Confidence Intervals for the Risk Ratio in Cohort Studies under Inverse Sampling

Three simple interval estimates for the risk ratio in inverse sampling are considered. The first two interval estimates are derived on the basis of Fieller's Theorem and the delta method with the logarithmic transformation, respectively. The third interval estimate is derived on the basis of an F-test statistic proposed by BENNETT (1981) for testing equal probabilities of a disease between two comparison groups when the disease is rare. To evaluate the performance of these three methods, a Monte Carlo simulation is used to compare the actual coverage probability with the nominal confidence level for each method and to estimate the expected length of the corresponding confidence interval in a variety of situations. On the basis of the results found in the simulation, we have concluded that the method with the logarithmic transformation is either equivalent to or better than the other two methods for all situations considered here.     

The oxo/peroxo debate: a nonheme iron perspective

The oxygen activation mechanisms proposed for nonheme iron systems generally follow the heme paradigm in invoking the involvement of iron-peroxo and iron-oxo species in their catalytic cycles. However, the nonheme ligand environments allow for end-on and side-on dioxygen coordination and impart greater flexibility in the modes of dioxygen activation. The currently available evidence for nonheme iron-peroxo and iron-oxo intermediates is summarized and discussed in light of the ongoing discussion on the nature of the oxidant(s) in heme enzymes.     

Reinforcement and learning

Evidence has been accumulating to support the process of reinforcement as a potential mechanism in speciation. In many species, mate choice decisions are influenced by cultural factors, including learned mating preferences (sexual imprinting) or learned mate attraction signals (e.g., bird song). It has been postulated that learning can have a strong impact on the likelihood of speciation and perhaps on the process of reinforcement, but no models have explicitly considered learning in a reinforcement context. We review the evidence that suggests that learning may be involved in speciation and reinforcement, and present a model of reinforcement via learned preferences. We show that not only can reinforcement occur when preferences are learned by imprinting, but that such preferences can maintain species differences easily in comparison with both autosomal and sex-linked genetically inherited preferences. We highlight the need for more explicit study of the connection between the behavioral process of learning and the evolutionary process of reinforcement in natural systems.     

Modela-r as a Froude and Strouhal dimensionless numbers combination for dynamic similarity in running

The aim of this study was to test the hypothesis that running at fixed fractions of Froude (Nfr) and Strouhal (Str) dimensionless numbers combinations induce dynamic similarity between humans of different sizes. Nineteen subjects ran in three experimental conditions, (i) constant speed, (ii) similar speed (Nfr) and (iii) similar speed and similar step frequency (Nfr and Str combination). In addition to anthropometric data, temporal, kinematic and kinetic parameters were assessed at each stage to measure dynamic similarity informed by dimensional scale factors and by the decrease of dimensionless mechanical parameter variability. Over a total of 54 dynamic parameters, dynamic similarity from scale factors was met for 16 (mean r=0.51), 32 (mean r=0.49) and 52 (mean r=0.60) parameters in the first, the second and the third experimental conditions, respectively. The variability of the dimensionless preceding parameters was lower in the third condition than in the others. This study shows that the combination of Nfr and Str, computed from the dimensionless energy ratio at the center of gravity (Modela-r) ensures dynamic similarity between different-sized subjects. The relevance of using similar experimental conditions to compare mechanical dimensionless parameters is also proved and will highlight the study of running techniques, or equipment, and will allow the identification of abnormal and pathogenic running patterns. Modela-r may be adapted to study other abilities requiring bounces in human or animal locomotion or to conduct investigations in comparative biomechanics.     

Estimating the human appropriation of land in Brazil by means of an Input–Output Economic Model and Ecological Footprint analysis

As we confront the current environmental crisis, determining the biophysical base (e.g., materials, energy, land, and water) of nations has become paramount. With advanced economies benefiting from the import of resource-intensive primary goods originating from poorer parts of the world, especially emerging nations, these are dilapidating their natural capital. Brazil is one of such emerging economies, whose mining and farming activities, propping up its export-led economic growth, exert great pressure on the environment. In particular, farming has been shown to have one of the world's greatest environmental impacts, especially as a consequence of land use associated with cattle ranching. Since a nation-wide evaluation of land-use types across the whole sectorial spectrum of the country's economy is still lacking, we used the most recently available Input–Output Economic Model for Brazil and the Ecological Footprint method to identify those economic sectors with the greatest potential for appropriating portions of the natural world.Our results show that: (i) the biggest chunk of Brazil's Ecological Footprint is due to its Carbon Footprint and, in particular, emissions from cattle; (ii) only a few economic sectors exhibit high Ecological Footprint values, chiefly those belonging to livestock farming and energy production based on fossil fuels; (iii) excluding the soybeans and slaughter sectors, export-oriented sectors have below-average Ecological Footprint values; and (iv) the percentage of Brazil's Ecological Footprint due to household consumption (excluding imports) is three times bigger than that attributable to exports, with sectors belonging to livestock farming contributing the most to such disparity.These results underscore that the environmental impact of the Brazilian economy can be drastically reduced by tackling the emission-intensive production processes of a few sectors only and disincentivizing the domestic consumption of a narrow range of products, especially with respect to the livestock segment.     

Influences of temporal independence of data on modelling species distributions

Modelling species distributions has been widely used to understand present and future potential distributions of species, and can provide adaptation and mitigation information as references for conservation and management under climate change. However, various methods of data splitting to develop and validate functions of the models do not get enough attention, which may mislead the interpretation of predicted results. We used the Taiwanese endemic birds to test the influences of temporal independence of datasets on model performance and prediction. Training and testing data were considered to be independent if they were collected during different survey periods (1993–2004 and 2009–2010). The results indicated no significant differences of six model performance measures (AUC, kappa, TSS, accuracy, sensitivity, and specificity) among the combinations of training and testing datasets. Both species- and grid cell-based assessments differed significantly between predictions by the annual and pooled training data. We also found an average of 85.8% similarity for species presences and absences in different survey periods. The remaining dissimilarity was mostly caused by species observed in the late survey period but not in the early one. The method of data splitting, yielding training and testing data, is critical for resulting model species distributions. Even if similar model performance exists, different methods can lead to different species distributional maps. More attention needs to be given to this issue, especially when amplifying these models to project species distributions in a changing world.     

Partition of lindane in synthetic and native membranes

Partition coefficients of the insecticide γ-1,2,3,4,5,6-hexachlorocyclohexane (trivially, lindane) were determined in model and native membranes. Partition in egg phosphatidylcholine bilayers decreases linearly with temperature, over a range (10–40°C) at which the lipid is in the liquid-crystalline state. Addition of 50 mol% cholesterol dramatically decreases partition (2100 falls to 100, at 10°C) and abolishes the temperature dependence. First-order phase transitions of dimyristoyl-, dipalmitoyl- and distearoylphosphatidylcholines (DMPC, DPPC and DSPC) are accompanied by a sharp increase in lindane partition. Apparently, the insecticide is easily accommodated in bilayers of short-aliphatic-chain lipids, since the partitions were 2450, 600 and 50 in DMPC, DPPC and DSPC, respectively, at temperatures 10 Cdeg below the midpoint of their transitions. The lindane partition sequence in native membranes is as follows: mitochondria, sarcoplasmic reticulum, myelin, brain microsomes and erythrocytes. This sequence correlates reasonably well with the relative content of cholesterol and is similar in liposomes of total extracted lipids, although the absolute partitions showed decreased values. Therefore, the presence of proteins in native membranes contributes to the insecticide partition, probably by favouring its interaction with lipids.     

Bayesian Estimation of the Distribution Function of the Poisson Model

This paper presents the Bayes estimators of the Poisson distribution function based on complete and truncated data under a natural conjugate prior. Laplace transform of the incomplete gamma function and the Gauss hypergeometric function have been employed in order to overcome the intractability of the integrals. Numerical examples from biosciences are given to illustrate the results. A Monte Carlo study has been carried out to compare Bayes estimators under complete data with the corresponding maximum liklihood estimators.