University College Cork

About: University College Cork is a education organization based out in Cork, Ireland. It is known for research contribution in the topics: Population & Context (language use). The organization has 12056 authors who have published 28452 publications receiving 958414 citations. The organization is also known as: Coláiste na hOllscoile Corcaigh & National University of Ireland, Cork.

Microbiota regulation of the Mammalian gut-brain axis.

Abstract: The realization that the microbiota-gut-brain axis plays a critical role in health and disease has emerged over the past decade. The brain-gut axis is a bidirectional communication system between the central nervous system (CNS) and the gastrointestinal tract. Regulation of the microbiota-brain-gut axis is essential for maintaining homeostasis, including that of the CNS. The routes of this communication are not fully elucidated but include neural, humoral, immune, and metabolic pathways. A number of approaches have been used to interrogate this axis including the use of germ-free animals, probiotic agents, antibiotics, or animals exposed to pathogenic bacterial infections. Together, it is clear that the gut microbiota can be a key regulator of mood, cognition, pain, and obesity. Understanding microbiota-brain interactions is an exciting area of research which may contribute new insights into individual variations in cognition, personality, mood, sleep, and eating behavior, and how they contribute to a range of neuropsychiatric diseases ranging from affective disorders to autism and schizophrenia. Finally, the concept of psychobiotics, bacterial-based interventions with mental health benefit, is also emerging.

Bacterial strain‐specific induction of Foxp3+ T regulatory cells is protective in murine allergy models

Abstract: BACKGROUND: The incidence of atopic disease has increased dramatically during recent decades and the potential immunoregulatory influence of the microbiota in these individuals is under investigation. OBJECTIVE: The aim of our study was to identify a bacterial strain that is protective in murine allergy models and to determine if microbial induction of T regulatory cells was associated with protection from allergic inflammation. METHODS: Three microbes (Bifidobacterium breve AH1205, B. longum AH1206 and Lactobacillus salivarius AH102) of human origin were fed to newborn, adult and germ-free animals. Induction of Foxp3(+) T regulatory cells was assessed by flow cytometry. Gene array analysis was performed on Peyer's patches. Strains were also examined for their protective effects in the ovalbumin (OVA) respiratory allergy model and the OVA-cholera toxin dietary allergy model. RESULTS: Bifidobacterium longum AH1206 consumption resulted in increased numbers of Foxp3(+) T regulatory cells in infant, adult and germ-free animals. B. breve AH1205 induced Foxp3(+) T regulatory cell expansion only in infant mice while L. salivarius AH102 did not alter T regulatory cell numbers in any animal model tested. B. longum AH1206 reduced the Peyer's patch gene expression associated with antigen presentation, TLR signalling and cytokine production while increasing the expression of genes associated with retinoic acid metabolism. B. longum AH1206 protected against airway inflammation in OVA-sensitized animals and B. longum AH1206 blocked the induction of IgE to orally administered OVA. Neither B. breve AH1205 nor L. salivarius AH102 had a protective effect in either model. CONCLUSION: Bacterial strain-specific induction of Foxp3(+) T regulatory cells in vivo is associated with protection from respiratory and oral allergy.

The generation of nisin variants with enhanced activity against specific Gram-positive pathogens

Abstract: Summary Nisin is the prototype of the lantibiotic group of anti- microbial peptides. It exhibits broad spectrum inhibi- tion of Gram-positive bacteria including important food pathogens and clinically relevant antibiotic- resistant bacteria. Significantly, the gene-encoded nature of nisin means that it can be subjected to gene-based bioengineering to generate novel derivatives. Here, we take advantage of this to gener- ate the largest bank of randomly mutated nisin derivatives reported to date, with the ultimate aim of identifying variants with enhanced bioactivity. This approach led to the identification of a nisin-producing strain with enhanced bioactivity against the mastitic pathogen Streptococcus agalactiae resulting from an amino acid change in the hinge region of the peptide (K22T). Prompted by this discovery, site-directed and site-saturation mutagenesis of the hinge region resi- dues was employed, resulting in the identification of additional derivatives, most notably N20P, M21V and K22S, with enhanced bioactivity and specific activity against Gram-positive pathogens including Listeria monocytogenes and/or Staphylococcus aureus. The identification of these derivatives represents a major step forward in the bioengineering of nisin, and lantibiotics in general, and confirms that peptide engineering can deliver derivatives with enhanced antimicrobial activity against specific problematic spoilage and pathogenic microbes or against Gram- positive bacteria in general.

An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities

Abstract: The term smart manufacturing refers to a future-state of manufacturing, where the real-time transmission and analysis of data from across the factory creates manufacturing intelligence, which can be used to have a positive impact across all aspects of operations. In recent years, many initiatives and groups have been formed to advance smart manufacturing, with the most prominent being the Smart Manufacturing Leadership Coalition (SMLC), Industry 4.0, and the Industrial Internet Consortium. These initiatives comprise industry, academic and government partners, and contribute to the development of strategic policies, guidelines, and roadmaps relating to smart manufacturing adoption. In turn, many of these recommendations may be implemented using data-centric technologies, such as Big Data, Machine Learning, Simulation, Internet of Things and Cyber Physical Systems, to realise smart operations in the factory. Given the importance of machine uptime and availability in smart manufacturing, this research centres on the application of data-driven analytics to industrial equipment maintenance. The main contributions of this research are a set of data and system requirements for implementing equipment maintenance applications in industrial environments, and an information system model that provides a scalable and fault tolerant big data pipeline for integrating, processing and analysing industrial equipment data. These contributions are considered in the context of highly regulated large-scale manufacturing environments, where legacy (e.g. automation controllers) and emerging instrumentation (e.g. internet-aware smart sensors) must be supported to facilitate initial smart manufacturing efforts.