Computer generation and quantitative morphometric analysis of virtual neurons

An important goal in computational neuroanatomy is the complete and accurate simulation of neuronal morphology. We are developing computational tools to model three-dimensional dendritic structures based on sets of stochastic rules. This paper reports an extensive, quantitative anatomical characterization of simulated motoneurons and Purkinje cells. We used several local and global algorithms implemented in the L-Neuron and ArborVitae programs to generate sets of virtual neurons. Parameters statistics for all algorithms were measured from experimental data, thus providing a compact and consistent description of these morphological classes. We compared the emergent anatomical features of each group of virtual neurons with those of the experimental database in order to gain insights on the plausibility of the model assumptions, potential improvements to the algorithms, and non-trivial relations among morphological parameters. Algorithms mainly based on local constraints (e.g., branch diameter) were successful in reproducing many morphological properties of both motoneurons and Purkinje cells (e.g. total length, asymmetry, number of bifurcations). The addition of global constraints (e.g., trophic factors) improved the angle-dependent emergent characteristics (average Euclidean distance from the soma to the dendritic terminations, dendritic spread). Virtual neurons systematically displayed greater anatomical variability than real cells, suggesting the need for additional constraints in the models. For several emergent anatomical properties, a specific algorithm reproduced the experimental statistics better than the others did. However, relative performances were often reversed for different anatomical properties and/or morphological classes. Thus, combining the strengths of alternative generative models could lead to comprehensive algorithms for the complete and accurate simulation of dendritic morphology.

The influence of individual characteristics on employee creative participation

In a knowledge-based economy and dynamic work environment retaining competitiveness is increasingly dependent on creativity, skills, individual abilities and appropriate motivation. For instance, the UK government explicitly stated in the recent "Review of Employee Engagement and Investment" report that new ways are required through which British companies could boost employee engagement at work, improving staff commitment and, thereby, increase workplace productivity. Although creativity and innovation have been studied extensively, little is known about employees' intrinsic willingness to contribute novel ideas and solutions (defined here as creative participation). For instance, the same individual can thrive in one organisation but be completely isolated in another and the question is to what extent this depends on individual characteristics and organisational settings. The main aim of this research is, therefore, to provide a conceptual framework for identification of individual characteristics that influence employees' willingness to contribute new ideas. In order to achieve this aim the investigation will be based on a developed psychological experiment, and will include personal-profiling inventory and a questionnaire. Understanding how these parameters influence willingness of an individual to put forward created ideas would offer an opportunity for companies to improve motivation practices and team efficiency, and can consequently lead to better overall performance.

The component polypeptide chains of bovine insulin nucleate or inhibit aggregation of the parent protein in a conformation-dependent manner

Amyloid fibrils are typically rigid, unbrariched structures with diameters of similar to 10 nm and lengths up to several micrometres, and are associated with more than 20 diseases including Alzheimer's disease and type II diabetes. Insulin is a small, predominantly alpha-helical protein consisting of 51 residues in two disulfide-linked polypeptide chains that readily assembles into amyloid fibrils under conditions of low PH and elevated temperature. We demonstrate here that both the A-chain and the B-chain of insulin are capable of forming amyloid fibrils in isolation under similar conditions, with fibrillar morphologies that differ from those composed of intact insulin. Both the A-chain and B-chain fibrils were found to be able to cross-seed the fibrillization of the parent protein, although these reactions were substantially less efficient than self-seeding with fibrils composed of full-length insulin. In both cases, the cross-seeded fibrils were morphologically distinct from the seeding, material, but shared common characteristics with typical insulin fibrils, including a very similar helical repeat. The broader distribution of heights of the cross-seeded fibrils compared to typical insulin fibrils, however, indicates that their underling protofilament hierarchy may be subtly different. In addition, and remarkably in view of this seeding behavior, the soluble forms of the A-chain and B-chain peptides were found to be capable of inhibiting insulin fibril formation. Studies using mass spectrometry suggest that this behavior might be attributable to complex formation between insulin and the A-chain and B-chain peptides. The finding that the same chemical form of a polypeptide chain in different physical states can either stimulate or inhibit the conversion of a protein into amyloid fibrils sheds new light on the mechanisms underlying fibril formation, fibril strain propagation and amyloid disease initiation and progression. (c) 2006 Elsevier Ltd. All rights reserved.

(-)Epicatechin stimulates ERK-dependent cyclic AMP response element activity and up-regulates GluR2 in cortical neurons

Emerging evidence suggests that the cellular actions of flavonoids relate not simply to their antioxidant potential but also to the modulation of protein kinase signalling pathways. We investigated in primary cortical neurons, the ability of the flavan-3-ol, (-)epicatechin, and its human metabolites at physiologically relevant concentrations, to stimulate phosphorylation of the transcription factor cAMP-response element binding protein (CREB), a regulator of neuronal viability and synaptic plasticity. (-)Epicatechin at 100-300 nmol/L stimulated a rapid, extracellular signal-regulated kinase (ERK)- and PI3K-dependent, increase in CREB phosphorylation. At micromolar concentrations, stimulation was no longer apparent and at the highest concentration tested (30 mu mol/L) (-)epicatechin was inhibitory. (-)Epicatechin also stimulated ERK and Akt phosphorylation with similar bell-shaped concentration-response characteristics. The human metabolite 3 '-O-methyl-(-)epicatechin was as effective as (-)epicatechin at stimulating ERK phosphorylation, but (-)epicatechin glucuronide was inactive. (-)Epicatechin and 3 '-O-methyl-(-)epicatechin treatments (100 nmol/L) increased CRE-luciferase activity in cortical neurons in a partially ERK-dependent manner, suggesting the potential to increase CREB-mediated gene expression. mRNA levels of the glutamate receptor subunit GluR2 increased by 60%, measured 18 h after a 15 min exposure to (-)epicatechin and this translated into an increase in GluR2 protein. Thus, (-)epicatechin has the potential to increase CREB-regulated gene expression and increase GluR2 levels and thus modulate neurotransmission, plasticity and synaptogenesis.

Variable selection algorithm for the construction of MIMO operating point dependent neurofuzzy networks

An input variable selection procedure is introduced for the identification and construction of multi-input multi-output (MIMO) neurofuzzy operating point dependent models. The algorithm is an extension of a forward modified Gram-Schmidt orthogonal least squares procedure for a linear model structure which is modified to accommodate nonlinear system modeling by incorporating piecewise locally linear model fitting. The proposed input nodes selection procedure effectively tackles the problem of the curse of dimensionality associated with lattice-based modeling algorithms such as radial basis function neurofuzzy networks, enabling the resulting neurofuzzy operating point dependent model to be widely applied in control and estimation. Some numerical examples are given to demonstrate the effectiveness of the proposed construction algorithm.

Summer drought in northern midlatitudes in a time-dependent CO2 climate experiment

A time-dependent climate-change experiment with a coupled ocean–atmosphere general circulation model has been used to study changes in the occurrence of drought in summer in southern Europe and central North America. In both regions, precipitation and soil moisture are reduced in a climate of greater atmospheric carbon dioxide. A detailed investigation of the hydrology of the model shows that the drying of the soil comes about through an increase in evaporation in winter and spring, caused by higher temperatures and reduced snow cover, and a decrease in the net input of water in summer. Evaporation is reduced in summer because of the drier soil, but the reduction in precipitation is larger. Three extreme statistics are used to define drought, namely the frequency of low summer precipitation, the occurrence of long dry spells, and the probability of dry soil. The last of these is arguably of the greatest practical importance, but since it is based on soil moisture, of which there are very few observations, the authors’ simulation of it has the least confidence. Furthermore, long time series for daily observed precipitation are not readily available from a sufficient number of stations to enable a thorough evaluation of the model simulation, especially for the frequency of long dry spells, and this increases the systematic uncertainty of the model predictions. All three drought statistics show marked increases owing to the sensitivity of extreme statistics to changes in their distributions. However, the greater likelihood of long dry spells is caused by a tendency in the character of daily rainfall toward fewer events, rather than by the reduction in mean precipitation. The results should not be taken as firm predictions because extreme statistics for small regions cannot be calculated reliably from the output of the current generation of GCMs, but they point to the possibility of large increases in the severity of drought conditions as a consequence of climate change caused by increased CO2.

Using meteorological wind data to estimate turbine generation output: a sensitivity analysis

Various studies investigating the future impacts of integrating high levels of renewable energy make use of historical meteorological (met) station data to produce estimates of future generation. Hourly means of 10m horizontal wind are extrapolated to a standard turbine hub height using the wind profile power or log law and used to simulate the hypothetical power output of a turbine at that location; repeating this procedure using many viable locations can produce a picture of future electricity generation. However, the estimate of hub height wind speed is dependent on the choice of the wind shear exponent a or the roughness length z0, and requires a number of simplifying assumptions. This paper investigates the sensitivity of this estimation on generation output using a case study of a met station in West Freugh, Scotland. The results show that the choice of wind shear exponent is a particularly sensitive parameter which can lead to significant variation of estimated hub height wind speed and hence estimated future generation potential of a region.

What limits insect fecundity? Body size- and temperature-dependent egg maturation and oviposition in a butterfly

1. Large female insects usually have high potential fecundity. Therefore selection should favour an increase in body size given that these females get opportunities to realize their potential advantage by maturing and laying more eggs. However, ectotherm physiology is strongly temperature-dependent, and activities are carried out sufficiently only within certain temperature ranges. Thus it remains unclear if the fecundity advantage of a large size is fully realized in natural environments, where thermal conditions are limiting. 2. Insect fecundity might be limited by temperature at two levels; first eggs need to mature, and then the female needs time for strategic ovipositing of the egg. Since a female cannot foresee the number of oviposition opportunities that she will encounter on a given day, the optimal rate of egg maturation will be governed by trade-offs associated with egg- and time-limited oviposition. As females of different sizes will have different amounts of body reserves, size-dependent allocation trade-offs between the mother’s condition and her egg production might be expected. 3. In the temperate butterfly Pararge aegeria , the time and temperature dependence of oviposition and egg maturation, and the interrelatedness of these two processes were investigated in a series of laboratory experiments, allowing a decoupling of the time budgets for the respective processes. 4. The results show that realized fecundity of this species can be limited by both the temperature dependence of egg maturation and oviposition under certain thermal regimes. Furthermore, rates of oviposition and egg maturation seemed to have regulatory effects upon each other. Early reproductive output was correlated with short life span, indicating a cost of reproduction. Finally, large females matured more eggs than small females when deprived of oviposition opportunities. Thus, the optimal allocation of resources to egg production seems dependent on female size. 5. This study highlights the complexity of processes underlying rates of egg maturation and oviposition in ectotherms under natural conditions. We further discuss the importance of temperature variation for egg- vs. time-limited fecundity and the consequences for the evolution of female body size in insects.

A study into the accuracy of using meteorological wind data to estimate turbine generation output

Meteorological (met) station data is used as the basis for a number of influential studies into the impacts of the variability of renewable resources. Real turbine output data is not often easy to acquire, whereas meteorological wind data, supplied at a standardised height of 10 m, is widely available. This data can be extrapolated to a standard turbine height using the wind profile power law and used to simulate the hypothetical power output of a turbine. Utilising a number of met sites in such a manner can develop a model of future wind generation output. However, the accuracy of this extrapolation is strongly dependent on the choice of the wind shear exponent alpha. This paper investigates the accuracy of the simulated generation output compared to reality using a wind farm in North Rhins, Scotland and a nearby met station in West Freugh. The results show that while a single annual average value for alpha may be selected to accurately represent the long term energy generation from a simulated wind farm, there are significant differences between simulation and reality on an hourly power generation basis, with implications for understanding the impact of variability of renewables on short timescales, particularly system balancing and the way that conventional generation may be asked to respond to a high level of variable renewable generation on the grid in the future.

Conditioning model output statistics of regional climate model precipitation on circulation patterns

Dynamical downscaling of Global Climate Models (GCMs) through regional climate models (RCMs) potentially improves the usability of the output for hydrological impact studies. However, a further downscaling or interpolation of precipitation from RCMs is often needed to match the precipitation characteristics at the local scale. This study analysed three Model Output Statistics (MOS) techniques to adjust RCM precipitation; (1) a simple direct method (DM), (2) quantile-quantile mapping (QM) and (3) a distribution-based scaling (DBS) approach. The modelled precipitation was daily means from 16 RCMs driven by ERA40 reanalysis data over the 1961–2000 provided by the ENSEMBLES (ENSEMBLE-based Predictions of Climate Changes and their Impacts) project over a small catchment located in the Midlands, UK. All methods were conditioned on the entire time series, separate months and using an objective classification of Lamb's weather types. The performance of the MOS techniques were assessed regarding temporal and spatial characteristics of the precipitation fields, as well as modelled runoff using the HBV rainfall-runoff model. The results indicate that the DBS conditioned on classification patterns performed better than the other methods, however an ensemble approach in terms of both climate models and downscaling methods is recommended to account for uncertainties in the MOS methods.

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.

Short communication: plasma concentration of glucose-dependent insulinotropic polypeptide may regulate milk energy production in lactating dairy cows

In dairy cows, an increase in plasma concentration of glucose-dependent insulinotropic polypeptide (GIP) is associated with an increase in metabolizable energy intake, but the role of GIP in energy partitioning of dairy cattle is not certain. The objective of this study was to examine the relationship between plasma GIP concentrations and energy partitioning toward milk production. Four mid-lactation, primiparous, rumenfistulated Holstein-Friesian cows were fed a control diet of 55% forage and 45% concentrate [dry matter (DM) basis] in a 4 × 4 Latin square design with 4-wk periods. The 4 treatments were (1) control diet fed at 1000 and 1600 h, and (2) once-daily (1000 h) feeding, (3) twice daily (1000 and 1600 h) feeding, and (4) 4 times/d (1000, 1600, 2200 and 0400 h) feeding of the control diet plus 1 dose (1.75 kg on a DM basis at 0955 h) into the rumen of supplemental vegetable proteins (Amino Green; SCA NuTec Ltd., Thirsk, UK). Measurements of respiratory exchange and energy balance were obtained over 4 d during the last week of each period while cows were housed in open-circuit respiration chambers. Blood was collected from the jugular vein every 30 min for 12 h, using indwelling catheters, starting at 0800 h on d 20 of each period. Plasma GIP concentration was measured in samples pooled over each 5 consecutive blood samplings. The relationships between plasma GIP, DM intake, heat production, respiratory quotient, milk yield, and milk energy output were analyzed using linear correlation procedures, with metabolizable intake as a partial variant. Plasma GIP concentration was not correlated with heat production, or milk yield, but was positively correlated with milk energy yield (correlation coefficient = 0.67) and negatively correlated with RQ (correlation coefficient = −0.72). The correlations between GIP and RQ and milk energy output do not imply causality, but suggest that a role for GIP may exist in the regulation of energy metabolism in dairy cows.

A gradual depth-dependent change of connectivity features of supragranular pyramidal cells in rat barrel cortex

Recent experimental evidence suggests a finer genetic, structural and functional subdivision of the layers which form a cortical column. The classical layer II/III (LII/III) of rodent neocortex integrates ascending sensory information with contextual cortical information for behavioral read-out. We systematically investigated to which extent regular-spiking supragranular pyramidal neurons, located at different depths within the cortex, show different input-output connectivity patterns. Combining glutamate-uncaging with whole-cell recordings and biocytin filling, we revealed a novel cellular organization of LII/III: (i) “Lower LII/III” pyramidal cells receive a very strong excitatory input from lemniscal LIV and much fewer inputs from paralemniscal LVa. They project to all layers of the home column, including a feedback projection to LIV whereas transcolumnar projections are relatively sparse. (ii) “Upper LII/III” pyramidal cells also receive their strongest input from LIV, but in addition, a very strong and dense excitatory input from LVa. They project extensively to LII/III as well as LVa and Vb of their home and neighboring columns, (iii) “Middle LII/III” pyramidal cell show an intermediate connectivity phenotype that stands in many ways in-between the features described for lower versus upper LII/III. “Lower LII/III” intracolumnarly segregates and transcolumnarly integrates lemniscal information whereas “upper LII/III” seems to integrate lemniscal with paralemniscal information. This suggests a finegrained functional subdivision of the supragranular compartment containing multiple circuits without any obvious cytoarchitectonic, other structural or functional correlate of a laminar border in rodent barrel cortex.

Parabens can enable hallmarks and characteristics of cancer in human breast epithelial cells: a review of the literature with reference to new exposure data and regulatory status

A framework for understanding the complexity of cancer development was established by Hanahan and Weinberg in their definition of the hallmarks of cancer. In this review, we consider the evidence that parabens can enable development in human breast epithelial cells of 4/6 of the basic hallmarks, 1/2 of the emerging hallmarks and 1/2 of the enabling characteristics. Hallmark 1: parabens have been measured as present in 99% of human breast tissue samples, possess oestrogenic activity and can stimulate sustained proliferation of human breast cancer cells at concentrations measurable in the breast. Hallmark 2: parabens can inhibit the suppression of breast cancer cell growth by hydroxytamoxifen, and through binding to the oestrogen-related receptor gamma (ERR) may prevent its deactivation by growth inhibitors. Hallmark 3: in the 10nM to 1M range, parabens give a dose-dependent evasion of apoptosis in high-risk donor breast epithelial cells. Hallmark 4: long-term exposure (>20weeks) to parabens leads to increased migratory and invasive activity in human breast cancer cells, properties which are linked to the metastatic process. Emerging hallmark: methylparaben has been shown in human breast epithelial cells to increase mTOR, a key regulator of energy metabolism. Enabling characteristic: parabens can cause DNA damage at high concentrations in the short term but more work is needed to investigate long-term low-doses of mixtures. The ability of parabens to enable multiple cancer hallmarks in human breast epithelial cells provides grounds for regulatory review of the implications of the presence of parabens in human breast tissue.

Characteristics of tropical cyclones in high-resolution models in the present climate

The global characteristics of tropical cyclones (TCs) simulated by several climate models are analyzed and compared with observations. The global climate models were forced by the same sea surface temperature (SST) fields in two types of experiments, using climatological SST and interannually varying SST. TC tracks and intensities are derived from each model's output fields by the group who ran that model, using their own preferred tracking scheme; the study considers the combination of model and tracking scheme as a single modeling system, and compares the properties derived from the different systems. Overall, the observed geographic distribution of global TC frequency was reasonably well reproduced. As expected, with the exception of one model, intensities of the simulated TC were lower than in observations, to a degree that varies considerably across models.