Non-invasive imaging of roots with high resolution X-ray micro-tomography

ray micro-tomography is a well-established technique for non-invasive imaging and evaluation of heterogeneous materials. An inexpensive X-ray micro-tomography system has been designed and built for the specific purposes of examining root growth and root/soil interactions. The system uses a silver target X-ray source with a focal spot diameter of 80 mum, an X-ray image intensifier with a sampling aperture of about 100 mum, and a sample with a diameter of 25 mm. Pre-germinated wheat and rape seeds were grown for up to 8-10 days in plastic containers in a sandy loam soil sieved to < 250 μm, and imaged with the X-ray system at regular intervals. The quality of 3 D image obtained was good allowing the development and growth of both root axes and some first-order laterals to be observed. The satisfactory discrimination between soil and roots enabled measurements of root diameter (wheat values were 0.48-1.22 mm) in individual tomographic slices and, by tracking from slice to slice, root lengths were also measured. The measurements obtained were generally within 10% of those obtained from destructive samples measured manually and with a flat-bed scanner. Further developments of the system will allow more detailed examination of the root: soil interface.

Bubble-included chocolate: relating structure with sensory response

Bubbles impart a very unique texture, chew, and mouth feel to foods. However, little is known about the relationship between structure of such products and consumer response in terms of mouth-feel and eating experience. The objective of this article is to investigate the sensory properties of 4 types of bubble-containing chocolates, produced by using different gases: carbon dioxide, nitrogen, nitrous oxide, and argon. The structure of these chocolates were characterized in terms of (1) gas hold-up values determined by density measurements and (2) bubble size distribution which was measured by undertaking an image analysis of X-ray microtomograph sections. Bubble size distributions were obtained by measuring bubble volumes after reconstructing 3D images from the tomographic sections. A sensory study was undertaken by a nonexpert panel of 20 panelists and their responses were analyzed using qualitative descriptive analysis (QDA). The results show that chocolates made from the 4 gases could be divided into 2 groups on the basis of bubble volume and gas hold-up: the samples produced using carbon dioxide and nitrous oxide had a distinctly higher gas hold-up containing larger bubbles in comparison with those produced using argon and nitrogen. The sensory study also demonstrated that chocolates made with the latter were perceived to be harder, less aerated, slow to melt in the mouth, and having overall flavor intensity. These products were further found to be creamier than the chocolates made by using carbon dioxide and nitrous oxide; the latter sample also showed a higher intensity of cocoa flavor.

A survey of terahertz applications in cultural heritage conservation science

The authors present a review of the advances that have been made to establish terahertz applications in the cultural heritage conservation sector over the last several years. This includes material spectroscopy, 2D and 3D imaging and tomographic studies, using a broad range of terahertz sources demonstrating the breadth and application of this burgeoning community.

Endothelin-converting enzyme-1 degrades internalized somatostatin-14

Agonist-induced internalization of somatostatin receptors (ssts) determines subsequent cellular responsiveness to peptide agonists and influences sst receptor scintigraphy. To investigate sst2A trafficking, rat sst2A tagged with epitope was expressed in human embryonic kidney cells and tracked by antibody labeling. Confocal microscopical analysis revealed that stimulation with sst and octreotide induced internalization of sst2A. Internalized sst2A remained sequestrated within early endosomes, and 60 min after stimulation, internalized sst2A still colocalized with beta-arrestin1-enhanced green fluorescence protein (EGFP), endothelin-converting enzyme-1 (ECE-1), and rab5a. Internalized (125)I-Tyr(11)-SST-14 was rapidly hydrolyzed by endosomal endopeptidases, with radioactive metabolites being released from the cell. Internalized (125)I-Tyr(1)-octreotide accumulated as an intact peptide and was released from the cell as an intact peptide ligand. We have identified ECE-1 as one of the endopeptidases responsible for inactivation of internalized SST-14. ECE-1-mediated cleavage of SST-14 was inhibited by the specific ECE-1 inhibitor, SM-19712, and by preventing acidification of endosomes using bafilomycin A(1). ECE-1 cleaved SST-14 but not octreotide in an acidic environment. The metallopeptidases angiotensin-1 converting enzyme and ECE-2 did not hydrolyze SST-14 or octreotide. Our results show for the first time that stimulation with SST-14 and octreotide induced sequestration of sst2A into early endosomes and that endocytosed SST-14 is degraded by endopeptidases located in early endosomes. Furthermore, octreotide was not degraded by endosomal peptidases and was released as an intact peptide. This mechanism may explain functional differences between octreotide and SST-14 after sst2A stimulation. Moreover, further investigation of endopeptidase-regulated trafficking of neuropeptides may result in novel concepts of neuropeptide receptor inactivation in cancer diagnosis.

Reduced-order modeling of light transport in tissue for real-time monitoring of brain hemodynamics using diffuse optical tomography

This paper proposes a new reconstruction method for diffuse optical tomography using reduced-order models of light transport in tissue. The models, which directly map optical tissue parameters to optical flux measurements at the detector locations, are derived based on data generated by numerical simulation of a reference model. The reconstruction algorithm based on the reduced-order models is a few orders of magnitude faster than the one based on a finite element approximation on a fine mesh incorporating a priori anatomical information acquired by magnetic resonance imaging. We demonstrate the accuracy and speed of the approach using a phantom experiment and through numerical simulation of brain activation in a rat's head. The applicability of the approach for real-time monitoring of brain hemodynamics is demonstrated through a hypercapnic experiment. We show that our results agree with the expected physiological changes and with results of a similar experimental study. However, by using our approach, a three-dimensional tomographic reconstruction can be performed in ∼3  s per time point instead of the 1 to 2 h it takes when using the conventional finite element modeling approach

Complex extreme learning machine applications in terahertz pulsed signals feature sets

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed algorithm should also be very useful in other applications requiring the classification of very large datasets.