Relation Embedding with Dihedral Group in Knowledge Graph

Link prediction is critical for the application of incomplete knowledge graph (KG) in the downstream tasks. As a family of effective approaches for link predictions, embedding methods try to learn low-rank representations for both entities and relations such that the bilinear form defined therein is a well-behaved scoring function. Despite of their successful performances, existing bilinear forms overlook the modeling of relation compositions, resulting in lacks of interpretability for reasoning on KG. To fulfill this gap, we propose a new model called DihEdral, named after dihedral symmetry group. This new model learns knowledge graph embeddings that can capture relation compositions by nature. Furthermore, our approach models the relation embeddings parametrized by discrete values, thereby decrease the solution space drastically. Our experiments show that DihEdral is able to capture all desired properties such as (skew-) symmetry, inversion and (non-) Abelian composition, and outperforms existing bilinear form based approach and is comparable to or better than deep learning models such as ConvE.Comment: ACL 201

3D tumor localization through real-time volumetric x-ray imaging for lung cancer radiotherapy

Recently we have developed an algorithm for reconstructing volumetric images and extracting 3D tumor motion information from a single x-ray projection. We have demonstrated its feasibility using a digital respiratory phantom with regular breathing patterns. In this work, we present a detailed description and a comprehensive evaluation of the improved algorithm. The algorithm was improved by incorporating respiratory motion prediction. The accuracy and efficiency were then evaluated on 1) a digital respiratory phantom, 2) a physical respiratory phantom, and 3) five lung cancer patients. These evaluation cases include both regular and irregular breathing patterns that are different from the training dataset. For the digital respiratory phantom with regular and irregular breathing, the average 3D tumor localization error is less than 1 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for 3D tumor localization from each projection ranges between 0.19 and 0.26 seconds, for both regular and irregular breathing, which is about a 10% improvement over previously reported results. For the physical respiratory phantom, an average tumor localization error below 1 mm was achieved with an average computation time of 0.13 and 0.16 seconds on the same GPU card, for regular and irregular breathing, respectively. For the five lung cancer patients, the average tumor localization error is below 2 mm in both the axial and tangential directions. The average computation time on the same GPU card ranges between 0.26 and 0.34 seconds

Poly(delta-gluconolactone) and Poly(delta-gluconolactone- ε

Poly(delta-gluconolactone) (PGL) and poly(delta-gluconolactone-ε-caprolactone) (P(GL-CL)) were synthesized through ring-opening polymerization (ROP) and characterized by FT-IR, NMR, XRD, intrinsic viscosity, GPC, DSC, and TGA. The crystallinity of P(GL-CL) with various d-GL/CL ratios (d-GL/CL = 5 : 5, 4 : 6, 3 : 7, 2 : 8, and 1 : 9) was 12.09 to 59.78% while PGL was amorphous. Melting temperature (Tm) of these polymers was 49.8 to 62.0°C and decomposition temperature was 282 to 489°C depending on the d-GL/CL ratios. In addition, all these polymers were degradable and the degradation rates could be controlled by adjusting d-GL/CL ratios. These results indicated that PGL and P(GL-CL) might be promising novel absorbable materials

GPU-based Fast Low-dose Cone Beam CT Reconstruction via Total Variation

Cone-beam CT (CBCT) has been widely used in image guided radiation therapy (IGRT) to acquire updated volumetric anatomical information before treatment fractions for accurate patient alignment purpose. However, the excessive x-ray imaging dose from serial CBCT scans raises a clinical concern in most IGRT procedures. The excessive imaging dose can be effectively reduced by reducing the number of x-ray projections and/or lowering mAs levels in a CBCT scan. The goal of this work is to develop a fast GPU-based algorithm to reconstruct high quality CBCT images from undersampled and noisy projection data so as to lower the imaging dose. The CBCT is reconstructed by minimizing an energy functional consisting of a data fidelity term and a total variation regularization term. We developed a GPU-friendly version of the forward-backward splitting algorithm to solve this model. A multi-grid technique is also employed. We test our CBCT reconstruction algorithm on a digital NCAT phantom and a head-and-neck patient case. The performance under low mAs is also validated using a physical Catphan phantom and a head-and-neck Rando phantom. It is found that 40 x-ray projections are sufficient to reconstruct CBCT images with satisfactory quality for IGRT patient alignment purpose. Phantom experiments indicated that CBCT images can be successfully reconstructed with our algorithm under as low as 0.1 mAs/projection level. Comparing with currently widely used full-fan head-and-neck scanning protocol of about 360 projections with 0.4 mAs/projection, it is estimated that an overall 36 times dose reduction has been achieved with our algorithm. Moreover, the reconstruction time is about 130 sec on an NVIDIA Tesla C1060 GPU card, which is estimated ~100 times faster than similar iterative reconstruction approaches.Comment: 20 pages, 10 figures, Paper was revised and more testing cases were added

Real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy

Purpose: To develop an algorithm for real-time volumetric image reconstruction and 3D tumor localization based on a single x-ray projection image for lung cancer radiotherapy. Methods: Given a set of volumetric images of a patient at N breathing phases as the training data, we perform deformable image registration between a reference phase and the other N-1 phases, resulting in N-1 deformation vector fields (DVFs). These DVFs can be represented efficiently by a few eigenvectors and coefficients obtained from principal component analysis (PCA). By varying the PCA coefficients, we can generate new DVFs, which, when applied on the reference image, lead to new volumetric images. We then can reconstruct a volumetric image from a single projection image by optimizing the PCA coefficients such that its computed projection matches the measured one. The 3D location of the tumor can be derived by applying the inverted DVF on its position in the reference image. Our algorithm was implemented on graphics processing units (GPUs) to achieve real-time efficiency. We generated the training data using a realistic and dynamic mathematical phantom with 10 breathing phases. The testing data were 360 cone beam projections corresponding to one gantry rotation, simulated using the same phantom with a 50% increase in breathing amplitude. Results: The average relative image intensity error of the reconstructed volumetric images is 6.9% +/- 2.4%. The average 3D tumor localization error is 0.8 mm +/- 0.5 mm. On an NVIDIA Tesla C1060 GPU card, the average computation time for reconstructing a volumetric image from each projection is 0.24 seconds (range: 0.17 and 0.35 seconds). Conclusions: We have shown the feasibility of reconstructing volumetric images and localizing tumor positions in 3D in near real-time from a single x-ray image.Comment: 8 pages, 3 figures, submitted to Medical Physics Lette

Pathogenic infection of Macaca nemestrina with a CCR5-tropic subtype-C simian-human immunodeficiency virus

Background: Although pig-tailed macaques (Macaca nemestrina) have been used in AIDS research for years, less is known about the early immunopathogenic events in this species, as compared to rhesus macaques (Macaca mulatta). Similarly, the events in early infection are well-characterized for simian immunodeficiency viruses (SIV), but less so for chimeric simian-human immunodeficiency viruses (SHIV), although the latter have been widely used in HIV vaccine studies. Here, we report the consequences of intrarectal infection with a CCR5-tropic clade C SHIV-1157ipd3N4 in pig-tailed macaques. Results: Plasma and cell-associated virus was detectable in peripheral blood and intestinal tissues of all four pig-tailed macaques following intrarectal inoculation with SHIV-1157ipd3N4. We also observed a rapid and irreversible loss of CD4+ T cells at multiple mucosal sites, resulting in a marked decrease of CD4:CD8 T cell ratios 0.5–4 weeks after inoculation. This depletion targeted subsets of CD4+ T cells expressing the CCR5 coreceptor and having a CD28-CD95+ effector memory phenotype, consistent with the R5-tropism of SHIV-1157ipd3N4. All three animals that were studied beyond the acute phase seroconverted as early as week 4, with two developing cross-clade neutralizing antibody responses by week 24. These two animals also demonstrated persistent plasma viremia for >48 weeks. One of these animals developed AIDS, as shown by peripheral blood CD4+ T-cell depletion starting at 20 weeks post inoculation. Conclusion: These findings indicate that SHIV-1157ipd3N4-induced pathogenesis in pig-tailed macaques followed a similar course as SIV-infected rhesus macaques. Thus, R5 SHIV-C-infection of pig-tailed macaques could provide a useful and relevant model for AIDS vaccine and pathogenesis research

Luminescent Solar Concentrators Fabricated by Dispersing Rare Earth Particles in PMMA Waveguide

Luminescent solar concentrators (LSCs) were fabricated by dispersing CaAlSiN3 : Eu2+ particles in a PMMA waveguide. A series of LSCs (dimension 5.0 cm × 5.0 cm × 0.5 cm) with different CaAlSiN3 : Eu2+ particle concentration were obtained and their performance was evaluated. The maximum optical concentration ratio is 1.23 with a power conversion efficiency of 1.44% for the LSC containing 0.5 wt% CaAlSiN3 : Eu2+ particles concentration. This strategy of dispersing rare earth particles in PMMA waveguide represents an alternative approach to producing highly durable LSCs

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies

PCA-based lung motion model

Organ motion induced by respiration may cause clinically significant targeting errors and greatly degrade the effectiveness of conformal radiotherapy. It is therefore crucial to be able to model respiratory motion accurately. A recently proposed lung motion model based on principal component analysis (PCA) has been shown to be promising on a few patients. However, there is still a need to understand the underlying reason why it works. In this paper, we present a much deeper and detailed analysis of the PCA-based lung motion model. We provide the theoretical justification of the effectiveness of PCA in modeling lung motion. We also prove that under certain conditions, the PCA motion model is equivalent to 5D motion model, which is based on physiology and anatomy of the lung. The modeling power of PCA model was tested on clinical data and the average 3D error was found to be below 1 mm.Comment: 4 pages, 1 figure. submitted to International Conference on the use of Computers in Radiation Therapy 201