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Estuarine submerged aquatic vegetation (SAV) provides vital habitat for macroinvertebrate communities that support diverse food webs and subsequent ecosystem services. Invasive SAV, however, has the potential to alter estuarine food webs through competition with native SAV, resulting in different associated biological communities. In the Mobile-Tensaw Delta (Alabama, USA), the invasive Eurasian milfoil, Myriophyllum spicatum, is fast becoming the dominant SAV, out-competing native SAV such as wild celery, Vallisneria americana. This study investigated the above- and belowground macroinvertebrate assemblages associated with these SAV habitats. We found significantly different assemblages between the SAV, with V. americana supporting more even and diverse epifaunal assemblages, and M. spicatum supporting greater total abundances of macroinvertebrates. Gammarid amphipods were more than 11 times more abundant in M. spicatum, while Polychaete species were threefold more abundant in V. americana. Our results suggest that V. americana may support a more diverse and even community compared to M. spicatum. If so, the continued decline in coverage of native V. americana and invasion of M. spicatum across the Mobile-Tensaw Delta could have system-wide ecological consequences. 

Selecting suitable architecture parameters and training hyperparameters is essential for enhancing machine learning (ML) model performance. Several recent empirical studies conduct large-scale correlational analysis on neural networks (NNs) to search for effective generalization metrics that can guide this type of model selection. Effective metrics are typically expected to correlate strongly with test performance. In this paper, we expand on prior analyses by examining generalization-metric-based model selection with the following objectives: (i) focusing on natural language processing (NLP) tasks, as prior work primarily concentrates on computer vision (CV) tasks; (ii) considering metrics that directly predict test error instead of the generalization gap; (iii) exploring metrics that do not need access to data to compute. From these objectives, we are able to provide the first model selection results on large pretrained Transformers from Huggingface using generalization metrics. Our analyses consider (I) hundreds of Transformers trained in different settings, in which we systematically vary the amount of data, the model size and the optimization hyperparameters, (II) a total of 51 pretrained Transformers from eight families of Huggingface NLP models, including GPT2, BERT, etc., and (III) a total of 28 existing and novel generalization metrics. Despite their niche status, we find that metrics derived from the heavy-tail (HT) perspective are particularly useful in NLP tasks, exhibiting stronger correlations than other, more popular metrics. To further examine these metrics, we extend prior formulations relying on power law (PL) spectral distributions to exponential (EXP) and exponentially-truncated power law (E-TPL) families. 

Abstract A working group from the Global Library of Underwater Biological Sounds effort collaborated with the World Register of Marine Species (WoRMS) to create an inventory of species confirmed or expected to produce sound underwater. We used several existing inventories and additional literature searches to compile a dataset categorizing scientific knowledge of sonifery for 33,462 species and subspecies across marine mammals, other tetrapods, fishes, and invertebrates. We found 729 species documented as producing active and/or passive sounds under natural conditions, with another 21,911 species deemed likely to produce sounds based on evaluated taxonomic relationships. The dataset is available on both figshare and WoRMS where it can be regularly updated as new information becomes available. The data can also be integrated with other databases (e.g., SeaLifeBase, Global Biodiversity Information Facility) to advance future research on the distribution, evolution, ecology, management, and conservation of underwater soniferous species worldwide. 

Identification of isomers using traditional mass spectroscopy methods has proven an interesting challenge due to their identical mass to charge ratios. This proves particularly consequential for gold clusters, as subtle variations in the ligand and cluster structure can have drastic effects on the cluster functionalization, solubility, and chemical properties. Biological nanopores have proven an effective tool in identifying subtle variations at the single molecule limit. This paper reports on the ability of an α-hemolysin (αHL) pore to differentiate between para -, meta -, and ortho - ( p -, m -, and o -, respectively) mercaptobenzoic acid ligands attached to gold clusters at the single cluster limit. Detecting differences between p -MBA and m -MBA requires pH-dependent studies that illustrate the role inter-ligand binding plays in stabilizing m -MBA-capped clusters. Additionally, this paper investigates the difference in behavior for these clusters when isolated, and when surrounded by small ligand–Au complexes (Au n L m , n = 0, 1, 2… and m = 1, 2,…) that are present following cluster synthesis. It is found that continuous exposure of clusters to freely diffusing ligand complexes stabilizes the clusters, while isolated clusters either disintegrate or exit the nanopore in seconds. This has implications for long term cluster stability. 

MnO 2 has been proposed as an electrode material in electrochemical energy storage devices. However, poor cycle life, especially in aqueous electrolytes, remains a detriment to commercialization. Prior studies have suggested a number of explanations for this capacity loss; however, experiments aimed at elucidating the details of the degradation process (es) are sparse. We describe here a microtube-membrane construct that allows for electrodeposition of monodisperse MnO 2 microparticles distributed across the membrane surface, and for subsequent electrochemical cycling of these MnO 2 particles. This allowed for a detailed analysis of the effect of cycling on the MnO 2 , by simply imaging the membrane surface before and after cycling. When an aqueous electrolyte was used, gross changes in particle shape, size and morphology were observed over the course of 500 cycles. Partial dissolution occurred as well. No such changes were observed when the MnO 2 particles were cycled (up to 500 times) in a propylene carbonate electrolyte solution.