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As a migratory, cryptic, foliage-roosting bat with a mostly solitary roosting behavior we have an incomplete understanding of the social behavior of the hoary bat, Lasiurus cinereus . In this species most social interactions between conspecifics are thought to involve mating behavior or territorial disputes. Developing a more complete understanding of the social behavior of this species would provide critical insight to address conservation challenges including high fatality rates from wind turbines during the period of fall migration. We tested the response of hoary bats to conspecific social call playback during the spring and fall migration to: (1) test whether conspecific social call broadcasting attracts or repels individual bats; (2) examine whether there are seasonal differences in these responses; (3) describe the structure and variation of recorded social calls; and (4) test whether conspecific social call playback can increase capture success. Hoary bats were attracted to social call broadcasting during both the spring and fall migration. Hoary bats produced social calls during the spring and fall migration, and when only males were present, suggesting a social function not associated with mating. While calls were variable in frequency and length, social calls tended to be a consistent upsloping shape. Attraction to social calls suggests social interactions not associated with mating behavior in hoary bats, and this technique proved successful as an acoustic lure to aid in capture and study of this elusive species.

The success of human cooperation crucially depends on mechanisms enabling individuals to detect unreliability in their conspecifics. Yet, how such epistemic vigilance is achieved from naturalistic sensory inputs remains unclear. Here we show that listeners’ perceptions of the certainty and honesty of other speakers from their speech are based on a common prosodic signature. Using a data-driven method, we separately decode the prosodic features driving listeners’ perceptions of a speaker’s certainty and honesty across pitch, duration and loudness. We find that these two kinds of judgments rely on a common prosodic signature that is perceived independently from individuals’ conceptual knowledge and native language. Finally, we show that listeners extract this prosodic signature automatically, and that this impacts the way they memorize spoken words. These findings shed light on a unique auditory adaptation that enables human listeners to quickly detect and react to unreliability during linguistic interactions. It remains unclear whether and how listeners can infer speakers’ reliability from the sound of their voice. Here, the authors show that listeners from several languages rely on a common prosodic signature to infer whether speakers are lying or doubting.

Mine closure operations aim to restore the ecosystem to a near-original state. Microorganisms are indispensable for soil equilibrium and restoration. Metabarcoding was employed to characterize the bacterial and fungal composition in pristine soils, stockpiled soils (topsoils), enriched stockpiled soils (technosoils), enriched and revegetated soils (revegetated technosoils), and pit ecosystems in an open pit gold mine. Chao1 analysis revealed highest richness in pristine and topsoils, followed by technosoils (-17.5%) and pits (-63%). Bacterial diversity surpassed fungal diversity (-40%) in soil samples, but fungal OTUs were more abundant in pit samples (+73.4%). The findings identified the dominant microbial communities and conducted a comparative analysis of the shared microbiota. Dominant genera differed notably between pristine, topsoil, and technosoil samples for bacteria and fungi. The ecological indices’ results indicated that the pristine soil microbial communities were distinct from those in the topsoils, revealing significant alterations during the stockpiling process. The revegetated technosoil showed more similarity to the pristine and topsoil samples than to the freshly prepared technosoil, suggesting that microbial restoration is an ongoing phenomenon. Microbial restoration analysis revealed that Bacterial communities recover faster than fungal communities highlighting the potential of managing technosoil physicochemical parameters to enhance microbial recovery similar to those found in pristine soils. Runoff water contribute to this rebalancing by transporting microorganisms between ecosystem. All pit samples exhibited significant differences in their microbial composition, with moisture and rock composition representing the primary axes of dissimilarity. The greater community complexity observed in soils is related to the availability of nutrients, physicochemical variations, and the possibility of interaction with other microbes. Pits represent extreme ecosystems that limit the growth of most microorganisms. The presented research provides a scientific basis for future restoration strategies to improve microbial diversity and ecosystem resilience in altered landscapes.

Cloud water, rainwater, and aerosol particles were collected in Puerto Rico from December 2004 to March 2007 in order to investigate their chemical composition, relation to sources, and removal processes. The species analyzed were inorganic ions, metals, total and dissolved organic carbon (TOC, DOC), total nitrogen (TN), and organic acids. For all samples, the dominant species were marine (Na+, Cl−), representing about 50%–65% of total content. Non‐sea‐salt fraction was dominated by SO42− (17%–25%), followed by water‐soluble organic (2%–8%) and total nitrogen (2% –6%) compounds. Organic acids represented contributions to the organic fraction in cloud water of 20% and 6% for aerosol particles. Inorganic species were predominant in total nitrogen portion. The chemical composition of cloud water, rainwater, and aerosol particles were observed to be sensitive to transport patterns. Air masses from northwest Africa showed the highest concentrations of nss‐Ca2+, Fe, and Al, suggesting a crustal origin. The pH values for cloud water and rainwater observed under this transport pattern were higher than background conditions, probably due to the alkalinity associated with nss‐Ca2+. The highest concentrations of Cl− and SO42−, with lower pH, were measured during periods of influence from Soufriere Hills volcano eruptions, most likely due to emitted SO2 and HCl. Air masses from North America had an anthropogenic influence, where levels of nss‐SO42−, TOC, and TN were higher (∼4 times) than in clean air masses. These results suggest that long‐range transport could be an extra source of metals and organic/nitrogen species to the Caribbean region ecosystems.

Growth and haematological parameters are used in aquaculture as indicators of the health of cultivated fish in response to changes in diet and the physicochemical conditions of the water. In this study, the physiological status of Totoaba macdonaldi was evaluated based on growth, haematological parameters and blood chemistry in response to two temperatures (23°C and 26°C) and the partial replacement of fishmeal with soybean meal (SBM—32%, 43% and 56%). In total, 288 fish were distributed among a control group and treatments in a 2 × 4 experimental design with three replicates. The diet was provided to apparent satiety three times a day for 61 days. Temperature significantly affected the final average weight of the fish acclimated to 23°C or 26°C (p < 0.05). The highest final average weight (264.2 ± 7.9 g) was recorded in fish acclimated to 26°C and fed a diet with 43% SBM, and the Fulton condition index ranged from 0.98 ± 0.01 to 1.05 ± 0.12. The albumin and aminotransferase enzyme concentrations and the albumin:globulin ratio were affected by the acclimation temperature (p < 0.05). The temperature–diet interaction affected the concentrations of leukocytes, albumin and triglycerides. Diets containing SBM significantly affected (p < 0.05) the concentrations of leukocytes, total proteins, albumin, globulins, glucose, triglycerides, aspartate and alanine aminotransferases, as well as the albumin:globulin ratio. Increases in these indicators were observed in fish fed diets with higher percentages of SBM. The results show that replacing fish meal with more than 32% SBM could negatively affect health‐related blood parameters in T. macdonaldi, with more pronounced effects when a diet–temperature interaction is present. These findings highlight the need for further research to evaluate the use of SBM in T. macdonaldi diets and its effect on their physiological performance under different thermal conditions.

An interview with Gabriel Reyes, the founder of FLi Sci, a program that provides research opportunities to first-generation and low-income science students, is presented. Reyes talks about issues in neuroscience, research on the impact of poverty on cognitive development, and how his personal experiences influenced his choice of research.

Modulation of frontal lobes activity is believed to be an important pathway trough which the hypothalamic-pituitary-adrenal (HPA) axis stress response impacts cognitive and emotional functioning. Here, we investigate the effects of stress on metacognition, which is the ability to monitor and control one's own cognition. As the frontal lobes have been shown to play a critical role in metacognition, we predicted that under activation of the HPA axis, participants should be less accurate in the assessment of their own performances in a perceptual decision task, irrespective of the effect of stress on the first order perceptual decision itself. To test this prediction, we constituted three groups of high, medium and low stress responders based on cortisol concentration in saliva in response to a standardized psycho-social stress challenge (the Trier Social Stress Test). We then assessed the accuracy of participants' confidence judgments in a visual discrimination task. As predicted, we found that high biological reactivity to stress correlates with lower sensitivity in metacognition. In sum, participants under stress know less when they know and when they do not know.

This study presents an AI-driven computational framework for optimizing the orientation and spatial deployment of photovoltaic (PV) systems installed on uneven terrain, with the objective of enhancing energy efficiency and supporting sustainable energy development. The proposed methodology integrates PVsyst-based numerical simulations with statistical modeling and bio-inspired heuristic optimization algorithms, forming a hybrid machine learning–assisted decision-making approach. A heuristic–parametric optimization strategy was employed to evaluate multiple tilt and azimuth configurations, aiming to maximize specific energy yield and overall system performance, expressed through the performance ratio (PR). The model was validated using site-specific climatic data from Veracruz, Mexico, and identified an optimal azimuth orientation of approximately 267.3°, corresponding to an estimated PR of 0.8318. The results highlight the critical influence of azimuth orientation on photovoltaic efficiency and demonstrate strong consistency between simulation outputs, statistical analysis, and intelligent optimization results. From an industrial perspective, the proposed framework reduces planning uncertainty and energy losses associated with suboptimal configurations, enabling more reliable and cost-effective photovoltaic system design, particularly for installations on uneven terrain. Moreover, the methodology significantly reduces planning time and potential installation costs by eliminating the need for preliminary physical testing, offering a scalable and reproducible AI-assisted tool that can contribute to lower levelized energy costs, enhanced system reliability, and more efficient deployment of photovoltaic technologies in the renewable energy industry. Future work will extend the model toward a multivariable machine learning framework incorporating tilt angle, climatic variability, and photovoltaic technology type, further strengthening its applicability in real-world environments and its contribution to Sustainable Development Goal 7: affordable and clean energy.

Datasets exist in real life in many formats (audio, music, image,...). In our case, we have them from various sources mixed together. Our mixtures represent noisy audio data that need to be extracted (features), compressed and analysed in order to be presented in a standard way. The resulted data will be used for the Blind Source Separation task. In this paper, we deal with two types of autoencoders: convolutional and denoising. The novelty of our work is to reconstruct the audio signal in the output of the neural network after extracting the meaningful features that present the pure and the powerful information. Simulation results show a great performance, yielding of 87% for the reconstructed signals that will be included in the automated system used for real word applications.