ライフサイエンスコーパス: lognormal

1 he underlying activity distribution remained lognormal.

2 We found that lognormal and gamma distribution models can perform poor

3 ssumption that the true exposure follows the lognormal and gamma distributions.

4 ve distributions, including the right Pareto-lognormal and lognormal distributions.

5 tral-limit theorem then explains the central lognormal, and a number of possible mechanisms could exp

6 The sequencing counts are described by a lognormal-beta-binomial hierarchical model, which provid

7 It consists of a mixture of a lognormal body and upper and lower power-law tails.

8 dilution curve is closely approximated by a lognormal curve and that loss of lithium in the lungs fo

9 Lognormal curve fitting was used to derive the areas und

10 A lognormal DFE best explains the data for D. melanogaster

11 M fail to fit empirical data better than the lognormal distribution 95% of the time, it also fails to

12 ss the neuronal population as reflected in a lognormal distribution and demonstrate that half of the

13 is based on a flexible multivariate Poisson-lognormal distribution and is seen to be a natural gener

14 iates considerably from the commonly assumed lognormal distribution and predicts considerably more ra

15 Both the fractal behavior and the lognormal distribution are intimately related to the fac

16 This distribution behaves like a lognormal distribution around the centre, and like a pow

17 A lognormal distribution best described the survival time,

18 ion of rates such as a gamma distribution or lognormal distribution has deservedly been popular, but

19 The role of their lognormal distribution in clonogenic cell survival was e

20 CD34(+) cells and blood vessels exhibited a lognormal distribution indicating a shared spatial niche

21 d that the fit does not suffer when a common lognormal distribution is assumed for all 18 genes compa

22 o in vivo reports, we found an approximately lognormal distribution of firing rates.

23 to increasing T cell search efficiency: 1) a lognormal distribution of step lengths, 2) motion that i

24 e movement patterns can be approximated by a lognormal distribution rather than a power-law distribut

25 Connection weights exhibit a heavy-tailed lognormal distribution spanning five orders of magnitude

26 Exon sizes in sequenced genomes show a lognormal distribution typical of a random Kolmogoroff f

27 The shape parameter of the lognormal distribution was found to be correlated to bot

28 le wild populations were best described by a lognormal distribution with power-law scaled tails, the

29 N-gons, in a type-I network are fitted by a lognormal distribution, whereas those in type-II display

30 me enhancements and inferred fluxes follow a lognormal distribution, with the top 10% emitters contri

31 seases follows a right-skewed, approximately lognormal distribution.

32 arse, and firing rates can be described by a lognormal distribution.

33 cortical neurons can also be described by a lognormal distribution.

34 buted in non-Gaussian distributions, e.g., a lognormal distribution.

35 We model these random periods by using a lognormal distribution.

36 s on Barro Colorado Island, Panama, than the lognormal distribution.

37 several empirical data sets better than the lognormal distribution.

38 er washout is also discussed in terms of the lognormal distribution.

39 ater concentration by randomly sampling from lognormal distributions for random error in the yearly p

40 ultiplicative cell growth, and the mixing of lognormal distributions having different variances, may

41 ng regimes of species-area relationships and lognormal distributions of species abundance with an exc

42 Both of these peaks display lognormal distributions.

43 ntent, and amount of starch/cell volume obey lognormal distributions.

44 ns, including the right Pareto-lognormal and lognormal distributions.

45 of small firms, data typically described by lognormal distributions.

46 ckouts in S. cerevisiae fits a double Pareto-lognormal (DPLN) distribution better than any of the alt

47 The double Pareto-lognormal (DPLN) distribution is an ideal candidate dist

48 With the observed confirmation of a lognormal emission distribution, this airborne observing

49 RPLN), left Pareto-lognormal (LPLN), normal, lognormal, exponential, and Pareto.

50 es rise to such correlations and yields both lognormal firing rates and synaptic efficacies.

51 monstrate that the mixture of the decomposed lognormal flight distributions associated with each moda

52 ifurcating point gives rise to an asymptotic lognormal flow distribution with a positive skewness.

53 connectivity profile) that was well fit by a lognormal function.

54 hibitor, many sets were fitted better by two lognormal functions.

55 th 210Po was shown to be well described by a lognormal (LN) distribution function with the aid of aut

56 s with a number of distributions: power law, lognormal, loglogistic, loggamma, right Pareto-lognormal

57 , right Pareto-lognormal (RPLN), left Pareto-lognormal (LPLN), normal, lognormal, exponential, and Pa

58 The Bernoulli-lognormal mixture assigns observations to subgroups with

59 We introduce a Bernoulli-lognormal mixture model for clustering DNA methylation d

60 We show that the lognormal mixture model performs best in terms of power

61 A compound Bayesian logit-lognormal model accounting for heteroscedasticity was us

62 In combining this scaling law with the lognormal model of biodiversity, we predict that Earth i

63 of mitotic deceleration (implemented with a lognormal model of replication).

64 between features in a modified zero-inflated lognormal model.

65 Lognormal models and censored-data methods produced esti

66 ral generalization of the univariate Poisson-lognormal models used in the ecological studies of biodi

67 better fit to the data than did conventional lognormal models.

68 rds, Weibull, exponential, log-logistic, and lognormal models.

69 imated by a unimodal distribution, such as a lognormal or a gamma distribution.

70 s can be considered independent draws from a Lognormal or a Gamma distribution.

71 tistical tests with Poisson, LN, and Poisson-lognormal (P-LN) models.

72 Many of the components in the central lognormal parts of the empirical distributions are unide

73 gnormal, loglogistic, loggamma, right Pareto-lognormal (PLN) and double PLN (dPLN).

74 alyses indicate an age of 110.9 (exponential/lognormal priors)/118.7 (uniform priors) million years (

75 nts were fitted to one and to two cumulative lognormal probability distribution functions.

76 The lognormal problem can be overcome by using cocktails of

77 ds applied to the log-transformed GFR (i.e., lognormal) quantify only rigid shifts in a given outcome

78 robability distributions: DPLN, right Pareto-lognormal (RPLN), left Pareto-lognormal (LPLN), normal,

79 Changes in the value of the lognormal shape parameter and slope of the cellular drug

80 tions observed at glacial calving fronts and lognormal size-frequency distributions observed globally

81 Their distributions are typically lognormal, suggesting that failure in chemotherapy and t

82 The lognormal survival model showed the best performance.

83 species abundance distribution resembling a lognormal with higher rarity, together with the observat

84 n of ~1.0 MJ/m, and that the distribution is lognormal with respect to energy per length and frequenc