Record performances at the Boston Marathon: Biometeorological factors

Air temperature and relative humidity have long been suspected of affecting the performance of marathon runners. Though these factors are important in their extremes, we show that other factors are even more indicative of race performances. Performances of the top 3 finishers in the last 30 Boston Marathons were correlated with hourly meteorological data for each race day. These 90 individual performances were classified as: record breaking performances (31), average performances (35), and unusually slow performances (24). The factors that help predict record breaking and unusually slow performances are: (i) wet bulb temperature, (ii) percent sky cover, and (iii) presence or absence of a light precipitation. Record breaking performances are characterized by a wet bulb temperature of <7.8°C, and 100% sky cover. A light drizzle is also conducive to better performances. On the other hand, unusually low performances are accompanied by a wet bulb temperature of >7.8°C, and a sky cover of 50% or less. No light, precipitation was recorded on any of the unusually slow race days. A graphic analysis clearly shows these relationships to exist. In addition, a multiple regression analysis confirms the importance of these variables. The authors advise that these are reliable predictors; however, when considering marathon races held in various geographical regions and differing climatic regimes, the exact numerical thresholds used here may not apply.     

Island urbanization and its ecological consequences: A case study in the Zhoushan Island,East China

Islands, which provide multiple ecosystem services, are subject to increasing urbanization pressure due to the ongoing marine development, especially in developing countries. Insights into the island urbanization mechanism and its ecological consequences are essential to sustainable development. In the present paper, the satellite images, nighttime lights, and topographic data were integrated to characterize the spatially explicit urbanization process and mechanism during 1995–2011 in the Zhoushan Island, East China. Furthermore, the corresponding spatially explicit changes in ecosystem services, including net primary productivity (NPP), carbon sequestration and oxygen production (CSOP), nutrient cycling, crop production, and habitat quality, were quantified based on the Carnegie–Ames–Stanford Approach (CASA) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) models. The results showed that the Zhoushan Island had experienced a rapid urbanization over the years, with significant urban encroachment on the farmland and tidal flat. Moreover, the urban land expansion was positively correlated with that of the nighttime lights and negatively correlated with the elevation, slope, and the distance to shoreline. These indicated that the urban expansion was resulted from the enhancement of socioeconomic activities, and concentrated in the near-shore areas with low altitude and gentle slope. The urban encroachment on other land use types resulted in a decrease of 3.4 Gg C a⁻¹ NPP, 8.7 Gg a⁻¹ CSOP, 13.2 Gg a⁻¹ nutrient cycling, and 12.3 t a⁻¹ crop production, respectively. In addition, the habitat quality in 11% area of this island degraded substantially. Therefore, to achieve sustainable development of islands, it is urgent to implement more stringent policies, such as island spatial regulation, environmental impact assessment, intensive land use, and urban greening, etc.     

An application of CIFAP for predicting the binding affinity of Chk1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide

Investigation of protein‐ligand interactions obtained from experiments has a crucial part in the design of newly discovered and effective drugs. Analyzing the data extracted from known interactions could help scientists to predict the binding affinities of promising ligands before conducting experiments. The objective of this study is to advance the CIFAP (compressed images for affinity prediction) method, which is relevant to a protein‐ligand model, identifying 2D electrostatic potential images by separating the binding site of protein‐ligand complexes and using the images for predicting the computational affinity information represented by pIC₅₀ values. The CIFAP method has 2 phases, namely, data modeling and prediction. In data modeling phase, the separated 3D structure of the binding pocket with the ligand inside is fitted into an electrostatic potential grid box, which is then compressed through 3 orthogonal directions into three 2D images for each protein‐ligand complex. Sequential floating forward selection technique is performed for acquiring prediction patterns from the images. In the prediction phase, support vector regression (SVR) and partial least squares regression are used for testing the quality of the CIFAP method for predicting the binding affinity of 45 CHK1 inhibitors derived from 2‐aminothiazole‐4‐carboxamide. The results show that the CIFAP method using both support vector regression and partial least squares regression is very effective for predicting the binding affinities of CHK1‐ligand complexes with low‐error values and high correlation. As a future work, the results could be improved by working on the pose of the ligands inside the grid.     

Indicators of sexual maturation and regression in the female cat flea, Ctenocephalides felis

Differences in the salivary glands, mesenteron epithelium and reproductive organs of female cat fleas, Ctenocephalides felis Bouché (Siphonaptera: Pulicidae), are related to the degree of reproductive maturation or regression. Contrary to previous ideas, blue bodies in the ovarioles are degenerate oocyte nuclei and their presence denotes failure of ripening oocytes to reach full maturity. A distinction between true corpora lutea and pseudo-corpora lutea is established, the presence of the former indicates successful oviposition, and of the latter, failure to complete maturation of eggs. Accurate indicators of sexual maturation and reproductive success are of potential value in assessing relative suitability of various hosts for a given flea species and therefore in assessing the degree of host specificity among fleas.     

Application of common predictive habitat techniques for post-border weed risk management

Weed risk assessment has become an accepted methodology for examining the likelihood and consequence of a plant species becoming invasive outside of its native range. Weed risk assessment draws upon biological and ecological information to estimate the likelihood and magnitude of the threats posed by introducing non-indigenous plants. In geographical terms, this has traditionally been understood as within a new country following importation of plant material. However, recent risk assessment development has focused more specifically on intracountry risk posed by already-present invasive plants and is referred to as post-border weed risk management. This form of assessment calls for fine-scale predictions of invasive species habitat suitability. This study applies some of the more popular and widely available habitat prediction models that represent a variety of different statistical approaches (linear regression, logistic regression, Bayesian probability, Classification and Regression Trees, Genetic Algorithm for Rule-set Production) to a single invasive plant, the vertebrate-dispersed, fleshy fruited European olive ( Olea europaea L.) in southern Australia. The relationships between the dependant ( O. europaea distribution) and independent (soil and climate) variables are used in the models to produce predictive maps for each model. Accuracy was calculated for each model output as well as a combined surface to examine whether recent calls for ensemble modelling of distributions produces improved predictions. Overall, the combined prediction demonstrated superior accuracy compared to any individual model outputs. The combined outputs can be likened to mapped gradations of predicted habitat suitability. The type of output produced in this study should form a critical component of post-border weed risk management but more importantly, the methodology will add to this important discipline.     

Analysis of Multiply Matched Retrospective Studies When Risk Factor Under Study is Discrete,With Two or More Levels

The odds ratio is known to closely approximate the relative risk when the disease is rare. Logistic regression models are often used to estimate such odds ratios, but here a different model is used which avoids the assumptions implicit in logistic modelling; it also has the advantage of providing a test of homogeneity for odds rat os in situations where the logistic model cannot.     

Power analysis of statistical methods for comparing treatment differences from limiting dilution assays

Summary Six different statistical methods for comparing limiting dilution assays were evaluated, using both real data and a power analysis of simulated data. Simulated data consisted of a series of 12 dilutions for two treatment groups with 24 cultures per dilution and 1,000 independent replications of each experiment. Data within each replication were generated by Monte Carlo simulation, based on a probability model of the experiment. Analyses of the simulated data revealed that the type I error rates for the six methods differed substantially, with only likelihood ratio and Taswell's weighted mean methods approximating the nominal 5% significance level. Of the six methods, likelihood ratio and Taswell's minimum Chi-square exhibited the best power (least probability of type II errors). Taswell's weighted mean test yielded acceptable type I and type II error rates, whereas the regression method was judged unacceptable for scientific work.