Semiparametric Latent Variable Regression Models for Spatio-temporal Modeling of Mobile Source Particles in the Greater Boston Area

Traffic particle concentrations show considerable spatial variability within a metropolitan area. We consider latent variable semiparametric regression models for modeling the spatial and temporal variability of black carbon and elemental carbon concentrations in the greater Boston area. Measurements of these pollutants, which are markers of traffic particles, were obtained from several individual exposure studies conducted at specific household locations as well as 15 ambient monitoring sites in the city. The models allow for both flexible, nonlinear effects of covariates and for unexplained spatial and temporal variability in exposure. In addition, the different individual exposure studies recorded different surrogates of traffic particles, with some recording only outdoor concentrations of black or elemental carbon, some recording indoor concentrations of black carbon, and others recording both indoor and outdoor concentrations of black carbon. A joint model for outdoor and indoor exposure that specifies a spatially varying latent variable provides greater spatial coverage in the area of interest. We propose a penalised spline formation of the model that relates to generalised kringing of the latent traffic pollution variable and leads to a natural Bayesian Markov Chain Monte Carlo algorithm for model fitting. We propose methods that allow us to control the degress of freedom of the smoother in a Bayesian framework. Finally, we present results from an analysis that applies the model to data from summer and winter separately

Reduced Bayesian Hierarchical Models: Estimating Health Effects of Simultaneous Exposure to Multiple Pollutants

Quantifying the health effects associated with simultaneous exposure to many air pollutants is now a research priority of the US EPA. Bayesian hierarchical models (BHM) have been extensively used in multisite time series studies of air pollution and health to estimate health effects of a single pollutant adjusted for potential confounding of other pollutants and other time-varying factors. However, when the scientific goal is to estimate the impacts of many pollutants jointly, a straightforward application of BHM is challenged by the need to specify a random-effect distribution on a high-dimensional vector of nuisance parameters, which often do not have an easy interpretation. In this paper we introduce a new BHM formulation, which we call "reduced BHM", aimed at analyzing clustered data sets in the presence of a large number of random effects that are not of primary scientific interest. At the first stage of the reduced BHM, we calculate the integrated likelihood of the parameter of interest (e.g. excess number of deaths attributed to simultaneous exposure to high levels of many pollutants). At the second stage, we specify a flexible random-effect distribution directly on the parameter of interest. The reduced BHM overcomes many of the challenges in the specification and implementation of full BHM in the context of a large number of nuisance parameters. In simulation studies we show that the reduced BHM performs comparably to the full BHM in many scenarios, and even performs better in some cases. Methods are applied to estimate location-specific and overall relative risks of cardiovascular hospital admissions associated with simultaneous exposure to elevated levels of particulate matter and ozone in 51 US counties during the period 1999-2005.

Potentially human pathogenic Acanthamoeba isolated from a heated indoor swimming pool in Switzerland

Some free-living amoebae, including some species of the genus Acanthamoeba, can cause infections in humans and animals. These organisms are known to cause granulomatous amebic encephalitis (GAE) in predominantly immune-deficient persons. In the present study, we isolated a potentially human pathogenic Acanthamoeba isolate originating from a public heated indoor swimming pool in Switzerland. The amoebae, thermophilically preselected by culture at 37 degrees C, subsequently displayed a high thermotolerance, being able to grow at 42 degrees C, and a marked cytotoxicity, based on a co-culture system using the murine cell line L929. Intranasal infection of Rag2-immunodeficient mice resulted in the death of all animals within 24 days. Histopathology of brains and lungs revealed marked tissue necrosis and hemorrhagic lesions going along with massive proliferation of amoebae. PCR and sequence analysis, based on 18S rDNA, identified the agent as Acanthamoeba lenticulata. In summary, the present study reports on an Acanthamoeba isolate from a heated swimming pool suggestive of being potentially pathogenic to immunocompromised persons.

Unexplained gonad alterations in whitefish (Coregonus spp.) from Lake Thun, Switzerland: levels of persistent organic pollutants in different morphs

Since 2000, a surprisingly high number of macroscopical gonad alterations has been reported in whitefish (Coregonus spp.) from Lake Thun, Switzerland. This unique phenomenon is still unexplained and has received much public attention. As one possible trigger for these effects, the presence of persistent, bioaccumulative and toxic compounds acting as endocrine disruptors in the lake has been discussed. In this study, concentrations of selected persistent organic pollutants were examined in two morphs of whitefish from Lake Thun and their link to the observed abnormalities was investigated. Analyzed compound classes included polychlorinated biphenyls, polychlorinated dibenzo-p-dioxins and dibenzofurans, polychlorinated naphthalenes, polybrominated diphenyl ethers and hexabromocyclododecanes. The target substances were identified in all samples and concentrations of the analyzed compounds were highly correlated among each other. These correlations show that the analyzed substances have the same distribution pattern throughout the lake and that uptake, accumulation and elimination processes are similar. Significant differences in contaminant levels within the samples existed between the two analyzed morphs of whitefish, most likely due to different age, food patterns and growth rate. No difference in contaminant levels was observed between fish with abnormal gonads and fish with normal gonads, suggesting no causal link between the investigated lipophilic organohalogen compounds present in fish and the observed gonad abnormalities in whitefish from Lake Thun. A comparison to existing data shows that concentrations in Lake Thun whitefish are at the lower bound of contaminant levels in whitefish from Swiss lakes or from European waters.

Optical Indoor Positioning of Vehicles

Indoor positioning is the backbone of many advanced intra-logistic applications. As opposed to unified outdoor satellite positioning systems, there are many different technical approaches to indoor positioning. Depending on the application, there are different trade-offs between accuracy, range, and costs. In this paper we present a new concept for a 4-degree-of-freedom (4-DOF) positioning system to be used for vehicle tracing in a logistic facility. The system employs optical data transmission between active infrastructure and receiver devices. Compared to existing systems, these optical technologies promise to achieve better accuracy at lower costs. We will introduce the positioning algorithm and an experimental setup of the system.

Hands-Free Navigation in Immersive Environments for the Evaluation of the Effectiveness of Indoor Navigation Systems

While navigation systems for cars are in widespread use, only recently, indoor navigation systems based on smartphone apps became technically feasible. Hence tools in order to plan and evaluate particular designs of information provision are needed. Since tests in real infrastructures are costly and environmental conditions cannot be held constant, one must resort to virtual infrastructures. This paper presents the development of an environment for the support of the design of indoor navigation systems whose center piece consists in a hands-free navigation method using the Microsoft Kinect in the four-sided Definitely Affordable Virtual Environment (DAVE). Navigation controls using the user's gestures and postures as the input to the controls are designed and implemented. The installation of expensive and bulky hardware like treadmills is avoided while still giving the user a good impression of the distance she has traveled in virtual space. An advantage in comparison to approaches using a head mounted display is that the DAVE allows the users to interact with their smartphone. Thus the effects of different indoor navigation systems can be evaluated already in the planning phase using the resulting system

Investigation on the evolution of an indoor robotic localization system based on wireless networks

This work addresses the evolution of an artificial neural network (ANN) to assist in the problem of indoor robotic localization. We investigate the design and building of an autonomous localization system based on information gathered from wireless networks (WN). The article focuses on the evolved ANN, which provides the position of a robot in a space, as in a Cartesian coordinate system, corroborating with the evolutionary robotic research area and showing its practical viability. The proposed system was tested in several experiments, evaluating not only the impact of different evolutionary computation parameters but also the role of the transfer functions on the evolution of the ANN. Results show that slight variations in the parameters lead to significant differences on the evolution process and, therefore, in the accuracy of the robot position.

SDR-based Passive Indoor Localization System for GSM

This study deals with indoor positioning using GSM radio, which has the distinct advantage of wide coverage over other wireless technologies. In particular, we focus on passive localization systems that are able to achieve high localization accuracy without any prior knowledge of the indoor environment or the tracking device radio settings. In order to overcome these challenges, newly proposed localization algorithms based on the exploitation of the received signal strength (RSS) are proposed. We explore the effects of non-line-of-sight communication links, opening and closing of doors, and human mobility on RSS measurements and localization accuracy. We have implemented the proposed algorithms on top of software defined radio systems and carried out detailed empirical indoor experiments. The performance results show that the proposed solutions are accurate with average localization errors between 2.4 and 3.2 meters.

Range-based Weighted-likelihood Particle Filter for RSS-based Indoor Tracking

Attractive business cases in various application fields contribute to the sustained long-term interest in indoor localization and tracking by the research community. Location tracking is generally treated as a dynamic state estimation problem, consisting of two steps: (i) location estimation through measurement, and (ii) location prediction. For the estimation step, one of the most efficient and low-cost solutions is Received Signal Strength (RSS)-based ranging. However, various challenges - unrealistic propagation model, non-line of sight (NLOS), and multipath propagation - are yet to be addressed. Particle filters are a popular choice for dealing with the inherent non-linearities in both location measurements and motion dynamics. While such filters have been successfully applied to accurate, time-based ranging measurements, dealing with the more error-prone RSS based ranging is still challenging. In this work, we address the above issues with a novel, weighted likelihood, bootstrap particle filter for tracking via RSS-based ranging. Our filter weights the individual likelihoods from different anchor nodes exponentially, according to the ranging estimation. We also employ an improved propagation model for more accurate RSS-based ranging, which we suggested in recent work. We implemented and tested our algorithm in a passive localization system with IEEE 802.15.4 signals, showing that our proposed solution largely outperforms a traditional bootstrap particle filter.

Fine-grained Indoor Tracking by Fusing Inertial Sensor and Physical Layer Information in WLANs

Indoor positioning has become an emerging research area because of huge commercial demands for location-based services in indoor environments. Channel State Information (CSI) as a fine-grained physical layer information has been recently proposed to achieve high positioning accuracy by using range-based methods, e.g., trilateration. In this work, we propose to fuse the CSI-based ranges and velocity estimated from inertial sensors by an enhanced particle filter to achieve highly accurate tracking. The algorithm relies on some enhanced ranging methods and further mitigates the remaining ranging errors by a weighting technique. Additionally, we provide an efficient method to estimate the velocity based on inertial sensors. The algorithms are designed in a network-based system, which uses rather cheap commercial devices as anchor nodes. We evaluate our system in a complex environment along three different moving paths. Our proposed tracking method can achieve 1:3m for mean accuracy and 2:2m for 90% accuracy, which is more accurate and stable than pedestrian dead reckoning and range-based positioning.

Robust indoor localization of narrowband signals

Many location-based services target users in indoor environments. Similar to the case of dense urban areas where many obstacles exist, indoor localization techniques suffer from outlying measurements caused by severe multipath propaga??tion and non-line-of-sight (NLOS) reception. Obstructions in the signal path caused by static or mobile objects downgrade localization accuracy. We use robust multipath mitigation techniques to detect and filter out outlying measurements in indoor environments. We validate our approach using a power-based lo??calization system with GSM. We conducted experiments without any prior knowledge of the tracked device's radio settings or the indoor radio environment. We obtained localization errors in the range of 3m even if the sensors had NLOS links to the target device.

Linking Ah receptor mediated effects of sediments and impacts on fish to key pollutants in the Yangtze Three Gorges Reservoir, China – A comprehensive perspective

The Three Gorges Reservoir (TGR), created in consequence of the Yangtze River's impoundment by the Three Gorges Dam, faces numerous anthropogenic impacts that challenge its unique ecosystem. Organic pollutants, particularly aryl hydrocarbon receptor (AhR) agonists, have been widely detected in the Yangtze River, but only little research was yet done on AhR-mediated activities. Hence, in order to assess effects of organic pollution, with particular focus on AhR-mediated activities, several sites in the TGR area were examined applying the "triad approach". It combines chemical analysis, in vitro, in vivo and in situ investigations to a holistic assessment. Sediments and the benthic fish species Pelteobagrus vachellii were sampled in 2011/2012, respectively, to identify relevant endpoints. Sediment was tested in vitro with the ethoxyresorufin-O-deethylase (EROD) induction assay, and in vivo with the Fish Embryo Toxicity Test and Sediment Contact Assay with Danio rerio. Activities of phase I (EROD) and phase II (glutathione-S-transferase) biotransformation enzymes, pollutant metabolites and histopathological alterations were studied in situ in P. vachellii. EROD induction was tested in vitro and in situ to evaluate possible relationships. Two sites, near Chongqing and Kaixian city, were identified as regional hot-spots and further investigated in 2013. The sediments induced in the in vitro/in vivo bioassays AhR-mediated activities and embryotoxic/teratogenic effects - particularly on the cardiovascular system. These endpoints could be significantly correlated to each other and respective chemical data. However, particle-bound pollutants showed only low bioavailability. The in situ investigations suggested a rather poor condition of P. vachellii, with histopathological alterations in liver and excretory kidney. Fish from Chongqing city exhibited significant hepatic EROD induction and obvious parasitic infestations. The polycyclic aromatic hydrocarbon (PAH) metabolite 1-hydroxypyrene was detected in bile of fish from all sites. All endpoints in combination with the chemical data suggest a pivotal role of PAHs in the observed ecotoxicological impacts.