Genotoxicity of inorganic lead salts and disturbance of microtubule function

Lead compounds are known genotoxicants, principally affecting the integrity of chromosomes. Lead chloride and lead acetate induced concentration-dependent increases in micronucleus frequency in V79 cells, starting at 1.1 μM lead chloride and 0.05 μM lead acetate. The difference between the lead salts, which was expected based on their relative abilities to form complex acetato-cations, was confirmed in an independent experiment. CREST analyses of the micronuclei verified that lead chloride and acetate were predominantly aneugenic (CREST-positive response), which was consistent with the morphology of the micronuclei (larger micronuclei, compared with micronuclei induced by a clastogenic mechanism). The effects of high concentrations of lead salts on the microtubule network of V79 cells were also examined using immunofluorescence staining. The dose effects of these responses were consistent with the cytotoxicity of lead(II), as visualized in the neutral-red uptake assay. In a cell-free system, 20-60 μM lead salts inhibited tubulin assembly dose-dependently. The no-observed-effect concentration of lead(II) in this assay was 10 μM. This inhibitory effect was interpreted as a shift of the assembly/disassembly steady-state toward disassembly, e.g., by reducing the concentration of assembly-competent tubulin dimers. The effects of lead salts on microtubule-associated motor-protein functions were studied using a kinesin-gliding assay that mimics intracellular transport processes in vitro by quantifying the movement of paclitaxel-stabilized microtubules across a kinesin-coated glass surface. There was a dose-dependent effect of lead nitrate on microtubule motility. Lead nitrate affected the gliding velocities of microtubules starting at concentrations above 10 μM and reached half-maximal inhibition of motility at about 50 μM. The processes reported here point to relevant interactions of lead with tubulin and kinesin at low dose levels.

The hereditary transmission of the glutathione transferase hGSTT1-1 conjugator phenotype in a large family

The polymorphism of human glutathione transferase hGSTT1-1 is expressed in three phenotypes. Experimentally, individuals can be classified as non-conjugators, low conjugators and 'high' conjugators depending on the enzyme activity in blood towards methylene chloride using a gas chromatographic assay. Non-conjugators do not have a functional hGSTT1 gene; however, little is known about the molecular basis of the three conjugator phenotypes. The higher hGSTT1-1 activity in high conjugators may be the result of enzyme induction or be genetically determined. Twenty-nine members of a large family, including three generations were phenotyped and genotyped with respect to hGSTT1-1. The hGSTT1-1 enzyme activity of high conjugators was twice as high as that of low conjugators. The distribution of hGSTT1-1 phenotypes strongly indicates a Mendelian intermediary inheritance, in which a gene-dosage effect results in a doubled enzyme expression in the presence of two functional alleles. The Mendelian intermediary inheritance is further supported by the findings of a semiquantitative polymerase chain reaction method designed to distinguish the three genotypes of hGSTT1 for rapid screening of large study groups.

A convolutional neural network for automatic analysis of aerial imagery

This paper introduces a new method to automate the detection of marine species in aerial imagery using a Machine Learning approach. Our proposed system has at its core, a convolutional neural network. We compare this trainable classifier to a handcrafted classifier based on color features, entropy and shape analysis. Experiments demonstrate that the convolutional neural network outperforms the handcrafted solution. We also introduce a negative training example-selection method for situations where the original training set consists of a collection of labeled images in which the objects of interest (positive examples) have been marked by a bounding box. We show that picking random rectangles from the background is not necessarily the best way to generate useful negative examples with respect to learning.

Model choice problems using approximate Bayesian computation with applications to pathogen transmission data sets

Analytically or computationally intractable likelihood functions can arise in complex statistical inferential problems making them inaccessible to standard Bayesian inferential methods. Approximate Bayesian computation (ABC) methods address such inferential problems by replacing direct likelihood evaluations with repeated sampling from the model. ABC methods have been predominantly applied to parameter estimation problems and less to model choice problems due to the added difficulty of handling multiple model spaces. The ABC algorithm proposed here addresses model choice problems by extending Fearnhead and Prangle (2012, Journal of the Royal Statistical Society, Series B 74, 1–28) where the posterior mean of the model parameters estimated through regression formed the summary statistics used in the discrepancy measure. An additional stepwise multinomial logistic regression is performed on the model indicator variable in the regression step and the estimated model probabilities are incorporated into the set of summary statistics for model choice purposes. A reversible jump Markov chain Monte Carlo step is also included in the algorithm to increase model diversity for thorough exploration of the model space. This algorithm was applied to a validating example to demonstrate the robustness of the algorithm across a wide range of true model probabilities. Its subsequent use in three pathogen transmission examples of varying complexity illustrates the utility of the algorithm in inferring preference of particular transmission models for the pathogens.

Treatment for prevention of HIV transmission in a localised epidemic : the case for South Australia

Background: Discussion is currently taking place among international HIV/AIDS groups around increasing HIV testing and initiating earlier use of antiretroviral therapy (ART) among people diagnosed with HIV as a method to reduce the spread of HIV. In this study, we explore the expected epidemiological impact of this strategy in a small population in which HIV transmission is predominantly confined to men who have sex with men (MSM). Methods: A deterministic mathematical transmission model was constructed to investigate the impacts of strategies that increase testing and treatment rates, and their likely potential to mitigate HIV epidemics among MSM. Our novel model distinguishes men in the population who are more easily accessible to prevention campaigns through engagement with the gay community from men who are not. This model is applied to the population of MSM in South Australia. Results: Our model-based findings suggest that increasing testing rates alone will have minimal impact on reducing the expected number of infections compared to current conditions. However, in combination with increases in treatment coverage, this strategy could lead to a 59–68% reduction in the number of HIV infections over the next 5 years. Targeting men who are socially engaged with the gay community would result in the majority of potential reductions in incidence, with only minor improvements possible by reaching all other MSM. Conclusions: Investing in strategies that will achieve higher coverage and earlier initiation of treatment to reduce infectiousness of HIV-infected individuals could be an effective strategy for reducing incidence in a population of MSM.

Spatial and temporal patterns of locally-acquired dengue transmission in northern Queensland, Australia, 1993-2012

BACKGROUND: Dengue has been a major public health concern in Australia since it re-emerged in Queensland in 1992-1993. We explored spatio-temporal characteristics of locally-acquired dengue cases in northern tropical Queensland, Australia during the period 1993-2012. METHODS: Locally-acquired notified cases of dengue were collected for northern tropical Queensland from 1993 to 2012. Descriptive spatial and temporal analyses were conducted using geographic information system tools and geostatistical techniques. RESULTS: 2,398 locally-acquired dengue cases were recorded in northern tropical Queensland during the study period. The areas affected by the dengue cases exhibited spatial and temporal variation over the study period. Notified cases of dengue occurred more frequently in autumn. Mapping of dengue by statistical local areas (census units) reveals the presence of substantial spatio-temporal variation over time and place. Statistically significant differences in dengue incidence rates among males and females (with more cases in females) (χ(2) = 15.17, d.f.  = 1, p<0.01). Differences were observed among age groups, but these were not statistically significant. There was a significant positive spatial autocorrelation of dengue incidence for the four sub-periods, with the Moran's I statistic ranging from 0.011 to 0.463 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the northern Queensland. CONCLUSIONS: Tropical areas are potential high-risk areas for mosquito-borne diseases such as dengue. This study demonstrated that the locally-acquired dengue cases have exhibited a spatial and temporal variation over the past twenty years in northern tropical Queensland, Australia. Therefore, this study provides an impetus for further investigation of clusters and risk factors in these high-risk areas.

Optimal HMM filtering and decision feedback equalisation for differential encoded transmission systems

In this paper conditional hidden Markov model (HMM) filters and conditional Kalman filters (KF) are coupled together to improve demodulation of differential encoded signals in noisy fading channels. We present an indicator matrix representation for differential encoded signals and the optimal HMM filter for demodulation. The filter requires O(N3) calculations per time iteration, where N is the number of message symbols. Decision feedback equalisation is investigated via coupling the optimal HMM filter for estimating the message, conditioned on estimates of the channel parameters, and a KF for estimating the channel states, conditioned on soft information message estimates. The particular differential encoding scheme examined in this paper is differential phase shift keying. However, the techniques developed can be extended to other forms of differential modulation. The channel model we use allows for multiplicative channel distortions and additive white Gaussian noise. Simulation studies are also presented.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

Identification of pre-spike network in patients with mesial temporal lobe epilepsy

Background: Seizures and interictal spikes in mesial temporal lobe epilepsy (MTLE) affect a network of brain regions rather than a single epileptic focus. Simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) studies have demonstrated a functional network in which hemodynamic changes are time-locked to spikes. However, whether this reflects the propagation of neuronal activity from a focus, or conversely the activation of a network linked to spike generation remains unknown. The functional connectivity (FC) changes prior to spikes may provide information about the connectivity changes that lead to the generation of spikes. We used EEG-fMRI to investigate FC changes immediately prior to the appearance of interictal spikes on EEG in patients with MTLE. Methods/principal findings: Fifteen patients with MTLE underwent continuous EEG-fMRI during rest. Spikes were identified on EEG and three 10 s epochs were defined relative to spike onset: spike (0–10 s), pre-spike (−10 to 0 s), and rest (−20 to −10 s, with no previous spikes in the preceding 45s). Significant spike-related activation in the hippocampus ipsilateral to the seizure focus was found compared to the pre-spike and rest epochs. The peak voxel within the hippocampus ipsilateral to the seizure focus was used as a seed region for FC analysis in the three conditions. A significant change in FC patterns was observed before the appearance of electrographic spikes. Specifically, there was significant loss of coherence between both hippocampi during the pre-spike period compared to spike and rest states. Conclusion/significance: In keeping with previous findings of abnormal inter-hemispheric hippocampal connectivity in MTLE, our findings specifically link reduced connectivity to the period immediately before spikes. This brief decoupling is consistent with a deficit in mutual (inter-hemispheric) hippocampal inhibition that may predispose to spike generation.

A Memetic Algorithm for the location-based continuously operating reference stations placement problem in Network Real-Time Kinematic

Network Real-Time Kinematic (NRTK) is a technology that can provide centimeter-level accuracy positioning services in real time, and it is enabled by a network of Continuously Operating Reference Stations (CORS). The location-oriented CORS placement problem is an important problem in the design of a NRTK as it will directly affect not only the installation and operational cost of the NRTK, but also the quality of positioning services provided by the NRTK. This paper presents a Memetic Algorithm (MA) for the location-oriented CORS placement problem, which hybridizes the powerful explorative search capacity of a genetic algorithm and the efficient and effective exploitative search capacity of a local optimization. Experimental results have shown that the MA has better performance than existing approaches. In this paper we also conduct an empirical study about the scalability of the MA, effectiveness of the hybridization technique and selection of crossover operator in the MA.

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.