Research progress on long non-coding RNAs for spinal cord injury

Abstract Spinal cord injury is a complex central nervous system disease with an unsatisfactory prognosis, often accompanied by multiple pathological processes. However, the underlying mechanisms of action of this disease are unclear, and there are no suitable targeted therapeutic options. Long non-coding RNA mediates a variety of neurological diseases and regulates various biological processes, including apoptosis and autophagy, inflammatory response, microenvironment, and oxidative stress. It is known that long non-coding RNAs have significant differences in gene expression in spinal cord injury. To further understand the mechanism of long non-coding RNA action in spinal cord injury and develop preventive and therapeutic strategies regarding spinal cord injury, this review outlines the current status of research between long non-coding RNAs and spinal cord injury and potential long non-coding RNAs targeting spinal cord injury

Ontology-Based Semantic Modeling for Automated Identification of Damage Mechanisms in Process Plants

Part 13: Semantics in Networks of Cognitive SystemsInternational audienceDamage mechanisms reduce the ability of equipment to deliver its intended function and, thus, increase the equipment’s probability of failure. Damage mechanism assessment is performed to identify the credible damage mechanisms of the equipment; thereby, appropriate measures can be applied to prevent failures. However, due to its high dependency on human cognition, damage mechanism assessment is error-prone and time-consuming. Additionally, due to its multi-disciplinary nature, the damage mechanism assessment process requires unambiguous communication and synchronization of perspectives among collaborating parties from different knowledge domains. Thus, the Damage Mechanism Identification Ontology (DMIO), supported by Web Ontology Language axioms and Semantic Web Rule Language rules, is proposed to conceptualize damage mechanism knowledge in both a human- and machine-interpretable manner and to enable automation of the damage mechanism identification task. The implementation of DMIO is expected to create a leaner damage mechanism assessment process by reducing the lead-time to perform the assessment, improving the quality of assessment results, and enabling more effective and efficient communication and collaboration among parties during the assessment process