Highly sensitive surface enhanced Raman scattering (SERS) detection platforms formed by large area self-assembled Au nanorod arrays

This thesis explores a new method to fabricate SERS detection platforms formed by large area self-assembled Au nanorod arrays. For the fabrication of these new SERS platforms a new droplet deposition method for the self-assembly of Au nanorods was developed. The method, based in the controlled evaporation of organic suspensions of Au nanorods, was used for the fabrication of horizontal and vertical arrays of Au nanorods over large areas (100μm2). The fabricated nanorods arrays showed a high degree of order measured by SEM and optical microscopy over mm2 areas, but unfortunately they detached from the support when immersed in any analyte solutions. In order to improve adhesion of arrays to the support and clean off residual organic matter, we introduced an additional stamping process. The stamping process allows the immobilization of the arrays on different flexible and rigid substrates, whose feasibility as SERS platforms were tested satisfactory with the model molecule 4ABT. Following the feasibility study, the substrates were used for the detection of the food contaminant Crystal Violet and the drug analogue Benzocaine as examples of recognition of health menaces in real field applications.

Sensing parasites: proteomic and advanced bio-detection alternatives

[EN] Parasitic diseases have a great impact in human and animal health. The gold standard for the diagnosis of the majority of parasitic infections is still conventional microscopy, which presents important limitations in terms of sensitivity and specificity and commonly requires highly trained technicians. More accurate molecular-based diagnostic tools are needed for the implementation of early detection, effective treatments and massive screenings with high-throughput capacities. In this respect, sensitive and affordable devices could greatly impact on sustainable control programmes which exist against parasitic diseases, especially in low income settings. Proteomics and nanotechnology approaches are valuable tools for sensing pathogens and host alteration signatures within microfluidic detection platforms. These new devices might provide novel solutions to fight parasitic diseases. Newly described specific parasite derived products with immune-modulatory properties have been postulated as the best candidates for the early and accurate detection of parasitic infections as well as for the blockage of parasite development. This review provides the most recent methodological and technological advances with great potential for biosensing parasites in their hosts, showing the newest opportunities offered by modern “-omics” and platforms for parasite detection and control.

Unique reporter-based sensor platforms to monitor signalling in cells

Introduction - In recent years much progress has been made in the development of tools for systems biology to study the levels of mRNA and protein, and their interactions within cells. However, few multiplexed methodologies are available to study cell signalling directly at the transcription factor level. Methods - Here we describe a sensitive, plasmid-based RNA reporter methodology to study transcription factor activation in mammalian cells, and apply this technology to profiling 60 transcription factors in parallel. The methodology uses two robust and easily accessible detection platforms; quantitative real-time PCR for quantitative analysis and DNA microarrays for parallel, higher throughput analysis. Findings - We test the specificity of the detection platforms with ten inducers and independently validate the transcription factor activation. Conclusions - We report a methodology for the multiplexed study of transcription factor activation in mammalian cells that is direct and not theoretically limited by the number of available reporters.

Sensing parasites: proteomic and advanced bio-detection alternatives

[EN] Parasitic diseases have a great impact in human and animal health. The gold standard for the diagnosis of the majority of parasitic infections is still conventional microscopy, which presents important limitations in terms of sensitivity and specificity and commonly requires highly trained technicians. More accurate molecular-based diagnostic tools are needed for the implementation of early detection, effective treatments and massive screenings with high-throughput capacities. In this respect, sensitive and affordable devices could greatly impact on sustainable control programmes which exist against parasitic diseases, especially in low income settings. Proteomics and nanotechnology approaches are valuable tools for sensing pathogens and host alteration signatures within micro fluidic detection platforms. These new devices might provide novel solutions to fight parasitic diseases. Newly described specific parasite derived products with immune-modulatory properties have been postulated as the best candidates for the early and accurate detection of parasitic infections as well as for the blockage of parasite development. This review provides the most recent methodological and technological advances with great potential for biosensing parasites in their hosts, showing the newest opportunities offered by modern “-omics” and platforms for parasite detection and control.

Assessment of a multiplexing high throughput immunochemical SPR biosensor in measuring multiple proteins on a single biosensor chip

Multiplexed immunochemical detection platforms offer the potential to decrease labour demands, increase sample throughput and decrease overall time to result. A prototype four channel multiplexed high throughput surface plasmon resonance biosensor was previously developed, for the detection of food related contaminants. A study focused on determining the instruments performance characteristics was undertaken. This was followed by the development of a multiplexed assay for four high molecular weight proteins. The protein levels were simultaneously evaluated in serum samples of 10-week-old veal calves (n = 24) using multiple sample preparation methods. Each of the biosensor's four channels were shown to be independent of one another and produced multiplexed within run repeatability (n = 6) ranging from 2.0 to 6.7%CV, for the four tested proteins, whilst between run reproducibility (n = 4) ranged from 1.5 to 8.9%CV. Four calibration curves were successfully constructed before serum sample preparation was optimised for each protein. Multiplexed concentration analysis was successfully performed on four channels revealing that each proteins concentration was consistent across the twenty-four tested animals. Signal reproducibility (n > 19) on a further long term study revealed coefficient of variation ranging from 1.1% to 7.3% and showed that the multiplexed assay was stable for at least 480 cycles. These findings indicate that the performance characteristics fall within the range of previously published data for singleplex optical biosensors and that the multiplexing biosensor is fit-for-purpose for simultaneous concentration analysis in many different types of applications such as the multiplexed detection of markers of growth-promoter abuse and multiplexed detection of residues of concern in food safety. © 2013 Elsevier B.V.

Validação de recursos de cargas viral do HIV-1 obtidos para insumos/kids/equipamentos de diferentes procedênias

Pós-graduação em Pesquisa e Desenvolvimento (Biotecnologia Médica) - FMB

Comparison of biosensor platforms for surface plasmon resonance based detection of paralytic shellfish toxins

Paralytic shellfish poisoning (PSP) toxins are produced by certain marine dinoflagellates and may accumulate in bivalve molluscs through filter feeding. The Mouse Bioassay (MBA) is the internationally recognised reference method of analysis, but it is prone to technical difficulties and regarded with increasing disapproval due to ethical reasons. As such, alternative methods are required. A rapid surface plasmon resonance (SPR) biosensor inhibition assay was developed to detect PSP toxins in shellfish by employing a saxitoxin polyclonal antibody (R895). Using an assay developed for and validated on the Biacore Q biosensor system, this project focused on transferring the assay to a high-throughput, Biacore T100 biosensor in another laboratory. This was achieved using a prototype PSP toxin kit and recommended assay parameters based on the Biacore Q method. A monoclonal antibody (GT13A) was also assessed. Even though these two instruments are based on SPR principles, they vary widely in their mode of operation including differences in the integrated mu-fluidic cartridges, autosampler system, and sensor chip compatibilities. Shellfish samples (n = 60), extracted using a simple, rapid procedure, were analysed using each platform, and results were compared to AOAC high performance liquid chromatography (HPLC) and MBA methods. The overall agreement, based on statistical 2 x 2 comparison tables, between each method ranged from 85% to 94.4% using R895 and 77.8% to 100% using GT13A. The results demonstrated that the antibody based assays with high sensitivity and broad specificity to PSP toxins can be applied to different biosensor platforms. (C) 2011 Elsevier B.V. All rights reserved.

Tethered DNA scaffolds on optical sensor platforms for detection of hipO gene from Campylobacter jejuni

DNA biosensors have gained increased attention over traditional diagnostic methods due to their fast and responsive operation and cost-effective design. The specificity of DNA biosensors relies on single-stranded oligonucleotide probes immobilized to a transduction platform. Here, we report the development of biosensors to detect the hippuricase gene (hipO) from Campylobacter jejuni using direct covalent coupling of thiol- and biotin-labeled single-stranded DNA (ssDNA) on both surface plasmon resonance (SPR) and diffraction optics technology (DOT, dotLab) transduction platforms. This is the first known report of the dotLab to detect targeted DNA. Application of 6-mercapto-1-hexanol as a spacer thiol for SPR gold surface created a self-assembled monolayer that removed unbound ssDNA and minimized non-specific detection. The detection limit of SPR sensors was shown to be 2.5 nM DNA while dotLab sensors demonstrated a slightly decreased detection limit of 5.0 nM (0.005 μM). It was possible to reuse the SPR sensor due to the negligible changes in sensor sensitivity (∼9.7 × 10 -7 ΔRU) and minimal damage to immobilized probes following use, whereas dotLab sensors could not be reused. Results indicated feasibility of optical biosensors for rapid and sensitive detection of the hipO gene of Campylobacter jejuni using specific ssDNA as a probe. © 2011 Elsevier B.V.

Airborne vision-based collision-detection system

Machine vision represents a particularly attractive solution for sensing and detecting potential collision-course targets due to the relatively low cost, size, weight, and power requirements of vision sensors (as opposed to radar and TCAS). This paper describes the development and evaluation of a real-time vision-based collision detection system suitable for fixed-wing aerial robotics. Using two fixed-wing UAVs to recreate various collision-course scenarios, we were able to capture highly realistic vision (from an onboard camera perspective) of the moments leading up to a collision. This type of image data is extremely scarce and was invaluable in evaluating the detection performance of two candidate target detection approaches. Based on the collected data, our detection approaches were able to detect targets at distances ranging from 400m to about 900m. These distances (with some assumptions about closing speeds and aircraft trajectories) translate to an advanced warning of between 8-10 seconds ahead of impact, which approaches the 12.5 second response time recommended for human pilots. We overcame the challenge of achieving real-time computational speeds by exploiting the parallel processing architectures of graphics processing units found on commercially-off-the-shelf graphics devices. Our chosen GPU device suitable for integration onto UAV platforms can be expected to handle real-time processing of 1024 by 768 pixel image frames at a rate of approximately 30Hz. Flight trials using manned Cessna aircraft where all processing is performed onboard will be conducted in the near future, followed by further experiments with fully autonomous UAV platforms.

Monitoring android for collaborative anomaly detection : a first architectural draft

Our daily lives become more and more dependent upon smartphones due to their increased capabilities. Smartphones are used in various ways, e.g. for payment systems or assisting the lives of elderly or disabled people. Security threats for these devices become more and more dangerous since there is still a lack of proper security tools for protection. Android emerges as an open smartphone platform which allows modification even on operating system level and where third-party developers first time have the opportunity to develop kernel-based low-level security tools. Android quickly gained its popularity among smartphone developers and even beyond since it bases on Java on top of "open" Linux in comparison to former proprietary platforms which have very restrictive SDKs and corresponding APIs. Symbian OS, holding the greatest market share among all smartphone OSs, was even closing critical APIs to common developers and introduced application certification. This was done since this OS was the main target for smartphone malwares in the past. In fact, more than 290 malwares designed for Symbian OS appeared from July 2004 to July 2008. Android, in turn, promises to be completely open source. Together with the Linux-based smartphone OS OpenMoko, open smartphone platforms may attract malware writers for creating malicious applications endangering the critical smartphone applications and owners privacy. Since signature-based approaches mainly detect known malwares, anomaly-based approaches can be a valuable addition to these systems. They base on mathematical algorithms processing data that describe the state of a certain device. For gaining this data, a monitoring client is needed that has to extract usable information (features) from the monitored system. Our approach follows a dual system for analyzing these features. On the one hand, functionality for on-device light-weight detection is provided. But since most algorithms are resource exhaustive, remote feature analysis is provided on the other hand. Having this dual system enables event-based detection that can react to the current detection need. In our ongoing research we aim to investigates the feasibility of light-weight on-device detection for certain occasions. On other occasions, whenever significant changes are detected on the device, the system can trigger remote detection with heavy-weight algorithms for better detection results. In the absence of the server respectively as a supplementary approach, we also consider a collaborative scenario. Here, mobile devices sharing a common objective are enabled by a collaboration module to share information, such as intrusion detection data and results. This is based on an ad-hoc network mode that can be provided by a WiFi or Bluetooth adapter nearly every smartphone possesses.

On the use of optical flow for scene change detection and description

We propose the use of optical flow information as a method for detecting and describing changes in the environment, from the perspective of a mobile camera. We analyze the characteristics of the optical flow signal and demonstrate how robust flow vectors can be generated and used for the detection of depth discontinuities and appearance changes at key locations. To successfully achieve this task, a full discussion on camera positioning, distortion compensation, noise filtering, and parameter estimation is presented. We then extract statistical attributes from the flow signal to describe the location of the scene changes. We also employ clustering and dominant shape of vectors to increase the descriptiveness. Once a database of nodes (where a node is a detected scene change) and their corresponding flow features is created, matching can be performed whenever nodes are encountered, such that topological localization can be achieved. We retrieve the most likely node according to the Mahalanobis and Chi-square distances between the current frame and the database. The results illustrate the applicability of the technique for detecting and describing scene changes in diverse lighting conditions, considering indoor and outdoor environments and different robot platforms.

Automatic UAV forced landing site detection using machine learning

The commercialization of aerial image processing is highly dependent on the platforms such as UAVs (Unmanned Aerial Vehicles). However, the lack of an automated UAV forced landing site detection system has been identified as one of the main impediments to allow UAV flight over populated areas in civilian airspace. This article proposes a UAV forced landing site detection system that is based on machine learning approaches including the Gaussian Mixture Model and the Support Vector Machine. A range of learning parameters are analysed including the number of Guassian mixtures, support vector kernels including linear, radial basis function Kernel (RBF) and polynormial kernel (poly), and the order of RBF kernel and polynormial kernel. Moreover, a modified footprint operator is employed during feature extraction to better describe the geometric characteristics of the local area surrounding a pixel. The performance of the presented system is compared to a baseline UAV forced landing site detection system which uses edge features and an Artificial Neural Network (ANN) region type classifier. Experiments conducted on aerial image datasets captured over typical urban environments reveal improved landing site detection can be achieved with an SVM classifier with an RBF kernel using a combination of colour and texture features. Compared to the baseline system, the proposed system provides significant improvement in term of the chance to detect a safe landing area, and the performance is more stable than the baseline in the presence of changes to the UAV altitude.

Whole exome sequencing is an efficient and sensitive method for detection of germline mutations in patients with phaeochromcytomas and paragangliomas

Background Genetic testing is recommended when the probability of a disease-associated germline mutation exceeds 10%. Germline mutations are found in approximately 25% of individuals with phaeochromcytoma (PCC) or paraganglioma (PGL); however, genetic heterogeneity for PCC/PGL means many genes may require sequencing. A phenotype-directed iterative approach may limit costs but may also delay diagnosis, and will not detect mutations in genes not previously associated with PCC/PGL. Objective To assess whether whole exome sequencing (WES) was efficient and sensitive for mutation detection in PCC/PGL. Methods Whole exome sequencing was performed on blinded samples from eleven individuals with PCC/PGL and known mutations. Illumina TruSeq™ (Illumina Inc, San Diego, CA, USA) was used for exome capture of seven samples, and NimbleGen SeqCap EZ v3.0 (Roche NimbleGen Inc, Basel, Switzerland) for five samples (one sample was repeated). Massive parallel sequencing was performed on multiplexed samples. Sequencing data were called using Genome Analysis Toolkit and annotated using annovar. Data were assessed for coding variants in RET, NF1, VHL, SDHD, SDHB, SDHC, SDHA, SDHAF2, KIF1B, TMEM127, EGLN1 and MAX. Target capture of five exome capture platforms was compared. Results Six of seven mutations were detected using Illumina TruSeq™ exome capture. All five mutations were detected using NimbleGen SeqCap EZ v3.0 platform, including the mutation missed using Illumina TruSeq™ capture. Target capture for exons in known PCC/PGL genes differs substantially between platforms. Exome sequencing was inexpensive (<$A800 per sample for reagents) and rapid (results <5 weeks from sample reception). Conclusion Whole exome sequencing is sensitive, rapid and efficient for detection of PCC/PGL germline mutations. However, capture platform selection is critical to maximize sensitivity.

Droplet digital PCR as a useful tool for the quantitative detection of Enterovirus 71

Hand, foot and mouth disease (HFMD) is a contagious viral disease that frequently affects infants and children and present with blisters and flu-like symptoms. This disease is caused by a group of enteroviruses such as enterovirus 71 (EV71) and coxsackievirus A16 (CA16). However, unlike other HFMD causing enteroviruses, EV71 have also been shown to be associated with more severe clinical manifestation such as aseptic meningitis, brainstem and cerebellar encephalitis which may lead to cardiopulmonary failure and death. Clinically, HFMD caused by EV71 is indistinguishable from other HFMD causing enteroviruses such as CA16. Molecular diagnosis methods such as the use of real-time PCR has been used commonly for the identification of EV71. In this study, two platforms namely the real-time PCR and the droplet digital PCR were compared for the detection quantitation of known EV71 viral copy number. The results reveal accurate and consistent results between the two platforms. In summary, the droplet digital PCR was demonstrated to be a promising technology for the identification and quantitation of EV71 viral copy number.

Raman spectroscopy techniques for detection of energetic materials at high and low concentration

This thesis presents the development of a rapid, sensitive and reproducible spectroscopic method for the detection of TNT in forensic and environmental applications. Simple nano sensors prepared by cost effective methods were utilized as sensitive platforms for the detection of TNT by surface enhanced Raman spectroscopy. The optimization of the substrate and the careful selection of a suitable recognition molecule contributed to the significant improvements of sensitive and selective targeting over current detection methods. The work presented in this thesis paves the way for effective detection and monitoring of explosives residues in law enforcement and environmental health applications.