We have synthesized semiconductor and metal nanoparticles (NPs) in the constrained geometry of polymer microgels. We used electrostatically driven attraction between the ionic groups of the microgels and the precursor cations in the bulk liquid medium to introduce the cations in the interior of the microgel. In the second step, the cations in the microgel interior reacted with the anion (to obtain semiconductor NPs) or they were treated with a reducing agent (to obtain metal NPs). Good control over the size and the concentration of the NPs in the microgel particles was achieved by changing the composition of the corresponding microgel. The doped microgel spheres were heated at pH 4 above the volume‐transition temperature of the polymer to expel the water from the microsphere interior; then the polymer was encapsulated with a hydrophobic polymeric shell. Hybrid core–shell particles were used as the building blocks of the nanostructured material with properties of a photonic crystal.

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Data mining can be described as "making better use of data". Every human being is increasingly faced with unmanageable amounts of data, hence, data mining or knowledge discovery apparently affects all of us. It is therefore recognized as one of the key research areas. Ideally, we would like to develop techniques for "making better use of any kind of data for any purpose". However, we argue that this goal is too demanding yet. It may sometimes be more promising to develop techniques applicable to specific data and with a specific goal in mind. In this paper, we describe such an application driven data mining system.Our aim is to predict stock markets using information contained in articles published on the Web. Mostly textual articles appearing in the leading and influential financial newspapers are taken as input. From those articles the daily closing values of major stock market indices in Asia, Europe and America are predicted. Textual statements contain not only the effect (e.g. the stocks plummet) but also why it happened (e.g. because of weakness in the dollar and consequently a weakening of the treasury bonds). Exploiting textual information in addition to numeric time series data increases the quality of the input. Hence improved predictions are expected. The forecasts are available real-time via www.cs.ust.hk/~beat/Predict daily at 7:45 am Hong Kong time. Hence all predictions are ready before Tokyo, Hong Kong and Singapore, the major Asian markets, start trading. The system's accuracy for this tremendously difficult but also extremely challenging application is highly promising.

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Polymorphisms of ss142022671, ss142022677 and ss142022679 in HRH4 are associated with AD.

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CNVs of the HRH4 gene are associated with SLE.

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