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  • Characterizing the trophic ecology of herbivorous coral reef fishes using stable isotope and fatty acid biomarkers

    by Rita García-Seoane, W. Lindsey White, Brett M. Taylor, Kendall D. Clements

    Understanding the trophic ecology of herbivorous and detritivorous fishes is essential for evaluating their ecological roles in coral reef ecosystems. In this study, we combined bulk stable isotope (δ15N and δ13C) and fatty acid analyses to investigate trophic partitioning and dietary resource use among herbivorous and detritivorous fishes from the Great Barrier Reef, Australia. Isotopic niches and fatty acid profiles confirmed significant trophic partitioning among algivores, detritivorous surgeonfishes, and parrotfishes. We also applied mixing models based on these ecological tracers to quantify the contributions of basal dietary sources to the fish. Our findings further support previous dietary knowledge for several species, including algivorous acanthurids, kyphosid chubs, and the rabbitfish Siganus doliatus. However, they also highlight trophic niche specializations within these groups, particularly in Naso unicornis, which assimilates substantial dietary protein from epiphytic cyanobacteria despite a macroalgal diet, and in the detritivorous Ctenochaetus striatus, which exhibited isotopic similarities to parrotfishes but differed in fatty acid composition, likely due to a higher intake of diatoms. Additionally, our analyses reinforce the distinctive dietary composition of parrotfishes, emphasizing the complexity of their feeding biology, in which microscopic photoautotrophs such as cyanobacteria and dinoflagellates play a key dietary role that has often been overlooked in previous studies on their nutritional ecology. Furthermore, these findings underscore the usefulness of multi-tracer approaches in refining our understanding of coral reef fish trophic ecology.

  • Enhancing student success prediction in higher education with swarm optimized enhanced efficientNet attention mechanism

    by Meshari Alazmi, Nasir Ayub

    Predicting student performance is crucial for providing personalized support and enhancing academic performance. Advanced machine-learning approaches are being used to understand student performance variables as educational data grows. A big dataset from several Chinese institutions and high schools is used to develop a credible student performance prediction technique. Moreover, the dataset includes 80 features and 200,000 records, and consequently, it represents one of the most extensive data collections available for educational research. Initially, data is passed through preprocessing to address outliers and missing values. In addition, we developed a novel hybrid feature selection model that combined correlation filtering with mutual information, Cross-Validation (CV) along with Recursive Feature Eliminatio (RFE) (R, and stability selection to identify the most impactful features. Moreover, This study develops the proposed EffiXNet, a more refined version of EfficientNet augmented with self-attention mechanisms, dynamic convolutions, improved normalization methods, and Sparrow Search Optimization Algorithm for hyperparameter optimization. The developed model was tested using an 80/20 train-test split, where 160,000 records were used for training and 40,000 for testing. The results reported, including accuracy, precision, recall, and F1-score, are based on the full test dataset. However, for better visualization, the confusion matrices display only a representative subset of test results. Furthermore, the EffiXNet value of AUC amounting to 0.99, a 25% reduction of logarithmic loss relative to the baseline models, precision of 97.8%, F1-score of 98.1%, and reliable optimization of memory usage. Significantly, the developed model showed a consistently high-performance level demonstrated by various metrics, which indicates that it is proficient in capturing intricate data patterns. The key insights the current research provides are the necessity of early intervention and directed training support in the educational domain. The EffiXNet framework offers a robust, scalable, and efficient solution for predicting student performance, with potential applications in academic institutions worldwide.

  • Intense low-frequency sound transiently biases human sound lateralisation

    by Carlos Jurado, Benedikt Grothe, Markus Drexl

    Intense low-frequency (LF) sound exposure transiently alters hearing thresholds and other markers of cochlear sensitivity, and for these changes the term ‘Bounce phenomenon’ (BP) has been coined. Under the BP, hearing thresholds slowly oscillate for several minutes involving both stages of hyper- and hyposensitivity and it is reasonable to assume that the perception of sounds at levels well above threshold will also be affected. Here, we evaluated the effect of the BP on auditory lateralisation in healthy human subjects. Sound lateralisation crucially depends on the processing of either interaural level- or time differences (ILDs and ITDs, respectively), depending on the spectral content of the sound. The ILD needed to perceive a virtual sound source in the middle of the head was tracked across time. Measurements were carried out without and with a previous exposure to an intense LF-sound in the left ear, to elicit the BP. In 65% of the recordings, significant time-variant deviations from the perceived midline were observed after cessation of the LF-sound. In other words, a binaural stimulus perceived in the middle moved perceptually to the side and often back to the middle after presentation of the intense LF-sound. This means that intense LF-sound exposure can lead to a biasing of ILD-based sound localisation.

  • Retraction: Validation of postnatal growth and retinopathy of prematurity (G-ROP) screening guidelines in a tertiary care hospital of Pakistan: A report from low-middle income country

    by The PLOS One Editors



  • Differentiating bacteria by their unique surface interactions

    by Nicholas K. Kotoulas, Stephanie Tan, Justin R. Nodwell, M. Cynthia Goh

    New, rapid, and accessible approaches to bacterial detection are necessary to help curb the rising impacts of antimicrobial resistance. In this study, we introduce a technique that distinguishes bacteria through their unique surface interactions. By measuring and combining the interaction strengths of a bacterium across a set of chemically defined surfaces, we produced a novel bacterial identifier termed the surface interaction profile (SIP). The interaction strengths of twelve test bacteria across three discrete polyelectrolyte multilayer films (PEMs) were measured, facilitated by introducing each bacterial suspension to individual PEMs in microfluidic channels over a 10-minute interaction period and rinsing to remove bulk and loosely bound bacteria. The remaining surface-bound cells were counted via microscopy and plotted against suspension concentrations to build attachment curves whose slopes were measured as the strength of interaction for a given bacteria-PEM combination. These slopes were collected, per bacterial type, to produce each SIP. SIPs were capable of distinguishing between our pathogenic strains (Klebsiella pneumoniae, Acinetobacter baumannii, Pseudomonas aeruginosa, Enterococcus faecalis, methicillin-resistant Staphylococcus aureus, and vancomycin-intermediate Staphylococcus aureus) by Gram stain and individual species, and each blind test pathogen was successfully identified through SIP comparison. Furthermore, SIPs were also successful at differentiating between select Staphylococcus aureus walKR mutants impacting cell wall metabolism and high-risk antibiotic resistance mutants (MRSA and VISA), highlighting the utility and future diagnostic potential of this technique.