163 research outputs found
Chemoenzymatic Probes for Detecting and Imaging Fucose-α(1-2)-galactose Glycan Biomarkers
The disaccharide motif fucose-α(1-2)-galactose (Fucα(1-2)Gal) is involved in many important physiological processes, such as learning and memory, inflammation, asthma, and tumorigenesis. However, the size and structural complexity of Fucα(1-2)Gal-containing glycans have posed a significant challenge to their detection. We report a new chemoenzymatic strategy for the rapid, sensitive detection of Fucα(1-2)Gal glycans. We demonstrate that the approach is highly selective for the Fucα(1-2)Gal motif, detects a variety of complex glycans and glycoproteins, and can be used to profile the relative abundance of the motif on live cells, discriminating malignant from normal cells. This approach represents a new potential strategy for biomarker detection and expands the technologies available for understanding the roles of this important class of carbohydrates in physiology and disease
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Spontaneous R-Parity Violation, Flavor Symmetry and Tribimaximal Mixing
We explore the possibility of spontaneous R parity violation in the context
of flavor symmetry. Our model contains singlet matter chiral superfields which are arranged as triplet of
and as well as few additional Higgs chiral superfields which are singlet
under MSSM gauge group and belong to triplet and singlet representation under
the flavor symmetry. R parity is broken spontaneously by the vacuum
expectation values of the different sneutrino fields and hence we have
neutrino-neutralino as well as neutrino-MSSM gauge singlet higgsino mixings in
our model, in addition to the standard model neutrino- gauge singlet neutrino,
gaugino-higgsino and higgsino-higgsino mixings. Because all of these mixings we
have an extended neutral fermion mass matrix. We explore the low energy
neutrino mass matrix for our model and point out that with some specific
constraints between the sneutrino vacuum expectation values as well as the MSSM
gauge singlet Higgs vacuum expectation values, the low energy neutrino mass
matrix will lead to a tribimaximal mixing matrix. We also analyze the potential
minimization for our model and show that one can realize a higher vacuum
expectation value of the singlet
sneutrino fields even when the other sneutrino vacuum expectation values are
extremely small or even zero.Comment: 18 page
Non-Standard Neutrino Propagation and Pion Decay
Motivated by the findings of the OPERA experiment, we discuss the hypothesis
that neutrino propagation does not obey Einstein special relativity. Under a
minimal set of modifications of the standard model Lagrangian, we consider the
implications of non standard neutrino propagation on the description of
neutrino interactions and, specifically, on the pion decay processes. We show
that all the different dispersion relations which have been proposed so far to
explain OPERA results, imply huge departures from the standard expectations.
The decay channel becomes significantly larger than
in the standard scenario, and may even dominate over . Moreover, the spectral distribution of neutrinos produced in the decay
processes and the probability that a pion decays in flight in neutrinos show
large deviations from the standard results.Comment: 17 pages, 10 figures, version accepted in JHE
Texture classification of proteins using support vector machines and bio-inspired metaheuristics
6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process
Lepton Number and Lepton Flavor Violation through Color Octet States
We discuss neutrinoless double beta decay and lepton flavor violating decays
such as in the colored seesaw scenario. In this mechanism,
neutrino masses are generated at one-loop via the exchange of TeV-scale
fermionic and scalar color octets. The same particles mediate lepton number and
flavor violating processes. We show that within this framework a dominant color
octet contribution to neutrinoless double beta decay is possible without being
in conflict with constraints from lepton flavor violating processes. We
furthermore compare the "direct" color octet contribution to neutrinoless
double beta decay with the "indirect" contribution, namely the usual standard
light Majorana neutrino exchange. For degenerate color octet fermionic states
both contributions are proportional to the usual effective mass, while for
non-degenerate octet fermions this feature is not present. Depending on the
model parameters, either of the contributions can be dominant.Comment: 17 pages, 16 figure
The Marker State Space (MSS) Method for Classifying Clinical Samples
The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al
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