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Fast and reliable detection of infectious SARS-CoV-2 virus loads is an important issue. Fluorescence spectroscopy is a sensitive tool to do so in clean environments. This presumes a comprehensive knowledge of fluorescence data.We aim at providing fully featured information on wavelength and time-dependent data of the fluorescence of the SARS-CoV-2 spike protein S1 subunit, its receptor-binding domain (RBD), and the human angiotensin-converting enzyme 2, especially with respect to possible optical detection schemes.Spectrally resolved excitation-emission maps of the involved proteins and measurements of fluorescence lifetimes were recorded for excitations from 220 to 295 nm. The fluorescence decay times were extracted by using a biexponential kinetic approach. The binding process in the SARS-CoV-2 RBD was likewise examined for spectroscopic changes.Distinct spectral features for each protein are pointed out in relevant spectra extracted from the excitation-emission maps. We also identify minor spectroscopic changes under the binding process. The decay times in the biexponential model are found to be ( 2.0 ± 0.1 ) ns and ( 8.6 ± 1.4 ) ns.Specific material data serve as an important background information for the design of optical detection and testing methods for SARS-CoV-2 loaded media.

Release of hazardous substances may cause severe consequences to humans and infrastructure. A fast detection system classifying these substances can be used to initiate countermeasures quickly and reduce damage to general public significantly. Nowadays, the investigation of such materials is time and money consuming but essential for damage limitation. Current procedures take long and require methods that involve measurements at close range and/or sampling for subsequent laboratory analyses. Both, sampling time and distance, can be improved using standoff laser induced fluorescence (LIF) spectroscopy which enables detection in seconds reaching distances over 100 m. By now the specificity of the technique is not sufficient to identify samples but it can be helpful for risk assessment and to guide first responders to salient regions for subsequent in situ measurements. This contribution presents an interactive graphical user interface as well as the practical workflow from generating training data and classification models to forecasting new records concurrently after the measurement. The foregoing modeling process is based on datasets generated previously with well-defined samples. Each sample from a considerable set of different chemical, botanical and bacterial substances can be distinguished using the LIF signals excited with short laser pulses of two UV wavelengths within a few seconds. Simultaneously, the fluorescence lifetime is recorded to provide additional information for a further enhanced discriminability. Estimating the sample species for new measurements consumes just a few seconds - including data acquisition, preprocessing, model application and visualization to the operator. As an example, the workflow is presented together with performance results for a test classification of 20 different substances.

After accidental or intended release of chemical or biological hazardous materials, fast detection and classification is mandatory to be able to rapidly take steps against negative consequences on humans. Standoff laser based devices are suited to localize and classify a thread on short timescales without the need of direct human contact to these materials. In this scope we present the classification prospects of our standoff detection system that utilizes laser induced fluorescence signals excited with laser pulses of two different UV excitation wavelengths. For fast data acquisition we use a rather limited spectral resolution of 32 channels per wavelength which is sufficient to achieve a good performance. The described system is able to perform a complete classification procedure including data acquisition, data analysis and classification and to get an assignment of the measured sample after a few seconds. The identification and classification prospects are evaluated by classifying measured sample spectra using a model which is based on a previously acquired reference database. Within the set, different representatives for bacterial and chemical agents as well as background materials as diesel and pollen are investigated. The performance, namely the classification accuracy and false alarm rates, is evaluated for differently sized classes down to an identification of the investigated set of materials.

Contaminated objects and areas must be handled carefully depending on the underlying pollution. There are methods which require short distances, others the collection of samples or even direct contact to the hazardous, and some of the established techniques take long to reach a conclusion. A fast standoff method for predicting the potential hazard can be achieved by examining the laser induced fluorescence spectra of the substances of interest. The samples are excited by low-energy laser pulses of two alternating wavelengths. The datasets are measured for almost 50 agents, including fuels, pesticides and bacteria and represent the basis for a subsequent classification procedure. Therefore, the investigated materials are grouped in seven classes depending on their origin and utilization. The majority of the dataset is used in a training phase to create predictive models, which are tested with the remaining signals to qualify the classification. After all, the single spectra of the test set are classifed with an error rate less than 0.1 % in predicting the correct class. With a statement like this frst responders would be able to choose the right preventive measure for a rescue or decontamination procedure.

To minimize the impact of an airborne bio-agent output, sensitive, specific and swift detection and identification are essential. A single method can hardly meet all of these requirements. Point sensors allow highly sensitive and specific identification but are localized and comparatively slow. Most laser-based standoff systems lack selectivity and specificity but provide real-time detection and classification in a wide region with additional information about location and propagation. A combination of both methods allows benefiting from their complementary assets and may be a promising solution to optimize detection and identification of hazardous substances. Here, we present progress for an outdoor bio-detector based on laser-induced fluorescence (LIF) developed at the DLR Lampoldshausen. After excitation at 280 and 355 nm, bacteria species express unique fluorescence spectra. Upon deactivation, the spectral features change depending on the applied method.

Prediction values for the novel proteins compared to their shuffled counterparts for each protein individually. (PDF 144Â kb)

Biological hazards, such as bacteria, represent a non-assessable threat in case of an accident or a terroristic attack. Rapid detection and highly sensitive identification of released, suspicious substances at low false alarm rates are challenging requirements which one single technology cannot cope with. It has been shown that standoff detection using laser-induced fluorescence (LIF) can provide information on the class of bioorganic substances in real-time1. In combination with traditional, highly sensitive, but non-standoff methods, the time for identification of the threat can be optimized. This work is aimed at the selectivity of LIF technology for different bacterial strains. A second important aspect examines how to deal with inactivated bacteria and how their fluorescence signature changes after deactivation. LIF spectra of closely and more distantly related bacterial strains are presented as well as spectra of bacteria treated by different inactivation methods.

SignalP prediction of signal peptides for proteins with one transmembrane domain. (PDF 74Â kb)

Cumulative distributions of PSI-Blast and HHblits family sizes. (PDF 8541Â kb)

Overview of bioinformatics predictions for Nog1. (PDF 110 kb)

E. coli strains and plasmids. (PDF 116 kb)

Full description of the metabolomics measurements. (PDF 125 kb)