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Field-effect EIS (electrolyte-insulator-semiconductor) sensors modified with a positively charged weak polyelectrolyte layer have been applied for the electrical detection of DNA (deoxyribonucleic acid) immobilization and hybridization by the intrinsic molecular charge. The EIS sensors are able to detect the existence of target DNA amplicons in PCR (polymerase chain reaction) samples and thus, can be used as tool for a quick verification of DNA amplification and the successful PCR process. Due to their miniaturized setup, compatibility with advanced micro- and nanotechnologies, and ability to detect biomolecules by their intrinsic molecular charge, those sensors can serve as possible platform for the development of label-free DNA chips. Possible application fields as well as challenges and limitations will be discussed.
Persistent Photoconductivity in Halogen-doped Cd1-X ZnX Te, Cd1-X MnX Te and Cd1-X-MgX -Te Layers
(1995)
Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle.
In vitro studies of the degradation kinetic of biopolymers are essential for the design and optimization of implantable biomedical devices. In the presented work, a field-effect capacitive sensor has been applied for the real-time and in situ monitoring of degradation processes of biopolymers for the first time. The polymer-covered field-effect sensor is, in principle, capable to detect any changes in bulk, surface and interface properties of the polymer induced by degradation processes. The feasibility of this approach has been experimentally proven by using the commercially available biomedical polymer poly(D,L-lactic acid) (PDLLA) as a model system. PDLLA films of different thicknesses were deposited on the Ta₂O₅-gate surface of the field-effect structure from a polymer solution by means of spin-coating method. The polymer-modified field-effect sensors have been characterized by means of capacitance–voltage and impedance-spectroscopy method. The degradation of the PDLLA was accelerated by changing the degradation medium from neutral (pH 7.2) to alkaline (pH 9) condition, resulting in drastic changes in the capacitance and impedance spectra of the polymer-modified field-effect sensor.
The characterization of the degradation kinetics of biodegradable polymers is mandatory with regard to their proper application. In the present work, polymer-modified electrolyte–insulator–semiconductor (PMEIS) field-effect sensors have been applied for in-situ monitoring of the pH-dependent degradation kinetics of the commercially available biopolymer poly(d,l-lactic acid) (PDLLA) in buffer solutions from pH 3 to pH 13. PDLLA films of 500 nm thickness were deposited on the surface of an Al–p-Si–SiO2–Ta2O5 structure from a polymer solution by means of spin-coating method. The PMEIS sensor is, in principle, capable to detect any changes in bulk, surface and interface properties of the polymer induced by degradation processes. A faster degradation has been observed for PDLLA films exposed to alkaline solutions (pH 9, pH 11 and pH 13).
Impedance spectroscopy: A tool for real-time in situ monitoring of the degradation of biopolymers
(2013)
Investigation of the degradation kinetics of biodegradable polymers is essential for the development of implantable biomedical devices with predicted biodegradability. In this work, an impedimetric sensor has been applied for real-time and in situ monitoring of degradation processes of biopolymers. The sensor consists of two platinum thin-film electrodes covered by a polymer film to be studied. The benchmark biomedical polymer poly(D,L-lactic acid) (PDLLA) was used as a model system. PDLLA films were deposited on the sensor structure from a polymer solution by using the spin-coating method. The degradation kinetics of PDLLA films have been studied in alkaline solutions of pH 9 and 12 by means of an impedance spectroscopy (IS) method. Any changes in a polymer capacitance/resistance induced by water uptake and/or polymer degradation will modulate the global impedance of the polymer-covered sensor that can be used as an indicator of the polymer degradation. The degradation rate can be evaluated from the time-dependent impedance spectra. As expected, a faster degradation has been observed for PDLLA films exposed to pH 12 solution.
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.
A sensor system for investigating (bio)degradationprocesses of polymers is presented. The system utilizes semiconductor field-effect sensors and is capable of monitoring the degradation process in-situ and in real-time. The degradation of the polymer poly(d,l-lactic acid) is exemplarily monitored in solutions with different pH value, pH-buffer solution containing the model enzyme lipase from Rhizomucormiehei and cell-culture medium containing supernatants from stimulated and non-stimulated THP-1-derived macrophages mimicking activation of the immune system.
A semiconductor field-effect device has been used for an enzymatically catalyzed degradation of biopolymers for the first time. This novel technique is capable to monitor the degradation process of multiple samples in situ and in real-time. As model system, the degradation of the biopolymer poly(D, L-lactic acid) has been monitored in the degradation medium containing the enzyme lipase from Rhizomucor miehei. The obtained results demonstrate the potential of capacitive field-effect sensors for degradation studies of biodegradable polymers.
Characterising an insect antenna as a receptor for a biosensor by means of impedance spectroscopy
(2001)
This paper presents NLP Lean Programming
framework (NLPf), a new framework
for creating custom natural language processing
(NLP) models and pipelines by utilizing
common software development build systems.
This approach allows developers to train and
integrate domain-specific NLP pipelines into
their applications seamlessly. Additionally,
NLPf provides an annotation tool which improves
the annotation process significantly by
providing a well-designed GUI and sophisticated
way of using input devices. Due to
NLPf’s properties developers and domain experts
are able to build domain-specific NLP
applications more efficiently. NLPf is Opensource
software and available at https://
gitlab.com/schrieveslaach/NLPf.
In this paper, carbon nanotubes (CNTs) were incorporated in penicillinase-phospholipid Langmuir and Langmuir–Blodgett (LB) films to enhance the enzyme catalytic properties. Adsorption of the penicillinase and CNTs at dimyristoylphosphatidic acid (DMPA) monolayers at the air–water interface was investigated by surface pressure–area isotherms, vibrational spectroscopy, and Brewster angle microscopy. The floating monolayers were transferred to solid supports through the LB technique, forming mixed DMPA-CNTs-PEN films, which were investigated by quartz crystal microbalance, vibrational spectroscopy, and atomic force microscopy. Enzyme activity was studied with UV–vis spectroscopy and the feasibility of the supramolecular device nanostructured as ultrathin films were essayed in a capacitive electrolyte–insulator–semiconductor (EIS) sensor device. The presence of CNTs in the enzyme–lipid LB film not only tuned the catalytic activity of penicillinase but also helped conserve its enzyme activity after weeks, showing increased values of activity. Viability as penicillin sensor was demonstrated with capacitance/voltage and constant capacitance measurements, exhibiting regular and distinctive output signals over all concentrations used in this work. These results may be related not only to the nanostructured system provided by the film, but also to the synergism between the compounds on the active layer, leading to a surface morphology that allowed a fast analyte diffusion because of an adequate molecular accommodation, which also preserved the penicillinase activity. This work therefore demonstrates the feasibility of employing LB films composed of lipids, CNTs, and enzymes as EIS devices for biosensing applications.
Muscle function is compromised by gravitational unloading in space affecting overall musculoskeletal health. Astronauts perform daily exercise programmes to mitigate these effects but knowing which muscles to target would optimise effectiveness. Accurate inflight assessment to inform exercise programmes is critical due to lack of technologies suitable for spaceflight. Changes in mechanical properties indicate muscle health status and can be measured rapidly and non-invasively using novel technology. A hand-held MyotonPRO device enabled monitoring of muscle health for the first time in spaceflight (> 180 days). Greater/maintained stiffness indicated countermeasures were effective. Tissue stiffness was preserved in the majority of muscles (neck, shoulder, back, thigh) but Tibialis Anterior (foot lever muscle) stiffness decreased inflight vs. preflight (p < 0.0001; mean difference 149 N/m) in all 12 crewmembers. The calf muscles showed opposing effects, Gastrocnemius increasing in stiffness Soleus decreasing. Selective stiffness decrements indicate lack of preservation despite daily inflight countermeasures. This calls for more targeted exercises for lower leg muscles with vital roles as ankle joint stabilizers and in gait. Muscle stiffness is a digital biomarker for risk monitoring during future planetary explorations (Moon, Mars), for healthcare management in challenging environments or clinical disorders in people on Earth, to enable effective tailored exercise programmes.
Pulmonary arterial cannulation is a common and effective method for percutaneous mechanical circulatory support for concurrent right heart and respiratory failure [1]. However, limited data exists to what effect the positioning of the cannula has on the oxygen perfusion throughout the pulmonary artery (PA). This study aims to evaluate, using computational fluid dynamics (CFD), the effect of different cannula positions in the PA with respect to the oxygenation of the different branching vessels in order for an optimal cannula position to be determined. The four chosen different positions (see Fig. 1) of the cannulas are, in the lower part of the main pulmonary artery (MPA), in the MPA at the junction between the right pulmonary artery (RPA) and the left pulmonary artery (LPA), in the RPA at the first branch of the RPA and in the LPA at the first branch of the LPA.
The integration of product data from heterogeneous sources and manufacturers into a single catalog is often still a laborious, manual task. Especially small- and medium-sized enterprises face the challenge of timely integrating the data their business relies on to have an up-to-date product catalog, due to format specifications, low quality of data and the requirement of expert knowledge. Additionally, modern approaches to simplify catalog integration demand experience in machine learning, word vectorization, or semantic similarity that such enterprises do not have. Furthermore, most approaches struggle with low-quality data. We propose Attribute Label Ranking (ALR), an easy to understand and simple to adapt learning approach. ALR leverages a model trained on real-world integration data to identify the best possible schema mapping of previously unknown, proprietary, tabular format into a standardized catalog schema. Our approach predicts multiple labels for every attribute of an inpu t column. The whole column is taken into consideration to rank among these labels. We evaluate ALR regarding the correctness of predictions and compare the results on real-world data to state-of-the-art approaches. Additionally, we report findings during experiments and limitations of our approach.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
Biodiversity and the coexistence of species have puzzled and fascinated biologists since decades and is a hotspot in todays’ natural sciences. Preserving this biodiversity is a great challenge as habitats and environments underlying tremendous changes like climate change and the loss of natural habitats, which are mainly due to anthropogenic influences. The coexistence of numerous species even in homogeneous environments is a stunning feature of natural communities and has been summarized under the term ‘paradox of plankton’. Up to now, there are several mechanisms discussed, which may contribute to local and global diversity of organisms. Several interspecific trade offs have been identified maintaining the coexistence of species like their abilities regarding competition and predator avoidance, their capability to disperse in space and time, and their ability to exploit variable resources. Further, micro-evolutionary dynamics supporting the coexistence of species have been added to our knowledge, and deriving from theoretical deterministic models, non-linear dynamics which describe the temporal fluctuation of abundances of organisms. Whereas competition and predation seem to be clue structural elements within interacting organisms, the intrinsic dynamic behavior – by means of temporal changes in abundance - plays an important role regarding coexistence within a community. The present work sheds light on different factors affecting the coexistence of species using experimental microbial model systems consisting of a bacterivorous ciliate as the predator and two bacteria strains as prey organism. Additionally, another experimental setup consisting of two up to five bacteria species competing for one limiting resource was investigated. Highly controllable chemostat systems were established to exclude extrinsic disturbances. According to theoretical analyses I was able to show - experimentally and theoretically - that phenotypic plasticity of one species within a microbial one-predator-two-prey food web enlarges the range of possible coexistence of all species under different dynamic conditions, compared to a food web without phenotypic plasticity. This was accompanied by non-linear (chaotic) population dynamics within all experimental systems showing phenotypic plasticity. The experiments on the interplay of competition, predation and invasion showed that all aspects have an influence on species coexistence. Under undisturbed controlled conditions all aspects were analyzed in detail and in combination. Populations showed oscillations which were shown by quasi-chaotic attractors in phase space diagrams. Competition experiments with two up to five bacteria species competing for one limiting resource showed that all organisms were able to coexist which was mediated by species oscillations entering a regime of chaos. Besides that fact it was found, that the productivity (biomass) as well as the total cell numbers – under the same nutrition supply – increased by an increasing number of species in the experimental systems. Up to now, the occurrence of non-linear dynamics in well controlled experimental studies has been recognized several times and this phenomenon seemed to be more common in natural systems than generally assumed.
Multimodal bioimage sensor
(2014)
To visualize the biochemical distribution two-dimensionally, we invented a solid-state-type ion image sensor that indicates the chemical activity of solutions and cells. The device, which consists of a CCD array covered with a functionalized membrane to detect charge accumulation, is highly sensitive to changes in the concentration and two-dimensional distribution of ions and biomaterials.
Activated carbons are known as excellent adsorbents. Their applications include the adsorptive removal of color, odor, taste, undesirable organic and inorganic pollutants from drinking and waste water; air purification in inhabited spaces; purification of many chemicals, pharmaceutical products and many others. This chapter elucidates the role of normal microflora in the maintenance of human health and presents materials on possible clinical displays of microecological infringements and ways of their correction. It presents new developments concerning new probiotics with immobilized Lactobacillus and Bacillus. The chapter considers the mechanisms of the intestine disbacteriosis correction by sorbed probiotics. It demonstrates the advantages and creation prospects of immobilized probiotics developed on the basis of carbonized rice husk. There are great prospects for the development of medical biotechnology due to use of carbon sorbents with a nanostructured surface. Microbial communities form a biocenosis of the biotope and together with the host organism create permanent or temporary ecosystems.