University of Seville

About: University of Seville is a education organization based out in Seville, Andalucía, Spain. It is known for research contribution in the topics: Population & Model predictive control. The organization has 20098 authors who have published 47317 publications receiving 947007 citations. The organization is also known as: Universidad de Sevilla.

Predictive Monitoring of Business Processes: A Survey

Abstract: Nowadays, process mining is becoming a growing area of interest in business process management (BPM). Process mining consists in the extraction of information from the event logs of a business process. From this information, we can discover process models, monitor and improve our processes. One of the applications of process mining, is the predictive monitoring of business process. The aim of these techniques is the prediction of quantifiable metrics of a running process instance with the generation of predictive models. The most representative approaches for the runtime prediction of business process are summarized in this paper. The different types of computational predictive methods, such as statistical techniques or machine learning approaches, and certain aspects as the type of predicted values and quality evaluation metrics, have been considered for the categorization of these methods. This paper also includes a summary of the basic concepts, as well as a global overview of the process predictive monitoring area, that can be used to support future efforts of researchers and practitioners in this research field.

DNA methylation heterogeneity defines a disease spectrum in Ewing sarcoma

Abstract: Developmental tumors in children and young adults carry few genetic alterations, yet they have diverse clinical presentation. Focusing on Ewing sarcoma, we sought to establish the prevalence and characteristics of epigenetic heterogeneity in genetically homogeneous cancers. We performed genome-scale DNA methylation sequencing for a large cohort of Ewing sarcoma tumors and analyzed epigenetic heterogeneity on three levels: between cancers, between tumors, and within tumors. We observed consistent DNA hypomethylation at enhancers regulated by the disease-defining EWS-FLI1 fusion protein, thus establishing epigenomic enhancer reprogramming as a ubiquitous and characteristic feature of Ewing sarcoma. DNA methylation differences between tumors identified a continuous disease spectrum underlying Ewing sarcoma, which reflected the strength of an EWS-FLI1 regulatory signature and a continuum between mesenchymal and stem cell signatures. There was substantial epigenetic heterogeneity within tumors, particularly in patients with metastatic disease. In summary, our study provides a comprehensive assessment of epigenetic heterogeneity in Ewing sarcoma and thereby highlights the importance of considering nongenetic aspects of tumor heterogeneity in the context of cancer biology and personalized medicine.

Inclusive education in higher education: challenges and opportunities

Abstract: Implementing the principles of inclusive education within higher education can be challenging. Inclusive education was originally developed for younger students, prior to its application within hig...

An intelligent system for false alarm reduction in infrared forest-fire detection

Abstract: Forest fires cause many environmental disasters, creating economical and ecological damage as well as endangering people's lives. Heightened interest in automatic surveillance and early forest-fire detection has taken precedence over traditional human surveillance because the latter's subjectivity affects detection reliability, which is the main issue for forest-fire detection systems. In current systems, the process is tedious, and human operators must manually validate many false alarms. Our approach, the False Alarm Reduction system, proposes an alternative real-time infrared-visual system that overcomes this problem. The FAR system consists of applying new infrared-image processing techniques and artificial neural networks (ANNs), using additional information from meteorological sensors and from a geographical information database, taking advantage of the information redundancy from visual and infrared cameras through a matching process, and designing a fuzzy expert rule base to develop a decision function. Furthermore, the system provides the human operator with new software tools to verify alarms.

Attachment of bacteria to the roots of higher plants.

Abstract: Attachment of soil bacteria to plant cells is supposedly the very early step required in plant-microbe interactions. Attachment also is an initial step for the formation of microbial biofilms on plant roots. For the rhizobia-legume symbiosis, various mechanisms and diverse surface molecules of both partners have been proposed to mediate in this process. The first phase of attachment is a weak, reversible, and unspecific binding in which plant lectins, a Ca(+2)-binding bacterial protein (rhicadhesin), and bacterial surface polysaccharide appear to be involved. The second attachment step requires the synthesis of bacterial cellulose fibrils that cause a tight and irreversible binding of the bacteria to the roots. Cyclic glucans, capsular polysaccharide, and cellulose fibrils also appear to be involved in the attachment of Agrobacterium to plant cells. Attachment of Azospirillum brasilense to cereals roots also can be divided in two different steps. Bacterial surface proteins, capsular polysaccharide and flagella appear to govern the first binding step while extracellular polysaccharide is involved in the second step. Outer cell surface proteins and pili are implicated in the adherence of Pseudomonas species to plant roots.