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We present an ocean-basin-scale dataset that includes tail fluke photographic identification (photo-ID) and encounter data for the majority of living individual humpback whales (Megaptera novaeangliae) in the North Pacific Ocean. The dataset was built through a broad collaboration combining 39 separate curated photo-ID catalogs supplemented with community science data. All available images were compared using a recently developed machine learning artificial intelligence image recognition algorithm able to rapidly and accurately detect matches between individuals. For the study period of 2001 to 2021, a total of 27,956 unique individuals were documented in 157,379 encounters, with each individual encountered, on average, in 5.6 sampling periods (i.e., breeding and feeding seasons), and with an annual average of 87.1% of whales encountered in more than one season. The combined dataset and image recognition tool represents a living and accessible resource for collaborative, basin-wide studies of a keystone marine mammal in a time of rapid ecological change.

Amyloid-β (Aβ) plays a relevant role in the neurodegenerative process of Alzheimer's disease (AD). The 25-35 peptide of amyloid-β (Aβ25-35) induces the inflammatory response in brain experimental models. Mucin-type O-glycosylation has been associated with inflammation of brain tissues in AD, thus in this work, we aimed at identifying changes in the glycosylation profile generated by the injection of Aβ25-35 into the CA1 of the hippocampus of rats, using histochemistry with lectins. Our results indicate that 100μM Aβ25-35 induce increased recognition of the Amaranthus leucocarpus lectin (ALL) (specific for Galβ1,3-GalNAcα1,0-Ser/Thr); whereas concanavalin A (Con A) (specific for α-Man) showed no differences among treated and control groups of rats. Jacalin and peanut agglutinin (Galβ1,3GalNAcα1,0-Ser/Thr) showed no recognition of brain cells of control or treated rats. After 6-h treatment of the tissue with trypsin or with 200mM GalNAc, the interaction with ALL was inhibited. Immunohistochemistry showed positive anti-NeuN and ALL-recognition of neurons; however, anti-GFAP and anti-CD11b showed no co-localization with ALL. The ALL+ neurons revealed the presence of cytochrome C in the cytosol and active caspase 3 in the cytosol and nucleus. Administration of the interleukin-1 receptor antagonist (IL-1RA) to Aβ25-35-treated rats diminished neuroinflammation and ALL recognition. These results suggest a close relationship among over-expression of mucin-type O-glycosylation, the neuroinflammatory process, and neuronal death.

International audience; — In passive acoustic monitoring, source localization using multipath propagation can be challenging whenever the source-receiver configuration leads to overlapping multipath arrival patterns. We propose a single-hydrophone model-based method with matched-impulse response (IR) processing that exploits unresolved multipath arrival patterns to locate the source in 3D, making the best use of the bathymetric information as well as the time differences of groups of arrivals. When only groups of paths are resolved, the identity of the paths constituting the groups remains unknown, preventing the use of classical localization methods. To overcome this critical limitation, we apply an approach based on the characterization of groups of paths to estimate the source location.

Baleen whale vocal activity can be the dominant underwater ambient noise source for certain locations and seasons. Previous wind-driven ambient-noise formulations have been adjusted to model ambient noise levels generated by random distributions of singing humpback whales in ocean waveguides and have been combined to a single model. This theoretical model predicts that changes in ambient noise levels with respect to fractional changes in singer population (defined as the noise "sensitivity") are relatively unaffected by the source level distributions and song spectra of individual humpback whales (Megaptera novaeangliae). However, the noise "sensitivity" does depend on frequency and on how the singers' spatial density changes with population size. The theoretical model was tested by comparing visual line transect surveys with bottom-mounted passive acoustic data collected during the 2013 and 2014 humpback whale breeding seasons off Los Cabos, Mexico. A generalized linear model (GLM) estimated the noise "sensitivity" across multiple frequency bands. Comparing the GLM estimates with the theoretical predictions suggests that humpback whales tend to maintain relatively constant spacing between one another while singing, but that individual singers either slightly increase their source levels or song duration, or cluster more tightly as the singing population increases.

In passive acoustic monitoring, source localization using multipath propagation can be challenging whenever the source-receiver configuration leads to unresolved paths. Here, we propose a single-hydrophone method to estimate the range of a source based on two steps. First, we define a time deformation operator (e.g., Bonnel et al. 2014) to estimate the signal’s impulse response (IR). Each group of paths, when warped at its time of arrival, is transformed into a tone that corresponds to a peak in the IR, which can be isolated in the warped time-frequency domain, permitting a significant improvement of the time resolution of the multipath, or groups of multipath. Next, relative arrival times are used to estimate the source range and depth. When only groups of paths are resolved, the identity of the paths constituting the group remains unknown, preventing the use of classical localization methods. To overcome this critical limitation, we apply an approach based on the characterization of groups of paths to estimate source location. We evaluate the method’s performance on synthesized data, and examine its potential use on humpback whale low-frequency modulated calls using data collected off Cabo San Lucas in 2013.