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Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been established as a powerful technique for the determination of metal and nonmetal distributions within biological systems with high sensitivity. An imaging LA-ICP-MS technique for Fe, Cu, Zn, and Mn was developed to produce large series of quantitative element maps in native brain sections of mice subchronically intoxicated with 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridin (MPTP) as a model of Parkinson’s disease. Images were calibrated using matrix-matched laboratory standards. A software solution allowing a precise delineation of anatomical structures was implemented. Coronal brain sections were analyzed crossing the striatum and the substantia nigra, respectively. Animals sacrificed 2 h, 7 d, or 28 d after the last MPTP injection and controls were investigated.
We observed significant decreases of Cu concentrations in the periventricular zone and the fascia dentata at 2 h and 7d and a recovery or overcompensation at 28 d, most pronounced in the rostral periventricular zone (+40%). In the cortex Cu decreased slightly to −10%. Fe increased in the interpeduncular nucleus (+40%) but not in the substantia nigra. This pattern is in line with a differential regulation of periventricular and parenchymal Cu, and with the histochemical localization of Fe, and congruent to regions of preferential MPTP binding described in the rodent brain.
The LA-ICP-MS technique yielded valid and statistically robust results in the present study on 39 slices from 19 animals. Our findings underline the value of routine micro-local analytical techniques in the life sciences and affirm a role of Cu availability in Parkinson’s disease.
Bioimaging of Metals by Laser Ablation Inductively Coupled Plasma Mass Spectrometry (LA-ICP-MS)
(2010)
The distribution analysis of (essential, beneficial, or toxic) metals (e.g., Cu, Fe, Zn, Pb, and others), metalloids, and non‐metals in biological tissues is of key interest in life science. Over the past few years, the development and application of several imaging mass spectrometric techniques has been rapidly growing in biology and medicine. Especially, in brain research metalloproteins are in the focus of targeted therapy approaches of neurodegenerative diseases such as Alzheimer's and Parkinson's disease, or stroke, or tumor growth. Laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) using double‐focusing sector field (LA‐ICP‐SFMS) or quadrupole‐based mass spectrometers (LA‐ICP‐QMS) has been successfully applied as a powerful imaging (mapping) technique to produce quantitative images of detailed regionally specific element distributions in thin tissue sections of human or rodent brain. Imaging LA‐ICP‐QMS was also applied to investigate metal distributions in plant and animal sections to study, for example, the uptake and transport of nutrient and toxic elements or environmental contamination. The combination of imaging LA‐ICP‐MS of metals with proteomic studies using biomolecular mass spectrometry identifies metal‐containing proteins and also phosphoproteins. Metal‐containing proteins were imaged in a two‐dimensional gel after electrophoretic separation of proteins (SDS or Blue Native PAGE). Recent progress in LA‐ICP‐MS imaging as a stand‐alone technique and in combination with MALDI/ESI‐MS for selected life science applications is summarized.
Laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) has been developed and established as an emerging technique in the generation of quantitative images of metal distributions in thin tissue sections of brain samples (such as human, rat and mouse brain), with applications in research related to neurodegenerative disorders. A new analytical protocol is described which includes sample preparation by cryo-cutting of thin tissue sections and matrix-matched laboratory standards, mass spectrometric measurements, data acquisition, and quantitative analysis. Specific examples of the bioimaging of metal distributions in normal rodent brains are provided. Differences to the normal were assessed in a Parkinson’s disease and a stroke brain model. Furthermore, changes during normal aging were studied. Powerful analytical techniques are also required for the determination and characterization of metal-containing proteins within a large pool of proteins, e.g., after denaturing or non-denaturing electrophoretic separation of proteins in one-dimensional and two-dimensional gels. LA-ICP-MS can be employed to detect metalloproteins in protein bands or spots separated after gel electrophoresis. MALDI-MS can then be used to identify specific metal-containing proteins in these bands or spots. The combination of these techniques is described in the second section.
The paper presents a research model and a measurement instrument for a research-in-progress study on the antecedents of success in IS offshoring projects. In this empirical-confirmatory study, we intend to analyse the impact of the constructs “offshoring expertise”, “trust in offshore service provider”, “project suitability”, “knowledge transfer”, and “liaison quality” on offshore project success. Constructs and indicators are derived from an extensive literature review. We plan to formulate a structural equation model and to test it using partial least squares (PLS) as an analysis technique. Our research model addresses the paucity of research that quantitatively examines offshoring success.
This paper empirically examines the current state of the IS offshoring phenomenon in Germany regarding project characteristics and success patterns. Relying on a sample of 304 projects conducted at various industry sectors and companies, results show that IS offshoring primarily occurs in sectors Telecommunications and IT at large corporations. Cost reduction is the main reason for going offshore and offshore projects are executed as part of a larger program at companies. Noticeably, most projects are delivered from India. Additionally, neither captive offshoring nor offshore outsourcing dominates as a de-livery option. Comparing different project subgroups regarding project success, the results reveal that projects delivered by an internal or partially owned service provider are more successful. Other project characteristics such as a project’s embedded-ness in a larger offshoring program, a project’s size, or a project’s offshoring degree in terms of relatively offshored labor hours show few significant differences. The paper addresses the paucity of empirical research on the current state of the IS offshoring phenomenon in Germany.
Identification of bats is most practically done by exploiting the characteristic features of their echolocation calls. This usually involves expert knowledge, expensive equipment and time-consuming post processing of previously recorded calls. Automated solutions exist, but are usually not as accurate as human experts. We present an automated solution for the processing of bat calls and identification of bat species with extremely high classification accuracy that can be used during live recording or in an automated post-processing software. Our algorithm is the first application of a Deep Convolutional Neural Network to classify bat species based on sound spectrogram images of their echolocation calls. We tested several deep CNN architectures including a modified Google Inception and a ResNet50 architecture. The nets were trained on a very large call database consisting of images of snippets of call spectrograms. All our software was developed in the Python programming language and an executable of the software is available on request.