Identification of genes important for growth of asymptomatic bacteriuria Escherichia coli in urine

Escherichia coli is the most important etiological agent of urinary tract infections (UTIs). Unlike uropathogenic E. coli, which causes symptomatic infections, asymptomatic bacteriuria (ABU) E. coli strains typically lack essential virulence factors and colonize the bladder in the absence of symptoms. While ABU E. coli can persist in the bladder for long periods of time, little is known about the genetic determinants required for its growth and fitness in urine. To identify such genes, we have employed a transposon mutagenesis approach using the prototypic ABU E. coli strain 83972 and the clinical ABU E. coli strain VR89. Six genes involved in the biosynthesis of various amino acids and nucleobases were identified (carB, argE, argC, purA, metE, and ilvC), and site-specific mutants were subsequently constructed in E. coli 83972 and E. coli VR89 for each of these genes. In all cases, these mutants exhibited reduced growth rates and final cell densities in human urine. The growth defects could be complemented in trans as well as by supplementation with the appropriate amino acid or nucleobase. When assessed in vivo in a mouse model, E. coli 83972carAB and 83972argC showed a significantly reduced competitive advantage in the bladder and/or kidney during coinoculation experiments with the parent strain, whereas 83972metE and 83972ilvC did not. Taken together, our data have identified several biosynthesis pathways as new important fitness factors associated with the growth of ABU E. coli in human urine.

Bisphenol A exposure is not associated with area-level socioeconomic index in Australian children using pooled urine samples

Bisphenol A (BPA) is used extensively in food-contact materials and has been detected routinely in populations worldwide, and this exposure has been linked to a range of negative health outcomes in humans. There is some evidence of an association between BPA and different socioeconomic variables which may be the result of different dietary patterns. The aim of this study was to conduct a preliminary investigation of the association between BPA and socioeconomic status in Australian children using pooled urine specimens and an area level socioeconomic index. Surplus pathology urine specimens collected from children aged 0-15 years in Queensland, Australia as samples of convenience (n = 469) were pooled by age, sex and area level socioeconomic index (n = 67 pools), and analysed for total BPA using online solid phase extraction LC-MS/MS. Concentration ranged from 1.08-27.4 ng/ml with geometric mean 2.57 ng/ml, and geometric mean exposure was estimated as 70.3 ng/kg d-1. Neither BPA concentration nor excretion was associated with age or sex, and the authors found no evidence of an association with socioeconomic status. These results suggest that BPA exposure is not associated with socioeconomic status in the Australian population due to relatively homogenous exposures in Australia, or that the socioeconomic gradient is relatively slight in Australia compared with other OECD countries.

Human vs. machine : identification of bat species from their echolocation calls by humans and by artificial neural networks

Automated remote ultrasound detectors allow large amounts of data on bat presence and activity to be collected. Processing of such data involves identifying bat species from their echolocation calls. Automated species identification has the potential to provide more consistent, predictable, and potentially higher levels of accuracy than identification by humans. In contrast, identification by humans permits flexibility and intelligence in identification, as well as the incorporation of features and patterns that may be difficult to quantify. We compared humans with artificial neural networks (ANNs) in their ability to classify short recordings of bat echolocation calls of variable signal to noise ratios; these sequences are typical of those obtained from remote automated recording systems that are often used in large-scale ecological studies. We presented 45 recordings (1–4 calls) produced by known species of bats to ANNs and to 26 human participants with 1 month to 23 years of experience in acoustic identification of bats. Humans correctly classified 86% of recordings to genus and 56% to species; ANNs correctly identified 92% and 62%, respectively. There was no significant difference between the performance of ANNs and that of humans, but ANNs performed better than about 75% of humans. There was little relationship between the experience of the human participants and their classification rate. However, humans with <1 year of experience performed worse than others. Currently, identification of bat echolocation calls by humans is suitable for ecological research, after careful consideration of biases. However, improvements to ANNs and the data that they are trained on may in future increase their performance to beyond those demonstrated by humans.

Identification of New Zealand bats in flight from analysis of echolocation calls by artificial neural networks.

Time-expanded and heterodyned echolocation calls of the New Zealand long-tailed Chalinolobus tuberculatus and lesser short-tailed bat Mystacina tuberculata were recorded and digitally analysed. Temporal and spectral parameters were measured from time-expanded calls and power spectra generated for both time-expanded and heterodyned calls. Artificial neural networks were trained to classify the calls of both species using temporal and spectral parameters and power spectra as input data. Networks were then tested using data not previously seen. Calls could be unambiguously identified using parameters and power spectra from time-expanded calls. A neural network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 40 kHz (the frequency with the most energy of the fundamental of C. tuberculatus call), could identify 99% and 84% of calls of C. tuberculatus and M. tuberculata, respectively. A second network, trained and tested using power spectra of calls from both species recorded using a heterodyne detector set to 27 kHz (the frequency with the most energy of the fundamental of M. tuberculata call), could identify 34% and 100% of calls of C. tuberculatus and M. tuberculata, respectively. This study represents the first use of neural networks for the identification of bats from their echolocation calls. It is also the first study to use power spectra of time-expanded and heterodyned calls for identification of chiropteran species. The ability of neural networks to identify bats from their echolocation calls is discussed, as is the ecology of both species in relation to the design of their echolocation calls.

Acoustic identification of 12 species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Acoustic identification of twelve species of echolocating bat by discriminant function analysis and artificial neural networks

We recorded echolocation calls from 14 sympatric species of bat in Britain. Once digitised, one temporal and four spectral features were measured from each call. The frequency-time course of each call was approximated by fitting eight mathematical functions, and the goodness of fit, represented by the mean-squared error, was calculated. Measurements were taken using an automated process that extracted a single call from background noise and measured all variables without intervention. Two species of Rhinolophus were easily identified from call duration and spectral measurements. For the remaining 12 species, discriminant function analysis and multilayer back-propagation perceptrons were used to classify calls to species level. Analyses were carried out with and without the inclusion of curve-fitting data to evaluate its usefulness in distinguishing among species. Discriminant function analysis achieved an overall correct classification rate of 79% with curve-fitting data included, while an artificial neural network achieved 87%. The removal of curve-fitting data improved the performance of the discriminant function analysis by 2 %, while the performance of a perceptron decreased by 2 %. However, an increase in correct identification rates when curve-fitting information was included was not found for all species. The use of a hierarchical classification system, whereby calls were first classified to genus level and then to species level, had little effect on correct classification rates by discriminant function analysis but did improve rates achieved by perceptrons. This is the first published study to use artificial neural networks to classify the echolocation calls of bats to species level. Our findings are discussed in terms of recent advances in recording and analysis technologies, and are related to factors causing convergence and divergence of echolocation call design in bats.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Autonomous helicopter hover using an artificial neural network

Details the developments to date of an unmanned air vehicle (UAV) based on a standard size 60 model helicopter. The design goal is to have the helicopter achieve stable hover with the aid of an INS and stereo vision. The focus of the paper is on the development of an artificial neural network (ANN) that makes use of only the INS data to generate hover commands, which are used to directly manipulate the flight servos. Current results show that networks incorporating some form of recurrency (state history) offer little advantage over those without. At this stage, the ANN has partially maintained periods of hover even with misaligned sensors.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Comparative proteomics of uropathogenic Escherichia coli during growth in human urine identify UCA-like (UCL) fimbriae as an adherence factor involved in biofilm formation and binding to uroepithelial cells

Uropathogenic Escherichia coli (UPEC) are the primary cause of urinary tract infection (UTI) in humans. For the successful colonisation of the human urinary tract, UPEC employ a diverse collection of secreted or surface-exposed virulence factors including toxins, iron acquisition systems and adhesins. In this study, a comparative proteomic approach was utilised to define the UPEC pan and core surface proteome following growth in pooled human urine. Identified proteins were investigated for subcellular origin, prevalence and homology to characterised virulence factors. Fourteen core surface proteins were identified, as well as eleven iron uptake receptor proteins and four distinct fimbrial types, including type 1, P, F1C/S and a previously uncharacterised fimbrial type, designated UCA-like (UCL) fimbriae in this study. These pathogenicity island (PAI)-associated fimbriae are related to UCA fimbriae of Proteus mirabilis, associated with UPEC and exclusively found in members of the E. coli B2 and D phylogroup. We further demonstrated that UCL fimbriae promote significant biofilm formation on abiotic surfaces and mediate specific attachment to exfoliated human uroepithelial cells. Combined, this study has defined the surface proteomic profiles and core surface proteome of UPEC during growth in human urine and identified a new type of fimbriae that may contribute to UTI.

Threats and Promises: The Promotion of Artificial Lighting to Women Consumers in the United States, 1880s – 1960s

This article examines the development of a specific gendered discourse in the United States in the first half of the twentieth century that united key beliefs about feminine beauty, identity, and the domestic interior with particular electric lighting technologies and effects. Largely driven by the electrical industry’s marketing rhetoric, American women were encouraged to adopt electric lighting as a beauty aid and ally in a host of domestic tasks. Drawing evidence from a number of primary texts, including women’s magazines, lighting and electrical industry trade journals, manufacturer-generated marketing materials, and popular home decoration and beauty advice literature, this study shifts the focus away from lighting as a basic utility, demonstrating the ways in which modern electric illumination was culturally constructed as a desirable personal and environmental beautifier as well as a means of harmonizing the domestic interior.

Concentrations of phthalates and DINCH metabolites in pooled urine from Queensland, Australia

Dialkyl phthalate esters (phthalates) are ubiquitous chemicals used extensively as plasticizers, solvents and adhesives in a range of industrial and consumer products. 1,2-Cyclohexane dicarboxylic acid, diisononyl ester (DINCH) is a phthalate alternative introduced due to a more favourable toxicological profile, but exposure is largely uncharacterised. The aim of this study was to provide the first assessment of exposure to phthalates and DINCH in the general Australian population. De-identified urine specimens stratified by age and sex were obtained from a community-based pathology laboratory and pooled (n = 24 pools of 100). Concentrations of free and total species were measured using online solid phase extraction isotope dilution high performance liquid chromatography tandem mass spectrometry. Concentrations ranged from 2.4 to 71.9 ng/mL for metabolites of di(2-ethylhexyl)phthalate, and from < 0.5 to 775 ng/mL for all other metabolites. Our data suggest that phthalate metabolites concentrations in Australia were at least two times higher than in the United States and Germany; and may be related to legislative differences among countries. DINCH metabolite concentrations were comparatively low and consistent with the limited data available. Ongoing biomonitoring among the general Australian population may help assess temporal trends in exposure and assess the effectiveness of actions aimed at reducing exposures.

Monitoring exposure to polycyclic aromatic hydrocarbons in an Australian population using pooled urine samples

Integrated exposure to polycyclic aromatic hydrocarbons (PAHs) can be assessed through monitoring of urinary mono-hydroxylated PAHs (OH-PAHs). The aim of this study was to provide the first assessment of exposure to PAHs in a large sample of the population in Queensland, Australia including exposure to infant (0-4. years). De-identified urine specimens, obtained from a pathology laboratory, were stratified by age and sex, and pooled (n. =. 24 pools of 100) and OH-PAHs were measured by gas chromatography-isotope dilution-tandem mass spectrometry. Geometric mean (GM) concentrations ranged from 30. ng/L (4-hydroxyphenanthrene) to 9221. ng/L (1-naphthol). GM of 1-hydroxypyrene, the most commonly used PAH exposure biomarker, was 142. ng/L. The concentrations of OH-PAHs found in this study are consistent with those in developed countries and lower than those in developing countries. We observed no association between sex and OH-PAH concentrations. However, we observed lower urinary concentrations of all OH-PAHs in samples from infants (0-4. years), children (5-14. years) and the elderly (>. 60. year old) compared with samples from other age groups (15-29, 30-44 and 45-59. years) which may be attributed to age-dependent behaviour-specific exposure sources.

Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

The Edge of Danger: artificial lighting and the dialectics of domestic occupation in Philip Johnson's Glass and Guest Houses

In the first half of the twentieth century the dematerializing of boundaries between enclosure and exposure problematized traditional acts of “occupation” and understandings of the domestic environment. As a space of escalating technological control, the modern domestic interior offered new potential to re-define the meaning and means of habitation. This shift is clearly expressed in the transformation of electric lighting technology and applications for the modern interior in the mid-twentieth century. Addressing these issues, this paper examines the critical role of electric lighting in regulating and framing both the public and private occupation of Philip Johnson’s New Canaan estate. Exploring the dialectically paired transparent Glass House and opaque Guest House (both 1949), this study illustrates how Johnson employed artificial light to control the visual environment of the estate as well as to aestheticize the performance of domestic space. Looking closely at the use of artificial light to create emotive effects as well as to intensify the experience of occupation, this revisiting of the iconic Glass House and lesser-known Guest House provides a more complex understanding of Johnson’s work and the means with which he inhabited his own architecture. Calling attention to the importance of Johnson serving as both architect and client, and his particular interest in exploring the new potential of architectural lighting in this period, this paper investigates Johnson’s use of electric light to support architectural narratives, maintain visual order and control, and to suit the nuanced desires of domestic occupation.