Improved detection of amnestic MCI by means of discriminative vector quantization of single-trial cognitive ERP responses

Cognitive event-related potentials (ERPs) are widely employed in the study of dementive disorders. The morphology of averaged response is known to be under the influence of neurodegenerative processes and exploited for diagnostic purposes. This work is built over the idea that there is additional information in the dynamics of single-trial responses. We introduce a novel way to detect mild cognitive impairment (MCI) from the recordings of auditory ERP responses. Using single trial responses from a cohort of 25 amnestic MCI patients and a group of age-matched controls, we suggest a descriptor capable of encapsulating single-trial (ST) response dynamics for the benefit of early diagnosis. A customized vector quantization (VQ) scheme is first employed to summarize the overall set of ST-responses by means of a small-sized codebook of brain waves that is semantically organized. Each ST-response is then treated as a trajectory that can be encoded as a sequence of code vectors. A subject's set of responses is consequently represented as a histogram of activated code vectors. Discriminating MCI patients from healthy controls is based on the deduced response profiles and carried out by means of a standard machine learning procedure. The novel response representation was found to improve significantly MCI detection with respect to the standard alternative representation obtained via ensemble averaging (13% in terms of sensitivity and 6% in terms of specificity). Hence, the role of cognitive ERPs as biomarker for MCI can be enhanced by adopting the delicate description of our VQ scheme.

Assessing the impact of multiple stressors on aquatic biota: the receptor's side matters.

Aquatic ecosystems are confronted with multiple stress factors. Current approaches to assess the risk of anthropogenic stressors to aquatic ecosystems are developed for single stressors and determine stressor effects primarily as a function of stressor properties. The cumulative impact of several stressors, however, may differ markedly from the impact of the single stressors and can result in nonlinear effects and ecological surprises. To meet the challenge of diagnosing and predicting multiple stressor impacts, assessment strategies should focus on properties of the biological receptors rather than on stressor properties. This change of paradigm is required because (i) multiple stressors affect multiple biological targets at multiple organizational levels, (ii) biological receptors differ in their sensitivities, vulnerabilities, and response dynamics to the individual stressors, and (iii) biological receptors function as networks, so that actions of stressors at disparate sites within the network can lead via indirect or cascading effects, to unexpected outcomes.

Frequency modulation of ERK activation dynamics rewires cell fate.

Transient versus sustained ERK MAP kinase (MAPK) activation dynamics induce proliferation versus differentiation in response to epidermal (EGF) or nerve (NGF) growth factors in PC-12 cells. Duration of ERK activation has therefore been proposed to specify cell fate decisions. Using a biosensor to measure ERK activation dynamics in single living cells reveals that sustained EGF/NGF application leads to a heterogeneous mix of transient and sustained ERK activation dynamics in distinct cells of the population, different than the population average. EGF biases toward transient, while NGF biases toward sustained ERK activation responses. In contrast, pulsed growth factor application can repeatedly and homogeneously trigger ERK activity transients across the cell population. These datasets enable mathematical modeling to reveal salient features inherent to the MAPK network. Ultimately, this predicts pulsed growth factor stimulation regimes that can bypass the typical feedback activation to rewire the system toward cell differentiation irrespective of growth factor identity.

Peri-ictal correlation dynamics of high-frequency (80-200 Hz) intracranial EEG

To assess (1) how large-scale correlation of intracranial EEG signals in the high-frequency range (80-200Hz) evolves from the pre-ictal, through the ictal into the postictal state and (2) whether the contribution of local neuronal activity to large-scale EEG correlation differentiates epileptogenic from non-epileptogenic brain tissue.

Recruitment dynamics of the grove-dominant tree Microberlinia bisulcata in African rain forest: extending the light response versus adult longevity trade-off concept

In groves of ectomycorrhizal caesalpiniaceous species in the Atlantic coastal forest of Central Africa the dominant tree Microberlinia bisulcata, which is shade-intolerant as a seedling but highly light-responding as a sapling, shows very limited regeneration. M. bisulcata saplings were mapped in an 82.5-ha plot at Korup and found to be located significantly far (>40 m) away from adults, a result confirmed by direct testing in a second 56-ha plot. Sapling growth over 6 years, the distribution of newly emerging seedlings around adults, recruitment of saplings in a large opening and the outward extent of seedlings at the grove edge were also investigated. Two processes appear to have been operating: (1) a very strong and consistent restriction of the very numerous seedlings establishing after masting close to adults, and (2) a strong but highly spatially variable promotion of distant survivors by increased light from the deaths of large trees of species other than M. bisulcata (which itself has very low mortality rate). This leads to an apparent escape-from-adults effect. To maintain saplings in the shade between multiple short periods of release ectomycorrhizal connections to other co-occurring caesalp species may enable a rachet-type mechanism. The recorded sapling dynamics currently contribute an essential part of the long-term cycling of the groves. M. bisulcata is an interesting example of an important group of tropical trees, particularly in Africa, which are both highly light-demanding when young yet capable also of forming very large forest emergents. To more comprehensively explain tropical tree responses, the case is made for adding a new dimension to the trade-off concept of early tree light-response versus adult longevity.

Uncovering the dynamics of cardiac systems using stochastic pacing and frequency domain analyses

Alternans of cardiac action potential duration (APD) is a well-known arrhythmogenic mechanism which results from dynamical instabilities. The propensity to alternans is classically investigated by examining APD restitution and by deriving APD restitution slopes as predictive markers. However, experiments have shown that such markers are not always accurate for the prediction of alternans. Using a mathematical ventricular cell model known to exhibit unstable dynamics of both membrane potential and Ca2+ cycling, we demonstrate that an accurate marker can be obtained by pacing at cycle lengths (CLs) varying randomly around a basic CL (BCL) and by evaluating the transfer function between the time series of CLs and APDs using an autoregressive-moving-average (ARMA) model. The first pole of this transfer function corresponds to the eigenvalue (λalt) of the dominant eigenmode of the cardiac system, which predicts that alternans occurs when λalt≤−1. For different BCLs, control values of λalt were obtained using eigenmode analysis and compared to the first pole of the transfer function estimated using ARMA model fitting in simulations of random pacing protocols. In all versions of the cell model, this pole provided an accurate estimation of λalt. Furthermore, during slow ramp decreases of BCL or simulated drug application, this approach predicted the onset of alternans by extrapolating the time course of the estimated λalt. In conclusion, stochastic pacing and ARMA model identification represents a novel approach to predict alternans without making any assumptions about its ionic mechanisms. It should therefore be applicable experimentally for any type of myocardial cell.

UPLC-ESI-TOFMS-based metabolomics and gene expression dynamics inspector self-organizing metabolomic maps as tools for understanding the cellular response to ionizing radiation

Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed dose- and time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.

Species mobility and frequency dynamics in a Swiss limestone grassland

Community dynamics in a calcareous grassland (Mesobrometum) in Egerkingen (Jura mountains, Switzerland) were investigated for 53 non-woody species in 25 1-m2 plots over 6 years. 50 0.0 1-m2 subplots per plot were recorded. The derived variables were spatial frequency, temporal frequency, frequency fluctuation, turnover, and cumulative frequency (each species), and cumulative species richness (all species). Spectra for 53 species of all variables were different for the two investigated spatial scales (0.0 1 m2, 1 m2). The comparison with other investigations of similar grass lands showed that the behaviour of some species is specific for this type of vegetation in general (e.g. Achillea millefolium, Arrhenatherum elatius, Bromus erectus ), but most species behaved in a stand-specific way, i.e. they may play another (similar or completely different) role in another grassland stand. Six spatio-temporal patterns were defined across species. To understand community dynamics, not only the dynamics of mobility but also of frequency fluctuations and spatial distribution of the species are fundamental. In addition, the understanding of temporal behaviour of all species present should be included. Averages always hide important information of vegetation dynamics, as was shown by the present investigation.