A Thousand Views of the Cathedral: The Law, Politics, and Statistics of Pandemic Dashboards

This Article explores the law, politics, and statistics of communicating data through the thousands of state, county, school district, and higher-education dashboards created in response to the COVID-19 global pandemic. Using a nationally distributed questionnaire and series of interviews with dashboard managers and stakeholders, we offer a wide-ranging view of data visualization practice in response to COVID-19. We pair this evidence with a survey of almost 3,000 entities responsible for public health communication, which resulted in collection of over 1,100 COVID-19 dashboards from a spectrum of government actors and private parties. We evaluate how legal issues were perceived and acted on, how data were politicized, and what technical challenges dashboard creators faced. We examine factors that led to creation of dashboards along with theory that explores the cognitive perception of dashboard elements. We explore the role of resources, and how those interplayed with specific software choices. We also examine the effect of local characteristics on dashboard creation, such as the presence of a county dashboard on data communication from local schools, the role of broadband access, technical occupations in the area, and so on. We conclude by suggesting a series of policies and practices that can be implemented to prepare for future data-based communication in a future public health crisis. These include practices to prepare for data visualization, enhanced resources devoted to public health communication, and the management of legal and political issues surrounding publicizing health information

Hippocampal LTP and contextual learning require surface diffusion of AMPA receptors

Long-term potentiation (LTP) of excitatory synaptic transmission has long been considered a cellular correlate for learning and memory. Early LTP (eLTP, <1 hour) had initially been explained either by presynaptic increases in glutamate release or by direct modification of post-synaptic α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR) function. Compelling models have more recently proposed that synaptic potentiation can occur by the recruitment of additional post-synaptic AMPARs, sourced either from an intracellular reserve pool by exocytosis or from nearby extra synaptic receptors pre-existing on the neuronal surface. However, the exact mechanism through which synapses can rapidly recruit new AMPARs during eLTP is still unknown. In particular, direct evidence for a pivotal role of AMPAR surface diffusion as a trafficking mechanism in synaptic plasticity is still lacking. Using AMPAR immobilization approaches, we show that interfering with AMPAR surface diffusion dramatically impaired synaptic potentiation of Schaffer collateral/commissural inputs to cornu ammonis area 1 (CA1) in cultured slices, acute slices and in vivo. Our data also identifies distinct contributions of various AMPAR trafficking routes to the temporal profile of synaptic potentiation. In addition, AMPAR immobilization in vivo in the dorsal hippocampus (DH) before fear conditioning, indicated that AMPAR diffusion is important for the early phase of contextual learning. Therefore, our results provide a direct demonstration that the recruitment of new receptors to synapses by surface diffusion is a critical mechanism for the expression of LTP and hippocampal learning. Since AMPAR surface diffusion is dictated by weak Brownian forces that are readily perturbed by protein-protein interactions, we anticipate that this fundamental trafficking mechanism will be a key target for modulating synaptic potentiation and learning

Gap Junctions and Epileptic Seizures – Two Sides of the Same Coin?

Electrical synapses (gap junctions) play a pivotal role in the synchronization of neuronal ensembles which also makes them likely agonists of pathological brain activity. Although large body of experimental data and theoretical considerations indicate that coupling neurons by electrical synapses promotes synchronous activity (and thus is potentially epileptogenic), some recent evidence questions the hypothesis of gap junctions being among purely epileptogenic factors. In particular, an expression of inter-neuronal gap junctions is often found to be higher after the experimentally induced seizures than before. Here we used a computational modeling approach to address the role of neuronal gap junctions in shaping the stability of a network to perturbations that are often associated with the onset of epileptic seizures. We show that under some circumstances, the addition of gap junctions can increase the dynamical stability of a network and thus suppress the collective electrical activity associated with seizures. This implies that the experimentally observed post-seizure additions of gap junctions could serve to prevent further escalations, suggesting furthermore that they are a consequence of an adaptive response of the neuronal network to the pathological activity. However, if the seizures are strong and persistent, our model predicts the existence of a critical tipping point after which additional gap junctions no longer suppress but strongly facilitate the escalation of epileptic seizures. Our results thus reveal a complex role of electrical coupling in relation to epileptiform events. Which dynamic scenario (seizure suppression or seizure escalation) is ultimately adopted by the network depends critically on the strength and duration of seizures, in turn emphasizing the importance of temporal and causal aspects when linking gap junctions with epilepsy

Uncertainty-aware estimation of population abundance using machine learning

Machine Learning is widely used for mining collections, such as images, sounds, or texts, by classifying their elements into categories. Automatic classification based on supervised learning requires groundtruth datasets for modeling the elements to classify, and for testing the quality of the classification. Because collecting groundtruth is tedious, a method for estimating the potential errors in large datasets based on limited groundtruth is needed. We propose a method that improves classification quality by using limited groundtruth data to extrapolate the po-tential errors in larger datasets. It significantly improves the counting of elements per class. We further propose visualization designs for understanding and evaluating the classification un-certainty. They support end-users in considering the impact of potential misclassifications for interpreting the classification output. This work was developed to address the needs of ecologists studying fish population abundance using computer vision, but generalizes to a larger range of applications. Our method is largely applicable for a variety of Machine Learning technologies, and our visualizations further support their transfer to end-users

TMS-Induced Cortical Potentiation during Wakefulness Locally Increases Slow Wave Activity during Sleep

BACKGROUND: Sleep slow wave activity (SWA) is thought to reflect sleep need, increasing in proportion to the length of prior wakefulness and decreasing during sleep. However, the process responsible for SWA regulation is not known. We showed recently that SWA increases locally after a learning task involving a circumscribed brain region, suggesting that SWA may reflect plastic changes triggered by learning. METHODOLOGY/PRINCIPAL FINDINGS: To test this hypothesis directly, we used transcranial magnetic stimulation (TMS) in conjunction with high-density EEG in humans. We show that 5-Hz TMS applied to motor cortex induces a localized potentiation of TMS-evoked cortical EEG responses. We then show that, in the sleep episode following 5-Hz TMS, SWA increases markedly (+39.1±17.4%, p<0.01, n = 10). Electrode coregistration with magnetic resonance images localized the increase in SWA to the same premotor site as the maximum TMS-induced potentiation during wakefulness. Moreover, the magnitude of potentiation during wakefulness predicts the local increase in SWA during sleep. CONCLUSIONS/SIGNIFICANCE: These results provide direct evidence for a link between plastic changes and the local regulation of sleep need

Ubiquitous molecular substrates for associative learning and activity-dependent neuronal facilitation.

Recent evidence suggests that many of the molecular cascades and substrates that contribute to learning-related forms of neuronal plasticity may be conserved across ostensibly disparate model systems. Notably, the facilitation of neuronal excitability and synaptic transmission that contribute to associative learning in Aplysia and Hermissenda, as well as associative LTP in hippocampal CA1 cells, all require (or are enhanced by) the convergence of a transient elevation in intracellular Ca2+ with transmitter binding to metabotropic cell-surface receptors. This temporal convergence of Ca2+ and G-protein-stimulated second-messenger cascades synergistically stimulates several classes of serine/threonine protein kinases, which in turn modulate receptor function or cell excitability through the phosphorylation of ion channels. We present a summary of the biophysical and molecular constituents of neuronal and synaptic facilitation in each of these three model systems. Although specific components of the underlying molecular cascades differ across these three systems, fundamental aspects of these cascades are widely conserved, leading to the conclusion that the conceptual semblance of these superficially disparate systems is far greater than is generally acknowledged. We suggest that the elucidation of mechanistic similarities between different systems will ultimately fulfill the goal of the model systems approach, that is, the description of critical and ubiquitous features of neuronal and synaptic events that contribute to memory induction

Climate and species affect fine root production with long-term fertilization in acidic tussock tundra near Toolik Lake, Alaska

Author Posting. © The Author(s), 2007. This is the author's version of the work. It is posted here by permission of Springer for personal use, not for redistribution. The definitive version was published in Oecologia 153 (2007): 643-652, doi:10.1007/s00442-007-0753-8.Long-term fertilization of acidic tussock tundra has led to changes in plant species composition, increases in aboveground production and biomass and substantial losses of soil organic carbon (SOC). Root litter is an important input to SOC pools, though little is known about fine root demography in tussock tundra. In this study, we examined the response of fine root production and live standing fine root biomass to short- and long-term fertilization, as changes in fine root demography may contribute to observed declines in SOC. Live standing fine root biomass increased with long-term fertilization, while fine root production declined, reflecting replacement of the annual fine root system of Eriophorum vaginatum, with the long-lived fine roots of Betula nana. Fine root production increased in fertilized plots during an unusually warm growing season, but remained unchanged in control plots, consistent with observations that B. nana shows a positive response to climate warming. Calculations based on a few simple assumptions suggest changes in fine root demography with long-term fertilization and species replacement could account for between 20 and 39% of observed declines in SOC stocks.This project was supported by National Science Foundation research grants 9810222, 9911681, 0221606 and 0528748

The Secrets of a Functional Synapse – From a Computational and Experimental Viewpoint

BACKGROUND: Neuronal communication is tightly regulated in time and in space. The neuronal transmission takes place in the nerve terminal, at a specialized structure called the synapse. Following neuronal activation, an electrical signal triggers neurotransmitter (NT) release at the active zone. The process starts by the signal reaching the synapse followed by a fusion of the synaptic vesicle and diffusion of the released NT in the synaptic cleft; the NT then binds to the appropriate receptor, and as a result, a potential change at the target cell membrane is induced. The entire process lasts for only a fraction of a millisecond. An essential property of the synapse is its capacity to undergo biochemical and morphological changes, a phenomenon that is referred to as synaptic plasticity. RESULTS: In this survey, we consider the mammalian brain synapse as our model. We take a cell biological and a molecular perspective to present fundamental properties of the synapse:(i) the accurate and efficient delivery of organelles and material to and from the synapse; (ii) the coordination of gene expression that underlies a particular NT phenotype; (iii) the induction of local protein expression in a subset of stimulated synapses. We describe the computational facet and the formulation of the problem for each of these topics. CONCLUSION: Predicting the behavior of a synapse under changing conditions must incorporate genomics and proteomics information with new approaches in computational biology