Quantification of Flavin-containing Monooxygenases 1, 3, and 5 in Human Liver Microsomes by UPLC-MRM-Based Targeted Quantitative Proteomics and Its Application to the Study of Ontogeny

Flavin-containing monooxygenases (FMOs) have a significant role in the metabolism of small molecule pharmaceuticals. Among the five human FMOs, FMO1, FMO3, and FMO5 are the most relevant to hepatic drug metabolism. Although age-dependent hepatic protein expression, based on immunoquantification, has been reported previously for FMO1 and FMO3, there is very little information on hepatic FMO5 protein expression. To overcome the limitations of immunoquantification, an ultra-performance liquid chromatography (UPLC)-multiple reaction monitoring (MRM)-based targeted quantitative proteomic method was developed and optimized for the quantification of FMO1, FMO3, and FMO5 in human liver microsomes (HLM). A post-in silico product ion screening process was incorporated to verify LC-MRM detection of potential signature peptides before their synthesis. The developed method was validated by correlating marker substrate activity and protein expression in a panel of adult individual donor HLM (age 39–67 years). The mean (range) protein expression of FMO3 and FMO5 was 46 (26–65) pmol/mg HLM protein and 27 (11.5–49) pmol/mg HLM protein, respectively. To demonstrate quantification of FMO1, a panel of fetal individual donor HLM (gestational age 14–20 weeks) was analyzed. The mean (range) FMO1 protein expression was 7.0 (4.9–9.7) pmol/mg HLM protein. Furthermore, the ontogenetic protein expression of FMO5 was evaluated in fetal, pediatric, and adult HLM. The quantification of FMO proteins also was compared using two different calibration standards, recombinant proteins versus synthetic signature peptides, to assess the ratio between holoprotein versus total protein. In conclusion, a UPLC-MRM-based targeted quantitative proteomic method has been developed for the quantification of FMO enzymes in HLM

Employing linked data and dialogue for modelling cultural awareness of a user

YesIntercultural competence is an essential 21st Century skill. A key issue for developers of cross-cultural training simulators is the need to provide relevant learning experience adapted to the learner’s abilities. This paper presents a dialogic approach for a quick assessment of the depth of a learner's current intercultural awareness as part of the EU ImREAL project. To support the dialogue, Linked Data is seen as a rich knowledge base for a diverse range of resources on cultural aspects. This paper investigates how semantic technologies could be used to: (a) extract a pool of concrete culturally-relevant facts from DBpedia that can be linked to various cultural groups and to the learner, (b) model a learner's knowledge on a selected set of cultural themes and (c) provide a novel, adaptive and user-friendly, user modelling dialogue for cultural awareness. The usability and usefulness of the approach is evaluated by CrowdFlower and Expert Inspection

Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement

YesWith the advancement of disruptive new technologies, there has been a considerable focus on personalisation as an important component in nurturing users' engagement. In the context of smart cities, Internet of Things (IoT) offer a unique opportunity to help empower citizens and improve societies' engagement with their governments at both micro and macro levels. This study aims to examine the role of perceived value of IoT in improving citizens' engagement with public services. A survey of 313 citizens in the UK, engaging in various public services, enabled through IoT, found that the perceived value of IoT is strongly influenced by empowerment, perceived usefulness and privacy related issues resulting in significantly affecting their continuous use intentions. The study offers valuable insights into the importance of perceived value of IoT-enabled services, while at the same time, providing an intersectional perspective of UK citizens towards the use of disruptive new technologies in the public sector

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

A note on exploration of IoT generated big data using semantics

yesWelcome to this special issue of the Future Generation Computer Systems (FGCS) journal. The special issue compiles seven technical contributions that significantly advance the state-of-the-art in exploration of Internet of Things (IoT) generated big data using semantic web techniques and technologies

Temporal Logic Control of POMDPs via Label-based Stochastic Simulation Relations

The synthesis of controllers guaranteeing linear temporal logic specifications on partially observable Markov decision processes (POMDP) via their belief models causes computational issues due to the continuous spaces. In this work, we construct a finite-state abstraction on which a control policy is synthesized and refined back to the original belief model. We introduce a new notion of label-based approximate stochastic simulation to quantify the deviation between belief models. We develop a robust synthesis methodology that yields a lower bound on the satisfaction probability, by compensating for deviations a priori, and that utilizes a less conservative control refinement

Interactive semantic feedback for intuitive ontology authoring

The complexity of ontology authoring and the difficulty to master the use of existing ontology authoring tools, put significant constraints on the involvement of both domain experts and knowledge engineers in ontology authoring. This often requires substantial effort for fixing ontologies defects (e.g. inconsistency, unsatisfiability, missing or unintended implications, redundancy, isolated entities). The paper argues that ontology authoring tools should provide immediate semantic feedback upon entering ontological constructs. We present a framework to analyse input axioms and provide meaningful feedback at a semantic level. The framework has been used to augment an existing Controlled Natural Language-based ontology authoring tool – ROO. An experimental study with ROO has been conducted to examine users' reactions to the semantic feedback and the effect on their ontology authoring behaviour. The study strongly supported responsive intuitive ontology authoring tools, and identified future directions to extend and integrate semantic feedback

MiR-15a/miR-16-1 expression inversely correlates with cyclin D1 levels in Men1 pituitary NETs

Multiple Endocrine Neoplasia type 1 (MEN1) is an autosomal dominant disorder characterised by the combined occurrence of parathyroid, pituitary and pancreatic islet tumours, and is due to mutations of the MEN1 gene, which encodes the tumour suppressor protein menin. Menin has multiple roles in genome stability, transcription, cell division and proliferation, but its mechanistic roles in tumourigenesis remain to be fully elucidated. MicroRNAs (miRNA) are non-coding single stranded RNAs that post-transcriptionally regulate gene expression and have been associated with tumour development, although the contribution of miRNAs to MEN1-associated tumourigenesis and their relationship with menin expression are not fully understood. Alterations in miRNA expression, including downregulation of three putative ‘tumour suppressor’ miRNAs, miR-15a, miR-16-1 and let 7a, have been reported in several tumour types including non-MEN1 pituitary adenomas. We have therefore investigated the expression of miR-15a, miR-16-1 and let-7a in pituitary tumours that developed after 12 months of age in female mice with heterozygous knock out of the Men1 gene (Men1+/- 41 mice). The miRNAs miR-15a, miR-16-1 and let-7a were significantly downregulated in pituitary tumours (by 2.3-fold, p<0.05; 2.1-fold p<0.01 and 1.6-fold p<0.05, respectively) of Men1+/- 43 mice, compared to normal wild type pituitaries. MiR-15a and miR-16-1 expression inversely correlated with expression of cyclin D1, a known pro-tumourigenic target of these miRNAs, and knock down of menin in a human cancer cell line (HeLa), and AtT20 mouse pituitary cell line resulted in significantly decreased expression of miR-15a (p<0.05), indicating that the decrease in miR-15a may be a direct result of lost menin expression

Developing Feedback Based Robotic Manufacturing Method for Earth-Based Materials

Although earth-based materials have the advantage of being locally sourced and have low embodied emissions, they can have an unpredictable material behavior due to their heterogeneous composition which potentially limits their use in manufacturing. As a result, it becomes challenging to standardise and maintain quality outcomes. Moreover, current industry methods are labour-intensive and require a high level of traditional knowledge. This research explores and develops a fabrication methodology for earthen materials that is location-agnostic. It involves an array of fabrication approaches, including the development of a robotic 'Impact Printing' setup using a UR10 robot and a custom tool to pick, place, and mechanically compact earth blocks. The 'Feedback System' employs Kinect 2.0 to scan the deformation of earth materials observed during fabrication and a computational algorithm to generate accurate and adapted toolpaths for the position and compaction of earthen blocks in real-time. To push the boundaries of architectural design for traditional building materials, the study investigates the construction of a closed Nubian vault using the aforementioned techniques and tools. Through the optimization of material behavior and manufacturing processes, the research opens up a pathway for automated onsite earth construction