaS2]2 (1), [C6H16N2][Ga2Se3(Se2)] (2), and mixed-metal phases with composition [C6H16N2][Ga2–xInxSe3(Se2)] (0 < x < 2)(3–5), is described. These materials have been characterised by single-crystal and powder X-ray diffraction, thermogravimetric analysis and UV/Vis diffuse reflectance spectroscopy. The materials contain one-dimensional anionic chains. In 1, these chains consist of edge-linked GaS4 tetrahedra, whilst in 2–5, the chains contain perselenide (Se2)2– units and comprise alternating four-membered [M2Se2] and five-membered [M2Se3] rings (where M = Ga, In). Compounds 3–5 represent the first examples of ternary mixed-metal [M2Se3(Se2)]2– chains.

A comparative corpus-assisted discourse study of the representations of hosts in promotional tourism discourse

Most studies concerned with the representations of local people in tourism discourse point to the prevalence of stereotypic images asserting that contemporary tourism perpetuates colonial legacy and gendered discursive practices. This claim has been, to some extent, contested in research that explores representations of hosts in local tourism materials claiming that tourism can also discursively resist the dominant Western imagery. While the evidence for the existence of hegemonic and diverging discourses about the local ‘Other’ seems compelling, the empirical basis of this research is rather small and often limited to one geographic context. The present study addresses these shortcomings by examining representations of hosts in a larger corpus of promotional tourism materials including texts produced by Western and local tourism industries. The data is investigated using the methodology of Corpus-Assisted Discourse Studies (CADS). By comparing external with internal (self) representations, this study verifies and refines some of the claims on the subject and offers a much more nuanced picture of representations that defies the black and white scenarios proposed in previous research

Equation for the microwave backscatter cross section of aggregate snowflakes using the self-similar Rayleigh–Gans approximation

In this paper an equation is derived for the mean backscatter cross section of an ensemble of snowflakes at centimeter and millimeter wavelengths. It uses the Rayleigh–Gans approximation, which has previously been found to be applicable at these wavelengths due to the low density of snow aggregates. Although the internal structure of an individual snowflake is random and unpredictable, the authors find from simulations of the aggregation process that their structure is “self-similar” and can be described by a power law. This enables an analytic expression to be derived for the backscatter cross section of an ensemble of particles as a function of their maximum dimension in the direction of propagation of the radiation, the volume of ice they contain, a variable describing their mean shape, and two variables describing the shape of the power spectrum. The exponent of the power law is found to be −. In the case of 1-cm snowflakes observed by a 3.2-mm-wavelength radar, the backscatter is 40–100 times larger than that of a homogeneous ice–air spheroid with the same mass, size, and aspect ratio.

Two-way CSI-assisted AF relaying with HPA nonlinearity

In this paper, we investigate half-duplex two-way dual-hop channel state information (CSI)-assisted amplify-and-forward (AF) relaying in the presence of high-power amplifier (HPA) nonlinearity at relays. The expression for the end-to-end signal-to-noise ratio (SNR) is derived as per the modified system model by taking into account the interference caused by relaying scheme and HPA nonlinearity. The system performance of the considered relaying network is evaluated in terms of average symbol error probability (SEP) in Nakagami-$m$ fading channels, by making use of the moment-generating function (MGF) approach. Numerical results are provided and show the effects of several parameters, such as quadrature amplitude modulation (QAM) order, number of relays, HPA parameters, and Nakagami parameter, on performance.

Enzyme assisted extraction of chitin from shrimp shells (Litopenaeus vannamei)

BACKGROUND: Chemical chitin extraction generates large amounts of wastes and increases partial deacetylation of the product. Therefore, the use of biological methods for chitin extraction is an interesting alternative. The effects of process conditions on enzyme assisted extraction of chitin from the shrimp shells in a systematic way were the focal points of this study. RESULTS: Demineralisation conditions of 25C, 20 min, shells-lactic acid ratio of 1:1.1 w/w; and shells-acetic acid ratio of 1:1.2 w/w, the maximum demineralisation values were 98.64 and 97.57% for lactic and acetic acids, respectively. A total protein removal efficiency of 91.10% by protease from Streptomyces griseus with enzyme-substrate ratio 55 U/g, pH 7.0 and incubation time 3 h is obtained when the particle size range is 50-25 μm, which was identified as the most critical factor. The X-ray diffraction and 13C NMR spectroscopy analysis showed that the lower percent crystallinity and higher degree of acetylation of chitin from enzyme assisted extraction may exhibit better solubility properties and less depolymerisation in comparison with chitin from the chemical extraction. CONCLUSION: The present work investigates the effects of individual factors on process yields, and it has shown that, if the particle size is properly controlled a reaction time of 3 h is more than enough for deproteination by protease. Physicochemical analysis indicated that the enzyme assisted production of chitin seems appropriate to extract chitin, possibly retaining its native structure.