Artificial immune systems based committee machine for classification application

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.A new adaptive learning Artificial Immune System (AIS) based committee machine is developed in this thesis. The new proposed approach efficiently tackles the general problem of clustering high-dimensional data. In addition, it helps on deriving useful decision and results related to other application domains such classification and prediction. Artificial Immune System (AIS) is a branch of computational intelligence field inspired by the biological immune system, and has gained increasing interest among researchers in the development of immune-based models and techniques to solve diverse complex computational or engineering problems. This work presents some applications of AIS techniques to health problems, and a thorough survey of existing AIS models and algorithms. The main focus of this research is devoted to building an ensemble model integrating different AIS techniques (i.e. Artificial Immune Networks, Clonal Selection, and Negative Selection) for classification applications to achieve better classification results. A new AIS-based ensemble architecture with adaptive learning features is proposed by integrating different learning and adaptation techniques to overcome individual limitations and to achieve synergetic effects through the combination of these techniques. Various techniques related to the design and enhancements of the new adaptive learning architecture are studied, including a neuro-fuzzy based detector and an optimizer using particle swarm optimization method to achieve enhanced classification performance. An evaluation study was conducted to show the performance of the new proposed adaptive learning ensemble and to compare it to alternative combining techniques. Several experiments are presented using different medical datasets for the classification problem and findings and outcomes are discussed. The new adaptive learning architecture improves the accuracy of the ensemble. Moreover, there is an improvement over the existing aggregation techniques. The outcomes, assumptions and limitations of the proposed methods with its implications for further research in this area draw this research to its conclusion

Implementation of machine learning for the evaluation of mastitis and antimicrobial resistance in dairy cows

Bovine mastitis is one of the biggest concerns in the dairy industry, where it affects sustainable milk production, farm economy and animal health. Most of the mastitis pathogens are bacterial in origin and accurate diagnosis of them enables understanding the epidemiology, outbreak prevention and rapid cure of the disease. This thesis aimed to provide a diagnostic solution that couples Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectroscopy coupled with machine learning (ML), for detecting bovine mastitis pathogens at the subspecies level based on their phenotypic characters. In Chapter 3, MALDI-TOF coupled with ML was performed to discriminate bovine mastitis-causing Streptococcus uberis based on transmission routes; contagious and environmental. S. uberis isolates collected from dairy farms across England and Wales were compared within and between farms. The findings of this chapter suggested that the proposed methodology has the potential of successful classification at the farm level. In Chapter 4, MALDI-TOF coupled with ML was performed to show proteomic differences between bovine mastitis-causing Escherichia coli isolates with different clinical outcomes (clinical and subclinical) and disease phenotype (persistent and non-persistent). The findings of this chapter showed that phenotypic differences can be detected by the proposed methodology even for genotypically identical isolates. In Chapter 5, MALDI-TOF coupled with ML was performed to differentiate benzylpenicillin signatures of bovine mastitis-causing Staphylococcus aureus isolates. The findings of this chapter presented that the proposed methodology enables fast, affordable and effective diag-nostic solution for targeting resistant bacteria in dairy cows. Having shown this methodology successfully worked for differentiating benzylpenicillin resistant and susceptible S. aureus isolates in Chapter 5, the same technique was applied to other mastitis agents Enterococcus faecalis and Enterococcus faecium and for profiling other antimicrobials besides benzylpenicillin in Chapter 6. The findings of this chapter demonstrated that MALDI-TOF coupled with ML allows monitoring the disease epidemiology and provides suggestions for adjusting farm management strategies. Taken together, this thesis highlights that MALDI-TOF coupled with ML is capable of dis-criminating bovine mastitis pathogens at subspecies level based on transmission route, clinical outcome and antimicrobial resistance profile, which could be used as a diagnostic tool for bo-vine mastitis at dairy farms

Implementation of machine learning for the evaluation of mastitis and antimicrobial resistance in dairy cows

Bovine mastitis is one of the biggest concerns in the dairy industry, where it affects sustainable milk production, farm economy and animal health. Most of the mastitis pathogens are bacterial in origin and accurate diagnosis of them enables understanding the epidemiology, outbreak prevention and rapid cure of the disease. This thesis aimed to provide a diagnostic solution that couples Matrix-Assisted Laser Desorption/Ionization-Time of Flight (MALDI-TOF) mass spectroscopy coupled with machine learning (ML), for detecting bovine mastitis pathogens at the subspecies level based on their phenotypic characters. In Chapter 3, MALDI-TOF coupled with ML was performed to discriminate bovine mastitis-causing Streptococcus uberis based on transmission routes; contagious and environmental. S. uberis isolates collected from dairy farms across England and Wales were compared within and between farms. The findings of this chapter suggested that the proposed methodology has the potential of successful classification at the farm level. In Chapter 4, MALDI-TOF coupled with ML was performed to show proteomic differences between bovine mastitis-causing Escherichia coli isolates with different clinical outcomes (clinical and subclinical) and disease phenotype (persistent and non-persistent). The findings of this chapter showed that phenotypic differences can be detected by the proposed methodology even for genotypically identical isolates. In Chapter 5, MALDI-TOF coupled with ML was performed to differentiate benzylpenicillin signatures of bovine mastitis-causing Staphylococcus aureus isolates. The findings of this chapter presented that the proposed methodology enables fast, affordable and effective diag-nostic solution for targeting resistant bacteria in dairy cows. Having shown this methodology successfully worked for differentiating benzylpenicillin resistant and susceptible S. aureus isolates in Chapter 5, the same technique was applied to other mastitis agents Enterococcus faecalis and Enterococcus faecium and for profiling other antimicrobials besides benzylpenicillin in Chapter 6. The findings of this chapter demonstrated that MALDI-TOF coupled with ML allows monitoring the disease epidemiology and provides suggestions for adjusting farm management strategies. Taken together, this thesis highlights that MALDI-TOF coupled with ML is capable of dis-criminating bovine mastitis pathogens at subspecies level based on transmission route, clinical outcome and antimicrobial resistance profile, which could be used as a diagnostic tool for bo-vine mastitis at dairy farms

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Extended–spectrum–beta-lactamases, cephalosporinases and carbapenemase-producing Escherichia coli in the human-dog interface

Tese de Doutoramento em Ciências Veterinárias na Especialidade de Ciências Biológicas e BiomédicasExtended–spectrum–beta-lactamases (ESBLs), cephalosporinases (encoded by the ESBLs and Ampc genes, respectively) and carbapenemase–producing Escherichia coli have become a major public health concern to both human and animal health. Urinary tract infections (UTI) are one of the most frequent bacterial infections in both human and companion animals. Uropathogenic E. coli (UPEC), belonging to extraintestinal pathogenic E. coli (ExPEC), is the most common bacterium isolated from companion animals. Moreover, the close contact of companion animals with humans creates opportunities for interspecies transmission of resistant bacteria and genes. E. coli from companion animals with UTI were found to harbour important antimicrobial resistance mechanisms and to belong to high-risk human clonal lineages, namely third-generation cephalosporin (3GC)-resistant E. coli O25b:H4-B2-ST131-H30/H30Rx, CC23 and ST648. In this work E. coli O25b:H4-ST131-H30/H30Rx was described for the first time in Europe in companion animals. Furthermore, the blaCMY-2 producing E. coli ST648 is the most common high-risk clonal lineage causing UTI in companion animals from the Lisbon area. Companion animals also seem to be reservoirs of bacteria and clinically important resistance genes, such β-lactams genes (classe A and C) which supports their role as reservoirs. The detection of faecal high-risk clone OXA-181-producing- E. coli ST410 strains that were closely related to uropathogenic clinical human strains was also an important finding and to our best knowledge was the first description in Portugal and Europe. These studies highligth the importance of companion animals as reservoirs of pathogenic E. coli harbouring important antimicrobial resistant genes. The emergence and spread of multidrug-resistant (MDR) E. coli in the natural environment by companion animal faecal contamination is also a concern towards animal and human health. These results point to need for control measures to prevent the dissemination of MDR ESBLs/AmpC and carbapenemases – producing bacteria from companion animals.RESUMO - Escherichia coli produtora de Beta-lactamases de Espectro Alargado e Carbapenemases na interface Homem-cão - A Escherichia coli produtora de beta-lactamases de espectro alargado (ESBLs / Ampc) e de carbapenemase tornou-se uma grande preocupação de saúde pública em termos de saúde humana e animal. As infeções do trato urinário (ITU) são uma das infeções bacterianas mais frequentes nos humanos e nos animais de companhia. A E. coli uropatogénica pertencente à família da E. coli patogénica extra-intestinal é a bactéria mais comum isolada em animais de companhia. Além disso, o contato próximo dos animais de companhia com os seres humanos permite oportunidades para a transmissão de bactérias resistentes e genes entre as espécies. Descobriu-se que E. coli de animais de companhia com ITU possuiem importantes mecanismos de resistência antimicrobiana e pertencem a linhagens clonais humanas de alto risco, nomeadamente, E. coli resistente as cefalosporinas de terceira geração (3GC) O25b: H4-B2-ST131-H30 / H30Rx, CC23 e ST648. Neste trabalho, a E. coli O25b: H4-ST131-H30 / H30Rx foi descrita pela primeira vez na Europa em animais de companhia. Além disso, a E. coli ST648 produtora de blaCMY-2 é a linhagem clonal de alto risco mais comum que causa ITU em animais de companhia na área de Lisboa. Os animais de companhia podem ser também potenciais reservatórios de bactérias e de genes de resistência clinicamente importantes, como os genes das β-lactamases (classes A e C). A detecção de estirpes fecais de linhagens clonais de alto risco E. coli produtora de OXA-181 ST410, relacionadas com estirpes clínicas uropatógenicas humanas foi também um achado importante e para nosso conhecimento foi a primeira descrição em Portugal e na Europa. Estes estudos destacam a importância dos animais de companhia como reservatórios de E. coli patogénica que contém importantes genes de resistência a antimicrobianos. O aparecimento e disseminação de E. coli multirresistente (MDR) no ambiente natural por contaminação fecal de animais de companhia é também uma preocupação para a saúde humana e animal. Estes resultados apontam para a necessidade de medidas de controlo para prevenir a disseminação de bactérias produtoras de ESBLs / AmpC e carbapenemases por animais de companhia.N/

Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) represents one of the most significant global health issues, given its high prevalence and the challenging nature and physiology of the liver and hepatic surgery, in its many forms. This means that the most appropriate management for HCC should incorporate a multidisciplinary approach, combining the expertise from several different specialties. This book showcases the various steps in the development, diagnosis, staging, and management of HCC and provides views and thoughts from true experts in the field. As such, it is a useful resource for any physician or surgeon, whether training or practicing, who is interested in caring for patients with HCC