Evidence for a dual function of EphB4 as tumor promoter and suppressor regulated by the absence or presence of the ephrin-B2 ligand

Overexpression of the receptor tyrosine kinase EphB4 is common in epithelial cancers and linked to tumor progression by promoting angiogenesis, increasing survival and facilitating invasion and migration. However, other studies have reported loss of EphB4 suggesting a tumor suppressor function in some cancers. These opposing roles may be regulated by (i) the presence of the primary ligand ephrin-B2 that regulates pathways involved in tumor suppression or (ii) the absence of ephrin-B2 that allows EphB4 signaling via ligand-independent pathways that contribute to tumor promotion. To explore this theory, EphB4 was overexpressed in the prostate cancer cell line 22Rv1 and the mammary epithelial cell line MCF-10A. Overexpressed EphB4 localized to lipid-rich regions of the plasma membrane and confirmed to be ligand-responsive as demonstrated by increased phosphorylation of ERK1/2 and internalization. EphB4 overexpressing cells demonstrated enhanced anchorage-independent growth, migration and invasion, all characteristics associated with an aggressive phenotype, and therefore supporting the hypothesis that overexpressed EphB4 facilitates tumor promotion. Importantly, these effects were reversed in the presence of ephrin-B2 which led to a reduction in EphB4 protein levels, demonstrating that ligand-dependent signaling is tumor suppressive. Furthermore, extended ligand stimulation caused a significant decrease in proliferation that correlated with a rise in caspase-3/7 and -8 activities. Together, these results demonstrate that overexpression of EphB4 confers a transformed phenotype in the case of MCF-10A cells and an increased metastatic phenotype in the case of 22Rv1 cancer cells and that both phenotypes can be restrained by stimulation with ephrin-B2, in part by reducing EphB4 levels.

Mesopic rod and S-cone interactions revealed by modulation thresholds

We analyzed mesopic rod and S-cone interactions in terms of their contributions to the blue-yellow opponent pathway. Stimuli were generated using a 4-primary colorimeter. Mixed rod and S-cone modulation thresholds (constant L-, M-cone excitation) were measured as a function of their phase difference. Modulation amplitude was equated using threshold units and contrast ratios. This study identified three interaction types: (1) A linear and antagonistic rod:S-cone interaction, (2) probability summation (3) and a previously unidentified mutual nonlinear reinforcement. Linear rod:S-cone interactions occur within the blue-yellow opponent pathway. Probability summation involves signaling by different post-receptoral pathways. The origin of the nonlinear reinforcement is possibly at the photoreceptors.

Relationship among CFH and ARMS2 genotypes, macular pigment optical density, and neuroretinal function in persons without age-related macular degeneration

Purpose: To determine whether there is a difference in neuroretinal function and in macular pigment optical density between persons with high- and low-risk gene variants for age-related macular degeneration (AMD) and no ophthalmoscopic signs of AMD, and to compare the results on neuroretinal function to patients with manifest early AMD. Methods and Participants: Neuroretinal function was assessed with the multifocal electroretinogram (mfERG) for 32 participants (22 healthy persons with no AMD and 10 early AMD patients). The 22 healthy participants with no AMD had high- or low-risk genotypes for either CFH (rs380390) and/or ARMS2 (rs10490924). Trough-to-peak response densities and peak-implicit times were analyzed in 5 concentric rings. Macular pigment optical densitometry was assessed by customized heterochromatic flicker photometry. Results: Trough-to-peak response densities for concentric rings 1 to 3 were, on average, significantly greater in participants with high-risk genotypes than in participants with low-risk genotypes and in persons with early AMD after correction for age and smoking (p<0.05). The group peak- implicit times for ring 1 were, on average, delayed in the patients with early AMD compared with the participants with high- or low-risk genotypes, although these differences were not significant. There was no significant correlation between genotypes and macular pigment optical density. Conclusion: Increased neuroretinal activity in persons who carry high-risk AMD genotypes may be due to genetically determined subclinical inflammatory and/or histological changes in the retina. Neuroretinal function in healthy persons genetically susceptible to AMD may be a useful additional early biomarker (in combination with genetics) before there is clinical manifestation.

Insights into the role and function of L2, the minor capsid protein of papillomaviruses

Human papillomaviruses (HPV) are responsible for the most common human sexually transmitted viral infections, and high-risk types are responsible for causing cervical and other cancers. The minor capsid protein L2 of HPV plays important roles in virus entry into cells, localisation of viral components to the nucleus, in DNA binding, capsid formation and stability. It also elicits antibodies that are more cross-reactive between HPV types than does the major capsid protein L1, making it an attractive potential target for new-generation, more broadly protective subunit vaccines against HPV infections. However, its low abundance in natural capsids-12-72 molecules per 360 copies of L1-limits its immunogenicity. This review will explore the biological roles of the protein, and prospects for its use in new vaccines. © 2009 Springer-Verlag.

Socioeconomic status and health outcomes in a developing country

Although the relationship between socioeconomic status (SES) and health is well documented for developed countries, less evidence has been presented for developing countries. The aim of this paper is to analyse this relationship at the household level for Fiji, a developing country in the South Pacific, using original household survey data. To allow for the endogeneity of SES status in the household health production function, we utilize a simultaneous equation approach where estimates are achieved by full information maximum likelihood. By restricting our sample to one, relatively small island, and including area and district hospital effects, physical geography effects are unpacked from income effects. We measure SES, as permanent income which is constructed using principal components analysis. An alternative specification considers transitory household income. We find that a 1% increase in wealth (our measure of permanent income) would lead to a 15% decrease in the probability of an incapacitating illness occurring intra-household. Although the presence of a strong relationship indicates that relatively small improvements in SES status can significantly improve health at the household level, it is argued that the design of appropriate policy would also require an understanding of the various mechanisms through which the relationship operates.

Post-match changes in neuromuscular function and the relationship to match demands in amateur rugby league matches

Objectives: The current study investigated the change in neuromuscular contractile properties following competitive rugby league matches and the relationship with physical match demands. Design: Eleven trained, male rugby league players participated in 2–3 amateur, competitive matches (n = 30). Methods: Prior to, immediately (within 15-min) and 2 h post-match, players performed repeated counter-movement jumps (CMJ) followed by isometric tests on the right knee extensors for maximal voluntary contraction (MVC), voluntary activation (VA) and evoked twitch contractile properties of peak twitch force (Pt), rate of torque development (RTD), contraction duration (CD) and relaxation rate (RR). During each match, players wore 1 Hz Global Positioning Satellite devices to record distance and speeds of matches. Further, matches were filmed and underwent notational analysis for number of total body collisions. Results: Total, high-intensity, very-high intensity distances covered and mean speed were 5585 ± 1078 m, 661 ± 265, 216 ± 121 m and 75 ± 14 m min−1, respectively. MVC was significantly reduced immediately and 2 h post-match by 8 ± 11 and 12 ± 13% from pre-match (p < 0.05). Moreover, twitch contractile properties indicated a suppression of Pt, RTD and RR immediately post-match (p < 0.05). However, VA was not significantly altered from pre-match (90 ± 9%), immediately-post (89 ± 9%) or 2 h post (89 ± 8%), (p > 0.05). Correlation analyses indicated that total playing time (r = −0.50) and mean speed (r = −0.40) were moderately associated to the change in post-match MVC, while mean speed (r = 0.35) was moderately associated to VA. Conclusions: The present study highlights the physical demands of competitive amateur rugby league result in interruption of peripheral contractile function, and post-match voluntary torque suppression may be associated with match playing time and mean speeds.

Estimating the probability of an invalid match for matching using the rank transform

A fundamental problem faced by stereo matching algorithms is the matching or correspondence problem. A wide range of algorithms have been proposed for the correspondence problem. For all matching algorithms, it would be useful to be able to compute a measure of the probability of correctness, or reliability of a match. This paper focuses in particular on one class for matching algorithms, which are based on the rank transform. The interest in these algorithms for stereo matching stems from their invariance to radiometric distortion, and their amenability to fast hardware implementation. This work differs from previous work in that it derives, from first principles, an expression for the probability of a correct match. This method was based on an enumeration of all possible symbols for matching. The theoretical results for disparity error prediction, obtained using this method, were found to agree well with experimental results. However, disadvantages of the technique developed in this chapter are that it is not easily applicable to real images, and also that it is too computationally expensive for practical window sizes. Nevertheless, the exercise provides an interesting and novel analysis of match reliability.

Network interactions and performance of a multi-function IEC 61850 process bus

New substation technology, such as non-conventional instrument transformers,and a need to reduce design and construction costs, are driving the adoption of Ethernet based digital process bus networks for high voltage substations. Protection and control applications can share a process bus, making more efficient use of the network infrastructure. This paper classifies and defines performance requirements for the protocols used in a process bus on the basis of application. These include GOOSE, SNMP and IEC 61850-9-2 sampled values. A method, based on the Multiple Spanning Tree Protocol (MSTP) and virtual local area networks, is presented that separates management and monitoring traffic from the rest of the process bus. A quantitative investigation of the interaction between various protocols used in a process bus is described. These tests also validate the effectiveness of the MSTP based traffic segregation method. While this paper focusses on a substation automation network, the results are applicable to other real-time industrial networks that implement multiple protocols. High volume sampled value data and time-critical circuit breaker tripping commands do not interact on a full duplex switched Ethernet network, even under very high network load conditions. This enables an efficient digital network to replace a large number of conventional analog connections between control rooms and high voltage switchyards.

Executive function development and stress effects on driving performance : preliminary findings from a young adult sample.

This chapter presents a pilot study examining the interactive contributions of executive function development/impairment and psychosocial stress to young adults’ (17-30 years old) driving behaviour in a simulator city scenario.

Utilising Reliability and Condition Monitoring Data for Asset Health Prognosis

The ability to forecast machinery health is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models which attempt to forecast machinery health based on condition data such as vibration measurements. This paper demonstrates how the population characteristics and condition monitoring data (both complete and suspended) of historical items can be integrated for training an intelligent agent to predict asset health multiple steps ahead. The model consists of a feed-forward neural network whose training targets are asset survival probabilities estimated using a variation of the Kaplan–Meier estimator and a degradation-based failure probability density function estimator. The trained network is capable of estimating the future survival probabilities when a series of asset condition readings are inputted. The output survival probabilities collectively form an estimated survival curve. Pump data from a pulp and paper mill were used for model validation and comparison. The results indicate that the proposed model can predict more accurately as well as further ahead than similar models which neglect population characteristics and suspended data. This work presents a compelling concept for longer-range fault prognosis utilising available information more fully and accurately.

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.

How activation, entanglement, and searching a semantic network contribute to event memory

Free association norms indicate that words are organized into semantic/associative neighborhoods within a larger network of words and links that bind the net together. We present evidence indicating that memory for a recent word event can depend on implicitly and simultaneously activating related words in its neighborhood. Processing a word during encoding primes its network representation as a function of the density of the links in its neighborhood. Such priming increases recall and recognition and can have long lasting effects when the word is processed in working memory. Evidence for this phenomenon is reviewed in extralist cuing, primed free association, intralist cuing, and single-item recognition tasks. The findings also show that when a related word is presented to cue the recall of a studied word, the cue activates it in an array of related words that distract and reduce the probability of its selection. The activation of the semantic network produces priming benefits during encoding and search costs during retrieval. In extralist cuing recall is a negative function of cue-to-distracter strength and a positive function of neighborhood density, cue-to-target strength, and target-to cue strength. We show how four measures derived from the network can be combined and used to predict memory performance. These measures play different roles in different tasks indicating that the contribution of the semantic network varies with the context provided by the task. We evaluate spreading activation and quantum-like entanglement explanations for the priming effect produced by neighborhood density.

Effect of cold water immersion after exercise in the heat on muscle function, body temperatures, and vessel diameter

Cold water immersion (CWI) is a popular recovery modality, but actual physiological responses to CWI after exercise in the heat have not been well documented. The purpose of this study was to examine effects of 20-min CWI (14 degrees C) on neuromuscular function, rectal (T(re)) and skin temperature (T(sk)), and femoral venous diameter after exercise in the heat. Ten well-trained male cyclists completed two bouts of exercise consisting of 90-min cycling at a constant power output (216+/-12W) followed by a 16.1km time trial (TT) in the heat (32 degrees C). Twenty-five minutes post-TT, participants were assigned to either CWI or control (CON) recovery conditions in a counterbalanced order. T(re) and T(sk) were recorded continuously, and maximal voluntary isometric contraction torque of the knee extensors (MVIC), MVIC with superimposed electrical stimulation (SMVIC), and femoral venous diameters were measured prior to exercise, 0, 45, and 90min post-TT. T(re) was significantly lower in CWI beginning 50min post-TT compared with CON, and T(sk) was significantly lower in CWI beginning 25min post-TT compared with CON. Decreases in MVIC, and SMVIC torque after the TT were significantly greater for CWI compared with CON; differences persisted 90min post-TT. Femoral vein diameter was approximately 9% smaller for CWI compared with CON at 45min post-TT. These results suggest that CWI decreases T(re), but has a negative effect on neuromuscular function.

Detecting Symbian OS malware through static function call analysis

Smartphones become very critical part of our lives as they offer advanced capabilities with PC-like functionalities. They are getting widely deployed while not only being used for classical voice-centric communication. New smartphone malwares keep emerging where most of them still target Symbian OS. In the case of Symbian OS, application signing seemed to be an appropriate measure for slowing down malware appearance. Unfortunately, latest examples showed that signing can be bypassed resulting in new malware outbreak. In this paper, we present a novel approach to static malware detection in resource-limited mobile environments. This approach can be used to extend currently used third-party application signing mechanisms for increasing malware detection capabilities. In our work, we extract function calls from binaries in order to apply our clustering mechanism, called centroid. This method is capable of detecting unknown malwares. Our results are promising where the employed mechanism might find application at distribution channels, like online application stores. Additionally, it seems suitable for directly being used on smartphones for (pre-)checking installed applications.