Light-Cone Quantization and Hadron Structure

In this talk, I review the use of the light-cone Fock expansion as a tractable and consistent description of relativistic many-body systems and bound states in quantum field theory and as a frame-independent representation of the physics of the QCD parton model. Nonperturbative methods for computing the spectrum and LC wavefunctions are briefly discussed. The light-cone Fock state representation of hadrons also describes quantum fluctuations containing intrinsic gluons, strangeness, and charm, and, in the case of nuclei, "hidden color". Fock state components of hadrons with small transverse size, such as those which dominate hard exclusive reactions, have small color dipole moments and thus diminished hadronic interactions; i.e., "color transparency". The use of light-cone Fock methods to compute loop amplitudes is illustrated by the example of the electron anomalous moment in QED. In other applications, such as the computation of the axial, magnetic, and quadrupole moments of light nuclei, the QCD relativistic Fock state description provides new insights which go well beyond the usual assumptions of traditional hadronic and nuclear physics.Comment: LaTex 36 pages, 3 figures. To obtain a copy, send e-mail to [email protected]

Effect of B on the microstructure and mechanical properties of mechanically milled TiAl alloys

The present study is concerned with gamma-(Ti52Al48)(100-x)B-x (x = 0, 0.5, 2, 5) alloys produced by mechanical milling/vacuum hot pressing (VHPing) using melt-extracted powders. Microstructure of the as-vacuum hot pressed (VHPed) alloys exhibits a duplex equiaxed microstructure of alpha(2) and gamma with a mean grain size of 200 nm. Besides alpha(2) and gamma phases, binary and 0.5 pet B alloys contain Ti,AIN and Al2O3 phases located along the grain boundaries and show appreciable coarsening in grain and dispersoid sizes during annealing treatment at 1300 degrees C for 5 hours. On the other hand, 2 pet B and 5 pet B alloys contain fine boride particles within the gamma grains and shaw minimal coarsening during annealing. Room-temperature compressing tests of the as-VHPed alloys show low ductility, but very high yield strength > 2100 MPa. After annealing treatment, mechanically milled alloys show much higher yield strength than conventional powder metallurgy and ingot metallurgy processed alloys, with equivalent ductility to ingot metallurgy processed alloys. The 5 pet B alloy with the smallest grain size shows higher yield strength than binary alloy up to the test temperature of 700 degrees C. At 850 degrees C, 5 pet B alloy shows much lower strength than the binary alloy, indicating that the deformation of fine 5 pet B alloy is dominated by the grain boundary sliding mechanism.ope

The role of metallothionein and astrocyte-neuron interactions in injury to the CNS

Metallothioneins (MTs) represent a large family of proteins characterized by high heavy metal content (mainly CuII and ZnII) and by an unusual cysteine abundance. They have a powerful protective function in all animal tissues, due most likely to their properties as free radical scavengers protecting against oxidative damage. Moreover, the presence and overexpression of MTs in various pathological conditions, such as metal dyshomeostasis, cell proliferation, neurological disorders, and chemotherapy and radiotherapy resistance, could be used as an important prognostic marker, as a histopathological diagnostic tool, and to follow specific pharmacological treatments

Two-Way Minimization: A Novel Treatment Allocation Method for Small Trials

Randomization is a hallmark of clinical trials. If a trial entails very few subjects and has many prognostic factors (or many factor levels) to be balanced, minimization is a more efficient method to achieve balance than a simple randomization. We propose a novel minimization method, the ‘two-way minimization’. The method separately calculates the ‘imbalance in the total numbers of subjects’ and the ‘imbalance in the distributions of prognostic factors’. And then to allocate a subject, it chooses—by probability—to minimize either one of these two aspects of imbalances. As such, it is a method that is both treatment-adaptive and covariate-adaptive. We perform Monte-Carlo simulations to examine its statistical properties. The two-way minimization (with proper regression adjustment of the force-balanced prognostic factors) has the correct type I error rates. It also produces point estimates that are unbiased and variance estimates that are accurate. When there are important prognostic factors to be balanced in the study, the method achieves the highest power and the smallest variance among randomization methods that are resistant to selection bias. The allocation can be done in real time and the subsequent data analysis is straightforward. The two-way minimization is recommended to balance prognostic factors in small trials

Discovering monotonic stemness marker genes from time-series stem cell microarray data

© 2015 Wang et al.; licensee BioMed Central Ltd. Background: Identification of genes with ascending or descending monotonic expression patterns over time or stages of stem cells is an important issue in time-series microarray data analysis. We propose a method named Monotonic Feature Selector (MFSelector) based on a concept of total discriminating error (DEtotal) to identify monotonic genes. MFSelector considers various time stages in stage order (i.e., Stage One vs. other stages, Stages One and Two vs. remaining stages and so on) and computes DEtotal of each gene. MFSelector can successfully identify genes with monotonic characteristics.Results: We have demonstrated the effectiveness of MFSelector on two synthetic data sets and two stem cell differentiation data sets: embryonic stem cell neurogenesis (ESCN) and embryonic stem cell vasculogenesis (ESCV) data sets. We have also performed extensive quantitative comparisons of the three monotonic gene selection approaches. Some of the monotonic marker genes such as OCT4, NANOG, BLBP, discovered from the ESCN dataset exhibit consistent behavior with that reported in other studies. The role of monotonic genes found by MFSelector in either stemness or differentiation is validated using information obtained from Gene Ontology analysis and other literature. We justify and demonstrate that descending genes are involved in the proliferation or self-renewal activity of stem cells, while ascending genes are involved in differentiation of stem cells into variant cell lineages.Conclusions: We have developed a novel system, easy to use even with no pre-existing knowledge, to identify gene sets with monotonic expression patterns in multi-stage as well as in time-series genomics matrices. The case studies on ESCN and ESCV have helped to get a better understanding of stemness and differentiation. The novel monotonic marker genes discovered from a data set are found to exhibit consistent behavior in another independent data set, demonstrating the utility of the proposed method. The MFSelector R function and data sets can be downloaded from: http://microarray.ym.edu.tw/tools/MFSelector/

Predictors of failed attendances in a multi-specialty outpatient centre using electronic databases.

BACKGROUND: Failure to keep outpatient medical appointments results in inefficiencies and costs. The objective of this study is to show the factors in an existing electronic database that affect failed appointments and to develop a predictive probability model to increase the effectiveness of interventions. METHODS: A retrospective study was conducted on outpatient clinic attendances at Tan Tock Seng Hospital, Singapore from 2000 to 2004. 22864 patients were randomly sampled for analysis. The outcome measure was failed outpatient appointments according to each patient's latest appointment. RESULTS: Failures comprised of 21% of all appointments and 39% when using the patients' latest appointment. Using odds ratios from the mutliple logistic regression analysis, age group (0.75 to 0.84 for groups above 40 years compared to below 20 years), race (1.48 for Malays, 1.61 for Indians compared to Chinese), days from scheduling to appointment (2.38 for more than 21 days compared to less than 7 days), previous failed appointments (1.79 for more than 60% failures and 4.38 for no previous appointments, compared with less than 20% failures), provision of cell phone number (0.10 for providing numbers compared to otherwise) and distance from hospital (1.14 for more than 14 km compared to less than 6 km) were significantly associated with failed appointments. The predicted probability model's diagnostic accuracy to predict failures is more than 80%. CONCLUSION: A few key variables have shown to adequately account for and predict failed appointments using existing electronic databases. These can be used to develop integrative technological solutions in the outpatient clinic

A transcriptome-driven analysis of epithelial brushings and bronchial biopsies to define asthma phenotypes in U-BIOPRED

RATIONALE AND OBJECTIVES: Asthma is a heterogeneous disease driven by diverse immunologic and inflammatory mechanisms. We used transcriptomic profiling of airway tissues to help define asthma phenotypes. METHODS: The transcriptome from bronchial biopsies and epithelial brushings of 107 moderate-to-severe asthmatics were annotated by gene-set variation analysis (GSVA) using 42 gene-signatures relevant to asthma, inflammation and immune function. Topological data analysis (TDA) of clinical and histological data was used to derive clusters and the nearest shrunken centroid algorithm used for signature refinement. RESULTS: 9 GSVA signatures expressed in bronchial biopsies and airway epithelial brushings distinguished two distinct asthma subtypes associated with high expression of T-helper type 2 (Th-2) cytokines and lack of corticosteroid response (Group 1 and Group 3). Group 1 had the highest submucosal eosinophils, high exhaled nitric oxide (FeNO) levels, exacerbation rates and oral corticosteroid (OCS) use whilst Group 3 patients showed the highest levels of sputum eosinophils and had a high BMI. In contrast, Group 2 and Group 4 patients had an 86% and 64% probability of having non-eosinophilic inflammation. Using machine-learning tools, we describe an inference scheme using the currently-available inflammatory biomarkers sputum eosinophilia and exhaled nitric oxide levels along with OCS use that could predict the subtypes of gene expression within bronchial biopsies and epithelial cells with good sensitivity and specificity. CONCLUSION: This analysis demonstrates the usefulness of a transcriptomic-driven approach to phenotyping that segments patients who may benefit the most from specific agents that target Th2-mediated inflammation and/or corticosteroid insensitivity