Polarization dependent photorefractive amplitude modulation production in MgO:LiNbO 3 phase modulators

We present experimental and theoretical results of the intensity dependence of residual amplitude modulation (RAM) production in electro-optic phase modulators. By utilizing the anisotropy of the medium, we show that RAM has a photorefractive origin.

Does occupational aggregation matter in determining the size of the gender wage gap?

The traditional decomposition of the gender wage gap distinguishes between a component attributable to gender differences in productivity-related characteristics and a residual component that is often taken as a measure of discrimination. This study of data from the 1989 Canadian Labour Market Activity Survey shows that when occupation is treated as a productivity-related characteristic, the proportion of the gender wage gap labeled explained increases with the number of occupational classifications distinguished. However, on the basis of evidence that occupational differences reflect the presence of barriers faced by women attempting to enter male-dominated occupations, the authors conclude that occupation should not be treated as a productivity-related characteristic; and in a decomposition of the gender wage gap that treats occupation as endogenously determined, they find that the level of occupational aggregation has little effect on the size of the "explained" component of the gap.

Deriving a preference-based measure for cancer using the EORTC QLQ-C30 : a confirmatory versus exploratory approach

Background: To derive preference-based measures from various condition-specific descriptive health-related quality of life (HRQOL) measures. A general 2-stage method is evolved: 1) an item from each domain of the HRQOL measure is selected to form a health state classification system (HSCS); 2) a sample of health states is valued and an algorithm derived for estimating the utility of all possible health states. The aim of this analysis was to determine whether confirmatory or exploratory factor analysis (CFA, EFA) should be used to derive a cancer-specific utility measure from the EORTC QLQ-C30. Methods: Data were collected with the QLQ-C30v3 from 356 patients receiving palliative radiotherapy for recurrent or metastatic cancer (various primary sites). The dimensional structure of the QLQ-C30 was tested with EFA and CFA, the latter based on a conceptual model (the established domain structure of the QLQ-C30: physical, role, emotional, social and cognitive functioning, plus several symptoms) and clinical considerations (views of both patients and clinicians about issues relevant to HRQOL in cancer). The dimensions determined by each method were then subjected to item response theory, including Rasch analysis. Results: CFA results generally supported the proposed conceptual model, with residual correlations requiring only minor adjustments (namely, introduction of two cross-loadings) to improve model fit (increment χ2(2) = 77.78, p < .001). Although EFA revealed a structure similar to the CFA, some items had loadings that were difficult to interpret. Further assessment of dimensionality with Rasch analysis aligned the EFA dimensions more closely with the CFA dimensions. Three items exhibited floor effects (>75% observation at lowest score), 6 exhibited misfit to the Rasch model (fit residual > 2.5), none exhibited disordered item response thresholds, 4 exhibited DIF by gender or cancer site. Upon inspection of the remaining items, three were considered relatively less clinically important than the remaining nine. Conclusions: CFA appears more appropriate than EFA, given the well-established structure of the QLQ-C30 and its clinical relevance. Further, the confirmatory approach produced more interpretable results than the exploratory approach. Other aspects of the general method remain largely the same. The revised method will be applied to a large number of data sets as part of the international and interdisciplinary project to develop a multi-attribute utility instrument for cancer (MAUCa).

Reduced expression of retinoblastoma gene product (pRB) and high expression of p53 are associated with poor prognosis in ovarian cancer

Paraffin sections (n = 168, 27 benign, 16 low malignant potential [LMP] and 125 malignant tumours) from epithelial ovarian tumours were evaluated immunohistochemically for expression of retinoblastoma gene product (pRB) and p53 protein, and the relationship among pRB, p53 and cyclin-dependent kinase inhibitor 2 (CDKN2) gene product p16INK4A (p16) was analysed, following our previous study of p16. Forty-one percent of the benign, 50% of the LMP and most (71%) of the malignant tumours showed high pRB expression. High expression of pRB (>50% pRB-positive cells) significantly correlated with non-mucinous histological subtypes. Reduced pRB expression, substage and residual disease were significant predictors for poor prognosis in stage I patients. All the benign and most of the LMP (81%) tumours were in either the p53-negative or low p53-positive category, but nearly half of the malignant tumours had high p53 expression. High p53 accumulation was found in non-mucinous, high grade and late stage tumours. For well-differentiated carcinomas, high p53 expression was a predictor of poor prognosis. However, even though high p53 expression was not associated with histological subtype, stage or the presence of residual disease, high p53 expression was not an independent predictor when all clinical parameters were combined. For all ovarian cancers, a close correlation was found between high p53 and high p16 expression. The relationship between the expression of pRB and p16 depended on tumour stage. In stage I tumours, high pRB was associated with low p16 reactivity. On the other hand, most advanced tumours showed both high pRB and high p16 reactivity. Int. J. Cancer 74:407–415, 1997. © 1997 Wiley-Liss, Inc.

A home-based progressive resistance exercise programme for patients with venous leg ulcers : a feasibility study

This study aimed to assess the feasibility of a home-based exercise program and examine the effects on the healing rates of venous leg ulcers. A 12 –week randomised controlled trial was conducted investigating the effects of an exercise intervention compared to a usual care group. Participants in both groups (n = 13) had active venous ulceration and were treated in a metropolitan hospital outpatients clinic in Australia. Data were collected on recruitment from medical records, clinical assessment and questionnaires. Follow-up data on progress in healing and treatments were collected fortnightly for 12 weeks. Calf muscle pump function data were collected at baseline and 12 weeks from recruitment. Range of ankle motion data were collected at baseline, 6 and 12 weeks from recruitment. This pilot study indicated that the intervention was feasible. Clinical significance was observed in the intervention group with a 32% greater decrease in ulcer size (p=0.34) than the control group, and a 10% (p=0.74) improvement in the number of participants healed in the intervention group compared to the control group. Significant differences between groups over time were observed in calf muscle pump function parameters; (ejection fraction [p = 0.05]; residual volume fraction [p = 0.04]) and range of ankle motion (p = 0.01). This pilot study is one of the first studies to examine and measure clinical healing rates for participants involved in a home-based progressive resistance exercise program. Further research is warranted with a larger multi-site study.

The importance of mosquito behavioural adaptations to malaria control in Africa

Over the past decade the use of long-lasting insecticidal nets (LLINs), in combination with improved drug therapies, indoor residual spraying (IRS) and better health infrastructure, has helped reduce malaria in many African countries for the first time in a generation. However, insecticide resistance in the vector is an evolving threat to these gains. We review emerging and historical data on behavioural resistance in response to LLINs and IRS. Overall the current literature suggests behavioural and species changes may be emerging, but the data are sparse and, at times unconvincing. However, preliminary modelling has demonstrated that behavioural resistance could have significant impacts on the effectiveness of malaria control. We propose seven recommendations to improve understanding of resistance in malaria vectors. Determining the public health impact of physiological and behavioural insecticide resistance is an urgent priority if we are to maintain the significant gains made in reducing malaria morbidity and mortality.

Spatial mapping of city-wide PBDE levels using an exponential decay model

Passive air samplers (PAS) consisting of polyurethane foam (PUF) disks were deployed at 6 outdoor air monitoring stations in different land use categories (commercial, industrial, residential and semi-rural) to assess the spatial distribution of polybrominated diphenyl ethers (PBDEs) in the Brisbane airshed. Air monitoring sites covered an area of 1143 km2 and PAS were allowed to accumulate PBDEs in the city's airshed over three consecutive seasons commencing in the winter of 2008. The average sum of five (∑5) PBDEs (BDEs 28, 47, 99, 100 and 209) levels were highest at the commercial and industrial sites (12.7 ± 5.2 ng PUF−1), which were relatively close to the city center and were a factor of 8 times higher than residential and semi-rural sites located in outer Brisbane. To estimate the magnitude of the urban ‘plume’ an empirical exponential decay model was used to fit PAS data vs. distance from the CBD, with the best correlation observed when the particulate bound BDE-209 was not included (∑5-209) (r2 = 0.99), rather than ∑5 (r2 = 0.84). At 95% confidence intervals the model predicts that regardless of site characterization, ∑5-209 concentrations in a PAS sample taken between 4–10 km from the city centre would be half that from a sample taken from the city centre and reach a baseline or plateau (0.6 to 1.3 ng PUF−1), approximately 30 km from the CBD. The observed exponential decay in ∑5-209 levels over distance corresponded with Brisbane's decreasing population density (persons/km2) from the city center. The residual error associated with the model increased significantly when including BDE-209 levels, primarily due to the highest level (11.4 ± 1.8 ng PUF−1) being consistently detected at the industrial site, indicating a potential primary source at this site. Active air samples collected alongside the PAS at the industrial air monitoring site (B) indicated BDE-209 dominated congener composition and was entirely associated with the particulate phase. This study demonstrates that PAS are effective tools for monitoring citywide regional differences however, interpretation of spatial trends for POPs which are predominantly associated with the particulate phase such as BDE-209, may be restricted to identifying ‘hotspots’ rather than broad spatial trends.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Cohort normalization based sparse representation for undersampled face recognition

Abstract. In recent years, sparse representation based classification(SRC) has received much attention in face recognition with multipletraining samples of each subject. However, it cannot be easily applied toa recognition task with insufficient training samples under uncontrolledenvironments. On the other hand, cohort normalization, as a way of mea-suring the degradation effect under challenging environments in relationto a pool of cohort samples, has been widely used in the area of biometricauthentication. In this paper, for the first time, we introduce cohort nor-malization to SRC-based face recognition with insufficient training sam-ples. Specifically, a user-specific cohort set is selected to normalize theraw residual, which is obtained from comparing the test sample with itssparse representations corresponding to the gallery subject, using poly-nomial regression. Experimental results on AR and FERET databases show that cohort normalization can bring SRC much robustness against various forms of degradation factors for undersampled face recognition.

An efficient online secondary path estimation for feedback active noise control systems

In practical cases for active noise control (ANC), the secondary path has usually a time varying behavior. For these cases, an online secondary path modeling method that uses a white noise as a training signal is required to ensure convergence of the system. The modeling accuracy and the convergence rate are increased when a white noise with a larger variance is used. However, the larger variance increases the residual noise, which decreases performance of the system and additionally causes instability problem to feedback structures. A sudden change in the secondary path leads to divergence of the online secondary path modeling filter. To overcome these problems, this paper proposes a new approach for online secondary path modeling in feedback ANC systems. The proposed algorithm uses the advantages of white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the algorithm and to prevent the instability effect of the white noise. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to correct the secondary path estimation. In addition, the proposed method models the secondary path without the need of using off-line estimation of the secondary path. Considering the above features increases the convergence rate and modeling accuracy, which results in a high system performance. Computer simulation results shown in this paper indicate effectiveness of the proposed method.

Prediction of age at menopause from assessment of ovarian reserve may be improved by using body mass index and smoking status

Objective: Menopause is the consequence of exhaustion of the ovarian follicular pool. AMH, an indirect hormonal marker of ovarian reserve, has been recently proposed as a predictor for age at menopause. Since BMI and smoking status are relevant independent factors associated with age at menopause we evaluated whether a model including all three of these variables could improve AMH-based prediction of age at menopause. Methods: In the present cohort study, participants were 375 eumenorrheic women aged 19–44 years and a sample of 2,635 Italian menopausal women. AMH values were obtained from the eumenorrheic women. Results: Regression analysis of the AMH data showed that a quadratic function of age provided a good description of these data plotted on a logarithmic scale, with a distribution of residual deviates that was not normal but showed significant leftskewness. Under the hypothesis that menopause can be predicted by AMH dropping below a critical threshold, a model predicting menopausal age was constructed from the AMH regression model and applied to the data on menopause. With the AMH threshold dependent on the covariates BMI and smoking status, the effects of these covariates were shown to be highly significant. Conclusions: In the present study we confirmed the good level of conformity between the distributions of observed and AMH-predicted ages at menopause, and showed that using BMI and smoking status as additional variables improves AMH-based prediction of age at menopause.

A new online secondary path modelling method for feedforward active noise control systems

An online secondary path modelling method using a white noise as a training signal is required in many applications of active noise control (ANC) to ensure convergence of the system. Not continually injection of white noise during system operation makes the system more desirable. The purposes of the proposed method are two folds: controlling white noise by preventing continually injection, and benefiting white noise with a larger variance. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. This paper proposes a new approach for online secondary path modelling in feedfoward ANC systems. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. Comparative simulation results shown in this paper indicate effectiveness of the proposed approach in controlling active noise.

Benefiting white noise in developing feedforward active noise control systems

In many applications of active noise control (ANC), an online secondary path modelling method using a white noise as a training signal is required to ensure convergence of the system. The modelling accuracy and the convergence rate increase when a white noise with larger variance is used, however larger the variance increases the residual noise, which decreases performance of the system. The proposed algorithm uses the advantages of the white noise with larger variance to model the secondary path, but the injection is stopped at the optimum point to increase performance of the system. In this approach, instead of continuous injection of the white noise, a sudden change in secondary path during the operation makes the algorithm to reactivate injection of the white noise to adjust the secondary path estimation. Comparative simulation results shown in this paper indicate effectiveness of the proposed method.

Antiproton induced DNA damage : proton like in flight, carbon-ion light near rest

Biological validation of new radiotherapy modalities is essential to understand their therapeutic potential. Antiprotons have been proposed for cancer therapy due to enhanced dose deposition provided by antiproton-nucleon annihilation. We assessed cellular DNA damage and relative biological effectiveness (RBE) of a clinically relevant antiproton beam. Despite a modest LET (~19 keV/μm), antiproton spread out Bragg peak (SOBP) irradiation caused significant residual γ-H2AX foci compared to X-ray, proton and antiproton plateau irradiation. RBE of ~1.48 in the SOBP and ~1 in the plateau were measured and used for a qualitative effective dose curve comparison with proton and carbon-ions. Foci in the antiproton SOBP were larger and more structured compared to X-rays, protons and carbon-ions. This is likely due to overlapping particle tracks near the annihilation vertex, creating spatially correlated DNA lesions. No biological effects were observed at 28–42 mm away from the primary beam suggesting minimal risk from long-range secondary particles.

Multifocal urothelial cancers with the mutator phenotype are of monoclonal origin and require panurothelial treatment for tumor clearance

Purpose: UC is a disease of the entire urothelium, characterized by multiplicity and multifocality. The clonal relationship among multiple UCs has implications regarding adjuvant chemotherapy. It has been investigated in studies of chromosomal alteration and single gene mutation. However, these genetic changes can occur in unrelated tumors under similar carcinogenic selection pressures. Tumors with high MSI have numerous DNA mutations, of which many provide no selection benefit. While these tumors represent an ideal model for studying UC clonality, their low frequency has prevented their previous investigation. Materials and Methods: We investigated 32 upper and lower urinary tract UCs with high MSI and 4 nonUC primary cancers in 9 patients. We used the high frequency and specificity of individual DNA mutations in these tumors (MSI at 17 loci) and the early timing of epigenetic events (methylation of 7 gene promoters) to investigate tumor clonality. Results: Molecular alterations varied among tumors from different primary organs but they appeared related in the UCs of all 9 patients. While 7 patients had a high degree of concordance among UCs, in 2 the UCs shared only a few similar alterations. Genetic and epigenetic abnormalities were frequently found in normal urothelial samples. Conclusions: Multiple UCs in each patient appeared to arise from a single clone. The molecular order of tumor development varied from the timing of clinical presentation and suggested that residual malignant cells persist in the urinary tract despite apparent curative surgery. These cells lead to subsequent tumor relapse and new methods are required to detect and eradicate them.