Optical Imaging of Odor-Evoked Glomerular Activity Patterns in the Antennal Lobes of the Ant Camponotus rufipes

 Ants have a well developed olfactory sense, which they need both for the perception of environmental chemicals, and for a highly sophisticated intraspecific communication system based on pheromones. The question arises therefore as to how different odors are coded in the antennal lobe, the first central neuropil to process olfactory information. We measured odor-evoked activity patterns using in vivo neuropil calcium recording in the antennal lobe of the ant Camponotus rufipes. We found that (a) odors elicit focal activity spots (diameter ca. 20 μm) which most probably represent the olfactory glomeruli; (b) different odors are coded in odor specific patterns of such activated spots, and a particular spot can participate in the pattern for different odors; (c) calcium increased in the activated spots within the 2-s stimulation period and slowly declined thereafter. Received: 10 March 1999 / Accepted in revised form: 5 July 1999     

基于特征和知识的精密注塑模设计和制造系统

采用先进的CAD/CAM技术对传统模具设计制造方法进行改造,是提高模具设计制造质量和效率的关键.此文提出基于特征和知识的才法构造精密注塑模设计和制造系统,并介绍了系统的总体方案和系统包含的各模块功能.这一系统是浙江省科委八五攻关项目“精密注塑模设计和制造(CAD/CAM)技术”的核心.     

The early bee catches the flower - circadian rhythmicity influences learning performance in honey bees, <Emphasis Type="Italic">Apis mellifera</Emphasis>

Circadian rhythmicity plays an important role for many aspects of honey bees’ lives. However, the question whether it also affects learning and memory remained unanswered. To address this question, we studied the effect of circadian timing on olfactory learning and memory in honey bees Apis mellifera using the olfactory conditioning of the proboscis extension reflex paradigm. Bees were differentially conditioned to odours and tested for their odour learning at four different “Zeitgeber” time points. We show that learning behaviour is influenced by circadian timing. Honey bees perform best in the morning compared to the other times of day. Additionally, we found influences of the light condition bees were trained at on the olfactory learning. This circadian-mediated learning is independent from feeding times bees were entrained to, indicating an inherited and not acquired mechanism. We hypothesise that a co-evolutionary mechanism between the honey bee as a pollinator and plants might be the driving force for the evolution of the time-dependent learning abilities of bees.     

Using OpenMI and a Model MAP to Integrate WaterML2 and NetCDF Data Sources into Flood Modeling of Genoa,Italy

Extreme hydrometeorological events such as flash floods have caused considerable loss of life and damage to infrastructure over recent years. Flood events in the Mediterranean region between 1990 and 2006 caused over 4,500 fatalities and cost over €29 billion in damage, with Italy one of the worst affected countries. The Distributed Computing Infrastructure for Hydro‐Meteorology (DRIHM) project is a European initiative aiming at providing an open, fully integrated eScience environment for predicting, managing, and mitigating the risks related to such extreme weather phenomena. Incorporating both modeled and observational data sources, it enables seamless access to a set of computing resources with the objective of providing a collection of services for performing experiments with numerical models in meteorology, hydrology, and hydraulics. The purpose of this article is to demonstrate how this flexible modeling architecture has been constructed using a set of standards including the NetCDF and WaterML2 file formats, in‐memory coupling with OpenMI, controlled vocabularies such as CF Standard Names, ISO19139 metadata, and a Model MAP (Metadata, Adaptors, Portability) gateway concept for preparing numerical models for standardized use. Hydraulic results, including the impact to buildings and hazards to people, are given for the use cases of the severe and fatal flash floods, which occurred in Genoa, Italy in November 2011 and October 2014.