72 research outputs found
Role of the meson in photoproduction off the deuteron
Neutral kaon photoproduction off the nucleon and deuteron has been
reinvestigated by utilizing the new experimental data on both targets. An
isobar model for elementary operator and impulse approximation for the reaction
on the deuteron have been used. The available free parameters in the elementary
model have been extracted from both elementary and deuteron data. In contrast
to the elementary reaction, fitting the deuteron data requires an inclusion of
weighting factor. The result indicates that the angular distribution of the
elementary process does not show backward peaking behavior.Comment: 4 pages, 4 figures, prepared for the Fifth Asia-Pacific Conference on
Few-Body Problems in Physics 2011 (APFB2011), Seoul, Korea, August 22-26,
201
Chiral Dynamics of Low-Energy Kaon-Baryon Interactions with Explicit Resonance
The processes involving low energy and interactions (where
or ) are studied in the framework of heavy baryon chiral
perturbation theory with the (1405) resonance appearing as an
independent field.
The leading and next-to-leading terms in the chiral expansion are taken into
account. We show that an approach which explicitly includes the (1405)
resonance as an elementary quantum field gives reasonable descriptions of both
the threshold branching ratios and the energy dependence of total cross
sections.Comment: 16 pages, 6 figure
Pseudoscalar meson photoproduction: from known to undiscovered resonances
The role of dynamics in spin observables for pseudoscalar meson
photoproduction is investigated using a density matrix approach in a multipole
truncated framework. Extraction of novel rules for and reactions based on resonance dominance, and on
other broad and reasonable dynamical assumptions, are discussed. Observables
that are particularly sensitive to missing nucleonic resonances predicted by
quark-based approaches, are singled out.Comment: 22 pages, latex, 3 figure
K0-Sigma+ Photoproduction with SAPHIR
Preliminary results of the analysis of the reaction p(gamma,K0)Sigma+ are
presented. We show the first measurement of the differential cross section and
much improved data for the total cross section than previous data. The data are
compared with model predictions from different isobar and quark models that
give a good description of p(gamma,K+)Lambda and p(gamma,K+)Sigma0 data in the
same energy range. Results of ChPT describe the data adequately at threshold
while isobar models that include hadronic form factors reproduce the data at
intermediate energies.Comment: 4 pages, Latex2e, 4 postscript figures. Talk given at the
International Conference on Hypernuclear and Strange Particle Physics
(HYP97), Brookhaven National Laboratory, USA, October 13-18, 1997. To be
published in Nucl. Phys. A. Revised version due to changes in experimental
dat
The Kaon-Photoproduction Of Nucleons In The Quark Model
In this paper, we develop a general framework to study the
meson-photoproductions of nucleons in the chiral quark model. The S and U
channel resonance contributions are expressed in terms of the
Chew-Goldberger-Low-Nambu (CGLN) amplitudes. The kaon-photoproduction
processes, , , and , are calculated. The initial results show that the quark
model provides a much improved description of the reaction mechanism for the
kaon-photoproductions of the nucleon with less parameters than the traditional
phenomenological approaches.Comment: 25 pages, 9 postscript figures can be obtained from the author
An Unified Approach To Pseudo Scalar Meson Photoproductions Off Nucleons In The Quark Model
An unified approach to the pseudo scalar meson (, and )
photoproduction off nucleons are presented. It begins with the low energy QCD
Lagrangian, and the resonances in the s- and u- channels are treated in the
framework of the quark model
The duality hypothesis is imposed to limit the number of the t-channel
exchanges. The CGLN amplitudes for each reaction are evaluated, which include
both proton and neutron targets. The important role by the S-wave resonances in
the second resonance region is discussed, it is particularly important for the
and photoproductions.Comment: 31 pages in Latex fil
The identification of informative genes from multiple datasets with increasing complexity
Background
In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes.
Results
In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes.
Conclusions
We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events
Nitric Oxide Facilitates Delivery and Mediates Improved Outcome of Autologous Bone Marrow Mononuclear Cells in a Rodent Stroke Model
Bone marrow mononuclear cells (MNC) represent an investigational treatment for stroke. The objective of this study was to determine the relevance of vasoactive mediators, generated in response to MNC injection, as factors regulating cerebral perfusion (CP), the biodistribution of MNC, and outcome in stroke.Long Evans rats underwent transient middle cerebral artery occlusion. MNC were extracted from the bone marrow at 22 hrs and injected via the internal carotid artery or the femoral vein 2 hours later. CP was measured with MRI or continuous laser Doppler flowmetry. Serum samples were collected to measure vasoactive mediators. Animals were treated with the Nitric Oxide (NO) inhibitor, L-NAME, to establish the relevance of NO-signaling to the effect of MNC. Lesion size, MNC biodistribution, and neurological deficits were assessed.CP transiently increased in the peri-infarct region within 30 min after injecting MNC compared to saline or fibroblast control. This CP increase corresponded temporarily to serum NO elevation and was abolished by L-NAME. Pre-treatment with L-NAME reduced brain penetration of MNC and prevented MNC from reducing infarct lesion size and neurological deficits.NO generation in response to MNC may represent a mechanism underlying how MNC enter the brain, reduce lesion size, and improve outcome in ischemic stroke
Predicting protein linkages in bacteria: Which method is best depends on task
<p>Abstract</p> <p>Background</p> <p>Applications of computational methods for predicting protein functional linkages are increasing. In recent years, several bacteria-specific methods for predicting linkages have been developed. The four major genomic context methods are: Gene cluster, Gene neighbor, Rosetta Stone, and Phylogenetic profiles. These methods have been shown to be powerful tools and this paper provides guidelines for when each method is appropriate by exploring different features of each method and potential improvements offered by their combination. We also review many previous treatments of these prediction methods, use the latest available annotations, and offer a number of new observations.</p> <p>Results</p> <p>Using <it>Escherichia coli </it>K12 and <it>Bacillus subtilis</it>, linkage predictions made by each of these methods were evaluated against three benchmarks: functional categories defined by COG and KEGG, known pathways listed in EcoCyc, and known operons listed in RegulonDB. Each evaluated method had strengths and weaknesses, with no one method dominating all aspects of predictive ability studied. For functional categories, as previous studies have shown, the Rosetta Stone method was individually best at detecting linkages and predicting functions among proteins with shared KEGG categories while the Phylogenetic profile method was best for linkage detection and function prediction among proteins with common COG functions. Differences in performance under COG versus KEGG may be attributable to the presence of paralogs. Better function prediction was observed when using a weighted combination of linkages based on reliability versus using a simple unweighted union of the linkage sets. For pathway reconstruction, 99 complete metabolic pathways in <it>E. coli </it>K12 (out of the 209 known, non-trivial pathways) and 193 pathways with 50% of their proteins were covered by linkages from at least one method. Gene neighbor was most effective individually on pathway reconstruction, with 48 complete pathways reconstructed. For operon prediction, Gene cluster predicted completely 59% of the known operons in <it>E. coli </it>K12 and 88% (333/418)in <it>B. subtilis</it>. Comparing two versions of the <it>E. coli </it>K12 operon database, many of the unannotated predictions in the earlier version were updated to true predictions in the later version. Using only linkages found by both Gene Cluster and Gene Neighbor improved the precision of operon predictions. Additionally, as previous studies have shown, combining features based on intergenic region and protein function improved the specificity of operon prediction.</p> <p>Conclusion</p> <p>A common problem for computational methods is the generation of a large number of false positives that might be caused by an incomplete source of validation. By comparing two versions of a database, we demonstrated the dramatic differences on reported results. We used several benchmarks on which we have shown the comparative effectiveness of each prediction method, as well as provided guidelines as to which method is most appropriate for a given prediction task.</p
Nucleon Form Factors in a Covariant Diquark-Quark Model
In a model where constituent quarks and diquarks interact through quark
exchange the Bethe-Salpeter equation in ladder approximation for the nucleon is
solved. Quark and diquark confinement is effectively parametrized by choosing
appropriately modified propagators. The coupling to external currents is
implemented via nontrivial vertex functions for quarks and diquarks to ensure
gauge invariance at the constituent level. Nucleon matrix elements are
evaluated in a generalised impulse approximation, and electromagnetic, pionic
and axial form factors are calculated.Comment: 33 Pages, 10 figures, modfied elsart.sty include
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