3D Intervertebral Disc Localization and Segmentation from MR Images by Data-Driven Regression and Classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Localization and Segmentation of 3D Intervertebral Discs in MR Images by Data Driven Estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

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'.

Electron and photon energy calibration with the ATLAS detector using LHC Run 1 data

This paper presents the electron and photon energy calibration achieved with the ATLAS detector using about 25 fb−1 of LHC proton–proton collision data taken at centre-of-mass energies of √s = 7 and 8 TeV. The reconstruction of electron and photon energies is optimised using multivariate algorithms. The response of the calorimeter layers is equalised in data and simulation, and the longitudinal profile of the electromagnetic showers is exploited to estimate the passive material in front of the calorimeter and reoptimise the detector simulation. After all corrections, the Z resonance is used to set the absolute energy scale. For electrons from Z decays, the achieved calibration is typically accurate to 0.05% in most of the detector acceptance, rising to 0.2% in regions with large amounts of passive material. The remaining inaccuracy is less than 0.2–1% for electrons with a transverse energy of 10 GeV, and is on average 0.3% for photons. The detector resolution is determined with a relative inaccuracy of less than 10% for electrons and photons up to 60 GeV transverse energy, rising to 40% for transverse energies above 500 GeV.

Search for Higgs boson decays to a photon and a Z boson in pp collisions at √s=7 and 8 TeV with the ATLAS detector

A search is reported for a neutral Higgs boson in the decay channel H → Zγ, Z → ℓ+ℓ− (ℓ = e, μ), using 4.5 fb−1 of pp collisions at √s = 7 TeV and 20.3 fb−1 of pp collisions at √s = 8 TeV, recorded by the ATLAS detector at the CERN Large Hadron Collider. The observed distribution of the invariantmass of the three final-state particles, mℓℓγ, is consistent with the Standard Model hypothesis in the investigated mass range of 120–150 GeV. For a Higgs boson with a mass of 125.5 GeV, the observed upper limit at the 95% confidence level is 11 times the Standard Model expectation. Upper limits are set on the cross section times branching ratio of a neutral Higgs boson with mass in the range 120–150 GeV between 0.13 and 0.5 pb for √s = 8 TeV at 95% confidence level.

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.

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.

Tracking individual membrane proteins and their biochemistry: The power of direct observation

The advent of single molecule fluorescence microscopy has allowed experimental molecular biophysics and biochemistry to transcend traditional ensemble measurements, where the behavior of individual proteins could not be precisely sampled. The recent explosion in popularity of new super-resolution and super-localization techniques coupled with technical advances in optical designs and fast highly sensitive cameras with single photon sensitivity and millisecond time resolution have made it possible to track key motions, reactions, and interactions of individual proteins with high temporal resolution and spatial resolution well beyond the diffraction limit. Within the purview of membrane proteins and ligand gated ion channels (LGICs), these outstanding advances in single molecule microscopy allow for the direct observation of discrete biochemical states and their fluctuation dynamics. Such observations are fundamentally important for understanding molecular-level mechanisms governing these systems. Examples reviewed here include the effects of allostery on the stoichiometry of ligand binding in the presence of fluorescent ligands; the observation of subdomain partitioning of membrane proteins due to microenvironment effects; and the use of single particle tracking experiments to elucidate characteristics of membrane protein diffusion and the direct measurement of thermodynamic properties, which govern the free energy landscape of protein dimerization. The review of such characteristic topics represents a snapshot of efforts to push the boundaries of fluorescence microscopy of membrane proteins to the absolute limit.

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.