Theoretical analysis on relationship between the neural activity and the EEG

Firstly, a collective oscillation mode of the neural activity is derived from the neural network system by using the multicompartment equation and the projection operator technique. This technique takes into account higher order interactions among neurons. The solution of the equation gives a chain structure with an infinite number of circuit loops in which each of them is only composed of four neurons. The obtained eigenvalues are quite similar to the spectrum of frequencies of the EEG. Secondly, the time-dependent behavior of the observed EEG is simulated by starting from the elementary process of action potential trains of neurons, which includes the effect of the collective oscillation mode mentioned above. This gives a comprehensive derivation of the EEG from the neural activity of action potentials. The simulation assumes that information of the action potential trains can be transmitted to the EEG through the intermediate states of the postsynaptic potential trains and the slow waves. The paper reports that a slightly modulated activity of a relatively small amount of neurons can cause a strong influence on the shape of the global EEG and that the calculated results reproduce the characteristic features of the EEG in a rat such as the theta rhythm, the spindle wave and the arousal wave.     

A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained

An increasing number of genetic variants have been identified for many complex diseases. However, it is controversial whether risk prediction based on genomic profiles will be useful clinically. Appropriate statistical measures to evaluate the performance of genetic risk prediction models are required. Previous studies have mainly focused on the use of the area under the receiver operating characteristic (ROC) curve, or AUC, to judge the predictive value of genetic tests. However, AUC has its limitations and should be complemented by other measures. In this study, we develop a novel unifying statistical framework that connects a large variety of predictive indices together. We showed that, given the overall disease probability and the level of variance in total liability (or heritability) explained by the genetic variants, we can estimate analytically a large variety of prediction metrics, for example the AUC, the mean risk difference between cases and non-cases, the net reclassification improvement (ability to reclassify people into high- and low-risk categories), the proportion of cases explained by a specific percentile of population at the highest risk, the variance of predicted risks, and the risk at any percentile. We also demonstrate how to construct graphs to visualize the performance of risk models, such as the ROC curve, the density of risks, and the predictiveness curve (disease risk plotted against risk percentile). The results from simulations match very well with our theoretical estimates. Finally we apply the methodology to nine complex diseases, evaluating the predictive power of genetic tests based on known susceptibility variants for each trait.     

A double-blind investigation of the relationship between seizure activity and the sleep EEG following EEG biofeedback training

The sleep EEGs of eight medically refractory epileptic patients were examined as part of a double-blind, ABA crossover study designed to determine the effectiveness of EEG biofeedback for the control of seizures. The patients were initially reinforced for one of three EEG criteria recorded from electrodes placed over sensorimotor cortex: (a) suppression of 3- to 7-Hz activity, (b) enhancement of 12- to 15-Hz activity, or (c) simultaneous suppression of 3- to 7-Hz and enhancement of 11- to 19-Hz activity. Reinforcement contingencies were reversed during the second or B phase, and then reinstated in their original form during the final A′ phase. All-night polysomnographic recordings were obtained at the end of each conditioning phase and were subjected to both visual and computer-based power spectral analyses. Four of the patients showed changes in their nocturnal paroxysmal activity that were either partially or totally consistent with the ABA′ contingencies of the study. The spectral data proved difficult to interpret, though two trends emerged from the analyses. Decreases in nocturnal 4- to 7-Hz activity were correlated with decreases in seizure activity, and increases in 8- to 11-Hz activity were correlated with decreases in seizure activity. These findings were shown to strengthen the hypothesis that EEG biofeedback may produce changes in the sleep EEG that are related to seizure incidence.     

A hypothesis and an experiment on the origin of the genetic coding process

From thermodynamic considerations it is argued that the earliest templates for protein synthesis were very unlike present nucleic acids. It is suggested that they were composed of linked uracil derivatives bearing hypothetical side chains that could specifically recognize and interact with individual amino acid side chains (Fig. 1). An experiment supporting the possibility of such a specific interaction is described. The hypothesis accounts for the grouping of amino acids in the present codon table according to their relative hydrophobic character.     

Neutral theory, phylogenies, and the relationship between phenotypic change and evolutionary rates

The neutral theory of molecular evolution predicts that rates of phenotypic change are largely independent from genotypic change. A recent study by Bromham et al. (2002) confirmed this expectation, finding no evidence for correlated phenotypic and molecular evolutionary rates in animals. We reevaluate this hypothesis, sampling at different taxonomic levels in plants and animals, using Bayesian inference to reconstruct phylogenetic trees and estimate rates of molecular evolution. We use independent contrasts in branch lengths to maximize the information extracted from each of the trees and nodal posterior probabilities to assess the influence of phylogenetic error. Our results indicate that in vascular plants between 2% and 11% of the variation in phenotypic rates of change can be explained by the rate of genotypic change. These results may be explained by the idea that processes that affect general evolutionary rates, such as body size, may also be expected to influence rates of morphological change.     

基于土地利用/覆被变化的流域景观格局与水沙响应关系研究

以黄河流域的2个典型流域为研究对象,借助GIS和Fragstats平台与长系列水沙数据,分析流域景观格局和水沙变化特征,并探讨景观指数与径流输沙的关系。结果表明:(1)两个流域优势景观类型为草地,1985—2010年间变化最大的景观类型分别为未利用土地(25a变幅为453.94 km~2)和耕地(25a变幅为52.85 km~2);(2)秃尾河流域景观均向规则、高连通和高度聚集的方向发展。孤山川控制流域内景观多样性和聚集度逐渐增加,整体向好。秃尾河流域景观稳定性指数高于孤山川流域,两流域草地和未利用土地地稳定性均呈增加趋势,而城乡工矿用地则相反。(3)流域年径流量和泥沙量均呈现逐年同步减小的趋势。秃尾河年径流量明显高于孤山川,但孤山川流域泥沙量与秃尾河流域相近。两流域径流泥沙相关关系显著,秃尾河流域相关系数(0.48)明显低于孤山川流域(0.85)。(4)景观指数与径流量、泥沙量呈显著线性相关,其中景观多样性相关的指数SHDI、SIDI、SHEI和SIEI均与径流呈极显著正相关,而泥沙仅与CONTAG、COHESION呈显著负相关。     

On the relationship between trophic position, body mass and temperature: reformulating the energy limitation hypothesis

Understanding the factors that constrain and drive changes in food chain length represents an open challenge in ecology. Although several explanatory hypotheses have been proposed, no synthesis has yet been achieved. The role of body size has been well-studied in recent years because the hierarchy of trophic connections – in which large animals consume small ones – suggests a positive relationship between trophic position and body size. Empirical evidence, however, supports the existence of both positive and negative associations, and some studies have even reported no significant relationship between trophic position and body size. These results suggest that the relationship may be non-monotonic and driven by several interacting mechanisms. Here, we analyze the effects of energetic limitations and structural constraints on species' trophic positions. We show that the trophic position of small-bodied animals can be limited by their ability to consume large prey, whereas energetic limitations strongly constrain trophic positions for large-bodied animals, with the intensity of this constraint depending on the amount of energy available to top predators. These differences in limiting mechanisms can account for the observed variability in the association between the trophic position of top predators and size. Furthermore, our derivation makes use of the Metabolic theory of ecology and predicts a negative relationship between temperature and the maximum achievable food chain length, providing a mechanistic foundation for the observed reductions in food chain length with temperature.     

The relationship between the poisson-boltzmann model and the condensation hypothesis: an analysis based on the low salt form of the Donnan coefficient

Two common models for the interaction of counterions with cylindrical polyions are considered in the context of the Donnan membrane equilibrium. General analytic expressions are obtained from the Poisson-Boltzmann equation for the Donnan coefficient in terms of the potential at the surface of the polyion or the local concentration of unbound ions at the surface. Analysis based on these expressions shows that if, and only if, the polyion charge density exceeds a certain critical value a large local concentration of ions will persist near the polyion surface at low ionic strengths. We therefore conclude that this principal hypothesis of the condensation model is consistent with the characteristics of the Poisson-Boltzmann potential at the surface of the polyion.     

Testing the hypothesis on the relationship between aerodynamic roughness length and albedo using vegetation structure parameters

Surface albedo (α) and aerodynamic roughness length (z ₀), which partition surface net radiation into energy fluxes, are critical land surface properties for biosphere–atmosphere interactions and climate variability. Previous studies suggested that canopy structure parameters influence both α and z ₀; however, no field data have been reported to quantify their relationships. Here, we hypothesize that a functional relationship between α and z ₀ exists for a vegetated surface, since both land surface parameters can be conceptually related to the characteristics of canopy structure. We test this hypothesis by using the observed data collected from 50 site-years of field measurements from sites worldwide covering various vegetated surfaces. On the basis of these data, a negative linear relationship between α and log(z ₀) was found, which is related to the canopy structural parameter. We believe that our finding is a big step toward the estimation of z ₀ with high accuracy. This can be used, for example, in the parameterization of land properties and the observation of z ₀ using satellite remote sensing.     

Effects of dipole position,orientation and noise on the accuracy of EEG source localization

Background  

The electroencephalogram (EEG) reflects the electrical activity in the brain on the surface of scalp. A major challenge in this field is the localization of sources in the brain responsible for eliciting the EEG signal measured at the scalp. In order to estimate the location of these sources, one must correctly model the sources, i.e., dipoles, as well as the volume conductor in which the resulting currents flow. In this study, we investigate the effects of dipole depth and orientation on source localization with varying sets of simulated random noise in 4 realistic head models.     

A relationship between 13C-chemical-shift displacements and counterion-condensation theory, in the binding of calcium ion by heparin

Characteristics of the interaction between heparin and calcium ion in the presence of sodium ion have been examined by monitoring the 13C-chemical shift changes as a function of the calcium ion concentration and the total ionic strength. The results indicated that the association between the polyanion and the divalent cation is a delocalized process, as opposed to one involving specific binding. The correspondence found between chemical shift and the number of Ca2+ ions bound per charged group, as derived from the Manning counterion-condensation model, showed that the stoichiometry is not a constant quantity but, rather, varies throughout the titration, and approaches a limiting value of 2 at high dilution. Additional measurements of T1 and line-width were consistent with an intramolecular order-disorder conformational process induced by the binding of calcium ion. Moreover, binding does not occur or is relatively weak with N-desulfated heparin, or chondroitin 4-sulfate and 6-sulfate, each of which possesses fewer sulfate groups than heparin. These differences serve to emphasize the importance of the charge-density parameter in the control of counterion condensation according to the Manning model, and suggest that the spacing between the negatively charged groups is an associated factor.     

Existence and stability criteria for phase-locked modes in ring neural networks based on the spike time resetting curve method

We developed a systematic and consistent mathematical approach to predicting 1:1 phase-locked modes in ring neural networks of spiking neurons based on the open loop spike time resetting curve (STRC) and its almost equivalent counterpart—the phase resetting curve (PRC). The open loop STRCs/PRCs were obtained by injecting into an isolated model neuron a triangular shaped time-dependent stimulus current closely resembling an actual synaptic input. Among other advantages, the STRC eliminates the confusion regarding the undefined phase for stimuli driving the neuron outside of the unperturbed limit cycle. We derived both open loop PRC and STRC-based existence and stability criteria for 1:1 phase-locked modes developed in ring networks of spiking neurons. Our predictions were in good agreement with the closed loop numerical simulations. Intuitive graphical methods for predicting phase-locked modes were also developed both for half-centers and for larger ring networks.     

A unifying concept for the dependence of whole-crop N : P ratio on biomass: theory and experiment

Background and Aims

Numerous estimates have been made of the concentrations of N and P required for good growth of crop species but they have not been defined by any unifying model. The aim of the present study was to develop such a model for the dependence of the N : P ratio on crop mass, to test its validity and to use it to identify elements of similarity between different crop species and wild plants.

Methods

A model was derived between plant N : P ratio (R_w) and its dry biomass per unit area (W) during growth with near optimum nutrition by considering that plants consist of growth-related tissue and storage-related tissue with N : P ratios R_g and R_s, respectively. Testing and calibration against experimental data on different crop species led to a simple equation between R_w and W which was tested against independent experimental data.

Key Results

The validity of the model and equation was supported by 365 measurements of R_w in 38 field experiments on crops. R_g and R_s remained approximately constant throughout growth, with average values of 11·8 and 5·8 by mass. The model also approximately predicted the relationships between leaf N and P concentrations in 124 advisory estimates on immature tissues and in 385 wild species from published global surveys.

Conclusions

The N : P ratio of the biomass of very different crops, during growth with near optimum levels of nutrients, is defined entirely in terms of crop biomass, an average N : P ratio of the storage/structure-related tissue of the crop and an average N : P ratio of the growth-related tissue. The latter is similar to that found in leaves of many wild plant species, and even micro-organisms and terrestrial and freshwater autotrophs.Key words: Biomass, growth rate, model, N : P ratio, plant allometry, plant mass, stoichiometry     

A hypothesis on the evolution of isoprenoid emission by oaks based on the correlation between emission type and Quercus taxonomy

We show that Mediterranean oaks that emit isoprene, monoterpenes or no isoprenoids belong to different subgenera as indicated by morpho-taxonomy and molecular genetics. On the other hand, oaks from North America and Asia that are taxonomically similar to the Mediterranean monoterpene emitter Q. ilex emit isoprene only. We surmise that isoprene emission is a genetic character which evolved ancestrally in the oak genus since this is the prevalent emission type in oaks widespread around the world and adapted to different environments. This ancestral character may have been either lost or modified in more recent clades such as those originating the Mediterranean oaks. If our hypothesis is correct then the taxonomy of European oaks is validated by this independent trait. Isoprenoid emission could serve as a chemo-taxonomical marker and could be used to reconstruct the phylogeny of oaks in association with molecular markers. Received: 30 November 1997  / Accepted: 28 February 1998     

The effects of morphine on the relationship between fetal EEG, breathing and blood pressure signals using fast wavelet transform

In this study, we introduce the fast wavelet transform (WT) as a method for investigating the effects of morphine on the electroencephalogram (EEG), respiratory activity and blood pressure in fetal lambs. Morphine was infused intravenously at 25 mg/h. The EEG, respiratory activity and blood pressure signals were analyzed using WT. We performed wavelet decomposition for five sets of parameters D _2j where -1 < j 5. The five series WTs represent the detail signal bandwidths: 1, 16–32 Hz; 2, 8–16 Hz; 3, 4–8 Hz; 4, 2–4 Hz; 5, 1–2 Hz. Before injection of the high-dose morphine, power in the EEG was high in all six frequency bandwidths. The respiratory and blood pressure signals showed common frequency components with respect to time and were coincident with the low-voltage fast activity (LVFA) EEG signal. Respiratory activity was observed during only some of the LVFA periods, and was completely absent during high-voltage slow activity (HVSA) EEG. The respiratory signal showed dominant power in the fourth wavelet band, and less power in the third and fifth bands. The blood pressure signal was also characterized by dominant power in the fourth wavelet band. This power was significantly increased during periods of respiratory activity. There was a strong relationship between fetal EEG, blood pressure and breathing movements. However, the injection of high-dose morphine resulted in a disruption of the normal cyclic pattern between the two EEG states and a significant increase in power in the first wavelet band. In addition, the high-dose drug resulted in a significant increase in the power of respiratory signal in the fourth and fifth wavelet bands, while power was reduced in the third wavelet band. Breathing activity was also continuous after the drug. The high-dose morphine also caused a temporary power shift from the third wavelet band to the fourth wavelet band for the 30-min period after injection of drug. Finally, high-dose morphine completely destroyed the correlation between EEG, breathing and blood pressure signals.     

Investigating the relationship between proteomic,compositional, and histologic biomarkers and cartilage biomechanics using artificial neural networks

A thorough understanding of the relationship between the biological and mechanical functions of articular cartilage is necessary to develop diagnostics and treatments for arthritic diseases. A key step in developing this understanding is the establishment of models which utilize large numbers of biomarkers to create comprehensive models of the interplay between cartilage biology and biomechanics, which will more accurately demonstrate the complex etiology and progression of tissue adaptation and degradation. It is the goal of this study to demonstrate the ability of artificial neural networks (ANNs) to utilize biomarkers to create predictive models of articular cartilage biomechanics, which will provide a basis for more sophisticated research in the future. Osteochondral plugs were collected from patients undergoing total knee arthroplasty, cultured, then analyzed to collect proteomic, compositional, and histologic biomarker data. Samples were subjected to stress relaxation testing as well as computational simulations using finite element analysis (FEA) modeling and optimization to determine key mechanical properties. The acquired data was fed into an ANN to generate a model which predicts the biomechanical properties of cartilage from given biomarkers. Using all significant inputs, the developed neural network predicted the ground substance modulus with a moderate degree of accuracy, but had difficulty predicting the collagen fiber modulus and cartilage permeability. Using only clinically attainable biomarkers, the best-performing model produced comparably accurate and more consistent predictions of all three mechanical properties. These models demonstrate the potential for ANNs to be included in clinical studies of articular cartilage.