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In recent years, there appeared a need for astronomical observations timed with submillisecond accuracy. These include, e.g., timing stellar occultations by small, subkilometer, or fast near-Earth asteroids and tracking artificial satellites in low-Earth orbit using optical sensors. Precise astrometry of fast-moving satellites and accurate timing of stellar occultations have parallel needs, requiring a reliable time source and good knowledge of camera delays. Thus, there is a need for an external device that would enable equipment and camera testing to check if they reach the required accuracy in time. We designed, constructed, and thoroughly tested a New EXposure Timing Analyser (NEXTA), a Global Navigation Satellite System–based precise timer allowing us to reach an accuracy of 0.1 ms, which is an order of magnitude better than in previously available tools. The device is a simple strip of blinking diodes to be imaged with a camera and compare the imaged time with the internal camera time stamp. Our tests spanned a range of scientific cameras widely used for stellar occultations and ground-based satellite tracking. The results revealed high reliability of both NEXTA and most of the tested cameras but also pointed out that practically all cameras had internal time biases of various levels. NEXTA can serve the community, being easily reproducible with inexpensive components. We provide all the necessary schemes and usage instructions.

Turbulent mixing of heat and momentum in the stably-stratified ocean interior occurs in discrete events driven by vertical variations of the horizontal velocity. Typically, these events have been modelled assuming an initially laminar stratified shear flow which develops wavelike instabilities, becomes fully turbulent, and then relaminarizes into a stable state. However, in the real ocean there is always some level of turbulence left over from previous events. Using direct numerical simulations, we show that the evolution of a stably-stratified shear layer may be significantly modified by pre-existing turbulence. The classical billow structure associated with Kelvin–Helmholtz instability is suppressed and eventually eliminated as the strength of the initial turbulence is increased. A corresponding energetics analysis shows that potential energy changes and dissipation of kinetic energy depend non-monotonically on initial turbulence strength, with the largest effects when initial turbulence is present but insufficient to prevent billow formation. The mixing efficiency decreases with increasing initial turbulence amplitude as the development of the Kelvin–Helmholtz billow, with its large pre-turbulent mixing efficiency, is arrested.

Long γ-ray bursts are associated with energetic, broad-lined, stripped-envelope supernovae 1 , 2 and as such mark the death of massive stars. The scarcity of such events nearby and the brightness of the γ-ray burst afterglow, which dominates the emission in the first few days after the burst, have so far prevented the study of the very early evolution of supernovae associated with γ-ray bursts 3 . In hydrogen-stripped supernovae that are not associated with γ-ray bursts, an excess of high-velocity (roughly 30,000 kilometres per second) material has been interpreted as a signature of a choked jet, which did not emerge from the progenitor star and instead deposited all of its energy in a thermal cocoon 4 . Here we report multi-epoch spectroscopic observations of the supernova SN 2017iuk, which is associated with the γ-ray burst GRB 171205A. Our spectra display features at extremely high expansion velocities (around 115,000 kilometres per second) within the first day after the burst 5 , 6 . Using spectral synthesis models developed for SN 2017iuk, we show that these features are characterized by chemical abundances that differ from those observed in the ejecta of SN 2017iuk at later times. We further show that the high-velocity features originate from the mildly relativistic hot cocoon that is generated by an ultra-relativistic jet within the γ-ray burst expanding and decelerating into the medium that surrounds the progenitor star 7 , 8 . This cocoon rapidly becomes transparent 9 and is outshone by the supernova emission, which starts to dominate the emission three days after the burst. Multi-epoch observations of a supernova associated with a γ-ray burst reveal spectral features at extremely high expansion velocities within the first day after the burst, indicative of a choked jet.

In recent years, there appeared a need for astronomical observations timed with submillisecond accuracy. These include, e.g., timing stellar occultations by small, subkilometer, or fast near-Earth asteroids and tracking artificial satellites in low-Earth orbit using optical sensors. Precise astrometry of fast-moving satellites and accurate timing of stellar occultations have parallel needs, requiring a reliable time source and good knowledge of camera delays. Thus, there is a need for an external device that would enable equipment and camera testing to check if they reach the required accuracy in time. We designed, constructed, and thoroughly tested a New EXposure Timing Analyser (NEXTA), a Global Navigation Satellite System–based precise timer allowing us to reach an accuracy of 0.1 ms, which is an order of magnitude better than in previously available tools. The device is a simple strip of blinking diodes to be imaged with a camera and compare the imaged time with the internal camera time stamp. Our tests spanned a range of scientific cameras widely used for stellar occultations and ground-based satellite tracking. The results revealed high reliability of both NEXTA and most of the tested cameras but also pointed out that practically all cameras had internal time biases of various levels. NEXTA can serve the community, being easily reproducible with inexpensive components. We provide all the necessary schemes and usage instructions.

Background Cancer survivors in Germany face considerable challenges related to the late and long-term effects of treatment and a lack of post-treatment support. Despite an increasing number of cancer survivors, existing healthcare systems are insufficiently adapted to meet their ongoing needs, particularly for long-term survivors who may experience physical, emotional, and socio-economic hardships. This study aims to address the knowledge gaps in the care situation of long-term cancer survivors, focusing on their experiences and the barriers they face in accessing care. Methods This study protocol outlines the methodology for a quantitative survey involving up to 3,300 long-term cancer survivors across various cancer types in Germany. The survey assesses their experiences with cancer care, focusing on diet, exercise, mental health, sleep, cognition, overall health-related quality of life, and somatic late effects. Special attention is given to survivors from diverse socio-demographic backgrounds, including those with a migration history, in order to explore the unique challenges they face. Discussion The results of the study will contribute to the development of needs-based care recommendations for cancer survivors, particularly those in potentially vulnerable groups. The findings will inform the design of more inclusive care strategies and interventions, leading to better long-term health outcomes for cancer survivors in Germany. Trial registration German Clinical Trials Register: DRKS00032146, registered on 03/12/2024.

The metabolic derangement is common in heart failure with reduced ejection fraction (HFrEF). The aim of the study was to check feasibility of the combined approach of untargeted metabolomics and machine learning to create a simple and potentially clinically useful diagnostic panel for HFrEF. The study included 67 chronic HFrEF patients (left ventricular ejection fraction-LVEF 24.3 ± 5.9%) and 39 controls without the disease. Fasting serum samples were fingerprinted by liquid chromatography-mass spectrometry. Feature selection based on random-forest models fitted to resampled data and followed by linear modelling, resulted in selection of eight metabolites (uric acid, two isomers of LPC 18:2, LPC 20:1, deoxycholic acid, docosahexaenoic acid and one unknown metabolite), demonstrating their predictive value in HFrEF. The accuracy of a model based on metabolites panel was comparable to BNP (0.85 vs 0.82), as verified on the test set. Selected metabolites correlated with clinical, echocardiographic and functional parameters. The combination of two innovative tools (metabolomics and machine-learning methods), both unrestrained by the gaps in the current knowledge, enables identification of a novel diagnostic panel. Its diagnostic value seems to be comparable to BNP. Large scale, multi-center studies using validated targeted methods are crucial to confirm clinical utility of proposed markers.

We investigate numerically transient linear growth of three-dimensional perturbations in a stratified shear layer to determine which perturbations optimize the growth of the total kinetic and potential energy over a range of finite target time intervals. The stratified shear layer has an initial parallel hyperbolic tangent velocity distribution with Reynolds number $\\def \\xmlpi #1{}\\def \\mathsfbi #1{\\boldsymbol {\\mathsf {#1}}}\\let \\le =\\leqslant \\let \\leq =\\leqslant \\let \\ge =\\geqslant \\let \\geq =\\geqslant \\def \\Pr {\\mathit {Pr}}\\def \\Fr {\\mathit {Fr}}\\def \\Rey {\\mathit {Re}}\\mathit{Re}=U_0 h/\\nu =1000$ and Prandtl number $\\nu /\\kappa =1$ , where $\\nu $ is the kinematic viscosity of the fluid and $\\kappa $ is the diffusivity of the density. The initial stable buoyancy distribution has constant buoyancy frequency $N_0$ , and we consider a range of flows with different bulk Richardson number ${\\mathit{Ri}}_b=N_0^2h^2/U_0^2$ , which also corresponds to the minimum gradient Richardson number ${\\mathit{Ri}}_g(z)=N_0^2/(\\mathrm{d}U/\\mathrm{d} z)^2$ at the midpoint of the shear layer. For short target times, the optimal perturbations are inherently three-dimensional, while for sufficiently long target times and small ${\\mathit{Ri}}_b$ the optimal perturbations are closely related to the normal-mode ‘Kelvin–Helmholtz’ (KH) instability, consistent with analogous calculations in an unstratified mixing layer recently reported by Arratia et al. (J. Fluid Mech., vol. 717, 2013, pp. 90–133). However, we demonstrate that non-trivial transient growth occurs even when the Richardson number is sufficiently high to stabilize all normal-mode instabilities, with the optimal perturbation exciting internal waves at some distance from the midpoint of the shear layer.

We describe a computational method for assessing functional connectivity in sensory neuronal networks. The method, which we term cross-trial correlation , can be applied to signals representing local field potentials (LFPs) evoked by sensory stimulations and utilizes their trial-to-trial variability. A set of single trial samples of a given post-stimulus latency from consecutive evoked potentials (EPs) recorded at a given site is correlated with such sets for all other latencies and recording sites. The results of this computation reveal how neuronal activities at various sites and latencies correspond to activation of other sites at other latencies. The method was used to investigate the functional connectivity of thalamo-cortical network of somatosensory system in behaving rats at two levels of alertness: habituated and aroused. We analyzed potentials evoked by vibrissal deflections recorded simultaneously from the ventrobasal thalamus and barrel cortex. The cross-trial correlation analysis applied to the early post-stimulus period (<25 ms) showed that the magnitude of the population spike recorded in the thalamus at 5 ms post-stimulus correlated with the cortical activation at 6–13 ms post-stimulus. This correlation value was reduced at 6–9 ms, i.e. at early postsynaptic cortical response, with increased level of the animals’ arousal. Similarly, the aroused state diminished positive thalamo-cortical correlation for subsequent early EP waves, whereas the efficacy of an indirect cortico-fugal inhibition (over 15 ms) did not change significantly. Thus we were able to characterize the state related changes of functional connections within the thalamo-cortical network of behaving animals.

In a stably stratified shear layer, multiple competing instabilities produce sensitivity to small changes in initial conditions, popularly called the butterfly effect (as a flapping wing may alter the weather). Three ensembles of 15 simulated mixing events, identical but for small perturbations to the initial state, are used to explore differences in the route to turbulence, the maximum turbulence level and the total amount and efficiency of mixing accomplished by each event. Comparisons show that a small change in the initial state alters the strength and timing of the primary Kelvin–Helmholtz instability, the subharmonic pairing instability and the various three-dimensional secondary instabilities that lead to turbulence. The effect is greatest in, but not limited to, the parameter regime where pairing and the three-dimensional secondary instabilities are in strong competition. Pairing may be accelerated or prevented; maximum turbulence kinetic energy may vary by up to a factor of 4.6, flux Richardson number by 12 %–15 % and net mixing by a factor of 2.

In this study, the results of investigations which enable an extension of the mathematical methods supporting air quality management in cities are presented. The actions were focused on the development of neural models of classification and prediction of the air quality classes (in respect of PM10 dust concentrations). The air quality class on a following day was predicted. The aim of modelling was to predict the air quality classes in the afternoon and in the evening when PM10 concentrations attained the daily maxima. The monitoring of PM10 concentration and the meteorological data for winter periods in 2004–2007 was used. The artificial neural network methods (ANN) with a simultaneous application of data compression methods were tested. The results of the air quality prediction are satisfactory. The accurate prognoses are predominant. The percentage of wrong prognoses is relatively small. The investigations confirm that neural prediction models allow good results to be obtained of the air quality class prediction. The results of the research prove that the tested models may be applied in the practice of air quality management in cities.