Visualization of the distribution of autophosphorylated calcium/calmodulin-dependent protein kinase II after tetanic stimulation in the CA1 area of the hippocampus

Autophosphorylation of calcium/calmodulin-dependent protein kinase II (CaMKII) at threonine-286 produces Ca2+-independent kinase activity and has been proposed to be involved in induction of long-term potentiation by tetanic stimulation in the hippocampus. We have used an immunocytochemical method to visualize and quantify the pattern of autophosphorylation of CaMKII in hippocampal slices after tetanization of the Schaffer collateral pathway. Thirty minutes after tetanic stimulation, autophosphorylated CaM kinase II (P-CaMKII) is significantly increased in area CA1 both in apical dendrites and in pyramidal cell somas. In apical dendrites, this increase is accompanied by an equally significant increase in staining for nonphosphorylated CaM kinase II. Thus, the increase in P-CaMKII appears to be secondary to an increase in the total amount of CaMKII. In neuronal somas, however, the increase in P-CaMKII is not accompanied by an increase in the total amount of CaMKII. We suggest that tetanic stimulation of the Schaffer collateral pathway may induce new synthesis of CaMKII molecules in the apical dendrites, which contain mRNA encoding its alpha-subunit. In neuronal somas, however, tetanic stimulation appears to result in long-lasting increases in P-CaMKII independent of an increase in the total amount of CaMKII. Our findings are consistent with a role for autophosphorylation of CaMKII in the induction and/or maintenance of long-term potentiation, but they indicate that the effects of tetanus on the kinase and its activity are not confined to synapses and may involve induction of new synthesis of kinase in dendrites as well as increases in the level of autophosphorylated kinase

Ultrastructure of Synapses in the Mammalian Brain

The morphology and molecular composition of synapses provide the structural basis for synaptic function. This article reviews the electron microscopy of excitatory synapses on dendritic spines, using data from rodent hippocampus, cerebral cortex, and cerebellar cortex. Excitatory synapses have a prominent postsynaptic density, in contrast with inhibitory synapses, which have less dense presynaptic or postsynaptic specializations and are usually found on the cell body or proximal dendritic shaft. Immunogold labeling shows that the presynaptic active zone provides a scaffold for key molecules involved in the release of neurotransmitter, whereas the postsynaptic density contains ligand-gated ionic channels, other receptors, and a complex network of signaling molecules. Delineating the structure and molecular organization of these axospinous synapses represents a crucial step toward understanding the mechanisms that underlie synaptic transmission and the dynamic modulation of neurotransmission associated with short- and long-term synaptic plasticity

College completion predicts lower depression but higher metabolic syndrome among disadvantaged minorities in young adulthood

College graduates enjoy healthier, longer lives compared with individuals who do not graduate from college. However, the health benefit of educational attainment is not as great for blacks as it is for whites. Moreover, college completion may not erase the detrimental effects of early-life disadvantage for blacks and Hispanics. We use nationally representative data on young adults to test whether American minorities experience differential returns to educational attainment. We find that college completion predicts lower rates of depression for all racial groups. It also predicts lower metabolic syndrome among whites. However, college completion predicts higher metabolic syndrome among black and Hispanic adults from disadvantaged backgrounds, suggesting upward mobility may come at a health cost to young minorities in America

Nanoconnectomic upper bound on the variability of synaptic plasticity

Information in a computer is quantified by the number of bits that can be stored and recovered. An important question about the brain is how much information can be stored at a synapse through synaptic plasticity, which depends on the history of probabilistic synaptic activity. The strong correlation between size and efficacy of a synapse allowed us to estimate the variability of synaptic plasticity. In an EM reconstruction of hippocampal neuropil we found single axons making two or more synaptic contacts onto the same dendrites, having shared histories of presynaptic and postsynaptic activity. The spine heads and neck diameters, but not neck lengths, of these pairs were nearly identical in size. We found that there is a minimum of 26 distinguishable synaptic strengths, corresponding to storing 4.7 bits of information at each synapse. Because of stochastic variability of synaptic activation the observed precision requires averaging activity over several minutes

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Encouraging Physical Activity Among Retirement Community Residents - The Role of Campus Commitment, Programming, Staffing, Promotion, Financing and Accreditation

Despite the well-established benefits of physical activity for older adults, seniors ages 75 and above are among the most sedentary of Americans. Continuing care retirement communities (CCRCs) seem ideal settings for creating physical activity promoting environments. We report on results of a nationally representative sample survey of CCRCs that are members of the American Association of Homes and Services for the Aging. Campuses with more physical activity opportunities on campus or in the outside community, more physical activity-related staff, better physical activity facilities and activities, more types of sources to finance the costs of physical activity, and more channels to promote physical activity tend to have more physically active residents. Campuses in which management places more importance on encouraging physical activity among residents also have more physically active residents.This project was funded by a grant from the Robert Wood Johnson Foundation

Computational reconstitution of spine calcium transients from individual proteins

We have built a stochastic model in the program MCell that simulates Ca^(2+) transients in spines from the principal molecular components believed to control Ca^(2+) entry and exit. Proteins, with their kinetic models, are located within two segments of dendrites containing 88 intact spines, centered in a fully reconstructed 6 × 6 × 5 μm^3 cube of hippocampal neuropil. Protein components include AMPA- and NMDA-type glutamate receptors, L- and R-type voltage-dependent Ca^(2+) channels, Na^+/Ca^(2+) exchangers, plasma membrane Ca^(2+) ATPases, smooth endoplasmic reticulum Ca^(2+) ATPases, immobile Ca2+ buffers, and calbindin. Kinetic models for each protein were taken from published studies of the isolated proteins in vitro. For simulation of electrical stimuli, the time course of voltage changes in the dendritic spine was generated with the desired stimulus in the program NEURON. Voltage-dependent parameters were then continuously re-adjusted during simulations in MCell to reproduce the effects of the stimulus. Nine parameters of the model were optimized within realistic experimental limits by a process that compared results of simulations to published data. We find that simulations in the optimized model reproduce the timing and amplitude of Ca^(2+) transients measured experimentally in intact neurons. Thus, we demonstrate that the characteristics of individual isolated proteins determined in vitro can accurately reproduce the dynamics of experimentally measured Ca^(2+) transients in spines. The model will provide a test bed for exploring the roles of additional proteins that regulate Ca^(2+) influx into spines and for studying the behavior of protein targets in the spine that are regulated by Ca^(2+) influx

BRAIN Initiative: Cutting-Edge Tools and Resources for the Community.

The overarching goal of the NIH BRAIN (Brain Research through Advancing Innovative Neurotechnologies) Initiative is to advance the understanding of healthy and diseased brain circuit function through technological innovation. Core principles for this goal include the validation and dissemination of the myriad innovative technologies, tools, methods, and resources emerging from BRAIN-funded research. Innovators, BRAIN funding agencies, and non-Federal partners are working together to develop strategies for making these products usable, available, and accessible to the scientific community. Here, we describe several early strategies for supporting the dissemination of BRAIN technologies. We aim to invigorate a dialogue with the neuroscience research and funding community, interdisciplinary collaborators, and trainees about the existing and future opportunities for cultivating groundbreaking research products into mature, integrated, and adaptable research systems. Along with the accompanying Society for Neuroscience 2019 Mini-Symposium, "BRAIN Initiative: Cutting-Edge Tools and Resources for the Community," we spotlight the work of several BRAIN investigator teams who are making progress toward providing tools, technologies, and services for the neuroscience community. These tools access neural circuits at multiple levels of analysis, from subcellular composition to brain-wide network connectivity, including the following: integrated systems for EM- and florescence-based connectomics, advances in immunolabeling capabilities, and resources for recording and analyzing functional connectivity. Investigators describe how the resources they provide to the community will contribute to achieving the goals of the NIH BRAIN Initiative. Finally, in addition to celebrating the contributions of these BRAIN-funded investigators, the Mini-Symposium will illustrate the broader diversity of BRAIN Initiative investments in cutting-edge technologies and resources