547 research outputs found
Translational high-dimesional drug interaction discovery and validation using health record databases and pharmacokinetics models
Indiana University-Purdue University Indianapolis (IUPUI)Polypharmacy leads to increased risk of drug-drug interactions (DDI’s). In this
dissertation, we create a database for quantifying fraction of metabolism (fm) of CYP450
isozymes for FDA approved drugs. A reproducible data collection protocol was
developed to extract key information from publicly available in vitro selective CYP
enzyme inhibition studies. The fm was then estimated from the curated data. Then,
proposed a random control selection approach for nested case-control design for
electronical health records (HER) and electronical medical records (EMR) databases. By
relaxing the matching by case’s index time restriction, random control dramatically
reduces the computational burden compared with traditional control selection
approaches. Using the Observational Medical Outcomes Partnership gold standard and
an EMR database, random control is demonstrated to have better performances as well.
Finally, combining epidemiological studies and pharmacokinetic modeling with fm
database, we detected and evaluated high-dimensional drug-drug interactions among
thirty high frequency drugs. Multi-drug combinations that increased risk of myopathy
were identified in the FAERS and EMR databases by a mixture drug-count response
model (MDCM) model. Twenty-eight 3-way and 43 4-way DDI’s increased ratio of area
under plasma concentration–time curve (AUCR) >2-fold and had significant myopathy
risk in both databases. The predicted AUCR of omeprazole in the presence of
fluconazole and clonidine was 9.35; and increased risk of myopathy was 6.41 (LFDR = 0.002) in FAERS and 18.46 (LFDR = 0.005) in EMR. We demonstrate that combining
health record informatics and pharmacokinetic modeling is a powerful translational
approach to detect high-dimensional DDI’s.2 year
Detach and Adapt: Learning Cross-Domain Disentangled Deep Representation
While representation learning aims to derive interpretable features for
describing visual data, representation disentanglement further results in such
features so that particular image attributes can be identified and manipulated.
However, one cannot easily address this task without observing ground truth
annotation for the training data. To address this problem, we propose a novel
deep learning model of Cross-Domain Representation Disentangler (CDRD). By
observing fully annotated source-domain data and unlabeled target-domain data
of interest, our model bridges the information across data domains and
transfers the attribute information accordingly. Thus, cross-domain joint
feature disentanglement and adaptation can be jointly performed. In the
experiments, we provide qualitative results to verify our disentanglement
capability. Moreover, we further confirm that our model can be applied for
solving classification tasks of unsupervised domain adaptation, and performs
favorably against state-of-the-art image disentanglement and translation
methods.Comment: CVPR 2018 Spotligh
The role of high-frequency data in volatility forecasting: evidence from the China stock market
This research investigates the role of high-frequency data in volatility forecasting of the China stock market by particularly feeding different frequency return series directly into a large number of GARCH versions. The contributions of this research are as follows. 1) We provide clear evidence to support that the superiority of traditional time series models in volatility forecasting remains by taking advantage of high-frequency data. 2) We incorporate different distribution assumptions in GARCH models to capture the stylized facts of high-frequency data. The result shows that: 1) data frequency in GARCH application substantially influence the accuracy of volatility forecasting, as the higher the frequency is of the return series, the better are the forecasts provided; 2) non-normal distributions such as skewed student-t and generalized error distribution are more capable at reproducing the stylized facts of both intraday and daily return series than normal distribution; and 3) GARCH estimated by 5-min returns not only outperforms other GARCH alternatives, but also considerably beats RV-based models such as HAR and ARFIMA at volatility forecasting
Semantic Segmentation Using Super Resolution Technique as Pre-Processing
Combining high-level and low-level visual tasks is a common technique in the
field of computer vision. This work integrates the technique of image super
resolution to semantic segmentation for document image binarization. It
demonstrates that using image super-resolution as a preprocessing step can
effectively enhance the results and performance of semantic segmentation
New Calibration Method Using Low Cost MEM IMUs to Verify the Performance of UAV-Borne MMS Payloads
Spatial information plays a critical role in remote sensing and mapping applications such as environment surveying and disaster monitoring. An Unmanned Aerial Vehicle (UAV)-borne mobile mapping system (MMS) can accomplish rapid spatial information acquisition under limited sky conditions with better mobility and flexibility than other means. This study proposes a long endurance Direct Geo-referencing (DG)-based fixed-wing UAV photogrammetric platform and two DG modules that each use different commercial Micro-Electro Mechanical Systems’ (MEMS) tactical grade Inertial Measurement Units (IMUs). Furthermore, this study develops a novel kinematic calibration method which includes lever arms, boresight angles and camera shutter delay to improve positioning accuracy. The new calibration method is then compared with the traditional calibration approach. The results show that the accuracy of the DG can be significantly improved by flying at a lower altitude using the new higher specification hardware. The new proposed method improves the accuracy of DG by about 20%. The preliminary results show that two-dimensional (2D) horizontal DG positioning accuracy is around 5.8 m at a flight height of 300 m using the newly designed tactical grade integrated Positioning and Orientation System (POS). The positioning accuracy in three-dimensions (3D) is less than 8 m
CCDWT-GAN: Generative Adversarial Networks Based on Color Channel Using Discrete Wavelet Transform for Document Image Binarization
To efficiently extract the textual information from color degraded document
images is an important research topic. Long-term imperfect preservation of
ancient documents has led to various types of degradation such as page
staining, paper yellowing, and ink bleeding; these degradations badly impact
the image processing for information extraction. In this paper, we present
CCDWT-GAN, a generative adversarial network (GAN) that utilizes the discrete
wavelet transform (DWT) on RGB (red, green, blue) channel splited images. The
proposed method comprises three stages: image preprocessing, image enhancement,
and image binarization. This work conducts comparative experiments in the image
preprocessing stage to determine the optimal selection of DWT with
normalization. Additionally, we perform an ablation study on the results of the
image enhancement stage and the image binarization stage to validate their
positive effect on the model performance. This work compares the performance of
the proposed method with other state-of-the-art (SOTA) methods on DIBCO and
H-DIBCO ((Handwritten) Document Image Binarization Competition) datasets. The
experimental results demonstrate that CCDWT-GAN achieves a top two performance
on multiple benchmark datasets, and outperforms other SOTA methods
Translational high-dimensional drug Interaction discovery and validation using health record databases and pharmacokinetics models
Polypharmacy increases the risk of drug-drug interactions (DDI's). Combining epidemiological studies with pharmacokinetic modeling, we detected and evaluated high-dimensional DDI's among thirty frequent drugs. Multi-drug combinations that increased risk of myopathy were identified in the FDA Adverse Event Reporting System (FAERS) and electronic medical record (EMR) databases by a mixture drug-count response model. CYP450 inhibition was estimated among the 30 drugs in the presence of 1 to 4 inhibitors using in vitro in vivo extrapolation. Twenty-eight 3-way and 43 4-way DDI's had significant myopathy risk in both databases and predicted increases in the area under the concentration time curve ratio (AUCR) >2-fold. The HD-DDI of omeprazole, fluconazole and clonidine was associated with a 6.41-fold (FAERS) and 18.46-fold (EMR) increase risk of myopathy (LFDR<0.005); the AUCR of omeprazole in this combination was 9.35.The combination of health record informatics and pharmacokinetic modeling is a powerful translational approach to detect high-dimensional DDI's
Scaly Ear Rash as the Herald of a Young Girl with Juvenile Systemic Lupus Erythematosus
Juvenile systemic lupus erythematosus (JSLE) is an autoimmune-mediated multiorgan disease. The cutaneous manifestation is one of the most common initial presentations in JSLE. A typical lesion is a facial malar rash, but a patient may sometimes present with nonclassical lesions. Herein, we report two cases of JSLE with similar persistent scaly ear rashes as the heralding cutaneous symptom preceding systemic symptoms. Identifying this atypical and underestimated cutaneous rash in juvenile patients might help the clinician make the correct diagnosis and provide earlier intervention, which may help prevent disease progression
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