Functional error modeling for uncertainty quantification in hydrogeology

Approximate models (proxies) can be employed to reduce the computational costs of estimating uncertainty. The price to pay is that the approximations introduced by the proxy model can lead to a biased estimation. To avoid this problem and ensure a reliable uncertainty quantification, we propose to combine functional data analysis and machine learning to build error models that allow us to obtain an accurate prediction of the exact response without solving the exact model for all realizations. We build the relationship between proxy and exact model on a learning set of geostatistical realizations for which both exact and approximate solvers are run. Functional principal components analysis (FPCA) is used to investigate the variability in the two sets of curves and reduce the dimensionality of the problem while maximizing the retained information. Once obtained, the error model can be used to predict the exact response of any realization on the basis of the sole proxy response. This methodology is purpose-oriented as the error model is constructed directly for the quantity of interest, rather than for the state of the system. Also, the dimensionality reduction performed by FPCA allows a diagnostic of the quality of the error model to assess the informativeness of the learning set and the fidelity of the proxy to the exact model. The possibility of obtaining a prediction of the exact response for any newly generated realization suggests that the methodology can be effectively used beyond the context of uncertainty quantification, in particular for Bayesian inference and optimization.

Dislocability of localization devices for nonpalpable breast lesions: experimental results

Purpose. For accurate resection of nonpalpable malignant breast lesions with a tumor-free resection rim, an exact and stable wire localization is essential. We tested the resistance towards traction force of different localization devices used in our clinic for breast lesions in two types of tissue. Materials and Methods. Eight different commercially available hook-wire devices were examined for resistance towards traction force using an analogue spring scale. Results. Most systems showed a high level of movement already under small traction force. Retractable systems with round hooks such as the Bard DuaLok , the Fil d'Ariane, and the RPLN Breast Localization Device withstood less traction force than the other systems. However, the Bard DuaLok system was very resistant towards a small traction force of 50 g when compared to the other systems. The Ultrawire Breast Localization Device withstood the most traction force in softer tissue and Kopans Breast Lesion Localization Needle withstood the most force in harder tissue. Conclusion. The Ultrawire Breast Localization Device and Kopans Breast Lesion Localization Needle withstood the most traction force. In general retractable systems withstand less traction force than nonretractable systems.

Small quantity but large effect — How minor phases control strain localization in upper mantle shear zones

Low viscosity domains such as localized shear zones exert an important control on the geodynamics of the uppermost mantle. Grain size reduction and subsequent strain localization related to a switch from dislocation to diffusion creep is one mechanism to form low viscosity domains. To sustain strain localization, the grain size of mantle minerals needs to be kept small over geological timescales. One way to keep olivine grain sizes small is by pinning of mobile grain boundaries during grain growth by other minerals (second phases). Detailed microstructural studies based on natural samples from three shear zones formed at different geodynamic settings, allowed the derivation of the olivine grain-size dependence on the second-phase content. The polymineralic olivine grain-size evolution with increasing strain is similar in the three shear zones. If the second phases are to pin the mobile olivine grain boundary the phases need to be well mixed before grain growth. We suggest that melt-rock and metamorphic reactions are crucial for the initial phase mixing in mantle rocks. With ongoing deformation and increasing strain, grain boundary sliding combined with mass transfer processes and nucleation of grains promotes phase mixing resulting in fine-grained polymineralic mixtures that deform by diffusion creep. Strain localization due to the presence of volumetrically minor minerals in polymineralic mantle rocks is only important at high strain deformation (ultramylonites) at low temperatures (<~800°C). At smaller strain and stress conditions and/or higher temperatures other parameters like overall energy available to deform a given rock volume, the inheritance of mechanical anisotropies or the presence of water or melts needs to be considered to explain strain localization in the upper mantle.

Localization and production of peptide endocannabinoids in the rodent CNS and adrenal medulla.

The endocannabinoid system (ECS) comprises the cannabinoid receptors CB1 and CB2 and their endogenous arachidonic acid-derived agonists 2-arachidonoyl glycerol and anandamide, which play important neuromodulatory roles. Recently, a novel class of negative allosteric CB1 receptor peptide ligands, hemopressin-like peptides derived from alpha hemoglobin, has been described, with yet unknown origin and function in the CNS. Using monoclonal antibodies we now identified the localization of RVD-hemopressin (pepcan-12) and N-terminally extended peptide endocannabinoids (pepcans) in the CNS and determined their neuronal origin. Immunohistochemical analyses in rodents revealed distinctive and specific staining in major groups of noradrenergic neurons, including the locus coeruleus (LC), A1, A5 and A7 neurons, which appear to be major sites of production/release in the CNS. No staining was detected in dopaminergic neurons. Peptidergic axons were seen throughout the brain (notably hippocampus and cerebral cortex) and spinal cord, indicative of anterograde axonal transport of pepcans. Intriguingly, the chromaffin cells in the adrenal medulla were also strongly stained for pepcans. We found specific co-expression of pepcans with galanin, both in the LC and adrenal gland. Using LC-MS/MS, pepcan-12 was only detected in non-perfused brain (∼40 pmol/g), suggesting that in the CNS it is secreted and present in extracellular compartments. In adrenal glands, significantly more pepcan-12 (400-700 pmol/g) was measured in both non-perfused and perfused tissue. Thus, chromaffin cells may be a major production site of pepcan-12 found in blood. These data uncover important areas of peptide endocannabinoid occurrence with exclusive noradrenergic immunohistochemical staining, opening new doors to investigate their potential physiological function in the ECS. This article is part of a Special Issue entitled 'Fluorescent Neuro-Ligands'.

A Passive WiFi Source Localization System based on Fine-grained Power-based Trilateration

Indoor localization systems become more interesting for researchers because of the attractiveness of business cases in various application fields. A WiFi-based passive localization system can provide user location information to third-party providers of positioning services. However, indoor localization techniques are prone to multipath and Non-Line Of Sight (NLOS) propagation, which lead to significant performance degradation. To overcome these problems, we provide a passive localization system for WiFi targets with several improved algorithms for localization. Through Software Defined Radio (SDR) techniques, we extract Channel Impulse Response (CIR) information at the physical layer. CIR is later adopted to mitigate the multipath fading problem. We propose to use a Nonlinear Regression (NLR) method to relate the filtered power information to propagation distances, which significantly improves the ranging accuracy compared to the commonly used log-distance path loss model. To mitigate the influence of ranging errors, a new trilateration algorithm is designed as well by combining Weighted Centroid and Constrained Weighted Least Square (WC-CWLS) algorithms. Experiment results show that our algorithm is robust against ranging errors and outperforms the linear least square algorithm and weighted centroid algorithm.

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.

Drosophila valois encodes a divergent WD protein that is required for Vasa localization and Oskar protein accumulation

valois (vls) was identified as a posterior group gene in the initial screens for Drosophila maternal-effect lethal mutations. Despite its early genetic identification, it has not been characterized at the molecular level until now. We show that vls encodes a divergent WD domain protein and that the three available EMS-induced point mutations cause premature stop codons in the vls ORF. We have generated a null allele that has a stronger phenotype than the EMS mutants. The vlsnull mutant shows that vls+ is required for high levels of Oskar protein to accumulate during oogenesis, for normal posterior localization of Oskar in later stages of oogenesis and for posterior localization of the Vasa protein during the entire process of pole plasm assembly. There is no evidence for vls being dependent on an upstream factor of the posterior pathway, suggesting that Valois protein (Vls) instead acts as a co-factor in the process. Based on the structure of Vls, the function of similar proteins in different systems and our phenotypic analysis, it seems likely that vls may promote posterior patterning by facilitating interactions between different molecules.

Localization of Hidden Insulinomas with 68Ga-DOTA-Exendin-4 PET/CT: A Pilot Study.

UNLABELLED (111)In-DOTA-exendin-4 SPECT/CT has been shown to be highly efficient in the detection of insulinomas. We aimed at determining whether novel PET/CT imaging with [Nle(14),Lys(40)(Ahx-DOTA-(68)Ga)NH2]exendin-4 ((68)Ga-DOTA-exendin-4) is feasible and sensitive in detecting benign insulinomas. METHODS (68)Ga-DOTA-exendin-4 PET/CT and (111)In-DOTA-exendin-4 SPECT/CT were performed in a randomized cross-over order on 5 patients with endogenous hyperinsulinemic hypoglycemia. The gold standard for comparison was the histologic diagnosis after surgery. RESULTS In 4 patients histologic diagnosis confirmed a benign insulinoma, whereas one patient refused surgery despite a positive (68)Ga-DOTA-exendin-4 PET/CT scan. In 4 of 5 patients, previously performed conventional imaging (CT or MR imaging) was not able to localize the insulinoma. (68)Ga-DOTA-exendin-4 PET/CT correctly identified the insulinoma in 4 of 4 patients, whereas (111)In-DOTA-exendin-4 SPECT/CT correctly identified the insulinoma in only 2 of 4 patients. CONCLUSION These preliminary data suggest that the use of (68)Ga-DOTA-exendin-4 PET/CT in detecting hidden insulinomas is feasible.

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.

Real-time localization using software defined radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system.