Understanding the effects of anesthetic agents on the EEG through neural field theory

Anesthetic and analgesic agents act through a diverse range of pharmacological mechanisms. Existing empirical data clearly shows that such "microscopic" pharmacological diversity is reflected in their "macroscopic" effects on the human electroencephalogram (EEG). Based on a detailed mesoscopic neural field model we theoretically posit that anesthetic induced EEG activity is due to selective parametric changes in synaptic efficacy and dynamics. Specifically, on the basis of physiologically constrained modeling, it is speculated that the selective modification of inhibitory or excitatory synaptic activity may differentially effect the EEG spectrum. Such results emphasize the importance of neural field theories of brain electrical activity for elucidating the principles whereby pharmacological agents effect the EEG. Such insights will contribute to improved methods for monitoring depth of anesthesia using the EEG.

Understanding the transition to seizure by modeling the epileptiform activity of general anesthetic agents

A central difficulty in modeling epileptogenesis using biologically plausible computational and mathematical models is not the production of activity characteristic of a seizure, but rather producing it in response to specific and quantifiable physiologic change or pathologic abnormality. This is particularly problematic when it is considered that the pathophysiological genesis of most epilepsies is largely unknown. However, several volatile general anesthetic agents, whose principle targets of action are quantifiably well characterized, are also known to be proconvulsant. The authors describe recent approaches to theoretically describing the electroencephalographic effects of volatile general anesthetic agents that may be able to provide important insights into the physiologic mechanisms that underpin seizure initiation.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Mechanism for synchronized motion between two humans in mutual tapping experiments: Transition from alternative mode to synchronization mode

We performed mutual tapping experiments between two humans to investigate the conditions required for synchronized motion. A transition from an alternative mode to a synchronization mode was discovered under the same conditions when a subject changed from a reactive mode to an anticipation mode in single tapping experiments. Experimental results suggest that the cycle time for each tapping motion is tuned by a proportional control that is based on synchronization errors and cycle time errors. As the tapping frequency increases, the mathematical model based on the feedback control in the sensory-motor closed loop predicts a discrete mode transition as the gain factors of the proportional control decease. The conditions of the synchronization were shown as a consequence of the coupled dynamics based on the subsequent feedback loop in the sensory-motor system.

An alternative approach to investigating lead-lag relationships between stock and stock index futures markets

In the absence of market frictions, the cost-of-carry model of stock index futures pricing predicts that returns on the underlying stock index and the associated stock index futures contract will be perfectly contemporaneously correlated. Evidence suggests, however, that this prediction is violated with clear evidence that the stock index futures market leads the stock market. It is argued that traditional tests, which assume that the underlying data generating process is constant, might be prone to overstate the lead-lag relationship. Using a new test for lead-lag relationships based on cross correlations and cross bicorrelations it is found that, contrary to results from using the traditional methodology, periods where the futures market leads the cash market are few and far between and when any lead-lag relationship is detected, it does not last long. Overall, the results are consistent with the prediction of the standard cost-of-carry model and market efficiency.

Portmanteau model diagnostics and tests for nonlinearity: a comparative Monte Carlo study of two alternative methods

This paper employs an extensive Monte Carlo study to test the size and power of the BDS and close return methods of testing for departures from independent and identical distribution. It is found that the finite sample properties of the BDS test are far superior and that the close return method cannot be recommended as a model diagnostic. Neither test can be reliably used for very small samples, while the close return test has low power even at large sample sizes

Sea surface temperature climate change initiative: alternative image classification algorithms for sea-ice affected oceans

We present a Bayesian image classification scheme for discriminating cloud, clear and sea-ice observations at high latitudes to improve identification of areas of clear-sky over ice-free ocean for SST retrieval. We validate the image classification against a manually classified dataset using Advanced Along Track Scanning Radiometer (AATSR) data. A three way classification scheme using a near-infrared textural feature improves classifier accuracy by 9.9 % over the nadir only version of the cloud clearing used in the ATSR Reprocessing for Climate (ARC) project in high latitude regions. The three way classification gives similar numbers of cloud and ice scenes misclassified as clear but significantly more clear-sky cases are correctly identified (89.9 % compared with 65 % for ARC). We also demonstrate the poetential of a Bayesian image classifier including information from the 0.6 micron channel to be used in sea-ice extent and ice surface temperature retrieval with 77.7 % of ice scenes correctly identified and an overall classifier accuracy of 96 %.

Using an individual-based model to select among alternative foraging strategies of woodpigeons: data support a memory-based model with a flocking mechanism

Population modelling is increasingly recognised as a useful tool for pesticide risk assessment. For vertebrates that may ingest pesticides with their food, such as woodpigeon (Columba palumbus), population models that simulate foraging behaviour explicitly can help predicting both exposure and population-level impact. Optimal foraging theory is often assumed to explain the individual-level decisions driving distributions of individuals in the field, but it may not adequately predict spatial and temporal characteristics of woodpigeon foraging because of the woodpigeons’ excellent memory, ability to fly long distances, and distinctive flocking behaviour. Here we present an individual-based model (IBM) of the woodpigeon. We used the model to predict distributions of foraging woodpigeons that use one of six alternative foraging strategies: optimal foraging, memory-based foraging and random foraging, each with or without flocking mechanisms. We used pattern-oriented modelling to determine which of the foraging strategies is best able to reproduce observed data patterns. Data used for model evaluation were gathered during a long-term woodpigeon study conducted between 1961 and 2004 and a radiotracking study conducted in 2003 and 2004, both in the UK, and are summarised here as three complex patterns: the distributions of foraging birds between vegetation types during the year, the number of fields visited daily by individuals, and the proportion of fields revisited by them on subsequent days. The model with a memory-based foraging strategy and a flocking mechanism was the only one to reproduce these three data patterns, and the optimal foraging model produced poor matches to all of them. The random foraging strategy reproduced two of the three patterns but was not able to guarantee population persistence. We conclude that with the memory-based foraging strategy including a flocking mechanism our model is realistic enough to estimate the potential exposure of woodpigeons to pesticides. We discuss how exposure can be linked to our model, and how the model could be used for risk assessment of pesticides, for example predicting exposure and effects in heterogeneous landscapes planted seasonally with a variety of crops, while accounting for differences in land use between landscapes.

Alternative generation of CNS neural stem cells and PNS derivatives from neural crest-derived peripheral stem cells

Neural crest-derived stem cells (NCSCs) from the embryonic peripheral nervous system (PNS) can be reprogrammed in neurosphere (NS) culture to rNCSCs that produce central nervous system (CNS) progeny, including myelinating oligodendrocytes. Using global gene expression analysis we now demonstrate that rNCSCs completely lose their previous PNS characteristics and acquire the identity of neural stem cells derived from embryonic spinal cord. Reprogramming proceeds rapidly and results in a homogenous population of Olig2-, Sox3-, and Lex-positive CNS stem cells. Low-level expression of pluripotency inducing genes Oct4, Nanog, and Klf4 argues against a transient pluripotent state during reprogramming. The acquisition of CNS properties is prevented in the presence of BMP4 (BMP NCSCs) as shown by marker gene expression and the potential to produce PNS neurons and glia. In addition, genes characteristic for mesenchymal and perivascular progenitors are expressed, which suggests that BMP NCSCs are directed toward a pericyte progenitor/mesenchymal stem cell (MSC) fate. Adult NCSCs from mouse palate, an easily accessible source of adult NCSCs, display strikingly similar properties. They do not generate cells with CNS characteristics but lose the neural crest markers Sox10 and p75 and produce MSC-like cells. These findings show that embryonic NCSCs acquire a full CNS identity in NS culture. In contrast, MSC-like cells are generated from BMP NCSCs and pNCSCs, which reveals that postmigratory NCSCs are a source for MSC-like cells up to the adult stage.

Molecular and phenotypic characterization of the alternative seasonal growth habit and flowering time in barley (Hordeum vulgare ssp. vulgare L.)

Barley can be classified into three major agronomic types, based on its seasonal growth habit (SGH): spring, winter and alternative. Winter varieties require exposure to vernalization to promote subsequent flowering and are autumn-sown. Spring varieties proceed to flowering in the absence of vernalization and are sown in the spring. The ‘alternative’ (also known as ‘facultative’) SGH is only loosely defined and can be sown in autumn or spring. Here, we investigate the molecular genetic basis of alternative barley. Analysis of the major barley vernalization (VRN-H1, VRN-H2) and photoperiod (PPD-H1, PPD-H2) response genes in a collection of 386 varieties found alternative SGH to be characterized by specific allelic combinations. Spring varieties possessed spring loci at one or both of the vernalization response loci, combined with long-day non-responsive ppd-H1 alleles and wild-type alleles at the short-day photoperiod response locus, PPD-H2. Winter varieties possessed winter alleles at both vernalization loci, in combination with the mutant ppd-H2 allele conferring delayed flowering under short-day photoperiods. In contrast, all alternative varieties investigated possessed a single spring allele (either at VRN-H1 or at VRN-H2) combined with mutant ppd-H2 alleles. This allelic combination is found only in alternative types and is diagnostic for alternative SGH in the collection studied. Analysis of flowering time under controlled environment found alternative varieties flowered later than spring control lines, with the difference most pronounced under short-day photoperiods. This work provides genetic characterization of the alternative SGH phenotype, allowing precise manipulation of SGH and flowering time within breeding programmes, and provides the molecular tools for classification of all three SGH categories within national variety registration processes.