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A Gram-negative, facultative anaerobic, rod-shaped, motile with peritrichous flagella, fluorescent bacterium, designated ‘ Candidatus Pseudomonas auctus’ sp. nov. JDE115, was isolated from soybean root nodules in Virginia and characterized using a comprehensive integrative methodology. Growth of JDE115 occurred with 0–5.0% (w/v) NaCl (optimum 1%), at pH 6.0–10.0 (optimum pH 7.0), and at 10–40°C (optimum 28°C) in LB broth. Phylogenetic analyses based on the 16S rRNA gene placed the isolate as a member of a novel species within the genus Pseudomonas. Phylogenetic analyses based on whole-genome sequences, 16S rRNA, showed JDE115 having the highest similarity to Pseudomonas glycinae MS586. Average Nucleotide Identity (ANI) analysis also revealed the highest similarity of JDE115 to Pseudomonas glycinae MS586 (94.59%), which is below the 95% threshold for species delineation. Genome-to-genome distance analysis (GGDC, Formula 2) showed a maximum value of 57.10% with the same strain, far below the 70% cutoff. The primary isoprenoid quinone detected in JDE115 was ubiquinone-9 (Q-9) and the DNA G + C content was 60.68 mol%. The whole-cell fatty acid profile was dominated by C16:0, C17:0 cyclo, and the summed features 3 (C16:1ω7c and/or C16:1ω6c) and 8 (C18:1ω7c and/or C18:1ω6c). Additional fatty acids detected included 12:0, 14:0, and 18:0. Based on these phenotypic, chemotaxonomic, and phylogenetic data, strain JDE115 is proposed to represent a new species in the genus Pseudomonas , for which the name ‘ Candidatus Pseudomonas auctus’ sp. nov. is proposed.

A Gram-negative, facultative anaerobic, rod-shaped, motile with peritrichous flagella, fluorescent bacterium, designated 'Candidatus Pseudomonas auctus' sp. nov. JDE115, was isolated from soybean root nodules in Virginia and characterized using a comprehensive integrative methodology. Growth of JDE115 occurred with 0-5.0% (w/v) NaCl (optimum 1%), at pH 6.0-10.0 (optimum pH 7.0), and at 10-40°C (optimum 28°C) in LB broth. Phylogenetic analyses based on the 16S rRNA gene placed the isolate as a member of a novel species within the genus Pseudomonas. Phylogenetic analyses based on whole-genome sequences, 16S rRNA, showed JDE115 having the highest similarity to Pseudomonas glycinae MS586. Average Nucleotide Identity (ANI) analysis also revealed the highest similarity of JDE115 to Pseudomonas glycinae MS586 (94.59%), which is below the 95% threshold for species delineation. Genome-to-genome distance analysis (GGDC, Formula 2) showed a maximum value of 57.10% with the same strain, far below the 70% cutoff. The primary isoprenoid quinone detected in JDE115 was ubiquinone-9 (Q-9) and the DNA G + C content was 60.68 mol%. The whole-cell fatty acid profile was dominated by C16:0, C17:0 cyclo, and the summed features 3 (C16:1ω7c and/or C16:1ω6c) and 8 (C18:1ω7c and/or C18:1ω6c). Additional fatty acids detected included 12:0, 14:0, and 18:0. Based on these phenotypic, chemotaxonomic, and phylogenetic data, strain JDE115 is proposed to represent a new species in the genus Pseudomonas, for which the name 'Candidatus Pseudomonas auctus' sp. nov. is proposed.

The rapid establishment of Fall Armyworm (FAW), Spodoptera frugiperda in Australia necessitates effective and sustainable management strategies. Pheromones offer a promising strategy for mitigating FAW damage through monitoring, mass trapping, and mating disruption. Understanding the pheromone composition of local FAW populations, as well as the variation in pheromone composition and production influenced by the mating status and age of FAW females, provides valuable insights into the factors contributing to pheromone production variability. This study investigated chemical composition of pheromone compounds of FAW population in Australia, temporal pattern of release, and the effect of mating status and age of FAW female on compounds release and production. Pheromone glands were collected by solvent extraction, whereas headspace volatiles were collected by solid phase microextraction (SPME) method. The sample contained four compounds ( Z )-7-dodecenyl acetate (Z7C12Ac), ( Z )-9-dodecenyl acetate (Z9C12Ac), ( Z )-9-tetradecenyl acetate (Z9C14Ac) and ( Z )-11-hexadecenyl acetate (Z11C16Ac), with Z9C14Ac present in a significantly higher amount. The maximum quantity of compounds from headspace volatiles was released between 4 to 6 h into the scotophase, while the compounds obtained from gland extraction varied across different phases of the scotophase. Younger FAW female released and produced higher amounts of compounds compared to the older individuals. Mated females have significantly higher compounds titre in their pheromone glands compared to the virgins, despite the latter releasing more volatile compounds. These findings highlight the impact of physiological factors on FAW pheromone compounds, offering valuable insights for developing sustainable strategies to manage FAW population in Australia.

The study aims to synthesize nano‐crystallite TSP using renewable, low‐cost, waste marine mollusk from three different species such as Babylonia japonica, Oliva sayana, and Conasprella bermudensis. The molar ratio of phosphate to calcium in triple superphosphate [TSP, Ca(H2PO4)2.H2O] significantly impacts its properties and fertilizer performance, in this case, we kept the ratio to 2. Raw TSP has a high phosphate content and lower calcium content. The synthesized TSP was analyzed using various techniques including TGA, XRD, EDX, FT‐IR, and SEM. The study utilized multiple XRD model equations to analyze crystallite size ( <100nm${\\char60 100\\hskip0.17em\\hskip0.17em{\\rm n}{\\rm m}}$), with all models except the Liner straight‐line method providing higher estimates for synthesized TSP. Furthermore, the values for stress (2×107 to 4×107 N/m2), strain (4×10−4 to 9×10−4), as well as energy density (4.54×103 to 16.27×103 J/m3) were also calculated for the synthesized product. However, the preferential growth calculation indicates that (010), (021), and (020) planes are the most thermodynamically stable planes for the growth of the synthesized TSP. Apart from that, FTIR result confirms that CaO, −OH, as well as PO43− functional groups are present in the synthesized products. This research suggests that marine mollusks can be utilized as a calcium precursor for P‐fertilizer and 60 % phosphoric acid, thereby reducing production costs by eliminating additional dehydrating. Additionally, waste marine mollusk shells could be utilized as an alternative to the production of phosphate‐based fertilizer. The study explores the synthesis of nano‐crystallite triple super phosphate (TSP) from marine mollusk waste, and in‐depth crystallographic characterization carries good evidence for fruitful application as nano‐fertilizer.

Background Internet addiction (IA) among adolescents is growing worldwide. Online temptation is particularly strong for adolescents due to rapid physical and cognitive development. IA may impair their mental, emotional, social, and physical health. Few traditional studies were conducted in Bangladesh. Thus, this study aimed to identify adolescents’ IA risk factors using advanced machine learning (ML). Methods A total of 385 individuals were convenience sampled and surveyed using the Patient Health Questionnaire‐9 (PHQ‐9), the UCLA Loneliness Scale (UCLA‐3), and Young's IA Test (IAT‐20) to measure the prevalence of depression, loneliness, and IA. Boruta found IA prevalence classifying factors. We evaluated decision tree (DT), support vector machine (SVM), logistic regression (LR), and random forest (RF) classification models using confusion matrix, receiver operating characteristic (ROC) curves, and k‐fold cross‐validation. Results Among 385 respondents, one‐third (30.1%) reported IA. Participants’ fathers’ education, favorite activity, loneliness, smoking status, depression, and internet use time were selected as important features classifying IA. The performance was tested on the basis of five different classification techniques overall: the SVM linear kernel model (accuracy = 0.819, specificity = 0.869, sensitivity = 0.687, precision = 0.666, area under the ROC curve [AUC] = 0.890, k‐fold accuracy = 0.801) performed better and authentically classified IA. Conclusion Raising awareness among adolescents and their parents is crucial because IA is frequent. The ML framework can identify significant prognostic indicators and classify this IA problem more accurately, helping policymakers, stakeholders, and families understand and prevent this crisis by improving policy‐making strategies and counseling services. The infographic outlines a study on classifying internet addiction (IA) among adolescents in Bangladesh using machine learning. The study involved 385 participants aged 13–19, focusing on key predictors such as depression, loneliness, smoking status, and internet use time. Data were collected using the IAT‐20, PHQ‐9, and UCLA‐3 scales. The Boruta algorithm was used with models like decision tree, logistic regression, and random forest, achieving an accuracy of 81.9%, sensitivity of 68.7%, specificity of 86.3%, and an AUC of 0.89. The findings highlight significant prevalence rates for internet addiction, depression, and high loneliness among participants.