Methods for Determining Frequency- and Region-Dependent Relationships Between Estimated LFPs and BOLD Responses in Humans

The relationship between electrophysiological and functional magnetic resonance imaging (fMRI) signals remains poorly understood. To date, studies have required invasive methods and have been limited to single functional regions and thus cannot account for possible variations across brain regions. Here we present a method that uses fMRI data and singe-trial electroencephalography (EEG) analyses to assess the spatial and spectral dependencies between the blood-oxygenation-level-dependent (BOLD) responses and the noninvasively estimated local field potentials (eLFPs) over a wide range of frequencies (0-256 Hz) throughout the entire brain volume. This method was applied in a study where human subjects completed separate fMRI and EEG sessions while performing a passive visual task. Intracranial LFPs were estimated from the scalp-recorded data using the ELECTRA source model. We compared statistical images from BOLD signals with statistical images of each frequency of the eLFPs. In agreement with previous studies in animals, we found a significant correspondence between LFP and BOLD statistical images in the gamma band (44-78 Hz) within primary visual cortices. In addition, significant correspondence was observed at low frequencies (<14 Hz) and also at very high frequencies (>100 Hz). Effects within extrastriate visual areas showed a different correspondence that not only included those frequency ranges observed in primary cortices but also additional frequencies. Results therefore suggest that the relationship between electrophysiological and hemodynamic signals thus might vary both as a function of frequency and anatomical region.

Applying machine learning methods to avalanche forecasting

Avalanche forecasting is a complex process involving the assimilation of multiple data sources to make predictions over varying spatial and temporal resolutions. Numerically assisted forecasting often uses nearest neighbour methods (NN), which are known to have limitations when dealing with high dimensional data. We apply Support Vector Machines to a dataset from Lochaber, Scotland to assess their applicability in avalanche forecasting. Support Vector Machines (SVMs) belong to a family of theoretically based techniques from machine learning and are designed to deal with high dimensional data. Initial experiments showed that SVMs gave results which were comparable with NN for categorical and probabilistic forecasts. Experiments utilising the ability of SVMs to deal with high dimensionality in producing a spatial forecast show promise, but require further work.

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.

Development of a Rational Characterization Method for Iowa Fly Ash, HR-286, 1988

This research project was conducted in an attempt to determine the cause of paste strength variability in Iowa fly ashes and to develop test methods to more adequately reflect fly ash physical and chemical characteristics. An extensive three year sampling and testing program was developed and initiated which incorporated fly ash from several Iowa power plants. Power plant design and operating data were collected. The variability was directly linked to power plant maintenance schedules and to sodium carbonate coal pretreatment. Fly ash physical and chemical properties can change drastically immediately before and after a maintenance outage. The concentrations of sulfate bearing minerals in the fly ash increases sharply during shutdown. Chemical, mineralogical, and physical testing indicated that the sodium, sulfate bearing minerals, lime and tricalcium aluminate contents of the fly ashes play important roles in the development of hydration reaction products in fly ash pastes. The weak pastes always contained ettringite as the major reaction product. The strong pastes contained straetlingite and monosulfoaluminate as the major reaction products along with minor amounts of ettringite. Recommendations for testing procedure changes and suggested interim test methods are presented.

Alternate Methods of Bridge Strengthening, HR-302, 1989

The need for upgrading a large number of understrength and obsolete bridges in the United States has been well documented in the literature. Through the performance of several Iowa DOT projects, the concept of strengthening bridges (simple and continuous spans) by post-tensioning has been developed. The purpose of this project was to investigate two additional strengthening alternatives that may be more efficient than post-tensioning in certain situations. The research program for each strengthening scheme included a literature review, laboratory testing of the strengthening scheme, and a finite-element analysis of the scheme. For clarity the two strengthening schemes are presented separately. In Part 1 of this report, the strengthening of existing steel stringers in composite steel beam concrete-deck bridges by providing partial end restraint was shown to be feasible. Part 2 of this report summarizes the research that was undertaken to strengthen the negative moment regions of continuous, composite bridges. Two schemes were investigated: post-compression of stringers and superimposed trusses within the stringers.

Implementing statistical learning methods through Bayesian networks. Part 1: a guide to Bayesian parameter estimation using forensic science data

As a thorough aggregation of probability and graph theory, Bayesian networks currently enjoy widespread interest as a means for studying factors that affect the coherent evaluation of scientific evidence in forensic science. Paper I of this series of papers intends to contribute to the discussion of Bayesian networks as a framework that is helpful for both illustrating and implementing statistical procedures that are commonly employed for the study of uncertainties (e.g. the estimation of unknown quantities). While the respective statistical procedures are widely described in literature, the primary aim of this paper is to offer an essentially non-technical introduction on how interested readers may use these analytical approaches - with the help of Bayesian networks - for processing their own forensic science data. Attention is mainly drawn to the structure and underlying rationale of a series of basic and context-independent network fragments that users may incorporate as building blocs while constructing larger inference models. As an example of how this may be done, the proposed concepts will be used in a second paper (Part II) for specifying graphical probability networks whose purpose is to assist forensic scientists in the evaluation of scientific evidence encountered in the context of forensic document examination (i.e. results of the analysis of black toners present on printed or copied documents).

Biomedical risk assessment as an aid for smoking cessation.

BACKGROUND: A possible strategy for increasing smoking cessation rates could be to provide smokers who have contact with healthcare systems with feedback on the biomedical or potential future effects of smoking, e.g. measurement of exhaled carbon monoxide (CO), lung function, or genetic susceptibility to lung cancer. OBJECTIVES: To determine the efficacy of biomedical risk assessment provided in addition to various levels of counselling, as a contributing aid to smoking cessation. SEARCH METHODS: For the most recent update, we searched the Cochrane Collaboration Tobacco Addiction Group Specialized Register in July 2012 for studies added since the last update in 2009. SELECTION CRITERIA: Inclusion criteria were: a randomized controlled trial design; subjects participating in smoking cessation interventions; interventions based on a biomedical test to increase motivation to quit; control groups receiving all other components of intervention; an outcome of smoking cessation rate at least six months after the start of the intervention. DATA COLLECTION AND ANALYSIS: Two assessors independently conducted data extraction on each paper, with disagreements resolved by consensus. Results were expressed as a relative risk (RR) for smoking cessation with 95% confidence intervals (CI). Where appropriate, a pooled effect was estimated using a Mantel-Haenszel fixed-effect method. MAIN RESULTS: We included 15 trials using a variety of biomedical tests. Two pairs of trials had sufficiently similar recruitment, setting and interventions to calculate a pooled effect; there was no evidence that carbon monoxide (CO) measurement in primary care (RR 1.06, 95% CI 0.85 to 1.32) or spirometry in primary care (RR 1.18, 95% CI 0.77 to 1.81) increased cessation rates. We did not pool the other 11 trials due to the presence of substantial clinical heterogeneity. Of the remaining 11 trials, two trials detected statistically significant benefits: one trial in primary care detected a significant benefit of lung age feedback after spirometry (RR 2.12, 95% CI 1.24 to 3.62) and one trial that used ultrasonography of carotid and femoral arteries and photographs of plaques detected a benefit (RR 2.77, 95% CI 1.04 to 7.41) but enrolled a population of light smokers and was judged to be at unclear risk of bias in two domains. Nine further trials did not detect significant effects. One of these tested CO feedback alone and CO combined with genetic susceptibility as two different interventions; none of the three possible comparisons detected significant effects. One trial used CO measurement, one used ultrasonography of carotid arteries and two tested for genetic markers. The four remaining trials used a combination of CO and spirometry feedback in different settings. AUTHORS' CONCLUSIONS: There is little evidence about the effects of most types of biomedical tests for risk assessment on smoking cessation. Of the fifteen included studies, only two detected a significant effect of the intervention. Spirometry combined with an interpretation of the results in terms of 'lung age' had a significant effect in a single good quality trial but the evidence is not optimal. A trial of carotid plaque screening using ultrasound also detected a significant effect, but a second larger study of a similar feedback mechanism did not detect evidence of an effect. Only two pairs of studies were similar enough in terms of recruitment, setting, and intervention to allow meta-analyses; neither of these found evidence of an effect. Mixed quality evidence does not support the hypothesis that other types of biomedical risk assessment increase smoking cessation in comparison to standard treatment. There is insufficient evidence with which to evaluate the hypothesis that multiple types of assessment are more effective than single forms of assessment.

Refinement of Rapid PCC Coarse Aggregate Testing Program, HR-1071, 1999

The characterization and categorization of coarse aggregates for use in portland cement concrete (PCC) pavements is a highly refined process at the Iowa Department of Transportation. Over the past 10 to 15 years, much effort has been directed at pursuing direct testing schemes to supplement or replace existing physical testing schemes. Direct testing refers to the process of directly measuring the chemical and mineralogical properties of an aggregate and then attempting to correlate those measured properties to historical performance information (i.e., field service record). This is in contrast to indirect measurement techniques, which generally attempt to extrapolate the performance of laboratory test specimens to expected field performance. The purpose of this research project was to investigate and refine the use of direct testing methods, such as X-ray analysis techniques and thermal analysis techniques, to categorize carbonate aggregates for use in portland cement concrete. The results of this study indicated that the general testing methods that are currently used to obtain data for estimating service life tend to be very reliable and have good to excellent repeatability. Several changes in the current techniques were recommended to enhance the long-term reliability of the carbonate database. These changes can be summarized as follows: (a) Limits that are more stringent need to be set on the maximum particle size in the samples subjected to testing. This should help to improve the reliability of all three of the test methods studied during this project. (b) X-ray diffraction testing needs to be refined to incorporate the use of an internal standard. This will help to minimize the influence of sample positioning errors and it will also allow for the calculation of the concentration of the various minerals present in the samples. (c) Thermal analysis data needs to be corrected for moisture content and clay content prior to calculating the carbonate content of the sample.

Comparing simulation methods for modelling the errors of stand inventory data

Abstract

Fatigue and Residual Stress Investigation of Composite Prestressed Steel Beams, HR-74, 1963

Two composite, prestressed, steel beams, fabricated by slightly different methods, were fatigue tested to destruction. Stresses and deflections were measured at regular intervals, and the behavior of each beam as failure progressed was recorded. Residual stresses were then evaluated by testing segments of each beam. An attempt was made to assess the effects of the residual stresses on fatigue strength.