Optical probing of sodium dynamics in neurons and astrocytes.

Changes in intracellular Na(+) concentration underlie essential neurobiological processes, but few reliable tools exist for their measurement. Here we characterize a new synthetic Na(+)-sensitive fluorescent dye, Asante Natrium Green (ANG), with unique properties. This indicator was excitable in the visible spectrum and by two-photon illumination, suffered little photobleaching and located to the cytosol were it remained for long durations without noticeable unwanted effects on basic cell properties. When used in brain tissue, ANG yielded a bright fluorescent signal during physiological Na(+) responses both in neurons and astrocytes. Synchronous electrophysiological and fluorometric recordings showed that ANG produced accurate Na(+) measurement in situ. This new Na(+) indicator opens innovative ways of probing neuronal circuits.

Simulation of interface dynamics across pore discontinuities

We study the dynamics of a water-oil meniscus moving from a smaller to a larger pore. The process is characterised by an abrupt change in the configuration, yielding a sudden energy release. A theoretic study for static conditions provides analytical solutions of the surface energy content of the system. Although the configuration after the sudden energy release is energetically more convenient, an energy barrier must be overcome before the process can happen spontaneously. The energy barrier depends on the system geometry and on the flow parameters. The analytical results are compared to numerical simulations that solve the full Navier-Stokes equation in the pore space and employ the Volume Of Fluid (VOF) method to track the evolution of the interface. First, the numerical simulations of a quasi-static process are validated by comparison with the analytical solutions for a static meniscus, then numerical simulations with varying injection velocity are used to investigate dynamic effects on the configuration change. During the sudden energy jump the system exhibits an oscillatory behaviour. Extension to more complex geometries might elucidate the mechanisms leading to a dynamic capillary pressure and to bifurcations in final distributions of fluid phases in porous

Diagnostic Tools for Identifying Sleepy Drivers in the Field, RB21-012, 2013

The overarching goal of this project was to identify and evaluate cognitive and behavioral indices that are sensitive to sleep deprivation and may help identify commercial motor vehicle drivers (CMV) who are at-risk for driving in a sleep deprived state and may prove useful in field tests administered by officers. To that end, we evaluated indices of driver physiognomy (e.g., yawning, droopy eyelids, etc.) and driver behavioral/cognitive state (e.g. distracted driving) and the sensitivity of these indices to objective measures of sleep deprivation. The measures of sleep deprivation were sampled on repeated occasions over a period of 3.5-months in each of 44 drivers diagnosed with Obstructive Sleep Apnea (OSA) and 22 controls (matched for gender, age within 5 years, education within 2 years, and county of residence for rural vs. urban driving). Comprehensive analyses showed that specific dimensions of driver physiognomy associated with sleepiness in previous research and face-valid composite scores of sleepiness did not: 1) distinguish participants with OSA from matched controls; 2) distinguish participants before and after PAP treatment including those who were compliant with their treatment; 3) predict levels of sleep deprivation acquired objectively from actigraphy watches, not even among those chronically sleep deprived. Those findings are consistent with large individual differences in driver physiognomy. In other words, when individuals were sleep deprived as confirmed by actigraphy watch output they did not show consistently reliable behavioral markers of being sleep deprived. This finding held whether each driver was compared to him/herself with adequate and inadequate sleep, and even among chronically sleep deprived drivers. The scientific evidence from this research study does not support the use of driver physiognomy as a valid measure of sleep deprivation or as a basis to judge whether a CMV driver is too fatigued to drive, as on the current Fatigued Driving Evaluation Checklist.. Fair and accurate determinations of CMV driver sleepiness in the field will likely require further research on alternative strategies that make use of a combination of information sources besides driver physiognomy, including work logs, actigraphy, in vehicle data recordings, GPS data on vehicle use, and performance tests.

Integration of local-scale hydrological and regional-scale geophysical based on a nonlinear Bayesian sequential simulation approach

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the regional scale represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed a downscaling procedure based on a non-linear Bayesian sequential simulation approach. The basic objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity, which is available throughout the model space. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariate kernel density function. This method is then applied to the stochastic integration of low-resolution, re- gional-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities. Finally, the overall viability of this downscaling approach is tested and verified by performing and comparing flow and transport simulation through the original and the downscaled hydraulic conductivity fields. Our results indicate that the proposed procedure does indeed allow for obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Analysis and spatial modeling of the indoor radon Swiss data

The present research deals with an important public health threat, which is the pollution created by radon gas accumulation inside dwellings. The spatial modeling of indoor radon in Switzerland is particularly complex and challenging because of many influencing factors that should be taken into account. Indoor radon data analysis must be addressed from both a statistical and a spatial point of view. As a multivariate process, it was important at first to define the influence of each factor. In particular, it was important to define the influence of geology as being closely associated to indoor radon. This association was indeed observed for the Swiss data but not probed to be the sole determinant for the spatial modeling. The statistical analysis of data, both at univariate and multivariate level, was followed by an exploratory spatial analysis. Many tools proposed in the literature were tested and adapted, including fractality, declustering and moving windows methods. The use of Quan-tité Morisita Index (QMI) as a procedure to evaluate data clustering in function of the radon level was proposed. The existing methods of declustering were revised and applied in an attempt to approach the global histogram parameters. The exploratory phase comes along with the definition of multiple scales of interest for indoor radon mapping in Switzerland. The analysis was done with a top-to-down resolution approach, from regional to local lev¬els in order to find the appropriate scales for modeling. In this sense, data partition was optimized in order to cope with stationary conditions of geostatistical models. Common methods of spatial modeling such as Κ Nearest Neighbors (KNN), variography and General Regression Neural Networks (GRNN) were proposed as exploratory tools. In the following section, different spatial interpolation methods were applied for a par-ticular dataset. A bottom to top method complexity approach was adopted and the results were analyzed together in order to find common definitions of continuity and neighborhood parameters. Additionally, a data filter based on cross-validation was tested with the purpose of reducing noise at local scale (the CVMF). At the end of the chapter, a series of test for data consistency and methods robustness were performed. This lead to conclude about the importance of data splitting and the limitation of generalization methods for reproducing statistical distributions. The last section was dedicated to modeling methods with probabilistic interpretations. Data transformation and simulations thus allowed the use of multigaussian models and helped take the indoor radon pollution data uncertainty into consideration. The catego-rization transform was presented as a solution for extreme values modeling through clas-sification. Simulation scenarios were proposed, including an alternative proposal for the reproduction of the global histogram based on the sampling domain. The sequential Gaussian simulation (SGS) was presented as the method giving the most complete information, while classification performed in a more robust way. An error measure was defined in relation to the decision function for data classification hardening. Within the classification methods, probabilistic neural networks (PNN) show to be better adapted for modeling of high threshold categorization and for automation. Support vector machines (SVM) on the contrary performed well under balanced category conditions. In general, it was concluded that a particular prediction or estimation method is not better under all conditions of scale and neighborhood definitions. Simulations should be the basis, while other methods can provide complementary information to accomplish an efficient indoor radon decision making.

Experimental analysis and computer prediction of CTF/NFI transcription factor DNA binding sites.

Accurate prediction of transcription factor binding sites is needed to unravel the function and regulation of genes discovered in genome sequencing projects. To evaluate current computer prediction tools, we have begun a systematic study of the sequence-specific DNA-binding of a transcription factor belonging to the CTF/NFI family. Using a systematic collection of rationally designed oligonucleotides combined with an in vitro DNA binding assay, we found that the sequence specificity of this protein cannot be represented by a simple consensus sequence or weight matrix. For instance, CTF/NFI uses a flexible DNA binding mode that allows for variations of the binding site length. From the experimental data, we derived a novel prediction method using a generalised profile as a binding site predictor. Experimental evaluation of the generalised profile indicated that it accurately predicts the binding affinity of the transcription factor to natural or synthetic DNA sequences. Furthermore, the in vitro measured binding affinities of a subset of oligonucleotides were found to correlate with their transcriptional activities in transfected cells. The combined computational-experimental approach exemplified in this work thus resulted in an accurate prediction method for CTF/NFI binding sites potentially functioning as regulatory regions in vivo.

MMPBSA decomposition of the binding energy throughout a molecular dynamics simulation of Amyloid-Beta (Aß10-35) aggregation

Recent experiments with amyloid-beta (Aß) peptides indicate that the formation of toxic oligomers may be an important contribution to the onset of Alzheimer's disease. The toxicity of Aß oligomers depend on their structure, which is governed by assembly dynamics. However, a detailed knowledge of the structure of at the atomic level has not been achieved yet due to limitations of current experimental techniques. In this study, replica exchange molecular dynamics simulations are used to identify the expected diversity of dimer conformations of Aß10-35 monomers. The most representative dimer conformation has been used to track the dimer formation process between both monomers. The process has been characterized by means of the evolution of the decomposition of the binding free energy, which provides an energetic profile of the interaction. Dimers undergo a process of reorganization driven basically by inter-chain hydrophobic and hydrophilic interactions and also solvation/desolvation processes.