107 research outputs found
PARMA-CC: Parallel Multiphase Approximate Cluster Combining
Clustering is a common component in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g. rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g. detecting critical situations, that are known to be accuracy-dependent), demand for novel approaches that respond faster while coping with large data sets. The latter is the challenge we address in this paper. We propose an algorithm, PARMA-CC, that complements existing density-based and distance-based clustering methods. PARMA-CC is based on approximate, data parallel cluster combining, where parallel threads can compute summaries of clusters of data (sub)sets and, through combining, together construct a comprehensive summary of the sets of clusters. By approximating clusters with their respective geometrical summaries, our technique scales well with increased data volumes, and, by computing and efficiently combining the summaries in parallel, it enables latency improvements. PARMA-CC combines the summaries using special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC can complement and outperform well-established methods, with significantly better scalability, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour. In the paper we also describe how PARMA-CC can facilitate time-critical applications through appropriate use of the summaries
Distinguishing N-acetylneuraminic acid linkage isomers on glycopeptides by ion mobility-mass spectrometry
Differentiating the structure of isobaric glycopeptides represents a major
challenge for mass spectrometry-based characterisation techniques. Here we
show that the regiochemistry of the most common N-acetylneuraminic acid
linkages of N-glycans can be identified in a site-specific manner from
individual glycopeptides using ion mobility-mass spectrometry analysis of
diagnostic fragment ions
High-resolution longitudinal N- and O-glycoprofiling of human monocyte-to-macrophage transition.
Protein glycosylation impacts the development and function of innate immune cells. The glycophenotypes and the glycan remodelling associated with the maturation of macrophages from monocytic precursor populations remain incompletely described. Herein, label-free porous graphitised carbon-liquid chromatography-tandem mass spectrometry (PGC-LC-MS/MS) was employed to profile with high resolution the N- and O-glycome associated with human monocyte-to-macrophage transition. Primary blood-derived CD14+ monocytes were differentiated ex vivo in the absence of strong anti- and proinflammatory stimuli using a conventional 7-day granulocyte-macrophage colony-stimulating factor differentiation protocol with longitudinal sampling. Morphology and protein expression monitored by light microscopy and proteomics validated the maturation process. Glycomics demonstrated that monocytes and macrophages display similar N-glycome profiles, comprising predominantly paucimannosidic (Man1-3GlcNAc2Fuc0-1, 22.1-30.8%), oligomannosidic (Man5-9GlcNAc2, 29.8-35.7%) and α2,3/6-sialylated complex-type N-glycans with variable core fucosylation (27.6-39.1%). Glycopeptide analysis validated conjugation of these glycans to human proteins, while quantitative proteomics monitored the glycoenzyme expression levels during macrophage differentiation. Significant interperson glycome variations were observed suggesting a considerable physiology-dependent or heritable heterogeneity of CD14+ monocytes. Only few N-glycome changes correlated with the monocyte-to-macrophage transition across donors including decreased core fucosylation and reduced expression of mannose-terminating (paucimannosidic-/oligomannosidic-type) N-glycans in macrophages, while lectin flow cytometry indicated that more dramatic cell surface glycan remodelling occurs during maturation. The less heterogeneous core 1-rich O-glycome showed a minor decrease in core 2-type O-glycosylation but otherwise remained unchanged with macrophage maturation. This high-resolution glycome map underpinning normal monocyte-to-macrophage transition, the most detailed to date, aids our understanding of the molecular makeup pertaining to two vital innate immune cell types and forms an important reference for future glycoimmunological studies
On the reliability of the theoretical internal conversion coefficients
Possible sources of uncertainties in the calculations of the internal
conversion coefficients are studied. The uncertainties induced by them are
estimated.Comment: 16 pages (including 3 figures inserted by 'epsfig' macro
FEBUKO and MODMEP: Field measurements and modelling of aerosol and cloud multiphase processes
An overview of the two FEBUKO aerosol–cloud interaction field experiments in the Thüringer Wald (Germany) in October 2001 and 2002 and the corresponding modelling project MODMEP is given. Experimentally, a variety of measurement methods were deployed to probe the gas phase, particles and cloud droplets at three sites upwind, downwind and within an orographic cloud with special emphasis on the budgets and interconversions of organic gas and particle phase constituents. Out of a total of 14 sampling periods within 30 cloud events three events (EI, EII and EIII) are selected for detailed analysis. At various occasions an impact of the cloud process on particle chemical composition such as on the organic compounds content, sulphate and nitrate and also on particle size distributions and particle mass is observed. Moreover, direct phase transfer of polar organic compound from the gas phase is found to be very important for the understanding of cloudwater composition. For the modelling side, a main result of the MODMEP project is the development of a cloud model, which combines a complex multiphase chemistry with detailed microphysics. Both components are described in a fine-resolved particle/drop spectrum. New numerical methods are developed for an efficient solution of the entire complex model. A further development of the CAPRAM mechanism has lead to a more detailed description of tropospheric aqueous phase organic chemistry. In parallel, effective tools for the reduction of highly complex reaction schemes are provided. Techniques are provided and tested which allow the description of complex multiphase chemistry and of detailed microphysics in multidimensional chemistry-transport models
Clustering Algorithms: Their Application to Gene Expression Data
Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure
Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics
Background: Network communities help the functional organization and
evolution of complex networks. However, the development of a method, which is
both fast and accurate, provides modular overlaps and partitions of a
heterogeneous network, has proven to be rather difficult. Methodology/Principal
Findings: Here we introduce the novel concept of ModuLand, an integrative
method family determining overlapping network modules as hills of an influence
function-based, centrality-type community landscape, and including several
widely used modularization methods as special cases. As various adaptations of
the method family, we developed several algorithms, which provide an efficient
analysis of weighted and directed networks, and (1) determine pervasively
overlapping modules with high resolution; (2) uncover a detailed hierarchical
network structure allowing an efficient, zoom-in analysis of large networks;
(3) allow the determination of key network nodes and (4) help to predict
network dynamics. Conclusions/Significance: The concept opens a wide range of
possibilities to develop new approaches and applications including network
routing, classification, comparison and prediction.Comment: 25 pages with 6 figures and a Glossary + Supporting Information
containing pseudo-codes of all algorithms used, 14 Figures, 5 Tables (with 18
module definitions, 129 different modularization methods, 13 module
comparision methods) and 396 references. All algorithms can be downloaded
from this web-site: http://www.linkgroup.hu/modules.ph
Development of highly sensitive and selective applications for glycoproteomics and clinical glycomics
Glycan structure elucidation, either on released oligosaccharides or still
attached to proteins/peptides, remains a challenging task mostly due to the
complexity embedded in these key molecules. Uncovering the functional
relevance of glycosylation in biological systems can be used for disease
diagnosis and treatment. The aim of this work was to develop highly sensitive
and selective clinical glycomics and glycoproteomics tools. A method for the
extraction of N-and O-glycans from histopathological tissue sections
(formalin-fixed & paraffin-embedded and/or Hematoxylin & Eosin stained) was
developed. The use of laser capture microdissection provided the opportunity
to gain spatial insights into the glyco-epitope distribution in hepatocellular
carcinoma and surrounding non-tumor tissue from as low as a few thousand
cells. For the first time detailed linkage information on structural isomers
(e.g. 2,3 vs. 2,6 linked sialic acids) and important glycan epitopes (e.g.
such as Lewis X) could be determined from minimal amounts of clinical tissue
specimens. This developed approach represents an enormous step forward in the
quest to identify disease specific glycan signatures. For the glycoproteomic
investigations defined glycopeptide standards were synthesized. These were
employed to systematically investigate glycopeptide fragmentation in
quadrupole-time-of-flight mass spectrometer to improve the information content
of tandem MS data for software-assisted glycopeptide analysis. Optimal
analysis conditions were used in a glycoproteomic analysis of the entire set
of human immunoglobulins. The glycopeptide standards were also key compounds
to explore the potential of ion mobility–mass spectrometry (IM-MS) for
providing glycan structure information from isobaric glycopeptides. IM-MS was
able to differentiate N-glycopeptide positions isomers. Even more importantly,
IM-MS could be used to differentiate α2-6 from α2-3 sialic acid (NeuAc)
linkage isomers. This was achieved by IM-MS separation of oxonium ions (NeuAc-
Gal-GlcNAc; 657 m/z) generated by collision induced dissociation of isolated
precursor ions but not on the level of intact glycopeptides. Using this
technology for the first time NeuAc linkages can be studied in a site-specific
manner within a single experiment. In future this capacity will provide novel
insights on the role of sialic acid in health and disease.Die detaillierte Charakterisierung von Glykanen, in gelöster Form oder in
Verbindung mit Proteinen/Peptiden, stellt aufgrund der ihnen innewohnenden
umfassenden Komplexität eine enorme Herausforderung dar. Die Aufklärung ihrer
biologischen Funktionen ist für den Kampf gegen Krankheiten von entscheidender
Bedeutung. Das Ziel dieser Arbeit war die Entwicklung von hochsensitiven und
selektiven Methoden zur Anwendung in der klinischen Glykomik und
Glykoproteomik. Es wurde ein Protokoll für die Extraktion von N- und
O-glykanen aus klinischen Gewebeproben (Formalin-fixiert & Paraffin-
eingebettet und/oder Hematoxilin & Eosin gefärbt) entwickelt. Mit Hilfe von
Laser-Mikrodissektionen war es zudem möglich räumliche Glykanprofile von
Gewebestücken von nur wenigen tausend Zellen (Leberzellkarzinom) und
angrenzenden Gebieten zu erstellen. Zum ersten Mal konnten detaillierte
Informationen über Glykan-Isomere (z.B. 2,3 und 2,6 verknüpfte Sialinsäuren)
und wichtige Epitope (z.B. Lewis X) von kleinsten Mengen klinischen Materials
gewonnen werden. Daher stellt diese Methode ein wertvolles Werkzeug bei der
Suche nach geeigneten Glykan Biomarkern dar. Für die glykoproteomischen
Arbeiten wurden definierte Glykopeptidstandards hergestellt. Diese wurden dann
benutzt um systematisch deren Fragmentierung in Quadrupol-
Flugzeitmassenspektrometern zu untersuchen und den Informationsgehalt der
entstandenen Fragment-Ion-Spektren für eine bessere computer-assistierte
Datenanalyse zu erhöhen. Die optimalen Parameter wurden dann u.a. mittels
diverser humaner Immunglobuline validiert. Die synthetischen Glykopeptide
wurden weiterhin verwendet, um das Potential der Ionen-Mobilitäts-
Massenspektrometrie zur Glykopeptid Charakterisierung zu untersuchen. Es hat
sich gezeigt, dass die untersuchten N-glykopeptid-Positionsisomere mittels der
Methode unterscheidbar waren. Weiterhin konnten isobare Glykopeptide, welche
nur in ihrer terminalen Sialinsäureverknüpfung variierten (α2-6 bzw. α2-3),
auf Fragment-Ebene unterschieden werden. Dies geschah an Hand von Oxonium-
Ionen (NeuAc-Gal-GlcNAc; m/z 657), welche zuvor durch kollisionsinduzierte
Dissoziation der Vorläuferionen generiert wurden. Zukünftig ermöglicht die
Methode die Bestimmung von Sialinsäureverknüpfungen auf Glykopeptiden in einem
Experiment, was die Untersuchung von Sialiansäuren in einem bestimmten
biologischen Kontext sehr vereinfachen wird
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