Adhesion and growth of electrically-active cortical neurons on polyethyleimine patterns microprinted on PEO-PPO-PEO triblockcopolymer-coated hydrophobic surfaces

This paper describes the adhesion and growth of dissociated cortical neurons on chemically patterned surfaces over a time period of 30 days. The presence of neurons was demonstrated by measurement of spontaneous bioelectrical activity on a micropatterned multielectrode array. Chemical patterns were prepared with a combination of neurophobic layers of polyethylenoxide-polypropylenoxide-polyethylenoxide (PEO-PPO-PEO) triblockcopolymers adsorbed onto hydrophobic surfaces and neurophilic microprinted tracks of polyethylenimine (PEI). Results showed that commercially available PEO-PPO-PEO triblockcopolymers F108 and F127 (Synperonics, ICI) significantly reduced the adhesion of neuronal tissue when adsorbed on hydrophobic Polyimide (PI) and Fluorocarbon (FC) surfaces over a time period of eight days. In general, both F108- and F127-coated PI displayed equal or better neurophobic background properties after 30 days. Viability of neuronal tissue after 30 days on PEI microprinted F108- and F127-coated PI was comparable with relatively high viability factors between 0.9 and 1 (scale from 0 to 1). Summarizing, the strategy to combine the neurophobic adsorbed triblock-copolymers F108 and F127 onto hydrophobic surfaces with neurophilic microprinted PEI resulted in relatively long-term neuronal pattern preservation with high numbers of viable neurons present after 30 days

Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV)

Results of ecological models differ, to some extent, more from measured data than from empirical knowledge. Existing techniques for validation based on quantitative assessments sometimes cause an underestimation of the performance of models due to time shifts, accelerations and delays or systematic differences between measurement and simulation. However, for the application of such models it is often more important to reproduce essential patterns instead of seemingly exact numerical values. This paper presents techniques to identify patterns and numerical methods to measure the consistency of patterns between observations and model results. An orthogonal set of deviance measures for absolute, relative and ordinal scale was compiled to provide informations about the type of difference. Furthermore, two different approaches accounting for time shifts were presented. The first one transforms the time to take time delays and speed differences into account. The second one describes known qualitative criteria dividing time series into interval units in accordance to their main features. The methods differ in their basic concepts and in the form of the resulting criteria. Both approaches and the deviance measures discussed are implemented in an R package. All methods are demonstrated by means of water quality measurements and simulation data. The proposed quality criteria allow to recognize systematic differences and time shifts between time series and to conclude about the quantitative and qualitative similarity of patterns.

The induction of vitellogenin mRNA in avian liver

Vitellogenine is het precursor eiwit van de belangrijkste dooiereiwitten lipovitelline en fosvitine; vitellogenine wordt gemaakt in de lever van leggende hennen onder invloed van oestrogenen. In hanen kan de synthese van vitellogenine worden geïnduceerd door inspuiting van 17B-oestradiol. Na inspuiting van dit hormoon neemt de snelheid van vitellogenine synthese enige dagen linear toe. Deze toename valt samen met een toename in de hoeveelheid vitellogenine-synthetiserende ribosomen. Dit suggereert dat oestradiol een accumulatie van aktieve vitellogenine mRNA moleculen in hanelevercellen veroorzaakt.... Zie: Samanvatting

Statistical Methods for the Qualitative Assessment of Dynamic Models with Time Delay (R Package qualV)

Results of ecological models differ, to some extent, more from measured data than from empirical knowledge. Existing techniques for validation based on quantitative assessments sometimes cause an underestimation of the performance of models due to time shifts, accelerations and delays or systematic differences between measurement and simulation. However, for the application of such models it is often more important to reproduce essential patterns instead of seemingly exact numerical values. This paper presents techniques to identify patterns and numerical methods to measure the consistency of patterns between observations and model results. An orthogonal set of deviance measures for absolute, relative and ordinal scale was compiled to provide informations about the type of difference. Furthermore, two different approaches accounting for time shifts were presented. The first one transforms the time to take time delays and speed differences into account. The second one describes known qualitative criteria dividing time series into interval units in accordance to their main features. The methods differ in their basic concepts and in the form of the resulting criteria. Both approaches and the deviance measures discussed are implemented in an R package. All methods are demonstrated by means of water quality measurements and simulation data. The proposed quality criteria allow to recognize systematic differences and time shifts between time series and to conclude about the quantitative and qualitative similarity of patterns