Dual Enlargement of Gold Nanoparticles: From Mechanism to Scanometric Detection of Pathogenic Bacteria

A mechanism of dual enlargement of gold nanoparticles (AuNPs) comprising two steps is described. In the first step, the AuNPs are enlarged by depositing Au atoms on their crystalline faces. In this process, the particles are not only enlarged but they are also observed to multiply: new Au nuclei are formed by the budding and division of the enlarged particles. In the second step, a silver enhancement is subsequently performed by the deposition of silver atoms on the enlarged and newly formed AuNPs to generate bimetallic Au@Ag core-shell structures. The dual nanocatalysis greatly enhances the electron density of the nanostructures, leading to a stronger intensity for colorimetric discrimination as well as better sensitivity for quantitative measurement. Based on this, a simple scanometric assay for the on-slide detection of the food-born pathogen Campylobacter jejuni is developed. After capturing the target bacteria, gold-tagged immunoprobes are added to create a signal on a solid substrate. The signal is then amplified by the dual enlargement process, resulting in a strong color intensity that can easily be recognized by the unaided eye, or measured by an inexpensive flatbed scanner. In this paper, dual nanocatalysis is reported for the first time. It provides a valuable mechanistic insight into the development of a simple and cost-effective detection format.

AN IMPROVED SCANOMETRIC IMMUNOASSAY BASED ON DUAL ENLARGEMENT OF GOLD NANOPARTICLES FOR RAPID AND LOW COST PATHOGEN DETECTION

In this study, we introduce a dual enlargement of gold nanoparticles (AuNPs) for the scanometric detection of pathogenic
bacteria. After capturing the target bacteria (Campylobacter jejuni cells), the gold immunoprobes were added to create signal on a solid substrate. The signal was then amplified dually by a gold growth process and a silver enhancement resulting in stronger intensity which can easily be recognized by an unaided eye, or measured by an inexpensive flatbed scanner. The dual-enhanced nanocatalysis is herein reported for the first time, it provides valuable insight into the development of a rapid, simple and cost-effective detection format.

Preparation of Highly Stable OEG derivatives-functionalized AuNPs and Its Application in LSPR-based Detection of PSA/ACT Complex

The zero-length crosslinker EDC has been widely used to make amide bonds between carboxylic acid and amine groups for bioconjugation because no residues remain in the crosslinked protein. During the conjugation process, EDC activates the carboxyl groups (negatively charged) and forms an unstable amine-reactive intermediate (positively charged). However, the process turns to be a problematic issue if it is applied to modify carboxyl-functionalized and –stabilized Au nanoparticles (AuNPs) due to the fact that the negatively repulsive forces which help to stabilize the AuNPs were disrupted leading to the colloid aggregation. Therefore, to modify the negatively carboxyl-terminated AuNPs while their stability can be maintained yet, we assume that functionalization of the AuNPs using 02 kinds of negatively charged groups which one serves as a linking agent, and the other one plays a role of negative charge maintainer could overcome the impediment.

In this study, the colloidal gold nanoparticles were synthesized by Turkevitch’s method, and then their surface was rationally functionalized with different molar ratios of HS(CH2)11(OCH2CH2)6OCH2COOH and HS(CH2)11(OCH2CH2)3OH (OEG6-COOH/OEG3-OH) by self assembling technique. As a result, the most appropriate molar ratio was found to be 1:10, and the AuNP aggregation was prevented not only in the activation process by EDC but also in the present of high concentration of NaCl as well as over in a wide pH range. This is the first time that extremely stable OEG derivatives-functionalized Au nanoparticles for protein bioconjugation using EDC chemistry is reported, and the results open the door for covalent bioconjugation of AuNPs in biological applications.

Optical coherence tomography (OCT) for detection of macular oedema in patients with diabetic retinopathy

Background

Diabetic macular oedema (DMO) is a thickening of the central retina, or the macula, and is associated with long-term visual loss in people with diabetic retinopathy (DR). Clinically significant macular oedema (CSMO) is the most severe form of DMO. Almost 30 years ago, the Early Treatment Diabetic Retinopathy Study (ETDRS) found that CSMO, diagnosed by means of stereoscopic fundus photography, leads to moderate visual loss in one of four people within three years. It also showed that grid or focal laser photocoagulation to the macula halves this risk. Recently, intravitreal injection of antiangiogenic drugs has also been used to try to improve vision in people with macular oedema due to DR.Optical coherence tomography (OCT) is based on optical reflectivity and is able to image retinal thickness and structure producing cross-sectional and three-dimensional images of the central retina. It is widely used because it provides objective and quantitative assessment of macular oedema, unlike the subjectivity of fundus biomicroscopic assessment which is routinely used by ophthalmologists instead of photography. Optical coherence tomography is also used for quantitative follow-up of the effects of treatment of CSMO.

Objectives

To determine the diagnostic accuracy of OCT for detecting DMO and CSMO, defined according to ETDRS in 1985, in patients referred to ophthalmologists after DR is detected. In the update of this review we also aimed to assess whether OCT might be considered the new reference standard for detecting DMO.

Search methods

We searched the Cochrane Database of Systematic Reviews (CDSR), the Database of Abstracts of Reviews of Effects (DARE), the Health Technology Assessment Database (HTA) and the NHS Economic Evaluation Database (NHSEED) (The Cochrane Library 2013, Issue 5), Ovid MEDLINE, Ovid MEDLINE In-Process and Other Non-Indexed Citations, Ovid MEDLINE Daily, Ovid OLDMEDLINE (January 1946 to June 2013), EMBASE (January 1950 to June 2013), Web of Science Conference Proceedings Citation Index - Science (CPCI-S) (January 1990 to June 2013), BIOSIS Previews (January 1969 to June 2013), MEDION and the Aggressive Research Intelligence Facility database (ARIF). We did not use any date or language restrictions in the electronic searches for trials. We last searched the electronic databases on 25 June 2013. We checked bibliographies of relevant studies for additional references.

Selection Criteria

We selected studies that assessed the diagnostic accuracy of any OCT model for detecting DMO or CSMO in patients with DR who were referred to eye clinics. Diabetic macular oedema and CSMO were diagnosed by means of fundus biomicroscopy by ophthalmologists or stereophotography by ophthalmologists or other trained personnel.

Data collection and analysis

Three authors independently extracted data on study characteristics and measures of accuracy. We assessed data using random-effects hierarchical sROC meta-analysis models.

Main results

We included 10 studies (830 participants, 1387 eyes), published between 1998 and 2012. Prevalence of CSMO was 19% to 65% (median 50%) in nine studies with CSMO as the target condition. Study quality was often unclear or at high risk of bias for QUADAS 2 items, specifically regarding study population selection and the exclusion of participants with poor quality images. Applicablity was unclear in all studies since professionals referring patients and results of prior testing were not reported. There was a specific 'unit of analysis' issue because both eyes of the majority of participants were included in the analyses as if they were independent.In nine studies providing data on CSMO (759 participants, 1303 eyes), pooled sensitivity was 0.78 (95% confidence interval (CI) 0.72 to 0.83) and specificity was 0.86 (95% CI 0.76 to 0.93). The median central retinal thickness cut-off we selected for data extraction was 250 µm (range 230 µm to 300 µm). Central CSMO was the target condition in all but two studies and thus our results cannot be applied to non-central CSMO.Data from three studies reporting accuracy for detection of DMO (180 participants, 343 eyes) were not pooled. Sensitivities and specificities were about 0.80 in two studies and were both 1.00 in the third study.Since this review was conceived, the role of OCT has changed and has become a key ingredient of decision-making at all levels of ophthalmic care in this field. Moreover, disagreements between OCT and fundus examination are informative, especially false positives which are referred to as subclinical DMO and are at higher risk of developing clinical CSMO.

Authors' conclusions

Using retinal thickness thresholds lower than 300 µm and ophthalmologist's fundus assessment as reference standard, central retinal thickness measured with OCT was not sufficiently accurate to diagnose the central type of CSMO in patients with DR referred to retina clinics. However, at least OCT false positives are generally cases of subclinical DMO that cannot be detected clinically but still suffer from increased risk of disease progression. Therefore, the increasing availability of OCT devices, together with their precision and the ability to inform on retinal layer structure, now make OCT widely recognised as the new reference standard for assessment of DMO, even in some screening settings. Thus, this review will not be updated further.

Synchrophasor-Based Islanding Detection for Distributed Generation Systems Using Systematic Principal Component Analysis Approaches

Systematic principal component analysis (PCA) methods are presented in this paper for reliable islanding detection for power systems with significant penetration of distributed generations (DGs), where synchrophasors recorded by Phasor Measurement Units (PMUs) are used for system monitoring. Existing islanding detection methods such as Rate-of-change-of frequency (ROCOF) and Vector Shift are fast for processing local information, however with the growth in installed capacity of DGs, they suffer from several drawbacks. Incumbent genset islanding detection cannot distinguish a system wide disturbance from an islanding event, leading to mal-operation. The problem is even more significant when the grid does not have sufficient inertia to limit frequency divergences in the system fault/stress due to the high penetration of DGs. To tackle such problems, this paper introduces PCA methods for islanding detection. Simple control chart is established for intuitive visualization of the transients. A Recursive PCA (RPCA) scheme is proposed as a reliable extension of the PCA method to reduce the false alarms for time-varying process. To further reduce the computational burden, the approximate linear dependence condition (ALDC) errors are calculated to update the associated PCA model. The proposed PCA and RPCA methods are verified by detecting abnormal transients occurring in the UK utility network.

A pre-embedding immunogold approach for detection of synaptic endocytic proteins in situ

During the past decade, many molecular components of clathrin-mediated endocytosis have been identified and proposed to play various hypothetical roles in the process [Nat. Rev. Neurosci. 1 (2000) 161; Nature 422 (2003) 37]. One limitation to the evaluation of these hypotheses is the efficiency and resolution of immunolocalization protocols currently in use. In order to facilitate the evaluation of these hypotheses and to understand more fully the molecular mechanisms of clathrin-mediated endocytosis, we have developed a protocol allowing enhanced and reliable subcellular immunolocalization of proteins in synaptic endocytic zones in situ. Synapses established by giant reticulospinal axons in lamprey are used as a model system for these experiments. These axons are unbranched and reach up to 80-100 microm in diameter. Synaptic active zones and surrounding endocytic zones are established on the surface of the axonal cylinder. To provide access for antibodies to the sites of synaptic vesicle recycling, axons are lightly fixed and cut along their longitudinal axis. To preserve the ultrastructure of the synaptic endocytic zone, antibodies are applied without the addition of detergents. Opened axons are incubated with primary antibodies, which are detected with secondary antibodies conjugated to gold particles. Specimens are then post-fixed and processed for electron microscopy. This approach allows preservation of the ultrastructure of the endocytic sites during immunolabeling procedures, while simultaneously achieving reliable immunogold detection of proteins on endocytic intermediates. To explore the utility of this approach, we have investigated the localization of a GTPase, dynamin, on clathrin-coated intermediates in the endocytic zone of the lamprey giant synapse. Using the present immunogold protocol, we confirm the presence of dynamin on late stage coated pits [Nature 422 (2003) 37] and also demonstrate that dynamin is recruited to the coat of endocytic intermediates from the very early stages of the clathrin coat formation. Thus, our experiments show that the current pre-embedding immunogold method is a useful experimental tool to study the molecular mechanisms of synaptic vesicle recycling.

Detection of Bridge Dynamic Parameters using an Instrumented Vehicle

Highway structures such as bridges are subject to continuous degradation primarily due to ageing and environmental factors. A rational transport policy requires the monitoring of this transport infrastructure to provide adequate maintenance and guarantee the required levels of transport service and safety. In Europe, this is now a legal requirement - a European Directive requires all member states of the European Union to implement a Bridge Management System. However, the process is expensive, requiring the installation of sensing equipment and data acquisition electronics on the bridge. This paper investigates the use of an instrumented vehicle fitted with accelerometers on its axles to monitor the dynamic behaviour of bridges as an indicator of its structural condition. This approach eliminates the need for any on-site installation of measurement equipment. A simplified half-car vehicle-bridge interaction model is used in theoretical simulations to test the possibility of extracting the dynamic parameters of the bridge from the spectra of the vehicle accelerations. The effect of vehicle speed, vehicle mass and bridge span length on the detection of the bridge dynamic parameters are investigated. The algorithm is highly sensitive to the condition of the road profile and simulations are carried out for both smooth and rough profiles

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.

Semantic feature-based visual attention model for pedestrian detection

Objective
Pedestrian detection under video surveillance systems has always been a hot topic in computer vision research. These systems are widely used in train stations, airports, large commercial plazas, and other public places. However, pedestrian detection remains difficult because of complex backgrounds. Given its development in recent years, the visual attention mechanism has attracted increasing attention in object detection and tracking research, and previous studies have achieved substantial progress and breakthroughs. We propose a novel pedestrian detection method based on the semantic features under the visual attention mechanism.
Method
The proposed semantic feature-based visual attention model is a spatial-temporal model that consists of two parts: the static visual attention model and the motion visual attention model. The static visual attention model in the spatial domain is constructed by combining bottom-up with top-down attention guidance. Based on the characteristics of pedestrians, the bottom-up visual attention model of Itti is improved by intensifying the orientation vectors of elementary visual features to make the visual saliency map suitable for pedestrian detection. In terms of pedestrian attributes, skin color is selected as a semantic feature for pedestrian detection. The regional and Gaussian models are adopted to construct the skin color model. Skin feature-based visual attention guidance is then proposed to complete the top-down process. The bottom-up and top-down visual attentions are linearly combined using the proper weights obtained from experiments to construct the static visual attention model in the spatial domain. The spatial-temporal visual attention model is then constructed via the motion features in the temporal domain. Based on the static visual attention model in the spatial domain, the frame difference method is combined with optical flowing to detect motion vectors. Filtering is applied to process the field of motion vectors. The saliency of motion vectors can be evaluated via motion entropy to make the selected motion feature more suitable for the spatial-temporal visual attention model.
Result
Standard datasets and practical videos are selected for the experiments. The experiments are performed on a MATLAB R2012a platform. The experimental results show that our spatial-temporal visual attention model demonstrates favorable robustness under various scenes, including indoor train station surveillance videos and outdoor scenes with swaying leaves. Our proposed model outperforms the visual attention model of Itti, the graph-based visual saliency model, the phase spectrum of quaternion Fourier transform model, and the motion channel model of Liu in terms of pedestrian detection. The proposed model achieves a 93% accuracy rate on the test video.
Conclusion
This paper proposes a novel pedestrian method based on the visual attention mechanism. A spatial-temporal visual attention model that uses low-level and semantic features is proposed to calculate the saliency map. Based on this model, the pedestrian targets can be detected through focus of attention shifts. The experimental results verify the effectiveness of the proposed attention model for detecting pedestrians.

Feature Selection for Anomaly Detection Using Optical Emission Spectroscopy

To maintain the pace of development set by Moore's law, production processes in semiconductor manufacturing are becoming more and more complex. The development of efficient and interpretable anomaly detection systems is fundamental to keeping production costs low. As the dimension of process monitoring data can become extremely high anomaly detection systems are impacted by the curse of dimensionality, hence dimensionality reduction plays an important role. Classical dimensionality reduction approaches, such as Principal Component Analysis, generally involve transformations that seek to maximize the explained variance. In datasets with several clusters of correlated variables the contributions of isolated variables to explained variance may be insignificant, with the result that they may not be included in the reduced data representation. It is then not possible to detect an anomaly if it is only reflected in such isolated variables. In this paper we present a new dimensionality reduction technique that takes account of such isolated variables and demonstrate how it can be used to build an interpretable and robust anomaly detection system for Optical Emission Spectroscopy data.