A number of scientific reports published to date contain data on endogenous levels of various phytohormones in potato (Solanum tuberosum L.) but a complete cytokinin profile of potato tissues, that would include data on all particular molecular forms of cytokinin, has still been missing. In this work, endogenous levels of all analytically detectable isoprenoid cytokinins, as well as the auxin indole-3-acetic acid (IAA), and abscisic acid (ABA) have been determined in shoots and roots of 30 day old in vitro grown potato (cv. Désirée). The results presented here are generally similar to other data reported for in vitro grown potato plants, whereas greenhouse-grown plants typically contain lower levels of ABA, possibly indicating that in vitro grown potato is exposed to chronic stress. Cytokinin N-glucosides, particularly N7-glucosides, are the dominant cytokinin forms in both shoots and roots of potato, whereas nucleobases, as the bioactive forms of cytokinins, comprise a low proportion of cytokinin levels in tissues of potato. Differences in phytohormone composition between shoots and roots of potato suggest specific patterns of transport and/or differences in tissue-specific metabolism of plant hormones. These results represent a contribution to understanding the hormonomics of potato, a crop species of extraordinary economic importance.

The main topic of this study is the bioremediation potential of the common duckweed, Lemna minor L., and selected rhizospheric bacterial strains in removing phenol from aqueous environments at extremely high initial phenol concentrations. To that end, fluorescence microscopy, MIC tests, biofilm formation, the phenol removal test (4-AAP method), the Salkowski essay, and studies of multiplication rates of sterile and inoculated duckweed in MS medium with phenol (200, 500, 750, and 1000 mg L⁻¹) were conducted. Out of seven bacterial strains, six were identified as epiphytes or endophytes that efficiently removed phenol. The phenol removal experiment showed that the bacteria/duckweed system was more efficient during the first 24 h compared to the sterile duckweed control group. At the end of this experiment, almost 90% of the initial phenol concentration was removed by both groups, respectively. The bacteria stimulated the duckweed multiplication even at a high bacterial population density (>10⁵ CFU mL⁻¹) over a prolonged period of time (14 days). All bacterial strains were sensitive to all the applied antibiotics and formed biofilms in vitro. The dual bacteria/duckweed system, especially the one containing strain 43-Hafnia paralvei C32-106/3, Accession No. MF526939, had a number of characteristics that are advantageous in bioremediation, such as high phenol removal efficiency, biofilm formation, safety (antibiotic sensitivity), and stimulation of duckweed multiplication.

In this work, we demonstrate that the rhizosphere of common duckweed (Lemna minor) is inhabited with various phenol-resistant bacterial strains. Based on 16S rRNA sequencing, we have identified 60 rhizosphere-associated bacterial isolates belonging to 10 different bacterial genera (Pseudomonas, Hafnia, Serratia, Enterobacter, Micrococcus, Stenotrophomonas, Xanthomonas, Bacillus, Staphylococcus and Klebsiella). All isolates have been tested for phenol resistance and ability to utilize phenol as the sole carbon source. 70% of all isolates survived high doses of phenol (≥200 mg/L) and at least 27% can be potentially acclimatized by gradual increase of phenol concentration. Finally, based on high phenol resistance, ability to utilize phenol as the sole carbon source and documented low pathogenicity, we propose 5 strains as potentially excellent candidates for bioremediation. These 5 strains taxonomically correspond to Klebsiella sp., Serratia sp., and Hafnia sp., respectively. To the best of our knowledge, this is the first attempt to assess decontamination capacity of Serratia nematodiphila and Hafnia sp. in the context of bioremediation of phenol-contaminated aqueous media. Although additional analyses are needed, interaction between the common duckweed and the selected bacterial strains may be utilized in future bioremediation strategies.

Sun-induced chlorophyll fluorescence (SIF) has proven to be advantageous in estimating gross primary production, despite the lack of a stable relationship. Satellite-based SIF measurements at Level 2 offer comprehensive global coverage and are available in near real time. However, these measurements are often limited by spatial and temporal sparsity, as well as discontinuities. These limitations primarily arise from incomplete satellite trajectories. Additionally, variability in cloud cover and periodic issues specific to the instruments can compromise data quality. Two families of methods have been developed to address data discontinuity: (1) machine learning-based gap-filling techniques and (2) geostatistical techniques (various forms of kriging). The former techniques utilize the relationships between ancillary data and SIF, while the latter usually rely on the available SIF data recordings and their covariance structure to provide estimates at unsampled locations. In this study, we create a synthetic approach for SIF gap filling by hybridizing the two approaches under the umbrella of kriging with external drift. We performed leave-one-out cross-validation of the OCO-2 SIF retrieval aggregates for the entire year of 2019, comparing three methods: ordinary kriging, ML-based estimation using ancillary data, and kriging with external drift. The Mean Absolute Error (MAE) for ML, ordinary kriging, and the hybrid approach was found to be 0.1399, 0.1318, and 0.1183 mW m² sr⁻¹ nm⁻¹, respectively. We demonstrate that the performance of the hybrid approach exceeds both parent techniques due to the incorporation of information from multiple resources. This use of multiple datasets enriches the hybrid model, making it more robust and accurate in handling the spatio-temporal variability and discontinuity of SIF data. The developed framework is portable and can be applied to SIF retrievals at various resolutions and from various sources (satellites), as well as extended to other satellite-measured variables.