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Spaced learning

Diane VonBehren, Molly M Killion, Carol Burke, Betsy Finkelmeier, Brigit Zamora
Three teams of perinatal expert nurses participated in planning and designing a new maternity unit, operationalizing the move to the new space, and evaluating care processes and workflows after the move. The hospitals involved were University of California, San Francisco Benioff Children's Hospital, Prentice Women's Hospital of Northwestern Memorial Healthcare in Chicago, IL, and Florida Hospital Orlando, Florida Hospital for Women. Although each team discussed specific details and lessons learned, there is remarkable consistency among the experiences of these teams and with the discussion of the process by the team at Mercy Hospital St...
November 2016: MCN. the American Journal of Maternal Child Nursing
Ashley Galati, Alyson Hock, Ramesh S Bhatt
Configural information (spacing between features) contributes to face-processing expertise in adulthood. We examined whether infants can be "trained" to process this information. In Experiment 1, 3.5-month-olds failed to discriminate changes in the spacing between facial features. However, in Experiments 2 and 3, infants processed the same information after being primed with faces in which the spacing was repeatedly altered. Experiment 4 found that priming was not effective with inverted faces or with faces depicting changes in features but not relations among features, indicating that the priming exhibited in Experiments 2 and 3 was specific to upright faces depicting spacing changes...
November 2016: Developmental Psychobiology
Kristen Tummeltshammer, Dima Amso, Robert M French, Natasha Z Kirkham
This study investigates whether infants are sensitive to backward and forward transitional probabilities within temporal and spatial visual streams. Two groups of 8-month-old infants were familiarized with an artificial grammar of shapes, comprising backward and forward base pairs (i.e. two shapes linked by strong backward or forward transitional probability) and part-pairs (i.e. two shapes with weak transitional probabilities in both directions). One group viewed the continuous visual stream as a temporal sequence, while the other group viewed the same stream as a spatial array...
October 16, 2016: Developmental Science
Evangelia I Zacharaki, Iosif Mporas, Kyriakos Garganis, Vasileios Megalooikonomou
Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections...
June 2016: Brain Informatics
Andreas Holzinger
Machine learning (ML) is the fastest growing field in computer science, and health informatics is among the greatest challenges. The goal of ML is to develop algorithms which can learn and improve over time and can be used for predictions. Most ML researchers concentrate on automatic machine learning (aML), where great advances have been made, for example, in speech recognition, recommender systems, or autonomous vehicles. Automatic approaches greatly benefit from big data with many training sets. However, in the health domain, sometimes we are confronted with a small number of data sets or rare events, where aML-approaches suffer of insufficient training samples...
June 2016: Brain Informatics
Cao Xiao, Jesse Bledsoe, Shouyi Wang, Wanpracha Art Chaovalitwongse, Sonya Mehta, Margaret Semrud-Clikeman, Thomas Grabowski
Today, diagnosis of attention deficit hyperactivity disorder (ADHD) still primarily relies on a series of subjective evaluations that highly rely on a doctor's experiences and intuitions from diagnostic interviews and observed behavior measures. An accurate and objective diagnosis of ADHD is still a challenge and leaves much to be desired. Many children and adults are inappropriately labeled with ADHD conditions, whereas many are left undiagnosed and untreated. Recent advances in neuroimaging studies have enabled us to search for both structural (e...
September 2016: Brain Informatics
Shanshan Wang, Jianbo Liu, Xi Peng, Pei Dong, Qiegen Liu, Dong Liang
Compressed sensing magnetic resonance imaging (CSMRI) employs image sparsity to reconstruct MR images from incoherently undersampled K-space data. Existing CSMRI approaches have exploited analysis transform, synthesis dictionary, and their variants to trigger image sparsity. Nevertheless, the accuracy, efficiency, or acceleration rate of existing CSMRI methods can still be improved due to either lack of adaptability, high complexity of the training, or insufficient sparsity promotion. To properly balance the three factors, this paper proposes a two-layer tight frame sparsifying (TRIMS) model for CSMRI by sparsifying the image with a product of a fixed tight frame and an adaptively learned tight frame...
2016: BioMed Research International
Yan Li, Ruiping Wang, Zhen Cui, Shiguang Shan, Xilin Chen
We address the problem of face video retrieval in TV-series which searches video clips based on the presence of specific character, given one face track of his/her. This is tremendously challenging because on one hand, faces in TV-series are captured in largely uncontrolled conditions with complex appearance variations, and on the other hand retrieval task typically needs efficient representation with low time and space complexity. To handle this problem, we propose a compact and discriminative representation for the huge body of video data, named Compact Video Code (CVC)...
October 10, 2016: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
C Brandon Ogbunugafor, Daniel L Hartl
Much of the public lacks a proper understanding of Darwinian evolution, a problem that can be addressed with new learning and teaching approaches to be implemented both inside the classroom and in less formal settings. Few analogies have been as successful in communicating the basics of molecular evolution as John Maynard Smith's protein space analogy (1970), in which he compared protein evolution to the transition between the terms WORD and GENE, changing one letter at a time to yield a different, meaningful word (in his example, the preferred path was WORD → WORE → GORE → GONE → GENE)...
October 2016: PLoS Computational Biology
Stylianos Hatzipanagos, Bernadette John, Yuan-Li Tiffany Chiu
BACKGROUND: Social media can support and sustain communities much better than previous generations of learning technologies, where institutional barriers undermined any initiatives for embedding formal and informal learning. Some of the many types of social media have already had an impact on student learning, based on empirical evidence. One of these, social networking, has the potential to support communication in formal and informal spaces. OBJECTIVE: In this paper we report on the evaluation of an institutional social network-King's Social Harmonisation Project (KINSHIP)-established to foster an improved sense of community, enhance communication, and serve as a space to model digital professionalism for students at King's College London, United Kingdom...
March 3, 2016: JMIR Med Educ
H Chai, J Zhang, G Yang, Z Ma
DNA-binding proteins play a pivotal role in various biological activities. Identification of DNA-binding residues (DBRs) is of great importance for understanding the mechanism of gene regulations and chromatin remodeling. Most traditional computational methods usually construct their predictors on static non-redundant datasets. They excluded many homologous DNA-binding proteins so as to guarantee the generalization capability of their models. However, those ignored samples may potentially provide useful clues when studying protein-DNA interactions, which have not obtained enough attention...
October 12, 2016: Molecular BioSystems
Bin Liu, Li Yao, Dapeng Han
Classification is an important part of resident space objects (RSOs) identification, which is a main focus of space situational awareness. Owing to the absence of some features caused by the limited and uncertain observations, RSO classification remains a difficult task. In this paper, an ontology for RSO classification named OntoStar is built upon domain knowledge and machine learning rules. Then data describing RSO are represented by OntoStar. A demo shows how an RSO is classified based on OntoStar. It is also shown in the demo that traceable and comprehensible reasons for the classification can be given, hence the classification can be checked and validated...
2016: SpringerPlus
Hui Yang, Fabio Leonelli
QRS morphology is commonly used in the electrocardiographic diagnosis of ventricular depolarization such as left bundle branch block (LBBB) and ventricular septal infarction. We investigated whether pattern matching of QRS loops in the 3-dimensional vectorcardiogram (VCG) will improve the grouping of patients whose space-time electrical activity akin to each other, thereby assisting in clinical decision making. First, pattern dissimilarity of VCG QRS loops is qualitatively measured and characterized among patients, resulting in a 93×93 distance matrix of patient-to-patient dissimilarity...
September 28, 2016: Computers in Biology and Medicine
Dominique C Hill
What are the hesitations, dangers, and potentialities to inviting students to peruse my body? What possibilities arise from centering and leading with the body in the teaching/learning process? What risks and possibilities does this enactment pose to a Black lesbian educator? This auto/ethnography journeys through and reflects upon my experience enacting what I have coined "embodied vulnerability" as a pedagogical practice. Within this essay, I explore the interrelationship of race, gender, and embodiment (or, the performance of self)...
October 10, 2016: Journal of Lesbian Studies
Hardy Hagena, Denise Manahan-Vaughan
Although the mossy fiber (MF) synapses of the hippocampal CA3 region display quite distinct properties in terms of the molecular mechanisms that underlie synaptic plasticity, they nonetheless exhibit persistent (>24 h) synaptic plasticity that is akin to that observed at the Schaffer collateral (SCH)-CA1 and perforant path (PP)-dentate gyrus (DG) synapses of freely behaving rats. In addition, they also respond to novel spatial learning with very enduring forms of long-term potentiation (LTP) and long-term depression (LTD)...
2016: Frontiers in Synaptic Neuroscience
Gabriel Aldaz, Sunil Puria, Larry J Leifer
BACKGROUND: Previous research has shown that hearing aid wearers can successfully self-train their instruments' gain-frequency response and compression parameters in everyday situations. Combining hearing aids with a smartphone introduces additional computing power, memory, and a graphical user interface that may enable greater setting personalization. To explore the benefits of self-training with a smartphone-based hearing system, a parameter space was chosen with four possible combinations of microphone mode (omnidirectional and directional) and noise reduction state (active and off)...
October 2016: Journal of the American Academy of Audiology
Danish Shaikh, John Hallam, Jakob Christensen-Dalsgaard
The peripheral auditory system of lizards has been extensively studied, because of its remarkable directionality. In this paper, we review the research that has been performed on this system using a biorobotic approach. The various robotic implementations developed to date, both wheeled and legged, of the auditory model exhibit strong phonotactic performance for two types of steering mechanisms-a simple threshold decision model and Braitenberg sensorimotor cross-couplings. The Braitenberg approach removed the need for a decision model, but produced relatively inefficient robot trajectories...
October 7, 2016: Biological Cybernetics
Erica L Middleton, Myrna F Schwartz, Katherine A Rawson, Hilary Traut, Jay Verkuilen
Purpose: The purpose of this article was to examine how different types of learning experiences affect naming impairment in aphasia. Methods: In 4 people with aphasia with naming impairment, we compared the benefits of naming treatment that emphasized retrieval practice (practice retrieving target names from long-term memory) with errorless learning (repetition training, which preempts retrieval practice) according to different schedules of learning. The design was within subjects...
October 7, 2016: Journal of Speech, Language, and Hearing Research: JSLHR
Hansaim Lim, Aleksandar Poleksic, Yuan Yao, Hanghang Tong, Di He, Luke Zhuang, Patrick Meng, Lei Xie
Target-based screening is one of the major approaches in drug discovery. Besides the intended target, unexpected drug off-target interactions often occur, and many of them have not been recognized and characterized. The off-target interactions can be responsible for either therapeutic or side effects. Thus, identifying the genome-wide off-targets of lead compounds or existing drugs will be critical for designing effective and safe drugs, and providing new opportunities for drug repurposing. Although many computational methods have been developed to predict drug-target interactions, they are either less accurate than the one that we are proposing here or computationally too intensive, thereby limiting their capability for large-scale off-target identification...
October 2016: PLoS Computational Biology
Alessandro Roncone, Matej Hoffmann, Ugo Pattacini, Luciano Fadiga, Giorgio Metta
This paper investigates a biologically motivated model of peripersonal space through its implementation on a humanoid robot. Guided by the present understanding of the neurophysiology of the fronto-parietal system, we developed a computational model inspired by the receptive fields of polymodal neurons identified, for example, in brain areas F4 and VIP. The experiments on the iCub humanoid robot show that the peripersonal space representation i) can be learned efficiently and in real-time via a simple interaction with the robot, ii) can lead to the generation of behaviors like avoidance and reaching, and iii) can contribute to the understanding the biological principle of motor equivalence...
2016: PloS One
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