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19. A peak into individuals' perceptions of surveillance

Author

Vignolles, A., Waiguny, M.K.J., Zhang, D.D., Boerman, S.C., Hendriks, H., Araujo, T.B., Voorveld, H.A.M., Vignolles, A., Waiguny, M.K.J., Zhang, D.D., Boerman, S.C., Hendriks, H., Araujo, T.B., and Voorveld, H.A.M.

Abstract

Item does not contain fulltext, In today's digital world, people leave countless digital traces online. Companies, governments, and other parties can use such data for different purposes. Personalized advertisements or content that are clearly based on their previous behavior can make consumers feel that they are being followed or monitored, and might describe it as "creepy" and that they feel "watched". Drawing upon a nationally representative sample of the Netherlands (N = 1,994), this study aims to shed light on individuals' perceptions of surveillance by identifying the perceived sources, purposes, and how it might differ in different media technologies among Dutch adults. Such knowledge can inform us which practices and contexts tend to trigger perceived surveillance. Moreover, we investigate the extent to which certain individual traits, including privacy concerns, technology trust, conspiracy mentality, and socio-demographic characteristics, relate to perceived surveillance. Our findings show that advertisers are the most commonly perceived source of surveillance and advertising is also the most commonly perceived purpose of surveillance. Perceived surveillance is the highest when people are using social media and web browsers comparing to other digital devices and services. Furthermore, privacy concerns and conspiracy mentality are positively related to perceived surveillance of all types of technologies. Finally, people who are younger and more highly educated perceive more surveillance when using social media, and younger people also feel more surveilled when using web browser.

Published
2023

26. SCORE2 risk prediction algorithms: new models to estimate 10-year risk of cardiovascular disease in Europe

Author

Hageman, S., Pennells, L., Ojeda, F., Kaptoge, S., Kuulasmaa, K., Vries, T. de, Xu, Z., Kee, F., Chung, R., Wood, A., McEvoy, J.W., Veronesi, G., Bolton, T., Dendale, P., Ference, B.A., Halle, M., Timmis, A., Vardas, P., Danesh, J., Graham, I., Salomaa, V., Visseren, F., Bacquer, D. de, Blankenberg, S., Dorresteijn, J., Angelantonio, E. di, Achenbach, S., Aleksandrova, K., Amiano, P., Amouyel, P., Andersson, J., Bakker, S.J.L., Costa, R.B.D., Beulens, J.W.J., Blaha, M., Bobak, M., Boer, J.M.A., Bonet, C., Bonnet, F., Boutron-Ruault, M.C., Braaten, T., Brenner, H., Brunner, F., Brunner, E.J., Brunstrom, M., Buring, J., Butterworth, A.S., Capkova, N., Cesana, G., Chrysohoou, C., Colorado-Yohar, S., Cook, N.R., Cooper, C., Dahm, C.C., Davidson, K., Dennison, E., Castelnuovo, A. di, Donfrancesco, C., Dorr, M., Dorynska, A., Eliasson, M., Engstrom, G., Ferrari, P., Ferrario, M., Ford, I., Fu, M., Gansevoort, R.T., Giampaoli, S., Gillum, R.F., Camara, A.G. de la, Grassi, G., Hansson, P.O., Huculeci, R., Hveem, K., Iacoviello, L., Ikram, M.K., Jorgensen, T., Joseph, B., Jousilahti, P., Jukema, J.W., Kaaks, R., Katzke, V., Kavousi, M., Kiechl, S., Klotsche, J., Konig, W., Kronmal, R.A., Kubinova, R., Kucharska-Newton, A., Lall, K., Lehmann, N., Leistner, D., Linneberg, A., Pablos, D.L., Lorenz, T., Lu, W.T., Luksiene, D., Lyngbakken, M., Magnussen, C., Malyutina, S., Ibanez, A.M., Masala, G., Mathiesen, E.B., Matsushita, K., Meade, T.W., Melander, O., Meyer, H.E., Moons, K.G.M., Moreno-Iribas, C., Muller, D., Munzel, T., Nikitin, Y., Nordestgaard, B.G., Omland, T., Onland, C., Overvad, K., Packard, C., Pajak, A., Palmieri, L., Panagiotakos, D., Panico, S., Perez-Cornago, A., Peters, A., Pietila, A., Pikhart, H., Psaty, B.M., Quarti-Trevano, F., Garcia, J.R.Q., Riboli, E., Ridker, P.M., Rodriguez, B., Rodriguez-Barranco, M., Rosengren, A., Roussel, R., Sacerdote, C., Sans, S., Sattar, N., Schiborn, C., Schmidt, B., Schottker, B., Schulze, M., Schwartz, J.E., Selmer, R.M., Shea, S., Shipley, M.J., Sieri, S., Soderberg, S., Sofat, R., Tamosiunas, A., Thorand, B., Tillmann, T., Tjonneland, A., Tong, T.Y.N., Trichopoulou, A., Tumino, R., Tunstall-Pedoe, H., Tybjaerg-Hansen, A., Tzoulaki, J., Heijden, A. van der, Schouw, Y.T. van der, Verschuren, W.M.M., Volzke, H., Waldeyer, C., Wareham, N.J., Weiderpass, E., Weidinger, F., Wild, P., Willeit, J., Willeit, P., Wilsgaard, T., Woodward, M., Zeller, T., Zhang, D.D., Zhou, B., SCORE2 Working Grp, ESC Cardiovasc Risk Collaboration, collaboration, SCORE2 working group and ESC Cardiovascular risk, Groningen Institute for Organ Transplantation (GIOT), Groningen Kidney Center (GKC), Cardiovascular Centre (CVC), Epidemiology, Neurology, Achenbach, S, Aleksandrova, K, Amiano, P, San Sebastian, D, Amouyel, P, Andersson, J, Bakker, S, Da Providencia Costa, R, Beulens, J, Blaha, M, Bobak, M, Boer, J, Bonet, C, Bonnet, F, Boutron-Ruault, M, Braaten, T, Brenner, H, Brunner, F, Brunner, E, Brunström, M, Buring, J, Butterworth, A, Capkova, N, Cesana, G, Chrysohoou, C, Colorado-Yohar, S, Cook, N, Cooper, C, Dahm, C, Davidson, K, Dennison, E, Di Castelnuovo, A, Donfrancesco, C, Dörr, M, Doryńska, A, Eliasson, M, Engström, G, Ferrari, P, Ferrario, M, Ford, I, Fu, M, Gansevoort, R, Giampaoli, S, Gillum, R, Gómez de la Cámara, A, Grassi, G, Hansson, P, Huculeci, R, Hveem, K, Iacoviello, L, Ikram, M, Jørgensen, T, Joseph, B, Jousilahti, P, Wouter Jukema, J, Kaaks, R, Katzke, V, Kavousi, M, Kiechl, S, Klotsche, J, König, W, Kronmal, R, Kubinova, R, Kucharska-Newton, A, Läll, K, Lehmann, N, Leistner, D, Linneberg, A, Pablos, D, Lorenz, T, Lu, W, Luksiene, D, Lyngbakken, M, Magnussen, C, Malyutina, S, Ibañez, A, Masala, G, Mathiesen, E, Matsushita, K, Meade, T, Melander, O, Meyer, H, Moons, K, Moreno-Iribas, C, Muller, D, Münzel, T, Nikitin, Y, Nordestgaard, B, Omland, T, Onland, C, Overvad, K, Packard, C, Pająk, A, Palmieri, L, Panagiotakos, D, Panico, S, Perez-Cornago, A, Peters, A, Pietilä, A, Pikhart, H, Psaty, B, Quarti-Trevano, F, Garcia, J, Riboli, E, Ridker, P, Rodriguez, B, Rodriguez-Barranco, M, Rosengren, A, Roussel, R, Sacerdote, C, S, S, Sattar, N, Schiborn, C, Schmidt, B, Schöttker, B, Schulze, M, Schwartz, J, Selmer, R, Shea, S, Shipley, M, Sieri, S, Söderberg, S, Sofat, R, Tamosiunas, A, Thorand, B, Tillmann, T, Tjønneland, A, Tong, T, Trichopoulou, A, Tumino, R, Tunstall-Pedoe, H, Tybjaerg-Hansen, A, Tzoulaki, J, van der Heijden, A, van der Schouw, Y, Verschuren, W, Völzke, H, Waldeyer, C, Wareham, N, Weiderpass, E, Weidinger, F, Wild, P, Willeit, J, Willeit, P, Wilsgaard, T, Woodward, M, Zeller, T, Zhang, D, Zhou, B, and Apollo - University of Cambridge Repository

Subjects
Male, Cardiology, RATIONALE, Blood Pressure, Disease, 030204 cardiovascular system & hematology, PROFILE, ACUTE CORONARY EVENTS, VALIDATION, Europe/epidemiology, 03 medical and health sciences, 0302 clinical medicine, SDG 3 - Good Health and Well-being, DESIGN, Clinical Research, Risk Factors, Diabetes mellitus, medicine, PARTICIPANTS, Humans, 030212 general & internal medicine, Risk factor, Aged, Primary prevention, business.industry, 10-year CVD risk, Incidence (epidemiology), Cardiovascular Diseases/epidemiology, Risk Prediction, Cardiovascular Disease, Primary Prevention, 10-year Cvd Risk, External validation, PRIMARY-CARE, Middle Aged, medicine.disease, Cardiovascular disease, Risk prediction, 3. Good health, Europe, Prediction algorithms, Blood pressure, Cardiovascular Diseases, Smoking status, Female, Cardiology and Cardiovascular Medicine, business, Algorithms, Demography
Abstract

Aims The aim of this study was to develop, validate, and illustrate an updated prediction model (SCORE2) to estimate 10-year fatal and non-fatal cardiovascular disease (CVD) risk in individuals without previous CVD or diabetes aged 40-69 years in Europe.Methods and results We derived risk prediction models using individual-participant data from 45 cohorts in 13 countries (677 684 individuals, 30 121 CVD events). We used sex-specific and competing risk-adjusted models, including age, smoking status, systolic blood pressure, and total- and HDL-cholesterol. We defined four risk regions in Europe according to country-specific CVD mortality, recalibrating models to each region using expected incidences and risk factor distributions. Region-specific incidence was estimated using CVD mortality and incidence data on 10 776 466 individuals. For external validation, we analysed data from 25 additional cohorts in 15 European countries (1 133 181 individuals, 43 492 CVD events). After applying the derived risk prediction models to external validation cohorts, C-indices ranged from 0.67 (0.65-0.68) to 0.81 (0.76-0.86). Predicted CVD risk varied several-fold across European regions. For example, the estimated 10-year CVD risk for a 50-year-old smoker, with a systolic blood pressure of 140 mmHg, total cholesterol of 5.5 mmol/L, and HDL-cholesterol of 1.3 mmol/L, ranged from 5.9% for men in low- risk countries to 14.0% for men in very high-risk countries, and from 4.2% for women in low-risk countries to 13.7% for women in very high-risk countries.Conclusion SCORE2-a new algorithm derived, calibrated, and validated to predict 10-year risk of first-onset CVD in European populations-enhances the identification of individuals at higher risk of developing CVD across Europe. Acknowledgements We thank investigators and participants of the several studies that contributed data to the Emerging Risk Factors Collaboration (ERFC). This research has been conducted using the UK Biobank Resource under Application Number 26865. Data from the Clinical Practice Research Datalink (CPRD) were obtained under licence from the UK Medicines and Healthcare products Regulatory Agency (protocol 162RMn2). CPRD uses data provided by patients and collected by the NHS as part of their care and support. We thank all EPIC participants and staff for their contribution to the study, the laboratory teams at the Medical Research Council Epidemiology Unit for sample management and Cambridge Genomic Services for genotyping, Sarah Spackman for data management and the team at the EPIC-CVD Coordinating Centre for study co-ordination and administration. Funding The ERFC co-ordinating centre was underpinned by programme grants from the British Heart Foundation (SP/09/002; RG/13/13/30194; RG/18/13/33946), BHF Centre of Research Excellence (RE/18/1/34212), the UK Medical Research Council (MR/L003120/1), and the National Institute for Health Research (NIHR) Cambridge Biomedical Research Centre (BRC1215-20014), with project-specific support received from the UK NIHR [*], British United Provident Association UK Foundation and an unrestricted educational grant from GlaxoSmithKline. A variety of funding sources have supported recruitment, follow-up, and laboratory measurements in the studies contributing data to the ERFC, which are listed on the ERFC website (www.phpc.cam.ac.uk/ceu/erfc/list-of-studies). *The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. This work was supported by Health Data Research UK, which is funded by the UK Medical Research Council, Engineering and Physical Sciences Research Council, Economic and Social Research Council, Department of Health and Social Care (England), Chief Scientist Office of the Scottish Government Health and Social Care Directorates, Health and Social Care Research and Development Division (Welsh Government), Public Health Agency (Northern Ireland), British Heart Foundation, and Wellcome. The MORGAM Project has received funding from EU projects MORGAM (Biomed BMH4-CT98-3183), GenomEUtwin (FP5, QLG2-CT-2002-01254), ENGAGE (FP7, HEALTH-F4-2007-201413),CHANCES (FP7, HEALTH-F3-2010-242244), BiomarCaRE (FP7,HEALTH-F2-2011-278913), euCanSHare (Horizon 2020, No. 825903) and AFFECT-EU (Horizon 2020, No. 847770); and Medical Research Council, London (G0601463, No. 80983: Biomarkers in the MORGAM Populations). This has supported central coordination, workshops and part of the activities of the MORGAM Data Centre, the MORGAM Laboratories and the MORGAM Participating Centres EPIC-CVD was funded by the European Research Council (268834), and the European Commission Framework Programme 7 (HEALTH-F2-2012-279233). This work was supported by the Estonian Research Council grant PUTs (PRG687, PUT1660, PUT1665, PRG184), by European Union through the European Regional Development Fund project no. MOBERA5 (Norface Network project no 462.16.107), by the Green ICT programme under Norway Grants 2014 – 2021 (grant number EU53928), by the European Union through Horizon 2020 grant no. 810645 and through the European Regional Development Fund (Project No. 2014-2020.4.01.16-0125) and by the PRECISE4Q consortium. PRECISE4Q project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 777107. This work was partly funded through the CoMorMent project. CoMorMent has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under Grant agreement 847776. The KORA study was initiated and financed by the Helmholtz Zentrum Mu¨nchen—German Research Center for Environmental Health, which is funded by the German Federal Ministry of Education and Research (BMBF) and by the State of Bavaria. The KORA study was supported by a research grant from the Virtual Institute of Diabetes Research (Helmholtz Zentrum Mu¨nchen), the Clinical Cooperation Group Diabetes between Ludwig-Maximilians-Universita¨t Mu¨nchen and Helmholtz Zentrum Mu¨nchen, and by the German Diabetes Center (DDZ). The HAPIEE project, Institute, was supported by grants from the Wellcome Trust (064947/Z/01/Z; WT081081) and US National Institute on Aging (1R01 and AG23522). The co-ordination of EPIC is financially supported by International Agency for Research on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, which has additional infrastructure support provided by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported by: Danish Cancer Society (Denmark); Ligue Contre le Cancer, Institut Gustave Roussy, Mutuelle Ge´ne´rale de l’Education Nationale, Institut National de la Sante´ et de la Recherche Me´dicale (INSERM) (France); German Cancer Aid, German Cancer Research Center (DKFZ), German Institute of Human Nutrition Potsdam Rehbruecke (DIfE), Federal Ministry of Education and Research (BMBF) (Germany); Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Compagnia di SanPaolo and National Research Council (Italy); Dutch 2448 SCORE2 working group and ESC Cardiovascular Risk Collaboration Ministry of Public Health, Welfare and Sports (VWS), Netherlands Cancer Registry (NKR), LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland), World Cancer Research Fund (WCRF), Statistics Netherlands (The Netherlands); Health Research Fund (FIS)—Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucı´a, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology—ICO (Spain); Swedish Cancer Society, Swedish Research Council and County Councils of Ska˚ne and Va¨sterbotten (Sweden); Cancer Research UK (14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (1000143 to EPIC-Norfolk; MR/M012190/1 to EPIC-Oxford) (United Kingdom)

Published
2021

30. Myc binding protein 2 suppresses M2‐like phenotypes in macrophages during zymosan‐induced inflammation in mice

Author

Pierre, S., Zhang, D.D., Suo, J., Kern, K., Tarighi, N., Scholich, K., and Publica

Abstract

MYCBP2 is an E3 ubiquitin ligase, which is well characterized as a key element in the inhibition of neuronal growth, synapse formation and synaptic strength by regulating several signaling pathways. Although MYCBP2 was suspected to be expressed also in immune cells, to date nothing is known about its role in inflammation. We used Multi‐epitope ligand cartography (MELC), a method for multiple sequential immunohistology, to show that MYCBP2 is strongly expressed in monocyte‐derived macrophages during zymosan‐induced inflammation. We generated a myeloid‐specific knockout mouse and found that loss of MYCBP2 in myeloid cells reduced nociceptive (painful) behavior during the resolution phase (1-3 days after zymosan injection). Quantitative MELC analyses and flow cytometric analysis showed an increased number of CD206‐expressing macrophages in the inflamed paw tissue. Fittingly, CD206 and arginase 1 expression was upregulated in MYCBP2‐deficient bone marrow‐derived macrophages after polarization with IL10 or IL4. The regulation of protein expression in these macrophages by MYCBP2 varied depending on the polarization signal. The increased IL10‐induced CD206 expression in MYCBP2‐deficient macrophages was mediated by p38 MAPK, while IL4‐induced CD206 expression in MYCBP2‐deficient macrophages was mediated by protein kinase A.

Published
2017

32. Erratum to: Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition) (Autophagy, 12, 1, 1-222, 10.1080/15548627.2015.1100356

Author

Klionsky, D.J. Abdelmohsen, K. Abe, A. Abedin, M.J. Abeliovich, H. Arozena, A.A. Adachi, H. Adams, C.M. Adams, P.D. Adeli, K. Adhihetty, P.J. Adler, S.G. Agam, G. Agarwal, R. Aghi, M.K. Agnello, M. Agostinis, P. Aguilar, P.V. Aguirre-Ghiso, J. Airoldi, E.M. Ait-Si-Ali, S. Akematsu, T. Akporiaye, E.T. Al-Rubeai, M. Albaiceta, G.M. Albanese, C. Albani, D. Albert, M.L. Aldudo, J. Algül, H. Alirezaei, M. Alloza, I. Almasan, A. Almonte-Beceril, M. Alnemri, E.S. Alonso, C. Altan-Bonnet, N. Altieri, D.C. Alvarez, S. Alvarez-Erviti, L. Alves, S. Amadoro, G. Amano, A. Amantini, C. Ambrosio, S. Amelio, I. Amer, A.O. Amessou, M. Amon, A. An, Z. Anania, F.A. Andersen, S.U. Andley, U.P. Andreadi, C.K. Andrieu-Abadie, N. Anel, A. Ann, D.K. Anoopkumar-Dukie, S. Antonioli, M. Aoki, H. Apostolova, N. Aquila, S. Aquilano, K. Araki, K. Arama, E. Aranda, A. Araya, J. Arcaro, A. Arias, E. Arimoto, H. Ariosa, A.R. Armstrong, J.L. Arnould, T. Arsov, I. Asanuma, K. Askanas, V. Asselin, E. Atarashi, R. Atherton, S.S. Atkin, J.D. Attardi, L.D. Auberger, P. Auburger, G. Aurelian, L. Autelli, R. Avagliano, L. Avantaggiati, M.L. Avrahami, L. Azad, N. Awale, S. Bachetti, T. Backer, J.M. Bae, D.-H. Bae, J.-S. Bae, O.-N. Bae, S.H. Baehrecke, E.H. Baek, S.-H. Baghdiguian, S. Bagniewska-Zadworna, A. Bai, H. Bai, J. Bai, X.-Y. Bailly, Y. Balaji, K.N. Balduini, W. Ballabio, A. Balzan, R. Banerjee, R. Bánhegyi, G. Bao, H. Barbeau, B. Barrachina, M.D. Barreiro, E. Bartel, B. Bartolomé, A. Bassham, D.C. Bassi, M.T. Bast, R.C., Jr. Basu, A. Batista, M.T. Batoko, H. Battino, M. Bauckman, K. Baumgarner, B.L. Bayer, K.U. Beale, R. Beaulieu, J.-F. Beck, G.R., Jr. Becker, C. Beckham, J.D. Bédard, P.-A. Bednarski, P.J. Begley, T.J. Behl, C. Behrends, C. Behrens, G.M.N. Behrns, K.E. Bejarano, E. Belaid, A. Belleudi, F. Bénard, G. Berchem, G. Bergamaschi, D. Bergami, M. Berkhout, B. Berliocchi, L. Bernard, A. Bernard, M. Bernassola, F. Bertolotti, A. Bess, A.S. Besteiro, S. Bettuzzi, S. Bhalla, S. Bhattacharyya, S. Bhutia, S.K. Biagosch, C. Bianchi, M.W. Biard-Piechaczyk, M. Billes, V. Bincoletto, C. Bingol, B. Bird, S.W. Bitoun, M. Bjedov, I. Blackstone, C. Blanc, L. Blanco, G.A. Blomhoff, H.K. Boada-Romero, E. Böckler, S. Boes, M. Boesze-Battaglia, K. Boise, L.H. Bolino, A. Boman, A. Bonaldo, P. Bordi, M. Bosch, J. Botana, L.M. Botti, J. Bou, G. Bouché, M. Bouchecareilh, M. Boucher, M.-J. Boulton, M.E. Bouret, S.G. Boya, P. Boyer-Guittaut, M. Bozhkov, P.V. Brady, N. Braga, V.M.M. Brancolini, C. Braus, G.H. Bravo-San-Pedro, J.M. Brennan, L.A. Bresnick, E.H. Brest, P. Bridges, D. Bringer, M.-A. Brini, M. Brito, G.C. Brodin, B. Brookes, P.S. Brown, E.J. Brown, K. Broxmeyer, H.E. Bruhat, A. Brum, P.C. Brumell, J.H. Brunetti-Pierri, N. Bryson-Richardson, R.J. Buch, S. Buchan, A.M. Budak, H. Bulavin, D.V. Bultman, S.J. Bultynck, G. Bumbasirevic, V. Burelle, Y. Burke, R.E. Burmeister, M. Bütikofer, P. Caberlotto, L. Cadwell, K. Cahova, M. Cai, D. Cai, J. Cai, Q. Calatayud, S. Camougrand, N. Campanella, M. Campbell, G.R. Campbell, M. Campello, S. Candau, R. Caniggia, I. Cantoni, L. Cao, L. Caplan, A.B. Caraglia, M. Cardinali, C. Cardoso, S.M. Carew, J.S. Carleton, L.A. Carlin, C.R. Carloni, S. Carlsson, S.R. Carmona-Gutierrez, D. Carneiro, L.A.M. Carnevali, O. Carra, S. Carrier, A. Carroll, B. Casas, C. Casas, J. Cassinelli, G. Castets, P. Castro-Obregon, S. Cavallini, G. Ceccherini, I. Cecconi, F. Cederbaum, A.I. Ceña, V. Cenci, S. Cerella, C. Cervia, D. Cetrullo, S. Chaachouay, H. Chae, H.-J. Chagin, A.S. Chai, C.-Y. Chakrabarti, G. Chamilos, G. Chan, E.Y.W. Chan, M.T.V. Chandra, D. Chandra, P. Chang, C.-P. Chang, R.C.-C. Chang, T.Y. Chatham, J.C. Chatterjee, S. Chauhan, S. Che, Y. Cheetham, M.E. Cheluvappa, R. Chen, C.-J. Chen, G. Chen, G.-C. Chen, G. Chen, H. Chen, J.W. Chen, J.-K. Chen, M. Chen, M. Chen, P. Chen, Q. Chen, Q. Chen, S.-D. Chen, S. Chen, S.S.-L. Chen, W. Chen, W.-J. Chen, W.Q. Chen, W. Chen, X. Chen, Y.-H. Chen, Y.-G. Chen, Y. Chen, Y. Chen, Y. Chen, Y.-J. Chen, Y.-Q. Chen, Y. Chen, Z. Chen, Z. Cheng, A. Cheng, C.H.K. Cheng, H. Cheong, H. Cherry, S. Chesney, J. Cheung, C.H.A. Chevet, E. Chi, H.C. Chi, S.-G. Chiacchiera, F. Chiang, H.-L. Chiarelli, R. Chiariello, M. Chieppa, M. Chin, L.-S. Chiong, M. Chiu, G.N.C. Cho, D.-H. Cho, S.-G. Cho, W.C. Cho, Y.-Y. Cho, Y.-S. Choi, A.M.K. Choi, E.-J. Choi, E.-K. Choi, J. Choi, M.E. Choi, S.-I. Chou, T.-F. Chouaib, S. Choubey, D. Choubey, V. Chow, K.-C. Chowdhury, K. Chu, C.T. Chuang, T.-H. Chun, T. Chung, H. Chung, T. Chung, Y.-L. Chwae, Y.-J. Cianfanelli, V. Ciarcia, R. Ciechomska, I.A. Ciriolo, M.R. Cirone, M. Claerhout, S. Clague, M.J. Cl� ria, J. Clarke, P.G.H. Clarke, R. Clementi, E. Cleyrat, C. Cnop, M. Coccia, E.M. Cocco, T. Codogno, P. Coers, J. Cohen, E.E.W. Colecchia, D. Coletto, L. Coll, N.S. Colucci-Guyon, E. Comincini, S. Condello, M. Cook, K.L. Coombs, G.H. Cooper, C.D. Cooper, J.M. Coppens, I. Corasaniti, M.T. Corazzari, M. Corbalan, R. Corcelle-Termeau, E. Cordero, M.D. Corral-Ramos, C. Corti, O. Cossarizza, A. Costelli, P. Costes, S. Cotman, S.L. Coto-Montes, A. Cottet, S. Couve, E. Covey, L.R. Cowart, L.A. Cox, J.S. Coxon, F.P. Coyne, C.B. Cragg, M.S. Craven, R.J. Crepaldi, T. Crespo, J.L. Criollo, A. Crippa, V. Cruz, M.T. Cuervo, A.M. Cuezva, J.M. Cui, T. Cutillas, P.R. Czaja, M.J. Czyzyk-Krzeska, M.F. Dagda, R.K. Dahmen, U. Dai, C. Dai, W. Dai, Y. Dalby, K.N. Valle, L.D. Dalmasso, G. D'amelio, M. Damme, M. Darfeuille-Michaud, A. Dargemont, C. Darley-Usmar, V.M. Dasarathy, S. Dasgupta, B. Dash, S. Dass, C.R. Davey, H.M. Davids, L.M. Dávila, D. Davis, R.J. Dawson, T.M. Dawson, V.L. Daza, P. de Belleroche, J. de Figueiredo, P. de Figueiredo, R.C.B.Q. de la Fuente, J. De Martino, L. De Matteis, A. De Meyer, G.R.Y. De Milito, A. De Santi, M. de Souza, W. De Tata, V. De Zio, D. Debnath, J. Dechant, R. Decuypere, J.-P. Deegan, S. Dehay, B. Del Bello, B. Del Re, D.P. Delage-Mourroux, R. Delbridge, L.M.D. Deldicque, L. Delorme-Axford, E. 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Published
2016

33. Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

Author

Klionsky, D.J. Abdelmohsen, K. Abe, A. Abedin, M.J. Abeliovich, H. Arozena, A.A. Adachi, H. Adams, C.M. Adams, P.D. Adeli, K. Adhihetty, P.J. Adler, S.G. Agam, G. Agarwal, R. Aghi, M.K. Agnello, M. Agostinis, P. Aguilar, P.V. Aguirre-Ghiso, J. Airoldi, E.M. Ait-Si-Ali, S. Akematsu, T. Akporiaye, E.T. Al-Rubeai, M. Albaiceta, G.M. Albanese, C. Albani, D. Albert, M.L. Aldudo, J. Algül, H. Alirezaei, M. Alloza, I. Almasan, A. Almonte-Beceril, M. Alnemri, E.S. Alonso, C. Altan-Bonnet, N. Altieri, D.C. Alvarez, S. Alvarez-Erviti, L. Alves, S. Amadoro, G. Amano, A. Amantini, C. Ambrosio, S. Amelio, I. Amer, A.O. Amessou, M. Amon, A. An, Z. Anania, F.A. Andersen, S.U. Andley, U.P. Andreadi, C.K. Andrieu-Abadie, N. Anel, A. Ann, D.K. Anoopkumar-Dukie, S. Antonioli, M. Aoki, H. Apostolova, N. Aquila, S. Aquilano, K. Araki, K. Arama, E. Aranda, A. Araya, J. Arcaro, A. Arias, E. Arimoto, H. Ariosa, A.R. Armstrong, J.L. Arnould, T. Arsov, I. Asanuma, K. Askanas, V. Asselin, E. Atarashi, R. Atherton, S.S. Atkin, J.D. Attardi, L.D. Auberger, P. Auburger, G. Aurelian, L. Autelli, R. Avagliano, L. Avantaggiati, M.L. Avrahami, L. Azad, N. Awale, S. Bachetti, T. Backer, J.M. Bae, D.-H. Bae, J.-S. Bae, O.-N. Bae, S.H. Baehrecke, E.H. Baek, S.-H. Baghdiguian, S. Bagniewska-Zadworna, A. Bai, H. Bai, J. Bai, X.-Y. Bailly, Y. Balaji, K.N. Balduini, W. Ballabio, A. Balzan, R. Banerjee, R. Bánhegyi, G. Bao, H. Barbeau, B. Barrachina, M.D. Barreiro, E. Bartel, B. Bartolomé, A. Bassham, D.C. Bassi, M.T. Bast, R.C., Jr. Basu, A. Batista, M.T. Batoko, H. Battino, M. Bauckman, K. Baumgarner, B.L. Bayer, K.U. Beale, R. Beaulieu, J.-F. Beck, G.R., Jr. Becker, C. Beckham, J.D. Bédard, P.-A. Bednarski, P.J. Begley, T.J. Behl, C. Behrends, C. Behrens, G.M.N. Behrns, K.E. Bejarano, E. Belaid, A. Belleudi, F. Bénard, G. Berchem, G. Bergamaschi, D. Bergami, M. Berkhout, B. Berliocchi, L. Bernard, A. Bernard, M. Bernassola, F. Bertolotti, A. Bess, A.S. Besteiro, S. Bettuzzi, S. Bhalla, S. 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Zhang, J. Zhang, J.-P. Zhang, L. Zhang, L. Zhang, L. Zhang, M.-Y. Zhang, X. Zhang, X.D. Zhang, Y. Zhang, Y. Zhang, Y. Zhang, Y. Zhang, Y. Zhao, M. Zhao, W.-L. Zhao, X. Zhao, Y.G. Zhao, Y. Zhao, Y. Zhao, Y.-X. Zhao, Z. Zhao, Z.J. Zheng, D. Zheng, X.-L. Zheng, X. Zhivotovsky, B. Zhong, Q. Zhou, G.-Z. Zhou, G. Zhou, H. Zhou, S.-F. Zhou, X.-J. Zhu, H. Zhu, H. Zhu, W.-G. Zhu, W. Zhu, X.-F. Zhu, Y. Zhuang, S.-M. Zhuang, X. Ziparo, E. Zois, C.E. Zoladek, T. Zong, W.-X. Zorzano, A. Zughaier, S.M.

Published
2016

34. Influence of Different Preparation Processes on the Mechanical Properties of Carbon Nanotube-Reinforced Copper Matrix Composites

Author

Guo, C.H., Zhan, Z.J., and Zhang, D.D.

Subjects
Научно-технический раздел
Abstract

Experiments on the uniform distribution of carbon nanotubes in the copper matrix using different ball milling methods were performed. The effect of different preparation processes for carbon nanotube (CNT)-reinforced copper matrix composites on their conductivity and mechanical properties was also analyzed. High-performance carbon nanotube-reinforced copper matrix composites containing 1 vol.% CNT by the best preparation process termed flake ball milling (FBM) possess good interface bonding strength not only with CNT evenly dispersed in the copper matrix but also with the CNT morphology and structure having the utmost integrity. Tensile strength increased by 30% with an elongation of 26% and electrical conductivity of 85% IACS as compared to a pure copper block prepared from the same FBM powders. Выполнены эксперименты по оценке равномерности распределения углеродных нанотрубок в медной матрице с использованием разных методов размола в шаровой мельнице. Проанализировано влияние различных методов изготовления композиционных материалов с медной матрицей, упрочненных углеродными нанотрубками, на их электропроводность и механические свойства. Высококачественные композиционные материалы (содержат 1 об.% углеродных нанотрубок), изготовляемые наиболее эффективным способом, получившим навание размол в шаровой мельнице для придания пластинчатой структуры , приобретают хорошую прочность сцепления на поверхности контакта не только при равномерном распределении углеродных нанотрубок в медной матрице, но и при максимальной целостности их морфологии и структуры. Предел прочности при растяжении возрастал на 30% при удлинении 26% и электропроводности 85% по стандарту IACS по сравнению с заготовкой из чистой меди, изготовленной из тех же пластинчатых порошков.

Published
2015

36. Stark Interference of Electric and Magnetic Dipole Transitions in the A - X Band of OH

Author

Schewe, H.C., Zhang, D.D., Meijer, G., Field, R.W., Sartakov, B.G., Groenenboom, G.C., Avoird, A. van der, Vanhaecke, N., Schewe, H.C., Zhang, D.D., Meijer, G., Field, R.W., Sartakov, B.G., Groenenboom, G.C., Avoird, A. van der, and Vanhaecke, N.

Abstract

Contains fulltext : 158683.pdf (publisher's version ) (Open Access)

Published
2016

37. Cold and intense OH radical beam sources

Author

Ploenes, L., Haas, D., Zhang, D.D., Meerakker, S.Y.T. van de, Willitsch, S., Ploenes, L., Haas, D., Zhang, D.D., Meerakker, S.Y.T. van de, and Willitsch, S.

Abstract

Item does not contain fulltext

Published
2016

39. Prostacyclin mediates neuropathic pain through interleukin 1v‐expressing resident macrophages

Author

Schuh, C.D., Pierre, S., Weigert, A., Weichand, B., Altenrath, K., Schreiber, Y., Ferreirós, N., Zhang, D.D., Suo, J., Treutlein, E.M., Henke, M., Kunkel, H., Grez, M., Nüsing, R., Brüne, B., Geisslinger, G., Scholich, K., and Publica

Abstract

Prostacyclin synthesized at the sites of nerve injuries regulates the accumulation of resident macrophages. These cells express IL1v, which supports the development of neuropathic pain. ABSTRACT: Prostacyclin is an important mediator of peripheral pain sensation. Here, we investigated its potential participation in mediating neuropathic pain and found that prostacyclin receptor (IP) knockout mice exhibited markedly decreased pain behavior. Application of an IP antagonist to the injury site or selective IP deficiency in myeloid cells mimicked the antinociceptive effect observed in IP knockout mice. At the site of nerve injury, IP was expressed in interleukin (IL) 1v‐containing resident macrophages, which were less common in IP knockout mice. Local administration of the IP agonist cicaprost inhibited macrophage migration in vitro and promoted accumulation of IP‐ and IL1v‐expressing cells as well as an increase of IL1v concentrations at the application site in vivo. Fittingly, the IL1‐receptor antagonist anakinra (IL‐1ra) decreased neuropathic pain behavior in wild‐type mice but not in IP knockout mice. Finally, continuous, but not single administration, of the cyclooxygenase inhibitor meloxicam early after nerve injury decreased pain behavior and the number of resident macrophages. Thus, early synthesis of prostacyclin at the site of injury causes accumulation of IL1v‐expressing macrophages as a key step in neuropathic pain after traumatic injury.

Published
2014

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