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Simulation learning

José Luis Díaz Agea, Alejandro Megías Nicolás, Juan Antonio García Méndez, María de Gracia Adánez Martínez, César Leal Costa
BACKGROUND: One of the main challenges faced by nursing educator is using the best strategy for students to learn. In MAES© (Self-learning methodology in simulated environments), the students are guided by a facilitator, and perform their simulations in a knowledge-specific area in a self-directed learning manner. METHOD: The performance by the students in the MAES© simulation was compared to traditional Simulation-based learning (SBL). With this aim in mind, a study was conducted which quantitatively analyzed and compared the scores in SBL and MAES© scenarios from 274 students enrolled in the 4th year of the nursing degree...
February 6, 2019: Nurse Education Today
Arunaz Kumar, Tarundeep Singh, Utkarsh Bansal, Jaivir Singh, Stacey Davie, Atul Malhotra
BACKGROUND: The developing world has a significantly high risk of women and babies dying during childbirth. Interprofessional simulation training has improved birth practices and outcomes by impacting clinical and non-technical skills like communication, teamwork, leadership and effective use of resources. While these programs have become a training requirement in many high-income countries, they have not been widely introduced in the low-income, low-resource settings. QUESTION: To explore the use of a structured obstetric and neonatal emergency simulation training program in India...
February 7, 2019: Midwifery
Samaneh Kouchaki, Avraam Tapinos, David L Robertson
Algorithms in bioinformatics use textual representations of genetic information, sequences of the characters A, T, G and C represented computationally as strings or sub-strings. Signal and related image processing methods offer a rich source of alternative descriptors as they are designed to work in the presence of noisy data without the need for exact matching. Here we introduce a method, multi-resolution local binary patterns (MLBP) adapted from image processing to extract local 'texture' changes from nucleotide sequence data...
February 15, 2019: Scientific Reports
Sören R Künzel, Jasjeet S Sekhon, Peter J Bickel, Bin Yu
There is growing interest in estimating and analyzing heterogeneous treatment effects in experimental and observational studies. We describe a number of metaalgorithms that can take advantage of any supervised learning or regression method in machine learning and statistics to estimate the conditional average treatment effect (CATE) function. Metaalgorithms build on base algorithms-such as random forests (RFs), Bayesian additive regression trees (BARTs), or neural networks-to estimate the CATE, a function that the base algorithms are not designed to estimate directly...
February 15, 2019: Proceedings of the National Academy of Sciences of the United States of America
Sean Commins, Dirk Fey
Navigation and spatial memory relies on the ability to use and recall environmental landmarks relative to important locations. Such learning is thought to result from the strengthening of associations between the goal location and environmental cues. Factors that contribute to the strength of this association include cue stability, saliency and cue location. Here we combine an autoregressive random walk model, that describes goal-directed swimming behaviour, with an associative learning model to provide an integrated model of landmark learning, using the water maze task...
February 14, 2019: Scientific Reports
Ayush Amin, Jason Salsamendi, Thomas Sullivan
With the ongoing changes in graduate medical education, emphasis has been placed on simulation models to increase clinical exposure and optimize learning. In specific, high-fidelity simulation presents as a potential option for procedural-skill development in interventional radiology. With improved haptic, visual, and tactile dynamics, high-fidelity endovascular simulators have gained increasing support from trainees and certified interventionalists alike. The 2 most common high-fidelity endovascular simulators utilized today are the Procedicus VIST and ANGIO Mentor, which contain notable differences in technical features, case availability, and cost...
March 2019: Techniques in Vascular and Interventional Radiology
Zoe A Miller, Ayush Amin, Joanthan Tu, Ana Echenique, Ronald S Winokur
The current model for medical education is based on the Master-Apprentice model which was adopted into practice over a century ago. Since then, there have been many changes in healthcare and the environment in which trainees learn, practice and become proficient in procedural and critical thinking skills. The current model for medical education has however, not changed considerably in this time frame, resulting in significant limitations to trainee education. Simulator-based training is a technique which can minimize the limitations of the apprenticeship model by mitigating the effect of time constraints, increased emphasis on patient safety and satisfaction and nonstandardization of Interventional Radiology (IR) curricula...
March 2019: Techniques in Vascular and Interventional Radiology
Ana Echenique, Evelyn P Wempe
Advanced practice registered nurses, such as Nurse Practitioners (NPs), can be found working in a variety of settings. Niche practices, such as Interventional Radiology, are highly specialized areas and are often specialties in which few NPs get to orient through during their graduate nursing program clinical rotations. For the NP transitioning into an Interventional Radiology practice, formal on-the-job orientation and training can assist in gaining specialty-based knowledge and competencies in performing interventional procedures...
March 2019: Techniques in Vascular and Interventional Radiology
Zoran Tiganj, Samuel J Gershman, Per B Sederberg, Marc W Howard
Natural learners must compute an estimate of future outcomes that follow from a stimulus in continuous time. Widely used reinforcement learning algorithms discretize continuous time and estimate either transition functions from one step to the next (model-based algorithms) or a scalar value of exponentially discounted future reward using the Bellman equation (model-free algorithms). An important drawback of model-based algorithms is that computational cost grows linearly with the amount of time to be simulated...
February 14, 2019: Neural Computation
Tess Allegra Forest, Alessandra Lichtenfeld, Bryan Alvarez, Amy S Finn
In synesthesia activation in one sensory domain, such as smell or sound, triggers an involuntary and unusual secondary sensory or cognitive experience. In the present study, we ask whether the added sensory experience of synesthesia can aid statistical learning-the ability to track environmental regularities in order to segment continuous information. To investigate this, we measured statistical learning outcomes, using an aurally presented artificial language, in two groups of synesthetes alongside controls and simulated the multimodal experience of synesthesia in non-synesthetes...
February 11, 2019: Cognition
Zubaer Ibna Mannan, Shyam Prasad Adhikari, Changju Yang, Ram Kaji Budhathoki, Hyongsuk Kim, Leon Chua
In this paper, a memristive artificial neural circuit imitating the excitatory chemical synaptic transmission of biological synapse is designed. The proposed memristor-based neural circuit exhibits synaptic plasticity, one of the important neurochemical foundations for learning and memory, which is demonstrated via the efficient imitation of short-term facilitation and long-term potentiation. Moreover, the memristive artificial circuit also mimics the distinct biological attributes of strong stimulation and deficient synthesis of neurotransmitters...
February 11, 2019: IEEE Transactions on Neural Networks and Learning Systems
Wenhan Luo, Peng Sun, Fangwei Zhong, Wei Liu, Tong Zhang, Yizhou Wang
We study active object tracking, where a tracker takes visual observations (i.e., frame sequences) as inputs and produces the corresponding camera control signals as outputs (e.g., move forward, turn left, etc.). Conventional methods tackle tracking and camera control tasks separately, and the resulting system is difficult to tune jointly. Such an approach also requires significant human efforts for image labeling and expensive trial-and-error system tuning in real-world. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning...
February 14, 2019: IEEE Transactions on Pattern Analysis and Machine Intelligence
Brian M Bonk, James W Weis, Bruce Tidor
Despite tremendous progress in understanding and engineering enzymes, knowledge of how enzyme structures and their dynamics induce observed catalytic properties is incomplete, and capabilities to engineer enzymes fall far short of industrial needs. Here we investigate the structural and dynamic drivers of enzyme catalysis for the rate-limiting step of the industrially important enzyme ketol-acid reductoisomerase (KARI) and identify a region of the conformational space of the bound enzyme-substrate complex that, when populated, leads to large increases in reactivity...
February 14, 2019: Journal of the American Chemical Society
James Gilbert, Nicole Pearcy, Rupert Norman, Thomas Millat, Klaus Winzer, John King, Charlie Hodgman, Nigel Minton, Jamie Twycross
Motivation: Genome scale metabolic models (GSMMs) are increasingly important for systems biology and metabolic engineering research as they are capable of simulating complex steady-state behaviour. Constraints based models of this form can include thousands of reactions and metabolites, with many crucial pathways that only become activated in specific simulation settings. However, despite their widespread use, power and the availability of tools to aid with the construction and analysis of large scale models, little methodology is suggested for their continued management...
February 13, 2019: Bioinformatics
Debswapna Bhattacharya
Motivation: Protein structure refinement aims to bring moderately accurate template-based protein models closer to the native state through conformational sampling. However, guiding the sampling towards the native state by effectively using restraints remains a major issue in structure refinement. Results: Here, we develop a machine learning based restrained relaxation protocol that uses deep discriminative learning based binary classifiers to predict multi-resolution probabilistic restraints from the starting structure and subsequently converts these restraints to be integrated into Rosetta all-atom energy function as additional scoring terms during structure refinement...
February 13, 2019: Bioinformatics
Paul D Hutchins, Jason D Russell, Joshua J Coon
Libraries of simulated lipid fragmentation spectra enable the identification of hundreds of unique lipids from complex lipid extracts, even when the corresponding lipid reference standards do not exist. Often, these in silico libraries are generated through expert annotation of spectra to extract and model fragmentation rules common to a given lipid class. Although useful for a given sample source or instrumental platform, the time-consuming nature of this approach renders it impractical for the growing array of dissociation techniques and instrument platforms...
February 12, 2019: Journal of the American Society for Mass Spectrometry
Maxence Ernoult, Julie Grollier, Damien Querlioz
One of the biggest stakes in nanoelectronics today is to meet the needs of Artificial Intelligence by designing hardware neural networks which, by fusing computation and memory, process and learn from data with limited energy. For this purpose, memristive devices are excellent candidates to emulate synapses. A challenge, however, is to map existing learning algorithms onto a chip: for a physical implementation, a learning rule should ideally be tolerant to the typical intrinsic imperfections of such memristive devices, and local...
February 12, 2019: Scientific Reports
Lisbeth Elvira de Vries, Michael May
This study presents an evaluation of virtual laboratory simulation for educational use in the AP Degree Programme in Chemical and Biotechnical Science at University College Copenhagen in Denmark. The purpose was to test if, and how, virtual laboratory simulation could be applied to a practically oriented education such as the education of laboratory technicians-the aim being to motivate students and improve the education with new teaching tools. The study investigated how specific virtual lab simulation cases (Labster-cases) may stimulate motivation, study intensity, and learning among laboratory technician students...
February 12, 2019: Biochemistry and Molecular Biology Education
Kerm Henriksen, David Rodrick, Erin N Grace, Marjorie Shofer, P Jeffrey Brady
OBJECTIVES: Despite endorsements for greater use of systems approaches and reports from national consensus bodies calling for closer engineering/health care partnerships to improve care delivery, there has been a scarcity of effort of actually engaging the design and engineering disciplines in patient safety projects. The article describes a grant initiative undertaken by the Agency for of Healthcare Research and Quality that brings these disciplines together to test new ideas that could make health care safer...
February 9, 2019: Journal of Patient Safety
Jaclyn Conelius, Sheila Grossman, Lisa Grossman Becht
BACKGROUND AND PURPOSE: Nurse practitioners (NPs) are expected to fill gaps in providing primary care in the United States and need vital skills to meet the growing need for primary care providers. One necessary skill is managing "on-call" clinical questions/concerns by patients across the life span. To date, there are no published studies that address "on-call" simulations for family NP (FNP) students across the life span. METHODS: This quasi-experimental, mixed-methods design used a confidence scale and Krippendorff's method for content analysis of discussion pages to determine the effectiveness and confidence of simulated "on-call" scenarios for FNP students during each of their clinical courses...
February 2019: Journal of the American Association of Nurse Practitioners
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