Metamaterials Nanosensor for Label-free Analysis of Conformation and Molecular Interaction of Biomolecules

Analysis of molecular interaction and conformational dynamics of biomolecules is of paramount importance in understanding of their vital functions in complex biological systems, disease detection, and new drug development. Plasmonic biosensors based upon surface plasmon resonance and localized surface plasmon resonance have become the predominant workhorse for detecting accumulated biomass caused by molecular binding events. However, unlike surface-enhanced Raman spectroscopy (SERS), the plasmonic biosensors indeed are not suitable tools to interrogate vibrational signatures of conformational transitions required for biomolecules to interact. Here, we show that plasmonic metamaterials can offer two transducing channels for parallel acquisition of optical transmission and sensitive SERS spectra at the biointerface, simultaneously probing the conformational states and binding affinity of biomolecules, e.g. G-quadruplexes, in different environments (Fig. 1). We further demonstrate the use of the metamaterials for fingerprinting and detection of arginine-glycine-glycine domain of nucleolin, a cancer biomarker which specifically binds to a G-quadruplex, with the picomolar sensitivity. The dual-mode nanosensor will significantly contribute to unraveling the complexes of the conformational dynamics of biomolecules as well as to improving specificity of biodetection assays.

Label-free Analysis of Conformational Dynamics and Molecular Interaction of Biomolecules by Vis-NIR Metasensor

Analysis of binding recognition and conformation of biomolecules is of paramount important in understanding of their vital functions in complex biological systems. By enabling sub-wavelength light localization and strong local field enhancement, plasmonic biosensors have become dominant tools used for such analysis owing to their label-free and real-time attributes1,2. However, the plasmonic biosensors are not well-suited to provide information regarding conformation or chemical fingerprint of biomolecules. Here, we show that plasmonic metamaterials, consisting of periodic arrays of artificial split-ring resonators (SRRs)3, can enable capabilities of both sensing and fingerprinting of biomolecules. We demonstrate that by engineering geometry of individual SRRs, localized surface plasmon resonance (LSPR) frequency of the metamaterials could be tuned to visible-near infrared regimes (Vis-NIR) such that they possess high local field enhancement for surface-enhanced Raman scattering spectroscopy (SERS). This will provide the basis for the development of a dual mode label-free conformational-resolving and quantitative detection platform. We present here the ability of each sensing mode to independently detect binding adsorption and to identify different conformational states of Guanine (G)-rich DNA monolayers in different environment milieu. Also shown is the use of the nanosensor for fingerprinting and detection of Arginine-Glycine-Glycine (RGG) peptide binding to the G-quadruplex aptamer. The dual-mode nanosensor will significantly contribute to unraveling the complexes of the conformational dynamics of biomolecules as well as to improving specificity of biodetection assays that the conventional, population-averaged plasmonic biosensors cannot achieve.

Metamaterials-Based Label-Free Nanosensor for Conformation and Affinity Biosensing

Analysis of molecular interaction and conformational dynamics of biomolecules is of paramount importance in understanding of their vital functions in complex biological systems, disease detection, and new drug development. Plasmonic biosensors based upon surface plasmon resonance and localized surface plasmon resonance have become the predominant workhorse for detecting accumulated biomass caused by molecular binding events. However, unlike surface-enhanced Raman spectroscopy (SERS), the plasmonic biosensors indeed are not suitable tools to interrogate vibrational signatures of conformational transitions required for biomolecules to interact. Here, we show that highly tunable plasmonic metamaterials can offer two transducing channels for parallel acquisition of optical transmission and sensitive SERS spectra at the biointerface, simultaneously probing the conformational states and binding affinity of biomolecules, e.g. G-quadruplexes, in different environments. We further demonstrate the use of the metamaterials for fingerprinting and detection of arginine-glycine-glycine domain of nucleolin, a cancer biomarker which specifically binds to a G-quadruplex, with the picomolar sensitivity.

Adaptive Fusion Of Particle Filtering And Spatio-Temporal Motion Energy For Human Tracking

Object tracking is an active research area nowadays due to its importance in human computer interface, teleconferencing and video surveillance. However, reliable tracking of objects in the presence of occlusions, pose and illumination changes is still a challenging topic. In this paper, we introduce a novel tracking approach that fuses two cues namely colour and spatio-temporal motion energy within a particle filter based framework. We conduct a measure of coherent motion over two image frames, which reveals the spatio-temporal dynamics of the target. At the same time, the importance of both colour and motion energy cues is determined in the stage of reliability evaluation. This determination helps maintain the performance of the tracking system against abrupt appearance changes. Experimental results demonstrate that the proposed method outperforms the other state of the art techniques in the used test datasets.

Adaptive uncertain information fusion to enhance plan selection in BDI agent systems

Correctly modelling and reasoning with uncertain information from heterogeneous sources in large-scale systems is critical when the reliability is unknown and we still want to derive adequate conclusions. To this end, context-dependent merging strategies have been proposed in the literature. In this paper we investigate how one such context-dependent merging strategy (originally defined for possibility theory), called largely partially maximal consistent subsets (LPMCS), can be adapted to Dempster-Shafer (DS) theory. We identify those measures for the degree of uncertainty and internal conflict that are available in DS theory and show how they can be used for guiding LPMCS merging. A simplified real-world power distribution scenario illustrates our framework. We also briefly discuss how our approach can be incorporated into a multi-agent programming language, thus leading to better plan selection and decision making.

An Event Driven Fusion Approach for Enjoyment Recognition in Real-time

Social signals and interpretation of carried information is of high importance in Human Computer Interaction. Often used for affect recognition, the cues within these signals are displayed in various modalities. Fusion of multi-modal signals is a natural and interesting way to improve automatic classification of emotions transported in social signals. Throughout most present studies, uni-modal affect recognition as well as multi-modal fusion, decisions are forced for fixed annotation segments across all modalities. In this paper, we investigate the less prevalent approach of event driven fusion, which indirectly accumulates asynchronous events in all modalities for final predictions. We present a fusion approach, handling short-timed events in a vector space, which is of special interest for real-time applications. We compare results of segmentation based uni-modal classification and fusion schemes to the event driven fusion approach. The evaluation is carried out via detection of enjoyment-episodes within the audiovisual Belfast Story-Telling Corpus.

IHG-1 increases mitochondrial fusion and bioenergetic function

Induced in high glucose-1 (IHG-1) is a conserved mitochondrial protein associated with diabetic nephropathy (DN) that amplifies profibrotic transforming growth factor (TGF)-β1 signaling and increases mitochondrial biogenesis. Here we report that inhibition of endogenous IHG-1 expression results in reduced mitochondrial respiratory capacity, ATP production, and mitochondrial fusion. Conversely, overexpression of IHG-1 leads to increased mitochondrial fusion and also protects cells from reactive oxygen species-induced apoptosis. IHG-1 forms complexes with known mediators of mitochondrial fusion-mitofusins (Mfns) 1 and 2-and enhances the GTP-binding capacity of Mfn2, suggesting that IHG-1 acts as a guanine nucleotide exchange factor. IHG-1 must be localized to mitochondria to interact with Mfn1 and Mfn2, and this interaction is necessary for increased IHG-1-mediated mitochondrial fusion. Together, these findings indicate that IHG-1 is a novel regulator of both mitochondrial dynamics and bioenergetic function and contributes to cell survival following oxidant stress. We propose that in diabetic kidney disease increased IHG-1 expression protects cell viability and enhances the actions of TGF-β, leading to renal proximal tubule dedifferentiation, an important event in the pathogenesis of this devastating condition.

Evaluation of information extraction techniques to label extracted data from e-commerce web page

Automatically determining and assigning shared and meaningful text labels to data extracted from an e-Commerce web page is a challenging problem. An e-Commerce web page can display a list of data records, each of which can contain a combination of data items (e.g. product name and price) and explicit labels, which describe some of these data items. Recent advances in extraction techniques have made it much easier to precisely extract individual data items and labels from a web page, however, there are two open problems: 1. assigning an explicit label to a data item, and 2. determining labels for the remaining data items. Furthermore, improvements in the availability and coverage of vocabularies, especially in the context of e-Commerce web sites, means that we now have access to a bank of relevant, meaningful and shared labels which can be assigned to extracted data items. However, there is a need for a technique which will take as input a set of extracted data items and assign automatically to them the most relevant and meaningful labels from a shared vocabulary. We observe that the Information Extraction (IE) community has developed a great number of techniques which solve problems similar to our own. In this work-in-progress paper we propose our intention to theoretically and experimentally evaluate different IE techniques to ascertain which is most suitable to solve this problem.

Towards a general framework for information fusion

Depending on the representation setting, different combination rules have been proposed for fusing information from distinct sources. Moreover in each setting, different sets of axioms that combination rules should satisfy have been advocated, thus justifying the existence of alternative rules (usually motivated by situations where the behavior of other rules was found unsatisfactory). These sets of axioms are usually purely considered in their own settings, without in-depth analysis of common properties essential for all the settings. This paper introduces core properties that, once properly instantiated, are meaningful in different representation settings ranging from logic to imprecise probabilities. The following representation settings are especially considered: classical set representation, possibility theory, and evidence theory, the latter encompassing the two other ones as special cases. This unified discussion of combination rules across different settings is expected to provide a fresh look on some old but basic issues in information fusion.

Evidential Fusion for Gender Profiling

Gender profiling is a fundamental task that helps CCTV systems to
provide better service for intelligent surveillance. Since subjects being detected
by CCTVs are not always cooperative, a few profiling algorithms are proposed
to deal with situations when faces of subjects are not available, among which
the most common approach is to analyze subjects’ body shape information. In
addition, there are some drawbacks for normal profiling algorithms considered
in real applications. First, the profiling result is always uncertain. Second, for a
time-lasting gender profiling algorithm, the result is not stable. The degree of
certainty usually varies, sometimes even to the extent that a male is classified
as a female, and vice versa. These facets are studied in a recent paper [16] using
Dempster-Shafer theory. In particular, Denoeux’s cautious rule is applied for
fusion mass functions through time lines. However, this paper points out that if
severe mis-classification is happened at the beginning of the time line, the result
of applying Denoeux’s rule could be disastrous. To remedy this weakness,
in this paper, we propose two generalizations to the DS approach proposed in
[16] that incorporates time-window and time-attenuation, respectively, in applying
Denoeux’s rule along with time lines, for which the DS approach is a special
case. Experiments show that these two generalizations do provide better results
than their predecessor when mis-classifications happen.

Fusion of Static and Temporal Information for Threat Evaluation in Sensor Networks

In many CCTV and sensor network based intelligent surveillance systems, a number of attributes or criteria are used to individually evaluate the degree of potential threat of a suspect. The outcomes for these attributes are in general from analytical algorithms where data are often pervaded with uncertainty and incompleteness. As a result, such individual threat evaluations are often inconsistent, and individual evaluations can change as time elapses. Therefore, integrating heterogeneous threat evaluations with temporal influence to obtain a better overall evaluation is a challenging issue. So far, this issue has rarely be considered by existing event reasoning frameworks under uncertainty in sensor network based surveillance. In this paper, we first propose a weighted aggregation operator based on a set of principles that constraints the fusion of individual threat evaluations. Then, we propose a method to integrate the temporal influence on threat evaluation changes. Finally, we demonstrate the usefulness of our system with a decision support event modeling framework using an airport security surveillance scenario.

Relationships between EEA1 binding partners and their role in endosome fusion

Homotypic fusion between early endosomes requires the phosphatidylinositol 3-phosphate (PI3P)-binding protein, Early Endosomal Autoantigen 1 (EEA1). We have investigated the role of other proteins that interact with EEA1 in the fusion of early endosomes derived from Baby Hamster Kidney (BHK) cells. We confirm a requirement for syntaxin 13, but additionally show that the assay is equally sensitive to reagents specifically targeted against syntaxin 6. Binding of EEA1 to immobilised GST-syntaxin 6 and 13 was directly compared; only syntaxin 6 formed a stable complex with EEA1. Early endosome fusion requires the release of intravesicular calcium, and calmodulin plays a vital role in the fusion pathway, as judged by sensitivity to antagonists. We demonstrate that both EEA1 and syntaxin 13 interact with calmodulin. In the case of EEA1, binding to calmodulin requires an IQ domain, which is adjacent to a C-terminal FYVE domain that specifically binds to PI3P. We have assessed the influence of protein binding partners on EEA1 interaction with PI3P and find that both calmodulin and rab5-GTP are antagonistic to PI3P binding, whilst syntaxins 6 and 13 have no effect. These studies reveal a complex network of interactions between the proteins required for endosome fusion.