Neutron-proton bremsstrahlung from intermediate energy heavy-ion reactions as a probe of the nuclear symmetry energy?

Hard photons from neutron-proton bremsstrahlung in intermediate energy heavy-ion reactions are examined as a potential probe of the nuclear symmetry energy within a transport model. Effects of the symmetry energy on the yields and spectra of hard photons are found to be generally smaller than those due to the currently existing uncertainties of both the in-medium nucleon-nucleon cross sections and the photon production probability in the elementary process $pn\to pn\gamma$. Very interestingly, nevertheless, the ratio of hard photon spectra $R_{1/2}(\gamma)$ from two reactions using isotopes of the same element is not only approximately independent of these uncertainties but also quite sensitive to the symmetry energy. For the head-on reactions of $^{132}Sn+^{124}Sn$ and $^{112}Sn+^{112}Sn$ at $E_{beam}/A=50$ MeV, for example, the $R_{1/2}(\gamma)$ displays a rise up to 15% when the symmetry energy is reduced by about 20% at $\rho=1.3\rho_0$ which is the maximum density reached in these reactions.Comment: Added new results in Fig. 6 and new references [27.28]. Phys. Lett. B in pres

A Korean Predictive Model for Postoperative Nausea and Vomiting

Postoperative nausea and vomiting (PONV) is one of the most common and distressing complications after surgery. An identification of risk factors associated with PONV would make it easier to select specific patients for effective antiemetic therapy. We designed a case-controlled study to identify the risk factors for PONV in 5,272 surgical patients. At postoperative 2 and 24 hr, patients were visited and interviewed on the presence and severity of PONV. Thirty nine percent of patients experienced one or more episodes of nausea or vomiting. Five risk factors were highly predictive of PONV: 1) female, 2) history of previous PONV or motion sickness, 3) duration of anesthesia more than 1 hour, 4) non-smoking status, and 5) use of opioid in the form of patient controlled analgesia (PCA), in the order of relevance. The formula to calculate the probability of PONV using the multiple regression analysis was as follows: P (probability of PONV)=1/1+e-Z, Z=-1.885+0.894 (gender)+0.661 (history)+0.584 (duration of anesthesia)+0.196 (smoking status) +0.186 (use of PCA-based opioid) where gender: female=1, male=0; history of previous PONV or motion sickness: yes=1, no=0; duration of anesthesia:more than 1 hr=1, less than or 1 hr=0; smoking status: no=1, yes=0; use of PCA-based opioid: yes=1, no=0

Combination of Antiemetics for the Prevention of Postoperative Nausea and Vomiting in High-Risk Patients

It was previously reported that the Korean predictive model could be used to identify patients at high risk of postoperative nausea and vomiting (PONV). This study investigated whether PONV in the high-risk and very high-risk patients identified by the Korean predictive model could be prevented by multiple prophylactic antiemetics. A total of 2,456 patients were selected from our previous PONV study and assigned to the control group, and 374 new patients were recruited consecutively to the treatment group. Patients in each group were subdivided into two risk groups according to the Korean predictive model: high-risk group and very high-risk group. Patients in the treatment group received an antiemetic combination of dexamethasone 5 mg (minutes after induction) and ondansetron 4 mg (30 min before the end of surgery). The incidences of PONV were examined at two hours after the surgery in the postanesthetic care unit and, additionally, at 24 hr after the surgery in the ward, and were analyzed for any differences between the control and treatment groups. The overall incidence of PONV decreased significantly from 52.1% to 23.0% (p≤0.001) after antiemetic prophylaxis. Specifically, the incidence decreased from 47.3% to 19.4% (p≤0.001) in the high-risk group and from 61.3% to 28.3% (p≤0.001) in the very high-risk group. Both groups showed a similar degree of relative risk reductions: 59.0% vs. 53.8% in the high-risk and very high-risk groups, respectively. The results of our study showed that the antiemetic prophylaxis with the combination of dexamethasone and ondansetron was effective in reducing the occurrence of PONV in both high-risk and very high-risk patients

Particle kinetic analysis of a polar jet from SECCHI COR data

Aims. We analyze coronagraph observations of a polar jet observed by the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) instrument suite onboard the Solar TErrestrial RElations Observatory (STEREO) spacecraft. Methods. In our analysis we compare the brightness distribution of the jet in white-light coronagraph images with a dedicated kinetic particle model. We obtain a consistent estimate of the time that the jet was launched from the solar surface and an approximate initial velocity distribution in the jet source. The method also allows us to check the consistency of the kinetic model. In this first application, we consider only gravity as the dominant force on the jet particles along the magnetic field. Results. We find that the kinetic model explains the observed brightness evolution well. The derived initiation time is consistent with the jet observations by the EUVI telescope at various wavelengths. The initial particle velocity distribution is fitted by Maxwellian distributions and we find deviations of the high energy tail from the Maxwellian distributions. We estimate the jet's total electron content to have a mass between 3.2 \times 1014 and 1.8 \times 1015 g. Mapping the integrated particle number along the jet trajectory to its source region and assuming a typical source region size, we obtain an initial electron density between 8 \times 109 and 5 \times 1010 cm-3 that is characteristic for the lower corona or the upper chromosphere. The total kinetic energy of all particles in the jet source region amounts from 2.1 \times 1028 to 2.4 \times 1029 erg.Comment: A&A, in pres

A direct probe of the in-medium pn scattering cross section

Hard photon production from neutron-proton bremsstrahlung in intermediate energy heavy-ion reactions is examined as a probe of the in-medium pn scattering cross section within a transport model. Uncertainty of photon production probability pn-pngamma is cancelled out by using the ratio of hard photon spectra R_{12C+12C/p+n}(gamma) from two reactions. The in medium pn scattering cross section is constrained by using the ratio of hard photon production cross sections of proton-induced reactions p+12C and p+2H. A reduction factor sigma_pn^medium/sigma_pn^free of about 0.5 ~ 0.7 around saturation density is obtained by comparing with the existing experimental data.Comment: 9 pages, 3 figures, accepted by PL

Crystal structure and substrate specificity of plant adenylate isopentenyltransferase from Humulus lupulus: distinctive binding affinity for purine and pyrimidine nucleotides

Cytokinins are important plant hormones, and their biosynthesis most begins with the transfer of isopentenyl group from dimethylallyl diphosphate (DMAPP) to the N6-amino group of adenine by either adenylate isopentenyltransferase (AIPT) or tRNA–IPT. Plant AIPTs use ATP/ADP as an isopentenyl acceptor and bacterial AIPTs prefer AMP, whereas tRNA–IPTs act on specific sites of tRNA. Here, we present the crystal structure of an AIPT–ATP complex from Humulus lupulus (HlAIPT), which is similar to the previous structures of Agrobacterium AIPT and yeast tRNA–IPT. The enzyme is structurally homologous to the NTP-binding kinase family of proteins but forms a solvent-accessible channel that binds to the donor substrate DMAPP, which is directed toward the acceptor substrate ATP/ADP. When measured with isothermal titration calorimetry, some nucleotides displayed different binding affinities to HlAIPT with an order of ATP > dATP ∼ ADP > GTP > CTP > UTP. Two basic residues Lys275 and Lys220 in HlAIPT interact with the β and γ-phosphate of ATP. By contrast, the interactions are absent in Agrobacterium AIPT because they are replaced by the acidic residues Asp221 and Asp171. Despite its structural similarity to the yeast tRNA–IPT, HlAIPT has evolved with a different binding strategy for adenylate