Microarrays for the screening of allergen-specific IgE in human serum

The described in vitro test system for allergy diagnosis is based on microscope glass slides activated with (3-glycidyloxypropyl)trimethoxysilane. Allergen solutions are immobilized as small droplets (approximately 10 nL) on the activated glass slides with a piezoelectric arrayer. In contrast to other tests for specific IgE, such as Pharmacia CAP FEIA, AlaSTAT, or FAST, only a 25-microL serum sample is needed for the screening of allergen-specific IgE against a multitude of allergens and the test can be performed in less than 1 h. Compared with multiallergen dipstick screening tests (e.g., IgEquick, CMG Immunodot) based on multiallergen-coated nitrocellulose strips, the measurement of the microarray-based system can be performed automatically. The chemiluminescence intensities are detected with a sensitive CCD camera. Allergen extracts and recombinant/purified allergens (24 preparations) have been used on the same modified surface for the screening of allergen-specific IgE. With these disposable microarray slides, it is possible to distinguish between patients with and without elevated levels of allergen-specific IgE. Repeated measurements of serum samples demonstrated a sufficient reproducibility. Detection limits (microg/L) of 0.35 (r Betvl), 0.16 (PLA2), and 1.9 (Der p1) were achieved.     

DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints

Recently, constraint-based mining of itemsets for questions like find all frequent itemsets whose total price is at least $50 has attracted much attention. Two classes of constraints, monotone and antimonotone, have been very useful in this area. There exist algorithms that efficiently take advantage of either one of these two classes, but no previous algorithms can efficiently handle both types of constraints simultaneously. In this paper, we present DualMiner, the first algorithm that efficiently prunes its search space using both monotone and antimonotone constraints. We complement a theoretical analysis and proof of correctness of DualMiner with an experimental study that shows the efficacy of DualMiner compared to previous work.     

Fair webs

Fair webs are energy-minimizing curve networks. Obtained via an extension of cubic splines or splines in tension to networks of curves, they are efficiently computable and possess a variety of interesting applications. We present properties of fair webs and their discrete counterparts, i.e., fair polygon networks. Applications of fair curve and polygon networks include fair surface design and approximation under constraints such as obstacle avoidance or guaranteed error bounds, aesthetic remeshing, parameterization and texture mapping, and surface restoration in geometric models.     

Creating comprehensible regression models

In this paper we will present a novel approach to data-driven fuzzy modeling which aims to create highly accurate but also easily comprehensible models. This is achieved by a three-stage approach which separates the definition of the underlying fuzzy sets, the learning of the initial fuzzy model, and finally a local or global optimization of the resulting model. The benefit of this approach is that it allows to use a language comprising of comprehensible fuzzy predicates and to incorporate expert knowledge by defining problem specific fuzzy predicates. Furthermore, we achieve highly accurate results by applying a regularized optimization technique.     

Cu materials—From crystal chemistry to magnetic model compounds

Based on electronic structure calculations within the density functional theory, we report a systematic approach for the modelling of low-dimensional Cu^II materials. Combining concepts of crystal chemistry with ab initio-based magnetic models, we present a systematic study of recently discovered compounds. Our calculation results are in good agreement with thermodynamic and magnetic measurements, suggesting the presented approach as a well-directed route to explore the magnetic phase diagram of low-dimensional Cu^II systems.     

Identifying novel biomarkers through data mining—A realistic scenario?

In this article we discuss the requirements to use data mining of published proteomics datasets to assist proteomics-based biomarker discovery, the use of external data integration to solve the issue of inadequate small sample sizes and finally, we try to estimate the probability that new biomarkers will be identified through data mining alone.     

Using VNIR and SWIR field imaging spectroscopy for drought stress monitoring of beech seedlings

Drought stress is expected to become a recurrent problem for central European forests due to regional climate change. In order to study the effects on one of the most common tree species in Germany, the European beech (Fagus sylvatica), young potted beech trees were exposed to drought stress in a controlled experiment and their reaction was observed using visible/near-infrared (VNIR) and shortwave infrared (SWIR) field imaging spectroscopy cameras mounted on a platform. Equivalent water thickness (EWT) and leaf chlorophyll content (LCC) were measured and partial least squares (PLS) regression models were trained using these reference measurements and reflectance spectra of the trees. The models were applied to create maps of these properties with a spatial resolution in the millimetre range. These maps can be used to study the spatial distribution of EWT and LCC for single leaves or even for intra-leaf variability. Both properties can be estimated using only the VNIR sensor, but EWT estimation improves considerably by also incorporating SWIR data. LCC estimations with SWIR data alone do not work satisfactorily.