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https://www.readbyqxmd.com/read/28336212/impact-of-a-continuing-professional-development-intervention-on-midwifery-academics-awareness-of-cultural-safety
#1
Tania Fleming, Debra K Creedy, Roianne West
BACKGROUND: Cultural safety in higher education learning and teaching environments is paramount to positive educational outcomes for Aboriginal and/or Torres Strait Islander (hereafter called First Peoples) students. There is a lack of research evaluating the impact of continuing professional development on midwifery academics' awareness of cultural safety. AIM: To implement and evaluate a continuing professional development intervention to improve midwifery academics' awareness of cultural safety in supporting First Peoples midwifery students success...
March 20, 2017: Women and Birth: Journal of the Australian College of Midwives
https://www.readbyqxmd.com/read/28333630/multilinear-spatial-discriminant-analysis-for-dimensionality-reduction
#2
Sen Yuan, Xia Mao, Lijiang Chen
In the last few years, great efforts have been made to extend linear projection technique (LPT) for multidimensional data (i.e. tensor), generally referred to as multilinear projection technique (MPT). The vectorized nature of LPT requires high dimensional data to be converted to vector, hence may lose spatial neighborhood information of raw data. MPT well addresses this problem by encoding multidimensional data as general tensors of second or even higher order. In this paper, we propose a novel multilinear projection technique, called multilinear spatial discriminant analysis (MSDA), to identify the underlying manifold of high-order tensor data...
March 21, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28333605/can-all-doctors-be-like-this-seven-stories-of-communication-transformation-told-by-physicians-rated-highest-by-patients
#3
Tom Janisse, Karen Tallman
INTRODUCTION: The top predictors of patient satisfaction with clinical visits are the quality of the physician-patient relationship and the communications contributing to their relationship. How do physicians improve their communication, and what effect does it have on them? This article presents the verbatim stories of seven high-performing physicians describing their transformative change in the areas of communication, connection, and well-being. METHODS: Data for this study are based on interviews from a previous study in which a 6-question set was posed, in semistructured 60-minute interviews, to 77 of the highest-performing Permanente Medical Group physicians in 4 Regions on the "Art of Medicine" patient survey...
2017: Permanente Journal
https://www.readbyqxmd.com/read/28333274/making-room-for-interactivity-using-the-cloud-based-audience-response-system-nearpod-to-enhance-engagement-in-lectures
#4
Stephen McClean, William Crowe
Active and collaborative learning provide distinct advantages for students in higher education, yet can often be hampered by the barrier of large class sizes. Solutions that combine a "bring your own device culture" (BOYD) with cloud-based technologies may facilitate a more interactive learning experience. In this pilot study we describe the use of one such technology, Nearpod, to enhance interactivity in lectures delivered to pharmacy and bioscience students at Ulster University. Existing material in PowerPoint or Keynote format is uploaded to the instructor area of Nearpod, interactive elements are added, and the lecture is then broadcast via the internet to student devices...
March 8, 2017: FEMS Microbiology Letters
https://www.readbyqxmd.com/read/28329757/neural-ensemble-dynamics-underlying-a-long-term-associative-memory
#5
Benjamin F Grewe, Jan Gründemann, Lacey J Kitch, Jerome A Lecoq, Jones G Parker, Jesse D Marshall, Margaret C Larkin, Pablo E Jercog, Francois Grenier, Jin Zhong Li, Andreas Lüthi, Mark J Schnitzer
The brain's ability to associate different stimuli is vital for long-term memory, but how neural ensembles encode associative memories is unknown. Here we studied how cell ensembles in the basal and lateral amygdala encode associations between conditioned and unconditioned stimuli (CS and US, respectively). Using a miniature fluorescence microscope, we tracked the Ca(2+) dynamics of ensembles of amygdalar neurons during fear learning and extinction over 6 days in behaving mice. Fear conditioning induced both up- and down-regulation of individual cells' CS-evoked responses...
March 22, 2017: Nature
https://www.readbyqxmd.com/read/28324937/data-driven-estimation-of-blood-pressure-using-photoplethysmographic-signals
#6
Shi Chao Gao, Peter Wittek, Li Zhao, Wen Jun Jiang
Noninvasive measurement of blood pressure by optical methods receives considerable interest, but the complexity of the measurement and the difficulty of adjusting parameters restrict applications. We develop a method for estimating the systolic and diastolic blood pressure using a single-point optical recording of a photoplethysmographic (PPG) signal. The estimation is data-driven, we use automated machine learning algorithms instead of mathematical models. Combining supervised learning with a discrete wavelet transform, the method is insensitive to minor irregularities in the PPG waveform, hence both pulse oximeters and smartphone cameras can record the signal...
August 2016: Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society
https://www.readbyqxmd.com/read/28324696/online-neural-monitoring-of-statistical-learning
#7
Laura J Batterink, Ken A Paller
The extraction of patterns in the environment plays a critical role in many types of human learning, from motor skills to language acquisition. This process is known as statistical learning. Here we propose that statistical learning has two dissociable components: (1) perceptual binding of individual stimulus units into integrated composites and (2) storing those integrated representations for later use. Statistical learning is typically assessed using post-learning tasks, such that the two components are conflated...
February 24, 2017: Cortex; a Journal Devoted to the Study of the Nervous System and Behavior
https://www.readbyqxmd.com/read/28319831/structure-based-prediction-of-host-pathogen-protein-interactions
#8
REVIEW
Rachelle Mariano, Stefan Wuchty
The discovery, validation, and characterization of protein-based interactions from different species are crucial for translational research regarding a variety of pathogens, ranging from the malaria parasite Plasmodium falciparum to HIV-1. Here, we review recent advances in the prediction of host-pathogen protein interfaces using structural information. In particular, we observe that current methods chiefly perform machine learning on sequence and domain information to produce large sets of candidate interactions that are further assessed and pruned to generate final, highly probable sets...
March 16, 2017: Current Opinion in Structural Biology
https://www.readbyqxmd.com/read/28316639/feature-extraction-and-classification-of-ehg-between-pregnancy-and-labour-group-using-hilbert-huang-transform-and-extreme-learning-machine
#9
Lili Chen, Yaru Hao
Preterm birth (PTB) is the leading cause of perinatal mortality and long-term morbidity, which results in significant health and economic problems. The early detection of PTB has great significance for its prevention. The electrohysterogram (EHG) related to uterine contraction is a noninvasive, real-time, and automatic novel technology which can be used to detect, diagnose, or predict PTB. This paper presents a method for feature extraction and classification of EHG between pregnancy and labour group, based on Hilbert-Huang transform (HHT) and extreme learning machine (ELM)...
2017: Computational and Mathematical Methods in Medicine
https://www.readbyqxmd.com/read/28314499/bringing-global-health-home-the-case-of-global-to-local-in-king-county-washington
#10
EDITORIAL
Adam Taylor, Fareeha Siddiqui
The article describes the experience of testing successful global health interventions in the cities of SeaTac and Tukwila, Washington-2 very diverse, underserved communities outside of Seattle that experience significant health disparities compared with surrounding areas in King County. Topics covered include an overview of the partnership that established Global to Local, the process of engaging Seattle-based global health institutions in identifying global health strategies to test, identifying communities experiencing health disparities that might benefit from global health-inspired interventions, engaging those local communities to understand the perceived drivers of poor health outcomes, tailoring global interventions to the local context, launching programs, and the successes and challenges that have emerged throughout this process...
November 2016: Annals of Global Health
https://www.readbyqxmd.com/read/28314498/exploring-social-justice-in-mixed-divided-cities-from-local-to-global-learning
#11
Corey Shdaimah, Jane Lipscomb, Roni Strier, Dassi Postan-Aizik, Susan Leviton, Jody Olsen
BACKGROUND: University of Haifa and the University of Maryland, Baltimore faculty developed a parallel binational, interprofessional American-Israeli course which explores social justice in the context of increasing urban, local, and global inequities. OBJECTIVES: This article describes the course's innovative approach to critically examine how social justice is framed in mixed/divided cities from different professional perspectives (social work, health, law). Participatory methods such as photo-voice, experiential learning, and theatre of the oppressed provide students with a shared language and multiple media to express and problematize their own and others' understanding of social (in)justice and to imagine social change...
November 2016: Annals of Global Health
https://www.readbyqxmd.com/read/28314495/global-service-learning-and-student-athletes-a-model-for-enhanced-academic-inclusion-at-the-university-of-washington
#12
Holly M Barker
BACKGROUND: The University of Washington (UW) continues to create opportunities to engage all students in transformational undergraduate educational opportunities, such as study abroad. OBJECTIVE: This article describes specific efforts to increase inclusion for student-athletes in study abroad, particularly for first-generation students, including low-income students of color. Given the overrepresentation of students of color in sports vis-à-vis the larger student body at predominantly white institutions (PWIs), like UW, service-learning in communities beyond campus boundaries provides opportunities to apply international learning to a local context and to create a continuum of learning...
November 2016: Annals of Global Health
https://www.readbyqxmd.com/read/28314196/governance-and-management-dynamics-of-landscape-restoration-at-multiple-scales-learning-from-successful-environmental-managers-in-sweden
#13
Lucas Dawson, Marine Elbakidze, Per Angelstam, Johanna Gordon
Due to a long history of intensive land and water use, habitat networks for biodiversity conservation are generally degraded in Sweden. Landscape restoration (LR) is an important strategy for achieving representative and functional green infrastructures. However, outcomes of LR efforts are poorly studied, particularly the dynamics of LR governance and management. We apply systems thinking methods to a series of LR case studies to analyse the causal structures underlying LR governance and management in Sweden...
March 14, 2017: Journal of Environmental Management
https://www.readbyqxmd.com/read/28306716/localization-and-diagnosis-framework-for-pediatric-cataracts-based-on-slit-lamp-images-using-deep-features-of-a-convolutional-neural-network
#14
Xiyang Liu, Jiewei Jiang, Kai Zhang, Erping Long, Jiangtao Cui, Mingmin Zhu, Yingying An, Jia Zhang, Zhenzhen Liu, Zhuoling Lin, Xiaoyan Li, Jingjing Chen, Qianzhong Cao, Jing Li, Xiaohang Wu, Dongni Wang, Haotian Lin
Slit-lamp images play an essential role for diagnosis of pediatric cataracts. We present a computer vision-based framework for the automatic localization and diagnosis of slit-lamp images by identifying the lens region of interest (ROI) and employing a deep learning convolutional neural network (CNN). First, three grading degrees for slit-lamp images are proposed in conjunction with three leading ophthalmologists. The lens ROI is located in an automated manner in the original image using two successive applications of Candy detection and the Hough transform, which are cropped, resized to a fixed size and used to form pediatric cataract datasets...
2017: PloS One
https://www.readbyqxmd.com/read/28295412/machine-learning-will-transform-radiology-significantly-within-the-next-five-years
#15
Ge Wang, Mannudeep Kalra, Colin G Orton
With impressive progress in machine learning, there has been increasingly more interest in its relevance to medical physics, which involves both medical imaging and radiation treatment planning. However, because it is still generally unclear how to identify unique niches, utilize big data, and optimize neural networks for machine learning, machine learning is yet to have a major impact on medical physics practice. Nevertheless, there are optimistic opinions that machine learning will have a major impact on medical physics and radiology within the next five years...
March 11, 2017: Medical Physics
https://www.readbyqxmd.com/read/28294468/memory-impairment-and-the-mediating-role-of-task-difficulty-in-patients-with-schizophrenia
#16
REVIEW
Kyrsten M Grimes, Anosha Zanjani, Konstantine K Zakzanis, C Psych
Using meta-analytic methods, we sought to synthesize the research literature on memory impairment in schizophrenia. Additionally, we compared performances across memory measures to determine if task difficulty (e.g., effortful encoding and retrieval versus non effortful encoding and retrieval) could account for variance across studies. Our primary measures of interest included the California Verbal Learning Test, Wechsler Memory Scale, Rey Auditory Verbal Learning Test, Hopkins Verbal Learning Test, Rey Osterrieth Complex Figure Test, and the Benton Visual Retention Test...
March 14, 2017: Psychiatry and Clinical Neurosciences
https://www.readbyqxmd.com/read/28294287/learning-gene-regulatory-networks-from-next-generation-sequencing-data
#17
Bochao Jia, Suwa Xu, Guanghua Xiao, Vishal Lamba, Faming Liang
In recent years, next generation sequencing (NGS) has gradually replaced microarray as the major platform in measuring gene expressions. Compared to microarray, NGS has many advantages, such as less noise and higher throughput. However, the discreteness of NGS data also challenges the existing statistical methodology. In particular, there still lacks an appropriate statistical method for reconstructing gene regulatory networks using NGS data in the literature. The existing local Poisson graphical model method is not consistent and can only infer certain local structures of the network...
March 10, 2017: Biometrics
https://www.readbyqxmd.com/read/28289601/3d-scattering-transforms-for-disease-classification-in-neuroimaging
#18
Tameem Adel, Taco Cohen, Matthan Caan, Max Welling
Classifying neurodegenerative brain diseases in MRI aims at correctly assigning discrete labels to MRI scans. Such labels usually refer to a diagnostic decision a learner infers based on what it has learned from a training sample of MRI scans. Classification from MRI voxels separately typically does not provide independent evidence towards or against a class; the information relevant for classification is only present in the form of complicated multivariate patterns (or "features"). Deep learning solves this problem by learning a sequence of non-linear transformations that result in feature representations that are better suited to classification...
2017: NeuroImage: Clinical
https://www.readbyqxmd.com/read/28287996/non-negative-spectral-learning-and-sparse-regression-based-dual-graph-regularized-feature-selection
#19
Ronghua Shang, Wenbing Wang, Rustam Stolkin, Licheng Jiao
Feature selection is an important approach for reducing the dimension of high-dimensional data. In recent years, many feature selection algorithms have been proposed, but most of them only exploit information from the data space. They often neglect useful information contained in the feature space, and do not make full use of the characteristics of the data. To overcome this problem, this paper proposes a new unsupervised feature selection algorithm, called non-negative spectral learning and sparse regression-based dual-graph regularized feature selection (NSSRD)...
March 6, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28279882/detection-of-an-invisible-needle-in-ultrasound-using-a-probabilistic-svm-and-time-domain-features
#20
Parmida Beigi, Robert Rohling, Tim Salcudean, Victoria A Lessoway, Gary C Ng
We propose a novel learning-based approach to detect an imperceptible hand-held needle in ultrasound images using the natural tremor motion. The minute tremor induced on the needle however is also transferred to the tissue in contact with the needle, making the accurate needle detection a challenging task. The proposed learning-based framework is based on temporal analysis of the phase variations of pixels to classify them according to the motion characteristics. In addition to the classification, we also obtain a probability map of the segmented pixels by cross-validation...
February 16, 2017: Ultrasonics
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