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.

Challenges to the implementation of behavioural survellance for HIV/STI in Europe

Background: Well-conducted behavioural surveillance (BS) is essential for policy planning and evaluation. Data should be comparable across countries. In 2008, the European Centre for Disease Prevention and Control (ECDC) began a programme to support Member States in the implementation of BS for Second Generation Surveillance. Methods: Data from a mapping exercise on current BS activities in EU/EFTA countries led to recommendations for establishing national BS systems and international coordination, and the definition of a set of core and transversal (UNGASS-Dublin compatible) indicators for BS in the general and eight specific populations. A toolkit for establishing BS has been developed and a BS needs-assessment survey has been launched in 30 countries. Tools for BS self-assessment and planning are currently being tested during interactive workshops with country representatives. Results: The mapping exercise revealed extreme diversity between countries. Around half had established a BS system, but this did not always correspond to the epidemiological situation. Challenges to implementation and harmonisation at all levels emerged from survey findings and workshop feedback. These include: absence of synergy between biological and behavioural surveillance and of actors having an overall view of all system elements; lack of awareness of the relevance of BS and of coordination between agencies; insufficient use of available data; financial constraints; poor sustainability, data quality and access to certain key populations; unfavourable legislative environments. Conclusions: There is widespread need in the region not only for technical support but also for BS advocacy: BS remains the neglected partner of second generation surveillance and requires increased political support and capacity-building in order to become effective. Dissemination of validated tools for BS, developed in interaction with country experts, proves feasible and acceptable.

Effect of interleukin-6 receptor inhibition with tocilizumab in patients with rheumatoid arthritis (OPTION study): a double-blind, placebo-controlled, randomised trial.

BACKGROUND: Interleukin 6 is involved in the pathogenesis of rheumatoid arthritis via its broad effects on immune and inflammatory responses. Our aim was to assess the therapeutic effects of blocking interleukin 6 by inhibition of the interleukin-6 receptor with tocilizumab in patients with rheumatoid arthritis. METHODS: In this double-blind, randomised, placebo-controlled, parallel group phase III study, 623 patients with moderate to severe active rheumatoid arthritis were randomly assigned with an interactive voice response system, stratified by site with a randomisation list provided by the study sponsor, to receive tocilizumab 8 mg/kg (n=205), tocilizumab 4 mg/kg (214), or placebo (204) intravenously every 4 weeks, with methotrexate at stable pre-study doses (10-25 mg/week). Rescue therapy with tocilizumab 8 mg/kg was offered at week 16 to patients with less than 20% improvement in both swollen and tender joint counts. The primary endpoint was the proportion of patients with 20% improvement in signs and symptoms of rheumatoid arthritis according to American College of Rheumatology criteria (ACR20 response) at week 24. Analyses were by intention to treat. This trial is registered with ClinicalTrials.gov, number NCT00106548. FINDINGS: The intention-to-treat analysis population consisted of 622 patients: one patient in the 4 mg/kg group did not receive study treatment and was thus excluded. At 24 weeks, ACR20 responses were seen in more patients receiving tocilizumab than in those receiving placebo (120 [59%] patients in the 8 mg/kg group, 102 [48%] in the 4 mg/kg group, 54 [26%] in the placebo group; odds ratio 4.0 [95% CI 2.6-6.1], p<0.0001 for 8 mg/kg vs placebo; and 2.6 [1.7-3.9], p<0.0001 for 4 mg/kg vs placebo). More people receiving tocilizumab than those receiving placebo had at least one adverse event (143 [69%] in the 8 mg/kg group; 151 [71%] in the 4 mg/kg group; 129 [63%] in the placebo group). The most common serious adverse events were serious infections or infestations, reported by six patients in the 8 mg/kg group, three in the 4 mg/kg group, and two in the placebo group. INTERPRETATION: Tocilizumab could be an effective therapeutic approach in patients with moderate to severe active rheumatoid arthritis. FUNDING: F Hoffmann-La Roche, Chugai Pharmaceutical.

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.

Interactive readings of children's literature in day care: Microgenetic analysis of the semiopictorial construction.

In line with educational issues involved in emergent literacy practices in preschool, in particular those concerning comprehension processes, this paper focuses on picture-based narrative comprehension during an interactive reading session of a wordless picture book, involving a group of children aged three and their teacher. Children are asked to make inferences about the meaning and outcome of the story, a procedure which gradually elicits their responses on how events link together, thus enhancing their capacity to use prior and implicit knowledge to build the story meaning. Moreover, this study highlights the importance of interaction for developing comprehension. Data collected was analysed following didactic microgenesis, an analytical approach showing that knowledge built during interaction depends on the joint construction of a zone of common meaning by which teacher and children try to adjust to each other. In order to help the process of merging different meanings of the story built online, a text written by researchers, following the narrative structure of the story, was read by the teacher after the picture-based reading. This led us to examine through interactional analysis which semiotic cues were used during recall on the following day, as an additional measure of knowledge construction.

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).

The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes.

Advanced neuroinformatics tools are required for methods of connectome mapping, analysis, and visualization. The inherent multi-modality of connectome datasets poses new challenges for data organization, integration, and sharing. We have designed and implemented the Connectome Viewer Toolkit - a set of free and extensible open source neuroimaging tools written in Python. The key components of the toolkit are as follows: (1) The Connectome File Format is an XML-based container format to standardize multi-modal data integration and structured metadata annotation. (2) The Connectome File Format Library enables management and sharing of connectome files. (3) The Connectome Viewer is an integrated research and development environment for visualization and analysis of multi-modal connectome data. The Connectome Viewer's plugin architecture supports extensions with network analysis packages and an interactive scripting shell, to enable easy development and community contributions. Integration with tools from the scientific Python community allows the leveraging of numerous existing libraries for powerful connectome data mining, exploration, and comparison. We demonstrate the applicability of the Connectome Viewer Toolkit using Diffusion MRI datasets processed by the Connectome Mapper. The Connectome Viewer Toolkit is available from http://www.cmtk.org/

Comparing simulation methods for modelling the errors of stand inventory data

Abstract