Positron bunching and electrostatic transport system for the production and emission of dense positronium clouds into vacuum

We describe a system designed to re-bunch positron pulses delivered by an accumulator supplied by a positron source and a Surko-trap. Positron pulses from the accumulator are magnetically guided in a 0.085 T field and are injected into a region free of magnetic fields through a μ -metal field terminator. Here positrons are temporally compressed, electrostatically guided and accelerated towards a porous silicon target for the production and emission of positronium into vacuum. Positrons are focused in a spot of less than 4 mm FWTM in bunches of ∼8 ns FWHM. Emission of positronium into the vacuum is shown by single shot positron annihilation lifetime spectroscopy.

Alteration of the pulmonary surfactant system in full-term infants with hereditary ABCA3 deficiency

RATIONALE: ABCA3 mutations are known to cause fatal surfactant deficiency. OBJECTIVE: We studied ABCA3 protein expression in full-term newborns with unexplained respiratory distress syndrome (URDS) as well as the relevance of ABCA3 mutations for surfactant homeostasis. METHODS: Lung tissue of infants with URDS was analyzed for the expression of ABCA3 in type II pneumocytes. Coding exons of the ABCA3 gene were sequenced. Surfactant protein expression was studied by immunohistochemistry, immunoelectron microscopy, and Western blotting. RESULTS: ABCA3 protein expression was found to be greatly reduced or absent in 10 of 14 infants with URDS. Direct sequencing revealed distinct ABCA3 mutations clustering within vulnerable domains of the ABCA3 protein. A strong expression of precursors of surfactant protein B (pro-SP-B) but only low levels and aggregates of mature surfactant protein B (SP-B) within electron-dense bodies in type II pneumocytes were found. Within the matrix of electron-dense bodies, we detected precursors of SP-C (pro-SP-C) and cathepsin D. SP-A was localized in small intracellular vesicles, but not in electron-dense bodies. SP-A and pro-SP-B were shown to accumulate in the intraalveolar space, whereas mature SP-B and SP-C were reduced or absent, respectively. CONCLUSION: Our data provide evidence that ABCA3 mutations are associated not only with a deficiency of ABCA3 but also with an abnormal processing and routing of SP-B and SP-C, leading to severe alterations of surfactant homeostasis and respiratory distress syndrome. To identify infants with hereditary ABCA3 deficiency, we suggest a combined diagnostic approach including immunohistochemical, ultrastructural, and mutation analysis.

A dose-controlled system for air-liquid interface cell exposure and application to zinc oxide nanoparticles

BACKGROUND: Engineered nanoparticles are becoming increasingly ubiquitous and their toxicological effects on human health, as well as on the ecosystem, have become a concern. Since initial contact with nanoparticles occurs at the epithelium in the lungs (or skin, or eyes), in vitro cell studies with nanoparticles require dose-controlled systems for delivery of nanoparticles to epithelial cells cultured at the air-liquid interface. RESULTS: A novel air-liquid interface cell exposure system (ALICE) for nanoparticles in liquids is presented and validated. The ALICE generates a dense cloud of droplets with a vibrating membrane nebulizer and utilizes combined cloud settling and single particle sedimentation for fast (~10 min; entire exposure), repeatable (<12%), low-stress and efficient delivery of nanoparticles, or dissolved substances, to cells cultured at the air-liquid interface. Validation with various types of nanoparticles (Au, ZnO and carbon black nanoparticles) and solutes (such as NaCl) showed that the ALICE provided spatially uniform deposition (<1.6% variability) and had no adverse effect on the viability of a widely used alveolar human epithelial-like cell line (A549). The cell deposited dose can be controlled with a quartz crystal microbalance (QCM) over a dynamic range of at least 0.02-200 mug/cm(2). The cell-specific deposition efficiency is currently limited to 0.072 (7.2% for two commercially available 6-er transwell plates), but a deposition efficiency of up to 0.57 (57%) is possible for better cell coverage of the exposure chamber. Dose-response measurements with ZnO nanoparticles (0.3-8.5 mug/cm(2)) showed significant differences in mRNA expression of pro-inflammatory (IL-8) and oxidative stress (HO-1) markers when comparing submerged and air-liquid interface exposures. Both exposure methods showed no cellular response below 1 mug/cm(2 )ZnO, which indicates that ZnO nanoparticles are not toxic at occupationally allowed exposure levels. CONCLUSION: The ALICE is a useful tool for dose-controlled nanoparticle (or solute) exposure of cells at the air-liquid interface. Significant differences between cellular response after ZnO nanoparticle exposure under submerged and air-liquid interface conditions suggest that pharmaceutical and toxicological studies with inhaled (nano-)particles should be performed under the more realistic air-liquid interface, rather than submerged cell conditions.

A Food Recognition System for Diabetic Patients based on an Optimized Bag of Features Model

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the Bag of Features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters. For the design and evaluation of the prototype system, a visual dataset with nearly 5,000 food images was created and organized into 11 classes. The optimized system computes dense local features, using the scale-invariant feature transform on the HSV color space, builds a visual dictionary of 10,000 visual words by using the hierarchical k-means clustering and finally classifies the food images with a linear support vector machine classifier. The system achieved classification accuracy of the order of 78%, thus proving the feasibility of the proposed approach in a very challenging image dataset.