The effect of pulsed microwaves on passive electrical properties and interspike intervals of snail neurons

The effects of pulsed microwaves (2.45 GHz, 10 μs, 100 pps, SAR: 81.5 kW/kg peak, 81.5 W/kg average) on membrane input resistance and action potential (AP) interval statistics were studied in spontaneously active ganglion neurons of land snails (Helix aspersa), at strictly constant temperature (20.8±.07°C worst case). Statistical comparison with sham-irradiated neurons revealed a significant increase in the mean input resistance of neurons exposed to pulsed microwaves (P ? .05 ). Pulsed microwaves had no visible effect on mean AP firing rate; this observation was confirmed by analysis of interspike intervals (ISIs). Using an integrator model for spontaneously active neurons, we found the net input current to be more variable in neurons exposed to pulsed microwaves. The mean input current was not affected. The standard deviation of ISIs and the autocorrelation of the input current were marginally affected, but these changes were not consistent across neurons. Although the observed effects were less obvious than those reported in other studies, they represent evidence of a direct interaction between neurons and pulsed microwaves, in the absence of macroscopic temperature changes. The data do not suggest a single, specific mechanism for such interaction. © 1993 Wiley-Liss, Inc.     

Statistical analysis of algal and water quality data

Data on cyanobacteria (blue-green algae) are generallycollected on a reactive basis, frequently in responseto bloom events. Such data presents a biased andincomplete snapshot of water quality. This paper looksat two typical data sets for UK waters showing thatwhile statistics may be used to describe the data theyare of limited use in forecasting. Suggestions ofappropriate tests for small and sparse data sets aremade.     

A regression analysis of the summer population dynamics of Polyarthra vulgaris in a northern Michigan bog lake

The population dynamics of a planktonic rotifer (Polyarthra vulgaris) were examined in a brown water, acid lake in northern Michigan, U.S.A. Predation by Chaoborus punctipennis and low food (Navicula spp. and Cyclotella spp.) concentrations were the main factors limiting P. vulgaris populations of all factors examined. The data presented here support a hypothesis for zooplankton limitation by an invertebrate predator.     

Allometric Models for Predicting Aboveground Biomass in Two Widespread Woody Plants in Hawaii

Allometric models are important for quantifying biomass and carbon storage in terrestrial ecosystems. Generalized allometry exists for tropical trees, but species‐ and site‐specific models are more accurate. We developed species‐specific models to predict aboveground biomass in two of the most ubiquitous natives in Hawaiian forests and shrublands, Metrosideros polymorpha and Dodonaea viscosa. The utility of the M. polymorpha allometry for predicting biomass across a range of sites was explored by comparing size structure (diameter at breast height vs. tree height) of the trees used to develop the models against trees from four M. polymorpha‐dominated forests along a precipitation gradient (1630–2380 mm). We also compared individual tree biomass estimated with the M. polymorpha model against existing generalized equations, and the D. viscosa model with an existing species‐specific model. Our models were highly significant and displayed minimal bias. Metrosideros polymorpha size structures from the three highest precipitation sites fell well within the 95% confidence intervals for the harvested trees, indicating that the models are applicable at these sites. However, size structure in the area with the lowest precipitation differed from those in the higher rainfall sites, emphasizing that care should be taken in applying the models too widely. Existing generalized allometry differed from the M. polymorpha model by up to 88 percent, particularly at the extremes of the data range examined, underestimating biomass in small trees and overestimating in large trees. The existing D. viscosa model underestimated biomass across all sizes by a mean of 43 percent compared to our model. The species‐specific models presented here should enable more accurate estimates of biomass and carbon sequestration in Hawaiian forests and shrublands.     

氚水在模拟水稻－水－土壤生态系统中的行为

采用模拟污染物的同位素示踪技术研究HTO(流水)在水稻-水-土壤模拟生态系统中的迁移、消长行为,并应用三库室开系统模型和非线性回归方法确定了水稻、水和土壤分室的拟合方程．结果表明,田表水中的HTO不仅在系统各分室间转移和分配,而且迅速向系统外散逸;HTO中的流以自由水氚和结合态流形式存在于水稻中,以吸湿性水流和结晶水氚存在于土壤,其中自由水氚(或吸湿性水氚)的比活度大于结合态流(或结晶水氚);水稻植株和土壤中HTO比活度随时间增加至最大值后又趋于下降,而结合态氚则呈缓慢增加;水稻茎秆中的总氚比活度高于其它各部位,而后逐渐趋于动态平衡．对实验数据进行回归分析得：田表水、土壤和水稻植株中的总氚比活度分别为Cw(t)=32.19c^-0.0353t 99.94c^-0.330t、Cs(t)=20.42(e^-0.0353t-e^-0.330t)和Cr(t)=38.49c^-0.0353t-10.13e^-0.330t-28.36c^-2.5744t。方差分析结果表明,各回归方程较好地反映了HTO在水稻-水-土壤生态系统中的行为．     

食块茎水鸟及水位对沙湖沉水植物冬芽分布的影响

分别于2012—2013、2013—2014年度越冬期候鸟越冬前(10月份)与越冬后(4月份)采用样方法调查沙湖沉水植物冬芽的种类、密度及生物量,分析不同水位条件的2个年度鄱阳湖碟形子湖沉水植物冬芽的分布及其对食块茎水鸟食物贡献的差异性,探讨越冬水鸟取食与水位变化对沉水植物冬芽分布的影响。结果表明:刺苦草(Vallisneria spinulosa)和罗氏轮叶黑藻(Hydrilla verticillata var.rosburghii)2种沉水植物的冬芽同域分布。2013年10月2种植物冬芽的密度与生物量均显著低于2012年同期,主要原因是鄱阳湖水位年际间变化剧烈,并对水质有显著影响:与2012年相比,2013年丰水期(4—9月)沙湖与主湖区连通的时间和日平均水深显著减小,但水体浊度显著增加,不利于沉水植物生长发育。2012—2013年度越冬水鸟迁出后2种冬芽的密度和生物量均明显下降,而2013—2014年度越冬期水鸟迁出后与迁入前相比两种植物冬芽的密度和生物量均无显著变化,很可能与食块茎水鸟的取食活动和高水位对食物可利用性的负面影响有密切关系。湖泊剧烈的水位变化导致越冬水鸟的食源具有年际波动的特征,而食块茎水鸟对鄱阳湖子湖的食物利用率受越冬季冬芽丰富度和食物可及性(accessibility)的共同影响。研究结果对鄱阳湖乃至长江中下游流域沉水植被恢复、越冬水鸟保护以及生态系统功能评估具有指导意义。     

Logistic regression when covariates are random effects from a non‐linear mixed model

In many studies, the association of longitudinal measurements of a continuous response and a binary outcome are often of interest. A convenient framework for this type of problems is the joint model, which is formulated to investigate the association between a binary outcome and features of longitudinal measurements through a common set of latent random effects. The joint model, which is the focus of this article, is a logistic regression model with covariates defined as the individual‐specific random effects in a non‐linear mixed‐effects model (NLMEM) for the longitudinal measurements. We discuss different estimation procedures, which include two‐stage, best linear unbiased predictors, and various numerical integration techniques. The proposed methods are illustrated using a real data set where the objective is to study the association between longitudinal hormone levels and the pregnancy outcome in a group of young women. The numerical performance of the estimating methods is also evaluated by means of simulation.     

A Multiphase Design Strategy for Dealing with Participation Bias

Summary A recently funded study of the impact of oral contraceptive use on the risk of bone fracture employed the randomized recruitment scheme of Weinberg and Wacholder (1990, Biometrics 46, 963–975). One potential complication in the bone fracture study is the potential for differential response rates between cases and controls; participation rates in previous, related studies have been around 70%. Although data from randomized recruitment schemes may be analyzed within the two‐phase study framework, ignoring potential differential participation may lead to biased estimates of association. To overcome this, we build on the two‐phase framework and propose an extension by introducing an additional stage of data collection aimed specifically at addressing potential differential participation. Four estimators that correct for both sampling and participation bias are proposed; two are general purpose and two are for the special case where covariates underlying the participation mechanism are discrete. Because the fracture study is ongoing, we illustrate the methods using infant mortality data from North Carolina.