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"content": "De novo genome assembly is the process of stitching short DNA sequences to generate longer DNA sequences, without using any reference sequence for alignment. It enables high-throughput genome sequencing and thus accelerates the discovery of new genomes. In this paper, we present a toolkit, called PPA-assembler, for de novo genome assembly in a distributed setting. The operations in our toolkit provide strong performance guarantees, and can be assembled to implement various sequencing strategies. PPA-assembler adopts the popular de Bruijn graph based approach for sequencing, and each operation is implemented as a program in Google's Pregel framework which can be easily deployed in a generic cluster. Experiments on large real and simulated datasets demonstrate that PPA-assembler is much more efficient than the state-of-the-arts while providing comparable sequencing quality. PPA-assembler has been open-sourced at https://github.com/yaobaiwei/PPA-Assembler.",
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"abstract": "Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgram level, which are not avaiblable in certain situations like clinical biopsies or single-cell genomes. Here, we developed an alternative method-CNAPE to computationally infer copy number alterations from gene expression data. A prior knowledge-aided machine learning model was proposed, trained and tested on 9,740 cancer samples from The Cancer Genome Atlas. We then applied CNAPE to study gliomas, the most common and aggressive brain cancer in adult. Particularly, using RNA sequencing data, CNAPE respectively predicted DNA copy number of chromosomes, chromosomal arms, and 12 commonly altered genes, and achieved over 80 percent accuracy in almost all broad regions and some focal regions. CNAPE was developed as an easy-to-use tool at https://github.com/WangLabHKUST/CNAPE.",
"abstracts": [
{
"abstractType": "Regular",
"content": "Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgram level, which are not avaiblable in certain situations like clinical biopsies or single-cell genomes. Here, we developed an alternative method-CNAPE to computationally infer copy number alterations from gene expression data. A prior knowledge-aided machine learning model was proposed, trained and tested on 9,740 cancer samples from The Cancer Genome Atlas. We then applied CNAPE to study gliomas, the most common and aggressive brain cancer in adult. Particularly, using RNA sequencing data, CNAPE respectively predicted DNA copy number of chromosomes, chromosomal arms, and 12 commonly altered genes, and achieved over 80 percent accuracy in almost all broad regions and some focal regions. CNAPE was developed as an easy-to-use tool at https://github.com/WangLabHKUST/CNAPE.",
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"normalizedAbstract": "Detection of DNA copy number alteration in cancer cells is critical to understanding cancer initiation and progression. Widely used methods, such as DNA arrays and genomic DNA sequencing, are relatively expensive and require DNA samples at a microgram level, which are not avaiblable in certain situations like clinical biopsies or single-cell genomes. Here, we developed an alternative method-CNAPE to computationally infer copy number alterations from gene expression data. A prior knowledge-aided machine learning model was proposed, trained and tested on 9,740 cancer samples from The Cancer Genome Atlas. We then applied CNAPE to study gliomas, the most common and aggressive brain cancer in adult. Particularly, using RNA sequencing data, CNAPE respectively predicted DNA copy number of chromosomes, chromosomal arms, and 12 commonly altered genes, and achieved over 80 percent accuracy in almost all broad regions and some focal regions. CNAPE was developed as an easy-to-use tool at https://github.com/WangLabHKUST/CNAPE.",
"title": "CNAPE: A Machine Learning Method for Copy Number Alteration Prediction from Gene Expression",
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"givenName": "Quanhua",
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"fullName": "Quanhua Mu",
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"id": "trans/tb/2012/04/05963634",
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"abstract": "Characterizing the interplay between the vortices and forces acting on a wind turbine's blades in a qualitative and quantitative way holds the potential for significantly improving large wind turbine design. This paper introduces an integrated pipeline for highly effective wind and force field analysis and visualization. We extract vortices induced by a turbine's rotation in a wind field, and characterize vortices in conjunction with numerically simulated forces on the blade surfaces as these vortices strike another turbine's blades downstream. The scientifically relevant issue to be studied is the relationship between the extracted, approximate locations on the blades where vortices strike the blades and the forces that exist in those locations. This integrated approach is used to detect and analyze turbulent flow that causes local impact on the wind turbine blade structure. The results that we present are based on analyzing the wind and force field data sets generated by numerical simulations, and allow domain scientists to relate vortex-blade interactions with power output loss in turbines and turbine life expectancy. Our methods have the potential to improve turbine design to save costs related to turbine operation and maintenance.",
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"content": "Characterizing the interplay between the vortices and forces acting on a wind turbine's blades in a qualitative and quantitative way holds the potential for significantly improving large wind turbine design. This paper introduces an integrated pipeline for highly effective wind and force field analysis and visualization. We extract vortices induced by a turbine's rotation in a wind field, and characterize vortices in conjunction with numerically simulated forces on the blade surfaces as these vortices strike another turbine's blades downstream. The scientifically relevant issue to be studied is the relationship between the extracted, approximate locations on the blades where vortices strike the blades and the forces that exist in those locations. This integrated approach is used to detect and analyze turbulent flow that causes local impact on the wind turbine blade structure. The results that we present are based on analyzing the wind and force field data sets generated by numerical simulations, and allow domain scientists to relate vortex-blade interactions with power output loss in turbines and turbine life expectancy. Our methods have the potential to improve turbine design to save costs related to turbine operation and maintenance.",
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"abstract": "Physiological responses to persuasion can help to increase our understanding of persuasive processes, and thereby the effectiveness of persuasive interventions. However, a clear relationship between psychophysiology and persuasion is not yet established. This article investigates if peripheral physiology predicts persuasion effectiveness, and whether peripheral physiology yields information that is not represented in other predictors of persuasion. We studied physiological reactions in the cardiovascular, electrodermal, facial muscle and respiratory systems of 75 participants while they read gain- or loss-framed persuasive messages advocating increased oral health care behavior. Persuasion effectiveness was measured as pre to post intervention changes in self-reported attitudes and intentions, as well as through changes in behavioral compliance over three weeks. Overall, participants showed stronger attitudes and intentions directly after the intervention (short-term persuasion), but did not show changes in behavior or attitudes two weeks later (no long-term persuasion). On an individual level, physiological reactivity parameters yielded additional information – next to self-report measures – to predict persuasion effectiveness on attitude, intention and behavioral compliance. To conclude, our findings suggested a positive relationship between physiological reactivity to persuasive messages and subsequent attitudes, intentions and behavior, and quantified the extra personalization that psychophysiological measures might bring to persuasive messaging.",
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"abstract": "We propose a method to calibrate viewpoint-dependent, channel-wise image blur of near-eye displays, especially of Optical See-Through Head-Mounted Displays (OST-HMDs). Imperfections in HMD optics cause channel-wise image shift and blur that degrade the image quality of the display at a user's viewpoint. If we can estimate such characteristics perfectly, we could mitigate the effect by applying correction techniques from the computational photography in computer vision as analogous to cameras. Unfortunately, directly applying existing calibration techniques of cameras to OST-HMDs is not a straightforward task. Unlike ordinary imaging systems, image blur in OST-HMDs is viewpoint-dependent, i.e., the optical characteristic of a display dynamically changes depending on the current viewpoint of the user. This constraint makes the problem challenging since we must measure image blur of an HMD, ideally, over the entire 3D eyebox in which a user can see an image. To overcome this problem, we model the viewpoint-dependent blur as a Gaussian Light Field (GLF) that stores spatial information of the display screen as a (4D) light field with depth information and the blur as point-spread functions in the form of Gaussian kernels, respectively. We first describe both our GLF model and a calibration procedure to learn a GLF for a given OST-HMD. We then apply our calibration method to two HMDs that use different optics: a cubic prism or holographic gratings. The results show that our method achieves significantly better accuracy in Point-Spread Function (PSF) estimations with an accuracy about 2 to 7 dB in Peak SNR.",
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"abstract": "Haptic perception essentially depends on the executed exploratory movements. It has been speculated that spontaneously executed movements are optimized for the computation of associated haptic properties. We investigated to what extent people strategically execute movements that are tuned for softness discrimination of objects with deformable surfaces. In Experiment 1, we investigated how movement parameters depend on expected stimulus compliance. In a discrimination task, we measured exploratory forces for less compliant (hard) stimuli and for more compliant (soft) stimuli. In Experiment 2, we investigated whether exploratory force also depends on the expected compliance difference between the two stimuli. The results indicate that participants apply higher forces when expecting harder objects as compared to softer objects, and they apply higher forces for smaller compliance differences than for larger ones. Experiment 3 examined how applied force influences differential sensitivity for softness as assessed by just noticeable differences (JNDs). For soft stimuli, JNDs did not depend on force. For hard stimuli, JNDs were “worse” (higher) if participants applied less force than they use naturally. We conclude that applying high force is a robust strategy to obtain high differential sensitivity, and that participants used this strategy if it was required for successful discrimination performance.",
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"content": "Haptic perception essentially depends on the executed exploratory movements. It has been speculated that spontaneously executed movements are optimized for the computation of associated haptic properties. We investigated to what extent people strategically execute movements that are tuned for softness discrimination of objects with deformable surfaces. In Experiment 1, we investigated how movement parameters depend on expected stimulus compliance. In a discrimination task, we measured exploratory forces for less compliant (hard) stimuli and for more compliant (soft) stimuli. In Experiment 2, we investigated whether exploratory force also depends on the expected compliance difference between the two stimuli. The results indicate that participants apply higher forces when expecting harder objects as compared to softer objects, and they apply higher forces for smaller compliance differences than for larger ones. Experiment 3 examined how applied force influences differential sensitivity for softness as assessed by just noticeable differences (JNDs). For soft stimuli, JNDs did not depend on force. For hard stimuli, JNDs were “worse” (higher) if participants applied less force than they use naturally. We conclude that applying high force is a robust strategy to obtain high differential sensitivity, and that participants used this strategy if it was required for successful discrimination performance.",
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"content": "Edge-based tracking is a fast and plausible approach for textureless 3D object tracking, but its robustness is still very challenging in highly cluttered backgrounds due to numerous local minima. To overcome this problem, we propose a novel method for fast and robust textureless 3D object tracking in highly cluttered backgrounds. The proposed method is based on optimal local searching of 3D-2D correspondences between a known 3D object model and 2D scene edges in an image with heavy background clutter. In our searching scheme, searching regions are partitioned into three levels (interior, contour, and exterior) with respect to the previous object region, and confident searching directions are determined by evaluating candidates of correspondences on their region levels; thus, the correspondences are searched among likely candidates in only the confident directions instead of searching through all candidates. To ensure the confident searching direction, we also adopt the region appearance, which is efficiently modeled on a newly defined local space (called a searching bundle). Experimental results and performance evaluations demonstrate that our method fully supports fast and robust textureless 3D object tracking even in highly cluttered backgrounds.",
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