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Developing a competency framework for managers to address suicide risk in the workplace
No full textEmployee mental health, and in particular, suicide risks, are things that managers often do not feel comfortable in addressing, leading to lack of knowledge, awareness, and support within an organization. The purpose of this research was to investigate the competencies required by managers to enable them to effectively address suicide risks arising with employees. Suicide-related ideations are thought to be characterized by experiences of
burdensomeness and thwarted connectedness. Drawing on clinical, managerial, and adaptive performance competencies, we examined competencies related to creating meaningfulness (as a counter to burdensomeness) and addressing employee\u27s need for relatedness (as a counter to thwarted connectedness) in terms of how managers assist employees presenting with suicide-related ideations in the workplace. To investigate this and develop a competency framework, we conducted qualitative interviews with 18 managers, drawing on existing interview protocols of critical incidents and behavioral event interviews for the elicitation of competencies. Competencies in adaptive performance (and particularly crisis management) emerged as important for facilitating managers’ interactions with employees who may present with suicidality. This research provides a first step in developing resources to equip managers with the necessary competencies that are needed to deal with employ experiencing suicide-related ideations
(i.e. perceived burdensomeness and thwarted connectedness). The framework is
also useful as an initial step to support human resource development (HRD) professionals develop interventions such as training and/or mentoring programs
for managers to address this very important issu
Understanding intracellular nanoparticle trafficking fates through spatiotemporally resolved magnetic nanoparticle recovery
No full textThe field of nanomedicine has the potential to be a game-changer in global health, with possible applications in prevention, diagnostics, and therapeutics. However, despite extensive research focus and funding, the forecasted explosion of novel nanomedicines is yet to materialize. We believe that clinical translation is ultimately hampered by a lack of understanding of how nanoparticles really interact with biological systems. When placed in a biological environment, nanoparticles adsorb a biomolecular layer that defines their biological identity. The challenge for bionanoscience is therefore to understand the evolution of the interactions of the nanoparticle–biomolecules complex as the nanoparticle is trafficked through the intracellular environment. However, to progress on this route, scientists face major challenges associated with isolation of specific intracellular compartments for analysis, complicated by the diversity of trafficking events happening simultaneously and the lack of synchronization between individual events. In this perspective article, we reflect on how magnetic nanoparticles can help to tackle some of these challenges as part of an overall workflow and act as a useful platform to investigate the bionano interactions within the cell that contribute to this nanoscale decision making. We discuss both established and emerging techniques for the magnetic extraction of nanoparticles and how they can potentially be used as tools to study the intracellular journey of nanomaterials inside the cell, and their potential to probe nanoscale decision-making events. We outline the inherent limitations of these techniques when investigating particular bio-nano interactions along with proposed strategies to improve both specificity and resolution. We conclude by describing how the integration of magnetic nanoparticle recovery with sophisticated analysis at the single-particle level could be applied to resolve key questions for this field in the future.Irish Research CouncilScience Foundation Ireland -- replace defaultCeltic Advanced Life Science Innovation Network (CALIN)Ireland-Wales ProgrammeUCD School of Biomolecular and Biomedical Scienc
A Three-Dimensional Cell Culture Platform for Long Time-Scale Observations of Bio-Nano Interactions
No full textWe know surprisingly little about the long-term outcomes for nanomaterials interacting with organisms. To date, most of what we know is derived from in vivo studies that limit the range of materials studied and the scope of advanced molecular biology tools applied. Long-term in vitro nanoparticle studies are hampered by a lack of suitable models, as standard cell culture techniques present several drawbacks, while technical limitations render current three-dimensional (3D) cellular spheroid models less suited. Now, by controlling the kinetic processes of cell assembly and division in a non-Newtonian culture medium, we engineer reproducible cell clusters of controlled size and phenotype, leading to a convenient and flexible long-term 3D culture that allows nanoparticle studies over many weeks in an in vitro setting. We present applications of this model for the assessment of intracellular polymeric and silica nanoparticle persistence and found that hydrocarbon-based polymeric nanoparticles undergo no apparent degradation over long time periods with no obvious biological impact, while amorphous silica nanoparticles degrade at different rates over several weeks, depending on their synthesis method.Irish Research CouncilPrincess Nourah Bint Abdulrahman University Projec
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