ABSTRACT Anthropogenic soundscape disturbance can constrain acoustic signalling, with potential consequences for reproductive behaviour in wild animals. We used passive acoustic monitoring to investigate how habitat, weather, and human-associated soundscape disturbance are associated with roaring activity of Iberian red deer (Cervus elaphus hispanicus) during the rut on Lousã Mountain, central Portugal. Twenty-nine AudioMoth recorders collected 150-s recordings every 7.5 min over 14 days, yielding 88,022 manually validated roars. We quantified anthrophony as relative acoustic energy in the 1–2 kHz band and modelled roar counts using a negative binomial generalised linear mixed model. Roaring activity was highest in shrublands and other open habitats, and decreased with increasing wind speed, rainfall and temperature. Importantly, roaring declined with increasing anthrophony and increased with distance from wind turbines, suggesting a possible avoidance of a broader disturbance gradient rather than only a purely acoustic effect. Daily roaring counts also tended to be lower towards the end of the week. Our results highlight that soundscape disturbance associated with human infrastructure and activity may influence both the location and timing of males’ vocalisations during the rut, demonstrating the value of soundscape metrics for identifying rutting areas potentially sensitive to disturbance and informing appropriate mitigation strategies.

ABSTRACT Passive Acoustic Monitoring (PAM) offers a powerful approach for detecting and assessing the presence of invasive species, thereby supporting the conservation of native ecosystems. In this study, we developed a species-specific classification model using convolutional neural networks (CNNs) to analyse the nocturnal calling activity patterns of Pelophylax nigromaculatus and Dryophytes leopardus in rice paddies, a microhabitat where interactions between translocated and native species are of ecological concern. Despite environmental noise from bird calls and wind, the mel-spectrogram-based model classified anuran vocalisations with high accuracy (88.45%). Misclassifications at dawn were mitigated by limiting the ecological analysis to specific nocturnal periods. The results revealed clearly distinct peak times of nocturnal calling activity between the two species at the study site. These findings demonstrate the usefulness of deep learning for describing fine-scale activity patterns in field-recorded amphibian soundscapes. This study provides methodological insights into the acoustic monitoring of domestically translocated and native species and highlights the potential of bioacoustics approaches for ecological assessment in paddy environments. Future research should focus on refining species classification models and integrating sound-source separation for more accurate species-specific assessments of calling activity patterns.

The effectiveness of passive acoustic monitoring (PAM) for studying marine biodiversity highly relies on comprehensive libraries of species-specific sounds. While sound production is well-documented in reef and freshwater fishes, the acoustic behaviour of ecologically and economically vital pelagic species like tunas still remains largely unexplored. This study provides a detailed characterization of sounds produced by the yellowfin tuna (Thunnus albacares), a widely distributed species sustaining a global fishery. Acoustic recordings were conducted in a large concrete tank to minimize acoustic artefacts and ensure species-specific attribution, where 19 juveniles were maintained in captivity during 16 days. Over 6400 sounds were detected and categorized into two distinct types: short sounds (average duration 47 ms) and long sounds (534.4 ms) consisting of trains of pulses, both with a peak frequency of 263.1 ± 31.3 Hz. There was a clear diel difference in yellowfin tuna sound production: long sounds were more abundant during the day than at night, whereas short sounds varied more modestly, peaking in the evening and declining overnight, with intermediate levels during the day. This characterization is a critical first step towards using PAM for monitoring this tropical tuna species, thereby providing a novel, fisheries-independent method to study its distribution, behaviour and abundance.

Understanding biodiversity in offshore benthic ecosystems is crucial as anthropogenic pressures like offshore wind development increasingly alter marine environments. Passive acoustic monitoring (PAM), widely used for marine mammal research, offers a promising yet underexplored tool for assessing broader faunal communities. Here, we investigate whether PAM data, collected initially for marine mammal monitoring, can reveal spatial variation in benthic biodiversity along Scotland's east coast. We analysed passive acoustic data from eight offshore sedimentary habitats, identifying 16 distinct biological sound types likely produced by fish and invertebrates. Acoustic indices were also calculated and compared with environmental variables and infaunal benthic richness derived from open-source biodiversity databases. Our results show that key habitat variables, including substrate type, current velocity, and spawning suitability, drive variation in acoustic communities. The findings of this pilot study demonstrate that PAM can be used to detect biologically meaningful patterns in benthic assemblages, and with future work focusing on investigating longer-term datasets, it could offer a cost-effective tool for biodiversity monitoring across space and time. While several acoustic indices correlated with phonic richness and benthic diversity, we currently do not recommend their use in biodiversity monitoring. This study highlights the ecological value of existing acoustic datasets and advances our understanding of soundscape ecology and species-habitat relationships in changing marine environments.

Micrometer-scale chemical sensors serve as pivotal functional components that bridge microscopic environments and macroscopic information systems, yet integrating multiple sensing modalities within a single microscale fiber without compromising performance remains a challenge in materials design. Herein, we report a continuous wet-spinning process to fabricate multifunctional microfibers that integrate chemically responsive polyaniline (PANI) with high-Seebeck-coefficient tellurium nanowires (TeNWs). The optimized PANI/TeNWs composite fiber (60 wt.% TeNWs) achieves a Seebeck coefficient of 59.9 µV K-1 and a power factor of 9.2 µW m-1 K-2, enabling passive temperature monitoring with a detection limit of 1 K, where thermal gradients directly generate the sensing signal. Simultaneously, the fiber demonstrates remarkable chemical sensing capabilities, exhibiting a Nernstian pH response (59.25mV pH-1) and rapid ammonia detection (0.96 s at 50ppm). By demonstrating both passive thermoelectric transduction and active chemical sensing in a single microfiber platform, this work establishes a versatile material system that combines energy-autonomous temperature sensing with high-performance chemical detection. This integrated approach offers substantial application prospects in precision medicine and environmental safety monitoring, where miniaturized sensors with diverse functionalities are urgently needed.

Pulmonary tuberculosis (TB) recurrence is a significant risk factor for the development of drug-resistant (DR) TB. Understanding the risk factors associated with pulmonary tuberculosis (PTB) recurrence is crucial for designing targeted interventions to end the TB epidemic. In Henan Province, China, where the PTB incidence rate was 37.09 per 100,000 in 2023, we conducted a retrospective cohort study. This study utilized passive monitoring to investigate the rate and predictors of bacteriological recurrence within 5 years after cure in a cohort of newly diagnosed PTB patients. A retrospective cohort analysis was carried out using data from the China Information System for Disease Control and Prevention (CISDCP), focusing on bacteriologically confirmed PTB patients who were diagnosed between 2014 and 2019 and cured in Henan Province, China. A five-year follow-up was conducted via passive monitoring to calculate the recurrence time and recurrence rate, as well as to identify risk factors associated with bacteriological recurrence. For univariate analysis, the Kaplan-Meier method was used. The log-rank test was employed for between-group comparisons. Multivariable analysis was performed using a Cox proportional hazards (PH) regression model, with results reported as adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs). Among the 53,124 cured PTB patients, 1346 experienced recurrence, yielding a 5-year cumulative recurrence rate of 2.53%. The recurrence density was 5.14 per 1,000 person-years (PYs). The median recurrence interval was 500.50 days (IQR: 217.00-940.75). Multivariable Cox regression revealed the top three risk factors associated with recurrence. Older age (≥ 45 years) was the strongest: compared with the reference group aged 15-24 years, the adjusted hazard ratio (aHR) was 2.659 (95% CI: 2.132-3.317) for patients aged 45-64 years and 1.958 (95% CI: 1.554-2.467) for those aged ≥ 65 years. Bacteriological positivity at month 2 or 3 of treatment (aHR = 1.877, 95% CI: 1.428-2.467) and male sex (aHR = 1.826, 95% CI: 1.573-2.119) were the other two key factors. Other significant factors associated with increased recurrence risk included the presence of comorbidities (aHR = 1.521), positive culture at month 0 (aHR = 1.319), being a member of the floating population (aHR = 1.205), and patient delay (aHR = 1.205). In contrast, receiving entire-episode directly observed therapy (DOT) served as a significant protective factor against PTB recurrence (aHR = 0.696, 95% CI: 0.545-0.889). The PTB recurrence rate in Henan is relatively low. Key risk factors included age ≥ 45 years, bacteriological positivity at month 2 or 3 of treatment, and male sex. Receiving entire-episode DOT was identified as a protective factor. Accordingly, public health education should be strengthened to promote timely medical consultation. Standardized treatment regimens, entire-episode DOT or video-observed therapy (VOT), early drug resistance testing, and comorbidity control are essential. Enhanced support should be provided to PTB patients who are male or belong to the floating population. Active follow-up and management procedures after cure should be implemented to reduce recurrence rates.

Occupational radiation exposure and radiation protection practices in interventional radiology and cardiology: A multicenter study in France.

Acoustic monitoring provides a powerful, non-invasive approach for studying marine ecosystems, offering insights into species presence, behaviour, and habitat use. Odontocetes rely heavily on sound for communication, navigation, and foraging, making their vocalisations key indicators of ecological and social processes. This study presents a curated, open-access dataset of odontocete acoustic recordings collected between 2022 and 2025 in the waters of the Madeira Archipelago, Eastern North Atlantic. The dataset helps to address a major geographic gap in publicly available bioacoustics resources from the Macaronesian region, a recognized biodiversity hotspot in the North Atlantic. Acoustic data were obtained using a SoundTrap 300HF hydrophone recording at a sampling rate of 288 kHz, during boat-based surveys of visually confirmed single-species sightings at close range. The collection includes annotated recordings from seven odontocete species, encompassing whistles, broadband pulsed calls and echolocation clicks. Each audio file is accompanied by detailed metadata that describes the species identity, group size, behaviour, and environmental context. This resource supports comparative bioacoustics research and the development of automated tools for passive acoustic monitoring.

Quantitative passive acoustic monitoring in benthic settings requires conversion of recorded amplitudes into absolute sound pressure levels (SPL, dB re 1 µPa). We tested whether HydroMoth (HM) recorders can provide sufficiently accurate SPL estimates in the low-frequency band relevant to benthic marine invertebrates and ship noise. Nine units were calibrated by a single-point offset using a 1000 Hz reference tone in an anechoic chamber and subsequently compared to a factory-calibrated SoundTrap ST600 in a controlled playback experiment (100-1000 Hz). In the pool dataset (HM1-HM5), deviations ranged from -2.6 to +1.8 dB with mean absolute errors typically within ~1-3 dB across frequencies; field measurements (HM6-HM9) showed deviations of similar magnitude, with mean deviations ranging from -0.5 to +1.0 dB across frequencies and mean absolute errors between 0.9 and 2.4 dB. Device orientation was fixed and identical across setups, reflecting benthic use cases where orientation is constrained and orientation related sensitivity differences act as a constant factor within deployments. These results show that HydroMoth recorders, under defined benthic conditions and within 100-1000 Hz, can provide SPL estimates with deviations comparable to those observed between commercial reference systems. This enables quantitative use of low-cost recorders in applications where absolute accuracy within a few decibels is sufficient.

Distributed acoustic sensing (DAS) has emerged as a powerful tool for passive whale monitoring, enabling both the detection of vocalizations and the simultaneous tracking of multiple individuals. However, a fundamental limitation of passive acoustic monitoring is that most methods rely on acoustic data, which is only available when whales vocalize. This clearly demonstrates the need for new sensing methods that can detect silent whales. In this paper, we detect hydrodynamic pressure and velocity fields in the low-frequency DAS data induced by a whale's motion and develop methods to analyze these signals. First, we use ships as proxies to demonstrate and calibrate the proposed method. Then, we show that a simple fluid mechanical model can be adapted to understand how whale swimming can be detected and analyzed using DAS. We detect multiple silent whales simultaneously, estimate their characteristics, and show that whale motion signals decay as one over distance cubed. Moreover, we demonstrate that we can observe hydrodynamic pressure and velocity signals from a cruise ship at 413 m water depth, and up to 550 m from the fiber cable. In comparison, the smaller blue whales can be observed when diving within 40 m of the fiber-optical cable. This sensing method enables an approach to monitoring one of the world's most endangered species.

This study systematically integrates acoustic methods and machine learning (ML) into marine ecosystem management, developing a comprehensive ML framework that combines passive acoustic monitoring (PAM) data with ecological survey observations to predict key coral reef ecological indicators, including fish abundance, fish species richness, and live coral cover. The framework extracts features from multiple acoustic frequency bands and deploys a complete ML workflow covering seven algorithms across three categories: tree-based models (Random Forest, LightGBM, Gradient Boosting), neural networks (Multilayer Perceptron, Recurrent Neural Networks, Bayesian Neural Networks), and an ensemble strategy (Voting Regressor). Evaluated on ten coral reef sites in Sanya, China, the framework was comprehensively compared in terms of predictive accuracy and computational efficiency. Results indicate that the LightGBM model achieves the highest predictive performance of these biological indicators, providing a more efficient, scalable, and non-invasive solution for marine fish monitoring, which can support decision-making in marine ecosystem management. The proposed machine learning-based framework has the potential to be integrated into decision-support tools for ecosystem management, enabling more efficient monitoring of coral reef ecosystems worldwide.

Interaction between organophosphorus nerve agents and algae/cyanobacteria: a review of algal ecotoxicology, biotransformation and application.

BackgroundOlder survivors of cancer face heightened risk of depression and anxiety related to cancer experiences, fear of recurrence, and aging-related difficulties. Conventional mental health monitoring approaches, such as clinical assessments and even electronic patient-reported outcomes, are limited by recall bias, patient burden, and infrequent data collection. Emerging patient-generated health data from wearables and smart home devices offer passive, low-burden, continuous monitoring, but their ability to capture mental health risks in older survivors of cancer remains unclear.ObjectiveThis study aims to explore whether patient-generated health data collected in the wild, either passively or actively, can classify older survivors of cancer as having or not having signs of anxiety and depression based on Patient Health Questionnaire-4 (PHQ-4) scores and to assess the potential added value of passive monitoring modalities, such as smart plugs.MethodsThis study recruited 41 older survivors of cancer (mean age 72.3, SD 6.81 years) from the LifeChamps project. Over a 12-week period, participants were monitored using an activity tracker to measure physical activity, sleep, and physiological metrics; a smart scale to capture weight and body composition; and a smart plug to track television use as a proxy for sedentary television viewing. Mental health status was self-reported via the PHQ-4 questionnaire in a mobile app. Machine learning models were trained to classify mental health risk based on features derived from each sensor modality, both independently and in combination.ResultsTree-based gradient boosting models showed good performance in classifying PHQ-4–defined mental health risk. The best-performing configuration, combining smart plug and smart scale features, achieved a mean F1-score of 0.77 (SD 0.15) and a mean area under the receiver operating characteristic curve (AUC) of 0.85 (SD 0.10) across 3 repeated train-test splits. Standalone smart plug models, based solely on passive television use patterns, achieved a mean F1-score of 0.66 (SD 0.04) and a mean AUC of 0.71 (SD 0.06), outperforming models that relied only on activity tracker data (mean F1 0.59, SD 0.2). Multimodal combinations tended to improve average performance but did not consistently yield large gains over the strongest single-modality configurations, likely reflecting adherence-related data loss for wearables and scales. Crucially, passive monitoring of television use patterns emerged as a promising behavioral proxy measure of mental health states.ConclusionsThis study pioneers the use of passively collected data (eg, smart plugs) for mental health monitoring in older survivors of cancer, demonstrating their potential. Smart plugs capture behavioral patterns without user burden, with reasonable standalone performance (mean AUC 0.71, SD 0.06), positioning them as a promising low-burden modality. Future work should validate findings in larger independent cohorts and in prospective clinical workflows. Such technologies could transform monitoring for vulnerable populations, enabling scalable, inclusive care while reducing health care burdens.

Delirium superimposed on dementia is associated with poor outcomes yet remains underdetected in home settings. Current detection relies on face-to-face clinical assessment (eg, the Confusion Assessment Method criteria), which is rarely applied outside hospitals. This proof-of-concept study developed a theory-driven framework for detecting delirium-consistent anomalous patterns in home-dwelling people with dementia, using passive smart home sensor data. The Technology Integrated Health Management dataset, an open access resource comprising a clinically derived cohort of older adults (aged 50 years) with a confirmed diagnosis of dementia or mild cognitive impairment, was used. The analysis included 13 patients who had at least 50% valid data for at least one 10-day analysis window, with data collected between April 1, 2019, and June 30, 2019. Individualized anomaly detection algorithms, including Isolation Forest and Long Short-Term Memory models, were applied to identify delirium-related anomalies within each participant. Predictor features consisted of theory-driven digital markers approximating key Confusion Assessment Method criteria, including agitation, disrupted sleep-wake cycles, and disorientation (indexed by activity entropy), along with clinically relevant indicators, such as physiological instability (early warning scores) and urinary tract infections. Using matched thresholds, the Isolation Forest identified 77 anomalies (anomaly rate: 15.65%), and the Long Short-Term Memory model identified 78 anomalies (anomaly rate: 15.85%), with anomalies typically occurring in short temporal clusters; agreement between methods ranged from 0% to 40% across individuals. Feature importance analyses indicated that activity entropy, sleep quality, and early warning scores were the most influential features, with stronger interfeature correlations observed during anomaly periods than during nonanomaly periods. This study demonstrates the technical feasibility of detecting delirium-related anomalies through passive smart home monitoring. While lacking ground truth validation, the approach shows promise for early intervention in community settings. Future validation studies with clinically confirmed delirium labels are essential.

Decoding coral reef soundscapes for monitoring and conservation.

Survival, growth rate, and welfare have always been the primary concerns of shrimp aquaculturists. Accordingly, a number of bioactive feed components have been supplemented in shrimp feed to improve health condition, feed intake, and growth. The present study evaluated the effects of supplementing a high ash, low protein marine bacterial biomass on feeding behavior, growth, proximate composition, hemolymph biochemistry, and digestive enzyme gene expression of juvenile Litopenaeus vannamei. Six isonitrogenous (36% crude protein) and isolipidic (6% crude lipid) diets were prepared to include graded levels (0%, 2.5%, 5%, 7.5%, 10%, and 20% of the diet) of a commercial bacterial biomass. Feed intake and behavior were assessed using passive acoustic monitoring prior to growth assessment. Following acoustic monitoring, juvenile L. vannamei (0.58 ± 0.02 g) were size‐sorted and stocked in an indoor recirculating aquaculture system. The experiment consisted of eight treatments, six of which were the diets prepared. The remaining two treatments were to offer shrimp the basal diet or the diet containing 10% bacterial biomass at 115% of the standard feeding ration. Each treatment consisted of five replicate aquaria, and the experiment was performed for 42 days. Results suggest that the inclusion of the bacterial biomass significantly affected shrimp feed consumption, growth, feed conversion ratio (FCR), alkaline phosphatase level, and protein and phosphorus retention (p < 0.05). Results were mainly influenced by bacterial biomass inclusion level. Shrimp offered the 10% bacterial biomass diet exhibited the most weight gain among shrimp offered the standard ration. However, offering shrimp the same diet but at 115% of the standard ration further improved weight gain. The present study suggests that supplementing shrimp feed with 10% of the commercial bacterial biomass we studied would lead to better growth, feed consumption, and feed conversion without adverse effects on hemolymph, overall health or digestion.

Triboelectric nanogenerators (TENGs) have emerged as promising platforms for self-powered sensing and real-time biomechanical monitoring. However, current ankle injury classification systems lack both machine learning intelligence and self-powered operation, limiting their effectiveness in dynamic environments. Here, we report a dual-filler strategy in electrospun PVDF-HFP nanofibers, incorporating copper sulfate and graphite to enhance the surface contact points by reducing fiber diameter, while improving dielectric polarization and stability This design yields a six-fold performance improvement compared to pristine fibers (50 V), delivering outputs of ∼302 V, 9.1 µA, and 80.6 nC, with excellent durability over 10 000 cycles, and a peak power density of ∼1.28 W m-2 sufficient to charge capacitors and power small electronics. Integrated into an ankle-worn platform, the fiber-based TENG device generated high-fidelity biomechanical signals. When analyzed using machine learning algorithms, the system achieves up to 99% accuracy across 700 datasets in detecting risky motions preceding sprains. This intelligence shifts the system from passive monitoring to proactive prevention, providing actionable feedback before injury onset. Beyond ankle injuries, this convergence of self-powered materials and artificial intelligence establishes a new class of intelligent wearables and paves the way for advanced musculoskeletal health monitoring and preventive medicine.

SynopsisInsect declines have been recorded in many parts of the world, however, the vast majority of taxa and ecosystems, particularly in the tropics, remain poorly documented. Monitoring insects in the tropics is challenging due to their immense diversity, and the limited resources for research. It is therefore crucial to maximally leverage existing scientific capacity. In complement to DNA-based approaches, or as an alternative that bypasses some of their shortcomings, automated, passive insect monitoring devices are a new tool that can assess insect diversity with standardization and scalability. An additional benefit of insect monitoring devices is the ability to program simultaneous, autonomous monitoring in remote regions that would be very resource intensive to monitor with traditional methodologies. Here, we describe the results of an insect monitoring expedition in Cerro Hoya National Park, Panama, which utilized 19 Mothboxes (automated light traps) deployed across an elevation gradient from 119 to 1534 m above sea level. Images were processed by the Mothbot computer vision system and manually validated at order level. For further validation, we sorted one order, – Coleoptera (beetles), to the level of morphospecies. Three days of sampling yielded 64,352 insect detections representing 17 orders. Within the Coleoptera, we detected 26 families and 142 species. Species richness and Shannon diversity decreased with increasing elevation, despite signs of anthropogenic disturbance at lower elevations. The number of detections (a proxy for activity patterns and abundance) also decreased with elevation except for the highest sampling points. Across all elevations, insect activity was greatest at the beginning of the night, with 40% of all insect detections occurring within an hour and a half of sunset, however trends differed between taxonomic groups. This study highlights the potential for automated insect monitors to enable large-scale insect monitoring in remote locations with small teams. Automated insect monitoring does not replace entomologists, but rather greatly expands their capacity for monitoring insect diversity at scale.

Adolescents are among the most frequent smartphone users worldwide. Yet, few studies have examined how smartphone use appears among minority adolescents, including sexual and gender minority (SGM) youth and children of immigrant parents, who often experience unique stressors and heightened mental-health risk. Passive smartphone monitoring provides a promising, low-burden method for continuously and objectively assessing real-world behavior, offering new opportunities to identify dynamic markers of mental health challenges, including suicide risk, in daily life. The present study evaluated the feasibility of a replicable framework for passive smartphone monitoring among adolescents at high risk for suicidal thoughts and behaviors (STB) and explored longitudinal differences in smartphone-derived behavioral features across minority subgroups. Ninety-nine adolescents aged 11-18 with recent STB completed baseline assessments and installed the iFeel app, which collected passive smartphone data for 6 months, including total and social-media screen time and phone-inactivity-based proxy sleep indicators inferred from nighttime phone inactivity. Participants contributed 1500 participant-weeks of data, with an average of 11.9 weeks of valid monitoring, supporting the feasibility and acceptability of this approach. Daily smartphone use time, social-media activity time, and sleep duration were comparable to normative adolescent data. No significant longitudinal differences emerged between SGM and non-SGM adolescents. However, immigrant-origin adolescents displayed shorter but more stable sleep patterns compared to non-immigrant origins, who exhibited longer baseline sleep with steeper declines over time. Findings highlight passive sensing as a feasible, inclusive, and scalable method for examining real-world behavioral processes associated with STB and mental health outcomes among diverse adolescents. This framework offers a scalable approach that future studies can apply to deepen real-time understanding of mental-health challenges and behavioral patterns among diverse adolescents.

Resting heart rate (RHR) is a key biomarker of cardiovascular health and mortality1-3, but passivelytracking it longitudinally generally requires a wearable device, limiting its availability. Here we present passive heart-rate monitoring (PHRM), a deep-learning system that uses facial video-based photoplethysmography for passive measurements of heart rate (HR) and RHR during everyday smartphone interactions. Our system was developed using 192,353 videos from 485 participants and validated on 162,546 videos from 211 participants in laboratory and free-living conditions, representing, to our knowledge, the largest validation study of its kind. PHRM outperformed state-of-the-art methods on our benchmarks. Compared with reference electrocardiograms, PHRM achieved a mean absolute percentage error (MAPE) lower than 10% for HR measurements across three skin-tone groups of light, medium and dark pigmentation, meeting industry accuracy standards; MAPE for each skin-tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error of less than five beats per minute, compared with a wearable HR tracker, and was associated with known risk factors for cardiovascular disease. These results highlight the potential of smartphones for enabling passive and equitable monitoring of heart health. Tofacilitate further research, we publicly release a large, annotated smartphone video dataset along with a pre-trained HR model.