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Systematic probing of local environments around biopolymers is important for understanding their functions. Therefore, there has been growing interest in in situ measurements of molecular granularity and heterogeneity through the systematic analysis of vibrational frequency shifts of carbonyl and nitrile infrared probes by vibrational Stark dipole theory. However, here we show that the nitrile vibrational frequency shift induced by its interaction with the surrounding molecules cannot be solely described by electric field-based theory because of the exchange-repulsion and dispersion interaction contributions. Considering a variety of molecular environments ranging from bulk solutions to protein environments, we explore the distinct scenarios of solute-environment contacts and their traces in vibrational frequency shifts. We believe that the present work could provide a set of clues that could be potentially used to design a rigorous theoretical model linking vibrational solvatochromism and molecular topology in complex heterogeneous environments.

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On the evening of 15 January 2022, the Hunga Tonga-Hunga Ha’apai volcano 1 unleashed a violent underwater eruption, blanketing the surrounding land masses in ash and debris 2 , 3 . The eruption generated tsunamis observed around the world. An event of this type last occurred in 1883 during the eruption of Krakatau 4 , and thus we have the first observations of a tsunami from a large emergent volcanic eruption captured with modern instrumentation. Here we show that the explosive eruption generated waves through multiple mechanisms, including: (1) air–sea coupling with the initial and powerful shock wave radiating out from the explosion in the immediate vicinity of the eruption; (2) collapse of the water cavity created by the underwater explosion; and (3) air–sea coupling with the air-pressure pulse that circled the Earth several times, leading to a global tsunami. In the near field, tsunami impacts are strongly controlled by the water-cavity source whereas the far-field tsunami, which was unusually persistent, can be largely described by the air-pressure pulse mechanism. Catastrophic damage in some harbours in the far field was averted by just tens of centimetres, implying that a modest sea level rise combined with a future, similar event would lead to a step-function increase in impacts on infrastructure. Piecing together the complexity of this event has broad implications for coastal hazards in similar geophysical settings, suggesting a currently neglected source of global tsunamis.

BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

Abstract:The application of deep learning, specifically deep convolutional neural networks (DCNNs), to the classification of remotely-sensed imagery of natural landscapes has the potential to greatly assist in the analysis and interpretation of geomorphic processes. However, the general usefulness of deep learning applied to conventional photographic imagery at a landscape scale is, at yet, largely unproven. If DCNN-based image classification is to gain wider application and acceptance within the geoscience community, demonstrable successes need to be coupled with accessible tools to retrain deep neural networks to discriminate landforms and land uses in landscape imagery. Here, we present an efficient approach to train/apply DCNNs with/on sets of photographic images, using a powerful graphical method called a conditional random field (CRF), to generate DCNN training and testing data using minimal manual supervision. We apply the method to several sets of images of natural landscapes, acquired from satellites, aircraft, unmanned aerial vehicles, and fixed camera installations. We synthesize our findings to examine the general effectiveness of transfer learning to landscape-scale image classification. Finally, we show how DCNN predictions on small regions of images might be used in conjunction with a CRF for highly accurate pixel-level classification of images.

Habitat diversity and heterogeneity play a fundamental role in structuring ecological communities. Dam emplacement and removal can fundamentally alter habitat characteristics, which, in turn, can affect associated biological communities. Beginning in the early 1900s, the Elwha and Glines Canyon dams in Washington, USA, withheld an estimated 30 million Mg of sediment from river, coastal, and nearshore habitats. During the staged removal of these dams, the largest dam removal project in history, over 14 million Mg of sediment were released from the former reservoirs. Our interdisciplinary study in coastal habitats, the first of its kind, shows how the physical changes to the river delta and estuary habitats during dam removal were linked to responses in biological communities. Sediment released during dam removal resulted in over a meter of sedimentation in the estuary and over 400 m of expansion of the river mouth delta landform. These changes increased the amount of supratidal and intertidal habitat, but also reduced the influx of seawater into the pre‐removal estuary complex. The effects of these geomorphic and hydrologic changes cascaded to biological systems, reducing the abundance of macroinvertebrates and fish in the estuary and shifting community composition from brackish to freshwater‐dominated species. Vegetation did not significantly change on the delta, but pioneer vegetation increased during dam removal, coinciding with the addition of newly available habitat. Understanding how coastal habitats respond to large‐scale human stressors, and in some cases the removal of those stressors, is increasingly important as human uses and restoration activities increase in these habitats.