Study of the discharge gas trapping during thin film growth

Discharge gas trapping in thin films produced by sputtering is known to be due to high energy neutrals bouncing back from the cathode. Qualitatively, the phenomenon is enhanced by raising the discharge voltage and is strongly dependent on the atomic masses of the discharge gas and of the cathode material. In addition to these known effects it is shown that, for a given gas, the trapped amount decreases with increasing the melting temperature of the deposited material. The results obtained both by sample melting and laser ablation are presented and discussed

Localized gut-associated lymphoid tissue hemorrhage induced by intravenous peptidoglycan-polysaccharide polymers.

A hemorrhage into gut-associated lymphoid tissue developed as early as 3 min after the intravenous injection of group A streptococcal peptidoglycan-polysaccharide polymers into rats. Extravasated erythrocytes were specifically located in the lamina propria and organized lymphoid follicles of the intestines and mesenteric lymph nodes and did not occur in the lungs, kidneys, liver, spleen, adrenal glands, or submandibular and popliteal lymph nodes, as determined by gross and histologic observations and measurement of radiolabeled erythrocytes. Petechial hemorrhage was preferentially located within the intestine to the distal ileum, Peyer's patches, and lymphoid aggregates of the colon. The hemorrhage was transient and occurred in a dose-dependent fashion. It was maximal 5 min after injection and resolved completely by 3 days. A unique feature of this altered vascular permeability was the absence of polymorphonuclear leukocytic infiltration, edema, vasculitis, and tissue necrosis

Suppression of experimental arthritis by gene transfer of interleukin 1 receptor antagonist cDNA.

Restoration of the impaired balance between pro- and antiinflammatory cytokines should provide effective treatment of rheumatoid arthritis. Gene therapy has been proposed as an approach for delivery of therapeutic proteins to arthritic joints. Here, we examined the efficacy of antiinflammatory gene therapy in bacterial cell wall-induced arthritis in rats. Human secreted interleukin 1 receptor antagonist (sIL-1ra) was expressed in joints of rats with recurrent bacterial cell wall-induced arthritis by using ex vivo gene transfer. To achieve this, primary synoviocytes were transduced in culture with a retroviral vector carrying the sIL-1ra cDNA. Transduced cells were engrafted in ankle joints of animals prior to reactivation of arthritis. Animals in control groups were engrafted with synoviocytes transduced with lacZ and neo marker genes. Cells continued to express transferred genes for at least 9 days after engraftment. We found that gene transfer of sIL-1ra significantly suppressed the severity of recurrence of arthritis, as assessed by measuring joint swelling and by the gross-observation score, and attenuated but did not abolish erosion of cartilage and bone. The effect of intraarticularly expressed sIL-1ra was essentially local, as there was no significant difference in severity of recurrence between unengrafted contralateral joints in control and experimental groups. We estimate that locally expressed sIL-1ra was about four orders of magnitude more therapeutically efficient than systemically administered recombinant sIL-1ra protein. These findings provide experimental evidence for the feasibility of antiinflammatory gene therapy for arthritis

Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics

Timm W, Scherbart A, Boecker S, Kohlbacher O, Nattkemper TW. Peak intensity prediction in MALDI-TOF mass spectrometry: A machine learning study to support quantitative proteomics. BMC Bioinformatics. 2008;9(1):443.Background: Mass spectrometry is a key technique in proteomics and can be used to analyze complex samples quickly. One key problem with the mass spectrometric analysis of peptides and proteins, however, is the fact that absolute quantification is severely hampered by the unclear relationship between the observed peak intensity and the peptide concentration in the sample. While there are numerous approaches to circumvent this problem experimentally (e. g. labeling techniques), reliable prediction of the peak intensities from peptide sequences could provide a peptide-specific correction factor. Thus, it would be a valuable tool towards label-free absolute quantification. Results: In this work we present machine learning techniques for peak intensity prediction for MALDI mass spectra. Features encoding the peptides' physico-chemical properties as well as string-based features were extracted. A feature subset was obtained from multiple forward feature selections on the extracted features. Based on these features, two advanced machine learning methods (support vector regression and local linear maps) are shown to yield good results for this problem (Pearson correlation of 0.68 in a ten-fold cross validation). Conclusion: The techniques presented here are a useful first step going beyond the binary prediction of proteotypic peptides towards a more quantitative prediction of peak intensities. These predictions in turn will turn out to be beneficial for mass spectrometry-based quantitative proteomics

LC-MSsim – a simulation software for liquid chromatography mass spectrometry data

<p>Abstract</p> <p>Background</p> <p>Mass Spectrometry coupled to Liquid Chromatography (LC-MS) is commonly used to analyze the protein content of biological samples in large scale studies. The data resulting from an LC-MS experiment is huge, highly complex and noisy. Accordingly, it has sparked new developments in Bioinformatics, especially in the fields of algorithm development, statistics and software engineering. In a quantitative label-free mass spectrometry experiment, crucial steps are the detection of peptide features in the mass spectra and the alignment of samples by correcting for shifts in retention time. At the moment, it is difficult to compare the plethora of algorithms for these tasks. So far, curated benchmark data exists only for peptide identification algorithms but no data that represents a ground truth for the evaluation of feature detection, alignment and filtering algorithms.</p> <p>Results</p> <p>We present <it>LC-MSsim</it>, a simulation software for LC-ESI-MS experiments. It simulates ESI spectra on the MS level. It reads a list of proteins from a FASTA file and digests the protein mixture using a user-defined enzyme. The software creates an LC-MS data set using a predictor for the retention time of the peptides and a model for peak shapes and elution profiles of the mass spectral peaks. Our software also offers the possibility to add contaminants, to change the background noise level and includes a model for the detectability of peptides in mass spectra. After the simulation, <it>LC-MSsim </it>writes the simulated data to mzData, a public XML format. The software also stores the positions (monoisotopic m/z and retention time) and ion counts of the simulated ions in separate files.</p> <p>Conclusion</p> <p><it>LC-MSsim </it>generates simulated LC-MS data sets and incorporates models for peak shapes and contaminations. Algorithm developers can match the results of feature detection and alignment algorithms against the simulated ion lists and meaningful error rates can be computed. We anticipate that <it>LC-MSsim </it>will be useful to the wider community to perform benchmark studies and comparisons between computational tools.</p

Enhancement of Cell Membrane Invaginations, Vesiculation and Uptake of Macromolecules by Protonation of the Cell Surface

The different pathways of endocytosis share an initial step involving local inward curvature of the cell’s lipid bilayer. It has been shown that to generate membrane curvature, proteins or lipids enforce transversal asymmetry of the plasma membrane. Thus it emerges as a general phenomenon that transversal membrane asymmetry is the common required element for the formation of membrane curvature. The present study demonstrates that elevating proton concentration at the cell surface stimulates the formation of membrane invaginations and vesiculation accompanied by efficient uptake of macromolecules (Dextran-FITC, 70 kD), relative to the constitutive one. The insensitivity of proton induced uptake to inhibiting treatments and agents of the known endocytic pathways suggests the entry of macromolecules to proceeds via a yet undefined route. This is in line with the fact that neither ATP depletion, nor the lowering of temperature, abolishes the uptake process. In addition, fusion mechanism such as associated with low pH uptake of toxins and viral proteins can be disregarded by employing the polysaccharide dextran as the uptake molecule. The proton induced uptake increases linearly in the extracellular pH range of 6.5 to 4.5, and possesses a steep increase at the range of 4> pH>3, reaching a plateau at pH≤3. The kinetics of the uptake implies that the induced vesicles release their content to the cytosol and undergo rapid recycling to the plasma membrane. We suggest that protonation of the cell’s surface induces local charge asymmetries across the cell membrane bilayer, inducing inward curvature of the cell membrane and consequent vesiculation and uptake