Planetary Gear Modal Vibration Experiments and Correlation against Lumped-Parameter and Finite Element Models

Experimental modal analysis techniques are applied to characterize the planar dynamic behavior of two spur planetary gears. Rotational and translational vibrations of the sun gear, carrier, and planet gears are measured. Experimentally obtained natural frequencies, mode shapes, and dynamic response are compared to the results from lumped-parameter and finite element models. Two qualitatively different classes of mode shapes in distinct frequency ranges are observed in the experiments and confirmed by the lumped-parameter model, which considers the accessory shafts and fixtures in the system to capture all of the natural frequencies and modes. The finite element model estimates the high-frequency modes that have significant tooth mesh deflection without considering the shafts and fixtures. The lumped-parameter and finite element models accurately predict the natural frequencies and modal properties established by experimentation. Rotational, translational, and planet mode types presented in published mathematical studies are confirmed experimentally. The number and types of modes in the low-frequency and high-frequency bands depend on the degrees of freedom in the central members and planet gears, respectively. The accuracy of natural frequency prediction is improved when the planet bearings have differing stiffnesses in the tangential and radial directions, consistent with the bearing load direction. (C) 2012 Elsevier Ltd. All rights reserved.

Tumour budding: a promising parameter in colorectal cancer

In 2011, the Tumour Node Metastasis (TNM) staging system still remains the gold standard for stratifying colorectal cancer (CRC) patients into prognostic subgroups, and is considered a solid basis for treatment management. Nevertheless, there is still a challenge with regard to therapeutic strategy; stage II patients are not typically selected for postoperative adjuvant chemotherapy, although some stage II patients have a comparable outcome to stage III patients who, themselves do receive such treatment. Consequently, there has been an inundation of 'prognostic biomarker' studies aiming to improve the prognostic stratification power of the TNM staging system. Most proposed biomarkers are not implemented because of lack of reproducibility, validation and standardisation. This problem can be partially resolved by following the REMARK guidelines. In search of novel prognostic factors for patients with CRC, one might glance at a table in the book entitled 'Prognostic Factors in Cancer' published by the International Union against Cancer (UICC) in 2006, in which TNM stage, L and V classifications are considered 'essential' prognostic factors, whereas tumour grade, perineural invasion, tumour budding and tumour-border configuration among others are proposed as 'additional' prognostic factors. Histopathology reports normally include the 'essential' features and are accompanied by tumour grade, histological subtype and information on perineural invasion, but interestingly, the tumour-border configuration (i.e., growth pattern) and especially tumour budding are rarely reported. Although scoring systems such as the 'BRE' in breast and 'Gleason' in prostate cancer are solidly based on histomorphological features and used in daily practice, no such additional scoring system to complement TNM staging is available for CRC. Regardless of differences in study design and methods for tumour-budding assessment, the prognostic power of tumour budding has been confirmed by dozens of study groups worldwide, suggesting that tumour budding may be a valuable candidate for inclusion into a future prognostic scoring system for CRC. This mini-review therefore attempts to present a short and concise overview on tumour budding, including morphological, molecular and prognostic aspects underlining its inter-disciplinary relevance.

Development of a morphometric magnetic resonance image parameter suitable for distinguishing between normal dogs and dogs with cerebellar atrophy

Neurodegenerative diseases affect the cerebellum of numerous dog breeds. Although subjective, magnetic resonance (MR) imaging has been used to detect cerebellar atrophy in these diseases, but there are few data available on the normal size range of the cerebellum relative to other brain regions. The purpose of this study was to determine whether the size of the cerebellum maintains a consistent ratio with other brain regions in different ages and breeds of normal dogs and to define a measurement that can be used to identify cerebellar atrophy on MR images. Images from 52 normal and 13 dogs with cerebellar degenerative diseases were obtained. Volume and mid-sagittal cross-sectional area of the forebrain, brainstem, and cerebellum were calculated for each normal dog and compared between different breeds and ages as absolute and relative values. The ratio of the cerebellum to total brain and of the brainstem to cerebellum mid-sagittal cross-sectional area was compared between normal and affected dogs and the sensitivity and specificity of these ratios at distinguishing normal from affected dogs was calculated. The percentage of the brain occupied by the cerebellum in diverse dog breeds between 1 and 5 years of age was not significantly different, and cerebellar size did not change with increasing age. Using a cut off of 89%, the ratio between the brainstem and cerebellum mid-sagittal cross-sectional area could be used successfully to differentiate affected from unaffected dogs with a sensitivity and specificity of 100%, making this ratio an effective tool for identifying cerebellar atrophy on MR images.

End-tidal CO(2). An important parameter for a correct interpretation in functional brain studies using speech tasks

The aim was to investigate the effect of different speech tasks, i.e. recitation of prose (PR), alliteration (AR) and hexameter (HR) verses and a control task (mental arithmetic (MA) with voicing of the result on end-tidal CO2 (PETCO2), cerebral hemodynamics and oxygenation. CO2 levels in the blood are known to strongly affect cerebral blood flow. Speech changes breathing pattern and may affect CO2 levels. Measurements were performed on 24 healthy adult volunteers during the performance of the 4 tasks. Tissue oxygen saturation (StO2) and absolute concentrations of oxyhemoglobin ([O2Hb]), deoxyhemoglobin ([HHb]) and total hemoglobin ([tHb]) were measured by functional near-infrared spectroscopy (fNIRS) and PETCO2 by a gas analyzer. Statistical analysis was applied to the difference between baseline before the task, 2 recitation and 5 baseline periods after the task. The 2 brain hemispheres and 4 tasks were tested separately. A significant decrease in PETCO2 was found during all 4 tasks with the smallest decrease during the MA task. During the recitation tasks (PR, AR and HR) a statistically significant (p < 0.05) decrease occurred for StO2 during PR and AR in the right prefrontal cortex (PFC) and during AR and HR in the left PFC. [O2Hb] decreased significantly during PR, AR and HR in both hemispheres. [HHb] increased significantly during the AR task in the right PFC. [tHb] decreased significantly during HR in the right PFC and during PR, AR and HR in the left PFC. During the MA task, StO2 increased and [HHb] decreased significantly during the MA task. We conclude that changes in breathing (hyperventilation) during the tasks led to lower CO2 pressure in the blood (hypocapnia), predominantly responsible for the measured changes in cerebral hemodynamics and oxygenation. In conclusion, our findings demonstrate that PETCO2 should be monitored during functional brain studies investigating speech using neuroimaging modalities, such as fNIRS, fMRI to ensure a correct interpretation of changes in hemodynamics and oxygenation.

The Optimal Confidence Region for a Random Parameter

Under a two-level hierarchical model, suppose that the distribution of the random parameter is known or can be estimated well. Data are generated via a fixed, but unobservable realization of this parameter. In this paper, we derive the smallest confidence region of the random parameter under a joint Bayesian/frequentist paradigm. On average this optimal region can be much smaller than the corresponding Bayesian highest posterior density region. The new estimation procedure is appealing when one deals with data generated under a highly parallel structure, for example, data from a trial with a large number of clinical centers involved or genome-wide gene-expession data for estimating individual gene- or center-specific parameters simultaneously. The new proposal is illustrated with a typical microarray data set and its performance is examined via a small simulation study.