We propose an approach for discriminating fibrillar collagen fibers from elastic fibers in the mouse cervix in Mueller matrix microscopy using convolutional neural networks (CNN) and K-nearest neighbor (K-NN) for classification. Second harmonic generation (SHG), two-photon excitation fluorescence (TPEF), and Mueller matrix polarimetry images of the mice cervix were collected with a self-validating Mueller matrix micro-mesoscope (SAMMM) system. The components and decompositions of each Mueller matrix were arranged as individual channels of information, forming one 3-D voxel per cervical slice. The classification algorithms analyzed each voxel and determined the amount of collagen and elastin, pixel by pixel, on each slice. SHG and TPEF were used as ground truths. To assess the accuracy of the results, mean-square error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) were used. Although the training and testing is limited to 11 and 5 cervical slices, respectively, MSE accuracy was above 85%, SNR was greater than 40 dB, and SSIM was larger than 90%.

Sea cucumber populations around the globe are experiencing marked declines caused by overexploitation and habitat degradation. Fisheries-independent data used to manage these ecologically and economically important species are frequently collected using diver- or snorkeler-based surveys, which have a number of limitations, including small spatial coverage and observer biases. In the present study, we explored how pairing traditional transect surveys with unmanned aerial vehicles (UAVs) and machine learning could improve sea cucumber density estimation in shallow environments. In July 2018, we conducted 24 simultaneous snorkeler–UAV transects in Tetiaroa, French Polynesia. All UAV images were independently reviewed by three observers and a convolution neural network (CNN) model: ResNet50. All three methods (snorkelers, manual review of UAV images, and ResNet50) produced similar counts, except at relatively high densities (∼75 sea cucumber 40 m−2), where UAVs and CNNs began to underestimate. Using a UAV-derived photomosaic of the study site, we simulated potential transect locations and determined a minimum of five samples were required to reliably estimate densities, while sample variance plateaued after 25 transects. Collectively, these results illustrate UAVs’ ability to survey small invertebrate species, while saving time, money, and labour compared to traditional methods, and highlights their potential to maximize efficiency when designing transect surveys.

No abstract

In 2004, destruction of a Gulf of Mexico oil platform by Hurricane Ivan initiated a discharge of oil and gas from a water depth of 135 m, where its bundle of well conductors was broken below the seafloor near the toppled wreckage. Discharge continued largely unabated until 2019, when findings partly reported herein prompted installation of a containment device that could trap oil before it entered the water column. In 2018, prior to containment, oil and gas bubbles formed plumes that rose to the surface, which were quantified by acoustic survey, visual inspection, and discrete collections in the water column. Continuous air sampling with a cavity ring-down spectrometer (CRDS) over the release site detected atmospheric methane concentrations as high as 11.7, ∼6 times greater than an ambient baseline of 1.95 ppmv. An inverse plume model, calibrated to tracer-gas release, estimated emission into the atmosphere of 9 g/s. In 2021, the containment system allowed gas to escape into the water at 120 m depth after passing through a separator that diverted oil into storage tanks. The CRDS detected transient peaks of methane as high as 15.9 ppmv ppm while oil was being recovered to a ship from underwater storage tanks. Atmospheric methane concentrations were elevated 1–2 ppmv over baseline when the ship was stationary within the surfacing plumes of gas after oil was removed from the flow. Oil rising to the surface was a greater source of methane to the atmosphere than associated gas bubbles.

Commercially available broadband echosounders have the potential to classify acoustic targets based on their scattering responses, which are a function of their species-specific morphological and physiological properties. This is particularly important in complex environments with biologically diverse fish assemblages. Using theoretical acoustic scattering models among 130 fishes across six species, we examine the potential to classify reef fish based on the fine-scale gas-bearing swim bladder morphology quantified from three-dimensional computed-tomography models. Modeled echoes of the swim bladder for an incident broadband sound source (30–200 kHz) and across a range of orientation angles (±44°) are acoustically simulated using the boundary element method. Backscatter models present characteristics that are consistent within species and distinguishable among them. Broadband and multifrequency echoes are classified and compared with Bayesian, support vector machine, k-nearest neighbor, and convolutional neural network estimators. Classifiers have higher accuracies (>70%) when noise is not present and perform better when applied to broadband spectra than multifrequency data (42, 70, 100, 132, 160, 184 kHz). The modeling and classification approaches presented indicate that a taxonomic distinction based on morphologically dependent scattering responses is possible and may provide the capacity to acoustically discriminate among fish species.