Use of autonomous audio recordings for the rapid inventory of birds in the white‐sand forests of the Peruvian Amazon

White‐sand forests are patchily distributed ecosystems covering just 5% of Amazonia that host many specialist species of birds not found elsewhere, and these forests are threatened due to their small size and human exploitation of sand for construction projects. As a result, many species of birds that are white‐sand specialists are at risk of extinction, and immediate conservation action is paramount for their survival. Our objective was to evaluate current survey methods and determine the relative effect of the size of patches of these forests on the presence or absence of white‐sand specialists. Using point counts and autonomous recorders, we surveyed avian assemblages occupying patches of white‐sand forest in the Peruvian Amazon in April 2018. Overall, we detected 126 species, including 21 white‐sand forest specialists. We detected significantly more species of birds per survey point with autonomous recorders than point counts. We also found a negative relationship between avian species richness and distance from the edge of patches of white‐sand forest, but a significant, positive relationship when only counting white‐sand specialists. Although we detected more species with autonomous recorders, point counts were more effective for detecting canopy‐dwelling passerines. Therefore, we recommend that investigators conducting surveys for rare and patchily distributed species in the tropics use a mixed‐method approach that incorporates both autonomous recorders and visual observation. Finally, our results suggest that conserving large, continuous patches of white‐sand forest may increase the likelihood of survival of species of birds that are white‐sand specialists.     

Profiling SARS-CoV-2 HLA-I peptidome reveals T cell epitopes from out-of-frame ORFs

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Immunocytochemical characterisation of endophytic bacteriaAzospirillum brasilense, Herbaspirillum seropedicae, Burkholderia ambifaria andGluconacetobacter diazotrophicus

In the present work an immunocytochemical characterisation of four endophytic bacterial species has been made by using polyclonal antiserum produced against each of the four bacterial strains previously heated at 60 °C. The aim of this researchsito identify common elements among bacteria associated with their endophytic behaviour. Analysis of extracts of each strain by immunoblotting and ELISA confirmed the presence of proteins from different bacterial strains made up of common epitopes. However, antisaproduced againstHerbaspirillum seropedicae andBurkholderia ambifaria show a high number of bands recognised on each extracts, while antisera againstAzospirillum brasilense andGluconacetobacter diazotrophicus show a low number of bands recognised on each extract. Immunogold labelling showed that epitopes are located both on the cell wall and in the cytoplasm; most likely they could be preursor cell wall proteins synthesized inside the cytoplasm and subsequently transported onto cell wall. Finally, the common bands amog bacterial strains revealed by immunoblotting could play a role as active hydrolases involved in host tissue penetration.     

Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior) and of the error (the loss function). The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality) seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed) distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.