Supervised data analysis and reliability estimation with exemplary application for spectral data

The analysis and classification of data is a common task in multiple fields of experimental research such as bioinformatics, medicine, satellite remote sensing or chemometrics leading to new challenges for an appropriate analysis. For this purpose different machine learning methods have been proposed. These methods usually do not provide information about the reliability of the classification. This, however, is a common requirement in, e.g. medicine and biology. In this line the present contribution offers an approach to enhance classifiers with reliability estimates in the context of prototype vector quantization. This extension can also be used to optimize precision or recall of the classifier system and to determine items which are not classifiable. This can lead to significantly improved classification results. The method is exemplarily presented on satellite remote spectral data but is applicable to a wider range of data sets.     

Support vector classification of proteomic profile spectra based on feature extraction with the bi-orthogonal discrete wavelet transform

Automatic classification of high-resolution mass spectrometry data has increasing potential to support physicians in diagnosis of diseases like cancer. The proteomic data exhibit variations among different disease states. A precise and reliable classification of mass spectra is essential for a successful diagnosis and treatment. The underlying process to obtain such reliable classification results is a crucial point. In this paper such a method is explained and a corresponding semi automatic parameterization procedure is derived. Thereby a simple straightforward classification procedure to assign mass spectra to a particular disease state is derived. The method is based on an initial preprocessing stage of the whole set of spectra followed by the bi-orthogonal discrete wavelet transform (DWT) for feature extraction. The approximation coefficients calculated from the scaling function exhibit a high peak pattern matching property and feature a denoising of the spectrum. The discriminating coefficients, selected by the Kolmogorov–Smirnov test are finally used as features for training and testing a support vector machine with both a linear and a radial basis kernel. For comparison the peak areas obtained with the it ClinProt-System ¹ [33] were analyzed using the same support vector machines. The introduced approach was evaluated on clinical MALDI-MS data sets with two classes each originating from cancer studies. The cross validated error rates using the wavelet coefficients where better than those obtained from the peak areas².     

Evolving trees for the retrieval of mass spectrometry-based bacteria fingerprints

In this paper, we investigate the application of Evolving Trees (ET) for the analysis of mass spectrometric data of bacteria. Evolving Trees are extensions of self-organizing maps (SOMs) developed for hierarchical classification systems. Therefore, they are well suited for taxonomic problems such as the identification of bacteria. Here, we focus on three topics, an appropriate pre-processing and encoding of the spectra, an adequate data model by means of a hierarchical Evolving Tree and an interpretable visualization. First, the high dimensionality of the data is reduced by a compact representation. Here, we employ sparse coding, specifically tailored for the processing of mass spectra. In the second step, the topographic information which is expected in the fingerprints is used for advanced tree evaluation and analysis. We adapted the original topographic product for SOMs for ET to achieve a judgment of topography. Additionally we transferred the concept of U-matrix for evaluation of the separability of SOMs to their analog in ET. We demonstrate these extensions for two mass spectrometric data sets of bacteria fingerprints and show their classification and evaluation capabilities in comparison to state of the art techniques.     

圆片级测试MEMS器件的解决之道

MEMS产业发展十分迅速,但是人们常常忽视对其的早期测试。乍一看来,MEMS器件和传统IC器件的制造十分相似,但是,由于MEMS器件具有额外的机械部分（大多是可活动的）和封装,这些部分的成本通常占其总成本的大部分,因此MEMS器件的特性比传统IC器件要复杂得多,而且各不相同。     

A chip-based electrophoresis system with electrochemical detection and hydrodynamic injection

A simple capillary electrophoresis system in the planar format that uses a new, hydrodynamic injection principle is described. The system was realized with poly(dimethylsiloxane)-glass chips and microdisk electrodes for amperometric detection. Using a double-tee injector, no precise voltage control of the electrolyte reservoirs was needed, thus making the microchip CE system more user-friendly. The analytical characteristics of chip-based CE-EC were evaluated using ascorbic acid as the model analyte. The reproducibility of migration time and signal height was expressed by relative standard deviations of 1.2% and 5.1%, respectively (n = 5). The limit of detection for ascorbic acid was approximately 5 microM at a signal-to-noise ratio of 3. Practical application concerning the determination of physiologically relevant compounds such as noradrenaline and L-dopa are discussed.     

Development of an HPLC-UV/Vis method for the determination of dyes in a gasoline sample employing different pre-treatments

A simple method was developed for the determination of the dyes Solvent Blue 14 (SB-14), Solvent Orange 7 (SO-7) and Solvent Red 24 (SR-24), based on high-performance liquid chromatography (HPLC) coupled with UV-Vis detection. The dyes were separated employing a reverse phase C-18 column, with gradient elution using a mobile phase containing acetonitrile/water. In conjunction with suitable sample pre-treatment protocols, the method was applied to the quantification of the dyes in gasoline samples.     

Validation of land surface temperature derived from MSG/SEVIRI with in situ measurements at Gobabeb,Namibia

Land surface temperature (LST) derived from Meteosat Second Generation/?Spinning-Enhanced Visible and Infrared Imager MSG/SEVIRI data is an operational product of the Land Surface Analysis Satellite Applications Facility (LSA SAF). The LST has a temporal resolution of 15 minutes, a sampling distance of 3 km at nadir, and a targeted accuracy of better than 2 K. Gobabeb (Namibia) is one of Karlsruhe Institute of Technology's (KIT's) four dedicated stations for LST validation. In March 2010, a field survey was performed to characterize the Gobabeb site more closely. SAF LST and in situ LST obtained over a period of 3 days from additional measurements with a telescopic mast on the Namib gravel plains were in good agreement with each other (bias 1.0 K). For the same period, the bias between SAF LST and Gobabeb main station LST was even smaller (0.4 K). A mobile measurement system was set up by fixing the telescopic mast to a four-wheel drive. Around solar noon, LST from in situ measurements along a 40 km track and LST from Gobabeb main station had a bias of 0.4 K and a standard deviation of 1.2 K, which means that in situ LSTs at Gobabeb main station are representative for large parts of the gravel plains. Exploiting this relationship, 2 years of LST from MSG/SEVIRI were compared with in situ LST from Gobabeb main station. The magnitude of the monthly biases between the two data sets was generally less than 1.0 K and root mean square errors were below 1.5 K. Furthermore, the bias appears to exhibit a seasonality, which could be accounted for in future validation work.     

晶圆级MEMS测试

MEMS产业正处于一个飞速发展的阶段,但MEMS的早期测试仍是一个很大程度上被忽略的领域。MEMS的制造与经典的IC制造类似,但MEMS器件通常含有机械部分,因此封装占整个MEMS器件成本的大     

Simultaneous imaging and chemical attack of a single living cell within a confluent cell monolayer by means of scanning electrochemical microscopy

Epithelial cell monolayers from rat kidney were imaged by scanning electrochemical microscopy (SECM) with sub-micrometer resolution in both lateral and vertical direction. Platinum disk ultra-microelectrodes (UMEs) with effective electrode radii between 200 and 600 nm were operated in the constant-height mode. The quality of the recorded SECM images compare favorably with those of phase contrast and confocal laser scanning microscopy. Besides the acquisition of SECM images, the UME was used to selectively attack a single living cell within the monolayer ensemble. Hydroxide ions were locally generated in the vicinity of a single target cell by the UME. The increase in pH induced cell necrosis that was subsequently imaged by SECM. It could be clearly demonstrated that the single target cell was selectively affected, whereas the adjacent reference cells remained unchanged.