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1. Scale-free behavioral dynamics directly linked with scale-free cortical dynamics

   https://doi.org/10.7554/eLife.79950  

   Jones, Sabrina A; Barfield, Jacob H; Norman, V Kindler; Shew, Woodrow L (January 2023, eLife)

   Naturally occurring body movements and collective neural activity both exhibit complex dynamics, often with scale-free, fractal spatiotemporal structure. Scale-free dynamics of both brain and behavior are important because each is associated with functional benefits to the organism. Despite their similarities, scale-free brain activity and scale-free behavior have been studied separately, without a unified explanation. Here, we show that scale-free dynamics of mouse behavior and neurons in the visual cortex are strongly related. Surprisingly, the scale-free neural activity is limited to specific subsets of neurons, and these scale-free subsets exhibit stochastic winner-take-all competition with other neural subsets. This observation is inconsistent with prevailing theories of scale-free dynamics in neural systems, which stem from the criticality hypothesis. We develop a computational model which incorporates known cell-type-specific circuit structure, explaining our findings with a new type of critical dynamics. Our results establish neural underpinnings of scale-free behavior and clear behavioral relevance of scale-free neural activity. 

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2. Deletion of the testosterone-sensitive TRPM8 Ca++ channel disrupts measures of anxiety and depression in mice: Implications for dopamine

   Shepler, B; Erdmier, A; Palmisano, H; Asuthkar, S; Koeltzow, TE (April 2024, Bradley University)

   Testosterone exerts high affinity for the Transient Receptor Potential Melastatin 8 (TRPM8) Ca2+ channel. TRPM8 -/- male mice exhibit disrupted sexual behavior (e.g., indiscriminate approach, delayed satiety), possibly due to decreased ventral tegmental area dopamine neuron activity. It is hypothesized that TRPM8 null mutant mice (Jackson Laboratories) will exhibit disruptions across a range of motivationally-relevant behaviors, including spontaneous locomotor activation, detection of novel stimuli, sucrose preference, and sensitivity to the psychomotor stimulant amphetamine. Initial findings indicate that male TRPM8 mutant mice (n=6) exhibit decreased nocturnal locomotor activity (F(1,12)=23.41, p<0.001), increased behavioral anxiety in the light/dark task (t(10)=2.44, p<0.05; d=1.4), and behavioral despair in the forced swim task (t(10)=3.70, p<0.005; d=2.1). In contrast, these mice tended to prefer a low concentration (0.1%) of sucrose compared to wildtype males (n=6; t(10)=1.35, p=0.09; d=0.83). Tests for sensitivity to amphetamine are in progress. These data suggest a pivotal role for TRPM8 in motivated behavior. 

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3. Moderate-separation Binary Companions May Influence Young Stellar X-Ray Luminosity

   https://doi.org/10.3847/2515-5172/ada14e  

   Clark, M; Sullivan, Kendall; Soares-Furtado, Melinda (December 2024, Research Notes of the AAS)

   Abstract Young pre-main sequence stars exhibit elevated X-ray levels due to their strong magnetic activity. Understanding young stars’ X-ray activity is essential for contextualizing the forming planets that they host. Binary stars present a unique environment that may influence planet formation and evolution. In this work, we assembled a sample of 65 systems with stellar characterization from the literature and X-ray fluxes from the XMM-Newton Extended Survey of the Taurus Molecular Cloud to investigate the potential relationship between binary separation and X-ray flux. We found that binary stars with separations smaller than the sample median may exhibit elevated X-ray flux compared to single stars. This suggests that binary companions could influence stellar and planetary evolution and warrants further investigation. 

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4. Encoding time in neural dynamic regimes with distinct computational tradeoffs

   https://doi.org/10.1371/journal.pcbi.1009271  

   Zhou, Shanglin; Masmanidis, Sotiris C.; Buonomano, Dean V. (March 2022, PLOS Computational Biology)

   Gutkin, Boris S. (Ed.)

   Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time. 

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5. Stratosphere‐Troposphere Coupling of Extreme Stratospheric Wave Activity in CMIP6 Models

   https://doi.org/10.1029/2023JD038811  

   Ding, Xiuyuan; Chen, Gang; Ma, Weiming (August 2023, Journal of Geophysical Research: Atmospheres)

   Abstract Extreme stratospheric wave activity has been suggested to be connected to surface temperature anomalies, but some key processes are not well understood. Using observations, we show that the stratospheric events featuring weaker‐than‐normal wave activity are associated with increased North American (NA) cold extreme risks before and near the event onset, accompanied by less frequent atmospheric river (AR) events on the west coast of the United States. Strong stratospheric wave events, on the other hand, exhibit a tropospheric weather regime transition. They are preceded by NA warm anomalies and increased AR frequency over the west coast, followed by increased risks of NA cold extremes and north‐shifted ARs over the Atlantic. Moreover, these links between the stratosphere and troposphere are attributed to the vertical structure of wave coupling. Weak wave events show a wave structure of westward tilt with increasing altitudes, while strong wave events feature a shift from westward tilt to eastward tilt during their life cycle. This wave phase shift indicates vertical wave coupling and likely regional planetary wave reflection. Further examinations of CMIP6 models show that models with a degraded representation of stratospheric wave structure exhibit biases in the troposphere during strong wave events. Specifically, models with a stratospheric ridge weaker than the reanalysis exhibit a weaker tropospheric signal. Our findings suggest that the vertical coupling of extreme stratospheric wave activity should be evaluated in the model representation of stratosphere‐troposphere coupling. 

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