@misc{NghinaunyeWaldeckJungetal., author = {Nghinaunye, Theopolina and Waldeck, Peter and Jung, Conrad H. G. and K{\"u}pper, Jan-Heiner and Jung, Friedrich and Braune, Steffen}, title = {Response of Arthrospira platensis to different temperatures regarding growth and biochemical composition}, series = {Clinical Hemorheology and Microcirculation}, journal = {Clinical Hemorheology and Microcirculation}, issn = {1386-0291}, doi = {10.3233/CH-238104}, pages = {1 -- 7}, abstract = {The growth of cyanobacteria can vary considerably depending on the ambient temperature. Since the optimal growth temperature for Arthrospira platensis (strain SAG21.99) is not yet known, this was investigated in the present study. The study revealed that a process temperature of 30°C seems to be optimal for the Arthrospira strain SAG21.99 cultivation in terms of a maximum biomass productivity. This was also true for the phycocyanin content which was at 30°C significantly higher than at 20 or 40°C.}, language = {en} } @misc{JungNghinaunyeWaldecketal., author = {Jung, Conrad H. G. and Nghinaunye, Theopolina and Waldeck, Peter and Braune, Steffen and Petrick, Ingolf and K{\"u}pper, Jan-Heiner and Jung, Friedrich}, title = {Decarbonization of Arthrospira platensis production by using atmospheric CO2 as an exclusive carbon source: proof of principle}, series = {International Journal of Environmental Science and Technology}, journal = {International Journal of Environmental Science and Technology}, issn = {1735-2630}, doi = {10.1007/s13762-023-05215-x}, abstract = {There is an urgent need to develop technologies for removing CO2 from the atmosphere to combat climate change. Microalgae and cyanobacteria, such as Arthrospira platensis (AP), have shown promise due to their high photoautotrophic biomass production. Conventional AP culture media are supplemented with high concentrations of NaHCO3 since AP utilizes as a carbon source. These culture conditions result in significant amounts of CO2 escaping into the atmosphere, instead of being sequestered during cultivation. Here, we investigated whether ambient air (0.042\% CO2) can be used for growing AP in a culture medium lacking a fossil-based carbon source. AP was cultured in 2 L glass bioreactors containing: (1) Zarrouk medium with 16.8 g/L NaHCO3 and aeration with 0.236 vvm air with 2\% CO2 ("NaHCO3/CO2-based") to compensate carbon loss due to CO2 outgassing, and (2) Zarrouk medium without NaHCO3 and a gas flow with ambient air (0.926 vvm) as the only carbon source ("air-based"). The air-based production resulted in the biofixation of 3.78 gCO2/L during the linear growth phase. With NaHCO3/CO2-based production, a comparable amount of 3.42 gCO2/L was obtained while 659.12 g of CO2 was released into the atmosphere. Total protein, phycocyanin, chlorophyll-a, and carotenoids were present in similar or increased amounts in AP produced by the air-based method. We concluded that cultivation of AP with Zarrouk medium lacking NaHCO3 but using ambient air with atmospheric CO2 as the only carbon source is possible without reducing productivity. These results improve our understanding of how atmospheric CO2 can be reduced by culturing AP.}, language = {en} } @misc{PidvalnyiKostenkoSudakovetal., author = {Pidvalnyi, Illia and Kostenko, Anna and Sudakov, Oleksandr and Isaev, Dmytro and Maximyuk, Oleksandr and Krishtal, Oleg and Iegorova, Olena and Kabin, Ievgen and Dyka, Zoya and Ortmann, Steffen and Langend{\"o}rfer, Peter}, title = {Classification of epileptic seizures by simple machine learning techniques : application to animals' electroencephalography signals}, series = {IEEE access}, volume = {13}, journal = {IEEE access}, publisher = {Institute of Electrical and Electronics Engineers (IEEE)}, address = {Piscataway, NJ}, issn = {2169-3536}, doi = {10.1109/ACCESS.2025.3527866}, pages = {8951 -- 8962}, abstract = {Detection and prediction of the onset of seizures are among the most challenging problems in epilepsy diagnostics and treatment. Small electronic devices capable of doing that will improve the quality of life for epilepsy patients while also open new opportunities for pharmacological intervention. This paper presents a novel approach using machine learning techniques to detect seizures onset using intracranial electroencephalography (EEG) signals. The proposed approach was tested on intracranial EEG data recorded in rats with pilocarpine model of temporal lobe epilepsy. A principal component analysis was applied for feature selection before using a support vector machine for the detection of seizures. Hjorth's parameters and Daubechies discrete wavelet transform coefficients were found to be the most informative features of EEG data. We found that the support vector machine approach had a classification sensitivity of 90\% and a specificity of 74\% for detecting ictal episodes. Changing the epoch parameter from one to twenty-one seconds results in changing the redistribution of principal components' values to 10\% but does not affect the classification result. Support vector machines are accessible and convenient methods for classification that have achieved promising classification quality, and are rather lightweight compared to other machine learning methods. So we suggest their future use in mobile devices for early epileptic seizure and preictal episode detection.}, language = {en} }