The biocontrol potential of three native soil cyanobacteria from biological soil crusts (Nostoc commune, Scytonema hyalinum, and Tolypothrix distorta) was tested by means of in vitro mycelial growth inhibition assays for eighteen cyanobacteria-based products against three phytopathogenic soilborne fungi (Phytophthora capsici, Pythium aphanidermatum, and Fusarium oxysporum f. sp. radicis-cucumerinum). Three cyanobacteria-based production factors were considered: (i) cyanobacterium strain, (ii) cyanobacterial culture growth phase, and (iii) different post-harvest treatments: raw cultures, cyanobacterial filtrates, and cyanobacterial extracts. Results showed that any of the factors considered are key points for successfully inhibiting fungal growth. N. commune showed the highest growth inhibition rates for the three phytopathogens; stationary phase treatments produced higher inhibition percentages than logarithmic ones; and all the post-harvest treatments of N. commune at the stationary phase inhibited the growth of P. capsici, up to 77.7%. Thus, N. commune products were tested in planta against P. capsici, but none of the products showed efficacy in delaying the onset nor reducing the damage due to P. capsici, demonstrating the complexity of the in planta assay\'s success and encouraging further research to design an appropriate scaling up methodology.

The gut microbiome plays a fundamental role in metabolism, as well as the immune and nervous systems. Microbial imbalance (dysbiosis) can contribute to subsequent physical and mental pathologies. As such, interest has been growing in the microbiota-gut-brain brain axis and the bioelectrical communication that could exist between bacterial and nervous cells. The aim of this study was to investigate the bioelectrical profile (electrome) of two bacterial species characteristic of the gut microbiome: a Proteobacteria Gram-negative bacillus Escherichia coli (E. coli), and a Firmicutes Gram-positive coccus Enterococcus faecalis (E. faecalis). We analyzed both bacterial strains to (i) validate the fluorescent probe bis-(1,3-dibutylbarbituric acid) trimethine oxonol, DiBAC4(3), as a reliable reporter of the changes in membrane potential (Vmem) for both bacteria; (ii) assess the evolution of the bioelectric profile throughout the growth of both strains; (iii) investigate the effects of two neural-type stimuli on Vmem changes: the excitatory neurotransmitter glutamate (Glu) and the inhibitory neurotransmitter γ-aminobutyric acid (GABA); (iv) examine the impact of the bioelectrical changes induced by neurotransmitters on bacterial growth, viability, and cultivability using absorbance, live/dead fluorescent probes, and viable counts, respectively. Our findings reveal distinct bioelectrical profiles characteristic of each bacterial species and growth phase. Importantly, neural-type stimuli induce Vmem changes without affecting bacterial growth, viability, or cultivability, suggesting a specific bioelectrical response in bacterial cells to neurotransmitter cues. These results contribute to understanding the bacterial response to external stimuli, with potential implications for modulating bacterial bioelectricity as a novel therapeutic target.

Phosphorylation of proteins at serine, threonine, and tyrosine residues plays an important role in physiological processes of bacteria, such as cell cycle, metabolism, virulence, dormancy, and stationary phase functions. Little is known about the targets and dynamics of protein phosphorylation in Streptococcus pyogenes, which possesses a single known transmembrane serine/threonine kinase belonging to the class of PASTA kinases. A proteomics and phosphoproteomics workflow was performed with S. pyogenes serotype M49 under different growth conditions, stationary phase, and starvation. The quantitative analysis of dynamic phosphorylation, which included a subset of 463 out of 815 identified phosphorylation sites, revealed two main types of phosphorylation events. A small group of phosphorylation events occurred almost exclusively at threonine residues of proteins related to the cell cycle and was enhanced in growing cells. The majority of phosphorylation events occurred during stationary phase or starvation, preferentially at serine residues. PASTA kinase-dependent cell cycle regulation processes found in related bacteria are conserved in S. pyogenes. Increased protein phosphorylation during the stationary phase has also been described for some other bacteria, and could therefore be a general feature in the physiology of bacteria, whose functions and the kinases involved need to be elucidated in further analyses.

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[This corrects the article DOI: 10.3389/fmicb.2022.1075621.].

Epidemiologists frequently adopt statistical process control tools, like control charts, to detect changes in the incidence or prevalence of a specific disease in real time, thereby protecting against outbreaks and emergent health concerns. Control charts have proven essential in instantly identifying fluctuations in infection rates, spotting emerging patterns, and enabling timely reaction measures in the context of COVID-19 monitoring. This study aims to review and select an optimal control chart in epidemiology to monitor variations in COVID-19 deaths and understand pandemic mortality patterns. An essential aspect of the present study is selecting an appropriate monitoring technique for distinct deaths in the USA in seven phases, including pre-growth, growth, and post-growth phases. Stage-1 evaluated control chart applications in epidemiology departments of 12 countries between 2000 and 2022. The study assessed various control charts and identified the optimal one based on maximum shift detection using sample data. This study considered at Shewhart ($\\bar X$, $R$, $C$) control charts and exponentially weighted moving average (EWMA) control chart with smoothing parameters λ = 0.25, 0.5, 0.75, and 1 were all investigated in this study. In Stage-2, we applied the EWMA control chart for monitoring because of its outstanding shift detection capabilities and compatibility with the present data. Daily deaths have been monitored from March 2020 to February 2023. Control charts in epidemiology show growing use, with the USA leading at 42% applications among top countries. During the application on COVID-19 deaths, the EWMA chart accurately depicted mortality dynamics from March 2020 to February 2022, indicating six distinct stages of death. The third and fifth waves were extremely catastrophic, resulting in a considerable loss of life. Significantly, a persistent sixth wave appeared from March 2022 to February 2023. The EWMA map effectively determined the peaks associated with each wave by thoroughly examining the time and amount of deaths, providing vital insights into the pandemic\'s progression. The severity of each wave was measured by the average number of deaths $W5(1899)\\,\\gt\\,W3(1881)\\,\\gt\\,W4(1393)\\,\\gt\\,W1(1036)\\,\\gt\\,W2(853)\\,\\gt\\,(W6(473)$. The USA entered a seventh phase (6th wave) from March 2022 to February 2023, marked by fewer deaths. While reassuring, it remains crucial to maintain vaccinations and pandemic control measures. Control charts enable early detection of daily COVID-19 deaths, providing a systematic strategy for government and medical staff. Incorporating the EWMA chart for monitoring immunizations, cases, and deaths is recommended.

Malassezia synthesizes and releases volatile organic compounds (VOCs), small molecules that allow them to carry out interaction processes. These lipid-dependent yeasts belong to the human skin mycobiota and are related to dermatological diseases. However, knowledge about VOC production and its function is lacking. This study aimed to determine the volatile profiles of Malassezia globosa, Malassezia restricta, and Malassezia sympodialis in the exponential and stationary growth phases. The compounds were separated and characterized in each growth phase through headspace solid-phase microextraction (HS-SPME) and gas chromatography-mass spectrometry (GC-MS). We found a total of 54 compounds, 40 annotated. Most of the compounds identified belong to alcohols and polyols, fatty alcohols, alkanes, and unsaturated aliphatic hydrocarbons. Unsupervised and supervised statistical multivariate analyses demonstrated that the volatile profiles of Malassezia differed between species and growth phases, with M. globosa being the species with the highest quantity of VOCs. Some Malassezia volatiles, such as butan-1-ol, 2-methylbutan-1-ol, 3-methylbutan-1-ol, and 2-methylpropan-1-ol, associated with biological interactions were also detected. All three species show at least one unique compound, suggesting a unique metabolism. The ecological functions of the compounds detected in each species and growth phase remain to be studied. They could interact with other microorganisms or be an important clue in understanding the pathogenic role of these yeasts.

As part of the lampenflora that inhabit limestone caves, microalgae play an important role in cave ecosystems but are understudied in tropical ecoregions. In the present study, the dominant eukaryotic and prokaryotic microalgae identified in lampenflora samples collected from Gua Tempurung, a cave in Malaysia, and growth stage-related microalgal attributes were determined. Stichococcus bacillaris, Synechococcus sp., and Trentepohlia aurea were selected and cultured in Bold\'s Basal Medium (S. bacillaris and T. aurea) or BG-11 medium (Synechococcus sp.) under laboratory conditions. The highest specific growth rate (0.72 ± 0.21 day-1) and dry weight (0.11 ± 0.04 mg L-1) were recorded in S. bacillaris in the early stationary phase. Trentepohlia aurea and Synechococcus sp. had the highest ash-free dry weight and total ash percentage (11.18 ± 4.64 mg L-1 and 8.55% ± 6.73%, respectively) in the early stationary phase. Stichococcus bacillaris had the highest moisture content (84.26% ± 0.64%) in the exponential phase. Chlorophylls a and b were highest in the early stationary phase in T. aurea (0.706 ± 0.40 mg L-1 and 1.094 ± 0.589 mg L-1, respectively). Carotenoid levels were highest in Synechococcus sp. in the early stationary stage (0.07 ± 0.02 mg L-1). Lipids were the major biochemical compound identified at the highest levels in Synechococcus sp. (67.87% ± 7.75%) in the early stationary phase, followed by protein recorded at the highest levels in T. aurea (57.99% ± 4.99%) in the early stationary phase. Carbohydrates were the compound identified least often with the highest recorded levels found in T. aurea (9.94% ± 0.49%) in the late stationary phase. Biomass, pigments, and biochemical accumulation varied at different growth stages in the studied microalgae, and this variation was species-specific. The present study provides a benchmark for the growth phases of aerophytic cave microalgae, which will be useful for determining their optimum harvest time and obtaining biochemical compounds of interest.

Early and accurate prediction of grain yield is of great significance for ensuring food security and formulating food policy. The exploration of key growth phases and features is beneficial to improving the efficiency and accuracy of yield prediction. In this study, a hybrid approach using the WOFOST model and deep learning was developed to forecast corn yield, which analysed yield prediction potential at different growth phases and features. The World Food Studies (WOFOST) model was used to build a comprehensive simulated dataset by inputting meteorological, soil, crop and management data. Different feature combinations at various growth phases were designed to forecast yield using machine learning and deep learning methods. The results show that the key features of corn\'s vegetative growth stage and reproductive growth stage were growth state features and water-related features, respectively. With the continuous advancement of the crop growth stage, the ability to predict yield continued to improve. Especially after entering the reproductive growth stage, corn kernels begin to form, and the yield prediction performance is significantly improved. The performance of the optimal yield prediction model in flowering (R2 = 0.53, RMSE = 554.84 kg/ha, MRE = 8.27%), in milk maturity (R2 = 0.89, RMSE = 268.76 kg/ha, MRE = 4.01%), and in maturity (R2 = 0.98, RMSE = 102.65 kg/ha, MRE = 1.53%) were given. Thus, our method improves the accuracy of yield prediction, and provides reliable analysis results for predicting yield at various growth phases, which is helpful for farmers and governments in agricultural decision making. This can also be applied to yield prediction for other crops, which is of great value to guide agricultural production.

Cyanobacterial harmful algal blooms (cHABs) dominated by Microcystis aeruginosa threaten the ecological integrity and beneficial uses of lakes globally. In addition to producing hepatotoxic microcystins (MC), M. aeruginosa exudates (MaE) contain various compounds with demonstrated toxicity to aquatic biota. Previously, we found that the ecotoxicity of MaE differed between MC-producing and MC-free strains at exponential (E-phase) and stationary (S-phase) growth phases. However, the components in these exudates and their specific harmful effects were unclear. In this study, we performed untargeted metabolomics based on liquid chromatography-mass spectrometry to reveal the constituents in MaE of a MC-producing and a MC-free strain at both E-phase and S-phase. A total of 409 metabolites were identified and quantified based on their relative abundance. These compounds included lipids, organoheterocyclic compounds, organic acid, benzenoids and organic oxygen compounds. Multivariate analysis revealed that strains and growth phases significantly influenced the metabolite profile. The MC-producing strain had greater total metabolites abundance than the MC-free strain at S-phase, whereas the MC-free strain released higher concentrations of benzenoids, lipids, organic oxygen, organic nitrogen and organoheterocyclic compounds than the MC-producing strain at E-phase. Total metabolites had higher abundance in S-phase than in E- phase in both strains. Analysis of differential metabolites (DMs) and pathways suggest that lipids metabolism and biosynthesis of secondary metabolites were more tightly coupled to growth phases than to strains. Abundance of some toxic lipids and benzenoids DMs were significantly higher in the MC-free strain than the MC-producing one. This study builds on the understanding of MaE chemicals and their biotoxicity, and adds to evidence that non-MC-producing strains of cyanobacteria may also pose a threat to ecosystem health.