Estimating Node Importance in Knowledge Graphs Using Graph Neural Networks

How can we estimate the importance of nodes in a knowledge graph (KG)? A KG is a multi-relational graph that has proven valuable for many tasks including question answering and semantic search. In this paper, we present GENI, a method for tackling the problem of estimating node importance in KGs, which enables several downstream applications such as item recommendation and resource allocation. While a number of approaches have been developed to address this problem for general graphs, they do not fully utilize information available in KGs, or lack flexibility needed to model complex relationship between entities and their importance. To address these limitations, we explore supervised machine learning algorithms. In particular, building upon recent advancement of graph neural networks (GNNs), we develop GENI, a GNN-based method designed to deal with distinctive challenges involved with predicting node importance in KGs. Our method performs an aggregation of importance scores instead of aggregating node embeddings via predicate-aware attention mechanism and flexible centrality adjustment. In our evaluation of GENI and existing methods on predicting node importance in real-world KGs with different characteristics, GENI achieves 5-17% higher NDCG@100 than the state of the art.Comment: KDD 2019 Research Track. 11 pages. Changelog: Type 3 font removed, and minor updates made in the Appendix (v2

Regge-like relation and a universal description of heavy-light systems

Using the Regge-like formula $(M-m_Q)^2=\pi\sigma L$ between hadron mass $M$ and angular momentum $L$ with a heavy quark mass $m_Q$ and a string tension $\sigma$, we analyze all the heavy-light systems, i.e., $D/D_s/B/B_s$ mesons and charmed and bottom baryons.Numerical plots are obtained for all the heavy-light mesons of experimental data whose slope becomes nearly equal to 1/2 of that for light hadrons. Assuming that charmed and bottom baryons consist of one heavy quark and one light cluster of two light quarks (diquark), we apply the formula to all the heavy-light baryons including recently discovered $\Omega_c$'s and find that these baryons experimentally measured satisfy the above formula. We predict the average mass values of $B$, $B_s$, $\Lambda_b$, $\Sigma_c$, $\Xi_c$, and $\Omega_c$ with $L=2$ as 6.01, 6.13, 6.15, 3.05, 3.07, and 3.34 GeV, respectively. Our results on baryons suggest that these baryons can be safely regarded as heavy quark-light cluster configuration. We also find a universal description for all the heavy-light mesons as well as baryons, i.e., one unique line is enough to describe both of charmed and bottom heavy-light systems. Our results suggest that instead of mass itself, gluon flux energy is essential to obtain a linear trajectory.Comment: 10 pages, 8 figures, 5 table

Patient-specific virtual stent-graft deployment for Type B aortic dissection: a pilot study of the impact of stent-graft length

Thoracic endovascular aortic repair (TEVAR) has been accepted as a standard treatment option for complicated type B aortic dissection. Distal stent-graft induced new entry (SINE) is recognized as one of the main post-TEVAR complications, which can lead to fatal prognosis. Previous retrospective cohort studies suggested that short stent-graft (SG) length (<165 mm) might correlate with increased risk of distal SINE. However, the influence of SG length on changes in local biomechanical conditions before and after TEVAR is unknown. In this paper, we aim to address this issue using a virtual SG deployment simulation model developed for application in type B aortic dissection. Our model incorporates detailed SG design and hyperelastic behaviour of the aortic wall. By making use of patient-specific geometry reconstructed from pre-TEVAR computed tomography angiography (CTA) scan, our model can predict post-TEVAR SG configuration and wall stress. Virtual SG deployment simulations were performed on a patient who underwent TEVAR with a short SG (158 mm in length), mimicking the actual clinical procedure. Further simulations were carried out on the same patient geometry but with different SG lengths (183 mm and 208 mm) in order to evaluate the effect of SG length on changes in local stress in the treated aorta

Is epinephrine still the drug of choice during cardiac arrest in the emergency department of the hospital? A meta-analysis

Epinephrine is the first-line emergency drug for cardiac arrest and anaphylactic reactions but is reported to be associated with many challenges resulting in its under- or improper utilization. Therefore, in this meta-analysis, the efficacy and safety of epinephrine as a first-line cardiac emergency drug for both out-of-hospital and in-hospital patients was assessed. Pertinent articles were searched in central databases like PubMed, Scopus, and Web of Science, using appropriate keywords as per the PRISMA guidelines. Retrospective and prospective studies were included according to the predefined PICOS criteria. RevMan and MedCalc software were used and statistical parameters such as odds ratio and risk ratio were calculated. Twelve clinical trials with a total of 208,690 cardiac arrest patients from 2000 to 2022 were included, in accordance with the chosen inclusion criteria. In the present meta-analysis, a high odds ratio (OR) value of 3.67 (95 % CI 2.32–5.81) with a tau2 value of 0.64, a chi2 value of 12,446.86, df value of 11, I2 value of 100 %, Z-value 5.53, and a p-value < 0.00001 were reported. Similarly, the risk ratio of 1.89 (95 % CI 1.47–2.43) with a tau2 value of 0.19, chi2 value of 11,530.67, df value of 11, I2 value of 100 %, Z-value of 4.95, and p-value < 0.000001. The present meta-analysis strongly prefers epinephrine injection as the first cardiac emergency drug for both out-of-hospital and in-hospital patients during cardiac arrest

Enhancing Reproductive Organ Segmentation in Pediatric CT via Adversarial Learning

Accurately segmenting organs in abdominal computed tomography (CT) scans is crucial for clinical applications such as pre-operative planning and dose estimation. With the recent advent of deep learning algorithms, many robust frameworks have been proposed for organ segmentation in abdominal CT images. However, many of these frameworks require large amounts of training data in order to achieve high segmentation accuracy. Pediatric abdominal CT images containing reproductive organs are particularly hard to obtain since these organs are extremely sensitive to ionizing radiation. Hence, it is extremely challenging to train automatic segmentation algorithms on organs such as the uterus and the prostate. To address these issues, we propose a novel segmentation network with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditionally generates additional features during training. The proposed CFG-SegNet (conditional feature generation segmentation network) is trained on a single loss function which combines adversarial loss, reconstruction loss, auxiliary classifier loss and segmentation loss. 2.5D segmentation experiments are performed on a custom data set containing 24 female CT volumes containing the uterus and 40 male CT volumes containing the prostate. CFG-SegNet achieves an average segmentation accuracy of 0.929 DSC (Dice Similarity Coefficient) on the prostate and 0.724 DSC on the uterus with 4-fold cross validation. The results show that our network is high-performing and has the potential to precisely segment difficult organs with few available training images

Nonsurgical repair of the ascending aorta: why less is more

Objective: Advanced endovascular options for acute and chronic pathology of the ascending aorta are emerging; however, several problems with stent grafts placed in the ascending aorta have been identified in patients unsuitable for surgical repair, such as migration and erosion at aorta interface. Method: Among the six cases analysed in this report, three were treated with a stent graft in the ascending aorta to manage chronic dissection in the proximal aorta; dimensions of those stent grafts varied between 34 and 45 mm in diameter, and from 77 to 100 mm in length. Three patients, matched by age, sex and their nature of pathology, were subjected to the focal closure of a single communicating entry by the use of an occluding device (Amplatzer ASD and PFO occluders between 14 and 18 mm disc diameter) with similar Charlson comorbidity score. Results: Both conceptually different nonsurgical management strategies were technically feasible; however, with stent grafts, an early or delayed erosion to full re-dissection was documented with stent grafts, in contrast to complete seal, with an induced remodelling and a long-term survival after the successful placing of coils and occluder devices. Moreover, aortic root motion was not impaired by the focal occlusion of a communication with an occluder, while free motion was impeded after stent graft placement. Conclusions: The intriguing observation in our small series was that stent grafts placed in the ascending aorta portends the risk of an either early (post-procedural) or delayed migration and erosion of aortic tissues at the landing site or biological interface between 12 and 16 months after the procedure, a phenomenon not seen with the use of focal occluding devices up to 5 years of follow-up. Obviously, the focal approach avoids the erosion of the aortic wall as the result of minimal interaction with the biological interface, such as a diseased aortic wall. Potential explanations may be related to a reduced motion of the aortic root after the placement of stent graft in the ascending aorta, whereas the free motion of aortic root was preserved with an occluder. The causality of erosion may however not be fully understood, as besides the stiffness and radial force of the stent graft, other factors such as the induced inflammatory reactions of aortic tissue and local adhesions within the chest may also play a role. With stent grafts failing to portend long-term success, they may still have a role as a temporizing solution for elective surgical conversion. Larger datasets from registries are needed to further explore this evolving field of interventions to the ascending aorta

MultiImport: Inferring Node Importance in a Knowledge Graph from Multiple Input Signals

Given multiple input signals, how can we infer node importance in a knowledge graph (KG)? Node importance estimation is a crucial and challenging task that can benefit a lot of applications including recommendation, search, and query disambiguation. A key challenge towards this goal is how to effectively use input from different sources. On the one hand, a KG is a rich source of information, with multiple types of nodes and edges. On the other hand, there are external input signals, such as the number of votes or pageviews, which can directly tell us about the importance of entities in a KG. While several methods have been developed to tackle this problem, their use of these external signals has been limited as they are not designed to consider multiple signals simultaneously. In this paper, we develop an end-to-end model MultiImport, which infers latent node importance from multiple, potentially overlapping, input signals. MultiImport is a latent variable model that captures the relation between node importance and input signals, and effectively learns from multiple signals with potential conflicts. Also, MultiImport provides an effective estimator based on attentive graph neural networks. We ran experiments on real-world KGs to show that MultiImport handles several challenges involved with inferring node importance from multiple input signals, and consistently outperforms existing methods, achieving up to 23.7% higher NDCG@100 than the state-of-the-art method.Comment: KDD 2020 Research Track. 10 page