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Imaging approach to mechanistic study of nanoparticle interactions with the blood-brain barrier
No full textUnderstanding nanoparticle interactions with the central nervous system, in particular the blood-brain barrier, is key to advances in therapeutics, as well as assessing the safety of nanoparticles. Challenges in achieving insights have been significant, even for relatively simple models. Here we use a combination of live cell imaging and computational analysis to directly study nanoparticle translocation across a human in vitro blood-brain barrier model. This approach allows us to identify and avoid problems in more conventional inferential in vitro measurements by identifying the catalogue of events of barrier internalization and translocation as they occur. Potentially this approach opens up the window of applicability of in vitro models, thereby enabling in depth mechanistic studies in the future. Model nanoparticles are used to illustrate the method. For those, we find that translocation, though rare, appears to take place. On the other hand, barrier uptake is efficient, and since barrier export is small, there is significant accumulation within the barrier. © 2014 American Chemical Society.European Commission - Seventh Framework Programme (FP7)Science Foundation Ireland -- replaceIrish Government’s Programme for Research in Third Level Institution
SCLpred-EMS: Subcellular localization prediction of endomembrane system and secretory pathway proteins by Deep N-to-1 Convolutional Neural Networks
No full textMotivation: The subcellular location of a protein can provide useful information for protein function prediction and drug design. Experimentally determining the subcellular location of a protein is an expensive and time-consuming task. Therefore, various computer-based tools have been developed, mostly using machine learning algorithms, to predict the subcellular location of proteins. Results: Here, we present a neural network-based algorithm for protein subcellular location prediction. We introduce SCLpred-EMS a subcellular localization predictor powered by an ensemble of Deep N-to-1 Convolutional Neural Networks. SCLpred-EMS predicts the subcellular location of a protein into two classes, the endomembrane system and secretory pathway versus all others, with a Matthews correlation coefficient of 0.75-0.86 outperforming the other state-of-the-art web servers we tested. Contact: [email protected] Research Counci
An exploration of the professional development needs of agricultural teachers in their role as educators
No full textAgricultural education plays a fundamental role in developing the future generation of young farmers. Over the past number of years, agriculture has undergone significant change with regard to production and consideration for the landscape and the environment within which farming communities reside. Conversely, education has experienced paradigm shifts relating to education programme delivery, instructional techniques, and the role of the teacher within the educational context. The purpose of this thesis is to explore the professional development needs of the agricultural educators involved in the delivery of vocational agricultural education and training programmes to the future generation of young farmers. Much research has been conducted regarding learners’ needs, the importance of vocational education and training programmes, and the learner experience within the vocational education and training sphere, however, little is known about the teachers’ experience in the delivery of such programmes. In addressing this intellectual gap, the thesis employs a mixed methods research paradigm consisting of three distinct research phases. The first phase employs an explanatory sequential mixed methods research design process to identify the specific professional development needs of the agricultural teaching population within the vocational education and training sector. The second phase furthers conceptual understanding of agricultural teacher professional development needs through the use of an exploratory sequential mixed methods research design to develop a professional development tool appropriate to the needs of the agricultural teaching population. Finally, the third research phase contributes a nuanced understanding of the primary and secondary motivators influencing young peoples’ further educational choice through the use of an in-depth exploratory research design. Findings from each phase in the data collection process are documented in the form of academic peer-reviewed paper publications developed based on findings within this thesis. In conclusion, this thesis contributes to theoretical and conceptual understandings of the study phenomenon relating to agricultural teacher professional development needs. The influence of policy, both nationally and internationally, are considered throughout the thesis given the effect of young farmer intervention on agricultural education programmes and the recruitment of agricultural teachers within the vocational education and training context.Teagas
Emergency response in educational policies during COVID-19 in Nepal: a critical review
No full textThe COVID-19 pandemic has brought chaos in education across the world, including developing countries like Nepal. To respond to this educational disruption in this South Asian country, different educational plans and policies were formulated by the Ministry of Education, Science and Technology, Government of Nepal. It is not known whether these policies were realistic and practicable, as there is no review of these documents to date. With this backdrop, this paper critically reviews the educational plans and policies that were developed to manage education during the crisis. It appraises the strengths of these policies in terms of their intent and practicalities of implementation in the given situation, and identifies gaps and challenges, and recommends some ways to realistically run the education system. The review reveals that these documents have several strengths, such as they plan to create data in terms of learners’ access to resources, value self-learning and parent education, and suggest several alternative ways to resume school. Yet, there are some gaps and challenges, the identification of which can guide the effective delivery of education in Nepal in any kind of crisis period both at present and in future. This paper is expected to help policy makers to revisit the existing policies or guide them when they form future educational policies that are designed to manage education in any kinds of crisis. It is also deemed helpful for teacher educators, practitioners and other educational stakeholders to understand about the educational plans and policies formed to deal with crises
Optimization of RIS-aided MIMO Systems via the Cutoff Rate
No full textThe main difficulty concerning optimizing the mutual information (MI) in reconfigurable intelligent surface (RIS)-aided communication systems with discrete signaling is the inability to formulate this optimization problem in an analytically tractable manner. Therefore, we propose to use the cutoff rate (CR) as a more tractable metric for optimizing the MI and introduce two optimization methods to maximize the CR. The first method is based on the projected gradient method (PGM), while the second method is derived from the principles of successive convex approximation (SCA). Simulation results show that the proposed optimization methods significantly enhance the CR and the corresponding MI.Science Foundation IrelandCheck issue date on checkdate -- J
Study protocol for TILDA COVID-19 survey. Altered lives in a time of crisis: preparing for recovery from the impact of the COVID-19 pandemic on the lives of older adults
No full textBackground: Older adults are the most at-risk of contracting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and among the most affected by measures put in place to prevent the spread of the virus. While the full effect of the public health measures, such as social distancing and wearing masks in public spaces, implemented since March 2020 are not yet known, it is expected that they will have a severely damaging effect on physical and psychological wellbeing. The Irish Longitudinal Study on Ageing (TILDA) has been researching the lives of older adults in Ireland since 2008 with data collection conducted at two-year intervals. With an established research infrastructure, TILDA was ideally placed to examine the effect of the coronavirus disease 2019 (COVID-19) pandemic on older adults. The aim of this study is to document the lives of older adults during the COVID-19 pandemic to understand the effect of the pandemic and public health responses on their wellbeing.
Methods: Data was collected from TILDA participants via self-completion-questionnaire (SCQ). The SCQ contains ten sections that capture information on many aspects of people’s lives during the pandemic including, changes in behaviour and social interactions, physical and psychological wellbeing indicators, healthcare utilisation, and exposure to SARS-CoV-2. Ethical approval was granted by the National Research Ethics Committee (NREC).
Conclusions: Research findings will be shared in a variety of formats including research reports and briefs, presentations, and academic papers. Data will be archived in the Irish Social Science Data Archive (ISSDA) and the Inter-university Consortium for Political and Social Research (ICPSR). As well as documenting the impact of the COVID-19 pandemic on older adults, findings from this study will provide important information to policy-makers as we respond to the damage caused by the COVID-19 pandemic
Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images
No full textBrain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed
Stigma is associated with illness self-concept in individuals with concealable chronic illnesses
No full textObjectives. Previous research suggests that chronic illnesses can elicit stigma, even
when those illnesses are concealable. Such stigmatization is assumed to lead to a
stigmatized identity. Additionally, chronic illness affects one’s self-concept, as one reconstructs a sense of self with illness incorporated. However, no research has examined the interplay between stigma and self-concept in those with concealable chronic illnesses. Therefore, we investigated the extent to which experienced, anticipated, and internalized stigma are associated with illness self-concept in individuals living with concealable chronic illnesses. Furthermore, we explored if the aforementioned aspects of stigma are associated with enrichment in the self-concept in the same cohort
Proof-of-concept techniques for generating synthetic thermal facial data for training of deep learning models
No full textThermal imaging has played a dynamic role in the diversified field of consumer technology applications. To build artificially intelligent thermal imaging systems, large scale thermal datasets are required for successful convergence of complex deep learning models. In this study, we have highlighted various techniques for generating large scale synthetic facial thermal data using both public and locally gathered datasets. It includes data augmentation, synthetic data generation using StyleGAN network, and 2D to 3D image reconstruction using deep learning architectures. Training and validation accuracy of Wide ResNet CNN for binary gender recognition task is improved by 4.6% and 4.4% using original and newly generated synthetic data with an overall test accuracy of 83.33%.This synthetic thermal data study for the training of deep
learning models to perform gender classification using the
public as well locally gathered dataset acquired from prototype
thermal camera is part of the project that has received funding
from the ECSEL Joint Undertaking (JU) under grant agreement
No 826131. The JU receives support from the European
Union’s Horizon 2020 research and innovation program and
National funding from France, Germany, Ireland (Enterprise
Ireland International Research Fund), and Italy
Novel Approaches to Probe the Activity of Deubiquitinating Enzymes
No full textUbiquitination is a highly conserved post-translational modification that regulates a
multitude of critical cellular events. This process is orchestrated by a complex
enzymatic network. Deubiquitinating enzymes (DUBs) are responsible for removal
of ubiquitin from its conjugates. There are over 100 DUBs expressed in eukaryotes
and several diseases are associated with their dysregulation, including cancer and
neurodegeneration. Activity-based probes (ABPs) have been developed as an
effective strategy to study DUBs.
ABPs targeting these enzymes are typically based on a ubiquitin scaffold and
incorporate an electrophilic group that reacts with an active site cysteine. These
probes have greatly enhanced the mechanistic and structural understanding of
DUBs but offer no external control over the timing of the reaction. In this work, ABPs
consisting of a monoubiquitin recognition element and warheads with reactivity
previously unexplored in the context of ABPs were designed and synthesised. The
reactivity of these novel ABPs with DUBs was examined and new labelling
strategies were developed to improve the study of these enzymes.
New electrophilic probes with fluoride reactive groups were designed and
synthesised. These probes were tested alongside previously reported electrophilic
probes and demonstrated negligible reactivity with DUBs.
Latent ubiquitin-based probes that target DUBs via a site selective, photoinitiated
thiol-ene coupling mechanism were developed. A novel labelling methodology was
developed for these alkene-functionalised probes and reactivity was demonstrated
against recombinant DUBs and endogenous DUBs within cell lysate. Specificity of
this probe labelling was confirmed using inhibitor and proteomic studies. Novel
assays to probe reversible and irreversible inhibitors for this enzyme were also
demonstrated.
In order to enhance the biocompatibility of this methodology, a milder source of UV
light was used to initiate the reaction between DUBs and the alkene-functionalised
probes. Alternative radical initiators were examined for a visible light-mediated
thiol-ene reaction. Successful labelling of recombinant DUBs was achieved in both
strategies, but visible light activation was limited by off-target reactivity in more
complex systems. The visible light-mediated thiol-ene reaction and the thiol-yne
reaction were also explored for non-templated protein conjugation, affording modest
coupling in both cases.
Overall, the novel electrophilic probes presented do not improve upon existing
probes of similar reactivity. However, the work on alkene functionalised probes
enables more finely resolved investigations of DUB activity in complex systems. In
contrast to existing cysteine reactive probes, control over the timing of the
enzyme-probe reaction is possible for the alkene functionalised probes as they are
completely inert under ambient conditions, even upon probe binding. This is
expected to help provide a better understanding of these enzymes and aid in the
study and development of novel inhibitors. The visible light-mediated thiol-ene and
thiol-yne reactions were found to have limited applications for bioconjugations