Cracks in the mirror hypothesis: High specularity does not reduce detection or predation risk

Abstract Some animals, including certain fish, beetles, spiders and Lepidoptera chrysalises, have such shiny or glossy surfaces that they appear almost mirror‐like. A compelling but unsubstantiated hypothesis is that a highly specular or mirror‐like appearance enhances survival by reflecting the surrounding environment and reducing detectability. We tested this hypothesis by asking human participants to wear a mobile eye‐tracking device and locate highly realistic mirror‐green and diffuse‐green replica beetles against a variety of backgrounds in a natural forest environment. We also tested whether a mirror‐like appearance enhances survival to wild predators by monitoring the survival of mirror‐green and diffuse‐green replica beetles in a forested habitat and an open habitat. Human participants showed no difference in the detection probability or detection latency of mirror versus diffuse replica beetles, indicating that mirror‐like appearance does not impair prey capture. The field predation experiment found no difference in survival between the mirror and diffuse replica beetles in forested environments. Similarly, there was no difference in survival when beetles were deployed in the open habitat where there is no background to reflect, indicating that predators detect and do not actively avoid mirror‐like beetles. Our results suggest that a mirror‐like appearance does not reduce attack by predators. Instead, highly specular, mirror‐like surfaces may have evolved for an alternate visual function or as a secondary consequence of selection for a non‐visual function, such as thermoregulation. A free Plain Language Summary can be found within the Supporting Information of this article

Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake–sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1–2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies

Validation of 'Somnivore', a Machine Learning Algorithm for Automated Scoring and Analysis of Polysomnography Data

Manual scoring of polysomnography data is labor-intensive and time-consuming, and most existing software does not account for subjective differences and user variability. Therefore, we evaluated a supervised machine learning algorithm, SomnivoreTM, for automated wake-sleep stage classification. We designed an algorithm that extracts features from various input channels, following a brief session of manual scoring, and provides automated wake-sleep stage classification for each recording. For algorithm validation, polysomnography data was obtained from independent laboratories, and include normal, cognitively-impaired, and alcohol-treated human subjects (total n = 52), narcoleptic mice and drug-treated rats (total n = 56), and pigeons (n = 5). Training and testing sets for validation were previously scored manually by 1-2 trained sleep technologists from each laboratory. F-measure was used to assess precision and sensitivity for statistical analysis of classifier output and human scorer agreement. The algorithm gave high concordance with manual visual scoring across all human data (wake 0.91 ± 0.01; N1 0.57 ± 0.01; N2 0.81 ± 0.01; N3 0.86 ± 0.01; REM 0.87 ± 0.01), which was comparable to manual inter-scorer agreement on all stages. Similarly, high concordance was observed across all rodent (wake 0.95 ± 0.01; NREM 0.94 ± 0.01; REM 0.91 ± 0.01) and pigeon (wake 0.96 ± 0.006; NREM 0.97 ± 0.01; REM 0.86 ± 0.02) data. Effects of classifier learning from single signal inputs, simple stage reclassification, automated removal of transition epochs, and training set size were also examined. In summary, we have developed a polysomnography analysis program for automated sleep-stage classification of data from diverse species. Somnivore enables flexible, accurate, and high-throughput analysis of experimental and clinical sleep studies

Time-gated luminescence acquisition for biochemical sensing: miRNA detection*Relacionar en OpeAire*

Luminescence emission is a multidimensional phenomenon comprising a time-domain layer defined by its excited-state kinetics and corresponding lifetime, which is specific to each luminophore and depends on environmental conditions. This feature allows for the discrimination of luminescence signals from species with a similar spectral profile but different lifetimes by time-gating (TG) the acquisition of luminescence. This approach represents an efficient tool for removing unwanted, usually short-lived, signals from scattered light and fluorescence interferents using luminophores with a long lifetime. Due to the emergence of time-resolved techniques using rapid excitation and acquisition methods (i.e., pulsed lasers and single-photon timing acquisition) and new long-lifetime luminophores (i.e., acridones, lanthanide complexes, nanoparticles, etc.), TG analyses can be easily applied to relevant chemical and biochemical issues. The successful application of TG to important biomedical topics has attracted the attention of the R&D industry due to its potential in the development and patenting of new probes, methods and techniques for drug discovery, immunoassays, biomarker discovery and biomolecular interactions, etc. Here, we review the technological efforts of innovative companies in the application of TG-based techniques. Among the many currently available biomarkers, circulating microRNAs (miRNAs) have received attention since they are highly specific and sensitive to different pathological stages of numerous diseases and easily accessible from biological fluids. qPCR is a powerful and routine technique used for the detection and quantification of miRNAs, but qPCR may introduce numerous artefacts and low reproducibility during the amplification process, particularly using complex samples. Thus, due to the efficiency of TG in separating short- lived sources of fluorescence common in biological fluids, TG is an ideal approach for the direct detection of miRNAs in liquid biopsies. Recently, great efforts in the use of TG have been achieved. Our contribution is the proposal of a direct detection approach using TG- imagining with single nucleobase resolution.European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 690866 (miRNA-DisEASY)Proyecto CTQ2017-85658-R. Ministerio de Economía y Competitividad/Agencia Estatal deInvestigación/Fondo Europeo de Desarrollo Regional (FEDER