A multi-satellite orbit determination problem in a parallel processing environment

The Engineering Orbit Analysis Unit at GE Valley Forge used an Intel Hypercube Parallel Processor to investigate the performance and gain experience of parallel processors with a multi-satellite orbit determination problem. A general study was selected in which major blocks of computation for the multi-satellite orbit computations were used as units to be assigned to the various processors on the Hypercube. Problems encountered or successes achieved in addressing the orbit determination problem would be more likely to be transferable to other parallel processors. The prime objective was to study the algorithm to allow processing of observations later in time than those employed in the state update. Expertise in ephemeris determination was exploited in addressing these problems and the facility used to bring a realism to the study which would highlight the problems which may not otherwise be anticipated. Secondary objectives were to gain experience of a non-trivial problem in a parallel processor environment, to explore the necessary interplay of serial and parallel sections of the algorithm in terms of timing studies, to explore the granularity (coarse vs. fine grain) to discover the granularity limit above which there would be a risk of starvation where the majority of nodes would be idle or under the limit where the overhead associated with splitting the problem may require more work and communication time than is useful

Fibroblast surface-associated FGF-2 promotes contact-dependent colorectal cancer cell migration and invasion through FGFR-SRC signaling and integrin αvβ5-mediated adhesion.

Carcinoma-associated fibroblasts were reported to promote colorectal cancer (CRC) invasion by secreting motility factors and extracellular matrix processing enzymes. Less is known whether fibroblasts may induce CRC cancer cell motility by contact-dependent mechanisms. To address this question we characterized the interaction between fibroblasts and SW620 and HT29 colorectal cancer cells in 2D and 3D co-culture models in vitro. Here we show that fibroblasts induce contact-dependent cancer cell elongation, motility and invasiveness independently of deposited matrix or secreted factors. These effects depend on fibroblast cell surface-associated fibroblast growth factor (FGF) -2. Inhibition of FGF-2 or FGF receptors (FGFRs) signaling abolishes these effects. FGFRs activate SRC in cancer cells and inhibition or silencing of SRC in cancer cells, but not in fibroblasts, prevents fibroblasts-mediated effects. Using an RGD-based integrin antagonist and function-blocking antibodies we demonstrate that cancer cell adhesion to fibroblasts requires integrin αvβ5. Taken together, these results demonstrate that fibroblasts induce cell-contact-dependent colorectal cancer cell migration and invasion under 2D and 3D conditions in vitro through fibroblast cell surface-associated FGF-2, FGF receptor-mediated SRC activation and αvβ5 integrin-dependent cancer cell adhesion to fibroblasts. The FGF-2-FGFRs-SRC-αvβ5 integrin loop might be explored as candidate therapeutic target to block colorectal cancer invasion

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

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

State-of-the art data normalization methods improve NMR-based metabolomic analysis

Extracting biomedical information from large metabolomic datasets by multivariate data analysis is of considerable complexity. Common challenges include among others screening for differentially produced metabolites, estimation of fold changes, and sample classification. Prior to these analysis steps, it is important to minimize contributions from unwanted biases and experimental variance. This is the goal of data preprocessing. In this work, different data normalization methods were compared systematically employing two different datasets generated by means of nuclear magnetic resonance (NMR) spectroscopy. To this end, two different types of normalization methods were used, one aiming to remove unwanted sample-to-sample variation while the other adjusts the variance of the different metabolites by variable scaling and variance stabilization methods. The impact of all methods tested on sample classification was evaluated on urinary NMR fingerprints obtained from healthy volunteers and patients suffering from autosomal polycystic kidney disease (ADPKD). Performance in terms of screening for differentially produced metabolites was investigated on a dataset following a Latin-square design, where varied amounts of 8 different metabolites were spiked into a human urine matrix while keeping the total spike-in amount constant. In addition, specific tests were conducted to systematically investigate the influence of the different preprocessing methods on the structure of the analyzed data. In conclusion, preprocessing methods originally developed for DNA microarray analysis, in particular, Quantile and Cubic-Spline Normalization, performed best in reducing bias, accurately detecting fold changes, and classifying samples

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

A multi-disciplinary commentary on preclinical research to investigate vascular contributions to dementia

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder

A Multi-disciplinary Commentary on Preclinical Research to investigate Vascular Contributions to Dementia

Although dementia research has been dominated by Alzheimer's disease (AD), most dementia in older people is now recognised to be due to mixed pathologies, usually combining vascular and AD brain pathology. Vascular cognitive impairment (VCI), which encompasses vascular dementia (VaD) is the second most common type of dementia. Models of VCI have been delayed by limited understanding of the underlying aetiology and pathogenesis. This review by a multidisciplinary, diverse (in terms of sex, geography and career stage), cross-institute team provides a perspective on limitations to current VCI models and recommendations for improving translation and reproducibility. We discuss reproducibility, clinical features of VCI and corresponding assessments in models, human pathology, bioinformatics approaches, and data sharing. We offer recommendations for future research, particularly focusing on small vessel disease as a main underpinning disorder.</p

Gene expression profiles in rat mesenteric lymph nodes upon supplementation with Conjugated Linoleic Acid during gestation and suckling

Background Diet plays a role on the development of the immune system, and polyunsaturated fatty acids can modulate the expression of a variety of genes. Human milk contains conjugated linoleic acid (CLA), a fatty acid that seems to contribute to immune development. Indeed, recent studies carried out in our group in suckling animals have shown that the immune function is enhanced after feeding them with an 80:20 isomer mix composed of c9,t11 and t10,c12 CLA. However, little work has been done on the effects of CLA on gene expression, and even less regarding immune system development in early life. Results The expression profile of mesenteric lymph nodes from animals supplemented with CLA during gestation and suckling through dam's milk (Group A) or by oral gavage (Group B), supplemented just during suckling (Group C) and control animals (Group D) was determined with the aid of the specific GeneChip® Rat Genome 230 2.0 (Affymettrix). Bioinformatics analyses were performed using the GeneSpring GX software package v10.0.2 and lead to the identification of 89 genes differentially expressed in all three dietary approaches. Generation of a biological association network evidenced several genes, such as connective tissue growth factor (Ctgf), tissue inhibitor of metalloproteinase 1 (Timp1), galanin (Gal), synaptotagmin 1 (Syt1), growth factor receptor bound protein 2 (Grb2), actin gamma 2 (Actg2) and smooth muscle alpha actin (Acta2), as highly interconnected nodes of the resulting network. Gene underexpression was confirmed by Real-Time RT-PCR. Conclusions Ctgf, Timp1, Gal and Syt1, among others, are genes modulated by CLA supplementation that may have a role on mucosal immune responses in early life