Gene expression profiles in the testis associated with testis-ova in adult Japanese medaka (Oryziaslatipes) exposed to 17α-ethinylestradiol

The occurrence of oocytes in the testis (testis-ova) of several fish species is often associated with exposure of estrogenic chemicals. However, induction mechanisms of the testis-ova remain to be elucidated. To develop marker genes for detecting testis-ova in the testis, adult male medaka were exposed to nominal concentration of 100 ng L⁻¹ of 17α-ethinylestradiol (EE2) for 3-5 weeks, and 800 ng estradiol benzoate (EB) for 3 weeks (experiment I), and a measured concentration of 20 ng L⁻¹ EE2 for 1-6 weeks (experiment II). Histological analysis was performed for the testis, and microarray analyses were performed for the testis, liver and brain. Microarray analysis in the estrogen-exposed medaka liver showed vitellogenin and choriogenin as estrogen responsive genes. Testis-ova were induced in the testis after 4 weeks of exposure to 100 ng L⁻¹ EE2, 3 weeks of exposure to 800 ng EB, and 6 weeks of exposure to 20 ng L⁻¹ EE2. Microarray analysis of estrogen-exposed testes revealed up-regulation of genes related to zona pellucida (ZP) and the oocytes marker gene, 42Sp50. Using quantitative RT-PCR we confirmed that Zpc5 gene can be used as a marker for the detection of testis-ova in male medaka.     

Effective diffusivity of the uranyl ion in a granite from Inada, Ibaraki, Japan

The effective diffusivity of uranium(VI) in Inada granite has been determined by through-diffusion. Experiments were performed at room temperature (20–25°C) in a 0.1 mol 1⁻¹ KCl solution where uranium is present predominantly as the poorly sorbing UO₂²⁺. An effective diffusivity (D_e) of (3.6 ± 1.6) × 10⁻¹⁴ m² s⁻¹ was obtained, close to that for uranine (nonsorbing organic tracer), but one order of magnitude lower than those obtained for Sr²⁺ and NpO₂⁺, and two orders of magnitude lower than that obtained for I⁻. According to well established theory, a proportional relationship exists between D_e and the diffusivity in the bulk of the solution (D_v). The effective diffusivity obtained in granite was not proportional to D_v. This agrees with results obtained for effective diffusivity in a Swedish granite. The ratio D_e/D_v was found to be not constant but increased with D_e or D_v. This result suggests a limit to the application of the theory.     

Prediction of physico-chemical properties for PCs/DFs using the UNIFAC model with an alternative approximation for group assignment

The original-type UNIFAC model was used to predict the environmentally important physico-chemical properties of PCDDs/DFs, such as aqueous solubility, Henry's law constant, and 1-octanol/water partition coefficient, through the UNIFAC-derived infinite dilution activity coefficient. In this application, we suggest an alternative approximation that the aromatic ether group AC-O in PCDD/DF molecules is replaced with the aliphatic ether group CH-O, because the AC-O group is not available in the conventional UNIFAC model. With this approximation, the ability of the UNIFAC model to predict those properties was examined by comparing with experimental data. The UNIFAC model provided comparatively good estimation results. From these results, it is shown that the alternative approximation is useful for the UNIFAC estimation of physico-chemical properties for PCDDs/DFs. Furthermore, the predicted solubilities of 2,3,7,8-T4CDD and O8CDD in organic solvents and the co-solvency effect on solubility of PCDDs in methanol/water mixture indicate that the UNIFAC calculation presented here could well predict the physico-chemical properties of PCDDs/DFs in various solution conditions.     

Global-scale quantitative assessment for biodiversity on forest land use: applying the Global No Net Loss approach

Here we propose a method to quantitatively assess and examine Global No Net Loss (GNNL) of forest biodiversity on a global scale. The method produces a GNNL index of existing forest and enables future predictions of forest loss under different assumptions. The method tests the feasibility of the GNNL index and enables discussion of policy for future global scale sustainable forest management up to 2050. The GNNL index was estimated from an equation including forest areas per country per forest type (primary forest, secondary forest and plantation forest), diversity of forest ecosystem, and species density. Estimates derived from historical data revealed an approximate 7% reduction in GNNL index between 1990 and 2005. Predictions of the GNNL index until 2050 emphasize the importance of regenerating large portions of forests felled for agricultural land (or other uses) with secondary forests.     

Polychlorinated biphenyls (PCBs) analysis using a miniaturized high-resolution time-of-flight mass spectrometer "MULTUM-S II"

In this study, we conducted polychlorinated biphenyls (PCBs) analysis using fast gas chromatography (GC)/high-resolution mass spectrometry (HRMS). Mass spectrometry (MS) was performed with a miniature multi-turn time-of-flight (TOF) analyzer called "MULTUM-S II". MULTUM-S II is truly a portable high resolution mass spectrometer. The mass spectrometer's high resolution capability is due to its theoretical infinite flight path utilizing perfect space and time focusing within a closed flight orbit. Mass resolution above 10?000 was easily achievable employing this portable system. This mass resolution is comparable to magnetic sector mass spectrometers, which have traditionally performed PCB analyses in the past. At a resolution of 10?000, a limit-of-detection of 1 ppb was determined using a heptachlorinated biphenyl standard sample. Using this fast GC/HRMS, 66 PCB congeners were analyzed within 5 min. In addition experiments aimed at confirming interference of PCB signal peaks and matrix peaks in diluted dielectric coolant fluids were performed. We found that the PCB signal peaks were detected without matrix interference via high mass resolution.     

Neglected biological patterns in the residuals

One of the fundamental assumptions underlying linear regression models is that the errors have a constant variance (i.e., homoscedastic). When this assumption is violated, standard errors from a regression can be biased and inconsistent, meaning that the associated p values and 95% confidence intervals cannot be trusted. The assumption of homoscedasticity is made for statistical reasons rather than biological reasons; in most real datasets, some form of heteroscedasticity is likely to exist. However, a survey of the behavioural ecology literature showed that only about 5% of articles explicitly mentioned heteroscedasticity, leaving 95% of articles in which heteroscedasticity was apparently absent. These results strongly indicate that the prevalence of heteroscedasticity is widely under-reported within behavioural ecology. The aim of this article is to raise awareness of heteroscedasticity amongst behavioural ecologists. Using topical examples from fields in behavioural ecology such as sexual dimorphism and animal personality, we highlight the biological importance of considering heteroscedasticity. We also emphasize that researchers should pay closer attention to the variance in their data and consider what factors could cause heteroscedasticity. In addition, we introduce some simple methods of dealing with heteroscedasticity. The two methods we focus on are: (1) incorporating variance functions within a generalised least squares (GLS) framework to model the functional form of heteroscedasticity and; (2) heteroscedasticity-consistent standard error (HCSE) estimators, which can be used when the functional form of heteroscedasticity is unknown. Using case studies, we show how both methods can influence the output from linear regression models. Finally, we hope that more researchers will consider heteroscedasticity as an important source of additional information about the particular biological process being studied, rather than an impediment to statistical analysis.     

A review of water electrolysis–based systems for hydrogen production using hybrid/solar/wind energy systems

Environmental Science and Pollution Research - Hydrogen energy, as clean and efficient energy, is considered significant support for the construction of a sustainable society in the face of global...     

Defining behavioural syndromes and the role of ‘syndrome deviation’ in understanding their evolution

This commentary highlights multivariate tools that have been used by evolutionary biologists in the study of syndromes and their evolution and discusses the insights that these methods provide into evolutionary processes relative to the metric ‘syndrome deviation’ that has recently been proposed by Herczeg and Garamszegi (Behav Ecol Sociobiol 66:161–169, 2012). We clarify that non-zero phenotypic correlations arise from the joint influences of within- and between-individual correlations, whereas only non-zero between-individual correlations represent behavioural syndromes, and discuss how acknowledgement of this subtle difference between phenotypic and between-individual correlations affects the applicability of syndrome deviation for the study of behavioural syndromes.     

Model averaging,missing data and multiple imputation: a case study for behavioural ecology

Model averaging, specifically information theoretic approaches based on Akaike’s information criterion (IT-AIC approaches), has had a major influence on statistical practices in the field of ecology and evolution. However, a neglected issue is that in common with most other model fitting approaches, IT-AIC methods are sensitive to the presence of missing observations. The commonest way of handling missing data is the complete-case analysis (the complete deletion from the dataset of cases containing any missing values). It is well-known that this results in reduced estimation precision (or reduced statistical power), biased parameter estimates; however, the implications for model selection have not been explored. Here we employ an example from behavioural ecology to illustrate how missing data can affect the conclusions drawn from model selection or based on hypothesis testing. We show how missing observations can be recovered to give accurate estimates for IT-related indices (e.g. AIC and Akaike weight) as well as parameters (and their standard errors) by utilizing ‘multiple imputation’. We use this paper to illustrate key concepts from missing data theory and as a basis for discussing available methods for handling missing data. The example is intended to serve as a practically oriented case study for behavioural ecologists deciding on how to handle missing data in their own datasets and also as a first attempt to consider the problems of conducting model selection and averaging in the presence of missing observations.