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Collaborative Research Center SFB 876 - Providing Information by Resource-Constrained Data Analysis


The collaborative research center SFB876 brings together data mining and embedded systems. On the one hand, embedded systems can be further improved using machine learning. On the other hand, data mining algorithms can be realized in hardware, e.g. FPGAs, or run on GPGPUs. The restrictions of ubiquitous systems in computing power, memory, and energy demand new algorithms for known learning tasks. These resource bounded learning algorithms may also be applied on extremely large data bases on servers.





Tracking down the coronavirus with biosensors: ISAS and TU Dortmund University assess the use of a measuring instrument

Whereas viruses are too small to be revealed visually, their interaction with antibodies can be made visible. The Leibniz Institute for Analytical Sciences (ISAS) and the Collaborative Research Centre (SFB) 876 of the Technical University (TU) Dortmund intend to apply a measurement method to the novel corona virus Sars-Cov-2.


The cooperation between ISAS and TU Dortmund University, which has been in place since 2010, could result in an effective method for the containment of the novel coronavirus (COVID-19). With the virus sensor, Dortmund physicists, computer scientists and mathematicians have developed an instrument that enables analysis procedures to be carried out in real time and on site. The sensor can also be used outside of special laboratories to determine the infection status of large groups of people, such as airport passengers or residents of entire housing estates. This measuring method can prevent the introduction, further spread and recurrence of viruses.

It is conceivable that the biosensor could now also be used to combat the novel coronavirus. To this end, scientists at ISAS and TU Dortmund University are currently working with anti-SARS-CoV-2 antibodies to prepare the Sensor for the corona viruses.

Indeed, our sensor works by exploiting a physical effect that bridges the gap between the micrometer and nanometer range: viruses - including corona viruses - are objects on the nanometer range and thus too small to be detected with optical microscopes, which are only accessible to the micrometer range. Microscopes lack the necessary magnifying power for the direct detection of viruses. The sensor, on the other hand, detects viruses indirectly by measuring changes in the so-called surface plasmon resonance that the viruses cause on the sensor. In principle, this is based on the detection of label-free biomolecular binding reactions on a gold surface, in a series of images taken with a CCD camera. Even though a virus is only nanometer in size, the resonance as an effect extends over the micrometer range. These characteristic changes are determined by image and signal analysis methods based on special neural networks and allow the identification of different viral pathogens with high detection rates in real time.

"By this, viruses become optically detectable, which allows a low-cost, mobile sensor and very fast tests," summarizes Dr. Roland Hergenröder, who heads the project group on the ISAS side. He hopes that with the availability of anti-SARS-CoV-2 antibodies, the Sensor will soon be able to be used for the detection of the novel coronavirus.

Sensor and analysis methods were developed in a cooperation of physicists, computer scientists and mathematicians of ISAS and the Chairs of Computer Graphics and Embedded Systems of TU Dortmund within the framework of the Collaborative Research Center 876, subproject B2 with the name "Resource optimizing real time analysis of artifactious image sequences for the detection of nano objects". Prof. Dr. Katharina Morik, speaker of the Collaborative Research Centre 876 summarizes: "We are proud of the biosensor; if it can now be used against corona, that's wonderful," Morik summarizes.

The B2 Virus Detection Projekt of SFB 876

The comprehensive real-time detection of the Coronavirus SARS-CoV-2 is a fundamental challenge. A biosensor called "Plasmon Assisted Microscopy of Nano-sized Objects" could make a valuable contribution here. The sensor represents a viable technology for mobile real-time detection and quantitative analysis of viruses and virus-like particles. A mobile system that can detect viruses in real time is urgently needed, due to the combination of virus emergence and evolution with increasing global travel and transport. It could be used for fast and reliable diagnoses in hospitals, airports, the open air, or other settings. The development of the sensor is part of the collaborative research center 876 funded by DFG (sfb876.tu-dortmund.de) and has been launched since 2010.

The biosensor permits the imaging of biological nano-vesicles (e.g. the Coronavirus) utilizing a Kretschmann’s scheme of plasmon excitation with an illumination of a gold sensor surface via a glass prism. The sensor applies anti-bodies to bind the nano-sized viruses on a gold layer. The presence of viruses can be detected by the intensity change of the reflection of a laser beam. For more technical details, we refer the reader to our survey paper by Shpacovitch, et al. (DOI: 10.3390/s17020244). Characteristics of these binding events are spatiotemporal blob-like structures with very low signal-to-noise ratio, which indicate particle bindings and can be automatically analyzed with image processing methods. We capture the intensity of the reflected laser beams using a CCD camera, which result in a series of artifactious images. For analysis of the images provided by the sensor, we have developed nanoparticle classification approaches based on deep neural network architectures. It is shown that the combination of the sensor and the application of deep learning enables a real-time data processing to automatically detect and quantify biological particles. With the availability of anti-SARS-CoV-2 antibodies, the sensor could thus also be used to detect the Coronavirus.

Subprojekt B2: Resource optimizing real time analysis of artifactious image sequences for the detection of nano objects


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