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Prochlorococcus marinus, the smallest picocyanobacterium, comprises multiple clades occupying distinct niches, currently across tropical and sub-tropical oligotrophic ocean regions, including Oxygen Minimum Zones. Ocean warming may open growth-permissive temperatures in new, poleward photic regimes, along with expanded Oxygen Minimum Zones. We used ocean metaproteomic data on current Prochlorococcus marinus niches, to guide testing of Prochlorococcus marinus growth across a matrix of peak irradiances, photoperiods, spectral bands and dissolved oxygen. MED4 from Clade HLI requires greater than 4 h photoperiod, grows at 25 μmol O2 L-1 and above, and exploits high cumulative diel photon doses. MED4, however, relies upon an alternative oxidase to balance electron transport, which may exclude it from growth under our lowest, 2.5 μmol O2 L-1, condition. SS120 from clade LLII/III is restricted to low light under full 250 μmol O2 L-1, shows expanded light exploitation under 25 μmol O2 L-1, but is excluded from growth under 2.5 μmol O2 L-1. Intermediate oxygen suppresses the cost of PSII photoinactivation, and possibly the enzymatic production of H2O2 in SS120, which has limitations on genomic capacity for PSII and DNA repair. MIT9313 from Clade LLIV is restricted to low blue irradiance under 250 μmol O2 L-1, but exploits much higher irradiance under red light, or under lower O2 concentrations, conditions which slow photoinactivation of PSII and production of reactive oxygen species. In warming oceans, range expansions and competition among clades will be governed not only by light levels. Short photoperiods governed by latitude, temperate winters, and depth attenuation of light, will exclude clade HLI (including MED4) from some habitats. In contrast, clade LLII/III (including SS120), and particularly clade LLIV (including MIT9313), may exploit higher light niches nearer the surface, under expanding OMZ conditions, where low O2 relieves the stresses of oxidation stress and PSII photoinhibition., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Savoie et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

CyanoCyc is a web portal that integrates an exceptionally rich database collection of information about cyanobacterial genomes with an extensive suite of bioinformatics tools. It was developed to address the needs of the cyanobacterial research and biotechnology communities. The 277 annotated cyanobacterial genomes currently in CyanoCyc are supplemented with computational inferences including predicted metabolic pathways, operons, protein complexes, and orthologs; and with data imported from external databases, such as protein features and Gene Ontology (GO) terms imported from UniProt. Five of the genome databases have undergone manual curation with input from more than a dozen cyanobacteria experts to correct errors and integrate information from more than 1,765 published articles. CyanoCyc has bioinformatics tools that encompass genome, metabolic pathway and regulatory informatics; omics data analysis; and comparative analyses, including visualizations of multiple genomes aligned at orthologous genes, and comparisons of metabolic networks for multiple organisms. CyanoCyc is a high-quality, reliable knowledgebase that accelerates scientists' work by enabling users to quickly find accurate information using its powerful set of search tools, to understand gene function through expert mini-reviews with citations, to acquire information quickly using its interactive visualization tools, and to inform better decision-making for fundamental and applied research., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Moore, Caspi, Campbell, Casey, Crevecoeur, Lea-Smith, Long, Omar, Paley, Schmelling, Torrado, Zehr and Karp.)

Cyanobium and Synechococcus are prominent, globally distributed cyanobacteria genera with ecological significance. Here, we report the genomes of the marine Synechococcus sp. CCMP836 and two strains of Cyanobium (CZS25K and CZS48M) along with the genomes of 17 co-occurring proteobacteria. These genomes will improve the strain-specific ecological positions., Competing Interests: The authors declare no conflict of interest.

Marine phytoplankton produce and scavenge Reactive Oxygen Species, to support cellular processes, while limiting damaging reactions. Some prokaryotic picophytoplankton have, however, lost all genes encoding scavenging of hydrogen peroxide. Such losses of metabolic function can only apply to Reactive Oxygen Species which potentially traverse the cell membrane outwards, before provoking damaging intracellular reactions. We hypothesized that cell radius influences which elements of Reactive Oxygen Species metabolism are partially or fully dispensable from a cell. We therefore investigated genomes and transcriptomes from diverse marine eukaryotic phytoplankton, ranging from 0.4 to 44 μm radius, to analyze the genomic allocations encoding enzymes metabolizing Reactive Oxygen Species. Superoxide has high reactivity, short lifetimes and limited membrane permeability. Genes encoding superoxide scavenging are ubiquitous across phytoplankton, but the fractional gene allocation decreased with increasing cell radius, consistent with a nearly fixed set of core genes for scavenging superoxide pools. Hydrogen peroxide has lower reactivity, longer intracellular and extracellular lifetimes and readily crosses cell membranes. Genomic allocations to both hydrogen peroxide production and scavenging decrease with increasing cell radius. Nitric Oxide has low reactivity, long intracellular and extracellular lifetimes and readily crosses cell membranes. Neither Nitric Oxide production nor scavenging genomic allocations changed with increasing cell radius. Many taxa, however, lack the genomic capacity for nitric oxide production or scavenging. The probability of presence of capacity to produce nitric oxide decreases with increasing cell size, and is influenced by flagella and colony formation. In contrast, the probability of presence of capacity to scavenge nitric oxide increases with increasing cell size, and is again influenced by flagella and colony formation., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Omar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Emotion identification is an essential task for human-computer interaction systems. Electroencephalogram (EEG) signals have been widely used in emotion recognition. So far, there have been several EEG-based emotion recognition datasets that the researchers have used to validate their developed models. Hence, we have used a new ICBrainDB EEG dataset to classify angry, neutral, happy, and sad emotions in this work. Signal processing-based wavelet transform (WT), tunable Q-factor wavelet transform (TQWT), and image processing-based histogram of oriented gradients (HOG), local binary pattern (LBP), and convolutional neural network (CNN) features have been used extracted from the EEG signals. The WT is used to extract the rhythms from each channel of the EEG signal. The instantaneous frequency and spectral entropy are computed from each EEG rhythm signal. The average, and standard deviation of instantaneous frequency, and spectral entropy of each rhythm of the signal are the final feature vectors. The spectral entropy in each channel of the EEG signal after performing the TQWT is used to create the feature vectors in the second signal side method. Each EEG channel is transformed into time-frequency plots using the synchrosqueezed wavelet transform. Then, the feature vectors are constructed individually using windowed HOG and LBP features. Also, each channel of the EEG data is fed to a pretrained CNN to extract the features. In the feature selection process, the ReliefF feature selector is employed. Various feature classification algorithms namely, k-nearest neighbor (KNN), support vector machines, and neural networks are used for the automated classification of angry, neutral, happy, and sad emotions. Our developed model obtained an average accuracy of 90.7% using HOG features and a KNN classifier with a tenfold cross-validation strategy., Competing Interests: Conflict of interestThe authors report no conflict of interest., (© The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.)

Marine phytoplankton vary widely in size across taxa, and in cell suspension densities across habitats and growth states. Cell suspension density and total biovolume determine the bulk influence of a phytoplankton community upon its environment. Cell suspension density also determines the intercellular spacings separating phytoplankton cells from each other, or from co-occurring bacterioplankton. Intercellular spacing then determines the mean diffusion paths for exchanges of solutes among co-occurring cells. Marine phytoplankton and bacterioplankton both produce and scavenge reactive oxygen species (ROS), to maintain intracellular ROS homeostasis to support their cellular processes, while limiting damaging reactions. Among ROS, hydrogen peroxide (H 2 O 2 ) has relatively low reactivity, long intracellular and extracellular lifetimes, and readily crosses cell membranes. Our objective was to quantify how cells can influence other cells via diffusional interactions, using H 2 O 2 as a case study. To visualize and constrain potentials for cell-to-cell exchanges of H 2 O 2 , we simulated the decrease of [H 2 O 2 ] outwards from representative phytoplankton taxa maintaining internal [H 2 O 2 ] above representative seawater [H 2 O 2 ]. [H 2 O 2 ] gradients outwards from static cell surfaces were dominated by volumetric dilution, with only a negligible influence from decay. The simulated [H 2 O 2 ] fell to background [H 2 O 2 ] within ~3.1 µm from a Prochlorococcus cell surface, but extended outwards 90 µm from a diatom cell surface. More rapid decays of other, less stable ROS, would lower these threshold distances. Bacterioplankton lowered simulated local [H 2 O 2 ] below background only out to 1. 2 µm from the surface of a static cell, even though bacterioplankton collectively act to influence seawater ROS. These small diffusional spheres around cells mean that direct cell-to-cell exchange of H 2 O 2 is unlikely in oligotrophic habits with widely spaced, small cells; moderate in eutrophic habits with shorter cell-to-cell spacing; but extensive within phytoplankton colonies.