Molecular dynamics at the receptor level of immunodominant myelin basic protein epitope 87-99 implicated in multiple sclerosis and its antagonists altered peptide ligands: triggering of immune response

This work reports molecular dynamics studies at the receptor level of the immunodominant myelin basic protein (MBP) epitope 87-99 implicated in multiple sclerosis, and its antagonists altered peptide ligands (APLs), namely [Arg91, Ala96] MBP87-99 and [Ala91,96] MBP87-99. The interaction of each peptide ligand with the receptor human leukocyte antigen HLA-DR2b was studied, starting from X-ray structure with pdb code: 1ymm. This is the first such study of APL-HLA-DR2b complexes, and hence the first attempt to gain a better understanding of the molecular recognition mechanisms that underlie TCR antagonism by these APLs. The amino acids His88 and Phe89 serve as T-cell receptor (TCR) anchors in the formation of the trimolecular complex TCR-peptide-HLA-DR2b, where the TCR binds in a diagonal, off-centered mode to the peptide-HLA complex. The present findings indicate that these two amino acids have a different orientation in the APLs [Arg91, Ala96] MBP87-99 and [Ala91,96] MBP87-99: His88 and Phe89 remain buried in HLA grooves and are not available for interaction with the TCR. We propose that this different topology could provide a possible mechanism of action for TCR antagonism.     

ReSpecT Nets: Towards an Analysis Methodology for ReSpecT Specifications

A key feature for infrastructures providing coordination services is the ability to define the behaviour of coordination abstractions according to the requirements identified at design-time. We take as a representative for this scenario the logic-based language ReSpecT (Reaction Specification Tuples), used to program the reactive behaviour of tuple centres. ReSpecT specifications are at the core of the engineering methodology underlying the TuCSoN infrastructure, and are therefore the “conceptual place” where formal methods can be fruitfully applied to guarantee relevant system properties.In this paper we introduce ReSpecT nets, a formalism that can be used to describe reactive behaviours that can succeed and fail, and that allows for an encoding to Petri nets with inhibitor arcs. ReSpecT nets are introduced to give a core model to a fragment of the ReSpecT language, and to pave the way for devising an analysis methodology including formal verification of safety and liveness properties. In particular, we provide a semantics to ReSpecT specifications through a mapping to ReSpecT nets. The potential of this approach for the analysis of ReSpecT specifications is discussed, presenting initial results for the analysis of safety properties.     

A low-power wireless video sensor node for distributed object detection

In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA, Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our hardware and software architecture, its performance and power characteristics. The case study considered in this paper is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing classification tasks locally.

Chemical cross-linking and mass spectrometry to map three-dimensional protein structures and protein-protein interactions

Closely related to studying the function of a protein is the analysis of its three-dimensional structure and the identification of interaction sites with its binding partners. An alternative approach to the high-resolution methods for three-dimensional protein structure analysis, such as X-ray crystallography and NMR spectroscopy, consists of covalently connecting two functional groups of the protein(s) under investigation. The location of the created cross-links imposes a distance constraint on the location of the respective side chains and allows one to draw conclusions on the three-dimensional structure of the protein or a protein complex. Recently, chemical cross-linking of proteins has been combined with a mass spectrometric analysis of the created cross-linked products. This review article describes the most popular cross-linking reagents for protein structure analysis and gives an overview of the different available strategies that employ chemical cross-linking and different mass spectrometric techniques. The challenges for mass spectrometry caused by the enormous complexity of the cross-linking reaction mixtures are emphasized. The various approaches described in the literature to facilitate the mass spectrometric detection of cross-linked products as well as computer software for data analyses are reviewed.     

Efficient additive kernels via explicit feature maps

Large scale nonlinear support vector machines (SVMs) can be approximated by linear ones using a suitable feature map. The linear SVMs are in general much faster to learn and evaluate (test) than the original nonlinear SVMs. This work introduces explicit feature maps for the additive class of kernels, such as the intersection, Hellinger's, and χ2 kernels, commonly used in computer vision, and enables their use in large scale problems. In particular, we: 1) provide explicit feature maps for all additive homogeneous kernels along with closed form expression for all common kernels; 2) derive corresponding approximate finite-dimensional feature maps based on a spectral analysis; and 3) quantify the error of the approximation, showing that the error is independent of the data dimension and decays exponentially fast with the approximation order for selected kernels such as χ2. We demonstrate that the approximations have indistinguishable performance from the full kernels yet greatly reduce the train/test times of SVMs. We also compare with two other approximation methods: Nystrom's approximation of Perronnin et al., which is data dependent, and the explicit map of Maji and Berg for the intersection kernel, which, as in the case of our approximations, is data independent. The approximations are evaluated on a number of standard data sets, including Caltech-101, Daimler-Chrysler pedestrians, and INRIA pedestrians.     

Multistage particle windows for fast and accurate object detection

The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.     

Ontological query answering under expressive Entity-Relationship schemata

The Entity-Relationship (ER) model is a fundamental tool for database design, recently extended and employed in knowledge representation and reasoning due to its expressiveness and comprehensibility. We address the problem of answering conjunctive queries under constraints representing schemata expressed in an extended version of the Entity-Relationship model. This extended model, called ER⁺, comprises is-a constraints among entities and relationships, plus functional and mandatory participation constraints. In particular, it allows for arbitrary permutations of the roles in is-a among relationships. A key notion that ensures high tractability in ER⁺ schemata is separability, i.e., the absence of interaction between the functional participation constraints and the other constructs of ER⁺. We provide a precise syntactic characterization of separable ER⁺ schemata by means of a necessary and sufficient condition. We present a complete complexity analysis of the conjunctive query answering problem under separable ER⁺ schemata, and also under several sublanguages of ER⁺. We show that the addition of so-called negative constraints does not increase the complexity of query answering. With such constraints, our model properly generalizes the most widely adopted tractable ontology languages, including those in the DL-Lite family.     

Counting clicks and beeps: Exploring numerosity based haptic and audio PIN entry

Haptic and audio cues now appear commonly in computer interfaces, partially due to inherent advantages such as their support for eyes-free interaction. Their invisible, unobservable nature also makes them ideal candidates for security interfaces in which users have to enter secret information such as passwords. In particular, researchers have explored this idea through the design of PIN entry authentication systems based on multi-modal combinations of visual and non-visual content or on the recognition of small sets of unimodal haptic or audio stimuli. This paper highlights the benefits and performance limitations of these approaches and introduces an alternative based on unimodal audio or haptic temporal numerosity – the ability to accurately and rapidly determine the number of cues presented in rapid temporal succession. In essence, in a numerosity interface, rather than recognizing distinct cues, users must count the number of times that a single cue occurs. In an iterative process of design and evaluation, three prototypes implementing this concept are presented and studies of their use reported. The results show the fastest PIN entry times and lowest error rates to be 8 s and 2%, figures that improve substantially on previous research. These results are attained while maintaining low levels of workload and substantial resistance to observation attack (as determined via camera attack security studies). In sum, this paper argues that unimodal audio and haptic numerosity is a valuable and relatively unexplored metaphor for non-visual input and demonstrates the validity of this claim in the demanding task of unobservable authentication systems.     

Self-Organising Maps: A new way to screen the level of satisfaction of dialysis patients

Evaluation of patient satisfaction has become an important indicator for assessing health care quality. Fresenius Medical Care (FME) as a global provider of dialysis services through its NephroCare network has a strong interest in monitoring patient satisfaction.The aim of the paper is to test and validate a methodology for detecting a residual area of low satisfaction in dialysis patients.The FME Patient Satisfaction Programme questionnaire was distributed to haemodialysis (HD) patients treated in 335 centers of its network. It contained 79 questions covering various satisfaction aspects regarding Dialysis Unit, Dialysis Arrangement, Nurses, Doctors, etc.To analyse the data provided by the questionnaire, the Self-Organising Map (SOM) method was used. SOM is a neural network model for clustering and projecting high-dimensional data into a low-dimensional space, preserving topological relationships of original high-dimensional data spaces.10,632 HD patients completed the questionnaire. Mean age was 63.05 ± 14.93 years with 56.69% males. Response rate was 66%. Overall level of satisfaction was 1.99 (range from ?3 to +3). On average patients were very satisfied with all issues. Nevertheless, a group of patients, around 60 years old, balanced gender ratio, whose level of satisfaction was lower than 1, were highlighted.In the NephroCare clinics patient satisfaction with service is rather high. While traditional analysis usually stops here, the SOM method allows identification of areas of potential improvement for specific patient groups.