Cerebro-costo-mandibular syndrome in a father and a female fetus: early prenatal ultrasonographic diagnosis and autosomal dominant transmission

Ultrasonography in a female fetus revealed cystic cervical hygroma, severe micrognathia, and vertebral and upper limb anomalies suggestive of cerebro-costo-mandibular syndrome (CCMS) which was diagnosed ultrasonographically at 16 weeks' gestation. The father is affected and presents with a Pierre Robin sequence, short stature and typical costovertebral anomalies. CCMS is a rare and severe disorder. The high frequency of sporadic cases, vertical transmission, and the excess of sibs affected via horizontal transmission suggest dominant autosomal mutation with possible germinal mosaicism. The vertical familial case detailed in the present report is a reminder of the high risk when one parent or one sibling is affected and the extreme variability of phenotype and costal ossification. Early prenatal ultrasound diagnosis is possible in a severely affected fetus. Copyright © 2001 John Wiley & Sons, Ltd.     

Effect of water temperature on the courtship behavior of the Alpine newt Triturus alpestris

Temperature is expected to have an effect on the behavioral patterns of all organisms, especially ectotherms. However, although several studies focused on the effect of temperature on acoustic displays in both insects and anurans, almost nothing is known about how environmental temperature may affect ectotherm visual courtship displays and sexual performance. The purpose of this study was to determine the effect of environmental temperature on the sexual behavior of Alpine newts (Triturus alpestris). We subjected T. alpestris to two different temperatures in controlled laboratory conditions. Temperature had a major effect on both male and female behaviors: at low temperature, the frequencies of several displays, including tail-raising during sperm deposition, are lowered. This variation is caused indirectly by temperature because it is due to female responsiveness, which is temperature-dependent. However, the fanning movement of the males tail during its main courtship display is independent of female behavior: at lower temperatures, the tail beats at a lower rate, but for a longer time. The similar reproductive success (i.e. sperm transfer) at the two temperature ranges indicates that breeding in cold water is not costly but instead allows males and females to mate early in the season. This is particularly adaptive because, in many habitats, the reproductive period is shortened by drying or freezing conditions, which may impair survival of branchiate offspring. This study also demonstrates the necessity of considering environmental parameters when modeling optimality and characteristics of ectotherm behaviors.Communicated by W. Cooper     

Sampling for weed spatial distribution mapping need not be adaptive

Weeds are species of interest for ecologists because they are competitors of the crop for resources but they also play an important role in maintaining biodiversity in agroecosystems. To study their spatial distribution at the field scale, only sampled observations are available due to the cost of sampling. Weeds sampling strategies are static. However, in the domain of spatial sampling, adaptive strategies have also been developed with, for some of them, an important on-line or off-line computational cost. In this article we provide answers to the following question: Are the current adaptive sampling methods efficient enough to motivate a wider use in practice when sampling a weed species at a field scale? We provide a comparison of the behaviour of 8 static strategies and 3 adaptive ones on four criteria: density class estimation, map restoration, spatial aggregation estimation, and sampling duration. From two weeds data sets, we estimated six contrasted Markov Random Field (MRF) models of weed density class spatial distribution and a model for sampling duration. The MRF models were then used to compare the strategies on a large set of simulated maps. Our main finding was that there is no clear gain in using adaptive sampling strategies rather than static ones for the three first criteria, and adaptive strategies were associated to longer sampling duration. This conclusion points out that for weed mapping, it is more important to build a good model of spatial distribution, than to propose complex adaptive sampling strategies.     

The performance of state-of-the-art modelling techniques depends on geographical distribution of species

We explored the effects of prevalence, latitudinal range and clumping (spatial autocorrelation) of species distribution patterns on the predictive accuracy of eight state-of-the-art modelling techniques: Generalized Linear Models (GLMs), Generalized Boosting Method (GBM), Generalized Additive Models (GAMs), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Multivariate Adaptive Regression Splines (MARS), Mixture Discriminant Analysis (MDA) and Random Forest (RF). One hundred species of Lepidoptera, selected from the Distribution Atlas of European Butterflies, and three climate variables were used to determine the bioclimatic envelope for each butterfly species. The data set consisting of 2620 grid squares 30′ × 60′ in size all over Europe was randomly split into the calibration and the evaluation data sets. The performance of different models was assessed using the area under the curve (AUC) of a receiver operating characteristic (ROC) plot. Observed differences in modelling accuracy among species were then related to the geographical attributes of the species using GAM. The modelling performance was negatively related to the latitudinal range and prevalence, whereas the effect of spatial autocorrelation on prediction accuracy depended on the modelling technique. These three geographical attributes accounted for 19–61% of the variation in the modelling accuracy. Predictive accuracy of GAM, GLM and MDA was highly influenced by the three geographical attributes, whereas RF, ANN and GBM were moderately, and MARS and CTA only slightly affected. The contrasting effects of geographical distribution of species on predictive performance of different modelling techniques represent one source of uncertainty in species spatial distribution models. This should be taken into account in biogeographical modelling studies and assessments of climate change impacts.     

The first fossil fungus gardens of Isoptera: oldest evidence of symbiotic termite fungiculture (Miocene, Chad basin)

Higher termites of the subfamily Macrotermitinae (fungus-growing termites) are known to build fungus gardens where a symbiotic fungus (Termitomyces sp.) is cultivated. The fungus grows on a substrate called fungus comb, a structure built with the termites’ own faeces. Here we present the first fossil fungus combs ever found in the world. They were extracted from 7-million-year-old continental sandstone (Chad basin). Fossilized fungus combs have an ovoid morphology with a more or less flattened concave base and a characteristic general alveolar aspect. Under lens, they display a typical millimetre-scale pelletal structure. The latter, as well as the general shape and alveolar aspect, are similar to the morphology of fungus combs from extant fungus-growing termites.     

A framework for testing meso‐mediational relationships in Organizational Behavior

We advance a framework for testing mediational relationships that traverse levels of analysis. We differentiate mediation that functions within levels, as well as different forms of meso‐mediation that operate downward across levels of analysis. We discuss the preconditions and evidential basis for these meso‐mediational type relationships. We then provide an empirical example that illustrates the different types of mediation and conclude with recommendations and extensions for studying more complex meso‐mediational organizational phenomena. Copyright © 2007 John Wiley & Sons, Ltd.     

How floral odours are learned inside the bumblebee (Bombus terrestris) nest

Recruitment in social insects often involves not only inducing nestmates to leave the nest, but also communicating crucial information about finding profitable food sources. Although bumblebees transmit chemosensory information (floral scent), the transmission mechanism is unknown as mouth-to-mouth fluid transfer (as in honeybees) does not occur. Because recruiting bumblebees release a pheromone in the nest that triggers foraging in previously inactive workers, we tested whether this pheromone helps workers learn currently rewarding floral odours, as found in food social learning in rats. We exposed colonies to artificial recruitment pheromone, paired with anise scent. The pheromone did not facilitate learning of floral scent. However, we found that releasing floral scent in the air of the colony was sufficient to trigger learning and that learning performance was improved when the chemosensory cue was provided in the nectar in honeypots; probably because it guarantees a tighter link between scent and reward, and possibly because gustatory cues are involved in addition to olfaction. Scent learning was maximal when anise-scented nectar was brought into the nest by demonstrator foragers, suggesting that previously unidentified cues provided by successful foragers play an important role in nestmates learning new floral odours.     

A dynamic simulation model to support reduction in illegal trade within legal wildlife markets

Sustainable wildlife trade is critical for biodiversity conservation, livelihoods, and food security. Regulatory frameworks are needed to secure these diverse benefits of sustainable wildlife trade. However, regulations limiting trade can backfire, sparking illegal trade if demand is not met by legal trade alone. Assessing how regulations affect wildlife market participants’ incentives is key to controlling illegal trade. Although much research has assessed how incentives at both the harvester and consumer ends of markets are affected by regulations, little has been done to understand the incentives of traders (i.e., intermediaries). We built a dynamic simulation model to support reduction in illegal wildlife trade within legal markets by focusing on incentives traders face to trade legal or illegal products. We used an Approximate Bayesian Computation approach to infer illegal trading dynamics and parameters that might be unknown (e.g., price of illegal products). We showcased the utility of the approach with a small-scale fishery case study in Chile, where we disentangled within-year dynamics of legal and illegal trading and found that the majority (∼77%) of traded fish is illegal. We utilized the model to assess the effect of policy interventions to improve the fishery's sustainability and explore the trade-offs between ecological, economic, and social goals. Scenario simulations showed that even significant increases (over 200%) in parameters proxying for policy interventions enabled only moderate improvements in ecological and social sustainability of the fishery at substantial economic cost. These results expose how unbalanced trader incentives are toward trading illegal over legal products in this fishery. Our model provides a novel tool for promoting sustainable wildlife trade in data-limited settings, which explicitly considers traders as critical players in wildlife markets. Sustainable wildlife trade requires incentivizing legal over illegal wildlife trade and consideration of the social, ecological, and economic impacts of interventions.     

Effects of Exposure Conditions on Antioxidant Depletion,Tensile Strength,and Creep Modulus of Corrugated HDPE Pipes Made With or Without Recycled Resins

Journal of Polymers and the Environment - The service lifetime of corrugated high-density polyethylene (HDPE) pipes used in transportation infrastructure applications has been the subject of...     

Metal particle seeding on urban surface samples

Environmental Science and Pollution Research - In order to estimate the resuspension of the particles empirically, it is necessary to carry out a homogeneous distribution of the particles on the...