Footbridge system identification using wireless inertial measurement units for force and response measurements

With the main focus on safety, design of structures for vibration serviceability is often overlooked or mismanaged, resulting in some high profile structures failing publicly to perform adequately under human dynamic loading due to walking, running or jumping. A standard tool to inform better design, prove fitness for purpose before entering service and design retrofits is modal testing, a procedure that typically involves acceleration measurements using an array of wired sensors and force generation using a mechanical shaker. A critical but often overlooked aspect is using input (force) to output (response) relationships to enable estimation of modal mass, which is a key parameter directly controlling vibration levels in service.

This paper describes the use of wireless inertial measurement units (IMUs), designed for biomechanics motion capture applications, for the modal testing of a 109 m footbridge. IMUs were first used for an output-only vibration survey to identify mode frequencies, shapes and damping ratios, then for simultaneous measurement of body accelerations of a human subject jumping to excite specific vibrations modes and build up bridge deck accelerations at the jumping location. Using the mode shapes and the vertical acceleration data from a suitable body landmark scaled by body mass, thus providing jumping force data, it was possible to create frequency response functions and estimate modal masses.

The modal mass estimates for this bridge were checked against estimates obtained using an instrumented hammer and known mass distributions, showing consistency among the experimental estimates. Finally, the method was used in an applied research application on a short span footbridge where the benefits of logistical and operational simplicity afforded by the highly portable and easy to use IMUs proved extremely useful for an efficient evaluation of vibration serviceability, including estimation of modal masses.

Protein expression profiling during chick retinal maturation: a proteomics-based approach

Background: The underlying pathways that drive retinal neurogenesis and synaptogenesis are still relatively poorly understood. Protein expression analysis can provide direct insight into these complex developmental processes. The aim of this study was therefore to employ proteomic analysis to study the developing chick retina throughout embryonic (E) development commencing at day 12 through 13, 17, 19 and post-hatch (P) 1 and 33 days.

Results: 2D proteomic and mass spectrometric analysis detected an average of 1514 spots per gel with 15 spots demonstrating either modulation or constitutive expression identified via MS. Proteins identified included alpha and beta-tubulin, alpha enolase, B-creatine kinase, gamma-actin, platelet-activating factor (PAF), PREDICTED: similar to TGF-beta interacting protein 1, capping protein (actin filament muscle Z line), nucleophosmin 1 (NPM1), dimethylarginine dimethylaminohydrolase, triosphoaphate isomerase, DJ1, stathmin, fatty acid binding protein 7 (FABP7/B-FABP), beta-synuclein and enhancer of rudimentary homologue.

Conclusion: This study builds upon previous proteomic investigations of retinal development and represents the addition of a unique data set to those previously reported. Based on reported bioactivity some of the identified proteins are most likely to be important to normal retinal development in the chick. Continued analysis of the dynamic protein populations present at the early stages and throughout retinal development will increase our understanding of the molecular events underpinning retinogenesis.

Genome scan meta-analysis of schizophrenia and bipolar disorder, part II:Schizophrenia

Schizophrenia is a common disorder with high heritability and a 10-fold increase in risk to siblings of probands. Replication has been inconsistent for reports of significant genetic linkage. To assess evidence for linkage across studies, rank-based genome scan meta-analysis (GSMA) was applied to data from 20 schizophrenia genome scans. Each marker for each scan was assigned to 1 of 120 30-cM bins, with the bins ranked by linkage scores (1 = most significant) and the ranks averaged across studies (R(avg)) and then weighted for sample size (N(sqrt)[affected casess]). A permutation test was used to compute the probability of observing, by chance, each bin's average rank (P(AvgRnk)) or of observing it for a bin with the same place (first, second, etc.) in the order of average ranks in each permutation (P(ord)). The GSMA produced significant genomewide evidence for linkage on chromosome 2q (PAvgRnk