Folate: Methods of analysis

Folates and its derivatives occur as polyglutamates in nature. The multiplicity of forms and the generally low levels in foods makes quantitative analysis of folate a difficult task. The assay of folates from foods generally involves three steps: liberation of folates from the cellular matrix; deconjugation from the polyglutamate to the mono and di-glutamate forms; and the detection of the biological activity or chemical concentration of the resulting folates. The detection methods used are the microbiological assay relying on the turbidimetric bacterial growth of Lactobacillus rhamnosus which is by far the most commonly used method; the HPLC and LC/MS techniques and bio-specific procedures. This review attempts to describe the methods along with the merits and demerits of using each of these methods.

Methods of Color Images Procession for Further Identification of the Object

The offered paper deals with the problems of color images preliminary procession. Among these are: interference control (local ones and noise) and extraction of the object from the background on the stage preceding the process of contours extraction. It was considered for a long time that execution of smoothing in segmentation through the boundary extraction is inadmissible, but the described methods and the obtained results evidence about expedience of using the noise control methods.

Comparison of ion pair and buffered mobile phase chromatography of haloacetic acids using direct and indirect UV and fluorescence detection

Reversed-phase high performance liquid chromatographic methods for the analysis of Haloacetic acids have been developed and compared to conventional direct detection methods. Haloacetic acids commonly found in drinking water, including monochloro-, dichloro-, bromo-, iodo- and trichloroacetic acids- have been studied. The ion pairing agent benzyltributylammonium ion was studied in detail using indirect UV and indirect fluorescence detection. Five different competing ions were evaluated to decrease analysis times and lower the detection limit by this new method. The direct detection method utilized an ammonium sulfate buffer and UV detection yielding a detection limit of 100 ppb. The indirect method developed has the advantage of being able to simultaneously analyze UV and non-UV absorbing ions and molecules but requires long equilibration times and demonstrated lower sensitivity than the direct method. ^

A behavior based approach to virus detection

Fast spreading unknown viruses have caused major damage on computer systems upon their initial release. Current detection methods have lacked capabilities to detect unknown viruses quickly enough to avoid mass spreading and damage. This dissertation has presented a behavior based approach to detecting known and unknown viruses based on their attempt to replicate. Replication is the qualifying fundamental characteristic of a virus and is consistently present in all viruses making this approach applicable to viruses belonging to many classes and executing under several conditions. A form of replication called self-reference replication, (SR-replication), has been formalized as one main type of replication which specifically replicates by modifying or creating other files on a system to include the virus itself. This replication type was used to detect viruses attempting replication by referencing themselves which is a necessary step to successfully replicate files. The approach does not require a priori knowledge about known viruses. Detection was accomplished at runtime by monitoring currently executing processes attempting to replicate. Two implementation prototypes of the detection approach called SRRAT were created and tested on the Microsoft Windows operating systems focusing on the tracking of user mode Win32 API system calls and Kernel mode system services. The research results showed SR-replication capable of distinguishing between file infecting viruses and benign processes with little or no false positives and false negatives. ^

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.^

A Behavior Based Approach to Virus Detection

Fast spreading unknown viruses have caused major damage on computer systems upon their initial release. Current detection methods have lacked capabilities to detect unknown virus quickly enough to avoid mass spreading and damage. This dissertation has presented a behavior based approach to detecting known and unknown viruses based on their attempt to replicate. Replication is the qualifying fundamental characteristic of a virus and is consistently present in all viruses making this approach applicable to viruses belonging to many classes and executing under several conditions. A form of replication called self-reference replication, (SR-replication), has been formalized as one main type of replication which specifically replicates by modifying or creating other files on a system to include the virus itself. This replication type was used to detect viruses attempting replication by referencing themselves which is a necessary step to successfully replicate files. The approach does not require a priori knowledge about known viruses. Detection was accomplished at runtime by monitoring currently executing processes attempting to replicate. Two implementation prototypes of the detection approach called SRRAT were created and tested on the Microsoft Windows operating systems focusing on the tracking of user mode Win32 API system calls and Kernel mode system services. The research results showed SR-replication capable of distinguishing between file infecting viruses and benign processes with little or no false positives and false negatives.

Development of a novel probe integrated with a micro-structured impedance sensor for the detection of breast cancer

The work described in this thesis focuses on the development of an innovative bioimpedance device for the detection of breast cancer using electrical impedance as the detection method. The ability for clinicians to detect and treat cancerous lesions as early as possible results in improved patient outcomes and can reduce the severity of the treatment the patient has to undergo. Therefore, new technology and devices are continually required to improve the specificity and sensitivity of the accepted detection methods. The gold standard for breast cancer detection is digital x-ray mammography but it has some significant downsides associated with it. The development of an adjunct technology to aid in the detection of breast cancers could represent a significant patient and economic benefit. In this project silicon substrates were pattern with two gold microelectrodes that allowed electrical impedance measurements to be recorded from intact tissue structures. These probes were tested and characterised using a range of in vitro and ex vivo experiments. The end application of this novel sensor device was in a first-in-human clinical trial. The initial results of this study showed that the silicon impedance device was capable of differentiating between normal and abnormal (benign and cancerous) breast tissue. The mean separation between the two tissue types 4,340 Ω with p < 0.001. The cancer type and grade at the site of the probe recordings was confirmed histologically and correlated with the electrical impedance measurements to determine if the different subtypes of cancer could each be differentiated. The results presented in this thesis showed that the novel impedance device demonstrated excellent electrochemical recording potential; was biocompatible with the growth of cultured cell lines and was capable of differentiating between intact biological tissues. The results outlined in this thesis demonstrate the potential feasibility of using electrical impedance for the differentiation of biological tissue samples. The novelty of this thesis is in the development of a new method of tissue determination with an application in breast cancer detection.

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.

Distribution, occurrence and biotoxin composition of the main shellfish toxin producing microalgae within European waters: A comparison of methods of analysis.

Harmful algal blooms (HABs) are a natural global phenomena emerging in severity and extent. Incidents have many economic, ecological and human health impacts. Monitoring and providing early warning of toxic HABs are critical for protecting public health. Current monitoring programmes include measuring the number of toxic phytoplankton cells in the water and biotoxin levels in shellfish tissue. As these efforts are demanding and labour intensive, methods which improve the efficiency are essential. This study compares the utilisation of a multitoxin surface plasmon resonance (multitoxin SPR) biosensor with enzyme-linked immunosorbent assay (ELISA) and analytical methods such as high performance liquid chromatography with fluorescence detection (HPLC-FLD) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) for toxic HAB monitoring efforts in Europe. Seawater samples (n = 256) from European waters, collected 2009–2011, were analysed for biotoxins: saxitoxin and analogues, okadaic acid and dinophysistoxins 1/2 (DTX1/DTX2) and domoic acid responsible for paralytic shellfish poisoning (PSP), diarrheic shellfish poisoning (DSP) and amnesic shellfish poisoning (ASP), respectively. Biotoxins were detected mainly in samples from Spain and Ireland. France and Norway appeared to have the lowest number of toxic samples. Both the multitoxin SPR biosensor and the RNA microarray were more sensitive at detecting toxic HABs than standard light microscopy phytoplankton monitoring. Correlations between each of the detection methods were performed with the overall agreement, based on statistical 2 × 2 comparison tables, between each testing platform ranging between 32% and 74% for all three toxin families illustrating that one individual testing method may not be an ideal solution. An efficient early warning monitoring system for the detection of toxic HABs could therefore be achieved by combining both the multitoxin SPR biosensor and RNA microarray.

Distribution, occurrence and biotoxin composition of the main shellfish toxin producing microalgae within European waters: A comparison of methods of analysis.

Harmful algal blooms (HABs) are a natural global phenomena emerging in severity and extent. Incidents have many economic, ecological and human health impacts. Monitoring and providing early warning of toxic HABs are critical for protecting public health. Current monitoring programmes include measuring the number of toxic phytoplankton cells in the water and biotoxin levels in shellfish tissue. As these efforts are demanding and labour intensive, methods which improve the efficiency are essential. This study compares the utilisation of a multitoxin surface plasmon resonance (multitoxin SPR) biosensor with enzyme-linked immunosorbent assay (ELISA) and analytical methods such as high performance liquid chromatography with fluorescence detection (HPLC-FLD) and liquid chromatography–tandem mass spectrometry (LC–MS/MS) for toxic HAB monitoring efforts in Europe. Seawater samples (n = 256) from European waters, collected 2009–2011, were analysed for biotoxins: saxitoxin and analogues, okadaic acid and dinophysistoxins 1/2 (DTX1/DTX2) and domoic acid responsible for paralytic shellfish poisoning (PSP), diarrheic shellfish poisoning (DSP) and amnesic shellfish poisoning (ASP), respectively. Biotoxins were detected mainly in samples from Spain and Ireland. France and Norway appeared to have the lowest number of toxic samples. Both the multitoxin SPR biosensor and the RNA microarray were more sensitive at detecting toxic HABs than standard light microscopy phytoplankton monitoring. Correlations between each of the detection methods were performed with the overall agreement, based on statistical 2 × 2 comparison tables, between each testing platform ranging between 32% and 74% for all three toxin families illustrating that one individual testing method may not be an ideal solution. An efficient early warning monitoring system for the detection of toxic HABs could therefore be achieved by combining both the multitoxin SPR biosensor and RNA microarray.

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.

Comparison of different methods to detect genetic barriers in a small mammal population

Habitat fragmentation and the consequently the loss of connectivity between populations can reduce the individuals interchange and gene flow, increasing the chances of inbreeding, and the increase the risk of local extinction. Landscape genetics is providing more and better tools to identify genetic barriers.. To our knowledge, no comparison of methods in terms of consistency has been made with observed data and species with low dispersal ability. The aim of this study is to examine the consistency of the results of five methods to detect barriers to gene flow in a Mediterranean pine vole population Microtus duodecimcostatus: F-statistics estimations, Non-Bayesian clustering, Bayesian clustering, Boundary detection and Simple/Partial Mantel tests. All methods were consistent in detecting the stream as a non-genetic barrier. However, no consistency in results among the methods were found regarding the role of the highway as a genetic barrier. Fst, Bayesian clustering assignment test and Partial Mantel test identifyed the highway as a filter to individual interchange. The Mantel tests were the most sensitive method. Boundary detection method (Monmonier’s Algorithm) and Non-Bayesian approaches did not detect any genetic differentiation of the pine vole due to the highway. Based on our findings we recommend that the genetic barrier detection in low dispersal ability populations should be analyzed with multiple methods such as Mantel tests, Bayesian clustering approaches because they show more sensibility in those scenarios and with boundary detection methods by having the aim of detect drastic changes in a variable of interest between the closest individuals. Although simulation studies highlight the weaknesses and the strengths of each method and the factors that promote some results, tests with real data are needed to increase the effectiveness of genetic barrier detection.

Chilean IPNV isolates: Robustness analysis of PCR detection

Background: The genomes of several infectious pancreatic necrosis viruses (IPNVs) isolated in Chile were sequenced with a single amplification approach for both segments A and B. The resulting sequences were then used to determine the conservation of the primer-binding regions used in polymerase chain reaction (PCR)-based diagnostic methods proposed in the literature. Thus, the robustness of each technique was studied, particularly the eventual effect of further mutations within the primer-binding sites. Results: On analysis, most methods currently used to detect Chilean IPNV varieties were deemed adequate. However, the primers were designed to be genogroup specific, implying that most detection methods pose some risk of detecting all strains prevalent in the country, due to the coexistence of genogroups 1 and 5. Conclusions: Negative resultsmust be interpreted carefully given the high genomic variability of IPNVs. Detection techniques (quantitative reverse transcription (qRT)-PCR) based on degenerate primers can be used to minimize the possibilities of false-negative detections.

Scedo-Select III: a new semi-selective culture medium for detection of the Scedosporium apiospermum species complex

International audience

Model-Based validation of Driver Drowsiness Detection System for ADAS

The work described in this Master’s Degree thesis was born after the collaboration with the company Maserati S.p.a, an Italian luxury car maker with its headquarters located in Modena, in the heart of the Italian Motor Valley, where I worked as a stagiaire in the Virtual Engineering team between September 2021 and February 2022. This work proposes the validation using real-world ECUs of a Driver Drowsiness Detection (DDD) system prototype based on different detection methods with the goal to overcome input signal losses and system failures. Detection methods of different categories have been chosen from literature and merged with the goal of utilizing the benefits of each of them, overcoming their limitations and limiting as much as possible their degree of intrusiveness to prevent any kind of driving distraction: an image processing-based technique for human physical signals detection as well as methods based on driver-vehicle interaction are used. A Driver-In-the-Loop simulator is used to gather real data on which a Machine Learning-based algorithm will be trained and validated. These data come from the tests that the company conducts in its daily activities so confidential information about the simulator and the drivers will be omitted. Although the impact of the proposed system is not remarkable and there is still work to do in all its elements, the results indicate the main advantages of the system in terms of robustness against subsystem failures and signal losses.