RESUMEN
The advancement of regenerative life support systems (RLSS) is crucial to allow long-distance space travel. Within the Micro-Ecological Life Support System Alternative (MELiSSA), efficient nitrogen recovery from urine and other waste streams is vital to produce liquid fertilizer to feed food and oxygen production in subsequent photoautotrophic processes. This study explores the effects of ionizing radiation on nitrogen cycle bacteria that transform urea to nitrate. In particular, we assess the radiotolerance of Comamonas testosteroni, Nitrosomonas europaea, and Nitrobacter winogradskyi after exposure to acute γ-irradiation. Moreover, a comprehensive whole transcriptome analysis elucidates the effects of spaceflight-analogue low-dose ionizing radiation on the individual axenic strains and on their synthetic community o. This research sheds light on how the spaceflight environment could affect ureolysis and nitrification processes from a transcriptomic perspective.
RESUMEN
Regenerative life support systems (RLSS) will play a vital role in achieving self-sufficiency during long-distance space travel. Urine conversion into a liquid nitrate-based fertilizer is a key process in most RLSS. This study describes the effects of simulated microgravity (SMG) on Comamonas testosteroni, Nitrosomonas europaea, Nitrobacter winogradskyi and a tripartite culture of the three, in the context of nitrogen recovery for the Micro-Ecological Life Support System Alternative (MELiSSA). Rotary cell culture systems (RCCS) and random positioning machines (RPM) were used as SMG analogues. The transcriptional responses of the cultures were elucidated. For CO2-producing C. testosteroni and the tripartite culture, a PermaLifeTM PL-70 cell culture bag mounted on an in-house 3D-printed holder was applied to eliminate air bubble formation during SMG cultivation. Gene expression changes indicated that the fluid dynamics in SMG caused nutrient and O2 limitation. Genes involved in urea hydrolysis and nitrification were minimally affected, while denitrification-related gene expression was increased. The findings highlight potential challenges for nitrogen recovery in space.
RESUMEN
Introduction: Paralyzed and physically impaired patients face communication difficulties, even when they are mentally coherent and aware. Electroencephalographic (EEG) brain-computer interfaces (BCIs) offer a potential communication method for these people without invasive surgery or physical device controls. Methods: Although virtual keyboard protocols are well documented in EEG BCI paradigms, these implementations are visually taxing and fatiguing. All English words combine 44 unique phonemes, each corresponding to a unique EEG pattern. In this study, a complete phoneme-based imagined speech EEG BCI was developed and tested on 16 subjects. Results: Using open-source hardware and software, machine learning models, such as k-nearest neighbor (KNN), reliably achieved a mean accuracy of 97 ± 0.001%, a mean F1 of 0.55 ± 0.01, and a mean AUC-ROC of 0.68 ± 0.002 in a modified one-versus-rest configuration, resulting in an information transfer rate of 304.15 bits per minute. In line with prior literature, the distinguishing feature between phonemes was the gamma power on channels F3 and F7. Discussion: However, adjustments to feature selection, trial window length, and classifier algorithms may improve performance. In summary, these are iterative changes to a viable method directly deployable in current, commercially available systems and software. The development of an intuitive phoneme-based EEG BCI with open-source hardware and software demonstrates the potential ease with which the technology could be deployed in real-world applications.