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1. Antimicrobial Resistance Patterns, Sequence Types, Virulence and Carbapenemase Genes of Carbapenem-Resistant Klebsiella pneumoniae Clinical Isolates from a Tertiary Care Teaching Hospital in Zunyi, China

Author

Shen M, Chen X, He J, Xiong L, Tian R, Yang G, Zha H, and Wu K

Subjects
klebsiella pneumonia, antimicrobial resistance gene, virulence gene, sequence type., Infectious and parasitic diseases, RC109-216
Abstract

Meijing Shen,* Xianghao Chen,* Jingyue He, Lin Xiong, Rengui Tian, Guangwu Yang, He Zha, Kaifeng Wu Department of Laboratory Medicine, the First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, People’s Republic of China*These authors contributed equally to this workCorrespondence: Kaifeng Wu; He Zha, Department of Laboratory Medicine, the First People’s Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, People’s Republic of China, Email kiphoonwu@126.com; zhahe666@126.comPurpose: Carbapenem-resistant Klebsiella pneumoniae (CRKP) has seriously threatened public health worldwide. This study aimed to investigate the antimicrobial resistance patterns, sequence types (STs), virulence and carbapenemase genes of CRKP isolates from patients in Zunyi, China.Methods: CRKP isolates were collected from the First People’s Hospital of Zunyi between January 2018 and December 2020. Antimicrobial susceptibility was determined using a VITEK® 2 analyzer and confirmed using either the broth dilution method, Kirby–Bauer method, or E-test assays. Carbapenemase production was examined using a modified carbapenem inactivation method. STs of the studied isolates were determined by multilocus sequence typing, and the presence of carbapenemase and virulence genes was examined using polymerase chain reaction assays.Results: In total, 94 CRKP isolates were collected. All studied isolates produced carbapenemase, and the most common carbapenemase gene was New Delhi metallo-β-lactamase (NDM; 72.3%), followed by Klebsiella pneumoniae carbapenemase (KPC; 24.5%), and Verona integron-encoded metallo-β-lactamase (VIM; 3.2%). Of the studied isolates, 74.3% exhibited multidrug-resistant (MDR) phenotype, and 25.7% were either pandrug-resistant (PDR) or extensively drug-resistant (XDR) phenotypes. The most prevalent sequence type was ST2407 (37.2%), followed by ST76 (21.3%) and ST11 (11.7%). The NDM gene was present in 97.1% of ST2407 isolates and 90.0% of ST76 isolates, whereas the KPC gene was present in 90.9% of ST11 isolates. The majority of the isolates carried wabG, uge, and fimH virulence genes, with prevalence rates of 94.7%, 92.6%, and 94.7%, respectively.Conclusion: This study describes NDM-producing ST2407 and ST76, as well as KPC-producing ST11, as the major clonal types of CRKP isolates in Zunyi, China. All CRKP isolates were resistant to multiple types of antibiotics, and the majority of isolates carried carbapenemase and virulence genes. Clonal spread of NDM-producing CRKP ST2407 and ST76, and KPC-producing CRKP ST11 should be strictly monitored.Keywords: Klebsiella pneumoniae, antimicrobial resistance patterns, virulence genes, sequence types, carbapenemase genes

Published
2023

2. Dose Selection of Ropivacaine for Spinal Anesthesia in Elderly Patients with Hip Fracture: An Up-Down Sequential Allocation Study

Author

Wang Y, Zha H, Fang X, Shen T, Pan K, Zhang J, He K, Wang S, and Hu L

Subjects
ropivacaine, dose selection, elder, spinal anesthesia, hip fracture, sequential design, Geriatrics, RC952-954.6
Abstract

Yu Wang,1,* Hanning Zha,1,* Xiang Fang,2 Tianjiao Shen,1 Kunyun Pan,1 Jianping Zhang,1 Keqiang He,1 Sheng Wang,1 Liguo Hu1 1Department of Anesthesiology, The First Affiliated Hospital of USTC (Anhui Provincial Hospital), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui, People’s Republic of China; 2Department of Anesthesiology, Hefei BOE Hospital, Hefei, Anhui, People’s Republic of China*These authors contributed equally to this workCorrespondence: Sheng Wang; Liguo Hu, Email iamsheng2020@ustc.edu.cn; hulg@163.comObjective: The dose selection of ropivacaine for spinal anesthesia in clinical work mainly depends on the experience of the anesthesiologist. In this study, a prospective and modified up-down sequential allocation design was used to provide the optimal dose selection of ropivacaine for spinal anesthesia.Patients and methods: This study was divided into two stages, and a total of 164 elderly patients with elective hip fractures were included. In stage I, the dose of ropivacaine was selected using the up-down sequential method of height correction, and the 50% effective dose (ED50) and 95% effective dose (ED95) were obtained. A nomogram for predicting satisfactory anesthesia and a formula for predicting the optimal dose was also given in this stage. In stage II, the dose of ropivacaine was calculated by using the optimal dose prediction formula, so as to evaluate the efficacy and safety of the model.Results: The ED50 and ED95 of the stage I were 7.036 mg (95%CI 6.549– 7.585 mg) and 8.709 mg (95%CI 7.902– 14.275 mg), respectively. And provided a nomogram predicting satisfactory anesthesia with a C-index of 0.847 (95%CI 0.774– 0.92). The optimal dose prediction formula of ropivacaine was calculated, including variables for age, gender, height, and weight. This formula was found to be 90% efficient. It is worth mentioning that the incidence of direct transfer to the ward in the two stages was as high as 86.84% and 93.33%, respectively, and no patients were transferred to the ICU in stage II.Conclusion: The ED50 and ED95 of ropivacaine were 7.036 mg and 8.709 mg, respectively, and the nomograms are sufficiently accurate to predict satisfactory anesthesia. Beyond that, the dose prediction equation provided in this study has high efficacy and safety, and can guide the dose selection of spinal anesthesia in elderly patients with hip fracture in clinical practice.Clinical trials registration: ChiCTR2100046982Keywords: ropivacaine, dose selection, elder, spinal anesthesia, Hip fracture, sequential design

Published
2022

3. Elevated Serum Levels of Progranulin and Soluble Vascular Cell Adhesion Molecule-1 in Patients with COVID-19

Author

Yao S, Luo N, Liu J, Zha H, Ai Y, Luo J, Shi S, and Wu K

Subjects
covid-19, pgrn, soluble adhesion molecules, pathogenesis, biomarker, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract

Shifei Yao,1,2 Nanning Luo,1,2 Jiaoyang Liu,1,2 He Zha,1 Yuanhang Ai,1,2 Juan Luo,1,2 Shi Shi,3 Kaifeng Wu1,2 1Department of Laboratory Medicine, Zunyi Medical University Third Affiliated Hospital/The First People’s Hospital of Zunyi, Zunyi, 563000, Guizhou, People’s Republic of China; 2Scientific Research Center, Zunyi Medical University Third Affiliated Hospital/The First People’s Hospital of Zunyi, Zunyi, 563000, Guizhou, People’s Republic of China; 3Department of Laboratory Medicine, The Fourth People’s Hospital of Zunyi, Zunyi, 563000, Guizhou, People’s Republic of ChinaCorrespondence: Kaifeng WuDepartment of Laboratory Medicine/Scientific Research Center, Zunyi Medical University Third Affiliated Hospital/The First People’s Hospital of Zunyi, Zunyi, 563000, Guizhou, People’s Republic of ChinaTel +86 85123116548Email kiphoonwu@126.comBackground: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is associated with the angiocentric inflammation and angiogenesis, yet the molecules involved in this process remain to be determined.Methods: We did a cross-sectional study of a cohort of patients with COVID-19 in Zunyi, China between February 1 and March 30, 2020. Serum concentrations of PGRN were determined by enzyme-linked immunosorbent assay in patients with COVID-19 at hospital admission and at discharge. In parallel, the serum levels of soluble adhesion molecules, vascular cell adhesion molecule-1 (sVCAM-1), intercellular adhesion molecule-1 (sICAM-1), P-selectin (sP-selectin), and E-selectin (sE-selectin) were assayed by a human adhesion molecule multiplex kit. The association between serum PGRN levels and other laboratory test results was analyzed by Spearman correlation analysis.Results: At baseline, the median serum PGRN levels in patients with COVID-19 were 94.8 ng/mL [interquartile range (IQR): 66.6– 119.6 ng/mL], which was significantly elevated compared with those in healthy controls (46.3 ng/mL, IQR: 41.8– 55.6 ng/mL). Moreover, the median serum sVCAM-1 levels were significantly higher in COVID-19 patients (1396.0 ng/mL, IQR: 1019.1– 1774.8 ng/mL) than those in healthy controls (612.4 ng/mL, IQR: 466.4– 689.3 ng/mL). However, the levels of sICAM-1, sP-selectin, and sE-selectin were not significantly elevated in patients with COVID-19 when compared to healthy controls. Further analysis showed that serum PGRN levels were significantly positively associated with sVCAM-1 (r= 0.675, P= 0.008) and inversely with sICAM-1 (r= − 0.609, P= 0.021) and aspartate aminotransferase levels (r= − 0.560, P= 0.037) in patients with COVID-19 at hospital admission. In COVID-19 patients, serum PGRN and sVCAM-1 levels fell significantly after successful treatment.Conclusion: The present study demonstrates elevated serum PGRN and sVCAM-1 levels in patients with COVID-19, which may provide clues as to the mechanisms underlying the pathogenesis of COVID-19. Further studies are warranted to evaluate the potential of PGRN and sVCAM-1 as biomarkers and investigate their role in the pathogenesis of COVID-19.Keywords: COVID-19, PGRN, soluble adhesion molecules, pathogenesis, biomarker

Published
2021

4. Centromere protein U expression promotes non-small-cell lung cancer cell proliferation through FOXM1 and predicts poor survival

Author

Wang X, Chen D, Gao J, Long H, Zha H, Zhang A, Shu C, Zhou L, Yang F, Zhu B, and Wu W

Subjects
CENPU, NSCLC, Proliferation, Forkhead box protein M1, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Xinxin Wang,1 Diangang Chen,1 Jianbao Gao,1 Haixia Long,1 Haoran Zha,2 Anmei Zhang,1 Chi Shu,1 Li Zhou,1 Fei Yang,1 Bo Zhu,1 Wei Wu3 1Institute of Cancer, Xinqiao Hospital, Army Medical University, Chongqing, China; 2Department of Oncology, The General Hospital of the People’s Liberation Army Rocket Force, Beijing, China; 3Department of Cardiothoracic Surgery, Southwest Hospital, Army Medical University, Chongqing, China Purpose: Centromere protein U (CENPU) abnormally exhibits high expression in various types of human tumor tissues and participates in tumor progression; however, its expression pattern and biological function in lung cancer have not yet been elucidated. In the present study, we explored the clinical significance and biological function of CENPU in lung cancer.Materials and methods: The Cancer Genome Atlas (TCGA) data analyses, quantitative real-time PCR (RT-PCR), and Western blotting were performed to quantify CENPU and FOXM1 expression in non-small-cell lung cancer (NSCLC) samples. Survival data were obtained from Kaplan–Meier plotter or PROGgene V2 prognostic database. The function of CENPU in lung cancer cell proliferation was determined using 5-ethynyl-2′-deoxyuridine (EdU), Cell Counting Kit-8 (CCK-8), and cell cycle assays, and the underlying mechanism was determined through bioinformatic analyses and validated by in vitro siRNA or plasmid transfection experiments.Results: CENPU was abnormally overexpressed in NSCLC samples compared with matched paired normal tissues. Higher expression of CENPU predicted worse overall survival (OS) and relapse-free survival (RFS) in NSCLC patients. Knockdown of CENPU expression by siRNA significantly inhibited proliferation and delayed cell cycle progression of lung cancer cells. To figure out the mechanism, bioinformatic analyses were performed and the results showed that the transcription factor, FOXM1, positively correlated with CENPU. Further in vitro experiments indicated that FOXM1 was the possible downstream transcription factor of CENPU as the knockdown of CENPU led to lower expression of FOXM1 and the overexpression of FOXM1 significantly reversed the inhibition of proliferation caused by CENPU knockdown. Furthermore, FOXM1 was highly expressed in NSCLC. The knockdown of FOXM1 also attenuated proliferation and induced G1 arrest in lung cancer cells.Conclusion: CENPU was highly expressed in NSCLC tissues, wherein it promoted lung cancer cell proliferation via the transcription factor, FOXM1, which could be a potential target for therapeutic strategies. Keywords: CENPU, NSCLC, proliferation, FOXM1

Published
2018

5. The Compact Linear Collider (CLIC) - 2018 Summary Report

Author

CLIC, The, collaborations, CLICdp, Charles, T. K., Giansiracusa, P. J., Lucas, T. G., Rassool, R. P., Volpi, M., Balazs, C., Afanaciev, K., Makarenko, V., Patapenka, A., Zhuk, I., Collette, C., Boland, M. J., Hoffman, A. C. Abusleme, Diaz, M. A., Garay, F., Chi, Y., He, X., Pei, G., Pei, S., Shu, G., Wang, X., Zhang, J., Zhao, F., Zhou, Z., Chen, H., Gao, Y., Huang, W., Kuang, Y. P., Li, B., Li, Y., Meng, X., Shao, J., Shi, J., Tang, C., Wang, P., Wu, X., Zha, H., Ma, L., Han, Y., Fang, W., Gu, Q., Huang, D., Huang, X., Tan, J., Wang, Z., Zhao, Z., Uggerhøj, U. I., Wistisen, T. N., Aabloo, A., Aare, R., Kuppart, K., Vigonski, S., Zadin, V., Aicheler, M., Baibuz, E., Brücken, E., Djurabekova, F., Eerola, P., Garcia, F., Haeggström, E., Huitu, K., Jansson, V., Kassamakov, I., Kimari, J., Kyritsakis, A., Lehti, S., Meriläinen, A., Montonen, R., Nordlund, K., Österberg, K., Saressalo, A., Väinölä, J., Veske, M., Farabolini, W., Mollard, A., Peauger, F., Plouin, J., Bambade, P., Chaikovska, I., Chehab, R., Delerue, N., Davier, M., Faus-Golfe, A., Irles, A., Kaabi, W., LeDiberder, F., Pöschl, R., Zerwas, D., Aimard, B., Balik, G., Blaising, J. -J., Brunetti, L., Chefdeville, M., Dominjon, A., Drancourt, C., Geoffroy, N., Jacquemier, J., Jeremie, A., Karyotakis, Y., Nappa, J. M., Serluca, M., Vilalte, S., Vouters, G., Bernhard, A., Bründermann, E., Casalbuoni, S., Hillenbrand, S., Gethmann, J., Grau, A., Huttel, E., Müller, A. -S., Peiffer, P., Perić, I., de Jauregui, D. Saez, Emberger, L., Graf, C., Simon, F., Szalay, M., van der Kolk, N., Brass, S., Kilian, W., Alexopoulos, T., Apostolopoulos, T., Gazis, E. N., Gazis, N., Kostopoulos, V., Kourkoulis, S., Heilig, B., Lichtenberger, J., Shrivastava, P., Dayyani, M. K., Ghasem, H., Hajari, S. S., Shaker, H., Ashkenazy, Y., Popov, I., Engelberg, E., Yashar, A., Abramowicz, H., Benhammou, Y., Borysov, O., Borysova, M., Levy, A., Levy, I., Alesini, D., Bellaveglia, M., Buonomo, B., Cardelli, A., Diomede, M., Ferrario, M., Gallo, A., Ghigo, A., Giribono, A., Piersanti, L., Stella, A., Vaccarezza, C., de Blas, J., Franceschini, R., D'Auria, G., Di Mitri, S., Abe, T., Aryshev, A., Fukuda, M., Furukawa, K., Hayano, H., Higashi, Y., Higo, T., Kubo, K., Kuroda, S., Matsumoto, S., Michizono, S., Naito, T., Okugi, T., Shidara, T., Tauchi, T., Terunuma, N., Urakawa, J., Yamamoto, A., Raboanary, R., Luiten, O. J., Stragier, X. F. D., Hart, R., van der Graaf, H., Eigen, G., Adli, E., Lindstrøm, C. A., Lillestøl, R., Malina, L., Pfingstner, J., Sjobak, K. N., Ahmad, A., Hoorani, H., Khan, W. A., Bugiel, S., Bugiel, R., Firlej, M., Fiutowski, T. A., Idzik, M., Moroń, J., Świentek, K. P., de Renstrom, P. Brückman, Krupa, B., Kucharczyk, M., Lesiak, T., Pawlik, B., Sopicki, P., Turbiarz, B., Wojtoń, T., Zawiejski, L. K., Kalinowski, J., Nowak, K., Żarnecki, A. F., Firu, E., Ghenescu, V., Neagu, A. T., Preda, T., Zgura, I. S., Aloev, A., Azaryan, N., Boyko, I., Budagov, J., Chizhov, M., Filippova, M., Glagolev, V., Gongadze, A., Grigoryan, S., Gudkov, D., Karjavine, V., Lyablin, M., Nefedov, Yu., Olyunin, A., Rymbekova, A., Samochkine, A., Sapronov, A., Shelkov, G., Shirkov, G., Soldatov, V., Solodko, E., Trubnikov, G., Tyapkin, I., Uzhinsky, V., Vorozhtov, A., Zhemchugov, A., Levichev, E., Mezentsev, N., Piminov, P., Shatilov, D., Vobly, P., Zolotarev, K., Jelisavčić, I. Božović, Kačarević, G., Dumbelović, G. Milutinović, Pandurović, M., Radulović, M., Stevanović, J., Vukasinović, N., Lee, D. -H., Ayala, N., Benedetti, G., Guenzel, T., Iriso, U., Marti, Z., Perez, F., Pont, M., Trenado, J., Ruiz-Jimeno, A., Vila, I., Calero, J., Dominguez, M., Garcia-Tabares, L., Gavela, D., Lopez, D., Toral, F., Gutierrez, C. Blanch, Boronat, M., Esperante, D., Fullana, E., Fuster, J., García, I., Gimeno, B., Lopez, P. Gomis, González, D., Perelló, M., Ros, E., Villarejo, M. A., Vnuchenko, A., Vos, M., Borgmann, Ch., Brenner, R., Ekelöf, T., Jacewicz, M., Olvegård, M., Ruber, R., Ziemann, V., Aguglia, D., Gonzalvo, J. Alabau, Leon, M. Alcaide, Tehrani, N. Alipour, Anastasopoulos, M., Andersson, A., Andrianala, F., Antoniou, F., Apyan, A., Arominski, D., Artoos, K., Assly, S., Atieh, S., Baccigalupi, C., Sune, R. Ballabriga, Caballero, D. Banon, Barnes, M. J., Garcia, J. Barranco, Bartalesi, A., Bauche, J., Bayar, C., Belver-Aguilar, C., Morell, A. Benot, Bernardini, M., Bett, D. R., Bettoni, S., Bettencourt, M., Bielawski, B., Garcia, O. Blanco, Kraljevic, N. Blaskovic, Bolzon, B., Bonnin, X. A., Bozzini, D., Branger, E., Brondolin, E., Brunner, O., Buckland, M., Bursali, H., Burkhardt, H., Caiazza, D., Calatroni, S., Campbell, M., Lasheras, N. Catalan, Cassany, B., Castro, E., Soares, R. H. Cavaleiro, Bastos, M. Cerqueira, Cherif, A., Chevallay, E., Cilento, V., Corsini, R., Costa, R., Cure, B., Curt, S., Gobbo, A. Dal, Dannheim, D., Daskalaki, E., Deacon, L., Degiovanni, A., De Michele, G., De Oliveira, L., Romano, V. Del Pozo, Delahaye, J. P., Delikaris, D., de Almeida, P. G. Dias, Dobers, T., Doebert, S., Doytchinov, I., Draper, M., Ramos, F. Duarte, Duquenne, M., Plaja, N. Egidos, Elsener, K., Esberg, J., Esposito, M., Evans, L., Fedosseev, V., Ferracin, P., Fiergolski, A., Foraz, K., Fowler, A., Friebel, F., Fuchs, J-F., Gaddi, A., Gamba, D., Fajardo, L. Garcia, Morales, H. Garcia, Garion, C., Gasior, M., Gatignon, L., Gayde, J-C., Gerbershagen, A., Gerwig, H., Giambelli, G., Gilardi, A., Goldblatt, A. N., Anton, S. Gonzalez, Grefe, C., Grudiev, A., Guerin, H., Guillot-Vignot, F. G., Gutt-Mostowy, M. L., Lutz, M. Hein, Hessler, C., Holma, J. K., Holzer, E. B., Hourican, M., Hynds, D., Ikarios, E., Levinsen, Y. Inntjore, Janssens, S., Jeff, A., Jensen, E., Jonker, M., Kamugasa, S. W., Kastriotou, M., Kemppinen, J. M. K., Khan, V., Kieffer, R. B., Klempt, W., Kokkinis, N., Kossyvakis, I., Kostka, Z., Korsback, A., Platia, E. Koukovini, Kovermann, J. W., Kozsar, C-I., Kremastiotis, I., Kröger, J., Kulis, S., Latina, A., Leaux, F., Lebrun, P., Lefevre, T., Leogrande, E., Linssen, L., Liu, X., Cudie, X. Llopart, Magnoni, S., Maidana, C., Maier, A. A., Durand, H. Mainaud, Mallows, S., Manosperti, E., Marelli, C., Lacoma, E. Marin, Marsh, S., Martin, R., Martini, I., Martyanov, M., Mazzoni, S., Mcmonagle, G., Mether, L. M., Meynier, C., Modena, M., Moilanen, A., Mondello, R., Cabral, P. B. Moniz, Irazabal, N. Mouriz, Munker, M., Muranaka, T., Nadenau, J., Navarro, J. G., Quirante, J. L. Navarro, Del Busto, E. Nebo, Nikiforou, N., Ninin, P., Nonis, M., Nisbet, D., Nuiry, F. X., Nürnberg, A., Ögren, J., Osborne, J., Ouniche, A. C., Pan, R., Papadopoulou, S., Papaphilippou, Y., Paraskaki, G., Pastushenko, A., Passarelli, A., Patecki, M., Pazdera, L., Pellegrini, D., Pepitone, K., Codina, E. Perez, Fontenla, A. Perez, Persson, T. H. B., Petrič, M., Pitman, S., Pitters, F., Pittet, S., Plassard, F., Popescu, D., Quast, T., Rajamak, R., Redford, S., Remandet, L., Renier, Y., Rey, S. F., Orozco, O. Rey, Riddone, G., Castro, E. Rodriguez, Roloff, P., Rossi, C., Rossi, F., Rude, V., Ruehl, I., Rumolo, G., Sailer, A., Sandomierski, J., Santin, E., Sanz, C., Bedolla, J. Sauza, Schnoor, U., Schmickler, H., Schulte, D., Senes, E., Serpico, C., Severino, G., Shipman, N., Sicking, E., Simoniello, R., Skowronski, P. K., Mompean, P. Sobrino, Soby, L., Sollander, P., Solodko, A., Sosin, M. P., Spannagel, S., Sroka, S., Stapnes, S., Sterbini, G., Stern, G., Ström, R., Stuart, M. J., Syratchev, I., Szypula, K., Tecker, F., Thonet, P. A., Thrane, P., Timeo, L., Tiirakari, M., Garcia, R. Tomas, Tomoiaga, C. I., Valerio, P., Vaňát, T., Vamvakas, A. L., Van Hoorne, J., Viazlo, O., Pinto, M. Vicente Barreto, Vitoratou, N., Vlachakis, V., Weber, M. A., Wegner, R., Wendt, M., Widorski, M., Williams, O. E., Williams, M., Woolley, B., Wuensch, W., Wulzer, A., Uythoven, J., Xydou, A., Yang, R., Zelios, A., Zhao, Y., Zisopoulos, P., Benoit, M., Sultan, D M S, Riva, F., Bopp, M., Braun, H. H., Craievich, P., Dehler, M., Garvey, T., Pedrozzi, M., Raguin, J. Y., Rivkin, L., Zennaro, R., Guillaume, S., Rothacher, M., Aksoy, A., Nergiz, Z., Yavas, Ö., Denizli, H., Keskin, U., Oyulmaz, K. Y., Senol, A., Ciftci, A. K., Baturin, V., Karpenko, O., Kholodov, R., Lebed, O., Lebedynskyi, S., Mordyk, S., Musienko, I., Profatilova, Ia., Storizhko, V., Bosley, R. R., Price, T., Watson, M. F., Watson, N. K., Winter, A. G., Goldstein, J., Green, S., Marshall, J. S., Thomson, M. A., Xu, B., You, T., Gillespie, W. A., Spannowsky, M., Beggan, C., Martin, V., Zhang, Y., Protopopescu, D., Robson, A., Apsimon, R. J., Bailey, I., Burt, G. C., Dexter, A. C., Edwards, A. V., Hill, V., Jamison, S., Millar, W. L., Papke, K., Casse, G., Vossebeld, J., Aumeyr, T., Bergamaschi, M., Bobb, L., Bosco, A., Boogert, S., Boorman, G., Cullinan, F., Gibson, S., Karataev, P., Kruchinin, K., Lekomtsev, K., Lyapin, A., Nevay, L., Shields, W., Snuverink, J., Towler, J., Yamakawa, E., Boisvert, V., West, S., Jones, R., Joshi, N., Bett, D., Bodenstein, R. M., Bromwich, T., Burrows, P. N., Christian, G. B., Gohil, C., Korysko, P., Paszkiewicz, J., Perry, C., Ramjiawan, R., Roberts, J., Coates, T., Salvatore, F., Bainbridge, A., Clarke, J. A., Krumpa, N., Shepherd, B. J. A., Walsh, D., Chekanov, S., Demarteau, M., Gai, W., Liu, W., Metcalfe, J., Power, J., Repond, J., Weerts, H., Xia, L., Zupan, J., Wells, J. D., Zhang, Z., Adolphsen, C., Barklow, T., Dolgashev, V., Franzi, M., Graf, N., Hewett, J., Kemp, M., Kononenko, O., Markiewicz, T., Moffeit, K., Neilson, J., Nosochkov, Y., Oriunno, M., Phinney, N., Rizzo, T., Tantawi, S., Wang, J., Weatherford, B., White, G., and Woodley, M.

Subjects
Physics - Accelerator Physics
Abstract

The Compact Linear Collider (CLIC) is a TeV-scale high-luminosity linear $e^+e^-$ collider under development at CERN. Following the CLIC conceptual design published in 2012, this report provides an overview of the CLIC project, its current status, and future developments. It presents the CLIC physics potential and reports on design, technology, and implementation aspects of the accelerator and the detector. CLIC is foreseen to be built and operated in stages, at centre-of-mass energies of 380 GeV, 1.5 TeV and 3 TeV, respectively. CLIC uses a two-beam acceleration scheme, in which 12 GHz accelerating structures are powered via a high-current drive beam. For the first stage, an alternative with X-band klystron powering is also considered. CLIC accelerator optimisation, technical developments and system tests have resulted in an increased energy efficiency (power around 170 MW) for the 380 GeV stage, together with a reduced cost estimate at the level of 6 billion CHF. The detector concept has been refined using improved software tools. Significant progress has been made on detector technology developments for the tracking and calorimetry systems. A wide range of CLIC physics studies has been conducted, both through full detector simulations and parametric studies, together providing a broad overview of the CLIC physics potential. Each of the three energy stages adds cornerstones of the full CLIC physics programme, such as Higgs width and couplings, top-quark properties, Higgs self-coupling, direct searches, and many precision electroweak measurements. The interpretation of the combined results gives crucial and accurate insight into new physics, largely complementary to LHC and HL-LHC. The construction of the first CLIC energy stage could start by 2026. First beams would be available by 2035, marking the beginning of a broad CLIC physics programme spanning 25-30 years., Comment: 112 pages, 59 figures; published as CERN Yellow Report Monograph Vol. 2/2018; corresponding editors: Philip N. Burrows, Nuria Catalan Lasheras, Lucie Linssen, Marko Petri\v{c}, Aidan Robson, Daniel Schulte, Eva Sicking, Steinar Stapnes

Published
2018
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6. Updated baseline for a staged Compact Linear Collider

Author

CLIC, The, collaborations, CLICdp, Boland, M. J., Felzmann, U., Giansiracusa, P. J., Lucas, T. G., Rassool, R. P., Balazs, C., Charles, T. K., Afanaciev, K., Emeliantchik, I., Ignatenko, A., Makarenko, V., Shumeiko, N., Patapenka, A., Zhuk, I., Hoffman, A. C. Abusleme, Gutierrez, M. A. Diaz, Gonzalez, M. Vogel, Chi, Y., He, X., Pei, G., Pei, S., Shu, G., Wang, X., Zhang, J., Zhao, F., Zhou, Z., Chen, H., Gao, Y., Huang, W., Kuang, Y. P., Li, B., Li, Y., Shao, J., Shi, J., Tang, C., Wu, X., Ma, L., Han, Y., Fang, W., Gu, Q., Huang, D., Huang, X., Tan, J., Wang, Z., Zhao, Z., Laštovička, T., Uggerhoj, U., Wistisen, T. N., Aabloo, A., Eimre, K., Kuppart, K., Vigonski, S., Zadin, V., Aicheler, M., Baibuz, E., Brücken, E., Djurabekova, F., Eerola, P., Garcia, F., Haeggström, E., Huitu, K., Jansson, V., Karimaki, V., Kassamakov, I., Kyritsakis, A., Lehti, S., Meriläinen, A., Montonen, R., Niinikoski, T., Nordlund, K., Österberg, K., Parekh, M., Törnqvist, N. A., Väinölä, J., Veske, M., Farabolini, W., Mollard, A., Napoly, O., Peauger, F., Plouin, J., Bambade, P., Chaikovska, I., Chehab, R., Davier, M., Kaabi, W., Kou, E., LeDiberder, F., Pöschl, R., Zerwas, D., Aimard, B., Balik, G., Baud, J. -P., Blaising, J. -J., Brunetti, L., Chefdeville, M., Drancourt, C., Geoffroy, N., Jacquemier, J., Jeremie, A., Karyotakis, Y., Nappa, J. M., Vilalte, S., Vouters, G., Bernard, A., Peric, I., Gabriel, M., Simon, F., Szalay, M., van der Kolk, N., Alexopoulos, T., Gazis, E. N., Gazis, N., Ikarios, E., Kostopoulos, V., Kourkoulis, S., Gupta, P. D., Shrivastava, P., Arfaei, H., Dayyani, M. K., Ghasem, H., Hajari, S. S., Shaker, H., Ashkenazy, Y., Abramowicz, H., Benhammou, Y., Borysov, O., Kananov, S., Levy, A., Levy, I., Rosenblat, O., D'Auria, G., Di Mitri, S., Abe, T., Aryshev, A., Higo, T., Makida, Y., Matsumoto, S., Shidara, T., Takatomi, T., Takubo, Y., Tauchi, T., Toge, N., Ueno, K., Urakawa, J., Yamamoto, A., Yamanaka, M., Raboanary, R., Hart, R., van der Graaf, H., Eigen, G., Zalieckas, J., Adli, E., Lillestøl, R., Malina, L., Pfingstner, J., Sjobak, K. N., Ahmed, W., Asghar, M. I., Hoorani, H., Bugiel, S., Dasgupta, R., Firlej, M., Fiutowski, T. A., Idzik, M., Kopec, M., Kuczynska, M., Moron, J., Swientek, K. P., Daniluk, W., Krupa, B., Kucharczyk, M., Lesiak, T., Moszczynski, A., Pawlik, B., Sopicki, P., Wojtoń, T., Zawiejski, L., Kalinowski, J., Krawczyk, M., Żarnecki, A. F., Firu, E., Ghenescu, V., Neagu, A. T., Preda, T., Zgura, I-S., Aloev, A., Azaryan, N., Budagov, J., Chizhov, M., Filippova, M., Glagolev, V., Gongadze, A., Grigoryan, S., Gudkov, D., Karjavine, V., Lyablin, M., Olyunin, A., Samochkine, A., Sapronov, A., Shirkov, G., Soldatov, V., Solodko, A., Solodko, E., Trubnikov, G., Tyapkin, I., Uzhinsky, V., Vorozhtov, A., Levichev, E., Mezentsev, N., Piminov, P., Shatilov, D., Vobly, P., Zolotarev, K., Jelisavcic, I. Bozovic, Kacarevic, G., Lukic, S., Milutinovic-Dumbelovic, G., Pandurovic, M., Iriso, U., Perez, F., Pont, M., Trenado, J., Aguilar-Benitez, M., Calero, J., Garcia-Tabares, L., Gavela, D., Gutierrez, J. L., Lopez, D., Toral, F., Moya, D., Jimeno, A. Ruiz, Vila, I., Argyropoulos, T., Gutierrez, C. Blanch, Boronat, M., Esperante, D., Faus-Golfe, A., Fuster, J., Martinez, N. Fuster, Muñoz, N. Galindo, García, I., Navarro, J. Giner, Ros, E., Vos, M., Brenner, R., Ekelöf, T., Jacewicz, M., Ögren, J., Olvegård, M., Ruber, R., Ziemann, V., Aguglia, D., Tehrani, N. Alipour, Andersson, A., Andrianala, F., Antoniou, F., Artoos, K., Atieh, S., Sune, R. Ballabriga, Barnes, M. J., Garcia, J. Barranco, Bartosik, H., Belver-Aguilar, C., Morell, A. Benot, Bett, D. R., Bettoni, S., Blanchot, G., Garcia, O. Blanco, Bonnin, X. A., Brunner, O., Burkhardt, H., Calatroni, S., Campbell, M., Lasheras, N. Catalan, Bastos, M. Cerqueira, Cherif, A., Chevallay, E., Constance, B., Corsini, R., Cure, B., Curt, S., Dalena, B., Dannheim, D., De Michele, G., De Oliveira, L., Deelen, N., Delahaye, J. P., Dobers, T., Doebert, S., Draper, M., Ramos, F. Duarte, Dubrovskiy, A., Elsener, K., Esberg, J., Esposito, M., Fedosseev, V., Ferracin, P., Fiergolski, A., Foraz, K., Fowler, A., Friebel, F., Fuchs, J-F., Rojas, C. A. Fuentes, Gaddi, A., Fajardo, L. Garcia, Morales, H. Garcia, Garion, C., Gatignon, L., Gayde, J-C., Gerwig, H., Goldblatt, A. N., Grefe, C., Grudiev, A., Guillot-Vignot, F. G., Gutt-Mostowy, M. L., Hauschild, M., Hessler, C., Holma, J. K., Holzer, E., Hourican, M., Hynds, D., Levinsen, Y. Inntjore, Jeanneret, B., Jensen, E., Jonker, M., Kastriotou, M., Kemppinen, J. M. K., Kieffer, R. B., Klempt, W., Kononenko, O., Korsback, A., Platia, E. Koukovini, Kovermann, J. W., Kozsar, C-I., Kremastiotis, I., Kulis, S., Latina, A., Leaux, F., Lebrun, P., Lefevre, T., Linssen, L., Cudie, X. Llopart, Maier, A. A., Durand, H. Mainaud, Manosperti, E., Marelli, C., Lacoma, E. Marin, Martin, R., Mazzoni, S., Mcmonagle, G., Mete, O., Mether, L. M., Modena, M., Münker, R. M., Muranaka, T., Del Busto, E. Nebot, Nikiforou, N., Nisbet, D., Nonglaton, J-M., Nuiry, F. X., Nürnberg, A., Olvegard, M., Osborne, J., Papadopoulou, S., Papaphilippou, Y., Passarelli, A., Patecki, M., Pazdera, L., Pellegrini, D., Pepitone, K., Codina, E. Perez, Fontenla, A. Perez, Persson, T. H. B., Petrič, M., Pitters, F., Pittet, S., Plassard, F., Rajamak, R., Redford, S., Renier, Y., Rey, S. F., Riddone, G., Rinolfi, L., Castro, E. Rodriguez, Roloff, P., Rossi, C., Rude, V., Rumolo, G., Sailer, A., Santin, E., Schlatter, D., Schmickler, H., Schulte, D., Shipman, N., Sicking, E., Simoniello, R., Skowronski, P. K., Mompean, P. Sobrino, Soby, L., Sosin, M. P., Sroka, S., Stapnes, S., Sterbini, G., Ström, R., Syratchev, I., Tecker, F., Thonet, P. A., Timeo, L., Timko, H., Garcia, R. Tomas, Valerio, P., Vamvakas, A. L., Vivoli, A., Weber, M. A., Wegner, R., Wendt, M., Woolley, B., Wuensch, W., Uythoven, J., Zha, H., Zisopoulos, P., Benoit, M., Pinto, M. Vicente Barreto, Bopp, M., Braun, H. H., Divall, M. Csatari, Dehler, M., Garvey, T., Raguin, J. Y., Rivkin, L., Zennaro, R., Aksoy, A., Nergiz, Z., Pilicer, E., Tapan, I., Yavas, O., Baturin, V., Kholodov, R., Lebedynskyi, S., Miroshnichenko, V., Mordyk, S., Profatilova, I., Storizhko, V., Watson, N., Winter, A., Goldstein, J., Green, S., Marshall, J. S., Thomson, M. A., Xu, B., Gillespie, W. A., Pan, R., Tyrk, M. A, Protopopescu, D., Robson, A., Apsimon, R., Bailey, I., Burt, G., Constable, D., Dexter, A., Karimian, S., Lingwood, C., Buckland, M. D., Casse, G., Vossebeld, J., Bosco, A., Karataev, P., Kruchinin, K., Lekomtsev, K., Nevay, L., Snuverink, J., Yamakawa, E., Boisvert, V., Boogert, S., Boorman, G., Gibson, S., Lyapin, A., Shields, W., Teixeira-Dias, P., West, S., Jones, R., Joshi, N., Bodenstein, R., Burrows, P. N., Christian, G. B., Gamba, D., Perry, C., Roberts, J., Clarke, J. A., Collomb, N. A., Jamison, S. P., Shepherd, B. J. A., Walsh, D., Demarteau, M., Repond, J., Weerts, H., Xia, L., Wells, J. D., Adolphsen, C., Barklow, T., Breidenbach, M., Graf, N., Hewett, J., Markiewicz, T., McCormick, D., Moffeit, K., Nosochkov, Y., Oriunno, M., Phinney, N., Rizzo, T., Tantawi, S., Wang, F., Wang, J., White, G., and Woodley, M.

Subjects
Physics - Accelerator Physics, High Energy Physics - Experiment
Abstract

The Compact Linear Collider (CLIC) is a multi-TeV high-luminosity linear e+e- collider under development. For an optimal exploitation of its physics potential, CLIC is foreseen to be built and operated in a staged approach with three centre-of-mass energy stages ranging from a few hundred GeV up to 3 TeV. The first stage will focus on precision Standard Model physics, in particular Higgs and top-quark measurements. Subsequent stages will focus on measurements of rare Higgs processes, as well as searches for new physics processes and precision measurements of new states, e.g. states previously discovered at LHC or at CLIC itself. In the 2012 CLIC Conceptual Design Report, a fully optimised 3 TeV collider was presented, while the proposed lower energy stages were not studied to the same level of detail. This report presents an updated baseline staging scenario for CLIC. The scenario is the result of a comprehensive study addressing the performance, cost and power of the CLIC accelerator complex as a function of centre-of-mass energy and it targets optimal physics output based on the current physics landscape. The optimised staging scenario foresees three main centre-of-mass energy stages at 380 GeV, 1.5 TeV and 3 TeV for a full CLIC programme spanning 22 years. For the first stage, an alternative to the CLIC drive beam scheme is presented in which the main linac power is produced using X-band klystrons., Comment: 57 pages, 27 figures, 12 tables, published as CERN Yellow Report. Updated version: Minor layout changes for print version

Published
2016
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7. Bipartite graph partitioning and data clustering

Author

Zha, H., He, X., Ding, C., Gu, M., and Simon, H.

Subjects
Computer Science - Information Retrieval, Computer Science - Learning, H.3.3, G.1.3, G.2.2
Abstract

Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, we propose a new data clustering method based on partitioning the underlying bipartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. We show that an approximate solution to the minimization problem can be obtained by computing a partial singular value decomposition (SVD) of the associated edge weight matrix of the bipartite graph. We point out the connection of our clustering algorithm to correspondence analysis used in multivariate analysis. We also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, we apply our clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency., Comment: Proceedings of ACM CIKM 2001, the Tenth International Conference on Information and Knowledge Management, 2001

Published
2001

14. Amplitude Factorization Polynomial Pre-distortion for Nonlinear LED

Author

CHENG Yuanbo, ZHA Haipeng, BAO Xian, ZHANG Kun, and LU Wenqiang

Subjects
VLC, LED, nonlinear distortion, memory effect, adaptive pre-distortion, Applied optics. Photonics, TA1501-1820
Abstract

【Objective】Light Emitting Diode (LED) is an important nonlinear optical signal generating device in Visible Light Communication (VLC) systems. The static nonlinearity of LED and the dynamic nonlinear memory effect caused by frequency response can lead to signal distortion and degrade the system performance.【Methods】The nonlinearity of LED can usually be compensated by estimating a pre-distortion model, and the wireless VLC is usually intensity modulated/directly detected.【Results】Therefore, the LED nonlinearity modeling is investigated, and an adaptive pre-distortion device based on Amplitude Factorization Polynomial (AFP) is proposed to compensate LED nonlinearity in this paper. Simulation results show that the proposed scheme exhibits good Bit Error Rate (BER), Amplitude Modulation/Amplitude Modulation (AM/AM) correction and constellation diagram performance with higher modeling accuracy.【Conclusion】The normalized mean square error performance improvement of more than 15 dB compared with the traditional memory polynomial is achieved.

Published
2024
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15. Automated assessment of mandibular shape asymmetry in 3-dimensions

Author

Fan, Y, Zhang, Y, Chen, G, He, W, Song, G, Matthews, H, Claes, P, Pei, Y, Zha, H, Penington, A, Kilpatrick, N, Schneider, P, Jiang, R, Xu, T, Fan, Y, Zhang, Y, Chen, G, He, W, Song, G, Matthews, H, Claes, P, Pei, Y, Zha, H, Penington, A, Kilpatrick, N, Schneider, P, Jiang, R, and Xu, T

Abstract

INTRODUCTION: This study aimed to develop an automatic pipeline for analyzing mandibular shape asymmetry in 3-dimensions. METHODS: Forty patients with skeletal Class I pattern and 80 patients with skeletal Class III pattern were used. The mandible was automatically segmented from the cone-beam computed tomography images using a U-net deep learning network. A total of 17,415 uniformly sampled quasi-landmarks were automatically identified on the mandibular surface via a template mapping technique. After alignment with the robust Procrustes superimposition, the pointwise surface-to-surface distance between original and reflected mandibles was visualized in a color-coded map, indicating the location of asymmetry. The degree of overall mandibular asymmetry and the asymmetry of subskeletal units were scored using the root-mean-squared-error between the left and right sides. These asymmetry parameters were compared between the skeletal Class I and skeletal Class III groups. RESULTS: The mandible shape was significantly more asymmetrical in patients with skeletal Class III pattern with positional asymmetry. The condyles were identified as the most asymmetric region in all groups, followed by the coronoid process and the ramus. CONCLUSIONS: This automated approach to quantify mandibular shape asymmetry will facilitate high-throughput image processing for big data analysis. The spatially-dense landmarks allow for evaluating mandibular asymmetry over the entire surface, which overcomes the information loss inherent in conventional linear distance or angular measurements. Precise quantification of the asymmetry can provide important information for individualized diagnosis and treatment planning in orthodontics and orthognathic surgery.

Published
2022

18. Acoustic light-sheet microscopy

Author

Wunderl, S., primary, Ishijima, A., additional, Susaki, E. A., additional, Xu, Z., additional, Song, H., additional, Zha, H., additional, Azuma, T., additional, Sakuma, I., additional, Fukuoka, H., additional, Okada, E., additional, Ueda, H. R., additional, Takagi, S., additional, and Nakagawa, K., additional

Published
2021
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21. CERN Yellow Reports: Monographs, Vol 2 (2018): The Compact Linear e+e− Collider (CLIC) : 2018 Summary Report

Author

Charles, T. K., Giansiracusa, P. J., Lucas, T. G., Rassool, R. P., Volpi, M., Balazs, C., Afanaciev, K., Makarenko, V., Patapenka, A., Zhuk, I., Collette, C., Boland, M. J., Hoffman, A. C. Abusleme, Diaz, M. A., Garay, F., Chi, Y., He, X., Pei, G., Pei, S., Shu, G., Wang, X., Zhang, J., Zhao, F., Zhou, Z., Chen, H., Gao, Y., Huang, W., Kuang, Y. P., Li, B., Li, Y., Meng, X., Shao, J., Shi, J., Tang, C., Wang, P., Wu, X., Zha, H., Ma, L., Han, Y., Fang, W., Gu, Q., Huang, D., Huang, X., Tan, J., Wang, Z., Zhao, Z., Uggerhøj, U. I., Wistisen, T. N., Aabloo, A., Aare, R., Kuppart, K., Vigonski, S., Zadin, V., Aicheler, M., Baibuz, E., Brücken, E., Djurabekova, F., Eerola, P., Garcia, F., Haeggström, E., Huitu, K., Jansson, V., Kassamakov, I., Kimari, J., Kyritsakis, A., Lehti, S., Meriläinen, A., Montonen, R., Nordlund, K., Österberg, K., Saressalo, A., Väinölä, J., Veske, M., Farabolini, W., Mollard, A., Peauger, F., Plouin, J., Bambade, P., Chaikovska, I., Chehab, R., Delerue, N., Davier, M., Faus-Golfe, A., Irles, A., Kaabi, W., LeDiberder, F., Pöschl, R., Zerwas, D., Aimard, B., Balik, G., Blaising, J. -J., Brunetti, L., Chefdeville, M., Dominjon, A., Drancourt, C., Geoffroy, N., Jacquemier, J., Jeremie, A., Karyotakis, Y., Nappa, J. M., Serluca, M., Vilalte, S., Vouters, G., Bernhard, A., Bründermann, E., Casalbuoni, S., Hillenbrand, S., Gethmann, J., Grau, A., Huttel, E., Müller, A. -S., Peiffer, P., Perić, I., de Jauregui, D. Saez, Emberger, L., Graf, C., Simon, F., Szalay, M., van der Kolk, N., Brass, S., Kilian, W., Alexopoulos, T., Apostolopoulos, T., Gazis, E. N., Gazis, N., Kostopoulos, V., Kourkoulis, S., Heilig, B., Lichtenberger, J., Shrivastava, P., Dayyani, M. K., Ghasem, H., Hajari, S. S., Shaker, H., Ashkenazy, Y., Popov, I., Engelberg, E., Yashar, A., Abramowicz, H., Benhammou, Y., Borysov, O., Borysova, M., Levy, A., Levy, I., Alesini, D., Bellaveglia, M., Buonomo, B., Cardelli, A., Diomede, M., Ferrario, M., Gallo, A., Ghigo, A., Giribono, A., Piersanti, L., Stella, A., Vaccarezza, C., de Blas, J., Franceschini, R., D'Auria, G., Di Mitri, S., Abe, T., Aryshev, A., Fukuda, M., Furukawa, K., Hayano, H., Higashi, Y., Higo, T., Kubo, K., Kuroda, S., Matsumoto, S., Michizono, S., Naito, T., Okugi, T., Shidara, T., Tauchi, T., Terunuma, N., Urakawa, J., Yamamoto, A., Raboanary, R., Luiten, O. J., Stragier, X. F. D., Hart, R., van der Graaf, H., Eigen, G., Adli, E., Lindstrøm, C. A., Lillestøl, R., Malina, L., Pfingstner, J., Sjobak, K. N., Ahmad, A., Hoorani, H., Khan, W. A., Bugiel, S., Bugiel, R., Firlej, M., Fiutowski, T. A., Idzik, M., Moroń, J., Świentek, K. P., Brückman de Renstrom, P., Krupa, B., Kucharczyk, M., Lesiak, T., Pawlik, B., Sopicki, P., Turbiarz, B., Wojtoń, T., Zawiejski, L. K., Kalinowski, J., Nowak, K., Żarnecki, A. F., Firu, E., Ghenescu, V., Neagu, A. T., Preda, T., Zgura, I. S., Aloev, A., Azaryan, N., Boyko, I., Budagov, J., Chizhov, M., Filippova, M., Glagolev, V., Gongadze, A., Grigoryan, S., Gudkov, D., Karjavine, V., Lyablin, M., Nefedov, Yu., Olyunin, A., Rymbekova, A., Samochkine, A., Sapronov, A., Shelkov, G., Shirkov, G., Soldatov, V., Solodko, E., Trubnikov, G., Tyapkin, I., Uzhinsky, V., Vorozhtov, A., Zhemchugov, A., Levichev, E., Mezentsev, N., Piminov, P., Shatilov, D., Vobly, P., Zolotarev, K., Jelisavčić, I. Božović, Kačarević, G., Milutinović Dumbelović, G., Pandurović, M., Radulović, M., Stevanović, J., Vukasinović, N., Lee, D. -H., Ayala, N., Benedetti, G., Guenzel, T., Iriso, U., Marti, Z., Perez, F., Pont, M., Trenado, J., Ruiz-Jimeno, A., Vila, I., Calero, J., Dominguez, M., Garcia-Tabares, L., Gavela, D., Lopez, D., Toral, F., Blanch Gutierrez, C., Boronat, M., Esperante, D., Fullana, E., Fuster, J., García, I., Gimeno, B., Lopez, P. Gomis, González, D., Perelló, M., Ros, E., Villarejo, M. A., Vnuchenko, A., Vos, M., Borgmann, Ch., Brenner, R., Ekelöf, T., Jacewicz, M., Olvegård, M., Ruber, R., Ziemann, V., Aguglia, D., Gonzalvo, J. Alabau, Leon, M. Alcaide, Alipour Tehrani, N., Anastasopoulos, M., Andersson, A., Andrianala, F., Antoniou, F., Apyan, A., Arominski, D., Artoos, K., Assly, S., Atieh, S., Baccigalupi, C., Sune, R. Ballabriga, Caballero, D. Banon, Barnes, M. J., Garcia, J. Barranco, Bartalesi, A., Bauche, J., Bayar, C., Belver-Aguilar, C., Morell, A. Benot, Bernardini, M., Bett, D. R., Bettoni, S., Bettencourt, M., Bielawski, B., Garcia, O. Blanco, Blaskovic Kraljevic, N., Bolzon, B., Bonnin, X. A., Bozzini, D., Branger, E., Brondolin, E., Brunner, O., Buckland, M., Bursali, H., Burkhardt, H., Caiazza, D., Calatroni, S., Campbell, M., Catalan Lasheras, N., Cassany, B., Castro, E., Soares, R. H. Cavaleiro, Cerqueira Bastos, M., Cherif, A., Chevallay, E., Cilento, V., Corsini, R., Costa, R., Cure, B., Curt, S., Gobbo, A. Dal, Dannheim, D., Daskalaki, E., Deacon, L., Degiovanni, A., De Michele, G., De Oliveira, L., Romano, V. Del Pozo, Delahaye, J. P., Delikaris, D., Dias De Almeida, P. G., Dobers, T., Doebert, S., Doytchinov, I., Draper, M., Duarte Ramos, F., Duquenne, M., Plaja, N. Egidos, Elsener, K., Esberg, J., Esposito, M., Evans, L., Fedosseev, V., Ferracin, P., Fiergolski, A., Foraz, K., Fowler, A., Friebel, F., Fuchs, J-F., Gaddi, A., Gamba, D., Morales, L. Garcia Fajardo H. Garcia, Garion, C., Gasior, M., Gatignon, L., Gayde, J-C., Gerbershagen, A., Gerwig, H., Giambelli, G., Gilardi, A., Goldblatt, A. N., Anton, S. Gonzalez, Grefe, C., Grudiev, A., Guerin, H., Guillot-Vignot, F. G., Gutt-Mostowy, M. L., Lutz, M. Hein, Hessler, C., Holma, J. K., Holzer, E. B., Hourican, M., Hynds, D., Ikarios, E., Levinsen, Y. Inntjore, Janssens, S., Jeff, A., Jensen, E., Jonker, M., Kamugasa, S. W., Kastriotou, M., Kemppinen, J. M. K., Khan, V., Kieffer, R. B., Klempt, W., Kokkinis, N., Kossyvakis, I., Kostka, Z., Korsback, A., Koukovini Platia, E., Kovermann, J. W., Kozsar, C-I., Kremastiotis, I., Kröger, J., Kulis, S., Latina, A., Leaux, F., Lebrun, P., Lefevre, T., Leogrande, E., Linssen, L., Liu, X., Llopart Cudie, X., Magnoni, S., Maidana, C., Maier, A. A., Mainaud Durand, H., Mallows, S., Manosperti, E., Marelli, C., Marin Lacoma, E., Marsh, S., Martin, R., Martini, I., Martyanov, M., Mazzoni, S., Mcmonagle, G., Mether, L. M., Meynier, C., Modena, M., Moilanen, A., Mondello, R., Cabral, P. B. Moniz, Irazabal, N. Mouriz, Munker, M., Muranaka, T., Nadenau, J., Navarro, J. G., Navarro Quirante, J. L., Del Busto, E. Nebo, Nikiforou, N., Ninin, P., Nonis, M., Nisbet, D., Nuiry, F. X., Nürnberg, A., Ögren, J., Osborne, J., Ouniche, A. C., Pan, R., Papadopoulou, S., Papaphilippou, Y., Paraskaki, G., Pastushenko, A., Passarelli, A., Patecki, M., Pazdera, L., Pellegrini, D., Pepitone, K., Perez Codina, E., Fontenla, A. Perez, Persson, T. H. B., Petrič, M., Pitman, S., Pitters, F., Pittet, S., Plassard, F., Popescu, D., Quast, T., Rajamak, R., Redford, S., Remandet, L., Renier, Y., Rey, S. F., Orozco, O. Rey, Riddone, G., Rodriguez Castro, E., Roloff, P., Rossi, C., Rossi, F., Rude, V., Ruehl, I., Rumolo, G., Sailer, A., Santin, J. Sandomierski E., Sanz, C., Bedolla, J. Sauza, Schnoor, U., Schmickler, H., Schulte, D., Senes, E., Serpico, C., Severino, G., Shipman, N., Sicking, E., Simoniello, R., Skowronski, P. K., Sobrino Mompean, P., Soby, L., Sollander, P., Solodko, A., Sosin, M. P., Spannagel, S., Sroka, S., Stapnes, S., Sterbini, G., Stern, G., Ström, R., Stuart, M. J., Syratchev, I., Szypula, K., Tecker, F., Thonet, P. A., Thrane, P., Timeo, L., Tiirakari, M., Garcia, R. Tomas, Tomoiaga, C. I., Valerio, P., Vaňát, T., Vamvakas, A. L., Van Hoorne, J., Viazlo, O., Vicente Barreto Pinto, M., Vitoratou, N., Vlachakis, V., Weber, M. A., Wegner, R., Wendt, M., Widorski, M., Williams, O. E., Williams, M., Woolley, B., Wuensch, W., Wulzer, A., Uythoven, J., Xydou, A., Yang, R., Zelios, A., Zhao, Y., Zisopoulos, P., Benoit, M., Sultan, D. M. S., Riva, F., Bopp, M., Braun, H. H., Craievich, P., Dehler, M., Garvey, T., Pedrozzi, M., Raguin, J. Y., Rivkin, L., Zennaro, R., Guillaume, S., Rothacher, M., Aksoy, A., Nergiz, Z., Yavas, Ö., Denizli, H., Keskin, U., Oyulmaz, K. Y., Senol, A., Ciftci, A. K., Baturin, V., Karpenko, O., Kholodov, R., Lebed, O., Lebedynskyi, S., Mordyk, S., Musienko, I., Profatilova, Ia., Storizhko, V., Bosley, R. R., Price, T., Watson, M. F., Watson, N. K., Winter, A. G., Goldstein, J., Green, S., Marshall, J. S., Thomson, M. A., Xu, B., You, T., Gillespie, W. A., Spannowsky, M., Beggan, C., Martin, V., Zhang, Y., Protopopescu, D., Robson, A., Apsimon, R. J., Bailey, I., Burt, G. C., Dexter, A. C., Edwards, A. V., Hill, V., Jamison, S., Millar, W. L., Papke, K., Casse, G., Vossebeld, J., Aumeyr, T., Bergamaschi, M., Bobb, L., Bosco, A., Boogert, S., Boorman, G., Cullinan, F., Gibson, S., Karataev, P., Kruchinin, K., Lekomtsev, K., Lyapin, A., Nevay, L., Shields, W., Snuverink, J., Towler, J., Yamakawa, E., Boisvert, V., West, S., Jones, R., Joshi, N., Bett, D., Bodenstein, R. M., Bromwich, T., Burrows, P. N., Christian, G. B., Gohil, C., Korysko, P., Paszkiewicz, J., Perry, C., Ramjiawan, R., Roberts, J., Coates, T., Salvatore, F., Bainbridge, A., Clarke, J. A., Krumpa, N., Shepherd, B. J. A., Walsh, D., Chekanov, S., Demarteau, M., Gai, W., Liu, W., Metcalfe, J., Power, J., Repond, J., Weerts, H., Xia, L., Zupan, J., Wells, J. D., Zhang, Z., Adolphsen, C., Barklow, T., Dolgashev, V., Franzi, M., Graf, N., Hewett, J., Kemp, M., Kononenko, O., Markiewicz, T., Moffeit, K., Neilson, J., Nosochkov, Y., Oriunno, M., Phinney, N., Rizzo, T., Tantawi, S., Wang, J., Weatherford, B., White, G., Woodley, M., Philip N. Burrows, Nuria Catalán Lasheras, Lucie Linssen, Marko Petrič, Aidan Robson, Daniel Schulte, Eva Sicking, Steinar Stapnes, Charles, T. K., Giansiracusa, P. J., Lucas, T. G., Rassool, R. P., Volpi, M., Balazs, C., Afanaciev, K., Makarenko, V., Patapenka, A., Zhuk, I., Collette, C., Boland, M. J., Hoffman, A. C. Abusleme, Diaz, M. A., Garay, F., Chi, Y., He, X., Pei, G., Pei, S., Shu, G., Wang, X., Zhang, J., Zhao, F., Zhou, Z., Chen, H., Gao, Y., Huang, W., Kuang, Y. P., Li, B., Li, Y., Meng, X., Shao, J., Shi, J., Tang, C., Wang, P., Wu, X., Zha, H., Ma, L., Han, Y., Fang, W., Gu, Q., Huang, D., Huang, X., Tan, J., Wang, Z., Zhao, Z., Uggerhøj, U. I., Wistisen, T. 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A., Lillestøl, R., Malina, L., Pfingstner, J., Sjobak, K. N., Ahmad, A., Hoorani, H., Khan, W. A., Bugiel, S., Bugiel, R., Firlej, M., Fiutowski, T. A., Idzik, M., Moroń, J., Świentek, K. P., Brückman de Renstrom, P., Krupa, B., Kucharczyk, M., Lesiak, T., Pawlik, B., Sopicki, P., Turbiarz, B., Wojtoń, T., Zawiejski, L. K., Kalinowski, J., Nowak, K., Żarnecki, A. F., Firu, E., Ghenescu, V., Neagu, A. T., Preda, T., Zgura, I. S., Aloev, A., Azaryan, N., Boyko, I., Budagov, J., Chizhov, M., Filippova, M., Glagolev, V., Gongadze, A., Grigoryan, S., Gudkov, D., Karjavine, V., Lyablin, M., Nefedov, Yu., Olyunin, A., Rymbekova, A., Samochkine, A., Sapronov, A., Shelkov, G., Shirkov, G., Soldatov, V., Solodko, E., Trubnikov, G., Tyapkin, I., Uzhinsky, V., Vorozhtov, A., Zhemchugov, A., Levichev, E., Mezentsev, N., Piminov, P., Shatilov, D., Vobly, P., Zolotarev, K., Jelisavčić, I. Božović, Kačarević, G., Milutinović Dumbelović, G., Pandurović, M., Radulović, M., Stevanović, J., Vukasinović, N., Lee, D. -H., Ayala, N., Benedetti, G., Guenzel, T., Iriso, U., Marti, Z., Perez, F., Pont, M., Trenado, J., Ruiz-Jimeno, A., Vila, I., Calero, J., Dominguez, M., Garcia-Tabares, L., Gavela, D., Lopez, D., Toral, F., Blanch Gutierrez, C., Boronat, M., Esperante, D., Fullana, E., Fuster, J., García, I., Gimeno, B., Lopez, P. Gomi, González, D., Perelló, M., Ros, E., Villarejo, M. A., Vnuchenko, A., Vos, M., Borgmann, Ch., Brenner, R., Ekelöf, T., Jacewicz, M., Olvegård, M., Ruber, R., Ziemann, V., Aguglia, D., Gonzalvo, J. Alabau, Leon, M. Alcaide, Alipour Tehrani, N., Anastasopoulos, M., Andersson, A., Andrianala, F., Antoniou, F., Apyan, A., Arominski, D., Artoos, K., Assly, S., Atieh, S., Baccigalupi, C., Sune, R. Ballabriga, Caballero, D. Banon, Barnes, M. J., Garcia, J. Barranco, Bartalesi, A., Bauche, J., Bayar, C., Belver-Aguilar, C., Morell, A. Benot, Bernardini, M., Bett, D. R., Bettoni, S., Bettencourt, M., Bielawski, B., Garcia, O. Blanco, Blaskovic Kraljevic, N., Bolzon, B., Bonnin, X. A., Bozzini, D., Branger, E., Brondolin, E., Brunner, O., Buckland, M., Bursali, H., Burkhardt, H., Caiazza, D., Calatroni, S., Campbell, M., Catalan Lasheras, N., Cassany, B., Castro, E., Soares, R. H. Cavaleiro, Cerqueira Bastos, M., Cherif, A., Chevallay, E., Cilento, V., Corsini, R., Costa, R., Cure, B., Curt, S., Gobbo, A. Dal, Dannheim, D., Daskalaki, E., Deacon, L., Degiovanni, A., De Michele, G., De Oliveira, L., Romano, V. Del Pozo, Delahaye, J. P., Delikaris, D., Dias De Almeida, P. G., Dobers, T., Doebert, S., Doytchinov, I., Draper, M., Duarte Ramos, F., Duquenne, M., Plaja, N. Egido, Elsener, K., Esberg, J., Esposito, M., Evans, L., Fedosseev, V., Ferracin, P., Fiergolski, A., Foraz, K., Fowler, A., Friebel, F., Fuchs, J-F., Gaddi, A., Gamba, D., Morales, L. Garcia Fajardo H. Garcia, Garion, C., Gasior, M., Gatignon, L., Gayde, J-C., Gerbershagen, A., Gerwig, H., Giambelli, G., Gilardi, A., Goldblatt, A. N., Anton, S. Gonzalez, Grefe, C., Grudiev, A., Guerin, H., Guillot-Vignot, F. G., Gutt-Mostowy, M. L., Lutz, M. Hein, Hessler, C., Holma, J. K., Holzer, E. B., Hourican, M., Hynds, D., Ikarios, E., Levinsen, Y. Inntjore, Janssens, S., Jeff, A., Jensen, E., Jonker, M., Kamugasa, S. W., Kastriotou, M., Kemppinen, J. M. K., Khan, V., Kieffer, R. B., Klempt, W., Kokkinis, N., Kossyvakis, I., Kostka, Z., Korsback, A., Koukovini Platia, E., Kovermann, J. W., Kozsar, C-I., Kremastiotis, I., Kröger, J., Kulis, S., Latina, A., Leaux, F., Lebrun, P., Lefevre, T., Leogrande, E., Linssen, L., Liu, X., Llopart Cudie, X., Magnoni, S., Maidana, C., Maier, A. A., Mainaud Durand, H., Mallows, S., Manosperti, E., Marelli, C., Marin Lacoma, E., Marsh, S., Martin, R., Martini, I., Martyanov, M., Mazzoni, S., Mcmonagle, G., Mether, L. M., Meynier, C., Modena, M., Moilanen, A., Mondello, R., Cabral, P. B. Moniz, Irazabal, N. Mouriz, Munker, M., Muranaka, T., Nadenau, J., Navarro, J. G., Navarro Quirante, J. L., Del Busto, E. Nebo, Nikiforou, N., Ninin, P., Nonis, M., Nisbet, D., Nuiry, F. X., Nürnberg, A., Ögren, J., Osborne, J., Ouniche, A. C., Pan, R., Papadopoulou, S., Papaphilippou, Y., Paraskaki, G., Pastushenko, A., Passarelli, A., Patecki, M., Pazdera, L., Pellegrini, D., Pepitone, K., Perez Codina, E., Fontenla, A. Perez, Persson, T. H. B., Petrič, M., Pitman, S., Pitters, F., Pittet, S., Plassard, F., Popescu, D., Quast, T., Rajamak, R., Redford, S., Remandet, L., Renier, Y., Rey, S. F., Orozco, O. Rey, Riddone, G., Rodriguez Castro, E., Roloff, P., Rossi, C., Rossi, F., Rude, V., Ruehl, I., Rumolo, G., Sailer, A., Santin, J. Sandomierski E., Sanz, C., Bedolla, J. Sauza, Schnoor, U., Schmickler, H., Schulte, D., Senes, E., Serpico, C., Severino, G., Shipman, N., Sicking, E., Simoniello, R., Skowronski, P. K., Sobrino Mompean, P., Soby, L., Sollander, P., Solodko, A., Sosin, M. P., Spannagel, S., Sroka, S., Stapnes, S., Sterbini, G., Stern, G., Ström, R., Stuart, M. J., Syratchev, I., Szypula, K., Tecker, F., Thonet, P. A., Thrane, P., Timeo, L., Tiirakari, M., Garcia, R. Toma, Tomoiaga, C. I., Valerio, P., Vaňát, T., Vamvakas, A. L., Van Hoorne, J., Viazlo, O., Vicente Barreto Pinto, M., Vitoratou, N., Vlachakis, V., Weber, M. A., Wegner, R., Wendt, M., Widorski, M., Williams, O. E., Williams, M., Woolley, B., Wuensch, W., Wulzer, A., Uythoven, J., Xydou, A., Yang, R., Zelios, A., Zhao, Y., Zisopoulos, P., Benoit, M., Sultan, D. M. S., Riva, F., Bopp, M., Braun, H. H., Craievich, P., Dehler, M., Garvey, T., Pedrozzi, M., Raguin, J. Y., Rivkin, L., Zennaro, R., Guillaume, S., Rothacher, M., Aksoy, A., Nergiz, Z., Yavas, Ö., Denizli, H., Keskin, U., Oyulmaz, K. Y., Senol, A., Ciftci, A. K., Baturin, V., Karpenko, O., Kholodov, R., Lebed, O., Lebedynskyi, S., Mordyk, S., Musienko, I., Profatilova, Ia., Storizhko, V., Bosley, R. R., Price, T., Watson, M. F., Watson, N. K., Winter, A. G., Goldstein, J., Green, S., Marshall, J. S., Thomson, M. A., Xu, B., You, T., Gillespie, W. A., Spannowsky, M., Beggan, C., Martin, V., Zhang, Y., Protopopescu, D., Robson, A., Apsimon, R. J., Bailey, I., Burt, G. C., Dexter, A. C., Edwards, A. V., Hill, V., Jamison, S., Millar, W. L., Papke, K., Casse, G., Vossebeld, J., Aumeyr, T., Bergamaschi, M., Bobb, L., Bosco, A., Boogert, S., Boorman, G., Cullinan, F., Gibson, S., Karataev, P., Kruchinin, K., Lekomtsev, K., Lyapin, A., Nevay, L., Shields, W., Snuverink, J., Towler, J., Yamakawa, E., Boisvert, V., West, S., Jones, R., Joshi, N., Bett, D., Bodenstein, R. M., Bromwich, T., Burrows, P. N., Christian, G. B., Gohil, C., Korysko, P., Paszkiewicz, J., Perry, C., Ramjiawan, R., Roberts, J., Coates, T., Salvatore, F., Bainbridge, A., Clarke, J. A., Krumpa, N., Shepherd, B. J. A., Walsh, D., Chekanov, S., Demarteau, M., Gai, W., Liu, W., Metcalfe, J., Power, J., Repond, J., Weerts, H., Xia, L., Zupan, J., Wells, J. D., Zhang, Z., Adolphsen, C., Barklow, T., Dolgashev, V., Franzi, M., Graf, N., Hewett, J., Kemp, M., Kononenko, O., Markiewicz, T., Moffeit, K., Neilson, J., Nosochkov, Y., Oriunno, M., Phinney, N., Rizzo, T., Tantawi, S., Wang, J., Weatherford, B., White, G., and Woodley, M.

Published
2018

25. FOXM1 promotes proliferation of lung adenocarcinoma cells by regulating HJURP

Author

ZHA Haoran and LI Zhaoxia

Subjects
hjurp, nsclc, foxm1, cancer cell proliferation, Medicine (General), R5-920
Abstract

Objective To investigate the relationship between the expression of Holliday junction recognition protein(HJURP) in lung cance tissues and prognosis of patients with lung adenocarcinoma(LUAD), and to explore the effect of HJURP on proliferation of LUAD cells(H1299 and SPC-A1). Methods Human non-small cell lung cancer (NSCLC) tissues and paired normal lung tissues(n=17) were collected from Department of Oncology of Rocket Force Medical Center from February 2019 to May 2020. RT-qPCR and Western blotting were applied to analyze the expression of HJURP in NSCLC tissues and normal tissues. Public available databases, the cancer genome atlas (TCGA) and Kaplan-Meier Plotter(KM-plot), were used to analyze the expression of HJURP of LUAD tissues and normal tissues and the predictive effect of prognosis in patients with LUAD. 5-ethynyl-2-deoxyuridine (EDU) incorporation assay was applied to investigate the effect of knock-dowm HJURP on the proliferation of cancer cell lines (H1299, SPC-A1). Pearson correlation analysis was used to analyze the relationship between HJURP and the expression of forkhead box protein M1(FOXM1). Chromatin immunoprecipitation(ChIP) assay was used to analyze the transcription regulation effect of FOXM1 on HJUPR. Dual-luciferase reporter assay was used to analyze the combination of FOXM1 and HJURP promoter. SiRNA was used to knock down FOXM1, which could detect the effect on the expression of HJURP. Results Compared with normal lung tissues, LUAD tissues had increased HJURP expression via RT-qPCR and Western blotting(P < 0.05). Increased HJURP expression in tumor tissues was correlated with a poor prognosis of patients with LUAD based on survival analysis of LUAD patients in TCGA and KM-plot(P < 0.05). Knock-down of HJURP inhibited the proliferation of H1299 (P < 0.05) and SPC-A1(P < 0.05) cells. The expression of FOXM1 correlated positively with HJURP in LUAD (medical center: r=0.581 3, P < 0.05; TCGA: r=0.877 6, P < 0.05; CCLE: r=0.455 1, P < 0.05). The results of ChIP assay and dual-luciferase reporter assay showed FOXM1 could combine the promoter region of HJURP. The expression of HJURP decreased after knocking down FOXM1(P < 0.05). Conclusion HJURP is involved in the regulation of the proliferation of LUAD cells. Increased HJURP expression in LUAD tissues is related to a poor prognosis. FOXM1 is the main upstream transcription factor for regulating HJURP.

Published
2023
Full Text
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26. Normalized Cut Algorithm for Automated Assignment of Protein Domains

Author

Samanta, M. P, Liang, S, Zha, H, and Biegel, Bryan A

Subjects
Numerical Analysis
Abstract

We present a novel computational method for automatic assignment of protein domains from structural data. At the core of our algorithm lies a recently proposed clustering technique that has been very successful for image-partitioning applications. This grap.,l-theory based clustering method uses the notion of a normalized cut to partition. an undirected graph into its strongly-connected components. Computer implementation of our method tested on the standard comparison set of proteins from the literature shows a high success rate (84%), better than most existing alternative In addition, several other features of our algorithm, such as reliance on few adjustable parameters, linear run-time with respect to the size of the protein and reduced complexity compared to other graph-theory based algorithms, would make it an attractive tool for structural biologists.

Published
2002

29. Role of tumor draining lymph nodes in generation of memory CD8+ T cells in a murine model of colon cancer

Author

XIE Shuanglong, ZHA Haoran, YANG Fei, ZHU Bo, and LIN Zhihua

Subjects
tumor draining lymph nodes, memory t cells, neoadjuvant immune checkpoint blockade, Medicine (General), R5-920
Abstract

Objective To investigate the role of tumor draining lymph nodes (TDLN) in the generation of memory CD8+ T cells under the context of neoadjuvant anti-PD-L1 therapy. Methods A mouse transplanted model of CT26/MC38-OVA colon cancer was established to recapitulate clinic neoadjuvant anti-PD-L1 therapy, and then the tumor masses and TDLN were removed in 15 d after transplantation. On day 30 after surgery, the effect of TDLN on the formation of immune memory was assessed under the condition of neoadjuvant PD-L1 antibody therapy by tumor re-challenge. The effect of neoadjuvant PD-L1 on tumor-specific CD8+ T cells was investigated by adoptive transfer CD45.1+ OT-Ⅰ cells. The distribution and phenotype of CD45+ CD8+T cells in various tissues were determined by flow cytometry at the time of surgery (effector phase) and 30 d after surgery (memory phase). Results The tumor mass was reduced significantly after receiving neoadjuvant anti-PD-L1 therapy (P < 0.05). After tumor re-challenge, tumor growth was significantly slowed in the mice treated with neoadjuvant PD-L antibody, whereas TDLN resection accelerated the tumor growth (P < 0.05). However, long-term survival of tumor re-challenge showed that all tumors were eventually subsided, whether or not the mice treated with neoadjuvant PD-L antibody. The results of adoptive reinjection indicated that the number of CD45.1+ CD8+ T cells being stored in TDLN and no-draining lymph nodes (NLN) was the highest (P < 0.05). Tcm (T central memory) cells and Trm (tissue-resident memory) cells were mainly distributed in TDLN and NLN in memory phase (P < 0.05). The distribution and memory phenotype of CD45.1+ CD8+ T cells in TDLN and NLN had no obvous changes after neoadjuvant anti-PD-L1 therapy. Conclusion TDLN is the primary site for the storage of memory CD8+ T cells, but under the condition of neoadjuvant PD-L1 antibody treatment, TDLN resection does not affect their long-term anti-tumor immunity.

Published
2023
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30. Pathology of tail fan necrosis in the spiny lobster, Jasus edwardsii

Author

Zha, H., Jones, B., Lewis, G., Dong, Y., Jeffs, A., Zha, H., Jones, B., Lewis, G., Dong, Y., and Jeffs, A.

Abstract

Tail fan necrosis (TFN) is the bacterial infection of the tail fan of spiny lobsters which leads to melanosis and erosion of the tail fan tissues. The condition is commonly found among spiny lobsters in aquaculture and commercial fisheries, and greatly reduces their commercial value. This study describes the pathology of TFN by examining the tail fans (telson, uropods) and internal organs (mid-gut, hepatopancreas, heart and gill) of 29 affected wild spiny lobsters (Jasus edwardsii) and 14 unaffected in New Zealand. Initial signs of TFN were observed around the margins of lacerations to the tail fan, with more extensive signs extending from these presumptive sites of initiation. The establishment of the condition at points of injury is consistent with the penetration of TFN through the cuticle and tissue layers of the affected tail fans, which is rarely seen in other forms of shell disease. Entry into these tissues was characterised initially by caseous necrosis and haemocyte accumulation, followed by the spread of these responses together with melanisation. Additional pathological changes to the tail fans included pseudomembrane formation, detachment of epidermis or cuticle, clotted haemolymph and fibrosis. Among internal organs, pathological changes were found in a total of two mid-gut, four heart and two gill samples from eight lobsters with TFN, while no suspected changes were found in the organs of lobsters without TFN. The causes of internal organ pathology associated with TFN in spiny lobsters warrants more detailed research.

Published
2018

32. Learning Registered Point Processes from Idiosyncratic Observations

Author

Xu, H., Lawrence Carin, and Zha, H.

Subjects
FOS: Computer and information sciences, Statistics - Machine Learning, Machine Learning (stat.ML)
Abstract

A parametric point process model is developed, with modeling based on the assumption that sequential observations often share latent phenomena, while also possessing idiosyncratic effects. An alternating optimization method is proposed to learn a "registered" point process that accounts for shared structure, as well as "warping" functions that characterize idiosyncratic aspects of each observed sequence. Under reasonable constraints, in each iteration we update the sample-specific warping functions by solving a set of constrained nonlinear programming problems in parallel, and update the model by maximum likelihood estimation. The justifiability, complexity and robustness of the proposed method are investigated in detail, and the influence of sequence stitching on the learning results is examined empirically. Experiments on both synthetic and real-world data demonstrate that the method yields explainable point process models, achieving encouraging results compared to state-of-the-art methods.

Published
2017
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33. The bHLH Protein Nulp1 is Essential for Femur Development Via Acting as a Cofactor in Wnt Signaling in Drosophila

Author

Zeng, Q., primary, Wan, Y., additional, Zhu, P., additional, Zhao, M., additional, Jiang, F., additional, Chen, J., additional, Tang, M., additional, Zhu, X., additional, Li, Y., additional, Zha, H., additional, Wang, Y., additional, Hu, M., additional, Mo, X., additional, Zhang, Y., additional, Chen, Y., additional, Ye, X., additional, Bodmer, R., additional, Ocorr, K., additional, Jiang, Z., additional, Zhuang, J., additional, Yuan, W., additional, and Wu, X., additional

Published
2018
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35. Key Technologies and Equipment for Smart Orchard Construction and Prospects

Author

HAN Leng, HE Xiongkui, WANG Changling, LIU Yajia, SONG Jianli, QI Peng, LIU Limin, LI Tian, ZHENG Yi, LIN Guihai, ZHOU Zhan, HUANG Kang, WANG Zhong, ZHA Hainie, ZHANG Guoshan, ZHOU Guotao, MA Yong, FU Hao, NIE Hongyuan, ZENG Aijun, and ZHANG Wei

Subjects
smart orchard, internet of things, intelligent agricultural equipment system, driverless machine, smart orchard management platform, information acquisition system, intelligent profiling sprayer, Agriculture (General), S1-972, Technology (General), T1-995
Abstract

Traditional orchard production is facing problems of labor shortage due to the aging, difficulties in the management of agricultural equipment and production materials, and low production efficiency which can be expected to be solved by building a smart orchard that integrates technologies of Internet of Things(IoT), big data, equipment intelligence, et al. In this study, based on the objectives of full mechanization and intelligent management, a smart orchard was built in Pinggu district, an important peaches and pears fruit producing area of Beijing. The orchard covers an aera of more than 30 hm2 in Xiying village, Yukou town. In the orchard, more than 10 kinds of information acquisition sensors for pests, diseases, water, fertilizers and medicines are applied, 28 kinds of agricultural machineries with intelligent technical support are equipped. The key technologies used include: intelligent information acquisition system, integrated water and fertilizer management system and intelligent pest management system. The intelligent operation equipment system includes: unmanned lawn mower, intelligent anti-freeze machine, trenching and fertilizer machine, automatic driving crawler, intelligent profiling variable sprayer, six-rotor branch-to-target drone, multi-functional picking platform and finishing and pruning machine, etc. At the same time, an intelligent management platform has been built in the smart orchard. The comparison results showed that, smart orchard production can reduce labor costs by more than 50%, save pesticide dosage by 30% ~ 40%, fertilizer dosage by 25% ~ 35%, irrigation water consumption by 60% ~ 70%, and comprehensive economic benefits increased by 32.5%. The popularization and application of smart orchards will further promote China's fruit production level and facilitate the development of smart agriculture in China.

Published
2022
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36. The technology of radio frequency fingerprint identification based on deep learning for 5G application

Author

Lin Yun, Wang Hanhong, and Zha Haoran

Subjects
ue authentication, radio frequency fingerprint identification, 5g security, deep neural networks, Electronic computers. Computer science, QA75.5-76.95
Abstract

User Equipment (UE) authentication holds paramount importance in upholding the security of wireless networks. A nascent technology, Radio Frequency Fingerprint Identification (RFFI), is gaining prominence as a means to bolster network security authentication. To expedite the integration of RFFI within fifth-generation (5G) networks, this research undertakes the creation of a comprehensive link-level simulation platform tailored for 5G scenarios. The devised platform emulates various device impairments, including an oscillator, IQ modulator, and power amplifier (PA) nonlinearities, alongside simulating channel distortions. Consequent to this, a plausibility analysis is executed, intertwining transmitter device impairments with 3rd Generation Partnership Project (3GPP) new radio (NR) protocols. Subsequently, an exhaustive exploration is conducted to assess the impact of transmitter impairments, deep neural networks (DNNs), and channel effects on RF fingerprinting performance. Notably, under a signal-to-noise ratio (SNR) of 15 dB, the deep learning approach demonstrates the capability to accurately classify 100 UEs with a commendable 91% accuracy rate. Through a multifaceted evaluation, it is ascertained that the Attention-based network architecture emerges as the optimal choice for the RFFI task, serving as the new benchmark model for RFFI applications.

Published
2024
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37. Updated baseline for a staged Compact Linear Collider

Author

Boland, M J, Felzmann, U, Giansiracusa, P J, Lucas, T G, Rassool, R P, Balazs, C, Charles, T K, Afanaciev, K, Emeliantchik, I, Ignatenko, A, Makarenko, V, Shumeiko, N, Patapenka, A, Zhuk, I, Abusleme Hoffman, A C, Diaz Gutierrez, M A, Gonzalez, M Vogel, Chi, Y, He, X, Pei, G, Pei, S, Shu, G, Wang, X, Zhang, J, Zhao, F, Zhou, Z, Chen, H, Gao, Y, Huang, W, Kuang, Y P, Li, B, Li, Y, Shao, J, Shi, J, Tang, C, Wu, X, Ma, L, Han, Y, Fang, W, Gu, Q, Huang, D, Huang, X, Tan, J, Wang, Z, Zhao, Z, Laštovička, T, Uggerhoj, U, Wistisen, T N, Aabloo, A, Eimre, K, Kuppart, K, Vigonski, S, Zadin, V, Aicheler, M, Baibuz, E, Brücken, E, Djurabekova, F, Eerola, P, Garcia, F, Haeggström, E, Huitu, K, Jansson, V, Karimaki, V, Kassamakov, I, Kyritsakis, A, Lehti, S, Meriläinen, A, Montonen, R, Niinikoski, T, Nordlund, K, Österberg, K, Parekh, M, Törnqvist, N A, Väinölä, J, Veske, M, Farabolini, W, Mollard, A, Napoly, O, Peauger, F, Plouin, J, Bambade, P, Chaikovska, I, Chehab, R, Davier, M, Kaabi, W, Kou, E, LeDiberder, F, Pöschl, R, Zerwas, D, Aimard, B, Balik, G, Baud, J-P, Blaising, J-J, Brunetti, L, Chefdeville, M, Drancourt, C, Geoffroy, N, Jacquemier, J, Jeremie, A, Karyotakis, Y, Nappa, J M, Vilalte, S, Vouters, G, Bernard, A, Peric, I, Gabriel, M, Simon, F, Szalay, M, van der Kolk, N, Alexopoulos, T, Gazis, E N, Gazis, N, Ikarios, E, Kostopoulos, V, Kourkoulis, S, Gupta, P D, Shrivastava, P, Arfaei, H, Dayyani, M K, Ghasem, H, Hajari, S S, Shaker, H, Ashkenazy, Y, Abramowicz, H, Benhammou, Y, Borysov, O, Kananov, S, Levy, A, Levy, I, Rosenblat, O, D'Auria, G, Di Mitri, S, Abe, T, Aryshev, A, Higo, T, Makida, Y, Matsumoto, S, Shidara, T, Takatomi, T, Takubo, Y, Tauchi, T, Toge, N, Ueno, K, Urakawa, J, Yamamoto, A, Yamanaka, M, Raboanary, R, Hart, R, van der Graaf, H, Eigen, G, Zalieckas, J, Adli, E, Lillestøl, R, Malina, L, Pfingstner, J, Sjobak, K N, Ahmed, W, Asghar, M I, Hoorani, H, Bugiel, S, Dasgupta, R, Firlej, M, Fiutowski, T A, Idzik, M, Kopec, M, Kuczynska, M, Moron, J, Swientek, K P, Daniluk, W, Krupa, B, Kucharczyk, M, Lesiak, T, Moszczynski, A, Pawlik, B, Sopicki, P, Wojtoń, T, Zawiejski, L, Kalinowski, J, Krawczyk, M, Żarnecki, A F, Firu, E, Ghenescu, V, Neagu, A T, Preda, T, Zgura, I-S, Aloev, A, Azaryan, N, Budagov, J, Chizhov, M, Filippova, M, Glagolev, V, Gongadze, A, Grigoryan, S, Gudkov, D, Karjavine, V, Lyablin, M, Olyunin, A, Samochkine, A, Sapronov, A, Shirkov, G, Soldatov, V, Solodko, A, Solodko, E, Trubnikov, G, Tyapkin, I, Uzhinsky, V, Vorozhtov, A, Levichev, E, Mezentsev, N, Piminov, P, Shatilov, D, Vobly, P, Zolotarev, K, Bozovic-Jelisavcic, I, Kacarevic, G, Lukic, S, Milutinovic-Dumbelovic, G, Pandurovic, M, Iriso, U, Perez, F, Pont, M, Trenado, J, Aguilar-Benitez, M, Calero, J, Garcia-Tabares, L, Gavela, D, Gutierrez, J L, Lopez, D, Toral, F, Moya, D, Ruiz-Jimeno, A, Vila, I, Argyropoulos, T, Blanch Gutierrez, C, Boronat, M, Esperante, D, Faus-Golfe, A, Fuster, J, Fuster Martinez, N, Galindo Muñoz, N, García, I, Giner Navarro, J, Ros, E, Vos, M, Brenner, R, Ekelöf, T, Jacewicz, M, Ögren, J, Olvegård, M, Ruber, R, Ziemann, V, Aguglia, D, Alipour Tehrani, N, Andersson, A, Andrianala, F, Antoniou, F, Artoos, K, Atieh, S, Ballabriga Sune, R, Barnes, M J, Barranco Garcia, J, Bartosik, H, Belver-Aguilar, C, Benot Morell, A, Bett, D R, Bettoni, S, Blanchot, G, Blanco Garcia, O, Bonnin, X A, Brunner, O, Burkhardt, H, Calatroni, S, Campbell, M, Catalan Lasheras, N, Cerqueira Bastos, M, Cherif, A, Chevallay, E, Constance, B, Corsini, R, Cure, B, Curt, S, Dalena, B, Dannheim, D, De Michele, G, De Oliveira, L, Deelen, N, Delahaye, J P, Dobers, T, Doebert, S, Draper, M, Duarte Ramos, F, Dubrovskiy, A, Elsener, K, Esberg, J, Esposito, M, Fedosseev, V, Ferracin, P, Fiergolski, A, Foraz, K, Fowler, A, Friebel, F, Fuchs, J-F, Fuentes Rojas, C A, Gaddi, A, Garcia Fajardo, L, Garcia Morales, H, Garion, C, Gatignon, L, Gayde, J-C, Gerwig, H, Goldblatt, A N, Grefe, C, Grudiev, A, Guillot-Vignot, F G, Gutt-Mostowy, M L, Hauschild, M, Hessler, C, Holma, J K, Holzer, E, Hourican, M, Hynds, D, Inntjore Levinsen, Y, Jeanneret, B, Jensen, E, Jonker, M, Kastriotou, M, Kemppinen, J M K, Kieffer, R B, Klempt, W, Kononenko, O, Korsback, A, Koukovini Platia, E, Kovermann, J W, Kozsar, C-I, Kremastiotis, I, Kulis, S, Latina, A, Leaux, F, Lebrun, P, Lefevre, T, Linssen, L, Llopart Cudie, X, Maier, A A, Mainaud Durand, H, Manosperti, E, Marelli, C, Marin Lacoma, E, Martin, R, Mazzoni, S, Mcmonagle, G, Mete, O, Mether, L M, Modena, M, Münker, R M, Muranaka, T, Nebot Del Busto, E, Nikiforou, N, Nisbet, D, Nonglaton, J-M, Nuiry, F X, Nürnberg, A, Olvegard, M, Osborne, J, Papadopoulou, S, Papaphilippou, Y, Passarelli, A, Patecki, M, Pazdera, L, Pellegrini, D, Pepitone, K, Perez Codina, E, Perez Fontenla, A, Persson, T H B, Petrič, M, Pitters, F, Pittet, S, Plassard, F, Rajamak, R, Redford, S, Renier, Y, Rey, S F, Riddone, G, Rinolfi, L, Rodriguez Castro, E, Roloff, P, Rossi, C, Rude, V, Rumolo, G, Sailer, A, Santin, E, Schlatter, D, Schmickler, H, Schulte, D, Shipman, N, Sicking, E, Simoniello, R, Skowronski, P K, Sobrino Mompean, P, Soby, L, Sosin, M P, Sroka, S, Stapnes, S, Sterbini, G, Ström, R, Syratchev, I, Tecker, F, Thonet, P A, Timeo, L, Timko, H, Tomas Garcia, R, Valerio, P, Vamvakas, A L, Vivoli, A, Weber, M A, Wegner, R, Wendt, M, Woolley, B, Wuensch, W, Uythoven, J, Zha, H, Zisopoulos, P, Benoit, M, Vicente Barreto Pinto, M, Bopp, M, Braun, H H, Csatari Divall, M, Dehler, M, Garvey, T, Raguin, J Y, Rivkin, L, Zennaro, R, Aksoy, A, Nergiz, Z, Pilicer, E, Tapan, I, Yavas, O, Baturin, V, Kholodov, R, Lebedynskyi, S, Miroshnichenko, V, Mordyk, S, Profatilova, I, Storizhko, V, Watson, N, Winter, A, Goldstein, J, Green, S, Marshall, J S, Thomson, M A, Xu, B, Gillespie, W A, Pan, R, Tyrk, M A, Protopopescu, D, Robson, A, Apsimon, R, Bailey, I, Burt, G, Constable, D, Dexter, A, Karimian, S, Lingwood, C, Buckland, M D, Casse, G, Vossebeld, J, Bosco, A, Karataev, P, Kruchinin, K, Lekomtsev, K, Nevay, L, Snuverink, J, Yamakawa, E, Boisvert, V, Boogert, S, Boorman, G, Gibson, S, Lyapin, A, Shields, W, Teixeira-Dias, P, West, S, Jones, R, Joshi, N, Bodenstein, R, Burrows, P N, Christian, G B, Gamba, D, Perry, C, Roberts, J, Clarke, J A, Collomb, N A, Jamison, S P, Shepherd, B J A, Walsh, D, Demarteau, M, Repond, J, Weerts, H, Xia, L, Wells, J D, Adolphsen, C, Barklow, T, Breidenbach, M, Graf, N, Hewett, J., Markiewicz, T, McCormick, D, Moffeit, K, Nosochkov, Y, Oriunno, M, Phinney, N, Rizzo, T, Tantawi, S, Wang, F, Wang, J, White, G, and Woodley, M

Subjects
hep-ex, Physics::Accelerator Physics, High Energy Physics::Experiment, Accelerators and Storage Rings, Particle Physics - Experiment, physics.acc-ph
Abstract

The Compact Linear Collider (CLIC) is a multi-TeV high-luminosity linear e+e- collider under development. For an optimal exploitation of its physics potential, CLIC is foreseen to be built and operated in a staged approach with three centre-of-mass energy stages ranging from a few hundred GeV up to 3 TeV. The first stage will focus on precision Standard Model physics, in particular Higgs and top-quark measurements. Subsequent stages will focus on measurements of rare Higgs processes, as well as searches for new physics processes and precision measurements of new states, e.g. states previously discovered at LHC or at CLIC itself. In the 2012 CLIC Conceptual Design Report, a fully optimised 3 TeV collider was presented, while the proposed lower energy stages were not studied to the same level of detail. This report presents an updated baseline staging scenario for CLIC. The scenario is the result of a comprehensive study addressing the performance, cost and power of the CLIC accelerator complex as a function of centre-of-mass energy and it targets optimal physics output based on the current physics landscape. The optimised staging scenario foresees three main centre-of-mass energy stages at 380 GeV, 1.5 TeV and 3 TeV for a full CLIC programme spanning 22 years. For the first stage, an alternative to the CLIC drive beam scheme is presented in which the main linac power is produced using X-band klystrons. The Compact Linear Collider (CLIC) is a multi-TeV high-luminosity linear e+e- collider under development. For an optimal exploitation of its physics potential, CLIC is foreseen to be built and operated in a staged approach with three centre-of-mass energy stages ranging from a few hundred GeV up to 3 TeV. The first stage will focus on precision Standard Model physics, in particular Higgs and top-quark measurements. Subsequent stages will focus on measurements of rare Higgs processes, as well as searches for new physics processes and precision measurements of new states, e.g. states previously discovered at LHC or at CLIC itself. In the 2012 CLIC Conceptual Design Report, a fully optimised 3 TeV collider was presented, while the proposed lower energy stages were not studied to the same level of detail. This report presents an updated baseline staging scenario for CLIC. The scenario is the result of a comprehensive study addressing the performance, cost and power of the CLIC accelerator complex as a function of centre-of-mass energy and it targets optimal physics output based on the current physics landscape. The optimised staging scenario foresees three main centre-of-mass energy stages at 380 GeV, 1.5 TeV and 3 TeV for a full CLIC programme spanning 22 years. For the first stage, an alternative to the CLIC drive beam scheme is presented in which the main linac power is produced using X-band klystrons.

Published
2016

38. Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks

Author

Du, N., Liang, Y., Balcan, M., Gomez Rodriguez, M., Zha, H., and Song, L.

Abstract

A typical viral marketing model identifies influential users in a social network to maximize a single product adoption assuming unlimited user attention, campaign budgets, and time. In reality, multiple products need campaigns, users have limited attention, convincing users incurs costs, and advertisers have limited budgets and expect the adoptions to be maximized soon. Facing these user, monetary, and timing constraints, we formulate the problem as a submodular maximization task in a continuous-time diffusion model under the intersection of a matroid and multiple knapsack constraints. We propose a randomized algorithm estimating the user influence in a network ($|\mathcal{V}|$ nodes, $|\mathcal{E}|$ edges) to an accuracy of $\epsilon$ with $n=\mathcal{O}(1/\epsilon^2)$ randomizations and $\tilde{\mathcal{O}}(n|\mathcal{E}|+n|\mathcal{V}|)$ computations. By exploiting the influence estimation algorithm as a subroutine, we develop an adaptive threshold greedy algorithm achieving an approximation factor $k_a/(2+2 k)$ of the optimal when $k_a$ out of the $k$ knapsack constraints are active. Extensive experiments on networks of millions of nodes demonstrate that the proposed algorithms achieve the state-of-the-art in terms of effectiveness and scalability.

Published
2016

45. Cross-Modal Similarity Learning via Pairs, Preferences, and Active Supervision

Author

Zhen, Y., Rai, P., Zha, H., and Lawrence Carin

Subjects
General Medicine
Abstract

We present a probabilistic framework for learning pairwise similarities between objects belonging to different modalities, such as drugs and proteins, or text and images. Our framework is based on learning a binary code based representation for objects in each modality, and has the following key properties: (i) it can leverage both pairwise as well as easy-to-obtain relative preference based cross-modal constraints, (ii) the probabilistic framework naturally allows querying for the most useful/informative constraints, facilitating an active learning setting (existing methods for cross-modal similarity learning do not have such a mechanism), and (iii) the binary code length is learned from the data. We demonstrate the effectiveness of the proposed approach on two problems that require computing pairwise similarities between cross-modal object pairs: cross-modal link prediction in bipartite graphs, and hashing based cross-modal similarity search.

Published
2015

46. Active Manifold Learning via Gershgorin Circle Guided Sample Selection

Author

Xu, H., Zha, H., REN-CANG LI, and Davenport, M. A.

Subjects
General Medicine
Abstract

In this paper, we propose an interpretation of active learning from a pure algebraic view and combine it with semi-supervised manifold learning. The proposed active manifold learning algorithm aims to learn the low-dimensional parameter space of the manifold with high accuracy from smartly labeled samples. We demonstrate that this problem is equivalent to a condition number minimization problem of the alignment matrix. Focusing on this problem, we first give a theoretical upper bound for the solution. Then we develop a heuristic but effective sample selection algorithm with the help of the Gershgorin circle theorem. We investigate the rationality, the feasibility, the universality and the complexity of the proposed method and demonstrate that our method yields encouraging active learning results.

Published
2015

47. Back to the Past: {S}ource Identification in Diffusion Networks from Partially Observed Cascades

Author

Farajtabar, M., Manuel Gomez Rodriguez, Du, N., Zamani, M., Zha, H., and Song, L.

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Physics - Physics and Society, FOS: Physical sciences, Computer Science - Social and Information Networks, Physics and Society (physics.soc-ph)
Abstract

When a piece of malicious information becomes rampant in an information diffusion network, can we identify the source node that originally introduced the piece into the network and infer the time when it initiated this? Being able to do so is critical for curtailing the spread of malicious information, and reducing the potential losses incurred. This is a very challenging problem since typically only incomplete traces are observed and we need to unroll the incomplete traces into the past in order to pinpoint the source. In this paper, we tackle this problem by developing a two-stage framework, which first learns a continuous-time diffusion network model based on historical diffusion traces and then identifies the source of an incomplete diffusion trace by maximizing the likelihood of the trace under the learned model. Experiments on both large synthetic and real-world data show that our framework can effectively go back to the past, and pinpoint the source node and its initiation time significantly more accurately than previous state-of-the-arts., Comment: To appear in the 18th International Conference on Artificial Intelligence and Statistics (AISTATS), 2015

Published
2015

48. Updated baseline for a staged Compact Linear Collider

Author

University of Helsinki, Helsinki Institute of Physics, University of Helsinki, Department of Physics, University of Helsinki, Department of Physics (-2009), Boland, M. J., Felzmann, U., Giansiracusa, P. J., Lucas, T. G., Rassool, R. P., Balazs, C., Charles, T. K., Afanaciev, K., Emeliantchik, I., Ignatenko, A., Makarenko, V., Shumeiko, N., Patapenka, A., Zhuk, I., Hoffman, A. C. Abusleme, Gutierrez, M. A. Diaz, Gonzalez, M. Vogel, Chi, Y., He, X., Pei, G., Pei, S., Shu, G., Wang, X., Zhang, J., Zhao, F., Zhou, Z., Chen, H., Gao, Y., Huang, W., Kuang, Y. P., Li, B., Li, Y., Shao, J., Shi, J., Tang, C., Wu, X., Ma, L., Han, Y., Fang, W., Gu, Q., Huang, D., Huang, X., Tan, J., Wang, Z., Zhao, Z., Laštovička, T., Uggerhoj, U., Wistisen, T. N., Aabloo, A., Eimre, K., Kuppart, K., Vigonski, S., Zadin, V., Aicheler, M., Baibuz, E., Brücken, E., Djurabekova, F., Eerola, P., Garcia, F., Haeggström, E., Huitu, K., Jansson, V., Karimaki, V., Kassamakov, I., Kyritsakis, A., Lehti, S., Meriläinen, A., Montonen, R., Niinikoski, T., Nordlund, K., Österberg, K., Parekh, M., Törnqvist, N. A., Väinölä, J., Veske, M., Farabolini, W., Mollard, A., Napoly, O., Peauger, F., Plouin, J., Bambade, P., Chaikovska, I., Chehab, R., Davier, M., Kaabi, W., Kou, E., LeDiberder, F., Pöschl, R., Zerwas, D., Aimard, B., Balik, G., Baud, J. -P., Blaising, J. -J., Brunetti, L., Chefdeville, M., Drancourt, C., Geoffroy, N., Jacquemier, J., Jeremie, A., Karyotakis, Y., Nappa, J. M., Vilalte, S., Vouters, G., Bernard, A., Peric, I., Gabriel, M., Simon, F., Szalay, M., Kolk, N. van der, Alexopoulos, T., Gazis, E. N., Gazis, N., Ikarios, E., Kostopoulos, V., Kourkoulis, S., Gupta, P. D., Shrivastava, P., Arfaei, H., Dayyani, M. K., Ghasem, H., Hajari, S. S., Shaker, H., Ashkenazy, Y., Abramowicz, H., Benhammou, Y., Borysov, O., Kananov, S., Levy, A., Levy, I., Rosenblat, O., D'Auria, G., Mitri, S. Di, Abe, T., Aryshev, A., Higo, T., Makida, Y., Matsumoto, S., Shidara, T., Takatomi, T., Takubo, Y., Tauchi, T., Toge, N., Ueno, K., Urakawa, J., Yamamoto, A., Yamanaka, M., Raboanary, R., Hart, R., Graaf, H. van der, Eigen, G., Zalieckas, J., Adli, E., Lillestøl, R., Malina, L., Pfingstner, J., Sjobak, K. N., Ahmed, W., Asghar, M. I., Hoorani, H., Bugiel, S., Dasgupta, R., Firlej, M., Fiutowski, T. A., Idzik, M., Kopec, M., Kuczynska, M., Moron, J., Swientek, K. P., Daniluk, W., Krupa, B., Kucharczyk, M., Lesiak, T., Moszczynski, A., Pawlik, B., Sopicki, P., Wojtoń, T., Zawiejski, L., Kalinowski, J., Krawczyk, M., Żarnecki, A. F., Firu, E., Ghenescu, V., Neagu, A. T., Preda, T., Zgura, I-S, Aloev, A., Azaryan, N., Budagov, J., Chizhov, M., Filippova, M., Glagolev, V., Gongadze, A., Grigoryan, S., Gudkov, D., Karjavine, V., Lyablin, M., Olyunin, A., Samochkine, A., Sapronov, A., Shirkov, G., Soldatov, V., Solodko, A., Solodko, E., Trubnikov, G., Tyapkin, I., Uzhinsky, V., Vorozhtov, A., Levichev, E., Mezentsev, N., Piminov, P., Shatilov, D., Vobly, P., Zolotarev, K., Jelisavcic, I. Bozovic, Kacarevic, G., Lukic, S., Milutinovic-Dumbelovic, G., Pandurovic, M., Iriso, U., Perez, F., Pont, M., Trenado, J., Aguilar-Benitez, M., Calero, J., Garcia-Tabares, L., Gavela, D., Gutierrez, J. L., Lopez, D., Toral, F., Moya, D., Jimeno, A. Ruiz, Vila, I., Argyropoulos, T., Gutierrez, C. Blanch, Boronat, M., Esperante, D., Faus-Golfe, A., Fuster, J., Martinez, N. Fuster, Muñoz, N. Galindo, García, I., Navarro, J. Giner, Ros, E., Vos, M., Brenner, R., Ekelöf, T., Jacewicz, M., Ögren, J., Olvegård, M., Ruber, R., Ziemann, V., Aguglia, D., Tehrani, N. Alipour, Andersson, A., Andrianala, F., Antoniou, F., Artoos, K., Atieh, S., Sune, R. Ballabriga, Barnes, M. J., Garcia, J. Barranco, Bartosik, H., Belver-Aguilar, C., Morell, A. Benot, Bett, D. R., Bettoni, S., Blanchot, G., Garcia, O. Blanco, Bonnin, X. A., Brunner, O., Burkhardt, H., Calatroni, S., Campbell, M., Lasheras, N. Catalan, Bastos, M. Cerqueira, Cherif, A., Chevallay, E., Constance, B., Corsini, R., Cure, B., Curt, S., Dalena, B., Dannheim, D., Michele, G. De, Oliveira, L. De, Deelen, N., Delahaye, J. P., Dobers, T., Doebert, S., Draper, M., Ramos, F. Duarte, Dubrovskiy, A., Elsener, K., Esberg, J., Esposito, M., Fedosseev, V., Ferracin, P., Fiergolski, A., Foraz, K., Fowler, A., Friebel, F., Fuchs, J-F, Rojas, C. A. Fuentes, Gaddi, A., Fajardo, L. Garcia, Morales, H. Garcia, Garion, C., Gatignon, L., Gayde, J-C, Gerwig, H., Goldblatt, A. N., Grefe, C., Grudiev, A., Guillot-Vignot, F. G., Gutt-Mostowy, M. L., Hauschild, M., Hessler, C., Holma, J. 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Abstract

The Compact Linear Collider (CLIC) is a multi-TeV high-luminosity linear e+e- collider under development. For an optimal exploitation of its physics potential, CLIC is foreseen to be built and operated in a staged approach with three centre-of-mass energy stages ranging from a few hundred GeV up to 3 TeV. The first stage will focus on precision Standard Model physics, in particular Higgs and top-quark measurements. Subsequent stages will focus on measurements of rare Higgs processes, as well as searches for new physics processes and precision measurements of new states, e.g. states previously discovered at LHC or at CLIC itself. In the 2012 CLIC Conceptual Design Report, a fully optimised 3 TeV collider was presented, while the proposed lower energy stages were not studied to the same level of detail. This report presents an updated baseline staging scenario for CLIC. The scenario is the result of a comprehensive study addressing the performance, cost and power of the CLIC accelerator complex as a function of centre-of-mass energy and it targets optimal physics output based on the current physics landscape. The optimised staging scenario foresees three main centre-of-mass energy stages at 380 GeV, 1.5 TeV and 3 TeV for a full CLIC programme spanning 22 years. For the first stage, an alternative to the CLIC drive beam scheme is presented in which the main linac power is produced using X-band klystrons.

Published
2016

49. Updated baseline for a staged Compact Linear Collider

Author

The, CLIC, collaborations, CLICdp, Boland, M. J., Felzmann, U., Giansiracusa, P. J., Lucas, T. G., Rassool, R. P., Balazs, C., Charles, T. K., Afanaciev, K., Emeliantchik, I., Ignatenko, A., Makarenko, V., Shumeiko, N., Patapenka, A., Zhuk, I., Hoffman, A. C. Abusleme, Gutierrez, M. A. Diaz, Gonzalez, M. Vogel, Chi, Y., Pei, G., Shu, G., Zhao, F., Huang, W., Kuang, Y. P., Tang, C., Ma, L., Fang, W., Gu, Q., Huang, D., Tan, J., Zhao, Z., Laštovička, T., Uggerhoj, U., Wistisen, T. N., Aabloo, A., Eimre, K., Kuppart, K., Vigonski, S., Zadin, V., Aicheler, M., Baibuz, E., Brücken, E., Djurabekova, F., Eerola, P., Haeggström, E., Huitu, K., Jansson, V., Karimaki, V., Kassamakov, I., Kyritsakis, A., Lehti, S., Meriläinen, A., Montonen, R., Niinikoski, T., Nordlund, K., Österberg, K., Parekh, M., Törnqvist, N. A., Väinölä, J., Veske, M., Farabolini, W., Mollard, A., Napoly, O., Peauger, F., Plouin, J., Bambade, P., Chaikovska, I., Chehab, R., Davier, M., Kaabi, W., Kou, E., LeDiberder, F., Pöschl, R., Zerwas, D., Aimard, B., Balik, G., Baud, J. -P., Blaising, J. -J., Brunetti, L., Chefdeville, M., Drancourt, C., Geoffroy, N., Jacquemier, J., Jeremie, A., Karyotakis, Y., Nappa, J. M., Vilalte, S., Vouters, G., Bernard, A., Peric, I., Gabriel, M., Simon, F., Szalay, M., Kolk, N. van der, Alexopoulos, T., Gazis, E. N., Gazis, N., Ikarios, E., Kostopoulos, V., Kourkoulis, S., Gupta, P. D., Shrivastava, P., Arfaei, H., Dayyani, M. K., Ghasem, H., Hajari, S. S., Shaker, H., Ashkenazy, Y., Abramowicz, H., Benhammou, Y., Borysov, O., Kananov, S., Levy, A., Levy, I., Rosenblat, O., D'Auria, G., Mitri, S. Di, Abe, T., Aryshev, A., Higo, T., Makida, Y., Matsumoto, S., Shidara, T., Takatomi, T., Takubo, Y., Tauchi, T., Toge, N., Ueno, K., Urakawa, J., Yamamoto, A., Yamanaka, M., Raboanary, R., Hart, R., Graaf, H. van der, Eigen, G., Zalieckas, J., Adli, E., Lillestøl, R., Malina, L., Pfingstner, J., Sjobak, K. N., Ahmed, W., Hoorani, H., Bugiel, S., Dasgupta, R., Firlej, M., Fiutowski, T. A., Idzik, M., Kopec, M., Kuczynska, M., Moron, J., Swientek, K. P., Daniluk, W., Krupa, B., Kucharczyk, M., Lesiak, T., Moszczynski, A., Pawlik, B., Sopicki, P., Wojtoń, T., Zawiejski, L., Kalinowski, J., Krawczyk, M., Żarnecki, A. F., Firu, E., Ghenescu, V., Neagu, A. 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Abstract

The Compact Linear Collider (CLIC) is a multi-TeV high-luminosity linear e+e- collider under development. For an optimal exploitation of its physics potential, CLIC is foreseen to be built and operated in a staged approach with three centre-of-mass energy stages ranging from a few hundred GeV up to 3 TeV. The first stage will focus on precision Standard Model physics, in particular Higgs and top-quark measurements. Subsequent stages will focus on measurements of rare Higgs processes, as well as searches for new physics processes and precision measurements of new states, e.g. states previously discovered at LHC or at CLIC itself. In the 2012 CLIC Conceptual Design Report, a fully optimised 3 TeV collider was presented, while the proposed lower energy stages were not studied to the same level of detail. This report presents an updated baseline staging scenario for CLIC. The scenario is the result of a comprehensive study addressing the performance, cost and power of the CLIC accelerator complex as a function of centre-of-mass energy and it targets optimal physics output based on the current physics landscape. The optimised staging scenario foresees three main centre-of-mass energy stages at 380 GeV, 1.5 TeV and 3 TeV for a full CLIC programme spanning 22 years. For the first stage, an alternative to the CLIC drive beam scheme is presented in which the main linac power is produced using X-band klystrons.

Published
2016

50. Shaping Social Activity by Incentivizing Users

Author

Farajtabar, M., Du, N., Manuel Gomez Rodriguez, Valera, I., Zha, H., and Song, L.

Subjects
Social and Information Networks (cs.SI), FOS: Computer and information sciences, Computer Science - Social and Information Networks
Abstract

Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.

Published
2014

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