Global distributions of Phaeocystis sp. abundance and biomass - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The planktonic haptophyte Phaeocystis has been suggested to play a fundamental role in the global biogeochemical cycling of carbon and sulphur, but little is known about its global biomass distribution. We have collected global microscopy data of the genus Phaeocystis and converted abundance data to carbon biomass using species-specific carbon conversion factors. Microscopic counts of single-celled and colonial Phaeocystis were obtained both through the mining of online databases and by accepting direct submissions (both published and unpublished) from Phaeocystis specialists. We recorded abundance data from a total of 1595 depth-resolved stations sampled between 1955-2009. The quality-controlled dataset includes 5057 counts of individual Phaeocystis cells resolved to species level and information regarding life-stages from 3526 samples. 83% of stations were located in the Northern Hemisphere while 17% were located in the Southern Hemisphere. Most data were located in the latitude range of 50-70° N. While the seasonal distribution of Northern Hemisphere data was well-balanced, Southern Hemisphere data was biased towards summer months. Mean species- and form-specific cell diameters were determined from previously published studies. Cell diameters were used to calculate the cellular biovolume of Phaeocystis cells, assuming spherical geometry. Cell biomass was calculated using a carbon conversion factor for Prymnesiophytes (Menden-Deuer and Lessard, 2000). For colonies, the number of cells per colony was derived from the colony volume. Cell numbers were then converted to carbon concentrations. An estimation of colonial mucus carbon was included a posteriori, assuming a mean colony size for each species. Carbon content per cell ranged from 9 pg (single-celled Phaeocystis antarctica) to 29 pg (colonial Phaeocystis globosa). Non-zero Phaeocystis cell biomasses (without mucus carbon) range from 2.9 - 10?5 µg l-1 to 5.4 - 103 µg l-1, with a mean of 45.7 µg l-1 and a median of 3.0 µg l-1. Highest biomasses occur in the Southern Ocean below 70° S (up to 783.9 µg l-1), and in the North Atlantic around 50° N (up to 5.4 - 103 µg l-1).

Dating of the quaternary terraces close to the Chenab river (Western Himalaya)

A morphotectonic study has been performed in the Pir Panjal Range (Southern Kashmir, Western Himalaya) to characterised the active tectonics. Along the Chenab River, we mapped 7 strath terraces at the hanging wall of the Medlicott Wadia Thrust, and dated 3 regional alluviation events using 3 methods (53 10Be samples, 12 OSL and 3 14C). The 3 methods are briefly presented and data are shown in a table. The three alluviation events correspond to the end of maximum monsoon phases.

On nonlinearity in neural encoding models applied to the primary visual cortex

Within the regression framework, we show how different levels of nonlinearity influence the instantaneous firing rate prediction of single neurons. Nonlinearity can be achieved in several ways. In particular, we can enrich the predictor set with basis expansions of the input variables (enlarging the number of inputs) or train a simple but different model for each area of the data domain. Spline-based models are popular within the first category. Kernel smoothing methods fall into the second category. Whereas the first choice is useful for globally characterizing complex functions, the second is very handy for temporal data and is able to include inner-state subject variations. Also, interactions among stimuli are considered. We compare state-of-the-art firing rate prediction methods with some more sophisticated spline-based nonlinear methods: multivariate adaptive regression splines and sparse additive models. We also study the impact of kernel smoothing. Finally, we explore the combination of various local models in an incremental learning procedure. Our goal is to demonstrate that appropriate nonlinearity treatment can greatly improve the results. We test our hypothesis on both synthetic data and real neuronal recordings in cat primary visual cortex, giving a plausible explanation of the results from a biological perspective.

First passages in bounded domains: When is the mean first passage time meaningful?

We study the first passage statistics to adsorbing boundaries of a Brownian motion in bounded two-dimensional domains of different shapes and configurations of the adsorbing and reflecting boundaries. From extensive numerical analysis we obtain the probability P(ω) distribution of the random variable ω=τ1/(τ1+τ2), which is a measure for how similar the first passage times τ1 and τ2 are of two independent realizations of a Brownian walk starting at the same location. We construct a chart for each domain, determining whether P(ω) represents a unimodal, bell-shaped form, or a bimodal, M-shaped behavior. While in the former case the mean first passage time (MFPT) is a valid characteristic of the first passage behavior, in the latter case it is an insufficient measure for the process. Strikingly we find a distinct turnover between the two modes of P(ω), characteristic for the domain shape and the respective location of absorbing and reflective boundaries. Our results demonstrate that large fluctuations of the first passage times may occur frequently in two-dimensional domains, rendering quite vague the general use of the MFPT as a robust measure of the actual behavior even in bounded domains, in which all moments of the first passage distribution exist.

Data mining applied to the cognitive rehabilitation of patients with acquired brain injury

Acquired brain injury (ABI) is one of the leading causes of death and disability in the world and is associated with high health care costs as a result of the acute treatment and long term rehabilitation involved. Different algorithms and methods have been proposed to predict the effectiveness of rehabilitation programs. In general, research has focused on predicting the overall improvement of patients with ABI. The purpose of this study is the novel application of data mining (DM) techniques to predict the outcomes of cognitive rehabilitation in patients with ABI. We generate three predictive models that allow us to obtain new knowledge to evaluate and improve the effectiveness of the cognitive rehabilitation process. Decision tree (DT), multilayer perceptron (MLP) and general regression neural network (GRNN) have been used to construct the prediction models. 10-fold cross validation was carried out in order to test the algorithms, using the Institut Guttmann Neurorehabilitation Hospital (IG) patients database. Performance of the models was tested through specificity, sensitivity and accuracy analysis and confusion matrix analysis. The experimental results obtained by DT are clearly superior with a prediction average accuracy of 90.38%, while MLP and GRRN obtained a 78.7% and 75.96%, respectively. This study allows to increase the knowledge about the contributing factors of an ABI patient recovery and to estimate treatment efficacy in individual patients.

Passenger Exposure to Magnetic Fields due to the Batteries of an Electric Vehicle

In electric vehicles, passengers sit very close to an electric system of significant power. The high currents achieved in these vehicles mean that the passengers could be exposed to significant magnetic fields. One of the electric devices present in the power train are the batteries. In this paper, a methodology to evaluate the magnetic field created by these batteries is presented. First, the magnetic field generated by a single battery is analyzed using finite elements simulations. Results are compared to laboratory measurements, taken from a real battery, in order to validate the model. After this, the magnetic field created by a complete battery pack is estimated and results are discussed.

Estrogen alters behavior and forebrain c-fos expression in ovariectomized rats subjected to the forced swim test

Estrogen has been implicated in brain functions related to affective state, including hormone-related affective disorders in women. Although some reports suggest that estrogen appears to decrease vulnerability to affective disorders in certain cases, the mechanisms involved are unknown. We used the forced swim test (FST), a paradigm used to test the efficacy of antidepressants, and addressed the hypotheses that estrogen alters behavior of ovariectomized rats in the FST and the FST-induced expression of c-fos, a marker for neuronal activity, in the rat forebrain. The behaviors displayed included struggling, swimming, and immobility. One hour after the beginning of the test on day 2, the animals were perfused, and the brains were processed for c-fos immunocytochemistry. On day 1, the estradiol benzoate-treated animals spent significantly less time struggling and virtually no time in immobility and spent most of the time swimming. Control rats spent significantly more time struggling or being immobile during a comparable period. On day 2, similar behavioral patterns with still more pronounced differences were observed between estradiol benzoate and ovariectomized control groups in struggling, immobility, and swimming. Analysis of the mean number of c-fos immunoreactive cell nuclei showed a significant reduction in the estradiol benzoate versus control groups in areas of the forebrain relating to sensory, contextual, and integrative processing. Our results suggest that estrogen-induced neurochemical changes in forebrain neurons may translate into an altered behavioral output in the affective domain.