Interrupted breeding in a songbird migrant triggers development of nocturnal locomotor activity

Abstract Long-distance avian migrants, e.g. Eurasian reed warblers (Acrocephalus scirpaceus), can precisely schedule events of their annual cycle. However, the proximate mechanisms controlling annual cycle and their interplay with environmental factors are poorly understood. We artificially interrupted breeding in reed warblers by bringing them into captivity and recording birds’ locomotor activity for 5–7 days. Over this time, most of the captive birds gradually developed nocturnal locomotor activity not observed in breeding birds. When the birds were later released and radio-tracked, the individuals with highly developed caged activity performed nocturnal flights. We also found that reed warblers kept indoors without access to local cues developed a higher level of nocturnal activity compared to the birds kept outdoors with an access to the familiar environment. Also, birds translocated from a distant site (21 km) had a higher motivation to fly at night-time after release compared to the birds captured within 1 km of a study site. Our study suggests that an interrupted breeding triggers development of nocturnal locomotor activity in cages, and the level of activity is correlated with motivation to perform nocturnal flights in the wild, which can be restrained by familiar environment

Principal nonlinear dynamical modes of climate variability

We are grateful to Michael Ghil and Dmitri Kondrashov for fruitful discussions. The study was supported by Government of Russian Federation (agreement #14.Z50.31.0033 with the Institute of Applied Physics of RAS).Peer reviewedPublisher PD

Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition

Currently, causes of the middle Pleistocene transition (MPT) – the onset of large-amplitude glacial variability with 100 kyr time scale instead of regular 41 kyr cycles before – are a challenging puzzle in Paleoclimatology. Here we show how a Bayesian data analysis based on machine learning approaches can help to reveal the main mechanisms underlying the Pleistocene variability, which most likely explain proxy records and can be used for testing existing theories. We construct a Bayesian data-driven model from benthic δ18O records (LR04 stack) accounting for the main factors which may potentially impact climate of the Pleistocene: internal climate dynamics, gradual trends, variations of insolation, and millennial variability. In contrast to some theories, we uncover that under long-term trends in climate, the strong glacial cycles have appeared due to internal nonlinear oscillations induced by millennial noise. We find that while the orbital Milankovitch forcing does not matter for the MPT onset, the obliquity oscillation phase-locks the climate cycles through the meridional gradient of insolation

Wavelet-based image decomposition method for NuSTAR stray light background studies

The large side aperture of the NuSTAR telescope for unfocused photons (so-called stray light) is a known source of rich astrophysical information. To support many studies based on the NuSTAR stray light data, we present a fully automatic method for determining detector area suitable for background analysis and free from any kind of focused X-ray flux. The method's main idea is `a trous' wavelet image decomposition, capable of detecting structures of any spatial scale and shape, which makes the method of general use. Applied to the NuSTAR data, the method provides a detector image region with the highest possible statistical quality, suitable for the NuSTAR stray light studies. We developed an open-source Python nuwavdet package, which implements the presented method. The package contains subroutines to generate detector image region for further stray light analysis and/or to produce a list of detector bad-flagged pixels for processing in the NuSTAR Data Analysis Software for conventional X-ray analysis.Comment: 9 pages, 10 figures. Published in Journal of Astronomical Telescopes, Instruments, and Systems (JATIS

Three-year survival rate and changes in the level of consciousness in outpatients after severe brain injuries

Introduction. There is a worldwide lack of statistical data about the patients with chronic disorders of consciousness (DOC). In Russia, there are no such data at all. Objective: to perform the first study in Russia to assess the survival rate and changes in the level of consciousness in outpatients with the chronic DOC after their hospital discharge as well as to identify the predictors of survival and improvement in the level of consciousness. Materials and methods. All the participants (n = 142) underwent their treatment and rehabilitation in Federal Research and Clinical Center of Intensive Care Medicine and Rehabilitology from January 2016 to January 2020. We recorded the changes in patient's vital status and their level of consciousness at the endpoints of 3, 6, 12, 24, and 36 months from the brain injury (both for hospital and outpatient stages). We used the KaplanMeier method to assess the survival rate. We also used the logistic regression model to determine the correlation between the predictors of the survival and the improvement in the level of consciousness at baseline and 36 months after the injury. Results. The mortality rate in the study group 3 years after the brain injury was 86.6%. Regardless of the survival rate, the level of consciousness had significantly improved (i.e., they regained communication) in 22.5% of patients within 3 years after the index event. The statistically significant final model of the regression analysis (for 142 patients) showed that younger age and higher overall CRS-R score improved the survival rate. The logistic regression model used to determine the predictors of the improvement in the level of consciousness among the survivors gave no significant results. Conclusions. High mortality rate among the outpatients, whose level of consciousness had improved at discharge, proves the ineffectiveness of the outpatient rehabilitation. Thus, we need to find a way to improve it. The authors hope that the data obtained in this study will form the basis of their research

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change