Protein identification by MALDI-TOF-MS peptide mapping: a new strategy

A new strategy for identifying proteins by MALDI-TOF-MS peptide mapping is reported. In contrast to current approaches, the strategy does not rely on a good relative or absolute mass accuracy as the criterion that discriminates false positive results. The protein sequence database is first searched for all proteins that match a minimum five of the submitted masses within the maximum expected relative errors when the default or externally determined calibration constants are used, for instance, +/-500 ppm. Typically, this search retrieves many thousand candidate sequences. Assuming initially that each of these is the correct protein, the relative errors of the matching peptide masses are calculated for each candidate sequence. Linear regression analysis is then performed of the calculated relative errors as a function of m/z for each candidate sequence, and the standard deviation to the regression is used to distinguish the correct sequence among the candidates. We show that this parameter is independent of whether the mass spectrometric data were internally or externally calibrated. The result is a search engine that renders internal spectrum calibration unnecessary and adapts to the quality of the raw data without user interference. This is made possible by a dynamic scoring algorithm, which takes into account the number of matching peptide masses, the percentage of the protein's sequence covered by these peptides and, as new parameter, the determined standard deviation. The lower the standard deviation, the less cleavage peptides are required for identification and vice versa. Performance of the new strategy is demonstrated and discussed. All necessary computing has been implemented in a computer program, free access to which is provided in the Internet.     

Process-Centered Software Engineering Environments, A Brief History and Future Challenges

Software engineering environments have a history of about two decades. Early environments provided support for small fragments of the software process (usually focusing on programming-in-the small). Then there was a trend towards support for more complete software processes (from early phases like requirements analysis and design down to testing and configuration management). Ten years ago the notion of process-centered software engineering environments initiated a new field in software engineering: software process research. The key idea is to use a model of a software process as input parameter for a software engineering environment. The environment is supposed to behave in accordance to the process model. Some aspects of this vision became true, others turned out to be of little practicability. In this article, we discuss the history of software engineering environments with a particular focus on process-centered software engineering environments (PCSEEs). We discuss the notion of distributed software processes (as one of the most substantial current trends in software process research) and we motivate the notion of a software process middleware which serves as basis of real-world software processes spread over various sites. In addition, we discuss some other trends in the software process research arena.     

A probabilistic model for binaural sound localization.

This paper proposes a biologically inspired and technically implemented sound localization system to robustly estimate the position of a sound source in the frontal azimuthal half-plane. For localization, binaural cues are extracted using cochleagrams generated by a cochlear model that serve as input to the system. The basic idea of the model is to separately measure interaural time differences and interaural level differences for a number of frequencies and process these measurements as a whole. This leads to two-dimensional frequency versus time-delay representations of binaural cues, so-called activity maps. A probabilistic evaluation is presented to estimate the position of a sound source over time based on these activity maps. Learned reference maps for different azimuthal positions are integrated into the computation to gain time-dependent discrete conditional probabilities. At every timestep these probabilities are combined over frequencies and binaural cues to estimate the sound source position. In addition, they are propagated over time to improve position estimation. This leads to a system that is able to localize audible signals, for example human speech signals, even in reverberating environments.     

Reaction behaviour of monomeric β-ketoesters

Summary Polymerizable enamines were synthesized by the reaction of 2-acetoacetoxyethyl methacrylate (AAEMA) with various aliphatic mono- and diamines. The enamines were characterized by elemental analyses, IR,¹H NMR and¹³C NMR spectroscopy. Radical polymerization of synthesized enamines yielded polymers with pendant enamine groups which were also prepared by the reaction of poly(AAEMA) with the corresponding amines.     

Superplasticity in eutectoid steel

A eutectoid steel exhibited abnormally large tensile extensibility in three quite dissimilar circumstances: i) micrograin plasticity, defined by a strain-rate sensitivity exponent m = 0.42, was found at 716°C for straining the ferrite-cementite aggregate at the rate è = 4.4 x 10^-3 min^-1, giving 133 pct elongation; ii) at a much greater strain rate, ε = 25 min^-1, superplasticity appeared in austenite strained at 927°C, giving 142 pct elongation; iii) a new type of transformation plasticity was predicted and experimentally verified: 490 pct elongation resulted from thermal cycling 21 times across Ae₁. Plastic stability analysis distinguishes it from micrograin plasticity by showing that it owes to strain-hard ening during plastically stable flow; hence, there are no restrictions ε or m. Furthermore, it is not necessary to perform the straining during the transformation, since the strain-hardening capacity can be regenerated by thermal cycling through the phase transformation if the transformation serves to recover the flow stress. Additional work showed that straining during austenitizing fails to increase m above pretransformation levels. Formerly Graduate Student, Syracuse University, Syracuse, N. Y.     

Human exposure to heavy metals in East Greenland I mercury

The concentration of mercury in 178 blood samples and 32 hair samples from the Angmagssalik district in East Greenland has been determined.For Greenlanders mercury concentrations are highly dependent on the amount of seal eaten. In the most heavily exposed group (eating more than six meals of seal per week), a significant positive correlation between blood mercury and age was demonstrated. No differences according to sex were demonstrated when the mean values were corrected for influence of age. In the most heavily exposed group, a mean value of 62.5 μg Hg/l was found, while in the group eating 1 meal of seal or less per week, the mean value was 22.2 μg Hg/l. In the control group consisting of Danes living temporarily in the district, the mean blood mercury concentration was 5.8 corresponding to the fact that they eat seal only occasionally.Hair mercury concentrations were found to correlate well with blood mercury concentrations (r = 0.9222). The hair/blood ratio was estimated to 289.It is concluded that the present relatively high mercury exposure in Greenland does not excert any immediate risk of intoxication to the adult population, but supplementary investigation on fetal exposure is needed.     

mRNA openers and closers: modulating AU-rich element-controlled mRNA stability by a molecular switch in mRNA secondary structure

Approximately 3 000 genes are regulated in a time-, tissue-, and stimulus-dependent manner by degradation or stabilization of their mRNAs. The process is mediated by interaction of AU-rich elements (AREs) in the mRNA's 3'-untranslated regions with trans-acting factors. AU-rich element-controlled genes of fundamentally different functional relevance depend for their activation on one positive regulator, HuR. Here we present a methodology to exploit this central regulatory process for specific manipulation of AU-rich element-controlled gene expression at the mRNA level. With a combination of single-molecule spectroscopy, computational biology, and molecular and cellular biochemistry, we show that mRNA recognition by HuR is dependent on the presentation of the sequence motif NNUUNNUUU in single-stranded conformation. The presentation of the HuR binding site in the mRNA secondary structure appears to act analogously to a regulatory on/off switch that specifically controls HuR access to mRNAs in cis. Based on this finding we present a methodology for manipulating ARE mRNA levels by actuating this conformational switch specifically in a target mRNA. Computationally designed oligonucleotides (openers) enhance the NNUUNNUUU accessibility by rearranging the mRNA conformation. Thereby they increase in vitro and endogenous HuR-mRNA complex formation which leads to specific mRNA stabilization (as demonstrated for TNFalpha and IL-2, respectively). Induced HuR binding both inside and outside the AU-rich element promotes functional IL-2 mRNA stabilization. This opener-induced mRNA stabilization mimics the endogenous IL-2 response to CD28 stimulation in human primary T-cells. We therefore propose that controlled modulation of the AU-rich element conformation by mRNA openers or closers allows message stabilization or destabilization in cis to be specifically triggered. The described methodology might provide a means for studying distinct pathways in a complex cellular network at the node of mRNA stability control. It allows ARE gene expression to be potentially silenced or boosted. This will be of particular value for drug-target validation, allowing the diseased phenotype to ameliorate or deteriorate. Finally, the mRNA openers provide a rational starting point for target-specific mRNA stability assays to screen for low-molecular-weight compounds acting as inhibitors or activators of an mRNA structure rearrangement.     

Stability-based validation of clustering solutions

Data clustering describes a set of frequently employed techniques in exploratory data analysis to extract "natural" group structure in data. Such groupings need to be validated to separate the signal in the data from spurious structure. In this context, finding an appropriate number of clusters is a particularly important model selection question. We introduce a measure of cluster stability to assess the validity of a cluster model. This stability measure quantifies the reproducibility of clustering solutions on a second sample, and it can be interpreted as a classification risk with regard to class labels produced by a clustering algorithm. The preferred number of clusters is determined by minimizing this classification risk as a function of the number of clusters. Convincing results are achieved on simulated as well as gene expression data sets. Comparisons to other methods demonstrate the competitive performance of our method and its suitability as a general validation tool for clustering solutions in real-world problems.     

Detection of skin cancer by classification of Raman spectra

Skin lesion classification based on in vitro Raman spectroscopy is approached using a nonlinear neural network classifier. The classification framework is probabilistic and highly automated. The framework includes a feature extraction for Raman spectra and a fully adaptive and robust feedforward neural network classifier. Moreover, classification rules learned by the neural network may be extracted and evaluated for reproducibility, making it possible to explain the class assignment. The classification performance for the present data set, involving 222 cases and five lesion types, was 80.5%+/-5.3% correct classification of malignant melanoma, which is similar to that of trained dermatologists based on visual inspection. The skin cancer basal cell carcinoma has a classification rate of 95.8%+/-2.7%, which is excellent. The overall classification rate of skin lesions is 94.8%+/-3.0%. Spectral regions, which are important for network classification, are demonstrated to reproduce. Small distinctive bands in the spectrum, corresponding to specific lipids and proteins, are shown to hold the discriminating information which the network uses to diagnose skin lesions.