Underwater noise, whether of natural or anthropogenic origin, has the ability to interfere with the way in which marine mammals receive acoustic signals (i.e., for communication, social interaction, foraging, navigation, etc.). This phenomenon, termed auditory masking, has been well studied in humans and terrestrial vertebrates (in particular birds), but less so in marine mammals. Anthropogenic underwater noise seems to be increasing in parts of the world's oceans and concerns about associated bioacoustic effects, including masking, are growing. In this article, we review our understanding of masking in marine mammals, summarise data on marine mammal hearing as they relate to masking (including audiograms, critical ratios, critical bandwidths, and auditory integration times), discuss masking release processes of receivers (including comodulation masking release and spatial release from masking) and anti-masking strategies of signalers (e.g. Lombard effect), and set a research framework for improved assessment of potential masking in marine mammals.

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Microscopic examination of blood smears remains the gold standard for laboratory inspection and diagnosis of malaria. Smear inspection is, however, time-consuming and dependent on trained microscopists with results varying in accuracy. We sought to develop an automated image analysis method to improve accuracy and standardization of smear inspection that retains capacity for expert confirmation and image archiving. Here, we present a machine learning method that achieves red blood cell (RBC) detection, differentiation between infected/uninfected cells, and parasite life stage categorization from unprocessed, heterogeneous smear images. Based on a pretrained Faster Region-Based Convolutional Neural Networks (R-CNN) model for RBC detection, our model performs accurately, with an average precision of 0.99 at an intersection-over-union threshold of 0.5. Application of a residual neural network-50 model to infected cells also performs accurately, with an area under the receiver operating characteristic curve of 0.98. Finally, combining our method with a regression model successfully recapitulates intraerythrocytic developmental cycle with accurate lifecycle stage categorization. Combined with a mobile-friendly web-based interface, called PlasmoCount, our method permits rapid navigation through and review of results for quality assurance. By standardizing assessment of Giemsa smears, our method markedly improves inspection reproducibility and presents a realistic route to both routine lab and future field-based automated malaria diagnosis.

The COVID-19 pandemic has highlighted the global need for reliable models of disease spread. We propose an AI-augmented forecast modeling framework that provides daily predictions of the expected number of confirmed COVID-19 deaths, cases, and hospitalizations during the following 4 weeks. We present an international, prospective evaluation of our models’ performance across all states and counties in the USA and prefectures in Japan. Nationally, incident mean absolute percentage error (MAPE) for predicting COVID-19 associated deaths during prospective deployment remained consistently <8% (US) and <29% (Japan), while cumulative MAPE remained <2% (US) and <10% (Japan). We show that our models perform well even during periods of considerable change in population behavior, and are robust to demographic differences across different geographic locations. We further demonstrate that our framework provides meaningful explanatory insights with the models accurately adapting to local and national policy interventions. Our framework enables counterfactual simulations, which indicate continuing Non-Pharmaceutical Interventions alongside vaccinations is essential for faster recovery from the pandemic, delaying the application of interventions has a detrimental effect, and allow exploration of the consequences of different vaccination strategies. The COVID-19 pandemic remains a global emergency. In the face of substantial challenges ahead, the approach presented here has the potential to inform critical decisions.

Standard audiometric data, such as audiograms and critical ratios, are often used to inform marine mammal noise-exposure criteria. However, these measurements are obtained using simple, artificial stimuli-i.e., pure tones and flat-spectrum noise-while natural sounds typically have more complex structure. In this study, detection thresholds for complex signals were measured in (I) quiet and (II) masked conditions for one California sea lion (Zalophus californianus) and one harbor seal (Phoca vitulina). In Experiment I, detection thresholds in quiet conditions were obtained for complex signals designed to isolate three common features of natural sounds: Frequency modulation, amplitude modulation, and harmonic structure. In Experiment II, detection thresholds were obtained for the same complex signals embedded in two types of masking noise: Synthetic flat-spectrum noise and recorded shipping noise. To evaluate how accurately standard hearing data predict detection of complex sounds, the results of Experiments I and II were compared to predictions based on subject audiograms and critical ratios combined with a basic hearing model. Both subjects exhibited greater-than-predicted sensitivity to harmonic signals in quiet and masked conditions, as well as to frequency-modulated signals in masked conditions. These differences indicate that the complex features of naturally occurring sounds enhance detectability relative to simple stimuli.

Ultrasonic coded transmitters (UCTs) are high-frequency acoustic tags that are often used to conduct survivorship studies of vulnerable fish species. Recent observations of differential mortality in tag control studies suggest that fish instrumented with UCTs may be selectively targeted by marine mammal predators, thereby skewing valuable survivorship data. In order to better understand the ability of pinnipeds to detect UCT outputs, behavioral high-frequency hearing thresholds were obtained from a trained harbor seal (Phoca vitulina) and a trained California sea lion (Zalophus californianus). Thresholds were measured for extended (500 ms) and brief (10 ms) 69 kHz narrowband stimuli, as well as for a stimulus recorded directly from a Vemco V16-3H UCT, which consisted of eight 10 ms, 69 kHz pure-tone pulses. Detection thresholds for the harbor seal were as expected based on existing audiometric data for this species, while the California sea lion was much more sensitive than predicted. Given measured detection thresholds of 113 dB re 1 μPa and 124 dB re 1 μPa, respectively, both species are likely able to detect acoustic outputs of the Vemco V16-3H under water from distances exceeding 200 m in typical natural conditions, suggesting that these species are capable of using UCTs to detect free-ranging fish.

Many species of large, mysticete whales are known to produce low-frequency communication sounds. These low-frequency sounds are susceptible to communication masking by shipping noise, which also tends to be low frequency in nature. The size of these species makes behavioral assessment of auditory capabilities in controlled, captive environments nearly impossible, and field-based playback experiments are expensive and necessarily limited in scope. Hence, it is desirable to produce a masking model for these species that can aid in determining the potential effects of shipping and other anthropogenic noises on these protected animals. The aim of this study was to build a model that combines a sophisticated representation of the auditory periphery with a spectrogram-based decision stage to predict masking levels. The output of this model can then be combined with a habitat-appropriate propagation model to calculate the potential effects of noise on communication range. For this study, the model was tested on three common North Atlantic right whale communication sounds, both to demonstrate the method and to probe how shipping noise affects the detection of sounds with varying spectral and temporal characteristics.

In order to better understand the ability of pinnipeds to detect acoustic signals from ultrasonic coded transmitters (UCTs) commonly used in fisheries research, high-frequency hearing thresholds were obtained from a trained Pacific harbor seal (Phoca vitulina) and a trained California sea lion (Zalophus californianus). Using a 69 kHz, 500 ms, narrow-band FM sweep stimulus, detection thresholds for the harbor seal and the sea lion were determined to be 106 dB and 112 dB re 1 μPa respectively. While the harbor seal threshold falls within the range of existing data, the sea lion threshold is 33 dB lower than expected based on previous reports. This finding indicates that sea lions may be more sensitive to the output of UCTs than previously thought, and allows for the possibility that acoustically tagged fish may be selectively targeted for predation by sea lions as well as seals. These hearing thresholds, combined with ongoing work on the effect of signal duration on high-frequency hearing, will help estimate the ranges at which certain UCTs can be detected by these species. Detection range estimations, in turn, will allow fisheries researchers to better understand how fish survivorship data obtained using UCTs may be skewed by pinniped predation.