Filtering Methods for Mass Spectrometry-based Peptide Identification Processes

Tandem mass spectrometry (MS/MS) is a powerful tool for identifying peptide sequences. In a typical experiment, incorrect peptide identifications may result due to noise contained in the MS/MS spectra and to the low quality of the spectra. Filtering methods are widely used to remove the noise and improve the quality of the spectra before the subsequent spectra identification process. However, existing filtering methods often use features and empirically assigned weights. These weights may not reflect the reality that the contribution (reflected by weight) of each feature may vary from dataset to dataset. Therefore, filtering methods that can adapt to different datasets have the potential to improve peptide identification results. This thesis proposes two adaptive filtering methods; denoising and quality assessment, both of which improve efficiency and effectiveness of peptide identification. First, the denoising approach employs an adaptive method for picking signal peaks that is more suitable for the datasets of interest. By applying the approach to two tandem mass spectra datasets, about 66% of peaks (likely noise peaks) can be removed. The number of peptides identified later by peptide identification on those datasets increased by 14% and 23%, respectively, compared to previous work (Ding et al., 2009a). Second, the quality assessment method estimates the probabilities of spectra being high quality based on quality assessments of the individual features. The probabilities are estimated by solving a constraint optimization problem. Experimental results on two datasets illustrate that searching only the high-quality tandem spectra determined using this method saves about 56% and 62% of database searching time and loses 9% of high-quality spectra. Finally, the thesis suggests future research directions including feature selection and clustering of peptides

Electronic band gaps and transport properties in periodically alternating mono- and bi-layer graphene superlattices

We investigate the electronic band structure and transport properties of periodically alternating mono- and bi-layer graphene superlattices (MBLG SLs). In such MBLG SLs, there exists a zero-averaged wave vector (zero-$\overline{k}$) gap that is insensitive to the lattice constant. This zero-$\overline{k}$ gap can be controlled by changing both the ratio of the potential widths and the interlayer coupling coefficient of the bilayer graphene. We also show that there exist extra Dirac points; the conditions for these extra Dirac points are presented analytically. Lastly, we demonstrate that the electronic transport properties and the energy gap of the first two bands in MBLG SLs are tunable through adjustment of the interlayer coupling and the width ratio of the periodic mono- and bi-layer graphene.Comment: More discussion is added and the English is polished. Accepted for publication in EP

Functional Gradient Material of Ti-6Al-4V and γ-TiAl Fabricated by Electron Beam Selective Melting

Additive Manufacturing (AM) technologies are very promising in fabricating functionally graded materials. Electron Beam Selective Manufacturing (EBSM) is one widely used AM technology capable of fabricating a variety of materials especially titanium alloys. Previous studies on EBSM process were focused on the manufacturing of one single material. In this study, a novel EBSM process capable of building gradient structures with dual metal materials was developed. Ti6Al4V powders and Ti47Al2Cr2Nb powders were used to fabricate Ti3Al/TiAl and Ti6Al4V/Ti3Al dual metal structures. The chemical compositions, microstructure and micro-hardness of the dual material samples were investigated employing Optical Microscope (OM), Scanning Electronic Microscope (SEM), Electron Probe Micro-Analyzer (EPMA). Results showed that the thickness of the transition zone was about 300μm. The transition zone was free of cracks, and the chemical compositions exhibited a staircase-like change. The microstructure and chemical compositions in different regions were studied. Microhardness was affected by the microstructure. The microstructures turned out to be full lamellar at the TiAl region and basket-weave structure at the Ti3Al and Ti6Al4V region.Mechanical Engineerin

The influence of users' Dark Triad on knowledge contribution behaviour on social Q&A sites

The users' knowledge contribution behavior is the driving force for the sustainable development of the social Q&A sites. This kind of user behavior is affected by various factors, among which users' personality traits are the prominent ones. The dark triad is a theory on the dark side of personality. This article explores the influence and mechanism of users' dark triad on their knowledge contribution in social Q&A sites. A questionnaire survey was conducted on 301 users with experience in social Q&A sites. The survey data were then analyzed by hierarchical regression and Bootstrap analysis. The dark triad significantly affects knowledge contribution on social Q & A sites. Online self-disclosure plays a completely mediating role in the relationship between the dark triad and knowledge contribution. The relational psychological contract has a moderating role between online self-disclosure and knowledge contribution. This study argues that the dark triad has a positive effect on knowledge contribution behavior in socialized Q&A communities by constructing a model of mediated effects that are moderated. The dark triad shows its altruistic side in the context of social Q&A sites. The role of the dark triad in different knowledge-intensive contexts should be viewed dialectically in future research. Based on these findings, we put forward some suggestions for encouraging users' knowledge contribution behavior in the social media context.Peer Reviewe

An unsupervised machine learning method for assessing quality of tandem mass spectra

<p>Abstract</p> <p>Background</p> <p>In a single proteomic project, tandem mass spectrometers can produce hundreds of millions of tandem mass spectra. However, majority of tandem mass spectra are of poor quality, it wastes time to search them for peptides. Therefore, the quality assessment (before database search) is very useful in the pipeline of protein identification via tandem mass spectra, especially on the reduction of searching time and the decrease of false identifications. Most existing methods for quality assessment are supervised machine learning methods based on a number of features which describe the quality of tandem mass spectra. These methods need the training datasets with knowing the quality of all spectra, which are usually unavailable for the new datasets.</p> <p>Results</p> <p>This study proposes an unsupervised machine learning method for quality assessment of tandem mass spectra without any training dataset. This proposed method estimates the conditional probabilities of spectra being high quality from the quality assessments based on individual features. The probabilities are estimated through a constraint optimization problem. An efficient algorithm is developed to solve the constraint optimization problem and is proved to be convergent. Experimental results on two datasets illustrate that if we search only tandem spectra with the high quality determined by the proposed method, we can save about 56 % and 62% of database searching time while losing only a small amount of high-quality spectra.</p> <p>Conclusions</p> <p>Results indicate that the proposed method has a good performance for the quality assessment of tandem mass spectra and the way we estimate the conditional probabilities is effective.</p