How Microbes Can Achieve Balanced Growth in a Fluctuating Environment

A microbial colony needs several essential nutrients in order to grow. Moreover, the colony requires these nutrients in fixed combinations, which are dictated by the chemical composition of its biomass. Unfortunately, ambient availabilities of the various nutrients vary all the time. This poses the question of how microbes can achieve balanced growth.The present solution to this problem is novel in that the allocation of molecular building blocks among assimilatory machineries within the cell is regarded as dynamic. This paper shows that allocation can be adapted so as to achieve balanced growth, nearly regardless of environmental conditions. Moreover, it is shown that a feedback mechanism, which monitors internal stores, is able to achieve this allocation.     

On small-sample confidence intervals for parameters in discrete distributions

The traditional definition of a confidence interval requires the coverage probability at any value of the parameter to be at least the nominal confidence level. In constructing such intervals for parameters in discrete distributions, less conservative behavior results from inverting a single two-sided test than inverting two separate one-sided tests of half the nominal level each. We illustrate for a variety of discrete problems, including interval estimation of a binomial parameter, the difference and the ratio of two binomial parameters for independent samples, and the odds ratio.     

Spatial population genetic structure in Trillium grandiflorum: the roles of dispersal, mating, history, and selection

The roles of the various potential ecological and evolutionary causes of spatial population genetic structure (SPGS) cannot in general be inferred from the extant structure alone. However, a stage-specific analysis can provide clues as to the causes of SPGS. We conducted a stage-specific SPGS analysis of a mapped population of about 2000 Trillium grandiflorum (Liliaceae), a long-lived perennial herb. We compared SPGS for juvenile (J), nonreproductive (NR), and reproductive (R) stages. Fisher's exact test showed that genotypes had Hardy-Weinberg frequencies at all loci and stage classes. Allele frequencies did not differ between stages. Bootstrapped 99% confidence intervals (99%CI) indicate that F-statistic values are indistinguishable from zero, (except for a slightly negative FIT for the R stage). Spatial autocorrelation was used to calculate f the average kinship coefficient between individuals within distance intervals. Null hypothesis 99%CIs for f were constructed by repeatedly randomizing genotypic locations. Significant positive fine-scale genetic structure was detected in the R and NR stages, but not in the J stage. This structure was most pronounced in the R stage, and declined by about half in each remaining stage: near-neighbor f = 0.122, 0.065, 0.027, for R, NR, and J, respectively. For R and NR, the near-neighbor f lies outside the null hypothesis 99%CI, indicating kinship at approximately the level of half-sibs and first cousins, respectively. We also simulated the expected SPGS of juveniles post dispersal, based on measured R-stage SPGS, the mating system, and measured pollen and seed dispersal properties. This provides a null hypothesis expectation (as a 99%CI) for the J-stage correlogram, against which to test the likelihood that post-dispersal events have influenced J-stage SPGS. The actual J correlogram lies within the null hypothesis 99%CI for the shortest distance interval and nearly all other distance intervals indicating that the observed low recruitment, random mating and seed dispersal patterns are sufficient to account for the disappearance of SPSG between the R and the J stages. The observed increase in SPGS between J and R stages has two potential explanations: history and local selection. The observed low total allelic diversity is consistent with a past bottleneck: a possible historical explanation. Only a longitudinal stage-specific study of SPGS structure can distinguish between historical events and local selection as causes of increased structure with increasing life history stage.     

福建省肖叶甲科物种垂直分布与区域分化的比较研究(鞘翅目:肖叶甲科)

研究福建省肖叶甲科物种的垂直分布和区域分化,通过对闽西北武夷山垂直分布的调查分析,认为肖叶甲科垂直分布呈现３个基本分带;海拔３５０ｍ以下,３５０－１２００ｍ和海拔１２００ｍ以上,其中以中间带种类最丰富,与闽中戴云山对比,发现物种的垂直分布范围（上限和下限）在戴云山比在武夷山高,有明显的上移趋势,主要影响因素是南北不同气流的作用和植被垂直分布变化,综合平面分布与垂直分布结果,福建省肖甲科区系以适应于     

云南西双版纳甲壳动物分布及区系特点(节肢动物门:甲壳动物总纲)

根据以往多年及近期对西双版纳甲壳动物包括介形类,枝角类,挠足类,陆生等足类,虾类,蟹类等的研究结果,将其种类组成,地理分布及区系特点予以分析探讨,指明该地区在世界动物地理区划中,应归入南亚－东南亚亚区中的印支－巽他分区,在中国动物地理区划占应为一个独立的小区。     

Three-dimensional structure of α-conotoxin EI determined by 1H NMR spectroscopy H resonance assignments of α-conotoxin EI

-Conotoxin EI is an 18-residue peptide (RDOCCYHPTCNMSNPQIC; 4–10, 5–18) isolated from the venom of Conus ermineus, the only fish-hunting cone snail of the Atlantic Ocean. This peptide targets specifically the nicotinic acetylcholine receptor (nAChR) found in mammalian skeletal muscle and the electric organ Torpedo, showing a novel selectivity profile when compared to other -conotoxins. The 3D structure of EI has been determined by 2D-NMR methods in combination with dynamical simulated annealing protocols. A total of 133 NOE-derived distances were used to produce 13 structures with minimum energy that complied with the NOE restraints. The structure of EI is characterized by a helical loop between Thr⁹ and Met¹² that is stabilized by the Cys⁴-Cys¹⁰ disulfide bond and turns involving Cys⁴-Cys⁵ and Asn¹⁴-Pro¹⁵. Other regions of the peptide appear to be flexible. The overall fold of EI is similar to that of other 4/7-conotoxins (PnIA/B, MII, EpI). However, unlike these other 4/7-conotoxins, EI targets the muscular type nAChR. The differences in selectivity can be attributed to differences in the surface charge distribution among these 4/7-conotoxins. The implications for binding of EI to the muscular nAChR are discussed with respect to the current NMR structure of EI.     

Generalized estimating equations for ordinal categorical data: arbitrary patterns of missing responses and missingness in a key covariate

We propose methods for regression analysis of repeatedly measured ordinal categorical data when there is nonmonotone missingness in these responses and when a key covariate is missing depending on observables. The methods use ordinal regression models in conjunction with generalized estimating equations (GEEs). We extend the GEE methodology to accommodate arbitrary patterns of missingness in the responses when this missingness is independent of the unobserved responses. We further extend the methodology to provide correction for possible bias when missingness in knowledge of a key covariate may depend on observables. The approach is illustrated with the analysis of data from a study in diagnostic oncology in which multiple correlated receiver operating characteristic curves are estimated and corrected for possible verification bias when the true disease status is missing depending on observables.     

Memory in coal tits: an alternative model

Jolliffe and Jolliffe (1997, Biometrics 53, 1136-1142) proposed various models for data from an experiment on memory in coal tits. This article describes an alternative model, which fits equally well and which may be simpler to interpret.