Development of Advanced Capillary Electrophoresis Techniques with UV and Mass Spectrometry Detection for Forensic, Pharmaceutical and Environmental Applications

Capillary electrophoresis (CE) is a modern analytical technique, which is electrokinetic separation generated by high voltage and taken place inside the small capillaries. In this dissertation, several advanced capillary electrophoresis methods are presented using different approaches of CE and UV and mass spectrometry are utilized as the detection methods. Capillary electrochromatography (CEC), as one of the CE modes, is a recent developed technique which is a hybrid of capillary electrophoresis and high performance liquid chromatography (HPLC). Capillary electrochromatography exhibits advantages of both techniques. In Chapter 2, monolithic capillary column are fabricated using in situ photoinitiation polymerization method. The column was then applied for the separation of six antidepressant compounds. Meanwhile, a simple chiral separation method is developed and presented in Chapter 3. Beta cycodextrin was utilized to achieve the goal of chiral separation. Not only twelve cathinone analytes were separated, but also isomers of several analytes were enantiomerically separated. To better understand the molecular information on the analytes, the TOF-MS system was coupled with the CE. A sheath liquid and a partial filling technique (PFT) were employed to reduce the contamination of MS ionization source. Accurate molecular information was obtained. It is necessary to propose, develop, and optimize new techniques that are suitable for trace-level analysis of samples in forensic, pharmaceutical, and environmental applications. Capillary electrophoresis (CE) was selected for this task, as it requires lower amounts of samples, it simplifies sample preparation, and it has the flexibility to perform separations of neutral and charged molecules as well as enantiomers. Overall, the study demonstrates the versatility of capillary electrophoresis methods in forensic, pharmaceutical, and environmental applications.

ADVANCED ISLANDING DETECTION METHODS FOR SINGLE PHASE GRID CONNECTED PV INVERTERS

Due to the growing concerns associated with fossil fuels, emphasis has been placed on clean and sustainable energy generation. This has resulted in the increase in Photovoltaics (PV) units being integrated into the utility system. The integration of PV units has raised some concerns for utility power systems, including the consequences of failing to detect islanding. Numerous methods for islanding detection have been introduced in literature. They can be categorized into local methods and remote methods. The local methods are categorically divided into passive and active methods. Active methods generally have smaller Non-Detection Zone (NDZ) but the injecting disturbances will slightly degrade the power quality and reliability of the power system. Slip Mode Frequency Shift Islanding Detection Method (SMS IDM) is an active method that uses positive feedback for islanding detection. In this method, the phase angle of the converter is controlled to have a sinusoidal function of the deviation of the Point of Common Coupling (PCC) voltage frequency from the nominal grid frequency. This method has a non-detection zone which means it fails to detect islanding for specific local load conditions. If the SMS IDM employs a different function other than the sinusoidal function for drifting the phase angle of the inverter, its non-detection zone could be smaller. In addition, Advanced Slip Mode Frequency Shift Islanding Detection Method (Advanced SMS IDM), which has been introduced in this thesis, eliminates the non-detection zone of the SMS IDM. In this method the parameters of SMS IDM change based on the local load impedance value. Moreover, the stability of the system is investigated by developing the dynamical equations of the system for two operation modes; grid connected and islanded mode. It is mathematically proven that for some loading conditions the nominal frequency is an unstable point and the operation frequency slides to another stable point, while for other loading conditions the nominal frequency is the only stable point of the system upon islanding occurring. Simulation and experimental results show the accuracy of the proposed methods in detection of islanding and verify the validity of the mathematical analysis.

The relationship of E. coli and total coliform culture to the quantitative molecular detection of various fecal indicators, sources, and pathogens in private drinking water wells

Water remains a predominant vector for human enteric pathogens not just for developing countries but also developed nations, where numerous infectious disease outbreaks, linked to the contamination of drinking water have been documented. Private drinking water wells are a source of drinking water that is largely unstudied even though a significant percentage of the population in Ontario relies on wells as their primary water source. As there exists little to no systematic surveillance for enteric infections or outbreaks related to well water sources, these individuals may be at higher risk of waterborne infectious diseases. The relationships between various fecal indicators in the water of private drinking water wells, including E. coli, Total Coliforms (TC) and Bacteroides, and enteric pathogens, including Campylobacter jejuni, Salmonella spp., and Shiga toxin producing E. coli, were studied. Convenience private well water samples collected from various regions of interest during the summer of 2014 underwent membrane filtration and culture to determine quantities of E. coli and TC colony forming units. 289 E. coli positive and 230 TC-only waters were successfully analyzed by individual qPCR assays for the aforementioned enteric pathogens. Microbial source tracking methods targeted to specific Bacteroides were used to determine the source of fecal contamination as either human or bovine. The source of fecal contamination varied by geographic region and is thought to be due to such things as differences in septic tank density and underlying geology, among others. Fecal indicators, E. coli and Bacteroides, were significantly correlated. E. coli as measured by qPCR was more strongly correlated to both total and human-specific Bacteroides genetic markers than culturable E. coli. Lastly, 1.9% of samples showed molecular evidence of contamination with enteric pathogens. Although low, this finding is significant given the limited volume of water available for testing, and suggests a potential health risk to consumers. Knowing the extent of contamination, as well as the biologic source, can better inform risk assessment and the development of potential intervention strategies for private well water in specific regions of Ontario.

Communication-efficient decentralized sequential detection

The problem of decentralized sequential detection is studied in this thesis, where local sensors are memoryless, receive independent observations, and no feedback from the fusion center. In addition to traditional criteria of detection delay and error probability, we introduce a new constraint: the number of communications between local sensors and the fusion center. This metric is able to reflect both the cost of establishing communication links as well as overall energy consumption over time. A new formulation for communication-efficient decentralized sequential detection is proposed where the overall detection delay is minimized with constraints on both error probabilities and the communication cost. Two types of problems are investigated based on the communication-efficient formulation: decentralized hypothesis testing and decentralized change detection. In the former case, an asymptotically person-by-person optimum detection framework is developed, where the fusion center performs a sequential probability ratio test based on dependent observations. The proposed algorithm utilizes not only reported statistics from local sensors, but also the reporting times. The asymptotically relative efficiency of proposed algorithm with respect to the centralized strategy is expressed in closed form. When the probabilities of false alarm and missed detection are close to one another, a reduced-complexity algorithm is proposed based on a Poisson arrival approximation. In addition, decentralized change detection with a communication cost constraint is also investigated. A person-by-person optimum change detection algorithm is proposed, where transmissions of sensing reports are modeled as a Poisson process. The optimum threshold value is obtained through dynamic programming. An alternative method with a simpler fusion rule is also proposed, where the threshold values in the algorithm are determined by a combination of sequential detection analysis and constrained optimization. In both decentralized hypothesis testing and change detection problems, tradeoffs in parameter choices are investigated through Monte Carlo simulations.

Design and standardization of PCR primers and protocols for detection of clonal immunoglobulin and T-cell receptor gene recombinations in suspect lymphoproliferations: report of the BIOMED-2 Concerted Action BMH4-CT98-3936.

In a European BIOMED-2 collaborative study, multiplex PCR assays have successfully been developed and standardized for the detection of clonally rearranged immunoglobulin (Ig) and T-cell receptor (TCR) genes and the chromosome aberrations t(11;14) and t(14;18). This has resulted in 107 different primers in only 18 multiplex PCR tubes: three VH-JH, two DH-JH, two Ig kappa (IGK), one Ig lambda (IGL), three TCR beta (TCRB), two TCR gamma (TCRG), one TCR delta (TCRD), three BCL1-Ig heavy chain (IGH), and one BCL2-IGH. The PCR products of Ig/TCR genes can be analyzed for clonality assessment by heteroduplex analysis or GeneScanning. The detection rate of clonal rearrangements using the BIOMED-2 primer sets is unprecedentedly high. This is mainly based on the complementarity of the various BIOMED-2 tubes. In particular, combined application of IGH (VH-JH and DH-JH) and IGK tubes can detect virtually all clonal B-cell proliferations, even in B-cell malignancies with high levels of somatic mutations. The contribution of IGL gene rearrangements seems limited. Combined usage of the TCRB and TCRG tubes detects virtually all clonal T-cell populations, whereas the TCRD tube has added value in case of TCRgammadelta(+) T-cell proliferations. The BIOMED-2 multiplex tubes can now be used for diagnostic clonality studies as well as for the identification of PCR targets suitable for the detection of minimal residual disease.

Sensitive and specific detection of viable Mycobacterium avium subsp. paratuberculosis in raw milk by the Peptide-mediated magnetic separation (PMS)-phage assay

This study describes further validation of a previously described Peptide-mediated magnetic separation (PMS)-Phage assay, and its application to test raw cows’ milk for presence of viable Mycobacterium avium subsp. paratuberculosis (MAP). The inclusivity and exclusivity of the PMS-phage assay were initially assessed, before the 50% limit of detection (LOD50) was determined and compared with those of PMS-qPCR (targeting both IS900 and f57) and PMS-culture. These methods were then applied in parallel to test 146 individual milk samples and 22 bulk tank milk samples from Johne’s affected herds. Viable MAP were detected by the PMS-phage assay in 31 (21.2%) of 146 individual milk samples (mean plaque count of 228.1 PFU/50 ml, range 6-948 PFU/50 ml), and 13 (59.1%) of 22 bulk tank milks (mean plaque count of 136.83 PFU/50 ml, range 18-695 PFU/50 ml). In contrast, only 7 (9.1%) of 77 individual milks and 10 (45.4%) of 22 bulk tank milks tested PMS-qPCR positive, and 17 (11.6%) of 146 individual milks and 11 (50%) of 22 bulk tank milks tested PMS-culture positive. The mean 50% limits of detection (LOD50) of the PMS-phage, PMS-IS900 qPCR and PMS-f57 qPCR assays, determined by testing MAP-spiked milk, were 0.93, 135.63 and 297.35 MAP CFU/50 ml milk, respectively. Collectively, these results demonstrate that, in our laboratory, the PMS-phage assay is a sensitive and specific method to quickly detect the presence of viable MAP cells in milk. However, due to its complicated, multi-step nature, the method would not be a suitable MAP screening method for the dairy industry.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background
It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.
Results
In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.
Conclusions
Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system

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.

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

LOPES-DOS-SANTOS, V. , CONDE-OCAZIONEZ, S. ; NICOLELIS, M. A. L. , RIBEIRO, S. T. , TORT, A. B. L. . Neuronal assembly detection and cell membership specification by principal component analysis. Plos One, v. 6, p. e20996, 2011.

The Effect of Mark Enhancement Techniques on the Subsequent Detection of Saliva

There appears to be a limited but growing body of research on the sequential analysis/treatment of multiple types of evidence. The development of an integrated forensic approach is necessary to maximise evidence recovery and to ensure that a particular treatment is not detrimental to other types of evidence. This study aims to assess the effect of latent and blood mark enhancement techniques (e.g. fluorescence, ninhydrin, acid violet 17, black iron-oxide powder suspension) on the subsequent detection of saliva. Saliva detection was performed by means of a presumptive test (Phadebas®) in addition to analysis by a rapid stain identification (RSID) kit test and confirmatory DNA testing. Additional variables included a saliva depletion series and a number of different substrates with varying porosities as well as different ageing periods. Examination and photography under white light and fluorescence was carried out prior to and after chemical enhancement All enhancement techniques (except Bluestar® Forensic Magnum luminol) employed in this study resulted in an improved visualisation of the saliva stains, although the inherent fluorescence of saliva was sometimes blocked after chemical treatment. The use of protein stains was, in general, detrimental to the detection of saliva. Positive results were less pronounced after the use of black iron-oxide powder suspension, cyanoacrylate fuming followed by BY40 and ninhydrin when compared to the respective positive controls. The application of Bluestar® Forensic Magnum luminol and black magnetic powder proved to be the least detrimental, with no significant difference between the test results and the positive controls. The use of non-destructive fluorescence examination provided good visualisation; however, only the first few marks in the depletion were observed. Of the samples selected for DNA analysis only depletion 1 samples contained sufficient DNA quantity for further processing using standard methodology. The 28 day delay between sample deposition and collection resulted in a 5-fold reduction in the amount of useable DNA. When sufficient DNA quantities were recovered, enhancement techniques did not have a detrimental effect on the ability to generate DNA profiles. This study aims to contribute to a strategy for maximising evidence recovery and efficiency for the detection of latent marks and saliva. The results demonstrate that most of the enhancement techniques employed in this study were not detrimental to the subsequent detection of saliva by means of presumptive, confirmative and DNA tests.

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Neuronal Assembly Detection and Cell Membership Specification by Principal Component Analysis

LOPES-DOS-SANTOS, V. , CONDE-OCAZIONEZ, S. ; NICOLELIS, M. A. L. , RIBEIRO, S. T. , TORT, A. B. L. . Neuronal assembly detection and cell membership specification by principal component analysis. Plos One, v. 6, p. e20996, 2011.

An Immune Inspired Approach to Anomaly Detection

The immune system provides a rich metaphor for computer security: anomaly detection that works in nature should work for machines. However, early artificial immune system approaches for computer security had only limited success. Arguably, this was due to these artificial systems being based on too simplistic a view of the immune system. We present here a second generation artificial immune system for process anomaly detection. It improves on earlier systems by having different artificial cell types that process information. Following detailed information about how to build such second generation systems, we find that communication between cells types is key to performance. Through realistic testing and validation we show that second generation artificial immune systems are capable of anomaly detection beyond generic system policies. The paper concludes with a discussion and outline of the next steps in this exciting area of computer security.

Optimizing Western Blots for the Detection of Endogenous α-Synuclein in the Enteric Nervous System

International audience

Detection of viral diseases in shrimp Litopenaeus vannamei in Iran with emphasis on white spot disease prevention by using marine alga extract

Nowadays, following was expanded shrimp breeding and culture; viral diseases have been main problem which threatened shrimp industry in the country. Therefore, shrimp samples were obtained from different stages of Litopenaeus vannmei life cycle (larval, post larval, juveniles, adults and broodstocks) based on clinical signs in the breeding center and shrimp farming from Bushehr, Khozestan and Sistan and Baluchestan provinces. Viral diseases were detected by PCR (Polymerase Chain Reaction), histopathology and transmission electron microscopy (TEM) methods. Results of the PCR were indicated present white spot virus (WSV) in juveniles, sub adults and adults shrimp with medium intensity from three provinces, but it was not showed in larval and post larval stages. Histopathological sections were indicated hypertrophy and basophilic Cowdry type A formation in nucleus cells of gill, haematopoietic, lymphoid and epithelial's cuticles and intestinal tissues which was associated with small vacuoles increased in B cells of hepatopancreas tissue of infection shrimps. Transmission electronic microscopic studies were demonstrated that the length and diameter virus was detected, respectively, 300 ± 20 nm and 75 ± 5 nm. Considerable, results of the PCR were only displayed IHHNV in juvenile, adult and broodstock shrimps from breeding and farming center of Bushehr province. The main lesion pathology was formed eosinophilic Cowdry type A in nucleus cells of gill, haematopoietic, lymphoid and epithelial's cuticles and intestinal tissues. Whereas penaeid shrimps are lack specific immune system, hence, in the present study was used of marine alga (Lurensia snideria) collected from along costal Persian Gulf of Bushehr province for viral diseases were prevented. Powder alga extract were added with a ratio of 1 % to shrimp diet. Total haemocyte count (THC) and total protein plasma (TPP) were increased after 5 days of oral administration diets. When shrimps were infected by with spot virus experimentally, THC and TPP gradually were increased in both two groups (shrimps fed with diet containing alga extract and without alga extract) after 48h. Nevertheless; THC, TPP and survival of shrimp fed with diet containing alga extract were more than shrimp control in 15 days. So, oral administration Lurensia snideria extract was capable prevention infected L. vannamei via stimulant specific immune system.