Baryon Number Fluctuations in Quasi-particle Model

Baryon number fluctuations are sensitive to the QCD phase transition and QCD critical point. According to the Feynman rules of finite-temperature field theory, we calculated various order moments and cumulants of the baryon number distributions in the quasi-particle model of quark gluon plasma. Furthermore, we compared our results with the experimental data measured by the STAR experiment at RHIC. It is found that the experimental data can be well described by the model for the colliding energies above 30 GeV and show large discrepancies at low energies. It can put new constraint on qQGP model and also provide a baseline for the QCD critical point search in heavy-ion collisions at low energies.Comment: 13 pages, 5 figure

Urea signalling to immediate-early gene transcription in renal medullary cells requires transactivation of the epidermal growth factor receptor.

Signalling by physiological levels of urea (e.g. 200 mM) in cells of the mammalian renal medulla is reminiscent of activation of a receptor tyrosine kinase. The epidermal growth factor (EGF) receptor may be transactivated by a variety of G-protein-coupled receptors, primarily through metalloproteinase-dependent cleavage of a membrane-anchored EGF precursor. In the murine inner medullary collecting duct (mIMCD3) cell line, urea (200 mM) induced prompt (1-5 min) tyrosine phosphorylation of the EGF receptor. Pharmacological inhibition of EGF receptor kinase activity with AG1478 or PD153035 blocked urea-inducible transcription and expression of the immediate-early gene, Egr-1. AG1478 blocked, either fully or partially, other hallmarks of urea signalling including Elk-1 activation and extracellular signal-regulated kinase phosphorylation. EGF receptor kinase inhibition also blocked the cytoprotective effect of urea observed in the context of hypertonicity-inducible apoptosis. EGF receptor transactivation was likely to be attributable to metalloproteinase-dependent ectodomain shedding of an EGF receptor agonist because both specific and non-specific inhibitors of metalloproteinases blocked the urea effect. Heparin-binding EGF (HB-EGF), in particular, was implicated because the diphtheria toxin analogue and highly specific antagonist of HB-EGF, CRM197, also blocked urea-inducible transcription. In aggregate, these data indicate that signalling in response to urea in renal medullary cells requires EGF receptor transactivation, probably through autocrine action of HB-EGF

Ultracompact Deep Neural Network for Ultrafast Optical Property Extraction in Spatial Frequency Domain Imaging (SFDI)

Spatial frequency domain imaging (SFDI) is a powerful, label-free imaging technique capable of the wide-field quantitative mapping of tissue optical properties and, subsequently, chromophore concentrations. While SFDI hardware acquisition methods have advanced towards video-rate, the inverse problem (i.e., the mapping of acquired diffuse reflectance to optical properties) has remained a bottleneck for real-time data processing and visualization. Deep learning methods are adept at fitting nonlinear patterns, and may be ideal for rapidly solving the SFDI inverse problem. While current deep neural networks (DNN) are growing increasingly larger and more complex (e.g., with millions of parameters or more), our study shows that it can also be beneficial to move in the other direction, i.e., make DNNs that are smaller and simpler. Here, we propose an ultracompact, two-layer, fully connected DNN structure (each layer with four and two neurons, respectively) for ultrafast optical property extractions, which is 30×–600× faster than current methods with a similar or improved accuracy, allowing for an inversion time of 5.5 ms for 696 × 520 pixels. We further demonstrated the proposed inverse model in numerical simulations, and comprehensive phantom characterization, as well as offering in vivo measurements of dynamic physiological processes. We further demonstrated that the computation time could achieve another 200× improvement with a GPU device. This deep learning structure will help to enable fast and accurate real-time SFDI measurements, which are crucial for pre-clinical, clinical, and industrial applications

Quantifying the Relative Importance of Climate Change and Human Activities on Selected Wetland Ecosystems in China

Climate change and human activities are important factors driving changes in wetland ecosystems. It is therefore crucial to quantitatively characterize the relative importance of these stressors in wetlands. Previous such analyses have generally not distinguished between wetland types, or have focused on individual wetland types. In this study, three representative wetland areas of the upper, middle and lower reaches of the Heilongjiang River Basin (HRB) were selected as the study area. An object-based classification was used with Landsat TM data to extract the spatial distribution of wetland in 1990, 2000 and 2010. We then quantified the relative importance of climate change and human activities on the wetlands by using the R package “relaimpo” package. The results indicated that: (1) the effects of human activities on wetland changes were greater (contribution rate of 63.57%) than climate change in the HRB. Specifically, there were differences in the relative importance of climate change and human activities for wetlands in different regions. Wetlands of the upper reaches were more affected by climate change, while wetlands in the middle and lower reaches were more affected by human activities; (2) climate change had a greater impact (contribution rate of 65.72%) on low intensity wetland loss, while human activities had a greater impact on moderate and severe intensity wetland loss, with respective contribution rates of 57.22% and 70.35%; (3) climate change had a larger effect on the shrub and forested wetland changes, with respective contribution rates of 58.33% and 52.58%. However, human activities had a larger effect on herbaceous wetland changes, with a contribution rate of 72.28%. Our study provides a useful framework for wetland assessment and management, and could be a useful tool for developing wetland utilization and protection approaches, particularly in sensitive environments in mid- and high-latitude areas