319 results on '"Nawrocki A"'
Search Results
2. Signature-based Adaptive Cloud Resource Usage Prediction Using Machine Learning and Anomaly Detection
- Author
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Sus, Wiktor, primary and Nawrocki, Piotr, additional
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- 2024
- Full Text
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3. Unmet non-medical needs of cancer patients in Poland: a quantitative and qualitative study
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Osowiecka, Karolina, primary, Szwiec, Marek, additional, Dolińska, Anna, additional, Gwara, Anna, additional, Kurowicki, Marcin, additional, Kołb-Sielecki, Jarosław, additional, Działach, Eliza, additional, Radecka, Weronika, additional, Nawrocki, Sergiusz, additional, and Rucińska, Monika, additional
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- 2024
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4. Integrated Geophysical and Geomorphological Studies of Caves in Calcarenite Limestones (Jaskinia pod Świecami Cave, Poland)
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Pasierb, Bernadetta, primary, Gajek, Grzegorz, additional, Urban, Jan, additional, and Nawrocki, Wiesław, additional
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- 2024
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5. Reconnaissance archaeomagnetic study of ancient bricks from Northern Poland
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Nawrocki, Jerzy, primary, Rosowiecka, Olga, additional, Wójcik, Krystian, additional, Werner, Tomasz, additional, Chadima, Martin, additional, Wasik, Bogusz, additional, and Wiewióra, Marcin, additional
- Published
- 2023
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6. Three age ranges of Cenozoic basaltic rocks from Lower Silesia (SW Poland) based on 40Ar/39Ar step-heating data
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Magdalena Pańczyk, Jerzy Nawrocki, Paweł Aleksandrowski, and Bogusław Przybylski
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General Earth and Planetary Sciences - Abstract
The precise ages of Cenozoic basaltic rocks from 20 localities (24 samples) in south-western Poland were studied by means of the 40Ar/39Ar isotope method. Three phases of volcanic activity were identified in this area. The older, Ruphelian phase took place 31–29 Ma ago and the younger, Aquitanian phase occurred 22–21 Ma ago. Significantly younger ages (4.8–4.6 Ma: Zanclean phase) were obtained for the basalts cropping out in the vicinity of Lądek Zdrój only. Most of the analyzed basalts from the Ruphelian phase are classified as nephelinites. They occur in the area of Lausitz Masif, south of Sudetic Marginal Fault and in the Opole Depression. The volcanic rocks from the Aquitanian phase crop out in the wide area north of the Sudetic Marginal Fault. They are composed of alkali basalts. The youngest phase is represented by the basanites only. The deep discontinuous tectonic structures intersecting the areas of volcanic activity in SW Poland, parallel or oblique to the tectonic stress directions previously reconstructed for the Oligocene and Miocene in the Bohemian Massif, were most probably reactivated by an extension approximately parallel or slightly oblique to them, thus opening pathways for the migration of basaltic magma in the Sudetes and their foreland.
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- 2022
7. 270 nm ultra-thin self-adhesive conformable and long-term air-stable complimentary organic transistors and amplifiers
- Author
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Mirshojaeian Hosseini, Mohammad Javad, primary, Yang, Yi, additional, Kruger, Walter, additional, Yokota, Tomoyuki, additional, Lee, Sunghoon, additional, Someya, Takao, additional, and Nawrocki, Robert A., additional
- Published
- 2023
- Full Text
- View/download PDF
8. Weak acids produced during anaerobic respiration suppress both photosynthesis and aerobic respiration
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Pang, Xiaojie, primary, Nawrocki, Wojciech J., additional, Cardol, Pierre, additional, Zheng, Mengyuan, additional, Jiang, Jingjing, additional, Fang, Yuan, additional, Yang, Wenqiang, additional, Croce, Roberta, additional, and Tian, Lijin, additional
- Published
- 2023
- Full Text
- View/download PDF
9. Asymptotic properties and convolutions of some almost periodic functions with applications
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Dariusz Bugajewski, Kosma Kasprzak, and Adam Nawrocki
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Applied Mathematics - Abstract
In this note we are going to continue investigation concerning the asymptotic behavior of some Levitan almost periodic functions or almost periodic functions in view of the Lebesgue measure as well as their convolutions with some functions naturally arising in the theory of ordinary linear differential equations. In particular, we obtain some estimations for functions (1), where $$\alpha$$ α is an irrational number of finite irrationality measure or a Liouville number. We also focus on the set of all irrational numbers for which the convolution of function (1) with function (2) exists. In particular, we characterize it from the topological and set-theoretical point of view.
- Published
- 2022
10. Anomaly detection in the context of long-term cloud resource usage planning
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Piotr Nawrocki and Wiktor Sus
- Subjects
Human-Computer Interaction ,Artificial Intelligence ,Hardware and Architecture ,Software ,Information Systems - Abstract
This paper describes a new approach to automatic long-term cloud resource usage planning with a novel hybrid anomaly detection mechanism. It analyzes existing anomaly detection solutions, possible improvements and the impact on the accuracy of resource usage planning. The proposed anomaly detection solution is an important part of the research, since it allows greater accuracy to be achieved in the long term. The proposed approach dynamically adjusts reservation plans in order to reduce the unnecessary load on resources and prevent the cloud from running out of them. The predictions are based on cloud analysis conducted using machine learning algorithms, which made it possible to reduce costs by about 50%. The solution was evaluated on real-life data from over 1700 virtual machines.
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- 2022
11. The structural basis of tRNA recognition by arginyl-tRNA-protein transferase
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Abeywansha, Thilini, primary, Huang, Wei, additional, Ye, Xuan, additional, Nawrocki, Allison, additional, Lan, Xin, additional, Jankowsky, Eckhard, additional, Taylor, Derek J., additional, and Zhang, Yi, additional
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- 2023
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12. The structural basis of tRNA recognition by arginyl-tRNA-protein transferase
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Thilini Abeywansha, Wei Huang, Xuan Ye, Allison Nawrocki, Xin Lan, Eckhard Jankowsky, Derek J. Taylor, and Yi Zhang
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Multidisciplinary ,General Physics and Astronomy ,General Chemistry ,General Biochemistry, Genetics and Molecular Biology - Abstract
Arginyl-tRNA-protein transferase 1 (ATE1) is a master regulator of protein homeostasis, stress response, cytoskeleton maintenance, and cell migration. The diverse functions of ATE1 arise from its unique enzymatic activity to covalently attach an arginine onto its protein substrates in a tRNA-dependent manner. However, how ATE1 (and other aminoacyl-tRNA transferases) hijacks tRNA from the highly efficient ribosomal protein synthesis pathways and catalyzes the arginylation reaction remains a mystery. Here, we describe the three-dimensional structures of Saccharomyces cerevisiae ATE1 with and without its tRNA cofactor. Importantly, the putative substrate binding domain of ATE1 adopts a previously uncharacterized fold that contains an atypical zinc-binding site critical for ATE1 stability and function. The unique recognition of tRNAArg by ATE1 is coordinated through interactions with the major groove of the acceptor arm of tRNA. Binding of tRNA induces conformational changes in ATE1 that helps explain the mechanism of substrate arginylation.
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- 2023
13. Data-Driven Adaptive Prediction of Cloud Resource Usage
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Nawrocki, Piotr, primary, Osypanka, Patryk, additional, and Posluszny, Beata, additional
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- 2023
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14. Small mammal diet indicates plant diversity, vegetation structure, and ecological integrity in a remote ecosystem
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Paul Schuette, Steve Ebbert, Amanda Droghini, and Timm Nawrocki
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Ecology ,Ecology, Evolution, Behavior and Systematics ,Nature and Landscape Conservation - Published
- 2022
15. Three age ranges of Cenozoic basaltic rocks from Lower Silesia (SW Poland) based on 40Ar/39Ar step-heating data
- Author
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Pańczyk, Magdalena, primary, Nawrocki, Jerzy, additional, Aleksandrowski, Paweł, additional, and Przybylski, Bogusław, additional
- Published
- 2022
- Full Text
- View/download PDF
16. Data-Driven Adaptive Prediction of Cloud Resource Usage
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Piotr Nawrocki, Patryk Osypanka, and Beata Posluszny
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Computer Networks and Communications ,Hardware and Architecture ,Software ,Information Systems - Abstract
Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.
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- 2023
17. Asymptotic properties and convolutions of some almost periodic functions with applications
- Author
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Bugajewski, Dariusz, primary, Kasprzak, Kosma, additional, and Nawrocki, Adam, additional
- Published
- 2022
- Full Text
- View/download PDF
18. Shift in bacterial etiology from the CAPNETZ cohort in patients with community-acquired pneumonia: data over more than a decade
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Dionne C.W. Braeken, Marcus Panning, Martin Witzenrath, Jan Rupp, Mathias W. Pletz, Norbert Suttorp, Andreas Essig, Tobias Welte, M Nawrocki, Klaus Dalhoff, Gernot Rohde, R Hoerster, MUMC+: MA Alg Ond Onderz Longziekten (9), RS: NUTRIM - R3 - Respiratory & Age-related Health, and Pulmonologie
- Subjects
0301 basic medicine ,Microbiology (medical) ,medicine.medical_specialty ,IMPACT ,Lung infection ,Culture ,030106 microbiology ,HAEMOPHILUS-INFLUENZAE ,medicine.disease_cause ,Haemophilus influenzae ,03 medical and health sciences ,0302 clinical medicine ,Community-acquired pneumonia ,Internal medicine ,Pneumonia, Bacterial ,medicine ,Humans ,In patient ,030212 general & internal medicine ,Diagnostics ,PATHOGENS ,Bacteria ,business.industry ,Brief Report ,General Medicine ,medicine.disease ,CAP ,Community-Acquired Infections ,Streptococcus pneumoniae ,Infectious Diseases ,Bacterial etiology ,PNEUMOCOCCAL VACCINATION ,Pneumococcal vaccination ,Cohort ,Pulmonary infiltrates ,business - Abstract
To determine the most relevant pathogens for CAP in Germany, patients with radiologically confirmed pulmonary infiltrates and at least one clinical sign of lung infection were prospectively recruited within the CAPNETZ cohort from 2004 until 2016. In 990 out of 4.672 patients (21%) receiving complete diagnostics the most prominent change of pathogens was a decrease of S. pneumoniae (58% in 2004 to 37.5% in 2016; p ≤ 0.001, ρ = − 0.148) and an increase of H. influenzae (12.2% to 20.8%; p = 0.001, ρ = 0.104).
- Published
- 2021
19. Anomaly detection in the context of long-term cloud resource usage planning
- Author
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Nawrocki, Piotr, primary and Sus, Wiktor, additional
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- 2022
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20. Vitrectomy with ILM peeling in diabetic macular edema in one eye vs. intravitreal anti-VEGF injections in the second eye: a comparative study
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Nawrocka, Zofia Anna, primary, Nawrocka, Zofia, additional, and Nawrocki, Jerzy, additional
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- 2022
- Full Text
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21. Erratum to: Measurement of exclusive $$\Upsilon $$ photoproduction from protons in pPb collisions at $${\sqrt{{s_{\mathrm{NN}}}}} = 5.02\,\hbox {TeV}$$
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Sirunyan, A., Tumasyan, A., Adam, W., Ambrogi, F., Asilar, E., Bergauer, T., Brandstetter, J., Brondolin, E., Dragicevic, M., Erö, J., Valle, A., Flechl, M., Friedl, M., Frühwirth, R., Ghete, V., Grossmann, J., Hrubec, J., Jeitler, M., König, A., Krammer, N., Krätschmer, I., Liko, D., Madlener, T., Mikulec, I., Pree, E., Rad, N., Rohringer, H., Schieck, J., Schöfbeck, R., Spanring, M., Spitzbart, D., Taurok, A., Waltenberger, W., Wittmann, J., Wulz, C.E., Zarucki, M., Chekhovsky, V., Mossolov, V., Gonzalez, J., Wolf, E., Croce, D., Janssen, X., Lauwers, J., Pieters, M., Klundert, M., Haevermaet, H., Mechelen, P., Remortel, N., Zeid, S., Blekman, F., D’Hondt, J., Bruyn, I., Clercq, J., Deroover, K., Flouris, G., Lontkovskyi, D., Lowette, S., Marchesini, I., Moortgat, S., Moreels, L., Python, Q., Skovpen, K., Tavernier, S., Doninck, W., Mulders, P., Parijs, I., Beghin, D., Bilin, B., Brun, H., Clerbaux, B., Lentdecker, G., Delannoy, H., Dorney, B., Fasanella, G., Favart, L., Goldouzian, R., Grebenyuk, A., Kalsi, A., Lenzi, T., Luetic, J., Seva, T., Starling, E., Velde, C., Vanlaer, P., Vannerom, D., Yonamine, R., Cornelis, T., Dobur, D., Fagot, A., Gul, M., Khvastunov, I., Poyraz, D., Roskas, C., Trocino, D., Tytgat, M., Verbeke, W., Vermassen, B., Vit, M., Zaganidis, N., Bakhshiansohi, H., Bondu, O., Brochet, S., Bruno, G., Caputo, C., Caudron, A., David, P., Visscher, S., Delaere, C., Delcourt, M., Francois, B., Giammanco, A., Krintiras, G., Lemaitre, V., Magitteri, A., Mertens, A., Musich, M., Piotrzkowski, K., Quertenmont, L., Saggio, A., Marono, M., Wertz, S., Zobec, J., Júnior, W., Alves, F., Alves, G.A., Brito, L., Silva, G., Hensel, C., Moraes, A., Pol, M., Teles, P., Chagas, E., Carvalho, W., Chinellato, J., Coelho, E., Costa, E., Silveira, G.G., Jesus Damiao, D., Souza, S., Malbouisson, H., Jaime, M., Almeida, M., Herrera, C., Mundim, L., Nogima, H., Rosas, L., Santoro, A., Sznajder, A., Thiel, M., Manganote, E., Silva De Araujo, F., Pereira, A., Ahuja, S., Bernardes, C., Calligaris, L., Tomei, T., Gregores, E., Mercadante, P., Novaes, S., Padula, Sandra, Abad, D., Vargas, J., Aleksandrov, A., Hadjiiska, R., Iaydjiev, P., Marinov, A., Misheva, M., Rodozov, M., Shopova, M., Sultanov, G., Dimitrov, A., Litov, L., Pavlov, B., Petkov, P., Fang, W., Gao, X., Yuan, L., Ahmad, M., Bian, J., Chen, G., Chen, H., Chen, M., Chen, Y., Jiang, C., Leggat, D., Liao, H., Liu, Z., Romeo, F., Shaheen, S., Spiezia, A., Tao, J., Wang, C., Wang, Z., Yazgan, E., Zhang, H., Zhao, J., Ban, Y., Li, J., Li, Q., Liu, S., Mao, Y., Qian, S., Wang, D., Xu, Z., Wang, Y., Avila, C., Cabrera, A., Montoya, C., Sierra, L., Florez, C., Hernández, C., Delgado, M., Courbon, B., Godinovic, N., Lelas, D., Puljak, I., Cipriano, P., Sculac, T., Antunovic, Z., Kovac, M., Brigljevic, V., Ferencek, D., Kadija, K., Mesic, B., Starodumov, A., Susa, T., Ather, M., Attikis, A., Mavromanolakis, G., Mousa, J., Nicolaou, C., Ptochos, F., Razis, P., Rykaczewski, H., Finger, M., Jarrin, E., Khalil, S., Mahmoud, M., Mohammed, Y., Bhowmik, S., Dewanjee, R., Kadastik, M., Perrini, L., Raidal, M., Veelken, C., Eerola, P., Kirschenmann, H., Pekkanen, J., Voutilainen, M., Havukainen, J., Heikkilä, J., Järvinen, T., Karimäki, V., Kinnunen, R., Lampén, T., Lassila-Perini, K., Laurila, S., Lehti, S., Lindén, T., Luukka, P., Mäenpää, T., Siikonen, H., Tuominen, E., Tuominiemi, J., Tuuva, T., Besancon, M., Couderc, F., Dejardin, M., Denegri, D., Faure, J., Ferri, F., Ganjour, S., Ghosh, S., Givernaud, A., Gras, P., Monchenault, G., Jarry, P., Leloup, C., Locci, E., Machet, M., Malcles, J., Negro, G., Rander, J., Rosowsky, A., Sahin, M., Titov, M., Abdulsalam, A., Amendola, C., Antropov, I., Baffioni, S., Beaudette, F., Busson, P., Cadamuro, L., Charlot, C., Cassagnac, R., Jo, M., Kucher, I., Lisniak, S., Lobanov, A., Blanco, J., Nguyen, M., Ochando, C., Ortona, G., Paganini, P., Pigard, P., Salerno, R., Sauvan, J., Sirois, Y., Leiton, A., Yilmaz, Y., Zabi, A., Zghiche, A., Agram, J.L., Andrea, J., Bloch, D., Brom, J.M., Chabert, E., Collard, C., Conte, E., Coubez, X., Drouhin, F., Fontaine, J.C., Gelé, D., Goerlach, U., Jansová, M., Juillot, P., Bihan, A.C., Tonon, N., Hove, P., Gadrat, S., Beauceron, S., Bernet, C., Boudoul, G., Chanon, N., Chierici, R., Contardo, D., Depasse, P., Mamouni, H., Fay, J., Finco, L., Gascon, S., Gouzevitch, M., Grenier, G., Ille, B., Lagarde, F., Laktineh, I., Lattaud, H., Lethuillier, M., Mirabito, L., Pequegnot, A., Perries, S., Popov, A., Sordini, V., Donckt, M., Viret, S., Zhang, S., Toriashvili, T., Tsamalaidze, Z., Autermann, C., Feld, L., Kiesel, M., Klein, K., Lipinski, M., Preuten, M., Rauch, M., Schomakers, C., Schulz, J., Teroerde, M., Wittmer, B., Zhukov, V., Albert, A., Duchardt, D., Endres, M., Erdmann, M., Erdweg, S., Esch, T., Fischer, R., Güth, A., Hebbeker, T., Heidemann, C., Hoepfner, K., Knutzen, S., Merschmeyer, M., Meyer, A., Millet, P., Mukherjee, S., Pook, T., Radziej, M., Reithler, H., Rieger, M., Scheuch, F., Teyssier, D., Thüer, S., Flügge, G., Kargoll, B., Kress, T., Künsken, A., Müller, T., Nehrkorn, A., Nowack, A., Pistone, C., Pooth, O., Stahl, A., Martin, M., Arndt, T., Asawatangtrakuldee, C., Beernaert, K., Behnke, O., Behrens, U., Martínez, A., Anuar, A., Borras, K., Botta, V., Campbell, A., Connor, P., Contreras-Campana, C., Costanza, F., Danilov, V., Wit, A., Pardos, C., Damiani, D., Eckerlin, G., Eckstein, D., Eichhorn, T., Elwood, A., Eren, E., Gallo, E., Garcia, J., Geiser, A., Luyando, J., Grohsjean, A., Gunnellini, P., Guthoff, M., Harb, A., Hauk, J., Hempel, M., Jung, H., Kasemann, M., Keaveney, J., Kleinwort, C., Knolle, J., Korol, I., Krücker, D., Lange, W., Lelek, A., Lenz, T., Lipka, K., Lohmann, W., Mankel, R., Melzer-Pellmann, I.A., Meyer, M., Missiroli, M., Mittag, G., Mnich, J., Mussgiller, A., Pitzl, D., Raspereza, A., Savitskyi, M., Saxena, P., Shevchenko, R., Stefaniuk, N., Tholen, H., Onsem, G., Walsh, R., Wen, Y., Wichmann, K., Wissing, C., Zenaiev, O., Aggleton, R., Bein, S., Blobel, V., Vignali, M., Dreyer, T., Garutti, E., Gonzalez, D., Haller, J., Hinzmann, A., Hoffmann, M., Karavdina, A., Kasieczka, G., Klanner, R., Kogler, R., Kovalchuk, N., Kurz, S., Kutzner, V., Lange, J., Marconi, D., Multhaup, J., Niedziela, M., Nowatschin, D., Peiffer, T., Perieanu, A., Reimers, A., Scharf, C., Schleper, P., Schmidt, A., Schumann, S., Schwandt, J., Sonneveld, J., Stadie, H., Steinbrück, G., Stober, F., Stöver, M., Troendle, D., Usai, E., Vanhoefer, A., Vormwald, B., Akbiyik, M., Barth, C., Baselga, M., Baur, S., Butz, E., Caspart, R., Chwalek, T., Colombo, F., Boer, W., Dierlamm, A., Faltermann, N., Freund, B., Friese, R., Giffels, M., Harrendorf, M., Hartmann, F., Heindl, S., Husemann, U., Kassel, F., Kudella, S., Mildner, H., Mozer, M., Müller, Th., Plagge, M., Quast, G., Rabbertz, K., Schröder, M., Shvetsov, I., Sieber, G., Simonis, H., Ulrich, R., Wayand, S., Weber, M., Weiler, T., Williamson, S., Wöhrmann, C., Wolf, R., Anagnostou, G., Daskalakis, G., Geralis, T., Kyriakis, A., Loukas, D., Topsis-Giotis, I., Karathanasis, G., Kesisoglou, S., Panagiotou, A., Saoulidou, N., Tziaferi, E., Kousouris, K., Papakrivopoulos, I., Evangelou, I., Foudas, C., Gianneios, P., Katsoulis, P., Kokkas, P., Mallios, S., Manthos, N., Papadopoulos, I., Paradas, E., Strologas, J., Triantis, F., Tsitsonis, D., Csanad, M., Filipovic, N., Pasztor, G., Surányi, O., Veres, G., Bencze, G., Hajdu, C., Horvath, D., Hunyadi, Á., Sikler, F., Vámi, T., Veszpremi, V., Vesztergombi, G., Beni, N., Czellar, S., Karancsi, J., Makovec, A., Molnar, J., Szillasi, Z., Bartók, M., Raics, P., Trocsanyi, Z., Ujvari, B., Choudhury, S., Komaragiri, J., Bahinipati, S., Mal, P., Mandal, K., Nayak, A., Sahoo, D., Swain, S., Bansal, S., Beri, S., Bhatnagar, V., Chauhan, S., Chawla, R., Dhingra, N., Gupta, R., Kaur, A., Kaur, M., Kaur, S., Kumar, R., Kumari, P., Lohan, M., Mehta, A., Sharma, S., Singh, J., Walia, G., Bhardwaj, A., Choudhary, B.C., Garg, R., Keshri, S., Kumar, A., Kumar, Ashok, Malhotra, S., Naimuddin, M., Ranjan, K., Shah, Aashaq, Sharma, R., Bhardwaj, R., Bhattacharya, R., Bhattacharya, S., Bhawandeep, U., Bhowmik, D., Dey, S., Dutt, S., Dutta, S., Majumdar, N., Mondal, K., Mukhopadhyay, S., Nandan, S., Purohit, A., Rout, P., Roy, A., Chowdhury, S., Sarkar, S., Sharan, M., Singh, B., Thakur, S., Behera, P., Chudasama, R., Dutta, D., Jha, V., Kumar, V., Mohanty, A., Netrakanti, P., Pant, L., Shukla, P., Topkar, A., Aziz, T., Dugad, S., Mahakud, B., Mitra, S., Mohanty, G., Sur, N., Sutar, B., Banerjee, S., Chatterjee, S., Das, P., Guchait, M., Jain, Sa., Kumar, S., Maity, M., Majumder, G., Mazumdar, K., Sahoo, N., Sarkar, T., Wickramage, N., Dube, S., Hegde, V., Kapoor, A., Kothekar, K., Pandey, S., Rane, A., Chenarani, S., Tadavani, E., Etesami, S., Khakzad, M., Najafabadi, M., Naseri, M., Mehdiabadi, S., Hosseinabadi, F., Safarzadeh, B., Zeinali, M., Felcini, M., Grunewald, M., Abbrescia, M., Calabria, C., Colaleo, A., Creanza, D., Cristella, L., Filippis, N., Palma, M., Florio, A., Errico, F., Fiore, L., Gelmi, A., Iaselli, G., Lezki, S., Maggi, G., Maggi, M., Marangelli, B., Miniello, G., My, S., Nuzzo, S., Pompili, A., Pugliese, G., Radogna, R., Ranieri, A., Selvaggi, G., Sharma, A., Silvestris, L., Venditti, R., Verwilligen, P., Zito, G., Abbiendi, G., Battilana, C., Bonacorsi, D., Borgonovi, L., Braibant-Giacomelli, S., Campanini, R., Capiluppi, P., Castro, A., Cavallo, F., Chhibra, S., Codispoti, G., Cuffiani, M., Dallavalle, G., Fabbri, F., Fanfani, A., Fasanella, D., Giacomelli, P., Grandi, C., Guiducci, L., Marcellini, S., Masetti, G., Montanari, A., Navarria, F., Odorici, F., Perrotta, A., Rossi, A., Rovelli, T., Siroli, G., Tosi, N., Albergo, S., Costa, S., Mattia, A., Giordano, F., Potenza, R., Tricomi, A., Tuve, C., Barbagli, G., Chatterjee, K., Ciulli, V., Civinini, C., D’Alessandro, R., Focardi, E., Latino, G., Lenzi, P., Meschini, M., Paoletti, S., Russo, L., Sguazzoni, G., Strom, D., Viliani, L., Benussi, L., Bianco, S., Piccolo, D., Primavera, F., Calvelli, V., Ferro, F., Ravera, F., Robutti, E., Tosi, S., Benaglia, A., Beschi, A., Brianza, L., Brivio, F., Ciriolo, V., Dinardo, M.E., Fiorendi, S., Gennai, S., Ghezzi, A., Govoni, P., Malberti, M., Malvezzi, S., Manzoni, R., Menasce, D., Moroni, L., Paganoni, M., Pauwels, K., Pedrini, D., Pigazzini, S., Ragazzi, S., Fatis, T., Buontempo, S., Cavallo, N., Guida, S., Fabozzi, F., Fienga, F., Galati, G., Iorio, A., Khan, W., Lista, L., Meola, S., Paolucci, P., Sciacca, C., Thyssen, F., Voevodina, E., Azzi, P., Bacchetta, N., Benato, L., Bisello, D., Boletti, A., Carlin, R., Oliveira, A., Checchia, P., Castro Manzano, P., Dorigo, T., Dosselli, U., Gasparini, F., Gasparini, U., Gozzelino, A., Lacaprara, S., Lujan, P., Margoni, M., Meneguzzo, A., Pozzobon, N., Ronchese, P., Rossin, R., Simonetto, F., Tiko, A., Zanetti, M., Zotto, P., Zumerle, G., Braghieri, A., Magnani, A., Montagna, P., Ratti, S., Re, V., Ressegotti, M., 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A., Tonwar, S., Abercrombie, D., Allen, B., Azzolini, V., Barbieri, R., Baty, A., Bauer, G., Bi, R., Brandt, S., Busza, W., Cali, I., D’Alfonso, M., Demiragli, Z., Ceballos, G., Goncharov, M., Harris, P., Hsu, D., Hu, M., Iiyama, Y., Innocenti, G., Klute, M., Kovalskyi, D., Lee, Y.J., Levin, A., Luckey, P., Maier, B., Marini, A.C., Mcginn, C., Mironov, C., Narayanan, S., Niu, X., Paus, C., Roland, C., Roland, G., Stephans, G., Sumorok, K., Tatar, K., Velicanu, D., Wang, T., Wyslouch, B., Zhaozhong, S., Benvenuti, A., Chatterjee, R., Evans, A., Hansen, P., Kalafut, S., Kubota, Y., Lesko, Z., Mans, J., Nourbakhsh, S., Ruckstuhl, N., Rusack, R., Turkewitz, J., Wadud, M., Acosta, J., Oliveros, S., Avdeeva, E., Bloom, K., Claes, D., Fangmeier, C., Golf, F., Suarez, R., Kamalieddin, R., Kravchenko, I., Monroy, J., Siado, J., Snow, G., Stieger, B., Godshalk, A., Harrington, C., Iashvili, I., Nguyen, D., Parker, A., Rappoccio, S., Roozbahani, B., Alverson, G., Barberis, E., Freer, C., Hortiangtham, A., Massironi, A., Morse, D.M., Orimoto, T., Lima, R., Wamorkar, T., Wang, B., Wisecarver, A., Wood, D., Charaf, O., Hahn, K., Mucia, N., Odell, N., Schmitt, M.H., Sung, K., Trovato, M., Velasco, M., Bucci, R., Dev, N., Hildreth, M., Anampa, K., Jessop, C., Karmgard, D., Kellams, N., Lannon, K., Li, W., Loukas, N., Marinelli, N., Meng, F., Mueller, C., Musienko, Y., Planer, M., Reinsvold, A., Ruchti, R., Siddireddy, P., Smith, G., Taroni, S., Wayne, M., Wightman, A., Wolf, M., Woodard, A., Alimena, J., Antonelli, L., Bylsma, B., Durkin, L., Flowers, S., Francis, B., Hart, A., Hill, C., Ji, W., Ling, T., Luo, W., Winer, B., Wulsin, H., Cooperstein, S., Driga, O., Elmer, P., Hardenbrook, J., Hebda, P., Higginbotham, S., Kalogeropoulos, A., Lange, D., Luo, J., Marlow, D., Mei, K., Ojalvo, I., Olsen, J., Palmer, C., Piroué, P., Salfeld-Nebgen, J., Stickland, D., Tully, C., Norberg, S., Barker, A., Barnes, V.E., Das, S., Gutay, L., Jones, M., Jung, A.W., Khatiwada, A., Miller, D., Neumeister, N., Peng, C., Qiu, H., Schulte, J., Sun, J., Wang, F., Xiao, R., Xie, W., Cheng, T., Dolen, J., Parashar, N., Chen, Z., Ecklund, K., Freed, S., Geurts, F.J.M., Guilbaud, M., Kilpatrick, M., Michlin, B., Padley, B.P., Roberts, J., Rorie, J., Shi, W., Tu, Z., Zabel, J., Zhang, A., Bodek, A., Barbaro, P., Demina, R., Duh, Y., Ferbel, T., Galanti, M., Garcia-Bellido, A., Han, J., Hindrichs, O., Khukhunaishvili, A., Lo, K., Tan, P., Verzetti, M., Ciesielski, R., Goulianos, K., Mesropian, C., Agapitos, A., Chou, J., Gershtein, Y., Espinosa, T.A., Halkiadakis, E., Heindl, M., Hughes, E., Kaplan, S., Elayavalli, R., Kyriacou, S., Lath, A., Montalvo, R., Nash, K., Osherson, M., Saka, H., Salur, S., Schnetzer, S., Sheffield, D., Somalwar, S., Stone, R., Thomas, S., Thomassen, P., Walker, M., Delannoy, A., Heideman, J., Riley, G., Rose, K., Spanier, S., Thapa, K., Bouhali, O., Hernandez, A., Celik, A., Dalchenko, M., Mattia, M., Delgado, A., Dildick, S., Eusebi, R., Gilmore, J., Huang, T., Kamon, T., Mueller, R., Pakhotin, Y., Patel, R., Perloff, A., Perniè, L., Rathjens, D., Safonov, A., Tatarinov, A., Akchurin, N., Damgov, J., Guio, F., Dudero, P., Faulkner, J., Gurpinar, E., Kunori, S., Lamichhane, K., Mengke, T., Muthumuni, S., Peltola, T., Undleeb, S., Volobouev, I., Greene, S., Gurrola, A., Janjam, R., Johns, W., Maguire, C., Melo, A., Ni, H., Padeken, K., Alvarez, J.D., Sheldon, P., Tuo, S., Velkovska, J., Xu, Q., Arenton, M., Barria, P., Cox, B., Hirosky, R., Joyce, M., Ledovskoy, A., Li, H., Neu, C., Sinthuprasith, T., Wolfe, E., Xia, F., Harr, R., Karchin, P., Poudyal, N., Sturdy, J., Thapa, P., Zaleski, S., Brodski, M., Buchanan, J., Caillol, C., Carlsmith, D., Dasu, S., Dodd, L., Duric, S., Gomber, B., Grothe, M., Herndon, M., Hervé, A., Hussain, U., Klabbers, P., Lanaro, A., Levine, A., Long, K., Loveless, R., Rekovic, V., Ruggles, T., Savin, A., Smith, N., Smith, W., and Woods, N.
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Physics and Astronomy (miscellaneous) ,CMS ,+muon%2B+muon%22">upsilon mesons --> muon+ muon ,rapidity [differential cross section] ,5.02*10**3 GeV ,(transverse momentum dependence) [differential cross section] ,+upsilon+mesons+p%22">photon p --> upsilon mesons p ,differential cross section: measured ,photoproduction [upsilon mesons] ,measured [differential cross section] ,upsilon mesons: photoproduction ,differential cross section: rapidity ,differential cross section: (transverse momentum dependence) ,ddc:530 ,UPC ,peripheral ,pPb ,Engineering (miscellaneous) ,decay modes [upsilon mesons] ,upsilon mesons: decay modes ,photoproduction - Abstract
The European physical journal / C 82(4), 343 (2022). doi:10.1140/epjc/s10052-022-10276-2, In this article the author name Luigi Calligaris was incorrectly written as A. Calligaris. The original article has been corrected., Published by Springer, Heidelberg
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- 2022
22. Targeted CUL4A inhibition synergizes with cisplatin to yield long-term survival in models of head and neck squamous cell carcinoma through a DDB2-mediated mechanism
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Jones, Trace M., primary, Espitia, Claudia M., additional, Ooi, Aikseng, additional, Bauman, Julie E., additional, Carew, Jennifer S., additional, and Nawrocki, Steffan T., additional
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- 2022
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23. Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning
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Bartlomiej Sniezynski and Piotr Nawrocki
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Schedule ,Service (systems architecture) ,Training set ,business.industry ,Computer science ,Context (computing) ,Supervised learning ,020206 networking & telecommunications ,Cloud computing ,02 engineering and technology ,Energy consumption ,Machine learning ,computer.software_genre ,Computer Science Applications ,Task (project management) ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Adaptation (computer science) ,Agent architecture ,computer ,Mobile device - Abstract
In this paper we present an original adaptive task scheduling system, which optimizes the energy consumption of mobile devices using machine learning mechanisms and context information. The system learns how to allocate resources appropriately: how to schedule services/tasks optimally between the device and the cloud, which is especially important in mobile systems. Decisions are made taking the context into account (e.g. network connection type, location, potential time and cost of executing the application or service). In this study, a supervised learning agent architecture and service selection algorithm are proposed to solve this problem. Adaptation is performed online, on a mobile device. Information about the context, task description, the decision made and its results such as power consumption are stored and constitute training data for a supervised learning algorithm, which updates the knowledge used to determine the optimal location for the execution of a given type of task. To verify the solution proposed, appropriate software has been developed and a series of experiments have been conducted. Results show that as a result of the experience gathered and the learning process performed, the decision module has become more efficient in assigning the task to either the mobile device or cloud resources.
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- 2020
24. No SARS-CoV-2 detection in the German CAPNETZ cohort of community acquired pneumonia before COVID-19 peak in March 2020
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Panning, Marcus, Wiener, Julius, Dreher, M., Cornelissen, Christian, Knüppel, W., Stolz, D., Suttorp, N., Bauer, W., Mikolajewska, A., Witzenrath, M., Pankow, W., Gläser, S., Rothe, Kathrin, Thiemig, D., Prediger, M., Schmager, S., Kolditz, M., Schulte-Hubbert, B., Langner, S., Rohde, G., Bellinghausen, C., Panning, M., Hoffmann, C., Schneider, Jochen, Welte, T., Freise, J., Barten, G., Kröner, W., Nawrocki, M., Naim, J., Illig, T., Klopp, N., Pletz, M., Kroegel, C., Pletz, Mathias W., Schleenvoigt, B., Forstner, C., Moeser, A., Drömann, D., Parschke, P., Franzen, K., Rupp, J., Käding, N., Wouters, M., Walraven, K., Rohde, Gernot, Braeken, D., Spinner, C., Zaruchas, A., Schaberg, D, Heigener, D., Hering, I., Albrich, W., Waldeck, F., Rassouli, F., Baldesberger, S., Rupp, Jan, Stenger, S., Wallner, M., Burgmann, H., Traby, L., Witzenrath, Martin, Spinner, Christoph D., and Members of the CAPNETZ study group
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0301 basic medicine ,Microbiology (medical) ,medicine.medical_specialty ,Respiratory pathogens ,Epidemiology ,viruses ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,030106 microbiology ,medicine.disease_cause ,03 medical and health sciences ,0302 clinical medicine ,Community-acquired pneumonia ,Internal medicine ,Correspondence ,Pandemic ,Medicine ,030212 general & internal medicine ,skin and connective tissue diseases ,Multiplex RT-PCR ,SARS-CoV-2 ,business.industry ,fungi ,General Medicine ,medicine.disease ,respiratory tract diseases ,Community acquired pneumonia ,ddc ,body regions ,Pneumonia ,Infectious Diseases ,Cohort ,Rhinovirus ,business ,Cohort study - Abstract
Purpose The first SARS-CoV-2 cases in Europe were reported in January 2020. Recently, concern arose on unrecognized infections before this date. For a better understanding of the pandemic, we retrospectively analyzed patient samples for SARS-CoV-2 from the prospective CAPNETZ study cohort. Methods We used nasopharyngeal swab samples from a cohort of well characterized patients with community acquired pneumonia of the CAPNETZ study group, recruited from different geographic regions across Germany, Austria, the Netherlands, and Switzerland between 02nd December 2019 and 28th April 2020. Multiplex real-time RT-PCR for a broad range of respiratory pathogens and SARS-CoV-2 real-time RT-PCR were performed on all samples. Results In our cohort, respiratory pathogens other than SARS-CoV-2 were detected in 21.5% (42/195) of patients with rhinovirus as the most frequently detected pathogen. The detection rate increased to 29.7% (58/195) when SARS-CoV-2 was included. No SARS-CoV-2 positive sample was detected before end of March 2020. Conclusions Respiratory viral pathogens accounted for a considerable number of positive results but no SARS-CoV-2 case was identified before the end of March 2020.
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- 2020
25. Accurate determination of solvation free energies of neutral organic compounds from first principles
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Leonid Pereyaslavets, Ganesh Kamath, Oleg Butin, Alexey Illarionov, Michael Olevanov, Igor Kurnikov, Serzhan Sakipov, Igor Leontyev, Ekaterina Voronina, Tyler Gannon, Grzegorz Nawrocki, Mikhail Darkhovskiy, Ilya Ivahnenko, Alexander Kostikov, Jessica Scaranto, Maria G. Kurnikova, Suvo Banik, Henry Chan, Michael G. Sternberg, Subramanian K. R. S. Sankaranarayanan, Brad Crawford, Jeffrey Potoff, Michael Levitt, Roger D. Kornberg, and Boris Fain
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Physics::Biological Physics ,Quantitative Biology::Biomolecules ,Computational chemistry ,Multidisciplinary ,Science ,General Physics and Astronomy ,General Chemistry ,Molecular dynamics ,Article ,General Biochemistry, Genetics and Molecular Biology ,Physics::Atomic and Molecular Clusters ,Physics::Chemical Physics ,Quantum chemistry - Abstract
The main goal of molecular simulation is to accurately predict experimental observables of molecular systems. Another long-standing goal is to devise models for arbitrary neutral organic molecules with little or no reliance on experimental data. While separately these goals have been met to various degrees, for an arbitrary system of molecules they have not been achieved simultaneously. For biophysical ensembles that exist at room temperature and pressure, and where the entropic contributions are on par with interaction strengths, it is the free energies that are both most important and most difficult to predict. We compute the free energies of solvation for a diverse set of neutral organic compounds using a polarizable force field fitted entirely to ab initio calculations. The mean absolute errors (MAE) of hydration, cyclohexane solvation, and corresponding partition coefficients are 0.2 kcal/mol, 0.3 kcal/mol and 0.22 log units, i.e. within chemical accuracy. The model (ARROW FF) is multipolar, polarizable, and its accompanying simulation stack includes nuclear quantum effects (NQE). The simulation tools’ computational efficiency is on a par with current state-of-the-art packages. The construction of a wide-coverage molecular modelling toolset from first principles, together with its excellent predictive ability in the liquid phase is a major advance in biomolecular simulation., Theoretical estimations of solvation free energy by continuum solvation models are generally not accurate. Here the authors report a polarizable force field fitted entirely to first-principles calculations for the estimation of free energy of solvation of arbitrary molecules.
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- 2022
26. Accurate determination of solvation free energies of neutral organic compounds from first principles
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Pereyaslavets, Leonid, primary, Kamath, Ganesh, additional, Butin, Oleg, additional, Illarionov, Alexey, additional, Olevanov, Michael, additional, Kurnikov, Igor, additional, Sakipov, Serzhan, additional, Leontyev, Igor, additional, Voronina, Ekaterina, additional, Gannon, Tyler, additional, Nawrocki, Grzegorz, additional, Darkhovskiy, Mikhail, additional, Ivahnenko, Ilya, additional, Kostikov, Alexander, additional, Scaranto, Jessica, additional, Kurnikova, Maria G., additional, Banik, Suvo, additional, Chan, Henry, additional, Sternberg, Michael G., additional, Sankaranarayanan, Subramanian K. R. S., additional, Crawford, Brad, additional, Potoff, Jeffrey, additional, Levitt, Michael, additional, Kornberg, Roger D., additional, and Fain, Boris, additional
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- 2022
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27. Small mammal diet indicates plant diversity, vegetation structure, and ecological integrity in a remote ecosystem
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Schuette, Paul, primary, Ebbert, Steve, additional, Droghini, Amanda, additional, and Nawrocki, Timm, additional
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- 2022
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28. The mitochondrially-localized nucleoside diphosphate kinase D (NME4) is a novel metastasis suppressor
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Lacombe, Marie-Lise, primary, Lamarche, Frederic, additional, De Wever, Olivier, additional, Padilla-Benavides, Teresita, additional, Carlson, Alyssa, additional, Khan, Imran, additional, Huna, Anda, additional, Vacher, Sophie, additional, Calmel, Claire, additional, Desbourdes, Céline, additional, Cottet-Rousselle, Cécile, additional, Hininger-Favier, Isabelle, additional, Attia, Stéphane, additional, Nawrocki-Raby, Béatrice, additional, Raingeaud, Joël, additional, Machon, Christelle, additional, Guitton, Jérôme, additional, Le Gall, Morgane, additional, Clary, Guilhem, additional, Broussard, Cedric, additional, Chafey, Philippe, additional, Thérond, Patrice, additional, Bernard, David, additional, Fontaine, Eric, additional, Tokarska-Schlattner, Malgorzata, additional, Steeg, Patricia, additional, Bièche, Ivan, additional, Schlattner, Uwe, additional, and Boissan, Mathieu, additional
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- 2021
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29. The novel autophagy inhibitor ROC-325 augments the antileukemic activity of azacitidine
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Faiz Anwer, Anjali S. Advani, Hetty E. Carraway, James G. Phillips, Claudia M. Espitia, Mikkael A. Sekeres, Jaroslaw P. Maciejewski, Bartlomiej P Przychodzen, Yingchun Han, Valeria Visconte, Jennifer S. Carew, Steffan T. Nawrocki, and Kevin R. Kelly
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Antimetabolites, Antineoplastic ,Cancer Research ,Extramural ,business.industry ,Azacitidine ,Autophagy ,Drug Synergism ,Hematology ,Xenograft Model Antitumor Assays ,Article ,Drug synergism ,Mice ,Oncology ,Cell culture ,Cell Line, Tumor ,medicine ,Cancer research ,Animals ,Humans ,business ,Hydroxychloroquine ,medicine.drug - Published
- 2019
30. VM Reservation Plan Adaptation Using Machine Learning in Cloud Computing
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Krzysztof Zieliński, Bartlomiej Sniezynski, Piotr Nawrocki, Michal Wilk, and Marcin Jarzab
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Resource scheduling ,Computer Networks and Communications ,business.industry ,Computer science ,Reservation ,Context (language use) ,Cloud computing ,Plan (drawing) ,Machine learning ,computer.software_genre ,Resource (project management) ,Hardware and Architecture ,Artificial intelligence ,Adaptation (computer science) ,business ,computer ,Software ,Information Systems - Abstract
In this paper we propose a novel reservation plan adaptation system based on machine learning. In the context of cloud auto-scaling, an important issue is the ability to define and use a resource reservation plan, which enables efficient resource scheduling. If necessary, the plan may allocate new resources upon reservation where a sufficient amount of resources is available. Our solution allows the updating of a reservation plan initially prepared by an administrator. It makes it possible to adapt reservation plans one or more weeks ahead. Hence, it allows time for the administrator to analyze the plan and discover potential problems with resource under-provisioning or over-provisioning, which may prevent server overload in the former case and unnecessary expenses in the latter. It also makes it possible to extract and analyze the knowledge learned, which may provide useful information about resource usage characteristics. The proposed solution is tested on OpenStack using real Wikipedia server traffic data. Experimental results demonstrate that machine learning enables an improvement in resource usage.
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- 2019
31. Changes in prostate-specific antigen midway through salvage radiotherapy may be associated with long-term outcomes
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Christopher A. Peters, Jason M. Homza, Harmar D. Brereton, and John T. Nawrocki
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medicine.medical_specialty ,business.industry ,medicine.medical_treatment ,Urology ,Bone metastasis ,Retrospective cohort study ,medicine.disease ,Confidence interval ,030218 nuclear medicine & medical imaging ,Radiation therapy ,Androgen deprivation therapy ,03 medical and health sciences ,Prostate cancer ,Prostate-specific antigen ,0302 clinical medicine ,030220 oncology & carcinogenesis ,medicine ,Clinical endpoint ,business - Abstract
To examine the relationship between changes in prostate-specific antigen (PSA) by midpoint of salvage radiotherapy (SRT) for biochemically recurrent prostate cancer and long-term clinical outcomes. We conducted a retrospective study of men treated with SRT for biochemically recurrent prostate cancer at a single practice from 2004 to 2016. Patients were grouped based on PSA response at treatment midpoint: group 0, no change; group 1, decrease; group 2, increase. The primary endpoint was clinical failure measured from time of SRT completion and defined as serum PSA ≥ 0.2 ng/mL above post-radiotherapy nadir, initiation of androgen deprivation therapy, development of bone metastasis, or death from prostate cancer. The Kaplan-Meier method was used to estimate freedom from clinical failure for each group, and differences between groups were examined using pairwise multiple comparison. Median follow-up was 51.6 months (range, 3.3–138.0). Of 76 eligible men, 13.1% experienced no change in PSA at midpoint of SRT (group 0), 68.4% experienced a decrease (group 1), and 18.4% experienced an increase (group 2). Four-year freedom from clinical failure rates were as follows: group 0, 60.0%; group 1, 58.3%; and group 2, 41.7%. Median time to clinical failure was 71.7 months (95% confidence interval, 46.9–96.5) for group 1; 26.8 months (95% confidence interval, 0.0–55.9) for group 2; and was not reached for group 0. There was a significant difference in four-year freedom from clinical failure between groups 1 and 2 (p = 0.036). PSA changes by the midpoint of SRT predict long-term prostate cancer control with increases in PSA associated with decreased freedom from disease progression.
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- 2019
32. When good animals love restored habitat in bad neighborhoods: ecological traps for eastern cottontails in agricultural landscapes
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Julia A. Nawrocki, Robert L. Schooley, and Michael P. Ward
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0106 biological sciences ,Ecology ,business.industry ,010604 marine biology & hydrobiology ,Wildlife ,Biodiversity ,Context (language use) ,010603 evolutionary biology ,01 natural sciences ,Predation ,Habitat destruction ,Geography ,Habitat ,Agriculture ,Ecological trap ,business ,Ecology, Evolution, Behavior and Systematics ,Nature and Landscape Conservation - Abstract
Large-scale restoration programs have been implemented worldwide to counteract habitat loss and fragmentation from expansion and intensification of agriculture. These agri-environment programs create habitat patches imbedded in an agricultural matrix. The assumption is these patches benefit wildlife and that individuals either avoid agricultural fields or use them to increase their fitness. We selected eastern cottontails (Sylvilagus floridanus) as a focal species on restored grasslands in Illinois, USA to assess survival and to determine if maladaptive habitat selection for agricultural fields could create an ecological trap. We also investigated whether a mismatch between real and perceived predation risk could explain their habitat selection. We radiotracked 95 cottontails and estimated habitat-specific survival using known-fate models, and measured habitat selection using Manly’s standardized selection ratios. We also assessed perceived predation risk with giving-up density experiments. Survival of cottontails was higher in restored grasslands relative to agricultural and developed areas. Habitat selection did not reflect this spatial variation in mortality risk, however, when crops were present on the landscape. Agricultural lands created an equal-preference trap. Cottontails did not accurately assess predation risk; they did not recognize that agricultural fields were risky. This mismatch could be due to cottontails’ emphasis on protecting themselves from avian predators rather than from coyotes (Canis latrans), which currently are their main predator. Our novel study demonstrates how dynamic landscape context can create ecological traps. Such unexpected outcomes can reduce the conservation benefits of restoration programs.
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- 2019
33. Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters
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Piotr Nawrocki and Patryk Osypanka
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Computer Networks and Communications ,Computer science ,business.industry ,Quality of service ,020206 networking & telecommunications ,Ranging ,Context (language use) ,Cloud computing ,02 engineering and technology ,Machine learning ,computer.software_genre ,Cost reduction ,Task (computing) ,Resource (project management) ,Hardware and Architecture ,0202 electrical engineering, electronic engineering, information engineering ,020201 artificial intelligence & image processing ,Anomaly detection ,Artificial intelligence ,business ,computer ,Software ,Information Systems - Abstract
Predicting demand for computing resources in any system is a vital task since it allows the optimized management of resources. To some degree, cloud computing reduces the urgency of accurate prediction as resources can be scaled on demand, which may, however, result in excessive costs. Numerous methods of optimizing cloud computing resources have been proposed, but such optimization commonly degrades system responsiveness which results in quality of service deterioration. This paper presents a novel approach, using anomaly detection and machine learning to achieve cost-optimized and QoS-constrained cloud resource configuration. The utilization of these techniques enables our solution to adapt to different system characteristics and different QoS constraints. Our solution was evaluated using a system located in Microsoft’s Azure cloud environment, and its efficiency in other providers’ computing clouds was estimated as well. Experiment results demonstrate a cost reduction ranging from 51% to 85% (for PaaS/IaaS) over the tested period.
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- 2021
34. Exploring the beliefs and perceptions of spending time in nature among U.S. youth
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Zamora, Astrid N., primary, Waselewski, Marika E., additional, Frank, Abby J., additional, Nawrocki, Jack R., additional, Hanson, Aspen R., additional, and Chang, Tammy, additional
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- 2021
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35. Ribovore: ribosomal RNA sequence analysis for GenBank submissions and database curation
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Schäffer, Alejandro A., primary, McVeigh, Richard, additional, Robbertse, Barbara, additional, Schoch, Conrad L., additional, Johnston, Anjanette, additional, Underwood, Beverly A., additional, Karsch-Mizrachi, Ilene, additional, and Nawrocki, Eric P., additional
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- 2021
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36. Reduced efficacy of a Src kinase inhibitor in crowded protein solution
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Kasahara, Kento, primary, Re, Suyong, additional, Nawrocki, Grzegorz, additional, Oshima, Hiraku, additional, Mishima-Tsumagari, Chiemi, additional, Miyata-Yabuki, Yukako, additional, Kukimoto-Niino, Mutsuko, additional, Yu, Isseki, additional, Shirouzu, Mikako, additional, Feig, Michael, additional, and Sugita, Yuji, additional
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- 2021
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37. R2DT is a framework for predicting and visualising RNA secondary structure using templates
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Sweeney, Blake A., primary, Hoksza, David, additional, Nawrocki, Eric P., additional, Ribas, Carlos Eduardo, additional, Madeira, Fábio, additional, Cannone, Jamie J., additional, Gutell, Robin, additional, Maddala, Aparna, additional, Meade, Caeden D., additional, Williams, Loren Dean, additional, Petrov, Anton S., additional, Chan, Patricia P., additional, Lowe, Todd M., additional, Finn, Robert D., additional, and Petrov, Anton I., additional
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- 2021
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38. Effects of mixed meal tolerance test on gastric emptying, glucose and lipid homeostasis in obese nonhuman primates
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Albarazanji, Kamal, primary, Nawrocki, Andrea R., additional, Gao, Bin, additional, Wang, Xiaoli, additional, Wang, Yixin, additional, and Xiao, Yong-Fu, additional
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- 2021
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39. CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes
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Martin R. Larsen, Richard Reynolds, Tim Kacprowski, Zsolt Illes, Jan Baumbach, Anja Hviid Simonsen, Romain Marignier, Pernille Lassen, Peter Høgh, Alyaa Yakut Rashid, Maria L. Elkjaer, Arkadiusz Nawrocki, Tobias Sejbaek, Helle Hvilsted Nielsen, Finn Sellebjerg, Lene Wermuth, and Multiple Sclerosis Society
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Adult ,Male ,Proteomics ,Pathology ,medicine.medical_specialty ,Proteome ,Apolipoprotein B ,Science ,Immunology ,Selenium-Binding Proteins ,Article ,Transcriptome ,medicine ,Humans ,Gene ,Cerebrospinal Fluid ,Science & Technology ,Multidisciplinary ,Molecular medicine ,biology ,Multiple sclerosis ,Brain ,Multiple Sclerosis, Chronic Progressive ,medicine.disease ,Multidisciplinary Sciences ,Remyelination ,Neurology ,Frzb ,biology.protein ,Science & Technology - Other Topics ,Medicine ,Female ,Alzheimer's disease ,Biomarkers ,Neuroscience - Abstract
To identify markers in the CSF of multiple sclerosis (MS) subtypes, we used a two-step proteomic approach: (i) Discovery proteomics compared 169 pooled CSF from MS subtypes and inflammatory/degenerative CNS diseases (NMO spectrum and Alzheimer disease) and healthy controls. (ii) Next, 299 proteins selected by comprehensive statistics were quantified in 170 individual CSF samples. (iii) Genes of the identified proteins were also screened among transcripts in 73 MS brain lesions compared to 25 control brains. F-test based feature selection resulted in 8 proteins differentiating the MS subtypes, and secondary progressive (SP)MS was the most different also from controls. Genes of 7 out these 8 proteins were present in MS brain lesions: GOLM was significantly differentially expressed in active, chronic active, inactive and remyelinating lesions, FRZB in active and chronic active lesions, and SELENBP1 in inactive lesions. Volcano maps of normalized proteins in the different disease groups also indicated the highest amount of altered proteins in SPMS. Apolipoprotein C-I, apolipoprotein A-II, augurin, receptor-type tyrosine-protein phosphatase gamma, and trypsin-1 were upregulated in the CSF of MS subtypes compared to controls. This CSF profile and associated brain lesion spectrum highlight non-inflammatory mechanisms in differentiating CNS diseases and MS subtypes and the uniqueness of SPMS.
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- 2021
40. Search for new phenomena in final states with two opposite-charge, same-flavor leptons, jets, and missing transverse momentum in pp collisions at $$ \sqrt{s}=13 $$ TeV
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Sirunyan, A. M., Tumasyan, A., Adam, W., Ambrogi, F., Asilar, E., Bergauer, T., Brandstetter, J., Brondolin, E., Dragicevic, M., Eroe, J., Flechl, M., Friedl, M., Fruehwirth, R., Ghete, V. M., Grossmann, J., Hrubec, J., Jeitler, M., Koenig, A., Krammer, N., Kraetschmer, I., Liko, D., Madlener, T., Mikulec, I., Pree, E., Rabady, D., Rad, N., Rohringer, H., Schieck, J., Schoefbeck, R., Spanring, M., Spitzbart, D., Waltenberger, W., Wittmann, J., Wulz, C. -E., Zarucki, M., Chekhovsky, V., Mossolov, V., Gonzalez, J. Suarez, De Wolf, E. 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H., Taylor, D., Woods, N., Lista, Luca, Iorio, Alberto Orso Maria, Fienga, Francesco, And, Others., Institut de Recherches sur les lois Fondamentales de l'Univers (IRFU), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Laboratoire Leprince-Ringuet (LLR), Centre National de la Recherche Scientifique (CNRS)-École polytechnique (X)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Institut Pluridisciplinaire Hubert Curien (IPHC), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Centre de Calcul de l'IN2P3 (CC-IN2P3), Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut de Physique Nucléaire de Lyon (IPNL), Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), CMS, Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-École polytechnique (X)-Centre National de la Recherche Scientifique (CNRS), Université de Strasbourg (UNISTRA)-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherches sur les lois Fondamentales de l'Univers ( IRFU ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay, Laboratoire Leprince-Ringuet ( LLR ), Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -École polytechnique ( X ) -Centre National de la Recherche Scientifique ( CNRS ), Institut Pluridisciplinaire Hubert Curien ( IPHC ), Centre National de la Recherche Scientifique ( CNRS ) -Université de Strasbourg ( UNISTRA ), Université de Haute-Alsace (UHA) Mulhouse - Colmar ( Université de Haute-Alsace (UHA) ), Centre de Calcul de l'IN2P3 ( CC-IN2P3 ), Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Institut de Physique Nucléaire de Lyon ( IPNL ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Physique Nucléaire et de Physique des Particules du CNRS ( IN2P3 ) -Centre National de la Recherche Scientifique ( CNRS ), Sirunyan, A, Tumasyan, A, Adam, W, Ambrogi, F, Asilar, E, Bergauer, T, Brandstetter, J, Brondolin, E, Dragicevic, M, Ero, J, Flechl, M, Friedl, M, Fruhwirth, R, Ghete, V, Grossmann, J, Hrubec, J, Jeitler, M, Konig, A, Krammer, N, Kratschmer, I, Liko, D, Madlener, T, Mikulec, I, Pree, E, Rabady, D, Rad, N, Rohringer, H, Schieck, J, Schofbeck, R, Spanring, M, Spitzbart, D, Waltenberger, W, Wittmann, J, Wulz, C, Zarucki, M, Chekhovsky, V, Mossolov, V, Suarez Gonzalez, J, De Wolf, E, Di Croce, D, Janssen, X, Lauwers, J, Van De Klundert, M, Van Haevermaet, H, Van Mechelen, P, Van Remortel, N, Abu Zeid, S, Blekman, F, D'Hondt, J, De Bruyn, I, De Clercq, J, Deroover, K, Flouris, G, Lontkovskyi, D, Lowette, S, Moortgat, S, Moreels, L, Python, Q, Skovpen, K, Tavernier, S, Van Doninck, W, Van Mulders, P, Van Parijs, I, Brun, H, Clerbaux, B, De Lentdecker, G, Delannoy, H, Fasanella, G, Favart, L, Goldouzian, R, Grebenyuk, A, Karapostoli, G, Lenzi, T, Luetic, J, Maerschalk, T, Marinov, A, Randle-conde, A, Seva, T, Vander Velde, C, Vanlaer, P, Vannerom, 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H., Taylor, D., Woods, N., Çukurova Üniversitesi, Fen-Edebiyat Fakültesi, Fizik Bölümü, Université de Strasbourg (UNISTRA)-Centre National de la Recherche Scientifique (CNRS), Centre National de la Recherche Scientifique (CNRS)-Institut National de Physique Nucléaire et de Physique des Particules du CNRS (IN2P3), Yerevan Phys Inst, Inst Hochenergiephys, Inst Nucl Problems, Univ Antwerp, Vrije Univ Brussel, Univ Libre Bruxelles, Univ Ghent, Catholic Univ Louvain, Univ Mons, Ctr Brasileiro Pesquisas Fis, Universidade do Estado do Rio de Janeiro (UERJ), Universidade Estadual Paulista (Unesp), Universidade Federal do ABC (UFABC), Bulgaria Acad Sci, Univ Sofia, Beihang Univ, Inst High Energy Phys, Peking Univ, Univ Andes, Univ Split, Inst Rudjer Boskov, Univ Cyprus, Charles Univ Prague, Univ San Francisco Quito, Acad Sci Res & Technol Arab Republ Egypt, NICPB, Univ Helsinki, Helsinki Inst Phys, Lappeenranta Univ Technol, Univ Paris Saclay, Univ Strasbourg, CNRS, Univ Claude Bernard Lyon 1, Georgian Tech Univ, Tbilisi State Univ, Rhein Westfal TH Aachen, Deutsch Elekt Synchrotron, Univ Hamburg, Inst Expt Kernphys, NCSR Demokritos, Univ Athens, Natl Tech Univ Athens, Univ Ioannina, Eotvos Lorand Univ, Wigner Res Ctr Phys, Inst Nucl Res ATOMKI, Univ Debrecen, Indian Inst Sci IISc, Natl Inst Sci Educ & Res, Panjab Univ, Univ Delhi, HBNI, Indian Inst Technol Madras, Bhabha Atom Res Ctr, Tata Inst Fundamental Res A, Tata Inst Fundamental Res B, IISER, Inst Res Fundamental Sci IPM, Univ Coll Dublin, Ist Nazl Fis Nucl, Univ Bari, Politecn Bari, Univ Bologna, Univ Catania, Univ Florence, Univ Genoa, Univ Milano Bicocca, Univ Napoli Federico II, Univ Basilicata, Univ G Marconi, Univ Padua, Univ Trento, Univ Pavia, Univ Perugia, Univ Pisa, Scuola Normale Super Pisa, Sapienza Univ Roma, Univ Turin, Univ Piemonte Orientale, Univ Trieste, Kyungpook Natl Univ, Chonbuk Natl Univ, Chonnam Natl Univ, Hanyang Univ, Korea Univ, Seoul Natl Univ, Univ Seoul, Sungkyunkwan Univ, Vilnius Univ, Univ Malaya, Ctr Invest & Estudios Avanzados IPN, Univ Iberoamer, Benemerita Univ Autonoma Puebla, Univ Nacl San Luis Potosi, Univ Auckland, Univ Canterbury, Quaid I Azam Univ, Natl Ctr Nucl Res, Univ Warsaw, Lab Instrumentacao & Fis Expt Particulas, Joint Inst Nucl Res, Petersburg Nucl Phys Inst, Inst Nucl Res, Inst Theoret & Expt Phys, Moscow Inst Phys & Technol, Natl Res Nucl Univ, PN Lebedev Phys Inst, Lomonosov Moscow State Univ, NSU, Univ Belgrade, Ctr Invest Energet Medioambientales & Tecnol CIEM, Univ Autonoma Madrid, Univ Oviedo, Univ Cantabria, European Org Nucl Res, Paul Scherrer Inst, Inst Particle Phys & Astrophys IPA, Univ Zurich, Natl Cent Univ, NTU, Chulalongkorn Univ, Cukurova Univ, Middle East Tech Univ, Bogazici Univ, Istanbul Tech Univ, Natl Acad Sci Ukraine, Kharkov Inst Phys & Technol, Univ Bristol, Rutherford Appleton Lab, Imperial Coll, Brunel Univ, Baylor Univ, Catholic Univ Amer, Univ Alabama, Boston Univ, Brown Univ, Univ Calif Davis, Univ Calif Los Angeles, Univ Calif Riverside, Univ Calif San Diego, Univ Calif Santa Barbara, CALTECH, Carnegie Mellon Univ, Univ Colorado, Cornell Univ, Fermilab Natl Accelerator Lab, Univ Florida, Florida Int Univ, Florida State Univ, Florida Inst Technol, UIC, Univ Iowa, Johns Hopkins Univ, Univ Kansas, Kansas State Univ, Lawrence Livermore Natl Lab, Univ Maryland, MIT, Univ Minnesota, Univ Mississippi, Univ Nebraska, SUNY Buffalo, Northeastern Univ, Northwestern Univ, Univ Notre Dame, Ohio State Univ, Princeton Univ, Univ Puerto Rico, Purdue Univ, Purdue Univ Northwest, Rice Univ, Univ Rochester, Rockefeller Univ, Rutgers State Univ, Univ Tennessee, Texas A&M Univ, Texas Tech Univ, Vanderbilt Univ, Univ Virginia, Wayne State Univ, Univ Wisconsin, Vienna Univ Technol, Universidade Estadual de Campinas (UNICAMP), Univ Fed Pelotas, Cairo Univ, Zewail City Sci & Technol, Univ Haute Alsace, Brandenburg Tech Univ Cottbus, Indian Inst Technol Bhubaneswar, Inst Phys, Univ Visva Bharati, Univ Ruhuna, Isfahan Univ Technol, Yazd Univ, Islamic Azad Univ, Univ Siena, Int Islamic Univ Malaysia, Agensi Nuklear Malaysia, Consejo Nacl Ciencia & Techol, Warsaw Univ Technol, St Petersburg State Polytech Univ, Budker Inst Nucl Phys, Riga Tech Univ, Stefan Meyer Inst Subat Phys SMI, Adiyaman Univ, Istanbul Aydin Univ, Mersin Univ, Cag Univ, Piri Reis Univ, Izmir Inst Technol, Necmettin Erbakan Univ, Marmara Univ, Kafkas Univ, Istanbul Bilgi Univ, Univ Southampton, Inst Astrofs Canarias, Utah Valley Univ, Beykent Univ, Bingol Univ, Erzincan Univ, Sinop Univ, Mimar Sinan Univ, Izmir Institute of Technology. Mathematics, DELLA RICCA, G., ET AL (the CMS, Collaboration), Centre National de la Recherche Scientifique (CNRS)-Université de Strasbourg (UNISTRA), Karapınar, Güler, Department of Physics, Helsinki Institute of Physics, Sirunyan, Albert M, And, Others, and CMS Collaboration
- Subjects
transverse momentum: missing-energy ,mass: lower limit [gluino] ,13000 GeV-cms ,mass: lower limit [neutralino] ,((n)jet dilepton) [final state] ,S046WNO ,gluino: pair production ,INVARIANT ,01 natural sciences ,Physics, Particles & Fields ,0302 clinical medicine ,Hadron-Hadron scattering (experiments) ,S046GNO ,scattering [p p] ,sbottom: mass: lower limit ,Nuclear Experiment ,supersymmetry: symmetry breaking ,Physics ,SUPERGAUGE TRANSFORMATIONS ,neutralino: mass: lower limit ,CERN LHC Coll ,kinematics ,gauge [mediation] ,Physical Sciences ,Physique des particules élémentaires ,PARTICLE PHYSICS ,colliding beams [p p] ,EXTENSION ,p p: scattering ,neutralino: massless ,Astrophysics::High Energy Astrophysical Phenomena ,pair production [gluino] ,114 Physical sciences ,decay modes ,03 medical and health sciences ,S046UPH ,[ PHYS.HEXP ] Physics [physics]/High Energy Physics - Experiment [hep-ex] ,strong coupling ,ddc:530 ,01 Mathematical Sciences ,Science & Technology ,pair production [squark] ,LARGE HADRON COLLIDER ,CMS ,final state: ((n)jet dilepton) ,010308 nuclear & particles physics ,Charge (physics) ,Beyond Standard Model ,Supersymmetry ,NEUTRINO ,p p: colliding beams ,CMS collaboration ,proton-proton collisions ,BSM ,supersymmetry ,dilepton: mass spectrum ,Lepton production ,Nuclear and High Energy Physics ,Physics::Instrumentation and Detectors ,Root (chord) ,S046SBT ,mass spectrum [dilepton] ,symmetry breaking [supersymmetry] ,High Energy Physics - Experiment ,030218 nuclear medicine & medical imaging ,ENERGY ,High Energy Physics - Experiment (hep-ex) ,model [electroweak interaction] ,[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex] ,HADRON COLLIDERS ,squark: pair production ,massless [neutralino] ,Flavor ,Beyond Standard ,Model ,MEASURING MASSES ,SUPERSYMMETRY ,PHYSICS ,MODEL ,02 Physical Sciences ,new physics: search for ,Settore FIS/01 - Fisica Sperimentale ,Nuclear & Particles Physics ,electroweak interaction: model ,Transverse momentum ,Particle Physics - Experiment ,Particle physics ,FOS: Physical sciences ,Computer Science::Digital Libraries ,mass: lower limit [chargino] ,search for [new physics] ,0103 physical sciences ,mass: lower limit [sbottom] ,mediation: gauge ,high energy physics ,experimental particle physics ,LHC ,physics ,beyond two generations ,excited top quark ,hadron-hadron scattering (experiments) ,beyond standard model ,lepton production ,chargino: mass: lower limit ,hep-ex ,High Energy Physics::Phenomenology ,missing-energy [transverse momentum] ,High Energy Physics::Experiment ,gluino: mass: lower limit ,experimental results ,Lepton - Abstract
Journal of high energy physics 1803(03), 076 (2018). doi:10.1007/s13130-018-7845-2, Search results are presented for physics beyond the standard model in final states with two opposite-charge, same-flavor leptons, jets, and missing transverse momentum. The data sample corresponds to an integrated luminosity of 35.9 fb$^{−1}$ of proton-proton collisions at $ \sqrt{s}=13 $ TeV collected with the CMS detector at the LHC in 2016. The analysis uses the invariant mass of the lepton pair, searching for a kinematic edge or a resonant-like excess compatible with the Z boson mass. The search for a kinematic edge targets production of particles sensitive to the strong force, while the resonance search targets both strongly and electroweakly produced new physics. The observed yields are consistent with the expectations from the standard model, and the results are interpreted in the context of simplified models of supersymmetry. In a gauge mediated supersymmetry breaking (GMSB) model of gluino pair production with decay chains including Z bosons, gluino masses up to 1500–1770 GeV are excluded at the 95% confidence level depending on the lightest neutralino mass. In a model of electroweak chargino-neutralino production, chargino masses as high as 610 GeV are excluded when the lightest neutralino is massless. In GMSB models of electroweak neutralino-neutralino production, neutralino masses up to 500-650 GeV are excluded depending on the decay mode assumed. Finally, in a model with bottom squark pair production and decay chains resulting in a kinematic edge in the dilepton invariant mass distribution, bottom squark masses up to 980–1200 GeV are excluded depending on the mass of the next-to-lightest neutralino., Published by Springer, Berlin
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- 2018
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41. Cloud Resource Demand Prediction using Machine Learning in the Context of QoS Parameters
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Nawrocki, Piotr, primary and Osypanka, Patryk, additional
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- 2021
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42. Erratum to: Study of dijet events with a large rapidity gap between the two leading jets in pp collisions at $$\sqrt{s}=7\,\text {Te}\text {V} $$
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CMS Collaboration, Sirunyan, A. M., Tumasyan, A., Adam, W., Asilar, E., Bergauer, T., Brandstetter, J., Brondolin, E., Dragicevic, M., Erö, J., Flechl, M., Friedl, M., Frühwirth, R., Ghete, V. M., Hartl, C., Hörmann, N., Hrubec, J., Jeitler, M., König, A., Krätschmer, I., Liko, D., Matsushita, T., Mikulec, I., Rabady, D., Rad, N., Rahbaran, B., Rohringer, H., Schieck, J., Strauss, J., Waltenberger, W., Wulz, C.-E., Dvornikov, O., Makarenko, V., Mossolov, V., Gonzalez, J. Suarez, Zykunov, V., Shumeiko, N., Alderweireldt, S., De Wolf, E. A., Janssen, X., Lauwers, J., Van De Klundert, M., Van Haevermaet, H., Van Mechelen, P., Van Remortel, N., Van Spilbeeck, A., Zeid, S. Abu, Blekman, F., D’Hondt, J., Daci, N., De Bruyn, I., Deroover, K., Lowette, S., Moortgat, S., Moreels, L., Olbrechts, A., Python, Q., Skovpen, K., Tavernier, S., Van Doninck, W., Van Mulders, P., Van Parijs, I., Brun, H., Clerbaux, B., De Lentdecker, G., Delannoy, H., Fasanella, G., Favart, L., Goldouzian, R., Grebenyuk, A., Karapostoli, G., Lenzi, T., Léonard, A., Luetic, J., Maerschalk, T., Marinov, A., Randle-conde, A., Seva, T., Vander Velde, C., Vanlaer, P., Vannerom, D., Yonamine, R., Zenoni, F., Zhang, F., Cimmino, A., Cornelis, T., Dobur, D., Fagot, A., Gul, M., Khvastunov, I., Poyraz, D., Salva, S., Schöfbeck, R., Tytgat, M., Van Driessche, W., Yazgan, E., Zaganidis, N., Bakhshiansohi, H., Beluffi, C., Bondu, O., Brochet, S., Bruno, G., Caudron, A., De Visscher, S., Delaere, C., Delcourt, M., Francois, B., Giammanco, A., Jafari, A., Komm, M., Krintiras, G., Lemaitre, V., Magitteri, A., Mertens, A., Musich, M., Piotrzkowski, K., Quertenmont, L., Selvaggi, M., Marono, M. 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- Subjects
Physics ,Physics and Astronomy (miscellaneous) ,010308 nuclear & particles physics ,Nuclear Theory ,01 natural sciences ,Charged particle ,Nuclear physics ,0103 physical sciences ,Physics::Accelerator Physics ,ddc:530 ,Rapidity ,Nuclear Experiment ,010306 general physics ,Engineering (miscellaneous) - Abstract
Events with no charged particles produced between the two leading jets are studied in proton-proton collisions at
- Published
- 2020
43. Erratum to: Study of dijet events with a large rapidity gap between the two leading jets in pp collisions at s√=7TeV
- Author
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Sirunyan, AM, Tumasyan, A, Adam, W, Asilar, E, Bergauer, T, Brandstetter, J, Brondolin, E, Dragicevic, M, Erö, J, Flechl, M, Friedl, M, Brianza, L, Sani, M, Sharma, V, Simon, S, Tadel, M, Vartak, A, Wasserbaech, S, Iaydjiev, P, Golovtsov, V, Welke, C, Calvert, B, Seva, T, Bylsma, B, Wood, J, Würthwein, F, Yagil, A, Della Porta, GZ, Amin, N, Bhandari, R, Bradmiller-Feld, J, Campagnari, C, Dishaw, A, Brivio, F, Ivanov, Y, Durkin, LS, Rodozov, M, Dutta, V, Sevilla, MF, George, C, Golf, F, Gouskos, L, Gran, J, Heller, R, Ciriolo, V, Incandela, J, Mullin, SD, Flowers, S, Kim, V, Ovcharova, A, Stoykova, S, Qu, H, Richman, J, Stuart, D, Suarez, I, Dinardo, ME, Yoo, J, Anderson, D, Bendavid, J, Francis, B, Bornheim, A, Kuznetsova, E, Bunn, J, Duarte, J, Sultanov, G, Lawhorn, JM, Fiorendi, S, Mott, A, Newman, HB, Pena, C, Spiropulu, M, Gomez, JP, Vlimant, JR, Xie, S, Murzin, V, Zhu, RY, Andrews, MB, Gennai, S, Ferguson, T, Vutova, M, Paulini, M, Russ, J, Sun, M, Hart, A, Vogel, H, Vorobiev, I, Weinberg, M, Oreshkin, V, Garcia, JMV, Cumalat, JP, Ford, WT, Jensen, F, Johnson, A, Dimitrov, A, Krohn, M, Hill, C, Leontsinis, S, Mulholland, T, Stenson, K, Ghezzi, A, Wagner, SR, Sulimov, V, Alexander, J, Chaves, J, Chu, J, Dittmer, S, Mcdermott, K, Ji, W, Glushkov, I, Mirman, N, Govoni, P, Kaufman, GN, Patterson, JR, Rinkevicius, A, Giammanco, A, Ryd, A, Skinnari, L, Soffi, L, Tan, SM, Liu, B, Tao, Z, Nourbakhsh, S, Bloom, K, Thom, J, Litov, L, Tucker, J, Wittich, P, Zientek, M, Vorobyev, A, Winn, D, Abdullin, S, Albrow, M, Gribushin, A, Malberti, M, Apollinari, G, Apresyan, A, Banerjee, S, Bauerdick, LAT, König, A, Pavlov, B, Beretvas, A, Andreev, Y, Berryhill, J, Bhat, PC, Malvezzi, S, Eno, SC, Luo, W, Bolla, G, Burkett, K, Butler, JN, Cheung, HWK, Chlebana, F, Cihangir, S, Cremonesi, M, Petkov, P, Manzoni, RA, Dermenev, A, Elvira, VD, Puigh, D, Fisk, I, Freeman, J, Gottschalk, E, Gray, L, Green, D, Grünendahl, S, Gutsche, O, Vander Velde, C, Hare, D, Harris, RM, Gninenko, S, Winer, BL, Fang, W, Hasegawa, S, Hirschauer, J, Hu, Z, Jayatilaka, B, Jindariani, S, Menasce, D, Johnson, M, Joshi, U, Klima, B, Kreis, B, Wulsin, HW, Golubev, N, Lammel, S, Ahmad, M, Linacre, J, Lincoln, D, Moroni, L, Lipton, R, Liu, M, Liu, T, De Sá, RL, Lykken, J, Cooperstein, S, Maeshima, K, Karneyeu, A, Magini, N, Marraffino, JM, Paganoni, M, Bian, JG, Maruyama, S, Mason, D, McBride, P, Merkel, P, Mrenna, S, Driga, O, Nahn, S, O’Dell, V, Kirsanov, M, Pedrini, D, Pedro, K, Prokofyev, O, Rakness, G, Chen, GM, Ristori, L, Sexton-Kennedy, E, Soha, A, Hernández, CFG, Spalding, WJ, Spiegel, L, Cabrillo, IJ, Stoynev, S, Krasnikov, N, Strait, J, Strobbe, N, Taylor, L, Tkaczyk, S, Chen, HS, Tran, NV, Elmer, P, Uplegger, L, Pompili, A, Pigazzini, S, Vaandering, EW, Vernieri, C, Verzocchi, M, Pashenkov, A, Vidal, R, Wang, M, Weber, HA, Whitbeck, A, Wu, Y, Hardenbrook, J, Zhokin, A, Chen, M, Acosta, D, Avery, P, Bortignon, P, Bourilkov, D, Tlisov, D, Brinkerhoff, A, Carnes, A, Carver, M, 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L, Konecki, M, Mannelli, M, Martelli, A, Meijers, F, Terkulov, A, Marcellini, S, Merlin, JA, Mersi, S, Meschi, E, Milenovic, P, Moortgat, F, Moraes, A, Morovic, S, Krolikowski, J, Mulders, M, Neugebauer, H, Masetti, G, Matsushita, T, Orfanelli, S, Orsini, L, Pape, L, Perez, E, Peruzzi, M, Petrilli, A, Petrucciani, G, Pol, ME, Misiura, M, Montanari, A, Pfeiffer, A, Avila, C, Pierini, M, Racz, A, Reis, T, Rolandi, G, Rovere, M, Sakulin, H, Sauvan, JB, Schäfer, C, Navarria, FL, Schwick, C, Olszewski, M, Trocino, D, Teles, PR, Seidel, M, Silva, P, Sphicas, P, Steggemann, J, Stoye, M, Perrotta, A, Takahashi, Y, Tosi, M, Treille, D, Wang, R-J, Walczak, M, Triossi, A, Chagas, EBBD, Tsirou, A, Veckalns, V, Veres, GI, Turkewitz, J, Verweij, M, Wardle, N, Wöhri, HK, Zagozdzinska, A, Wood, D, Zeuner, WD, De Visscher, S, Bertl, W, Deiters, K, Carvalho, W, Rossi, AM, Erdmann, W, Horisberger, R, Ingram, Q, Kaestli, HC, Kotlinski, D, Anelli, C, Langenegger, U, Rohe, T, Bargassa, P, Rovelli, T, Wiederkehr, SA, Bachmair, F, Bäni, L, Hartl, C, Chinellato, J, Bianchini, L, Casal, B, Charaf, O, Dissertori, G, Dittmar, M, Siroli, GP, Donegà, M, Da Cruz E. Silva, CB, Grab, C, Heidegger, C, Hits, D, Hoss, J, Kasieczka, G, Custódio, A, Hahn, KA, Lustermann, W, Cortezon, EP, Mangano, B, Marionneau, M, del Arbol, PMR, Calpas, B, Masciovecchio, M, Meinhard, MT, Meister, D, Micheli, F, Musella, P, Tosi, N, Nessi-Tedaldi, F, Da Costa, EM, Pandolfi, F, Pata, J, Pauss, F, Di Francesco, A, Perrin, G, Perrozzi, L, Quittnat, M, Rossini, M, Maerschalk, T, Mucia, N, Schönenberger, M, Starodumov, A, Tavolaro, VR, Da Silveira, GG, Theofilatos, K, Wallny, R, Faccioli, P, Aarrestad, TK, Amsler, C, Albergo, S, Caminada, L, Baskakov, A, Canelli, MF, De Cosa, A, Galloni, C, Hinzmann, A, Hreus, T, De Jesus Damiao, D, Kilminster, B, Gallinaro, M, Costa, S, Ngadiuba, J, Pinna, D, Odell, N, Rauco, G, Robmann, P, Salerno, D, Seitz, C, Yang, Y, Zucchetta, A, Candelise, V, Di Mattia, A, De Oliveira Martins, C, Hollar, J, Doan, TH, Pollack, B, Khurana, R, Konyushikhin, M, Kuo, CM, Lin, W, Pozdnyakov, A, Giordano, F, Yu, SS, Chang, P, Leonardo, N, Cabrera, A, De Souza, SF, Chang, YH, Chao, Y, Chen, KF, Chen, PH, Acosta, JG, Fiori, F, Hou, W-S, Hsiung, Y, Liu, YF, Lu, R-S, Schmitt, MH, Iglesias, LL, Moya, MM, Guativa, LMH, Paganis, E, Potenza, R, Psallidas, A, Tsai, JF, Asavapibhop, B, Singh, G, Srimanobhas, N, Suwonjandee, N, Baden, A, Sung, K, Adiguzel, A, Nemallapudi, MV, Tricomi, A, Cerci, S, Damarseckin, S, Malbouisson, H, Demiroglu, ZS, Dozen, C, Dumanoglu, I, Girgis, S, Gokbulut, G, Trovato, M, Guler, Y, Tuve, C, Hos, I, Delaere, C, Kangal, EE, Kara, O, Topaksu, AK, Figueiredo, DM, Kiminsu, U, Oglakci, M, Onengut, G, Velasco, M, Barbagli, G, Ozdemir, K, Cerci, DS, Topakli, H, Seixas, J, Turkcapar, S, Zorbakir, IS, Zorbilmez, C, Bilin, B, Hörmann, N, Herrera, CM, Cruz, SS, Dev, N, Bilmis, S, Isildak, B, Karapinar, G, Yalvac, M, Toldaiev, O, Zeyrek, M, Gülmez, E, Kaya, M, Kaya, O, Ciulli, V, Yetkin, EA, Hildreth, M, Yetkin, T, Mundim, L, Cakir, A, Cankocak, K, Sen, S, Vadruccio, D, Grynyov, B, Levchuk, L, Civinini, C, Sorokin, P, Aggleton, R, Anampa, KH, Ball, F, Beck, L, Brooke, JJ, Nogima, H, Burns, D, Clement, E, Varela, J, Marinov, A, Cussans, D, Flacher, H, Goldstein, J, Belyaev, A, Grimes, M, Heath, GP, Heath, HF, Jacob, J, Kreczko, L, Da Silva, WLP, D’Alessandro, R, Lucas, C, Afanasiev, S, Newbold, DM, Paramesvaran, S, Jessop, C, Poll, A, Sakuma, T, Nasr-storey, SSE, Smith, D, Smith, VJ, Focardi, E, Bell, KW, Santoro, A, Bunin, P, Brew, C, Karmgard, DJ, Brown, RM, Calligaris, L, Cieri, D, Cockerill, DJA, Oliveros, S, Coughlan, JA, Harder, K, Harper, S, Olaiya, E, Petyt, D, Gavrilenko, M, Kellams, N, Sznajder, A, Shepherd-Themistocleous, CH, Thea, A, Lenzi, P, Tomalin, IR, Williams, T, Baber, M, Bainbridge, R, Buchmuller, O, Bundock, A, Burton, D, Baron, O, Sierra, LFC, Golutvin, I, Meschini, M, Casasso, S, Manganote, EJT, Citron, M, Colling, D, Corpe, L, Dauncey, P, Davies, G, De Wit, A, Della Negra, M, Lannon, K, Paoletti, S, Di Maria, R, Gorbunov, I, Dunne, P, Elwood, A, De Araujo, FTDS, Futyan, D, Haddad, Y, Hall, G, Iles, G, James, T, Russo, L, Marinelli, N, Lane, R, Laner, C, Kamenev, A, Lucas, R, Lyons, L, Magnan, A-M, Pereira, AV, Mastrolorenzo, L, Sguazzoni, G, Nash, J, Meng, F, Nikitenko, A, Pela, J, Penning, B, Delcourt, M, Pesaresi, M, Raymond, DM, Richards, A, Rose, A, Andrés, IS, Ahuja, S, Scott, E, Mueller, C, Seez, C, Summers, S, Tapper, A, Uchida, K, Karjavin, V, Acosta, MV, Virdee, T, Strom, D, Wright, J, Zenz, SC, Cole, JE, Musienko, Y, Hrubec, J, Bernardes, CA, Hobson, PR, Khan, A, Kyberd, P, Lanev, A, Viliani, L, Reid, ID, Symonds, P, Teodorescu, L, Turner, M, Planer, M, Borzou, A, Call, K, Dittmann, J, Dogra, S, Hatakeyama, K, Benussi, L, Liu, H, Malakhov, A, Pastika, N, Bartek, R, Dominguez, A, Boos, E, Buccilli, A, Cooper, SI, Henderson, C, Rumerio, P, Randle-conde, A, West, C, Tomei, TRFP, Arcaro, D, Matveev, V, Avetisyan, A, Bose, T, Reinsvold, A, Gastler, D, Rankin, D, Richardson, C, Qian, SJ, Rohlf, J, Sulak, L, Zou, D, Benelli, G, Gregores, EM, Palichik, V, Cutts, D, Ruchti, R, Garabedian, A, Hakala, J, Bianco, S, Heintz, U, Hogan, JM, Jesus, O, Kwok, KHM, Laird, E, Landsberg, G, Mao, Z, Perelygin, V, Belloni, A, Rupprecht, N, Mercadante, PG, Narain, M, Piperov, S, Sagir, S, Spencer, E, Syarif, R, Breedon, R, De La Barca Sanchez, MC, Piccolo, D, Smith, G, Shmatov, S, Chertok, M, Moon, CS, Conway, J, Conway, R, Cox, PT, Erbacher, R, Flores, C, Funk, G, Primavera, F, Gardner, M, Florez, C, Ko, W, Shulha, S, Lander, R, Mclean, C, Novaes, SF, Mulhearn, M, Pellett, D, Pilot, J, Calvelli, V, Shalhout, S, Shi, M, Taroni, S, Smith, J, Squires, M, Skatchkov, N, Stolp, D, Tos, K, Tripathi, M, Padula, SS, Ferro, F, Bachtis, M, Bravo, C, Cousins, R, Wayne, M, Dasgupta, A, Florent, A, Hauser, J, Smirnov, V, Ignatenko, M, Mccoll, N, Vischia, P, Saltzberg, D, Schnaible, C, Abad, DR, Valuev, V, Wolf, M, Bouvier, E, Burt, K, Clare, R, Francois, B, Monge, MR, Ellison, J, Gary, JW, Shirazi, SMAG, Hanson, G, Heilman, J, Woodard, A, Vargas, JCR, Jandir, P, Kennedy, E, Lacroix, F, Robutti, E, Long, OR, Voytishin, N, Negrete, MO, Paneva, MI, Shrinivas, A, Si, W, Alimena, J, Wei, H, Wimpenny, S, Jeitler, M, Tosi, S, Aleksandrov, A, Yates, BR, Branson, JG, Zarubin, A, Cerati, GB, Cittolin, S, Derdzinski, M, Ershov, A, Gerosa, R, Holzner, A, Avdeeva, E, Klein, D, Krutelyov, V, Letts, J, Hadjiiska, R, Macneill, I, Chtchipounov, L, Olivito, D, Padhi, S, Antonelli, L, and Pieri, M
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Astrophysics::High Energy Astrophysical Phenomena ,High Energy Physics::Experiment - Abstract
Events with no charged particles produced between the two leading jets are studied in proton–proton collisions at s√=7 TeV. The jets were required to have transverse momentum pjetT>40 GeV and pseudorapidity 1.50.2 GeV in the interval −1
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- 2020
44. Study protocol: watchful observation of patients with limited small cell lung cancer instead of the PCI—prospective, multi-center one-arm study
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Anna Sugajska and Sergiusz Nawrocki
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0301 basic medicine ,Cancer Research ,medicine.medical_specialty ,Lung Neoplasms ,Population ,SRT ,Neuroimaging ,Radiosurgery ,lcsh:RC254-282 ,Small-cell lung cancer ,law.invention ,Benton Visual Retention Test ,Study Protocol ,03 medical and health sciences ,0302 clinical medicine ,Randomized controlled trial ,law ,Stereotactic radiotherapy ,Genetics ,medicine ,Clinical endpoint ,Humans ,Multicenter Studies as Topic ,education ,education.field_of_study ,Brain Neoplasms ,business.industry ,PCI ,Chemoradiotherapy ,lcsh:Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,Mental Status and Dementia Tests ,Magnetic Resonance Imaging ,Small Cell Lung Carcinoma ,Survival Rate ,Observational Studies as Topic ,030104 developmental biology ,Oncology ,030220 oncology & carcinogenesis ,Conventional PCI ,Radiology ,Cranial Irradiation ,Prophylactic cranial irradiation ,business ,Neurocognitive - Abstract
Background Prophylactic cranial irradiation (PCI) is a current standard of care after confirmed response to radical chemoradiotherapy for limited disease small cell lung cancer (LD-SCLC). This standard is mostly based on results of old randomized studies when brain imaging with magnetic resonance (MRI) was not available. Survival benefit of PCI in extended disease SCLC was recently challenged by the results of randomized phase III study from Japan. Methods Eighty patients with LD-SCLC after response to chest chemoradiotherapy will be enrolled. Patients will be followed up by brain MRI every 3 to 6 months up to 3 years. Neurocognitive function tests will be performed at baseline and after 12 and 24 months. Patients who develop brain metastases will be irradiated with stereotactic (SRT) or whole brain RT (WBRT). The primary endpoint is overall survival. The secondary endpoints are: response rate to radiotherapy of early detected brain metastases, analysis of efficacy of SRT and WBRT; assessment and analysis of neurocognitive functions and QoL in the studied cohorts: QLQ-C30 questionnaire and the California Verbal Learning Test, Color connection test, Benton visual retention test, and verbal fluency test will be carried out. Discussion The results of this trial may contribute to changing of LD-SCLC clinical management by deescalating the treatment. There is a lack of prospective, recent studies in LD-SCLC patients with omission of PCI and modern radiation therapy technologies for developed brain metastases. The comprehensive neurocognitive function testing will help to assess the impact of modern radiotherapy (SRT) compared with WBRT and no-PCI in SCLC patients. A subgroup of long-term survivors, who will not develop brain metastases, will not be exposed to unnecessary brain irradiation with its deleterious consequences. The limitation of our study is a lack of parallel randomized control arm. This is a potential source of bias; however, randomized study will be difficult to complete for two major reasons: (1) limited population of LD-SCLC eligible for the study and (2) opinions of our patients, who after information and discussion about benefits and potential harms of PCI, often choose to omit PCI in our practice. Trial registration ClinicalTrials.gov Identifier: NCT04168281, 19 Nov. 2019.
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- 2020
45. VADR: validation and annotation of virus sequence submissions to GenBank
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Massachusetts Institute of Technology. Department of Biology, Schäffer, Alejandro A., Hatcher, Eneida L., Yankie, Linda, Shonkwiler, Lara, Brister, J. Rodney, Karsch-Mizrachi, Ilene, Nawrocki, Eric P., Massachusetts Institute of Technology. Department of Biology, Schäffer, Alejandro A., Hatcher, Eneida L., Yankie, Linda, Shonkwiler, Lara, Brister, J. Rodney, Karsch-Mizrachi, Ilene, and Nawrocki, Eric P.
- Abstract
Background GenBank contains over 3 million viral sequences. The National Center for Biotechnology Information (NCBI) previously made available a tool for validating and annotating influenza virus sequences that is used to check submissions to GenBank. Before this project, there was no analogous tool in use for non-influenza viral sequence submissions. Results We developed a system called VADR (Viral Annotation DefineR) that validates and annotates viral sequences in GenBank submissions. The annotation system is based on the analysis of the input nucleotide sequence using models built from curated RefSeqs. Hidden Markov models are used to classify sequences by determining the RefSeq they are most similar to, and feature annotation from the RefSeq is mapped based on a nucleotide alignment of the full sequence to a covariance model. Predicted proteins encoded by the sequence are validated with nucleotide-to-protein alignments using BLAST. The system identifies 43 types of “alerts” that (unlike the previous BLAST-based system) provide deterministic and rigorous feedback to researchers who submit sequences with unexpected characteristics. VADR has been integrated into GenBank’s submission processing pipeline allowing for viral submissions passing all tests to be accepted and annotated automatically, without the need for any human (GenBank indexer) intervention. Unlike the previous submission-checking system, VADR is freely available (https://github.com/nawrockie/vadr) for local installation and use. VADR has been used for Norovirus submissions since May 2018 and for Dengue virus submissions since January 2019. Since March 2020, VADR has also been used to check SARS-CoV-2 sequence submissions. Other viruses with high numbers of submissions will be added incrementally. Conclusion VADR improves the speed with which non-flu virus submissions to GenBank can be checked and improves the content and quality of the GenBank annotations. The availability and portability of the soft
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- 2020
46. On spaces of vector-valued functions modeled on the Hardy and Bergman algebras
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Marek Nawrocki
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Mathematics::Functional Analysis ,Pure mathematics ,Algebra and Number Theory ,Applied Mathematics ,010102 general mathematics ,Banach space ,01 natural sciences ,010101 applied mathematics ,Convex structure ,Computational Mathematics ,Geometry and Topology ,0101 mathematics ,Vector-valued function ,Analysis ,Analytic function ,Mathematics - Abstract
The strong $$N^p(X), \mathcal {N}^p(X)$$ and weak $$wN^p(X), w\mathcal {N}^p(X)$$ spaces of analytic functions with values in a Banach space X, modeled on the large Hardy $$N^p$$ and Bergman $$\mathcal {N}^p$$ algebras respectively, are studied. The Frechet envelopes and the continuous linear functionals on these spaces are described. We show that, although that strong and weak spaces are different, they have the same locally convex structure (the Frechet envelopes are equal).
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- 2019
47. CSF proteome in multiple sclerosis subtypes related to brain lesion transcriptomes
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Elkjaer, Maria L., primary, Nawrocki, Arkadiusz, additional, Kacprowski, Tim, additional, Lassen, Pernille, additional, Simonsen, Anja Hviid, additional, Marignier, Romain, additional, Sejbaek, Tobias, additional, Nielsen, Helle H., additional, Wermuth, Lene, additional, Rashid, Alyaa Yakut, additional, Høgh, Peter, additional, Sellebjerg, Finn, additional, Reynolds, Richard, additional, Baumbach, Jan, additional, Larsen, Martin R., additional, and Illes, Zsolt, additional
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- 2021
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48. Adaptive Context-Aware Energy Optimization for Services on Mobile Devices with Use of Machine Learning
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Nawrocki, Piotr, primary and Sniezynski, Bartlomiej, additional
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- 2020
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49. VADR: validation and annotation of virus sequence submissions to GenBank
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Schäffer, Alejandro A., primary, Hatcher, Eneida L., additional, Yankie, Linda, additional, Shonkwiler, Lara, additional, Brister, J. Rodney, additional, Karsch-Mizrachi, Ilene, additional, and Nawrocki, Eric P., additional
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- 2020
- Full Text
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50. Resolution of NASH and hepatic fibrosis by the GLP-1R and GCGR dual-agonist cotadutide via modulating mitochondrial function and lipogenesis
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Boland, Michelle L., primary, Laker, Rhianna C., additional, Mather, Karly, additional, Nawrocki, Arkadiusz, additional, Oldham, Stephanie, additional, Boland, Brandon B., additional, Lewis, Hilary, additional, Conway, James, additional, Naylor, Jacqueline, additional, Guionaud, Silvia, additional, Feigh, Michael, additional, Veidal, Sanne S., additional, Lantier, Louise, additional, McGuinness, Owen P., additional, Grimsby, Joseph, additional, Rondinone, Cristina M., additional, Jermutus, Lutz, additional, Larsen, Martin R., additional, Trevaskis, James L., additional, and Rhodes, Christopher J., additional
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- 2020
- Full Text
- View/download PDF
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