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Buck E, Burt J, Karampatsas K, Hsia Y, Whyte G, Amirthalingam G, Skirrow H, Le Doare K. 'Unable to have a proper conversation over the phone about my concerns': a multimethods evaluation of the impact of COVID-19 on routine childhood vaccination services in London, UK. Public Health 2023; 225:229-236. [PMID: 37944278 DOI: 10.1016/j.puhe.2023.09.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2023] [Revised: 09/25/2023] [Accepted: 09/27/2023] [Indexed: 11/12/2023]
Abstract
OBJECTIVES Investigating the completion rate of 12-month vaccinations and parental perspectives on vaccine services during COVID-19. STUDY-DESIGN Service evaluation including parental questionnaire. METHODS Uptake of 12-month vaccinations in three London general practices during three periods: pre-COVID (1/3/2018-28/2/2019, n = 826), during COVID (1/3/2019-28/2/2020, n = 775) and post-COVID first wave (1/8/2020-31/1/2021, n = 419). Questionnaire of parents whose children were registered at the practices (1/4/2019-1/22/2021, n = 1350). RESULTS Comparing pre-COVID and both COVID cohorts, the completion rates of 12-month vaccines were lower. Haemophilus influenzae type B/meningococcal group C (Hib/MenC) vaccination uptake was 5.6% lower (89.0% vs 83.4%, P=<0.001), meningococcal group B (MenB) booster uptake was 4.4% lower (87.3% vs 82.9%, P = 0.006), pneumococcal conjugate vaccine (PCV) booster uptake was 6% lower (88.0% vs 82.0%, P < 0.001) and measles, mumps and rubella (MMR) vaccine uptake was 5.2% lower (89.1% vs 83.9%, P = 0.003). Black/Black-British ethnicity children had increased odds of missing their 12-month vaccinations compared to White ethnicity children (adjusted odds ratio 0.43 [95% confidence interval 0.24-0.79, P = 0.005; 0.36 [0.20-0.65], P < 0.001; 0.48 [0.27-0.87], P = 0.01; 0.40 [0.22-0.73], P = 0.002; for Hib/MenC, MenB booster, PCV booster and MMR. Comparing pre-COVID and COVID periods, vaccinations coded as not booked increased for MMR (10%), MenB (7%) and PCV booster (8%). Parents reported changes to vaccination services during COVID-19, including difficulties booking and attending appointments and lack of vaccination reminders. CONCLUSION A sustained decrease in 12-month childhood vaccination uptake disproportionally affected Black/Black British ethnicity infants during the first wave of the pandemic. Vaccination reminders and availability of healthcare professionals to discuss parental vaccine queries are vital to maintaining uptake.
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Affiliation(s)
- Eleanor Buck
- St George's Hospital Medical School, St. George's, University of London, London, United Kingdom.
| | - J Burt
- Ashford and St Peter's Hospital NHS Foundation Trust, United Kingdom
| | - K Karampatsas
- Centre for Neonatal and Paediatric Infection, Institute of Infection and Immunity, St. George's, University of London, London, United Kingdom
| | - Y Hsia
- Centre for Neonatal and Paediatric Infection, Institute of Infection and Immunity, St. George's, University of London, London, United Kingdom; School of Pharmacy, Queen's University Belfast, Belfast, United Kingdom
| | - G Whyte
- North Croydon Medical Centre, United Kingdom
| | - G Amirthalingam
- Immunisation and Vaccine Preventable Diseases Division, UK Health Security Agency, United Kingdom
| | - H Skirrow
- School of Public Health, Imperial College London, United Kingdom
| | - K Le Doare
- Centre for Neonatal and Paediatric Infection, Institute of Infection and Immunity, St. George's, University of London, London, United Kingdom; MRC/UVRI @LHSTM Uganda Research Unit, Entebbe, Uganda; Pathogen Immunity Group, UK Health Security Agency, Porton Down, United Kingdom
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Courbet A, Hansen J, Hsia Y, Bethel N, Park YJ, Xu C, Moyer A, Boyken S, Ueda G, Nattermann U, Nagarajan D, Silva D, Sheffler W, Quispe J, Nord A, King N, Bradley P, Veesler D, Kollman J, Baker D. Computational design of mechanically coupled axle-rotor protein assemblies. Science 2022; 376:383-390. [PMID: 35446645 PMCID: PMC10712554 DOI: 10.1126/science.abm1183] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Natural molecular machines contain protein components that undergo motion relative to each other. Designing such mechanically constrained nanoscale protein architectures with internal degrees of freedom is an outstanding challenge for computational protein design. Here we explore the de novo construction of protein machinery from designed axle and rotor components with internal cyclic or dihedral symmetry. We find that the axle-rotor systems assemble in vitro and in vivo as designed. Using cryo-electron microscopy, we find that these systems populate conformationally variable relative orientations reflecting the symmetry of the coupled components and the computationally designed interface energy landscape. These mechanical systems with internal degrees of freedom are a step toward the design of genetically encodable nanomachines.
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Affiliation(s)
- A. Courbet
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, USA
| | - J. Hansen
- Department of Biochemistry, University of Washington, Seattle, USA
| | - Y. Hsia
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - N. Bethel
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, USA
| | - YJ. Park
- Department of Biochemistry, University of Washington, Seattle, USA
| | - C. Xu
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, USA
| | - A. Moyer
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - S.E. Boyken
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - G. Ueda
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - U. Nattermann
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - D. Nagarajan
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - D. Silva
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
- Division of Life Science, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong
- Monod Bio, Inc, Seattle, USA
| | - W. Sheffler
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - J. Quispe
- Department of Biochemistry, University of Washington, Seattle, USA
| | - A. Nord
- Centre de Biologie Structurale (CBS), INSERM, CNRS, Université Montpellier, Montpellier, France
| | - N. King
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
| | - P. Bradley
- Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, USA
| | - D. Veesler
- Department of Biochemistry, University of Washington, Seattle, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, USA
| | - J. Kollman
- Department of Biochemistry, University of Washington, Seattle, USA
| | - D. Baker
- Department of Biochemistry, University of Washington, Seattle, USA
- Institute for Protein Design, University of Washington, Seattle, USA
- Howard Hughes Medical Institute, University of Washington, Seattle, USA
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Chuang WY, Chang SH, Yu WH, Yang CK, Yeh CJ, Ueng SH, Liu YJ, Chen TD, Chen KH, Hsieh YY, Hsia Y, Wang TH, Hsueh C, Kuo CF, Yeh CY. Successful Identification of Nasopharyngeal Carcinoma in Nasopharyngeal Biopsies Using Deep Learning. Cancers (Basel) 2020; 12:cancers12020507. [PMID: 32098314 PMCID: PMC7072217 DOI: 10.3390/cancers12020507] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2020] [Revised: 02/16/2020] [Accepted: 02/19/2020] [Indexed: 12/14/2022] Open
Abstract
Pathologic diagnosis of nasopharyngeal carcinoma (NPC) can be challenging since most cases are nonkeratinizing carcinoma with little differentiation and many admixed lymphocytes. Our aim was to evaluate the possibility to identify NPC in nasopharyngeal biopsies using deep learning. A total of 726 nasopharyngeal biopsies were included. Among them, 100 cases were randomly selected as the testing set, 20 cases as the validation set, and all other 606 cases as the training set. All three datasets had equal numbers of NPC cases and benign cases. Manual annotation was performed. Cropped square image patches of 256 × 256 pixels were used for patch-level training, validation, and testing. The final patch-level algorithm effectively identified NPC patches, with an area under the receiver operator characteristic curve (AUC) of 0.9900. Using gradient-weighted class activation mapping, we demonstrated that the identification of NPC patches was based on morphologic features of tumor cells. At the second stage, whole-slide images were sequentially cropped into patches, inferred with the patch-level algorithm, and reconstructed into images with a smaller size for training, validation, and testing. Finally, the AUC was 0.9848 for slide-level identification of NPC. Our result shows for the first time that deep learning algorithms can identify NPC.
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Affiliation(s)
- Wen-Yu Chuang
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Center for Vascularized Composite Allotransplantation, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan
| | - Shang-Hung Chang
- Center for Big Data Analytics and Statistics, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan;
| | - Wei-Hsiang Yu
- aetherAI, Co., Ltd., No. 3-2, Yuan-Qu Street, Nangang District, Taipei City 115, Taiwan; (W.-H.Y.); (C.-K.Y.)
| | - Cheng-Kun Yang
- aetherAI, Co., Ltd., No. 3-2, Yuan-Qu Street, Nangang District, Taipei City 115, Taiwan; (W.-H.Y.); (C.-K.Y.)
| | - Chi-Ju Yeh
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Shir-Hwa Ueng
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Chang Gung Molecular Medicine Research Center, Chang Gung University, No. 259, Wenhua First Road, Guishan District, Taoyuan City 333, Taiwan
| | - Yu-Jen Liu
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Tai-Di Chen
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Kuang-Hua Chen
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Yi-Yin Hsieh
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
| | - Yi Hsia
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Department of Pathology, MacKay Memorial Hospital, No. 92, Section 2, Zhongshan North Road, Zhongshan District, Taipei City 104, Taiwan
| | - Tong-Hong Wang
- Tissue Bank, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan;
| | - Chuen Hsueh
- Department of Pathology, Chang Gung Memorial Hospital and Chang Gung University, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan; (W.-Y.C.); (C.-J.Y.); (S.-H.U.); (Y.-J.L.); (T.-D.C.); (K.-H.C.); (Y.-Y.H.); (Y.H.); (C.H.)
- Chang Gung Molecular Medicine Research Center, Chang Gung University, No. 259, Wenhua First Road, Guishan District, Taoyuan City 333, Taiwan
| | - Chang-Fu Kuo
- Center for Artificial Intelligence in Medicine, Chang Gung Memorial Hospital, No. 5, Fuxing Street, Guishan District, Taoyuan City 333, Taiwan;
| | - Chao-Yuan Yeh
- aetherAI, Co., Ltd., No. 3-2, Yuan-Qu Street, Nangang District, Taipei City 115, Taiwan; (W.-H.Y.); (C.-K.Y.)
- Correspondence: ; Tel.: +886-2-27856892
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Hsia Y, Hsu P, Chang S. An alternative modality for the treatment of lip cancer or oral commissure cancer. Int J Oral Maxillofac Surg 2015. [DOI: 10.1016/j.ijom.2015.08.129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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Abstract
We undertook a meta-analysis of randomized controlled trials to summarize the efficacy of anti-obesity drugs in reducing BMI and improving health in children and adolescents. Data sources included Medline, Embase, the Cochrane controlled trials register and other registers of controlled trials, together with reference lists of identified articles. All data sources were searched from January 1996 to July 2008. We searched for double blind randomized placebo controlled trials of approved anti-obesity drugs used in children and adolescents (age < 20) with primary obesity for > or = 6 months. Six trials, 4 of sibutramine (total patients = 686) and 2 of orlistat (n = 573) met inclusion criteria. No trials of rimonabant were identified. Compared with placebo, sibutramine together with behavioural support reduced BMI by 2.20 kg/m(2) (95% CI: 1.57 to 2.83) and orlistat together with behavioural support reduced BMI by 0.83 kg/m(2) (95% CI 0.47 to 1.19). Sibutramine improved waist circumference, triglycerides and high density lipoprotein (HDL)-cholesterol, but raised systolic and diastolic blood pressure and pulse. Orlistat increased rates of gastrointestinal side-effects. We conclude that sibutramine in adolescents produces clinically meaningful reductions in BMI and waist circumference of approximately 0.63 SD, with improvements in cardiometabolic risk. Orlistat modestly reduces BMI (effect size approximately 0.24 SD) with a high prevalence of gastrointestinal adverse effects.
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Affiliation(s)
- R M Viner
- UCL Institute of Child Health, University College London, UK.
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Graham CH, Sperling HG, Hsia Y, Coulson AH. The Determination of Some Visual Functions of a Unilaterally Color-Blind Subject: Methods and Results. The Journal of Psychology 2010. [DOI: 10.1080/00223980.1961.9916458] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
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Trachtenberg E, Vinson M, Hayes E, Hsu YM, Houtchens K, Erlich H, Klitz W, Hsia Y, Hollenbach J. HLA class I (A, B, C) and class II (DRB1, DQA1, DQB1, DPB1) alleles and haplotypes in the Han from southern China. ACTA ACUST UNITED AC 2007; 70:455-63. [PMID: 17900288 DOI: 10.1111/j.1399-0039.2007.00932.x] [Citation(s) in RCA: 96] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
In this study, polymerase chain reaction-sequence-specific oligonucleotide prode (SSOP) typing results for the human leukocyte antigen (HLA) class I (A, B, and C) and class II (DRB1, DQA1, DQB1, and DPB1) loci in 264 individuals of the Han ethnic group from the Canton region of southern China are presented. The data are examined at the allele, genotype, and haplotype level. Common alleles at each of the loci are in keeping with those observed in similar populations, while the high-resolution typing methods used give additional details about allele frequency distributions not shown in previous studies. Twenty distinct alleles are seen at HLA-A in this population. The locus is dominated by the A*1101 allele, which is found here at a frequency of 0.266. The next three most common alleles, A*2402, A*3303, and A*0203, are each seen at frequencies of greater than 10%, and together, these four alleles account for roughly two-thirds of the total for HLA-A in this population. Fifty alleles are observed for HLA-B, 21 of which are singleton copies. The most common HLA-B alleles are B*4001 (f= 0.144), B*4601 (f= 0.119), B*5801 (f= 0.089), B*1301 (f= 0.068), B*1502 (f= 0.073), and B*3802 (f= 0.070). At the HLA-C locus, there are a total of 20 alleles. Four alleles (Cw*0702, Cw*0102, Cw*0801, and Cw*0304) are found at frequencies of greater than 10%, and together, these alleles comprise over 60% of the total. Overall, the class II loci are somewhat less diverse than class I. Twenty-eight distinct alleles are seen at DRB1, and the most common three, DRB1*0901, *1202, and *1501, are each seen at frequencies of greater than 10%. The DR4 lineage also shows extensive expansion in this population, with seven subtypes, representing one quarter of the diversity at this locus. Eight alleles are observed at DQA1; DQA1*0301 and 0102 are the most common alleles, with frequencies over 20%. The DQB1 locus is dominated by four alleles of the 03 lineage, which make up nearly half of the total. The two most common DQB1 alleles in this population are DQB1*0301 (f= 0.242) and DQB1*0303 (f= 0.15). Eighteen alleles are observed at DPB1; DPB1*0501 is the most common allele, with a frequency of 37%. The class I allele frequency distributions, expressed in terms of Watterson's (homozygosity) F-statistic, are all within expectations under neutrality, while there is evidence for balancing selection at DRB1, DQA1, and DQB1. Departures from Hardy-Weinberg expectations are observed for HLA-C and DRB1 in this population. Strong individual haplotypic associations are seen for all pairs of loci, and many of these occur at frequencies greater than 5%. In the class I region, several examples of HLA-B and -C loci in complete or near complete linkage disequilibrium (LD) are present, and the two most common, B*4601-Cw*0102 and B*5801-Cw*0302 account for more than 20% of the B-C haplotypes. Similarly, at class II, nearly all of the most common DR-DQ haplotypes are in nearly complete LD. The most common DRB1-DQB1 haplotypes are DRB1*0901-DQB1*0303 (f= 0.144) and DRB1*1202-DQB1*0301 (f= 0.131). The most common four locus class I and class II combined haplotypes are A*3303-B*5801-DRB1*0301-DPB1*0401 (f= 0.028) and A*0207-B*4601-DRB1*0901-DPB1*0501 (f= 0.026). The presentation of complete DNA typing for the class I loci and haplotype analysis in a large sample such as this can provide insights into the population history of the region and give useful data for HLA matching in transplantation and disease association studies in the Chinese population.
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Affiliation(s)
- E Trachtenberg
- Children's Hospital Oakland Research Institute, Oakland, CA 94609, USA.
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Affiliation(s)
- Y Hsia
- Department of Psychology, Columbia University
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Affiliation(s)
- C H Graham
- DEPARTMENT OF PSYCHOLOGY, COLUMBIA UNIVERSITY
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Affiliation(s)
- Y Hsia
- DEPARTMENT OF PSYCHOLOGY, COLUMBIA UNIVERSITY
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Bavel Z, Grzymala-Busse J, Hsia Y, Mancisidor-Landa R. Tier automation representation of communication protocols. SIGCOMM Comput Commun Rev 1986. [DOI: 10.1145/1013812.18189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
The tier automation is presented as a model for communication protocols. Several advantages of the model are cited, among which are universality of representation and manipulability. A scheme for using the tier automation to model specific distributed architectures and their protocols is described. The scheme is then used on a sample protocol and a transmission session with the sample protocol as exhibited.
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Affiliation(s)
- Z Bavel
- Department of Computer Science, University of Kansas, Lawrence, Kansas
| | - J Grzymala-Busse
- Department of Computer Science, University of Kansas, Lawrence, Kansas
| | - Y Hsia
- Department of Computer Science, University of Kansas, Lawrence, Kansas
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Gravel RA, Lam KF, Scully KJ, Hsia Y. Genetic complementation of propionyl-CoA carboxylase deficiency in cultured human fibroblasts. Am J Hum Genet 1977; 29:378-88. [PMID: 195466 PMCID: PMC1685391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
Propionyl-CoA carboxylase (PCC) deficiency is an inherited metabolic disorder showing considerable variability of expression. We have investigated the possibility that there is a genetic basis for the clinical heterogeneity in this disorder by examining complementation in Sendai virus mediated heterokaryons of mutant fibroblast strains. Restoration of PCC activity was monitored in individual multinucleate cells in situ using a radioautographic procedure which detects the incorporation of 14C-propionate into trichloracetic acid precipitable material. Each mutant strain incorporated negligible amounts of radioactivity compared to control strains. Activity was not restored when different mutants were mixed without virus or when homokaryons were produced by self-fusion. Seven mutant strains were fused in all pairwise combinations and examined for increased 14C-propionate incorporation in heterokaryons. Two main complementation groups were revealed. One group was composed of three mutants. The other was a complex group composed of four mutants in which intragroup complementation was demonstrated. Two mutants showing excellent complementation by radioautography were examined for complementation by the direct assay of PCC ACTIVITY. The enzyme activity of virus-treated preparations with 23% multinucleate cells was 183 U (pmol/min/mg protein) compared to 16 U for the untreated mixture (normal range 450-850 u). We conclude that PCC deficiency resulted from mutations of heterogeneous origin, although the classification of mutants into complementation groups did not correlate with patterns of clinical heterogeneity.
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Oyama T, Hsia Y. Compensatory hue shift in simultaneous color contrast as a function of separation between inducing and test fields. J Exp Psychol 1966; 71:405-13. [PMID: 5908823 DOI: 10.1037/h0022958] [Citation(s) in RCA: 20] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
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