Mangrove sediments-associated bacterium (Bacillus sp. SW7) with multiple plant growth-promoting traits promotes the growth of tomato (Solanum Lycopersicum)

Global food production intensification presents a major hurdle to ensuring food security amidst a growing world population. Widespread use of chemical fertilizers in recent decades has risked soil fertility, compounded by the challenges posed by climate change, particularly in arid regions. To address these issues, adopting plant growth-promoting (PGP) bacteria stands out as a promising solution, offering multifaceted benefits to arid agroecosystems. We isolated a bacterial strain, SW7, from mangrove sediment, characterised the entire genome followed by phylogenetic analyses, and evaluated its in-vitro PGP activity. Subsequently, we examined its impact on tomato seed germination and plant growth. The strain SW7 exhibited growth on 11% NaCl, survival at 50°C, and possessed multiple PGP traits such as significant increase in seed germination rate (60.60 ± 38.85%), phosphate (83.3 g L−1) and potassium (39.6 g L−1) solubilization and produced indole acetic acid (3.60 ppm). Additionally, strain SW7 tested positive for ammonia, catalase, and oxidase enzyme production. The strain SW7 genome consists of 5.1 MB with 35.18% G+C content. Through genome-based phylogenetic and orthoANI analyses, the strain was identified as a novel Bacillus species, designated herein as Bacillus sp. SW7. In an eight-week shade-house experiment, inoculation of strain SW7 improved, leaf number, leaf density, leaf area index and mass water of tomatoes. Additional parameters, like chlorophyll a, chlorophyll b and carotenoids were not affected in SW7-inoculated tomatoes. In conclusion, Bacillus sp. SW7 exhibits multiple PGP traits and an adaptive capacity to high temperature and salinity, positioning it as a potential candidate for elevating the productivity of arid agroecosystems

Applications of Machine Learning in Chemical and Biological Oceanography

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.Comment: 58 Pages, 5 Figure

Bacterial resistome in different stages of wastewater treatment plant is highly impacted by the abundance of the Pseudomonadota community

Wastewater treatment plants (WWTPs) are significant sources of antimicrobial resistance (AMR). We studied bacterial communities and antibiotic-resistance genes (ARGs) in different stages of a WWTP in the UAE. We found high levels of extended-spectrum beta-lactamase bacteria in the returned activated sludge (RAS) stage, including Escherichia coli and Aeromonas sobria, resistant to ESBL, ampicillin, and fosfomycin. Bacterial richness was highest in the primary effluent (PE) stage, with distinct community structures influenced by environmental factors. Pseudomonadota dominated across all stages, with Bacteroidota and Bacillota in PE, and Actinomycetota and Pseudomonadota in AS and RAS. Acidovorax sp. showed strong connections with ARGs in PE and RAS, while Delftia acidovorans had the most associations with ARGs in AS. These findings underscore the role of WWTP stages in shaping bacterial communities and ARG abundance, highlighting the potential of certain bacteria in AMR development and dissemination

Mangrove sediments-associated bacterium (Bacillus sp. SW7) with multiple plant growth-promoting traits promotes the growth of tomato (Solanum Lycopersicum)

Global food production intensification presents a major hurdle to ensuring food security amidst a growing world population. Widespread use of chemical fertilizers in recent decades has risked soil fertility, compounded by the challenges posed by climate change, particularly in arid regions. To address these issues, adopting plant growth-promoting (PGP) bacteria stands out as a promising solution, offering multifaceted benefits to arid agroecosystems. We isolated a bacterial strain, SW7, from mangrove sediment, characterised the entire genome followed by phylogenetic analyses, and evaluated its in-vitro PGP activity. Subsequently, we examined its impact on tomato seed germination and plant growth. The strain SW7 exhibited growth on 11% NaCl, survival at 50°C, and possessed multiple PGP traits such as significant increase in seed germination rate (60.60 ± 38.85%), phosphate (83.3 g L−1) and potassium (39.6 g L−1) solubilization and produced indole acetic acid (3.60 ppm). Additionally, strain SW7 tested positive for ammonia, catalase, and oxidase enzyme production. The strain SW7 genome consists of 5.1 MB with 35.18% G+C content. Through genome-based phylogenetic and orthoANI analyses, the strain was identified as a novel Bacillus species, designated herein as Bacillus sp. SW7. In an eight-week shade-house experiment, inoculation of strain SW7 improved, leaf number, leaf density, leaf area index and mass water of tomatoes. Additional parameters, like chlorophyll a, chlorophyll b and carotenoids were not affected in SW7-inoculated tomatoes. In conclusion, Bacillus sp. SW7 exhibits multiple PGP traits and an adaptive capacity to high temperature and salinity, positioning it as a potential candidate for elevating the productivity of arid agroecosystems