Proteomic analysis and molecular characterization of Anisakis pegreffii allergenic and immunogenic proteins

In the nematode genus Anisakis, nine species are currently genetically recognized among which Anisakis simplex (s. s.) and Anisakis pegreffii have been recognised to be relevant for humans as a result of their zoonotic role in causing the disease, Anisakiasis. In addition to infection with this parasite, Anisakis can also cause allergic sensitisation. To date, A. simplex allergens have been described to represent the largest number for any parasite nematode accepted by the WHO/IUIS nomenclature committee. However, few data exist on the existence of such proteins in the sibling species, A. pegreffii. A. pegreffii has been reported as the causative agent of invasive anisakiasis in Europe, Japan and South Korea. It is reported as the most widespread anisakid species known to affect commercial fish from Mediterranean waters. Studies on A. pegreffii and identification of molecules released at the interface of host-parasite relationship are of crucial importance and may provide a basis for designing better novel diagnostic and therapeutic strategies. A detailed review on the current knowledge regarding Anisakis spp, particularly A. simplex, and its immunogenic proteins is presented in Chapter 1. This chapter presents an understanding of the current status on the increasing number of Anisakis simplex molecules identified to attack key pathways in the mammalian immune system. Through phylogenetic analysis, relationships of these proteins with homologs in other nematodes and invertebrates are presented and major A. simplex allergenic protein structures were modelled. This provides the foundation for further investigation of these proteins and their presence in A. pegreffii, and further guides their biological and genomic explorations. Chapter 2 describes the materials and methods generally used in this study while Chapter 3 follows up on the information provided in Chapter 1 on A. simplex immunogenic proteins. These proteins were investigated in A. pegreffii using high throughput mass spectrometry (LC/MS-MS). This method analysed and identified proteins present in the crude extract (CE) as well as excretory/secretory (ES) products of A. pegreffii. The results obtained showed that over 90 % of allergenic and immunogenic proteins identified in A. pegreffii proteome have also been described in A. simplex. Furthermore, most of the proteins identified in A. pegreffii ES (~80%) were found to be homologs of proteins in the ES of other helminths. The results of this chapter therefore emphasizes the cryptic speciation of the two sibling species, A. simplex and A. pegreffii, as well as affirming the notion that parasites employ a conserved set of proteins for parasite–host interaction mechanisms and host immune response evasion. Furthermore, the result from this chapter also suggests the probable absence of allergy-reducing molecules in A. pegreffii, which may be a contributing factor as to why Anisakis nematodes are able to elicit overt hypersensitivity reactions (allergy); in addition to inducing a Th-2 biased immune response. One of the main discoveries in the proteomic analysis of A. pegreffii CE and ES in chapter 3 was the observation that a number of proteins identified as part of A. pegreffii ES molecules in this study were not predicted to be secreted molecules. This raised the thought that such proteins must have reached the exterior or released by novel or alternative mechanisms, Hence, Chapter 4 was initiated to investigate and identify by LC-MS/MS, the exosomes of A. pegreffii and their cargo content. Abundant round-shaped materials with the expected size of exosomes were obtained after ultracentrifugation and they were visualized by transmission electron microscopy (TEM). Among the proteins identified were key exosome markers which include Heat Shock protein (HSP)-70, enolase and elongation factor 1-alpha. The result from this chapter constitutes the first report of the existence and composition of exosome-like vesicles in the L3 larvae of the parasite, A. pegreffii. The identified structures appear to play critical role in transportation of immunomodulatory and allergenic proteins such as leucine aminopeptidase (LAP) and tropomyosin (TM), respectively. In addition, high portions of proteins enriched in A. pegreffii exosomes were implicated in carbohydrate metabolism, indicative that the parasite's main energy source is probably derived from carbohydrate metabolism. Exosomes might be involved in transporting proteins needed for this function within the parasites and to the host for parasite survival. These proteins are stabilized against degradation by encapsulation within vesicles. It is demonstrated in this study for the first time, that parasite exosomes contain high concentrations of allergens, including the pan-allergen tropomyosin, providing evidence for the route of allergic sensitisation to live parasites. The result of this chapter, suggests that the secretion of certain proteins in this parasite, follow non-conventional pathways. Chapter 5 investigates, through immunoproteomic analysis, proteins from the CE and ES of A. pegreffii that are cross-reactive with serum IgE antibody of confirmed shellfish allergic patients. In the ES, we identified 2 different reactive proteins that satisfied the criteria for putative cross-reactive allergens as defined for this study and these were fructose bisphosphate aldolase 1 and enolase. In the CE, these proteins were also identified- tropomyosin as fructose bisphosphate aldolase 1 and enolase. Tropomyosin, one of the three proteins identified, had been previously described as a cross-reactive allergen in both shellfish and Anisakis parasite. The two other novel putative cross- reactive allergens described in this chapter are proteins with close homologues in fish. Finally, in Chapter 6, a protease, leucine aminopeptidase (LAP) reported to be implicated in immunomodulation in other helminths, was characterized. LAP of Anisakis was cloned, expressed and purified by IMAC in a bacterial host. The activity of the enzyme was investigated and its location in A. pegreffii determined using histochemical methods. This protease was found predominantly in the gut lumen of A. pegreffii and in addition was shown to interact with cathepsin proteases by cleaving in particular, the inactivated cathepsin L5 of Fasciola hepatica and releasing the activated form. The result of this study depicts Anisakis LAP as a protein of interest in immunomodulatory activities and further investigation of this enzyme as a potential therapeutic candidate could be explored. In summary, the results of this study highlights the proteins that are enriched in the proteome of A. pegreffii and the mechanisms employed by this parasite to release secreted molecules to sites of activity. It also demonstrates that A. pegreffii secretes specific sets of proteins that are preserved against degradation by being enclosed within vesicles. In addition, putative cross-reactive allergens were defined for A. pegreffii and an immunogenic protein (LAP) was characterized. Opportunities for further exploitation of the proteins identified in A. pegreffii, in a therapeutic context, are provided by the results of this study

Hybrid algorithm for NARX network parameters' determination using differential evolution and genetic algorithm

A hybrid optimization algorithm using Differential Evolution (DE) and Genetic Algorithm (GA) is proposed in this study to address the problem of network parameters determination associated with the Nonlinear Autoregressive with eXogenous inputs Network (NARX-network). The proposed algorithm involves a two level optimization scheme to search for both optimal network architecture and weights. The DE at the upper level is formulated as combinatorial optimization to search for the network architecture while the associated network weights that minimize the prediction error is provided by the GA at the lower level. The performance of the algorithm is evaluated on identification of a laboratory rotary motion system. The system identification results show the effectiveness of the proposed algorithm for nonparametric model development

Automatic diagnosis of diabetic retinopathy from fundus images using digital signal and image processing techniques

Automatic diagnosis and display of diabetic retinopathy from images of retina using the techniques of digital signal and image processing is presented in this paper. The acquired images undergo pre-processing to equalize uneven illumination associated with the acquired fundus images. This stage also removes noise present in the image. Segmentation stage clusters the image into two distinct classes while the abnormalities detection stage was used to distinguish between candidate lesions and other information. Methods of diagnosis of red spots, bleeding and detection of vein-artery crossover points have also been developed in this work using the color information, shape, size, object length to breadth ration as contained in the acquired digital fundus image. The algorithm was tested with a separate set of 25 fundus images. From this, the result obtained for Microaneurysms and Haemorrhages diagnosis shows the appropriateness of the method

A new method of correcting uneven illumination problem in fundus image

Recent advancements in signal and image processing have reduced the time of diagnoses, effort and pressure on the screeners by providing auto diagnostic tools for different diseases. The success rate of these tools greatly depend on the quality of acquired images. Bad image quality can significantly reduce the specificity and the sensitivity which in turn forces screeners back to their tedious job of manual diagnoses. In acquired fundus images, some areas appear to be brighter than the other, that is areas close to the center of the image are always well illuminated, hence appear very bright while areas far from the center are poorly illuminated hence appears to be very dark. Several techniques including the simple thresholding, Naka Rushton (NR) filtering technique and histogram equalization (HE) method have been suggested by various researchers to overcome this problem. However, each of these methods has limitations at their own and hence the need to develop a more robust technique that will provide better performance with greater flexibility. A new method of compensating uneven (irregular) illumination in fundus images termed global-local adaptive histogram equalization using partially-overlapped windows (GLAPOW) is proposed in this paper. The developed algorithm has been tested and the results obtained show superior performance when compared to other known techniques for uneven illumination correction

Detection of vascular intersection in retina fundus image using modified cross point number and neural network technique

Vascular intersection can be used as one of the symptoms for monitoring and diagnosis of diabetic retinopathy from fundus images. In this work we apply the knowledge of digital image processing, fuzzy logic and neural network technique to detect bifurcation and vein-artery cross-over points in fundus images. The acquired images undergo preprocessing stage for illumination equalization and noise removal. Segmentation stage clusters the image into two distinct classes by the use of fuzzy c-means technique, neural network technique and modified cross-point number (MCN) methods were employed for the detection of bifurcation and cross-over points. MCN uses a 5x5 window with 16 neighboring pixels for efficient detection of bifurcation and cross over points in fundus images. Result obtained from applying this hybrid method on both real and simulated vascular points shows that this method perform better than the existing simple cross-point number (SCN) method, thus an improvement to the vascular point detection and a good tool in the monitoring and diagnosis of diabetic retinopathy

Assessment of Mould Growth on Building Materials using Spatial and Frequency Domain Analysis Techniques

The phenomenon of Sick Building Syndrome (SBS), Building Related Illness (BRI) and some other indoor related diseases have been attributed to mould and fungi exposure in the indoor environment. Despite the growing concern over mould and fungi infestations on building materials, little has been reported in the literature on the development of an objective tool and criteria for measuring and characterizing the shape and the level of severity of such parasitic phenomenon. In this paper, an objective based approach of mould and fungi growth assessment using spatial and frequency domain information is proposed. The spatial domain analysis of the acquired Mould Infested Images (MII) is achieved using Ratio Test (RT), Compactness Test (CT) and Visual Test (VT) while the frequency domain analysis uses the popular Discrete Fourier Transform (DFT) implemented in the form of Fast Fourier Transform (FFT) in analyzing the boundary pixel sequence. The resulting frequency components (Fourier Descriptors (FD)) can now be analyzed or stored for reconstruction purposes. Application of structural similarity measures on the reconstructed MII in spatial domain shows that the use of relative low number of FD is sufficient for analyzing, characterizing and reconstruction of the original spatial domain boundary pixels

Development of solar powered irrigation system

Development of a solar powered irrigation system has been discussed in this paper. This system would be SCADA-based and quite useful in areas where there is plenty of sunshine but insufficient water to carry out farming activities, such as rubber plantation, strawberry plantation, or any plantation, that requires frequent watering. The system is powered by solar system as a renewable energy which uses solar panel module to convert Sunlight into electricity. The development and implementation of an automated SCADA controlled system that uses PLC as a controller is significant to agricultural, oil and gas monitoring and control purpose purposes. In addition, the system is powered by an intelligent solar system in which solar panel targets the radiation from the Sun. Other than that, the solar system has reduced energy cost as well as pollution. The system is equipped with four input sensors; two soil moisture sensors, two level detection sensors. Soil moisture sensor measures the humidity of the soil, whereas the level detection sensors detect the level of water in the tank. The output sides consist of two solenoid valves, which are controlled respectively by two moistures sensors

Vascular intersection detection in retina fundus images using a new hybrid approach

The use of vascular intersection aberration as one of the signs when monitoring and diagnosing diabetic retinopathy from retina fundus images (FIs) has been widely reported in the literature. In this paper, a new hybrid approach called the combined cross-point number (CCN) method able to detect the vascular bifurcation and intersection points in FIs is proposed. The CCN method makes use of two vascular intersection detection techniques, namely the modified cross-point number (MCN) method and the simple cross-point number (SCN) method. Our proposed approach was tested on images obtained from two different and publicly available fundus image databases. The results show a very high precision, accuracy, sensitivity and low false rate in detecting both bifurcation and crossover points compared with both the MCN and the SCN method

Damage index: Assessment of mould growth on building materials using digital image processing technique

There is a growing concern over the adverse health effects of exposure to high concentration of mould spores in the indoor environments. Copious epidemiological studies have shown a direct relationship between the exposure to indoor mould and several adverse health effects. The phenomenon of Sick building syndrome (SBS) and Building Related Illness (BRI) have also been attributed to moulds exposure in the indoor environment. In spite of this growing concern, little have been reported on the development of an objective mould assessment particularly criteria for visual inspection of mould growth on building materials. The main premise of this study is that visual inspection related with mould damaged material can lead to objective ranking of the severity of damaged material, and reduce the subjective nature of mould dam-aged estimation by the use digital image processing (DIP) techniques. A four stage technique procedure, involving image preprocessing, Image segmentation and mould analysis and classification stage for the detection of mould growth is examined in this paper. Results obtained when this proposed algorithm was applied to acquired digital images collected from different infested building materials indicates the appropriateness of this method in enhancing the visual assessment and grading associated with mould growth on building material

Federated deep learning for intrusion detection in Consumer-Centric Internet of Things

Consumer-centric Internet of Things (CIoT) will play a pivotal role in the fifth industrial revolution (Industry 5.0) but it exhibits vulnerabilities that can render it susceptible to various cyberattacks. Recent studies have explored the potential of Federated Learning (FL) for privacy-preserving intrusion detection in IoT. However, the development of the FL models relied on unrealistic and irrelevant network traffic data, while also exhibiting limitations in terms of covered attack types and classification scenarios. In this paper, we develop Federated Deep Learning (FDL) models using three recent and highly relevant datasets, covering a wide range of attack types as well as binary and multi-class classification scenarios. Our findings demonstrate that the FDL models not only achieve high classification performance, comparable to traditional Centralized Deep Learning (CDL) models, in terms of accuracy (99.60±0.46%), precision (92.50±8.40%), recall (95.42±6.24%), and F1 score (93.51±7.76%) but also exhibit superior computational efficiency compared to their CDL counterparts. The FDL approach reduces the training time by 30.52-75.87%. These classification performance and computational efficiency were achieved through multiple rounds of distributed local training in FDL. Therefore, the proposed FDL framework presents a robust security solution for designing and deploying a resilient CIoT