All-optical physiology resolves a synaptic basis for behavioral timescale plasticity.

Learning has been associated with modifications of synaptic and circuit properties, but the precise changes storing information in mammals have remained largely unclear. We combined genetically targeted voltage imaging with targeted optogenetic activation and silencing of pre- and post-synaptic neurons to study the mechanisms underlying hippocampal behavioral timescale plasticity. In mice navigating a virtual-reality environment, targeted optogenetic activation of individual CA1 cells at specific places induced stable representations of these places in the targeted cells. Optical elicitation, recording, and modulation of synaptic transmission in behaving mice revealed that activity in presynaptic CA2/3 cells was required for the induction of plasticity in CA1 and, furthermore, that during induction of these place fields in single CA1 cells, synaptic input from CA2/3 onto these same cells was potentiated. These results reveal synaptic implementation of hippocampal behavioral timescale plasticity and define a methodology to resolve synaptic plasticity during learning and memory in behaving mammals.

An encompassed representation of timescale hierarchies in first-order reaction network.

Complex networks are pervasive in various fields such as chemistry, biology, and sociology. In chemistry, first-order reaction networks are represented by a set of first-order differential equations, which can be constructed from the underlying energy landscape. However, as the number of nodes increases, it becomes more challenging to understand complex kinetics across different timescales. Hence, how to construct an interpretable, coarse-graining scheme that preserves the underlying timescales of overall reactions is of crucial importance. Here, we develop a scheme to capture the underlying hierarchical subsets of nodes, and a series of coarse-grained (reduced-dimensional) rate equations between the subsets as a function of time resolution from the original reaction network. Each of the coarse-grained representations guarantees to preserve the underlying slow characteristic timescales in the original network. The crux is the construction of a lumping scheme incorporating a similarity measure in deciphering the underlying timescale hierarchy, which does not rely on the assumption of equilibrium. As an illustrative example, we apply the scheme to four-state Markovian models and Claisen rearrangement of allyl vinyl ether (AVE), and demonstrate that the reduced-dimensional representation accurately reproduces not only the slowest but also the faster timescales of overall reactions although other reduction schemes based on equilibrium assumption well reproduce the slowest timescale but fail to reproduce the second-to-fourth slowest timescales with the same accuracy. Our scheme can be applied not only to the reaction networks but also to networks in other fields, which helps us encompass their hierarchical structures of the complex kinetics over timescales.

Mindful attention promotes control of brain network dynamics for self-regulation and discontinues the past from the present.

Mindful attention is characterized by acknowledging the present experience as a transient mental event. Early stages of mindfulness practice may require greater neural effort for later efficiency. Early effort may self-regulate behavior and focalize the present, but this understanding lacks a computational explanation. Here we used network control theory as a model of how external control inputs-operationalizing effort-distribute changes in neural activity evoked during mindful attention across the white matter network. We hypothesized that individuals with greater network controllability, thereby efficiently distributing control inputs, effectively self-regulate behavior. We further hypothesized that brain regions that utilize greater control input exhibit shorter intrinsic timescales of neural activity. Shorter timescales characterize quickly discontinuing past processing to focalize the present. We tested these hypotheses in a randomized controlled study that primed participants to either mindfully respond or naturally react to alcohol cues during fMRI and administered text reminders and measurements of alcohol consumption during 4 wk postscan. We found that participants with greater network controllability moderated alcohol consumption. Mindful regulation of alcohol cues, compared to one's own natural reactions, reduced craving, but craving did not differ from the baseline group. Mindful regulation of alcohol cues, compared to the natural reactions of the baseline group, involved more-effortful control of neural dynamics across cognitive control and attention subnetworks. This effort persisted in the natural reactions of the mindful group compared to the baseline group. More-effortful neural states had shorter timescales than less effortful states, offering an explanation for how mindful attention promotes being present.

The Limits of the Constant-rate Birth-Death Prior for Phylogenetic Tree Topology Inference.

Birth-death models are stochastic processes describing speciation and extinction through time and across taxa and are widely used in biology for inference of evolutionary timescales. Previous research has highlighted how the expected trees under the constant-rate birth-death (crBD) model tend to differ from empirical trees, for example, with respect to the amount of phylogenetic imbalance. However, our understanding of how trees differ between the crBD model and the signal in empirical data remains incomplete. In this Point of View, we aim to expose the degree to which the crBD model differs from empirically inferred phylogenies and test the limits of the model in practice. Using a wide range of topology indices to compare crBD expectations against a comprehensive dataset of 1189 empirically estimated trees, we confirm that crBD model trees frequently differ topologically compared with empirical trees. To place this in the context of standard practice in the field, we conducted a meta-analysis for a subset of the empirical studies. When comparing studies that used Bayesian methods and crBD priors with those that used other non-crBD priors and non-Bayesian methods (i.e., maximum likelihood methods), we do not find any significant differences in tree topology inferences. To scrutinize this finding for the case of highly imbalanced trees, we selected the 100 trees with the greatest imbalance from our dataset, simulated sequence data for these tree topologies under various evolutionary rates, and re-inferred the trees under maximum likelihood and using the crBD model in a Bayesian setting. We find that when the substitution rate is low, the crBD prior results in overly balanced trees, but the tendency is negligible when substitution rates are sufficiently high. Overall, our findings demonstrate the general robustness of crBD priors across a broad range of phylogenetic inference scenarios but also highlight that empirically observed phylogenetic imbalance is highly improbable under the crBD model, leading to systematic bias in data sets with limited information content.

Conformational Rigidity and Protein Dynamics at Distinct Timescales Regulate PTP1B Activity and Allostery.

Protein function originates from a cooperation of structural rigidity, dynamics at different timescales, and allostery. However, how these three pillars of protein function are integrated is still only poorly understood. Here we show how these pillars are connected in Protein Tyrosine Phosphatase 1B (PTP1B), a drug target for diabetes and cancer that catalyzes the dephosphorylation of numerous substrates in essential signaling pathways. By combining new experimental and computational data on WT-PTP1B and ≥10 PTP1B variants in multiple states, we discovered a fundamental and evolutionarily conserved CH/π switch that is critical for positioning the catalytically important WPD loop. Furthermore, our data show that PTP1B uses conformational and dynamic allostery to regulate its activity. This shows that both conformational rigidity and dynamics are essential for controlling protein activity. This connection between rigidity and dynamics at different timescales is likely a hallmark of all enzyme function.

Neural event segmentation of continuous experience in human infants.

How infants experience the world is fundamental to understanding their cognition and development. A key principle of adult experience is that, despite receiving continuous sensory input, we perceive this input as discrete events. Here we investigate such event segmentation in infants and how it differs from adults. Research on event cognition in infants often uses simplified tasks in which (adult) experimenters help solve the segmentation problem for infants by defining event boundaries or presenting discrete actions/vignettes. This presupposes which events are experienced by infants and leaves open questions about the principles governing infant segmentation. We take a different, data-driven approach by studying infant event segmentation of continuous input. We collected whole-brain functional MRI (fMRI) data from awake infants (and adults, for comparison) watching a cartoon and used a hidden Markov model to identify event states in the brain. We quantified the existence, timescale, and organization of multiple-event representations across brain regions. The adult brain exhibited a known hierarchical gradient of event timescales, from shorter events in early visual regions to longer events in later visual and associative regions. In contrast, the infant brain represented only longer events, even in early visual regions, with no timescale hierarchy. The boundaries defining these infant events only partially overlapped with boundaries defined from adult brain activity and behavioral judgments. These findings suggest that events are organized differently in infants, with longer timescales and more stable neural patterns, even in sensory regions. This may indicate greater temporal integration and reduced temporal precision during dynamic, naturalistic perception.

Hierarchical timescales in the neocortex: Mathematical mechanism and biological insights.

A cardinal feature of the neocortex is the progressive increase of the spatial receptive fields along the cortical hierarchy. Recently, theoretical and experimental findings have shown that the temporal response windows also gradually enlarge, so that early sensory neural circuits operate on short timescales whereas higher-association areas are capable of integrating information over a long period of time. While an increased receptive field is accounted for by spatial summation of inputs from neurons in an upstream area, the emergence of timescale hierarchy cannot be readily explained, especially given the dense interareal cortical connectivity known in the modern connectome. To uncover the required neurobiological properties, we carried out a rigorous analysis of an anatomically based large-scale cortex model of macaque monkeys. Using a perturbation method, we show that the segregation of disparate timescales is defined in terms of the localization of eigenvectors of the connectivity matrix, which depends on three circuit properties: 1) a macroscopic gradient of synaptic excitation, 2) distinct electrophysiological properties between excitatory and inhibitory neuronal populations, and 3) a detailed balance between long-range excitatory inputs and local inhibitory inputs for each area-to-area pathway. Our work thus provides a quantitative understanding of the mechanism underlying the emergence of timescale hierarchy in large-scale primate cortical networks.

Cortical timescales and the modular organization of structural and functional brain networks.

Recent years have seen growing interest in characterizing the properties of regional brain dynamics and their relationship to other features of brain structure and function. In particular, multiple studies have observed regional differences in the "timescale" over which activity fluctuates during periods of quiet rest. In the cerebral cortex, these timescales have been associated with both local circuit properties as well as patterns of inter-regional connectivity, including the extent to which each region exhibits widespread connectivity to other brain areas. In the current study, we build on prior observations of an association between connectivity and dynamics in the cerebral cortex by investigating the relationship between BOLD fMRI timescales and the modular organization of structural and functional brain networks. We characterize network community structure across multiple scales and find that longer timescales are associated with greater within-community functional connectivity and diverse structural connectivity. We also replicate prior observations of a positive correlation between timescales and structural connectivity degree. Finally, we find evidence for preferential functional connectivity between cortical areas with similar timescales. We replicate these findings in an independent dataset. These results contribute to our understanding of functional brain organization and structure-function relationships in the human brain, and support the notion that regional differences in cortical dynamics may in part reflect the topological role of each region within macroscale brain networks.

MRI Assessment of Intrinsic Neural Timescale and Gray Matter Volume in Parkinson's Disease.

BACKGROUND: Numerous studies have indicated altered temporal features of the brain function in Parkinson's disease (PD), and the autocorrelation magnitude of intrinsic neural signals, called intrinsic neural timescales, were often applied to estimate how long neural information stored in local brain areas. However, it is unclear whether PD patients at different disease stages exhibit abnormal timescales accompanied with abnormal gray matter volume (GMV). PURPOSE: To assess the intrinsic timescale and GMV in PD. STUDY TYPE: Prospective. POPULATION: 74 idiopathic PD patients (44 early stage (PD-ES) and 30 late stage (PD-LS), as determined by the Hoehn and Yahr (HY) severity classification scale), and 73 healthy controls (HC). FIELD STRENGTH/SEQUENCE: 3.0 T MRI scanner; magnetization prepared rapid acquisition gradient echo and echo planar imaging sequences. ASSESSMENT: The timescales were estimated by using the autocorrelation magnitude of neural signals. Voxel-based morphometry was performed to calculate GMV in the whole brain. Severity of motor symptoms and cognitive impairments were assessed using the unified PD rating scale, the HY scale, the Montreal cognitive assessment, and the mini-mental state examination. STATISTICAL TEST: Analysis of variance; two-sample t-test; Spearman rank correlation analysis; Mann-Whitney U test; Kruskal-Wallis' H test. A P value <0.05 was considered statistically significant. RESULTS: The PD group had significantly abnormal intrinsic timescales in the sensorimotor, visual, and cognitive-related areas, which correlated with the symptom severity (ρ = -0.265, P = 0.022) and GMV (ρ = 0.254, P = 0.029). Compared to the HC group, the PD-ES group had significantly longer timescales in anterior cortical regions, whereas the PD-LS group had significantly shorter timescales in posterior cortical regions. CONCLUSION: This study suggested that PD patients have abnormal timescales in multisystem and distinct patterns of timescales and GMV in cerebral cortex at different disease stages. This may provide new insights for the neural substrate of PD. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Towards a qualitative theory of the interruption of eating behavior change.

The poor maintenance of eating behavior change is one of the main obstacles to minimizing weight regain after weight loss during diets for non-surgical care of obese or overweight patients. We start with a known informal explanation of interruption in eating behavior change during severe restriction and formalize it as a causal network involving psychological variables, which we extend with energetic variables governed by principles of thermodynamics. The three core phenomena of dietary behavior change, i.e., non-initiation, initiation followed by discontinuation and initiation followed by non-discontinuation, are expressed in terms of the value of the key variable representing mood or psychological energy, the fluctuation of which is the result of three causal relationships. Based on our experimental knowledge of the time evolution profile of the three causal input variables, we then proceed to a qualitative analysis of the resulting theory, i.e., we consider an over-approximation of it which, after discretization, can be expressed in the form of a finite integer-based model. Using Answer Set Programming, we show that our formal model faithfully reproduces the three phenomena and, under a certain assumption, is minimal. We generalize this result by providing all the minimal models reproducing these phenomena when the possible causal relationships exerted on mood are extended to all the other variables (not just those assumed in the informal explanation), with arbitrary causality signs. Finally, by a direct analytical resolution of an under-approximation of our theory, obtained by assuming linear causalities, as a system of linear ODEs, we find exactly the same minimal models, proving that they are also equal to the actual minimal models of our theory since these are framed below and above by the models of the under-approximation and the over-approximation. We determine which parameters need to be person-specific and which can be considered invariant, i.e., we explain inter-individual variability. Our approach could pave the way for universally accepted theories in the field of behavior change and, more broadly, in other areas of psychology.

Network ecology: Tie fitness in social context(s).

Social relations are embedded in material, cultural, and institutional settings that affect network dynamics and the resulting topologies. For example, romantic entanglements are subject to social and cultural norms, interfirm alliances are constrained by country-specific legislation, and adolescent friendships are conditioned by classroom settings and neighborhood effects. In short, social contexts shape social relations and the networks they give rise to. However, how and when they do so remain to be established. This paper presents network ecology as a general framework for identifying how the proximal environment shapes social networks by focusing interactions and social relations, and how these interactions and relations in turn shape the environment in which social networks form. Tie fitness is introduced as a metric that quantifies how well particular dyadic social relations would align with the setting. Using longitudinal networks collected on two cohorts each in 18 North American schools, i.e., 36 settings, we develop five generalizable observations about the time-varying fitness of adolescent friendship. Across all 252 analyzed networks, tie fitness predicted new tie formation, tie longevity, and tie survival. Dormant fit ties cluster in relational niches, thereby establishing a resource base for social identities competing for increased representation in the relational system.

Invariant timescale hierarchy across the cortical somatosensory network.

The ability of cortical networks to integrate information from different sources is essential for cognitive processes. On one hand, sensory areas exhibit fast dynamics often phase-locked to stimulation; on the other hand, frontal lobe areas with slow response latencies to stimuli must integrate and maintain information for longer periods. Thus, cortical areas may require different timescales depending on their functional role. Studying the cortical somatosensory network while monkeys discriminated between two vibrotactile stimulus patterns, we found that a hierarchical order could be established across cortical areas based on their intrinsic timescales. Further, even though subareas (areas 3b, 1, and 2) of the primary somatosensory (S1) cortex exhibit analogous firing rate responses, a clear differentiation was observed in their timescales. Importantly, we observed that this inherent timescale hierarchy was invariant between task contexts (demanding vs. nondemanding). Even if task context severely affected neural coding in cortical areas downstream to S1, their timescales remained unaffected. Moreover, we found that these time constants were invariant across neurons with different latencies or coding. Although neurons had completely different dynamics, they all exhibited comparable timescales within each cortical area. Our results suggest that this measure is demonstrative of an inherent characteristic of each cortical area, is not a dynamical feature of individual neurons, and does not depend on task demands.

Multi-timescale analysis of midbrain dopamine neuronal firing activities.

Midbrain dopamine (DA) neurons exhibit spiking and bursting patterns under physiological conditions. Based on the data on electrophysiological recordings, Yu et al. developed a 13-dimensional mathematical model to capture the detailed characteristics of the DA neuronal firing activities. We use the fitting method to simplify the original model into a 4-dimensional model. Then, the spiking-to-bursting transition is detected from a simple and robust mathematical condition. Physiologically, this condition is a balance of the restorative and the regenerative ion channels at resting potential. Geometrically, this condition imposes a transcritical bifurcation. Moreover, we combine singularity theory and singular perturbation methods to capture the geometry of three-timescale firing attractors in a universal unfolding of a cusp singularity. In particular, the planar description of the corresponding firing patterns can generate the corresponding firing attractors. This analysis provides a new idea for understanding the firing activities of the DA neuron and the specific mechanisms for the switching and dynamic regulation among different patterns.

A roadmap for ladybird conservation and recovery.

Ladybirds (Coleoptera: Coccinellidae) provide services that are critical to food production, and they fulfill an ecological role as a food source for predators. The richness, abundance, and distribution of ladybirds, however, are compromised by many anthropogenic threats. Meanwhile, a lack of knowledge of the conservation status of most species and the factors driving their population dynamics hinders the development and implementation of conservation strategies for ladybirds. We conducted a review of the literature on the ecology, diversity, and conservation of ladybirds to identify their key ecological threats. Ladybird populations are most affected by climate factors, landscape composition, and biological invasions. We suggest mitigating actions for ladybird conservation and recovery. Short-term actions include citizen science programs and education, protective measures for habitat recovery and threatened species, prevention of the introduction of non-native species, and the maintenance and restoration of natural areas and landscape heterogeneity. Mid-term actions involve the analysis of data from monitoring programs and insect collections to disentangle the effect of different threats to ladybird populations, understand habitat use by taxa on which there is limited knowledge, and quantify temporal trends of abundance, diversity, and biomass along a management-intensity gradient. Long-term actions include the development of a worldwide monitoring program based on standardized sampling to fill data gaps, increase explanatory power, streamline analyses, and facilitate global collaborations.

Las catarinas (Coleoptera: Coccinellidae) proporcionan servicios que son críticos para la producción de alimento, y juegan un papel ecológico como fuente de alimento para depredadores. Sin embargo, la riqueza, abundancia y distribución de catarinas están en peligro debido a muchas amenazas antropogénicas. La carencia de conocimiento sobre el estatus de conservación de la mayoría de las especies y los factores que inciden en su dinámica poblacional dificulta el desarrollo e implementación de estrategias de conservación para las catarinas. Realizamos una revisión de la literatura sobre la ecología, diversidad y conservación de catarinas para identificar sus amenazas ecológicas clave. Las poblaciones de catarinas fueron afectadas mayormente por factores climáticos, composición del paisaje e invasiones biológicas. Proponemos acciones de mitigación para la conservación y recuperación de catarinas. Acciones a corto plazo incluyen programas de ciencia y educación ciudadana, medidas de protección para la recuperación de hábitat y de especies amenazadas, prevención de la introducción de especies no nativas y el mantenimiento y restauración de áreas naturales y la heterogeneidad del paisaje. Acciones a mediano plazo implican el análisis de datos obtenidos de programas de monitoreo y colecciones de insectos para desenmarañar el efecto de las diferentes amenazas a las poblaciones de catarinas, comprender el uso del hábitat por taxa de los que se tiene conocimiento limitado y cuantifica las tendencias temporales de la abundancia, diversidad y biomasa a lo largo de un gradiente de intensidad de manejo. Acciones a largo plazo incluyen el desarrollo de un programa de monitoreo a nivel mundial basado en muestreos estandarizados para subsanar la falta de datos, incrementar el poder explicativo, optimizar los análisis y facilitar colaboraciones globales.

Natural Parameter Conditions for Singular Perturbations of Chemical and Biochemical Reaction Networks.

We consider reaction networks that admit a singular perturbation reduction in a certain parameter range. The focus of this paper is on deriving "small parameters" (briefly for small perturbation parameters), to gauge the accuracy of the reduction, in a manner that is consistent, amenable to computation and permits an interpretation in chemical or biochemical terms. Our work is based on local timescale estimates via ratios of the real parts of eigenvalues of the Jacobian near critical manifolds. This approach modifies the one introduced by Segel and Slemrod and is familiar from computational singular perturbation theory. While parameters derived by this method cannot provide universal quantitative estimates for the accuracy of a reduction, they represent a critical first step toward this end. Working directly with eigenvalues is generally unfeasible, and at best cumbersome. Therefore we focus on the coefficients of the characteristic polynomial to derive parameters, and relate them to timescales. Thus, we obtain distinguished parameters for systems of arbitrary dimension, with particular emphasis on reduction to dimension one. As a first application, we discuss the Michaelis-Menten reaction mechanism system in various settings, with new and perhaps surprising results. We proceed to investigate more complex enzyme catalyzed reaction mechanisms (uncompetitive, competitive inhibition and cooperativity) of dimension three, with reductions to dimension one and two. The distinguished parameters we derive for these three-dimensional systems are new. In fact, no rigorous derivation of small parameters seems to exist in the literature so far. Numerical simulations are included to illustrate the efficacy of the parameters obtained, but also to show that certain limitations must be observed.

Extreme weather events recorded by daily to hourly resolution biogeochemical proxies of marine giant clam shells.

Paleoclimate research has built a framework for Earth's climate changes over the past 65 million years or even longer. However, our knowledge of weather-timescale extreme events (WEEs, also named paleoweather), which usually occur over several days or hours, under different climate regimes is almost blank because current paleoclimatic records rarely provide information with temporal resolution shorter than monthly scale. Here we show that giant clam shells (Tridacna spp.) from the tropical western Pacific have clear daily growth bands, and several 2-y-long (from January 29, 2012 to December 9, 2013) daily to hourly resolution biological and geochemical records, including daily growth rate, hourly elements/Ca ratios, and fluorescence intensity, were obtained. We found that the pulsed changes of these ultra-high-resolution proxy records clearly matched with the typical instrumental WEEs, for example, tropical cyclones during the summer-autumn and cold surges during the winter. When a tropical cyclone passes through or approaches the sampling site, the growth rate of Tridacna shell decreases abruptly due to the bad weather. Meanwhile, enhanced vertical mixing brings nutrient-enriched subsurface water to the surface, resulting in a high Fe/Ca ratio and strong fluorescence intensity (induced by phytoplankton bloom) in the shell. Our results demonstrate that Tridacna shell has the potential to be used as an ultra-high-resolution archive for paleoweather reconstructions. The fossil shells living in different geological times can be built as a Geological Weather Station network to lengthen the modern instrumental data and investigate the WEEs under various climate conditions.

Separating Long-Term Equilibrium Adaptation from Short-Term Self-Regulation Dynamics Using Latent Differential Equations.

Self-regulating systems change along different timescales. Within a given week, a depressed person's affect might oscillate around a low equilibrium point. However, when the timeframe is expanded to capture the year during which they onboarded antidepressant medication, their equilibrium and oscillatory patterns might reorganize around a higher affective point. To simultaneously account for the meaningful change processes that happen at different time scales in complex self-regulatory systems, we propose a single model that combines a second-order linear differential equation for short timescale regulation and a first-order linear differential equation for long timescale adaptation of equilibrium. This model allows for individual-level moderation of short-timescale model parameters. The model is tested in a simulation study which shows that, surprisingly, the short and long timescales can fully overlap and the model still converges to the reasonable estimates. Finally, an application of this model to self-regulation of emotional well-being in recent widows is presented and discussed.

Does geopolitical risk matter in carbon and crude oil markets from a multi-timescale perspective?

The ongoing Russia-Ukraine conflict serves as a prime illustration of geopolitical risk, manifesting implications beyond global energy price surges and regional energy deficits; it also resonates within the carbon trading market. This study delves into the non-linear and asymmetric impacts of geopolitical risk on oil-carbon linkages spanning 2013 to 2022. Commencing with an exploration of dynamic correlations within the carbon and crude oil markets, our findings indicate that market movements in carbon or crude oil are subject not only to their respective historical volatility trends but also to reciprocal influences from the alternate market, highlighting the existence of asymmetric spillovers between the carbon and crude oil markets. Subsequently, a threshold vector error correction model (TVECM) sheds light on the long-term transmission dynamics of geopolitical risk to oil-carbon linkages. It is concluded that the oil-carbon linkages have a limited impact on geopolitical risk and are subject to shocks from geopolitical risk. Finally, the maximum overlap discrete wavelet transform (MODWT) method unveils heterogeneity across temporal scales: in the short term, geopolitical risk fluctuations are influenced by oil-carbon linkages, and in the middle and long run it becomes independent. Such insights furnish investors with an enriched comprehension of the interplay between crude oil and carbon markets amid geopolitical disturbances, while also offering policymakers a foundation upon which to sculpt informed responses to geopolitical risk.

Simultaneous Hydrogen Generation and Exciplex Stimulated Emission in Photobasic Carbon Dots.

Photocatalytic water splitting is a promising approach to generating sustainable hydrogen. However, the transport of photoelectrons to the catalyst sites, usually within ps-to-ns timescales, is much faster than proton delivery (∼µs), which limits the activity. Therefore, the acceleration of abstraction of protons from water molecules towards the catalytic sites to keep up with the electron transfer rate can significantly promote hydrogen production. The photobasic effect that is the increase in proton affinity upon excitation offers means to achieve this objective. Herein, we design photobasic carbon dots and identify that internal pyridinic N sites are intrinsically photobasic. This is supported by steady-state and ultrafast spectroscopic measurements that demonstrate proton abstraction within a few picoseconds of excitation. Furthermore, we show that in water, they form a unique four-level lasing scheme with optical gain and stimulated emission. The latter competes with photocatalysis, revealing a rather unique mechanism for efficiency loss, such that the stimulated emission can act as a toggle for photocatalytic activity. This provides additional means of controlling the photocatalytic process and helps the rational design of photocatalytic materials.

Timescales of Local and Cross-Area Interactions during Neuroprosthetic Learning.

How does the brain integrate signals with different timescales to drive purposeful actions? Brain-machine interfaces (BMIs) offer a powerful tool to causally test how distributed neural networks achieve specific neural patterns. During neuroprosthetic learning, actuator movements are causally linked to primary motor cortex (M1) neurons, i.e., "direct" neurons that project to the decoder and whose firing is required to successfully perform the task. However, it is unknown how such direct M1 neurons interact with both "indirect" local (in M1 but not part of the decoder) and across area neural populations (e.g., in premotor cortex/M2), all of which are embedded in complex biological recurrent networks. Here, we trained male rats to perform a M1-BMI task and simultaneously recorded the activity of indirect neurons in both M2 and M1. We found that both M2 and M1 indirect neuron populations could be used to predict the activity of the direct neurons (i.e., "BMI-potent activity"). Interestingly, compared with M1 indirect activity, M2 neural activity was correlated with BMI-potent activity across a longer set of time lags, and the timescale of population activity patterns evolved more slowly. M2 units also predicted the activity of both M1 direct and indirect neural populations, suggesting that M2 population dynamics provide a continuous modulatory influence on M1 activity as a whole, rather than a moment-by-moment influence solely on neurons most relevant to a task. Together, our results indicate that longer timescale M2 activity provides modulatory influence over extended time lags on shorter-timescale control signals in M1.SIGNIFICANCE STATEMENT A central question in the study of motor control is whether primary motor cortex (M1) and premotor cortex (M2) interact through task-specific subpopulations of neurons, or whether tasks engage broader correlated networks. Brain-machine interfaces (BMIs) are powerful tools to study cross-area interactions. Here, we performed simultaneous recordings of M1 and M2 in a BMI task using a subpopulation of M1 neurons (direct neurons). We found that activity outside of direct neurons in M1 and M2 was predictive of M1-BMI task activity, and that M2 activity evolved at slower timescales than M1. These findings suggest that M2 provides a continuous modulatory influence on M1 as a whole, supporting a model of interactions through broad correlated networks rather than task-specific neural subpopulations.