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.

A comparison of individual cow versus group concentrate allocation strategies on dry matter intake, milk production, tissue changes, and fertility of Holstein-Friesian cows offered a grass silage diet

A diverse range of concentrate allocation strategies are adopted on dairy farms. The objectives of this study were to examine the effects on cow performance [dry matter (DM) intake (DMI), milk yield and composition, body tissue changes, and fertility] of adopting 2 contrasting concentrate allocation strategies over the first 140 d of lactation. Seventy-seven Holstein-Friesian dairy cows were allocated to 1 of 2 concentrate allocation strategies at calving, namely group or individual cow. Cows on the group strategy were offered a mixed ration comprising grass silage and concentrates in a 50:50 ratio on a DM basis. Cows on the individual cow strategy were offered a basal mixed ration comprising grass silage and concentrates (the latter included in the mix to achieve a mean intake of 6 kg/cow per day), which was formulated to meet the cow’s energy requirements for maintenance plus 24 kg of milk/cow per day. Additional concentrates were offered via an out-of-parlor feeding system, with the amount offered adjusted weekly based on each individual cow’s milk yield during the previous week. In addition, all cows received a small quantity of straw in the mixed ration part of the diet (approximately 0.3 kg/cow per day), plus 0.5 kg of concentrate twice daily in the milking parlor. Mean concentrate intakes over the study period were similar with each of the 2 allocation strategies (11.5 and 11.7 kg of DM/cow per day for group and individual cow, respectively), although the pattern of intake with each treatment differed over time. Concentrate allocation strategy had no effect on either milk yield (39.3 and 38.0 kg/d for group and individual cow, respectively), milk composition, or milk constituent yield. The milk yield response curves with each treatment were largely aligned with the concentrate DMI curves. Cows on the individual cow treatment had a greater range of concentrate DMI and milk yields than those on the group treatment. With the exception of a tendency for cows on the individual cow treatment to lose more body weight to nadir than cows on the group treatment, concentrate allocation strategy had little effect on either body weight or body condition score over the experimental period. Cows on the individual cow treatment had a higher pregnancy rate to first and second service and tended to have a higher 100-d in calf rate than cows on the group treatment. This study demonstrates that concentrate allocation strategy had little effect on overall production performance.

Frequency of mutations and polymorphisms in borderline ovarian tumors of known cancer genes.

Borderline ovarian tumors represent an understudied subset of ovarian tumors. Most studies investigating aberrations in borderline tumors have focused on KRAS/BRAF mutations. In this study, we conducted an extensive analysis of mutations and single-nucleotide polymorphisms (SNPs) in borderline ovarian tumors. Using the Sequenom MassArray platform, we investigated 160 mutations/polymorphisms in 33 genes involved in cell signaling, apoptosis, angiogenesis, cell cycle regulation and cellular senescence. Of 52 tumors analyzed, 33 were serous, 18 mucinous and 1 endometrioid. KRAS c.35G>A p.Gly12Asp mutations were detected in eight tumors (six serous and two mucinous), BRAF V600E mutations in two serous tumors, and PIK3CA H1047Y and PIK3CA E542K mutations in a serous and an endometrioid BOT, respectively. CTNNB1 mutation was detected in a serous tumor. Potentially functional polymorphisms were found in vascular endothelial growth factor (VEGF), ABCB1, FGFR2 and PHLPP2. VEGF polymorphisms were the most common and detected at four loci. PHLPP2 polymorphisms were more frequent in mucinous as compared with serous tumors (P=0.04), with allelic imbalance in one case. This study represents the largest and most comprehensive analysis of mutations and functional SNPs in borderline ovarian tumors to date. At least 25% of borderline ovarian tumors harbor somatic mutations associated with potential response to targeted therapeutics.

Mutational Status of the TP53 Gene As a Predictor of Response and Survival in Patients With Chronic Lymphocytic Leukemia: Results From the LRF CLL4 Trial

PurposeTP53 mutations have been described in chronic lymphocytic leukemia (CLL) and have been associated with poor prognosis in retrospective studies. We aimed to address the frequency and prognostic value of TP53 abnormalities in patients with CLL in the context of a prospective randomized trial.Patients and MethodsWe analyzed 529 CLL samples from the LRF CLL4 (Leukaemia Research Foundation Chronic Lymphocytic Leukemia 4) trial (chlorambucil v fludarabine with or without cyclophosphamide) at the time of random assignment for mutations in the TP53 gene. TP53 mutation status was correlated with response and survival data.ResultsMutations of TP53 were found in 40 patients (7.6%), including 25 (76%) of 33 with 17p deletion and 13 (3%) of 487 without that deletion. There was no significant correlation between TP53 mutations and age, stage, IGHV gene mutations, CD38 and ZAP-70 expression, or any other chromosomal abnormality other than 17p deletion, in which concordance was high (96%). TP53 mutations were significantly associated with poorer overall response rates (27% v 83%; P <.001) and shorter progression-free survival (PFS) and overall survival (OS; 5-year PFS: 5% v 17%; 5-year OS: 20% v 59%; P <.001 for both). Multivariate analysis that included baseline clinical variables, treatment, and known adverse genetic factors confirmed that TP53 mutations have added prognostic value.ConclusionTP53 mutations are associated with impaired response and shorter survival in patients with CLL. Analysis of TP53 mutations should be performed in patients with CLL who have progressive disease before starting first-line treatment, and those with mutations should be selected for novel experimental therapies. J Clin Oncol 29: 2223-2229. (C) 2011 by American Society of Clinical Oncology