Abstract:Systems of oscillators often converge to a state of synchrony when sufficiently interconnected. Twenty years ago, the mathematical analysis of models of coupled oscillators revealed the possibility for complex phases that exhibit a coexistence of synchronous and asynchronous clusters, known as “chimera states.” Beyond their recurrence in theoretical models, chimeras have been observed under specifically designed experimental conditions, yet their emergence in nature has remained elusive. Here, we report eviden… Show more
“…This work enables accurate identification and classification of firefly species in order to ensure their protection and long-term survival. Figure 1 ( A ) Our standardized data collection method (top) films fireflies in their natural habitat with two cameras arranged in a stereoscopic vision configuration 22 (photo reproduced and modified with permission from 23 ). Flash streaks (center, colored by time) in the resulting videos are triangulated and concatenated into trajectories, based on proximity and velocity (bottom).…”
Many nocturnally active fireflies use precisely timed bioluminescent patterns to identify mates, making them especially vulnerable to light pollution. As urbanization continues to brighten the night sky, firefly populations are under constant stress, and close to half of the species are now threatened. Ensuring the survival of firefly biodiversity depends on a large-scale conservation effort to monitor and protect thousands of populations. While species can be identified by their flash patterns, current methods require expert measurement and manual classification and are infeasible given the number and geographic distribution of fireflies. Here we present the application of a recurrent neural network (RNN) for accurate automated firefly flash pattern classification. Using recordings from commodity cameras, we can extract flash trajectories of individuals within a swarm and classify their species with an accuracy of approximately seventy percent. In addition to its potential in population monitoring, automated classification provides the means to study firefly behavior at the population level. We employ the classifier to measure and characterize the variability within and between swarms, unlocking a new dimension of their behavior. Our method is open source, and deployment in community science applications could revolutionize our ability to monitor and understand firefly populations.
“…This work enables accurate identification and classification of firefly species in order to ensure their protection and long-term survival. Figure 1 ( A ) Our standardized data collection method (top) films fireflies in their natural habitat with two cameras arranged in a stereoscopic vision configuration 22 (photo reproduced and modified with permission from 23 ). Flash streaks (center, colored by time) in the resulting videos are triangulated and concatenated into trajectories, based on proximity and velocity (bottom).…”
Many nocturnally active fireflies use precisely timed bioluminescent patterns to identify mates, making them especially vulnerable to light pollution. As urbanization continues to brighten the night sky, firefly populations are under constant stress, and close to half of the species are now threatened. Ensuring the survival of firefly biodiversity depends on a large-scale conservation effort to monitor and protect thousands of populations. While species can be identified by their flash patterns, current methods require expert measurement and manual classification and are infeasible given the number and geographic distribution of fireflies. Here we present the application of a recurrent neural network (RNN) for accurate automated firefly flash pattern classification. Using recordings from commodity cameras, we can extract flash trajectories of individuals within a swarm and classify their species with an accuracy of approximately seventy percent. In addition to its potential in population monitoring, automated classification provides the means to study firefly behavior at the population level. We employ the classifier to measure and characterize the variability within and between swarms, unlocking a new dimension of their behavior. Our method is open source, and deployment in community science applications could revolutionize our ability to monitor and understand firefly populations.
“…Synchronizing human networks can happen spontaneously [1,2] or intentionally [3,4]. The synchronization of the network is essential for coordinating ideas [5,6], and the well-being of its members [7][8][9], and is seen in other organisms as well [10][11][12]. Understanding the dynamics of human networks has implications for politics, economics, social sciences, and pandemic control.…”
Finding the global minimum in complex networks while avoiding local minima is challenging in many types of networks. We study the dynamics of complex human networks and observed that humans have different methods to avoid local minima than other networks. Humans can change the coupling strength between them or change their tempo. This leads to different dynamics than other networks and makes human networks more robust and better resilient against perturbations. We observed high-order vortex states, oscillation death, and amplitude death, due to the unique dynamics of the network. This research may have implications in politics, economics, pandemic control, decision-making, and predicting the dynamics of networks with artificial intelligence.
“…This particular species emits collectively synchronized flashes to attract the opposite sex in what is often described by spectators as a once-in-a-lifetime experience. The stunning display of bioluminescence has long attracted the interest of researchers, with both the synchrony of P. carolinus fireflies’ flashes − and the responsible biochemical processes being actively investigated topics to date. While the ethological background and exact mechanism of synchrony have yet to be fully unraveled, the bioluminescent glow has been traced back to the firefly compound luciferin (Figure , A): Upon exposure to air in the presence of adenosine triphosphate (ATP) as the energy supply and the luciferase enzyme, luciferin incorporates dioxygen to form a high-energy dioxetanone, which fuels the bioluminescent reaction .…”
We report the preparation and infrared spectroscopic identification of 1,2-dioxetanedione, which is one of the two possible cyclic dimers of carbon dioxide. We prepared this hitherto experimentally incompletely characterized species in a solid nitrogen matrix at 3 K from the reaction of oxalyl dichloride with the urea•hydrogen peroxide complex. Surprisingly, irradiation at 254 nm does not lead to its dissociation into carbon dioxide but rather yields cyclic carbon trioxide. We further assert our spectroscopic assignments by 18 O isotopic labeling and high-level N-electron valence state perturbation theory and coupled-cluster computations. The successful isolation of 1,2-dioxetanedione supports its viability as the postulated high-energy intermediate in the well-known and ubiquitously exploited "peroxyoxalate" chemiluminescent system.
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