878 resultados para RU(BPY)(3)(3 )-BASED CHEMILUMINESCENCE DETECTION
Resumo:
Los métodos de detección rápida de microorganismos se están convirtiendo en una herramienta esencial para el control de calidad en el área de la biotecnología, como es el caso de las industrias de alimentos y productos farmacéuticos y bioquímicos. En este escenario, el objetivo de esta tesis doctoral es desarrollar una técnica de inspección rápida de microoganismos basada en ultrasonidos. La hipótesis propuesta es que la combinación de un dispositivo ultrasónico de medida y un medio líquido diseñado específicamente para producir y atrapar burbujas, pueden constituir la base de un método sensible y rápido de detección de contaminaciones microbianas. La técnica presentada es efectiva para bacterias catalasa-positivas y se basa en la hidrólisis del peróxido de hidrógeno inducida por la catalasa. El resultado de esta reacción es un medio con una creciente concentración de burbujas. Tal medio ha sido estudiado y modelado desde el punto de vista de la propagación ultrasónica. Las propiedades deducidas a partir del análisis cinemático de la enzima se han utilizado para evaluar el método como técnica de inspección microbiana. En esta tesis, se han investigado aspectos teóricos y experimentales de la hidrólisis del peróxido de hidrógeno. Ello ha permitido describir cuantitativamente y comprender el fenómeno de la detección de microorganismos catalasa-positivos mediante la medida de parámetros ultrasónicos. Más concretamente, los experimentos realizados muestran cómo el oxígeno que aparece en forma de burbujas queda atrapado mediante el uso de un gel sobre base de agar. Este gel fue diseñado y preparado especialmente para esta aplicación. A lo largo del proceso de hidrólisis del peróxido de hidrógeno, se midió la atenuación de la onda y el “backscattering” producidos por las burbujas, utilizando una técnica de pulso-eco. Ha sido posible detectar una actividad de la catalasa de hasta 0.001 unidades/ml. Por otra parte, este estudio muestra que por medio del método propuesto, se puede lograr una detección microbiana para concentraciones de 105 células/ml en un periodo de tiempo corto, del orden de unos pocos minutos. Estos resultados suponen una mejora significativa de tres órdenes de magnitud en comparación con otros métodos de detección por ultrasonidos. Además, la sensibilidad es competitiva con modernos y rápidos métodos microbiológicos como la detección de ATP por bioluminiscencia. Pero sobre todo, este trabajo muestra una metodología para el desarrollo de nuevas técnicas de detección rápida de bacterias basadas en ultrasonidos. ABSTRACT In an industrial scenario where rapid microbiological methods are becoming essential tools for quality control in the biotechnological area such as food, pharmaceutical and biochemical; the objective of the work presented in this doctoral thesis is to develop a rapid microorganism inspection technique based on ultrasounds. It is proposed that the combination of an ultrasonic measuring device with a specially designed liquid medium, able to produce and trap bubbles could constitute the basis of a sensitive and rapid detection method for microbial contaminations. The proposed technique is effective on catalase positive microorganisms. Well-known catalase induced hydrogen peroxide hydrolysis is the fundamental of the developed method. The physical consequence of the catalase induced hydrogen peroxide hydrolysis is an increasingly bubbly liquid medium. Such medium has been studied and modeled from the point of view of ultrasonic propagation. Properties deduced from enzyme kinematics analysis have been extrapolated to investigate the method as a microbial inspection technique. In this thesis, theoretical and experimental aspects of the hydrogen peroxide hydrolysis were analyzed in order to quantitatively describe and understand the catalase positive microorganism detection by means of ultrasonic measurements. More concretely, experiments performed show how the produced oxygen in form of bubbles is trapped using the new gel medium based on agar, which was specially designed for this application. Ultrasonic attenuation and backscattering is measured in this medium using a pulse-echo technique along the hydrogen peroxide hydrolysis process. Catalase enzymatic activity was detected down to 0.001 units/ml. Moreover, this study shows that by means of the proposed method, microbial detection can be achieved down to 105 cells/ml in a short time period of the order of few minutes. These results suppose a significant improvement of three orders of magnitude compared to other ultrasonic detection methods for microorganisms. In addition, the sensitivity reached is competitive with modern rapid microbiological methods such as ATP detection by bioluminescence. But above all, this work points out a way to proceed for developing new rapid microbial detection techniques based on ultrasound.
Resumo:
Sensing systems in living bodies offer a large variety of possible different configurations and philosophies able to be emulated in artificial sensing systems. Motion detection is one of the areas where different animals adopt different solutions and, in most of the cases, these solutions reflect a very sophisticated form. One of them, the mammalian visual system, presents several advantages with respect to the artificial ones. The main objective of this paper is to present a system, based on this biological structure, able to detect motion, its sense and its characteristics. The configuration adopted responds to the internal structure of the mammalian retina, where just five types of cells arranged in five layers are able to differentiate a large number of characteristics of the image impinging onto it. Its main advantage is that the detection of these properties is based purely on its hardware. A simple unit, based in a previous optical logic cell employed in optical computing, is the basis for emulating the different behaviors of the biological neurons. No software is present and, in this way, no possible interference from outside affects to the final behavior. This type of structure is able to work, once the internal configuration is implemented, without any further attention. Different possibilities are present in the architecture to be presented: detection of motion, of its direction and intensity. Moreover, some other characteristics, as symmetry may be obtained.
Resumo:
The number and grade of injured neuroanatomic structures and the type of injury determine the degree of impairment after a brain injury event and the recovery options of the patient. However, the body of knowledge and clinical intervention guides are basically focused on functional disorder and they still do not take into account the location of injuries. The prognostic value of location information is not known in detail either. This paper proposes a feature-based detection algorithm, named Neuroanatomic-Based Detection Algorithm (NBDA), based on SURF (Speeded Up Robust Feature) to label anatomical brain structures on cortical and sub-cortical areas. Themain goal is to register injured neuroanatomic structures to generate a database containing patient?s structural impairment profile. This kind of information permits to establish a relation with functional disorders and the prognostic evolution during neurorehabilitation procedures.
Resumo:
The aim of automatic pathological voice detection systems is to serve as tools, to medical specialists, for a more objective, less invasive and improved diagnosis of diseases. In this respect, the gold standard for those system include the usage of a optimized representation of the spectral envelope, either based on cepstral coefficients from the mel-scaled Fourier spectral envelope (Mel-Frequency Cepstral Coefficients) or from an all-pole estimation (Linear Prediction Coding Cepstral Coefficients) forcharacterization, and Gaussian Mixture Models for posterior classification. However, the study of recently proposed GMM-based classifiers as well as Nuisance mitigation techniques, such as those employed in speaker recognition, has not been widely considered inpathology detection labours. The present work aims at testing whether or not the employment of such speaker recognition tools might contribute to improve system performance in pathology detection systems, specifically in the automatic detection of Obstructive Sleep Apnea. The testing procedure employs an Obstructive Sleep Apnea database, in conjunction with GMM-based classifiers looking for a better performance. The results show that an improved performance might be obtained by using such approach.
Resumo:
In this paper we propose an innovative approach to tackle the problem of traffic sign detection using a computer vision algorithm and taking into account real-time operation constraints, trying to establish intelligent strategies to simplify as much as possible the algorithm complexity and to speed up the process. Firstly, a set of candidates is generated according to a color segmentation stage, followed by a region analysis strategy, where spatial characteristic of previously detected objects are taken into account. Finally, temporal coherence is introduced by means of a tracking scheme, performed using a Kalman filter for each potential candidate. Taking into consideration time constraints, efficiency is achieved two-fold: on the one side, a multi-resolution strategy is adopted for segmentation, where global operation will be applied only to low-resolution images, increasing the resolution to the maximum only when a potential road sign is being tracked. On the other side, we take advantage of the expected spacing between traffic signs. Namely, the tracking of objects of interest allows to generate inhibition areas, which are those ones where no new traffic signs are expected to appear due to the existence of a TS in the neighborhood. The proposed solution has been tested with real sequences in both urban areas and highways, and proved to achieve higher computational efficiency, especially as a result of the multi-resolution approach.
Resumo:
This paper presents a strategy for solving the feature matching problem in calibrated very wide-baseline camera settings. In this kind of settings, perspective distortion, depth discontinuities and occlusion represent enormous challenges. The proposed strategy addresses them by using geometrical information, specifically by exploiting epipolar-constraints. As a result it provides a sparse number of reliable feature points for which 3D position is accurately recovered. Special features known as junctions are used for robust matching. In particular, a strategy for refinement of junction end-point matching is proposed which enhances usual junction-based approaches. This allows to compute cross-correlation between perfectly aligned plane patches in both images, thus yielding better matching results. Evaluation of experimental results proves the effectiveness of the proposed algorithm in very wide-baseline environments.
Resumo:
In this paper we propose an innovative method for the automatic detection and tracking of road traffic signs using an onboard stereo camera. It involves a combination of monocular and stereo analysis strategies to increase the reliability of the detections such that it can boost the performance of any traffic sign recognition scheme. Firstly, an adaptive color and appearance based detection is applied at single camera level to generate a set of traffic sign hypotheses. In turn, stereo information allows for sparse 3D reconstruction of potential traffic signs through a SURF-based matching strategy. Namely, the plane that best fits the cloud of 3D points traced back from feature matches is estimated using a RANSAC based approach to improve robustness to outliers. Temporal consistency of the 3D information is ensured through a Kalman-based tracking stage. This also allows for the generation of a predicted 3D traffic sign model, which is in turn used to enhance the previously mentioned color-based detector through a feedback loop, thus improving detection accuracy. The proposed solution has been tested with real sequences under several illumination conditions and in both urban areas and highways, achieving very high detection rates in challenging environments, including rapid motion and significant perspective distortion
Resumo:
This paper proposes an automatic expert system for accuracy crop row detection in maize fields based on images acquired from a vision system. Different applications in maize, particularly those based on site specific treatments, require the identification of the crop rows. The vision system is designed with a defined geometry and installed onboard a mobile agricultural vehicle, i.e. submitted to vibrations, gyros or uncontrolled movements. Crop rows can be estimated by applying geometrical parameters under image perspective projection. Because of the above undesired effects, most often, the estimation results inaccurate as compared to the real crop rows. The proposed expert system exploits the human knowledge which is mapped into two modules based on image processing techniques. The first one is intended for separating green plants (crops and weeds) from the rest (soil, stones and others). The second one is based on the system geometry where the expected crop lines are mapped onto the image and then a correction is applied through the well-tested and robust Theil–Sen estimator in order to adjust them to the real ones. Its performance is favorably compared against the classical Pearson product–moment correlation coefficient.
Resumo:
This work proposes an optimization of a semi-supervised Change Detection methodology based on a combination of Change Indices (CI) derived from an image multitemporal data set. For this purpose, SPOT 5 Panchromatic images with 2.5 m spatial resolution have been used, from which three Change Indices have been calculated. Two of them are usually known indices; however the third one has been derived considering the Kullbak-Leibler divergence. Then, these three indices have been combined forming a multiband image that has been used in as input for a Support Vector Machine (SVM) classifier where four different discriminant functions have been tested in order to differentiate between change and no_change categories. The performance of the suggested procedure has been assessed applying different quality measures, reaching in each case highly satisfactory values. These results have demonstrated that the simultaneous combination of basic change indices with others more sophisticated like the Kullback-Leibler distance, and the application of non-parametric discriminant functions like those employees in the SVM method, allows solving efficiently a change detection problem.
Resumo:
Nonlinear analysis tools for studying and characterizing the dynamics of physiological signals have gained popularity, mainly because tracking sudden alterations of the inherent complexity of biological processes might be an indicator of altered physiological states. Typically, in order to perform an analysis with such tools, the physiological variables that describe the biological process under study are used to reconstruct the underlying dynamics of the biological processes. For that goal, a procedure called time-delay or uniform embedding is usually employed. Nonetheless, there is evidence of its inability for dealing with non-stationary signals, as those recorded from many physiological processes. To handle with such a drawback, this paper evaluates the utility of non-conventional time series reconstruction procedures based on non uniform embedding, applying them to automatic pattern recognition tasks. The paper compares a state of the art non uniform approach with a novel scheme which fuses embedding and feature selection at once, searching for better reconstructions of the dynamics of the system. Moreover, results are also compared with two classic uniform embedding techniques. Thus, the goal is comparing uniform and non uniform reconstruction techniques, including the one proposed in this work, for pattern recognition in biomedical signal processing tasks. Once the state space is reconstructed, the scheme followed characterizes with three classic nonlinear dynamic features (Largest Lyapunov Exponent, Correlation Dimension and Recurrence Period Density Entropy), while classification is carried out by means of a simple k-nn classifier. In order to test its generalization capabilities, the approach was tested with three different physiological databases (Speech Pathologies, Epilepsy and Heart Murmurs). In terms of the accuracy obtained to automatically detect the presence of pathologies, and for the three types of biosignals analyzed, the non uniform techniques used in this work lightly outperformed the results obtained using the uniform methods, suggesting their usefulness to characterize non-stationary biomedical signals in pattern recognition applications. On the other hand, in view of the results obtained and its low computational load, the proposed technique suggests its applicability for the applications under study.
Resumo:
A novel GPU-based nonparametric moving object detection strategy for computer vision tools requiring real-time processing is proposed. An alternative and efficient Bayesian classifier to combine nonparametric background and foreground models allows increasing correct detections while avoiding false detections. Additionally, an efficient region of interest analysis significantly reduces the computational cost of the detections.
Resumo:
Video analytics play a critical role in most recent traffic monitoring and driver assistance systems. In this context, the correct detection and classification of surrounding vehicles through image analysis has been the focus of extensive research in the last years. Most of the pieces of work reported for image-based vehicle verification make use of supervised classification approaches and resort to techniques, such as histograms of oriented gradients (HOG), principal component analysis (PCA), and Gabor filters, among others. Unfortunately, existing approaches are lacking in two respects: first, comparison between methods using a common body of work has not been addressed; second, no study of the combination potentiality of popular features for vehicle classification has been reported. In this study the performance of the different techniques is first reviewed and compared using a common public database. Then, the combination capabilities of these techniques are explored and a methodology is presented for the fusion of classifiers built upon them, taking into account also the vehicle pose. The study unveils the limitations of single-feature based classification and makes clear that fusion of classifiers is highly beneficial for vehicle verification.
Resumo:
We have investigated mRNA 3′-end-processing signals in each of six eukaryotic species (yeast, rice, arabidopsis, fruitfly, mouse, and human) through the analysis of more than 20,000 3′-expressed sequence tags. The use and conservation of the canonical AAUAAA element vary widely among the six species and are especially weak in plants and yeast. Even in the animal species, the AAUAAA signal does not appear to be as universal as indicated by previous studies. The abundance of single-base variants of AAUAAA correlates with their measured processing efficiencies. As found previously, the plant polyadenylation signals are more similar to those of yeast than to those of animals, with both common content and arrangement of the signal elements. In all species examined, the complete polyadenylation signal appears to consist of an aggregate of multiple elements. In light of these and previous results, we present a broadened concept of 3′-end-processing signals in which no single exact sequence element is universally required for processing. Rather, the total efficiency is a function of all elements and, importantly, an inefficient word in one element can be compensated for by strong words in other elements. These complex patterns indicate that effective tools to identify 3′-end-processing signals will require more than consensus sequence identification.
Resumo:
The Arp2/3 complex is implicated in actin polymerization-driven movement of Listeria monocytogenes. Here, we find that Arp2p and Arc15p, two subunits of this complex, show tight, actin-independent association with isolated yeast mitochondria. Arp2p colocalizes with mitochondria. Consistent with this result, we detect Arp2p-dependent formation of actin clouds around mitochondria in intact yeast. Cells bearing mutations in ARP2 or ARC15 genes show decreased velocities of mitochondrial movement, loss of all directed movement and defects in mitochondrial morphology. Finally, we observe a decrease in the velocity and extent of mitochondrial movement in yeast in which actin dynamics are reduced but actin cytoskeletal structure is intact. These results support the idea that the movement of mitochondria in yeast is actin polymerization driven and that this movement requires Arp2/3 complex.