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A novel photoexcitation method for the light-addressable potentiometric sensor (LAPS) realized a higher spatial resolution of chemical imaging. In this method, a modulated light probe, which generates the alternating photocurrent signal, is surrounded by a ring of constant light, which suppresses the lateral diffusion of photocarriers by enhancing recombination. A device simulation verified that a higher spatial resolution could be obtained by adjusting the gap between the modulated and constant light. It was also found that a higher intensity and a longer wavelength of constant light was more effective. However, there exists a tradeoff between the spatial resolution and the amplitude of the photocurrent, and thus, the signal-to-noise ratio. A tilted incidence of constant light was applied, which could achieve even higher resolution with a smaller loss of photocurrent.
The chemical imaging sensor, which is based on the principle of the light-addressable potentiometric sensor (LAPS), is a powerful tool to visualize the spatial distribution of chemical species on the sensor surface. The spatial resolution of this sensor depends on the diffusion of photocarriers excited by a modulated light. In this study, a novel hybrid fiber-optic illumination was developed to enhance the spatial resolution. It consists of a modulated light probe to generate a photocurrent signal and a ring of constant light, which suppresses the lateral diffusion of minority carriers excited by the modulated light. It is demonstrated that the spatial resolution was improved from 92 μm to 68 μm.
Molecular-genetic identification of emerged novel invasive pathogens of Asiatic Elm Ulmus pumila L
(2014)
The dwarf elm Ulmus pumila L. (Ulmaceae) is one of indigenous species of flora in Kazakhstan and forms a basis of dendroflora in virtually all settlements of the region. In the past decade, multiple outbreaks of previously unknown diseases of the small-leaved elm have been registered. In our study, by the molecular-genetic analysis it was found that the pathogens responsible for the outbreaks are microfungi belonging to the genus Fusarium – F. solani and F. oxysporum. The nucleotide sequences (ITS regions) isolated from the diseased trees showed very high similarity with the GenBank control numbers EU625403.1 and FJ478128.1 (100.0 and 99.0 % respectively). Oncoming research will focus on the search of natural microbial antagonists of the discovered phytopathogens.
Network theory provides novel concepts that promise an improved characterization of interacting dynamical systems. Within this framework, evolving networks can be considered as being composed of nodes, representing systems, and of time-varying edges, representing interactions between these systems. This approach is highly attractive to further our understanding of the physiological and pathophysiological dynamics in human brain networks. Indeed, there is growing evidence that the epileptic process can be regarded as a large-scale network phenomenon. We here review methodologies for inferring networks from empirical time series and for a characterization of these evolving networks. We summarize recent findings derived from studies that investigate human epileptic brain networks evolving on timescales ranging from few seconds to weeks. We point to possible pitfalls and open issues, and discuss future perspectives.
The scope of this study is the measurement of endotoxin adsorption rate for carbonized rice husk. It showed good adsorption properties for LPS. During the batch experiments, several techniques were used and optimized for improving the material’s adsorption behavior. Also, with the results obtained it was possible to differentiate the materials according to their adsorption capacity and kinetic characteristics.
Background
True date palms (Phoenix dactylifera L.) are impressive trees and have served as an indispensable source of food for mankind in tropical and subtropical countries for centuries. The aim of this study is to differentiate date palm tree varieties by analysing leaflet cross sections with technical/optical methods and artificial neural networks (ANN).
Results
Fluorescence microscopy images of leaflet cross sections have been taken from a set of five date palm tree cultivars (Hewlat al Jouf, Khlas, Nabot Soltan, Shishi, Um Raheem). After features extraction from images, the obtained data have been fed in a multilayer perceptron ANN with backpropagation learning algorithm.
Conclusions
Overall, an accurate result in prediction and differentiation of date palm tree cultivars was achieved with average prediction in tenfold cross-validation is 89.1% and reached 100% in one of the best ANN.