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Zhu Z, Sun X, Chen K, Zhang M, Wu G. Comprehensive evaluation of advanced platelet-rich fibrin in common complications following sagittal split ramus osteotomy: a double-blind, split-mouth, randomized clinical trial. Int J Oral Maxillofac Surg 2025; 54:134-142. [PMID: 38839533 DOI: 10.1016/j.ijom.2024.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Revised: 03/06/2024] [Accepted: 03/12/2024] [Indexed: 06/07/2024]
Abstract
The sagittal split ramus osteotomy (SSRO) carries potential risks and complications. A double-blind, split-mouth, randomized clinical trial was performed, involving 30 patients undergoing mandibular setback. Advanced platelet-rich fibrin (A-PRF) was applied to one side, and the other side served as a control. The volume of postoperative drainage over 24 h was recorded. At 1, 2, and 5 days, and 3 months postsurgery, nerve recovery was assessed using the two-point discrimination test (TPD), while pain was evaluated using a visual analogue scale (VAS pain). Facial swelling was evaluated by taking linear measurements from facial reference points at the same time intervals. In the treatment group, the 24-hour drainage volume was lower (P = 0.011), pain was better on day 5 (P = 0.011), and TPD was better on day 2 (P = 0.011), day 5 (P = 0.007), and 3 months postoperatively (P = 0.020) than in the control group. There was also less facial swelling in the treatment group when compared to the baseline of 3 months postoperative (day 1, P = 0.012; day 2, P = 0.001; day 5, P = 0.011). The difference in bone mineral density (HU) at 3 months between the treatment group (469.7 ± 134.2) and the control group (348.3 ± 127.2) was statistically significant (P = 0.011), in favour of the treatment group. A-PRF may reduce postoperative complications such as neurosensory disturbance of the inferior alveolar nerve, pain, swelling, and drainage while enhancing bone healing in the osteotomy gap following SSRO. TRIAL REGISTRATION: The study was registered with the Chinese Clinical Trial Register (ChiCTR2200064534).
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Xu Q, Jia N, Sun X, Zhang MB, Guo XW, Zhang HD, Ling RJ, Liu YM, Wang Z, Li G, Yin Y, Shao H, Li J, Zhang HD, Qiu B, Wang DY, Zeng Q, Wang RG, Ye Y, Xiao B, Zou H, Chen JC, Li DX, Liu YQ, Shi QH, Liu JX, Jiang EF, Qi J, Mei LY, Zhao XF, Yang MM, Wang ZX. [Structural equation analysis and modeling of adverse ergonomic factors for lower back pain]. ZHONGHUA LAO DONG WEI SHENG ZHI YE BING ZA ZHI = ZHONGHUA LAODONG WEISHENG ZHIYEBING ZAZHI = CHINESE JOURNAL OF INDUSTRIAL HYGIENE AND OCCUPATIONAL DISEASES 2024; 42:937-945. [PMID: 39746684 DOI: 10.3760/cma.j.cn121094-20240422-00177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Objective: To establish a structural equation model of lower back pain and its risk factors in occupational population in China based on a large-sample occupational epidemiological survey and structural equation analysis, so as to lay the foundation for the prevention of this disease. Methods: A total of 73497 occupational epidemiological cases were selected from June 2018 to December 2023 using the Chinese version of the Musculoskeletal Disorders Electronic Questionnaire. The results were analyzed using ergonomic hazard factors and their risk source classification criteria. Confirmatory factor analysis was then applied to extract individual factors, work organization, working postures, work types, muscle fatigue, and lower back pain. These factors were subsequently classified into six categories and structural equation model was established. Results: The main structural reliability and validity indexes of the structural equation model of lower back pain and its adverse ergonomic factors after adjustment were basically up to standard[goodness of fit index (GFI) was 0.956, the adjusted goodness of fit index (AGFI) was 0.944, the root mean square error of approximation (RMSEA) was 0.043, the normed fit index (NFI) was 0.922, the comparative fit index (CFI) was 0.923, and the Tucker-Lewis index (TLI) was 0.908], and the model fit was good. There were correlations among four exogenous latent variables: individual factors, work organization, working postures and work types. Among them, individual factors had a weak negative correlation with the three exogenous potential variables of work organization, working postures and work types (r=-0.30, -0.11, -0.18), and work organization had a moderate positive correlation with working postures and work types (r=0.49, 0.55). There was a strong positive correlation between work types and working postures (r=0.72). The direct path coefficients of individual factors, working postures and muscle fatigue were 0.07, 0.11 and 0.40, respectively. The direct path coefficients of work types were -0.07. There was an indirect path mediated by muscle fatigue between work types, working postures and work organization and lower back pain, and the indirect path coefficients were: work types-muscle fatigue-lower back pain (0.15), working postures-muscle fatigue-lower back pain (-0.04) and work organization-muscle fatigue-lower back pain (0.27) . Conclusion: When carrying out the prevention and control of lower back pain, the pathogenesis of lower back pain induced by workers' muscle fatigue caused by work organization, working postures, and work types factors should be comprehensively considered, so as to provide a theoretical reference for improving the prevention and control level of lower back pain.
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Jia N, Zhang MB, Xu Q, Zhang HD, Ling RJ, Liu YM, Wang Z, Li G, Yin Y, Shao H, Li J, Zhang HD, Qiu B, Wang DY, Zeng Q, Wang RG, Ye Y, Xiao B, Zou H, Chen JC, Li DX, Liu YQ, Shi QH, Liu JX, Jiang EF, Qi J, Mei LY, Zhao XF, Yang MM, Guo XW, Sun X, Wang ZX. [Study on the epidemiological characteristics of work-related musculoskeletal disorders among 29 industries or occupational groups in China]. ZHONGHUA LAO DONG WEI SHENG ZHI YE BING ZA ZHI = ZHONGHUA LAODONG WEISHENG ZHIYEBING ZAZHI = CHINESE JOURNAL OF INDUSTRIAL HYGIENE AND OCCUPATIONAL DISEASES 2024; 42:884-895. [PMID: 39746679 DOI: 10.3760/cma.j.cn121094-20240731-00358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Objective: To analyze the distribution characteristics of work-related musculoskeletal disorders (WMSDs) among the occupational population in China's key industries during the period from 2018 to 2023, and to provide data support for the formulation of targeted prevention strategies. Methods: Between January 1, 2018, and December 31, 2023, a cross-sectional epidemiological survey and a retrospective investigation were conducted in seven geographical regions of North China, East China, Central China, South China, Southwest China, Northwest China, and Northeast China, using a combination of epidemiological cross-sectional and retrospective surveys. The industries were stratified according to the degree of closeness to WMSDs, the size of the occupational population, and the importance of the national economy, and then cluster sampling was conducted according to the size of the enterprises (large, medium, and small) within each stratum. Representative enterprises were selected, and workers who had been employed for more than one year and met the inclusion criteria were selected as the study subjects. The survey subjects completed the Chinese version of the Musculoskeletal Disorders Survey Questionnaire online by scanning a QR code. A total of 88, 609 valid questionnaires were collected. Continuous data were described by means and standard deviations, and the standardized incidence rates of WMSDs were evaluated using chi-square tests to compare the standardized incidence rates of different body parts. Results: The standardized prevalence rate of WMSDs among the frontline working population in China was 36.7%, with the top three being the neck (21.37%), shoulders (18.23%), and lower back (14.92%). There was a statistically significant difference in the standardized prevalence rate of WMSDs between different body parts (χ(2)=47577.82, P<0.05). The lowest standardized prevalence rate of WMSDs was found in the southeast coastal region of China (32.54%), while the highest was in the northwest (49.70%) and northeast regions (46.16%). The standardized prevalence rate of WMSDs decreased from 42.88% in 2018 to 29.94% in 2023. The micro-enterprise observation group had a higher concentration of WMSDs in the neck (20.66%), lower back (17.95%), and shoulders (17.79%), while the large enterprise observation group had a higher concentration in the neck (20.54%), shoulders (17.52%), and lower back (14.65%). Among the industries surveyed, the highest standardized prevalence rate of WMSDs was found in the healthcare industry (53.18%), followed by the toy manufacturing industry (50.54%), the automotive manufacturing industry (43.39%), general aviation services (42.71%), and the ship and related equipment manufacturing industry (40.56%). The standardized prevalence rate of WMSDs among women was significantly higher than that of men (OR value=1.44, P<0.05). In addition, the standardized prevalence rate of WMSDs with multiple affected body parts (four or more body parts affected simultaneously) was higher, and the risk of WMSDs increased with subjective fatigue, showing a significant "S-shaped" trend (the OR value was 2.19, 3.16, 4.71, 5.49, and 3.97 when the RPE was 13, 15, 17, 19, and 20, respectively. χ(2)(trend)=17.23, P<0.001) . Conclusion: The distribution characteristics of WMSDs in key industries in China show obvious differences in different parts, regions, time, enterprise scale, industry and population attributes. Agriculture, pharmaceutical manufacturing, packaging and decoration and other printing industries have a high proportion of WMSDs in multiple parts at the same time and in a single site. It is necessary to study and implement precise preventive measures according to specific regions, industries and enterprise scales, as well as the characteristics of multi-site WMSDs, so as to effectively protect the health and well-being of the working population.
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Jia N, Zhang MB, Xu Q, Zhang HD, Ling RJ, Liu YM, Wang Z, Li G, Yin Y, Shao H, Li J, Zhang HD, Qiu B, Wang DY, Zeng Q, Wang RG, Ye Y, Xiao B, Zou H, Chen JC, Li DX, Liu YQ, Shi QH, Liu JX, Jiang EF, Qi J, Mei LY, Zhao XF, Yang MM, Guo XW, Sun X, Wang ZX. [Study on the relationship between occupational exposure characteristics and musculoskeletal disorders in 29 industries or workforce groups in China]. ZHONGHUA LAO DONG WEI SHENG ZHI YE BING ZA ZHI = ZHONGHUA LAODONG WEISHENG ZHIYEBING ZAZHI = CHINESE JOURNAL OF INDUSTRIAL HYGIENE AND OCCUPATIONAL DISEASES 2024; 42:896-910. [PMID: 39746680 DOI: 10.3760/cma.j.cn121094-20240904-00414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Objective: To explore the occupational contact characteristics of work-related musculoskeletal disorders (WMSDs) among key occupational groups in China, assess the impact of occupational factors on the occurrence of WMSDs, and propose corresponding preventive strategies. Methods: Between January 1, 2018, and December 31, 2023, a cross-sectional epidemiological survey method was employed to retrospectively investigate the occurrence of WMSDs and related occupational factors. Valid questionnaire data from 88609 on-the-job workers in 441 enterprises across 29 industries or job types in seven regions of China were collected, and the relationship between WMSDs and various occupational factors was discussed using multifactorial logistic regression analysis. Odds ratios (OR) and their 95% confidence intervals (CI) were calculated to assess the impact of different factors on the risk of WMSDs occurrence, with all statistical tests performed as two-tailed tests, using P<0.05 as the criterion for statistical significance. Results: Among the surveyed subjects, 59989 were male, accounting for 67.7% (59989/88609) ; 28620 women, accounting for 32.3% (28620/88609). The standardized incidence rate of work related musculoskeletal disorders (WMSDs) in 9 parts of the body was statistically significant (P<0.05) among different working age groups without considering specific parts. The average length of service for onset of WMSDs (regardless of location) is (4.52±2.67) years, and there is a statistically significant difference in the length of service for onset of WMSDs in different locations (P<0.05). As the frequency of heavy object handling increases, the standardization rate and OR value of WMSDs in various parts show a significant increase trend. The incidence of WMSDs is significantly correlated with maintaining poor work posture for a long time, frequent repetitive operations, and heavy object handling (OR=1.52, 1.45, 1.63, P<0.01) . Conclusion: This study reveals that the risk of WMSDs in China's occupational population shows a non-linear change with increasing length of service, especially with a significant increase in 10 to 25 years of service. Poor work posture, frequent repetitive operations, and heavy object handling are significantly correlated with WMSDs, providing a scientific basis for developing more accurate and effective prevention strategies for WMSDs.
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Guo XW, Wang ZX, Sun X, Zhang MB, Xu Q, Zhang HD, Ling RJ, Liu YM, Wang Z, Li G, Yin Y, Shao H, Li J, Zhang HD, Qiu B, Wang DY, Zeng Q, Wang RG, Ye Y, Xiao B, Zou H, Chen JC, Li DX, Liu YQ, Shi QH, Liu JX, Jiang EF, Qi J, Mei LY, Zhao XF, Yang MM, Jia N. [Structural equation analysis and modeling of wrist WMSDs and its adverse ergonomic factors]. ZHONGHUA LAO DONG WEI SHENG ZHI YE BING ZA ZHI = ZHONGHUA LAODONG WEISHENG ZHIYEBING ZAZHI = CHINESE JOURNAL OF INDUSTRIAL HYGIENE AND OCCUPATIONAL DISEASES 2024; 42:927-936. [PMID: 39746683 DOI: 10.3760/cma.j.cn121094-20240507-00201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/04/2025]
Abstract
Objective: To investigate the relationships and impacts between various occupational ergonomic hazards and hand and wrist fatigue, as well as work-related musculoskeletal disorders (WMSDs) of the hand and wrist, and to propose targeted preventive and intervention measures for adverse occupational ergonomic factors causing WMSDs of the hand and wrist. Methods: From 2018 to December 2023, a nationwide epidemiologic survey study of wrist WMSDs was conducted using the Chinese version of the electronic questionnaire system for musculoskeletal disorders.A total of 88, 609 valid questionnaires were collected. The exclusion criteria were as follows: patients with congenital spinal deformities, as well as those with wrist WMSDs caused by external injuries, infectious diseases, and malignant tumors. A total of 73, 497 questionnaires were finally included in the study analysis, with an effective questionnaire return rate of 82.5%. Exploratory factor analysis was used to analyze and summarize the factors affecting wrist WMSDs from the questionnaires, including individual factors, work organization, work type, wrist work posture, wrist fatigue, and wrist WMSDs as latent variables, and to hypothesize, fit, and validate the structural aspect model, as well as to conduct mediation effect analysis. Results: The incidence of WMSDs and fatigue in wrist was 12.19% and 16.30% respectively. The fitting indexes of the modified structural equation model were basically up to the standard (GFI was 0.981, AGFI was 0.973, RMSEA was 0.031, NFI was 0.863, IFI and CFI were 0.865). There is a correlation between individual factors, work organization, wrist working posture and work type. There was a low negative correlation between individual factors and other factors, and there was a positive correlation between work organization, work type and wrist WMSDs (r=0.346, 0.295), and these two factors were positively correlated with wrist fatigue height (r=0.862, 0.599), and were positively correlated with wrist working posture (r=0.443, 0.620). There was moderate positive correlation between wrist working posture and wrist fatigue (r=0.469). The three most influential factors on wrist WMSDs were work organization, individual factor and work type, and the path coefficients were 0.247, 0.210 and 0.136, respectively. The first two factors that have the greatest influence on wrist fatigue are work organization and work type, and the path coefficients are 0.758 and 0.188, respectively. Individual factors, work type, wrist working posture and work organization had direct effects on wrist WMSDs, and the effect values were 0.093, 0.253, 0.718 and 0.583, respectively. Wrist fatigue played a partial mediating role between individual factors, work type, wrist working posture and work organization and wrist WMSDs, with the indirect effect ratio of 25.6%, 45.8%, 3.2% and 65.5%. Conclusion: Wrist fatigue plays an important mediating role in the path of various factors affecting wrist WMSDs, especially in the path of work organization, work type, individual factors and wrist WMSDs. Poor wrist working posture is an important risk factor that directly affects the occurrence of wrist WMSDs.
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Hayrapetyan A, Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, Damanakis K, Dragicevic M, Hussain PS, Jeitler M, Krammer N, Li A, Liko D, Mikulec I, Schieck J, Schöfbeck R, Schwarz D, Sonawane M, Templ S, Waltenberger W, Wulz CE, Darwish MR, Janssen T, Laer TV, Mechelen PV, Breugelmans N, D’Hondt J, Dansana S, De Moor A, Delcourt M, Heyen F, Lowette S, Makarenko I, Müller D, Tavernier S, Tytgat M, Onsem GPV, Van Putte S, Vannerom D, Bilin B, Clerbaux B, Das AK, De Lentdecker G, Evard H, Favart L, Gianneios P, Jaramillo J, Khalilzadeh A, Khan FA, Lee K, Mahdavikhorrami M, Malara A, Paredes S, Shahzad MA, Thomas L, Bemden MV, Velde CV, Vanlaer P, De Coen M, Dobur D, Gokbulut G, Hong Y, Knolle J, Lambrecht L, Marckx D, Amarilo KM, Samalan A, Skovpen K, Bossche NVD, Linden JVD, Wezenbeek L, Benecke A, Bethani A, Bruno G, Caputo C, De Favereau De Jeneret J, Delaere C, Donertas IS, Giammanco A, Guzel AO, Jain S, Lemaitre V, Lidrych J, Mastrapasqua P, Tran TT, Wertz S, Alves GA, Pereira MAG, Coelho E, Silva GC, Hensel C, De Oliveira TM, Herrera CM, Moraes A, Teles PR, Soeiro M, Pereira AV, Júnior WLA, Filho MBF, Malbouisson HB, Carvalho W, Chinellato J, Da Costa EM, Da Silveira GG, De Jesus Damiao D, De Souza SF, De Souza RG, Macedo M, Martins J, Mundim L, Nogima H, Pinheiro JP, Santoro A, Sznajder A, Thiel M, Bernardes CA, Calligaris L, Tomei TRFP, Gregores EM, Da Costa BL, Silverio IM, Mercadante PG, Novaes SF, Orzari B, Padula SS, Aleksandrov A, Antchev G, Hadjiiska R, Iaydjiev P, Misheva M, Shopova M, Sultanov G, Dimitrov A, Litov L, Pavlov B, Petkov P, Petrov A, Shumka E, Keshri S, Thakur S, Cheng T, Javaid T, Yuan L, Hu Z, Liang Z, Liu J, Yi K, Chen GM, Chen HS, Chen M, Iemmi F, Jiang CH, Kapoor A, Liao H, Liu ZA, Sharma R, Song JN, Tao J, Wang C, Wang J, Wang Z, Zhang H, Zhao J, Agapitos A, Ban Y, Deng S, Guo B, Jiang C, Levin A, Li C, Li Q, Mao Y, Qian S, Qian SJ, Qin X, Sun X, Wang D, Yang H, Zhang L, Zhao Y, Zhou C, Yang S, You Z, Jaffel K, Lu N, Bauer G, Li B, Zhang J, Gao X, Lin Z, Lu C, Xiao M, Avila C, Trujillo DAB, Cabrera A, Florez C, Fraga J, Vega JAR, Ramirez F, Rendón C, Rodriguez M, Barbosa AAR, Alvarez JDR, Giljanovic D, Godinovic N, Lelas D, Sculac A, Kovac M, Petkovic A, Sculac T, Bargassa P, Brigljevic V, Chitroda BK, Ferencek D, Jakovcic K, Mishra S, Starodumov A, Susa T, Attikis A, Christoforou K, Hadjiagapiou A, Leonidou C, Mousa J, Nicolaou C, Paizanos L, Ptochos F, Razis PA, Rykaczewski H, Saka H, Stepennov A, Finger M, Finger M, Kveton A, Jarrin EC, Assran Y, El-mahdy B, Elgammal S, Lotfy A, Mahmoud MA, Ehataht K, Kadastik M, Lange T, Nandan S, Nielsen C, Pata J, Raidal M, Tani L, Veelken C, Kirschenmann H, Osterberg K, Voutilainen M, Bharthuar S, Norjoharuddeen NB, Brücken E, Garcia F, Inkaew P, Kallonen KTS, Lampén T, Lassila-Perini K, Lehti S, Lindén T, Martikainen L, Myllymäki M, Rantanen MM, Siikonen H, Tuominiemi J, Luukka P, Petrow H, Besancon M, Couderc F, Dejardin M, Denegri D, Faure JL, Ferri F, Ganjour S, Gras P, de Monchenault GH, Kumar M, Lohezic V, Malcles J, Orlandi F, Portales L, Rosowsky A, Sahin MÖ, Savoy-Navarro A, Simkina P, Titov M, Tornago M, Beaudette F, Boldrini G, Busson P, Cappati A, Charlot C, Chiusi M, Damas F, Davignon O, De Wit A, Ehle IT, Alves BAFS, Ghosh S, Gilbert A, de Cassagnac RG, Hakimi A, Harikrishnan B, Kalipoliti L, Liu G, Nguyen M, Ochando C, Salerno R, Sauvan JB, Sirois Y, Gómez LU, Vernazza E, Zabi A, Zghiche A, Agram JL, Andrea J, Apparu D, Bloch D, Brom JM, Chabert EC, Collard C, Falke S, Goerlach U, Haeberle R, Bihan ACL, Meena M, Poncet O, Saha G, Sessini MA, Hove PV, Vaucelle P, Florio AD, Amram D, Beauceron S, Blancon B, Boudoul G, Chanon N, Contardo D, Depasse P, Dozen C, Mamouni HE, Fay J, Gascon S, Gouzevitch M, Greenberg C, Grenier G, Ille B, Jourd‘huy E, Laktineh IB, Lethuillier M, Mirabito L, Perries S, Purohit A, Donckt MV, Verdier P, Xiao J, Lomidze I, Toriashvili T, Tsamalaidze Z, Botta V, Rodríguez SC, Feld L, Klein K, Lipinski M, Meuser D, Pauls A, Adán DP, Röwert N, Teroerde M, Diekmann S, Dodonova A, Eich N, Eliseev D, Engelke F, Erdmann J, Erdmann M, Fackeldey P, Fischer B, Hebbeker T, Hoepfner K, Ivone F, Jung A, Lee M, Mausolf F, Merschmeyer M, Meyer A, Mukherjee S, Noll D, Nowotny F, Pozdnyakov A, Rath Y, Redjeb W, Rehm F, Reithler H, Sarkisovi V, Schmidt A, Sharma A, Spah JL, Stein A, Da Silva De Araujo FT, Wiedenbeck S, Zaleski S, Dziwok C, Flügge G, Kress T, Nowack A, Pooth O, Stahl A, Ziemons T, Zotz A, Petersen HA, Martin MA, Alimena J, Amoroso S, An Y, Bach J, Baxter S, Bayatmakou M, Gonzalez HB, Behnke O, Belvedere A, Blekman F, Borras K, Campbell A, Cardini A, Cheng C, Colombina F, De Silva M, Eckerlin G, Eckstein D, Banos LIE, Filatov O, Gallo E, Geiser A, Guglielmi V, Guthoff M, Hinzmann A, Jeppe L, Kaech B, Kasemann M, Kleinwort C, Kogler R, Komm M, Krücker D, Lange W, Pernia DL, Lipka K, Lohmann W, Lorkowski F, Mankel R, Melzer-Pellmann IA, Morentin MM, Meyer AB, Milella G, Figueroa KM, Mussgiller A, Nair LP, Niedziela J, Nürnberg A, Otarid Y, Park J, Ranken E, Raspereza A, Rastorguev D, Rübenach J, Rygaard L, Saggio A, Scham M, Schnake S, Schütze P, Schwanenberger C, Selivanova D, Sharko K, Shchedrolosiev M, Stafford D, Vazzoler F, Barroso AV, Walsh R, Wang D, Wang Q, Wen Y, Wichmann K, Wiens L, Wissing C, Yang Y, Santos AZC, Albrecht A, Albrecht S, Antonello M, Bein S, Benato L, Bollweg S, Bonanomi M, Connor P, Morabit KE, Fischer Y, Garutti E, Grohsjean A, Haller J, Jabusch HR, Kasieczka G, Keicher P, Klanner R, Korcari W, Kramer T, Kuo CC, Kutzner V, Labe F, Lange J, Lobanov A, Matthies C, Moureaux L, Mrowietz M, Nigamova A, Nissan Y, Paasch A, Rodriguez KJP, Quadfasel T, Raciti B, Rieger M, Savoiu D, Schindler J, Schleper P, Schröder M, Schwandt J, Sommerhalder M, Stadie H, Steinbrück G, Tews A, Wolf M, Brommer S, Burkart M, Butz E, Chwalek T, Dierlamm A, Droll A, Elicabuk U, Faltermann N, Giffels M, Gottmann A, Hartmann F, Hofsaess R, Horzela M, Husemann U, Kieseler J, Klute M, Koppenhöfer R, Lawhorn JM, Link M, Lintuluoto A, Maier B, Maier S, Mitra S, Mormile M, Müller T, Neukum M, Oh M, Pfeffer E, Presilla M, Quast G, Rabbertz K, Regnery B, Shadskiy N, Shvetsov I, Simonis HJ, Sowa L, Stockmeier L, Tauqeer K, Toms M, Trevisani N, Cube RFV, Wassmer M, Wieland S, Wittig F, Wolf R, Zuo X, Anagnostou G, Daskalakis G, Kyriakis A, Papadopoulos A, Stakia A, Kontaxakis P, Melachroinos G, Painesis Z, Papavergou I, Paraskevas I, Saoulidou N, Theofilatos K, Tziaferi E, Vellidis K, Zisopoulos I, Bakas G, Chatzistavrou T, Karapostoli G, Kousouris K, Papakrivopoulos I, Siamarkou E, Tsipolitis G, Zacharopoulou A, Adamidis K, Bestintzanos I, Evangelou I, Foudas C, Kamtsikis C, Katsoulis P, Kokkas P, Kioseoglou PGK, Manthos N, Papadopoulos I, Strologas J, Hajdu C, Horvath D, Márton K, Rádl AJ, Sikler F, Veszpremi V, Csanád M, Farkas K, Fehérkuti A, Gadallah MMA, Kadlecsik Á, Major P, Pásztor G, Veres GI, Ujvari B, Zilizi G, Bencze G, Czellar S, Molnar J, 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Houghton C, Jaroslawski D, Konstantinou S, Laflotte I, Lath A, Montalvo R, Nash K, Reichert J, Routray H, Saha P, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Vora J, Wang H, Ally D, Delannoy AG, Fiorendi S, Higginbotham S, Holmes T, Kanuganti AR, Karunarathna N, Lee L, Nibigira E, Spanier S, Aebi D, Ahmad M, Akhter T, Bouhali O, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Gogate N, Hegde V, Hussain A, Kazhykarim Y, Lamichhane K, Lee SW, Mankel A, Peltola T, Volobouev I, Appelt E, Chen Y, Greene S, Gurrola A, Johns W, Elayavalli RK, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Chung H, Cox B, Hakala J, Hirosky R, Ledovskoy A, Neu C, Bhattacharya S, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Sreekala JM, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pétré L, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Alexakhin V, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Blinov V, Boos E, Borshch V, Budkouski D, Bunichev V, Chekhovsky V, Chistov R, Danilov M, Dermenev A, Dimova T, Druzhkin D, Dubinin M, Dudko L, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanov Y, Kachanov V, Karjavine V, Karneyeu A, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Klyukhin V, Kodolova O, Konstantinov D, Korenkov V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oreshkin V, Palichik V, Perelygin V, Petrushanko S, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Sergeychik V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Sulimov V, Terkulov A, Teryaev O, Tlisova I, Toropin A, Tulupov A, Uvarov L, Uzunian A, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Search for CP violation in D 0 → K S 0 K S 0 decays in proton-proton collisions at s = 13 Te V. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS 2024; 84:1264. [PMID: 39651650 PMCID: PMC11624237 DOI: 10.1140/epjc/s10052-024-13244-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2024] [Accepted: 08/13/2024] [Indexed: 12/11/2024]
Abstract
A search is reported for charge-parity CP violation inD 0 → K S 0 K S 0 decays, using data collected in proton-proton collisions ats = 13 Te V recorded by the CMS experiment in 2018. The analysis uses a dedicated data set that corresponds to an integrated luminosity of 41.6fb - 1 , which consists of about 10 billion events containing a pair of b hadrons, nearly all of which decay to charm hadrons. The flavor of the neutral D meson is determined by the pion charge in the reconstructed decaysD ∗ + → D 0 π + andD ∗ - → D ¯ 0 π - . The CP asymmetry inD 0 → K S 0 K S 0 is measured to beA CP ( K S 0 K S 0 ) = ( 6.2 ± 3.0 ± 0.2 ± 0.8 ) % , where the three uncertainties represent the statistical uncertainty, the systematic uncertainty, and the uncertainty in the measurement of the CP asymmetry in theD 0 → K S 0 π + π - decay. This is the first CP asymmetry measurement by CMS in the charm sector as well as the first to utilize a fully hadronic final state.
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Grants
- SC
- Austrian Federal Ministry of Education, Science and Research
- Austrian Science Fund
- Belgian Fonds de la Recherche Scientifique
- Belgian Fonds voor Wetenschappelijk Onderzoek
- CNPq
- CAPES
- FAPERJ
- FAPERGS
- FAPESP
- Bulgarian Ministry of Education and Science
- Bulgarian National Science Fund
- CERN
- Chinese Academy of Sciences
- Ministry of Science and Technology
- Chinese National Natural Science Foundation of China
- Colombian Funding Agency (MINICIENCIAS)
- Croatian Ministry of Science, Education and Sport
- Croatian Science Foundation
- Research and Innovation Foundation
- SENESCYT
- Estonian Research Council via PRG780, PRG803, RVTT3 and TK202
- Ministry of Education and Research via TK202
- Academy of Finland
- Finnish Ministry of Education and Culture
- Helsinki Institute of Physics
- Institut National de Physique Nucléaire et de Physique des Particules
- Centre National de la Recherche Scientifique
- Commissariat à l’Énergie Atomique et aux Énergies Alternatives
- Shota Rustaveli National Science Foundation
- Bundesministerium für Bildung und Forschung
- Deutsche Forschungsgemeinschaft
- Helmholtz-Gemeinschaft Deutscher Forschungszentren
- General Secretariat for Research and Innovation
- National Research, Development and Innovation Office
- Department of Atomic Energy
- Department of Science and Technology
- ICSC -National Research Centre for High Performance Computing, Big Data and Quantum Computing, funded by the NextGenerationEU program
- FAIR - Future Artificial Intelligence Research; funded by the NextGenerationEU program
- Institute for Research in Fundamental Studies
- Science Foundation
- Istituto Nazionale di Fisica Nucleare
- Korean Ministry of Education, Science and Technology
- National Research Foundation of Korea (NRF)
- MES
- Research Council of Lithuania (LMTLT), agreement No. VS-19
- Ministry of Education
- University of Malaya
- BUAP
- CINVESTAV
- CONACYT
- LNS
- SEP
- UASLP
- MOS
- Ministry of Business, Innovation and Employment
- Pakistan Atomic Energy Commission
- Ministry of Educaton and Science
- National Science Centre
- Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019
- JINR, Dubna
- Ministry of Education and Science of the Russian Federation
- Federal Agency of Atomic Energy of the Russian Federation
- Russian Academy of Sciences
- Russian Foundation for Basic Research
- National Research Center “Kurchatov Institute"
- Ministry of Education, Science and Technological Development of Serbia
- MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”
- Fondo Europeo de Desarrollo Regional, Spain
- Plan de Ciencia, Tecnología e Innovación del Principado de Asturias
- MOSTR
- ETH Board
- ETH Zurich
- PSI
- SNF
- UniZH
- Canton Zurich
- SER
- Ministry of Science and Technology
- Ministry of Higher Education, Science, Research and Innovation
- National Science and Technology Development Agency of Thailand
- Scientific and Technical Research Council of Turkey
- Turkish Atomic Energy Authority
- National Academy of Sciences of Ukraine
- Science and Technology Facilities Council
- US Department of Energy
- US National Science Foundation
- Marie-Curie programme
- European Research Council and EPLANET (European Union)
- European Research Council/European Cooperation in Science and Technology), Action CA16108
- Leventis Foundation
- Alfred P. Sloan Foundation
- Alexander von Humboldt Foundation
- Science Committee, project no. 22rl-037
- Belgian Federal Science Policy Office
- Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- Belgian Fonds de la Recherche Scientifique, "Excellence of Science - EOS" - be.h project n. 30820817
- Belgian Fonds voor Wetenschappelijk Onderzoek, "Excellence of Science - EOS" - be.h project n. 30820817
- Beijing Municipal Science & Technology Commission, No. Z191100007219010
- Fundamental Research Funds for the Central Universities
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy - EXC 2121 “Quantum Universe” – 390833306
- Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497
- Hellenic Foundation for Research and Innovation, Project Number 2288
- Hungarian Academy of Sciences
- New National Excellence Program - ÚNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64
- Council of Scientific and Industrial Research, India
- Latvian Council of Science
- Ministy of Education and Science, project no. 2022/WK/14
- National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
- Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018
- National Priorities Research Program by Qatar National Research Fund
- Ministry of Science and Higher Education, project no. FSWU-2023-0073 and FSWW-2020-0008
- Russian Science Foundation Project Number 24-22-00445
- Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00
- Programa Severo Ochoa del Principado de Asturias
- CUAASC
- National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B37G660013
- Kavli Foundation
- Nvidia Corporation
- Welch Foundation, contract C-1845
- Weston Havens Foundation
- Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna
- Inter-University Institute for High Energies, Brussels
- Université Catholique de Louvain, Louvain-la-Neuve
- São Paulo Research and Analysis Center, São Paulo
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro
- University of Sofia, Sofia
- Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing
- National Institute of Chemical Physics and Biophysics, Tallinn
- Helsinki Institute of Physics, Helsinki
- Grille de Recherche d’Ile de France (GRIF), Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette, France and Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
- Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne
- Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg
- Deutsches Elektronen-Synchrotron, Hamburg
- Karlsruher Institut für Technologie, Karlsruhe
- RWTH Aachen University, Aachen
- University of Ioánnina, Ioánnina
- Wigner Research Centre for Physics, Budapest
- Tata Institute of Fundamental Research, Mumbai
- INFN CNAF, Bologna
- INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari
- INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa
- INFN Sezione di Roma, Sapienza Università di Roma, Rome
- INFN Sezione di Trieste, Università di Trieste, Trieste
- Laboratori Nazionali di Legnaro, Legnaro
- National Centre for Physics, Quaid-I-Azam University, Islamabad
- Akademickie Centrum Komputerowe Cyfronet AGH, Krakow
- National Centre for Nuclear Research, Swierk
- Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa
- Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’, Protvino
- Institute for Nuclear Research (INR) of the Russian Academy of Sciences, Troitsk
- Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ’Kurchatov Institute’, Moscow
- Joint Institute for Nuclear Research, Dubna
- Korea Institute of Science and Technology Information (KISTI), Daejeon
- Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid
- Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander
- Port d’Informació Científica, Bellaterra
- CERN, European Organization for Nuclear Research, Geneva
- CSCS - Swiss National Supercomputing Centre, Lugano
- National Center for High-performance Computing (NCHC), Hsinchu City
- Middle East Technical University, Physics Department, Ankara
- National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov
- GridPP, Brunel University, Uxbridge
- GridPP, Imperial College, London
- GridPP, Queen Mary University of London, London
- GridPP, Royal Holloway, University of London, London
- GridPP, Rutherford Appleton Laboratory, Didcot
- GridPP, University of Bristol, Bristol
- GridPP, University of Glasgow, Glasgow
- Baylor University, Waco
- California Institute of Technology, Pasadena
- Fermi National Accelerator Laboratory, Batavia
- Massachusetts Institute of Technology, Cambridge
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley
- Open Science Grid (OSG) Consortium
- Pittsburgh Supercomputing Center (PSC), Pittsburgh
- Purdue University, West Lafayette
- San Diego Supercomputer Center (SDSC), La Jolla
- Texas Advanced Computing Center (TACC), Austin
- University of California, San Diego, La Jolla
- University of Colorado Boulder, Boulder
- University of Florida, Gainesville
- University of Nebraska-Lincoln, Lincoln
- University of Wisconsin - Madison, Madison
- Vanderbilt University, Nashville
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7
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Yan L, Sun X, Lou B, Zhang Y, Zhuang D, Jia J, Zhang L, He Y, Xu L, Wu S, Zhou Q, Chen C, Yu X, Li H. Carrier frequency and incidence estimation of deficiency of adenosine deaminase 2 in the Chinese population based on massive exome sequencing data. Clin Immunol 2024; 269:110394. [PMID: 39481740 DOI: 10.1016/j.clim.2024.110394] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2024] [Revised: 10/25/2024] [Accepted: 10/27/2024] [Indexed: 11/02/2024]
Abstract
Deficiency of adenosine deaminase 2 (DADA2) is an autosomal recessive autoinflammatory disease characterised by early onset stroke, recurrent fever, and diverse vascular pathologies, caused by loss-of-function homozygous or compound heterozygous variants of ADA2. This research aimed to determine the carrier frequency and expected incidence of DADA2 in China, using massive exome sequencing (ES) data. A total of 50 likely pathogenic/pathogenic variants (LP/PVs) were identified among 69,413 Chinese individuals, including 20 novel and rare variants (<0.0022 % allele frequency), expanding the known spectrum of PVs in ADA2. The overall carrier frequency in the Chinese population was 1.05 % (732/69,413) and the estimated incidence of DADA2 was approximately one in 92,251 individuals. The present study provides an accurate estimation of the prevalence of DADA2 in China, supporting genetic counseling, early diagnosis treatment, and prognostic evaluation.
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Zhang D, Wu H, Wang Y, Xu Z, Sun X, Liswaniso S, Qin N, Xu R. The inhibition roles of RAB23 gene in granulosa cell proliferation and progesterone synthesis of hen ovarian prehierarchical follicles. Br Poult Sci 2024; 65:690-698. [PMID: 39036858 DOI: 10.1080/00071668.2024.2377748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2024] [Accepted: 06/14/2024] [Indexed: 07/23/2024]
Abstract
1. The proliferation of granulosa cells is vital for the development and recruitment of hen ovarian prehierarchical follicles (PF). The RAB23 protein is a member of the Rab family, belonging to the GTPase family. This study studied the regulatory roles of the RAB23 gene in PF.2. The expression of RAB23 was significantly increased in granulosa cells (GC) during PF growth and was highest in GC at 6-8 mm diameter (p < 0.05). The RAB23 protein was mainly expressed in the GC, oocytes (OC) as well as somatic cells (SC) of the PF.3. The mRNA expression of FSHR, CCND1,CYP11A1, StAR and HSD3B1 was significantly increased in the siRNA RAB23 group (p < 0.05). Additionally, protein expression of FSHR, CCND1, CYP11A1, HSD3B1 was significantly increased (p < 0.05) after GC were transfected with RAB23-specific siRNA. Protein expression of StAR in the siRNA RAB23 group was numerically higher than that in the positive control (PC) and negative control (NC) groups. The GC proliferation rate and progesterone synthesis of the prehierarchical follicles in hen ovaries were markedly increased in vitro (p < 0.05).4.This study revealed that RAB23 might play an inhibitory role in GC proliferation and progesterone synthesis during the prehierarchical follicles development in vitro.
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Ma J, Li Z, Xu J, Lai J, Zhao J, Ma L, Sun X. PRDM1 promotes the ferroptosis and immune escape of thyroid cancer by regulating USP15-mediated SELENBP1 deubiquitination. J Endocrinol Invest 2024; 47:2981-2997. [PMID: 39014173 DOI: 10.1007/s40618-024-02385-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/30/2024] [Accepted: 04/25/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND The deubiquitinating enzyme Ubiquitin-specific peptidase 15 (USP15) is upregulated in various cancers and promotes tumor progression by increasing the expression of several oncogenes. This project is designed to explore the role and mechanism of USP15 in thyroid cancer (TC) progression. METHODS Selenium-binding protein 1 (SELENBP1), USP15, CCL2/5, CXCL10/11, IL-4, and TGF-β1 mRNA levels were detected using real-time quantitative polymerase chain reaction (RT-qPCR). SELENBP1, USP15, GPX4, IL-10, Arg-1, Granzyme B, TNF-α, and PR domain zinc finger protein 1 (PRDM1) protein levels were examined by western blot assay. Fe+ level, malondialdehyde (MDA), and lipid-ROS levels were determined using special kits. The proportion of CD11b+CD206+ positive cells was detected using a flow cytometry assay. The role of SELENBP1 on TC cell growth was examined using a xenograft tumor model in vivo. After GeneMANIA prediction, the interaction between USP15 and SELENBP1 was verified using Co-immunoprecipitation (CoIP) assay. The binding between PRDM1 and USP15 promoter was predicted by JASPAR and validated using Chromatin immunoprecipitation (ChIP) and dual-luciferase reporter assays. RESULTS SELENBP1 was increased in TC subjects and cell lines, and its knockdown repressed TC cell proliferation, migration, invasion, immune escape, and induced ferroptosis in vitro, as well as blocked tumor growth in vivo. In mechanism, USP15 interacted with SELENBP1 and maintained its stabilization by removing ubiquitin. Meanwhile, the upregulation of USP15 was induced by the transcription factor PRDM1. CONCLUSION USP15 transcriptionally mediated by PRDM1 might boost TC cell malignant behaviors through deubiquitinating SELENBP1, providing a promising therapeutic target for TC treatment.
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Sun X, Pawlyk B, Xu X, Liu X, Bulgakov OV, Adamian M, Sandberg MA, Khani SC, Tan MH, Smith AJ, Ali RR, Li T. Retraction Note: Gene therapy with a promoter targeting both rods and cones rescues retinal degeneration caused by AIPL1 mutations. Gene Ther 2024:10.1038/s41434-024-00505-6. [PMID: 39548238 DOI: 10.1038/s41434-024-00505-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2024]
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Ma X, Peng L, Zhu X, Chu T, Yang C, Zhou B, Sun X, Gao T, Zhang M, Chen P, Chen H. Isolation, identification, and challenges of extracellular vesicles: emerging players in clinical applications. Apoptosis 2024:10.1007/s10495-024-02036-2. [PMID: 39522104 DOI: 10.1007/s10495-024-02036-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/23/2024] [Indexed: 11/16/2024]
Abstract
Extracellular vesicles (EVs) serve as critical mediators of intercellular communication, encompassing exosomes, microvesicles, and apoptotic vesicles that play significant roles in diverse physiological and pathological contexts. Numerous studies have demonstrated that EVs derived from mesenchymal stem cells (MSC-EVs) play a pivotal role in facilitating tissue and organ repair, alleviating inflammation and apoptosis, enhancing the proliferation of endogenous stem cells within tissues and organs, and modulating immune function-these functions have been extensively utilized in clinical applications. The precise classification, isolation, and identification of MSC-EVs are essential for their clinical applications. This article provides a comprehensive overview of the biological properties of EVs, emphasizing both their advantages and limitations in isolation and identification methodologies. Additionally, we summarize the protein markers associated with MSC-EVs, emphasizing their significance in the treatment of various diseases. Finally, this article addresses the current challenges and dilemmas in developing clinical applications for MSC-EVs, aiming to offer valuable insights for future research.
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Hayrapetyan A, Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, Damanakis K, Dragicevic M, Hussain PS, Jeitler M, Krammer N, Li A, Liko D, Mikulec I, Schieck J, Schöfbeck R, Schwarz D, Sonawane M, Templ S, Waltenberger W, Wulz CE, Darwish MR, Janssen T, Van Mechelen P, Breugelmans N, D'Hondt J, Dansana S, De Moor A, Delcourt M, Heyen F, Lowette S, Makarenko I, Müller D, Tavernier S, Tytgat M, Van Onsem GP, Van Putte S, Vannerom D, Clerbaux B, Das AK, De Lentdecker G, Evard H, Favart L, Gianneios P, Hohov D, Jaramillo J, Khalilzadeh A, Khan FA, Lee K, Mahdavikhorrami M, Malara A, Paredes S, Shahzad MA, Thomas L, Vanden Bemden M, Vander Velde C, Vanlaer P, De Coen M, Dobur D, Gokbulut G, Hong Y, Knolle J, Lambrecht L, Marckx D, Mestdach G, Mota Amarilo K, Samalan A, Skovpen K, Van Den Bossche N, van der Linden J, Wezenbeek L, Benecke A, Bethani A, Bruno G, Caputo C, De Favereau De Jeneret J, Delaere C, Donertas IS, Giammanco A, Guzel AO, Jain S, Lemaitre V, Lidrych J, Mastrapasqua P, Tran TT, Wertz S, Alves GA, Alves Gallo Pereira M, Coelho E, Correia Silva G, Hensel C, Menezes De Oliveira T, Moraes A, Rebello Teles P, Soeiro M, Vilela Pereira A, Aldá Júnior WL, Barroso Ferreira Filho M, Brandao Malbouisson H, Carvalho W, Chinellato J, Da Costa EM, Da Silveira GG, De Jesus Damiao D, Fonseca De Souza S, Gomes De Souza R, Macedo M, Martins J, Mora Herrera C, Mundim L, Nogima H, Pinheiro JP, Santoro A, Sznajder A, Thiel M, Bernardes CA, Calligaris L, Fernandez Perez Tomei TR, Gregores EM, Maietto Silverio I, Mercadante PG, Novaes SF, Orzari B, Padula SS, Aleksandrov A, Antchev G, Hadjiiska R, Iaydjiev P, Misheva M, Shopova M, Sultanov G, Dimitrov A, Litov L, Pavlov B, Petkov P, Petrov A, Shumka E, Keshri S, Thakur S, Cheng T, Javaid T, Yuan L, Hu Z, Liang Z, Liu J, Yi K, Chen GM, Chen HS, Chen M, Iemmi F, Jiang CH, Kapoor A, Liao H, Liu ZA, Sharma R, Song JN, Tao J, Wang C, Wang J, Wang Z, Zhang H, Zhao J, Agapitos A, Ban Y, Deng S, Guo B, Jiang C, Levin A, Li C, Li Q, Mao Y, Qian S, Qian SJ, Qin X, Sun X, Wang D, Yang H, Zhang L, Zhao Y, Zhou C, Yang S, You Z, Jaffel K, Lu N, Bauer G, Li B, Zhang J, Gao X, Lin Z, Lu C, Xiao M, Avila C, Barbosa Trujillo DA, Cabrera A, Florez C, Fraga J, Reyes Vega JA, Ramirez F, Rendón C, Rodriguez M, Ruales Barbosa AA, Ruiz Alvarez JD, Giljanovic D, Godinovic N, Lelas D, Sculac A, Kovac M, Petkovic A, Sculac T, Bargassa P, Brigljevic V, Chitroda BK, Ferencek D, Jakovcic K, Mishra S, Starodumov A, Susa T, Attikis A, Christoforou K, Hadjiagapiou A, Leonidou C, Mousa J, Nicolaou C, Paizanos L, Ptochos F, Razis PA, Rykaczewski H, Saka H, Stepennov A, Finger M, Finger M, Kveton A, Carrera Jarrin E, Assran Y, El-Mahdy B, Elgammal S, Mahmoud MA, Mohammed Y, Ehataht K, Kadastik M, Lange T, Nandan S, Nielsen C, Pata J, Raidal M, Tani L, Veelken C, Kirschenmann H, Osterberg K, Voutilainen M, Bharthuar S, Bin Norjoharuddeen N, Brücken E, Garcia F, Inkaew P, Kallonen KTS, Lampén T, Lassila-Perini K, Lehti 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Savva K, Seez C, Shukla R, Tapper A, Uchida K, Uttley GP, Vage LH, Virdee T, Vojinovic M, Wardle N, Winterbottom D, Coldham K, Cole JE, Khan A, Kyberd P, Reid ID, Abdullin S, Brinkerhoff A, Caraway B, Collins E, Dittmann J, Hatakeyama K, Hiltbrand J, McMaster B, Samudio J, Sawant S, Sutantawibul C, Wilson J, Bartek R, Dominguez A, Huerta Escamilla C, Simsek AE, Uniyal R, Vargas Hernandez AM, Bam B, Buchot Perraguin A, Chudasama R, Cooper SI, Crovella C, Gleyzer SV, Pearson E, Perez CU, Rumerio P, Usai E, Yi R, Akpinar A, Cosby C, De Castro G, Demiragli Z, Erice C, Fangmeier C, Fernandez Madrazo C, Fontanesi E, Gastler D, Golf F, Jeon S, O'cain J, Reed I, Rohlf J, Salyer K, Sperka D, Spitzbart D, Suarez I, Tsatsos A, Zecchinelli AG, Benelli G, Coubez X, Cutts D, Gouskos L, Hadley M, Heintz U, Hogan JM, Kwon T, Landsberg G, Lau KT, Li D, Luo J, Mondal S, Narain M, Pervan N, Russell T, Sagir S, Simpson F, Stamenkovic M, Venkatasubramanian N, Yan X, Zhang W, Abbott S, Brainerd C, Breedon R, Cai H, Calderon De La Barca Sanchez M, Chertok M, Citron M, Conway J, Cox PT, Erbacher R, Jensen F, Kukral O, Mocellin G, Mulhearn M, Ostrom S, Wei W, Yao Y, Yoo S, Zhang F, Bachtis M, Cousins R, Datta A, Flores Avila G, Hauser J, Ignatenko M, Iqbal MA, Lam T, Manca E, Nunez Del Prado A, Saltzberg D, Valuev V, Clare R, Gary JW, Gordon M, Hanson G, Si W, Wimpenny S, Aportela A, Arora A, Branson JG, Cittolin S, Cooperstein S, Diaz D, Duarte J, Giannini L, Gu Y, Guiang J, Kansal R, Krutelyov V, Lee R, Letts J, Masciovecchio M, Mokhtar F, Mukherjee S, Pieri M, Quinnan M, Sathia Narayanan BV, Sharma V, Tadel M, Vourliotis E, Würthwein F, Xiang Y, Yagil A, Barzdukas A, Brennan L, Campagnari C, Downham K, Grieco C, Incandela J, Kim J, Li AJ, Masterson P, Mei H, Richman J, Santpur SN, Sarica U, Schmitz R, Setti F, Sheplock J, Stuart D, Vámi TÁ, Wang S, Zhang D, Bornheim A, Cerri O, Latorre A, Mao J, Newman HB, Reales Gutiérrez G, Spiropulu M, Vlimant JR, Wang C, Xie S, Zhu RY, Alison J, An S, Andrews MB, Bryant P, Cremonesi M, Dutta V, Ferguson T, Gómez Espinosa TA, Harilal A, Kallil Tharayil A, Liu C, Mudholkar T, Murthy S, Palit P, Park K, Paulini M, Roberts A, Sanchez A, Terrill W, Cumalat JP, Ford WT, Hart A, Hassani A, Karathanasis G, Manganelli N, Perloff A, Savard C, Schonbeck N, Stenson K, Ulmer KA, Wagner SR, Zipper N, Zuolo D, Alexander J, Bright-Thonney S, Chen X, Cranshaw DJ, Fan J, Fan X, Hogan S, Kotamnives P, Monroy J, Oshiro M, Patterson JR, Reid M, Ryd A, Thom J, Wittich P, Zou R, Albrow M, Alyari M, Amram O, Apollinari G, Apresyan A, Bauerdick LAT, Berry D, Berryhill J, Bhat PC, Burkett K, Butler JN, Canepa A, Cerati GB, Cheung HWK, Chlebana F, Cummings G, Di Petrillo KF, Dickinson J, Dutta I, Elvira VD, Feng Y, Freeman J, Gandrakota A, Gecse Z, Gray L, Green D, Grummer A, Grünendahl S, Guerrero D, Gutsche O, Harris RM, Heller R, Herwig TC, Hirschauer J, Jayatilaka B, Jindariani S, Johnson M, Joshi U, Klijnsma T, Klima B, Kwok KHM, Lammel S, Lincoln D, Lipton R, Liu T, Madrid C, Maeshima K, Mantilla C, Mason D, McBride P, Merkel P, Mrenna S, Nahn S, Ngadiuba J, Noonan D, Norberg S, Papadimitriou V, Pastika N, Pedro K, Pena C, Ravera F, Reinsvold Hall A, Ristori L, Safdari M, Sexton-Kennedy E, Smith N, Soha A, Spiegel L, Stoynev S, Strait J, Taylor L, Tkaczyk S, Tran NV, Uplegger L, Vaandering EW, Zoi I, Aruta C, Avery P, Bourilkov D, Chang P, Cherepanov V, Field RD, Koenig E, Kolosova M, Konigsberg J, Korytov A, Matchev K, Menendez N, Mitselmakher G, Mohrman K, Muthirakalayil Madhu A, Rawal N, Rosenzweig S, Takahashi Y, Wang J, Adams T, Al Kadhim A, Askew A, Bower S, Habibullah R, Hagopian V, Hashmi R, Kim RS, Kim S, Kolberg T, Martinez G, Prosper H, Prova PR, Wulansatiti M, Yohay R, Zhang J, Alsufyani B, Baarmand MM, Butalla S, Das S, Elkafrawy T, Hohlmann M, Rahmani M, Yanes E, Adams MR, Baty A, Bennett C, Cavanaugh R, Escobar Franco R, Evdokimov O, Gerber CE, Hawksworth M, Hingrajiya A, Hofman DJ, Lee JH, Lemos DS, Merrit AH, Mills C, Nanda S, Oh G, Ozek B, Pilipovic D, Pradhan R, Prifti E, Roy T, Rudrabhatla S, Tonjes MB, Varelas N, Wadud MA, Ye Z, Yoo J, Alhusseini M, Blend D, Dilsiz K, Emediato L, Karaman G, Köseyan OK, Merlo JP, Mestvirishvili A, Neogi O, Ogul H, Onel Y, Penzo A, Snyder C, Tiras E, Blumenfeld B, Corcodilos L, Davis J, Gritsan AV, Kang L, Kyriacou S, Maksimovic P, Roguljic M, Roskes J, Sekhar S, Swartz M, Abreu A, Alcerro Alcerro LF, Anguiano J, Arteaga Escatel S, Baringer P, Bean A, Flowers Z, Grove D, King J, Krintiras G, Lazarovits M, Le Mahieu C, Marquez J, Minafra N, Murray M, Nickel M, Pitt M, Popescu S, Rogan C, Royon C, Salvatico R, Sanders S, Smith C, Wilson G, Allmond B, Gujju Gurunadha R, Ivanov A, Kaadze K, Maravin Y, Natoli J, Roy D, Sorrentino G, Baden A, Belloni A, Bistany-Riebman J, Chen YM, Eno SC, Hadley NJ, Jabeen S, Kellogg RG, Koeth T, Kronheim B, Lai Y, Lascio S, Mignerey AC, Nabili S, Palmer C, Papageorgakis C, Paranjpe MM, Wang L, Bendavid J, Cali IA, Chou PC, D'Alfonso M, Eysermans J, Freer C, Gomez-Ceballos G, Goncharov M, Grosso G, Harris P, Hoang D, Kovalskyi D, Krupa J, Lavezzo L, Lee YJ, Long K, Mcginn C, Novak A, Paus C, Rankin D, Roland C, Roland G, Rothman S, Stephans GSF, Van Steenweghen P, Wang Z, Wyslouch B, Yang TJ, Crossman B, Joshi BM, Kapsiak C, Krohn M, Mahon D, Mans J, Marzocchi B, Revering M, Rusack R, Saradhy R, Strobbe N, Bloom K, Claes DR, Haza G, Hossain J, Joo C, Kravchenko I, Siado JE, Tabb W, Vagnerini A, Wightman A, Yan F, Yu D, Bandyopadhyay H, Hay L, Hsia HW, Iashvili I, Kalogeropoulos A, Kharchilava A, Morris M, Nguyen D, Rappoccio S, Rejeb Sfar H, Williams A, Young P, Alverson G, Barberis E, Bonilla J, Dervan J, Haddad Y, Han Y, Krishna A, Li J, Lu M, Madigan G, Mccarthy R, Morse DM, Nguyen V, Orimoto T, Parker A, Skinnari L, Wood D, Bueghly J, Dittmer S, Hahn KA, Liu Y, Miao Y, Monk DG, Schmitt MH, Taliercio A, Velasco M, Agarwal G, Band R, Bucci R, Castells S, Das A, Goldouzian R, Hildreth M, Ho KW, Hurtado Anampa K, Ivanov T, Jessop C, Lannon K, Lawrence J, Loukas N, Lutton L, Mariano J, Marinelli N, Mcalister I, McCauley T, Mcgrady C, Moore C, Musienko Y, Nelson H, Osherson M, Piccinelli A, Ruchti R, Townsend A, Wan Y, Wayne M, Yockey H, Zarucki M, Zygala L, Basnet A, Bylsma B, Carrigan M, Durkin LS, Hill C, Joyce M, Nunez Ornelas M, Wei K, Winer BL, Yates BR, Bouchamaoui H, Das P, Dezoort G, Elmer P, Frankenthal A, Greenberg B, Haubrich N, Kennedy K, Kopp G, Kwan S, Lange D, Loeliger A, Marlow D, Ojalvo I, Olsen J, Shevelev A, Stickland D, Tully C, Malik S, Bakshi AS, Chandra S, Chawla R, Gu A, Gutay L, Jones M, Jung AW, Koshy AM, Liu M, Negro G, Neumeister N, Paspalaki G, Piperov S, Scheurer V, Schulte JF, Stojanovic M, Thieman J, Virdi AK, Wang F, Xie W, Dolen J, Parashar N, Pathak A, Acosta D, Carnahan T, Ecklund KM, Fernández Manteca PJ, Freed S, Gardner P, Geurts FJM, Li W, Lin J, Miguel Colin O, Padley BP, Redjimi R, Rotter J, Yigitbasi E, Zhang Y, Bodek A, de Barbaro P, Demina R, Dulemba JL, Garcia-Bellido A, Hindrichs O, Khukhunaishvili A, Parmar N, Parygin P, Popova E, Taus R, Chiarito B, Chou JP, Clark SV, Gadkari D, Gershtein Y, Halkiadakis E, Heindl M, Houghton C, Jaroslawski D, Karacheban O, Konstantinou S, Laflotte I, Lath A, Montalvo R, Nash K, Reichert J, Routray H, Saha P, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Vora J, Wang H, Acharya H, Ally D, Delannoy AG, Fiorendi S, Higginbotham S, Holmes T, Kanuganti AR, Karunarathna N, Lee L, Nibigira E, Spanier S, Aebi D, Ahmad M, Akhter T, Bouhali O, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Gogate N, Hegde V, Hussain A, Kazhykarim Y, Lamichhane K, Lee SW, Mankel A, Peltola T, Volobouev I, Appelt E, Chen Y, Greene S, Gurrola A, Johns W, Kunnawalkam Elayavalli R, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Hakala J, Hirosky R, Ledovskoy A, Neu C, Bhattacharya S, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Madhusudanan Sreekala J, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pétré L, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Alexakhin V, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Blinov V, Boos E, Borshch V, Budkouski D, Bunichev V, Chekhovsky V, Chistov R, Danilov M, Dermenev A, Dimova T, Druzhkin D, Dubinin M, Dudko L, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanov Y, Kachanov V, Karjavine V, Karneyeu A, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Klyukhin V, Kodolova O, Konstantinov D, Korenkov V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oreshkin V, Palichik V, Perelygin V, Petrushanko S, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Search for Soft Unclustered Energy Patterns in Proton-Proton Collisions at 13 TeV. PHYSICAL REVIEW LETTERS 2024; 133:191902. [PMID: 39576923 DOI: 10.1103/physrevlett.133.191902] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Accepted: 09/06/2024] [Indexed: 11/24/2024]
Abstract
The first search for soft unclustered energy patterns (SUEPs) is performed using an integrated luminosity of 138 fb^{-1} of proton-proton collision data at sqrt[s]=13 TeV, collected in 2016-2018 by the CMS detector at the LHC. Such SUEPs are predicted by hidden valley models with a new, confining force with a large 't Hooft coupling. In events with boosted topologies, selected by high-threshold hadronic triggers, the multiplicity and sphericity of clustered tracks are used to reject the background from standard model quantum chromodynamics. With no observed excess of events over the standard model expectation, limits are set on the cross section for production via gluon fusion of a scalar mediator with SUEP-like decays.
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Wang M, Li S, Peng R, Räisänen SE, Serviento AM, Sun X, Wang K, Yu F, Niu M. Learning end-to-end respiratory rate prediction of dairy cows from red, green, and blue videos. J Dairy Sci 2024; 107:9862-9874. [PMID: 39067761 DOI: 10.3168/jds.2023-24601] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Accepted: 07/03/2024] [Indexed: 07/30/2024]
Abstract
Respiratory rate (RR) is an important indicator of the health and welfare status of dairy cows. In recent years, progress has been made in monitoring the RR of dairy cows using video data and learning methods. However, existing approaches often involve multiple processing modules, such as region of interest (ROI) detection and tracking, which can introduce errors that propagate through successive steps. The objective of this study was to develop an end-to-end computer vision method to predict RR of dairy cows continuously and automatically. The method leverages the capabilities of a state-of-the-art Transformer model, VideoMAE, which divides video frames into patches as input tokens, enabling the automated selection and featurization of relevant regions, such as a cow's abdomen, for predicting RR. The original encoder of VideoMAE was retained, and a classification head was added on top of it. Further, the weights of the first 11 layers of the pre-trained model were kept, whereas the weights of the final layer and classifier were fine-tuned using video data collected in a tiestall barn from 6 dairy cows. Respiratory rates measured using a respiratory belt for individual cows were serving as the ground truth (GT). The evaluation of the developed model was conducted using multiple metrics, including mean absolute error (MAE) of 2.58 breaths per minute (bpm), root mean squared error (RMSE) of 3.52 bpm, root mean squared prediction error (RMSPE; as a proportion of observed mean) of 15.03%, and Pearson r of 0.86. Compared with a conventional method involving multiple processing modules, the end-to-end approach performed better in terms of MAE, RMSE, and RMSPE. These results suggest the potential to implement the developed computer vision method for an end-to-end solution, for monitoring RR of dairy cows automatically in a tiestall setting. Future research on integrating this method with other behavioral detection and animal identification algorithms for animal monitoring in a freestall dairy barn can be beneficial for a broader application.
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Khorchani M, Schmer M, Freidenreich A, Awada T, Birru G, Christofferson S, Drijber R, Erickson G, Jin V, McDermott R, Suyker A, Watson A, Woodbury B, Xiong Y, Hiller J, Sun X, Li L. The LTAR Grazing Land Common Experiment at Platte River High Plains Aquifer. JOURNAL OF ENVIRONMENTAL QUALITY 2024; 53:948-956. [PMID: 39384547 DOI: 10.1002/jeq2.20629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 08/26/2024] [Indexed: 10/11/2024]
Abstract
The Long-Term Agroecosystem Research (LTAR) network of the United States Department of Agriculture (USDA) consists presently of 18 sites within the contiguous United States that are managed by the Agricultural Research Service (ARS) and its partners. The LTAR network focuses on developing national strategies for more efficient, resilient, and profitable agricultural production systems, improved environmental quality, and enhanced rural prosperity. The Platte River High Plains Aquifer (PRHPA) LTAR site is managed jointly by the University of Nebraska-Lincoln (UNL) and USDA-ARS and is one of the LTAR sites that conduct research on both integrated cropping and grazing systems. The PRHPA region encompasses multiple land resource areas and diverse agricultural production systems. The PRHPA sites, predominantly located in eastern Nebraska, are designated as an integrated system focused specifically on the region's dominant production practices of row crop (corn and soybean), managed pastures, and beef cattle production. Here, we focus on C3 cool-season smooth bromegrass (Bromus inermis Leyss.) pasture grazing systems under prevailing and alternative management practices for the region. The sites evaluate continuous and rotational grazing with and without pasture fertilization (prevailing practices). In an additional treatment, cattle are supplemented with dry distillers grains plus solubles, while manure supplies fertilization (alternative practice). Main measurements at the site evaluate plant and animal productivity, forage quality, greenhouse gas fluxes, and soil physical, chemical, and biological properties. This paper describes the regional characteristics of the PRHPA site, ongoing LTAR research related to pasture and livestock production, stakeholder engagement, and future research plans.
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Abdulhamid MI, Aboona BE, Adam J, Adams JR, Agakishiev G, Aggarwal I, Aggarwal MM, Ahammed Z, Aitbaev A, Alekseev I, Alpatov E, Aparin A, Aslam S, Atchison J, Averichev GS, Bairathi V, Cap JGB, Barish K, Bhagat P, Bhasin A, Bhatta S, Bhosale SR, Bordyuzhin IG, Brandenburg JD, Brandin AV, Broodo C, Cai XZ, Caines H, Calderón de la Barca Sánchez M, Cebra D, Ceska J, Chakaberia I, Chan BK, Chang Z, Chatterjee A, Chen D, Chen J, Chen JH, Chen Z, Cheng J, Cheng Y, Christie W, Chu X, Crawford HJ, Csanád M, Dale-Gau G, Das A, Dedovich TG, Deppner IM, Derevschikov AA, Dhamija A, Dixit P, Dong X, Drachenberg JL, Duckworth E, Dunlop JC, Engelage J, Eppley G, Esumi S, Evdokimov O, Eyser O, Fatemi R, Fazio S, Feng CJ, Feng Y, Finch E, Fisyak Y, Flor FA, Fu C, Gao T, Geurts F, Ghimire N, Gibson A, Gopal K, Gou X, Grosnick D, Gupta A, Hamed A, Han Y, Harasty MD, Harris JW, Harrison-Smith H, He W, He XH, He Y, Hu C, Hu Q, Hu Y, Huang H, Huang HZ, Huang SL, Huang T, Huang Y, Huang Y, Humanic TJ, Isshiki M, Jacobs WW, Jalotra A, Jena C, Ji Y, Jia J, Jin C, Ju X, Judd EG, Kabana S, Kalinkin D, Kang K, Kapukchyan D, Kauder K, Keane D, Kechechyan A, Khanal A, Kiselev A, Knospe AG, Ko HS, Kochenda L, Korobitsin AA, Yu. Kraeva A, Kravtsov P, Kumar L, Labonte MC, Lacey R, Landgraf JM, Lebedev A, Lednicky R, Lee JH, Leung YH, Li C, Li D, Li HS, Li H, Li W, Li X, Li Y, Li Y, Li Z, Liang X, Liang Y, Lin T, Lin Y, Liu C, Liu G, Liu H, Liu L, Liu T, Liu X, Liu Y, Liu Z, Ljubicic T, Lomicky O, Longacre RS, Loyd EM, Lu T, Luo J, Luo XF, Luong VB, Ma L, Ma R, Ma YG, Magdy N, Manikandhan R, Margetis S, Matonoha O, McNamara G, Mezhanska O, Mi K, Minaev NG, Mohanty B, Mondal B, Mondal MM, Mooney I, Morozov DA, Mudrokh A, Nagy MI, Nain AS, Nam JD, Nasim M, Nedorezov E, Neff D, Nelson JM, Nie M, Nigmatkulov G, Niida T, Nogach LV, Nonaka T, Odyniec G, Ogawa A, Oh S, Okorokov VA, Okubo K, Page BS, Pal S, Pandav A, Panday A, Panebratsev Y, Pani T, Parfenov P, Paul A, Perkins C, Pokhrel BR, Posik M, Povarov A, Protzman T, Pruthi NK, Putschke J, Qin Z, Qiu H, Racz C, Radhakrishnan SK, Rana A, Ray RL, Robertson CW, Rogachevsky OV, Rosales Aguilar MA, Roy D, Ruan L, Sahoo AK, Sahoo NR, Sako H, Salur S, Samigullin E, Sato S, Schaefer BC, Schmidke WB, Schmitz N, Seger J, Seto R, Seyboth P, Shah N, Shahaliev E, Shanmuganathan PV, Shao T, Sharma M, Sharma N, Sharma R, Sharma SR, Sheikh AI, Shen D, Shen DY, Shen K, Shi SS, Shi Y, Shou QY, Si F, Singh J, Singha S, Sinha P, Skoby MJ, Söhngen Y, Song Y, Srivastava B, Stanislaus TDS, Stewart DJ, Strikhanov M, Su Y, Sun C, Sun X, Sun Y, Sun Y, Surrow B, Svirida DN, Sweger ZW, Tamis AC, Tang AH, Tang Z, Taranenko A, Tarnowsky T, Thomas JH, Tlusty D, Todoroki T, Tokarev MV, Trentalange S, Tribedy P, Tsai OD, Tsang CY, Tu Z, Tyler J, Ullrich T, Underwood DG, Upsal I, Van Buren G, Vasiliev AN, Verkest V, Videbæk F, Vokal S, Voloshin SA, Wang G, Wang JS, Wang J, Wang K, Wang X, Wang Y, Wang Y, Wang Y, Wang Z, Webb JC, Weidenkaff PC, Westfall GD, Wieman H, Wilks G, Wissink SW, Wu J, Wu J, Wu X, Wu X, Xi B, Xiao ZG, Xie G, Xie W, Xu H, Xu N, Xu QH, Xu Y, Xu Y, Xu Z, Xu Z, Yan G, Yan Z, Yang C, Yang Q, Yang S, Yang Y, Ye Z, Ye Z, Yi L, Yu Y, Zha W, Zhang C, Zhang D, Zhang J, Zhang S, Zhang W, Zhang X, Zhang Y, Zhang Y, Zhang Y, Zhang Y, Zhang ZJ, Zhang Z, Zhang Z, Zhao F, Zhao J, Zhao M, Zhou S, Zhou Y, Zhu X, Zurek M, Zyzak M. Imaging shapes of atomic nuclei in high-energy nuclear collisions. Nature 2024; 635:67-72. [PMID: 39506156 PMCID: PMC11541211 DOI: 10.1038/s41586-024-08097-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 09/23/2024] [Indexed: 11/08/2024]
Abstract
Atomic nuclei are self-organized, many-body quantum systems bound by strong nuclear forces within femtometre-scale space. These complex systems manifest a variety of shapes1-3, traditionally explored using non-invasive spectroscopic techniques at low energies4,5. However, at these energies, their instantaneous shapes are obscured by long-timescale quantum fluctuations, making direct observation challenging. Here we introduce the collective-flow-assisted nuclear shape-imaging method, which images the nuclear global shape by colliding them at ultrarelativistic speeds and analysing the collective response of outgoing debris. This technique captures a collision-specific snapshot of the spatial matter distribution within the nuclei, which, through the hydrodynamic expansion, imprints patterns on the particle momentum distribution observed in detectors6,7. We benchmark this method in collisions of ground-state uranium-238 nuclei, known for their elongated, axial-symmetric shape. Our findings show a large deformation with a slight deviation from axial symmetry in the nuclear ground state, aligning broadly with previous low-energy experiments. This approach offers a new method for imaging nuclear shapes, enhances our understanding of the initial conditions in high-energy collisions and addresses the important issue of nuclear structure evolution across energy scales.
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Bi D, Gao H, Al-Watary MQH, Sun X, Zhao Q, Li J. Changes in mandibular angle and intergonial width after bilateral sagittal split ramus osteotomy or bimaxillary surgery with/without counterclockwise rotation. Int J Oral Maxillofac Surg 2024:S0901-5027(24)00384-9. [PMID: 39462705 DOI: 10.1016/j.ijom.2024.09.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 09/21/2024] [Accepted: 09/24/2024] [Indexed: 10/29/2024]
Abstract
The aim of this retrospective study was to evaluate the morphological changes in the mandibular angle area after orthognathic surgery with or without mandibular counterclockwise rotation in Class II deformity patients, and to investigate the associated factors. Computed tomography scans obtained preoperatively (T0), within 1 month postoperatively (T1), and 6 months postoperatively (T2) were collected from 58 patients who underwent either bilateral sagittal split ramus osteotomy (group I), bimaxillary surgery with mandibular counterclockwise rotation (group II), or bimaxillary surgery without mandibular counterclockwise rotation (group III). The intergonial width increased after surgery, by 2.78 ± 2.02 mm in group I, 2.86 ± 2.81 mm in group II, and 2.53 ± 2.42 mm in group III (all P < 0.001). The mandibular angle (MA) increased in group I (ΔMA 4.76 ± 2.79°; P < 0.001) and group III (ΔMA 3.50 ± 2.58°; P < 0.001); however no significant increase was observed in group II. The increase in intergonial width was positively correlated with the lateral displacement of the proximal segment. Counterclockwise rotation of the proximal segment resulted in an increase in MA, while counterclockwise rotation resulted in a reduction in this increasing trend. The results indicate that reducing the displacement and rotation of the proximal segments is key to minimizing changes in the mandibular angle area.
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Yan X, Zhu J, Wang J, Lu Y, Ye X, Sun X, Jiang H, Li Z, He C, Zhai W, Dong Q, Chen W, Yu Z, Pan Y, Huang D. Development and validation of a novel prognostic prediction system based on GLIM-defined malnutrition for colorectal cancer patients post-radical surgery. Front Nutr 2024; 11:1425317. [PMID: 39502874 PMCID: PMC11536661 DOI: 10.3389/fnut.2024.1425317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Accepted: 09/20/2024] [Indexed: 11/08/2024] Open
Abstract
Background Malnutrition often occurs in patients with colorectal cancer. This study aims to develop a predictive model based on GLIM criteria for patients with colorectal cancer who underwent radical surgery. Methods From December 2015 to May 2021, patients with colorectal cancer who underwent radical surgery at our center were recruited for this study. We prospectively collected data on GLIM-defined malnutrition and other clinicopathological characteristics. Using Cox regeneration, we developed a novel nomogram for prognostic prediction, which was validated and compared to traditional nutritional factors for predictive accuracy. Results Among the 983 patients enrolled in this study, malnutrition was identified in 233 (23.70%) patients. Multivariate analysis indicated that GLIM-defined malnutrition is the independent risk factor for overall survival (HR = 1.793, 95% CI = 1.390-2.313 for moderate malnutrition and HR = 3.485, 95% CI = 2.087-5.818 for severe malnutrition). The novel nomogram based on the GLIM criteria demonstrated a better performance than existing criteria, with AUC of 0.729, 0.703, and 0.683 for 1-year, 3-year, and 5-year OS, respectively, in the validation cohort. In addition, the risk score determined by this system exhibited significantly poorer short-term and long-term clinical outcomes in high-risk groups in both malnourished and well-nourished patients. Conclusion Combining handgrip strength, serum albumin level, and TNM stage would help improve the predictive effect of GLIM criteria for colorectal cancer patients post-radical surgery and benefit the individual prognostic prediction of colorectal cancer.
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S, Oreshkin V, Palichik V, Perelygin V, Perfilov M, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vorobyev A, Vorotnikov G, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Search for the Z Boson Decay to ττμμ in Proton-Proton Collisions at sqrt[s]=13 TeV. PHYSICAL REVIEW LETTERS 2024; 133:161805. [PMID: 39485967 DOI: 10.1103/physrevlett.133.161805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2024] [Accepted: 08/14/2024] [Indexed: 11/03/2024]
Abstract
The first search for the Z boson decay to ττμμ at the CERN LHC is presented, based on data collected by the CMS experiment at the LHC in proton-proton collisions at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb^{-1}. The data are compatible with the predicted background. For the first time, an upper limit at the 95% confidence level of 6.9 times the standard model expectation is placed on the ratio of the Z→ττμμ to Z→4μ branching fractions. Limits are also placed on the six flavor-conserving four-lepton effective-field-theory operators involving two muons and two tau leptons, for the first time testing all such operators.
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Nibigira E, Spanier S, Aebi D, Ahmad M, Bouhali O, Dalchenko M, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Malhotra S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Hussain A, Kazhykarim Y, Lamichhane K, Lee SW, Mankel A, Mengke T, Muthumuni S, Peltola T, Volobouev I, Whitbeck A, Appelt E, Greene S, Gurrola A, Johns W, Elayavalli RK, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Hakala J, Hirosky R, Ledovskoy A, Li A, Neu C, Lara CEP, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Sreekala JM, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Chekhovsky V, Chistov R, Danilov M, Dermenev A, Dimova T, 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Observation of the Λ b 0 → J / ψ Ξ - K + decay. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS 2024; 84:1062. [PMID: 39422214 PMCID: PMC11480154 DOI: 10.1140/epjc/s10052-024-13114-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 07/08/2024] [Indexed: 10/19/2024]
Abstract
Using proton-proton collision data corresponding to an integrated luminosity of 140 fb - 1 collected by the CMS experiment ats = 13 Te V , theΛ b 0 → J / ψ Ξ - K + decay is observed for the first time, with a statistical significance exceeding 5 standard deviations. The relative branching fraction, with respect to theΛ b 0 → ψ ( 2 S ) Λ decay, is measured to be B ( Λ b 0 → J / ψ Ξ - K + ) / B ( Λ b 0 → ψ ( 2 S ) Λ ) = [ 3.38 ± 1.02 ± 0.61 ± 0.03 ] % , where the first uncertainty is statistical, the second is systematic, and the third is related to the uncertainties in B ( ψ ( 2 S ) → J / ψ π + π - ) and B ( Ξ - → Λ π - ) .
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Grants
- SC
- Austrian Federal Ministry of Education, Science and Research
- Austrian Science Fund
- Belgian Fonds de la Recherche Scientifique
- Belgian Fonds voor Wetenschappelijk Onderzoek
- CNPq
- CAPES
- FAPERJ
- FAPERGS
- FAPESP
- Bulgarian Ministry of Education and Science
- Bulgarian National Science Fund
- CERN
- Chinese Academy of Sciences
- Ministry of Science and Technology
- Chinese National Natural Science Foundation of China
- Colombian Funding Agency (MINICIENCIAS)
- Croatian Ministry of Science, Education and Sport
- Croatian Science Foundation
- Research and Innovation Foundation
- SENESCYT
- Estonian Research Council via PRG780, PRG803, RVTT3 and TK202
- Academy of Finland
- Finnish Ministry of Education and Culture
- Helsinki Institute of Physics
- Institut National de Physique Nucléaire et de Physique des Particules
- Centre National de la Recherche Scientifique
- Commissariat á l’Énergie Atomique et aux Énergies Alternatives
- Shota Rustaveli National Science Foundation
- Bundesministerium für Bildung und Forschung
- Deutsche Forschungsgemeinschaft
- Helmholtz-Gemeinschaft Deutscher Forschungszentren
- General Secretariat for Research and Innovation
- National Research, Development and Innovation Office
- Department of Atomic Energy
- Department of Science and Technology
- ICSC -National Research Centre for High Performance Computing, Big Data and Quantum Computing, funded by the EU NexGeneration program
- Institute for Research in Fundamental Studies
- Science Foundation
- Istituto Nazionale di Fisica Nucleare
- Korean Ministry of Education, Science and Technology
- National Research Foundation of Korea (NRF)
- MES
- Lithuanian Academy of Sciences
- Ministry of Education
- University of Malaya
- BUAP
- CINVESTAV
- CONACYT
- LNS
- SEP
- UASLP
- MOS
- Ministry of Business, Innovation and Employment
- Pakistan Atomic Energy Commission
- Ministry of Educaton and Science
- National Science Centre
- Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019
- JINR, Dubna
- Ministry of Education and Science of the Russian Federation
- Federal Agency of Atomic Energy of the Russian Federation
- Russian Academy of Sciences
- Russian Foundation for Basic Research
- National Research Center “Kurchatov Institute"
- Ministry of Education, Science and Technological Development of Serbia
- MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”
- Fondo Europeo de Desarrollo Regional, Spain
- Plan de Ciencia, Tecnología e Innovación del Principado de Asturias
- MOSTR
- ETH Board
- ETH Zurich
- PSI
- SNF
- UniZH
- Canton Zurich
- SER
- Ministry of Science and Technology
- Ministry of Higher Education, Science, Research and Innovation
- National Science and Technology Development Agency of Thailand
- Scientific and Technical Research Council of Turkey
- Turkish Atomic Energy Authority
- National Academy of Sciences of Ukraine
- Science and Technology Facilities Council
- US Department of Energy
- US National Science Foundation
- Marie-Curie programme
- European Research Council and EPLANET (European Union)
- European Research Council/European Cooperation in Science and Technology), Action CA16108
- Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093 (European Union)
- Leventis Foundation
- Alfred P. Sloan Foundation
- Alexander von Humboldt Foundation
- Science Committee, project no. 22rl-037
- Belgian Federal Science Policy Office
- Fonds pour la Formation á la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- Belgian Fonds de la Recherche Scientifique, "Excellence of Science - EOS" - be.h project n. 30820817
- Belgian Fonds voor Wetenschappelijk Onderzoek, "Excellence of Science - EOS" - be.h project n. 30820817
- Beijing Municipal Science & Technology Commission, No. Z191100007219010
- Fundamental Research Funds for the Central Universities
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy - EXC 2121 "Quantum Universe" – 390833306
- Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497
- Hellenic Foundation for Research and Innovation, Project Number 2288
- Hungarian Academy of Sciences
- New National Excellence Program - ÚNKP, the NKFIH research grants K 124845, K 124850, K 128713, K 128786, K 129058, K 131991, K 133046, K 138136, K 143460, K 143477, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64
- Council of Scientific and Industrial Research, India
- Latvian Council of Science
- Ministy of Education and Science, project no. 2022/WK/14
- National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
- Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018
- National Priorities Research Program by Qatar National Research Fund
- Ministry of Science and Higher Education, project no. FSWU-2023-0073 and FSWW-2020-0008
- Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00
- Programa Severo Ochoa del Principado de Asturias
- CUAASC
- National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B37G660013
- Kavli Foundation
- Nvidia Corporation
- Welch Foundation, contract C-1845
- Weston Havens Foundation
- Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna
- Inter-University Institute for High Energies, Brussels
- Université Catholique de Louvain, Louvain-la-Neuve
- São Paulo Research and Analysis Center, São Paulo
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro
- University of Sofia, Sofia
- Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing
- National Institute of Chemical Physics and Biophysics, Tallinn
- Helsinki Institute of Physics, Helsinki
- Grille de Recherche d’Ile de France (GRIF), Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette, France and Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
- Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette
- Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne
- Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg
- Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris, Palaiseau
- Deutsches Elektronen-Synchrotron, Hamburg
- Karlsruher Institut für Technologie, Karlsruhe
- RWTH Aachen University, Aachen
- University of Ioánnina, Ioánnina
- Wigner Research Centre for Physics, Budapest
- Tata Institute of Fundamental Research, Mumbai
- INFN CNAF, Bologna
- INFN Sezione di Bari, Universitá di Bari, Politecnico di Bari, Bari
- INFN Sezione di Pisa, Universitá di Pisa, Scuola Normale Superiore di Pisa, Pisa
- INFN Sezione di Roma, Sapienza Universitá di Roma, Rome
- INFN Sezione di Trieste, Universitá di Trieste, Trieste
- Laboratori Nazionali di Legnaro, Legnaro
- Kyungpook National University, Daegu
- National Centre for Physics, Quaid-I-Azam University, Islamabad
- Akademickie Centrum Komputerowe Cyfronet AGH, Krakow
- National Centre for Nuclear Research, Swierk
- Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa
- Institute for High Energy Physics of National Research Centre ‘Kurchatov Institute’, Protvino
- Institute for Nuclear Research (INR) of the Russian Academy of Sciences, Troitsk
- Institute for Theoretical and Experimental Physics named by A.I. Alikhanov of NRC ’Kurchatov Institute’, Moscow
- Joint Institute for Nuclear Research, Dubna
- Korea Institute of Science and Technology Information (KISTI), Daejeon
- Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid
- Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander
- Port d’Informació Científica, Bellaterra
- CERN, European Organization for Nuclear Research, Geneva
- CSCS - Swiss National Supercomputing Centre, Lugano
- Instrumentation and Detector Consortium, Taipei
- National Center for High-performance Computing (NCHC), Hsinchu City
- Middle East Technical University, Physics Department, Ankara
- National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov
- GridPP, Brunel University, Uxbridge
- GridPP, Imperial College, London
- GridPP, Queen Mary University of London, London
- GridPP, Royal Holloway, University of London, London
- GridPP, Rutherford Appleton Laboratory, Didcot
- GridPP, University of Bristol, Bristol
- GridPP, University of Glasgow, Glasgow
- GridPP, University of Oxford, Oxford
- Baylor University, Waco
- California Institute of Technology, Pasadena
- Fermi National Accelerator Laboratory, Batavia
- Massachusetts Institute of Technology, Cambridge
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley
- Open Science Grid (OSG) Consortium
- Pittsburgh Supercomputing Center (PSC), Pittsburgh
- Purdue University, West Lafayette
- San Diego Supercomputer Center (SDSC), La Jolla
- Texas Advanced Computing Center (TACC), Austin
- University of California, San Diego, La Jolla
- University of Colorado Boulder, Boulder
- University of Florida, Gainesville
- University of Nebraska-Lincoln, Lincoln
- University of Wisconsin-Madison, Madison
- Vanderbilt University, Nashville
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Madigan G, Mccarthy R, Morse DM, Nguyen V, Orimoto T, Parker A, Skinnari L, Tishelman-Charny A, Wang B, Wood D, Bhattacharya S, Bueghly J, Chen Z, Dittmer S, Hahn KA, Liu Y, Miao Y, Monk DG, Schmitt MH, Taliercio A, Velasco M, Agarwal G, Band R, Bucci R, Castells S, Das A, Goldouzian R, Hildreth M, Ho KW, Hurtado Anampa K, Ivanov T, Jessop C, Lannon K, Lawrence J, Loukas N, Lutton L, Mariano J, Marinelli N, Mcalister I, McCauley T, Mcgrady C, Moore C, Musienko Y, Nelson H, Osherson M, Piccinelli A, Ruchti R, Townsend A, Wan Y, Wayne M, Yockey H, Zarucki M, Zygala L, Basnet A, Bylsma B, Carrigan M, Durkin LS, Hill C, Joyce M, Nunez Ornelas M, Wei K, Winer BL, Yates BR, Addesa FM, Bouchamaoui H, Das P, Dezoort G, Elmer P, Frankenthal A, Greenberg B, Haubrich N, Kopp G, Kwan S, Lange D, Loeliger A, Marlow D, Ojalvo I, Olsen J, Shevelev A, Stickland D, Tully C, Malik S, Bakshi AS, Barnes VE, Chandra S, Chawla R, Das S, Gu A, Gutay L, Jones M, Jung AW, Kondratyev D, Koshy AM, Liu M, Negro G, Neumeister N, Paspalaki G, Piperov S, Scheurer V, Schulte JF, Stojanovic M, Thieman J, Virdi AK, Wang F, Xie W, Dolen J, Parashar N, Pathak A, Acosta D, Carnahan T, Ecklund KM, Fernández Manteca PJ, Freed S, Gardner P, Geurts FJM, Li W, Miguel Colin O, Padley BP, Redjimi R, Rotter J, Yigitbasi E, Zhang Y, Bodek A, de Barbaro P, Demina R, Dulemba JL, Garcia-Bellido A, Hindrichs O, Khukhunaishvili A, Parmar N, Parygin P, Popova E, Taus R, Goulianos K, Chiarito B, Chou JP, Gershtein Y, Halkiadakis E, Heindl M, Jaroslawski D, Karacheban O, Laflotte I, Lath A, Montalvo R, Nash K, Routray H, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Vora J, Wang H, Acharya H, Ally D, Delannoy AG, Fiorendi S, Higginbotham S, Holmes T, Kanuganti AR, Karunarathna N, Lee L, Nibigira E, Spanier S, Aebi D, Ahmad M, Bouhali O, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Hussain A, Kazhykarim Y, Lamichhane K, Lee SW, Mankel A, Peltola T, Volobouev I, Whitbeck A, Appelt E, Chen Y, Greene S, Gurrola A, Johns W, Kunnawalkam Elayavalli R, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Hakala J, Hirosky R, Ledovskoy A, Neu C, Perez Lara CE, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Madhusudanan Sreekala J, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Chekhovsky V, Chistov R, Danilov M, Dermenev A, Dimova T, Druzhkin D, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanov Y, Kachanov V, Kaminskiy A, Karjavine V, Karneyeu A, Khein L, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Kodolova O, Korenkov V, Korotkikh V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oreshkin V, Palichik V, Perelygin V, Petrushanko S, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vardanyan I, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Observation of Enhanced Long-Range Elliptic Anisotropies Inside High-Multiplicity Jets in pp Collisions at sqrt[s]=13 TeV. PHYSICAL REVIEW LETTERS 2024; 133:142301. [PMID: 39423390 DOI: 10.1103/physrevlett.133.142301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 06/20/2024] [Accepted: 08/27/2024] [Indexed: 10/21/2024]
Abstract
A search for collective effects inside jets produced in proton-proton collisions is performed via correlation measurements of charged particles using the CMS detector at the CERN LHC. The analysis uses data collected at a center-of-mass energy of sqrt[s]=13 TeV, corresponding to an integrated luminosity of 138 fb^{-1}. Jets are reconstructed with the anti-k_{T} algorithm with a distance parameter of 0.8 and are required to have transverse momentum greater than 550 GeV and pseudorapidity |η^{jet}|<1.6. Two-particle correlations among the charged particles within the jets are studied as functions of the particles' azimuthal angle and pseudorapidity separations (Δϕ^{*} and Δη^{*}) in a jet coordinate basis, where particles' η^{*}, ϕ^{*} are defined relative to the direction of the jet. The correlation functions are studied in classes of in-jet charged-particle multiplicity up to N_{ch}^{j}≈100. Fourier harmonics are extracted from long-range azimuthal correlation functions to characterize azimuthal anisotropy for |Δη^{*}|>2. For low-N_{ch}^{j} jets, the long-range elliptic anisotropic harmonic, v_{2}^{*}, is observed to decrease with N_{ch}^{j}. This trend is well described by Monte Carlo event generators. However, a rising trend for v_{2}^{*} emerges at N_{ch}^{j}≳80, hinting at a possible onset of collective behavior, which is not reproduced by the models tested. This observation yields new insights into the dynamics of jet evolution in the vacuum.
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Ma X, Räisänen SE, Wang K, Amelchanka S, Giller K, Islam MZ, Li Y, Peng R, Reichenbach M, Serviento AM, Sun X, Niu M. Evaluating GreenFeed and respiration chambers for daily and intraday measurements of enteric gaseous exchange in dairy cows housed in tie-stalls. J Dairy Sci 2024:S0022-0302(24)01166-4. [PMID: 39343233 DOI: 10.3168/jds.2024-25246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2024] [Accepted: 08/20/2024] [Indexed: 10/01/2024]
Abstract
The objective of this study was to evaluate the GreenFeed (GF) and respiration chambers (RC) for daily and intraday measurements of the enteric gaseous exchange, as well as the metabolic heat production, lying behavior, and feed intake (FI) rate of dairy cows at these 2 respective housing conditions [tie-stall barn (TS) vs. RC] during the summer periods. Sixteen multiparous lactating dairy cows were recruited and arranged in a randomized complete block design with a baseline period established for each cow. Cows were given a basal diet (CON) for a baseline period of 7 d and were then fed a 3-nitrooxypropanol (3-NOP)-containing feed for the subsequent 26 d as experimental period. During both the baseline and the last 7 d of treatment period, gaseous exchanges of each animal were measured in the TS using GF for 8 6-hourly staggered measurements over 3 d, immediately followed by the measurement in RC for 2 d. Corresponding DMI, milk yield, and behavior parameters (e.g., lying behavior and FI rate) in TS and RC were recorded. The correlation coefficients of CH4 and H2 using raw data were 0.84 and 0.85, respectively. For all gases, correlation coefficients between GF and RC on individual cow level decreased when the marginal fixed effects (e.g., inhibitor and breed) were corrected by a mixed model. There were no differences in daily CH4 production or intensity between GF and RC (442 vs. 443 g CH4/d or 16.6 vs. 16.2 g CH4 /kg MY). However, greater CH4 yield was measured by GF than RC (19.0 vs. 17.8 g CH4/kg DMI), driven by a lower DMI (23.3 vs. 24.6 kg/d) when cows were housed in TS sampled by GF compared with cows being housed and sampled in RC. The correlations for CO2 production and O2 consumption were moderate and expected due to the variation associated with the mild heat stress condition during GF measurements in the TS (Thermal humidity index (THI) 56 vs. 68), as indicated by the reduced lying time (-2.1 h/d). At the intraday level, there was an interaction between techniques and hour-of-day for CH4 production, as indicated by the discrepancies in post-prandial CH4 emissions between techniques. In summary, this set of results showed that there were strong positive correlations for CH4 and H2 emissions between GF and RC based on individual cow data. However, such relationship should be interpreted with caution, given the data clustering resulting from the use of inhibitor 3-NOP. On treatment level, these 2 techniques detected similar inhibitor effect on the estimated daily CH4 emissions. The intraday patterns of CH4 and H2 production captured by GF provided a close approximation for those measured by RC. Nevertheless, potential underestimation may occur, especially following fresh feed delivery. For measuring CO2 production and O2 consumption, the GF captured similar intraday variations to those in the RC. However, the estimated daily production and consumption were not directly comparable, which was expected due to the variable thermal conditions during the summer. Further evaluations under the same weather conditions are warranted.
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Hayrapetyan A, Tumasyan A, Adam W, Andrejkovic JW, Bergauer T, Chatterjee S, Damanakis K, Dragicevic M, Hussain PS, Jeitler M, Krammer N, Li A, Liko D, Mikulec I, Schieck J, Schöfbeck R, Schwarz D, Sonawane M, Templ S, Waltenberger W, Wulz CE, Darwish MR, Janssen T, Mechelen PV, Bols ES, D'Hondt J, Dansana S, De Moor A, Delcourt M, Faham HE, Lowette S, Makarenko I, Müller D, Sahasransu AR, Tavernier S, Tytgat M, Onsem GPV, Putte SV, Vannerom D, Clerbaux B, Das AK, De Lentdecker G, Favart L, Gianneios P, Hohov D, Jaramillo J, Khalilzadeh A, Khan FA, Lee K, Mahdavikhorrami M, Malara A, Paredes S, Thomas L, Bemden MV, Velde CV, Vanlaer P, De Coen M, Dobur D, Hong Y, Knolle J, Lambrecht L, Mestdach G, Amarilo KM, Rendón C, Samalan A, Skovpen K, Bossche NVD, Linden JVD, Wezenbeek L, Benecke A, Bethani A, Bruno G, Caputo C, Delaere C, Donertas IS, Giammanco A, Jaffel K, Jain S, Lemaitre V, Lidrych J, Mastrapasqua P, Mondal K, Tran TT, Wertz S, Alves GA, Coelho E, Hensel C, De Oliveira TM, Moraes A, Teles PR, Soeiro M, Júnior WLA, Pereira MAG, Filho MBF, Malbouisson HB, Carvalho W, Chinellato J, Da Costa EM, Da Silveira GG, De Jesus Damiao D, De Souza SF, De Souza RG, Martins J, Herrera CM, Mundim L, Nogima H, Pinheiro JP, Santoro A, Sznajder A, Thiel M, Pereira AV, Bernardes CA, Calligaris L, Tomei TRFP, Gregores EM, Mercadante PG, Novaes SF, Orzari B, Padula SS, Aleksandrov A, Antchev G, Hadjiiska R, Iaydjiev P, Misheva M, Shopova M, Sultanov G, Dimitrov A, Litov L, Pavlov B, Petkov P, Petrov A, Shumka E, Keshri S, Thakur S, Cheng T, Javaid T, Yuan L, Hu Z, Liu J, Yi K, Chen GM, Chen HS, Chen M, Iemmi F, Jiang CH, Kapoor A, Liao H, Liu ZA, Sharma R, Song JN, Tao J, Wang C, Wang J, Wang Z, Zhang H, Agapitos A, Ban Y, Levin A, Li C, Li Q, Mao Y, Qian SJ, Sun X, Wang D, Yang H, Zhang L, Zhou C, You Z, Lu N, Bauer G, Gao X, Leggat D, Okawa H, Lin Z, Lu C, Xiao M, Avila C, Trujillo DAB, Cabrera A, Florez C, Fraga J, Vega JAR, Guisao JM, Ramirez F, Rodriguez M, Alvarez JDR, Giljanovic D, Godinovic N, Lelas D, Sculac A, Kovac M, Sculac T, Bargassa P, Brigljevic V, Chitroda BK, Ferencek D, Mishra S, Starodumov A, Susa T, Attikis A, Christoforou K, Konstantinou S, Mousa J, Nicolaou C, Ptochos F, Razis PA, Rykaczewski H, Saka H, Stepennov A, Finger M, Finger M, Kveton A, Ayala E, Jarrin EC, Abdelalim AA, Salama E, Mahmoud MA, Mohammed Y, Ehataht K, Kadastik M, Lange T, Nandan S, Nielsen C, Pata J, Raidal M, Tani L, Veelken C, Kirschenmann H, Osterberg K, Voutilainen M, Bharthuar S, Brücken E, Garcia F, Kallonen KTS, Kinnunen R, Lampén T, Lassila-Perini K, Lehti S, Lindén T, Martikainen L, Myllymäki M, Rantanen MM, Siikonen H, Tuominen E, Tuominiemi J, Luukka P, Petrow H, Besancon M, Couderc F, Dejardin M, Denegri D, Faure JL, Ferri F, Ganjour S, Gras P, de Monchenault GH, Lohezic V, Malcles J, Rander J, Rosowsky A, Sahin MÖ, Savoy-Navarro A, Simkina P, Titov M, Tornago M, Barrera CB, Beaudette F, Perraguin AB, Busson P, Cappati A, Charlot C, Chiusi M, Damas F, Davignon O, De Wit A, Alves BAFS, Ghosh S, Gilbert A, de Cassagnac RG, Hakimi A, Harikrishnan B, Kalipoliti L, Liu G, Motta J, Nguyen M, Ochando C, Portales L, Salerno R, Sauvan JB, Sirois Y, Tarabini A, Vernazza E, Zabi A, Zghiche A, Agram JL, Andrea J, Apparu D, Bloch D, Brom JM, Chabert EC, Collard C, Falke S, Goerlach U, Grimault C, Haeberle R, Bihan ACL, Meena M, Saha G, Sessini MA, Hove PV, Beauceron S, Blancon B, Boudoul G, Chanon N, Choi J, Contardo D, Depasse P, Dozen C, Mamouni HE, Fay J, Gascon S, Gouzevitch M, Greenberg C, Grenier G, Ille B, Laktineh IB, Lethuillier M, Mirabito L, Perries S, Purohit A, Donckt MV, Verdier P, Xiao J, Bagaturia I, Lomidze I, Tsamalaidze Z, Botta V, Feld L, Klein K, Lipinski M, Meuser D, Pauls A, Röwert N, Teroerde M, Diekmann S, Dodonova A, Eich N, Eliseev D, Engelke F, Erdmann J, Erdmann M, Fackeldey P, Fischer B, Hebbeker T, Hoepfner K, Ivone F, Jung A, Lee MY, Mausolf F, Merschmeyer M, Meyer A, Mukherjee S, Noll D, Nowotny F, Pozdnyakov A, Rath Y, Redjeb W, Rehm F, Reithler H, Sarkar U, Sarkisovi V, Schmidt A, Sharma A, Spah JL, Stein A, Da Silva De Araujo FT, Vigilante L, Wiedenbeck S, Zaleski S, Dziwok C, Flügge G, Ahmad WH, Kress T, Nowack A, Pooth O, Stahl A, Ziemons T, Zotz A, Petersen HA, Martin MA, Alimena J, Amoroso S, An Y, Baxter S, Bayatmakou M, Gonzalez HB, Behnke O, Belvedere A, Bhattacharya S, Blekman F, Borras K, Campbell A, Cardini A, Cheng C, Colombina F, Rodríguez SC, Silva GC, De Silva M, Eckerlin G, Eckstein D, Banos LIE, Filatov O, Gallo E, Geiser A, Giraldi A, Guglielmi V, Guthoff M, Hinzmann A, Jafari A, Jeppe L, Jomhari NZ, Kaech B, Kasemann M, Kleinwort C, Kogler R, Komm M, Krücker D, Lange W, Pernia DL, Lipka K, Lohmann W, Mankel R, Melzer-Pellmann IA, Morentin MM, Meyer AB, Milella G, Mussgiller A, Nair LP, Nürnberg A, Otarid Y, Park J, Adán DP, Ranken E, Raspereza A, Lopes BR, Rübenach J, Saggio A, Scham M, Schnake S, Schütze P, Schwanenberger C, Selivanova D, Sharko K, Shchedrolosiev M, Ricardo RES, Stafford D, Vazzoler F, Barroso AV, Walsh R, Wang Q, Wen Y, Wichmann K, Wiens L, Wissing C, Yang Y, Santos AZC, Albrecht A, Albrecht S, Antonello M, Bein S, Benato L, Bollweg S, Bonanomi M, Connor P, Eich M, Morabit KE, Fischer Y, Garbers C, Garutti E, Grohsjean A, Haller J, Jabusch HR, Kasieczka G, Keicher P, Klanner R, Korcari W, Kramer T, Kutzner V, Labe F, Lange J, Lobanov A, Matthies C, Mehta A, Moureaux L, Mrowietz M, Nigamova A, Nissan Y, Paasch A, Rodriguez KJP, Quadfasel T, Raciti B, Rieger M, Savoiu D, Schindler J, Schleper P, Schröder M, Schwandt J, Sommerhalder M, Stadie H, Steinbrück G, Tews A, Wolf M, Brommer S, Burkart M, Butz E, Chwalek T, Dierlamm A, Droll A, Faltermann N, Giffels M, Gottmann A, Hartmann F, Hofsaess R, Horzela M, Husemann U, Kieseler J, Klute M, Koppenhöfer R, Lawhorn JM, Link M, Lintuluoto A, Maier S, Mitra S, Mormile M, Müller T, Neukum M, Oh M, Presilla M, Quast G, Rabbertz K, Regnery B, Shadskiy N, Shvetsov I, Simonis HJ, Toms M, Trevisani N, Cube RFV, Wassmer M, Wieland S, Wittig F, Wolf R, Zuo X, Anagnostou G, Daskalakis G, Kyriakis A, Papadopoulos A, Stakia A, Kontaxakis P, Melachroinos G, Panagiotou A, Papavergou I, Paraskevas I, Saoulidou N, Theofilatos K, Tziaferi E, Vellidis K, Zisopoulos I, Bakas G, Chatzistavrou T, Karapostoli G, Kousouris K, Papakrivopoulos I, Siamarkou E, Tsipolitis G, Zacharopoulou A, Adamidis K, Bestintzanos I, Evangelou I, Foudas C, Kamtsikis C, Katsoulis P, Kokkas P, Kioseoglou PGK, Manthos N, Papadopoulos I, Strologas J, Bartók M, Hajdu C, Horvath D, Márton K, Sikler F, Veszpremi V, Csanád M, Farkas K, Gadallah MMA, Kadlecsik Á, Major P, Mandal K, Pásztor G, Rádl AJ, Veres GI, Raics P, Ujvari B, Zilizi G, Bencze G, Czellar S, Molnar J, Szillasi Z, Csorgo T, Nemes F, Novak T, Babbar J, Bansal S, Beri SB, Bhatnagar V, Chaudhary G, Chauhan S, Dhingra N, Kaur A, Kaur A, Kaur H, Kaur M, Kumar S, Sandeep K, Sheokand T, Singh JB, Singla A, Ahmed A, Bhardwaj A, Chhetri A, Choudhary BC, Kumar A, Kumar A, Naimuddin M, Ranjan K, Saumya S, Baradia S, Barman S, Bhattacharya S, Dutta S, Dutta S, Sarkar S, Ameen MM, Behera PK, Behera SC, Chatterjee S, Jana P, Kalbhor P, Komaragiri JR, Kumar D, Pujahari PR, Saha NR, Sharma A, Sikdar AK, Verma S, Dugad S, Kumar M, Mohanty GB, Suryadevara P, Bala A, Banerjee S, Chatterjee RM, Dewanjee RK, Guchait M, Jain S, Jaiswal A, Karmakar S, Kumar S, Majumder G, Mazumdar K, Parolia S, Thachayath A, Bahinipati S, Kar C, Maity D, Mal P, Mishra T, Bindhu VKMN, Naskar K, Nayak A, Sadangi P, Saha P, Swain SK, Varghese S, Vats D, Acharya S, Alpana A, Dube S, Gomber B, Kansal B, Laha A, Sahu B, Sharma S, Vaish KY, Bakhshiansohi H, Khazaie E, Zeinali M, Chenarani S, Etesami SM, Khakzad M, Najafabadi MM, Grunewald M, Abbrescia M, Aly R, Colaleo A, Creanza D, D'Anzi B, De Filippis N, De Palma M, Florio AD, Elmetenawee W, Fiore L, Iaselli G, Louka M, Maggi G, Maggi M, Margjeka I, Mastrapasqua V, My S, Nuzzo S, Pellecchia A, Pompili A, Pugliese G, 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Gurrola A, Johns W, Elayavalli RK, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Hakala J, Hirosky R, Ledovskoy A, Neu C, Lara CEP, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Sreekala JM, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pétré L, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Chadeeva M, Chekhovsky V, Chistov R, Demiyanov A, Dermenev A, Dimova T, Druzhkin D, Dubinin M, Dudko L, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Gribushin A, Ivanov Y, Kachanov V, Karjavine V, Karneyeu A, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Klyukhin V, Kodolova O, Korenkov V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oreshkin V, Palichik V, Perelygin V, Petrushanko S, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service. COMPUTING AND SOFTWARE FOR BIG SCIENCE 2024; 8:17. [PMID: 39248308 PMCID: PMC11374919 DOI: 10.1007/s41781-024-00124-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 07/23/2024] [Indexed: 09/10/2024]
Abstract
Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.
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Ma X, Räisänen SE, Garcia-Ascolani ME, Bobkov M, He T, Islam MZ, Li Y, Peng R, Reichenbach M, Serviento AM, Soussan E, Sun X, Wang K, Yang S, Zeng Z, Niu M. Effects of 3-nitrooxypropanol (Bovaer10) and whole cottonseed on milk production and enteric methane emissions from dairy cows under Swiss management conditions. J Dairy Sci 2024; 107:6817-6833. [PMID: 38762115 DOI: 10.3168/jds.2023-24460] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Accepted: 03/29/2024] [Indexed: 05/20/2024]
Abstract
The objective of this study was to determine the potential effect and interaction of 3-nitrooxypropanol (3-NOP; Bovaer, DSM-Firmenich Nutrition Products Ltd.) and whole cottonseed (WCS) on lactational performance and enteric methane (CH4) emission of dairy cows. A total of 16 multiparous cows, including 8 Holstein Friesian (HF) and 8 Brown Swiss (BS; 224 ± 36 DIM, 26 ± 3.7 kg milk yield, mean ± SD), were used in a split-plot design, where the main plot was the breed of cows. Within each subplot, cows were randomly assigned to a treatment sequence in a replicated 4 × 4 Latin square design with 2 × 2 factorial arrangements of treatments with four 24-d periods. The experimental treatments were as follows: (1) control (basal TMR), (2) 3-NOP (60 mg/kg TMR DM), (3) WCS (5% TMR DM), and (4) 3-NOP + WCS. The treatment diets were balanced for ether extract, crude protein, and NDF contents (4%, 16%, and 43% of TMR DM, respectively). The basal diets were fed twice daily at 0800 and 1800 h. Dry matter intake and milk yield were measured daily, and enteric gas emissions were measured (using the GreenFeed System, C-Lock Inc.) during the last 3 d of each 24-d experimental period when animals were housed in tiestalls. There was no difference in DMI on treatment level, whereas the WCS treatment increased ECM yield and milk fat yield. No interaction of 3-NOP and WCS occurred for any of the enteric gas emission parameters, but 3-NOP decreased CH4 production (g/d), CH4 yield (g/kg DMI), and CH4 intensity (g/kg ECM) by 13%, 14%, and 13%, respectively. Further, an unexpected interaction of breed by 3-NOP was observed for different enteric CH4 emission metrics: HF cows had a greater CH4 mitigation effect compared with BS cows for CH4 production (g/d; 18% vs. 8%), CH4 intensity (g/kg milk yield; 19% vs. 3%), and CH4 intensity (g/kg ECM; 19% vs. 4%). Hydrogen production was increased by 2.85-fold in HF and 1.53-fold in BS cows receiving 3-NOP. Further, a 3-NOP × time interaction occurred for both breeds. In BS cows, 3-NOP tended to reduce CH4 production by 18% at approximately 4 h after morning feeding, but no effect was observed at other time points. In HF cows, the greatest mitigation effect of 3-NOP (29.6%) was observed immediately after morning feeding, and it persisted at around 23% to 26% for 10 h until the second feed provision, and 3 h thereafter, in the evening. In conclusion, supplementing 3-NOP at 60 mg/kg DM to a high-fiber diet resulted in 18% to 19% reduction in enteric CH4 emission in Swiss HF cows. The lower response to 3-NOP by BS cows was unexpected and has not been observed in other studies. These results should be interpreted with caution due to the low number of cows per breed. Finally, supplementing WCS at 5% of DM improved ECM and milk fat yield but did not enhance the CH4 inhibition effect of 3-NOP of dairy cows.
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AMV, Bam B, Chudasama R, Cooper SI, Gleyzer SV, Perez CU, Rumerio P, Usai E, Yi R, Akpinar A, Arcaro D, Cosby C, Demiragli Z, Erice C, Fangmeier C, Madrazo CF, Fontanesi E, Gastler D, Golf F, Jeon S, Reed I, Rohlf J, Salyer K, Sperka D, Spitzbart D, Suarez I, Tsatsos A, Yuan S, Zecchinelli AG, Benelli G, Coubez X, Cutts D, Hadley M, Heintz U, Hogan JM, Kwon T, Landsberg G, Lau KT, Li D, Luo J, Mondal S, Narain M, Pervan N, Sagir S, Simpson F, Stamenkovic M, Wong WY, Yan X, Zhang W, Abbott S, Bonilla J, Brainerd C, Breedon R, De La Barca Sanchez MC, Chertok M, Citron M, Conway J, Cox PT, Erbacher R, Jensen F, Kukral O, Mocellin G, Mulhearn M, Pellett D, Wei W, Yao Y, Zhang F, Bachtis M, Cousins R, Datta A, Avila GF, Hauser J, Ignatenko M, Iqbal MA, Lam T, Manca E, Del Prado AN, Saltzberg D, Valuev V, Clare R, Gary JW, Gordon M, Hanson G, Si W, Wimpenny S, Branson JG, Cittolin S, Cooperstein S, Diaz D, Duarte J, Giannini L, Guiang J, Kansal R, Krutelyov V, Lee R, Letts J, Masciovecchio M, Mokhtar F, Mukherjee S, Pieri M, Quinnan M, Narayanan BVS, Sharma V, Tadel M, Vourliotis E, Würthwein F, Xiang Y, Yagil A, Barzdukas A, Brennan L, Campagnari C, Dorsett A, Incandela J, Kim J, Li AJ, Masterson P, Mei H, Richman J, Sarica U, Schmitz R, Setti F, Sheplock J, Stuart D, Vámi TÁ, Wang S, Bornheim A, Cerri O, Latorre A, Mao J, Newman HB, Spiropulu M, Vlimant JR, Wang C, Xie S, Zhu RY, Alison J, An S, Andrews MB, Bryant P, Cremonesi M, Dutta V, Ferguson T, Harilal A, Liu C, Mudholkar T, Murthy S, Paulini M, Roberts A, Sanchez A, Terrill W, Cumalat JP, Ford W, Hassani A, Karathanasis G, MacDonald E, Manganelli N, Perloff A, Savard C, Schonbeck N, Stenson K, Ulmer KA, Wagner SR, Zipper N, Alexander J, Bright-Thonney S, Chen X, Cranshaw DJ, Fan J, Fan X, Gadkari D, Hogan S, Kotamnives P, Monroy J, Oshiro M, Patterson JR, Reichert J, Reid M, Ryd A, Thom J, Wittich P, Zou R, Albrow M, Alyari M, Amram O, Apollinari G, Apresyan A, Bauerdick LAT, Berry D, Berryhill J, Bhat PC, Burkett K, Butler JN, Canepa A, Cerati GB, Cheung HWK, Chlebana F, Cummings G, Dickinson J, Dutta I, Elvira VD, Feng Y, Freeman J, Gandrakota A, Gecse Z, Gray L, Green D, Grummer A, Grünendahl S, Guerrero D, Gutsche O, Harris RM, Heller R, Herwig TC, Hirschauer J, Horyn L, Jayatilaka B, Jindariani S, Johnson M, Joshi U, Klijnsma T, Klima B, Kwok KHM, Lammel S, Lincoln D, Lipton R, Liu T, Madrid C, Maeshima K, Mantilla C, Mason D, McBride P, Merkel P, Mrenna S, Nahn S, Ngadiuba J, Noonan D, Papadimitriou V, Pastika N, Pedro K, Pena C, Ravera F, Hall AR, Ristori L, Sexton-Kennedy E, Smith N, Soha A, Spiegel L, Stoynev S, Strait J, Taylor L, Tkaczyk S, Tran NV, Uplegger L, Vaandering EW, Zoi I, Aruta C, Avery P, Bourilkov D, Cadamuro L, Chang P, Cherepanov V, Field RD, Koenig E, Kolosova M, Konigsberg J, Korytov A, Lo KH, Matchev K, Menendez N, Mitselmakher G, Mohrman K, Madhu AM, Rawal N, Rosenzweig D, Rosenzweig S, Shi K, Wang J, Adams T, Al Kadhim A, Askew A, Bower N, Habibullah R, Hagopian V, Hashmi R, Kim RS, Kim S, Kolberg T, Martinez G, Prosper H, Prova PR, Wulansatiti M, Yohay R, Zhang J, Alsufyani B, Baarmand MM, Butalla S, Elkafrawy T, Hohlmann M, Verma RK, Rahmani M, Yanes E, Adams MR, Baty A, Bennett C, Cavanaugh R, Franco RE, Evdokimov O, Gerber CE, Hofman DJ, Lee JH, Lemos DS, Merrit AH, Mills C, Nanda S, Oh G, Ozek B, Pilipovic D, Pradhan R, Roy T, Rudrabhatla S, Tonjes MB, Varelas N, Ye Z, Yoo J, Alhusseini M, Blend D, Dilsiz K, Emediato L, Karaman G, Köseyan OK, Merlo JP, Mestvirishvili A, Nachtman J, Neogi O, Ogul H, Onel Y, Penzo A, Snyder C, Tiras E, Blumenfeld B, Corcodilos L, Davis J, Gritsan AV, Kang L, Kyriacou S, Maksimovic P, Roguljic M, Roskes J, Sekhar S, Swartz M, Abreu A, Alcerro LFA, Anguiano J, Baringer P, Bean A, Flowers Z, Grove D, King J, Krintiras G, Lazarovits M, Le Mahieu C, Lindsey C, Marquez J, Minafra N, Murray M, Nickel M, Pitt M, Popescu S, Rogan C, Royon C, Salvatico R, Sanders S, Smith C, Wang Q, Wilson G, Allmond B, Ivanov A, Kaadze K, Kalogeropoulos A, Kim D, Maravin Y, Nam K, Natoli J, Roy D, Sorrentino G, Rebassoo F, Wright D, Baden A, Belloni A, Chen YM, Eno SC, Hadley NJ, Jabeen S, Kellogg RG, Koeth T, Lai Y, Lascio S, Mignerey AC, Nabili S, Palmer C, Papageorgakis C, Paranjpe MM, Wang L, Bendavid J, Busza W, Cali IA, D’Alfonso M, Eysermans J, Freer C, Gomez-Ceballos G, Goncharov M, Grosso G, Harris P, Hoang D, Kovalskyi D, Krupa J, Lavezzo L, Lee YJ, Long K, Mironov C, Paus C, Rankin D, Roland C, Roland G, Rothman S, Stephans GSF, Wang Z, Wyslouch B, Yang TJ, Crossman B, Joshi BM, Kapsiak C, Krohn M, Mahon D, Mans J, Marzocchi B, Pandey S, Revering M, Rusack R, Saradhy R, Schroeder N, Strobbe N, Wadud MA, Cremaldi LM, Bloom K, Claes DR, Haza G, Hossain J, Joo C, Kravchenko I, Siado JE, Tabb W, Vagnerini A, Wightman A, Yan F, Yu D, Bandyopadhyay H, Hay L, Iashvili I, Kharchilava A, Morris M, Nguyen D, Rappoccio S, Sfar HR, Williams A, Alverson G, Barberis E, Dervan J, Haddad Y, Han Y, Krishna A, Li J, Lu M, Madigan G, Mccarthy R, Morse DM, Nguyen V, Orimoto T, Parker A, Skinnari L, Tishelman-Charny A, Wang B, Wood D, Bhattacharya S, Bueghly J, Chen Z, Dittmer S, Hahn KA, Liu Y, Miao Y, Monk DG, Schmitt MH, Taliercio A, Velasco M, Agarwal G, Band R, Bucci R, Castells S, Das A, Goldouzian R, Hildreth M, Ho KW, Anampa KH, Ivanov T, Jessop C, Lannon K, Lawrence J, Loukas N, Lutton L, Mariano J, Marinelli N, Mcalister I, McCauley T, Mcgrady C, Moore C, Musienko Y, Nelson H, Osherson M, Piccinelli A, Ruchti R, Townsend A, Wan Y, Wayne M, Yockey H, Zarucki M, Zygala L, Basnet A, Bylsma B, Carrigan M, Durkin L, Hill C, Joyce M, Ornelas MN, Wei K, Winer BL, Yates BR, Addesa FM, Bouchamaoui H, Das P, Dezoort G, Elmer P, Frankenthal A, Greenberg B, Haubrich N, Kopp G, Kwan S, Lange D, Loeliger A, Marlow D, Ojalvo I, Olsen J, Shevelev A, Stickland D, Tully C, Malik S, Bakshi AS, Barnes VE, Chandra S, Chawla R, Das S, Gu A, Gutay L, Jones M, Jung AW, Kondratyev D, Koshy AM, Liu M, Negro G, Neumeister N, Paspalaki G, Piperov S, Scheurer V, Schulte JF, Stojanovic M, Thieman J, Virdi AK, Wang F, Xie W, Dolen J, Parashar N, Pathak A, Acosta D, Carnahan T, Ecklund KM, Manteca PJF, Freed S, Gardner P, Geurts FJM, Li W, Colin OM, Padley BP, Redjimi R, Rotter J, Yigitbasi E, Zhang Y, Bodek A, de Barbaro P, Demina R, Dulemba JL, Garcia-Bellido A, Hindrichs O, Khukhunaishvili A, Parmar N, Parygin P, Popova E, Taus R, Goulianos K, Chiarito B, Chou JP, Gershtein Y, Halkiadakis E, Hart A, Heindl M, Jaroslawski D, Karacheban O, Laflotte I, Lath A, Montalvo R, Nash K, Routray H, Salur S, Schnetzer S, Somalwar S, Stone R, Thayil SA, Thomas S, Vora J, Wang H, Acharya H, Ally D, Delannoy AG, Fiorendi S, Higginbotham S, Holmes T, Kanuganti AR, Karunarathna N, Lee L, Nibigira E, Spanier S, Aebi D, Ahmad M, Bouhali O, Eusebi R, Gilmore J, Huang T, Kamon T, Kim H, Luo S, Mueller R, Overton D, Rathjens D, Safonov A, Akchurin N, Damgov J, Hegde V, Hussain A, Kazhykarim Y, Lamichhane K, Lee SW, Mankel A, Peltola T, Volobouev I, Whitbeck A, Appelt E, Chen Y, Greene S, Gurrola A, Johns W, Elayavalli RK, Melo A, Romeo F, Sheldon P, Tuo S, Velkovska J, Viinikainen J, Cardwell B, Cox B, Hakala J, Hirosky R, Ledovskoy A, Neu C, Lara CEP, Karchin PE, Aravind A, Banerjee S, Black K, Bose T, Dasu S, De Bruyn I, Everaerts P, Galloni C, He H, Herndon M, Herve A, Koraka CK, Lanaro A, Loveless R, Sreekala JM, Mallampalli A, Mohammadi A, Mondal S, Parida G, Pinna D, Savin A, Shang V, Sharma V, Smith WH, Teague D, Tsoi HF, Vetens W, Warden A, Afanasiev S, Andreev V, Andreev Y, Aushev T, Azarkin M, Babaev A, Belyaev A, Blinov V, Boos E, Borshch V, Budkouski D, Chadeeva M, Chekhovsky V, Chistov R, Dermenev A, Dimova T, Druzhkin D, Dubinin M, Dudko L, Ershov A, Gavrilov G, Gavrilov V, Gninenko S, Golovtcov V, Golubev N, Golutvin I, Gorbunov I, Ivanov Y, Kachanov V, Karjavine V, Karneyeu A, Kim V, Kirakosyan M, Kirpichnikov D, Kirsanov M, Klyukhin V, Kodolova O, Korenkov V, Kozyrev A, Krasnikov N, Lanev A, Levchenko P, Lukina O, Lychkovskaya N, Makarenko V, Malakhov A, Matveev V, Murzin V, Nikitenko A, Obraztsov S, Oreshkin V, Palichik V, Perelygin V, Petrushanko S, Polikarpov S, Popov V, Radchenko O, Savina M, Savrin V, Shalaev V, Shmatov S, Shulha S, Skovpen Y, Slabospitskii S, Smirnov V, Snigirev A, Sosnov D, Sulimov V, Tcherniaev E, Terkulov A, Teryaev O, Tlisova I, Toropin A, Uvarov L, Uzunian A, Vardanyan I, Vorobyev A, Voytishin N, Yuldashev BS, Zarubin A, Zhizhin I, Zhokin A. Measurement of multijet azimuthal correlations and determination of the strong coupling in proton-proton collisions at s = 13 Te V. THE EUROPEAN PHYSICAL JOURNAL. C, PARTICLES AND FIELDS 2024; 84:842. [PMID: 39185726 PMCID: PMC11339110 DOI: 10.1140/epjc/s10052-024-13116-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 07/11/2024] [Indexed: 08/27/2024]
Abstract
A measurement is presented of a ratio observable that provides a measure of the azimuthal correlations among jets with large transverse momentum p T . This observable is measured in multijet events over the range ofp T = 360 - 3170 Ge V based on data collected by the CMS experiment in proton-proton collisions at a centre-of-mass energy of 13Te V , corresponding to an integrated luminosity of 134fb - 1 . The results are compared with predictions from Monte Carlo parton-shower event generator simulations, as well as with fixed-order perturbative quantum chromodynamics (pQCD) predictions at next-to-leading-order (NLO) accuracy obtained with different parton distribution functions (PDFs) and corrected for nonperturbative and electroweak effects. Data and theory agree within uncertainties. From the comparison of the measured observable with the pQCD prediction obtained with the NNPDF3.1 NLO PDFs, the strong coupling at the Z boson mass scale isα S ( m Z ) = 0.1177 ± 0.0013 (exp) - 0.0073 + 0.0116 (theo) = 0 . 1177 - 0.0074 + 0.0117 , where the total uncertainty is dominated by the scale dependence of the fixed-order predictions. A test of the running of α S in theTe V region shows no deviation from the expected NLO pQCD behaviour.
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Grants
- SC
- Austrian Federal Ministry of Education, Science and Research
- Austrian Science Fund
- Belgian Fonds de la Recherche Scientifique
- Belgian Fonds voor Wetenschappelijk Onderzoek
- CNPq
- CAPES
- FAPERJ
- FAPERGS
- FAPESP
- Bulgarian Ministry of Education and Science
- Bulgarian National Science Fund
- CERN
- Chinese Academy of Sciences
- Ministry of Science and Technology
- Chinese National Natural Science Foundation of China
- Colombian Funding Agency (MINICIENCIAS)
- Croatian Ministry of Science, Education and Sport
- Croatian Science Foundation
- Research and Innovation Foundation
- SENESCYT
- Estonian Research Council via PRG780, PRG803, RVTT3 and TK202
- Ministry of Education and Research via TK202
- Academy of Finland
- Finnish Ministry of Education and Culture
- Helsinki Institute of Physics
- Institut National de Physique Nucléaire et de Physique des Particules
- Centre National de la Recherche Scientifique
- Commissariat à l’Énergie Atomique et aux Énergies Alternatives
- Shota Rustaveli National Science Foundation
- Bundesministerium für Bildung und Forschung
- Deutsche Forschungsgemeinschaft
- Helmholtz-Gemeinschaft Deutscher Forschungszentren
- General Secretariat for Research and Innovation
- National Research, Development and Innovation Office
- Department of Atomic Energy
- Department of Science and Technology
- ICSC -National Research Centre for High Performance Computing, Big Data and Quantum Computing, funded by the EU NexGeneration program
- FAIR - Future Artificial Intelligence Research; funded by the EU NexGeneration program
- Institute for Research in Fundamental Studies
- Science Foundation
- Istituto Nazionale di Fisica Nucleare
- Korean Ministry of Education, Science and Technology
- National Research Foundation of Korea (NRF)
- MES
- Research Council of Lithuania (LMTLT), agreement No. VS-19
- Ministry of Education
- University of Malaya
- BUAP
- CINVESTAV
- CONACYT
- LNS
- SEP
- UASLP
- MOS
- Ministry of Business, Innovation and Employment
- Pakistan Atomic Energy Commission
- Ministry of Educaton and Science
- National Science Centre
- Fundação para a Ciência e a Tecnologia, CERN/FIS-PAR/0025/2019 and CERN/FIS-INS/0032/2019
- Ministry of Education, Science and Technological Development of Serbia
- MCIN/AEI/10.13039/501100011033, ERDF “a way of making Europe”
- Fondo Europeo de Desarrollo Regional, Spain
- Plan de Ciencia, Tecnología e Innovación del Principado de Asturias
- MOSTR
- ETH Board
- ETH Zurich
- PSI
- SNF
- UniZH
- Canton Zurich
- SER
- Ministry of Higher Education, Science, Research and Innovation
- National Science and Technology Development Agency of Thailand
- Scientific and Technical Research Council of Turkey
- Turkish Atomic Energy Authority
- National Academy of Sciences of Ukraine
- Science and Technology Facilities Council
- US Department of Energy
- US National Science Foundation
- Marie-Curie programme
- European Research Council and EPLANET (European Union)
- European Research Council/European Cooperation in Science and Technology), Action CA16108
- Horizon 2020 Grant, contract Nos. 675440, 724704, 752730, 758316, 765710, 824093, 101115353, 101002207 (European Union)
- Leventis Foundation
- Alfred P. Sloan Foundation
- Alexander von Humboldt Foundation
- Science Committee, project no. 22rl-037
- Belgian Federal Science Policy Office
- Fonds pour la Formation à la Recherche dans l’Industrie et dans l’Agriculture (FRIA-Belgium)
- Agentschap voor Innovatie door Wetenschap en Technologie (IWT-Belgium)
- Belgian Fonds de la Recherche Scientifique, "Excellence of Science - EOS" - be.h project n. 30820817
- Belgian Fonds voor Wetenschappelijk Onderzoek, "Excellence of Science - EOS" - be.h project n. 30820817
- Beijing Municipal Science & Technology Commission, No. Z191100007219010
- Fundamental Research Funds for the Central Universities
- Ministry of Education, Youth and Sports (MEYS) of the Czech Republic
- Deutsche Forschungsgemeinschaft (DFG) under Germany’s Excellence Strategy - EXC 2121 "Quantum Universe" – 390833306
- Deutsche Forschungsgemeinschaft (DFG), project number 400140256 - GRK2497
- Hellenic Foundation for Research and Innovation, Project Number 2288
- Hungarian Academy of Sciences
- New National Excellence Program - ÚNKP, the NKFIH research grants K 131991, K 133046, K 138136, K 143460, K 143477, K 146913, K 146914, K 147048, 2020-2.2.1-ED-2021-00181, and TKP2021-NKTA-64 (Hungary);
- Council of Scientific and Industrial Research, India
- Latvian Council of Science
- Ministy of Education and Science, project no. 2022/WK/14
- National Science Center, Opus 2021/41/B/ST2/01369 and 2021/43/B/ST2/01552
- Fundação para a Ciência e a Tecnologia, CEECIND/01334/2018
- National Priorities Research Program by Qatar National Research Fund
- Programa Estatal de Fomento de la Investigación Científica y Técnica de Excelencia María de Maeztu, grant MDM-2017-0765 and projects PID2020-113705RB, PID2020-113304RB, PID2020-116262RB and PID2020-113341RB-I00
- Programa Severo Ochoa del Principado de Asturias
- CUAASC
- National Science, Research and Innovation Fund via the Program Management Unit for Human Resources & Institutional Development, Research and Innovation, grant B37G660013
- Kavli Foundation
- Nvidia Corporation
- Welch Foundation, contract C-1845
- Weston Havens Foundation
- Institut für Hochenergiephysik (HEPHY) using the Cloud Infrastructure Platform (CLIP), Vienna
- Inter-University Institute for High Energies, Brussels
- Université Catholique de Louvain, Louvain-la-Neuve
- São Paulo Research and Analysis Center, São Paulo
- Universidade do Estado do Rio de Janeiro, Rio de Janeiro
- University of Sofia, Sofia
- Institute of High Energy Physics of the Chinese Academy of Sciences, Beijing
- National Institute of Chemical Physics and Biophysics, Tallinn
- Helsinki Institute of Physics, Helsinki
- Grille de Recherche d’Ile de France (GRIF), Institut de recherche sur les lois fondamentales de l’Univers, CEA, Université Paris-Saclay, Gif-sur-Yvette, France and Laboratoire Leprince-Ringuet, CNRS/IN2P3, Ecole Polytechnique, Institut Polytechnique de Paris
- Institut national de physique nucléaire et de physique des particules, IN2P3, Villeurbanne
- Institut Pluridisciplinaire Hubert Curien (IPHC), Strasbourg
- Deutsches Elektronen-Synchrotron, Hamburg
- Karlsruher Institut für Technologie, Karlsruhe
- RWTH Aachen University, Aachen
- University of Ioánnina, Ioánnina
- Wigner Research Centre for Physics, Budapest
- Tata Institute of Fundamental Research, Mumbai
- INFN CNAF, Bologna
- INFN Sezione di Bari, Università di Bari, Politecnico di Bari, Bari
- INFN Sezione di Pisa, Università di Pisa, Scuola Normale Superiore di Pisa, Pisa
- INFN Sezione di Roma, Sapienza Università di Roma, Rome
- INFN Sezione di Trieste, Università di Trieste, Trieste
- Laboratori Nazionali di Legnaro, Legnaro
- National Centre for Physics, Quaid-I-Azam University, Islamabad
- Akademickie Centrum Komputerowe Cyfronet AGH, Krakow
- National Centre for Nuclear Research, Swierk
- Laboratório de Instrumentação e Física Experimental de Partículas, Lisboa
- Korea Institute of Science and Technology Information (KISTI), Daejeon
- Centro de Investigaciones Energéticas Medioambientales y Tecnológicas (CIEMAT), Madrid
- Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander
- Port d’Informació Científica, Bellaterra
- CERN, European Organization for Nuclear Research, Geneva
- Instrumentation and Detector Consortium, Taipei
- National Center for High-performance Computing (NCHC), Hsinchu City
- Middle East Technical University, Physics Department, Ankara
- National Scientific Center, Kharkov Institute of Physics and Technology, Kharkov
- GridPP, Brunel University, Uxbridge
- GridPP, Imperial College, London
- GridPP, Queen Mary University of London, London
- GridPP, Royal Holloway, University of London, London
- GridPP, Rutherford Appleton Laboratory, Didcot
- GridPP, University of Bristol, Bristol
- GridPP, University of Glasgow, Glasgow
- Baylor University, Waco
- California Institute of Technology, Pasadena
- Fermi National Accelerator Laboratory, Batavia
- Massachusetts Institute of Technology, Cambridge
- National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility, Berkeley
- Open Science Grid (OSG) Consortium
- Pittsburgh Supercomputing Center (PSC), Pittsburgh
- Purdue University, West Lafayette
- San Diego Supercomputer Center (SDSC), La Jolla
- Texas Advanced Computing Center (TACC), Austin
- University of California, San Diego, La Jolla
- University of Colorado Boulder, Boulder
- University of Florida, Gainesville
- University of Nebraska-Lincoln, Lincoln
- University of Wisconsin - Madison, Madison
- Vanderbilt University, Nashville
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Cong X, Chen T, Li S, Wang Y, Zhou L, Li X, Zhang P, Sun X, Zhao S. [Dihydroartemisinin enhances sensitivity of nasopharyngeal carcinoma HNE1/DDP cells to cisplatin-induced apoptosis by promoting ROS production]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:1553-1560. [PMID: 39276051 PMCID: PMC11378052 DOI: 10.12122/j.issn.1673-4254.2024.08.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 09/16/2024]
Abstract
OBJECTIVE To investigate the effect of dihydroartemisinin (DHA) for enhancing the inhibitory effect of cisplatin (DDP) on DDP-resistant nasopharyngeal carcinoma cell line HNE1/DDP and explore the mechanism. METHODS CCK-8 method was used to assess the survival rate of HNE1/DDP cells treated with DHA (0, 5, 10, 20, 40, 80, and 160 μmol/L) and DDP (0, 4, 8, 16, 32, 64, 128 μmol/L) for 24 or 48 h, and the combination index of DHA and DDP was calculated using Compusyn software. HNE1/DDP cells treated with DHA, DDP, or their combination for 24 h were examined for cell viability, proliferation and colony formation ability using CCK-8, EdU and colony-forming assays. Flow cytometry was used to detect cell apoptosis and intracellular reactive oxygen species (ROS). The expression levels of apoptosis-related proteins cleaved PARP, cleaved caspase-9 and cleaved caspase-3 were detected by Western blotting. The effects of N-acetyl-cysteine (a ROS inhibitor) on proliferation and apoptosis of HNE1/DDP cells with combined treatment with DHA and DDP were analyzed. RESULTS Different concentrations of DHA and DDP alone both significantly inhibited the viability of HNE1/DDP cells. The combination index of DHA (5 μmol/L) combined with DDP (8, 16, 32, 64, 128 μmol/L) were all below 1. Compared with DHA or DDP alone, their combined treatment more potently decreased the cell viability, colony-forming ability and the number of EdU-positive cells, and significantly increased the apoptotic rate, intracellular ROS level, and the expression levels of cleaved PARP, cleaved caspase-9 and cleaved caspase-3 in HNE1/DDP cells. N-acetyl-cysteine pretreatment obviously attenuated the inhibitory effect on proliferation and apoptosis-inducing effect of DHA combined with DDP in HNE1/DDP cells (P<0.01). CONCLUSION DHA enhances the growth-inhibitory and apoptosis-inducing effect of DDP on HNE1/DDP cells possibly by promoting accumulation of intracellular ROS.
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