Effects of sciatic nerve transection on ultrastructure, NADPH-diaphorase reaction and serotonin-, tyrosine hydroxylase-, c-Fos-, glucose transporter 1- and 3-like immunoreactivities in frog dorsal root ganglion

Frogs have been used as an alternative model to study pain mechanisms. Since we did not find any reports on the effects of sciatic nerve transection (SNT) on the ultrastructure and pattern of metabolic substances in frog dorsal root ganglion (DRG) cells, in the present study, 18 adult male frogs (Rana catesbeiana) were divided into three experimental groups: naive (frogs not subjected to surgical manipulation), sham (frogs in which all surgical procedures to expose the sciatic nerve were used except transection of the nerve), and SNT (frogs in which the sciatic nerve was exposed and transected). After 3 days, the bilateral DRG of the sciatic nerve was collected and used for transmission electron microscopy. Immunohistochemistry was used to detect reactivity for glucose transporter (Glut) types 1 and 3, tyrosine hydroxylase, serotonin and c-Fos, as well as nicotinamide adenine dinucleotide phosphate diaphorase (NADPH-diaphorase). SNT induced more mitochondria with vacuolation in neurons, satellite glial cells (SGCs) with more cytoplasmic extensions emerging from cell bodies, as well as more ribosomes, rough endoplasmic reticulum, intermediate filaments and mitochondria. c-Fos immunoreactivity was found in neuronal nuclei. More neurons and SGCs surrounded by tyrosine hydroxylase-like immunoreactivity were found. No change occurred in serotonin- and Glut1- and Glut3-like immunoreactivity. NADPH-diaphorase occurred in more neurons and SGCs. No sign of SGC proliferation was observed. Since the changes of frog DRG in response to nerve injury are similar to those of mammals, frogs should be a valid experimental model for the study of the effects of SNT, a condition that still has many unanswered questions.

An interleukin-33/ST2 signaling deficiency reduces overt pain-like behaviors in mice

Interleukin (IL)-33, the most recent member of the IL family of cytokines, signals through the ST2 receptor. IL-33/ST2 signaling mediates antigen challenge-induced mechanical hyperalgesia in the joints and cutaneous tissues of immunized mice. The present study asked whether IL-33/ST2 signaling is relevant to overt pain-like behaviors in mice. Acetic acid and phenyl-p-benzoquinone induced significant writhing responses in wild-type (WT) mice; this overt nociceptive behavior was reduced in ST2-deficient mice. In an antigen-challenge model, ST2-deficient immunized mice had reduced induced flinch and licking overt pain-like behaviors. In the formalin test, ST2-deficient mice also presented reduced flinch and licking responses, compared with WT mice. Naive WT and ST2-deficient mice presented similar responses in the rota-rod, hot plate, and electronic von Frey tests, indicating no impairment of motor function or alteration in basal nociceptive responses. The results demonstrate that IL-33/ST2 signaling is important in the development of overt pain-like behaviors.

Development and validation of a genotype 3 recombinant protein-based immunoassay for hepatitis E virus serology in swine

Hepatitis E virus (HEV) is classified within the family Hepeviridae, genus Hepevirus. HEV genotype 3 (Gt3) infections are endemic in pigs in Western Europe and in North and South America and cause zoonotic infections in humans. Several serological assays to detect HEV antibodies in pigs have been developed, at first mainly based on HEV genotype 1 (Gt1) antigens. To develop a sensitive HEV Gt3 ELISA, a recombinant baculovirus expression product of HEV Gt3 open reading frame-2 was produced and coated onto polystyrene ELISA plates. After incubation of porcine sera, bound HEV antibodies were detected with anti-porcine anti-IgG and anti-IgM conjugates. For primary estimation of sensitivity and specificity of the assay, sets of sera were used from pigs experimentally infected with HEV Gt3. For further validation of the assay and to set the cutoff value, a batch of 1100 pig sera was used. All pig sera were tested using the developed HEV Gt3 assay and two other serologic assays based on HEV Gt1 antigens. Since there is no gold standard available for HEV antibody testing, further validation and a definite setting of the cutoff of the developed HEV Gt3 assay were performed using a statistical approach based on Bayes' theorem. The developed and validated HEV antibody assay showed effective detection of HEV-specific antibodies. This assay can contribute to an improved detection of HEV antibodies and enable more reliable estimates of the prevalence of HEV Gt3 in swine in different regions.

Deficiency of sex hormones does not affect 17-ß-estradiol-induced coronary vasodilation in the isolated rat heart

The relaxation of coronary arteries by estrogens in the coronary vascular beds of naive and hypertensive rats has been well described. However, little is known about this action in gonadectomized rats. We investigated the effect of 17-ß-estradiol (E2) in coronary arteries from gonadectomized rats, as well as the contributions of endothelium-derived factors and potassium channels. Eight-week-old female and male Wistar rats weighing 220-300 g were divided into sham-operated and gonadectomized groups (n=9−12 animals per group). The baseline coronary perfusion pressure (CPP) was determined, and the vasoactive effects of 10 μM E2 were assessed by bolus administration before and after endothelium denudation or by perfusion with NG-nitro-L-arginine methyl ester (L-NAME), indomethacin, clotrimazole, L-NAME plus indomethacin, L-NAME plus clotrimazole or tetraethylammonium (TEA). The CPP differed significantly between the female and sham-operated male animals. Gonadectomy reduced the CPP only in female rats. Differences in E2-induced relaxation were observed between the female and male animals, but male castration did not alter this response. For both sexes, the relaxation response to E2 was, at least partly, endothelium-dependent. The response to E2 was reduced only in the sham-operated female rats treated with L-NAME. However, in the presence of indomethacin, clotrimazole, L-NAME plus indomethacin or L-NAME plus clotrimazole, or TEA, the E2 response was significantly reduced in all groups. These results highlight the importance of prostacyclin, endothelium-derived hyperpolarizing factor, and potassium channels in the relaxation response of coronary arteries to E2 in all groups, whereas nitric oxide may have had an important role only in the sham-operated female group.

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.

Evaluation of Machine Learning Classifiers for Mobile Network Intrusion Detection Systems

Mobile malwares are increasing with the growing number of Mobile users. Mobile malwares can perform several operations which lead to cybersecurity threats such as, stealing financial or personal information, installing malicious applications, sending premium SMS, creating backdoors, keylogging and crypto-ransomware attacks. Knowing the fact that there are many illegitimate Applications available on the App stores, most of the mobile users remain careless about the security of their Mobile devices and become the potential victim of these threats. Previous studies have shown that not every antivirus is capable of detecting all the threats; due to the fact that Mobile malwares use advance techniques to avoid detection. A Network-based IDS at the operator side will bring an extra layer of security to the subscribers and can detect many advanced threats by analyzing their traffic patterns. Machine Learning(ML) will provide the ability to these systems to detect unknown threats for which signatures are not yet known. This research is focused on the evaluation of Machine Learning classifiers in Network-based Intrusion detection systems for Mobile Networks. In this study, different techniques of Network-based intrusion detection with their advantages, disadvantages and state of the art in Hybrid solutions are discussed. Finally, a ML based NIDS is proposed which will work as a subsystem, to Network-based IDS deployed by Mobile Operators, that can help in detecting unknown threats and reducing false positives. In this research, several ML classifiers were implemented and evaluated. This study is focused on Android-based malwares, as Android is the most popular OS among users, hence most targeted by cyber criminals. Supervised ML algorithms based classifiers were built using the dataset which contained the labeled instances of relevant features. These features were extracted from the traffic generated by samples of several malware families and benign applications. These classifiers were able to detect malicious traffic patterns with the TPR upto 99.6% during Cross-validation test. Also, several experiments were conducted to detect unknown malware traffic and to detect false positives. These classifiers were able to detect unknown threats with the Accuracy of 97.5%. These classifiers could be integrated with current NIDS', which use signatures, statistical or knowledge-based techniques to detect malicious traffic. Technique to integrate the output from ML classifier with traditional NIDS is discussed and proposed for future work.

Wood species identification by visual characterization of fibers

Kandidaatintyö tehtiin osana PulpVision-tutkimusprojektia, jonka tarkoituksena on kehittää kuvapohjaisia laskenta- ja luokittelumetodeja sellun laaduntarkkailuun paperin valmistuksessa. Tämän tutkimusprojektin osana on aiemmin kehitetty metodi, jolla etsittiin kaarevia rakenteita kuvista, ja tätä metodia hyödynnettiin kuitujen etsintään kuvista. Tätä metodia käytettiin lähtökohtana kandidaatintyölle. Työn tarkoituksena oli tutkia, voidaanko erilaisista kuitukuvista laskettujen piirteiden avulla tunnistaa kuvassa olevien kuitujen laji. Näissä kuitukuvissa oli kuituja neljästä eri puulajista ja yhdestä kasvista. Nämä lajit olivat akasia, koivu, mänty, eukalyptus ja vehnä. Jokaisesta lajista valittiin 100 kuitukuvaa ja nämä kuvat jaettiin kahteen ryhmään, joista ensimmäistä käytettiin opetusryhmänä ja toista testausryhmänä. Opetusryhmän avulla jokaiselle kuitulajille laskettiin näitä kuvaavia piirteitä, joiden avulla pyrittiin tunnistamaan testausryhmän kuvissa olevat kuitulajit. Nämä kuvat oli tuottanut CEMIS-Oulu (Center for Measurement and Information Systems), joka on mittaustekniikkaan keskittynyt yksikkö Oulun yliopistossa. Yksittäiselle opetusryhmän kuitukuvalle laskettiin keskiarvot ja keskihajonnat kolmesta eri piirteestä, jotka olivat pituus, leveys ja kaarevuus. Lisäksi laskettiin, kuinka monta kuitua kuvasta löydettiin. Näiden piirteiden eri yhdistelmien avulla testattiin tunnistamisen tarkkuutta käyttämällä k:n lähimmän naapurin menetelmää ja Naiivi Bayes -luokitinta testausryhmän kuville. Testeistä saatiin lupaavia tuloksia muun muassa pituuden ja leveyden keskiarvoja käytettäessä saavutettiin jopa noin 98 %:n tarkkuus molemmilla algoritmeilla. Tunnistuksessa kuitujen keskimäärinen pituus vaikutti olevan kuitukuvia parhaiten kuvaava piirre. Käytettyjen algoritmien välillä ei ollut suurta vaihtelua tarkkuudessa. Testeissä saatujen tulosten perusteella voidaan todeta, että kuitukuvien tunnistaminen on mahdollista. Testien perusteella kuitukuvista tarvitsee laskea vain kaksi piirrettä, joilla kuidut voidaan tunnistaa tarkasti. Käytetyt lajittelualgoritmit olivat hyvin yksinkertaisia, mutta ne toimivat testeissä hyvin.

Activity of brainstem cholinergic neurons during 22 kHz ultrasonic vocalization in rats /

Adult rats emit 22 kHz ultrasonic alann calls in aversive situations. This type of call IS a component of defensive behaviour and it functions predominantly to warn conspecifics about predators. Production of these calls is dependent on the central cholinergic system. The laterodorsal tegmental nucleus (LDT) and pedunculopontine tegmental nucleus (PPT) contain largely cholinergic neurons, which create a continuous column in the brainstem. The LDT projects to structures in the forebrain, and it has been implicated in the initiation of 22 kHz alarm calls. It was hypothesized that release of acetylcholine from the ascending LDT terminals in mesencephalic and diencephalic areas initiates 22 kHz alarm vocalization. Therefore, the tegmental cholinergic neurons should be more active during emission of alarm calls. The aim of this study was to demonstrate increased activity of LDT cholinergic neurons during emission of 22 kHz calls induced by air puff stimuli. Immunohistochemical staining of the enzyme choline acetyltransferase identified cell bodies of cholinergic neurons, and c-Fos immunolabeling identified active cells. Double labeled cells were regarded as active cholinergic cells. There were significantly more (pnaIve non-airpuffed) animals. Although the numbers were low, there were also significantly more (p

Decoding algorithms using side-effect machines

Bioinformatics applies computers to problems in molecular biology. Previous research has not addressed edit metric decoders. Decoders for quaternary edit metric codes are finding use in bioinformatics problems with applications to DNA. By using side effect machines we hope to be able to provide efficient decoding algorithms for this open problem. Two ideas for decoding algorithms are presented and examined. Both decoders use Side Effect Machines(SEMs) which are generalizations of finite state automata. Single Classifier Machines(SCMs) use a single side effect machine to classify all words within a code. Locking Side Effect Machines(LSEMs) use multiple side effect machines to create a tree structure of subclassification. The goal is to examine these techniques and provide new decoders for existing codes. Presented are ideas for best practices for the creation of these two types of new edit metric decoders.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

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

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

Bayesian Analysis for a Theory of Random Consumer Demand: The Case of Indivisible Goods

McCausland (2004a) describes a new theory of random consumer demand. Theoretically consistent random demand can be represented by a \"regular\" \"L-utility\" function on the consumption set X. The present paper is about Bayesian inference for regular L-utility functions. We express prior and posterior uncertainty in terms of distributions over the indefinite-dimensional parameter set of a flexible functional form. We propose a class of proper priors on the parameter set. The priors are flexible, in the sense that they put positive probability in the neighborhood of any L-utility function that is regular on a large subset bar(X) of X; and regular, in the sense that they assign zero probability to the set of L-utility functions that are irregular on bar(X). We propose methods of Bayesian inference for an environment with indivisible goods, leaving the more difficult case of indefinitely divisible goods for another paper. We analyse individual choice data from a consumer experiment described in Harbaugh et al. (2001).

L'exception de non-subrogation: l'influence de ses principes justificatifs sur sa mise en oeuvre

À la lecture de l'article 2365 c.c.Q., le créancier et la caution ne peuvent pas percevoir les droits et les libertés que ce texte concrétise à leur encontre ou à leur profit. Pour pallier ce problème, les auteurs et la jurisprudence ont alors laissé place à leur imagination afin de tenter de classifier cette disposition à l'intérieur d'institutions juridiques éprouvées, le tout en vue de démythifier le contenu de la règle de droit. Pour notre part, nous considérons que l'exception de non-subrogation est une notion originale en soi, qui trouve sa source à l'intérieur même de son institution. La thèse que nous soutenons est que l'exception de non-subrogation, mode de libération qui a pour mission de combattre le comportement opportuniste, cristallise l'obligation de bonne foi en imposant implicitement au créancier une obligation de bonne subrogation. Tout manquement du créancier à cette obligation a comme conséquence de rendre le droit de créance du créancier irrecevable à l'égard de la caution devant les tribunaux. Ce précepte éclaircit le contexte de l'article 2365 C.c.Q. et, par le fait même, il permet de délimiter le contour de son domaine et de préciser ses conditions d'application. L'exception de non-subrogation est un mécanisme juridique qui date de l'époque romaine. Elle est maintenant intégrée dans presque tous les systèmes juridiques du monde, tant en droit civil qu'en common law. Dans la législation québécoise, elle s'est cristallisée à l'article 2365 C.c.Q. Il s'agit d'une disposition d'ordre public qui ne peut être invoquée que par la caution. Son application dépend du cumul de quatre conditions: 1) le fait du créancier; 2) la perte d'un droit subrogatoire; 3) le préjudice de la caution; 4) le lien causal entre les trois derniers éléments. Lorsque ces quatre conditions sont remplies, la caution est libérée de son engagement dans la mesure du préjudice qu'elle subit.

Rôle de la molécule CD47 sur le lymphocyte T dans la régulation de la réponse immunitaire

L’importance respective des lymphocytes T régulateurs naturels générés dans le thymus ou induits en périphérie dans la régulation immunitaire et la résolution de l’inflammation est désormais bien établie. Nous avons contribué à mettre en évidence une nouvelle voie d’induction de lymphocytes T régulateurs périphériques à partir de cellules T humaines CD4+CD25- naïves et mémoires. Nous avons montré que l’engagement de la molécule ubiquitaire transmembranaire CD47 sur la cellule T par un anticorps monoclonal ou par le peptide 4N1K (peptide dérivé du domaine carboxy-terminal de la thrombospondine-1 et spécifique du site de liaison à CD47) induisait des lymphocytes T CD4+ régulateurs exerçant une fonction suppressive sur les lymphocytes T effecteurs. Les propriétés suppressives induites par la thrombospondine-1 confortent les fonctions anti-inflammatoires de cette protéine de la matrice extracellulaire. L’inhibition exercée par les lymphocytes T régulateurs induits dépend du contact intercellulaire entre les cellules T régulatrices et leurs cibles, et est indépendante du TGF-. Nos résultats démontrent également le rôle de CD47 sur le lymphocyte T CD4+ dans la réponse immunitaire spécifique de l’antigène in vivo. En effet, les souris BALB/c déficientes pour CD47 présentent un biais de la sécrétion d’anticorps et de cytokines de type Th1, alors que les souris BALB/c sont décrites comme exprimant un profil de production de cytokines de type Th2. Nos travaux mettent en évidence le rôle de CD47 dans l’inhibition du développement d’une réponse cellulaire et humorale de type Th1 in vivo, confirmant de précédentes études in vitro réalisées avec des cellules T CD4+ humaines. Nous présentons également le rôle inhibiteur de l’engagement de CD28 in vitro sur la différenciation en cellules Th17 des lymphocytes T CD4+ naïfs isolés de souris BALB/c. Le mécanisme proposé est dépendant de la production de l’IL-2 et de l’IFN- et indépendant de la présence de lymphocytes T régulateurs. Notre étude du rôle de deux molécules transmembranaires CD47 et CD28 exprimées sur la cellule T CD4+, contribue à une meilleure connaissance des mécanismes impliqués dans la tolérance immunologique, la résolution de l’inflammation et la différenciation des cellules T "helper" CD4+.

Analyse par apprentissage automatique des réponses fMRI du cortex auditif à des modulations spectro-temporelles

L'application de classifieurs linéaires à l'analyse des données d'imagerie cérébrale (fMRI) a mené à plusieurs percées intéressantes au cours des dernières années. Ces classifieurs combinent linéairement les réponses des voxels pour détecter et catégoriser différents états du cerveau. Ils sont plus agnostics que les méthodes d'analyses conventionnelles qui traitent systématiquement les patterns faibles et distribués comme du bruit. Dans le présent projet, nous utilisons ces classifieurs pour valider une hypothèse portant sur l'encodage des sons dans le cerveau humain. Plus précisément, nous cherchons à localiser des neurones, dans le cortex auditif primaire, qui détecteraient les modulations spectrales et temporelles présentes dans les sons. Nous utilisons les enregistrements fMRI de sujets soumis à 49 modulations spectro-temporelles différentes. L'analyse fMRI au moyen de classifieurs linéaires n'est pas standard, jusqu'à maintenant, dans ce domaine. De plus, à long terme, nous avons aussi pour objectif le développement de nouveaux algorithmes d'apprentissage automatique spécialisés pour les données fMRI. Pour ces raisons, une bonne partie des expériences vise surtout à étudier le comportement des classifieurs. Nous nous intéressons principalement à 3 classifieurs linéaires standards, soient l'algorithme machine à vecteurs de support (linéaire), l'algorithme régression logistique (régularisée) et le modèle bayésien gaussien naïf (variances partagées).