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https://www.readbyqxmd.com/read/28100775/cool-associated-tyrosine-phosphorylated-protein-1-is-required-for-the-anchorage-independent-growth-of-cervical-carcinoma-cells-by-binding-paxillin-and-promoting-akt-activation
#1
Sungsoo M Yoo, Arash Latifkar, Richard A Cerione, Marc A Antonyak
Cool-associated tyrosine phosphorylated protein-1 (Cat-1) is a signaling scaffold, as well as an ADP-ribosylation factor-GTPase-activating protein (ARF-GAP). Although best known for its role in cell migration, we recently showed that the ability of Cat-1 to bind paxillin, a major constituent of focal complexes, was also essential for the anchorage-independent growth of HeLa cervical carcinoma cells. Here, we set out to learn more about the underlying mechanism by which Cat-paxillin interactions mediate this effect...
January 18, 2017: Journal of Biological Chemistry
https://www.readbyqxmd.com/read/28097899/the-need-for-narrative-reflection-and-experiential-learning-in-medical-education-a-lesson-learned-through-an-urban-indigenous-health-elective
#2
Lindsay S Herzog
In this personal view article, I discuss a formative experience I had during an Urban Indigenous Health elective in which I participated while in my final year of medical school. The elective was developed on the foundation of an experiential learning model, which is central to Indigenous pedagogy and emphasizes learning through experience and narrative reflection. By transforming medical education into a place where such concepts are integrated and valued, I argue that we will create physicians who are self-aware, compassionate and able to provide culturally safe care to all patient populations they will serve in their future practices...
January 18, 2017: Medical Teacher
https://www.readbyqxmd.com/read/28093555/variance-and-invariance-of-neuronal-long-term-representations
#3
REVIEW
Claudia Clopath, Tobias Bonhoeffer, Mark Hübener, Tobias Rose
The brain extracts behaviourally relevant sensory input to produce appropriate motor output. On the one hand, our constantly changing environment requires this transformation to be plastic. On the other hand, plasticity is thought to be balanced by mechanisms ensuring constancy of neuronal representations in order to achieve stable behavioural performance. Yet, prominent changes in synaptic strength and connectivity also occur during normal sensory experience, indicating a certain degree of constitutive plasticity...
March 5, 2017: Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences
https://www.readbyqxmd.com/read/28092555/progressive-shape-distribution-encoder-for-3d-shape-retrieval
#4
Jin Xie, Fan Zhu, Guoxian Dai, Ling Shao, Yi Fang
Since there are complex geometric variations with 3D shapes, extracting efficient 3D shape features is one of the most challenging tasks in shape matching and retrieval. In this paper, we propose a deep shape descriptor by learning shape distributions at different diffusion time via a progressive shape-distribution-encoder (PSDE). First, we develop a shape distribution representation with the kernel density estimator to characterize the intrinsic geometry structures of 3D shapes. Then, we propose to learn a deep shape feature through an unsupervised PSDE...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092552/discriminative-elastic-net-regularized-linear-regression
#5
Zheng Zhang, Zhihui Lai, Yong Xu, Ling Shao, Jian Wu, Guo-Sen Xie
In this paper, we aim at learning compact and discriminative linear regression models. Linear regression has been widely used in different problems. However, most of the existing linear regression methods exploit the conventional zeroone matrix as the regression targets, which greatly narrows the flexibility of the regression model. Another major limitation of theses methods is that the learned projection matrix fails to precisely project the image features to the target space due to their weak discriminative capability...
January 11, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092529/sub-category-classifiers-for-multiple-instance-learning-and-its-application-to-retinal-nerve-fiber-layer-visibility-classification
#6
Siyamalan Manivannan, Caroline Cobb, Stephen Burgess, Emanuele Trucco
We propose a novel multiple instance learning method to assess the visibility (visible/not visible) of the retinal nerve fiber layer (RNFL) in fundus camera images. Using only image-level labels, our approach learns to classify the images as well as to localize the RNFL visible regions. We transform the original feature space to a discriminative subspace, and learn a region-level classifier in that subspace. We propose a margin-based loss function to jointly learn this subspace and the region-level classifier...
January 16, 2017: IEEE Transactions on Medical Imaging
https://www.readbyqxmd.com/read/28088356/advancing-the-prediction-accuracy-of-protein-protein-interactions-by-utilizing-evolutionary-information-from-position-specific-scoring-matrix-and-ensemble-classifier
#7
Lei Wang, Zhu-Hong You, Shi-Xiong Xia, Feng Liu, Xing Chen, Xin Yan, Yong Zhou
Protein-Protein Interactions (PPIs) are essential to most biological processes and play a critical role in most cellular functions. With the development of high-throughput biological techniques and in silico methods, a large number of PPI data have been generated for various organisms, but many problems remain unsolved. These factors promoted the development of the in silico methods based on machine learning to predict PPIs. In this study, we propose a novel method by combining ensemble Rotation Forest (RF) classifier and Discrete Cosine Transform (DCT) algorithm to predict the interactions among proteins...
January 11, 2017: Journal of Theoretical Biology
https://www.readbyqxmd.com/read/28087243/using-deep-learning-to-investigate-the-neuroimaging-correlates-of-psychiatric-and-neurological-disorders-methods-and-applications
#8
REVIEW
Sandra Vieira, Walter H L Pinaya, Andrea Mechelli
Deep learning (DL) is a family of machine learning methods that has gained considerable attention in the scientific community, breaking benchmark records in areas such as speech and visual recognition. DL differs from conventional machine learning methods by virtue of its ability to learn the optimal representation from the raw data through consecutive nonlinear transformations, achieving increasingly higher levels of abstraction and complexity. Given its ability to detect abstract and complex patterns, DL has been applied in neuroimaging studies of psychiatric and neurological disorders, which are characterised by subtle and diffuse alterations...
January 10, 2017: Neuroscience and Biobehavioral Reviews
https://www.readbyqxmd.com/read/28077906/how-to-improve-visibility-of-scientific-biomedical-sources
#9
REVIEW
Asim Kurjak
With the rapid development of information and communications technologies, industrial nations are transforming into societies in which knowledge is the most contested and valuable good. The increased speed at which we have to acquire new knowledge, insights, and abilities is forcing us to divide up learning into novel, shorter phases. The traditional choreography of learning with its long, rigid defined school, job, and university educational periods is already obsolete today. Self-organized, lifelong learning is becoming a must...
December 2016: Acta Informatica Medica: AIM
https://www.readbyqxmd.com/read/28065214/the-evolution-of-human-uniqueness
#10
Robert Boyd
The human species is an outlier in the natural world. Two million years ago our ancestors were a slightly odd apes. Now we occupy the largest ecological and geographical range of any species, have larger biomass, and process more energy. Usually, this transformation is explained in terms of cognitive ability-people are just smarter than all the rest. In this paper I argue that culture, our ability to learn from each other, and cooperation, our ability to make common cause with large groups of unrelated individuals are the real roots of human uniqueness, and sketch an evolutionary account of how these crucial abilities co-evolved with each other and with other features of our life histories...
January 9, 2017: Spanish Journal of Psychology
https://www.readbyqxmd.com/read/28063694/post-wbrt-cognitive-impairment-and-hippocampal-neuronal-depletion-measured-by-in-vivo-metabolic-mr-spectroscopy-results-of-prospective-investigational-study
#11
Petr Pospisil, Tomas Kazda, Ludmila Hynkova, Martin Bulik, Marie Dobiaskova, Petr Burkon, Nadia N Laack, Pavel Slampa, Radim Jancalek
BACKGROUND AND PURPOSE: The aim of this prospective study is to evaluate post-whole brain radiotherapy (WBRT) changes in hippocampal concentration of N-acetylaspartate (h-tNAA) as a marker of neuronal loss and to correlate those changes to neurocognitive function. MATERIAL AND METHODS: Thirty-five patients with brain metastases underwent baseline single slice multi-voxel MR spectroscopy (MRS) examination for measurement of hippocampal h-tNAA together with baseline battery of neurocognitive tests focused on memory (Auditory Verbal Learning Test and Brief Visuospatial Memory Test - Revised) as well as quality of life questionnaires (EORTC QLQ-C30 a EORTC QLQ-BN20)...
January 4, 2017: Radiotherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology
https://www.readbyqxmd.com/read/28062814/from-our-practices-to-yours-key-messages-for-the-journey-to-integrated-behavioral-health
#12
Stephanie B Gold, Larry A Green, C J Peek
BACKGROUND: The historic, cultural separation of primary care and behavioral health has caused the spread of integrated care to lag behind other practice transformation efforts. The Advancing Care Together study was a 3-year evaluation of how practices implemented integrated care in their local contexts; at its culmination, practice leaders ("innovators") identified lessons learned to pass on to others. METHODS: Individual feedback from innovators, key messages created by workgroups of innovators and the study team, and a synthesis of key messages from a facilitated discussion were analyzed for themes via immersion/crystallization...
January 2017: Journal of the American Board of Family Medicine: JABFM
https://www.readbyqxmd.com/read/28059984/transforming-roles-of-nursing-professional-development-practitioners
#13
Joan Insalaco Warren, Mary G Harper
This research was undertaken to delineate the future role of nursing professional development (NPD) specialists. Using a modified e-Delphi technique, seven key roles for NPD (partner for practice transitions, learning facilitator, change agent, mentor, leader, champion of scientific inquiry, and advocate for NPD specialty) and their concomitant competencies evolved. Results of this study informed the update of the NPD Scope and Standards of Practice and may be used to identify competencies for the specialty...
January 2017: Journal for Nurses in Professional Development
https://www.readbyqxmd.com/read/28059691/establishing-data-intensive-healthcare-the-case-of-hospital-electronic-prescribing-and-medicines-administration-systems-in-scotland
#14
Kathrin Cresswell, Pam Smith, Charles Swainson, Angela Timoney, Aziz Sheikh
BACKGROUND: Creating learning health systems, characterised by the use and repeated reuse of demographic, process and clinical data to improve the safety, quality and efficiency of care, is a key aim in realising the potential benefits and efficiency savings associated with the implementation of health information technology. OBJECTIVES: We sought to investigate stakeholder perspectives on and experiences of the implementation of hospital electronic prescribing and medicines administration (HEPMA) systems in Scotland and use these to inform political decisions on approaches to promoting the use and reuse of digitised prescribing and medication administration data in order to improve care processes and outcomes...
October 4, 2016: Journal of Innovation in Health Informatics
https://www.readbyqxmd.com/read/28056090/accurate-de-novo-prediction-of-protein-contact-map-by-ultra-deep-learning-model
#15
Sheng Wang, Siqi Sun, Zhen Li, Renyu Zhang, Jinbo Xu
MOTIVATION: Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. METHOD: This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks...
January 5, 2017: PLoS Computational Biology
https://www.readbyqxmd.com/read/28055940/deep-nonlinear-metric-learning-for-3-d-shape-retrieval
#16
Jin Xie, Guoxian Dai, Fan Zhu, Ling Shao, Yi Fang
Effective 3-D shape retrieval is an important problem in 3-D shape analysis. Recently, feature learning-based shape retrieval methods have been widely studied, where the distance metrics between 3-D shape descriptors are usually hand-crafted. In this paper, motivated by the fact that deep neural network has the good ability to model nonlinearity, we propose to learn an effective nonlinear distance metric between 3-D shape descriptors for retrieval. First, the locality-constrained linear coding method is employed to encode each vertex on the shape and the encoding coefficient histogram is formed as the global 3-D shape descriptor to represent the shape...
December 28, 2016: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28055922/kernel-based-multilayer-extreme-learning-machines-for-representation-learning
#17
Chi Man Wong, Chi Man Vong, Pak Kin Wong, Jiuwen Cao
Recently, multilayer extreme learning machine (ML-ELM) was applied to stacked autoencoder (SAE) for representation learning. In contrast to traditional SAE, the training time of ML-ELM is significantly reduced from hours to seconds with high accuracy. However, ML-ELM suffers from several drawbacks: 1) manual tuning on the number of hidden nodes in every layer is an uncertain factor to training time and generalization; 2) random projection of input weights and bias in every layer of ML-ELM leads to suboptimal model generalization; 3) the pseudoinverse solution for output weights in every layer incurs relatively large reconstruction error; and 4) the storage and execution time for transformation matrices in representation learning are proportional to the number of hidden layers...
December 29, 2016: IEEE Transactions on Neural Networks and Learning Systems
https://www.readbyqxmd.com/read/28055849/hetero-manifold-regularisation-for-cross-modal-hashing
#18
Feng Zheng, Yi Tang, Ling Shao
Recently, cross-modal search has attracted considerable attention but remains a very challenging task because of the integration complexity and heterogeneity of the multi-modal data. To address both challenges, in this paper, we propose a novel method termed hetero-manifold regularisation (HMR) to supervise the learning of hash functions for efficient cross-modal search. A hetero-manifold integrates multiple sub-manifolds defined by homogeneous data with the help of cross-modal supervision information. Taking advantages of the hetero-manifold, the similarity between each pair of heterogeneous data could be naturally measured by three order random walks on this hetero-manifold...
December 28, 2016: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28052254/pan-cancer-immunogenomic-analyses-reveal-genotype-immunophenotype-relationships-and-predictors-of-response-to-checkpoint-blockade
#19
Pornpimol Charoentong, Francesca Finotello, Mihaela Angelova, Clemens Mayer, Mirjana Efremova, Dietmar Rieder, Hubert Hackl, Zlatko Trajanoski
The Cancer Genome Atlas revealed the genomic landscapes of human cancers. In parallel, immunotherapy is transforming the treatment of advanced cancers. Unfortunately, the majority of patients do not respond to immunotherapy, making the identification of predictive markers and the mechanisms of resistance an area of intense research. To increase our understanding of tumor-immune cell interactions, we characterized the intratumoral immune landscapes and the cancer antigenomes from 20 solid cancers and created The Cancer Immunome Atlas (https://tcia...
January 3, 2017: Cell Reports
https://www.readbyqxmd.com/read/28049151/modulation-of-microrna-mrna-target-pairs-by-human-papillomavirus-16-oncoproteins
#20
Mallory E Harden, Nripesh Prasad, Anthony Griffiths, Karl Munger
: The E6 and E7 proteins are the major oncogenic drivers encoded by high-risk human papillomaviruses (HPVs). While many aspects of the transforming activities of these proteins have been extensively studied, there are fewer studies that have investigated how HPV E6/E7 expression affects the expression of cellular noncoding RNAs. The goal of our study was to investigate HPV16 E6/E7 modulation of cellular microRNA (miR) levels and to determine the potential consequences for cellular gene expression...
January 3, 2017: MBio
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