CosMIC: a consistent metric for spike inference from calcium imaging

In recent years, the development of algorithms to detect neuronal spiking activity from two-photon calcium imaging data has received much attention. Meanwhile, few researchers have examined the metrics used to assess the similarity of detected spike trains with the ground truth. We highlight the limitations of the two most commonly used metrics, the spike train correlation and success rate, and propose an alternative, which we refer to as CosMIC. Rather than operating on the true and estimated spike trains directly, the proposed metric assesses the similarity of the pulse trains obtained from convolution of the spike trains with a smoothing pulse. The pulse width, which is derived from the statistics of the imaging data, reflects the temporal tolerance of the metric. The final metric score is the size of the commonalities of the pulse trains as a fraction of their average size. Viewed through the lens of set theory, CosMIC resembles a continuous Sørensen-Dice coefficient — an index commonly used to assess the similarity of discrete, presence/absence data. We demonstrate the ability of the proposed metric to discriminate the precision and recall of spike train estimates. Unlike the spike train correlation, which appears to reward overestimation, the proposed metric score is maximised when the correct number of spikes have been detected. Furthermore, we show that CosMIC is more sensitive to the temporal precision of estimates than the success rate

Investigation and demonstration of dry carbon-based sorbent injection for mercury control. Quarterly technical report, October 1--December 31, 1996

The U.S. Department of Energy (DOE) has issued Public Service Company of Colorado (PSCo) a cost sharing contract to evaluate carbon-based sorbents for mercury control on a 600 acfm laboratory scale particulate control module (PCM). The PCM can simulate an electrostatic precipitator, a pulse-jet fabric filter, and a reverse air fabric filter and uses actual flue gas from an operating coal-fired power plant. Up to 3 different dry carbon-based sorbents will be tested to determine the mercury removal capability in the different configurations. The project is currently in the fifth quarter of an eight quarter Phase I project. The PCM has been fabricated and mercury removal testing with the ESP configuration has been completed. Original plans included the use on an on-line meercury analyzer to collect the test data. However, due to very low baseline mercury concentration, on-line measurement did not provide accurate data. The project has continued using a modified MESA method grab sample technique to determine inlet and outlet mercury concentrations. A major concern during sorbent evaluations has been the natural ability of the flyash at the test site to remove mercury. This has made determination of sorbent only mercury removal difficult. Overall vapor-phase mercury removals of 15 to 70% have been obtained but this includes mercury removals in the range of 30% by the flyash. It is believed that a maximum of approximately 40% removal due to the sorbent only has been obtained. A number of test and sampling modifications are in progress to increase the data confidence and many questions remain. Startup of the pulse jet configuration began in early November but results of this testing are not available at this time. The project team has decided to proceed with pulse jet testing using flue gas that does not contain significant flyash quantities to further investigate the sorbent only mercury removal

A specific case in the classification of woods by FTIR and chemometric: discrimination of Fagales from Malpighiales

Fourier transform infrared (FTIR) spectroscopic data was used to classify wood samples from nine species within the Fagales and Malpighiales using a range of multivariate statistical methods. Taxonomic classification of the family Fagaceae and Betulaceae from Angiosperm Phylogenetic System Classification (APG II System) was successfully performed using supervised pattern recognition techniques. A methodology for wood sample discrimination was developed using both sapwood and heartwood samples. Ten and eight biomarkers emerged from the dataset to discriminate order and family, respectively. In the species studied FTIR in combination with multivariate analysis highlighted significant chemical differences in hemicelluloses, cellulose and guaiacyl (lignin) and shows promise as a suitable approach for wood sample classification

Endoplasmic Reticulum Stress Is Reduced in Tissues of Obese Subjects After Weight Loss

OBJECTIVE—Obesity is associated with insulin resistance and type 2 diabetes, although the mechanisms linking these pathologies remain undetermined. Recent studies in rodent models revealed endoplasmic reticulum (ER) stress in adipose and liver tissues and demonstrated that ER stress could cause insulin resistance. Therefore, we tested whether these stress pathways were also present in obese human subjects and/or regulated by weight loss

A Dynamic Model of Interactions of Ca^(2+), Calmodulin, and Catalytic Subunits of Ca^(2+)/Calmodulin-Dependent Protein Kinase II

During the acquisition of memories, influx of Ca^(2+) into the postsynaptic spine through the pores of activated N-methyl-D-aspartate-type glutamate receptors triggers processes that change the strength of excitatory synapses. The pattern of Ca^(2+) influx during the first few seconds of activity is interpreted within the Ca^(2+)-dependent signaling network such that synaptic strength is eventually either potentiated or depressed. Many of the critical signaling enzymes that control synaptic plasticity, including Ca^(2+)/calmodulin-dependent protein kinase II (CaMKII), are regulated by calmodulin, a small protein that can bind up to 4 Ca^(2+) ions. As a first step toward clarifying how the Ca^(2+)-signaling network decides between potentiation or depression, we have created a kinetic model of the interactions of Ca^(2+), calmodulin, and CaMKII that represents our best understanding of the dynamics of these interactions under conditions that resemble those in a postsynaptic spine. We constrained parameters of the model from data in the literature, or from our own measurements, and then predicted time courses of activation and autophosphorylation of CaMKII under a variety of conditions. Simulations showed that species of calmodulin with fewer than four bound Ca^(2+) play a significant role in activation of CaMKII in the physiological regime, supporting the notion that processing ofCa^(2+) signals in a spine involves competition among target enzymes for binding to unsaturated species of CaM in an environment in which the concentration of Ca^(2+) is fluctuating rapidly. Indeed, we showed that dependence of activation on the frequency of Ca^(2+) transients arises from the kinetics of interaction of fluctuating Ca^(2+) with calmodulin/CaMKII complexes. We used parameter sensitivity analysis to identify which parameters will be most beneficial to measure more carefully to improve the accuracy of predictions. This model provides a quantitative base from which to build more complex dynamic models of postsynaptic signal transduction during learning