Detection and pattern recognition applied to leaves and chromosomes

The Project you are about to see it is based on the technologies used on object detection and recognition, especially on leaves and chromosomes. To do so, this document contains the typical parts of a scientific paper, as it is what it is. It is composed by an Abstract, an Introduction, points that have to do with the investigation area, future work, conclusions and references used for the elaboration of the document. The Abstract talks about what are we going to find in this paper, which is technologies employed on pattern detection and recognition for leaves and chromosomes and the jobs that are already made for cataloguing these objects. In the introduction detection and recognition meanings are explained. This is necessary as many papers get confused with these terms, specially the ones talking about chromosomes. Detecting an object is gathering the parts of the image that are useful and eliminating the useless parts. Summarizing, detection would be recognizing the objects borders. When talking about recognition, we are talking about the computers or the machines process, which says what kind of object we are handling. Afterwards we face a compilation of the most used technologies in object detection in general. There are two main groups on this category: Based on derivatives of images and based on ASIFT points. The ones that are based on derivatives of images have in common that convolving them with a previously created matrix does the treatment of them. This is done for detecting borders on the images, which are changes on the intensity of the pixels. Within these technologies we face two groups: Gradian based, which search for maximums and minimums on the pixels intensity as they only use the first derivative. The Laplacian based methods search for zeros on the pixels intensity as they use the second derivative. Depending on the level of details that we want to use on the final result, we will choose one option or the other, because, as its logic, if we used Gradian based methods, the computer will consume less resources and less time as there are less operations, but the quality will be worse. On the other hand, if we use the Laplacian based methods we will need more time and resources as they require more operations, but we will have a much better quality result. After explaining all the derivative based methods, we take a look on the different algorithms that are available for both groups. The other big group of technologies for object recognition is the one based on ASIFT points, which are based on 6 image parameters and compare them with another image taking under consideration these parameters. These methods disadvantage, for our future purposes, is that it is only valid for one single object. So if we are going to recognize two different leaves, even though if they refer to the same specie, we are not going to be able to recognize them with this method. It is important to mention these types of technologies as we are talking about recognition methods in general. At the end of the chapter we can see a comparison with pros and cons of all technologies that are employed. Firstly comparing them separately and then comparing them all together, based on our purposes. Recognition techniques, which are the next chapter, are not really vast as, even though there are general steps for doing object recognition, every single object that has to be recognized has its own method as the are different. This is why there is not a general method that we can specify on this chapter. We now move on into leaf detection techniques on computers. Now we will use the technique explained above based on the image derivatives. Next step will be to turn the leaf into several parameters. Depending on the document that you are referring to, there will be more or less parameters. Some papers recommend to divide the leaf into 3 main features (shape, dent and vein] and doing mathematical operations with them we can get up to 16 secondary features. Next proposition is dividing the leaf into 5 main features (Diameter, physiological length, physiological width, area and perimeter] and from those, extract 12 secondary features. This second alternative is the most used so it is the one that is going to be the reference. Following in to leaf recognition, we are based on a paper that provides a source code that, clicking on both leaf ends, it automatically tells to which specie belongs the leaf that we are trying to recognize. To do so, it only requires having a database. On the tests that have been made by the document, they assure us a 90.312% of accuracy over 320 total tests (32 plants on the database and 10 tests per specie]. Next chapter talks about chromosome detection, where we shall pass the metaphasis plate, where the chromosomes are disorganized, into the karyotype plate, which is the usual view of the 23 chromosomes ordered by number. There are two types of techniques to do this step: the skeletonization process and swiping angles. Skeletonization progress consists on suppressing the inside pixels of the chromosome to just stay with the silhouette. This method is really similar to the ones based on the derivatives of the image but the difference is that it doesnt detect the borders but the interior of the chromosome. Second technique consists of swiping angles from the beginning of the chromosome and, taking under consideration, that on a single chromosome we cannot have more than an X angle, it detects the various regions of the chromosomes. Once the karyotype plate is defined, we continue with chromosome recognition. To do so, there is a technique based on the banding that chromosomes have (grey scale bands] that make them unique. The program then detects the longitudinal axis of the chromosome and reconstructs the band profiles. Then the computer is able to recognize this chromosome. Concerning the future work, we generally have to independent techniques that dont reunite detection and recognition, so our main focus would be to prepare a program that gathers both techniques. On the leaf matter we have seen that, detection and recognition, have a link as both share the option of dividing the leaf into 5 main features. The work that would have to be done is to create an algorithm that linked both methods, as in the program, which recognizes leaves, it has to be clicked both leaf ends so it is not an automatic algorithm. On the chromosome side, we should create an algorithm that searches for the beginning of the chromosome and then start to swipe angles, to later give the parameters to the program that searches for the band profiles. Finally, on the summary, we explain why this type of investigation is needed, and that is because with global warming, lots of species (animals and plants] are beginning to extinguish. That is the reason why a big database, which gathers all the possible species, is needed. For recognizing animal species, we just only have to have the 23 chromosomes. While recognizing a plant, there are several ways of doing it, but the easiest way to input a computer is to scan the leaf of the plant. RESUMEN. El proyecto que se puede ver a continuación trata sobre las tecnologías empleadas en la detección y reconocimiento de objetos, especialmente de hojas y cromosomas. Para ello, este documento contiene las partes típicas de un paper de investigación, puesto que es de lo que se trata. Así, estará compuesto de Abstract, Introducción, diversos puntos que tengan que ver con el área a investigar, trabajo futuro, conclusiones y biografía utilizada para la realización del documento. Así, el Abstract nos cuenta qué vamos a poder encontrar en este paper, que no es ni más ni menos que las tecnologías empleadas en el reconocimiento y detección de patrones en hojas y cromosomas y qué trabajos hay existentes para catalogar a estos objetos. En la introducción se explican los conceptos de qué es la detección y qué es el reconocimiento. Esto es necesario ya que muchos papers científicos, especialmente los que hablan de cromosomas, confunden estos dos términos que no podían ser más sencillos. Por un lado tendríamos la detección del objeto, que sería simplemente coger las partes que nos interesasen de la imagen y eliminar aquellas partes que no nos fueran útiles para un futuro. Resumiendo, sería reconocer los bordes del objeto de estudio. Cuando hablamos de reconocimiento, estamos refiriéndonos al proceso que tiene el ordenador, o la máquina, para decir qué clase de objeto estamos tratando. Seguidamente nos encontramos con un recopilatorio de las tecnologías más utilizadas para la detección de objetos, en general. Aquí nos encontraríamos con dos grandes grupos de tecnologías: Las basadas en las derivadas de imágenes y las basadas en los puntos ASIFT. El grupo de tecnologías basadas en derivadas de imágenes tienen en común que hay que tratar a las imágenes mediante una convolución con una matriz creada previamente. Esto se hace para detectar bordes en las imágenes que son básicamente cambios en la intensidad de los píxeles. Dentro de estas tecnologías nos encontramos con dos grupos: Los basados en gradientes, los cuales buscan máximos y mínimos de intensidad en la imagen puesto que sólo utilizan la primera derivada; y los Laplacianos, los cuales buscan ceros en la intensidad de los píxeles puesto que estos utilizan la segunda derivada de la imagen. Dependiendo del nivel de detalles que queramos utilizar en el resultado final nos decantaremos por un método u otro puesto que, como es lógico, si utilizamos los basados en el gradiente habrá menos operaciones por lo que consumirá más tiempo y recursos pero por la contra tendremos menos calidad de imagen. Y al revés pasa con los Laplacianos, puesto que necesitan más operaciones y recursos pero tendrán un resultado final con mejor calidad. Después de explicar los tipos de operadores que hay, se hace un recorrido explicando los distintos tipos de algoritmos que hay en cada uno de los grupos. El otro gran grupo de tecnologías para el reconocimiento de objetos son los basados en puntos ASIFT, los cuales se basan en 6 parámetros de la imagen y la comparan con otra imagen teniendo en cuenta dichos parámetros. La desventaja de este método, para nuestros propósitos futuros, es que sólo es valido para un objeto en concreto. Por lo que si vamos a reconocer dos hojas diferentes, aunque sean de la misma especie, no vamos a poder reconocerlas mediante este método. Aún así es importante explicar este tipo de tecnologías puesto que estamos hablando de técnicas de reconocimiento en general. Al final del capítulo podremos ver una comparación con los pros y las contras de todas las tecnologías empleadas. Primeramente comparándolas de forma separada y, finalmente, compararemos todos los métodos existentes en base a nuestros propósitos. Las técnicas de reconocimiento, el siguiente apartado, no es muy extenso puesto que, aunque haya pasos generales para el reconocimiento de objetos, cada objeto a reconocer es distinto por lo que no hay un método específico que se pueda generalizar. Pasamos ahora a las técnicas de detección de hojas mediante ordenador. Aquí usaremos la técnica explicada previamente explicada basada en las derivadas de las imágenes. La continuación de este paso sería diseccionar la hoja en diversos parámetros. Dependiendo de la fuente a la que se consulte pueden haber más o menos parámetros. Unos documentos aconsejan dividir la morfología de la hoja en 3 parámetros principales (Forma, Dentina y ramificación] y derivando de dichos parámetros convertirlos a 16 parámetros secundarios. La otra propuesta es dividir la morfología de la hoja en 5 parámetros principales (Diámetro, longitud fisiológica, anchura fisiológica, área y perímetro] y de ahí extraer 12 parámetros secundarios. Esta segunda propuesta es la más utilizada de todas por lo que es la que se utilizará. Pasamos al reconocimiento de hojas, en la cual nos hemos basado en un documento que provee un código fuente que cucando en los dos extremos de la hoja automáticamente nos dice a qué especie pertenece la hoja que estamos intentando reconocer. Para ello sólo hay que formar una base de datos. En los test realizados por el citado documento, nos aseguran que tiene un índice de acierto del 90.312% en 320 test en total (32 plantas insertadas en la base de datos por 10 test que se han realizado por cada una de las especies]. El siguiente apartado trata de la detección de cromosomas, en el cual se debe de pasar de la célula metafásica, donde los cromosomas están desorganizados, al cariotipo, que es como solemos ver los 23 cromosomas de forma ordenada. Hay dos tipos de técnicas para realizar este paso: Por el proceso de esquelotonización y barriendo ángulos. El proceso de esqueletonización consiste en eliminar los píxeles del interior del cromosoma para quedarse con su silueta; Este proceso es similar a los métodos de derivación de los píxeles pero se diferencia en que no detecta bordes si no que detecta el interior de los cromosomas. La segunda técnica consiste en ir barriendo ángulos desde el principio del cromosoma y teniendo en cuenta que un cromosoma no puede doblarse más de X grados detecta las diversas regiones de los cromosomas. Una vez tengamos el cariotipo, se continua con el reconocimiento de cromosomas. Para ello existe una técnica basada en las bandas de blancos y negros que tienen los cromosomas y que son las que los hacen únicos. Para ello el programa detecta los ejes longitudinales del cromosoma y reconstruye los perfiles de las bandas que posee el cromosoma y que lo identifican como único. En cuanto al trabajo que se podría desempeñar en el futuro, tenemos por lo general dos técnicas independientes que no unen la detección con el reconocimiento por lo que se habría de preparar un programa que uniese estas dos técnicas. Respecto a las hojas hemos visto que ambos métodos, detección y reconocimiento, están vinculados debido a que ambos comparten la opinión de dividir las hojas en 5 parámetros principales. El trabajo que habría que realizar sería el de crear un algoritmo que conectase a ambos ya que en el programa de reconocimiento se debe clicar a los dos extremos de la hoja por lo que no es una tarea automática. En cuanto a los cromosomas, se debería de crear un algoritmo que busque el inicio del cromosoma y entonces empiece a barrer ángulos para después poder dárselo al programa que busca los perfiles de bandas de los cromosomas. Finalmente, en el resumen se explica el por qué hace falta este tipo de investigación, esto es que con el calentamiento global, muchas de las especies (tanto animales como plantas] se están empezando a extinguir. Es por ello que se necesitará una base de datos que contemple todas las posibles especies tanto del reino animal como del reino vegetal. Para reconocer a una especie animal, simplemente bastará con tener sus 23 cromosomas; mientras que para reconocer a una especie vegetal, existen diversas formas. Aunque la más sencilla de todas es contar con la hoja de la especie puesto que es el elemento más fácil de escanear e introducir en el ordenador.

Peripheral vision and pattern recognition : a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

Pattern recognition analyses of brain activation elicited by happy and neutral faces in unipolar and bipolar depression

Objectives: Recently, pattern recognition approaches have been used to classify patterns of brain activity elicited by sensory or cognitive processes. In the clinical context, these approaches have been mainly applied to classify groups of individuals based on structural magnetic resonance imaging (MRI) data. Only a few studies have applied similar methods to functional MRI (fMRI) data. Methods: We used a novel analytic framework to examine the extent to which unipolar and bipolar depressed individuals differed on discrimination between patterns of neural activity for happy and neutral faces. We used data from 18 currently depressed individuals with bipolar I disorder (BD) and 18 currently depressed individuals with recurrent unipolar depression (UD), matched on depression severity, age, and illness duration, and 18 age- and gender ratio-matched healthy comparison subjects (HC). fMRI data were analyzed using a general linear model and Gaussian process classifiers. Results: The accuracy for discriminating between patterns of neural activity for happy versus neutral faces overall was lower in both patient groups relative to HC. The predictive probabilities for intense and mild happy faces were higher in HC than in BD, and for mild happy faces were higher in HC than UD (all p < 0.001). Interestingly, the predictive probability for intense happy faces was significantly higher in UD than BD (p = 0.03). Conclusions: These results indicate that patterns of whole-brain neural activity to intense happy faces were significantly less distinct from those for neutral faces in BD than in either HC or UD. These findings indicate that pattern recognition approaches can be used to identify abnormal brain activity patterns in patient populations and have promising clinical utility as techniques that can help to discriminate between patients with different psychiatric illnesses.

Peripheral vision and pattern recognition:a review

We summarize the various strands of research on peripheral vision and relate them to theories of form perception. After a historical overview, we describe quantifications of the cortical magnification hypothesis, including an extension of Schwartz's cortical mapping function. The merits of this concept are considered across a wide range of psychophysical tasks, followed by a discussion of its limitations and the need for non-spatial scaling. We also review the eccentricity dependence of other low-level functions including reaction time, temporal resolution, and spatial summation, as well as perimetric methods. A central topic is then the recognition of characters in peripheral vision, both at low and high levels of contrast, and the impact of surrounding contours known as crowding. We demonstrate how Bouma's law, specifying the critical distance for the onset of crowding, can be stated in terms of the retinocortical mapping. The recognition of more complex stimuli, like textures, faces, and scenes, reveals a substantial impact of mid-level vision and cognitive factors. We further consider eccentricity-dependent limitations of learning, both at the level of perceptual learning and pattern category learning. Generic limitations of extrafoveal vision are observed for the latter in categorization tasks involving multiple stimulus classes. Finally, models of peripheral form vision are discussed. We report that peripheral vision is limited with regard to pattern categorization by a distinctly lower representational complexity and processing speed. Taken together, the limitations of cognitive processing in peripheral vision appear to be as significant as those imposed on low-level functions and by way of crowding.

The application of PCA as a movement pattern recognition technique: A proof of principle

Quantitative methods can help us understand how underlying attributes contribute to movement patterns. Applying principal components analysis (PCA) to whole-body motion data may provide an objective data-driven method to identify unique and statistically important movement patterns. Therefore, the primary purpose of this study was to determine if athletes’ movement patterns can be differentiated based on skill level or sport played using PCA. Motion capture data from 542 athletes performing three sport-screening movements (i.e. bird-dog, drop jump, T-balance) were analyzed. A PCA-based pattern recognition technique was used to analyze the data. Prior to analyzing the effects of skill level or sport on movement patterns, methodological considerations related to motion analysis reference coordinate system were assessed. All analyses were addressed as case-studies. For the first case study, referencing motion data to a global (lab-based) coordinate system compared to a local (segment-based) coordinate system affected the ability to interpret important movement features. Furthermore, for the second case study, where the interpretability of PCs was assessed when data were referenced to a stationary versus a moving segment-based coordinate system, PCs were more interpretable when data were referenced to a stationary coordinate system for both the bird-dog and T-balance task. As a result of the findings from case study 1 and 2, only stationary segment-based coordinate systems were used in cases 3 and 4. During the bird-dog task, elite athletes had significantly lower scores compared to recreational athletes for principal component (PC) 1. For the T-balance movement, elite athletes had significantly lower scores compared to recreational athletes for PC 2. In both analyses the lower scores in elite athletes represented a greater range of motion. Finally, case study 4 reported differences in athletes’ movement patterns who competed in different sports, and significant differences in technique were detected during the bird-dog task. Through these case studies, this thesis highlights the feasibility of applying PCA as a movement pattern recognition technique in athletes. Future research can build on this proof-of-principle work to develop robust quantitative methods to help us better understand how underlying attributes (e.g. height, sex, ability, injury history, training type) contribute to performance.

Age estimation in common sole Solea solea larvae: validation of daily increments and evaluation of a pattern recognition technique

Close similarities have been found between the otoliths of sea-caught and laboratory-reared larvae of the common sole Solea solea (L.), given appropriate temperatures and nourishment of the latter. But from hatching to mouth formation. and during metamorphosis, sole otoliths have proven difficult to read because the increments may be less regular and low contrast. In this study, the growth increments in otoliths of larvae reared at 12 degrees C were counted by light microscopy to test the hypothesis of daily deposition, with some results verified using scanning electron microscopy (SEM), and by image analysis in order to compare the reliability of the 2 methods in age estimation. Age was first estimated (in days posthatch) from light micrographs of whole mounted otoliths. Counts were initiated from the increment formed at the time of month opening (Day 4). The average incremental deposition rate was consistent with the daily hypothesis. However, the light-micrograph readings tended to underestimate the mean ages of the larvae. Errors were probably associated with the low-contrast increments: those deposited after the mouth formation during the transition to first feeding, and those deposited from the onset of eye migration (about 20 d posthatch) during metamorphosis. SEM failed to resolve these low-contrast areas accurately because of poor etching. A method using image analysis was applied to a subsample of micrograph-counted otoliths. The image analysis was supported by an algorithm of pattern recognition (Growth Demodulation Algorithm, GDA). On each otolith, the GDA method integrated the growth pattern of these larval otoliths to averaged data from different radial profiles, in order to demodulate the exponential trend of the signal before spectral analysis (Fast Fourier Transformation, FFT). This second method both allowed more precise designation of increments, particularly for low-contrast areas, and more accurate readings but increased error in mean age estimation. The variability is probably due to a still rough perception of otolith increments by the GDA method, counting being achieved through a theoretical exponential pattern and mean estimates being given by FFT. Although this error variability was greater than expected, the method provides for improvement in both speed and accuracy in otolith readings.

Restoration of Pattern Recognition Receptor Costimulation to Treat Chromoblastomycosis, a Chronic Fungal Infection of the Skin

Chromoblastomycosis is a chronic skin infection caused by the fungus Fonsecaea pedrosoi. Exploring the reasons underlying the chronic nature of F. pedrosoi infection in a murine model of chromoblastomycosis, we find that chronicity develops due to a lack of pattern recognition receptor (PRR) costimulation. F. pedrosoi was recognized primarily by C-type lectin receptors (CLRs), but not by Toll-like receptors (TLRs), which resulted in the defective induction of proinflammatory cytokines. Inflammatory responses to F. pedrosoi could be reinstated by TLR costimulation, but also required the CLR Mincle and signaling via the Syk/CARD9 pathway. Importantly, exogenously administering TLR ligands helped clear F. pedrosoi infection in vivo. These results demonstrate how a failure in innate recognition can result in chronic infection, highlight the importance of coordinated PRR signaling, and provide proof of the principle that exogenously applied PRR agonists can be used therapeutically.

The pattern recognition receptors Nod1 and Nod2 account for neutrophil recruitment to the lungs of mice infected with Legionella pneumophila

The intracellular bacterium Legionella pneumophila induces a severe form of pneumonia called Legionnaires diseases, which is characterized by a strong neutrophil (NE) infiltrate to the lungs of infected individuals. Although the participation of pattern recognition receptors, such as Toll-like receptors, was recently demonstrated, there is no information on the role of nod-like receptors (NLRs) for bacterial recognition in vivo and for NE recruitment to the lungs. Here, we employed a murine model of Legionnaires disease to evaluate host and bacterial factors involved in NE recruitment to the mice lungs. We found that L. pneumophila type four secretion system, known as Dot/Icm, was required for NE recruitment as dot/icm mutants fail to trigger NE recruitment in a process independent of bacterial multiplication. By using mice deficient for Nod1, Nod2, and Rip2, we found that these receptors accounted for NE recruitment to the lungs of infected mice. In addition, Rip2-dependent responses were important for cytokine production and bacterial clearance. Collectively, these studies show that Nod1, Nod2, and Rip2 account for generation of innate immune responses in vivo, which are important for NE recruitment and bacterial clearance in a murine model of Legionnaires diseases. (C) 2010 Institut Pasteur. Published by Elsevier Masson SAS. All rights reserved.

Stratigraphic interpretation of Well-Log data of the Athabasca Oil Sands of Alberta Canada through Pattern recognition and Artificial Intelligence

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Software for pattern recognition of the larvae of Aedes aegypti and Aedes albopictus

Software for pattern recognition of the larvae of mosquitoes Aedes aegypti and Aedes albopictus, biological vectors of dengue and yellow fever, has been developed. Rapid field identification of larva using a digital camera linked to a laptop computer equipped with this software may greatly help prevention campaigns.

Development of a semi-automatic self learning pattern recognition system for the control of electronic boards

This project was funded under the Applied Research Grants Scheme administered by Enterprise Ireland. The project was a partnership between Galway - Mayo Institute of Technology and an industrial company, Tyco/Mallinckrodt Galway. The project aimed to develop a semi - automatic, self - learning pattern recognition system capable of detecting defects on the printed circuits boards such as component vacancy, component misalignment, component orientation, component error, and component weld. The research was conducted in three directions: image acquisition, image filtering/recognition and software development. Image acquisition studied the process of forming and digitizing images and some fundamental aspects regarding the human visual perception. The importance of choosing the right camera and illumination system for a certain type of problem has been highlighted. Probably the most important step towards image recognition is image filtering, The filters are used to correct and enhance images in order to prepare them for recognition. Convolution, histogram equalisation, filters based on Boolean mathematics, noise reduction, edge detection, geometrical filters, cross-correlation filters and image compression are some examples of the filters that have been studied and successfully implemented in the software application. The software application developed during the research is customized in order to meet the requirements of the industrial partner. The application is able to analyze pictures, perform the filtering, build libraries, process images and generate log files. It incorporates most of the filters studied and together with the illumination system and the camera it provides a fully integrated framework able to analyze defects on printed circuit boards.

Statistical pattern recognition for audio-forensics : empirical investigations on the application scenarios audio steganalysis and microphone forensics

Magdeburg, Univ., Fak. für Informatik, Diss., 2013

Intracellular pattern-recognition receptors

The last ten years of research in the field of innate immunity have been incredibly fertile: the transmembrane Toll-like receptors (TLRs) were discovered as guardians protecting the host against microbial attacks and the emerging pathways characterized in detail. More recently, cytoplasmic sensors were identified, which are capable of detecting not only microbial, but also self molecules. Importantly, while such receptors trigger crucial host responses to microbial insult, over-activity of some of them has been linked to autoinflammatory disorders, hence demonstrating the importance of tightly regulating their actions over time and space. Here, we provide an overview of recent findings covering this area of innate and inflammatory responses that originate from the cytoplasm

Expression of non-TLR pattern recognition receptors in the spleen of BALB/c mice infected with Plasmodium yoelii and Plasmodium chabaudi chabaudi AS

The spleen plays a crucial role in the development of immunity to malaria, but the role of pattern recognition receptors (PRRs) in splenic effector cells during malaria infection is poorly understood. In the present study, we analysed the expression of selected PRRs in splenic effector cells from BALB/c mice infected with the lethal and non-lethal Plasmodium yoelii strains 17XL and 17X, respectively, and the non-lethal Plasmodium chabaudi chabaudi AS strain. The results of these experiments showed fewer significant changes in the expression of PRRs in AS-infected mice than in 17X and 17XL-infected mice. Mannose receptor C type 2 (MRC2) expression increased with parasitemia, whereas Toll-like receptors and sialoadhesin (Sn) decreased in mice infected with P. chabaudi AS. In contrast, MRC type 1 (MRC1), MRC2 and EGF-like module containing mucin-like hormone receptor-like sequence 1 (F4/80) expression decreased with parasitemia in mice infected with 17X, whereas MRC1 an MRC2 increased and F4/80 decreased in mice infected with 17XL. Furthermore, macrophage receptor with collagenous structure and CD68 declined rapidly after initial parasitemia. SIGNR1 and Sn expression demonstrated minor variations in the spleens of mice infected with either strain. Notably, macrophage scavenger receptor (Msr1) and dendritic cell-associated C-type lectin 2 expression increased at both the transcript and protein levels in 17XL-infected mice with 50% parasitemia. Furthermore, the increased lethality of 17X infection in Msr1 -/- mice demonstrated a protective role for Msr1. Our results suggest a dual role for these receptors in parasite clearance and protection in 17X infection and lethality in 17XL infection.

Early pattern recognition in severe perinatal asphyxia: a prospective MRI study.

On the basis of MRI examinations in 88 neonates and infants with perinatal asphyxia, we defined 6 different patterns on T2-weighted images: pattern A--scattered hyperintensity of both hemispheres of the telencephalon with blurred border zones between cortex and white matter, indicating diffuse brain injury; pattern B--parasagittal hyperintensity extending into the corona radiata, corresponding to the watershed zones; pattern C--hyper- and hypointense lesions in thalamus and basal ganglia, which relate to haemorrhagic necrosis or iron deposition in these areas; pattern D--periventricular hyperintensity, mainly along the lateral ventricles, i.e. periventricular leukomalacia (PVL), originating from the matrix zone; pattern E--small multifocal lesions varying from hyper--to hypointense, interpreted as necrosis and haemorrhage; pattern F--periventricular centrifugal hypointense stripes in the centrum semiovale and deep white matter of the frontal and occipital lobes. Contrast was effectively inverted on T1-weighted images. Patterns A, B and C were found in 17%, 25% and 37% of patients, and patterns D, E and F in 19%, 17% and 35%, respectively. In 49 patients a combination of patterns was observed, but 30% of the initial images were normal. At follow-up, persistent abnormalities were seen in all children with patterns A and D, but in only 52% of those with pattern C. Myelination was retarded most often in patients with diffuse brain injury and PVL (patterns A and D).