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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.
Cell-based sensors for the detection of gases have long been underrepresented, due to the cellular requirement of being cultured in a liquid environment. In this work we established a cell-based gas biosensor for the detection of toxic substances in air, by adapting a commercial sensor chip (Bionas®), previously used for the measurement of pollutants in liquids. Cells of the respiratory tract (A549, RPMI 2650, V79), which survive at a gas phase in a natural context, are used as biological receptors. The physiological cell parameters acidification, respiration and morphology are continuously monitored in parallel. Ammonia was used as a highly water-soluble model gas to test the feasibility of the sensor system. Infrared measurements confirmed the sufficiency of the medium draining method. This sensor system provides a basis for many sensor applications such as environmental monitoring, building technology and public security.
Designing novel or optimizing existing biodegradable polymers for biomedical applications requires numerous tests on the effect of substances on the degradation process. In the present work, polymer-modified electrolyte–insulator–semiconductor (PMEIS) sensors have been applied for monitoring an enzymatically catalyzed degradation of polymers for the first time. The thin films of biodegradable polymer poly(d,l-lactic acid) and enzyme lipase were used as a model system. During degradation, the sensors were read-out by means of impedance spectroscopy. In order to interpret the data obtained from impedance measurements, an electrical equivalent circuit model was developed. In addition, morphological investigations of the polymer surface have been performed by means of in situ atomic force microscopy. The sensor signal change, which reflects the progress of degradation, indicates an accelerated degradation in the presence of the enzyme compared to hydrolysis in neutral pH buffer media. The degradation rate increases with increasing enzyme concentration. The obtained results demonstrate the potential of PMEIS sensors as a very promising tool for in situ and real-time monitoring of degradation of polymers.
In the present work, a novel method for monitoring sterilisation processes with gaseous H2O2 in combination with heat activation by means of a specially designed calorimetric gas sensor was evaluated. Therefore, the sterilisation process was extensively studied by using test specimens inoculated with Bacillus atrophaeus spores in order to identify the most influencing process factors on its microbicidal effectiveness. Besides the contact time of the test specimens with gaseous H2O2 varied between 0.2 and 0.5 s, the present H2O2 concentration in a range from 0 to 8% v/v (volume percent) had a strong influence on the microbicidal effectiveness, whereas the change of the vaporiser temperature, gas flow and humidity were almost negligible. Furthermore, a calorimetric H2O2 gas sensor was characterised in the sterilisation process with gaseous H2O2 in a wide range of parameter settings, wherein the measurement signal has shown a linear response against the H2O2 concentration with a sensitivity of 4.75 °C/(% v/v). In a final step, a correlation model by matching the measurement signal of the gas sensor with the microbial inactivation kinetics was established that demonstrates its suitability as an efficient method for validating the microbicidal effectiveness of sterilisation processes with gaseous H2O2.
Exposure to prolonged periods in microgravity is associated with deconditioning of the musculoskeletal system due to chronic changes in mechanical stimulation. Given astronauts will operate on the Lunar surface for extended periods of time, it is critical to quantify both external (e.g., ground reaction forces) and internal (e.g., joint reaction forces) loads of relevant movements performed during Lunar missions. Such knowledge is key to predict musculoskeletal deconditioning and determine appropriate exercise countermeasures associated with extended exposure to hypogravity.
This work describes a procedure to yield attenuation maps from MR images which are used for the absorption correction (AC) of brain PET data. Such an approach could be mandatory for future combined PET and MRI scanners, which probably do not include a transmission facility. T1-weighted MR images were segmented into brain tissue, bone, soft tissue, and sinus; attenuation coefficients corresponding to elemental composition and density as well as to 511 keV photon energy were respectively assigned. Attenuation maps containing up to four compartments were created and forward projected into sinograms with attenuation factors which then were used for AC during reconstruction of FDG-PET data. The commonly used AC based on a radioactive (68Ge) transmission scan served as reference. The reconstructed radioactivity values obtained with the MRI-based AC were about 20% lower than those obtained with PET-based AC if the skull was not taken into account. Considering the skull the difference was still about 10%. Our investigations demonstrate the feasibility of a MRI-based AC, but revealed also the necessity of a satisfying delineation of bone thickness which tends to be underestimated in our first approach of T1-weighted MR image segmentation.
Multi-attribute relation extraction (MARE): simplifying the application of relation extraction
(2021)
Natural language understanding’s relation extraction makes innovative and encouraging novel business concepts possible and facilitates new digitilized decision-making processes. Current approaches allow the extraction of relations with a fixed number of entities as attributes. Extracting relations with an arbitrary amount of attributes requires complex systems and costly relation-trigger annotations to assist these systems. We introduce multi-attribute relation extraction (MARE) as an assumption-less problem formulation with two approaches, facilitating an explicit mapping from business use cases to the data annotations. Avoiding elaborated annotation constraints simplifies the application of relation extraction approaches. The evaluation compares our models to current state-of-the-art event extraction and binary relation extraction methods. Our approaches show improvement compared to these on the extraction of general multi-attribute relations.
Multi-parameter detection for supporting monitoring and control of biogas processes in agriculture
(2014)
High-k perovskite oxide of barium strontium titanate (BST) represents a very attractive multi-functional transducer material for the development of (bio-)chemical sensors. In this work, a Si-based sensor chip containing Pt interdigitated electrodes covered with a thin BST layer (485 nm) has been developed for multi-parameter chemical sensing. The chip has been applied for the contactless measurement of the electrolyte conductivity, the detection of adsorbed charged macromolecules (positively charged polyelectrolytes of polyethylenimine) and the concentration of hydrogen peroxide (H2O2) vapor. The experimental results of functional testing of individual sensors are presented. The mechanism of the BST sensitivity to charged polyelectrolytes and H2O2 vapor has been proposed and discussed.
In this work, a multi-sensor chip for the investigation of the sensing properties of different types of metal oxides towards hydrogen peroxide in the ppm range is presented. The fabrication process and physical characterization of the multi-sensor chip are described. Pure SnO2 and WO3 as well as Pd- and Pt-doped SnO2 films are characterized in terms of their sensitivity to H2O2. The sensing films have been prepared by drop-coating of water-dispensed nano-powders. A physical characterization, including scanning electron microscopy and X-ray diffraction analysis of the deposited metal-oxide films, was done. From the measurements in hydrogen peroxide atmosphere, it could be shown, that all of the tested metal oxide films are suitable for the detection of H2O2 in the ppm range. The highest sensitivity and reproducibility was achieved using Pt-doped SnO2.
Calibration plot of a SnO2, WO3, Pt-, and Pd-doped SnO2 gas sensor for H2O2 concentrations in the ppm range.