Meteorological and Hydrological Data collected at Gununo SCRP Research Station from 1996 to 2004

Nine years of meteorological and hydrological data collected at Gununo SCRP Research Station are ecoded and available

Quantitative summer and winter temperature reconstructions from pollen and chironomid data between 15 and 8 ka BP in the Baltic–Belarus area

New pollen based reconstructions of summer (May-to-August) and winter (December-to-February) temperatures between 15 and 8 ka BP along a S-N transect in the Baltic-Belarus (BB) area display trends in temporal and spatial changes in climate variability. These results are completed by two chironomid-based July mean temperature reconstructions. The magnitude of change compared with modern temperatures was more prominent in the northern part of BB area. The 4 C degrees winter and 2 C degrees summer warming at the start of GI-1 was delayed in the BB area and Lateglacial maximum temperatures were reached at ca 13.6 ka BP, being 4 C degrees colder than the modern mean. The Younger Dryas cooling in the area was 5 C degrees colder than present, as inferred by all proxies. In addition, our analyses show an early Holocene divergence in winter temperature trends with modern values reaching 1 ka earlier (10 ka BP) in southern BB compared to the northern part of the region (9 ka BP).

A fixed-links modeling approach to assess individual differences in the attentional blink: Analysis of behavioral and psychophysiological data

The attentional blink (AB) is a fundamental limitation of the ability to select relevant information from irrelevant information. It can be observed with the detection rate in an AB task as well as with the corresponding P300 amplitude of the event-related potential. In previous research, however, correlations between these two levels of observation were weak and rather inconsistent. A possible explanation of this finding might be that multiple processes underlie the AB and, thus, obscure a possible relationship between AB-related detection rate and the corresponding P300 amplitude. The present study investigated this assumption by applying a fixed-links modeling approach to represent behavioral individual differences in the AB as a latent variable. Concurrently, this approach enabled us to control for additional sources of variance in AB performance by deriving two additional latent variables. The correlation between the latent variable reflecting behavioral individual differences in AB magnitude and a corresponding latent variable derived from the P300 amplitude was high (r=.70). Furthermore, this correlation was considerably stronger than the correlations of other behavioral measures of the AB magnitude with their psychophysiological counterparts (all rs<.40). Our findings clearly indicate that the systematic disentangling of various sources of variance by utilizing the fixed-links modeling approach is a promising tool to investigate behavioral individual differences in the AB and possible psychophysiological correlates of these individual differences.

Multiple imputation for missing continuous and dichotomous data: The effects of patterns of missingness and variable correlations on type I error with small samples

The purpose of this study is to investigate the effects of predictor variable correlations and patterns of missingness with dichotomous and/or continuous data in small samples when missing data is multiply imputed. Missing data of predictor variables is multiply imputed under three different multivariate models: the multivariate normal model for continuous data, the multinomial model for dichotomous data and the general location model for mixed dichotomous and continuous data. Subsequent to the multiple imputation process, Type I error rates of the regression coefficients obtained with logistic regression analysis are estimated under various conditions of correlation structure, sample size, type of data and patterns of missing data. The distributional properties of average mean, variance and correlations among the predictor variables are assessed after the multiple imputation process. ^ For continuous predictor data under the multivariate normal model, Type I error rates are generally within the nominal values with samples of size n = 100. Smaller samples of size n = 50 resulted in more conservative estimates (i.e., lower than the nominal value). Correlation and variance estimates of the original data are retained after multiple imputation with less than 50% missing continuous predictor data. For dichotomous predictor data under the multinomial model, Type I error rates are generally conservative, which in part is due to the sparseness of the data. The correlation structure for the predictor variables is not well retained on multiply-imputed data from small samples with more than 50% missing data with this model. For mixed continuous and dichotomous predictor data, the results are similar to those found under the multivariate normal model for continuous data and under the multinomial model for dichotomous data. With all data types, a fully-observed variable included with variables subject to missingness in the multiple imputation process and subsequent statistical analysis provided liberal (larger than nominal values) Type I error rates under a specific pattern of missing data. It is suggested that future studies focus on the effects of multiple imputation in multivariate settings with more realistic data characteristics and a variety of multivariate analyses, assessing both Type I error and power. ^

Identification of maltreatment type in children with disabilities using the National Child Abuse and Neglect Data System (NCANDS)

This study was a retrospective design and used secondary data from the National Child Abuse and Neglect Data System (NCANDS), provided by the National Data Archive on Child Abuse and Neglect Family Life Development Center administered by Cornell University. The dataset contained information for the year 2005 on children from birth to 18 years of age. Child abuse and neglect for disabled children, was evaluated in-depth in the present study. Descriptive and statistical analysis was performed using the children with and without disabilities. It was found that children with disabilities have a lower rate of substantiation that likely indicates the interference of reporting due to their handicap. The results of this research demonstrate the important need to teach professionals and laypersons alike on how to recognize and substantiate abuse among disabled children.^

Mixture modeling for joint analysis of survival, discrete, and continuous data

Mixture modeling is commonly used to model categorical latent variables that represent subpopulations in which population membership is unknown but can be inferred from the data. In relatively recent years, the potential of finite mixture models has been applied in time-to-event data. However, the commonly used survival mixture model assumes that the effects of the covariates involved in failure times differ across latent classes, but the covariate distribution is homogeneous. The aim of this dissertation is to develop a method to examine time-to-event data in the presence of unobserved heterogeneity under a framework of mixture modeling. A joint model is developed to incorporate the latent survival trajectory along with the observed information for the joint analysis of a time-to-event variable, its discrete and continuous covariates, and a latent class variable. It is assumed that the effects of covariates on survival times and the distribution of covariates vary across different latent classes. The unobservable survival trajectories are identified through estimating the probability that a subject belongs to a particular class based on observed information. We applied this method to a Hodgkin lymphoma study with long-term follow-up and observed four distinct latent classes in terms of long-term survival and distributions of prognostic factors. Our results from simulation studies and from the Hodgkin lymphoma study demonstrated the superiority of our joint model compared with the conventional survival model. This flexible inference method provides more accurate estimation and accommodates unobservable heterogeneity among individuals while taking involved interactions between covariates into consideration.^

Experimental growth characteristics, cell size, yessotoxin values and bioassay data of Protoceratium reticulatum (Dinophyceae)

Harmful algal blooms are mainly caused by marine dinoflagellates and are known to produce potent toxins that may affect the ecosystem, human activities and health. Such events have increased in frequency and intensity worldwide in the past decades. Numerous processes involved in Global Change are amplified in the Arctic, but little is known about species specific responses of arctic dinoflagellates. The aim of this work was to perform an exhaustive morphological, phylogenetical and toxinological characterization of Greenland Protoceratium reticulatum and, in addition, to test the effect of temperature on growth and production of bioactive secondary metabolites. Seven clonal isolates, the first isolates of P. reticulatum available from arctic waters, were phylogenetically characterized by analysis of the LSU rDNA. Six isolates were further characterized morphologically and were shown to produce both yessotoxins (YTX) and lytic compounds, representing the first report of allelochemical activity in P. reticulatum. As shown for one of the isolates, growth was strongly affected by temperature with a maximum growth rate at 15 °C, a significant but slow growth at 1 °C, and cell death at 25 °C, suggesting an adaptation of P. reticulatum to temperate waters. Temperature had no major effect on total YTX cell quota or lytic activity but both were affected by the growth phase with a significant increase at stationary phase. A comparison of six isolates at a fixed temperature of 10 °C showed high intraspecific variability for all three physiological parameters tested. Growth rate varied from 0.06 to 0.19 per day, and total YTX concentration ranged from 0.3 to 15.0 pg YTX/cell and from 0.5 to 31.0 pg YTX/cell at exponential and stationary phase, respectively. All six isolates performed lytic activity; however, for two isolates lytic activity was only detectable at higher cell densities in stationary phase.