Omgaan met robotisering en digitalisering. We hoeven het wiel niet opnieuw uit te vinden

Contains fulltext : 147369.pdf (author's version ) (Open Access)Veel van de huidige discussies over robotisering en arbeidsmarkt doen denken aan discussies van vroeger: over ‘wetenschappelijk bedrijfsbeheer’ in de jaren tien en twintig van de vorige eeuw, over automatisering in de jaren zestig en over micro-elektronica in de jaren tachtig. De omstandigheden waren steeds anders, maar de dilemma’s min of meer dezelfde. We hebben daarvan kunnen leren dat ‘winnaars en verliezers’ niet zozeer het gevolg zijn van technologische ontwikkelingen alswel van hoe belanghebbenden arbeid en arbeidsmarkt organiseren. De gekozen oplossingen zijn steeds min of meer hetzelfde: functieverbetering, werkoverleg, competentieontwikkeling en medezeggenschap op organisatieniveau, sociale dialoog op sector- en landelijk niveau, aangevuld met stimuleringsregelingen van de overheid, onderzoek en maatschappelijk debat. En natuurlijk moet het onderwijs meebewegen en anticiperen. We hoeven dus niet het wiel opnieuw uit te vinden. Hoe de toekomst eruit zal zien is moeilijk te voorspellen. We kunnen dan ook maar beter inzetten op het creëren van het optimale innovatie- en veranderpotentieel. Een samenleving die dat breed heeft weten te realiseren kan elke ontwikkeling en verrassing aan

Application of Artificial Neural Networks in Pharmacokinetics

Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development

Sociale innovatie als inspiratie

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