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Wang K, Guo K, Ji Z, Liu Y, Chen F, Wu S, Zhang Q, Yao Y, Zhou Q. Association of Preeclampsia with Incident Dementia and Alzheimer’s Disease among Women in the Framingham Offspring Study. J Prev Alzheimers Dis 2022; 9:725-730. [DOI: 10.14283/jpad.2022.62] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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Budd Haeberlein S, Aisen PS, Barkhof F, Chalkias S, Chen T, Cohen S, Dent G, Hansson O, Harrison K, von Hehn C, Iwatsubo T, Mallinckrodt C, Mummery CJ, Muralidharan KK, Nestorov I, Nisenbaum L, Rajagovindan R, Skordos L, Tian Y, van Dyck CH, Vellas B, Wu S, Zhu Y, Sandrock A. Two Randomized Phase 3 Studies of Aducanumab in Early Alzheimer's Disease. J Prev Alzheimers Dis 2022; 9:197-210. [PMID: 35542991 DOI: 10.14283/jpad.2022.30] [Citation(s) in RCA: 177] [Impact Index Per Article: 88.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
BACKGROUND Alzheimer's disease is a progressive, irreversible, and fatal disease for which accumulation of amyloid beta is thought to play a key role in pathogenesis. Aducanumab is a human monoclonal antibody directed against aggregated soluble and insoluble forms of amyloid beta. OBJECTIVES We evaluated the efficacy and safety of aducanumab in early Alzheimer's disease. DESIGN EMERGE and ENGAGE were two randomized, double-blind, placebo-controlled, global, phase 3 studies of aducanumab in patients with early Alzheimer's disease. SETTING These studies involved 348 sites in 20 countries. PARTICIPANTS Participants included 1638 (EMERGE) and 1647 (ENGAGE) patients (aged 50-85 years, confirmed amyloid pathology) who met clinical criteria for mild cognitive impairment due to Alzheimer's disease or mild Alzheimer's disease dementia, of which 1812 (55.2%) completed the study. INTERVENTION Participants were randomly assigned 1:1:1 to receive aducanumab low dose (3 or 6 mg/kg target dose), high dose (10 mg/kg target dose), or placebo via IV infusion once every 4 weeks over 76 weeks. MEASUREMENTS The primary outcome measure was change from baseline to week 78 on the Clinical Dementia Rating Sum of Boxes (CDR-SB), an integrated scale that assesses both function and cognition. Other measures included safety assessments; secondary and tertiary clinical outcomes that assessed cognition, function, and behavior; and biomarker endpoints. RESULTS EMERGE and ENGAGE were halted based on futility analysis of data pooled from the first approximately 50% of enrolled patients; subsequent efficacy analyses included data from a larger data set collected up to futility declaration and followed prespecified statistical analyses. The primary endpoint was met in EMERGE (difference of -0.39 for high-dose aducanumab vs placebo [95% CI, -0.69 to -0.09; P=.012; 22% decrease]) but not in ENGAGE (difference of 0.03, [95% CI, -0.26 to 0.33; P=.833; 2% increase]). Results of biomarker substudies confirmed target engagement and dose-dependent reduction in markers of Alzheimer's disease pathophysiology. The most common adverse event was amyloid-related imaging abnormalities-edema. CONCLUSIONS Data from EMERGE demonstrated a statistically significant change across all four primary and secondary clinical endpoints. ENGAGE did not meet its primary or secondary endpoints. A dose- and time-dependent reduction in pathophysiological markers of Alzheimer's disease was observed in both trials.
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Edmonston D, Wu S, Li Y, Khan R, Boop F, Merchant T. Limited Surgery and Conformal Photon Radiation Therapy for Pediatric Craniopharyngioma: Long-Term Results From the RT1 Protocol. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.201] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Peng Y, Wu S, Liu Y, Chen M, Miao J, Zhao C, Chen S, Qi Z, Deng X. Synthetic CT Generation From Multi-Sequence MR Images for Head and Neck MRI-Only Radiotherapy via Cycle-Consistent Generative Adversarial Network. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.1445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Zhao X, Xuan L, Yin J, Tang Y, Sun H, Wu S, Jing H, Fang H, Song Y, Jin J, Liu Y, Chen B, Qi S, Li N, Tang Y, Lu N, Yang Y, Li Y, Sun B, Wang S. Radiotherapy in Breast Cancer Patients With Isolated Regional Recurrence After Mastectomy: A Joint Analysis of 144 Cases From Two Institutions. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.714] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Konieczkowski D, Otani K, Drumm M, Wu S, Saylor P, Wu C, Efstathiou J, Miyamoto D. Impact of AR-V7 and Other Androgen Receptor Splice Variant Expression on Outcomes of Post-Prostatectomy Salvage Therapy. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.131] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Wang R, Wu S, Qian D, Zhang Y, Fan B, Hu M. A Lung Cancer Auxiliary Diagnostic Method: Deep Learning Based Mediastinal Lymphatic Partitions Segmentation for Cancer Staging. Int J Radiat Oncol Biol Phys 2021. [DOI: 10.1016/j.ijrobp.2021.07.474] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Chen M, Wu S, Zhao W, Zhou Y, Zhou Y, Wang G. Application of deep learning to auto-delineation of target volumes and organs at risk in radiotherapy. Cancer Radiother 2021; 26:494-501. [PMID: 34711488 DOI: 10.1016/j.canrad.2021.08.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2021] [Revised: 07/30/2021] [Accepted: 08/04/2021] [Indexed: 11/28/2022]
Abstract
The technological advancement heralded the arrival of precision radiotherapy (RT), thereby increasing the therapeutic ratio and decreasing the side effects from treatment. Contour of target volumes (TV) and organs at risk (OARs) in RT is a complicated process. In recent years, automatic contouring of TV and OARs has rapidly developed due to the advances in deep learning (DL). This technology has the potential to save time and to reduce intra- or inter-observer variability. In this paper, the authors provide an overview of RT, introduce the concept of DL, summarize the data characteristics of the included literature, summarize the possible challenges for DL in the future, and discuss the possible research directions.
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Jiang C, Wu S, Wang M, Li H, Zhao X. J-shaped relationship between admission diastolic blood pressure and 2-year cardiovascular mortality in elderly patients with acute coronary syndrome. Eur Heart J 2021. [DOI: 10.1093/eurheartj/ehab724.1363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Objective
To investigate the relationship between admission diastolic blood pressure (DBP) and subsequent cardiovascular and all-cause mortality in elderly patients with acute coronary syndrome (ACS).
Methods
This is a retrospective observational study. Consecutive patients ≥65 years of age admitted for ACS at a 2,300-bed tertiary hospital from December 2012 to July 2019 were included. The association between admission DBP and cardiovascular and all-cause mortality during hospitalization and over the follow-up period among this population were analyzed using multivariate COX regression model. Results were presented according to DBP quartiles: Q1, less than 67 mm Hg; Q2, from 67 to 72 mm Hg; Q3, from 73 to 80 mm Hg; and Q4, above 80 mm Hg.
Results
A total of 6 785 patients were included in this cohort study. Mean (SD) patient age was 74.0 (6.5) years, and 47.6% were women. Mean (SD) follow-up time was 2.54 (1.82) years. A non-linear relation was observed between DBP at admission and cardiovascular and all-cause mortality during hospitalization and over the follow-up period using restricted cubic splines. After adjustment for potential confounders, patients in Q3 or Q2 had lower risk for 2-year cardiovascular death by Cox proportional hazard model compared with patients in Q4 (hazard ratio [HR] 0.66; 95% confidence interval [CI], 0.48–0.90, P=0.010, for Q3 vs Q4; and HR 0.72; 95% CI, 0.53–0.99, P=0.041, for Q2vs Q4), while patients in Q1 had similar risk for cardiovascular death with that of patients in Q4. Meanwhile, when compared with patients in Q1, patients in Q3 had lower risk for 2-year cardiovascular death (HR, 0.72; 95% CI, 0.53–0.97, P=0.033). However, lower or higher admission DBP was not an independent predictor of 2-year all-cause mortality in this population.
Conclusion
Among patients aged ≥65 years admitted for ACS, there is a J-curve relationship between supine admission DBP and risk for 2-year cardiovascular death, with a nadir at 73–80 mm Hg.
Funding Acknowledgement
Type of funding sources: Other. Main funding source(s): the Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support Study population and selectionAdjusted multivariate COX regression
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Guo W, Liang N, Ma Q, Chen X, Liu R, Wu S, Bao H, Wu X, Shao Y, Qiu B, Wang D, Tan F, Gao Y, Xue Q, Gao S. MA07.07 Detecting Stage I Lung Cancer with High Sensitivity Using Genome-wide Multi-dimensional Fragmentomic Profiles of Cell Free DNA. J Thorac Oncol 2021. [DOI: 10.1016/j.jtho.2021.08.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Rogg J, Ben Ma Z, Pandya M, Wu S, Sharma K. 284 An Analysis of a Novel Telemedicine Intervention to Decrease Emergency Department Visits in a County Hospital System. Ann Emerg Med 2021. [DOI: 10.1016/j.annemergmed.2021.09.297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Zhi X, Liu J, Wu S, Niu C. A generalized l 2,p-norm regression based feature selection algorithm. J Appl Stat 2021; 50:703-723. [PMID: 36819074 PMCID: PMC9930865 DOI: 10.1080/02664763.2021.1975662] [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: 09/30/2020] [Accepted: 08/28/2021] [Indexed: 10/20/2022]
Abstract
Feature selection is an important data dimension reduction method, and it has been used widely in applications involving high-dimensional data such as genetic data analysis and image processing. In order to achieve robust feature selection, the latest works apply the l 2 , 1 or l 2 , p -norm of matrix to the loss function and regularization terms in regression, and have achieved encouraging results. However, these existing works rigidly set the matrix norms used in the loss function and the regularization terms to the same l 2 , 1 or l 2 , p -norm, which limit their applications. In addition, the algorithms for solutions they present either have high computational complexity and are not suitable for large data sets, or cannot provide satisfying performance due to the approximate calculation. To address these problems, we present a generalizedl 2 , p -norm regression based feature selection ( l 2 , p -RFS) method based on a new optimization criterion. The criterion extends the optimization criterion of ( l 2 , p -RFS) when the loss function and the regularization terms in regression use different matrix norms. We cast the new optimization criterion in a regression framework without regularization. In this framework, the new optimization criterion can be solved using an iterative re-weighted least squares (IRLS) procedure in which the least squares problem can be solved efficiently by using the least square QR decomposition (LSQR) algorithm. We have conducted extensive experiments to evaluate the proposed algorithm on various well-known data sets of both gene expression and image data sets, and compare it with other related feature selection methods.
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Yang L, Wang Z, Wu S, Lu WJ, Xiong H. [The correlation between post-allo-HSCT CMV infection and the difference affinity of donor HLA-type recognition of CMV antigen peptide in children]. ZHONGHUA XUE YE XUE ZA ZHI = ZHONGHUA XUEYEXUE ZAZHI 2021; 42:757-762. [PMID: 34753231 PMCID: PMC8607047 DOI: 10.3760/cma.j.issn.0253-2727.2021.09.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Download PDF] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Indexed: 11/07/2022]
Abstract
Objective: To explore the correlation between the affinity while donor's HLA type recognizing different cytomegalovirus (CMV) antigen peptide and the occurrence of CMV infection after allogeneic hematopoietic stem cell transplantation (allo-HSCT) in children. Methods: To investigate the relationship between CMV reactivation, CMV infection or CMV related tissue/organ diseases and the different HLA-type recognition antigen peptide of the donors, we retrospectively analyzed the clinical data of 146 children with CMV infection for 6 months since from the time they underwent transplantation in Wuhan Children's Hospital. Results: Among 146 patients, the HLA type of 82 (56.16%) cases had high affinity with PP65 alone, and 34 cases of CMV infection occurred after transplantation (41.46%) . None of 5 cases that had a high affinity with IE-1 alone got CMV infection. None of 2 cases with no clear high-affinity peptide had CMV infection. Three of 5 cases that had a high affinity with PP65 and PP50 had CMV infection. Thirteen of 52 cases that had a high affinity with PP65 and IE-1 had CMV infection (25.00%) . HLA with exclusive PP50 high affinity was not encountered. Donors with a high-affinity HLA locus associated with IE-1 showed a lower incidence of CMV infection after HSCT compared to those carrying only the PP65 high-affinity allele (22.81% vs 41.46%, P=0.029) . Conclusion: HLA type with PP65 and IE-1 high-affinity covers approximately 99.8% of the donors. Stem cells generated from HLA donors with high affinity with the CMV antigen peptide IE-1 can reduce the risk of post-transplantation CMV-activated infection in children.
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Wang N, Wu S, Zhao J, Chen M, Zeng J, Lu G, Wang J, Zhang J, Liu J, Shi Y. Bile acids increase intestinal marker expression via the FXR/SNAI2/miR-1 axis in the stomach. Cell Oncol (Dordr) 2021; 44:1119-1131. [PMID: 34510400 PMCID: PMC8516775 DOI: 10.1007/s13402-021-00622-z] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 06/24/2021] [Indexed: 12/24/2022] Open
Abstract
Purpose Intestinal metaplasia (IM) is a precancerous lesion that increases the risk of subsequent gastric cancer (GC) development. Previously, miR-1 has been shown to play an essential role in the initiation of bile acid (BA)-induced IM. The objective of the present study was to investigate the mechanism underlying miR-1 inhibition by BA in gastric cells. Methods Ingenuity pathway analysis (IPA) was used to identify molecules acting upstream of miR-1. The effects of deoxycholic acid (DCA), FXR and SNAI2 on the expression of intestinal markers were assessed using quantitative real-time PCR (qRT-PCR) and Western blotting. The expression level of major molecules was detected by immunohistochemistry (IHC) in tissue microarrays. The transcriptional regulation of miR-1 was verified using luciferase reporter and chromatin immunoprecipitation (ChIP) assays. Results We found that BA treatment caused aberrant expression of FXR and intestinal markers in gastric cells. Augmented FXR led to transcriptional activation of SNAI2, which in turn suppressed the miR-1 promoter. Moreover, we found that compared with normal tissues, the expression levels of both FXR and SNAI2 were increased and positively correlated with each other in IM tissues. Additionally, their expression showed an inverse correlation with that of miR-1 in IM tissues. Conclusions Our findings indicate that FXR may be responsible for a series of molecular changes in gastric cells after BA treatment, and that the FXR/SNAI2/miR-1 axis exhibits a crucial role in BA-induced progression of IM. Blocking the FXR-oriented axis may provide a promising approach for IM or even GC treatment. Supplementary Information The online version contains supplementary material available at 10.1007/s13402-021-00622-z.
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Zhen Q, Zhang Y, Yu Y, Yang H, Zhang T, Li X, Mo X, Li B, Wu J, Liang Y, Ge H, Xu Q, Chen W, Qian W, Xu H, Chen G, Bai B, Zhang J, Lu Y, Chen S, Zhang H, Zhang Y, Chen X, Li X, Jin X, Lin X, Yong L, Fang M, Zhao J, Lu Y, Wu S, Jiang D, Shi J, Cao H, Qiu Y, Li S, Kang X, Shen J, Ma H, Sun S, Fan Y, Chen W, Bai M, Jiang Q, Li W, Lv C, Li S, Chen M, Li F, Li Y, Sun L. Three Novel Structural Variations at MHC and IL12B Predisposing to Psoriasis. Br J Dermatol 2021; 186:307-317. [PMID: 34498260 DOI: 10.1111/bjd.20752] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/07/2021] [Indexed: 11/29/2022]
Abstract
BACKGROUND Structural variations (SVs, defined as DNA variants ≥50 bp) have been associated with various complex human diseases. However, research to screen the whole genome for SVs predisposing to psoriasis is still lacking. OBJECTIVES This study aimed to investigate the association of SVs and psoriasis. METHODS We performed a genome-wide screen on SVs using an imputation method on 5 independent cohorts with 45,386 subjects from the Chinese Han population. Fine mapping analysis, genetic interaction analysis and RNA expression analysis were conducted to explore the mechanism of SVs. RESULTS We obtained 4,535 SVs in total and identified 2 novel deletions (esv3608550, OR=2.73, P<2.00×10-308 ; esv3608542, OR=0.47, P=7.40×10-28 ) at 6q21.33 (MHC), 1 novel Alu element insertion (esv3607339, OR=1.22, P=1.18×10-35 ) at 5q33.3 (IL12B), and confirmed 1 previously reported deletion (esv3587563, OR=1.30, P=9.52×10-60 ) at 1q21.2 (LCE) for psoriasis. Fine mapping analysis including SNPs and small Insertions/Deletions (InDels) revealed that esv3608550 and esv3608542 were independently associated with psoriasis, and a novel independent SNP (rs9378188, OR=1.65, P=3.46×10-38 ) was identified at 6q21.33. By genetic interaction analysis and RNA expression analysis, we speculate that the association of 2 deletions at 6q21.33 with psoriasis might relate to their influence on the expression of HLA-C. CONCLUSIONS Our study constructed the most comprehensive SV map for psoriasis thus far and enriched the genetic architecture and pathogenesis of psoriasis as well as highlighted the nonnegligible impact of SVs on complex diseases.
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Aharonian F, An Q, Axikegu, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai H, Cai JT, Cao Z, Cao Z, Chang J, Chang JF, Chang XC, Chen BM, Chen J, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen XL, Chen Y, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, Dai BZ, Dai HL, Dai ZG, Danzengluobu, Volpe DD, Piazzoli BD, Dong XJ, Fan JH, Fan YZ, Fan ZX, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng YL, Gao B, Gao CD, Gao Q, Gao W, Ge MM, Geng LS, Gong GH, Gou QB, Gu MH, Guo JG, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JC, He SL, He XB, He Y, Heller M, Hor YK, Hou C, Hou X, Hu HB, Hu S, Hu SC, Hu XJ, Huang DH, Huang QL, Huang WH, Huang XT, Huang Y, Huang ZC, Ji F, Ji XL, Jia HY, Jiang K, Jiang ZJ, Jin C, Kuleshov D, Levochkin K, Li BB, Li C, Li C, Li F, Li HB, Li HC, Li HY, Li J, Li K, Li WL, Li X, Li X, Li XR, Li Y, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu H, Liu HD, Liu J, Liu JL, Liu JS, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu YN, Liu ZX, Long WJ, Lu R, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Masood A, Mitthumsiri W, Montaruli T, Nan YC, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Ruffolo D, Rulev V, Sáiz A, Shao L, Shchegolev O, Sheng XD, Shi JR, Song HC, Stenkin YV, Stepanov V, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang ZB, Tian WW, Wang BD, Wang C, Wang H, Wang HG, Wang JC, Wang JS, Wang LP, Wang LY, Wang RN, Wang W, Wang W, Wang XG, Wang XJ, Wang XY, Wang YD, Wang YJ, Wang YP, Wang Z, Wang Z, Wang ZH, Wang ZX, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu WX, Wu XF, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao G, Xiao HB, Xin GG, Xin YL, Xing Y, Xu DL, Xu RX, Xue L, Yan DH, Yang CW, Yang FF, Yang JY, Yang LL, Yang MJ, Yang RZ, Yang SB, Yao YH, Yao ZG, Ye YM, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhai XX, Zhang BB, Zhang HM, Zhang HY, Zhang JL, Zhang JW, Zhang L, Zhang L, Zhang LX, Zhang PF, Zhang PP, Zhang R, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang Y, Zhang Y, Zhang YF, Zhang YL, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zheng Y, Zhou B, Zhou H, Zhou JN, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. A dynamic range extension system for LHAASO WCDA-1. RADIATION DETECTION TECHNOLOGY AND METHODS 2021. [DOI: 10.1007/s41605-021-00275-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Zhang X, Wang Z, Wang X, Tang W, Liu R, Bao H, Chen X, Wu S, Wu X, Shao Y, Fan J, Zhou J. 950P Ultra-sensitive and cost-effective method for early stage hepatocellular carcinoma and intrahepatic cholangiocarcinoma detection using plasma cfDNA fragmentomic profiles. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Li A, Zhang W, Wu S, Li J, Yu Y. 1021P Long non-coding RNAs influence cancer immunotherapy efficacy through regulating T-cell infiltration and activity or tumor antigenicity. Ann Oncol 2021. [DOI: 10.1016/j.annonc.2021.08.1405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Wu S, Liu S, Chen A, Qian D, Lu Y. OC-0477 Self-attention Condition GAN for Synthetic CT Generation from CBCT for Head and Neck Radiotherapy. Radiother Oncol 2021. [DOI: 10.1016/s0167-8140(21)06924-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Jabri MA, Wu S, Zhang Y, Wang H, Pan Y, Ma J, Wang L. Accuracy of Bolton's Analysis among Different Malocclusion Patients Plaster Models and Digital Models Obtained by Ex Vivo Scanning with iTero Scanner in Chinese Han Population. Niger J Clin Pract 2021; 24:1086-1091. [PMID: 34290188 DOI: 10.4103/njcp.njcp_307_20] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Aim This investigation aimed to compare the accuracy of Bolton's analysis on plaster models of various malocclusion groups by utilizing digital calipers and iTero scanner. Materials and Methods The data consisted maxillary and mandibular plaster study casts of 61 patients (Class I-20, Class II-20, Class III-21) there were 31 males and 30 females. iTero®element scanner was utilized to scan the models and Bolton's analysis was performed on digital models. Also, the Digital caliper was utilized to perform the Manual measurements. Mesiodistal tooth widths, Anterior and Overall Bolton ratio was measured utilizing OrthoCAD™ software on digital models and plaster models with digital calipers. Statistical analysis was performed utilizing One-way ANOVA and independent T-test. Results Results revealed anterior and overall Bolton ratios showed significant differences (P < 0.05) for the measurements performed utilizing digital models. Anterior ratio for (Group 1) iTero measurements depicted the statistical significant value (P < 0.03) and overall ratio for (Group 2) digital caliper measurements depicted the statistical significant value (P < 0.02). Conclusion With the introduction of intra-oral laser scanners it has become more convenient for the practitioner to perform the intra-oral digital scanning and carry out the model analysis digitally and iTero scanner can also be utilized extra-orally to perform the scanning and model analysis. Our study concludes that intra-oral laser scanner like iTero is more convenient for an orthodontist, and can be utilized for extra-oral scanning of orthodontic dental models as the measurements obtained on digital models was as accurate as the conventional method.
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Cao Z, Aharonian F, An Q, Bai LX, Bai YX, Bao YW, Bastieri D, Bi XJ, Bi YJ, Cai H, Cai JT, Cao Z, Chang J, Chang JF, Chen BM, Chen ES, Chen J, Chen L, Chen L, Chen L, Chen MJ, Chen ML, Chen QH, Chen SH, Chen SZ, Chen TL, Chen XL, Chen Y, Cheng N, Cheng YD, Cui SW, Cui XH, Cui YD, D'Ettorre Piazzoli B, Dai BZ, Dai HL, Dai ZG, Della Volpe D, Dong XJ, Duan KK, Fan JH, Fan YZ, Fan ZX, Fang J, Fang K, Feng CF, Feng L, Feng SH, Feng YL, Gao B, Gao CD, Gao LQ, Gao Q, Gao W, Ge MM, Geng LS, Gong GH, Gou QB, Gu MH, Guo FL, Guo JG, Guo XL, Guo YQ, Guo YY, Han YA, He HH, He HN, He JC, He SL, He XB, He Y, Heller M, Hor YK, Hou C, Hou X, Hu HB, Hu S, Hu SC, Hu XJ, Huang DH, Huang QL, Huang WH, Huang XT, Huang XY, Huang ZC, Ji F, Ji XL, Jia HY, Jiang K, Jiang ZJ, Jin C, Ke T, Kuleshov D, Levochkin K, Li BB, Li C, Li C, Li F, Li HB, Li HC, Li HY, Li J, Li J, Li K, Li WL, Li XR, Li X, Li X, Li Y, Li YZ, Li Z, Li Z, Liang EW, Liang YF, Lin SJ, Liu B, Liu C, Liu D, Liu H, Liu HD, Liu J, Liu JL, Liu JS, Liu JY, Liu MY, Liu RY, Liu SM, Liu W, Liu Y, Liu YN, Liu ZX, Long WJ, Lu R, Lv HK, Ma BQ, Ma LL, Ma XH, Mao JR, Masood A, Min Z, Mitthumsiri W, Montaruli T, Nan YC, Pang BY, Pattarakijwanich P, Pei ZY, Qi MY, Qi YQ, Qiao BQ, Qin JJ, Ruffolo D, Rulev V, Saiz A, Shao L, Shchegolev O, Sheng XD, Shi JY, Song HC, Stenkin YV, Stepanov V, Su Y, Sun QN, Sun XN, Sun ZB, Tam PHT, Tang ZB, Tian WW, Wang BD, Wang C, Wang H, Wang HG, Wang JC, Wang JS, Wang LP, Wang LY, Wang RN, Wang W, Wang W, Wang XG, Wang XJ, Wang XY, Wang Y, Wang YD, Wang YJ, Wang YP, Wang ZH, Wang ZX, Wang Z, Wang Z, Wei DM, Wei JJ, Wei YJ, Wen T, Wu CY, Wu HR, Wu S, Wu WX, Wu XF, Xi SQ, Xia J, Xia JJ, Xiang GM, Xiao DX, Xiao G, Xiao HB, Xin GG, Xin YL, Xing Y, Xu DL, Xu RX, Xue L, Yan DH, Yan JZ, Yang CW, Yang FF, Yang JY, Yang LL, Yang MJ, Yang RZ, Yang SB, Yao YH, Yao ZG, Ye YM, Yin LQ, Yin N, You XH, You ZY, Yu YH, Yuan Q, Zeng HD, Zeng TX, Zeng W, Zeng ZK, Zha M, Zhai XX, Zhang BB, Zhang HM, Zhang HY, Zhang JL, Zhang JW, Zhang LX, Zhang L, Zhang L, Zhang PF, Zhang PP, Zhang R, Zhang SR, Zhang SS, Zhang X, Zhang XP, Zhang YF, Zhang YL, Zhang Y, Zhang Y, Zhao B, Zhao J, Zhao L, Zhao LZ, Zhao SP, Zheng F, Zheng Y, Zhou B, Zhou H, Zhou JN, Zhou P, Zhou R, Zhou XX, Zhu CG, Zhu FR, Zhu H, Zhu KJ, Zuo X. Peta-electron volt gamma-ray emission from the Crab Nebula. Science 2021; 373:425-430. [PMID: 34261813 DOI: 10.1126/science.abg5137] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Accepted: 06/23/2021] [Indexed: 11/03/2022]
Abstract
The Crab Nebula is a bright source of gamma rays powered by the Crab Pulsar's rotational energy through the formation and termination of a relativistic electron-positron wind. We report the detection of gamma rays from this source with energies from 5 × 10-4 to 1.1 peta-electron volts with a spectrum showing gradual steepening over three energy decades. The ultrahigh-energy photons imply the presence of a peta-electron volt electron accelerator (a pevatron) in the nebula, with an acceleration rate exceeding 15% of the theoretical limit. We constrain the pevatron's size between 0.025 and 0.1 parsecs and the magnetic field to ≈110 microgauss. The production rate of peta-electron volt electrons, 2.5 × 1036 ergs per second, constitutes 0.5% of the pulsar spin-down luminosity, although we cannot exclude a contribution of peta-electron volt protons to the production of the highest-energy gamma rays.
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Wu S, Han J, Zhen L, Ma Y, Li D, Liao C. Prospective ultrasound diagnosis of orofacial clefts in the first trimester. ULTRASOUND IN OBSTETRICS & GYNECOLOGY : THE OFFICIAL JOURNAL OF THE INTERNATIONAL SOCIETY OF ULTRASOUND IN OBSTETRICS AND GYNECOLOGY 2021; 58:134-137. [PMID: 32530100 DOI: 10.1002/uog.22123] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 05/23/2020] [Accepted: 06/01/2020] [Indexed: 06/11/2023]
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Lehnfeld J, Dukashin Y, Mark J, White GD, Wu S, Katzur V, Müller R, Ruhl S. Saliva and Serum Protein Adsorption on Chemically Modified Silica Surfaces. J Dent Res 2021; 100:1047-1054. [PMID: 34157899 PMCID: PMC8381597 DOI: 10.1177/00220345211022273] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Biomaterials, once inserted in the oral cavity, become immediately covered by a layer of adsorbed proteins that consists mostly of salivary proteins but also of plasma proteins if the biomaterial is placed close to the gingival margin or if it becomes implanted into tissue and bone. It is often this protein layer, rather than the pristine biomaterial surface, that is subsequently encountered by colonizing bacteria or attaching tissue cells. Thus, to study this important initial protein adsorption from human saliva and serum and how it might be influenced through chemical modification of the biomaterial surface, we have measured the amount of protein adsorbed and analyzed the composition of the adsorbed protein layer using gel electrophoresis and western blotting. Here, we have developed an in vitro model system based on silica surfaces, chemically modified with 7 silane-based self-assembled monolayers that span a broad range of physicochemical properties, from hydrophilic to hydrophobic surfaces (water contact angles from 15° to 115°), low to high surface free energy (12 to 57 mN/m), and negative to positive surface charge (zeta potentials from –120 to +40 mV at physiologic pH). We found that the chemical surface functionalities exerted a substantial effect on the total amounts of proteins adsorbed; however, no linear correlation of the adsorbed amounts with the physicochemical surface parameters was observed. Only the adsorption behavior of a few singular protein components, from which physicochemical data are available, seems to follow physicochemical expectations. Examples are albumin in serum and lysozyme in saliva; in both, adsorption was favored on countercharged surfaces. We conclude from these findings that in complex biofluids such as saliva and serum, adsorption behavior is dominated by the overall protein-binding capacity of the surface rather than by specific physicochemical interactions of single protein entities with the surface.
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Abbott R, Abbott TD, Abraham S, Acernese F, Ackley K, Adams A, Adams C, Adhikari RX, Adya VB, Affeldt C, Agarwal D, Agathos M, Agatsuma K, Aggarwal N, Aguiar OD, Aiello L, Ain A, Ajith P, Akutsu T, Aleman KM, Allen G, Allocca A, Altin PA, Amato A, Anand S, Ananyeva A, Anderson SB, Anderson WG, Ando M, Angelova SV, Ansoldi S, Antelis JM, Antier S, Appert S, Arai K, Arai K, Arai Y, Araki S, Araya A, Araya MC, Areeda JS, Arène M, Aritomi N, Arnaud N, Aronson SM, Asada H, Asali Y, Ashton G, Aso Y, Aston SM, Astone P, Aubin F, Auclair P, Aufmuth P, AultONeal K, Austin C, Babak S, Badaracco F, Bader MKM, Bae S, Bae Y, Baer AM, Bagnasco S, Bai Y, Baiotti L, Baird J, Bajpai R, Ball M, Ballardin G, Ballmer SW, Bals M, Balsamo A, Baltus G, Banagiri S, Bankar D, Bankar RS, Barayoga JC, Barbieri C, Barish BC, Barker D, Barneo P, Barnum S, Barone F, Barr B, Barsotti L, Barsuglia M, Barta D, Bartlett J, Barton MA, Bartos I, Bassiri R, Basti A, Bawaj M, Bayley JC, Baylor AC, Bazzan M, Bécsy B, Bedakihale VM, Bejger M, Belahcene I, Benedetto V, Beniwal D, Benjamin MG, Bennett TF, Bentley JD, BenYaala M, Bergamin F, Berger BK, Bernuzzi S, Bersanetti D, Bertolini A, Betzwieser J, Bhandare R, Bhandari AV, Bhattacharjee D, Bhaumik S, Bidler J, Bilenko IA, Billingsley G, Birney R, Birnholtz O, Biscans S, Bischi M, Biscoveanu S, Bisht A, Biswas B, Bitossi M, Bizouard MA, Blackburn JK, Blackman J, Blair CD, Blair DG, Blair RM, Bobba F, Bode N, Boer M, Bogaert G, Boldrini M, Bondu F, Bonilla E, Bonnand R, Booker P, Boom BA, Bork R, Boschi V, Bose N, Bose S, Bossilkov V, Boudart V, Bouffanais Y, Bozzi A, Bradaschia C, Brady PR, Bramley A, Branch A, Branchesi M, Breschi M, Briant T, Briggs JH, Brillet A, Brinkmann M, Brockill P, Brooks AF, Brooks J, Brown DD, Brunett S, Bruno G, Bruntz R, Bryant J, Buikema A, Bulik T, Bulten HJ, Buonanno A, Buscicchio R, Buskulic D, Cadonati L, Caesar M, Cagnoli G, Cahillane C, Cain HW, Calderón Bustillo J, Callaghan JD, Callister TA, Calloni E, Camp JB, Canepa M, Cannavacciuolo M, Cannon KC, Cao H, Cao J, Cao Z, Capocasa E, Capote E, Carapella G, Carbognani F, Carlin JB, Carney MF, Carpinelli M, Carullo G, Carver TL, Casanueva Diaz J, Casentini C, Castaldi G, Caudill S, Cavaglià M, Cavalier F, Cavalieri R, Cella G, Cerdá-Durán P, Cesarini E, Chaibi W, Chakravarti K, Champion B, Chan CH, Chan C, Chan CL, Chan M, Chandra K, Chanial P, Chao S, Charlton P, Chase EA, Chassande-Mottin E, Chatterjee D, Chaturvedi M, Chatziioannou K, Chen A, Chen C, Chen HY, Chen J, Chen K, Chen X, Chen YB, Chen YR, Chen Z, Cheng H, Cheong CK, Cheung HY, Chia HY, Chiadini F, Chiang CY, Chierici R, Chincarini A, Chiofalo ML, Chiummo A, Cho G, Cho HS, Choate S, Choudhary RK, Choudhary S, Christensen N, Chu H, Chu Q, Chu YK, Chua S, Chung KW, Ciani G, Ciecielag P, Cieślar M, Cifaldi M, Ciobanu AA, Ciolfi R, Cipriano F, Cirone A, Clara F, Clark EN, Clark JA, Clarke L, Clearwater P, Clesse S, Cleva F, Coccia E, Cohadon PF, Cohen DE, Cohen L, Colleoni M, Collette CG, Colpi M, Compton CM, Constancio M, Conti L, Cooper SJ, Corban P, Corbitt TR, Cordero-Carrión I, Corezzi S, Corley KR, Cornish N, Corre D, Corsi A, Cortese S, Costa CA, Cotesta R, Coughlin MW, Coughlin SB, Coulon JP, Countryman ST, Cousins B, Couvares P, Covas PB, Coward DM, Cowart MJ, Coyne DC, Coyne R, Creighton JDE, Creighton TD, Criswell AW, Croquette M, Crowder SG, Cudell JR, Cullen TJ, Cumming A, Cummings R, Cuoco E, Curyło M, Canton TD, Dálya G, Dana A, DaneshgaranBajastani LM, D'Angelo B, Danilishin SL, D'Antonio S, Danzmann K, Darsow-Fromm C, Dasgupta A, Datrier LEH, Dattilo V, Dave I, Davier M, Davies GS, Davis D, Daw EJ, Dean R, Deenadayalan M, Degallaix J, De Laurentis M, Deléglise S, Del Favero V, De Lillo F, De Lillo N, Del Pozzo W, DeMarchi LM, De Matteis F, D'Emilio V, Demos N, Dent T, Depasse A, De Pietri R, De Rosa R, De Rossi C, DeSalvo R, De Simone R, Dhurandhar S, Díaz MC, Diaz-Ortiz M, Didio NA, Dietrich T, Di Fiore L, Di Fronzo C, Di Giorgio C, Di Giovanni F, Di Girolamo T, Di Lieto A, Ding B, Di Pace S, Di Palma I, Di Renzo F, Divakarla AK, Dmitriev A, Doctor Z, D'Onofrio L, Donovan F, Dooley KL, Doravari S, Dorrington I, Drago M, Driggers JC, Drori Y, Du Z, Ducoin JG, Dupej P, Durante O, D'Urso D, Duverne PA, Dwyer SE, Easter PJ, Ebersold M, Eddolls G, Edelman B, Edo TB, Edy O, Effler A, Eguchi S, Eichholz J, Eikenberry SS, Eisenmann M, Eisenstein RA, Ejlli A, Enomoto Y, Errico L, Essick RC, Estellés H, Estevez D, Etienne Z, Etzel T, Evans M, Evans TM, Ewing BE, Fafone V, Fair H, Fairhurst S, Fan X, Farah AM, Farinon S, Farr B, Farr WM, Farrow NW, Fauchon-Jones EJ, Favata M, Fays M, Fazio M, Feicht J, Fejer MM, Feng F, Fenyvesi E, Ferguson DL, Fernandez-Galiana A, Ferrante I, Ferreira TA, Fidecaro F, Figura P, Fiori I, Fishbach M, Fisher RP, Fishner JM, Fittipaldi R, Fiumara V, Flaminio R, Floden E, Flynn E, Fong H, Font JA, Fornal B, Forsyth PWF, Franke A, Frasca S, Frasconi F, Frederick C, Frei Z, Freise A, Frey R, Fritschel P, Frolov VV, Fronzé GG, Fujii Y, Fujikawa Y, Fukunaga M, Fukushima M, Fulda P, Fyffe M, Gabbard HA, Gadre BU, Gaebel SM, Gair JR, Gais J, Galaudage S, Gamba R, Ganapathy D, Ganguly A, Gao D, Gaonkar SG, Garaventa B, García-Núñez C, García-Quirós C, Garufi F, Gateley B, Gaudio S, Gayathri V, Ge G, Gemme G, Gennai A, George J, Gergely L, Gewecke P, Ghonge S, Ghosh A, Ghosh A, Ghosh S, Ghosh S, Ghosh S, Giacomazzo B, Giacoppo L, Giaime JA, Giardina KD, Gibson DR, Gier C, Giesler M, Giri P, Gissi F, Glanzer J, Gleckl AE, Godwin P, Goetz E, Goetz R, Gohlke N, Goncharov B, González G, Gopakumar A, Gosselin M, Gouaty R, Grace B, Grado A, Granata M, Granata V, Grant A, Gras S, Grassia P, Gray C, Gray R, Greco G, Green AC, Green R, Gretarsson AM, Gretarsson EM, Griffith D, Griffiths W, Griggs HL, Grignani G, Grimaldi A, Grimes E, Grimm SJ, Grote H, Grunewald S, Gruning P, Guerrero JG, Guidi GM, Guimaraes AR, Guixé G, Gulati HK, Guo HK, Guo Y, Gupta A, Gupta A, Gupta P, Gustafson EK, Gustafson R, Guzman F, Ha S, Haegel L, Hagiwara A, Haino S, Halim O, Hall ED, Hamilton EZ, Hammond G, Han WB, Haney M, Hanks J, Hanna C, Hannam MD, Hannuksela OA, Hansen H, Hansen TJ, Hanson J, Harder T, Hardwick T, Haris K, Harms J, Harry GM, Harry IW, Hartwig D, Hasegawa K, Haskell B, Hasskew RK, Haster CJ, Hattori K, Haughian K, Hayakawa H, Hayama K, Hayes FJ, Healy J, Heidmann A, Heintze MC, Heinze J, Heinzel J, Heitmann H, Hellman F, Hello P, Helmling-Cornell AF, Hemming G, Hendry M, Heng IS, Hennes E, Hennig J, Hennig MH, Hernandez Vivanco F, Heurs M, Hild S, Hill P, Himemoto Y, Hines AS, Hiranuma Y, Hirata N, Hirose E, Hochheim S, Hofman D, Hohmann JN, Holgado AM, Holland NA, Hollows IJ, Holmes ZJ, Holt K, Holz DE, Hong Z, Hopkins P, Hough J, Howell EJ, Hoy CG, Hoyland D, Hreibi A, Hsieh B, Hsu Y, Huang GZ, Huang HY, Huang P, Huang YC, Huang YJ, Huang YW, Hübner MT, Huddart AD, Huerta EA, Hughey B, Hui DCY, Hui V, Husa S, Huttner SH, Huxford R, Huynh-Dinh T, Ide S, Idzkowski B, Iess A, Ikenoue B, Imam S, Inayoshi K, Inchauspe H, Ingram C, Inoue Y, Intini G, Ioka K, Isi M, Isleif K, Ito K, Itoh Y, Iyer BR, Izumi K, JaberianHamedan V, Jacqmin T, Jadhav SJ, Jadhav SP, James AL, Jan AZ, Jani K, Janssens K, Janthalur NN, Jaranowski P, Jariwala D, Jaume R, Jenkins AC, Jeon C, Jeunon M, Jia W, Jiang J, Jin HB, Johns GR, Jones AW, Jones DI, Jones JD, Jones P, Jones R, Jonker RJG, Ju L, Jung K, Jung P, Junker J, Kaihotsu K, Kajita T, Kakizaki M, Kalaghatgi CV, Kalogera V, Kamai B, Kamiizumi M, Kanda N, Kandhasamy S, Kang G, Kanner JB, Kao Y, Kapadia SJ, Kapasi DP, Karathanasis C, Karki S, Kashyap R, Kasprzack M, Kastaun W, Katsanevas S, Katsavounidis E, Katzman W, Kaur T, Kawabe K, Kawaguchi K, Kawai N, Kawasaki T, Kéfélian F, Keitel D, Key JS, Khadka S, Khalili FY, Khan I, Khan S, Khazanov EA, Khetan N, Khursheed M, Kijbunchoo N, Kim C, Kim JC, Kim J, Kim K, Kim WS, Kim YM, Kimball C, Kimura N, King PJ, Kinley-Hanlon M, Kirchhoff R, Kissel JS, Kita N, Kitazawa H, Kleybolte L, Klimenko S, Knee AM, Knowles TD, Knyazev E, Koch P, Koekoek G, Kojima Y, Kokeyama K, Koley S, Kolitsidou P, Kolstein M, Komori K, Kondrashov V, Kong AKH, Kontos A, Koper N, Korobko M, Kotake K, Kovalam M, Kozak DB, Kozakai C, Kozu R, Kringel V, Krishnendu NV, Królak A, Kuehn G, Kuei F, Kumar A, Kumar P, Kumar R, Kumar R, Kume J, Kuns K, Kuo C, Kuo HS, Kuromiya Y, Kuroyanagi S, Kusayanagi K, Kwak K, Kwang S, Laghi D, Lalande E, Lam TL, Lamberts A, Landry M, Lane BB, Lang RN, Lange J, Lantz B, La Rosa I, Lartaux-Vollard A, Lasky PD, Laxen M, Lazzarini A, Lazzaro C, Leaci P, Leavey S, Lecoeuche YK, Lee HK, Lee HM, Lee HW, Lee J, Lee K, Lee R, Lehmann J, Lemaître A, Leon E, Leonardi M, Leroy N, Letendre N, Levin Y, Leviton JN, Li AKY, Li B, Li J, Li KL, Li TGF, Li X, Lin CY, Lin FK, Lin FL, Lin HL, Lin LCC, Linde F, Linker SD, Linley JN, Littenberg TB, Liu GC, Liu J, Liu K, Liu X, Llorens-Monteagudo M, Lo RKL, Lockwood A, Lollie ML, London LT, Longo A, Lopez D, Lorenzini M, Loriette V, Lormand M, Losurdo G, Lough JD, Lousto CO, Lovelace G, Lück H, Lumaca D, Lundgren AP, Luo LW, Macas R, MacInnis M, Macleod DM, MacMillan IAO, Macquet A, Magaña Hernandez I, Magaña-Sandoval F, Magazzù C, Magee RM, Maggiore R, Majorana E, Maksimovic I, Maliakal S, Malik A, Man N, Mandic V, Mangano V, Mango JL, Mansell GL, Manske M, Mantovani M, Marchesoni F, Marchio M, Marion F, Mark Z, Márka S, Márka Z, Markakis C, Markosyan AS, Markowitz A, Maros E, Marquina A, Marsat S, Martelli F, Martin IW, Martin RM, Martinez M, Martinez V, Martinovic K, Martynov DV, Marx EJ, Masalehdan H, Mason K, Massera E, Masserot A, Massinger TJ, Masso-Reid M, Mastrogiovanni S, Matas A, Mateu-Lucena M, Matichard F, Matiushechkina M, Mavalvala N, McCann JJ, McCarthy R, McClelland DE, McClincy P, McCormick S, McCuller L, McGhee GI, McGuire SC, McIsaac C, McIver J, McManus DJ, McRae T, McWilliams ST, Meacher D, Mehmet M, Mehta AK, Melatos A, Melchor DA, Mendell G, Menendez-Vazquez A, Menoni CS, Mercer RA, Mereni L, Merfeld K, Merilh EL, Merritt JD, Merzougui M, Meshkov S, Messenger C, Messick C, Meyers PM, Meylahn F, Mhaske A, Miani A, Miao H, Michaloliakos I, Michel C, Michimura Y, Middleton H, Milano L, Miller AL, Millhouse M, Mills JC, Milotti E, Milovich-Goff MC, Minazzoli O, Minenkov Y, Mio N, Mir LM, Mishkin A, Mishra C, Mishra T, Mistry T, Mitra S, Mitrofanov VP, Mitselmakher G, Mittleman R, Miyakawa O, Miyamoto A, Miyazaki Y, Miyo K, Miyoki S, Mo G, Mogushi K, Mohapatra SRP, Mohite SR, Molina I, Molina-Ruiz M, Mondin M, Montani M, Moore CJ, Moraru D, Morawski F, More A, Moreno C, Moreno G, Mori Y, Morisaki S, Moriwaki Y, Mours B, Mow-Lowry CM, Mozzon S, Muciaccia F, Mukherjee A, Mukherjee D, Mukherjee S, Mukherjee S, Mukund N, Mullavey A, Munch J, Muñiz EA, Murray PG, Musenich R, Nadji SL, Nagano K, Nagano S, Nakamura K, Nakano H, Nakano M, Nakashima R, Nakayama Y, Nardecchia I, Narikawa T, Naticchioni L, Nayak B, Nayak RK, Negishi R, Neil BF, Neilson J, Nelemans G, Nelson TJN, Nery M, Neunzert A, Ng KY, Ng SWS, Nguyen C, Nguyen P, Nguyen T, Nguyen Quynh L, Ni WT, Nichols SA, Nishizawa A, Nissanke S, Nocera F, Noh M, Norman M, North C, Nozaki S, Nuttall LK, Oberling J, O'Brien BD, Obuchi Y, O'Dell J, Ogaki W, Oganesyan G, Oh JJ, Oh K, Oh SH, Ohashi M, Ohishi N, Ohkawa M, Ohme F, Ohta H, Okada MA, Okutani Y, Okutomi K, Olivetto C, Oohara K, Ooi C, Oram R, O'Reilly B, Ormiston RG, Ormsby ND, Ortega LF, O'Shaughnessy R, O'Shea E, Oshino S, Ossokine S, Osthelder C, Otabe S, Ottaway DJ, Overmier H, Pace AE, Pagano G, Page MA, Pagliaroli G, Pai A, Pai SA, Palamos JR, Palashov O, Palomba C, Pan K, Panda PK, Pang H, Pang PTH, Pankow C, Pannarale F, Pant BC, Paoletti F, Paoli A, Paolone A, Parisi A, Park J, Parker W, Pascucci D, Pasqualetti A, Passaquieti R, Passuello D, Patel M, Patricelli B, Payne E, Pechsiri TC, Pedraza M, Pegoraro M, Pele A, Peña Arellano FE, Penn S, Perego A, Pereira A, Pereira T, Perez CJ, Périgois C, Perreca A, Perriès S, Petermann J, Petterson D, 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AB, Ying M, Yokogawa K, Yokoyama J, Yokozawa T, Yoon A, Yoshioka T, Yu H, Yu H, Yuzurihara H, Zadrożny A, Zanolin M, Zeidler S, Zelenova T, Zendri JP, Zevin M, Zhan M, Zhang H, Zhang J, Zhang L, Zhang R, Zhang T, Zhao C, Zhao G, Zhao Y, Zhao Y, Zhou Z, Zhu XJ, Zhu ZH, Zucker ME, Zweizig J. Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run. PHYSICAL REVIEW LETTERS 2021; 126:241102. [PMID: 34213926 DOI: 10.1103/physrevd.97.102002] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/31/2021] [Accepted: 05/23/2021] [Indexed: 05/21/2023]
Abstract
We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10^{-15}. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.
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AB, Ying M, Yokogawa K, Yokoyama J, Yokozawa T, Yoon A, Yoshioka T, Yu H, Yu H, Yuzurihara H, Zadrożny A, Zanolin M, Zeidler S, Zelenova T, Zendri JP, Zevin M, Zhan M, Zhang H, Zhang J, Zhang L, Zhang R, Zhang T, Zhao C, Zhao G, Zhao Y, Zhao Y, Zhou Z, Zhu XJ, Zhu ZH, Zucker ME, Zweizig J. Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run. PHYSICAL REVIEW LETTERS 2021; 126:241102. [PMID: 34213926 DOI: 10.1103/physrevlett.126.241102] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 03/31/2021] [Accepted: 05/23/2021] [Indexed: 06/13/2023]
Abstract
We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection. We also use the stochastic gravitational-wave background energy density upper limits derived from the O3 data to constrain the cosmic string tension Gμ as a function of the number of kinks, or the number of cusps, for two cosmic string loop distribution models. Additionally, we develop and test a third model that interpolates between these two models. Our results improve upon the previous LIGO-Virgo constraints on Gμ by 1 to 2 orders of magnitude depending on the model that is tested. In particular, for the one-loop distribution model, we set the most competitive constraints to date: Gμ≲4×10^{-15}. In the case of cosmic strings formed at the end of inflation in the context of grand unified theories, these results challenge simple inflationary models.
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