Detection and quantification of Roundup Ready® soybean residues in sausage samples by conventional and real-time PCR

The increasing presence of products derived from genetically modified (GM) plants in human and animal diets has led to the development of detection methods to distinguish biotechnology-derived foods from conventional ones. The conventional and real-time PCR have been used, respectively, to detect and quantify GM residues in highly processed foods. DNA extraction is a critical step during the analysis process. Some factors such as DNA degradation, matrix effects, and the presence of PCR inhibitors imply that a detection or quantification limit, established for a given method, is restricted to a matrix used during validation and cannot be projected to any other matrix outside the scope of the method. In Brazil, sausage samples were the main class of processed products in which Roundup Ready® (RR) soybean residues were detected. Thus, the validation of methodologies for the detection and quantification of those residues is absolutely necessary. Sausage samples were submitted to two different methods of DNA extraction: modified Wizard and the CTAB method. The yield and quality were compared for both methods. DNA samples were analyzed by conventional and real-time PCR for the detection and quantification of Roundup Ready® soybean in the samples. At least 200 ng of total sausage DNA was necessary for a reliable quantification. Reactions containing DNA amounts below this value led to large variations on the expected GM percentage value. In conventional PCR, the detection limit varied from 1.0 to 500 ng, depending on the GM soybean content in the sample. The precision, performance, and linearity were relatively high indicating that the method used for analysis was satisfactory.

Use of real-time PCR to evaluate two DNA extraction methods from food

The DNA extraction is a critical step in Genetically Modified Organisms analysis based on real-time PCR. In this study, the CTAB and DNeasy methods provided good quality and quantity of DNA from the texturized soy protein, infant formula, and soy milk samples. Concerning the Certified Reference Material consisting of 5% Roundup Ready® soybean, neither method yielded DNA of good quality. However, the dilution test applied in the CTAB extracts showed no interference of inhibitory substances. The PCR efficiencies of lectin target amplification were not statistically different, and the coefficients of correlation (R²) demonstrated high degree of correlation between the copy numbers and the threshold cycle (Ct) values. ANOVA showed suitable adjustment of the regression and absence of significant linear deviations. The efficiencies of the p35S amplification were not statistically different, and all R² values using DNeasy extracts were above 0.98 with no significant linear deviations. Two out of three R² values using CTAB extracts were lower than 0.98, corresponding to lower degree of correlation, and the lack-of-fit test showed significant linear deviation in one run. The comparative analysis of the Ct values for the p35S and lectin targets demonstrated no statistical significant differences between the analytical curves of each target.

Real-time simulation of multibody systems with applications for working mobile vehicles

This dissertation describes an approach for developing a real-time simulation for working mobile vehicles based on multibody modeling. The use of multibody modeling allows comprehensive description of the constrained motion of the mechanical systems involved and permits real-time solving of the equations of motion. By carefully selecting the multibody formulation method to be used, it is possible to increase the accuracy of the multibody model while at the same time solving equations of motion in real-time. In this study, a multibody procedure based on semi-recursive and augmented Lagrangian methods for real-time dynamic simulation application is studied in detail. In the semirecursive approach, a velocity transformation matrix is introduced to describe the dependent coordinates into relative (joint) coordinates, which reduces the size of the generalized coordinates. The augmented Lagrangian method is based on usage of global coordinates and, in that method, constraints are accounted using an iterative process. A multibody system can be modelled as either rigid or flexible bodies. When using flexible bodies, the system can be described using a floating frame of reference formulation. In this method, the deformation mode needed can be obtained from the finite element model. As the finite element model typically involves large number of degrees of freedom, reduced number of deformation modes can be obtained by employing model order reduction method such as Guyan reduction, Craig-Bampton method and Krylov subspace as shown in this study The constrained motion of the working mobile vehicles is actuated by the force from the hydraulic actuator. In this study, the hydraulic system is modeled using lumped fluid theory, in which the hydraulic circuit is divided into volumes. In this approach, the pressure wave propagation in the hoses and pipes is neglected. The contact modeling is divided into two stages: contact detection and contact response. Contact detection determines when and where the contact occurs, and contact response provides the force acting at the collision point. The friction between tire and ground is modelled using the LuGre friction model, which describes the frictional force between two surfaces. Typically, the equations of motion are solved in the full matrices format, where the sparsity of the matrices is not considered. Increasing the number of bodies and constraint equations leads to the system matrices becoming large and sparse in structure. To increase the computational efficiency, a technique for solution of sparse matrices is proposed in this dissertation and its implementation demonstrated. To assess the computing efficiency, augmented Lagrangian and semi-recursive methods are implemented employing a sparse matrix technique. From the numerical example, the results show that the proposed approach is applicable and produced appropriate results within the real-time period.

Real-time adaptive control of ultra-fast laser scribing process with spectrometer online monitoring

There exist several researches and applications about laser welding monitoring and parameter control but not a single one have been created for controlling of laser scribing processes. Laser scribing is considered to be very fast and accurate process and thus it would be necessary to develop accurate turning and monitoring system for such a process. This research focuses on finding out whether it would be possible to develop real-time adaptive control for ultra-fast laser scribing processes utilizing spectrometer online monitoring. The thesis accurately presents how control code for laser parameter tuning is developed using National Instrument's LabVIEW and how spectrometer is being utilized in online monitoring. Results are based on behavior of the control code and accuracy of the spectrometer monitoring when scribing different steel materials. Finally control code success is being evaluated and possible development ideas for future are presented.

Real-time monitoring of laser scribing process utilizing high-speed camera

Currently, laser scribing is growing material processing method in the industry. Benefits of laser scribing technology are studied for example for improving an efficiency of solar cells. Due high-quality requirement of the fast scribing process, it is important to monitor the process in real time for detecting possible defects during the process. However, there is a lack of studies of laser scribing real time monitoring. Commonly used monitoring methods developed for other laser processes such a laser welding, are sufficient slow and existed applications cannot be implemented in fast laser scribing monitoring. The aim of this thesis is to find a method for laser scribing monitoring with a high-speed camera and evaluate reliability and performance of the developed monitoring system with experiments. The laser used in experiments is an IPG ytterbium pulsed fiber laser with 20 W maximum average power and Scan head optics used in the laser is Scanlab’s Hurryscan 14 II with an f100 tele-centric lens. The camera was connected to laser scanner using camera adapter to follow the laser process. A powerful fully programmable industrial computer was chosen for executing image processing and analysis. Algorithms for defect analysis, which are based on particle analysis, were developed using LabVIEW system design software. The performance of the algorithms was analyzed by analyzing a non-moving image from the scribing line with resolution 960x20 pixel. As a result, the maximum analysis speed was 560 frames per second. Reliability of the algorithm was evaluated by imaging scribing path with a variable number of defects 2000 mm/s when the laser was turned off and image analysis speed was 430 frames per second. The experiment was successful and as a result, the algorithms detected all defects from the scribing path. The final monitoring experiment was performed during a laser process. However, it was challenging to get active laser illumination work with the laser scanner due physical dimensions of the laser lens and the scanner. For reliable error detection, the illumination system is needed to be replaced.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

Development and use of a multiplex real-time quantitative polymerase chain reaction assay for detection and differentiation of Porcine circovirus-2 genotypes 2a and 2b in an epidemiological survey.

By the end of 2004, the Canadian swine population had experienced a severe 2 increase in the incidence of Porcine circovirus-associated disease (PCVAD), a problem that was 3 associated with the emergence of a new Porcine circovirus-2 genotype (PCV-2b), previously 4 unrecovered in North America. Thus it became important to develop a diagnostic tool that could 5 differentiate between the old and new circulating genotypes (PCV-2a and -2b, respectively). 6 Consequently, a multiplex real-time quantitative polymerase chain reaction (mrtqPCR) assay that 7 could sensitively and specifically identify and differentiate PCV-2 genotypes was developed. A 8 retrospective epidemiological survey that used the mrtqPCR assay was performed to determine if 9 cofactors could affect the risk of PCVAD. From 121 PCV-2–positive cases gathered for this 10 study, 4.13%, 92.56% and 3.31% were positive for PCV-2a, PCV-2b, and both genotypes, 11 respectively. In a data analysis using univariate logistic regressions, PCVAD compatible 12 (PCVAD/c) score was significantly associated with the presence of Porcine reproductive and 13 respiratory syndrome virus (PRRSV), PRRSV viral load, PCV-2 viral load, and PCV-2 14 immunohistochemistry (IHC) results. Polytomous logistic regression analysis revealed that 15 PCVAD/c score was affected by PCV-2 viral load (P = 0.0161) and IHC (P = 0.0128), but not by 16 the PRRSV variables (P > 0.9); suggesting that mrtqPCR in tissue is a reliable alternative to IHC. 17 Logistic regression analyses revealed that PCV-2 increased the odds ratio of isolating 2 major 18 swine pathogens of the respiratory tract, Actinobacillus pleuropneumoniae and Streptococcus 19 suis serotypes 1/2, 1, 2, 3, 4, and 7, which are serotypes commonly associated with clinical 20 diseases.

Data Security in Fault Tolerant Hard Real Time Systems- Use of Time Dependant Multiple Random Cipher Code

The present research problem is to study the existing encryption methods and to develop a new technique which is performance wise superior to other existing techniques and at the same time can be very well incorporated in the communication channels of Fault Tolerant Hard Real time systems along with existing Error Checking / Error Correcting codes, so that the intention of eaves dropping can be defeated. There are many encryption methods available now. Each method has got it's own merits and demerits. Similarly, many crypt analysis techniques which adversaries use are also available.

Permutation entropy based real-time chatter detection using audio signal in turning process

Machine tool chatter is an unfavorable phenomenon during metal cutting, which results in heavy vibration of cutting tool. With increase in depth of cut, the cutting regime changes from chatter-free cutting to one with chatter. In this paper, we propose the use of permutation entropy (PE), a conceptually simple and computationally fast measurement to detect the onset of chatter from the time series using sound signal recorded with a unidirectional microphone. PE can efficiently distinguish the regular and complex nature of any signal and extract information about the dynamics of the process by indicating sudden change in its value. Under situations where the data sets are huge and there is no time for preprocessing and fine-tuning, PE can effectively detect dynamical changes of the system. This makes PE an ideal choice for online detection of chatter, which is not possible with other conventional nonlinear methods. In the present study, the variation of PE under two cutting conditions is analyzed. Abrupt variation in the value of PE with increase in depth of cut indicates the onset of chatter vibrations. The results are verified using frequency spectra of the signals and the nonlinear measure, normalized coarse-grained information rate (NCIR).

Real Time Raga Detection and Analysis Using Computer

Department of Computer Applications, Cochin University of Science and Technology

Evaluation Of A Novel Method Of Real Time Computer Assisted Spine Surgery

In this paper the effectiveness of a novel method of computer assisted pedicle screw insertion was studied using testing of hypothesis procedure with a sample size of 48. Pattern recognition based on geometric features of markers on the drill has been performed on real time optical video obtained from orthogonally placed CCD cameras. The study reveals the exactness of the calculated position of the drill using navigation based on CT image of the vertebra and real time optical video of the drill. The significance value is 0.424 at 95% confidence level which indicates good precision with a standard mean error of only 0.00724. The virtual vision method is less hazardous to both patient and the surgeon

Femtosecond real-time probing of reactions. 12. Vectorial dynamics of transition states

Femtosecond time-resolved techniques with KETOF (kinetic energy time-of-flight) detection in a molecular beam are developed for studies of the vectorial dynamics of transition states. Application to the dissociation reaction of IHgI is presented. For this system, the complex [I---Hg---I](++)* is unstable and, through the symmetric and asymmetric stretch motions, yields different product fragments: [I---Hg---I](++)* -> HgI(X^2/sigma^+) + I(^2P_3/2) [or I*(^2P_l/2)] (1a); [I---Hg---I](++)* -> Hg(^1S_0) + I(^2P_3/2) + I(^2P_3/2) [or I* (^2P_1/2)] (1 b). These two channels, (1a) and (1b), lead to different kinetic energy distributions in the products. It is shown that the motion of the wave packet in the transition-state region can be observed by MPI mass detection; the transient time ranges from 120 to 300 fs depending on the available energy. With polarized pulses, the vectorial properties (transition moments alignment relative to recoil direction) are studied for fragment separations on the femtosecond time scale. The results indicate the nature of the structure (symmetry properties) and the correlation to final products. For 311-nm excitation, no evidence of crossing between the I and I* potentials is found at the internuclear separations studied. (Results for 287-nm excitation are also presented.) Molecular dynamics simulations and studies by laser-induced fluorescence support these findings.

Femtosecond real-time probing of reactions. 13. Multiphoton dynamics of IHgI

Real-time studies of the dynamics were performed on the reaction of HgI_2 in a molecular beam. Excitation was by either one or multi pump photons (311 nm), leading to two separate sets of dynamics, each of which could be investigated by a time-delayed probe laser (622 nm) that ionized the parent molecule and the fragments by REMPI processes. These dynamics were distinguished by combining the information from transients taken at each mass (HgI_2, HgI, I_2, Hg, and I) with the results of pump (and probe) power dependence studies on each mass. A method of plotting the slope of the intensity dependence against the pump-probe time delay proved essential. In the preceding publication, we detailed the dynamics of the reaction initiated by a one photon excitation to the A-continuum. Here, we present studies of higher-energy states. Multiphoton excitation accesses predissociative states of HgI_2, for which there are crossings into the symmetric and asymmetric stretch coordinates. The dynamics of these channels, which lead to atomic (I or Hg) and diatomic (HgI) fragments, are discussed and related to the nature of the intermediates along the reaction pathway.

Femtosecond real-time probing of reactions

Femtosecond reaction dynamics of OClO in a supersonic molecular beam are reported. The system is excited to the A^2A_2 state with a femtosecond pulse, covering a range of excitation in the symmetric stretch between v_1 = 17 to v_1 = 11 (308-352 nm). A time-delayed femtosecond probe pulse ionizes the OClO, and OClO^+ is detected. This ion has not been observed in previous experiments because of its ultrafast fragmentation. Transients are reported for the mass of the parent OClO as well as the mass of the ClO. Apparent biexponential decays are observed and related to the fragmentation dynamics: OClO+hv \rightarrow (OClO)^{(++)*} \rightarrow ClO+O \rightarrow Cl+O_2. Clusters of OClO with water (OClO)_n (H_2 0)_m with n from 1 to 3 and m from 0 to 3 are also observed. The dynamics of the fragmentation reveal the nuclear motions and the electronic coupling between surfaces. The time scale for bond breakage is in the range of 300-500 fs, depending on v_1; surface crossing to form new intermediates is a pathway for the two channels of fragmentation: ClO+O (primary) and Cl+O_2 (minor). Comparisons with results of ab initio calculations are made.

Adaptive Real-time Anomaly-based Intrusion Detection using Data Mining and Machine Learning Techniques

Die zunehmende Vernetzung der Informations- und Kommunikationssysteme führt zu einer weiteren Erhöhung der Komplexität und damit auch zu einer weiteren Zunahme von Sicherheitslücken. Klassische Schutzmechanismen wie Firewall-Systeme und Anti-Malware-Lösungen bieten schon lange keinen Schutz mehr vor Eindringversuchen in IT-Infrastrukturen. Als ein sehr wirkungsvolles Instrument zum Schutz gegenüber Cyber-Attacken haben sich hierbei die Intrusion Detection Systeme (IDS) etabliert. Solche Systeme sammeln und analysieren Informationen von Netzwerkkomponenten und Rechnern, um ungewöhnliches Verhalten und Sicherheitsverletzungen automatisiert festzustellen. Während signatur-basierte Ansätze nur bereits bekannte Angriffsmuster detektieren können, sind anomalie-basierte IDS auch in der Lage, neue bisher unbekannte Angriffe (Zero-Day-Attacks) frühzeitig zu erkennen. Das Kernproblem von Intrusion Detection Systeme besteht jedoch in der optimalen Verarbeitung der gewaltigen Netzdaten und der Entwicklung eines in Echtzeit arbeitenden adaptiven Erkennungsmodells. Um diese Herausforderungen lösen zu können, stellt diese Dissertation ein Framework bereit, das aus zwei Hauptteilen besteht. Der erste Teil, OptiFilter genannt, verwendet ein dynamisches "Queuing Concept", um die zahlreich anfallenden Netzdaten weiter zu verarbeiten, baut fortlaufend Netzverbindungen auf, und exportiert strukturierte Input-Daten für das IDS. Den zweiten Teil stellt ein adaptiver Klassifikator dar, der ein Klassifikator-Modell basierend auf "Enhanced Growing Hierarchical Self Organizing Map" (EGHSOM), ein Modell für Netzwerk Normalzustand (NNB) und ein "Update Model" umfasst. In dem OptiFilter werden Tcpdump und SNMP traps benutzt, um die Netzwerkpakete und Hostereignisse fortlaufend zu aggregieren. Diese aggregierten Netzwerkpackete und Hostereignisse werden weiter analysiert und in Verbindungsvektoren umgewandelt. Zur Verbesserung der Erkennungsrate des adaptiven Klassifikators wird das künstliche neuronale Netz GHSOM intensiv untersucht und wesentlich weiterentwickelt. In dieser Dissertation werden unterschiedliche Ansätze vorgeschlagen und diskutiert. So wird eine classification-confidence margin threshold definiert, um die unbekannten bösartigen Verbindungen aufzudecken, die Stabilität der Wachstumstopologie durch neuartige Ansätze für die Initialisierung der Gewichtvektoren und durch die Stärkung der Winner Neuronen erhöht, und ein selbst-adaptives Verfahren eingeführt, um das Modell ständig aktualisieren zu können. Darüber hinaus besteht die Hauptaufgabe des NNB-Modells in der weiteren Untersuchung der erkannten unbekannten Verbindungen von der EGHSOM und der Überprüfung, ob sie normal sind. Jedoch, ändern sich die Netzverkehrsdaten wegen des Concept drif Phänomens ständig, was in Echtzeit zur Erzeugung nicht stationärer Netzdaten führt. Dieses Phänomen wird von dem Update-Modell besser kontrolliert. Das EGHSOM-Modell kann die neuen Anomalien effektiv erkennen und das NNB-Model passt die Änderungen in Netzdaten optimal an. Bei den experimentellen Untersuchungen hat das Framework erfolgversprechende Ergebnisse gezeigt. Im ersten Experiment wurde das Framework in Offline-Betriebsmodus evaluiert. Der OptiFilter wurde mit offline-, synthetischen- und realistischen Daten ausgewertet. Der adaptive Klassifikator wurde mit dem 10-Fold Cross Validation Verfahren evaluiert, um dessen Genauigkeit abzuschätzen. Im zweiten Experiment wurde das Framework auf einer 1 bis 10 GB Netzwerkstrecke installiert und im Online-Betriebsmodus in Echtzeit ausgewertet. Der OptiFilter hat erfolgreich die gewaltige Menge von Netzdaten in die strukturierten Verbindungsvektoren umgewandelt und der adaptive Klassifikator hat sie präzise klassifiziert. Die Vergleichsstudie zwischen dem entwickelten Framework und anderen bekannten IDS-Ansätzen zeigt, dass der vorgeschlagene IDSFramework alle anderen Ansätze übertrifft. Dies lässt sich auf folgende Kernpunkte zurückführen: Bearbeitung der gesammelten Netzdaten, Erreichung der besten Performanz (wie die Gesamtgenauigkeit), Detektieren unbekannter Verbindungen und Entwicklung des in Echtzeit arbeitenden Erkennungsmodells von Eindringversuchen.