Thomas Burger

Deciphering Thylakoid Sub-compartments using a Mass Spectrometry-based Approach

Photosynthesis has shaped atmospheric and ocean chemistries and probably changed the climate as well, as oxygen is released from water as part of the photosynthetic process. In photosynthetic eukaryotes, this process occurs in the chloroplast, an organelle containing the most abundant biological membrane, the thylakoids. The thylakoids of plants and some green algae are structurally inhomogeneous, consisting of two main domains: the grana, which are piles of membranes gathered by stacking forces, and the stroma-lamellae, which are unstacked thylakoids connecting the grana. The major photosynthetic complexes are unevenly distributed within these compartments because of steric and electrostatic constraints. Although proteomic analysis of thylakoids has been instrumental to define its protein components, no extensive proteomic study of subthylakoid localization of proteins in the BBY (grana) and the stroma-lamellae fractions has been achieved so far. To fill this gap, we performed a complete survey of the protein composition of these thylakoid subcompartments using thylakoid membrane fractionations. We employed semiquantitative proteomics coupled with a data analysis pipeline and manual annotation to differentiate genuine BBY and stroma-lamellae proteins from possible contaminants. About 300 thylakoid (or potentially thylakoid) proteins were shown to be enriched in either the BBY or the stroma-lamellae fractions. Overall, present findings corroborate previous observations obtained for photosynthetic proteins that used nonproteomic approaches. The originality of the present proteomic relies in the identification of photosynthetic proteins whose differential distribution in the thylakoid subcompartments might explain already observed phenomenon such as LHCII docking. Besides, from the present localization results we can suggest new molecular actors for photosynthesis-linked activities. For instance, most PsbP-like subunits being differently localized in stroma-lamellae, these proteins could be linked to the PSI-NDH complex in the context of cyclic electron flow around PSI. In addition, we could identify about a hundred new likely minor thylakoid (or chloroplast) proteins, some of them being potential regulators of the chloroplast physiology.

Toward an Axiomatic Definition of Conflict Between Belief Functions

Recently, the problem of measuring the conflict between two bodies of evidence represented by belief functions has known a regain of interest. In most works related to this issue, Dempsters rule plays a central role. In this paper, we propose to study the notion of conflict from a different perspective. We start by examining consistency and conflict on sets and extract from this settings basic properties that measures of consistency and conflict should have. We then extend this basic scheme to belief functions in different ways. In particular, we do not make any a priori assumption about sources (in)dependence and only consider such assumptions as possible additional information.

Mass-spectrometry-based spatial proteomics data analysis using pRoloc and pRolocdata.

Motivation: Experimental spatial proteomics, i.e. the high-throughput assignment of proteins to sub-cellular compartments based on quantitative proteomics data, promises to shed new light on many biological processes given adequate computational tools. Results: Here we present pRoloc, a complete infrastructure to support and guide the sound analysis of quantitative mass-spectrometry-based spatial proteomics data. It provides functionality for unsupervised and supervised machine learning for data exploration and protein classification and novelty detection to identify new putative sub-cellular clusters. The software builds upon existing infrastructure for data management and data processing. Availability: pRoloc is implemented in the R language and available under an open-source license from the Bioconductor project (http://www.bioconductor.org/). A vignette with a complete tutorial describing data import/export and analysis is included in the package. Test data is available in the companion package pRolocdata. Contact: lg390@cam.ac.uk

A Dempster-Shafer Theory based combination of handwriting recognition systems with multiple rejection strategies

Dempster-Shafer Theory (DST) is particularly efficient in combining multiple information sources providing incomplete, imprecise, biased, and conflictive knowledge. In this work, we focused on the improvement of the accuracy rate and the reliability of a HMM based handwriting recognition system, by the use of Dempster-Shafer Theory (DST). The system proceeds in two steps: First, an evidential combination method is proposed to finely combine the probabilistic outputs of the HMM classifiers. Second, a global post-processing module is proposed to improve the reliability of the system thanks to a set of acceptance/rejection decision strategies. In the end, an alternative treatment of the rejected samples is proposed using multi-stream HMM to improve the word recognition rate as well as the reliability of the recognition system, while not causing significant delays in the recognition process. Experiments carried out on two publically available word databases (RIMES for Latin script and IFN/ENIT for Arabic script) show the benefit of the proposed strategies. HighlightsA two level handwritten word recognition system is proposed.Combining Hidden Markov models classifier based on Dempster-Shafer Theory.A post-processing module based on different DST based acceptance/rejection strategies.We improve the accuracy rate and the reliability of a Handwriting recognition system.

A Foundation for Reliable Spatial Proteomics Data Analysis

Quantitative mass-spectrometry-based spatial proteomics involves elaborate, expensive, and time-consuming experimental procedures, and considerable effort is invested in the generation of such data. Multiple research groups have described a variety of approaches for establishing high-quality proteome-wide datasets. However, data analysis is as critical as data production for reliable and insightful biological interpretation, and no consistent and robust solutions have been offered to the community so far. Here, we introduce the requirements for rigorous spatial proteomics data analysis, as well as the statistical machine learning methodologies needed to address them, including supervised and semi-supervised machine learning, clustering, and novelty detection. We present freely available software solutions that implement innovative state-of-the-art analysis pipelines and illustrate the use of these tools through several case studies involving multiple organisms, experimental designs, mass spectrometry platforms, and quantitation techniques. We also propose sound analysis strategies for identifying dynamic changes in subcellular localization by comparing and contrasting data describing different biological conditions. We conclude by discussing future needs and developments in spatial proteomics data analysis.

DAPAR & ProStaR: software to perform statistical analyses in quantitative discovery proteomics

Abstract Summary DAPAR and ProStaR are software tools to perform the statistical analysis of label-free XIC-based quantitative discovery proteomics experiments. DAPAR contains procedures to filter, normalize, impute missing value, aggregate peptide intensities, perform null hypothesis significance tests and select the most likely differentially abundant proteins with a corresponding false discovery rate. ProStaR is a graphical user interface that allows friendly access to the DAPAR functionalities through a web browser. Availability and implementation DAPAR and ProStaR are implemented in the R language and are available on the website of the Bioconductor project (http://www.bioconductor.org/). A complete tutorial and a toy dataset are accompanying the packages.

A Consistency-Specificity Trade-Off to Select Source Behavior in Information Fusion

Combining pieces of information provided by several sources without or with little prior knowledge about the behavior of the sources is an old yet still important and rather open problem in the belief function theory. In this paper, we propose an approach to select the behavior of sources based on a very general and expressive fusion scheme, that has the important advantage of making clear the assumptions made about the sources. The selection process itself relies on two cornerstones that are the notions of specificity and consistency of a knowledge representation, and that we adapt to the considered fusion scheme. We illustrate our proposal on different examples and show that the proposed approach actually encompasses some important existing fusion strategies.

How to randomly generate mass functions

As Dempster–Shafer theory spreads in different application fields, and as mass functions are involved in more and more complex systems, the need for algorithms randomly generating mass functions arises. Such algorithms can be used, for instance, to evaluate some statistical properties or to simulate the uncertainty in some systems (e.g., data base content, training sets). As such random generation is often perceived as secondary, most of the proposed algorithms use straightforward procedures whose sample statistical properties can be difficult to characterize. Thus, although such algorithms produce randomly generated mass functions, they do not always produce what could be expected from them (for example, uniform sampling in the set of all possible mass functions). In this paper, we briefly review some well-known algorithms, explaining why their statistical properties are hard to characterize. We then provide relatively simple algorithms and procedures to perform efficient random generation of mass functions whose sampling properties are controlled.

Image and video for hearing impaired people

We present a global overview of image- and video-processing-based methods to help the communication of hearing impaired people. Two directions of communication have to be considered: from a hearing person to a hearing impaired person and vice versa. In this paper, firstly, we describe sign language (SL) and the cued speech (CS) language which are two different languages used by the deaf community. Secondly, we present existing tools which employ SL and CS video processing and recognition for the automatic communication between deaf people and hearing people. Thirdly, we present the existing tools for reverse communication, from hearing people to deaf people that involve SL and CS video synthesis.

Revisiting the Notion of Conflicting Belief Functions

The problem of conflict measurement between information sources knows a regain of interest. In most works related to this issue, Dempter’s rule plays a central role. In this paper, we propose to revisit conflict from a different perspective. We do not make a priori assumption about dependencies and start from the definition of conflicting sets, studying its possible extensions to the framework of belief functions.