Thermal and electrical conductivity of melt mixed polycarbonate hybrid composites co-filled with multi-walled carbon nanotubes and graphene nanoplatelets

"This is the peer reviewed version of the following article: Wegrzyn, M., Ortega, A., Benedito, A., & Gimenez, E. (2015). Thermal and electrical conductivity of melt mixed polycarbonate hybrid composites co‐filled with multi‐walled carbon nanotubes and graphene nanoplatelets. Journal of Applied Polymer Science, 132(37), which has been published in final form at https://doi.org/10.1002/app.42536. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] In this work, we present thermoplastic nanocomposites of polycarbonate (PC) matrix with hybrid nanofillers system formed by a melt-mixing approach. Various concentrations of multi-walled carbon nanotubes (MWCNT) and graphene nanoplatelets (GnP) were mixed in to PC and the melt was homogenized. The nanocomposites were compression molded and characterized by different techniques. Torque dependence on the nanofiller composition increased with the presence of carbon nanotubes. The synergy of carbon nanotubes and GnP showed exponential increase of thermal conductivity, which was compared to logarithmic increase for nanocomposite with no MWCNT. Decrease of Shore A hardness at elevated loads present for all investigated nanocomposites was correlated with the expected low homogeneity caused by a low shear during melt-mixing. Mathematical model was used to calculate elastic modulus from Shore A tests results. Vicat softening temperature (VST) showed opposite pattern for hybrid nanocomposites and for PC-MWCNT increasing in the latter case. Electrical conductivity boost was explained by the collective effect of high nanofiller loads and synergy of MWCNT and GnP. (c) 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42536.This work is funded by the European Community's Seventh Framework Program (FP7-PEOPLE-ITN-2008) within the CONTACT project Marie Curie Fellowship under grant number 238363.Wegrzyn, M.; Ortega, A.; Benedito, A.; Giménez Torres, E. (2015). Thermal and electrical conductivity of melt mixed polycarbonate hybrid composites co-filled with multi-walled carbon nanotubes and graphene nanoplatelets. Journal of Applied Polymer Science. 132(37):42536-1-42536-8. https://doi.org/10.1002/app.42536S42536-142536-813237Su, D. S., & Schlögl, R. (2010). Nanostructured Carbon and Carbon Nanocomposites for Electrochemical Energy Storage Applications. ChemSusChem, 3(2), 136-168. doi:10.1002/cssc.200900182Yang, L., Liu, F., Xia, H., Qian, X., Shen, K., & Zhang, J. (2011). Improving the electrical conductivity of a carbon nanotube/polypropylene composite by vibration during injection-moulding. Carbon, 49(10), 3274-3283. doi:10.1016/j.carbon.2011.03.054Singh, I. V., Tanaka, M., & Endo, M. (2007). Effect of interface on the thermal conductivity of carbon nanotube composites. International Journal of Thermal Sciences, 46(9), 842-847. doi:10.1016/j.ijthermalsci.2006.11.003Kuan, H.-C., Ma, C.-C. M., Chang, W.-P., Yuen, S.-M., Wu, H.-H., & Lee, T.-M. (2005). Synthesis, thermal, mechanical and rheological properties of multiwall carbon nanotube/waterborne polyurethane nanocomposite. Composites Science and Technology, 65(11-12), 1703-1710. doi:10.1016/j.compscitech.2005.02.017Arasteh, R., Omidi, M., Rousta, A. H. A., & Kazerooni, H. (2011). A Study on Effect of Waviness on Mechanical Properties of Multi-Walled Carbon Nanotube/Epoxy Composites Using Modified Halpin–Tsai Theory. Journal of Macromolecular Science, Part B, 50(12), 2464-2480. doi:10.1080/00222348.2011.579868Cai, D., Jin, J., Yusoh, K., Rafiq, R., & Song, M. (2012). High performance polyurethane/functionalized graphene nanocomposites with improved mechanical and thermal properties. Composites Science and Technology, 72(6), 702-707. doi:10.1016/j.compscitech.2012.01.020Yu, D., & Dai, L. (2009). Self-Assembled Graphene/Carbon Nanotube Hybrid Films for Supercapacitors. The Journal of Physical Chemistry Letters, 1(2), 467-470. doi:10.1021/jz9003137Haslam, M. D., & Raeymaekers, B. (2013). A composite index to quantify dispersion of carbon nanotubes in polymer-based composite materials. Composites Part B: Engineering, 55, 16-21. doi:10.1016/j.compositesb.2013.05.038Pötschke, P., Dudkin, S. M., & Alig, I. (2003). Dielectric spectroscopy on melt processed polycarbonate—multiwalled carbon nanotube composites. Polymer, 44(17), 5023-5030. doi:10.1016/s0032-3861(03)00451-8Stankovich, S., Dikin, D. A., Dommett, G. H. B., Kohlhaas, K. M., Zimney, E. J., Stach, E. A., … Ruoff, R. S. (2006). Graphene-based composite materials. Nature, 442(7100), 282-286. doi:10.1038/nature04969Sathyanarayana, S., Olowojoba, G., Weiss, P., Caglar, B., Pataki, B., Mikonsaari, I., … Henning, F. (2012). Compounding of MWCNTs with PS in a Twin-Screw Extruder with Varying Process Parameters: Morphology, Interfacial Behavior, Thermal Stability, Rheology, and Volume Resistivity. Macromolecular Materials and Engineering, 298(1), 89-105. doi:10.1002/mame.201200018Ye, L., Wu, Q., & Qu, B. (2009). Synergistic effects and mechanism of multiwalled carbon nanotubes with magnesium hydroxide in halogen-free flame retardant EVA/MH/MWNT nanocomposites. Polymer Degradation and Stability, 94(5), 751-756. doi:10.1016/j.polymdegradstab.2009.02.010Kalaitzidou, K., Fukushima, H., & Drzal, L. T. (2007). Multifunctional polypropylene composites produced by incorporation of exfoliated graphite nanoplatelets. Carbon, 45(7), 1446-1452. doi:10.1016/j.carbon.2007.03.029Mu, Q., Feng, S., & Diao, G. (2007). Thermal conductivity of silicone rubber filled with ZnO. Polymer Composites, 28(2), 125-130. doi:10.1002/pc.20276Pötschke, P., Bhattacharyya, A. R., & Janke, A. (2004). Melt mixing of polycarbonate with multiwalled carbon nanotubes: microscopic studies on the state of dispersion. European Polymer Journal, 40(1), 137-148. doi:10.1016/j.eurpolymj.2003.08.008King, J. A., Barton, R. L., Hauser, R. A., & Keith, J. M. (2008). Synergistic effects of carbon fillers in electrically and thermally conductive liquid crystal polymer based resins. Polymer Composites, 29(4), 421-428. doi:10.1002/pc.20446Hwang, Y., Kim, M., & Kim, J. (2013). Improvement of the mechanical properties and thermal conductivity of poly(ether-ether-ketone) with the addition of graphene oxide-carbon nanotube hybrid fillers. Composites Part A: Applied Science and Manufacturing, 55, 195-202. doi:10.1016/j.compositesa.2013.08.010Babaei, H., Keblinski, P., & Khodadadi, J. M. (2013). Thermal conductivity enhancement of paraffins by increasing the alignment of molecules through adding CNT/graphene. International Journal of Heat and Mass Transfer, 58(1-2), 209-216. doi:10.1016/j.ijheatmasstransfer.2012.11.013Yang, S.-Y., Lin, W.-N., Huang, Y.-L., Tien, H.-W., Wang, J.-Y., Ma, C.-C. M., … Wang, Y.-S. (2011). Synergetic effects of graphene platelets and carbon nanotubes on the mechanical and thermal properties of epoxy composites. Carbon, 49(3), 793-803. doi:10.1016/j.carbon.2010.10.014Pascual, J., Peris, F., Boronat, T., Fenollar, O., & Balart, R. (2011). Study of the effects of multi-walled carbon nanotubes on mechanical performance and thermal stability of polypropylene. Polymer Engineering & Science, 52(4), 733-740. doi:10.1002/pen.22128Yasin, T., Nisar, M., Shafiq, M., Nho, Y.-C., & Ahmad, R. (2013). Influence of sepiolite and electron beam irradiation on the structural and physicochemical properties of polyethylene/starch nanocomposites. Polymer Composites, 34(3), 408-416. doi:10.1002/pc.22431Zhang, W. D., Shen, L., Phang, I. Y., & Liu, T. (2004). Carbon Nanotubes Reinforced Nylon-6 Composite Prepared by Simple Melt-Compounding. Macromolecules, 37(2), 256-259. doi:10.1021/ma035594fZhang, C., Tjiu, W. W., Liu, T., Lui, W. Y., Phang, I. Y., & Zhang, W.-D. (2011). Dramatically Enhanced Mechanical Performance of Nylon-6 Magnetic Composites with Nanostructured Hybrid One-Dimensional Carbon Nanotube−Two-Dimensional Clay Nanoplatelet Heterostructures. The Journal of Physical Chemistry B, 115(13), 3392-3399. doi:10.1021/jp112284kLin, J., Wang, L., & Chen, G. (2010). Modification of Graphene Platelets and their Tribological Properties as a Lubricant Additive. Tribology Letters, 41(1), 209-215. doi:10.1007/s11249-010-9702-5Qiu, L., Yang, X., Gou, X., Yang, W., Ma, Z.-F., Wallace, G. G., & Li, D. (2010). Dispersing Carbon Nanotubes with Graphene Oxide in Water and Synergistic Effects between Graphene Derivatives. Chemistry - A European Journal, 16(35), 10653-10658. doi:10.1002/chem.201001771Tian, L., Meziani, M. J., Lu, F., Kong, C. Y., Cao, L., Thorne, T. J., & Sun, Y.-P. (2010). Graphene Oxides for Homogeneous Dispersion of Carbon Nanotubes. ACS Applied Materials & Interfaces, 2(11), 3217-3222. doi:10.1021/am100687nPotts, J. R., Dreyer, D. R., Bielawski, C. W., & Ruoff, R. S. (2011). Graphene-based polymer nanocomposites. Polymer, 52(1), 5-25. doi:10.1016/j.polymer.2010.11.042Song, Y. S., & Youn, J. R. (2005). Influence of dispersion states of carbon nanotubes on physical properties of epoxy nanocomposites. Carbon, 43(7), 1378-1385. doi:10.1016/j.carbon.2005.01.007Huang, H., Liu, C. H., Wu, Y., & Fan, S. (2005). Aligned Carbon Nanotube Composite Films for Thermal Management. Advanced Materials, 17(13), 1652-1656. doi:10.1002/adma.200500467Shokrieh, M. M., Hosseinkhani, M. R., Naimi-Jamal, M. R., & Tourani, H. (2013). Nanoindentation and nanoscratch investigations on graphene-based nanocomposites. Polymer Testing, 32(1), 45-51. doi:10.1016/j.polymertesting.2012.09.001Schiøtz , J. nd Dinesen , A. R. 2001 127Hornbostel, B., Pötschke, P., Kotz, J., & Roth, S. (2008). Mechanical properties of triple composites of polycarbonate, single-walled carbon nanotubes and carbon fibres. Physica E: Low-dimensional Systems and Nanostructures, 40(7), 2434-2439. doi:10.1016/j.physe.2007.08.100Gupta, T. K., Singh, B. P., Mathur, R. B., & Dhakate, S. R. (2014). Multi-walled carbon nanotube–graphene–polyaniline multiphase nanocomposite with superior electromagnetic shielding effectiveness. Nanoscale, 6(2), 842-851. doi:10.1039/c3nr04565jPan, M., Shi, X., Li, X., Hu, H., & Zhang, L. (2004). Morphology and properties of PVC/clay nanocomposites viain situ emulsion polymerization. Journal of Applied Polymer Science, 94(1), 277-286. doi:10.1002/app.20896Wegrzyn, M., Juan, S., Benedito, A., & Giménez, E. (2013). The influence of injection molding parameters on electrical properties of PC/ABS-MWCNT nanocomposites. Journal of Applied Polymer Science, 130(3), 2152-2158. doi:10.1002/app.39412Godavarti, S., & Karwe, M. (1997). Determination of Specific Mechanical Energy Distribution on a Twin-Screw Extruder. Journal of Agricultural Engineering Research, 67(4), 277-287. doi:10.1006/jaer.1997.017

An analysis and comparison of multinational officers of the watch in the global maritime labor market

Officer of the watch (OOW) is an important part of the maritime labor market. For many years, countries have been improving their development of OOWs for the maritime market, in terms of both quantity and quality. As the supply of qualified OOWs for maritime transportation is such an important issue, shipping companies recruit multinational OOWs for both economic and socio-cultural reasons. This study aims to identify the qualifications of an ideal officer that holds office on commercial ships, and to make a comparison among Filipino, Chinese, Indian, Eastern European and Turkish OOWs. The research takes into account expert opinions of a number of shipping companies that employ multinational seafarers. A Fuzzy Analytic Hierarchy Process (FAHP) technique is applied in this study to assist in the comparison of officers. A number of main and sub-criteria are outlined to determine both positive and negative aspects of OOWs from the selected countries for decision making purposes. This study allows maritime countries to evaluate their maritime education and training policies for selection and assessment of OOWs. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group

Magnetic hot spots in closely spaced thick gold nanorings

This document is the Accepted Manuscript version of a Published Work that appeared in final form in Nano Letters, copyright © American Chemical Society after peer review and technical editing by the publisher. To access the final edited and published work see http://pubs.acs.org/page/policy/articlesonrequest/index.htmlLigh-matter interaction at optical frequencies is mostly mediated by the electric component of the electromagnetic field, with the magnetic component usually being considered negligible. Recently, it has been shown that properly engineered metallic nanostructures can provide a magnetic response at optical frequencies originated from real or virtual flows of electric current in the structure. In this work, we demonstrate a magnetic plasmonic mode which emerges in closely spaced thick gold nanorings. The plasmonic resonance obtains a magnetic dipole character by sufficiently increasing the height of the nanorings. Numerical simulations show that a virtual current loop appears at resonance for sufficiently thick nanorings, resulting in a strong concentration of the magnetic field in the gap region (magnetic hot spot). We find that there is an optimum thickness that provides the maximum magnetic intensity enhancement (over 200-fold enhancement) and give an explanation of this observation. This strong magnetic resonance, observed both experimentally and theoretically, can be used to build new metamaterials and resonant loop nanoantennas at optical frequencies.This work has been supported by Spanish Government and European Union (EU) funds under contracts CSD2008-00066 and TEC2011-28664-C02-02 and Universitat Politecnica de Valencia (program INNOVA 2011). The authors extend special thanks to Mr. J. Ross Aitken for his contribution to this work.Lorente Crespo, M.; Wang, L.; Ortuño Molinero, R.; García Meca, C.; Ekinci, Y.; Martínez Abietar, AJ. (2013). Magnetic hot spots in closely spaced thick gold nanorings. Nano Letters. 13(6):2654-2661. https://doi.org/10.1021/nl400798sS2654266113

Pharmacokinetic aspects of retinal drug delivery

Drug delivery to the posterior eye segment is an important challenge in ophthalmology, because many diseases affect the retina and choroid leading to impaired vision or blindness. Currently, intravitreal injections are the method of choice to administer drugs to the retina, but this approach is applicable only in selected cases (e.g. anti-VEGF antibodies and soluble receptors). There are two basic approaches that can be adopted to improve retinal drug delivery: prolonged and/or retina targeted delivery of intravitreal drugs and use of other routes of drug administration, such as periocular, suprachoroidal, sub-retinal, systemic, or topical. Properties of the administration route, drug and delivery system determine the efficacy and safety of these approaches. Pharmacokinetic and pharmacodynamic factors determine the required dosing rates and doses that are needed for drug action. In addition, tolerability factors limit the use of many materials in ocular drug delivery. This review article provides a critical discussion of retinal drug delivery, particularly from the pharmacokinetic point of view. This article does not include an extensive review of drug delivery technologies, because they have already been reviewed several times recently. Instead, we aim to provide a systematic and quantitative view on the pharmacokinetic factors in drug delivery to the posterior eye segment. This review is based on the literature and unpublished data from the authors' laboratory.Peer reviewe

Global, regional, and national burden of traumatic brain injury and spinal cord injury, 1990-2016: a systematic analysis for the Global Burden of Disease Study 2016.

Traumatic brain injury (TBI) and spinal cord injury (SCI) are increasingly recognised as global health priorities in view of the preventability of most injuries and the complex and expensive medical care they necessitate. We aimed to measure the incidence, prevalence, and years of life lived with disability (YLDs) for TBI and SCI from all causes of injury in every country, to describe how these measures have changed between 1990 and 2016, and to estimate the proportion of TBI and SCI cases caused by different types of injury. METHODS: We used results from the Global Burden of Diseases, Injuries, and Risk Factors (GBD) Study 2016 to measure the global, regional, and national burden of TBI and SCI by age and sex. We measured the incidence and prevalence of all causes of injury requiring medical care in inpatient and outpatient records, literature studies, and survey data. By use of clinical record data, we estimated the proportion of each cause of injury that required medical care that would result in TBI or SCI being considered as the nature of injury. We used literature studies to establish standardised mortality ratios and applied differential equations to convert incidence to prevalence of long-term disability. Finally, we applied GBD disability weights to calculate YLDs. We used a Bayesian meta-regression tool for epidemiological modelling, used cause-specific mortality rates for non-fatal estimation, and adjusted our results for disability experienced with comorbid conditions. We also analysed results on the basis of the Socio-demographic Index, a compound measure of income per capita, education, and fertility. FINDINGS: In 2016, there were 27·08 million (95% uncertainty interval [UI] 24·30-30·30 million) new cases of TBI and 0·93 million (0·78-1·16 million) new cases of SCI, with age-standardised incidence rates of 369 (331-412) per 100 000 population for TBI and 13 (11-16) per 100 000 for SCI. In 2016, the number of prevalent cases of TBI was 55·50 million (53·40-57·62 million) and of SCI was 27·04 million (24·98-30·15 million). From 1990 to 2016, the age-standardised prevalence of TBI increased by 8·4% (95% UI 7·7 to 9·2), whereas that of SCI did not change significantly (-0·2% [-2·1 to 2·7]). Age-standardised incidence rates increased by 3·6% (1·8 to 5·5) for TBI, but did not change significantly for SCI (-3·6% [-7·4 to 4·0]). TBI caused 8·1 million (95% UI 6·0-10·4 million) YLDs and SCI caused 9·5 million (6·7-12·4 million) YLDs in 2016, corresponding to age-standardised rates of 111 (82-141) per 100 000 for TBI and 130 (90-170) per 100 000 for SCI. Falls and road injuries were the leading causes of new cases of TBI and SCI in most regions. INTERPRETATION: TBI and SCI constitute a considerable portion of the global injury burden and are caused primarily by falls and road injuries. The increase in incidence of TBI over time might continue in view of increases in population density, population ageing, and increasing use of motor vehicles, motorcycles, and bicycles. The number of individuals living with SCI is expected to increase in view of population growth, which is concerning because of the specialised care that people with SCI can require. Our study was limited by data sparsity in some regions, and it will be important to invest greater resources in collection of data for TBI and SCI to improve the accuracy of future assessments

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (, , ) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types

Integrated genomic characterization of pancreatic ductal adenocarcinoma

We performed integrated genomic, transcriptomic, and proteomic profiling of 150 pancreatic ductal adenocarcinoma (PDAC) specimens, including samples with characteristic low neoplastic cellularity. Deep whole-exome sequencing revealed recurrent somatic mutations in KRAS, TP53, CDKN2A, SMAD4, RNF43, ARID1A, TGFβR2, GNAS, RREB1, and PBRM1. KRAS wild-type tumors harbored alterations in other oncogenic drivers, including GNAS, BRAF, CTNNB1, and additional RAS pathway genes. A subset of tumors harbored multiple KRAS mutations, with some showing evidence of biallelic mutations. Protein profiling identified a favorable prognosis subset with low epithelial-mesenchymal transition and high MTOR pathway scores. Associations of non-coding RNAs with tumor-specific mRNA subtypes were also identified. Our integrated multi-platform analysis reveals a complex molecular landscape of PDAC and provides a roadmap for precision medicine

Radioactivity control strategy for the JUNO detector

602siopenJUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day (cpd), therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration to reduce at minimum the impact of natural radioactivity. We describe our efforts for an optimized experimental design, a careful material screening and accurate detector production handling, and a constant control of the expected results through a meticulous Monte Carlo simulation program. We show that all these actions should allow us to keep the background count rate safely below the target value of 10 Hz (i.e. ∼1 cpd accidental background) in the default fiducial volume, above an energy threshold of 0.7 MeV. [Figure not available: see fulltext.]openAbusleme A.; Adam T.; Ahmad S.; Ahmed R.; Aiello S.; Akram M.; An F.; An Q.; Andronico G.; Anfimov N.; Antonelli V.; Antoshkina T.; Asavapibhop B.; de Andre J.P.A.M.; Auguste D.; Babic A.; Baldini W.; Barresi A.; Basilico D.; Baussan E.; Bellato M.; Bergnoli A.; Birkenfeld T.; Blin S.; Blum D.; Blyth S.; Bolshakova A.; Bongrand M.; Bordereau C.; Breton D.; Brigatti A.; Brugnera R.; Bruno R.; Budano A.; Buscemi M.; Busto J.; Butorov I.; Cabrera A.; Cai H.; Cai X.; Cai Y.; Cai Z.; Cammi A.; Campeny A.; Cao C.; Cao G.; Cao J.; Caruso R.; Cerna C.; Chang J.; Chang Y.; Chen P.; Chen P.-A.; Chen S.; Chen X.; Chen Y.-W.; Chen Y.; Chen Y.; Chen Z.; Cheng J.; Cheng Y.; Chetverikov A.; Chiesa D.; Chimenti P.; Chukanov A.; Claverie G.; Clementi C.; Clerbaux B.; Conforti Di Lorenzo S.; Corti D.; Cremonesi O.; Dal Corso F.; Dalager O.; De La Taille C.; Deng J.; Deng Z.; Deng Z.; Depnering W.; Diaz M.; Ding X.; Ding Y.; Dirgantara B.; Dmitrievsky S.; Dohnal T.; Dolzhikov D.; Donchenko G.; Dong J.; Doroshkevich E.; Dracos M.; Druillole F.; Du S.; Dusini S.; Dvorak M.; Enqvist T.; Enzmann H.; Fabbri A.; Fajt L.; Fan D.; Fan L.; Fang J.; Fang W.; Fargetta M.; Fedoseev D.; Fekete V.; Feng L.-C.; Feng Q.; Ford R.; Formozov A.; Fournier A.; Gan H.; Gao F.; Garfagnini A.; Giammarchi M.; Giaz A.; Giudice N.; Gonchar M.; Gong G.; Gong H.; Gornushkin Y.; Gottel A.; Grassi M.; Grewing C.; Gromov V.; Gu M.; Gu X.; Gu Y.; Guan M.; Guardone N.; Gul M.; Guo C.; Guo J.; Guo W.; Guo X.; Guo Y.; Hackspacher P.; Hagner C.; Han R.; Han Y.; Hassan M.S.; He M.; He W.; Heinz T.; Hellmuth P.; Heng Y.; Herrera R.; Hor Y.K.; Hou S.; Hsiung Y.; Hu B.-Z.; Hu H.; Hu J.; Hu J.; Hu S.; Hu T.; Hu Z.; Huang C.; Huang G.; Huang H.; Huang W.; Huang X.; Huang X.; Huang Y.; Hui J.; Huo L.; Huo W.; Huss C.; Hussain S.; Ioannisian A.; Isocrate R.; Jelmini B.; Jen K.-L.; Jeria I.; Ji X.; Ji X.; Jia H.; Jia J.; Jian S.; Jiang D.; Jiang X.; Jin R.; Jing X.; Jollet C.; Joutsenvaara J.; Jungthawan S.; Kalousis L.; Kampmann P.; Kang L.; Karaparambil R.; Kazarian N.; Khan W.; Khosonthongkee K.; Korablev D.; Kouzakov K.; Krasnoperov A.; Kruth A.; Kutovskiy N.; Kuusiniemi P.; Lachenmaier T.; Landini C.; Leblanc S.; Lebrin V.; Lefevre F.; Lei R.; Leitner R.; Leung J.; Li D.; Li F.; Li F.; Li H.; Li H.; Li J.; Li M.; Li M.; Li N.; Li N.; Li Q.; Li R.; Li S.; Li T.; Li W.; Li W.; Li X.; Li X.; Li X.; Li Y.; Li Y.; Li Z.; Li Z.; Li Z.; Liang H.; Liang H.; Liao J.; Liebau D.; Limphirat A.; Limpijumnong S.; Lin G.-L.; Lin S.; Lin T.; Ling J.; Lippi I.; Liu F.; Liu H.; Liu H.; Liu H.; Liu H.; Liu H.; Liu J.; Liu J.; Liu M.; Liu Q.; Liu Q.; Liu R.; Liu S.; Liu S.; Liu S.; Liu X.; Liu X.; Liu Y.; Liu Y.; Lokhov A.; Lombardi P.; Lombardo C.; Loo K.; Lu C.; Lu H.; Lu J.; Lu J.; Lu S.; Lu X.; Lubsandorzhiev B.; Lubsandorzhiev S.; Ludhova L.; Luo F.; Luo G.; Luo P.; Luo S.; Luo W.; Lyashuk V.; Ma B.; Ma Q.; Ma S.; Ma X.; Ma X.; Maalmi J.; Malyshkin Y.; Mantovani F.; Manzali F.; Mao X.; Mao Y.; Mari S.M.; Marini F.; Marium S.; Martellini C.; Martin-Chassard G.; Martini A.; Mayer M.; Mayilyan D.; Mednieks I.; Meng Y.; Meregaglia A.; Meroni E.; Meyhofer D.; Mezzetto M.; Miller J.; Miramonti L.; Montini P.; Montuschi M.; Muller A.; Nastasi M.; Naumov D.V.; Naumova E.; Navas-Nicolas D.; Nemchenok I.; Nguyen Thi M.T.; Ning F.; Ning Z.; Nunokawa H.; Oberauer L.; Ochoa-Ricoux J.P.; Olshevskiy A.; Orestano D.; Ortica F.; Othegraven R.; Pan H.-R.; Paoloni A.; Parmeggiano S.; Pei Y.; Pelliccia N.; Peng A.; Peng H.; Perrot F.; Petitjean P.-A.; Petrucci F.; Pilarczyk O.; Pineres Rico L.F.; Popov A.; Poussot P.; Pratumwan W.; Previtali E.; Qi F.; Qi M.; Qian S.; Qian X.; Qian Z.; Qiao H.; Qin Z.; Qiu S.; Rajput M.U.; Ranucci G.; Raper N.; Re A.; Rebber H.; Rebii A.; Ren B.; Ren J.; Ricci B.; Robens M.; Roche M.; Rodphai N.; Romani A.; Roskovec B.; Roth C.; Ruan X.; Ruan X.; Rujirawat S.; Rybnikov A.; Sadovsky A.; Saggese P.; Sanfilippo S.; Sangka A.; Sanguansak N.; Sawangwit U.; Sawatzki J.; Sawy F.; Schever M.; Schwab C.; Schweizer K.; Selyunin A.; Serafini A.; Settanta G.; Settimo M.; Shao Z.; Sharov V.; Shaydurova A.; Shi J.; Shi Y.; Shutov V.; Sidorenkov A.; Simkovic F.; Sirignano C.; Siripak J.; Sisti M.; Slupecki M.; Smirnov M.; Smirnov O.; Sogo-Bezerra T.; Sokolov S.; Songwadhana J.; Soonthornthum B.; Sotnikov A.; Sramek O.; Sreethawong W.; Stahl A.; Stanco L.; Stankevich K.; Stefanik D.; Steiger H.; Steinmann J.; Sterr T.; Stock M.R.; Strati V.; Studenikin A.; Sun S.; Sun X.; Sun Y.; Sun Y.; Suwonjandee N.; Szelezniak M.; Tang J.; Tang Q.; Tang Q.; Tang X.; Tietzsch A.; Tkachev I.; Tmej T.; Treskov K.; Triossi A.; Troni G.; Trzaska W.; Tuve C.; Ushakov N.; van den Boom J.; van Waasen S.; Vanroyen G.; Vassilopoulos N.; Vedin V.; Verde G.; Vialkov M.; Viaud B.; Vollbrecht M.C.; Volpe C.; Vorobel V.; Voronin D.; Votano L.; Walker P.; Wang C.; Wang C.-H.; Wang E.; Wang G.; Wang J.; Wang J.; Wang K.; Wang L.; Wang M.; Wang M.; Wang M.; Wang R.; Wang S.; Wang W.; Wang W.; Wang W.; Wang X.; Wang X.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Y.; Wang Z.; Wang Z.; Wang Z.; Wang Z.; Waqas M.; Watcharangkool A.; Wei L.; Wei W.; Wei W.; Wei Y.; Wen L.; Wiebusch C.; Wong S.C.-F.; Wonsak B.; Wu D.; Wu F.; Wu Q.; Wu Z.; Wurm M.; Wurtz J.; Wysotzki C.; Xi Y.; Xia D.; Xie X.; Xie Y.; Xie Z.; Xing Z.; Xu B.; Xu C.; Xu D.; Xu F.; Xu H.; Xu J.; Xu J.; Xu M.; Xu Y.; Xu Y.; Yan B.; Yan T.; Yan W.; Yan X.; Yan Y.; Yang A.; Yang C.; Yang C.; Yang H.; Yang J.; Yang L.; Yang X.; Yang Y.; Yang Y.; Yao H.; Yasin Z.; Ye J.; Ye M.; Ye Z.; Yegin U.; Yermia F.; Yi P.; Yin N.; Yin X.; You Z.; Yu B.; Yu C.; Yu C.; Yu H.; Yu M.; Yu X.; Yu Z.; Yu Z.; Yuan C.; Yuan Y.; Yuan Z.; Yuan Z.; Yue B.; Zafar N.; Zambanini A.; Zavadskyi V.; Zeng S.; Zeng T.; Zeng Y.; Zhan L.; Zhang A.; Zhang F.; Zhang G.; Zhang H.; Zhang H.; Zhang J.; Zhang J.; Zhang J.; Zhang J.; Zhang J.; Zhang P.; Zhang Q.; Zhang S.; Zhang S.; Zhang T.; Zhang X.; Zhang X.; Zhang X.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Y.; Zhang Z.; Zhang Z.; Zhao F.; Zhao J.; Zhao R.; Zhao S.; Zhao T.; Zheng D.; Zheng H.; Zheng M.; Zheng Y.; Zhong W.; Zhou J.; Zhou L.; Zhou N.; Zhou S.; Zhou T.; Zhou X.; Zhu J.; Zhu K.; Zhu K.; Zhu Z.; Zhuang B.; Zhuang H.; Zong L.; Zou J.Abusleme, A.; Adam, T.; Ahmad, S.; Ahmed, R.; Aiello, S.; Akram, M.; An, F.; An, Q.; Andronico, G.; Anfimov, N.; Antonelli, V.; Antoshkina, T.; Asavapibhop, B.; de Andre, J. P. A. M.; Auguste, D.; Babic, A.; Baldini, W.; Barresi, A.; Basilico, D.; Baussan, E.; Bellato, M.; Bergnoli, A.; Birkenfeld, T.; Blin, S.; Blum, D.; Blyth, S.; Bolshakova, A.; Bongrand, M.; Bordereau, C.; Breton, D.; Brigatti, A.; Brugnera, R.; Bruno, R.; Budano, A.; Buscemi, M.; Busto, J.; Butorov, I.; Cabrera, A.; Cai, H.; Cai, X.; Cai, Y.; Cai, Z.; Cammi, A.; Campeny, A.; Cao, C.; Cao, G.; Cao, J.; Caruso, R.; Cerna, C.; Chang, J.; Chang, Y.; Chen, P.; Chen, P. -A.; Chen, S.; Chen, X.; Chen, Y. -W.; Chen, Y.; Chen, Y.; Chen, Z.; Cheng, J.; Cheng, Y.; Chetverikov, A.; Chiesa, D.; Chimenti, P.; Chukanov, A.; Claverie, G.; Clementi, C.; Clerbaux, B.; Conforti Di Lorenzo, S.; Corti, D.; Cremonesi, O.; Dal Corso, F.; Dalager, O.; De La Taille, C.; Deng, J.; Deng, Z.; Deng, Z.; Depnering, W.; Diaz, M.; Ding, X.; Ding, Y.; Dirgantara, B.; Dmitrievsky, S.; Dohnal, T.; Dolzhikov, D.; Donchenko, G.; Dong, J.; Doroshkevich, E.; Dracos, M.; Druillole, F.; Du, S.; Dusini, S.; Dvorak, M.; Enqvist, T.; Enzmann, H.; Fabbri, A.; Fajt, L.; Fan, D.; Fan, L.; Fang, J.; Fang, W.; Fargetta, M.; Fedoseev, D.; Fekete, V.; Feng, L. -C.; Feng, Q.; Ford, R.; Formozov, A.; Fournier, A.; Gan, H.; Gao, F.; Garfagnini, A.; Giammarchi, M.; Giaz, A.; Giudice, N.; Gonchar, M.; Gong, G.; Gong, H.; Gornushkin, Y.; Gottel, A.; Grassi, M.; Grewing, C.; Gromov, V.; Gu, M.; Gu, X.; Gu, Y.; Guan, M.; Guardone, N.; Gul, M.; Guo, C.; Guo, J.; Guo, W.; Guo, X.; Guo, Y.; Hackspacher, P.; Hagner, C.; Han, R.; Han, Y.; Hassan, M. S.; He, M.; He, W.; Heinz, T.; Hellmuth, P.; Heng, Y.; Herrera, R.; Hor, Y. K.; Hou, S.; Hsiung, Y.; Hu, B. -Z.; Hu, H.; Hu, J.; Hu, J.; Hu, S.; Hu, T.; Hu, Z.; Huang, C.; Huang, G.; Huang, H.; Huang, W.; Huang, X.; Huang, X.; Huang, Y.; Hui, J.; Huo, L.; Huo, W.; Huss, C.; Hussain, S.; Ioannisian, A.; Isocrate, R.; Jelmini, B.; Jen, K. -L.; Jeria, I.; Ji, X.; Ji, X.; Jia, H.; Jia, J.; Jian, S.; Jiang, D.; Jiang, X.; Jin, R.; Jing, X.; Jollet, C.; Joutsenvaara, J.; Jungthawan, S.; Kalousis, L.; Kampmann, P.; Kang, L.; Karaparambil, R.; Kazarian, N.; Khan, W.; Khosonthongkee, K.; Korablev, D.; Kouzakov, K.; Krasnoperov, A.; Kruth, A.; Kutovskiy, N.; Kuusiniemi, P.; Lachenmaier, T.; Landini, C.; Leblanc, S.; Lebrin, V.; Lefevre, F.; Lei, R.; Leitner, R.; Leung, J.; Li, D.; Li, F.; Li, F.; Li, H.; Li, H.; Li, J.; Li, M.; Li, M.; Li, N.; Li, N.; Li, Q.; Li, R.; Li, S.; Li, T.; Li, W.; Li, W.; Li, X.; Li, X.; Li, X.; Li, Y.; Li, Y.; Li, Z.; Li, Z.; Li, Z.; Liang, H.; Liang, H.; Liao, J.; Liebau, D.; Limphirat, A.; Limpijumnong, S.; Lin, G. -L.; Lin, S.; Lin, T.; Ling, J.; Lippi, I.; Liu, F.; Liu, H.; Liu, H.; Liu, H.; Liu, H.; Liu, H.; Liu, J.; Liu, J.; Liu, M.; Liu, Q.; Liu, Q.; Liu, R.; Liu, S.; Liu, S.; Liu, S.; Liu, X.; Liu, X.; Liu, Y.; Liu, Y.; Lokhov, A.; Lombardi, P.; Lombardo, C.; Loo, K.; Lu, C.; Lu, H.; Lu, J.; Lu, J.; Lu, S.; Lu, X.; Lubsandorzhiev, B.; Lubsandorzhiev, S.; Ludhova, L.; Luo, F.; Luo, G.; Luo, P.; Luo, S.; Luo, W.; Lyashuk, V.; Ma, B.; Ma, Q.; Ma, S.; Ma, X.; Ma, X.; Maalmi, J.; Malyshkin, Y.; Mantovani, F.; Manzali, F.; Mao, X.; Mao, Y.; Mari, S. M.; Marini, F.; Marium, S.; Martellini, C.; Martin-Chassard, G.; Martini, A.; Mayer, M.; Mayilyan, D.; Mednieks, I.; Meng, Y.; Meregaglia, A.; Meroni, E.; Meyhofer, D.; Mezzetto, M.; Miller, J.; Miramonti, L.; Montini, P.; Montuschi, M.; Muller, A.; Nastasi, M.; Naumov, D. V.; Naumova, E.; Navas-Nicolas, D.; Nemchenok, I.; Nguyen Thi, M. T.; Ning, F.; Ning, Z.; Nunokawa, H.; Oberauer, L.; Ochoa-Ricoux, J. P.; Olshevskiy, A.; Orestano, D.; Ortica, F.; Othegraven, R.; Pan, H. -R.; Paoloni, A.; Parmeggiano, S.; Pei, Y.; Pelliccia, N.; Peng, A.; Peng, H.; Perrot, F.; Petitjean, P. -A.; Petrucci, F.; Pilarczyk, O.; Pineres Rico, L. F.; Popov, A.; Poussot, P.; Pratumwan, W.; Previtali, E.; Qi, F.; Qi, M.; Qian, S.; Qian, X.; Qian, Z.; Qiao, H.; Qin, Z.; Qiu, S.; Rajput, M. U.; Ranucci, G.; Raper, N.; Re, A.; Rebber, H.; Rebii, A.; Ren, B.; Ren, J.; Ricci, B.; Robens, M.; Roche, M.; Rodphai, N.; Romani, A.; Roskovec, B.; Roth, C.; Ruan, X.; Ruan, X.; Rujirawat, S.; Rybnikov, A.; Sadovsky, A.; Saggese, P.; Sanfilippo, S.; Sangka, A.; Sanguansak, N.; Sawangwit, U.; Sawatzki, J.; Sawy, F.; Schever, M.; Schwab, C.; Schweizer, K.; Selyunin, A.; Serafini, A.; Settanta, G.; Settimo, M.; Shao, Z.; Sharov, V.; Shaydurova, A.; Shi, J.; Shi, Y.; Shutov, V.; Sidorenkov, A.; Simkovic, F.; Sirignano, C.; Siripak, J.; Sisti, M.; Slupecki, M.; Smirnov, M.; Smirnov, O.; Sogo-Bezerra, T.; Sokolov, S.; Songwadhana, J.; Soonthornthum, B.; Sotnikov, A.; Sramek, O.; Sreethawong, W.; Stahl, A.; Stanco, L.; Stankevich, K.; Stefanik, D.; Steiger, H.; Steinmann, J.; Sterr, T.; Stock, M. R.; Strati, V.; Studenikin, A.; Sun, S.; Sun, X.; Sun, Y.; Sun, Y.; Suwonjandee, N.; Szelezniak, M.; Tang, J.; Tang, Q.; Tang, Q.; Tang, X.; Tietzsch, A.; Tkachev, I.; Tmej, T.; Treskov, K.; Triossi, A.; Troni, G.; Trzaska, W.; Tuve, C.; Ushakov, N.; van den Boom, J.; van Waasen, S.; Vanroyen, G.; Vassilopoulos, N.; Vedin, V.; Verde, G.; Vialkov, M.; Viaud, B.; Vollbrecht, M. C.; Volpe, C.; Vorobel, V.; Voronin, D.; Votano, L.; Walker, P.; Wang, C.; Wang, C. -H.; Wang, E.; Wang, G.; Wang, J.; Wang, J.; Wang, K.; Wang, L.; Wang, M.; Wang, M.; Wang, M.; Wang, R.; Wang, S.; Wang, W.; Wang, W.; Wang, W.; Wang, X.; Wang, X.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Y.; Wang, Z.; Wang, Z.; Wang, Z.; Wang, Z.; Waqas, M.; Watcharangkool, A.; Wei, L.; Wei, W.; Wei, W.; Wei, Y.; Wen, L.; Wiebusch, C.; Wong, S. C. -F.; Wonsak, B.; Wu, D.; Wu, F.; Wu, Q.; Wu, Z.; Wurm, M.; Wurtz, J.; Wysotzki, C.; Xi, Y.; Xia, D.; Xie, X.; Xie, Y.; Xie, Z.; Xing, Z.; Xu, B.; Xu, C.; Xu, D.; Xu, F.; Xu, H.; Xu, J.; Xu, J.; Xu, M.; Xu, Y.; Xu, Y.; Yan, B.; Yan, T.; Yan, W.; Yan, X.; Yan, Y.; Yang, A.; Yang, C.; Yang, C.; Yang, H.; Yang, J.; Yang, L.; Yang, X.; Yang, Y.; Yang, Y.; Yao, H.; Yasin, Z.; Ye, J.; Ye, M.; Ye, Z.; Yegin, U.; Yermia, F.; Yi, P.; Yin, N.; Yin, X.; You, Z.; Yu, B.; Yu, C.; Yu, C.; Yu, H.; Yu, M.; Yu, X.; Yu, Z.; Yu, Z.; Yuan, C.; Yuan, Y.; Yuan, Z.; Yuan, Z.; Yue, B.; Zafar, N.; Zambanini, A.; Zavadskyi, V.; Zeng, S.; Zeng, T.; Zeng, Y.; Zhan, L.; Zhang, A.; Zhang, F.; Zhang, G.; Zhang, H.; Zhang, H.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, J.; Zhang, P.; Zhang, Q.; Zhang, S.; Zhang, S.; Zhang, T.; Zhang, X.; Zhang, X.; Zhang, X.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Y.; Zhang, Z.; Zhang, Z.; Zhao, F.; Zhao, J.; Zhao, R.; Zhao, S.; Zhao, T.; Zheng, D.; Zheng, H.; Zheng, M.; Zheng, Y.; Zhong, W.; Zhou, J.; Zhou, L.; Zhou, N.; Zhou, S.; Zhou, T.; Zhou, X.; Zhu, J.; Zhu, K.; Zhu, K.; Zhu, Z.; Zhuang, B.; Zhuang, H.; Zong, L.; Zou, J

Constraints on the CKM angle gamma in B^0-->[overline D]0K^(*0) and B^0-->D^0K^(*0) from a Dalitz analysis of D^0 and [overline D]^0 decays to K_Sπ^+π^-

We present constraints on the angle gamma of the unitarity triangle with a Dalitz analysis of neutral D decays to K_Sπ^+π^- from the processes B^0-->[overline D]^0K^(*0) ([overline B]^0-->D^0[overline K]^(*0)) and B^0-->D^0K^(*0) ([overline B]^0-->[overline D]^0[overline K]^(*0)) with K^(*0)-->K^+π^- ([overline K]^(*0)-->K-π^+). Using a sample of 371×10^6 B[overline B] pairs collected with the BABAR detector at PEP-II, we constrain the angle gamma as a function of r_S, the magnitude of the average ratio between b-->u and b-->c amplitudes

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability