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Institute
- Fachbereich Medizintechnik und Technomathematik (2052) (remove)
A graphene-functionalized carbon fiber electrode was modified with adsorbed polyethylenimine to introduce amino functionalities and then with trigonelline and 4-carboxyphenylboronic acid covalently bound to the amino groups. The trigonelline species containing quarterized pyridine groups produced positive charge on the electrode surface regardless of the pH value, while the phenylboronic acid species were neutral below pH 8 and negatively charged above pH 9 (note that their pKa=8.4). The total charge on the monolayer-modified electrode was positive at the neutral pH and negative at pH > 9. Note that 4-carboxyphenylboronic acid was attached to the electrode surface in molar excess to trigonelline, thus allowing the negative charge to dominate on the electrode surface at basic pH. Negatively charged fluorescent dye-labeled insulin (insulin-FITC) was loaded on the modified electrode surface at pH 7.0 due to its electrostatic attraction to the positively charged interface. The local pH in close vicinity to the electrode surface was increased to ca. 9–10 due to consumption of H+ ions upon electrochemical reduction of oxygen proceeding at the potential of −1.0 V (vs. Ag/AgCl) applied on the modified electrode. The process resulted in recharging of the electrode surface to the negative value due to the formation of the negative charge on the phenylboronic acid groups, thus resulting in the electrostatic repulsion of insulin-FITC and stimulating its release from the electrode surface. The insulin release was characterized by fluorescence spectroscopy (using the FITC-labeled insulin), by electrochemical measurements on an iridium oxide, IrOx, electrode and by mass spectrometry. The graphene-functionalized carbon fiber electrode demonstrated significant advantages in the signal-stimulated insulin release comparing with the carbon fiber electrode without the graphene species.
Robotergestütztes System für ein verbessertes neuromuskuläres Aufbautraining der Beinstrecker
(2016)
Neuromuskuläres Aufbautraining der Beinstrecker ist ein wichtiger Bestandteil in der Rehabilitation und Prävention von Muskel-Skelett-Erkrankungen. Effektives Training erfordert hohe Muskelkräfte, die gleichzeitig hohe Belastungen von bereits geschädigten Strukturen bedeuten. Um trainingsinduzierte Schädigungen zu vermeiden, müssen diese Kräfte kontrolliert werden. Mit heutigen Trainingsgeräten können diese Ziele allerdings nicht erreicht werden. Für ein sicheres und effektives Training sollen durch den Einsatz der Robotik, Sensorik, eines Regelkreises sowie Muskel-Skelett-Modellen Belastungen am Zielgewebe direkt berechnet und kontrolliert werden. Auf Basis zweier Vorstudien zu möglichen Stellgrößen wird der Aufbau eines robotischen Systems vorgestellt, das sowohl für Forschungszwecke als auch zur Entwicklung neuartiger Trainingsgeräte verwendet werden kann.
Für die Verarbeitung von natürlicher Sprache ist ein wichtiger Zwischenschritt das Parsing, bei dem für Sätze der natürlichen Sprache Ableitungsbäume bestimmt werden. Dieses Verfahren ist vergleichbar zum Parsen formaler Sprachen, wie z. B. das Parsen eines Quelltextes. Die Parsing-Methoden der formalen Sprachen, z. B. Bottom-up-Parser, können nicht auf das Parsen der natürlichen Sprache übertragen werden, da keine Formalisierung der natürlichen Sprachen existiert [3, 12, 23, 30].
In den ersten Programmen, die natürliche Sprache verarbeiten [32, 41], wurde versucht die natürliche Sprache mit festen Regelmengen zu verarbeiten. Dieser Ansatz stieß jedoch schnell an seine Grenzen, da die Regelmenge nicht vollständig sowie nicht minimal ist und wegen der benötigten Menge an Regeln schwer zu verwalten ist. Die Korpuslinguistik [22] bot die Möglichkeit, die Regelmenge durch Supervised-Machine-Learning-Verfahren [2] abzulösen.
Teil der Korpuslinguistik ist es, große Textkorpora zu erstellen und diese mit sprachlichen Strukturen zu annotieren. Zu diesen Strukturen gehören sowohl die Wortarten als auch die Ableitungsbäume der Sätze. Vorteil dieser Methodik ist es, dass repräsentative Daten zur Verfügung stehen. Diese Daten werden genutzt, um mit Supervised-Machine-Learning-Verfahren die Gesetzmäßigkeiten der natürliche Sprachen zu erlernen.
Das Maximum-Entropie-Verfahren ist ein Supervised-Machine-Learning-Verfahren, das genutzt wird, um natürliche Sprache zu erlernen. Ratnaparkhi [25] nutzt Maximum-Entropie, um Ableitungsbäume für Sätze der natürlichen Sprache zu erlernen. Dieses Verfahren macht es möglich, die natürliche Sprache (abgebildet als Σ∗) trotz einer fehlenden formalen Grammatik zu parsen.
The LAPS (light-addressable potentiometric sensor) platform is one of the most attractive approaches for chemical and biological sensing with many applications ranging from pH and ion/analyte concentration measurements up to cell metabolism detection and chemical imaging. However, although it is generally accepted that LAPS measurements are spatially resolved, the light-addressability feature of LAPS devices has not been discussed in detail so far. In this work, an extended electrical equivalent-circuit model of the LAPS has been presented, which takes into account possible cross-talk effects due to the capacitive coupling of the non-illuminated region. A shunting effect of the non-illuminated area on the measured photocurrent and addressability of LAPS devices has been studied. It has been shown, that the measured photocurrent will be determined not only by the local interfacial potential in the illuminated region but also by possible interfacial potential changes in the non-illuminated region, yielding cross-talk effects. These findings were supported by the experimental investigations of a penicillin-sensitive multi-spot LAPS and a metal-insulator-semiconductor LAPS as model systems.
The enormous diversity of seed traits is an intriguing feature and critical for the overwhelming success of higher plants. In particular, seed mass is generally regarded to be key for seedling development but is mostly approximated by using scanning methods delivering only two-dimensional data, often termed seed size. However, three-dimensional traits, such as the volume or mass of single seeds, are very rarely determined in routine measurements. Here, we introduce a device named phenoSeeder, which enables the handling and phenotyping of individual seeds of very different sizes. The system consists of a pick-and-place robot and a modular setup of sensors that can be versatilely extended. Basic biometric traits detected for individual seeds are two-dimensional data from projections, three-dimensional data from volumetric measures, and mass, from which seed density is also calculated. Each seed is tracked by an identifier and, after phenotyping, can be planted, sorted, or individually stored for further evaluation or processing (e.g. in routine seed-to-plant tracking pipelines). By investigating seeds of Arabidopsis (Arabidopsis thaliana), rapeseed (Brassica napus), and barley (Hordeum vulgare), we observed that, even for apparently round-shaped seeds of rapeseed, correlations between the projected area and the mass of seeds were much weaker than between volume and mass. This indicates that simple projections may not deliver good proxies for seed mass. Although throughput is limited, we expect that automated seed phenotyping on a single-seed basis can contribute valuable information for applications in a wide range of wild or crop species, including seed classification, seed sorting, and assessment of seed quality.
Label-free Electrostatic Detection of DNA Amplification by PCR Using Capacitive Field-effect Devices
(2016)
A capacitive field-effect EIS (electrolyte-insulator-semiconductor) sensor modified with a positively charged weak polyelectrolyte of poly(allylamine hydrochloride) (PAH)/single-stranded probe DNA (ssDNA) bilayer has been used for a label-free electrostatic detection of pathogen-specific DNA amplification via polymerase chain reaction (PCR). The sensor is able to distinguish between positive and negative PCR solutions, to detect the existence of target DNA amplicons in PCR samples and thus, can be used as tool for a quick verification of DNA amplification and the successful PCR process.
Enzyme-based logic gates and circuits - analytical applications and interfacing with electronics
(2017)
The paper is an overview of enzyme-based logic gates and their short circuits, with specific examples of Boolean AND and OR gates, and concatenated logic gates composed of multi-step enzyme-biocatalyzed reactions. Noise formation in the biocatalytic reactions and its decrease by adding a “filter” system, converting convex to sigmoid response function, are discussed. Despite the fact that the enzyme-based logic gates are primarily considered as components of future biomolecular computing systems, their biosensing applications are promising for immediate practical use. Analytical use of the enzyme logic systems in biomedical and forensic applications is discussed and exemplified with the logic analysis of biomarkers of various injuries, e.g., liver injury, and with analysis of biomarkers characteristic of different ethnicity found in blood samples on a crime scene. Interfacing of enzyme logic systems with modified electrodes and semiconductor devices is discussed, giving particular attention to the interfaces functionalized with signal-responsive materials. Future perspectives in the design of the biomolecular logic systems and their applications are discussed in the conclusion.
A New Class of Biosensors Based on Tobacco Mosaic Virus and Coat Proteins as Enzyme Nanocarrier
(2016)