Flow at the SPS and RHIC as a Quark Gluon Plasma Signature

Radial and elliptic flow in non-central heavy ion collisions can constrain the effective Equation of State(EoS) of the excited nuclear matter. To this end, a model combining relativistic hydrodynamics and a hadronic transport code(RQMD [17]) is developed. For an EoS with a first order phase transition, the model reproduces both the radial and elliptic flow data at the SPS. With the EoS fixed from SPS data, we quantify predictions at RHIC where the Quark Gluon Plasma(QGP) pressure is expected to drive additional radial and elliptic flow. Currently, the strong elliptic flow observed in the first RHIC measurements does not conclusively signal this nascent QGP pressure. Additional measurements are suggested to pin down the EoS.Comment: 4 pages, 4 figures. Revised. Included discussed of v_2 (p_t) vs. b and comparison to STAR dat

Metabolomic Profiling of Plasma from Melioidosis Patients Using UHPLC-QTOF MS Reveals Novel Biomarkers for Diagnosis

© 2016 by the authors; licensee MDPI, Basel, Switzerland.To identify potential biomarkers for improving diagnosis of melioidosis, we compared plasma metabolome profiles of melioidosis patients compared to patients with other bacteremia and controls without active infection, using ultra-high-performance liquid chromatography-electrospray ionization-quadruple time-of-flight mass spectrometry. Principal component analysis (PCA) showed that the metabolomic profiles of melioidosis patients are distinguishable from bacteremia patients and controls. Using multivariate and univariate analysis, 12 significant metabolites from four lipid classes, acylcarnitine (n = 6), lysophosphatidylethanolamine (LysoPE) (n = 3), sphingomyelins (SM) (n = 2) and phosphatidylcholine (PC) (n = 1), with significantly higher levels in melioidosis patients than bacteremia patients and controls, were identified. Ten of the 12 metabolites showed area-under-receiver operating characteristic curve (AUC) >0.80 when compared both between melioidosis and bacteremia patients, and between melioidosis patients and controls. SM(d18:2/16:0) possessed the largest AUC when compared, both between melioidosis and bacteremia patients (AUC 0.998, sensitivity 100% and specificity 91.7%), and between melioidosis patients and controls (AUC 1.000, sensitivity 96.7% and specificity 100%). Our results indicate that metabolome profiling might serve as a promising approach for diagnosis of melioidosis using patient plasma, with SM(d18:2/16:0) representing a potential biomarker. Since the 12 metabolites were related to various pathways for energy and lipid metabolism, further studies may reveal their possible role in the pathogenesis and host response in melioidosis.published_or_final_versio

Observation of increases in emission from modern vehicles over time in Hong Kong using remote sensing

Author name used in this manuscript: W. T. Hung2011-2012 > Academic research: refereed > Publication in refereed journalAccepted ManuscriptPublishe

Acetate Kinase Isozymes Confer Robustness in Acetate Metabolism

Acetate kinase (ACK) (EC no: 2.7.2.1) interconverts acetyl-phosphate and acetate to either catabolize or synthesize acetyl-CoA dependent on the metabolic requirement. Among all ACK entries available in UniProt, we found that around 45% are multiple ACKs in some organisms including more than 300 species but surprisingly, little work has been done to clarify whether this has any significance. In an attempt to gain further insight we have studied the two ACKs (AckA1, AckA2) encoded by two neighboring genes conserved in Lactococcus lactis (L. lactis) by analyzing protein sequences, characterizing transcription structure, determining enzyme characteristics and effect on growth physiology. The results show that the two ACKs are most likely individually transcribed. AckA1 has a much higher turnover number and AckA2 has a much higher affinity for acetate in vitro. Consistently, growth experiments of mutant strains reveal that AckA1 has a higher capacity for acetate production which allows faster growth in an environment with high acetate concentration. Meanwhile, AckA2 is important for fast acetate-dependent growth at low concentration of acetate. The results demonstrate that the two ACKs have complementary physiological roles in L. lactis to maintain a robust acetate metabolism for fast growth at different extracellular acetate concentrations. The existence of ACK isozymes may reflect a common evolutionary strategy in bacteria in an environment with varying concentrations of acetate

Normalized periprostatic fat MRI measurements can predict prostate cancer aggressiveness in men undergoing radical prostatectomy for clinically localised disease

Periprostatic and pelvic fat have been shown to influence prostate cancer behaviour through the secretion of chemokines and growth factors, acting in a paracrine mode. We have measured periprostatic fat volume (PFV) with normalisation to prostate gland volume on pelvic magnetic resonance imaging (MRI) and have correlated this with grade (Gleason score; GS) and pathological staging (pT) of prostate cancer (PCa) following radical prostatectomy (RP). PFV was determined using a segmentation technique on contiguous T1-weighted axial MRI slices from the level of the prostate base to the apex. The abdominal fat area (AFA) and subcutaneous fat thickness (SFT) were measured using T1-weighted axial slices at the level of the umbilicus and the upper border of the symphysis pubis, respectively. PFV was normalised to prostate volume (PV) to account for variations in PV (NPFV=PFV/PV). Patients were stratified into three risk groups according to post-operative GS: ≤6, 7(3+4), and ≥7(4+3). NPFV was significantly different between the groups (p=0.001) and positively correlated with post-operative GS (ρ=0.294, p<0.001). There was a difference in NPFV between those with upgrading of GS from 6 post prostatectomy (2.43±0.98; n=26) compared to those who continued to be low grade (1.99±0.82; n=17); however, this did not reach statistical significance (p=0.11)

Phage therapy is effective against infection by Mycobacterium ulcerans in a murine footpad model

Author Summary: Buruli Ulcer (BU), caused by Mycobacterium ulcerans, is a necrotizing disease of the skin, subcutaneous tissue and bone. Standard treatment of BU patients consists of a combination of the antibiotics rifampicin and streptomycin for 8 weeks. However, in advanced stages of the disease, surgical resection of the destroyed skin is still required. The use of bacterial viruses (bacteriophages) for the control of bacterial infections has been considered as an alternative or a supplement to antibiotic chemotherapy. By using a mouse model of M. ulcerans footpad infection, we show that mice treated with a single subcutaneous injection of the mycobacteriophage D29 present decreased footpad pathology associated with a reduction of the bacterial burden. In addition, D29 treatment induced increased levels of IFN-γ and TNF in M. ulcerans -infected footpads, correlating with a predominance of a mononuclear infiltrate. These findings suggest the potential use of phage therapy in BU, as a novel therapeutic approach against this disease, particularly in advanced stages where bacteria are found primarily in an extracellular location in the subcutaneous tissue, and thus immediately accessible by lytic phages.This work was supported by a grant from the Health Services of Fundacao Calouste Gulbenkian, and the Portuguese Science and Technology Foundation (FCT) fellowships SFRH/BPD/64032/2009, SFRH/BD/41598/2007, and SFRH/BPD/68547/2010 to GT, TGM, and AGF, respectively. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

The clinical and functional significance of c-Met in breast cancer: a review

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.CMH-Y is funded by a Cancer Research UK Clinical Research Fellowship. JLJ is funded by the Breast Cancer Campaign Tissue Bank

Executive cash compensation and tax aggressiveness of Chinese firms

We examine the influence of corporate compensation policies on firms’ tax aggressiveness in an emerging market where executive compensation is primarily in cash form. Based on a hand-collected dataset of 958 firm-year observations of Chinese listed firms for the 2006-2012 period, we find that firms paying higher executive cash compensation are associated with lower tax aggressiveness. This relationship also holds for the excess cash compensation measures which control for executive shareholding, firm profitability, size, growth opportunity, and board independence. We further document that mutual funds ownership pressure firms paying higher compensation to reduce their tax aggressiveness, suggesting adverse selection by mutual funds on firms exhibiting risky tax avoidance activities. High leverage offsets the negative link between cash compensation and tax aggressiveness, indicating a complementary effect between debt and tax avoidance, and, hence, suggesting that creditor monitoring is weak. These results are robust to the system-GMM estimation, which simultaneously account for the endogeneity of executive compensation, tax aggressiveness, ownership and control, leverage, and corporate governance. Our findings on Chinese firms have important policy implications for developing countries around the world with concentrated ownership structure, weak institutional environment, widespread corruption, ineffective rule of law, and ongoing significant social and political transformation

Forehead EEG in Support of Future Feasible Personal Healthcare Solutions: Sleep Management, Headache Prevention, and Depression Treatment

© 2013 IEEE. There are current limitations in the recording technologies for measuring EEG activity in clinical and experimental applications. Acquisition systems involving wet electrodes are time-consuming and uncomfortable for the user. Furthermore, dehydration of the gel affects the quality of the acquired data and reliability of long-term monitoring. As a result, dry electrodes may be used to facilitate the transition from neuroscience research or clinical practice to real-life applications. EEG signals can be easily obtained using dry electrodes on the forehead, which provides extensive information concerning various cognitive dysfunctions and disorders. This paper presents the usefulness of the forehead EEG with advanced sensing technology and signal processing algorithms to support people with healthcare needs, such as monitoring sleep, predicting headaches, and treating depression. The proposed system for evaluating sleep quality is capable of identifying five sleep stages to track nightly sleep patterns. Additionally, people with episodic migraines can be notified of an imminent migraine headache hours in advance through monitoring forehead EEG dynamics. The depression treatment screening system can predict the efficacy of rapid antidepressant agents. It is evident that frontal EEG activity is critically involved in sleep management, headache prevention, and depression treatment. The use of dry electrodes on the forehead allows for easy and rapid monitoring on an everyday basis. The advances in EEG recording and analysis ensure a promising future in support of personal healthcare solutions