Role of the K1K_1 meson in K0K^0 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 $K^0\Lambda$ 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 $\bar{K}N$ and $Y\pi$ interactions (where $Y= \Sigma$ or $\Lambda$) are studied in the framework of heavy baryon chiral perturbation theory with the $\Lambda$(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 $\Lambda$(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

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

Multiple var2csa-Type PfEMP1 Genes Located at Different Chromosomal Loci Occur in Many Plasmodium falciparum Isolates

BACKGROUND:The var2csa gene encodes a Plasmodium falciparum adhesion receptor which binds chondroitin sulfate A (CSA). This var gene is more conserved than other PfEMP1/var genes and is found in all P. falciparum isolates. In isolates 3D7, FCR3/It4 and HB3, var2csa is transcribed from a sub-telomeric position on the left arm of chromosome 12, but it is not known if this location is conserved in all parasites. Genome sequencing indicates that the var2csa gene is duplicated in HB3, but whether this is true in natural populations is uncertain. METHODOLOGY/PRINCIPAL FINDINGS:To assess global variation in the VAR2CSA protein, sequence variation in the DBL2X region of var2csa genes in 54 P.falciparum samples was analyzed. Chromosome mapping of var2csa loci was carried out and a quantitative PCR assay was developed to estimate the number of var2csa genes in P.falciparum isolates from the placenta of pregnant women and from the peripheral circulation of other malaria patients. Sequence analysis, gene mapping and copy number quantitation in P.falciparum isolates indicate that there are at least two loci and that both var2csa-like genes can be transcribed. All VAR2CSA DBL2X domains fall into one of two distinct phylogenetic groups possessing one or the other variant of a large (approximately 26 amino acid) dimorphic motif, but whether either motif variant is linked to a specific locus is not known. CONCLUSIONS/SIGNIFICANCE:Two or more related but distinct var2csa-type PfEMP1/var genes exist in many P. falciparum isolates. One gene is on chromosome 12 but additional var2csa-type genes are on different chromosomes in different isolates. Multiplicity of var2csa genes appears more common in infected placentae than in samples from non-pregnant donors indicating a possible advantage of this genotype in pregnancy associated malaria

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

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, $\gamma p\to K^+ \Lambda$, $\gamma p\to K^+ \Sigma^0$, and $\gamma p\to K^0\Sigma^+$, 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

BibGlimpse: The case for a light-weight reprint manager in distributed literature research

Background While text-mining and distributed annotation systems both aim at capturing knowledge and presenting it in a standardized form, there have been few attempts to investigate potential synergies between these two fields. For instance, distributed annotation would be very well suited for providing topic focussed, expert knowledge enriched text corpora. A key limitation for this approach is the availability of literature annotation systems that can be routinely used by groups of collaborating researchers on a day to day basis, not distracting from the main focus of their work. Results For this purpose, we have designed BibGlimpse. Features like drop-to-file, SVM based automated retrieval of PubMed bibliography for PDF reprints, and annotation support make BibGlimpse an efficient, light-weight reprint manager that facilitates distributed literature research for work groups. Building on an established open search engine, full-text search and structured queries are supported, while at the same time making shared collections of annotated reprints accessible to literature classification and text-mining tools. Conclusion BibGlimpse offers scientists a tool that enhances their own literature management. Moreover, it may be used to create content enriched, annotated text corpora for research in text-mining

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

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

Spallation reactions. A successful interplay between modeling and applications

The spallation reactions are a type of nuclear reaction which occur in space by interaction of the cosmic rays with interstellar bodies. The first spallation reactions induced with an accelerator took place in 1947 at the Berkeley cyclotron (University of California) with 200 MeV deuterons and 400 MeV alpha beams. They highlighted the multiple emission of neutrons and charged particles and the production of a large number of residual nuclei far different from the target nuclei. The same year R. Serber describes the reaction in two steps: a first and fast one with high-energy particle emission leading to an excited remnant nucleus, and a second one, much slower, the de-excitation of the remnant. In 2010 IAEA organized a worskhop to present the results of the most widely used spallation codes within a benchmark of spallation models. If one of the goals was to understand the deficiencies, if any, in each code, one remarkable outcome points out the overall high-quality level of some models and so the great improvements achieved since Serber. Particle transport codes can then rely on such spallation models to treat the reactions between a light particle and an atomic nucleus with energies spanning from few tens of MeV up to some GeV. An overview of the spallation reactions modeling is presented in order to point out the incomparable contribution of models based on basic physics to numerous applications where such reactions occur. Validations or benchmarks, which are necessary steps in the improvement process, are also addressed, as well as the potential future domains of development. Spallation reactions modeling is a representative case of continuous studies aiming at understanding a reaction mechanism and which end up in a powerful tool.Comment: 59 pages, 54 figures, Revie