ライフサイエンスコーパス: multifactor

1 dry air, and pulmonary disease mechanics by multifactor analysis of variance.

2 and disappointing dead ends, indicating the multifactored and complex nature of the disorder.

3 Data were subjected to evaluation using multifactor ANOVA and principal component analysis (PCA)

4 Multifactor ANOVA revealed that levels of those volatile

5 Multifactor ANOVA, considering the content of total anth

6 tail plays a stimulatory role in cooperative multifactor assembly.

7 95% confidence intervals obtained by using a multifactor bootstrap-resampling approach contain the tr

8 val around the calculated time by applying a multifactor bootstrap-resampling approach.

9 expression changes were driven by a stepwise multifactor cascading control mechanism, where the speci

10 Multifactor ChIP-Seq analysis in primary human cells cou

11 Thus, NAP-1 appears to be one component of a multifactor chromatin assembly machinery that mediates t

12 However, multisite, multifactor climate manipulation studies that have exami

13 mains that have been implicated in the yeast multifactor complex (eIF1-eIF3-eIF5-eIF2-GTP-Met-tRNA(i)

14 erated from purified human proteins a stable multifactor complex (MFC) comprising eIF1, eIF2, eIF3 an

15 tions that disrupt eIF2-eIF3 contacts in the multifactor complex (MFC) diminished 40S-bound TC, indic

16 e eIF2.tRNA(i)(Met.)GTP complex (TC) and the multifactor complex (MFC) required for translation initi

17 Initiation factor 3 (eIF3) forms a multifactor complex (MFC) with eIF1, eIF2, and eIF5 that

18 It is recruited to the 43 S complex in the multifactor complex (MFC) with eIF2, eIF3, and eIF5 via

19 (eIF3) of Saccharo myces cerevisiae forms a multifactor complex (MFC) with eIFs 1, 2, 5 and Met-tRNA

20 nd eIF3c, thereby mediating formation of the multifactor complex (MFC), an important intermediate for

21 duced 40S binding of all constituents of the multifactor complex (MFC), comprised of these three fact

22 binds the eIF3/eIF1/eIF5 complex to form the multifactor complex (MFC), whereas eIF2.GDP binds the pe

23 acts with eIF3-eIF1-eIF5 complex to form the multifactor complex (MFC), while eIF2GDP associates with

24 RNA(i)(Met) ternary complex (TC) to form the multifactor complex (MFC).

25 to, and stabilizes, the eIF3-eIF5- eIF1-eIF2 multifactor complex and is required for the normal level

26 to interact with eIF1, eIF2, and eIF3 in the multifactor complex and with eIF4G in the 48S complex.

27 C loading on 40S subunits or destabilize the multifactor complex containing eIF1, eIF3, eIF5, and TC,

28 F3, was shown to bind to, and stabilize, the multifactor complex containing eIFs 1, 2, 3, and 5 and M

29 interaction between eIF2 and eIF3/eIF1 in a multifactor complex containing Met-tRNA(i)(Met).

30 initiation factors are capable of forming a multifactor complex in vitro.

31 mutation in eIF5-CTD, which destabilizes the multifactor complex in vivo, reduced the binding of Met-

32 We propose that the multifactor complex is an important intermediate in tran

33 to the silencer leads to the formation of a multifactor complex that induces silencer function and r

34 which mRNAs undergo polyadenylation; CPSF, a multifactor complex that interacts with the near-ubiquit

35 The multifactor complex was disrupted by the tif5-7A mutatio

36 ith CARM1, CBP, c-Jun, and Sp1 and that this multifactor complex was formed in a p53-dependent manner

37 It is recruited to the 40 S subunit in a multifactor complex with Met-tRNA(i)(Met), eIF2, eIF3, a

38 est the occurrence of an eIF3/eIF1/eIF5/eIF2 multifactor complex, which was observed in cell extracts

39 These results suggest that the stability of multifactor complexes at promoters and regulatory elemen

40 ted from a failure to treat job control as a multifactor concept.

41 cation, few have examined these effects in a multifactor context or recorded how these effects vary s

42 ntly, the context provided by multimodal, or multifactor delivery represents a key element of most bi

43 ly to characterize count data and allows for multifactor design.

44 mbinatorial approach, namely the generalized multifactor dimensionality reduction (GMDR) method, whic

45 The computationally efficient multifactor dimensionality reduction (MDR) approach has

46 Multifactor Dimensionality Reduction (MDR) has been intr

47 Multifactor dimensionality reduction (MDR) is a powerful

48 this problem, we have previously developed a multifactor dimensionality reduction (MDR) method for co

49 To address this problem, we have developed a multifactor dimensionality reduction (MDR) method for co

50 everal combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, have

51 Recent combinatorial approaches, such as the multifactor dimensionality reduction (MDR) method, the c

52 MDR-Phenomics, a novel approach based on the multifactor dimensionality reduction (MDR) method, to de

53 and BAT3) were investigated by entropy-based multifactor dimensionality reduction (MDR), classificati

54 f the AMBIENCE algorithm was compared to the multifactor dimensionality reduction (MDR), generalized

55 3.95, P = 7.8 x 10(-5) [FDR </=0.05], P for multifactor dimensionality reduction = 5.9 x 10(-45)).

56 AMBROSIA was compared with multifactor dimensionality reduction across several dive

57 Subsequent multifactor dimensionality reduction and classification

58 Multifactor dimensionality reduction identified a gene-g

59 teractions were assessed through model-based multifactor dimensionality reduction in the PIAMA study,

60 Multifactor dimensionality reduction indicated that the

61 Parallel multifactor dimensionality reduction is a tool for large

62 Multifactor dimensionality reduction revealed that genot

63 FITF also outperformed multifactor dimensionality reduction when interactions i

64 ts show that SIPI has higher power than MDR (Multifactor Dimensionality Reduction), AA_Full, Geno_Ful

65 o-head comparisons with the relevance-chain, multifactor dimensionality reduction, and PDT methods, t

66 o-head comparisons with the relevance-chain, multifactor dimensionality reduction, and the pedigree d

67 We used Multifactor- dimensionality reduction (MDR) program as a

68 ve refined independent marker sets, extended multifactor-dimensionality reduction (EMDR) analysis was

69 We introduce multifactor-dimensionality reduction (MDR) as a method f

70 e, long-term ecosystem-scale studies testing multifactor effects on plants and soils are urgently req

71 These findings indicate the importance of a multifactor experimental approach to understanding ecosy

72 ariable climate and atmosphere simulator for multifactor experimentation on natural or artificial eco

73 Multifactor experiments are often advocated as important

74 Our goals were to investigate how multifactor experiments can be used to constrain models

75 ctive importance of these processes requires multifactor experiments under realistic field conditions

76 The multifactor gatekeeping model has been proposed to expla

77 In 2006, we established a long-term, multifactor global change experiment to determine the in

78 Much concern has been raised about how multifactor global change has affected food security and

79 n the longest-running, best-replicated, most-multifactor global-change experiment at the ecosystem sc

80 A variety of multifactor indexes have been proposed for preoperative

81 It is likely that optimal multifactor initiation complexes exist that allow for op

82 story of reflux is an important risk for EA, multifactor interactions also play important roles in EA

83 f analytical tools for identifying nonlinear multifactor interactions and unraveling the genetic arch

84 Determining multifactor interactions is the primary topic of interes

85 Widespread multifactor interactions present a significant challenge

86 ocesses may influence their responses to the multifactor interactions.

87 upervisors and 179 supervisees completed the Multifactor Leadership Questionnaire and a demographic s

88 A multifactor logistic regression analysis was performed t

89 Multifactor logistic regression analysis was used to ide

90 tions of soil carbon dynamics and results of multifactor manipulations to calibrate a model that can

91 Across all multifactor manipulations, elevated carbon dioxide suppr

92 These results identify a multifactored mechanism to control LKB1 localization, an

93 trophic magnification factors (TMFs), and a multifactor model that included delta(15)N-derived troph

94 range overlap of environmental variables in multifactor models controlling for phylogeny to simultan

95 g the classification and prediction error of multifactor models.

96 on the magnitude of each interacting factor, multifactor, multilevel experiments are required, but ar

97 We applied 10 TBMs to the multifactor Prairie Heating and CO2 Enrichment (PHACE) e

98 distinguish metazoan cells are specified by multifactor regulatory complexes containing distinct com

99 ngle factors (i.e., Ea or DeltaS alone); (2) multifactor scenarios of photosynthetic temperature accl

100 , these data reveal the power of integrating multifactor sequencing of chromatin immunoprecipitates w

101 perature ranges to determine whether and how multifactor thermal acclimation of photosynthesis occurs

102 by competing with activating ETS factors in multifactor transcriptional complexes.