The Narrowing of Charge Balance Function and Hadronization Time in Relativistic Heavy Ion Collisions

The widths of charge balance function in high energy hadron-hadron and relativistic heavy ion collisions are studied using the Monte Carlo generators PYTHIA and AMPT, respectively. The narrowing of balance function as the increase of multiplicity is found in both cases. The mean parton-freeze-out time of a heavy-ion-collision event is used as the characteristic hadronization time of the event. It turns out that for a fixed multiplicity interval the width of balance function is consistent with being independent of hadronization time.Comment: 4 pages, 7 figure

Effect of an equilibrium phase transition on multiphase transport in relativistic heavy ion collisions

The hadronization scheme for parton transport in relativistic heavy ion collisions is considered in detail. It is pointed out that the traditional scheme for particles being freezed out one by one leads to serious problem on unreasonable long lifetime of partons. A collective phase transition following a supercooling is implemented in a simple way. It turns out that the modified model with a sudden phase transition is able to reproduce the experimental longitudinal distributions of final state particles better than the original one does. The encouraging results indicate that equilibrium phase transition should be taken into proper account in parton transport models for relativistic heavy ion collisions

MRI signal phase oscillates with neuronal activity in cerebral cortex: implications for neuronal current imaging

Neuronal activity produces transient ionic currents that may be detectable using magnetic resonance imaging (MRI). We examined the feasibility of MRI-based detection of neuronal currents using computer simulations based on the laminar cortex model (LCM). Instead of simulating the activity of single neurons, we decomposed neuronal activity to action potentials (AP) and postsynaptic potentials (PSP). The geometries of dendrites and axons were generated dynamically to account for diverse neuronal morphologies. Magnetic fields associated with APs and PSPs were calculated during spontaneous and stimulated cortical activity, from which the neuronal current induced MRI signal was determined. We found that the MRI signal magnitude change (< 0.1 ppm) is below currently detectable levels but that the signal phase change is likely to be detectable. Furthermore, neuronal MRI signals are sensitive to temporal and spatial variations in neuronal activity but independent of the intensity of neuronal activation. Synchronised neuronal activity produces large phase changes (in the order of 0.1 mrad). However, signal phase oscillates with neuronal activity. Consequently, MRI scans need to be synchronised with neuronal oscillations to maximise the likelihood of detecting signal phase changes due to neuronal currents. These findings inform the design of MRI experiments to detect neuronal currents

Two-particle azimuthal angle correlations and azimuthal charge balance function in relativistic heavy ion collisions

The two-particle azimuthal angle correlation (TPAC) and azimuthal charge balance function (ACBF) are used to study the anisotropic expansion in relativistic heavy ion collisions. It is demonstrated by the relativistic quantum molecular dynamics (RQMD) model and a multi-phase transport (AMPT) model that the small-angle correlation in TPAC indeed presents anisotropic expansion, and the large-angle (or back-to-back) correlation is mainly due to global momentum conservations. The AMPT model reproduces the observed TPAC, but the RQMD model fails to reproduce the strong correlations in both small and large azimuthal angles. The width of ACBF from RQMD and AMPT models decreases from peripheral to central collisions, consistent with experimental data, but in contrast to the expectation from thermal model calculations. The ACBF is insensitive to anisotropic expansion. It is a probe for the mechanism of hadronization, similar to the charge balance function in rapidity

Language Ability Accounts for Ethnic Difference in Mathematics Achievement

The mathematics achievement of minority students has always been a focal point of educators in China. This study investigated the differences in mathematics achievement between Han and minority pupils to determine if there is any cognitive mechanism that can account for the discrepancy. We recruited 236 Han students and 272 minority students (including Uygur and Kazak) from the same primary schools. They were tested on mathematics achievement, language abilities, and general cognitive abilities. The results showed that Han pupils had better mathematics achievement scores and better Chinese language ability than minority students. After controlling for age, gender, and general cognitive abilities, there were still significant differences in mathematics achievement between Han and minority students. However, these differences disappeared after controlling for language ability. These results suggest that the relatively poor levels of mathematics achievement observed in minority students is related to poor Chinese language skills

Computational Prediction of Human Salivary Proteins from Blood Circulation and Application to Diagnostic Biomarker Identification

Proteins can move from blood circulation into salivary glands through active transportation, passive diffusion or ultrafiltration, some of which are then released into saliva and hence can potentially serve as biomarkers for diseases if accurately identified. We present a novel computational method for predicting salivary proteins that come from circulation. The basis for the prediction is a set of physiochemical and sequence features we found to be discerning between human proteins known to be movable from circulation to saliva and proteins deemed to be not in saliva. A classifier was trained based on these features using a support-vector machine to predict protein secretion into saliva. The classifier achieved 88.56% average recall and 90.76% average precision in 10-fold cross-validation on the training data, indicating that the selected features are informative. Considering the possibility that our negative training data may not be highly reliable (i.e., proteins predicted to be not in saliva), we have also trained a ranking method, aiming to rank the known salivary proteins from circulation as the highest among the proteins in the general background, based on the same features. This prediction capability can be used to predict potential biomarker proteins for specific human diseases when coupled with the information of differentially expressed proteins in diseased versus healthy control tissues and a prediction capability for blood-secretory proteins. Using such integrated information, we predicted 31 candidate biomarker proteins in saliva for breast cancer