The Impact of Sleep Apnea on Conventional Doppler Indices

AbstractObjectiveTo prospectively explore the impact of obstructive sleep apnea (OSA) on conventional Doppler indices and to identify possible negative prognostic factors for left ventricular diastolic dysfunction. Materials and Methods: All included subjects had overnight polysomnog-raphy. All subjects underwent a comprehensive echocardiography examination to evaluate systolic and diastolic function of the left ventricle. A multiple logistic regression model was created to identify potential negative prognostic factors for left ventricular dysfunction.ResultsA significant decrease in the ratio of early and atrial mitral flow velocity (E/A ratio) in OSA patients was found. Patients with moderate-to-severe OSA had a significant increase in the odds ratio for development of an abnormal E/A ratio (p=0.014, multivariate logistic regression). There was a significant negative correlation between E/A ratio and apnea-hypopnea index (p = 0.01). Non-obese OSA patients and obese-OSA patients carried significantly increased odds ratios for the development of a reduced E/A ratio (p = 0.02 and 0.038, respectively).ConclusionSubjects with OSA had reduced mitral E/A ratios, which implies possible impaired diastolic heart function. Further study to reverse impaired diastolic function via lifestyle modifications and treatment with nasal continuous positive airway pressure or surgery is warranted

Will Your Project Get the Green Light? Predicting the Success of Crowdfunding Campaigns

Capital is always essential for a business project over times. After emerging in 2000, crowdfunding gradually becomes one of the most popular fundraising resources. However, the mechanism of crowdfunding significantly differs from traditional capital-collecting approaches. As long as the amount of pledged money reaches the goal in time, the project succeeds, its initiator receives the funds, the platform gains the revenue, and its backers acquire rewards. Reaching the goal by deadline becomes an important issue. The goal of our study is to develop an effective technique for predicting whether a crowdfunding campaign will succeed or fail. On the basis of a dataset collected from Kickstarter, our empirical evaluation results suggest that our proposed technique significantly outperforms the benchmark method. In addition, with the use of time-dependent factors, the prediction accuracy improves from 72.89% at day 0 to 87.13% at the first day and eventually to 89.62% at day 7

NNVA: Neural Network Assisted Visual Analysis of Yeast Cell Polarization Simulation

Complex computational models are often designed to simulate real-world physical phenomena in many scientific disciplines. However, these simulation models tend to be computationally very expensive and involve a large number of simulation input parameters which need to be analyzed and properly calibrated before the models can be applied for real scientific studies. We propose a visual analysis system to facilitate interactive exploratory analysis of high-dimensional input parameter space for a complex yeast cell polarization simulation. The proposed system can assist the computational biologists, who designed the simulation model, to visually calibrate the input parameters by modifying the parameter values and immediately visualizing the predicted simulation outcome without having the need to run the original expensive simulation for every instance. Our proposed visual analysis system is driven by a trained neural network-based surrogate model as the backend analysis framework. Surrogate models are widely used in the field of simulation sciences to efficiently analyze computationally expensive simulation models. In this work, we demonstrate the advantage of using neural networks as surrogate models for visual analysis by incorporating some of the recent advances in the field of uncertainty quantification, interpretability and explainability of neural network-based models. We utilize the trained network to perform interactive parameter sensitivity analysis of the original simulation at multiple levels-of-detail as well as recommend optimal parameter configurations using the activation maximization framework of neural networks. We also facilitate detail analysis of the trained network to extract useful insights about the simulation model, learned by the network, during the training process.Comment: Published at IEEE Transactions on Visualization and Computer Graphic

Using a kernel density estimation based classifier to predict species-specific microRNA precursors

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules participating in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches obtain more attention because that they can discover species-specific pre-miRNAs. Most <it>ab initio </it>approaches proposed novel features to characterize RNA molecules. However, there were fewer discussions on the associated classification mechanism in a miRNA predictor.</p> <p>Results</p> <p>This study focuses on the classification algorithm for miRNA prediction. We develop a novel <it>ab initio </it>method, miR-KDE, in which most of the features are collected from previous works. The classification mechanism in miR-KDE is the relaxed variable kernel density estimator (RVKDE) that we have recently proposed. When compared to the famous support vector machine (SVM), RVKDE exploits more local information of the training dataset. MiR-KDE is evaluated using a training set consisted of only human pre-miRNAs to predict a benchmark collected from 40 species. The experimental results show that miR-KDE delivers favorable performance in predicting human pre-miRNAs and has advantages for pre-miRNAs from the genera taxonomically distant to humans.</p> <p>Conclusion</p> <p>We use a novel classifier of which the characteristic of exploiting local information is particularly suitable to predict species-specific pre-miRNAs. This study also provides a comprehensive analysis from the view of classification mechanism. The good performance of miR-KDE encourages more efforts on the classification methodology as well as the feature extraction in miRNA prediction.</p

The Taiwan ECDFS Near-Infrared Survey: Ultra-deep J and Ks Imaging in the Extended Chandra Deep Field-South

We present ultra-deep J and Ks imaging observations covering a 30' * 30' area of the Extended Chandra Deep Field-South (ECDFS) carried out by our Taiwan ECDFS Near-Infrared Survey (TENIS). The median 5-sigma limiting magnitudes for all detected objects in the ECDFS reach 24.5 and 23.9 mag (AB) for J and Ks, respectively. In the inner 400 arcmin^2 region where the sensitivity is more uniform, objects as faint as 25.6 and 25.0 mag are detected at 5-sigma. So this is by far the deepest J and Ks datasets available for the ECDFS. To combine the TENIS with the Spitzer IRAC data for obtaining better spectral energy distributions of high-redshift objects, we developed a novel deconvolution technique (IRACLEAN) to accurately estimate the IRAC fluxes. IRACLEAN can minimize the effect of blending in the IRAC images caused by the large point-spread functions and reduce the confusion noise. We applied IRACLEAN to the images from the Spitzer IRAC/MUSYC Public Legacy in the ECDFS survey (SIMPLE) and generated a J+Ks selected multi-wavelength catalog including the photometry of both the TENIS near-infrared and the SIMPLE IRAC data. We publicly release the data products derived from this work, including the J and Ks images and the J+Ks selected multiwavelength catalog.Comment: 25 pages, 25 figures, ApJS in pres

Anxiety and mood disorder in young males with mitral valve prolapse

For-Wey Lung1&amp;ndash;4, Chih-Tao Cheng5, Wei-To Chang6, Bih-Ching Shu71Department of Psychiatry, Kaohsiung Armed Forces General Hospital, Kaohsiung, Taiwan; 2Graduate Institute of Behavioral Sciences, Kaohsiung Medical Center, Taiwan; 3Department of Psychiatry, National Defense Medical Center, Taipei, Taiwan; 4Calo Psychiatric Center, Pingtung County, Taiwan; 5School of public Health, University of California, Berkeley, CA, USA; 6Liu Chia-Hsiu Hospital, Kaohsiung County, Taiwan; 7Institute of Allied Health Sciences and Department of Nursing, National Cheng Kung University, Tainan, TaiwanObjective: This study explored the prevalence of panic disorder and other psychiatric disorders in young Han Chinese males with mitral valve prolapse (MVP). With the factors of age, sex, and ethnicity controlled, the specific&amp;nbsp;role of MVP in panic disorder was analyzed. Methods: Subjects with chest pain aged between 18 and 25 years were assessed with the echocardiograph for MVP and the Chinese version of the Mini-International Neuropsychiatric Interview for panic disorder (n = 39).Results: Of the 39 participants, 35.9% met the diagnosis of anxiety disorder, 46.2% met at least one criterion of anxiety disorder, and 23.1% met the diagnostic criteria of major depressive disorder. There was no statistically significant difference in the prevalence of panic disorder between one of the (8.3%) MVP patients, and two (7.4%) control participants.Conclusions: There is a high prevalence of psychiatric disorder, including anxiety disorder and major depressive disorder, in those who report pain symptoms, so that diagnosis and treatment of these patients is of great importance. In addition, individuals with MVP did not have an increased risk for panic disorder. Whether MVP may be a modifier or mediating factor for panic disorder needs to be further assessed in a larger scale study.Keywords: mitral valve prolapse, panic disorder, Han Chinese males, major depressive disorder, echocardiograph, MIN

BN-embedded monolayer graphene with tunable electronic and topological properties

Finding an effective and controllable way to create a sizable energy gap in graphene-based systems has been a challenging topic of intensive research. We propose that the hybrid of boron nitride and graphene (h-BNC) at low BN doping serves as an ideal platform for band-gap engineering and valleytronic applications. We report a systematic first-principles study of the atomic configurations and band gap opening for energetically favorable BN patches embedded in graphene. Based on first-principles calculations, we construct a tight-binding model to simulate general doping configurations in large supercells. Unexpectedly, the calculations find a linear dependence of the band gap on the effective BN concentration at low doping, arising from an induced effective on-site energy difference at the two C sublattices as they are substituted by B and N dopants alternately. The significant and tunable band gap of a few hundred meVs, with preserved topological properties of graphene and feasible sample preparation in the laboratory, presents great opportunities to realize valley physics applications in graphene systems at room temperature

A Comprehensive Review of Machine Learning Advances on Data Change: A Cross-Field Perspective

Recent artificial intelligence (AI) technologies show remarkable evolution in various academic fields and industries. However, in the real world, dynamic data lead to principal challenges for deploying AI models. An unexpected data change brings about severe performance degradation in AI models. We identify two major related research fields, domain shift and concept drift according to the setting of the data change. Although these two popular research fields aim to solve distribution shift and non-stationary data stream problems, the underlying properties remain similar which also encourages similar technical approaches. In this review, we regroup domain shift and concept drift into a single research problem, namely the data change problem, with a systematic overview of state-of-the-art methods in the two research fields. We propose a three-phase problem categorization scheme to link the key ideas in the two technical fields. We thus provide a novel scope for researchers to explore contemporary technical strategies, learn industrial applications, and identify future directions for addressing data change challenges