Confrontation of Developing Melanophore Bars of Dark and White Axolotls With Endogenous Dark-Embryo Mannose-Binding Lectin Correlates With Melanophore Bar Disruption

Lectins are carbohydrate-binding proteins suggested to be important in embryonic cell adhesion/differentiation. Dark and white axolotls contain an endogenous mannosebinding lectin that is especially prevalent during larval melanophore pattern formation (Martha et al., 1990). To determine if this lectin can alter melanophore patterning, lectin extracts have been isolated from Dark embryos by affinity chromatography. The main protein band is 44K on SDS-PAGE. Dark and white embryos at the early chromatophore migration stage have been confronted with Dark lectin or its nonmetabolized inhibitor, 2-deoxyglucose (2-DG). The barred melanophore pattern of both genotypes is disrupted by lectin or 2-DG treatment suggesting that endogenous mannose-binding lectin and its receptor participate in bar formation.     

Cuticular structure of the agnostine trilobite Homagnostus obesus

Study of the exoskeletal surface microstructure of specimens of Homagnostus obesus (Belt, 1867) from the Upper Cambrian of Sweden has given information about the structure of agnostine cuticle. It is likely that the very thin cuticle of agnostines (5-15 μm), unlike that of polymerid trilobites, was constructed only of a prismatic layer. The exoskeletons were strengthened by reticulation on the external surface, the ridges forming up to 15% of the total cuticle thickness. Pits on the visceral surface of the exoskeleton of H. obesus may have contained photoreceptors as their morphology is similar to that of the Nileus glabellar 'tubercle'. This would have allowed the animal to monitor changes in light intensity. Possible sensory receptors in other agnostine trilobites are reviewed. Most sense organs were positioned on the unmineralized ventral surface of the organism. □ Trilobita, Agnostina, exoskeleton. cuticle, micro-structure, sensory receptors.     

Diagnostic assessment of European gross primary production

We present an approach to estimate gross primary production (GPP) using a remotely sensed biophysical vegetation product (fraction of absorbed photosynthetically active radiation, FAPAR) from the European Commission Joint Research Centre (JRC) in conjunction with GPP estimates from eddy covariance measurement towers in Europe. By analysing the relationship between the cumulative growing season FAPAR and annual GPP by vegetation type, we find that the former can be used to accurately predict the latter. The root mean square error of prediction is of the order of 250 gC m⁻² yr⁻¹. The cumulative growing season FAPAR integrates over a number of effects relevant for GPP such as the length of the growing season, the vegetation's response to environmental conditions and the amount of light harvested that is available for photosynthesis. We corroborate the proposed GPP estimate (noted FAPAR-based productivity assessment+land cover, FPA+LC) on the continental scale with results from the MOD17+radiation-use efficiency model, an artificial neural network up-scaling approach (ANN) and the Lund–Potsdam–Jena managed Land biosphere model (LPJmL). The closest agreement of the mean spatial GPP pattern among the four models is between FPA+LC and ANN (R²= 0.74). At least some of the discrepancy between FPA-LC and the other models result from biases of meteorological forcing fields for MOD17+, ANN and LPJmL. Our analysis further implies that meteorological information is to a large degree redundant for GPP estimation when using the JRC-FAPAR. A major advantage of the FPA+LC approach presented in this paper lies in its simplicity and that it requires no additional meteorological input driver data that commonly introduce substantial uncertainty. We find that results from different data-oriented models may be robust enough to evaluate process-oriented models regarding the mean spatial pattern of GPP, while there is too little consensus among the diagnostic models for such purpose regarding inter-annual variability.