Ocular exposure to UV-B in sunlight: the Melbourne Visual Impairment Project model

Quantification of ocular exposure to ultraviolet-B radiation (UV-B) has become an important public health issue, with reports that the ozone layer is being depleted worldwide. Ocular exposure to UV-B is determined by ambient UV-B levels, the duration of outdoor exposure, the proportion of ambient UV-B that reaches the eye, and the use of ocular protection. We have developed a simplified model for quantifying lifetime ocular UV-B exposure that can be used in large epidemiological surveys. Exposure to UV-B is assessed and quantified using a model based on personal exposure over the six summer months. Data available for a population-based sample of 1150 people in the age range 40-98 years revealed a distribution in average annual lifetime ocular UV-B exposure similar to that reported in a previous study on which this model is based, and also demonstrate that people can recall lifetime personal behaviour related to ocular protection. It takes 12 minutes on average to collect these data. This model can be employed by researchers worldwide for uniform assessment of ocular UV-B exposure.

Fault detection and diagnosis of induction motors using motor current signature analysis and a hybrid FMM-CART model

In this paper, a novel approach to detect and classify comprehensive fault conditions of induction motors using a hybrid fuzzy min-max (FMM) neural network and classification and regression tree (CART) is proposed. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. A series of real experiments is conducted, whereby the motor current signature analysis method is applied to form a database comprising stator current signatures under different motor conditions. The signal harmonics from the power spectral density are extracted as discriminative input features for fault detection and classification with FMM-CART. A comprehensive list of induction motor fault conditions, viz., broken rotor bars, unbalanced voltages, stator winding faults, and eccentricity problems, has been successfully classified using FMM-CART with good accuracy rates. The results are comparable, if not better, than those reported in the literature. Useful explanatory rules in the form of a decision tree are also elicited from FMM-CART to analyze and understand different fault conditions of induction motors.

Condition monitoring of broken rotor bars using a hybrid FMM-GA model

A condition monitoring system for induction motors using a hybrid Fuzzy Min-Max (FMM) neural network and Genetic Algorithm (GA) is presented in this paper. Two types of experiments, one from the finite element method and another from real laboratory tests of broken rotor bars in an induction motor are conducted. The induction motor with broken rotor bars is operated under different load conditions. FMM is first used for learning and distinguishing between a healthy motor and one with broken rotor bars. The GA is then utilized for extracting fuzzy if-then rules using the don’t care approach in minimizing the number of rules. The results clearly demonstrate the effectiveness of the hybrid FMM-GA model in condition monitoring of broken rotor bars in induction motors.

A simplified stroke rehabilitation assessment of movement instrument

Background and Purpose. An efficient, reliable, and valid instrument for assessing motor function in patients with stroke is needed by both clinicians and researchers. To improve administration efficiency, we applied the multidimensional Rasch model to the 30-item, 3-subscale Stroke Rehabilitation Assessment of Movement (STREAM) instrument to produce a concise, reliable, and valid instrument (simplified STREAM [S-STREAM]) for measuring motor function in patients with stroke. Subjects and Methods. The STREAM (consisting of 3 subscales: upper-limb movements, lower-limb movements, and mobility) was administered to 351 subjects with first stroke occurrence and a median time after stroke of 19.5 months. The unidimensionality of each subscale of the STREAM first was verified with unidimensional Rasch analysis. Each subscale of the STREAM then was simplified by deleting redundant items on the basis of expert opinion and the results of the Rasch analysis. The Rasch reliability of the S-STREAM and the concurrent validity of the S-STREAM with the STREAM were examined with multidimensional Rasch analysis and the intraclass correlation coefficient (ICC), respectively. Results. After deleting the items that did not fit the Rasch model, we found that the 8-item upper-limb movement subscale, the 9-item lower-limb movement subscale, and the 10-item mobility subscale assessed single, unidimensional upper-limb movements, lower-limb movements, and mobility, respectively. We selected 5 items from each subscale to construct the S-STREAM and found that the reliability of each subscale of the resulting simplified instrument was high (Rasch reliability coefficients of [greater than or equal to] .91). The agreement between the subscale scores (Rasch estimates) of the S-STREAM and those of the STREAM was excellent (ICC of [greater than or equal to] .99, with a lower limit for the 95% confidence interval of [greater than or equal to] .985), indicating good concurrent validity of the S-STREAM with the STREAM. Discussion and Conclusion. The S-STREAM demonstrates high Rasch reliability, unidimensionality, and concurrent validity with the STREAM in patients with stroke. Furthermore, the S-STREAM is efficient to administer, as it consists of only half the number of items in the original STREAM. Additional studies to examine other psychometric properties (eg, predictive validity and responsiveness) of the S-STREAM or its psychometric properties in various recovery stages after stroke are needed to further establish its utility in both clinical and research settings.

Online motor fault detection and diagnosis using a hybrid FMM-CART model

In this brief, a hybrid model combining the fuzzy min-max (FMM) neural network and the classification and regression tree (CART) for online motor detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, exploits the advantages of both FMM and CART for undertaking data classification and rule extraction problems. To evaluate the applicability of the proposed FMM-CART model, an evaluation with a benchmark data set pertaining to electrical motor bearing faults is first conducted. The results obtained are equivalent to those reported in the literature. Then, a laboratory experiment for detecting and diagnosing eccentricity faults in an induction motor is performed. In addition to producing accurate results, useful rules in the form of a decision tree are extracted to provide explanation and justification for the predictions from FMM-CART. The experimental outcome positively shows the potential of FMM-CART in undertaking online motor fault detection and diagnosis tasks.

Simplified physically based model for estimating effective floc density

This paper applies dimensional analysis to propose an alternative model for estimating the effective density of flocs (Δρf). The model takes into account the effective density of the primary particles, in addition to the sizes of the floc and primary particles, and does not consider the concept of self-similarity. The model contains three dimensionless products and two empirical parameters (αf and βf), which were calibrated by using data available in the literature. Values of αf=0.7 and βf=0.8 were obtained. The average value of the primary particle size (Dp) for the data used in the analysis, inferred from the new model, was found to vary from 0.05 μm to 100 μm with a mean value of 2.5 μm. Good comparisons were obtained in comparing the estimated floc-settling velocity on the basis of the proposed model for effective floc density with the measured value.

Motor fault detection and diagnosis using a hybrid FMM-CART model with online learning

In this paper, a hybrid online learning model that combines the fuzzy min-max (FMM) neural network and the Classification and Regression Tree (CART) for motor fault detection and diagnosis tasks is described. The hybrid model, known as FMM-CART, incorporates the advantages of both FMM and CART for undertaking data classification (with FMM) and rule extraction (with CART) problems. In particular, the CART model is enhanced with an importance predictor-based feature selection measure. To evaluate the effectiveness of the proposed online FMM-CART model, a series of experiments using publicly available data sets containing motor bearing faults is first conducted. The results (primarily prediction accuracy and model complexity) are analyzed and compared with those reported in the literature. Then, an experimental study on detecting imbalanced voltage supply of an induction motor using a laboratory-scale test rig is performed. In addition to producing accurate results, a set of rules in the form of a decision tree is extracted from FMM-CART to provide explanations for its predictions. The results positively demonstrate the usefulness of FMM-CART with online learning capabilities in tackling real-world motor fault detection and diagnosis tasks. © 2014 Springer Science+Business Media New York.

Oral treatment with Cu(II)(atsm) increases mutant SOD1 in vivo but protects motor neurons and improves the phenotype of a transgenic mouse model of amyotrophic lateral sclerosis

Mutations in the metallo-protein Cu/Zn-superoxide dismutase (SOD1) cause amyotrophic lateral sclerosis (ALS) in humans and an expression level-dependent phenotype in transgenic rodents. We show that oral treatment with the therapeutic agent diacetyl-bis(4-methylthiosemicarbazonato)copper(II) [Cu(II)(atsm)] increased the concentration of mutant SOD1 (SOD1G37R) in ALS model mice, but paradoxically improved locomotor function and survival of the mice. To determine why the mice with increased levels of mutant SOD1 had an improved phenotype, we analyzed tissues by mass spectrometry. These analyses revealed most SOD1 in the spinal cord tissue of the SOD1G37R mice was Cu deficient. Treating with Cu(II)(atsm) decreased the pool of Cu-deficient SOD1 and increased the pool of fully metallated (holo) SOD1. Tracking isotopically enriched (65)Cu(II)(atsm) confirmed the increase in holo-SOD1 involved transfer of Cu from Cu(II)(atsm) to SOD1, suggesting the improved locomotor function and survival of the Cu(II)(atsm)-treated SOD1G37R mice involved, at least in part, the ability of the compound to improve the Cu content of the mutant SOD1. This was supported by improved survival of SOD1G37R mice that expressed the human gene for the Cu uptake protein CTR1. Improving the metal content of mutant SOD1 in vivo with Cu(II)(atsm) did not decrease levels of misfolded SOD1. These outcomes indicate the metal content of SOD1 may be a greater determinant of the toxicity of the protein in mutant SOD1-associated forms of ALS than the mutations themselves. Improving the metal content of SOD1 therefore represents a valid therapeutic strategy for treating ALS caused by SOD1.

Environmental enrichment ameliorates a motor coordination deficit in a mouse model of Rett syndrome Mecp2 gene dosage effects and BDNF expression

Rett syndrome, commonly associated with mutations of the methyl CpG-binding protein 2 (MECP2) gene, is characterised by an apparently normal early postnatal development followed by deterioration of acquired cognitive and motor coordination skills in early childhood. To evaluate whether environmental factors may influence the disease outcome of Rett syndrome, we tested the effect of environmental enrichment from 4 weeks of age on the behavioural competence of mutant mice harboring a Mecp2 tm1Tam-null allele. Our findings show that enrichment improves motor coordination in heterozygous Mecp2 +/− females but not Mecp2 −/y males. Standard-housed Mecp2 +/− mice had an initial motor coordination deficit on the accelerating rotarod, which improved with training then deteriorated in subsequent weeks. Enrichment resulted in a significant reduction in this coordination deficit in Mecp2 +/− mice, returning the performance to wild-type levels. Brain-derived neurotrophic factor (BDNF) protein levels were 75 and 85% of wild-type controls in standard-housed and environmentally enriched Mecp2 +/− cerebellum, respectively. Mecp2 −/y mice showed identical deficits of cerebellar BDNF (67% of wild-type controls) irrespective of their housing environment. Our findings demonstrate a positive impact of environmental enrichment in a Rett syndrome model; this impact may be dependent on the existence of one functional copy of Mecp2.

Effect of mean stress on multiaxial ratcheting life: a simplified life prediction model based on average equivalent stress amplitude

This article proposes a model to predict uniaxial and multiaxial ratcheting life by addressing the three primary parameters that influence failure life: fatigue damage, ratcheting damage and the multiaxial loading path. These three factors are addressed in the present model by (a) the stress amplitude for fatigue damage, (b) mean stress-dependent Goodman equation for ratcheting damage and (c) an inherent weight factor based on average equivalent stress to account for the multiaxial loading. The proposed model requires only two material constants which can be easily determined from uniaxial symmetric stress-controlled fatigue tests. Experimental ratcheting life data collected from the literature for 1025 and 42CrMo steel under multiaxial proportional and nonproportional constant amplitude loading ratcheting with triangular sinusoidal and trapezoidal waveform (i.e. linear, rhombic, circular, elliptical and square stress paths) have shown good agreement with the proposed model.

Characterizing the molecular phenotype of an Atp7a(T985I) conditional knock in mouse model for X-linked distal hereditary motor neuropathy (dHMNX)

ATP7A is a P-type ATPase essential for cellular copper (Cu) transport and homeostasis. Loss-of-function ATP7A mutations causing systemic Cu deficiency are associated with severe Menkes disease or its milder allelic variant, occipital horn syndrome. We previously identified two rare ATP7A missense mutations (P1386S and T994I) leading to a non-fatal form of motor neuron disorder, X-linked distal hereditary motor neuropathy (dHMNX), without overt signs of systemic Cu deficiency. Recent investigations using a tissue specific Atp7a knock out model have demonstrated that Cu plays an essential role in motor neuron maintenance and function, however the underlying pathogenic mechanisms of ATP7A mutations causing axonal degeneration remain unknown. We have generated an Atp7a conditional knock in mouse model of dHMNX expressing Atp7a(T985I), the orthologue of the human ATP7A(T994I) identified in dHMNX patients. Although a degenerative motor phenotype is not observed, the knock in Atp7a(T985I/Y) mice show altered Cu levels within the peripheral and central nervous systems, an increased diameter of the muscle fibres and altered myogenin and myostatin gene expression. Atp7a(T985I/Y) mice have reduced Atp7a protein levels and recapitulate the defective trafficking and altered post-translational regulatory mechanisms observed in the human ATP7A(T994I) patient fibroblasts. Our model provides a unique opportunity to characterise the molecular phenotype of dHMNX and the time course of cellular events leading to the process of axonal degeneration in this disease.