The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

Improving Access and Mental Health for Youth Through Virtual Models of Care

The overall objective of this research is to evaluate the use of a mobile health smartphone application (app) to improve the mental health of youth between the ages of 14–25 years, with symptoms of anxiety/depression. This project includes 115 youth who are accessing outpatient mental health services at one of three hospitals and two community agencies. The youth and care providers are using eHealth technology to enhance care. The technology uses mobile questionnaires to help promote self-assessment and track changes to support the plan of care. The technology also allows secure virtual treatment visits that youth can participate in through mobile devices. This longitudinal study uses participatory action research with mixed methods. The majority of participants identified themselves as Caucasian (66.9%). Expectedly, the demographics revealed that Anxiety Disorders and Mood Disorders were highly prevalent within the sample (71.9% and 67.5% respectively). Findings from the qualitative summary established that both staff and youth found the software and platform beneficial

The Impact of Digital Technologies on Public Health in Developed and Developing Countries

This open access book constitutes the refereed proceedings of the 18th International Conference on String Processing and Information Retrieval, ICOST 2020, held in Hammamet, Tunisia, in June 2020.* The 17 full papers and 23 short papers presented in this volume were carefully reviewed and selected from 49 submissions. They cover topics such as: IoT and AI solutions for e-health; biomedical and health informatics; behavior and activity monitoring; behavior and activity monitoring; and wellbeing technology. *This conference was held virtually due to the COVID-19 pandemic

A survey of multiple classifier systems as hybrid systems

A current focus of intense research in pattern classification is the combination of several classifier systems, which can be built following either the same or different models and/or datasets building approaches. These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers. This paper presents an up-to-date survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems. The article discusses major issues, such as diversity and decision fusion methods, providing a vision of the spectrum of applications that are currently being developed

Genetic studies on the mosquito vector Culex pipiens.

As duas espécies do complexo Culex pipiens com maior distribuição geográfica, Culex quinquefasciatus e Culex pipiens sensu stricto, são importantes vectores de filárias e arbovírus. Culex pipiens s.s. apresenta categorias intra-específicas definidas por características ecológicas e fisiológicas, das quais as formas pipiens e molestus têm sido implicadas na transmissão do vírus da Febre do Nilo Ocidental na Europa e América do Norte. Hibridação entre Cx. quinquefasciatus e Cx. pipiens s.s. foi documentada em algumas regiões geográficas onde ambas espécies coexistem simpatricamente. Este fenómeno também foi descrito entre as formas molestus e pipiens, em áreas de simpatria e quando existe contacto limitado em certas épocas do ano. No entanto, o impacto da hibridação na divergência genética entre as espécies ou formas está por clarificar. Além disso, a hibridação pode afectar características ecológicas/fisiológicas das espécies/formas, que podem influenciar a sua capacidade vectorial. Neste contexto, foram analisadas populações do complexo Cx. pipiens da Europa, EUA e da Macaronésia com objectivo de determinar níveis de diferenciação genética e taxas de hibridação entre os membros do complexo. As amostras de mosquitos foram obtidas por diferentes métodos de colheita no terreno e a partir de colónias laboratoriais, entre 2005 e 2011. As análises genéticas realizadas foram baseadas em microssatélites e por polimorfismos de comprimento de fragmentos amplificados. Foram efectuadas comparações abordando questões específicas a diferentes níveis taxonómicos, que estão descritas nos cinco capítulos de resultados da tese. A distribuição e níveis de hibridação entre Cx. quinquefasciatus e Cx. pipiens s.s. foram avaliados nas ilhas da Macaronésia, o que permitiu detectar híbridos (~40%) em duas ilhas do arquipélago de Cabo Verde. A distribuição destas espécies na região reflecte a biogeografia e aspectos históricos da colonização humana. A coexistência em habitats de superfície das formas molestus e pipiens na região da Comporta (Portugal), foi demostrada pela combinação de análises fenotípicas e genéticas. As análises moleculares também sugerem a existência de um padrão de introgressão assimétrica, de molestus para pipiens. Estudos adicionais, sugerem uma maior tendência da forma molestus para explorar habitats intradomiciliares/antropogénicos quando comparada com a forma pipiens. Em ambas as formas, mais de 90% das refeições sanguíneas foram realizadas em aves. Foi ainda efectuada a primeira análise genómica focada na divergência entre os genomas das formas molestus e pipiens. Esta análise indicou uma baixa divergência entre os dois genomas (1,4%–3,1%), o que é consistente com um processo de especiação simpátrica com fluxo génico. Finalmente, foram realizadas análises genéticas em amostras de Cx. pipiens s.s. colhidas na Grécia durante um surto de Febre do Nilo Ocidental, em 2010. Populações simpátricas de molestus e pipiens com introgressão assimétrica foram identificadas na região onde o surto ocorreu, enquanto uma população homogénea de molestus foi encontrada numa região sem transmissão do vírus. Estes resultados evidenciam a importância da caracterização da variação genética e das relações evolutivas entre os membros do complexo Cx. pipiens para entender o seu potencial como vectores de doenças. Também abrem novas perspectivas para a investigação da ecologia e evolução deste complexo de espécies com importância médica.The two widespread species of the Culex pipiens complex, Culex quinquefasciatus and Culex pipiens sensu stricto, are major vectors of filarial worms and arboviruses. Culex pipiens s.s. is also divided into intraspecific categories defined by ecological and physiological traits. Of these, two forms, denoted pipiens and molestus, have been implicated in West Nile virus transmission in Europe and North America. Inter-specific hybridisation between Cx. quinquefasciatus and Cx. pipiens s.s.has been documented in some geographic regions where both species occur sympatrically. Likewise, hybridisation between molestus and pipiens forms has been described in areas of sympatry or when the forms become in contact during certain times of the year. However, the impact of hybridisation on the extent of genetic divergence between species or forms remains uncertain. Moreover, hybridisation may affect ecological and physiological traits of the species/forms, which may influence their vectorial capacity. In this context, the degree of genetic differentiation and hybridisation between members of the Cx. pipiens complex was studied in populations from Europe, USA and Macaronesian islands. Mosquito samples were obtained from field collections or laboratory colonies between 2005 and 2011. Genetic analyses were based on microsatellite genotypes and amplified fragment length polymorphisms. Comparisons were made at different taxonomic levels, addressing specific questions. These are described in the five results chapters of this thesis. The distribution and hybridisation between Cx. quinquefasciatus and Cx. pipiens s.s. were assessed in Macaronesian islands. Hybrid rates ~40% were detected in two islands of the Cape Verde archipelago. The distribution of the species reflects both the islands' biogeography and historical aspects of human colonization. A combination of phenotypic and genetic analyses conducted in Comporta (Portugal) revealed the co-occurrence of molestus and pipiens forms of Cx. pipiens s.s. in aboveground habitats.Moreover, a pattern of asymmetric introgression from molestus into pipiens was found. Subsequent molecular and ecological analyses carried out in the same region suggested that the molestus form has a higher tendency to explore indoor/anthropogenic habitats, when compared with the sympatric pipiens form. In both forms, over 90% of blood meals were made on avian hosts. The first genomic scan addressing levels of genome divergence between molestus and pipiens forms was implemented. Low levels of inter-form genomic divergence (1.4%–3.1%) were detected, consistent with a process of sympatric speciation with gene flow. Finally, Cx. pipiens s.s. samples collected in Greece during a WNV outbreak in 2010 were genetically characterised. Sympatric molestus and pipiens populations with asymmetric introgression were detected in the region where the outbreak occurred, whereas a more genetically homogenous molestus population was found in a region with no WNV transmission. These results highlight the importance of characterizing patterns of genetic variation and evolutionary relations among members of the Cx. pipiens complex as a requirement for understanding the potential of these species to act as disease vectors. They also open new perspectives for further research on the ecology and evolution of this species complex of medical importance

How complex analyses of large multidimensional datasets advance psychology – examples from large-scale studies on behavior, brain imaging, and genetics

Psychology investigates the interplay of human mind, body, and its environment in health and disease. Fully understanding these complex interrelations requires comprehensive analyses across multiple modalities and multidimensional datasets. Large-scale analyses on complex datasets are the exception rather than the rule in current psychological research. At the same time, large and complex datasets are becoming increasingly available. This thesis points out benefits, challenges and adequate approaches for analyzing complex multidimensional datasets in psychology. We applied these approaches and analysis strategies in two studies. In the first publication, we reduced the dimensionality of brain activation during a working memory task based on data from a very large sample. We observed that a mainly parietally-centered brain network was associated with working memory performance and global measures of white matter integrity. In the second publication, we exhaustively assessed pairwise interaction effects of genetic markers onto epigenetic modifications of the genome. Such modifications are complex traits that can be influenced by the environment and in turn affect development and behavior. The lack of observed strong interaction effects in our study suggested that focusing on additive effects is a suitable approach for investigating the link between genetic markers and epigenetic modifications. Both studies demonstrate how psychological scientists can exploit large complex datasets by applying adequate research practices and methodologies. Further adopting these approaches will prepare psychological research for harnessing large and complex datasets, leading towards a better understanding of mental health and disease

Large-scale inference in the focally damaged human brain

Clinical outcomes in focal brain injury reflect the interactions between two distinct anatomically distributed patterns: the functional organisation of the brain and the structural distribution of injury. The challenge of understanding the functional architecture of the brain is familiar; that of understanding the lesion architecture is barely acknowledged. Yet, models of the functional consequences of focal injury are critically dependent on our knowledge of both. The studies described in this thesis seek to show how machine learning-enabled high-dimensional multivariate analysis powered by large-scale data can enhance our ability to model the relation between focal brain injury and clinical outcomes across an array of modelling applications. All studies are conducted on internationally the largest available set of MR imaging data of focal brain injury in the context of acute stroke (N=1333) and employ kernel machines at the principal modelling architecture. First, I examine lesion-deficit prediction, quantifying the ceiling on achievable predictive fidelity for high-dimensional and low-dimensional models, demonstrating the former to be substantially higher than the latter. Second, I determine the marginal value of adding unlabelled imaging data to predictive models within a semi-supervised framework, quantifying the benefit of assembling unlabelled collections of clinical imaging. Third, I compare high- and low-dimensional approaches to modelling response to therapy in two contexts: quantifying the effect of treatment at the population level (therapeutic inference) and predicting the optimal treatment in an individual patient (prescriptive inference). I demonstrate the superiority of the high-dimensional approach in both settings

MEASURING & MONITORING Plant Populations

The root of the word monitoring means to warn, and an essential purpose of monitoring is to raise a warning flag that the current course of action is not working. Monitoring is a powerful tool for identifying problems in the early stages, before they become dramatically obvious or crises. If identified early, problems can be addressed while cost-effective solutions are still available. For example, an invasive species that threatens a rare plant population is much easier to control at the initial stages of invasion, compared to eradicating it once it is well established at a site. Monitoring is also critical for measuring management success. Good monitoring can demonstrate that the current management approach is working and provide evidence supporting the continuation of current management

Machine Learning for Biomedical Application

Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images