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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.
Persistent Photoconductivity in Halogen-doped Cd1-X ZnX Te, Cd1-X MnX Te and Cd1-X-MgX -Te Layers
(1995)
Proceedings of the 2nd Humboldt Kolleg, Hammamet, Tunisia Organizer: Alexander von Humboldt Stiftung, Germany. pdf 184 p. Welcome Address Dear Participants, Welcome to the 2nd Humboldt Kolleg in “Nanoscale Science and Technology” (NS&T’12) in Tunisia, sponsored by the "Alexander von Humboldt" foundation. The NS&T’12 multidisciplinary scientific program includes seven "hot" topics dealing with "Nanoscale Science and Technology" covering basic and application-oriented research as well as industrial (market) aspects: - Molecular Biophyics, Spectroscopy Techniques, Imaging Microscopy - Nanomaterials Synthesis for Medicine and Bio-chemical Sensors - Nanostructures, Semiconductors, Photonics and Nanodevices - New Technologies in Market Industry - Environment, Electro-chemistry, Bio-polymers and Fuel Cells - Nanomaterials, Photovoltaic, Modelling, Quantum Physics - Microelectronics, Sensors Networks and Embedded Systems We are deeply indebted to all members of the Scientific Committee and General Chairs for joint Sessions and to all speakers and chairmen, who have dedicated invaluable time and efforts for the realization of this event. On behalf of the Organizing Committee, we are cordially inviting you to join the conference and hope that your stay will be fruitful, rewarding and enjoyable. Prof. Dr. Michael J. Schöning, Prof. Dr. Adnane Abdelghani
The ANM’09 multi-disciplinary scientific program includes topics in the fields of "Nanotechnology and Microelectronics" ranging from "Bio/Micro/Nano Materials and Interfacing" aspects, "Chemical and Bio-Sensors", "Magnetic and Superconducting Devices", "MEMS and Microfluidics" over "Theoretical Aspects, Methods and Modelling" up to the important bridging "Academics meet Industry".
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.