Refine
Year of publication
Institute
- Fachbereich Medizintechnik und Technomathematik (1689) (remove)
Language
- English (1689) (remove)
Document Type
- Article (1358)
- Conference Proceeding (210)
- Book (43)
- Part of a Book (43)
- Doctoral Thesis (18)
- Other (6)
- Patent (4)
- Preprint (3)
- Lecture (2)
- Habilitation (1)
Keywords
- Biosensor (25)
- Finite-Elemente-Methode (12)
- Einspielen <Werkstoff> (10)
- CAD (8)
- civil engineering (8)
- Bauingenieurwesen (7)
- FEM (6)
- Limit analysis (6)
- Shakedown analysis (6)
- shakedown analysis (6)
Motile cilia are hair-like cell extensions that beat periodically to generate fluid flow along various epithelial tissues within the body. In dense multiciliated carpets, cilia were shown to exhibit a remarkable coordination of their beat in the form of traveling metachronal waves, a phenomenon which supposedly enhances fluid transport. Yet, how cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine experiments, novel analysis tools, and theory to address this knowledge gap. To investigate collective dynamics of cilia, we studied zebrafish multiciliated epithelia in the nose and the brain. We focused mainly on the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Even though synchronization is local only, we observed global patterns of traveling metachronal waves across the zebrafish multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right noses, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions, i.e., cilia colliding with each other, and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment coincide and generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.
Motile cilia are hair-like cell extensions present in multiple organs of the body. How cilia coordinate their regular beat in multiciliated epithelia to move fluids remains insufficiently understood, particularly due to lack of rigorous quantification. We combine here experiments, novel analysis tools, and theory to address this knowledge gap. We investigate collective dynamics of cilia in the zebrafish nose, due to its conserved properties with other ciliated tissues and its superior accessibility for non-invasive imaging. We revealed that cilia are synchronized only locally and that the size of local synchronization domains increases with the viscosity of the surrounding medium. Despite the fact that synchronization is local only, we observed global patterns of traveling metachronal waves across the multiciliated epithelium. Intriguingly, these global wave direction patterns are conserved across individual fish, but different for left and right nose, unveiling a chiral asymmetry of metachronal coordination. To understand the implications of synchronization for fluid pumping, we used a computational model of a regular array of cilia. We found that local metachronal synchronization prevents steric collisions and improves fluid pumping in dense cilia carpets, but hardly affects the direction of fluid flow. In conclusion, we show that local synchronization together with tissue-scale cilia alignment are sufficient to generate metachronal wave patterns in multiciliated epithelia, which enhance their physiological function of fluid pumping.
Algal polysaccharides (extracellular polysaccharides) and carbon nanotubes (CNTs) were adsorbed on dioctadecyldimethylammonium bromide Langmuir monolayers to serve as a matrix for the incorporation of urease. The physicochemical properties of the supramolecular system as a monolayer at the air–water interface were investigated by surface pressure–area isotherms, surface potential–area isotherms, interfacial shear rheology, vibrational spectroscopy, and Brewster angle microscopy. The floating monolayers were transferred to hydrophilic solid supports, quartz, mica, or capacitive electrolyte–insulator–semiconductor (EIS) devices, through the Langmuir–Blodgett (LB) technique, forming mixed films, which were investigated by quartz crystal microbalance, fluorescence spectroscopy, and field emission gun scanning electron microscopy. The enzyme activity was studied with UV–vis spectroscopy, and the feasibility of the thin film as a urea sensor was essayed in an EIS sensor device. The presence of CNT in the enzyme–lipid LB film not only tuned the catalytic activity of urease but also helped to conserve its enzyme activity. Viability as a urease 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 are related to the synergism between the compounds on the active layer, leading to a surface morphology that allowed fast analyte diffusion owing to an adequate molecular accommodation, which also preserved the urease activity. This work demonstrates the feasibility of employing LB films composed of lipids, CNT, algal polysaccharides, and enzymes as EIS devices for biosensing applications.
Monitoring of organic acids (OA) and volatile fatty acids (VFA) is crucial for the control of anaerobic digestion. In case of unstable process conditions, an accumulation of these intermediates occurs. In the present work, two different enzyme-based biosensor arrays are combined and presented for facile electrochemical determination of several process-relevant analytes. Each biosensor utilizes a platinum sensor chip (14 × 14 mm²) with five individual working electrodes. The OA biosensor enables simultaneous measurement of ethanol, formate, d- and l-lactate, based on a bi-enzymatic detection principle. The second VFA biosensor provides an amperometric platform for quantification of acetate and propionate, mediated by oxidation of hydrogen peroxide. The cross-sensitivity of both biosensors toward potential interferents, typically present in fermentation samples, was investigated. The potential for practical application in complex media was successfully demonstrated in spiked sludge samples collected from three different biogas plants. Thereby, the results obtained by both of the biosensors were in good agreement to the applied reference measurements by photometry and gas chromatography, respectively. The proposed hybrid biosensor system was also used for long-term monitoring of a lab-scale biogas reactor (0.01 m³) for a period of 2 months. In combination with typically monitored parameters, such as gas quality, pH and FOS/TAC (volatile organic acids/total anorganic carbonate), the amperometric measurements of OA and VFA concentration could enhance the understanding of ongoing fermentation processes.
Three amperometric biosensors have been developed for the detection of L-malic acid, fumaric acid, and L -aspartic acid, all based on the combination of a malate-specific dehydrogenase (MDH, EC 1.1.1.37) and diaphorase (DIA, EC 1.8.1.4). The stepwise expansion of the malate platform with the enzymes fumarate hydratase (FH, EC 4.2.1.2) and aspartate ammonia-lyase (ASPA, EC 4.3.1.1) resulted in multi-enzyme reaction cascades and, thus, augmentation of the substrate spectrum of the sensors. Electrochemical measurements were carried out in presence of the cofactor β-nicotinamide adenine dinucleotide (NAD+) and the redox mediator hexacyanoferrate (III) (HCFIII). The amperometric detection is mediated by oxidation of hexacyanoferrate (II) (HCFII) at an applied potential of + 0.3 V vs. Ag/AgCl. For each biosensor, optimum working conditions were defined by adjustment of cofactor concentrations, buffer pH, and immobilization procedure. Under these improved conditions, amperometric responses were linear up to 3.0 mM for L-malate and fumarate, respectively, with a corresponding sensitivity of 0.7 μA mM−1 (L-malate biosensor) and 0.4 μA mM−1 (fumarate biosensor). The L-aspartate detection system displayed a linear range of 1.0–10.0 mM with a sensitivity of 0.09 μA mM−1. The sensor characteristics suggest that the developed platform provides a promising method for the detection and differentiation of the three substrates.
REM sleep without atonia (RSWA) is a key feature for the diagnosis of rapid eye movement (REM) sleep behaviour disorder (RBD). We introduce RBDtector, a novel open-source software to score RSWA according to established SINBAR visual scoring criteria. We assessed muscle activity of the mentalis, flexor digitorum superficialis (FDS), and anterior tibialis (AT) muscles. RSWA was scored manually as tonic, phasic, and any activity by human scorers as well as using RBDtector in 20 subjects. Subsequently, 174 subjects (72 without RBD and 102 with RBD) were analysed with RBDtector to show the algorithm’s applicability. We additionally compared RBDtector estimates to a previously published dataset. RBDtector showed robust conformity with human scorings. The highest congruency was achieved for phasic and any activity of the FDS. Combining mentalis any and FDS any, RBDtector identified RBD subjects with 100% specificity and 96% sensitivity applying a cut-off of 20.6%. Comparable performance was obtained without manual artefact removal. RBD subjects also showed muscle bouts of higher amplitude and longer duration. RBDtector provides estimates of tonic, phasic, and any activity comparable to human scorings. RBDtector, which is freely available, can help identify RBD subjects and provides reliable RSWA metrics.
Ambitious climate targets affect the competitiveness of industries in the international market. To prevent such industries from moving to other countries in the wake of increased climate protection efforts, cost adjustments may become necessary. Their design requires knowledge of country-specific production costs. Here, we present country-specific cost figures for different production routes of steel, paying particular attention to transportation costs. The data can be used in floor price models aiming to assess the competitiveness of different steel production routes in different countries (Rübbelke, 2022).
The European Union's aim to become climate neutral by 2050 necessitates ambitious efforts to reduce carbon emissions. Large reductions can be attained particularly in energy intensive sectors like iron and steel. In order to prevent the relocation of such industries outside the EU in the course of tightening environmental regulations, the establishment of a climate club jointly with other large emitters and alternatively the unilateral implementation of an international cross-border carbon tax mechanism are proposed. This article focuses on the latter option choosing the steel sector as an example. In particular, we investigate the financial conditions under which a European cross border mechanism is capable to protect hydrogen-based steel production routes employed in Europe against more polluting competition from abroad. By using a floor price model, we assess the competitiveness of different steel production routes in selected countries. We evaluate the climate friendliness of steel production on the basis of specific GHG emissions. In addition, we utilize an input-output price model. It enables us to assess impacts of rising cost of steel production on commodities using steel as intermediates. Our results raise concerns that a cross-border tax mechanism will not suffice to bring about competitiveness of hydrogen-based steel production in Europe because the cost tends to remain higher than the cost of steel production in e.g. China. Steel is a classic example for a good used mainly as intermediate for other products. Therefore, a cross-border tax mechanism for steel will increase the price of products produced in the EU that require steel as an input. This can in turn adversely affect competitiveness of these sectors. Hence, the effects of higher steel costs on European exports should be borne in mind and could require the cross-border adjustment mechanism to also subsidize exports.
A solid-state amperometric hydrogen sensor based on a protonated Nafion membrane and catalytic active electrode operating at room temperature was fabricated and tested. Ionic conducting polymer-metal electrode interfaces were prepared chemically by using the impregnation-reduction method. The polymer membrane was impregnated with tetra-ammine platinum chloride hydrate and the metal ions were subsequently reduced by using either sodium tetrahydroborate or potassium tetrahydroborate. The hydrogen sensing characteristics with air as reference gas is reported. The sensors were capable of detecting hydrogen concentrations from 10 ppm to 10% in nitrogen. The response time was in the range of 10-30 s and a stable linear current output was observed. The thin Pt films were characterized by XRD, Infrared Spectroscopy, Optical Microscopy, Atomic Force Microscopy, Scanning Electron Microscopy and EDAX.
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.
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.
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.
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.
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.
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.
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.
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.
Research collaborations provide opportunities for both practitioners and researchers: practitioners need solutions for difficult business challenges and researchers are looking for hard problems to solve and publish. Nevertheless, research collaborations carry the risk that practitioners focus on quick solutions too much and that researchers tackle theoretical problems, resulting in products which do not fulfill the project requirements.
In this paper we introduce an approach extending the ideas of agile and lean software development. It helps practitioners and researchers keep track of their common research collaboration goal: a scientifically enriched software product which fulfills the needs of the practitioner’s business model.
This approach gives first-class status to application-oriented metrics that measure progress and success of a research collaboration continuously. Those metrics are derived from the collaboration requirements and help to focus on a commonly defined goal.
An appropriate tool set evaluates and visualizes those metrics with minimal effort, and all participants will be pushed to focus on their tasks with appropriate effort. Thus project status, challenges and progress are transparent to all research collaboration members at any time.
Characterising an insect antenna as a receptor for a biosensor by means of impedance spectroscopy
(2001)
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.
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.
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.
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.
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).
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.
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.