A new peak detection algorithm for MALDI mass spectrometry data based on a modified Asymmetric Pseudo-Voigt model

Background Mass Spectrometry (MS) is a ubiquitous analytical tool in biological research and is used to measure the mass-to-charge ratio of bio-molecules. Peak detection is the essential first step in MS data analysis. Precise estimation of peak parameters such as peak summit location and peak area are critical to identify underlying bio-molecules and to estimate their abundances accurately. We propose a new method to detect and quantify peaks in mass spectra. It uses dual-tree complex wavelet transformation along with Stein's unbiased risk estimator for spectra smoothing. Then, a new method, based on the modified Asymmetric Pseudo-Voigt (mAPV) model and hierarchical particle swarm optimization, is used for peak parameter estimation. Results Using simulated data, we demonstrated the benefit of using the mAPV model over Gaussian, Lorentz and Bi-Gaussian functions for MS peak modelling. The proposed mAPV model achieved the best fitting accuracy for asymmetric peaks, with lower percentage errors in peak summit location estimation, which were 0.17% to 4.46% less than that of the other models. It also outperformed the other models in peak area estimation, delivering lower percentage errors, which were about 0.7% less than its closest competitor - the Bi-Gaussian model. In addition, using data generated from a MALDI-TOF computer model, we showed that the proposed overall algorithm outperformed the existing methods mainly in terms of sensitivity. It achieved a sensitivity of 85%, compared to 77% and 71% of the two benchmark algorithms, continuous wavelet transformation based method and Cromwell respectively. Conclusions The proposed algorithm is particularly useful for peak detection and parameter estimation in MS data with overlapping peak distributions and asymmetric peaks. The algorithm is implemented using MATLAB and the source code is freely available at http://mapv.sourceforge.net

Event triggered adaptive differential modulation: A new method for traffic reduction in networked control systems

Congestion in a networked control system (NCS) has many undesirable effects that can make a control system unstable if severe enough. These include delays and packet drops. Therefore, reducing network traffic, including traffic generated by the NCS itself is a necessity. In event based control (EBC) a control signal is generated when a specific event is triggered such as the control error exceeding a predetermined threshold. When the rate of change of a variable is bounded, event triggering reduces the effective frequency in which control signals have to be generated when compared to a system with periodic sampling. This paper proposes a new method of event triggering called event triggered adaptive differential modulation (ETADM) that combines the bandwidth reduction strategies of event triggering and human speech coding techniques. The proposed method can be applied to nonlinear systems with a stabilizing feedback. In addition, it can be shown to be robust to packet drops. Stability for this method can be analyzed in terms of input to state stability (ISS) for a given bound of the signal reconstruction error

A comparative study of manufacturing practices and performance variables

The reported study was conducted to compare and contrast current manufacturing practices between two countries, Australia and Malaysia, and identify the practices that significantly influence their manufacturing performances. The results are based on data collected from surveys using a standard questionnaire in both countries. Evidence indicates that product quality and reliability is the main competitive factor for manufacturers. Maintaining a supplier rating system and regularly updating it with field failure and warranty data and making use of product data management are found to be effective manufacturing practices. In terms of the investigated manufacturing performance, Australian manufacturers are marginally ahead of their Malaysian counterparts. However, Malaysian manufacturers came out ahead on most dimensions of advanced quality and manufacturing practices, particularly in the adoption of product data management, effective supply chains and relationships with suppliers and customers.

Unsupervised learning for exploring MALDI imaging mass spectrometry 'omics' data

Matrix Assisted Laser Desorption Ionization-Imaging Mass Spectrometry (MALDI-IMS) is an emerging data acquisition technology in biological research. It has gained its popularity in `omics' sciences because of its ability to explore the spatial distributions of various bio-molecules in detail. The sheer volume of data generated through this technology and the often limited a priori knowledge about the molecular compositions of biological samples, call for efficient data analysis methods. In this paper, first we review the available computational methods for analyzing the high-dimensional imaging datasets highlighting their advantages and limitations. Then, we propose a more recent unsupervised method as a means of exploring MALDI-IMS data and demonstrate its competency by extracting hidden significant spatial distribution patterns of a rat brain imaging dataset. Finally, we explain the potential future advances of `omics' research associated with MALDI-IMS and the foreseeable challenges in analyzing the resultant data