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https://www.readbyqxmd.com/read/28092795/moderate-aerobic-exercise-training-for-improving-reproductive-function-in-infertile-patients-a-randomized-controlled-trial
#1
Behzad Hajizadeh Maleki, Bakhtyar Tartibian
This study investigated for the first time the changes in seminal markers of inflammation, oxidative stress status, semen parameters, sperm DNA integrity as well as pregnancy rate following 24weeks of moderate aerobic exercise in infertile patients. A total of 1026 sedentary men (aged 25-40years) attending the infertility clinic with history of more than one year of infertility, were screened and 419 were randomized to either exercise (EX, n=210) or non-exercise (NON-EX, n=209) groups. Exercise training favorably attenuated seminal markers of both inflammation (IL-1β, IL-6, IL-8, and TNF-α) and oxidative stress (ROS, MDA, 8-Isoprostane) as well as enhanced antioxidant defense system (SOD, catalase and TAC) (P<0...
January 13, 2017: Cytokine
https://www.readbyqxmd.com/read/28092773/how-does-a-newly-encountered-face-become-familiar-the-effect-of-within-person-variability-on-adults-and-children-s-perception-of-identity
#2
Kristen A Baker, Sarah Laurence, Catherine J Mondloch
Adults and children aged 6years and older easily recognize multiple images of a familiar face, but often perceive two images of an unfamiliar face as belonging to different identities. Here we examined the process by which a newly encountered face becomes familiar, defined as accurate recognition of multiple images that capture natural within-person variability in appearance. In Experiment 1 we examined whether exposure to within-person variability in appearance helps children learn a new face. Children aged 6-13years watched a 10-min video of a woman reading a story; she was filmed on a single day (low variability) or over three days, across which her appearance and filming conditions (e...
January 13, 2017: Cognition
https://www.readbyqxmd.com/read/28092648/does-brief-chronic-pain-management-education-change-opioid-prescribing-rates-a-pragmatic-trial-in-australian-early-career-general-practitioners
#3
Simon Mark Holliday, Chris Hayes, Adrian J Dunlop, Simon Morgan, Amanda Tapley, Kim M Henderson, Mieke L van Driel, Elizabeth G Holliday, Jean I Ball, Andrew Davey, Neil Allan Spike, Lawrence Andrew McArthur, Parker John Magin
We aimed to evaluate the effect of pain education on opioid prescribing by early-career general practitioners. A brief training workshop was delivered to general practice registrars of a single regional training provider. The workshop significantly reduced "hypothetical" opioid prescribing (in response to paper-based vignettes) in an earlier evaluation. The effect of the training on "actual" prescribing was evaluated using a nonequivalent control group design nested within the Registrar Clinical Encounters in Training (ReCEnT) cohort study: 4 other regional training providers were controls...
February 2017: Pain
https://www.readbyqxmd.com/read/28092591/relational-regularized-discriminative-sparse-learning-for-alzheimer-s-disease-diagnosis
#4
Baiying Lei, Peng Yang, Tianfu Wang, Siping Chen, Dong Ni
Accurate identification and understanding informative feature is important for early Alzheimer's disease (AD) prognosis and diagnosis. In this paper, we propose a novel discriminative sparse learning method with relational regularization to jointly predict the clinical score and classify AD disease stages using multimodal features. Specifically, we apply a discriminative learning technique to expand the class-specific difference and include geometric information for effective feature selection. In addition, two kind of relational information are incorporated to explore the intrinsic relationships among features and training subjects in terms of similarity learning...
January 16, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092587/learning-domain-invariant-subspace-using-domain-features-and-independence-maximization
#5
Ke Yan, Lu Kou, David Zhang
Domain adaptation algorithms are useful when the distributions of the training and the test data are different. In this paper, we focus on the problem of instrumental variation and time-varying drift in the field of sensors and measurement, which can be viewed as discrete and continuous distributional change in the feature space. We propose maximum independence domain adaptation (MIDA) and semi-supervised MIDA to address this problem. Domain features are first defined to describe the background information of a sample, such as the device label and acquisition time...
January 16, 2017: IEEE Transactions on Cybernetics
https://www.readbyqxmd.com/read/28092585/saliency-based-lesion-segmentation-via-background-detection-in-dermoscopic-images
#6
Euijoon Ahn, Jinman Kim, Lei Bi, Ashnil Kumar, Changyang Li, Michael Fulham, Dagan Feng
The segmentation of skin lesions in dermoscopic images is a fundamental step in automated computer-aided diagnosis (CAD) of melanoma. Conventional segmentation methods, however, have difficulties when the lesion borders are indistinct and when contrast between the lesion and the surrounding skin is low. They also perform poorly when there is a heterogeneous background or a lesion that touches the image boundaries; this then results in under- and over-segmentation of the skin lesion. We suggest that saliency detection using the reconstruction errors derived from a sparse representation model coupled with a novel background detection can more accurately discriminate the lesion from surrounding regions...
January 16, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28092583/deep-learning-and-insomnia-assisting-clinicians-with-their-diagnosis
#7
Mostafa Shahin, Beena Ahmed, Sana Tmar-Ben Hamida, Fathima Mulaffer, Martin Glos, Thomas Penzel
Effective sleep analysis is hampered by the lack of automated tools catering for disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this. The first approach used EEG data from the whole sleep recording irrespective of the sleep stage (stage-independent classification), while the second used only EEG data from insomnia-impacted specific sleep stages (stage-dependent classification)...
January 9, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28092582/automatic-prediction-of-health-status-using-smartphone-derived-behaviour-profiles
#8
Daniel Kelly, Kevin Curran, Brian Caulfield
OBJECTIVE: Current methods of assessing the affect a patients' health has on their daily life are extremely limited. The aim of this work is to develop a sensor based approach to health status measurement in order to objectively measure health status. METHODS: Techniques to generate human behaviour profiles, derived from smartphone accelerometer and gyroscope sensors, are proposed. Experiments, using SVM regression models, are then conducted in order to evaluate the use of the proposed behaviour profiles as a predictor of health status...
January 9, 2017: IEEE Journal of Biomedical and Health Informatics
https://www.readbyqxmd.com/read/28092576/multiple-instance-learning-for-medical-image-and-video-analysis
#9
Gwenole Quellec, Guy Cazuguel, Beatrice Cochener, Mathieu Lamard
Multiple-Instance Learning (MIL) is a recent machine learning paradigm that is particularly well suited to Medical Image and Video Analysis (MIVA) tasks. Based solely on class labels assigned globally to images or videos, MIL algorithms learn to detect relevant patterns locally in images or videos. These patterns are then used for classification at a global level. Because supervision relies on global labels, manual segmentations are not needed to train MIL algorithms, unlike traditional Single-Instance Learning (SIL) algorithms...
January 10, 2017: IEEE Reviews in Biomedical Engineering
https://www.readbyqxmd.com/read/28092571/machine-learned-replacement-of-n-labels-for-basecalled-sequences-in-dna-barcoding
#10
Eddie Ma, Sujeevan Ratnasingham, Stefan Kremer
This study presents a machine learning method that increases the number of identified bases in Sanger Sequencing. The system post-processes a KB basecalled chromatogram. It selects a recoverable subset of N-labels in the KB-called chromatogram to replace with basecalls (A,C,G,T). An N-label correction is defined given an additional read of the same sequence, and a human finished sequence. Corrections are added to the dataset when an alignment determines the additional read and human agree on the identity of the N-label...
August 11, 2016: IEEE/ACM Transactions on Computational Biology and Bioinformatics
https://www.readbyqxmd.com/read/28092564/static-vs-dynamic-decoding-algorithms-in-a-non-invasive-body-machine-interface
#11
Ismael Seanez-Gonzalez, Camilla Pierella, Ali Farshchiansadegh, Elias Barry Thorp, Farnaz Abdollahi, Jessica P Pedersen, Ferdinando A Mussa-Ivaldi
In this study, we consider a non-invasive bodymachine interface that captures body motions still available to people with spinal cord injury (SCI) and maps them into a set of signals for controlling a computer user interface while engaging in a sustained level of mobility and exercise. We compare the effectiveness of two decoding algorithms that transform a highdimensional body-signal vector into a lower dimensional control vector on 6 subjects with high-level SCI and 8 controls. One algorithm is based on a static map from current body signals to the current value of the control vector set through principal component analysis (PCA), the other on dynamic mapping a segment of body signals to the value and the temporal derivatives of the control vector set through a Kalman filter...
December 15, 2016: IEEE Transactions on Neural Systems and Rehabilitation Engineering
https://www.readbyqxmd.com/read/28092563/effects-of-different-types-of-virtual-reality-display-on-presence-and-learning-in-a-safety-training-scenario
#12
Fabio Buttussi, Luca Chittaro
The increasing availability of head-mounted displays (HMDs) for home use motivates the study of the possible effects that adopting this new hardware might have on users. Moreover, while the impact of display type has bee respectively representative of: (i) desktop VR (a standard desktop monitor), (ii) many setups for immersive VR used in the literature (an HMD with narrow field of view and a 3-DOF tracker), and (iii) new setups for immersive home VR (an HMD with wide field of view and 6-DOF tracker). We assessed effects on knowledge gain, and different self-reported measures (self-efficacy, engagement, presence)...
January 16, 2017: IEEE Transactions on Visualization and Computer Graphics
https://www.readbyqxmd.com/read/28092557/single-image-super-resolution-using-global-regression-based-on-multiple-local-linear-mappings
#13
Jae-Seok Choi, Munchurl Kim
Super-resolution (SR) has become more vital, because of its capability to generate high-quality ultra-high definition (UHD) high-resolution (HR) images from low-resolution (LR) input images. Conventional SR methods entail high computational complexity, which makes them difficult to be implemented for up-scaling of full-high-definition (FHD) input images into UHD-resolution images. Nevertheless, our previous super-interpolation (SI) method showed a good compromise between PSNR performances and computational complexity...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092545/heterogeneous-face-recognition-a-common-encoding-feature-discriminant-approach
#14
Dihong Gong, Zhifeng Li, Weilin Huang, Xuelong Li, Dacheng Tao
Heterogeneous face recognition is an important yet challenging problem in face recognition community. It refers to matching a probe face image to a gallery of face images taken from alternate imaging modality. The major challenge of heterogeneous face recognition lies in the great discrepancies between different image modalities. Conventional face feature descriptors, e.g. LBP, HOG and SIFT, are mostly designed in a handcrafted way and thus generally fail to extract the common discriminant information from the heterogeneous face images...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092540/robust-and-discriminative-labeling-for-multi-label-active-learning-based-on-maximum-correntropy-criterion
#15
Bo Du, Zengmao Wang, Lefei Zhang, Liangpei Zhang, Dacheng Tao
Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092535/super-resolution-person-re-identification-with-semi-coupled-low-rank-discriminant-dictionary-learning
#16
Xiao-Yuan Jing, Xiaoke Zhu, Fei Wu, Ruimin Hu, Xinge You, Yunhong Wang, Hui Feng, Jing-Yu Yang
Person re-identification has been widely studied due to its importance in surveillance and forensics applications. In practice, gallery images are high-resolution (HR) while probe images are usually low-resolution (LR) in the identification scenarios with large variation of illumination, weather or quality of cameras. Person re-identification in this kind of scenarios, which we call super-resolution (SR) person re-identification, has not been well studied. In this paper, we propose a semi-coupled low-rank discriminant dictionary learning (SLD2L) approach for SR person re-identification task...
January 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
https://www.readbyqxmd.com/read/28092522/active-self-paced-learning-for-cost-effective-and-progressive-face-identification
#17
Liang Lin, Keze Wang, Deyu Meng, Wangmeng Zuo, Lei Zhang
This paper aims to develop a novel cost-effective framework for face identification, which progressively maintains a batch of classifiers with the increasing face images of different individuals. By naturally combining two recently rising techniques: active learning (AL) and self-paced learning (SPL), our framework is capable of automatically annotating new instances and incorporating them into training under weak expert recertification. We first initialize the classifier using a few annotated samples for each individual, and extract image features using the convolutional neural nets...
January 16, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28092521/measuring-and-predicting-tag-importance-for-image-retrieval
#18
Shangwen Li, Sanjay Purushotham, Chen Chen, Yuzhuo Ren, C-C Jay Kuo
Textual data such as tags, sentence descriptions are combined with visual cues to reduce the semantic gap for image retrieval applications in today's Multimodal Image Retrieval (MIR) systems. However, all tags are treated as equally important in these systems, which may result in misalignment between visual and textual modalities during MIR training. This will further lead to degenerated retrieval performance at query time. To address this issue, we investigate the problem of tag importance prediction, where the goal is to automatically predict the tag importance and use it in image retrieval...
January 11, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28092518/compositional-model-based-fisher-vector-coding-for-image-classi%C3%AF-cation
#19
Lingqiao Liu, Peng Wang, Chunhua Shen, Lei Wang, Anton van den Hengel, Chao Wang, Heng Tao Shen
Deriving from the gradient vector of a generative model of local features, Fisher vector coding (FVC) has been identified as an effective coding method for image classification. Most, if not all, FVC implementations employ the Gaussian mixture model (GMM) as the generative model for local features. However, the representative power of a GMM can be limited because it essentially assumes that local features can be characterized by a fixed number of feature prototypes, and the number of prototypes is usually small in FVC...
January 10, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
https://www.readbyqxmd.com/read/28092492/self-care-in-palliative-care-nursing-and-medical-professionals-a-cross-sectional-survey
#20
Jason Mills, Timothy Wand, Jennifer A Fraser
BACKGROUND: Self-care is an important consideration for palliative care professionals. To date, few details have been recorded about the nature or uptake of self-care practices in the palliative care workforce. As part of a broader mixed methods study, this article reports findings from a national survey of nurses and doctors. OBJECTIVE: The objective of this study was to examine perceptions, education, and practices relating to self-care among palliative care nursing and medical professionals...
January 16, 2017: Journal of Palliative Medicine
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