The increased use of wireless technology causes a significant exposure increase for all living organisms to radio frequency electromagnetic fields (RF-EMF). This comprises bacteria, animals, and also plants. Unfortunately, our understanding of how RF-EMF influences plants and plant physiology remains inadequate. In this study, we examined the effects of RF-EMF radiation on lettuce plants (Lactuca sativa) in both indoor and outdoor environments using the frequency ranges of 1890–1900 MHz (DECT) at 2.4 GHz and 5 GHz (Wi-Fi). Under greenhouse conditions, RF-EMF exposure had only a minor impact on fast chlorophyll fluorescence kinetics and no effect on plant flowering time. In contrast, lettuce plants exposed to RF-EMF in the field showed a significant and systemic decrease in photosynthetic efficiency and accelerated flowering time compared to the control groups. Gene expression analysis revealed significant down-regulation of two stress-related genes in RF-EMF-exposed plants: violaxanthin de-epoxidase (VDE) and zeaxanthin epoxidase (ZEP). RF-EMF-exposed plants had lower Photosystem II’s maximal photochemical quantum yield (F_V/F_M) and non-photochemical quenching (NPQ) than control plants under light stress conditions. In summary, our results imply that RF-EMF might interfere with plant stress responses and reduced plant stress tolerance.

In Chlamydomonas reinhardtii, ketocarotenoid biosynthesis is limited to the diploid zygospore stage. In this study, we attempted to engineer the ketocarotenoid pathway into Chlamydomonas haploid vegetative green cells by overexpressing the key enzyme ß-carotene ketolase (CrBKT). We chose strain CC-4102 for the approach; competitive pathways, α-carotene biosynthesis and xanthophyll cycle are silenced in this strain. Driven by the strong constitutive HSP70/RBCS2 promoter CrBKT overexpression resulted in the production of canthaxanthin, the ketolation product from ß-carotene as well as a drastic reduction in the chlorophyll concentration. Intriguingly, these phenotypes could only be detected from lines transformed and grown heterotrophically in the dark. Once exposed to light, these transformants lost the aforementioned phenotypes as well as their antibiotic resistance. This phenomenon is in agreement with the fact that we were unable to recover any canthaxanthin-producing line among light-selected transformants.

Modern agriculture demands for comprehensive information about the plants themselves. Conventional chemistry-based analytical methods—due to their low throughput and high associated costs—are no longer capable of providing these data. In recent years, remote reflectance-based characterisation has become one of the most promising solutions for rapid assessments of plant attributes. However, in many cases, expensive equipment is required because accurate quantifications need assessments of the full reflectance spectrum. In this experimental study, we examined the versatility of visible spectral sensors as alternative reflectance measuring devices for biological/biochemical quantifications of sweet basil (Ocimum basilicum). Our results confirm the applicability and scope of visible spectral sensors for analysis and quantification of important plant properties, in particular the contents of valuable substances, such as phenolic compounds and flavonoids.

The analysis of fast fluorescence kinetics, specifically through the JIP test, is a valuable tool for identifying and characterizing plant stress. However, interpreting OJIP data requires a comprehensive understanding of their underlying theory. This study proposes a Machine Learning-based approach using a One-Class Support Vector Machine anomaly detection model to effectively categorize OJIP measurements into “normal”, representing healthy plants, and “anomalies”. This approach was validated using a previously published dataset. A subgroup of the identified “anomalies” was clearly linked to stress-induced reductions in photosynthesis. Furthermore, the percentage of these “anomalies” showed a meaningful correlation with both the progression and severity of stress. The results highlight the still largely unexploited potential of Machine Learning in OJIP analysis.

Aquaporins are important and well-studied water channel membrane proteins. However, being membrane proteins, sample preparation for functional analysis is tedious and time-consuming. In this paper, we report a new approach for the co-translational insertion of two aquaporins from Escherichia coli and Nicotiana tabacum using the CFPS system. This was done in the presence of liposomes with a modified procedure to form homogenous proteo-liposomes suitable for functional analysis of water permeability using stopped-flow spectrophotometry. Two model aquaporins, AqpZ and NtPIP2;1, were successfully incorporated into the liposome in their active forms. Shifted green fluorescent protein was fused to the C-terminal part of AqpZ to monitor its insertion and status in the lipid environment. This new fast approach offers a fast and straightforward method for the functional analysis of aquaporins in both prokaryotic and eukaryotic organisms.

The proliferation of telecommunication devices in recent decades has resulted in a substantial increase in exposure risk to manmade radiofrequency electromagnetic fields (RF-EMFs) for both animals and plants. The physiological effects of these exposures remain to be fully elucidated. In this study, we measured and analyzed the chlorophyll fluorescence rise kinetics of lettuce plants in the presence of RF-EMFs and after a short drought treatment. The analysis of the fluorescence data was conducted using two different strategies: a conventional JIP test and a novel machine learning-assisted anomaly-detection approach. Our results suggest that exposure to RF-EMFs weakens the plant’s hormetic responses induced by drought treatment, both in terms of the response’s magnitude and its extent. These findings provide further evidence supporting the hypothesis that RF-EMFs interfere with plant stress responses.