A low-voltage activated, transient calcium current is responsible for the time-dependent depolarizing inward rectification of rat neocortical neurons in vitro

Intracellular recordings were obtained from rat neocortical neurons in vitro. The current-voltage-relationship of the neuronal membrane was investigated using current- and single-electrode-voltage-clamp techniques. Within the potential range up to 25 mV positive to the resting membrane potential (RMP: –75 to –80 mV) the steady state slope resistance increased with depolarization (i.e. steady state inward rectification in depolarizing direction). Replacement of extracellular NaCl with an equimolar amount of choline chloride resulted in the conversion of the steady state inward rectification to an outward rectification, suggesting the presence of a voltage-dependent, persistent sodium current which generated the steady state inward rectification of these neurons. Intracellularly injected outward current pulses with just subthreshold intensities elicited a transient depolarizing potential which invariably triggered the first action potential upon an increase in current strength. Single-electrode-voltage-clamp measurements reveled that this depolarizing potential was produced by a transient calcium current activated at membrane potentials 15–20 mV positive to the RMP and that this current was responsible for the time-dependent increase in the magnitude of the inward rectification in depolarizing direction in rat neocortical neurons. It may be that, together with the persistent sodium current, this calcium current regulates the excitability of these neurons via the adjustment of the action potential threshold

Over-Fitting in Model Selection with Gaussian Process Regression

Model selection in Gaussian Process Regression (GPR) seeks to determine the optimal values of the hyper-parameters governing the covariance function, which allows flexible customization of the GP to the problem at hand. An oft-overlooked issue that is often encountered in the model process is over-fitting the model selection criterion, typically the marginal likelihood. The over-fitting in machine learning refers to the fitting of random noise present in the model selection criterion in addition to features improving the generalisation performance of the statistical model. In this paper, we construct several Gaussian process regression models for a range of high-dimensional datasets from the UCI machine learning repository. Afterwards, we compare both MSE on the test dataset and the negative log marginal likelihood (nlZ), used as the model selection criteria, to find whether the problem of overfitting in model selection also affects GPR. We found that the squared exponential covariance function with Automatic Relevance Determination (SEard) is better than other kernels including squared exponential covariance function with isotropic distance measure (SEiso) according to the nLZ, but it is clearly not the best according to MSE on the test data, and this is an indication of over-fitting problem in model selection

Slab melting as a barrier to deep carbon subduction

Interactions between crustal and mantle reservoirs dominate the surface inventory of volatile elements over geological time, moderating atmospheric composition and maintaining a lifesupporting planet1. While volcanoes expel volatile components into surface reservoirs, subduction of oceanic crust is responsible for replenishment of mantle reservoirs2,3. Many natural, ‘superdeep’ diamonds originating in the deep upper mantle and transition zone host mineral inclusions, indicating an affinity to subducted oceanic crust4–7. Here we show that the majority of slab geotherms will intersect a deep depression along the melting curve of carbonated oceanic crust at depths of approximately 300 to 700 kilometres, creating a barrier to direct carbonate recycling into the deep mantle. Low-degree partial melts are alkaline carbonatites that are highly reactive with reduced ambient mantle, producing diamond. Many inclusions in superdeep diamonds are best explained by carbonate melt–peridotite reaction. A deep carbon barrier may dominate the recycling of carbon in the mantle and contribute to chemical and isotopic heterogeneity of the mantle reservoir

Activation in a Frontoparietal Cortical Network Underlies Individual Differences in the Performance of an Embedded Figures Task

The Embedded Figures Test (EFT) requires observers to search for a simple geometric shape hidden inside a more complex figure. Surprisingly, performance in the EFT is negatively correlated with susceptibility to illusions of spatial orientation, such as the Roelofs effect. Using fMRI, we previously demonstrated that regions in parietal cortex are involved in the contextual processing associated with the Roelofs task. In the present study, we found that similar parietal regions (superior parietal cortex and precuneus) were more active during the EFT than during a simple matching task. Importantly, these parietal activations overlapped with regions found to be involved during contextual processing in the Roelofs illusion. Additional parietal and frontal areas, in the right hemisphere, showed strong correlations between brain activity and behavioral performance during the search task. We propose that the posterior parietal regions are necessary for processing contextual information across many different, but related visuospatial tasks, with additional parietal and frontal regions serving to coordinate this processing in participants proficient in the task

Shedding light on the elusive role of endothelial cells in cytomegalovirus dissemination.

Cytomegalovirus (CMV) is frequently transmitted by solid organ transplantation and is associated with graft failure. By forming the boundary between circulation and organ parenchyma, endothelial cells (EC) are suited for bidirectional virus spread from and to the transplant. We applied Cre/loxP-mediated green-fluorescence-tagging of EC-derived murine CMV (MCMV) to quantify the role of infected EC in transplantation-associated CMV dissemination in the mouse model. Both EC- and non-EC-derived virus originating from infected Tie2-cre(+) heart and kidney transplants were readily transmitted to MCMV-naïve recipients by primary viremia. In contrast, when a Tie2-cre(+) transplant was infected by primary viremia in an infected recipient, the recombined EC-derived virus poorly spread to recipient tissues. Similarly, in reverse direction, EC-derived virus from infected Tie2-cre(+) recipient tissues poorly spread to the transplant. These data contradict any privileged role of EC in CMV dissemination and challenge an indiscriminate applicability of the primary and secondary viremia concept of virus dissemination

Imaging the 2013 explosive crater excavation and new dome formation at Volcán de Colima with TerraSAR-X, time-lapse cameras and modelling

The summit region of steep volcanoes hosting lava domes often displays rapid geomorphologic and structural changes, which are important for monitoring the source region of hazards. Explosive crater excavation is often followed by new lava-dome growth, which is one of the most dynamic morphometric changes that may occur at volcanoes. However, details of these crater formations, and the ensuing new dome growth remain poorly studied. A common problem is the lack of observational data due to hazardous field access and the limited resolution of satellite remote sensing techniques. This paper describes the destructive-constructive crater activity at Volcán de Colima, Mexico, which occurred between January and March 2013. The crater geometry and early dome formation were observed through a combination of high-resolution TerraSAR-X spotmode satellite radar images and permanently installed monitoring cameras. This combined time-lapse imagery was used to identify ring-shaped gas emissions prior to the explosion and to distinguish between the sequential explosion and crater excavation stages, which were followed by dome growth. By means of particle image velocimetry, the digital flow field is computed from consecutive camera images, showing that vertical dome growth is dominant at the beginning. The upward growth is found to grade into spreading and a lateral growth domain. After approximately two months of gradually filling the excavated craters with new magma, the dome overflows the western margin of the crater and develops into a flow that produces block and ash flow hazards. We discuss and compare the observations to discrete element models, allowing us to mimic the vertical and lateral growth history of the dome and to estimate the maximum strength of the bulk rock mass. Moreover, our results allow a discussion on the controls of a critical dome height that may be reached prior to its gravitational spreading

Swift detection of the super-swift switch-on of the super-soft phase in nova V745 Sco (2014)

V745 Sco is a recurrent nova, with the most recent eruption occurring in February 2014. V745 Sco was first observed by Swift a mere 3.7 hr after the announcement of the optical discovery, with the super-soft X-ray emission being detected around four days later and lasting for only ~two days, making it both the fastest follow-up of a nova by Swift and the earliest switch-on of super-soft emission yet detected. Such an early switch-on time suggests a combination of a very high velocity outflow and low ejected mass and, together with the high effective temperature reached by the super-soft emission, a high mass white dwarf (>1.3 M_sun). The X-ray spectral evolution was followed from an early epoch where shocked emission was evident, through the entirety of the super-soft phase, showing evolving column density, emission lines, absorption edges and thermal continuum temperature. UV grism data were also obtained throughout the super-soft interval, with the spectra showing mainly emission lines from lower ionization transitions and the Balmer continuum in emission. V745 Sco is compared with both V2491 Cyg (another nova with a very short super-soft phase) and M31N 2008-12a (the most rapidly recurring nova yet discovered). The longer recurrence time compared to M31N 2008-12a could be due to a lower mass accretion rate, although inclination of the system may also play a part. Nova V745 Sco (2014) revealed the fastest evolving super-soft source phase yet discovered, providing a detailed and informative dataset for study

Primary carbonatite melt from deeply subducted oceanic crust

Partial melting in the Earth's mantle plays an important part in generating the geochemical and isotopic diversity observed in volcanic rocks at the surface. Identifying the composition of these primary melts in the mantle is crucial for establishing links between mantle geochemical 'reservoirs' and fundamental geodynamic processes. Mineral inclusions in natural diamonds have provided a unique window into such deep mantle processes. Here we provide experimental and geochemical evidence that silicate mineral inclusions in diamonds from Juina, Brazil, crystallized from primary and evolved carbonatite melts in the mantle transition zone and deep upper mantle. The incompatible trace element abundances calculated for a melt coexisting with a calcium-titanium-silicate perovskite inclusion indicate deep melting of carbonated oceanic crust, probably at transition-zone depths. Further to perovskite, calcic-majorite garnet inclusions record crystallization in the deep upper mantle from an evolved melt that closely resembles estimates of primitive carbonatite on the basis of volcanic rocks. Small-degree melts of subducted crust can be viewed as agents of chemical mass-transfer in the upper mantle and transition zone, leaving a chemical imprint of ocean crust that can possibly endure for billions of years.4 page(s

Simple estimators of the intensity of seasonal occurrence

<p>Abstract</p> <p>Background</p> <p>Edwards's method is a widely used approach for fitting a sine curve to a time-series of monthly frequencies. From this fitted curve, estimates of the seasonal intensity of occurrence (i.e., peak-to-low ratio of the fitted curve) can be generated.</p> <p>Methods</p> <p>We discuss various approaches to the estimation of seasonal intensity assuming Edwards's periodic model, including maximum likelihood estimation (MLE), least squares, weighted least squares, and a new closed-form estimator based on a second-order moment statistic and non-transformed data. Through an extensive Monte Carlo simulation study, we compare the finite sample performance characteristics of the estimators discussed in this paper. Finally, all estimators and confidence interval procedures discussed are compared in a re-analysis of data on the seasonality of monocytic leukemia.</p> <p>Results</p> <p>We find that Edwards's estimator is substantially biased, particularly for small numbers of events and very large or small amounts of seasonality. For the common setting of rare events and moderate seasonality, the new estimator proposed in this paper yields less finite sample bias and better mean squared error than either the MLE or weighted least squares. For large studies and strong seasonality, MLE or weighted least squares appears to be the optimal analytic method among those considered.</p> <p>Conclusion</p> <p>Edwards's estimator of the seasonal relative risk can exhibit substantial finite sample bias. The alternative estimators considered in this paper should be preferred.</p