Reutilization of diagnostic cases by adaptation of knowledge models.

International audienceThis paper deals with design of knowledge oriented diagnostic system. Two challenges are addressed. The first one concerns the elicitation of expert practice and the proposition of a methodology for developing four knowledge containers of case based reasoning system. The second one concerns the proposition of a general adaptation phase to reuse case solving diagnostic problems in a different context. In most cases, adaptation methods are application-specific and the challenge in this work is to make a general adaptation method for the field of industrial diagnostics applications. This paper is a contribution to fill this gap in the field of fault diagnostic and repair assistance of equipment. The proposed adaptation algorithm relies on hierarchy descriptors, an implied context model and dependencies between problems and solutions of the source cases. In addition, one can note that the first retrieved case is not necessarily the most adaptable case, and to take into account this report, an adaptation-guided retrieval step based on a similarity measure associated with an adaptation measure is realized on the diagnostic problem. These two measures allow selecting the most adaptable case among the retrieved cases. The two retrieval and adaptation phases are applied on real industrial system called Supervised industrial system of Transfer of pallets (SISTRE)

PETRA: Process Evolution using a TRAce-based system on a maintenance platform

To meet increasing needs in the field of maintenance, we studied the dynamic aspect of process and services on a maintenance platform, a major challenge in process mining and knowledge engineering. Hence, we propose a dynamic experience feedback approach to exploit maintenance process behaviors in real execution of the maintenance platform. An active learning process exploiting event log is introduced by taking into account the dynamic aspect of knowledge using trace engineering. Our proposal makes explicit the underlying knowledge of platform users by means of a trace-based system called “PETRA”. The goal of this system is to extract new knowledge rules about transitions and activities in maintenance processes from previous platform executions as well as its user (i.e. maintenance operators) interactions. While following a Knowledge Traces Discovery process and handling the maintenance ontology IMAMO, “PETRA” is composed of three main subsystems: tracking, learning and knowledge capitalization. The capitalized rules are shared in the platform knowledge base in order to be reused in future process executions. The feasibility of this method is proven through concrete use cases involving four maintenance processes and their simulation

Metabolic adaptations of Uropathogenic E. coli in the urinary tract

© 2017 Mann, Mediati, Duggin, Harry and Bottomley. Escherichia coli ordinarily resides in the lower gastrointestinal tract in humans, but some strains, known as Uropathogenic E. coli (UPEC), are also adapted to the relatively harsh environment of the urinary tract. Infections of the urine, bladder and kidneys by UPEC may lead to potentially fatal bloodstream infections. To survive this range of conditions, UPEC strains must have broad and flexible metabolic capabilities and efficiently utilize scarce essential nutrients. Whole-organism (or “omics”) methods have recently provided significant advances in our understanding of the importance of metabolic adaptation in the success of UPECs. Here we describe the nutritional and metabolic requirements for UPEC infection in these environments, and focus on particular metabolic responses and adaptations of UPEC that appear to be essential for survival in the urinary tract

Experience feedback in product lifecycle management

Given the popularity of industrial enterprises for Product Lifecycle Management (PLM) information systems capable of supporting the entire product development process, we see the emergence of new needs and new research directions in the operation of these dynamic complex environments. Reference standards are applicable to the services and industries which bring innovation and technologies to a fastgrowing and demanding market. To obtain perfect control of business risks and performance and to ensure “zero defect”, standards specific to the fields of transport, emergency (IRIS IN 9100 ...) and generic standards (ISO 9001 ...) are more restrictive. They involve full transparency and rigor in flawless quality management processes and monitoring products. In this field, knowledge management is paramount; it helps improve overall performance of industrial systems by structuring the information assets acquired by the company stakeholders. In a way, it is the substantive development of our research. We detailed the approach adopted to implement the Experience Feedback (EF) system dedicated to the product in the PLM business. We presented a first action with the objective of formalizing the implicit experiences generated following the response to a triggering event. In this work, we mainly considered negative events for which the information to be collected are clearly identified. We propose an approach combining Problem Solving and EF adapting the level of commitment to the criticality or importance of the problem addressed. To instantiate this approach in PLM, we have chosen to rely on the Change Management Process (CMP) because, firstly, it involves changes in product data and, secondly, it usually concerns driving developments for correction or improvement of the technical specifications related to the production process

Functional diversity of 2-oxoglutarate/Fe(II)-dependent dioxygenases in plant metabolism

Oxidative enzymes catalyze many different reactions in plant metabolism. Among this suite of enzymes are the 2-oxoglutarate/Fe(II)-dependent dioxygenases (2-ODDs). Cytochromes P450 (CYPs) as often considered the most versatile oxidative enzymes in nature, but the diversity and complexity of reactions catalyzed by 2-ODDs is superior to the CYPs. The list of oxidative reactions catalyzed by 2-ODDs includes hydroxylations, demethylations, desaturations, ring closure, ring cleavage, epimerization, rearrangement, halogenation, and demethylenation. Furthermore, recent work, including the discovery of 2-ODDs involved in epigenetic regulation, and others catalyzing several characteristic steps in specialized metabolic pathways, support the argument that 2-ODDs are among the most versatile and important oxidizing biological catalysts. In this review, we survey and summarize the pertinent literature with a focus on several key reactions catalyzed by 2-ODDs, and discuss the significance and impact of these enzymes in plant metabolism

Desarrollo e integración de ontologías para la representación de pacientes COVID-19

Desde que se identificó el primer caso conocido de síndrome respiratorio agudo severo coronavirus 2 (SARS-CoV-2) en Wuhan, China, en diciembre de 2019, la enfermedad se ha extendido por todo el mundo, dando lugar a la pandemia actual de COVID-19 que causa síntomas variables en las personas, pero a menudo incluyen fiebre, tos seca, dolor de cabeza, fatiga, dificultad para respirar, perdida del olfato y perdida del gusto, de aquellos que desarrollan síntomas lo suficientemente notables como para ser clasificados como pacientes, la mayoría desarrolla síntomas leves a moderados, mientras que el 14% desarrolla síntomas graves y el 5% sufre síntomas críticos, por lo que deben ser atendidos en centros especializados. De modo que cada paciente reacciona de forma diferente ante la enfermedad, el saber cuáles son los factores que determinan la condición de un paciente es importante para conocer el comportamiento de la enfermedad, por ello se considera implementar bases de conocimientos que permitan generar nuevo conocimiento en base a los hechos establecidos de la información de pacientes, por ejemplo, el tratamiento de los pacientes. Por tal motivo, en este proyecto de investigación se reporta el diseño y desarrollo un sistema de modelos ontológicos integrados para la representación y administración de perfiles de pacientes con COVID-19: diagnóstico y tratamiento, donde los resultados de las diferentes evaluaciones realizadas a dicho modelo, muestran la factibilidad de utilizar esta base de conocimiento integrada por ontologías para la representación de la información clínica del paciente y obtener nuevo conocimiento y servir como base para proyectos de investigación a futuro.Since the first known case of severe acute respiratory syndrome coronavirus 2 (SARSCoV- 2) was identified in Wuhan, China, in December 2019, the disease has spread throughout the world, giving rise to the current COVID pandemic. -19 which causes variable symptoms in people, but often include fever, dry cough, headache, fatigue, shortness of breath, loss of smell, and loss of taste, of those who develop symptoms notable enough to be classified as patients, the majority develop mild to moderate symptoms, while 14% develop severe symptoms and 5% suffer critical symptoms, so they must be treated in specialized centers. So that each patient reacts differently to the disease, adequate knowledge of the factors that determine the condition of a patient is important to know the behavior of the disease, for this reason, it is considered to implement knowledge bases that allow generating new knowledge is based on established facts of patient information, for example, treatment of patients. For this reason, this research project reports the design and development of a system of integrated ontological models for the representation and administration of profiles of patients with COVID-19: diagnosis and treatment, where the results of the different evaluations carried out on the said model, show the feasibility of using this knowledge base integrated by ontologies to represent the patient’s clinical information and obtain new knowledge and serve as a basis for future research projects